diff --git a/Build_C_code.m b/Build_C_code.m
index b400391..8ec6699 100644
--- a/Build_C_code.m
+++ b/Build_C_code.m
@@ -1,42 +1,48 @@
-function Build_C_code()
-
-    % This function biulds the required C MEX fucntions.
-    % NOTE, b default matlab uses -o2 optimisations.
-    % Chnaging this to -ofast reduces the doIRFconvolution.c
-    % runtime from 0.017534s to 0.010353s on a x64 Linux maxtine (in 2018) (41% faster).
-    %
-    % To change the Linux compile settings open the file ~/.matlab/R2016a/mex_C_glnxa64.xml
-    % and change:
-    %   - 'COPTIMFLAGS="-O -DNDEBUG"' to 'COPTIMFLAGS="-Ofast -DNDEBUG"'
-    %   - 'LDOPTIMFLAGS="-O"' to 'LDOPTIMFLAGS="-Ofast"'
-    %
-    % To chnage Windows Visual studio compier, open the file 
-    % mex_C_win64.xml and chnage: 
-    %  - 'OPTIMFLAGS="/Ofast /Oy- /DNDEBUG"' to 'OPTIMFLAGS="/O2 /Oy- /DNDEBUG"'
-
-    arch=computer('arch');
-    mexopts = {'-O' '-v' ['-' arch]};
-    % 64-bit platform
-    if ~isempty(strfind(computer(),'64'))
-        mexopts(end+1) = {'-largeArrayDims'};
-    end
-
-    % invoke MEX compilation tool
-    if ispc
-        mex(mexopts{:},'algorithms\models\TransferNoise\ForcingTransformation\forcingTransform_soilMoisture.c');
-        mex(mexopts{:},'algorithms\models\TransferNoise\doIRFconvolution.c');
-        
-        movefile('doIRFconvolution.mexw64', 'algorithms\models\TransferNoise\doIRFconvolution.mexw64')
-        movefile('forcingTransform_soilMoisture.mexw64', 'algorithms\models\TransferNoise\ForcingTransformation\forcingTransform_soilMoisture.mexw64')
-
-    else        
-        mex(mexopts{:},'algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c');
-        mex(mexopts{:},'algorithms/models/TransferNoise/doIRFconvolution.c');        
-        
-        movefile('doIRFconvolution.mexa64', 'algorithms/models/TransferNoise')
-        movefile('forcingTransform_soilMoisture.mexa64', 'algorithms/models/TransferNoise/ForcingTransformation')
-    end
-
-    
-end
-
+function Build_C_code()
+
+
+    addpath(genpath([pwd, filesep, 'algorithms']));
+    addpath(genpath([pwd, filesep, 'dataPreparationAnalysis']));
+    addpath(genpath([pwd, filesep, 'Examples']));
+    addpath(genpath([pwd, filesep, 'GUI']));
+    
+    % This function biulds the required C MEX fucntions.
+    % NOTE, b default matlab uses -o2 optimisations.
+    % Chnaging this to -ofast reduces the doIRFconvolution.c
+    % runtime from 0.017534s to 0.010353s on a x64 Linux maxtine (in 2018) (41% faster).
+    %
+    % To change the Linux compile settings open the file ~/.matlab/R2016a/mex_C_glnxa64.xml
+    % and change:
+    %   - 'COPTIMFLAGS="-O -DNDEBUG"' to 'COPTIMFLAGS="-Ofast -DNDEBUG"'
+    %   - 'LDOPTIMFLAGS="-O"' to 'LDOPTIMFLAGS="-Ofast"'
+    %
+    % To chnage Windows Visual studio compier, open the file 
+    % mex_C_win64.xml and chnage: 
+    %  - 'OPTIMFLAGS="/Ofast /Oy- /DNDEBUG"' to 'OPTIMFLAGS="/O2 /Oy- /DNDEBUG"'
+
+    arch=computer('arch');
+    mexopts = {'-O' '-v' ['-' arch]};
+    % 64-bit platform
+    if ~isempty(strfind(computer(),'64'))
+        mexopts(end+1) = {'-largeArrayDims'};
+    end
+
+    % invoke MEX compilation tool
+    if ispc
+        mex(mexopts{:},'algorithms\models\TransferNoise\ForcingTransformation\c');
+        mex(mexopts{:},'algorithms\models\TransferNoise\doIRFconvolution.c');
+        
+        movefile('doIRFconvolution.mexw64', 'algorithms\models\TransferNoise\doIRFconvolution.mexw64')
+        movefile('forcingTransform_soilMoisture.mexw64', 'algorithms\models\TransferNoise\ForcingTransformation\forcingTransform_soilMoisture.mexw64')
+
+    else        
+        mex(mexopts{:},'algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c');
+        mex(mexopts{:},'algorithms/models/TransferNoise/doIRFconvolution.c');        
+        
+        movefile('doIRFconvolution.mexa64', 'algorithms/models/TransferNoise')
+        movefile('forcingTransform_soilMoisture.mexa64', 'algorithms/models/TransferNoise/ForcingTransformation')
+    end
+
+    
+end
+
diff --git a/ChangeLog.txt b/ChangeLog.txt
index e307267..5255ef6 100644
--- a/ChangeLog.txt
+++ b/ChangeLog.txt
@@ -1,179 +1,179 @@
-This file documents changes to HydroSight from version 1.2.6 onwards.
-----------------------------------------------------------------------
-
-13 Feb 2016:
-* doIRFconvolution.c: Simpsons 3/8 integration was fund to produce numerical artefacts at daily simulations. This is due to the switching of the integraion for even and odd number of convolution days. This was overcome by use of the Simpsons extended rule. NOTE: the Xeon Phi verion has NOT yet been recompiled.
-
-20 Feb 2017:
-* HydroSightModel:integrateData(): local search procedure for kriging edit to ensure points both sides of the estimation time point are included in the temporal kriging.
-
-2 Mar 2017:
-* HydroSightModel:integrateData(): Universal kriging equations changed from variogram values to covariances.
-* HydroSightModel:calibrateModel(): residuals converted to single data type only when there are >1 parameter sets.
-
-17 Mar 2017
-* doDataQualityAnalysis(): rate of change analysis edits to look at rate of change forward in time. Originally a bug caused it to look backward.
-
-17 Mar 2017
-* HydroSight_GUI. Version number updated to 1.2.7
-
-22 May 2017
-* Error fixed in estimate of residual noise, sigma_n, for TFN and exp. models. It is now derived using the innovations, not residuals.
-* Outlier detection now uses an estimate of the residual noise that is a function of time i.e the time between prior observation and the point being assesed.
-
-4 August 2017
-* pumpingRate_SAestimation() updated and SP-UCI code and model_TFN.m edited to allow downscaling of infrequent (>daily) groundwater pumping data to daily, weeekly or monthly rates.
-
-16 August 2017
-* Bug fixed with SCE-UI while-loop for invalid params.
-
-18 August 2017
-* responseFunction_Pearsons.getParameters_physicalLimit() upper limit for 'A' reduced to log(1/1000) in an attempt to improve est. of free drainage.
-
-18 August 2017
-* Alpha version of a new calibration results GUI. It now allows the plotting of the forcing data.
-
-24 August 2017
-* Alpha version of a new calibration results GUI extended to handle MCMC outputs and plot model specific outputs eg weighting fucntion plots.
-* Bug fixed - deleting data preparation rows now deletes the actual model object.
-* Boot time significantly increased by removing use of createtable(). Tables can no longer be sorted.
-* Alpha version of a new calibration results GUI edited to work with exponential model ('get' functions for forcing data and derived data moved into HydroSightModel())
-* Alpha version of a new calibration results GUI tabs turn on/off when relevant data is/is not available.
-
-1 September 2017
-* Tool tips added to selected TFN weighting function and derived weighting function option tables.
-* Parameter bounds added to TFN Pearson's weighting function GUI table.
-* climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer() k_sat parameter bounds reduced to reflect sensible soil values.
-* climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer() gamma parameter chnnged to have log10() transform (as had been stated in class description).
-* Bug fixed in pumpingRate_SAestimation.m that sometimes caused errors on last day of forcing.
-* The approach for SPUCI calibration of models with stochastic forcing was changed to update the stochastic forcing only prior to the updating of a complex. The change reduced the calibration time.
-
-15 September 2017
-* Bug fixed in DimEst.m causing a while loop to loop indefinitely.
-* Bug fixed in responseFunction_Pearsons.m plotting.
-* Back transfored parameters returned by climateTransform_soilMoistureModels_v2() and climateTransform_soilMoistureModels_2layer_v2().
-* New classes climateTransform_soilMoistureModels_v2() and climateTransform_soilMoistureModels_2layer_v2() added that constrains the simulated ET.
-* GUI derived parameters plot x-axis label trimmed.
-
-15 March 2018
-* Bug fixed on ODE solver for forcingTransform_soilMoisture.c that is likely to reduce the estimated recharge from models using climateTransform_soilMoistureModels_v2().
-* The calibration GUI now allows setting of most calibrations settings for all methods and prints the command window output to the GUI.
-* 'Calibration setting' column removed from the calibration table.
-* Error fixed in the estimation of effective precip and the runoff rate in climateTransform_soilMoistureModels().
-* All forcing fluxes from calibrated models can be explored and plotted over a user-set time-step and period.
-* Data can now be exported from all results tables by right clicking on the table.
-* The selection of pumping bores for pumpingRate_SAestimation() downscaling is now aided by a 'wizard'. The wizard selects pumping bores within a user set distance of the obs bore.
-* Models using pumpingRate_SAestimation() can now be calibrated with an evaluation period. Note, the evaluation period should have daily metered usage.
-
-29 March 2018
-* Calibration window, SCU tab, extended to allow the input of an upper and lower maximum number of complexes (ngs). This is useful when calibrating many models each having a different number of parameters. 
-* All inputs within the calibration window now allow the input of a empty value. When so done, the default will be used, specifically the seed value will be random. This is useful for calibrating many models each with a different initial seed.
-* Within climateTransform_soilMoistureModels(), and all inheriting soil models, the soil moisture capacity parameter physical limit was reduced from inf to 1000 mm. This was done to improve calibration run time and to constrain the solution to a plausible range.
-* Bug fixed in copying rows within the GUI.
-* Bug fixed in DREAM.m that caused the calibration output text to not be shown in the calibration window.
-* Wait box removed from DREAM.m 
-
-10 April 2018
-* GUI now allows the importing of time-series models (not data-preparation models) from another project. 
-* Calibration on a HPC cluster is now working with the GUI calibration window. Note, offloading calibration to a cluster is only available when HydroSight is run from within Matlab.
-* HPC calibration now supports the SLURM HPC system. 
-
-1 May 2018
-* Bug fixed in pumpingRate_SAestimation() that caused a new downscaled time series to only be applied when an SP-UCI calibration evolution completed. The outcome of this is that the pumping downscaling is now better 'tuned' to the obs. hydrograph.
-* Computational efficiency of pumpingRate_SAestimation.updateStochForcingData() is improved by replacing a for-loop with a vectorised form.
-* climateTransform_soilMoistureModels.setTransformedForcing() is now only undertaken when the soil parameters change. This was undertaken to improve computational efficiency and should not change the results from any model.
-* Calibration scheme SP-UCI tweaked to produce more efficient calibration when stochastic forcing data is being derived (eg from pumpingRate_SAestimation)
-* Bug fixed in HydroSight_GUI() that caused model construction to crash when (somewhow) there are duplicate model labels in the calibration table.
-
-22 May 2018
-* Bug fixed in calibration scheme SP-UCI when calibration when stochastic forcing data. The issue resolved was that after each evolutionary loop the objective function value was not updated using the final stochastic forcing. This caused instability in the calibration.
-* Formatting of GUI tool tips reformatted.
-
-23 May 2018
-* GUI help moved to using that at https://github.com/peterson-tim-j/HydroSight/wiki
-* MEX .C files recompiled for Windows.
-
-22 May 2018
-* Bug fixed in calibration scheme SP-UCI when calibration when stochastic forcing data. The issue resolved was that after the end of each complex, the final stochastic forcing was not returned. This caused the stochastic forcing to not be updated.
-* Calibration GUI now has a progress bar.
-* Calibration GUI now modal ie the user can't switch to other HydroSight windows.
-
-13 Aug 2018
-* Feature added to GUI opetions for pumpingRate_SAestimation() allowing the wizard selected bores to be pushed to the pumping weighting component.
-* Bug fixed in calibration GUI daily data results table. Now the day of the month is correct.
-* Significant impovement in the calibration time of models containing pumpingRate_SAestimation(). 
-
-15 Aug 18
-* Bug fixed in model_TFN GUI for pushing transformed forcing data names to weightimg functions.
-* Dropdown option for multi-model removed becasue this feature is has been delayed.
-
-16 Aug 2018
-* Build_C_code.m added to build mex functions. NOTE, see file for compiler optimisations tips.
-* MEX functions compiled using -0fast and -03 optimisation (Linux/windows respectively)
-* Bug fixed in building models within an otherwise empty project.
-
-23 Aug 2018
-* calibGUI_interface.m added to GIT. This class was added to HydroSight GUI in mid-2018 but unfortunately I forgot to add it to GIT.
-* findjobj() can be VERY slow in linux. This Mathworks File Exchange function was updated to the v1.50.0.0, which is reported as being faster.
-
-4 October 2018
-* Daily integrated ET and drainage results and daily mass balance approximations are now provided by climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer (and V2 versions). This was added because the standard aforementioned fluxes are instantaneous values at the end of each day and are not daily sums. 
-
-12 Nov 2018
-* Bug fixed in data quality analysis algorithm that caused outlier detection analysis to crash.
-* Bug fixed in quality analysis GUI to ensure variable field names can be created from the bore ID.
-* Bugs fixed in appending quality analysis GUI table data.
-* GUI for quality analysis tweaked to allow for time-step of seconds.
-
-23 Nov 2018
-* All plots can be exported to a PNG image file using a new icon within the GUI.
-
-20 Dec 2018
-* Requirement for instalaltion of the Financial Toolbox (for use of year.m, month.m, day.m etc) has been removed by the addition of package specific files year.m, month.m, day.m, minute.m and second.m
-* The default number of standard deviations for the auto-outlier detetection has been increased from 3 to 4.
-
-28 Feb 2019
-* GUI become disfunctional under Windows 10. This was resolved by changing the GUI construction from using https://au.mathworks.com/matlabcentral/fileexchange/27758-gui-layout-toolbox to https://au.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox
-* The code for GUI construction (https://au.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox) has been removed from the package. For those running HydroSight from within Matlab, the user must now install the toolbox from the prior link.
-
-5 April 2019
-* Mex file for Windows OS (forcingTransform_soilMoisture.mexa64) added.
-* Error message for missing above file shortened to be visible within the GUI. 
-* GUI on Windows 10 can be incorrectly built. In an attempt to reslove this, HydroSight.m tries to use the Matlab add-on "GUI Layout Toolbox" but only if installed.
-* Bug fixed. Starting a new project now allows the constrcution table to have >0 rows.
-* Version number updated
-
-16 April 2019
-* Bug fixed in listing TFN weighting input data types. 
-
-9 May 2019
-* Bug fixed in forward and backward data preparation which cases this feature to fail
-* Bug fixed in editing outlier points which caused the plot to no longer be displayed.
-
-29 May 2019
-* Bug fixed that caused the GUI to not open in matlab 2018b+. Importantly, when using 2018b+ the plot zoom etc tools now appear within the plot, not within the toolbar (see GitHub issue #40).
-* Bug fixed that caused the Pearson's fucntion lag time to the peak weighting to be incorrect (see GitHub issue #41).
-* Slider added that allows efficient changing of the GUI table the width (see GitHub issue #42).
-* Bug fixed that caused duplicate site ID names within the coordinates file to cause an unhandled errors (see GitHub issue #43).
-* GUI left hand panels changed to fixed height panels.
-* The function which loads the GUI now reports unhandled errors (Hydrosight.m).
-* The name of the tab 'Date Preparation' has been changed to 'Outlier Removal'
-* Bug fixed in example_TFN_model.m.
-
-30 May 2019
-* Bug fixed in offloading models from RAM to HDD. This feature now works.
-
-19 June 2019
-* Bug fixed in building model_TFN derived weighting model (using the GUI).
-
-11 July 2019
-* The "climateTransform_soilMoistureModels", and the varients for 2 layers and with the constraint on soil ET, have been changed to use 3 
-  subdaily steps when solving the soil moisture differential equations. This was undertaken because estimation of the flux over a day (rather
-  than the instantaneous estimate of the flux) cannot be reliabilty integrated. The substeps now provide 4 values per day, which are 
-  numerically integrated (using trapazoidal rule) to produce a daily flux that is then used with the daily timestep weighting function. 
-  This approach to estimating fluxes produced much lower soil model mass balance errors, but achieving such required a reduction in the ODE 
-  solver error tolerance from 1e-2 to 1e-6. Other relevant change include: (i) removal of the fluxes with the "_integrated" suffix - all are 
-  now integrated; (ii) "interflow" flux has ben removed becuase of complications in estimating the runoff with multiple layers, (iii) the 
-  "effectice precipitation" is now listed as a flux (iv) when the deep layer of a two layer model fills, runoff is produced from  it.
-* Bug fixed in the filtering of flux data by a start and end date.
+This file documents changes to HydroSight from version 1.2.6 onwards.
+----------------------------------------------------------------------
+
+13 Feb 2016:
+* doIRFconvolution.c: Simpsons 3/8 integration was fund to produce numerical artefacts at daily simulations. This is due to the switching of the integraion for even and odd number of convolution days. This was overcome by use of the Simpsons extended rule. NOTE: the Xeon Phi verion has NOT yet been recompiled.
+
+20 Feb 2017:
+* HydroSightModel:integrateData(): local search procedure for kriging edit to ensure points both sides of the estimation time point are included in the temporal kriging.
+
+2 Mar 2017:
+* HydroSightModel:integrateData(): Universal kriging equations changed from variogram values to covariances.
+* HydroSightModel:calibrateModel(): residuals converted to single data type only when there are >1 parameter sets.
+
+17 Mar 2017
+* doDataQualityAnalysis(): rate of change analysis edits to look at rate of change forward in time. Originally a bug caused it to look backward.
+
+17 Mar 2017
+* HydroSight_GUI. Version number updated to 1.2.7
+
+22 May 2017
+* Error fixed in estimate of residual noise, sigma_n, for TFN and exp. models. It is now derived using the innovations, not residuals.
+* Outlier detection now uses an estimate of the residual noise that is a function of time i.e the time between prior observation and the point being assesed.
+
+4 August 2017
+* pumpingRate_SAestimation() updated and SP-UCI code and model_TFN.m edited to allow downscaling of infrequent (>daily) groundwater pumping data to daily, weeekly or monthly rates.
+
+16 August 2017
+* Bug fixed with SCE-UI while-loop for invalid params.
+
+18 August 2017
+* responseFunction_Pearsons.getParameters_physicalLimit() upper limit for 'A' reduced to log(1/1000) in an attempt to improve est. of free drainage.
+
+18 August 2017
+* Alpha version of a new calibration results GUI. It now allows the plotting of the forcing data.
+
+24 August 2017
+* Alpha version of a new calibration results GUI extended to handle MCMC outputs and plot model specific outputs eg weighting fucntion plots.
+* Bug fixed - deleting data preparation rows now deletes the actual model object.
+* Boot time significantly increased by removing use of createtable(). Tables can no longer be sorted.
+* Alpha version of a new calibration results GUI edited to work with exponential model ('get' functions for forcing data and derived data moved into HydroSightModel())
+* Alpha version of a new calibration results GUI tabs turn on/off when relevant data is/is not available.
+
+1 September 2017
+* Tool tips added to selected TFN weighting function and derived weighting function option tables.
+* Parameter bounds added to TFN Pearson's weighting function GUI table.
+* climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer() k_sat parameter bounds reduced to reflect sensible soil values.
+* climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer() gamma parameter chnnged to have log10() transform (as had been stated in class description).
+* Bug fixed in pumpingRate_SAestimation.m that sometimes caused errors on last day of forcing.
+* The approach for SPUCI calibration of models with stochastic forcing was changed to update the stochastic forcing only prior to the updating of a complex. The change reduced the calibration time.
+
+15 September 2017
+* Bug fixed in DimEst.m causing a while loop to loop indefinitely.
+* Bug fixed in responseFunction_Pearsons.m plotting.
+* Back transfored parameters returned by climateTransform_soilMoistureModels_v2() and climateTransform_soilMoistureModels_2layer_v2().
+* New classes climateTransform_soilMoistureModels_v2() and climateTransform_soilMoistureModels_2layer_v2() added that constrains the simulated ET.
+* GUI derived parameters plot x-axis label trimmed.
+
+15 March 2018
+* Bug fixed on ODE solver for forcingTransform_soilMoisture.c that is likely to reduce the estimated recharge from models using climateTransform_soilMoistureModels_v2().
+* The calibration GUI now allows setting of most calibrations settings for all methods and prints the command window output to the GUI.
+* 'Calibration setting' column removed from the calibration table.
+* Error fixed in the estimation of effective precip and the runoff rate in climateTransform_soilMoistureModels().
+* All forcing fluxes from calibrated models can be explored and plotted over a user-set time-step and period.
+* Data can now be exported from all results tables by right clicking on the table.
+* The selection of pumping bores for pumpingRate_SAestimation() downscaling is now aided by a 'wizard'. The wizard selects pumping bores within a user set distance of the obs bore.
+* Models using pumpingRate_SAestimation() can now be calibrated with an evaluation period. Note, the evaluation period should have daily metered usage.
+
+29 March 2018
+* Calibration window, SCU tab, extended to allow the input of an upper and lower maximum number of complexes (ngs). This is useful when calibrating many models each having a different number of parameters. 
+* All inputs within the calibration window now allow the input of a empty value. When so done, the default will be used, specifically the seed value will be random. This is useful for calibrating many models each with a different initial seed.
+* Within climateTransform_soilMoistureModels(), and all inheriting soil models, the soil moisture capacity parameter physical limit was reduced from inf to 1000 mm. This was done to improve calibration run time and to constrain the solution to a plausible range.
+* Bug fixed in copying rows within the GUI.
+* Bug fixed in DREAM.m that caused the calibration output text to not be shown in the calibration window.
+* Wait box removed from DREAM.m 
+
+10 April 2018
+* GUI now allows the importing of time-series models (not data-preparation models) from another project. 
+* Calibration on a HPC cluster is now working with the GUI calibration window. Note, offloading calibration to a cluster is only available when HydroSight is run from within Matlab.
+* HPC calibration now supports the SLURM HPC system. 
+
+1 May 2018
+* Bug fixed in pumpingRate_SAestimation() that caused a new downscaled time series to only be applied when an SP-UCI calibration evolution completed. The outcome of this is that the pumping downscaling is now better 'tuned' to the obs. hydrograph.
+* Computational efficiency of pumpingRate_SAestimation.updateStochForcingData() is improved by replacing a for-loop with a vectorised form.
+* climateTransform_soilMoistureModels.setTransformedForcing() is now only undertaken when the soil parameters change. This was undertaken to improve computational efficiency and should not change the results from any model.
+* Calibration scheme SP-UCI tweaked to produce more efficient calibration when stochastic forcing data is being derived (eg from pumpingRate_SAestimation)
+* Bug fixed in HydroSight_GUI() that caused model construction to crash when (somewhow) there are duplicate model labels in the calibration table.
+
+22 May 2018
+* Bug fixed in calibration scheme SP-UCI when calibration when stochastic forcing data. The issue resolved was that after each evolutionary loop the objective function value was not updated using the final stochastic forcing. This caused instability in the calibration.
+* Formatting of GUI tool tips reformatted.
+
+23 May 2018
+* GUI help moved to using that at https://github.com/peterson-tim-j/HydroSight/wiki
+* MEX .C files recompiled for Windows.
+
+22 May 2018
+* Bug fixed in calibration scheme SP-UCI when calibration when stochastic forcing data. The issue resolved was that after the end of each complex, the final stochastic forcing was not returned. This caused the stochastic forcing to not be updated.
+* Calibration GUI now has a progress bar.
+* Calibration GUI now modal ie the user can't switch to other HydroSight windows.
+
+13 Aug 2018
+* Feature added to GUI opetions for pumpingRate_SAestimation() allowing the wizard selected bores to be pushed to the pumping weighting component.
+* Bug fixed in calibration GUI daily data results table. Now the day of the month is correct.
+* Significant impovement in the calibration time of models containing pumpingRate_SAestimation(). 
+
+15 Aug 18
+* Bug fixed in model_TFN GUI for pushing transformed forcing data names to weightimg functions.
+* Dropdown option for multi-model removed becasue this feature is has been delayed.
+
+16 Aug 2018
+* Build_C_code.m added to build mex functions. NOTE, see file for compiler optimisations tips.
+* MEX functions compiled using -0fast and -03 optimisation (Linux/windows respectively)
+* Bug fixed in building models within an otherwise empty project.
+
+23 Aug 2018
+* calibGUI_interface.m added to GIT. This class was added to HydroSight GUI in mid-2018 but unfortunately I forgot to add it to GIT.
+* findjobj() can be VERY slow in linux. This Mathworks File Exchange function was updated to the v1.50.0.0, which is reported as being faster.
+
+4 October 2018
+* Daily integrated ET and drainage results and daily mass balance approximations are now provided by climateTransform_soilMoistureModels() and climateTransform_soilMoistureModels_2layer (and V2 versions). This was added because the standard aforementioned fluxes are instantaneous values at the end of each day and are not daily sums. 
+
+12 Nov 2018
+* Bug fixed in data quality analysis algorithm that caused outlier detection analysis to crash.
+* Bug fixed in quality analysis GUI to ensure variable field names can be created from the bore ID.
+* Bugs fixed in appending quality analysis GUI table data.
+* GUI for quality analysis tweaked to allow for time-step of seconds.
+
+23 Nov 2018
+* All plots can be exported to a PNG image file using a new icon within the GUI.
+
+20 Dec 2018
+* Requirement for instalaltion of the Financial Toolbox (for use of year.m, month.m, day.m etc) has been removed by the addition of package specific files year.m, month.m, day.m, minute.m and second.m
+* The default number of standard deviations for the auto-outlier detetection has been increased from 3 to 4.
+
+28 Feb 2019
+* GUI become disfunctional under Windows 10. This was resolved by changing the GUI construction from using https://au.mathworks.com/matlabcentral/fileexchange/27758-gui-layout-toolbox to https://au.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox
+* The code for GUI construction (https://au.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox) has been removed from the package. For those running HydroSight from within Matlab, the user must now install the toolbox from the prior link.
+
+5 April 2019
+* Mex file for Windows OS (forcingTransform_soilMoisture.mexa64) added.
+* Error message for missing above file shortened to be visible within the GUI. 
+* GUI on Windows 10 can be incorrectly built. In an attempt to reslove this, HydroSight.m tries to use the Matlab add-on "GUI Layout Toolbox" but only if installed.
+* Bug fixed. Starting a new project now allows the constrcution table to have >0 rows.
+* Version number updated
+
+16 April 2019
+* Bug fixed in listing TFN weighting input data types. 
+
+9 May 2019
+* Bug fixed in forward and backward data preparation which cases this feature to fail
+* Bug fixed in editing outlier points which caused the plot to no longer be displayed.
+
+29 May 2019
+* Bug fixed that caused the GUI to not open in matlab 2018b+. Importantly, when using 2018b+ the plot zoom etc tools now appear within the plot, not within the toolbar (see GitHub issue #40).
+* Bug fixed that caused the Pearson's fucntion lag time to the peak weighting to be incorrect (see GitHub issue #41).
+* Slider added that allows efficient changing of the GUI table the width (see GitHub issue #42).
+* Bug fixed that caused duplicate site ID names within the coordinates file to cause an unhandled errors (see GitHub issue #43).
+* GUI left hand panels changed to fixed height panels.
+* The function which loads the GUI now reports unhandled errors (Hydrosight.m).
+* The name of the tab 'Date Preparation' has been changed to 'Outlier Removal'
+* Bug fixed in example_TFN_model.m.
+
+30 May 2019
+* Bug fixed in offloading models from RAM to HDD. This feature now works.
+
+19 June 2019
+* Bug fixed in building model_TFN derived weighting model (using the GUI).
+
+11 July 2019
+* The "climateTransform_soilMoistureModels", and the varients for 2 layers and with the constraint on soil ET, have been changed to use 3 
+  subdaily steps when solving the soil moisture differential equations. This was undertaken because estimation of the flux over a day (rather
+  than the instantaneous estimate of the flux) cannot be reliabilty integrated. The substeps now provide 4 values per day, which are 
+  numerically integrated (using trapazoidal rule) to produce a daily flux that is then used with the daily timestep weighting function. 
+  This approach to estimating fluxes produced much lower soil model mass balance errors, but achieving such required a reduction in the ODE 
+  solver error tolerance from 1e-2 to 1e-6. Other relevant change include: (i) removal of the fluxes with the "_integrated" suffix - all are 
+  now integrated; (ii) "interflow" flux has ben removed becuase of complications in estimating the runoff with multiple layers, (iii) the 
+  "effectice precipitation" is now listed as a flux (iv) when the deep layer of a two layer model fills, runoff is produced from  it.
+* Bug fixed in the filtering of flux data by a start and end date.
 * Model examples re-run to reflect changes to the soil moisture model.
\ No newline at end of file
diff --git a/GUI/HydroSight_GUI.m b/GUI/HydroSight_GUI.m
index 24ec8e3..e8c6e60 100644
--- a/GUI/HydroSight_GUI.m
+++ b/GUI/HydroSight_GUI.m
@@ -1,9191 +1,9191 @@
-classdef HydroSight_GUI < handle  
-    %HydroSight_GUI Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    %class properties - access is private so nothing else can access these
-    %variables. Useful in different sitionations
-    properties
-        % Version number
-        versionNumber = '1.3.5';
-        versionDate= '11 September 2019';
-        
-        % Model types supported
-        %modelTypes = {'model_TFN','model_TFN_LOA', 'ExpSmooth'};
-        modelTypes = {'model_TFN', 'ExpSmooth'};
-        
-        % GUI properies for the overall figure.
-        FigureSplash
-        Figure;        
-        figure_Menu
-        figure_contextMenu
-        figure_examples
-        figure_Help
-        figure_Layout 
-        
-        % GUI properties for the individual tabs.
-        tab_Project
-        tab_DataPrep;
-        tab_ModelConstruction;
-        tab_ModelCalibration;        
-        tab_ModelSimulation;
-        
-        % Store model data
-        models=[];
-        
-        % Store a record of the model lables (as row names in a tavle) and
-        % if the models are calibrated.
-        model_labels=[];
-        
-        % Store the data preparation analysis results;
-        dataPrep
-        
-        % Copies of data
-        copiedData = {};
-        
-        % Setting for off loading calibration to HPC cluster
-        HPCoffload = {};
-        
-        % File name for the current set of models
-        project_fileName = '';
-        
-        % Are the models to be stored on HDD
-        modelsOnHDD = '';
-    end
-    
-    % Events
-    events
-       quitModelCalibration 
-    end
-    
-    methods
-        
-        function this = HydroSight_GUI
-            
-%             % Check the toolbox for GUIs exists
-%             if ~isdeployed && isempty(ver('layout'))
-%                 msgbox({'The following toolbox file must be installed within Matlab to use.', ...
-%                     'HydroSight. Please download and install it and then re-start', ...
-%                     'HydroSight. Also, a web browser will now open at the toolbox site.', ...
-%                     '', ...
-%                     'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox'}, ...
-%                 'Toolbox missing: gui-layout-toolbox', 'error');
-%             
-%                 web( 'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox' ) 
-%                 
-%                 return
-%             end
-
-            
-            %--------------------------------------------------------------
-            % Open a window and add some menus
-            this.Figure = figure( ...
-                'Name', 'HydroSight', ...
-                'NumberTitle', 'off', ...
-                'MenuBar', 'none', ...
-                'HandleVisibility', 'off', ...
-                'Visible','off', ...
-                'Toolbar','figure', ...
-                'CloseRequestFcn',@this.onExit); 
-            
-            % Show splash (if code not deployed)
-            if ~isdeployed
-               onAbout(this, [],[]);
-            end
-            
-            % Set window Size
-            windowHeight = this.Figure.Parent.ScreenSize(4);
-            windowWidth = this.Figure.Parent.ScreenSize(3);
-            figWidth = 0.8*windowWidth;
-            figHeight = 0.6*windowHeight;            
-            this.Figure.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];
-            %this.Figure.Visible = 'off';
-            
-            
-            % Set default panel color
-            %warning('off');
-            %uiextras.set( this.Figure, 'DefaultBoxPanelTitleColor', [0.7 1.0 0.7] );
-            %warning('on');
-            
-            % + File menu
-            this.figure_Menu = uimenu( this.Figure, 'Label', 'File' );
-            uimenu( this.figure_Menu, 'Label', 'New Project', 'Callback', @this.onNew);
-            uimenu( this.figure_Menu, 'Label', 'Set Project Folder ...', 'Callback', @this.onSetProjectFolder);
-            uimenu( this.figure_Menu, 'Label', 'Open Project...', 'Callback', @this.onOpen);
-            uimenu( this.figure_Menu, 'Label', 'Import Model(s) ...', 'Callback', @this.onImportModel);
-            uimenu( this.figure_Menu, 'Label', 'Save Project as ...', 'Callback', @this.onSaveAs );
-            uimenu( this.figure_Menu, 'Label', 'Save Project', 'Callback', @this.onSave,'Enable','off');
-            uimenu( this.figure_Menu, 'Label', 'Move models from RAM to HDD...', 'Callback', @this.onMoveModels,'Separator','on', 'Enable','off');
-            uimenu( this.figure_Menu, 'Label', 'Exit', 'Callback', @this.onExit,'Separator','on' );
-
-            % + Examples menu
-            this.figure_examples = uimenu( this.Figure, 'Label', 'Examples' );
-            uimenu( this.figure_examples, 'Label', 'TFN model - Landuse change', 'Tag','TFN - LUC','Callback', @this.onExamples );
-            uimenu( this.figure_examples, 'Label', 'TFN model - Pumping and climate', 'Tag','TFN - Pumping','Callback', @this.onExamples );
-
-            % + Help menu
-            this.figure_Help = uimenu( this.Figure, 'Label', 'Help' );
-            uimenu(this.figure_Help, 'Label', 'Overview', 'Tag','doc_Overview','Callback', @this.onDocumentation);
-            uimenu(this.figure_Help, 'Label', 'User Interface', 'Tag','doc_GUI','Callback', @this.onDocumentation);
-            uimenu(this.figure_Help, 'Label', 'Data requirements', 'Tag','doc_data_req','Callback', @this.onDocumentation);            
-            uimenu(this.figure_Help, 'Label', 'Time-series algorithms', 'Tag','doc_timeseries_algorithms','Callback', @this.onDocumentation);                
-            uimenu(this.figure_Help, 'Label', 'Tutorials', 'Tag','doc_tutes','Callback', @this.onDocumentation);                            
-            uimenu(this.figure_Help, 'Label', 'Calibration Fundementals','Tag','doc_Calibration','Callback', @this.onDocumentation);            
-            uimenu(this.figure_Help, 'Label', 'Support', 'Tag','doc_Support','Callback', @this.onDocumentation);                            
-            uimenu(this.figure_Help, 'Label', 'Publications', 'Tag','doc_Publications','Callback', @this.onDocumentation);                
-            
-            uimenu(this.figure_Help, 'Label', 'Check for updates at GitHub', 'Tag','doc_GitHubUpdate','Callback', @this.onGitHub,'Separator','on');
-            uimenu(this.figure_Help, 'Label', 'Submit bug report to GitHub', 'Tag','doc_GitHubIssue','Callback', @this.onGitHub);
-            
-            
-            uimenu(this.figure_Help, 'Label', 'License and Disclaimer', 'Tag','doc_Publications','Callback', @this.onLicenseDisclaimer,'Separator','on');
-            uimenu(this.figure_Help, 'Label', 'Version', 'Tag','doc_Version','Callback', @this.onVersion);
-            uimenu(this.figure_Help, 'Label', 'About', 'Callback', @this.onAbout );
-                        
-            % Get toolbar object
-            hToolbar = findall(this.Figure,'tag','FigureToolBar');
-            
-            % Hide toolbar button not used (2018B prior and after)
-            hToolbutton = findall(hToolbar,'tag','Annotation.InsertLegend');            
-            hToolbutton.Visible = 'off';
-            hToolbutton.UserData = 'Never';
-            hToolbutton.Separator = 'off';
-            hToolbutton = findall(hToolbar,'tag','Annotation.InsertColorbar');            
-            hToolbutton.Visible = 'off';
-            hToolbutton.UserData = 'Never';
-            hToolbutton.Separator = 'off';
-            hToolbutton = findall(hToolbar,'tag','DataManager.Linking');            
-            hToolbutton.Visible = 'off';
-            hToolbutton.UserData = 'Never';	
-            hToolbutton.Separator = 'off';      
-            hToolbutton = findall(hToolbar,'tag','Standard.EditPlot');            
-            hToolbutton.Visible = 'off';
-            hToolbutton.UserData = 'Plot';
-            hToolbutton.Separator = 'off';            
-            
-            % Redefine print button
-            hToolbutton = findall(hToolbar,'tag','Standard.PrintFigure');            
-            set(hToolbutton, 'ClickedCallback',@this.onPrint, 'TooltipString','Open the print preview window for the displayed plot ...');
-            hToolbutton.Visible = 'off';
-            hToolbutton.UserData = 'Plot';
-            hToolbutton.Separator = 'off';
-            icon = 'implay_export.gif';
-            [cdata,map] = imread(icon);
-            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
-            cdata = ind2rgb(cdata,map);
-            
-            
-            % Check if version is 2018b or later. From this point the
-            % plot toolbar buttons moved into the plot.
-            v=version();
-            isBefore2018b = str2double(v(1:3))<9.5;           
-            
-            % Hide property inspector
-            try 
-                hToolbutton = findall(hToolbar,'tag','Standard.OpenInspector');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';            
-            catch
-                % do nothing
-            end
-                
-            
-            % Add tool bar          
-            if isBefore2018b
-                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOn');                        
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';	
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOff');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Never';	
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.Brushing');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.DataCursor');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.Rotate');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Never';
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.Pan');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomOut');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomIn');            
-                hToolbutton.Visible = 'off';
-                hToolbutton.UserData = 'Plot';
-                hToolbutton.Separator = 'off';
-                uipushtool(hToolbar,'cdata',cdata, 'tooltip','Export displayed plot to PNG file ...', ...
-                    'ClickedCallback',@this.onExportPlot, ...
-                    'tag','Export.plot', 'Visible','off');
-            end
-
-            hToolbutton = findall(hToolbar,'tag','Standard.NewFigure');            
-            hToolbutton.Visible = 'on';
-            hToolbutton.UserData = 'Always';	                            
-            set(hToolbutton, 'ClickedCallback',@this.onNew, 'TooltipString','Start a new project.');            
-            
-            hToolbutton = findall(hToolbar,'tag','Standard.FileOpen');            
-            hToolbutton.Visible = 'on';
-            hToolbutton.UserData = 'Always';	                            
-            set(hToolbutton, 'ClickedCallback',@this.onOpen, 'TooltipString','Open a new project ...');
-
-            hToolbutton = findall(hToolbar,'tag','Standard.SaveFigure');            
-            hToolbutton.Visible = 'on';
-            hToolbutton.UserData = 'Always';	                            
-            set(hToolbutton, 'ClickedCallback',@this.onSave, 'TooltipString','Save current project ...');            
-            
-            % Get hidden state and show hidden children
-            oldState = get(0,'ShowHiddenHandles');
-            set(0,'ShowHiddenHandles','on')
-            
-            % Add new button for 'st folder and shift new button to the far left.
-            icon = 'foldericon.gif';
-            [cdata,map] = imread(icon);
-            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
-            cdata = ind2rgb(cdata,map);
-            uipushtool(hToolbar,'cdata',cdata, 'tooltip','Set the project folder ...','Tag','Standard.SetFolder','ClickedCallback',@this.onSetProjectFolder);
-            hToolbutton = findall(hToolbar);            
-            set(hToolbar,'Children',hToolbutton([3:length(hToolbutton), 2]));            
-                                    
-            % Add new button for help.
-            icon = 'helpicon.gif';
-            [cdata,map] = imread(icon);
-            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
-            cdata = ind2rgb(cdata,map);
-            uipushtool(hToolbar,'cdata',cdata, 'tooltip','Open help for the current tab ...', 'ClickedCallback',@this.onDocumentation);
-            
-            % Add separator.
-            if isBefore2018b
-                hToolbar.Children(13).Separator = 'on';
-            end
-            
-            % Reset hidden state
-            set(0,'ShowHiddenHandles',oldState);
-            
-            %Create Panels for different windows       
-            this.figure_Layout = uiextras.TabPanel( 'Parent', this.Figure, 'Padding', ...
-                5, 'TabSize',127,'FontSize',8);
-            this.tab_Project.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ProjectDescription');            
-            this.tab_DataPrep.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','DataPreparation');
-            this.tab_ModelConstruction.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelConstruction');
-            this.tab_ModelCalibration.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelCalibration');
-            this.tab_ModelSimulation.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelSimulation');
-            this.figure_Layout.TabNames = {'Project Description', 'Outlier Removal','Model Construction', 'Model Calibration','Model Simulation'};
-            this.figure_Layout.SelectedChild = 1;
-           
-%%          Layout Tab1 - Project description
-            %------------------------------------------------------------------
-            % Project title
-            hbox1t1 = uiextras.VBoxFlex('Parent', this.tab_Project.Panel,'Padding', 3, 'Spacing', 3);
-            uicontrol(hbox1t1,'Style','text','String','Project Title: ','HorizontalAlignment','left', 'Units','normalized');            
-            this.tab_Project.project_name = uicontrol(hbox1t1,'Style','edit','HorizontalAlignment','left', 'Units','normalized',...
-                'TooltipString','Input a project title. This is an optional input to assist project management.');            
-            
-            % Empty row spacer
-            uicontrol(hbox1t1,'Style','text','String','','Units','normalized');                      
-                        
-            % Project description
-            uicontrol(hbox1t1,'Style','text','String','Project Description: ','HorizontalAlignment','left', 'Units','normalized');                      
-            this.tab_Project.project_description = uicontrol(hbox1t1,'Style','edit','HorizontalAlignment','left', 'Units','normalized', ...
-                'Min',1,'Max',100,'TooltipString','Input an extended project description. This is an optional input to assist project management.');            
-            
-            % Set sizes
-            set(hbox1t1, 'Sizes', [20 20 20 20 -1]);            
-            
-            
-
-%%          Layout Tab2 - Data Preparation
-            % -----------------------------------------------------------------
-            % Declare panels        
-            hbox1t2 = uiextras.HBoxFlex('Parent', this.tab_DataPrep.Panel,'Padding', 3, 'Spacing', 3,'Tag','Outlier detection outer hbox');
-            vbox1t2 = uiextras.VBox('Parent',hbox1t2,'Padding', 3, 'Spacing', 3);
-            %hbox3t2 = uiextras.HButtonBox('Parent',vbox1t2,'Padding', 3, 'Spacing', 3);                
-            hbox3t2 = uiextras.HBox('Parent',vbox1t2,'Padding', 3, 'Spacing', 3);
-            hboxBtn1 = uiextras.HButtonBox('Parent',hbox3t2 ,'Padding', 3, 'Spacing', 3);             
-            hboxBtn2 = uiextras.HButtonBox('Parent',hbox3t2 ,'Padding', 3, 'Spacing', 3);              
-            
-            % Create table for model construction
-            cnames1t2 ={'<html><center>Select<br />Bore</center></html>', ...                                                 
-                        '<html><center>Obs. Head<br />File</center></html>', ...   
-                        '<html><center>Bore<br />ID</center></html>', ...   
-                        '<html><center>Bore Depth<br />(Below Surface)</center></html>', ...   
-                        '<html><center>Surface<br />Elevation</center></html>', ...   
-                        '<html><center>Casing Length<br />(above surface)</center></html>', ...   
-                        '<html><center>Construction<br />Date (dd/mm/yy)</center></html>', ...   
-                        '<html><center>Check<br />Start Date?</center></html>', ...                           
-                        '<html><center>Check<br />End Date?</center></html>', ...                                                   
-                        '<html><center>Check Above<br />Bore Depth?</center></html>', ...                      
-                        '<html><center>Check Below <br />Casing?</center></html>', ...                      
-                        '<html><center>Threshold for Max. Daily abs(Head)<br />Change</center></html>', ...
-                        '<html><center>Threshold Duration for<br />Constant Head (days)?</center></html>', ...
-                        '<html><center>Auto-Outlier<br />Num. St. dev?</center></html>', ...                        
-                        '<html><center>Auto-Outlier foward<br />backward analysis?</center></html>', ...                        
-                        '<html><center>Analysis<br />Status</center></html>', ...
-                        '<html><center>No. Erroneous<br />Obs.</center></html>', ...
-                        '<html><center>No. Outlier<br />Obs.</center></html>', ...
-                        };
-            cformats1t2 = {'logical', 'char', 'char','numeric','numeric','numeric','char','logical','logical','logical','logical','numeric','numeric','numeric','logical','char','char','char'};
-            cedit1t2 = logical([1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0]);            
-            rnames1t2 = {[1]};
-            toolTipStr = ['<html>Optional detection and removal<br>' ...
-                  'of erroneous groundwater level observations.</ul>'];
-            
-            % Initialise data.
-            data = {false, '', '',0, 0, 0, '01/01/1900',true, true, true, true, 10, 120, 4, 1,...
-                '<html><font color = "#FF0000">Bore not analysed.</font></html>', ...
-                ['<html><font color = "#808080">','(NA)','</font></html>'], ...
-                ['<html><font color = "#808080">','(NA)','</font></html>']};
-            
-            % Add table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.            
-%             this.tab_DataPrep.Table = createTable(vbox1t2,cnames1t2,data, false, ...
-%                 'ColumnEditable',cedit1t2,'ColumnFormat',cformats1t2,'RowName', rnames1t2, ...
-%                 'CellSelectionCallback', @this.dataPrep_tableSelection,...
-%                 'Tag','Data Preparation', 'TooltipString', toolTipStr);     
-
-            this.tab_DataPrep.Table = uitable(vbox1t2 , 'ColumnName', cnames1t2,'Data', data, ...
-                'ColumnEditable',cedit1t2,'ColumnFormat',cformats1t2,'RowName', rnames1t2, ...
-                'CellSelectionCallback', @this.dataPrep_tableSelection,...
-                'Tag','Data Preparation', 'TooltipString', toolTipStr);
-                        
-%             % Find java sorting object in table
-%             try
-%                 this.figure_Layout.Selection = 2;
-%                 drawnow update;
-%                 jscrollpane = findjobj(this.tab_DataPrep.Table);
-%                 jtable = jscrollpane.getViewport.getView;
-% 
-%                 % Turn the JIDE sorting on
-%                 jtable.setSortable(true);
-%                 jtable.setAutoResort(true);
-%                 jtable.setMultiColumnSortable(true);
-%                 jtable.setPreserveSelectionsAfterSorting(true);            
-%             catch
-%                 warndlg('Creating of the GUI row-sorting module failed for the data preparation table.');
-%             end            
-                        
-            % Add buttons to top left panel               
-            uicontrol('Parent',hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Data Preparation', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Use this feature to efficiently analyse a large number of bores.') );
-            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Data Preparation', 'TooltipString', sprintf('Export a .csv file of the table below.') );
-            uicontrol('Parent',hboxBtn2,'String','Analyse Selected Bores','Callback', @this.onAnalyseBores, 'Tag','Data Preparation', 'TooltipString', sprintf('Use the tick-box below to select the models to analyse then click here. \n After analysing, the status is given in the right most column.') );            
-            uicontrol('Parent',hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Data Preparation', 'TooltipString', sprintf('Export a .csv file of the analyses results. \n After analysing, the .csv file can be used in the time-series modelling.') );            
-            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
-                'Callback', {@this.onChangeTableWidth, 'Outlier detection outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
-            hboxBtn1.ButtonSize(1) = 225;
-            hboxBtn2.ButtonSize(1) = 225;
-            set(hbox3t2,'Sizes',[90 -1])  
-            
-            % Create vbox for the various model options
-            this.tab_DataPrep.modelOptions.vbox = uiextras.VBoxFlex('Parent',hbox1t2,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
-            
-            % Add model options panel for bore IDs
-            dynList = [];
-            vbox4t2 = uiextras.VBox('Parent',this.tab_DataPrep.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
-            uicontrol( 'Parent', vbox4t2,'Style','text','String',sprintf('%s\n%s%s','Please select the Bore ID for the analysis:'), 'Units','normalized');            
-            this.tab_DataPrep.modelOptions.boreIDList = uicontrol('Parent',vbox4t2,'Style','list','BackgroundColor','w', ...
-                'String',dynList(:),'Value',1,'Callback',...
-                @this.dataPrep_optionsSelection, 'Units','normalized');     
-            
-             % Create vbox for showing a table of results and plotting hydrographs
-            this.tab_DataPrep.modelOptions.resultsOptions.box = uiextras.VBoxFlex('Parent', this.tab_DataPrep.modelOptions.vbox,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
-            panelt2 = uipanel('Parent',this.tab_DataPrep.modelOptions.resultsOptions.box);
-            this.tab_DataPrep.modelOptions.resultsOptions.plots = axes( 'Parent', panelt2); 
-
-            % Add table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.            
-%             this.tab_DataPrep.modelOptions.resultsOptions.table = createTable(this.tab_DataPrep.modelOptions.resultsOptions.box, ...
-%                 {'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'}, ... 
-%                 cell(0,13), false, ...
-%                 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','logical','logical','logical','logical','logical','logical'}, ...
-%                 'ColumnEditable', [false(1,6) true(1,7)], ...
-%                 'Tag','Data Preparation - results table', ...
-%                 'CellEditCallback', @this.dataPrep_resultsTableEdit, ...,
-%                 'TooltipString', 'Results data from the bore data analysis for erroneous observations and outliers.');   
-%             
-%             
-            this.tab_DataPrep.modelOptions.resultsOptions.table = uitable(this.tab_DataPrep.modelOptions.resultsOptions.box, ...
-                'ColumnName',{'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'}, ... 
-                'Data',cell(0,13), ...
-                'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','logical','logical','logical','logical','logical','logical'}, ...
-                'ColumnEditable', [false(1,6) true(1,7)], ...
-                'Tag','Data Preparation - results table', ...
-                'CellEditCallback', @this.dataPrep_resultsTableEdit, ...,
-                'TooltipString', 'Results data from the bore data analysis for erroneous observations and outliers.');   
-                        
-            
-            % Resize the panels            
-            set(vbox1t2, 'Sizes', [30 -1]);
-            set(vbox4t2, 'Sizes', [30 -1]);            
-            set(this.tab_DataPrep.modelOptions.resultsOptions.box, 'Sizes',[-1 -1]);
-            set(this.tab_DataPrep.modelOptions.vbox, 'Sizes',[0 0]);
-            
-            
-            % Hide the panel for the analysis opens and results
-            set(this.tab_DataPrep.modelOptions.vbox,'Heights',[0 0]);
-            
-%           Add context menu
-            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
-            
-            % Add items
-            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
-            
-            % Attach menu to the construction table
-            set(this.tab_DataPrep.Table,'UIContextMenu',this.Figure.UIContextMenu);
-                        
-            % Add table name to .UserData
-            set(this.tab_DataPrep.Table.UIContextMenu,'UserData','this.tab_DataPrep.Table');            
-            
-%%          Layout Tab3 - Model Construction
-            %------------------------------------------------------------------
-            % Declare panels        
-            hbox1t3 = uiextras.HBoxFlex('Parent', this.tab_ModelConstruction.Panel,'Padding', 3, 'Spacing', 3, 'Tag', 'Model Construction outer hbox');
-            vbox1t3 = uiextras.VBox('Parent',hbox1t3,'Padding', 3, 'Spacing', 3);
-            hbox1t4 = uiextras.HBox('Parent',vbox1t3,'Padding', 3, 'Spacing', 3);
-            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t4 ,'Padding', 3, 'Spacing', 3);             
-            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t4 ,'Padding', 3, 'Spacing', 3);             
-            
-            % Create table for model construction
-            cnames1t3 ={'<html><center>Select<br />Model</center></html>', ... 
-                        '<html><center>Model<br />Label</center></html>', ...   
-                        '<html><center>Obs. Head<br />File</center></html>', ...   
-                        '<html><center>Forcing Data<br />File</center></html>', ...   
-                        '<html><center>Coordinates<br />File</center></html>', ...   
-                        '<html><center>Bore<br />ID</center></html>', ...   
-                        '<html><center>Model<br />Type</center></html>', ...                           
-                        '<html><center>Model<br />Options</center></html>', ...                           
-                        '<html><center>Build<br />Status</center></html>'};
-            cformats1t3 = {'logical', 'char', 'char','char','char','char',this.modelTypes,'char','char'};
-            cedit1t3 = logical([1 1 1 1 1 1 1 1 0]);            
-            rnames1t3 = {[1]};
-            toolTipStr = 'Define the model label, input data, bore ID and model structure for each model.';
-            
-            
-            % Initialise data
-            data = cell(1,9);
-            data{1,9} = '<html><font color = "#FF0000">Model not built.</font></html>';
-
-            % Add table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.
-%             this.tab_ModelConstruction.Table = createTable(vbox1t3,cnames1t3,data, false, ...
-%                 'ColumnEditable',cedit1t3,'ColumnFormat',cformats1t3,'RowName', rnames1t3, ...
-%                 'CellSelectionCallback', @this.modelConstruction_tableSelection,...
-%                 'CellEditCallback', @this.modelConstruction_tableEdit,...
-%                 'Tag','Model Construction', ...
-%                 'TooltipString', toolTipStr);                        
-%             
-            this.tab_ModelConstruction.Table = uitable(vbox1t3,'ColumnName',cnames1t3,'Data',data,  ...
-                'ColumnEditable',cedit1t3,'ColumnFormat',cformats1t3,'RowName', rnames1t3, ...
-                'CellSelectionCallback', @this.modelConstruction_tableSelection,...
-                'CellEditCallback', @this.modelConstruction_tableEdit,...
-                'Tag','Model Construction', ...
-                'TooltipString', toolTipStr);                        
-                        
-            
-%             % Find java sorting object in table
-%             try
-%                 this.figure_Layout.Selection = 3;
-%                 drawnow update;
-%                 jscrollpane = findjobj(this.tab_ModelConstruction.Table);
-%                 jtable = jscrollpane.getViewport.getView;
-% 
-%                 % Turn the JIDE sorting on
-%                 jtable.setSortable(true);
-%                 jtable.setAutoResort(true);
-%                 jtable.setMultiColumnSortable(true);
-%                 jtable.setPreserveSelectionsAfterSorting(true);            
-%             catch
-%                 warndlg('Creating of the GUI row-sorting module failed for the model construction table.');
-%             end
-                        
-            % Add buttons to top left panel               
-            uicontrol('Parent',hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Model Construction', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Use this feature to efficiently build a large number of models.') );
-            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Construction', 'TooltipString', sprintf('Export a .csv file of the table below.') );
-            uicontrol('Parent',hboxBtn2,'String','Build Selected Models','Callback', @this.onBuildModels, 'Tag','Model Construction', 'TooltipString', sprintf('Use the tick-box below to select the models to build then click here. \n After building, the status is given in the right most column.') );                        
-            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
-                'Callback', {@this.onChangeTableWidth, 'Model Construction outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
-            hboxBtn1.ButtonSize(1) = 225;
-            hboxBtn2.ButtonSize(1) = 225;
-            set(hbox1t4,'Sizes',[60 -1])
-            
-            % Create vbox for the various model options
-            this.tab_ModelConstruction.modelOptions.vbox = uiextras.VBoxFlex('Parent',hbox1t3,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
-            
-            % Add model options panel for bore IDs
-            dynList = [];
-            vbox4t3 = uiextras.VBox('Parent',this.tab_ModelConstruction.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
-            uicontrol( 'Parent', vbox4t3,'Style','text','String',sprintf('%s\n%s%s','Please select the Bore ID for the model:'), 'Units','normalized');            
-            this.tab_ModelConstruction.boreIDList = uicontrol('Parent',vbox4t3,'Style','list','BackgroundColor','w', ...
-                'String',dynList(:),'Value',1,'Callback',...
-                @this.modelConstruction_optionsSelection, 'Units','normalized');            
-
-            % Add model options panel for decriptions of each model type
-            dynList = [];
-            vbox5t3 = uiextras.VBox('Parent',this.tab_ModelConstruction.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
-            uicontrol( 'Parent', vbox5t3,'Style','text','String',sprintf('%s\n%s%s','Below is a decsription of the selected model type:'), 'Units','normalized');            
-            this.tab_ModelConstruction.modelDescriptions = uicontrol( 'Parent', vbox5t3,'Style','text','String','(No model type selected.)', 'HorizontalAlignment','left','Units','normalized');                        
-            set(vbox5t3, 'Sizes', [30 -1]);
-            
-            % Resize the panels
-            set(vbox1t3, 'Sizes', [30 -1]);
-            set(hbox1t3, 'Sizes', [-2 -1]);
-            set(vbox4t3, 'Sizes', [30 -1]);            
-            
-            % Build model options for each model type                
-            includeModelOption = false(length(this.modelTypes),1);
-            for i=1:length(this.modelTypes);
-                switch this.modelTypes{i}
-                    case 'model_TFN'
-                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox = uiextras.HBox('Parent',this.tab_ModelConstruction.modelOptions.vbox,'Padding', 3, 'Spacing', 3);
-                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons = uiextras.VButtonBox('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox,'Padding', 3, 'Spacing', 3);
-                        uicontrol('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons,'String','<','Callback', @this.onApplyModelOptions, 'TooltipString','Copy model option to current model.');
-                        uicontrol('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons,'String','<<','Callback', @this.onApplyModelOptions_selectedBores, 'TooltipString','Copy model option to selected models (of the current model type).');
-                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).obj = model_TFN_gui( this.tab_ModelConstruction.modelTypes.model_TFN.hbox);
-                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox.Widths=[40 -1];
-                        includeModelOption(i) = true;
-                    case 'ExpSmooth'  
-                        this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox = uiextras.HBox('Parent',this.tab_ModelConstruction.modelOptions.vbox,'Padding', 3, 'Spacing', 3);                        
-                        uicontrol( 'Parent', this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox,'Style','text', ...
-                        'String','The exponential smoothing model does not have any model option.','HorizontalAlignment','center', 'Units','normalized');            
-                    
-                        % Setup GUI.
-                        this.tab_ModelConstruction.modelTypes.ExpSmooth.obj = ExpSmooth_gui( this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox);
-                        
-                        includeModelOption(i) = true;
-                        
-                    otherwise
-                        warndlg({['The following model type is not integrated into the user interface: ', this.modelTypes{i}], ...
-                            '', 'It could not be included in the user interface.'},'Model type unavailable ...');
-                        includeModelOption(i) = false;
-                end                
-            end
-            
-            % Redefine model options to include only those that are
-            % established in the user interface.
-            this.tab_ModelConstruction.Table.ColumnFormat{7} = this.modelTypes(includeModelOption);
-            
-            % Hide all modle option vboxes 
-            this.tab_ModelConstruction.modelOptions.vbox.Heights = zeros(size(this.tab_ModelConstruction.modelOptions.vbox.Heights));
-
-%           Add context menu
-            % Create menu
-            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
-            
-            % Add items
-            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
-                        
-            % Attach menu to the construction table
-            set(this.tab_ModelConstruction.Table,'UIContextMenu',this.Figure.UIContextMenu);
-                        
-            % Add table name to .UserData
-            set(this.tab_ModelConstruction.Table.UIContextMenu,'UserData','this.tab_ModelConstruction.Table');
-            
-            
-%%          Layout Tab4 - Calibrate models
-            %------------------------------------------------------------------
-            hbox1t4 = uiextras.HBoxFlex('Parent', this.tab_ModelCalibration.Panel,'Padding', 3, 'Spacing', 3,'Tag','Model Calibration outer hbox');
-            vbox1t4 = uiextras.VBox('Parent',hbox1t4,'Padding', 3, 'Spacing', 3);
-            hbox1t5 = uiextras.HBox('Parent',vbox1t4,'Padding', 3, 'Spacing', 3);
-            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t5 ,'Padding', 3, 'Spacing', 3);             
-            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t5 ,'Padding', 3, 'Spacing', 3);             
-            
-            
-            % Add table
-            cnames1t4 = {   '<html><center>Select<br />Model</center></html>', ...   
-                            '<html><center>Model<br />Label</center></html>', ...   
-                            '<html><center>Bore<br />ID</center></html>', ...   
-                            '<html><center>Head<br />Start Date</center></html>', ...
-                            '<html><center>Head<br />End Date</center></html>', ...
-                            '<html><center>Calib.<br />Start Date</center></html>', ...
-                            '<html><center>Calib.<br />End Date</center></html>', ...
-                            '<html><center>Calib.<br />Method</center></html>', ...
-                            '<html><center>Calib.<br />Status</center></html>', ...
-                            '<html><center>Calib.<br />Period CoE</center></html>', ...
-                            '<html><center>Eval. Period<br />Unbiased CoE</center></html>', ...
-                            '<html><center>Calib.<br />Period AICc</center></html>', ...
-                            '<html><center>Calib.<br />Period BIC</center></html>'};
-            data = cell(0,14);            
-            rnames1t4 = {[1]};
-            cedit1t4 = logical([1 0 0 0 0 1 1 1 0 0 0 0 0]);            
-            %cformats1t4 = {'logical', 'char', 'char','char','char','char','char', {'SP-UCI' 'CMA-ES' 'DREAM' 'Multi-model'},'char','char','char','char','char'};
-            cformats1t4 = {'logical', 'char', 'char','char','char','char','char', {'SP-UCI' 'CMA-ES' 'DREAM' },'char','char','char','char','char'};
-      
-            toolTipStr = 'Calibration of models that have been successfully built.';              
-            
-            % Add table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.
-            this.tab_ModelCalibration.resultsOptions.currentTab = [];
-            this.tab_ModelCalibration.resultsOptions.currentPlot = [];                        
-%             this.tab_ModelCalibration.Table = createTable(vbox1t4,cnames1t4, data, false, ...
-%                 'ColumnFormat', cformats1t4, 'ColumnEditable', cedit1t4, ...
-%                 'RowName', rnames1t4, 'Tag','Model Calibration', ...
-%                 'CellSelectionCallback', @this.modelCalibration_tableSelection,...
-%                 'TooltipString', toolTipStr, ...
-%                 'Interruptible','off');              
-            this.tab_ModelCalibration.Table = uitable(vbox1t4,'ColumnName',cnames1t4,'Data',data,  ...
-                'ColumnFormat', cformats1t4, 'ColumnEditable', cedit1t4, ...
-                'RowName', rnames1t4, 'Tag','Model Calibration', ...
-                'CellSelectionCallback', @this.modelCalibration_tableSelection,...
-                'TooltipString', toolTipStr, ...
-                'Interruptible','off');   
-            
-%             % Find java sorting object in table
-%             try
-%                 this.figure_Layout.Selection = 4;
-%                 drawnow update;
-%                 jscrollpane = findjobj(this.tab_ModelCalibration.Table);
-%                 jtable = jscrollpane.getViewport.getView;
-% 
-%                 % Turn the JIDE sorting on
-%                 jtable.setSortable(true);
-%                 jtable.setAutoResort(true);
-%                 jtable.setMultiColumnSortable(true);
-%                 jtable.setPreserveSelectionsAfterSorting(true);            
-%             catch
-%                 warndlg('Creating of the GUI row-sorting module failed for the model calibration table.');
-%             end                
-            
-            % Add button for calibration
-            uicontrol('Parent',hboxBtn2,'String','Import Table Data','Callback', @this.onImportTable, 'Tag','Model Calibration', 'TooltipString', sprintf('Import a .csv file of table data to the table below. \n Only rows with a model label and bore ID matching a row within the table will be imported.') );
-            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Calibration', 'TooltipString', sprintf('Export a .csv file of the table below.') );            
-            uicontrol('Parent',hboxBtn2,'String','Calibrate Selected Models','Callback', @this.onCalibModels,'Tag','useLocal', 'TooltipString', sprintf('Use the tick-box below to select the models to calibrate then click here. \n During and after calibration, the status is given in the 9th column.') );            
-            uicontrol('Parent',hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Model Calibration', 'TooltipString', sprintf('Export a .csv file of the calibration results from all models.') );            
-            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
-                'Callback', {@this.onChangeTableWidth, 'Model Calibration outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
-            hboxBtn1.ButtonSize(1) = 225;
-            hboxBtn2.ButtonSize(1) = 225;
-            set(hbox1t5,'Sizes',[90 -1])                 
-            
-            % Size boxe
-            set(vbox1t4, 'Sizes', [30 -1]);
-                        
-            % Create vbox for the various model options            
-            resultsvbox = uiextras.VBoxFlex('Parent',hbox1t4,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
-                        
-            % Add tabs for various types of results            
-            this.tab_ModelCalibration.resultsTabs = uiextras.TabPanel( 'Parent',resultsvbox, 'Padding', 5, 'TabSize',127,'FontSize',8);
-            this.tab_ModelCalibration.resultsOptions.calibPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
-                'Tag','CalibrationResultsTab');            
-            this.tab_ModelCalibration.resultsOptions.forcingPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
-                'Tag','ForcingDataTab');          
-            this.tab_ModelCalibration.resultsOptions.paramsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
-                'Tag','ParametersTab');
-            this.tab_ModelCalibration.resultsOptions.derivedParamsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
-                'Tag','DerivedParametersTab');                         
-            this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
-                'Tag','ModelSpecifics');                       
-            this.tab_ModelCalibration.resultsTabs.TabNames = {'Calib. Results','Forcing Data','Parameters', ...
-                'Derived Parameters','Model Specifics'};
-            this.tab_ModelCalibration.resultsTabs.SelectedChild = 1;
-                        
-            % Build calibration results tab            
-            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.calibPanel,'Padding', 3, 'Spacing', 3);
-            resultsvboxTable = uiextras.Grid('Parent', resultsvbox ,'Padding', 3, 'Spacing', 3);            
-            tbl = uitable(resultsvboxTable , 'ColumnName',{'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head','Model Err.','Noise Lower','Noise Upper'}, ... 
-                'Data',cell(0,11), 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','numeric','numeric','numeric','numeric'}, ...
-                'ColumnEditable', true(1,11), 'Tag','Model Calibration - results table', ...
-                'TooltipString',['<html>This table shows the calibration & evaluation results. <br>', ... 
-                'A range of plots can be used to explore aspects of the calibration.']);   
-
-            % Build calibration results table contect menu
-            contextMenu = uicontextmenu(this.Figure,'Visible','on');
-            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - results table export', 'Callback',@this.onExportResults);                 
-            set(tbl,'UIContextMenu',contextMenu);
-            set(tbl.UIContextMenu,'UserData','Model Calibration - results table');
-            
-            resultsvboxDropDown = uiextras.Grid('Parent', resultsvboxTable ,'Padding', 3, 'Spacing', 3);            
-            uicontrol(resultsvboxDropDown,'Style','text','String','Select the plot type:','HorizontalAlignment','left' );
-            uicontrol(resultsvboxDropDown,'Style','popupmenu', ...
-                'String',{  'Time-series of Heads', ...
-                            'Time-series of Residuals', ...
-                            'Histogram of Calib. Residuals', ...
-                            'Histogram of Eval. Residuals', ...
-                            'Obs. Head vs Modelled Head', ...
-                            'Obs. Head vs Residuals', ...
-                            'Variogram of Residuals', ...
-                            '(none)'}, ...
-                'Value',1,'Callback', @this.modelCalibration_onUpdatePlotSetting);                                                
-            set(resultsvboxTable, 'ColumnSizes', -1, 'RowSizes', [-1 20] );
-            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
-                'Tag','Model Calibration - results plot');  
-            set(resultsvbox, 'Sizes', [-1 -1]);
-            
-            % Building forcing data
-            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.forcingPanel,'Padding', 3, 'Spacing', 3);
-            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);
-            uicontrol(resultsvboxOptions,'Style','text','String','Time step:','HorizontalAlignment','left' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'daily','weekly','monthly','quarterly','annual','full-record'}, ...
-                'Value',1, 'Callback', @this.modelCalibration_onUpdateForcingData, ...
-                'TooltipString', ['<html>Select the time-scale for presentation of the forcing data.  <br>', ...
-                'Note, all time-scales >daily are reported as daily mean.'],'HorizontalAlignment','right');    
-            uicontrol(resultsvboxOptions,'Style','text','String','Time step metric:','HorizontalAlignment','left' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', ...
-                'String',{'sum','mean','st. dev.','variance','skew','min','5th %ile','10th %ile','25th %ile','50th %ile','75th %ile','90th %ile','95th %ile', 'max', ...
-                'inter-quantile range', 'No. zero days', 'No. <0 days', 'No. >0 day'}, ...
-                'Value',1, 'Callback', @this.modelCalibration_onUpdateForcingData, ...
-                'TooltipString', ['<html>Select calculation to apply when aggregating the daily data.  <br>', ...
-                'Note, all plots will use the resulting data.'],'HorizontalAlignment','right');    
-
-            uicontrol(resultsvboxOptions,'Style','text','String','Start date:','HorizontalAlignment','left');
-            uicontrol(resultsvboxOptions,'Style','edit','String','01/01/0001','TooltipString','Filter the data and plot to that above a data (as dd/mm/yyyy).', 'Callback', @this.modelCalibration_onUpdateForcingData );
-
-            uicontrol(resultsvboxOptions,'Style','text','String','End date:','HorizontalAlignment','left');
-            uicontrol(resultsvboxOptions,'Style','edit','String','31/12/9999','TooltipString','Filter the data and plot to that below a data (as dd/mm/yyyy).', 'Callback', @this.modelCalibration_onUpdateForcingData );
-                   
-            set(resultsvboxOptions, 'ColumnSizes', [-1 -1 -1 -1 -1 -1 -1 -1], 'RowSizes', [25] );
-            
-            tbl = uitable(resultsvbox, 'ColumnName',{'Year','Month', 'Day'}, ... 
-                'Data',cell(0,3), 'ColumnFormat', {'numeric','numeric','numeric'}, ...
-                'ColumnEditable', true(1,3), 'Tag','Model Calibration - forcing table', ...
-                'TooltipString',['<html>This table allows exploration of the forcing data used for the calibration <br>', ... 
-                     '& evaluation and forcing data derived from the model (e.g. from a soil moisture <br>', ... 
-                     'transformation model). Use the table to explore forcing dynamics at a range of time-steps.']);  
-            contextMenu = uicontextmenu(this.Figure,'Visible','on');
-            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - forcing table export', 'Callback',@this.onExportResults);                 
-            set(tbl,'UIContextMenu',contextMenu);
-            set(tbl.UIContextMenu,'UserData','Model Calibration - forcing table');
-                 
-                 
-            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);
-            uicontrol(resultsvboxOptions,'Style','text','String','Plot type:' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'line','scatter','bar','histogram','cdf','box-plot (daily metric)', ...
-                'box-plot (monthly metric)','box-plot (quarterly metric)','box-plot (annually metric)'}, 'Value',1,'HorizontalAlignment','right', ...
-                'Callback',@this.modelCalibration_onUpdateForcingPlotType);    
-            uicontrol(resultsvboxOptions,'Style','text','String','x-axis:' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'Date', '(none)'}, 'Value',1,'HorizontalAlignment','right');    
-            uicontrol(resultsvboxOptions,'Style','text','String','y-axis:' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'Date', '(none)'}, 'Value',1,'HorizontalAlignment','right');                
-            uicontrol(resultsvboxOptions,'Style','pushbutton','String','Build plot','Callback', @this.modelCalibration_onUpdateForcingPlot, ...
-                'Tag','Model Calibration - forcing plot', 'TooltipString', 'Build the forcing plot.','ForegroundColor','blue');            
-            set(resultsvboxOptions, 'ColumnSizes', [-1 -2 -1 -2 -1 -2 -2], 'RowSizes', [25] );
-            %hbuttons = uiextras.HButtonBox('Parent',resultsvboxOptions,'Padding', 3, 'Spacing', 3);  
-            %uicontrol('Parent',hbuttons,'String','Build plot','Callback', @this.modelCalibration_onUpdatePlotSetting, ...
-            %    'Tag','Model Calibration - forcing plot', 'TooltipString', 'Build the forcing plot.','ForegroundColor','blue');            
-            
-            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1] );             
-            set(resultsvbox, 'Sizes', [30 -1 30 -1]);
-            
-            % set selected tab and plot to 
-            this.tab_ModelCalibration.resultsOptions.currentTab = 1;
-            this.tab_ModelCalibration.resultsOptions.currentPlot = 7;
-            
-            % Build parameters tab
-            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.paramsPanel,'Padding', 3, 'Spacing', 3);  
-            tbl = uitable(resultsvbox, ...
-                'ColumnName',{'Component Name','Parameter Name','Value'}, ... 
-                'ColumnFormat', {'char','char','numeric'}, ...
-                'ColumnEditable', true(1,3), ...
-                'Tag','Model Calibration - parameter table', ...
-                'TooltipString',['<html>This table allows exploration of the calibrated model parameters. <br>', ... 
-                 'Use this table to inform assessment of the validity of the model.']);            
-             
-            contextMenu = uicontextmenu(this.Figure,'Visible','on');
-            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - parameter table export', 'Callback',@this.onExportResults);                 
-            set(tbl,'UIContextMenu',contextMenu);
-            set(tbl.UIContextMenu,'UserData','Model Calibration - parameter table');                         
-
-            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
-                'Tag','Model Calibration - parameter plot');  
-            set(resultsvbox, 'Sizes', [-1 -2]);
-            
-            % Build derived parameters tab
-            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.derivedParamsPanel,'Padding', 3, 'Spacing', 3);  
-            tbl = uitable(resultsvbox, ...
-                'ColumnName',{'Component Name','Parameter Name','Derived Value'}, ... 
-                'ColumnFormat', {'char','char','numeric'}, ...
-                'ColumnEditable', true(1,3), ...
-                'Tag','Model Calibration - derived parameter table', ...
-                'TooltipString',['<html>This table allows exploration of the parameters derived from the calibrated <br>', ... 
-                 'parameters. The parameters shown are dependent upon the model structure. <br>', ...
-                 'For example, TFN models having using, say, the Ferris Knowles weighting function <br>', ...
-                 'will show the transmissivity and storativity.']); 
-             
-            contextMenu = uicontextmenu(this.Figure,'Visible','on');
-            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - derived parameter table export', 'Callback',@this.onExportResults);                 
-            set(tbl,'UIContextMenu',contextMenu);
-            set(tbl.UIContextMenu,'UserData','Model Calibration - derived parameter table');                         
-             
-            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
-                'Tag','Model Calibration - derived parameter plot');  
-            set(resultsvbox, 'Sizes', [-1 -2]);
-                        
-            % Build model specific outputs tab.
-            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel,'Padding', 3, 'Spacing', 3);                        
-            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);            
-            uicontrol(resultsvboxOptions,'Style','text','String','Select model specific output:' );
-            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'(none)'},'Value',1, ...
-                'Tag','Model Calibration - derived data dropdown', ...
-                'Callback', @this.modelCalibration_onUpdateDerivedData);                         
-            tbl = uitable(resultsvbox, ...
-                'ColumnName',{'Variabe 1','Variabe 2'}, ... 
-                'ColumnFormat', {'numeric','numeric'}, ...
-                'ColumnEditable', true(1,2), ...
-                'Tag','Model Calibration - derived data table', ...
-                'TooltipString',['<html>This table allows exploration of the data derived from the calibrated. <br>', ... 
-                 'The data shown are very dependent upon the model structure. <br>', ...
-                 'For example, TFN models having using, say, the Pearsons weighting function <br>', ...
-                 'will show the weighting data and a plot.']);     
-            contextMenu = uicontextmenu(this.Figure,'Visible','on');
-            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - derived data table export', 'Callback',@this.onExportResults);                 
-            set(tbl,'UIContextMenu',contextMenu);
-            set(tbl.UIContextMenu,'UserData','Model Calibration - derived data table export');               
-            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
-                'Tag','Model Calibration - derived data plot');             
-            set(resultsvbox, 'Sizes', [30 -1 -2]);                                                            
-                        
-%           Add context menu
-            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
-            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
-            
-            % Attach menu to the construction table
-            set(this.tab_ModelCalibration.Table,'UIContextMenu',this.Figure.UIContextMenu);
-                        
-            % Add table name to .UserData
-            set(this.tab_ModelCalibration.Table.UIContextMenu,'UserData','this.tab_ModelCalibration.Table');
-
-            
-
-%%          Layout Tab5 - Model Simulation
-            %------------------------------------------------------------------            
-            hbox1t5 = uiextras.HBoxFlex('Parent', this.tab_ModelSimulation.Panel,'Padding', 3, 'Spacing', 3, 'Tag','Model Simulation outer hbox');
-            vbox1t5 = uiextras.VBox('Parent',hbox1t5,'Padding', 3, 'Spacing', 3);
-            vbox2t5 = uiextras.VBox('Parent',hbox1t5,'Padding', 3, 'Spacing', 3);
-            hbox1t6 = uiextras.HBox('Parent',vbox1t5,'Padding', 3, 'Spacing', 3);
-            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t6 ,'Padding', 3, 'Spacing', 3);             
-            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t6 ,'Padding', 3, 'Spacing', 3);                 
-            
-                        
-            % Add button for calibration
-            % Add buttons to top left panel               
-            uicontrol(hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Model Simulation', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Only rows where the model label is for a model that have been calibrated will be imported.') );
-            uicontrol(hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Simulation', 'TooltipString', sprintf('Export a .csv file of the table below.') );                        
-            uicontrol(hboxBtn2,'String','Simulate Selected Models','Callback', @this.onSimModels, 'TooltipString', sprintf('Use the tick-box below to select the models to simulate then click here. \n During and after simulation, the status is given in the 9th column.') );            
-            uicontrol(hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Model Simulation', 'TooltipString', sprintf('Export a .csv file of the simulation results from all models.') );            
-            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
-                'Callback', {@this.onChangeTableWidth, 'Model Simulation outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
-            hboxBtn1.ButtonSize(1) = 225;
-            hboxBtn2.ButtonSize(1) = 225;
-            set(hbox1t6,'Sizes',[90 -1])              
-            
-            % Add table
-            cnames1t5 = {   '<html><center>Select<br />Model</center></html>', ...   
-                            '<html><center>Model<br />Label</center></html>', ...                               
-                            '<html><center>Bore<br />ID</center></html>', ...   
-                            '<html><center>Head<br />Start Date</center></html>', ...
-                            '<html><center>Head<br />End Date</center></html>', ...
-                            '<html><center>Simulation<br />Label</center></html>', ...   
-                            '<html><center>Forcing Data<br />File</center></html>', ...
-                            '<html><center>Simulation<br />Start Date</center></html>', ...
-                            '<html><center>Simulation<br />End Date</center></html>', ...
-                            '<html><center>Simulation<br />Time step</center></html>', ...                            
-                            '<html><center>Krig<br />Sim. Residuals?</center></html>', ... 
-                            '<html><center>Simulation<br />Status</center></html>'};
-            data = cell(1,12);            
-            rnames1t5 = {[1]};
-            cedit1t5 = logical([1 1 0 0 0 1 1 1 1 1 1 0]);            
-            cformats1t5 = {'logical', {'(none calibrated)'}', 'char','char','char','char','char','char', 'char',{'Daily' 'Weekly' 'Monthly' 'Yearly'}, 'logical','char' };
-            toolTipStr = 'Run simulations of calibrated models';
-              
-            % Add table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.
-%             this.tab_ModelSimulation.Table = createTable(vbox1t5,cnames1t5, data, false, ...
-%                 'ColumnFormat', cformats1t5, 'ColumnEditable', cedit1t5, ...
-%                 'RowName', rnames1t5, 'Tag','Model Simulation', ...
-%                 'CellSelectionCallback', @this.modelSimulation_tableSelection,...
-%                 'CellEditCallback', @this.modelSimulation_tableEdit,...
-%                 'TooltipString', toolTipStr);   
-
-            this.tab_ModelSimulation.Table = uitable(vbox1t5, ...
-                'ColumnName', cnames1t5, 'Data', data, ...
-                'ColumnFormat', cformats1t5, 'ColumnEditable', cedit1t5, ...
-                'RowName', rnames1t5, 'Tag','Model Simulation', ...
-                'CellSelectionCallback', @this.modelSimulation_tableSelection,...
-                'CellEditCallback', @this.modelSimulation_tableEdit,...
-                'TooltipString', toolTipStr);            
-                                 
-            
-%             % Find java sorting object in table
-%             try
-%                 this.figure_Layout.Selection = 5;
-%                 drawnow update;
-%                 jscrollpane = findjobj(this.tab_ModelSimulation.Table);
-%                 jtable = jscrollpane.getViewport.getView;
-% 
-%                 % Turn the JIDE sorting on
-%                 jtable.setSortable(true);
-%                 jtable.setAutoResort(true);
-%                 jtable.setMultiColumnSortable(true);
-%                 jtable.setPreserveSelectionsAfterSorting(true);            
-%             catch
-%                 warndlg('Creating of the GUI row-sorting module failed for the model simulation table.');
-%             end                         
-                        
-            % Add drop-down for the results box
-            uicontrol(vbox2t5,'Style','text','String','Select simulation results to display:' );
-            this.tab_ModelSimulation.resultsOptions.popup = uicontrol('Parent',vbox2t5,'Style','popupmenu', ...
-                'String',{'Simulation data', 'Simulation & decomposition plots','(none)'}, ...
-                'Value',3,'Callback', @this.modelSimulation_onResultsSelection);         
-            
-            this.tab_ModelSimulation.resultsOptions.dataTable.box = uiextras.Grid('Parent', vbox2t5,'Padding', 3, 'Spacing', 3);
-
-            % Add results table. Importantly, this is done using createTable, not
-            % uitable. This was required to achieve acceptable perforamnce
-            % for large tables.
-%             this.tab_ModelSimulation.resultsOptions.dataTable.table = createTable(this.tab_ModelSimulation.resultsOptions.dataTable.box, ...
-%                 {'Year','Month', 'Day','Hour','Minute', 'Sim. Head','Noise Lower','Noise Upper'}, ...
-%                 cell(0,8), false, ...
-%                 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','numeric','numeric'}, ...
-%                 'ColumnEditable', true(1,8), ...
-%                 'Tag','Model Simulation - results table', ...
-%                 'TooltipString', 'Results data from the model simulation.');     
-            this.tab_ModelSimulation.resultsOptions.dataTable.table = uitable(this.tab_ModelSimulation.resultsOptions.dataTable.box, ...
-                'ColumnName',{'Year','Month', 'Day','Hour','Minute', 'Sim. Head','Noise Lower','Noise Upper'}, ...
-                'Data',cell(0,8), ...
-                'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','numeric','numeric'}, ...
-                'ColumnEditable', true(1,8), ...
-                'Tag','Model Simulation - results table', ...
-                'TooltipString', 'Results data from the model simulation.');                                 
-            
-            this.tab_ModelSimulation.resultsOptions.box = vbox2t5;
-            this.tab_ModelSimulation.resultsOptions.plots.panel = uiextras.BoxPanel('Parent', vbox2t5);                                              
-            
-            % Set box sizes
-            set(hbox1t5, 'Sizes', [-2 -1]);
-            set(vbox1t5, 'Sizes', [30 -1]);
-            set(vbox2t5, 'Sizes', [30 20 0 0]);
-                        
-%           Add context menu
-            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
-            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
-            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
-            
-            % Attach menu to the construction table
-            set(this.tab_ModelSimulation.Table,'UIContextMenu',this.Figure.UIContextMenu);
-                        
-            % Add table name to .UserData
-            set(this.tab_ModelSimulation.Table.UIContextMenu,'UserData','this.tab_ModelSimulation.Table');
-            
-%%          Store this.models on HDD
-            %----------------------------------------------------
-            this.modelsOnHDD = '';
-            this.models = [];
-            
-%%          Close the splash window and show the app
-            %----------------------------------------------------
-            if ~isdeployed
-               close(this.FigureSplash);
-            end                        
-            this.figure_Layout.Selection = 1;
-            set(this.Figure,'Visible','on');
-        end
-
-        % Show show plotting icons
-        function plotToolbarState(this,iconState)
-            % Check if version is 2018b or later. From this point the
-            % plot toolbar buttons moved into the plot.
-            v=version();
-            isBefore2018b = str2double(v(1:3))<9.5;
-
-            if isBefore2018b 
-                hToolbar = findall(this.Figure,'tag','FigureToolBar');
-                hToolbutton = findall(hToolbar,'tag','Exploration.Brushing');            
-                hToolbutton.Visible = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.DataCursor');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(hToolbar,'tag','Exploration.Rotate');            
-                hToolbutton.Visible = 'off';
-                hToolbutton = findall(hToolbar,'tag','Exploration.Pan');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomOut');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomIn');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(hToolbar,'tag','Standard.PrintFigure');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(this.Figure,'tag','Export.plot');            
-                hToolbutton.Visible = iconState;
-                hToolbutton = findall(hToolbar,'tag','Standard.EditPlot');            
-                hToolbutton.Visible = 'off';            
-                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOn');            
-                hToolbutton.Visible = 'off';                          
-            end
-        end        
-        
-        % Set project folder
-        function onSetProjectFolder(this,hObject,eventdata)
-
-            % Initialise project names
-            projectName = '';
-            projectExt = '';
-            
-            % Get project folder
-            if isempty(this.project_fileName)
-                projectPath = uigetdir('Select project folder.');    
-            else
-                % Get project folder and file name (if a dir)
-                if isdir(this.project_fileName)
-                    projectPath = this.project_fileName;
-                else
-                    [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
-                end
-                                
-                if isempty(projectPath)
-                    projectPath = uigetdir('Select project folder.'); 
-                else
-                    projectPath = uigetdir(projectPath, 'Select project folder.'); 
-                end
-                
-            end
-
-            if projectPath~=0;
-
-                % Get the current project folder. If the project folder has
-                % changed then warn the user that all infput file names
-                % must be within the new project folder.
-                if ~isempty(this.project_fileName)
-                    currentProjectFolder = fileparts(this.project_fileName);
-                    
-                    if ~strcmp(currentProjectFolder, projectPath)
-                        warndlg({'The project folder is different to that already set.';''; ...
-                                 'Importantly, all file names in the project are relative'; ...
-                                 'to the project folder and so all input .csv files must be'; ...
-                                 'within the project folder or a sub-folder within it.'},'Input file name validity.','modal');
-                    end
-                end
-                
-                % Update project folder
-                this.project_fileName = projectPath;
-                                
-                % Update GUI title
-                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
-                drawnow update;
-            end            
-            
-        end
-            
-        function onMoveModels(this,hObject,eventdata)
-            
-            % The project must be saved to a file. Check the project file
-            % is defined.
-            if isempty(this.project_fileName) || isdir(this.project_fileName)
-                warndlg({'The project must first be saved to a file.';'Please first save the project.'}, 'Project not saved...')
-                return
-            end
-               
-            % Tell the user what is going to be done.
-            if ~isempty(this.modelsOnHDD)
-                response = questdlg({'Moving the models to the RAM will shift all built, calibrated and '; ...
-                                   'simulated models from the hard-drive of the project file to the RAM.'; ...
-                                   ''; ...
-                                   'This allows significantly fewer models to be analysed within one '; ...
-                                   'project. However, any operation requiring access to the models '; ...
-                                   'may be muxh faster. Such operations include changing settings or '; ...
-                                   'plot and exporting results.'; ...
-                                   ''; ...
-                                   'Additionally, changes made to a model (e.g. rebuilting, calibrating'; ...
-                                   'or simulating) will NOT be automatically saved to the project file.'; ...
-                                   ''; ...
-                                   'To move the models to RAM, click OK.'},'Move the project models to RAM ...','OK','Cancel','Cancel');                
-            else
-                response = questdlg({'Moving the models to the HDD will shift all built, calibrated and '; ...
-                                   'simulated models from the RAM to the project file on the hard disk.'; ...
-                                   ''; ...
-                                   'This allows significantly more models to be analysed within one '; ...
-                                   'project. However, any operation requiring access to the models '; ...
-                                   'may be very slow. Such operations include changing settings or '; ...
-                                   'plot and exporting results. Hence, it is highly advisable to '; ...
-                                   'store the project file on a local hard-drive, and not a network'; ...
-                                   'location.'; ...
-                                   ''; ...
-                                   'Additionally, changes made to a model (e.g. rebuilting, calibrating'; ...
-                                   'or simulating) will be automatically saved to the project file. The'; ...
-                                   'project data tables within each tab will however not be automatically'; ...
-                                   'saved.'; ...
-                                   ''; ...
-                                   'To move the models to the hard-drive, click OK.'},'Move the project models to hard-drive ...','OK','Cancel','Cancel');
-            end
-                           
-             if ~strcmp(response,'OK')
-                 return
-             end
-             
-             % Change cursor
-             set(this.Figure, 'pointer', 'watch');                
-             drawnow update;                    
-
-             % Store original - in case of error
-             modelsOnHDD_orig = this.modelsOnHDD;
-             
-             if ~isempty(this.modelsOnHDD)    
-                 % Move models to RAM. The following loads each model
-                 % object and then assigns si to the project.
-                 try                      
-                     model_labels = fieldnames(this.models);
-                     nModels = length(model_labels);
-                     for i=1:nModels
-                        tmpModels.(model_labels{i}) = getModel(this, model_labels{i});
-                     end
-                     this.models = tmpModels;
-                     this.modelsOnHDD = '';
-                     
-                     % Change file menu label
-                     for i=1:size(this.figure_Menu.Children,1)
-                        if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                        strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                            set(this.figure_Menu.Children(i),'Label','Move models from RAM to HDD...');
-                        end       
-                     end   
-
-                     % Change cursor
-                     set(this.Figure, 'pointer', 'arrow');                
-                     drawnow update;                    
-                                   
-                     msgbox('Models were successfully moved to the RAM.','Successful model relocation','help');                     
-                 catch ME
-                     this.modelsOnHDD = modelsOnHDD_orig;
-                     msgbox({'Models relocation failed!','','Check access to the project file and RAM availability.'},'Relcation of models failed.','error');
-                 end
-             else               % Move models to HDD
-                try 
-                    
-                    % Get the folder for the project files
-                    folderName = uigetdir(fileparts(this.project_fileName) ,'Select folder for the model files.');    
-                    if isempty(folderName)
-                        return;
-                    end                                                        
-                    
-                    % remove project folder from file paths
-                    [ind_start,ind_end]=regexp(folderName ,fileparts(this.project_fileName));
-                    folderName = folderName(ind_end+2:end);
-                    
-                    % Check models are to go into a subfolder.
-                    if isempty(folderName)
-                        msgbox({'Models relocation failed!','','The models must be offloaded to a sub-folder of the project.'},'Relcation of models failed.','error');
-                    
-                        this.modelsOnHDD = modelsOnHDD_orig;
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;   
-                        
-                        return
-                    end
-                    
-                    % Store sub-folder to models in project
-                    this.modelsOnHDD = folderName;
-                    
-                    % Move models to HDD
-                    model_labels = fieldnames(this.models);
-                    nModels = length(model_labels);
-                    for i=1:nModels
-                        model = this.models.(model_labels{i});                        
-                        setModel( this, model_labels{i}, model);
-                    end
-
-                    % Change file menu label
-                    for i=1:size(this.figure_Menu.Children,1)
-                        if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                        strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                            set(this.figure_Menu.Children(i),'Label','Move models from HDD to RAM...');
-                        end       
-                    end                       
-
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');                
-                    drawnow update;                    
-                    
-                    msgbox('Models were successfully moved to the hard-drive.','Successful model relocation','help');
-                catch ME
-                    this.modelsOnHDD = modelsOnHDD_orig;
-                    msgbox({'Models relocation failed!','','Check access to the project file and RAM availability.'},'Relcation of models failed.','error');
-                end
-             end
-             
-             % Change cursor
-             set(this.Figure, 'pointer', 'arrow');                
-             drawnow update;               
-        end
-        
-        % Open saved model
-        function onNew(this,hObject,eventdata)
-
-            % Check if all of the GUI tables are empty. If not, warn the
-            % user the opening the example will delete the existing data.
-            if ~isempty(this.tab_Project.project_name.String) || ...
-            ~isempty(this.tab_Project.project_description.String) || ...
-            (size(this.tab_ModelConstruction.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
-            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
-            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
-                response = questdlg({'Started a new project will close the current project.','','Do you want to continue?'}, ...
-                 'Close the current project?','Yes','No','No');
-             
-                if strcmp(response,'No')
-                    return;
-                end
-            end              
-            
-            % Initialise project data
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update;              
-            this.project_fileName ='';
-            this.model_labels=[];
-            this.models=[];              
-            this.tab_Project.project_name.String = '';
-            this.tab_Project.project_description.String = '';
-            this.tab_DataPrep.Table.Data = {};
-            this.tab_DataPrep.Table.RowName = {}; 
-            this.tab_ModelConstruction.Table.Data = { [],[],[],[],[],[],[],[], '<html><font color = "#FF0000">Model not built.</font></html>'};
-            this.tab_ModelConstruction.Table.RowName = {}; 
-            this.tab_ModelCalibration.Table.Data = {};
-            this.tab_ModelCalibration.Table.RowName = {};
-            this.tab_ModelSimulation.Table.Data = {};
-            this.tab_ModelSimulation.Table.RowName = {};
-            this.dataPrep = [];
-            this.copiedData={};
-            this.HPCoffload={};
-            this.modelsOnHDD='';
-            set(this.Figure,'Name','HydroSight');
-            drawnow update;   
-                
-            % Enable file menu items
-            for i=1:size(this.figure_Menu.Children,1)
-                if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
-                    set(this.figure_Menu.Children(i),'Enable','on');
-                elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                    if this.modelsOnHDD
-                        set(this.figure_Menu.Children(i),'Label', 'Move models from HDD to RAM...');
-                    else
-                        set(this.figure_Menu.Children(i),'Label', 'Move models from RAM to HDD...');
-                    end
-                    set(this.figure_Menu.Children(i),'Enable','on');
-                end       
-            end
-
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;                               
-        end
-        
-        % Open saved model
-        function onOpen(this,hObject,eventdata)
-            
-            % Check if all of the GUI tables are empty. If not, warn the
-            % user the opening the example will delete the existing data.
-            if ~isempty(this.tab_Project.project_name.String) || ...
-            ~isempty(this.tab_Project.project_description.String) || ...
-            (size(this.tab_ModelConstruction.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
-            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
-            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
-                response = questdlg({'Opening a new project will close the current project.','','Do you want to continue?'}, ...
-                 'Close the current project?','Yes','No','No');
-             
-                if strcmp(response,'No')
-                    return;
-                end
-            end            
-            
-            % Set initial folder to the project folder (if set)
-            currentProjectFolder='';
-            if ~isempty(this.project_fileName)                                
-                try    
-                    if isdir(this.project_fileName)
-                        currentProjectFolder = this.project_fileName;
-                    else
-                        currentProjectFolder = fileparts(this.project_fileName);
-                    end 
-                    
-                    currentProjectFolder = [currentProjectFolder,filesep];
-                    cd(currentProjectFolder);
-                catch
-                    % do nothing
-                end
-            end
-            
-            % Show folder selection dialog
-            [fName,pName] = uigetfile({'*.mat'},'Select project to open.');    
-
-            if fName~=0;
-                
-                % Assign the file name 
-                this.project_fileName = fullfile(pName,fName);
-
-                % Ask if the performance stats in the GUI calib table are
-                % to be rebuilt
-                doPerforamnceMeasureRestimation=false;
-                if ~isdeployed
-                    response = questdlg({'Would you like the calibration performance metrics to be re-calculated?','','Note, the calculations can be slow if there are 100''s of models'},'Re-calculate performance metrics?','Yes','No','Cancel','No');
-                    if strcmp(response,'Cancel')
-                        return;
-                    elseif strcmp(response,'Yes')
-                        doPerforamnceMeasureRestimation=true;
-                    end
-                end
-                
-                % Change cursor
-                set(this.Figure, 'pointer', 'watch');                
-                drawnow update;
-                
-                % Analyse the variables in the file
-                %----------------------------------------------------------
-                % Get variables in file
-                try
-                    vars= whos('-file',this.project_fileName);
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    errordlg(['The following project file could not be opened:',this.project_fileName],'Project file read error ...');
-                    return;
-                end
-
-                % Filter out 'models' variable
-                j=0;
-                hasDataVar = false;
-                hasModelsVar = false;
-                for i=1:size(vars)
-                   if ~strcmp(vars(i).name,'data') && ~strcmp(vars(i).name,'label') && ~strcmp(vars(i).name,'models') 
-                       j=j+1;
-                       varNames{j} = vars(i).name;
-                   end
-                   if strcmp(vars(i).name,'data')
-                       hasDataVar = true;
-                   end
-                   if strcmp(vars(i).name,'models')
-                       hasModelsVar = true;
-                   end                   
-                end                
-                
-                % GET MODEL DATA
-                %----------------------------------------------------------                
-                % Add HPC settings
-                savedData = load(this.project_fileName, 'settings','-mat');
-                try
-                    this.HPCoffload = savedData.settings.HPCoffload;
-                catch
-                    this.HPCoffload = {};
-                end
-                
-                % Add model on HDD setting
-                try 
-                    if islogical(savedData.settings.modelsOnHDD)
-                        this.modelsOnHDD = '';
-                    else
-                        this.modelsOnHDD = savedData.settings.modelsOnHDD;
-                    end
-                catch
-                    this.modelsOnHDD = '';
-                end
-
-                % Add model label settings
-                try
-                    savedData = load(this.project_fileName, 'model_labels','-mat');
-                    if isempty(fieldnames(savedData))
-                        this.model_labels=[];
-                    else
-                        this.model_labels=savedData.model_labels;                
-                    end
-                catch ME
-                    this.model_labels=[];
-                end
-                
-                % Clear models in project.                        
-                this.models=[];                                
-                
-                % Assign built models.
-                try
-                    % Load data and labels assuming they are not in a
-                    % field of 'models'.                        
-                    if hasModelsVar
-                        savedData = load(this.project_fileName, 'models','-mat');
-                        savedData = savedData.models;                                                        
-                    else
-                        savedData = load(this.project_fileName, '-mat');                            
-
-                        % Remove GUI data fields
-                        try
-                        	filt = strcmp(fieldnames(savedData),'dataPrep') | strcmp(fieldnames(savedData),'settings') |  strcmp(fieldnames(savedData),'tableData') |  strcmp(fieldnames(savedData),'model_labels');
-                            removedFields = fieldnames(savedData);
-                            removedFields = removedFields(filt);                            
-                            savedData = rmfield(savedData,removedFields);
-                        catch ME
-                            % do nothing
-                        end                            
-                    end
-
-                    if ~isempty(this.modelsOnHDD)
-                        
-                        % Setup matfile link to 'this'. NOTE: for
-                        % offloadeed models setModel() can handle the third input being the 
-                        % file name to the matfile link. 
-                        % Note 2: The models only need to be loaded if
-                        % this.model_labels does not contain calib. status.
-                        model_labels = fieldnames(savedData);
-                        nModels = length(model_labels);
-                        nErr=0;
-                        if isempty(this.model_labels) || size(this.model_labels,1)<nModels
-                            this.model_labels=[];
-                            h = waitbar(0,'Re-building list of calibrated models because of error. Please wait ...');                        
-                            for i=1:nModels             
-                                waitbar(i/nModels);
-                                try                                
-                                    setModel(this, model_labels{i,1}, savedData.(model_labels{i,1}));
-                                catch ME
-                                    nErr = nErr+1;
-                                end                                
-                            end
-                            close(h);
-                        else
-                            % Update object hold the relative path to the .mat file                            
-                            for i=1:nModels   
-                                model_label_tmp = HydroSight_GUI.modelLabel2FieldName(model_labels{i,1});
-                                this.models.(model_label_tmp) = fullfile(this.modelsOnHDD, [model_label_tmp,'.mat']);                                
-                            end
-                        end
-                        
-                        if nErr>0
-                            warndlg(['The HDD stored .mat files could not be loaded for ', num2str(nErr), ' models. Check the .mat files exist for all calibrated models in the project'],'Model load errors...');
-                        end
-                    else
-                        
-                        if iscell(savedData)
-                            nModels = size(savedData,1);
-                            for i=1:nModels                                
-                                setModel(this, savedData{i,1}.model_label, savedData{i,1});
-                            end
-                        else
-                            model_labels = fieldnames(savedData);
-                            nModels = length(model_labels);
-                            for i=1:nModels
-                                setModel(this, savedData.(model_labels{i,1}).model_label, savedData.(model_labels{i,1}));
-                            end
-                        end                 
-                    end
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Loaded models could not be assigned to the user interface.','File model data error');
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;
-                end                  
-                
-                % GET GUI TABLE DATA
-                %----------------------------------------------------------
-                % Load file (except 'model')
-                try
-                    savedData = load(this.project_fileName, varNames{:}, '-mat');
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Project file could not be loaded.','File error');
-                    return;
-                end
-                
-                % Assign loaded data to the tables and models.
-                try
-                    this.tab_Project.project_name.String = savedData.tableData.tab_Project.title;
-                    this.tab_Project.project_description.String = savedData.tableData.tab_Project.description;
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Data could not be assigned to the user interface table: Project Description','File table data error');
-                     set(this.Figure, 'pointer', 'watch');
-                     drawnow update;
-                end
-                try
-                    if size(savedData.tableData.tab_DataPrep,2)==17
-                       savedData.tableData.tab_DataPrep = [ savedData.tableData.tab_DataPrep(:,1:14), ...
-                                                            repmat(false,size(savedData.tableData.tab_DataPrep,1),1), ...
-                                                            savedData.tableData.tab_DataPrep(:,15:17) ];
-                    end
-                    this.tab_DataPrep.Table.Data = savedData.tableData.tab_DataPrep;
-                    
-                    % Update row numbers
-                    nrows = size(this.tab_DataPrep.Table.Data,1);
-                    this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                    
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Data could not be assigned to the user interface table: Data Preparation','File table data error');
-                     set(this.Figure, 'pointer', 'watch');
-                     drawnow update;
-                end               
-                try
-
-                    this.tab_ModelConstruction.Table.Data = savedData.tableData.tab_ModelConstruction;
-                    
-                    % Update row numbers
-                    nrows = size(this.tab_ModelConstruction.Table.Data,1);
-                    this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));     
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Data could not be assigned to the user interface table: Model Construction','File table data error');
-                     set(this.Figure, 'pointer', 'watch');
-                     drawnow update;
-                end     
-                
-                try       
-                    
-                    % Check if the input calibration table has 14 columns.
-                    % If so, then delete the calib settings column.
-                    if size(savedData.tableData.tab_ModelCalibration,2)==14
-                        savedData.tableData.tab_ModelCalibration = savedData.tableData.tab_ModelCalibration(:,[1:8,10:14]);
-                    end
-                    
-                    % The following loop addresses a problem that first
-                    % arose with the MCMC calibration (ie DREAM) whereby
-                    % the calib perforamnce states were shown for posterior all
-                    % parameter sets. The code eblow simple takes the coe
-                    % values from the GUI table and recalculates the mean.
-                    % Obviously if there is only one value listed then
-                    % this code does not chnage the result.
-                    if doPerforamnceMeasureRestimation
-                        % This code allows the recalculation of the model
-                        % performance statistics. Uncomment if required.
-                        h = waitbar(0,'Recalculating performance metrics. Please wait ...');
-                        for i=1:size(savedData.tableData.tab_ModelCalibration,1)
-                            
-                            % update progress bar
-                            waitbar(i/size(savedData.tableData.tab_ModelCalibration,1));
-                            
-                            % Get model lable
-                            model_Label = savedData.tableData.tab_ModelCalibration{i,2};
-                            model_Label = HydroSight_GUI.removeHTMLTags(model_Label); 
-                            model_Label = HydroSight_GUI.modelLabel2FieldName(model_Label);
-                            try
-                                tmpModel = getModel(this,model_Label);                        
-                            catch ME
-                                savedData.tableData.tab_ModelCalibration{i,10} = '<html><font color = "#808080">(NA)</font></html>';
-                                savedData.tableData.tab_ModelCalibration{i,12} = '<html><font color = "#808080">(NA)</font></html>';
-                                savedData.tableData.tab_ModelCalibration{i,13} = '<html><font color = "#808080">(NA)</font></html>';                            
-                                continue;
-                            end
-
-                            if ~isempty(tmpModel.calibrationResults) && tmpModel.calibrationResults.isCalibrated
-                                head_calib_resid = tmpModel.calibrationResults.data.modelledHead_residuals;
-                                SSE = sum(head_calib_resid.^2);
-                                RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
-                                tmpModel.calibrationResults.performance.RMSE = RMSE;                        
-
-                                % CoE
-                                obsHead =  tmpModel.calibrationResults.data.obsHead;
-                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
-                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(:,2));            
-                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
-                                                                
-                                CoE_cal = median(tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
-                                AICc = median(tmpModel.calibrationResults.performance.AICc);
-                                BIC = median(tmpModel.calibrationResults.performance.BIC);
-
-                                savedData.tableData.tab_ModelCalibration{i,10} = ['<html><font color = "#808080">',num2str(CoE_cal),'</font></html>'];
-
-                                savedData.tableData.tab_ModelCalibration{i,12} = ['<html><font color = "#808080">',num2str(AICc),'</font></html>'];
-                                savedData.tableData.tab_ModelCalibration{i,13} = ['<html><font color = "#808080">',num2str(BIC),'</font></html>'];
-
-                                if ~isempty(tmpModel.evaluationResults)
-                                    
-                                    head_eval_resid = tmpModel.evaluationResults.data.modelledHead_residuals;
-                                    obsHead =  tmpModel.evaluationResults.data.obsHead;
-                                    
-                                    % Mean error
-                                    tmpModel.evaluationResults.performance.mean_error = mean(head_eval_resid); 
-
-                                    %RMSE
-                                    SSE = sum(head_eval_resid.^2);
-                                    tmpModel.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
-
-                                    % Unbiased CoE
-                                    residuals_unbiased = bsxfun(@minus,head_eval_resid, tmpModel.evaluationResults.performance.mean_error);
-                                    SSE = sum(residuals_unbiased.^2);
-                                    tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
-                                    
-                                    CoE_eval = median(tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
-                                    savedData.tableData.tab_ModelCalibration{i,11} = ['<html><font color = "#808080">',num2str(CoE_eval),'</font></html>'];
-                                else
-                                    savedData.tableData.tab_ModelCalibration{i,11} = '<html><font color = "#808080">(NA)</font></html>';
-                                end  
-                                
-                                % Save model
-                                setModel(this,model_Label, tmpModel);                        
-                            else
-                                savedData.tableData.tab_ModelCalibration{i,10} = '<html><font color = "#808080">(NA)</font></html>';
-                                savedData.tableData.tab_ModelCalibration{i,12} = '<html><font color = "#808080">(NA)</font></html>';
-                                savedData.tableData.tab_ModelCalibration{i,13} = '<html><font color = "#808080">(NA)</font></html>';
-                            end
-                        end
-                       close (h);
-                    else
-                        colInd=[10:13];
-                        for i=colInd                    
-                            performanceStat = HydroSight_GUI.removeHTMLTags(savedData.tableData.tab_ModelCalibration(:,i));
-                            ind = cellfun(@(x) isempty(x) || strcmp(x,'(NA)'), performanceStat);
-                            performanceStat(ind) = repmat({'<html><font color = "#808080">(NA)</font></html>'},sum(ind),1);
-                            performanceStat(~ind) = cellfun( @(x) ['<html><font color = "#808080">',num2str(median(str2num(x))),'</font></html>'],performanceStat(~ind),'UniformOutput',false);
-                            savedData.tableData.tab_ModelCalibration(:,i) = performanceStat;
-                        end
-                    end
-                    this.tab_ModelCalibration.Table.Data = savedData.tableData.tab_ModelCalibration;
-                    
-                    % Update row numbers
-                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));           
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Data could not be assigned to the user interface table: Model Calibration','File table data error');
-                     set(this.Figure, 'pointer', 'watch');
-                     drawnow update;
-                end                
-                try
-                    this.tab_ModelSimulation.Table.Data = savedData.tableData.tab_ModelSimulation;
-                    
-                    % Update row numbers
-                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                    this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                       
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Data could not be assigned to the user interface table: Model Simulation','File table data error');
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;
-                end                              
-                    
-                % Assign analysed bores.
-                try
-                    this.dataPrep = savedData.dataPrep;
-                catch ME
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    warndlg('Loaded data analysis results could not be assigned to the user interface.','File model data error');
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;
-                end          
-                
-                % Update GUI title
-                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
-                drawnow update;   
-                
-                % Enable file menu items
-                for i=1:size(this.figure_Menu.Children,1)
-                    if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
-                        set(this.figure_Menu.Children(i),'Enable','on');
-                    elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                    strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                        if this.modelsOnHDD
-                            set(this.figure_Menu.Children(i),'Label', 'Move models from HDD to RAM...');
-                        else
-                            set(this.figure_Menu.Children(i),'Label', 'Move models from RAM to HDD...');
-                        end
-                        set(this.figure_Menu.Children(i),'Enable','on');
-                    end       
-                end
-
-            end
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;                    
-        end
-
-        function onImportModel(this,hObject,eventdata)
-
-            % Get current project folder
-            projectPath='';                
-            if isdir(this.project_fileName)
-                projectPath = this.project_fileName;
-            else
-                projectPath = fileparts(this.project_fileName);
-            end
-            
-            % Show open file window
-             if isempty(projectPath)
-                [fName,pName] = uigetfile({'*.mat'},'Select project file for importing...'); 
-            else
-                [fName,pName] = uigetfile({'*.mat'},'Select project file for importing...', projectPath); 
-            end            
-            if fName~=0;
-                % Assign file name to date cell array
-                fName = fullfile(pName,fName);
-            else
-                return;
-            end            
-            
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update;   
-            
-            % Check models are not stored off the HDD
-            newProject = load(this.project_fileName, 'settings','-mat');
-            if isfield(newProject.settings,'modelsOnHDD') && ~isempty(newProject.settings.modelsOnHDD)
-                warndlg({'The new project has the models offloaded to the hard-drive..';''; ...
-                         'Importing of such models s not yet supported.'},'Models cannot be imported...','modal');                
-                return
-            end    
-            
-            newProjectGUITables = load(fName, 'tableData','-mat');
-            
-            % Load models
-            newProject = load(fName, '-mat');
-            filt = strcmp(fieldnames(newProject),'dataPrep') | ...
-                   strcmp(fieldnames(newProject),'settings') |  ...
-                   strcmp(fieldnames(newProject),'tableData') |  ...
-                   strcmp(fieldnames(newProject),'model_labels');
-            removedFields = fieldnames(newProject);
-            removedFields = removedFields(filt);                            
-            newProject = rmfield(newProject,removedFields);
-            
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;               
-            
-            % Show list models
-            newModelNames = fieldnames(newProject);
-            [listSelection,ok] = listdlg('PromptString',{'Below is a list of the built models within the selected project.','Select the models(s) to import.'}, ...
-                                    'ListString',newModelNames, ...
-                                    'ListSize',[400,300], ...
-                                    'Name','Select models to import ...');
-            if ok~=1  
-                return
-            end
-                
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update;               
-            
-            % Import selected models  
-            importedModels = cell(0,1);
-            nModels = 0;
-            for i=1:length(listSelection)
-                
-                % Build new model label                
-                newModelLabel = newModelNames{listSelection(i)};
-                ModelLabel = newModelLabel;
-                nModels = nModels +1;
-                importedModels{nModels,1} = newModelLabel;
-                
-                % Check if model exists
-                if isfield(this.models,newModelLabel)
-                    newModelLabel = [newModelLabel,'_imported'];
-                end
-                
-                % Find the model within the GUI tables.
-                filt = strcmp(newProjectGUITables.tableData.tab_ModelConstruction(:,2), ModelLabel);
-                if ~isempty(filt)
-                    newTableData = newProjectGUITables.tableData.tab_ModelConstruction(filt,:);
-                    if size(newTableData,2) ~=size(this.tab_ModelConstruction.Table.Data,2)
-                        importedModels{nModels,1} = [importedModels{nModels,1},': Error - construction table inconsistency'];
-                        continue
-                    end
-                    newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
-                    this.tab_ModelConstruction.Table.Data(end+1,:)=newTableData(1,:);
-                    this.tab_ModelConstruction.Table.RowName{end+1} = num2str(str2num(this.tab_ModelConstruction.Table.RowName{end})+1);
-                end
-                newProjectTableLabels = HydroSight_GUI.removeHTMLTags(newProjectGUITables.tableData.tab_ModelCalibration(:,2));
-                filt = strcmp(newProjectTableLabels, ModelLabel);
-                if ~isempty(filt)
-                    newTableData = newProjectGUITables.tableData.tab_ModelCalibration(filt,:);
-                    if size(newTableData,2) ~=size(this.tab_ModelCalibration.Table.Data,2)
-                        importedModels{nModels,1} = [importedModels{nModels,1},': Error - calib. table inconsistency'];
-                        continue
-                    end
-                    newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
-                    this.tab_ModelCalibration.Table.Data(end+1,:)=newTableData(1,:);
-                    this.tab_ModelCalibration.Table.RowName{end+1} = num2str(str2num(this.tab_ModelCalibration.Table.RowName{end})+1);
-                end
-                newProjectTableLabels = HydroSight_GUI.removeHTMLTags(newProjectGUITables.tableData.tab_ModelSimulation(:,2));
-                filt = strcmp(newProjectTableLabels, ModelLabel);                
-                filt = find(strcmp(newProjectGUITables.tableData.tab_ModelSimulation, ModelLabel));
-                if ~isempty(filt)
-                    for j=filt
-                        newTableData = newProjectGUITables.tableData.tab_ModelSimulation(j,:);
-                        if size(newTableData,2) ~=size(this.tab_ModelSimulation.Table.Data,2)
-                            importedModels{nModels,1} = [importedModels{nModels,1},': Warning - simulation table inconsistency'];
-                            break
-                        end
-                        newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
-                        this.tab_ModelSimulation.Table.Data(end+1,:)=newTableData(1,:);
-                        this.tab_ModelSimulation.Table.RowName{end+1} = num2str(str2num(this.tab_ModelSimulation.Table.RowName{end})+1);
-                    end              
-                end
-                
-                % Add model object and label.
-                try          
-                    setModel(this, newModelLabel, newProject.(ModelLabel));
-                catch ME
-                    importedModels{nModels,1} = [importedModels{nModels,1},': Error - model import failure'];
-                    continue
-                end
-                    
-                % Record model was added
-                importedModels{nModels,1} = [importedModels{nModels,1},': Successfully imported'];
-            end
-                
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;               
-            
-            msgbox({'Below is a summary of the importation:','',importedModels{:}}, ...
-                         'Model import summary...','modal');    
-        end
-        
-        % Save as current model        
-        function onSaveAs(this,hObject,eventdata)
-            
-            % set current folder to the project folder (if set)
-            currentProjectFolder='';
-            if ~isempty(this.project_fileName)                                
-                try    
-                    if isdir(this.project_fileName)
-                        currentProjectFolder = this.project_fileName;
-                    else
-                        currentProjectFolder = fileparts(this.project_fileName);
-                    end 
-                    
-                    currentProjectFolder = [currentProjectFolder,filesep];
-                    cd(currentProjectFolder);
-                catch
-                    % do nothing
-                end
-            end
-            
-            [fName,pName] = uiputfile({'*.mat'},'Save models as ...');    
-            if fName~=0;
-                
-                % Get the current project folder. If the project folder has
-                % changed then warn the user that all infput file names
-                % must be within the new project folder.
-                if ~isempty(currentProjectFolder)                    
-                    if ~strcmp(currentProjectFolder, pName)
-                        warndlg({'The project folder is different to that already set.';''; ...
-                                 'Importantly, all file names in the project are relative'; ...
-                                 'to the project folder and so all input .csv files must be'; ...
-                                 'within the project folder or a sub-folder within it.'},'Input file name invalid.','modal');
-                    end
-                end
-
-                % Change cursor
-                set(this.Figure, 'pointer', 'watch');    
-                drawnow update;
-                
-                % Assign file name to date cell array
-                this.project_fileName = fullfile(pName,fName);
-                
-                % Collate the tables of data to a temp variable.
-                tableData.tab_Project.title = this.tab_Project.project_name.String;
-                tableData.tab_Project.description = this.tab_Project.project_description.String;
-                tableData.tab_DataPrep = this.tab_DataPrep.Table.Data;
-                tableData.tab_ModelConstruction = this.tab_ModelConstruction.Table.Data;
-                tableData.tab_ModelCalibration = this.tab_ModelCalibration.Table.Data;
-                tableData.tab_ModelSimulation = this.tab_ModelSimulation.Table.Data;
-                                
-                % Get the data preparation results
-                dataPrep = this.dataPrep;               
-                
-                % Get settings
-                settings.HPCoffload = this.HPCoffload;
-                settings.modelsOnHDD = this.modelsOnHDD;
-                
-                % Get model labels & calib. status
-                model_labels = this.model_labels;                
-                
-                % Save the GUI tables to the file.
-                save(this.project_fileName, 'tableData', 'dataPrep', 'model_labels','settings', '-v7.3');  
-                
-                % Save models. NOTE: If the models are offloaded to the
-                % HDD, then only the file path to the individual model
-                % needs to be saved.
-                if ~isempty(this.models)
-                    try
-                        tmpModels = this.models;
-                        save(this.project_fileName, '-struct', 'tmpModels', '-append');
-                        clear tmpModels;                                       
-
-                        % Enable file menu items
-                        for i=1:size(this.figure_Menu.Children,1)
-                            if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
-                                set(this.figure_Menu.Children(i),'Enable','on');
-                            elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                            strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                                set(this.figure_Menu.Children(i),'Enable','on');
-                            end       
-                        end
-
-                    catch ME
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;
-                        warndlg('The project could not be saved. Please check you have write access to the directory.','Project not saved ...');
-                        return;
-                    end     
-                end
-                
-                % Update GUI title
-                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);                                        
-                
-            end
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;
-            
-        end
-        
-        % Save model        
-        function onSave(this,hObject,eventdata)
-        
-            if isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2;
-                onSaveAs(this,hObject,eventdata);
-            else               
-                % Change cursor
-                set(this.Figure, 'pointer', 'watch');   
-                drawnow update;
-                
-                % Collate the tables of data to a temp variable.
-                tableData.tab_Project.title = this.tab_Project.project_name.String;
-                tableData.tab_Project.description = this.tab_Project.project_description.String;
-                tableData.tab_DataPrep = this.tab_DataPrep.Table.Data;
-                tableData.tab_ModelConstruction = this.tab_ModelConstruction.Table.Data;
-                tableData.tab_ModelCalibration = this.tab_ModelCalibration.Table.Data;
-                tableData.tab_ModelSimulation = this.tab_ModelSimulation.Table.Data;
-                                
-                % Get the data preparation results
-                dataPrep = this.dataPrep;
-                
-                % Get settings
-                settings.HPCoffload = this.HPCoffload;
-                settings.modelsOnHDD = this.modelsOnHDD;                               
-                
-                % Get model labels & calib. status
-                model_labels = this.model_labels;
-                
-                % Save the GUI tables to the file.
-                save(this.project_fileName, 'tableData', 'dataPrep', 'model_labels','settings', '-v7.3');  
-                if ~isempty(this.models)
-                    try
-                        % Save each model as a single variable in file. If the
-                        % models have been offloaded to HDD then only the paths
-                        % to the files will be saved.
-                        tmpModels = this.models;
-                        save(this.project_fileName, '-struct', 'tmpModels', '-append');
-                        clear tmpModels;
-                    catch
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;
-                        warndlg('The project models could not be saved. Please check you have write access to the directory.','Project not saved ...');
-                        return;                    
-                    end
-                end
-            end            
-            
-            set(this.Figure, 'pointer', 'arrow');   
-            drawnow update;
-        end    
-        
-        % This function runs when the app is closed        
-        function onExit(this,hObject,eventdata)    
-            
-            ans = questdlg('Do you want to save the project before exiting?','Save project?','Yes','No','Cancel','Yes');
-            
-            % Save project
-            if strcmp(ans,'Yes')
-                onSave(this,hObject,eventdata);
-            end
-            
-            % Check that it was saved (ie if saveas was called from save() )
-            if ~strcmp(ans,'No') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
-                warndlg('HydroSight cannot exit because the project does not appear to have been saved.','Project save error ...');
-                return
-            end
-            
-            % Exit
-            if strcmp(ans,'Yes') || strcmp(ans,'No')
-                delete(this.Figure);
-            end
-        end
-
-        function dataPrep_tableSelection(this, hObject, eventdata)
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);            
-            data=get(hObject,'Data'); % get the data cell array of the table
-            
-            % Fix bug in col 15 bng integer, not logical
-            if ~islogical(data{1,15})
-                if data{1,15}==1
-                    data{1,15}=true;
-                else
-                    data{1,15}=false;
-                end
-                set(hObject,'Data',data);
-            end
-            
-            % Undertake column specific operations.
-            if ~isempty(icol) && ~isempty(irow)
-                
-                % Record the current row and column numbers
-                this.tab_DataPrep.currentRow = irow;
-                this.tab_DataPrep.currentCol = icol;
-            
-                % Remove HTML tags from the column name
-                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
-                
-                switch columnName;
-                    case 'Obs. Head File'
-                        this.tab_DataPrep.modelOptions.vbox.Heights = [0; 0];
-
-                        % Get file name and remove project folder from
-                        % preceeding full path.
-                        fName = getFileName(this, 'Select the Observed Head file.');                                                
-                        if fName~=0;
-                            % Assign file name to date cell array
-                            data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
-                            
-                            % Input file name to the table
-                            set(hObject,'Data',data);
-                        end                        
-                        
-                    case 'Bore ID'
-                         % Check the obs. head file is listed
-                         fname = data{eventdata.Indices(1),2};
-                         if isempty(fname)
-                            warndlg('The observed head file name must be input before selecting the bore ID');
-                            return;
-                         end
-                         
-                         % Construct full file path name
-                         if isdir(this.project_fileName)                             
-                            fname = fullfile(this.project_fileName,fname); 
-                         else
-                            fname = fullfile(fileparts(this.project_fileName),fname);  
-                         end                         
-                         
-                         % Check the bore ID file exists.
-                         if exist(fname,'file') ~= 2;
-                            warndlg('The observed head file does not exist.');
-                            return;
-                         end
-                         
-                         % Read in the observed head file.
-                         try                            
-                            tbl = readtable(fname);
-                         catch
-                            warndlg('The observed head file could not be read in. It must a .csv file of 6 columns');
-                            return;
-                         end
-                        
-                         % Check there are the correct number of columns
-                         if length(tbl.Properties.VariableNames) < 5 || length(tbl.Properties.VariableNames) >8
-                            warndlg({'The observed head file must be in one of the following formats:', ...'
-                                '  -boreID, year, month, day, head', ...
-                                '  -boreID, year, month, day, hour, minute, head', ...
-                                '  -boreID, year, month, day, hour, minute, second, head'});
-                            return;
-                         end
-                             
-                         % Check columns 2 to 6 are numeric.
-                         if any(any(~isnumeric(tbl{:,2:end})))
-                            warndlg('Columns 2 to 6 within the observed head file must contain only numeric data.');
-                            return;
-                         end
-                            
-                         % Find the unique bore IDs   
-                         boreIDs = unique(tbl{:,1});
-                         
-                         % Free up memory
-                         clear tbl;
-                         
-                         % Input the unique bore IDs to the list box.
-                         set(this.tab_DataPrep.modelOptions.boreIDList,'String',boreIDs);  
-                         
-                         % Show the list box.
-                         this.tab_DataPrep.modelOptions.vbox.Heights = [-1; 0];                        
-                    otherwise
-                        % Show the results if the bore has been analysed.
-                        
-                        boreID = data{eventdata.Indices(1),3};
-                        modelStatus = HydroSight_GUI.removeHTMLTags(data{eventdata.Indices(1),16});
-                        
-                        if ~isempty(this.dataPrep) && ~isempty(boreID) && ...
-                        isfield(this.dataPrep,boreID) && ~isempty(this.dataPrep.(boreID)) && ...
-                        strcmp(modelStatus,'Bore analysed.')
-                            this.tab_DataPrep.modelOptions.vbox.Heights = [0; -1];                    
-                            set(this.tab_DataPrep.modelOptions.resultsOptions.box, 'Sizes',[-1 -1]);
-                            
-                            % Get the analysis results.
-                            headData = this.dataPrep.(boreID);
-                            
-                            % Add head data to the uitable                            
-                            nrows = size(headData,1);
-                            ncols = size(headData,2);
-                            this.tab_DataPrep.modelOptions.resultsOptions.table.Data = table2cell(headData);
-
-                            % Convert to a matrix
-                            headData = table2array(headData);                            
-                            
-                            % Create a time vector form head data.
-                            dateVec = datenum(headData(:,1),headData(:,2),headData(:,3),headData(:,4),headData(:,5),zeros(size(headData,1),1));
-                            
-                            % Plot the hydrograph and the errors and outliers
-                            isError = any(headData(:,7:end)==1,2);
-                            h= plot(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(~isError), headData(~isError,6),'b.-');
-                            legendstring{1} = 'Obs. head';
-                            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
-                            
-                            % Date errors
-                            col = 7;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'Date error'};
-                            end
-                            
-                            col = 8;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'Duplicate error'};
-                            end
-
-                            col = 9;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'Min head error'};
-                            end   
-                            
-                            col = 10;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'Max head error'};
-                            end  
-                            
-                            col = 11;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} '|dh/dt| error'};
-                            end                              
-                            
-                            col = 12;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'dh/dt=0 error'};
-                            end                              
-                            
-                            col = 13;
-                            isError = headData(:,col)==1;
-                            if any(isError)
-                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                                legendstring = {legendstring{:} 'Outlier obs.'};
-                            end  
-                            
-                            % Format plot
-                            %datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x','YY');
-                            datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x');
-                            xlabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Year');
-                            ylabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Head');
-                            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'off');
-                            box(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
-                            axis(this.tab_DataPrep.modelOptions.resultsOptions.plots,'tight');
-                            legend(this.tab_DataPrep.modelOptions.resultsOptions.plots, legendstring,'Location','eastoutside');
-                            
-                            % SHow results
-                            this.tab_DataPrep.modelOptions.resultsOptions.box.Heights = [-1 -1];
-                            
-                            % SHow plot icons
-                            plotToolbarState(this,'on');
-                        else
-                            this.tab_DataPrep.modelOptions.vbox.Heights = [0; 0];
-                        end
-                end
-            end
-        end
-        
-        % Data preparation results table edit.
-        function dataPrep_resultsTableEdit(this, hObject, eventdata)
-
-            % Check the row was found.
-            if ~isfield(this.tab_DataPrep,'currentRow') || isempty(this.tab_DataPrep.currentRow)
-                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
-                return;
-            end            
-            
-            % Get the current row from the main data preparation table.
-            irow = this.tab_DataPrep.currentRow;
-            boreID = this.tab_DataPrep.Table.Data{irow, 3};
-                
-            % Check the row was found.
-            if isempty(boreID)
-                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
-                return;
-            end
-
-            % Get the new table of data.
-            headData=get(hObject,'Data');            
-            
-            % Check the number of rows in the data strcuture equal that of
-            % the results table.
-            if size(this.dataPrep.(boreID),1) ~= size(headData,1)
-                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
-                return;
-            end
-                        
-            % Update the data analysis data structure.
-            this.dataPrep.(boreID)(:,:) = headData;
-            
-            %Convert to matrix.
-            headData = table2array(this.dataPrep.(boreID)(:,:));
-            
-            % Update table statistics
-            numErroneouObs = sum(any(headData(:,7:12),2));
-            numOutlierObs = sum(headData(:,13));                    
-            this.tab_DataPrep.Table.Data{irow,17} = ['<html><font color = "#808080">',num2str(numErroneouObs),'</font></html>'];
-            this.tab_DataPrep.Table.Data{irow,18} = ['<html><font color = "#808080">',num2str(numOutlierObs),'</font></html>'];            
-            
-            % Redraw the plot.
-            %--------------------------------------------------------------
-
-            
-            % Create a time vector form head data.
-            dateVec = datenum(headData(:,1),headData(:,2),headData(:,3),headData(:,4),headData(:,5),zeros(size(headData,1),1));
-
-            % Plot the hydrograph and the errors and outliers
-            isError = any(headData(:,7:end)==1,2);
-            h= plot(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(~isError), headData(~isError,6),'b.-');
-            legendstring{1} = 'Obs. head';
-            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
-
-            % Date errors
-            col = 7;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'Date error'};
-            end
-
-            col = 8;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'Duplicate error'};
-            end
-
-            col = 9;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'Min head error'};
-            end   
-
-            col = 10;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'Max head error'};
-            end  
-
-            col = 11;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} '|dh/dt| error'};
-            end                              
-
-            col = 12;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'dh/dt=0 error'};
-            end                              
-
-            col = 13;
-            isError = headData(:,col)==1;
-            if any(isError)
-                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
-                legendstring = {legendstring{:} 'Outlier obs.'};
-            end  
-
-            % Format plot
-            datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x','YY');
-            xlabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Year');
-            ylabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Head');
-            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'off');
-            box(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
-            axis(this.tab_DataPrep.modelOptions.resultsOptions.plots,'tight');
-            legend(this.tab_DataPrep.modelOptions.resultsOptions.plots, legendstring,'Location','eastoutside');            
-            
-            
-            
-        end
-        
-        function dataPrep_optionsSelection(this, hObject, eventdata)
-            try                         
-                switch this.tab_DataPrep.currentCol
-                    case 3 % Bore ID column
-                        
-                         % Get selected bores
-                         listSelection = get(hObject,'Value');
-
-                         % Get data from model construction table
-                         data=get(this.tab_DataPrep.Table,'Data'); 
-
-                         % Get the selected bore row index.
-                         index_selected = this.tab_DataPrep.currentRow;
-
-                         % Check if the nwe bore ID is unique.
-                         newBoreID = hObject.String(listSelection,1);
-                         if any(strcmp( data(:,3) , newBoreID) & index_selected~=[1:size(data,1)]')
-                             warndlg('The bore ID must be unique.','Bore ID error...');
-                             return;
-                         end
-                         
-                         % Check the bore ID is a valid field name.
-                         tmp=struct();
-                         try 
-                             tmp.(newBoreID{1}) = 1;
-                         catch ME
-                             warndlg('The bore ID is of an invalid format. It must not start with a number. Consider appending a non-numeric prefix, eg "Bore_"','Bore ID error...');
-                             return;
-                         end
-                         
-                         
-                         % Add selected bore ID is cell array at the currently
-                         % selected bore.
-                         data(index_selected,3) =  hObject.String(listSelection,1);
-
-                         % Set bore ID 
-                         set(this.tab_DataPrep.Table,'Data', data); 
-                end
-            catch ME
-                return;
-            end        
-        end        
-        
-        %Cell selection response for tab 1 - model construction
-        function modelConstruction_tableSelection(this, hObject, eventdata)
-            
-            % Hide plotting toolbars
-            plotToolbarState(this, 'off');
-            
-            % Get table indexes
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);                        
-            
-            % Undertake column specific operations.
-            if isempty(icol) && isempty(irow)
-                return
-            end
-            
-            % Hide the adjacent panels
-            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0; 0; 0];
-                        
-            % Remove HTML tags from the column name
-            columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
-
-            if ~(strcmp(columnName, 'Obs. Head File') || ...
-            strcmp(columnName, 'Forcing Data File') || ...
-            strcmp(columnName, 'Coordinates File') || ...
-            strcmp(columnName, 'Bore ID') || ...
-            strcmp(columnName, 'Model Type') || ...
-            strcmp(columnName, 'Model Options'))
-                return;
-            end
-            
-            % Record the current row and column numbers
-            this.tab_ModelConstruction.currentRow = irow;
-            this.tab_ModelConstruction.currentCol = icol;
-                        
-            % Get the data cell array of the table            
-            data=get(hObject,'Data'); 
-            
-            switch columnName;
-                case 'Obs. Head File'
-                    % Get file name and remove project folder from
-                    % preceeding full path.                    
-                    fName = getFileName(this, 'Select the Observed Head file.');               
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
-
-                        % Input file name to the table
-                        set(hObject,'Data',data);
-                    end
-
-                     % Hide the panels.
-                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];                        
-
-                case 'Forcing Data File'
-
-                    % Get file name and remove project folder from
-                    % preceeding full path.
-                    fName = getFileName(this, 'Select the Forcing Data file.');                                                
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
-
-                        % Input file name to the table
-                        set(hObject,'Data',data);
-                    end
-                    % Hide the panels.
-                    this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];                        
-
-                case 'Coordinates File'
-                    % Get file name and remove project folder from
-                    % preceeding full path.
-                    fName = getFileName(this, 'Select the Coordinates file.');                                                
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
-
-                        % Input file name to the table
-                        set(hObject,'Data',data);
-                    end
-
-                    % Hide the panels.
-                    this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];
-
-                case 'Bore ID'
-                     % Check the obs. head file is listed
-                     fname = data{eventdata.Indices(1),3};
-                     if isempty(fname)
-                        warndlg('The observed head file name must be input before selecting the bore ID');
-                        return;
-                     end
-
-                     % Construct full file name.
-                     if isdir(this.project_fileName)                             
-                        fname = fullfile(this.project_fileName,fname); 
-                     else
-                        fname = fullfile(fileparts(this.project_fileName),fname);  
-                     end
-
-                     % Check the bore ID file exists.
-                     if exist(fname,'file') ~= 2;
-                        warndlg('The observed head file does not exist.');
-                        return;
-                     end
-
-                     % Read in the observed head file.
-                     try
-                        tbl = readtable(fname);
-                     catch
-                        warndlg('The observed head file could not be read in. It must a .csv file of 6 columns');
-                        return;
-                     end
-
-                     % Check there are the correct number of columns
-                     if length(tbl.Properties.VariableNames) <5 || length(tbl.Properties.VariableNames) >8
-                        warndlg({'The observed head file in one of the following structure:', ...
-                            '- boreID, year, month, day, head', ...
-                            '- boreID, year, month, day, hour, head', ...
-                            '- boreID, year, month, day, hour, minute, head', ...
-                            '- boreID, year, month, day, hour, minute, second, head', ...
-                            });
-                        return;
-                     end
-
-                     % Check columns 2 to 6 are numeric.
-                     if any(any(~isnumeric(tbl{:,2:length(tbl.Properties.VariableNames)})))
-                        warndlg(['Columns 2 to ',num2str(length(tbl.Properties.VariableNames)),' within the observed head file must contain only numeric data.']);
-                        return;
-                     end
-
-                     % Find the unique bore IDs   
-                     boreIDs = unique(tbl{:,1});
-
-                     % Free up memory
-                     clear tbl;
-
-                     % Input the unique bore IDs to the list box.
-                     set(this.tab_ModelConstruction.boreIDList,'String',boreIDs);  
-
-                     % Show the list box.
-                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [-1; 0 ; 0; 0];
-                case 'Model Type'                        
-                     % Get the current model type.
-                     modelType = data{irow,7};
-
-                     % Get description for the current model type
-                     try                         
-                        modelDecription  =eval([modelType,'.description()']);                          
-                     catch
-                        modelDecription = 'No decription is available for the selected model.';
-                     end
-
-                     % Assign model decription to GUI string box                         
-                     this.tab_ModelConstruction.modelDescriptions.String = modelDecription; 
-
-                     % Show the description.
-                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; -1 ; 0; 0];
-
-                case 'Model Options'
-                    % Check the preceeding inputs have been defined.
-                    if  any(cellfun(@(x) isempty(x), data(irow,3:7)))
-                        warndlg('The observed head data file, forcing data file, coordinates file, bore ID and model type must be input before the model option can be set.');
-                        return;
-                    end
-
-                    % Set the forcing data, bore ID, coordinates and options
-                    % for the given model type.
-                    try          
-                        modelType = eventdata.Source.Data{irow,7};
-
-                        if isempty(this.project_fileName)
-                            warningdlg('The project folder must be set before the model options can be viewed or edited.')
-                            return
-                        end
-                        if isdir(this.project_fileName)
-                            dirname = this.project_fileName;
-                        else
-                            dirname = fileparts(this.project_fileName);
-                        end                            
-
-                        fname = fullfile(dirname,data{irow,4}); 
-                        setForcingData(this.tab_ModelConstruction.modelTypes.(modelType).obj, fname);
-                        fname = fullfile(dirname,data{irow,5});
-                        setCoordinatesData(this.tab_ModelConstruction.modelTypes.(modelType).obj, fname);                        
-                        setBoreID(this.tab_ModelConstruction.modelTypes.(modelType).obj, data{irow,6});
-
-                        % If the model options are empty, then add a
-                        % default empty cell, else set the existing
-                        % options into the model type GUI RHS panel.
-                        if isempty(eventdata.Source.Data{irow,8}) || strcmp(eventdata.Source.Data{irow,8},'{}')
-                            %eventdata.Source.Data{irow,8} = [];
-                            data{irow,8} = [];
-                        end
-                        setModelOptions(this.tab_ModelConstruction.modelTypes.(modelType).obj, data{irow,8})
-
-                    catch ME
-                        warndlg('Unknown model type selected or the GUI for the selected model crashed.');
-                        this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0;  0; 0];
-                    end
-
-                     % Show model type options.
-                     switch modelType
-                         case 'model_TFN'
-                            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0;-1; 0];
-                         case 'ExpSmooth'
-                            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0; 0; 0];
-                         otherwise
-                             this.tab_ModelConstruction.modelOptions.vbox.Heights =[0; 0; 0; 0];
-                     end
-
-                otherwise
-                     % Hide the panels.
-                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];
-                end
-
-        end
-            
-        function modelConstruction_optionsSelection(this, hObject, eventdata)
-            try                         
-                switch this.tab_ModelConstruction.currentCol
-                    case 6
-                         % Get selected bores
-                         listSelection = get(hObject,'Value');
-
-                         % Get data from model construction table
-                         data=get(this.tab_ModelConstruction.Table,'Data'); 
-
-                         % Get the selected bore row index.
-                         index_selected = this.tab_ModelConstruction.currentRow;
-
-                         % Add selected bore ID is cell array at the currently
-                         % selected bore.
-                         data(index_selected,6) =  hObject.String(listSelection,1);
-
-                         % Set bore ID 
-                         set(this.tab_ModelConstruction.Table,'Data', data); 
-                end
-            catch ME
-                return;
-            end        
-        end
-        
-        function modelConstruction_tableEdit(this, hObject, eventdata)
-            
-            % Hide plotting toolbars
-            plotToolbarState(this, 'off');            
-            
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);                        
-            
-            % Undertake column specific operations.
-            if ~isempty(icol) && ~isempty(irow)
-
-                % Record the current row and column numbers
-                this.tab_ModelConstruction.currentRow = irow;
-                this.tab_ModelConstruction.currentCol = icol;
-            
-                % Remove HTML tags from the column name
-                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});                
-                                
-                % Warn the user if the model is already built and the
-                % inputs are to change - reuiring the model object to be
-                % removed.
-                if ~isempty(this.models) && ~strcmp(eventdata.PreviousData, eventdata.NewData) && icol~=1 && ~isempty(eventdata.PreviousData)
-                    
-                    % Get original model label
-                    if strcmp(columnName, 'Model Label')
-                        modelLabel=eventdata.PreviousData;
-                    else
-                        modelLabel = hObject.Data{irow,2};
-                    end
-                    
-                    
-                    % Convert model label to field label.
-                    model_labelAsField = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-                    
-                    % Check if the model object exists                    
-                    if any(strcmp(fieldnames(this.models), model_labelAsField))
-                       
-                        % Check if the model is calibrated
-                        try
-                            isCalibrated = this.model_labels{model_labelAsField,1};
-                        catch
-                            isCalibrated = false;
-                        end
-                        
-                        % Create warnign message and display
-                        if isCalibrated
-                            msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
-                                    '', ...                                
-                                   'Do you want to continue with the changes to the model construction?'};
-                        else
-                            msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
-                                    '', ...
-                                   'Do you want to continue with the changes to the model construction?'};
-                        end                            
-                               
-                        response = questdlg(msg,'Overwrite exiting model?','Yes','No','No');
-                        
-                        % Check if 'cancel, else delete the model object
-                        if strcmp(response,'No')
-                            return
-                        else
-                            this.models = rmfield(this.models,model_labelAsField);
-                        end
-                                                                        
-                        % Change status of the model object.
-                        hObject.Data{irow,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
-
-                        % Delete model from calibration table.
-                        modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
-                        modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
-                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
-                        this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
-
-                        % Update row numbers
-                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                     
-                        
-                        % Delete models from simulations table.
-                        modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);                            
-                        modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
-                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
-                        this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);                        
-                        
-                        % Update row numbers
-                        nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                        this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                     
-                    end
-                    
-                end
-
-                
-                switch columnName;
-                    
-                    case 'Model Label'                    
-                        % Get current and all modle labels
-                        allLabels = hObject.Data(:,2);
-                        newLabel = eventdata.NewData;
-                        
-                        % Check the model label can be converted to an
-                        % appropriate field name (for saving)                        
-                        if isempty(HydroSight_GUI.modelLabel2FieldName(newLabel))
-                            return;
-                        end
-                        
-                        % Check that the model label is unique.
-                        newLabel = HydroSight_GUI.createUniqueLabel(allLabels, newLabel, irow);                          
-                        
-                        % Report error if required
-                        if ~strcmp(newLabel, hObject.Data{irow,2})
-                            warndlg('The model label must be unique! An extension has been added to the label.','Model label error ...');
-                        end                        
-                        
-                        % Input model label to GUI
-                        hObject.Data{irow,2} = newLabel;
-                        
-                    case 'Model Options'
-                        if any(strcmp(hObject.Data{irow,7},{'model_TFN'}))
-                            modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(hObject.Data{irow,7}).obj);
-                        elseif strcmp(hObject.Data{irow,7},'expSmooth') 
-                            % do nothing
-                        else
-                            error('Model type unmkown.')
-                        end
-                        hObject.Data(irow,icol) = modelOptionsArray;
-
-                    case 'Model Type'                        
-                         % Get the current model type.
-                         modelType = hObject.Data{irow,7};
-                         
-                         % Get description for the current model type
-                         try                         
-                            modelDecription  =eval([modelType,'.description()']);                          
-                         catch
-                         	modelDecription = 'No decription is available for the selected model.';
-                         end
-                             
-                         
-                         % Assign model decription to GUI string b8ox                         
-                         this.tab_ModelConstruction.modelDescriptions.String = modelDecription; 
-                         
-                         % Show the description.
-                         this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; -1 ; 0; 0];                        
-                    otherwise
-                            % Do nothing                                             
-                end
-            end
-        end
-        
-        function modelCalibration_tableSelection(this, hObject, eventdata)
-                        
-            % Get indexes to table data
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);            
-            data=get(hObject,'Data'); % get the data cell array of the table
-            
-            % Reset stored daily forcing data.
-            this.tab_ModelCalibration.resultsOptions.forcingData.data_input = [];
-            this.tab_ModelCalibration.resultsOptions.forcingData.data_derived = [];
-            this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input = {};
-            this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived = {};
-            this.tab_ModelCalibration.resultsOptions.forcingData.filt=[];
-            
-            % Exit if no cell is selected.
-            if isempty(icol) && isempty(irow)
-                return
-            end
-                
-            % Record the current row and column numbers
-            this.tab_ModelCalibration.currentRow = irow;
-            this.tab_ModelCalibration.currentCol = icol;
-
-            % Remove HTML tags from the column name
-            columnName  = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
-
-            % Exit if no results are to be viewed and none of the following are to columns edits
-            if ~(strcmp(columnName, 'Calib. Start Date') || strcmp(columnName, 'Calib. End Date'))                  
-                if isfield(this.tab_ModelCalibration,'resultsOptions')
-                    if isempty(this.tab_ModelCalibration.resultsOptions.currentTab) || ...
-                    isempty(this.tab_ModelCalibration.resultsOptions.currentPlot)
-                        return;                    
-                    end
-                else
-                    return
-                end
-            end
-
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');   
-            drawnow update;   
-
-            switch columnName;
-                case 'Calib. Start Date'
-                    % Get start and end dates of the observed head
-                    % data, remove HTML tags and then convert to a
-                    % date number.
-                    startDate = data{irow,4};
-                    endDate = data{irow,5};
-                    startDate = HydroSight_GUI.removeHTMLTags(startDate);
-                    endDate = HydroSight_GUI.removeHTMLTags(endDate);                        
-                    startDate = datenum(startDate,'dd-mmm-yyyy');
-                    endDate = datenum(endDate,'dd-mmm-yyyy');
-
-                    % Open the calander with the already input date,
-                    % else use the start date of the obs. head.
-                    if isempty(data{irow,icol})
-                        inputDate = startDate;                            
-                    else
-                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
-                    end
-                    selectedDate = uical(inputDate, 'English',startDate, endDate);
-
-                    % Get the calibration end date 
-                    calibEndDate = datenum( data{irow,icol+1},'dd-mmm-yyyy');                        
-
-                    % Check date is between start and end date of obs
-                    % head.
-                    if selectedDate < startDate || selectedDate > endDate    
-                        warndlg('The calibration start date must be within the range of the observed head data.');
-                    elseif calibEndDate<=selectedDate
-                        warndlg('The calibration start date must be less than the calibration end date.');
-                    else
-                        data{eventdata.Indices(1),eventdata.Indices(2)} = datestr(selectedDate,'dd-mmm-yyyy');
-                    end
-                    set(hObject,'Data',data);
-                case 'Calib. End Date'
-                    % Get start and end dates of the observed head
-                    % data, remove HTML tags and then convert to a
-                    % date number.
-                    startDate = data{irow,4};
-                    endDate = data{irow,5};
-                    startDate = HydroSight_GUI.removeHTMLTags(startDate);
-                    endDate = HydroSight_GUI.removeHTMLTags(endDate);
-                    startDate = datenum(startDate,'dd-mmm-yyyy');
-                    endDate = datenum(endDate,'dd-mmm-yyyy');
-
-                    % Open the calander with the already input date,
-                    % else use the start date of the obs. head.
-                    if isempty(data{irow,icol})
-                        inputDate = endDate;                            
-                    else
-                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
-                    end
-                    selectedDate = uical(inputDate, 'English',startDate, endDate);
-
-                    % Get the calibration start date 
-                    calibStartDate = datenum( data{irow,icol-1},'dd-mmm-yyyy');                        
-
-                    % Check date is between start and end date of obs
-                    % head.
-                    if selectedDate < startDate || selectedDate > endDate    
-                        warndlg('The calibration end date must be within the range of the observed head data.');
-                    elseif calibStartDate>=selectedDate
-                        warndlg('The calibration end date must be greater than the calibration start date.');
-                    else
-                        data{eventdata.Indices(1),eventdata.Indices(2)} = datestr(selectedDate,'dd-mmm-yyyy');
-                    end
-                    set(hObject,'Data',data);
-
-                otherwise
-                    % Do nothing
-            end
-            
-            % Check there is any table data
-            if isempty(data)
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                   
-                return;
-            end            
-                         
-                        
-            % Find index to the calibrated model label within the
-            % list of constructed models.
-            if isempty(irow)
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                                   
-                return;
-            end
-            
-            % Find the curretn model label
-            calibLabel = HydroSight_GUI.removeHTMLTags(data{this.tab_ModelCalibration.currentRow,2});             
-            this.tab_ModelCalibration.currentModel = calibLabel;
-            
-            % Get a copy of the model object. This is only done to
-            % minimise HDD read when the models are off loaded to HDD using
-            % matfile();
-            tmpModel = getModel(this, calibLabel);
-            
-            
-            % Update table of data
-            %-------------------------------------------------------------
-            % Display the requested calibration results if the model object
-            % exists and there are calibration results.
-            if ~isempty(tmpModel) ...
-            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
-            && tmpModel.calibrationResults.isCalibrated        
-        
-                    % Show a table of calibration data
-                    %---------------------------------
-                    % Get the model calibration data.
-                    tableData = tmpModel.calibrationResults.data.obsHead;
-                    hasModelledDistn = false;
-                    if size(tmpModel.calibrationResults.data.modelledHead,2)>2
-                        hasModelledDistn = true;
-                    end
-                    if hasModelledDistn
-                        tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
-                            tmpModel.calibrationResults.data.modelledHead(:,3),tmpModel.calibrationResults.data.modelledHead(:,4), ...                                
-                            double(tmpModel.calibrationResults.data.modelledHead_residuals(:,2:4)), ...
-                            tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];                            
-                    else
-                        tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
-                            double(tmpModel.calibrationResults.data.modelledHead_residuals(:,end)), ...
-                            tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];
-                    end
-                    % Get evaluation data
-                    if isfield(tmpModel.evaluationResults,'data')
-                        % Get data
-                        evalData = tmpModel.evaluationResults.data.obsHead;
-                        if hasModelledDistn
-                            evalData = [evalData, zeros(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
-                                tmpModel.evaluationResults.data.modelledHead(:,3), tmpModel.evaluationResults.data.modelledHead(:,4), ...
-                                double(tmpModel.evaluationResults.data.modelledHead_residuals(:,2:4)), ...
-                                tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
-
-                        else
-                            evalData = [evalData, zeros(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
-                                double(tmpModel.evaluationResults.data.modelledHead_residuals(:,end)), ...
-                                tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
-                        end
-                        % Append to table of calibration data and sort
-                        % by time.
-                        tableData = [tableData; evalData];
-                        tableData = sortrows(tableData, 1);
-                    end
-
-                    % Calculate year, month, day etc
-                    tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
-
-                    % COnvert table to a cell array so that the logical
-                    % variables can be displayed.
-                    tableData = [num2cell(tableData(:,1:6)), num2cell( tableData(:,7)==true), num2cell(tableData(:,8:end))];
-
-                    % Add data to the table.
-                    this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Contents(1).Contents(1).Data = tableData;  
-
-                    if hasModelledDistn
-                        this.tab_ModelCalibration.resultsOptions.dataTable.table.ColumnName = {'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head (50th %ile)','Mod. Head (5th %ile)','Mod. Head (95th %ile)','Model Residual (50th %ile)','Model Residual (5th %ile)','Model Residual (95th %ile)','Total Err. (5th %ile)','Total Err. (95th %ile)'};
-                    else
-                        this.tab_ModelCalibration.resultsOptions.dataTable.table.ColumnName = {'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head','Model Residual','Total Err. (5th %ile)','Total Err. (95th %ile)'};
-                    end
-                    
-                    % Add drop-down options for the derived data and update
-                    % plot etc
-                    %---------------------------------            
-                    % Get index to the model specific outpus tab
-                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Model Specifics');
-                    
-                    % Set the model derived drop-down options.
-                    obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data dropdown');                    
-                    derivedData_types = getDerivedDataTypes(tmpModel);  
-                    if isempty(derivedData_types)                        
-                        obj.String = {'(none)'};
-                        obj.Value = 1;
-                        
-                        % Disable tab
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind}='off';                        
-                    else
-                        % Enable tab
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind}='on';
-                        
-                        % Build drop-down data
-                        derivedData_types = strcat(derivedData_types(:,1), ':', derivedData_types(:,2));
-
-                        obj.String = derivedData_types;
-                        if obj.Value>length(obj.String)
-                            obj.Value = min(1,length(obj.String));
-                        end   
-
-                        modelCalibration_onUpdateDerivedData(this, hObject, eventdata)                
-                    end
-    
-                    %---------------------------------
-                    
-                    % Update calibration plot
-                    modelCalibration_onUpdatePlotSetting(this);
-                    
-                    % Show model parameter data
-                    %---------------------------------
-                    %Get parameters and names 
-                    [paramValues, paramsNames] = getParameters(tmpModel.model);  
-
-                    % Add to the table
-                    obj = findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel, 'Tag','Model Calibration - parameter table');
-                    obj.Data = cell(size(paramValues,1),size(paramValues,2)+2);
-                    obj.Data(:,1) = paramsNames(:,1);
-                    obj.Data(:,2) = paramsNames(:,2);
-                    obj.Data(:,3:end) = num2cell(paramValues);
-
-                    nparams=size(paramValues,2);                        
-                    colnames = cell(nparams+2,1);
-                    colnames{1,1}='Component Name';
-                    colnames{2,1}='Parameter Name';
-                    colnames(3:end,1) = strcat(repmat({'Parm. Set '},1,nparams)',num2str([1:nparams]'));
-                    obj.ColumnName = colnames;
-                    %---------------------------------                    
-                                        
-                    % Show derived parameter data
-                    %---------------------------------
-                    % Get table
-                    obj = findobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel, 'Tag','Model Calibration - derived parameter table');
-                    
-                    %Get parameters and names 
-                    [derivedParamValues, derivedParamsNames] = getDerivedParameters(tmpModel);        
-
-                    % Get tab index for the forcing data tab.
-                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Derived Parameters');                                        
-                    
-                    % Add to the table (if not empty)
-                    if isempty(derivedParamValues)
-                        obj.Data = [];
-                        obj.ColumnName = {};
-                        
-                        % Disable tab if there is no data 
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'off';  
-                    else
-                        % Enable tab if there is no data 
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'on';
-                        
-                        obj.Data = cell(size(derivedParamValues,1),size(derivedParamValues,2)+2);
-                        obj.Data(:,1) = derivedParamsNames(:,1);
-                        obj.Data(:,2) = derivedParamsNames(:,2);
-                        obj.Data(:,3:end) = num2cell(derivedParamValues);
-
-                        nderivedParams=size(derivedParamValues,2);                        
-                        colnames = cell(nderivedParams+2,1);
-                        colnames{1,1}='Component Name';
-                        colnames{2,1}='Parameter Name';
-                        colnames(3:end,1) = strcat(repmat({'Parm. Set '},1,nderivedParams)',num2str([1:nderivedParams]'));
-                        obj.ColumnName = colnames;
-                    end
-                    %---------------------------------                               
-                    
-                    % Show model forcing data
-                    %---------------------------------                                        
-                    % Get the input forcing data
-                    [tableData, forcingData_colnames] = getForcingData(tmpModel);
-                    
-                    % Get tab index for the forcing data tab.
-                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Forcing Data');                    
-                    
-                    % Get the model derived forcing data (if not empty)
-                    if isempty(tableData)
-                        tableData = [];
-                        tableData_derived = [];
-                        forcingData_colnames = {};
-                        forcingData_colnames_derived= {};
-                        this.tab_ModelCalibration.resultsOptions.forcingData.filt = [];
-                        
-                        % Disable tab if there is no data 
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'off';
-                    else
-                        % Enable tab if there is no data 
-                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'on';
-                        
-                        
-                        if size(paramValues,2)>1
-                            % Get forcing data from the first parameter set
-                            setParameters(tmpModel.model,paramValues(:,1), paramsNames);  
-                            [tableData_derived_tmp, forcingData_colnames_derived] = getDerivedForcingData(tmpModel,tableData(:,1));                                        
-
-                            % Initialise derived forcing data matrix
-                            tableData_derived = nan(size(tableData_derived_tmp,1),size(tableData_derived_tmp,2), size(paramValues,2));
-                            tableData_derived(:,:,1) = tableData_derived_tmp;
-                            clear tableData_derived_tmp;
-
-                            % Change cursor
-                            set(this.Figure, 'pointer', 'watch');   
-                            drawnow update;                         
-
-                            % Derive forcing using parfor. 
-                            % Note, the use of a waitbar with parfor is adapted
-                            % from http://au.mathworks.com/matlabcentral/newsreader/view_thread/166139                        
-                            poolobj = gcp('nocreate');
-                            matlabpoolsize = max(1,poolobj.NumWorkers);
-                            nparamSets = size(paramValues,2);
-                            nLoops = 10;
-                            nparamSetsPerLoop =  ceil(nparamSets/nLoops);
-                            startInd = 0;
-                            h = waitbar(0, ['Calculating transformed forcing for ', num2str(size(paramValues,2)), ' parameter sets. Please wait ...']);
-                            t = tableData(:,1);                        
-                            % do a for loop that is big enough to do all necessary iterations
-
-                            for n=1:nLoops ;
-                                startInd = min(startInd + (n-1)*nparamSetsPerLoop+1,nparamSets);
-                                endInd = min(startInd +nparamSetsPerLoop,nparamSets);
-                                parfor z = startInd:endInd 
-                                    setParameters(tmpModel.model,paramValues(:,z), paramsNames);  
-                                    tableData_derived(:,:,z) = getDerivedForcingData(tmpModel,t);    
-                                end
-                                % update waitbar each "matlabpoolsize"th iteration
-                                waitbar(n/nLoops);
-                            end
-                            close(h);                                                                    
-
-                            % Change cursor
-                            set(this.Figure, 'pointer', 'watch');   
-                            drawnow update;                         
-
-                            % Reset all parameters
-                            setParameters(tmpModel.model,paramValues, paramsNames);  
-
-                            % Clear model object
-                            clear tmpModel
-
-                        else
-                            [tableData_derived, forcingData_colnames_derived] = getDerivedForcingData(tmpModel,tableData(:,1)); 
-                        end
-
-                        % Calculate year, month, day etc
-                        t = datetime(tableData(:,1), 'ConvertFrom','datenum');
-                        tableData = [year(t), quarter(t), month(t), week(t,'weekofyear'), day(t), tableData(:,2:end)];                    
-                        forcingData_colnames = {'Year','Quarter','Month','Week','Day', forcingData_colnames{2:end}};                        
-                    end
-
-                    
-                    % Store the daily forcing data. This is just done to
-                    % avoid re-loading the model within updateForcingData()
-                    % and updateForcinfPlot().
-                    this.tab_ModelCalibration.resultsOptions.forcingData.data_input = tableData;
-                    this.tab_ModelCalibration.resultsOptions.forcingData.data_derived = tableData_derived;
-                    this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input = forcingData_colnames;
-                    this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived = forcingData_colnames_derived;
-                    this.tab_ModelCalibration.resultsOptions.forcingData.filt=true(size(tableData,1),1);
-                                  
-                    % Free up RAM
-                    clear tableData_derived tableData
-                    
-                    % Update table and plots
-                    if ~isempty(this.tab_ModelCalibration.resultsOptions.forcingData.data_input)
-                        modelCalibration_onUpdateForcingData(this)
-                    end
-                    %---------------------------------
-            end
-                           
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');      
-            drawnow update;
-        end                
-        
-        function modelCalibration_onUpdatePlotSetting(this, hObject, eventdata)
-            
-            % Get selected popup menu item
-            plotID = this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Contents(1).Contents(2).Contents(2).Value;
-            
-            % Get a copy of the model object. This is only done to
-            % minimise HDD read when the models are off loaded to HDD using
-            % matfile();
-            tmpModel = getModel(this, this.tab_ModelCalibration.currentModel);
-
-            % Exit if model not found.
-            if isempty(tmpModel)    
-                % Turn off plot icons
-                plotToolbarState(this,'off');  
-                
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                                                   
-                return;
-            end                                             
-            
-            % Display the requested calibration results if the model object
-            % exists and there are calibration results.
-            if ~isempty(tmpModel) ...
-            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
-            && tmpModel.calibrationResults.isCalibrated        
-                
-                % Plot calibration result.
-                %-----------------------
-                % Create an axis handle for the figure.
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.calibPanel,'Tag','Model Calibration - results plot');
-                delete( findobj(obj ,'type','axes'));
-                delete( findobj(obj ,'type','legend'));     
-                
-                obj = uipanel('Parent', obj);
-                axisHandle = axes( 'Parent', obj);
-                % Show the calibration plots. NOTE: QQ plot type
-                % fails so is skipped
-                if plotID<=4
-                    calibrateModelPlotResults(tmpModel, plotID, axisHandle);
-                else
-                    calibrateModelPlotResults(tmpModel, plotID+1, axisHandle);
-                end                                
-                
-                % Plot parameters
-                %-----------------------
-                [paramValues, paramsNames] = getParameters(tmpModel.model);                          
-                paramsNames  = strrep(paramsNames(:,2), '_',' ');
-
-                % Create an axis handle for the figure.
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel, 'Tag','Model Calibration - parameter plot');
-                delete( findobj(obj ,'type','axes'));
-                %delete( findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2),'type','legend'));     
-                %delete( findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2),'type','uipanel'));     
-                %h = uipanel('Parent', this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2));
-                axisHandle = axes( 'Parent',obj);
-
-                if size(paramValues,2)==1
-                    bar(axisHandle,paramValues);
-                    set(axisHandle, 'xTickLabel', paramsNames,'FontSize',8,'xTickLabelRotation',45);
-                    ylabel(axisHandle,'Param. value');
-                else
-                    % A bug seems to occur when the builtin plotmatrix is ran to produce a plot inside a GUI 
-                    % whereby the default fig menu items and icons
-                    % appear. The version of plotmatrix below has a
-                    % a few lines commented out to supress this
-                    % proble,m (see lines 232-236)                            
-                    [~, ax] = plotmatrix(axisHandle,paramValues', '.');      
-                    for i=1:size(ax,1)
-                        ylabel(ax(i,1), paramsNames(i),'FontSize',8);
-                        xlabel(ax(end,i), paramsNames(i),'FontSize',8);
-                    end
-                end                
-                
-                % Plot derived parameters
-                %-----------------------
-                % Create an axis handle for the figure.
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel, 'Tag','Model Calibration - derived parameter plot');
-                delete( findobj(obj ,'type','axes'));
-                axisHandle = axes( 'Parent',obj);
-
-                % Get data and show
-                [paramValues, paramsNames] = getDerivedParameters(tmpModel);                          
-                if ~isempty(paramValues)
-                    
-                    paramsNames = paramsNames(:,2);
-                    ind=strfind(paramsNames,':');
-                    for i=1:length(ind)
-                       if ~isempty(ind{i})
-                           paramsNames{i} = paramsNames{i}(1:max(1,ind{i}-1));
-                       end
-                    end
-                    paramsNames  = strrep(paramsNames, '_',' ');
-
-                    if size(paramValues,2)==1
-                        bar(axisHandle,paramValues);
-                        set(axisHandle, 'xTick', 1:length(paramsNames));
-                        set(axisHandle, 'xTickLabel', paramsNames,'FontSize',8,'xTickLabelRotation',45);
-                        ylabel(axisHandle,'Derived param. value');
-                    else
-                        % A bug seems to occur when the builtin plotmatrix is ran to produce a plot inside a GUI 
-                        % whereby the default fig menu items and icons
-                        % appear. The version of plotmatrix below has a
-                        % a few lines commented out to supress this
-                        % proble,m (see lines 232-236)                            
-                        [~, ax] = plotmatrix(axisHandle,paramValues', '.');      
-                        for i=1:size(ax,1)
-                            ylabel(ax(i,1), paramsNames(i),'FontSize',8);
-                            xlabel(ax(end,i), paramsNames(i),'FontSize',8);
-                        end
-                    end                       
-                    
-                end                                           
-                %-----------------------         
-                                
-                drawnow update;
-            end
-           
-            % Turn on plot icons
-            plotToolbarState(this,'on');            
-
-            % Chane cursor
-            set(this.Figure, 'pointer', 'arrow');   
-            drawnow update;           
-        end
-        
-        function modelCalibration_onUpdateForcingData(this, hObject, eventdata)
-            
-            % Get time step value
-            timestepID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(2).Value;
-            
-            % Get the calculate for the time stepa aggregation
-            calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
-            calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
-            calcString = calcString{calcID};
-            
-            % check the start and end dates
-            sdate = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(6).String;
-            edate = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(8).String;
-            try
-                if isempty(sdate)
-                    sdate = datetime('01/01/0001','InputFormat','dd/MM/yyyy');
-                else
-                    sdate=datetime(sdate,'InputFormat','dd/MM/yyyy');
-                end
-            catch me
-               errordlg('The start date does not appear to be in the correct format of dd/mm/yyy.','Input date error ...');
-               return;
-            end
-            try
-                if isempty(edate)
-                    edate= datetime('31/12/9999','InputFormat','dd/MM/yyyy');
-                else                
-                    edate = datetime(edate,'InputFormat','dd/MM/yyyy');
-                end
-            catch me
-               errordlg('The end date does not appear to be in the correct format of dd/mm/yyy.','Input date error ...');
-               return;
-            end            
-            if sdate > edate
-               errordlg('The end date must be after the start date.','Input date error ...');
-               return;                
-            end            
-                        
-            % Get daily input forcing data.
-            tableData = this.tab_ModelCalibration.resultsOptions.forcingData.data_input;
-            forcingData_colnames = this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input;
-                       
-            % Exit if no data
-            if isempty(tableData)
-                return;
-            end
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');      
-            drawnow update;
-            
-            % Get daily model derived forcing data.
-            tableData_derived = this.tab_ModelCalibration.resultsOptions.forcingData.data_derived;
-            forcingData_colnames_derived = this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived;            
-                        
-            % Filter the table data by the input dates
-            t = datetime(tableData(:,1),tableData(:,3),tableData(:,5));
-            filt = t>=sdate & t<=edate;
-            tableData = tableData(filt,:);
-            if length(size(tableData_derived))==3
-                tableData_derived = tableData_derived(filt,:,:);
-            else
-                tableData_derived = tableData_derived(filt,:);
-            end
-            
-            % Add filter to object for plotting            
-            this.tab_ModelCalibration.resultsOptions.forcingData.filt = filt;                        
-            
-            % Build forcing data at requested time step
-            switch timestepID
-                case 1  %daily
-                    ind = [1:size(tableData,1)]';
-                case 2  % weekly
-                     [~,~,ind] = unique(tableData(:,[1,4]),'rows');                           
-                case 3  % monthly
-                    [~,~,ind] = unique(tableData(:,[1,3]),'rows');                        
-                case 4  % quarterly
-                    [~,~,ind] = unique(tableData(:,[1,2]),'rows');                        
-                case 5  % annually
-                    [~,~,ind] = unique(tableData(:,1),'rows');                        
-                case 6  % all data
-                    ind = ones(size(tableData,1),1);
-                otherwise
-                    error('Unknown forcing data time ste.')
-            end
-
-            % Set function for aggregation equation
-            switch calcID             
-                case 1  % sum
-                    fhandle = @sum;                    
-                case 2
-                    fhandle = @mean;
-                case 3
-                    fhandle = @std;
-                case 4
-                    fhandle = @var;
-                case 5
-                    fhandle = @skewness;
-                case 6
-                    fhandle = @min;
-                case {7,8,9,10,11,12,13}
-                    p = str2num(calcString(1:length(calcString)-7));
-                    fhandle = @(x) prctile(x,p);
-                case 14
-                    fhandle = @max;
-                case 15
-                    fhandle = @iqr;                    
-                case 16
-                    fhandle = @(x) sum(x==0);
-                case 17
-                    fhandle = @(x) sum(x<0);
-                case 18
-                    fhandle = @(x) sum(x>0);
-                otherwise
-                    error('Equation for the aggregation of daily data is unkown.');
-            end
-            
-            % Upscale input data
-            %----------
-            % Only upacale the data if the time step is greater than daily
-            if timestepID>1            
-                tableData_noDates = tableData(:,6:end);
-                [rowidx, colidx] = ndgrid(ind, 1:size(tableData_noDates, 2)) ;
-                upscaledData = accumarray([rowidx(:) colidx(:)], tableData_noDates(:), [], fhandle);                
-            else
-                upscaledData = tableData(:,6:end);
-            end
-            
-            % Upscale derived data and compine with upscaled input data
-            if length(size(tableData_derived))==3
-                % Build colume names without %iles
-                forcingData_colnames_yaxis = {forcingData_colnames{:}, forcingData_colnames_derived{:}};
-                
-                % Upscale derived data
-                for i=1:1:size(tableData_derived, 2)
-                    % Only upscale the data if the time step is greater than daily
-                    if timestepID>1                                
-                        [rowidx, colidx, depthidx] = ndgrid(ind, 1, 1:size(tableData_derived, 3));
-                        upscaledData_derived_prctiles = tableData_derived(:,i,:);
-                        upscaledData_derived_prctiles = accumarray([rowidx(:) colidx(:) depthidx(:)], upscaledData_derived_prctiles(:), [], fhandle);                
-                    else
-                        upscaledData_derived_prctiles = tableData_derived(:,i,:);
-                    end
-                
-                    % Calculate percentiles for the upscaled data
-                    upscaledData_derived_prctiles = prctile( upscaledData_derived_prctiles,[5 10 25 50 75 90 95],3);
-                    upscaledData_derived_prctiles = permute(upscaledData_derived_prctiles,[1 3 2]);
-
-                    % Merge input forcing and percentiles of upsaled derived
-                    % forcing
-                    upscaledData = [upscaledData, upscaledData_derived_prctiles];
-                    
-                    
-                    % Build column names
-                    forcingData_colnames = {forcingData_colnames{:}, ...
-                                            [forcingData_colnames_derived{i},'-05th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-10th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-25th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-50th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-75th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-90th%ile'], ...
-                                            [forcingData_colnames_derived{i},'-95th%ile']};                
-                end
-                                                            
-            else    % Upscale derived data
-                                
-                % Only upscale the data if the time step is greater than daily
-                if timestepID>1                                                
-                    [rowidx, colidx] = ndgrid(ind, 1:size(tableData_derived, 2)) ;
-                    upscaledData_derived = accumarray([rowidx(:) colidx(:)], tableData_derived(:), [], fhandle);                
-                    upscaledData = [upscaledData, upscaledData_derived];
-                else
-                    upscaledData = [upscaledData, tableData_derived];
-                end
-                
-                forcingData_colnames = {forcingData_colnames{:}, forcingData_colnames_derived{:}};
-                forcingData_colnames_yaxis = forcingData_colnames;
-            end
-            
-            % Build date column.
-            tableData_year = accumarray(ind,tableData(:,1),[],@max);
-            tableData_quarter = accumarray(ind,tableData(:,2),[],@max);
-            tableData_month = accumarray(ind,tableData(:,3),[],@max);            
-            tableData_week = accumarray(ind,tableData(:,4),[],@max);                            
-            tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
-            
-            % Build new table
-            tableData = [tableData_year, tableData_quarter, tableData_month, tableData_week, tableData_day, upscaledData];
-            
-            % Add to the table
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).Data = tableData;
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).ColumnName = forcingData_colnames;
-
-            % Get the plotting type
-            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
-            plotType = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
-            plotType = plotType{plotType_val};            
-            
-            % Get the currently selected x and y axis options
-            forcingData_colnames_xaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String;
-            forcingData_colnames_yaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String;
-            forcingData_value_xaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value;
-            forcingData_value_yaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value;            
-                        
-            % Update the drop-down x and y axis options
-            if any(strfind(plotType,'box-plot'))
-                forcingData_colnames_xaxis = {'Date','(none)'};
-            else
-                %ind = strfind(forcingData_colnames,'th%ile');
-                %forcingData_colnames(k>0)=forcingData_colnames(k>0){1:k-3};
-                forcingData_colnames_xaxis = {'Date', forcingData_colnames_yaxis{6:end},'(none)'};
-            end
-            forcingData_colnames_yaxis = {'Date', forcingData_colnames_yaxis{6:end},'(none)'};
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String = forcingData_colnames_xaxis;
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String = forcingData_colnames_yaxis;
-
-            % Update the selected x anbd y axis items. To do this, the
-            % original item is selected, else reset to 1.
-            if ~isempty(forcingData_value_xaxis_prior ) && forcingData_value_xaxis_prior >0
-                ind = find(strcmp(forcingData_colnames_xaxis, forcingData_colnames_xaxis_prior{forcingData_value_xaxis_prior}));
-                if ~isempty(ind)
-                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = ind;    
-                else
-                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
-                end                    
-            else
-                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
-            end
-            if ~isempty(forcingData_value_yaxis_prior ) && forcingData_value_yaxis_prior >0
-                ind = find(strcmp(forcingData_colnames_yaxis, forcingData_colnames_yaxis_prior{forcingData_value_yaxis_prior}));
-                if ~isempty(ind)
-                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = ind;    
-                else
-                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = 1;
-                end                    
-            else
-                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = 1;
-            end            
-            % Update forcing plot
-            modelCalibration_onUpdateForcingPlot(this)
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');      
-            drawnow update;
-            
-        end
-
-        function modelCalibration_onUpdateForcingPlotType(this, hObject, eventdata)
-            
-            % Get the forcing data columns
-            forcingData_colnames = {this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input{:}, ...
-                this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived{:} };            
-            
-           % Remove those with %iles
-           filt = cellfun(@(x) ~isempty(strfind(x,'-05th%ile')) || ...
-                  ~isempty(strfind(x,'-10th%ile')) || ...
-                  ~isempty(strfind(x,'-25th%ile')) || ...                                   
-                  ~isempty(strfind(x,'-50th%ile')) || ...                                   
-                  ~isempty(strfind(x,'-75th%ile')) || ...                                   
-                  ~isempty(strfind(x,'-90th%ile')) || ...                                   
-                  ~isempty(strfind(x,'-95th%ile')) ...                                   
-                  ,forcingData_colnames);       
-            if any(filt)
-                forcingData_colnames_wpcntiles = forcingData_colnames(filt);  
-                forcingData_colnames_wpcntiles = cellfun(@(x) x(1:length(x) - 9),forcingData_colnames_wpcntiles);
-                forcingData_colnames_wpcntiles = unique(forcingData_colnames_wpcntiles );
-                forcingData_colnames = {forcingData_colnames{~filt}, forcingData_colnames_wpcntiles{:}};
-            end
-              
-              
-            % Get the plotting type
-            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
-            plotType = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
-            plotType = plotType{plotType_val};
-            
-            % Update the drop-down x and y axis options
-            if any(strfind(plotType,'box-plot'))
-                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
-                forcingData_colnames_xaxis = {'Date','(none)'};
-            else
-                forcingData_colnames_xaxis = {'Date', forcingData_colnames{6:end},'(none)'};
-            end
-            forcingData_colnames_yaxis = {'Date', forcingData_colnames{6:end},'(none)'};
-            
-            
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String = forcingData_colnames_xaxis ;
-            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String = forcingData_colnames_yaxis;
-            
-        end
-        
-        function modelCalibration_onUpdateForcingPlot(this, hObject, eventdata)
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');      
-            drawnow update;
-                        
-            % Turn on plot icons
-            plotToolbarState(this,'on');
-            
-            % Get the calculate for the time step aggregation
-            calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
-            calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
-            calcString = calcString{calcID};            
-            
-            % Get the user plotting settings
-            plotType_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
-            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
-            
-            xaxis_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String;
-            xaxis_val = min(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value, ...
-                        length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String));
-            
-            yaxis_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String;
-            yaxis_val = min(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value, ...
-                        length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String));
-                        
-            % Get the data
-            tableData = this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).Data;
-            forcingData_colnames = this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).ColumnName;
-            
-            % Calc date
-            if xaxis_val==1 || yaxis_val==1
-                forcingDates = datetime(tableData(:,1), tableData(:,3), tableData(:,5));
-            end
-            
-            %Check if a box plot is to be created
-            plotType_isBoxPlot = false;
-            if plotType_val>=6 && plotType_val<=9
-                plotType_isBoxPlot = true;
-            end
-            
-            % Get axis data
-            xdataHasErrorVals =  false;
-            ydataHasErrorVals =  false;
-            if xaxis_val==1
-                xdata = forcingDates;
-                xdataLabel = 'Date';
-            elseif xaxis_val~=length(xaxis_options)
-                
-               % Get the data type to plot
-               xdataLabel = xaxis_options{xaxis_val};
-                
-               % Find the  data columns with the requsted name and extract
-               % comulns of data.
-               filt = cellfun(@(x) ~isempty(strfind(x,[xdataLabel,'-05th%ile'])) || ...
-                      ~isempty(strfind(x,[xdataLabel,'-10th%ile'])) || ...
-                      ~isempty(strfind(x,[xdataLabel,'-25th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[xdataLabel,'-50th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[xdataLabel,'-75th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[xdataLabel,'-90th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[xdataLabel,'-95th%ile'])) ...                                   
-                      ,forcingData_colnames);   
-               if ~any(filt)
-                    filt =  strcmp(forcingData_colnames,xdataLabel);
-               end                  
-               xdata =  tableData(:,filt);
-               xdataHasErrorVals = sum(filt)>1;
-                               
-               % Build the y-label
-               xSeriesLabel = forcingData_colnames(filt);               
-               xSeriesLabel = strrep(xSeriesLabel,'_',' ');
-               xdataLabel = strrep(xdataLabel,'_',' ');
-               if ~plotType_isBoxPlot 
-                xdataLabel = [calcString,' of ',xaxis_options{xaxis_val}];
-                xdataLabel = strrep(xdataLabel,'_',' ');
-               end
-            else
-               xdata = [];
-               xdataLabel = '(none)';
-            end
-            if yaxis_val==1
-                ydata = forcingDates;
-                ydataLabel = 'Date';
-            elseif yaxis_val~=length(yaxis_options)
-               % Get the data type to plot
-               ydataLabel = yaxis_options{yaxis_val};
-                
-               % Find the  data columns with the requsted name and extract
-               % comulns of data.
-               filt = cellfun(@(x) ~isempty(strfind(x,[ydataLabel,'-05th%ile'])) || ...
-                      ~isempty(strfind(x,[ydataLabel,'-10th%ile'])) || ...
-                      ~isempty(strfind(x,[ydataLabel,'-25th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[ydataLabel,'-50th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[ydataLabel,'-75th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[ydataLabel,'-90th%ile'])) || ...                                   
-                      ~isempty(strfind(x,[ydataLabel,'-95th%ile'])) ...                                   
-                      ,forcingData_colnames);               
-               if ~any(filt)
-                    filt =  strcmp(forcingData_colnames,ydataLabel);
-               end
-               ydata =  tableData(:,filt);
-               ydataHasErrorVals = sum(filt)>1;
-               
-               % Build the y-label and series label
-               ySeriesLabel = forcingData_colnames(filt);               
-               ySeriesLabel = strrep(ySeriesLabel,'_',' ');
-               ydataLabel = strrep(ydataLabel,'_',' ');
-               if ~plotType_isBoxPlot 
-                ydataLabel = [calcString,' of ',ydataLabel];
-               end
-            else
-               ydata = [];
-               ydataLabel = '(none)';                
-            end
-
-            % Create an axis handle for the figure.
-            delete( findobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4),'type','axes'));
-            axisHandle = axes( 'Parent',this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4));            
-            
-            % Exit of no data to plot (and not plotting a distrbution)
-            if isempty(ydata) && plotType_val<4
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');      
-                drawnow update;       
-                
-                return
-            end
-            
-            % Check if using date for either axis            
-            xdata_isdate=false;
-            ydata_isdate=false;
-            if isdatetime(xdata)                        
-                xdata_isdate=true;
-            end
-            if isdatetime(ydata)            
-                ydata_isdate=true;
-            end            
-            
-                        
-            switch plotType_val
-                case {1,2}     
-                    plotSymbol = 'b.-';
-                    if plotType_val==2
-                        plotSymbol = 'b.';
-                    end
-                    if xdataHasErrorVals && ydataHasErrorVals
-                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
-                        hold(axisHandle,'on');
-                        try
-                            errorbar(axisHandle,xdata(:,4), ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)), ...
-                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);                        
-                        catch
-                            errorbar(axisHandle,xdata(:,4), ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)), ...
-                                'linestyle','none','color',[0.6 0.6 0.6]);                                                    
-                        end
-                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
-                        hold(axisHandle,'off');
-                    elseif ~xdataHasErrorVals && xdata_isdate && ydataHasErrorVals
-
-                        if xdata_isdate
-                            xdata = datenum(xdata);
-                        end
-                        XFill = [xdata' fliplr(xdata')];
-                        YFill = [ydata(:,1)', fliplr(ydata(:,7)')];                   
-                        fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
-                        hold(axisHandle,'on');                    
-                        YFill = [ydata(:,2)', fliplr(ydata(:,6)')];                   
-                        fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
-                        hold(axisHandle,'on');
-                        YFill = [ydata(:,3)', fliplr(ydata(:,5)')];                   
-                        fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
-                        hold(axisHandle,'on');
-                        clear XFill YFill     
-
-                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
-                        hold(axisHandle,'off');
-                        
-                        % Date date axis. NOTE, code adopted from dateaxis.m.
-                        % Pre-2016B dateaxis did not allow input of axis
-                        % handle.                        
-                        if xdata_isdate;
-                            dateaxis_local(axisHandle,'x');
-                        end
-                        if ydata_isdate;
-                            dateaxis_local(axisHandle,'y');
-                        end                
-                        legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');
-                    elseif ~xdataHasErrorVals && ~xdata_isdate && ydataHasErrorVals                        
-                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
-                        hold(axisHandle,'on');
-                        errorbar(axisHandle,xdata, ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);
-                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
-                        hold(axisHandle,'off');                 
-                    elseif xdataHasErrorVals && ~ydataHasErrorVals            
-                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
-                        try
-                            hold(axisHandle,'on');
-                            errorbar(axisHandle,xdata(:,4), ydata, ...
-                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)), 'ornt','horizontal','linestyle','none','color',[0.6 0.6 0.6]);                                                
-                        catch
-                            % do nothing
-                        end           
-                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
-                        hold(axisHandle,'off');
-                    else
-                        plot(axisHandle,xdata, ydata,plotSymbol);
-                        axis(axisHandle,'tight');                                                
-                    end
-                    xlabel(axisHandle,xdataLabel);  
-                    ylabel(axisHandle,ydataLabel);             
-                    
-                case 3
-                    if strcmp(xdataLabel,'(none)') || strcmp(ydataLabel,'(none)')
-                        errordlg('A bar plot requires both x-axis and y-axis inputs.','Axis selection error...');
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');      
-                        drawnow update;
-                                                
-                        return;
-                    end
-                    if xdata_isdate
-                        xdata=datenum(xdata);
-                        if ydataHasErrorVals
-                            bar(axisHandle,xdata, ydata(:,4)); 
-                        else
-                            bar(axisHandle,xdata, ydata);
-                        end
-                    end
-                    if ydata_isdate
-                        ydata=datenum(ydata);
-                        if xdataHasErrorVals
-                            barh(axisHandle,ydata, xdata(:,4));
-                        else
-                            barh(axisHandle,ydata, xdata);
-                        end
-                    end                
-                    if ~xdata_isdate && ~ydata_isdate
-                        errordlg('A bar plot requires either the x-axis or y-axis to be "Date".','Axis selection error...');
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');      
-                        drawnow update;                        
-                        
-                        return;
-                    end
-                    
-                    if xdataHasErrorVals && ~xdata_isdate
-                        try
-                            hold(axisHandle,'on');
-                            errorbar(axisHandle,xdata(:,4), ydata, ...
-                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)), 'ornt','horizontal','linestyle','none','color',[0.6 0.6 0.6]);                                                
-                            legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
-                        catch
-                            % do nothing
-                        end           
-                        hold(axisHandle,'off');
-                    elseif ydataHasErrorVals && ~ydata_isdate
-                        hold(axisHandle,'on');
-                        errorbar(axisHandle,xdata, ydata(:,4), ...
-                        abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);                                                
-                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
-                        hold(axisHandle,'off');
-                    end
-                                                            
-                    xlabel(axisHandle,xdataLabel);  
-                    ylabel(axisHandle,ydataLabel);    
-                    % Date date axis. NOTE, code adaopted from dateaxis.m.
-                    % Pre-2016B dateaxis did not allow input of axis
-                    % handle.
-                    if xdata_isdate;
-                        dateaxis_local(axisHandle,'x');
-                    end
-                    if ydata_isdate;
-                        dateaxis_local(axisHandle,'y');
-                    end
-                    axis(axisHandle,'tight');
-                case 4                    
-                    if strcmp(ydataLabel,'(none)')
-                        if xdataHasErrorVals
-                            xdata=xdata(:,4);
-                        end
-                        histogram(axisHandle,xdata, floor(sqrt(length(xdata))),'Normalization','probability');
-                        ylabel(axisHandle,'Probability');             
-                        xlabel(axisHandle,xdataLabel);     
-                        if xdataHasErrorVals
-                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
-                        end
-                    elseif strcmp(xdataLabel,'(none)')
-                        if ydataHasErrorVals
-                            ydata=ydata(:,4);
-                        end                        
-                        histogram(axisHandle,ydata, floor(sqrt(length(ydata))),'Normalization','probability');
-                        ylabel(axisHandle,'Probability');             
-                        xlabel(axisHandle,ydataLabel);                  
-                        if ydataHasErrorVals
-                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
-                        end                        
-                    elseif ~strcmp(xdataLabel,'(none)') && ~strcmp(xdataLabel,'(none)')
-                        % Plot median value
-                        if xdataHasErrorVals
-                            xdata=xdata(:,4);
-                        end                        
-                        if ydataHasErrorVals
-                            ydata=ydata(:,4);
-                        end                        
-                                                
-                        % Convert date is to be plotted
-                        if xdata_isdate
-                            xdata=datenum(xdata);
-                        end
-                        if ydata_isdate
-                            ydata=datenum(ydata);
-                        end
-                        
-                        % Make bivariate histogram
-                        histogram2(axisHandle,xdata,ydata, floor(sqrt(length(xdata))), 'DisplayStyle','tile','ShowEmptyBins','on','Normalization','probability');
-                        h = colorbar(axisHandle);
-                        xlabel(axisHandle,xdataLabel);  
-                        ylabel(axisHandle,ydataLabel);                  
-                        ylabel(h,'Probability'); 
-                        box(axisHandle,'on');
-                        
-                        % Date date axis. NOTE, code adaopted from dateaxis.m.
-                        % Pre-2016B dateaxis did not allow input of axis
-                        % handle.                        
-                        if xdata_isdate;
-                            dateaxis_local(axisHandle,'x');
-                        end
-                        if ydata_isdate;
-                            dateaxis_local(axisHandle,'y');
-                        end
-                        if xdataHasErrorVals || ydataHasErrorVals
-                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
-                        end                        
-                    end
-                case 5
-                    % Convert date is to be plotted
-                    if xdata_isdate
-                        xdata=datenum(xdata);
-                    end
-                    if ydata_isdate
-                        ydata=datenum(ydata);
-                    end
-
-                    % Make CDF plot
-                    if strcmp(ydataLabel,'(none)')
-                        if xdataHasErrorVals
-                            [f, xtmp] = ecdf(xdata(:,1));                            
-                            plot(axisHandle, xtmp, f,'linestyle',':','color',[0.8 0.8 0.8]);                            
-                            hold(axisHandle,'on');
-                            [f, xtmp] = ecdf(xdata(:,2));                            
-                            plot(axisHandle, xtmp, f,'linestyle','-.','color',[0.6 0.6 0.6]);                            
-                            [f, xtmp] = ecdf(xdata(:,3));                            
-                            plot(axisHandle, xtmp, f,'linestyle','--','color',[0.4 0.4 0.4]);                                                        
-                            [f, xtmp] = ecdf(xdata(:,4));
-                            plot(axisHandle, xtmp, f,'b.-');
-                            [f, xtmp] = ecdf(xdata(:,5));                            
-                            plot(axisHandle, xtmp, f,'linestyle','--','color',[0.4 0.4 0.4]);                            
-                            [f, xtmp] = ecdf(xdata(:,6));                            
-                            plot(axisHandle, xtmp, f,'linestyle','-.','color',[0.6 0.6 0.6]);                                                        
-                            [f, xtmp] = ecdf(xdata(:,7));                            
-                            plot(axisHandle, xtmp, f,'linestyle',':','color',[0.8 0.8 0.8]);                            
-                            legend(axisHandle,' 5th%ile','10th%ile','25th%ile','50th%ile','75th%ile','90th%ile','95th%ile','Location', 'northeastoutside');
-                            hold(axisHandle,'off');
-                        else
-                            [f, xdata] = ecdf(xdata);
-                            stairs(axisHandle, xdata, f,'b.-');
-                        end
-                        ylabel(axisHandle,'Probability');             
-                        xlabel(axisHandle,xdataLabel);                  
-                    elseif strcmp(xdataLabel,'(none)')
-                        [f, xdata] = ecdf(ydata);
-                        stairs(axisHandle, xdata, f,'b.-');
-                        ylabel(axisHandle,'Probability');  
-                        xlabel(axisHandle,ydataLabel);                  
-                    elseif ~strcmp(xdataLabel,'(none)') && ~strcmp(xdataLabel,'(none)')
-                        % Plot median value
-                        if xdataHasErrorVals
-                            xdata=xdata(:,4);
-                        end                        
-                        if ydataHasErrorVals
-                            ydata=ydata(:,4);
-                        end                        
-                                                
-                        histogram2(axisHandle,xdata,ydata, floor(sqrt(length(xdata))), 'DisplayStyle','tile','ShowEmptyBins','on','Normalization','cdf');
-                        h = colorbar(axisHandle);
-                        xlabel(axisHandle,xdataLabel);                  
-                        ylabel(axisHandle,ydataLabel);                  
-                        ylabel(h,'Probability'); 
-                        box(axisHandle,'on');
-                        
-                        if xdataHasErrorVals || ydataHasErrorVals
-                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
-                        end                            
-                    end
-                    
-                    % Date date axis. NOTE, code adaopted from dateaxis.m.
-                    % Pre-2016B dateaxis did not allow input of axis
-                    % handle.                        
-                    if xdata_isdate;
-                        dateaxis_local(axisHandle,'x');
-                    end
-                    if ydata_isdate;
-                        dateaxis_local(axisHandle,'y');
-                    end                       
-                case {6,7,8,9}      % Box plots at daily sum, monthly sum, 1/4 sum, annual sum
-                    
-                    if ydataHasErrorVals || xdataHasErrorVals
-                        errordlg('HydroSight cannot create box plots of ensemble data (ie as derived from DREAM calibation).', 'Feature unavailable ...')
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');      
-                        drawnow update;                        
-                        
-                        return
-                    end
-                    
-                    % Check the x-axis is date
-                    if xdata_isdate                  
-                        xdata=datenum(xdata);
-                    else
-                        errordlg('The x-axis must plot the "Date" for box plots.','Axis selection error...');
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');      
-                        drawnow update;                        
-                        
-                        return;
-                    end
-                    if isdatetime(ydata)
-                        errordlg('Only the x-axis can set to "Date" for box plots.','Axis selection error...');
-
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');      
-                        drawnow update;                        
-                        
-                        return;
-                    end
-
-                    % Get the daily data and apply time filter
-                    tableData = [this.tab_ModelCalibration.resultsOptions.forcingData.data_input, ...
-                                 this.tab_ModelCalibration.resultsOptions.forcingData.data_derived];                    
-                    forcingData_colnames = {this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input{:}, ...
-                                this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived{:} };            
-
-                    filt  = this.tab_ModelCalibration.resultsOptions.forcingData.filt;
-                    tableData = tableData(filt,:);
-                    
-                    % re-extract ydata
-                    ydata = tableData(:,yaxis_val+4);
-    
-                    % Calculate time steps                    
-                    tableData = [tableData(:,1:5),ydata];                    
-                    forcingData_colnames = {forcingData_colnames{1:5}, ydataLabel};                    
-                 
-                    % Get the calculate for the time stepa aggregation
-                    calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
-                    calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
-                    calcString = calcString{calcID};                    
-                    
-                    % Set function for aggregation equation
-                    switch calcID             
-                        case 1  % sum
-                            fhandle = @sum;                    
-                        case 2
-                            fhandle = @mean;
-                        case 3
-                            fhandle = @std;
-                        case 4
-                            fhandle = @var;
-                        case 5
-                            fhandle = @skewness;
-                        case 6
-                            fhandle = @min;
-                        case {7,8,9,10,11,12,13}
-                            p = str2num(calcString(1:length(calcString)-7));
-                            fhandle = @(x) prctile(x,p);
-                        case 14
-                            fhandle = @max;
-                        case 15
-                            fhandle = @iqr;                    
-                        otherwise
-                            error('Equation for the aggregation of daily data is unkown.');
-                    end
-
-                    
-                    % Sum the data to the required sum time step
-                    switch plotType_val
-                        case 6  %daily
-                            ind = [1:size(tableData,1)]';
-                            ydataLabel = [ydataLabel, ' (daily rate)'];
-                        case 7  % monthly
-                            [~,~,ind] = unique(tableData(:,[1,3]),'rows');
-                            ydataLabel = [ydataLabel, ' (monthly ',calcString,')'];
-                        case 8  % quarterly
-                            [~,~,ind] = unique(tableData(:,[1,2]),'rows');   
-                            ydataLabel = [ydataLabel, ' (quarterly ',calcString,')'];
-                        case 9  % annually
-                            [~,~,ind] = unique(tableData(:,1),'rows');                        
-                            ydataLabel = [ydataLabel, ' (annual ',calcString,')'];
-                        otherwise
-                            error('Unknown type of box plot.')
-                    end
-                    tableData_sum = accumarray(ind,tableData(:,end),[],fhandle);
-
-                    % Build date column and new tableData.
-                    tableData_year = accumarray(ind,tableData(:,1),[],@max);
-                    tableData_quarter = accumarray(ind,tableData(:,2),[],@max);
-                    tableData_month = accumarray(ind,tableData(:,3),[],@max);            
-                    tableData_week = accumarray(ind,tableData(:,4),[],@max);                            
-                    tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
-                    tableData = [tableData_year, tableData_quarter, tableData_month, tableData_week, tableData_day, tableData_sum];
-                    
-                    % Get time step value
-                    timestepID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(2).Value;
-                    
-                    % Group the y-data to the next greatest time step
-                    % Build foring data at requtested time step
-
-                    % Build foring data at requtested time step
-                    switch timestepID
-                        case 1  %daily
-                            ind = [1:size(tableData,1)]';                            
-                        case 2  % weekly
-                             [~,~,ind] = unique(tableData(:,[1,4]),'rows');                         
-                             xdataTickLabels = 'dd/mm/yy'; 
-                             xdataLabel = 'Date';
-                        case 3  % monthly
-                            [~,~,ind] = unique(tableData(:,[1,3]),'rows');                        
-                            xdataTickLabels = 'mmyy';      
-                            xdataLabel = 'Month-Year';
-                        case 4  % quarterly
-                            [~,~,ind] = unique(tableData(:,[1,2]),'rows');                            
-                            xdataTickLabels = 'QQ-YY';
-                            xdataLabel = 'Quarter-Year';
-                        case 5  % annually
-                            [~,~,ind] = unique(tableData(:,1),'rows');                              
-                            xdataTickLabels = 'YY';
-                            xdataLabel = 'Year';
-                        case 6  % all data
-                            ind = ones(size(tableData,1),1);
-                            xdataTickLabels = '';
-                            xdataLabel = '';
-                        otherwise
-                            error('Unknown forcing data time step.')
-                    end                                        
-
-                    % Build box plot
-                    if plotType_val==6
-                        boxplot(axisHandle,tableData(:,end), ind,'notch','on','ExtremeMode','clip','Jitter',0.75,'symbol','.');                    
-                    else
-                        boxplot(axisHandle,tableData(:,end), ind,'notch','off','ExtremeMode','clip','Jitter',0.75,'symbol','.');                    
-                    end
-                    
-                    %Add x tick labels
-                    if timestepID<6
-                        tableData_year = accumarray(ind,tableData(:,1),[],@max);
-                        tableData_month = accumarray(ind,tableData(:,3),[],@max);            
-                        tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
-                        t = unique(datenum(tableData_year, tableData_month, tableData_day));
-                        xdataTickLabels = datestr(t,xdataTickLabels);
-                        set(axisHandle, 'XTickLabel',xdataTickLabels);
-                        xlabel(axisHandle,xdataLabel); 
-                    else
-                        set(axisHandle, 'XTickLabel',xdataTickLabels);
-                        xlabel(axisHandle,xdataLabel);                         
-                    end                   
-                    ylabel(axisHandle,ydataLabel);                  
-                    
-                otherwise
-                    error('Unknown forcing data plot type.')                    
-            end                    
-            
-            box(axisHandle,'on');
-            axis(axisHandle,'tight');            
-            hold(axisHandle,'off');
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');      
-            drawnow update;            
-            
-            function dateaxis_local(ax, tickaxis)
-                % Determine range of data and choose appropriate label format 
-                Lim= get(ax, [tickaxis,'lim']);
-                Cond = Lim(2)-Lim(1); 
-
-                if Cond <= 14 % Range less than 15 days, day of week   
-                    dateform = 7;  
-                elseif Cond > 14 && Cond <= 31 % Range less than 32 days, day of month 
-                    dateform = 6; 
-                elseif Cond > 31 && Cond <= 180 % Range less than 181 days, month/day 
-                    dateform = 5; 
-                elseif Cond > 180 && Cond <= 365  % Range less than 366 days, 3 letter month 
-                    dateform = 3; 
-                elseif Cond > 365 && Cond <= 365*3 % Range less than 3 years, month year  
-                    dateform = 11; 
-                else % Range greater than 3 years, 2 digit year 
-                    dateform = 10; 
-                end 
-
-                % Get axis tick values and add appropriate start date. 
-                xl = get(ax,[tickaxis,'tick'])'; 
-                set(ax,[tickaxis,'tickmode'],'manual',[tickaxis,'limmode'],'manual') 
-                n = length(xl); 
-
-                % Guarantee that the day, month, and year strings have the 
-                % the same number of characters
-                switch dateform
-                  case {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16} 
-                    dstr = datestr(xl,dateform);
-                  case 17 % Year/Month/Day  (ISO format)    
-                    dstr = datestr(xl,25);
-                  otherwise
-                    error(message('finance:calendar:dateAxis'))       
-                end 
-
-                % Set axis tick labels 
-                set(ax,[tickaxis,'ticklabel'],dstr)                         
-            end
-        end       
-        
-        % Get derived data AND add data table and plot.
-        function modelCalibration_onUpdateDerivedData(this, hObject, eventdata)
-       
-            % Record the current row and column numbers
-            irow = this.tab_ModelCalibration.currentRow;
-            icol = this.tab_ModelCalibration.currentCol;
-
-            % Get the calibration table data
-            obj = this.tab_ModelCalibration.Table;
-            data = obj.Data;
-            
-            % Find index to the calibrated model label within the
-            % list of constructed models.
-            if isempty(irow)
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                                   
-                return;
-            end
-            
-            % Find the curretn model label
-            calibLabel = HydroSight_GUI.removeHTMLTags(data{irow,2});             
-            this.tab_ModelCalibration.currentModel = calibLabel;
-            
-            % Get a copy of the model object. This is only done to
-            % minimise HDD read when the models are off loaded to HDD using
-            % matfile();
-            tmpModel = getModel(this, calibLabel);            
-            
-            % Display the requested calibration results if the model object
-            % exists and there are calibration results.
-            if ~isempty(tmpModel) ...
-            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
-            && tmpModel.calibrationResults.isCalibrated  
-            
-                % Get model specific derived data and plot etc
-                %-----------------------             
-                % Create an axis handle for the figure.
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data plot'); 
-                delete( findobj(obj ,'type','axes'));
-                axisHandle = axes( 'Parent',obj);                
-
-                % Get length of forcing data.
-                t = getForcingData(tmpModel);
-                t = [1:max(t(:,1))-min(t(1,1))]';
-
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data dropdown');                    
-                derivedData_type = obj.String;
-                derivedData_type = derivedData_type{obj.Value};
-                ind = strfind(derivedData_type,':');
-                modelComponant = derivedData_type(1:ind(1)-1);
-                derivedData_variable = derivedData_type(ind(1)+1:end);
-                [derivedData, derivedData_names] = getDerivedData(tmpModel, modelComponant, derivedData_variable, t, axisHandle);
-
-                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data table');                    
-                obj.Data = derivedData;
-                obj.ColumnName = derivedData_names;
-                drawnow update;            
-            else
-                errordlg(['The following selected model must be calibrated to display the results:',calibLabel],'Model not calibrated ...');
-            end            
-        end
-        
-        function modelSimulation_tableEdit(this, hObject, eventdata)
-
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');                
-            drawnow update;            
-            
-            % Get GUI table indexes
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);            
-            data=get(hObject,'Data'); % get the data cell array of the table
-                        
-            % Undertake column specific operations.
-            if ~isempty(icol) && ~isempty(irow)
-                
-                % Record the current row and column numbers
-                this.tab_ModelSimulation.currentRow = irow;
-                this.tab_ModelSimulation.currentCol = icol;
-            
-                % Remove HTML tags from the column name
-                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
-                
-                switch columnName;
-                    % Get the selected model.
-                    case 'Model Label'
-                        
-                        % Get the selected model for simulation
-                        calibLabel = eventdata.EditData;
-
-                        % Check if any models are calibrated.
-                        if strcmp(calibLabel,'(none calibrated)')
-                            return;
-                        end
-                        
-                        % Get a copy of the model object. This is only done to
-                        % minimise HDD read when the models are off loaded to HDD using
-                        % matfile();
-                        tmpModel = getModel(this, calibLabel);
-
-                        % Exit if model is model not found
-                        if isempty(tmpModel) || ~tmpModel.calibrationResults.isCalibrated
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                            
-                            return
-                        end                        
-                        
-                        % Assign data from the calbration toable to the simulation
-                        % table.
-                        irow = eventdata.Indices(:,1);     
-                        headData = getObservedHead(tmpModel);
-                        obshead_start = floor(min(headData(:,1)));
-                        obshead_end = max(headData(:,1));
-                        boreID = tmpModel.bore_ID;
-                        if size(hObject.Data,1)<irow
-                            hObject.Data = [hObject.Data; cell(1,size(hObject.Data,2))];
-
-                        end
-                        hObject.Data{irow,1} = false;
-                        hObject.Data{irow,2} = calibLabel;
-                        hObject.Data{irow,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
-                        hObject.Data{irow,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
-                        hObject.Data{irow,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];
-                        hObject.Data{irow,6} = '';
-                        hObject.Data{irow,7} = '';
-                        hObject.Data{irow,8} = '';
-                        hObject.Data{irow,9} = '';
-                        hObject.Data{irow,10} = '';   
-                        hObject.Data{irow,11} = false;   
-                        hObject.Data{irow,12} = '<html><font color = "#FF0000">Not Simulated.</font></html>';
-                        
-                        % Update status in GUI
-                        drawnow update                        
-                        
-                    % Check the input model simulation label is unique for the selected model.    
-                    case 'Simulation Label'                        
-                        %  Check if the new model label is unique and
-                        %  create a new label if not.
-                        allLabels = hObject.Data(:,[2,6]);
-                        newLabel = {hObject.Data{irow, 2}, eventdata.EditData};
-                        newLabel = HydroSight_GUI.createUniqueLabel(allLabels, newLabel, irow);                        
-                        hObject.Data{irow,6} = newLabel{2};
-
-                        % Warn user if the label has chnaged.
-                        if ~strcmp(newLabel, eventdata.EditData)
-                            warndlg('The model and simulation label pair must be unique. An modified label has been input','Error ...');
-                        end                        
-                end
-            end
-            
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;                            
-
-        end        
-        
-        function modelSimulation_tableSelection(this, hObject, eventdata)
-            
-            % Hide plotting toolbar
-            plotToolbarState(this,'off');
-                       
-            % Get GUI table indexes            
-            icol=eventdata.Indices(:,2);
-            irow=eventdata.Indices(:,1);                        
-            
-            % Exit of no cells are selected
-            if isempty(icol) && isempty(irow)
-                return
-            end
-
-            % Remove HTML tags from the column name
-            columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});            
-            
-            % Exit if no cells edits are to be dealt with and no results
-            % are to viewed.
-            if ~(strcmp(columnName, 'Model Label') ||  strcmp(columnName, 'Forcing Data File') || ...
-            strcmp(columnName, 'Simulation Start Date') || strcmp(columnName, 'Simulation End Date'))
-        
-                if isfield( this.tab_ModelSimulation,'resultsOptions')
-                    if this.tab_ModelSimulation.resultsOptions.popup.Value==3
-                        return
-                    end
-                else
-                    return
-                end
-            end
-
-            % Record the current row and column numbers
-            this.tab_ModelSimulation.currentRow = irow;
-            this.tab_ModelSimulation.currentCol = icol;            
-            
-            % Get table data
-            data=get(hObject,'Data'); % get the data cell array of the table
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');                
-            drawnow update;            
-
-            switch columnName;
-                case 'Model Label'
-                    % Get list of models
-                    model_label = fieldnames(this.models);
-                    
-                    % Get list of those that are calibrated
-                    filt = false(length(model_label),1);
-                    for i=1:length(model_label)                           
-                        
-                        % Convert model label to field label.
-                        model_labelAsField = HydroSight_GUI.modelLabel2FieldName(model_label{i});
-                        
-                        % Check if model is calibrated
-                        filt(i) = this.model_labels{model_labelAsField, 'isCalibrated'};
-                    end                    
-                    model_label = model_label(filt);
-
-                    % Assign calib model labels to drop down
-                    hObject.ColumnFormat{2} = model_label';   
-
-                    % Update status in GUI
-                    drawnow update
-
-                case 'Forcing Data File'
-
-                    % Get file name and remove project folder from
-                    % preceeding full path.
-                    fName = getFileName(this, 'Select the Forcing Data file.');
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        data{irow,icol} = fName;
-
-                        % Input file name to the table
-                        set(hObject,'Data',data);
-                    end
-
-                case {'Simulation Start Date', 'Simulation End Date'}
-                    % Get the selected model for simulation
-                    calibLabel = data{irow,2};
-
-                    % Get calibrated model field name
-                    calibLabel = HydroSight_GUI.modelLabel2FieldName(calibLabel);            
-
-                    % Check the model is calibrated.
-                    if ~this.model_labels{calibLabel, 'isCalibrated'};
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                            
-                        warndlg('A calibrated model must be first selected from the "Model Label" column.', 'Error ...');
-                        return;
-                    end
-
-                    % Get the forcing data for the model.
-                    % If a new forcing data file is given, then open it
-                    % up and get the start and end dates from it.
-                    if isempty( data{irow,7}) || strcmp(data{irow,7},'');                            
-                        % Get a copy of the model object. This is only done to
-                        % minimise HDD read when the models are off loaded to HDD using
-                        % matfile();
-                        tmpModel = getModel(this, calibLabel);
-
-                        % Get forcing data
-                        forcingData = getForcingData(tmpModel);
-
-                        % Get start and end dates of the forcing
-                        % data, remove HTML tags and then convert to a
-                        % date number.
-                        startDate = min(forcingData(:,1));
-                        endDate = max(forcingData(:,1));                            
-                    else
-                        % Import forcing data
-                        % Check fname file exists.
-                        fname = data{irow,7};
-                        if exist(fname,'file') ~= 2;   
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                                  
-                            warndlg('The new forcing date file could not be open for examination of the start and end dates.', 'Error ...');
-                            return;
-                        end
-
-                        % Read in the file.
-                        try
-                           forcingData = readtable(fname);
-                        catch           
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                                  
-                            warndlg('The new forcing date file could not be imported for extraction of the start and end dates. Please check its format.', 'Error ...');
-                            return;
-                        end    
-
-                        % Calculate the start and end dates
-                        try
-                           forcingData_dates = datenum(forcingData{:,1}, forcingData{:,2}, forcingData{:,3});
-                           startDate = min(forcingData_dates);
-                           endDate = max(forcingData_dates);
-                        catch
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                                  
-                            warndlg('The dates from the new forcing data file could not be calculated. Please check its format.', 'Error ...');
-                            return;                                
-                        end
-                    end
-
-                    % Open the calander with the already input date,
-                    % else use the start date of the obs. head.
-                    if isempty(data{irow,icol})
-                        inputDate = startDate;                            
-                    else
-                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
-                    end
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                          
-                    selectedDate = uical(inputDate, 'English',startDate, endDate);
-
-                    % Check if user cancelled uical
-                    if isempty(selectedDate)
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                              
-                        return;
-                    end
-
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;                                                  
-
-                    % Check the selected date
-                    if strcmp(columnName, 'Simulation Start Date')
-                        % Get the end date 
-                        simEndDate=inf;
-                        if ~isempty(data{irow,icol+1})
-                            simEndDate = datenum( data{irow,icol+1},'dd-mmm-yyyy');
-                        end
-
-                        % Check date is between start and end date of obs
-                        % head.
-                        if selectedDate < startDate || selectedDate > endDate 
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                                                          
-                            warndlg('The simulation start date must be within the range of the observed forcing data.');
-                            return;
-                        elseif selectedDate>=simEndDate
-                            set(this.Figure, 'pointer', 'arrow');
-                            drawnow update;                                                          
-                            warndlg('The simulation start date must be less than the simulation end date.');
-                            return;
-                        else
-                            data{irow,icol} = datestr(selectedDate,'dd-mmm-yyyy');
-                            set(hObject,'Data',data);
-                        end
-
-                        set(this.Figure, 'pointer', 'watch');
-                        drawnow update;                               
-
-                    else
-                        % Get the end date 
-                        simStartDate = -inf;
-                        if ~isempty(data{irow,icol-1})
-                            simStartDate = datenum( data{irow,icol-1},'dd-mmm-yyyy');                        
-                        end
-
-                        % Check date is between start and end date of obs
-                        % head.
-                        if selectedDate < startDate || selectedDate > endDate    
-                            warndlg('The simulation end date must be within the range of the observed forcing data.');
-                        elseif selectedDate<=simStartDate
-                            warndlg('The simulation end date must be less than the simulation end date.');
-                        else
-                            data{irow,icol} = datestr(selectedDate,'dd-mmm-yyyy');
-                            set(hObject,'Data',data);
-                        end
-
-                    end
-
-                otherwise
-                    % Do nothing
-            end
-                        
-            % Check a row and column are selected.
-            if isempty(irow) || isempty(icol)
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update;                 
-                return
-            end
-            
-            % Find index to the calibrated model label within the list of calibrated
-            % models.
-            modelLabel = data{irow,2};
-            if isempty(modelLabel)
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update;                 
-                return;
-            end          
-            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);                 
-            
-            % Check if there is a simulation label within the
-            % identified calibrated model.            
-            simLabel = data{irow,6};
-            if isempty(simLabel)
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update;                 
-                return;
-            end
-            
-            % Get a copy of the model object. This is only done to
-            % minimise HDD read when the models are off loaded to HDD using
-            % matfile();
-            tmpModel = getModel(this, modelLabel);
-
-            % Exit if model is model not found or model is empty
-            if isempty(tmpModel) || isempty(tmpModel.simulationResults)
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update;                            
-                return
-            end                        
-            
-            % Find index to the simulation label within the
-            % identified calibrated model.                        
-            simInd = cellfun(@(x) strcmp(simLabel, x.simulationLabel), tmpModel.simulationResults);
-            if all(~simInd)    % Exit if model not found.
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update; 
-                return;
-            end          
-            simInd = find(simInd);
-            
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update; 
-                                
-            % Display the requested simulation results if the model object
-            % exists and there are simulation results.
-            if ~isempty(simInd) && isfield(tmpModel.simulationResults{simInd,1},'head') && ...
-            ~isempty(tmpModel.simulationResults{simInd,1}.head )               
-                
-                % Get pop up menu item for the selection of results to
-                % display.
-                results_item = this.tab_ModelSimulation.resultsOptions.popup.Value;
-                        
-                switch results_item
-                    case 1
-                        % Show a table of calibration data
-                        
-                        % Get the model simulation data.
-                        tableData = tmpModel.simulationResults{simInd,1}.head;                        
-
-                        % Calculate year, month, day etc
-                        tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
-
-                        % Convert to a table data type and add data to the table.
-                        this.tab_ModelSimulation.resultsOptions.dataTable.table.Data = tableData;
-                        
-                        
-                        if size(tmpModel.calibrationResults.parameters.params_final,2)==1
-                            this.tab_ModelSimulation.resultsOptions.dataTable.table.ColumnName = {'Year','Month','Day','Hour','Minute',tmpModel.simulationResults{simInd,1}.colnames{2:end}};                            
-                        else
-                            % Create column names
-                            colnames={};
-                            for i=2:length(tmpModel.simulationResults{simInd,1}.colnames)
-                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-50th %ile']];
-                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-5th %ile']];
-                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-95th %ile']];
-                            end
-                            
-                            % Convert to a table data type and add data to the table.                            
-                            this.tab_ModelSimulation.resultsOptions.dataTable.table.ColumnName = {'Year','Month','Day','Hour','Minute',colnames{:}};                            
-                        end
-                        
-
-                    case 2
-                       % Show plotting toolbar
-                       plotToolbarState(this,'on');
-                        
-                       % Determine the number of plots to create.
-                       if size(tmpModel.calibrationResults.parameters.params_final,2)==1
-                            nsubPlots = size(tmpModel.simulationResults{simInd,1}.head,2) - 1;
-                       else                            
-                            nsubPlots = (size(tmpModel.simulationResults{simInd,1}.head,2)-1)/3 ;
-                       end                        
-                        
-                        % Delete existing panels
-                        delete( findobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children,'type','panel'));
-                        delete(findobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children,'type','uipanel'))
-                                                
-                        % Add uipanel and axes for each plot
-                        for i=1:nsubPlots
-                            h = uipanel('Parent',this.tab_ModelSimulation.resultsOptions.plots.panel, 'Visible','off');
-                            axisHandles{i} = axes( 'Parent',h);
-                        end
-                        
-                        % Edit position and visibility of each panel
-                        for i=1:nsubPlots                            
-                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'BorderType','none');
-                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Units','normalized');
-                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Position',[0.01, 1-i/nsubPlots, 0.99, 1/nsubPlots - 0.01]);
-                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Visible','on');
-                        end
-                            
-                        % Plot the simulation data using the axis handles
-                        solveModelPlotResults(tmpModel, simLabel, axisHandles);
-                        
-                    case 3
-                        % do nothing
-                end
-            else
-                this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 0];
-            end            
-            
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;             
-        end        
-        
-        function modelSimulation_onResultsSelection(this, hObject, eventdata)
-            % Get selected popup menu item
-            listSelection = get(hObject,'Value');
-                         
-            switch listSelection
-                case 1 %Data 
-                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 -1 0];
-                case 2 %Summary plots
-                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 -1];
-                case 3 %None
-                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 0];
-            end
-        end
-        
-        % Get the model options cell array (as a string).
-        function onApplyModelOptions(this, hObject, eventdata)
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');   
-            drawnow update;    
-            
-            try
-                % get the new model options
-                irow = this.tab_ModelConstruction.currentRow;
-                modelType = this.tab_ModelConstruction.Table.Data{irow,7};
-                modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(modelType).obj);            
-
-                % Warn the user if the model is already built and the
-                % inputs are to change - reuiring the model object to be
-                % removed.                            
-                if ~isempty(this.tab_ModelConstruction.Table.Data{irow,8}) && ...
-                ~strcmp(modelOptionsArray, this.tab_ModelConstruction.Table.Data{irow,8} )
-
-                    % Get original model label
-                    modelLabel = this.tab_ModelConstruction.Table.Data{irow,2};
-
-                    % Check if the model object exists
-                    modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);            
-                    if ~isempty(this.models) && any(strcmp(fieldnames(this.models), modelLabel))
-                        
-                        % Get model
-                        tmpModel = getModel(this, modelLabel);
-                                               
-                        % Check if the model is calibrated
-                        isCalibrated =  isfield(tmpModel.calibrationResults,'isCalibrated') ... 
-                            & tmpModel.calibrationResults.isCalibrated;
-
-                        % Create warnign message and display
-                        if isCalibrated
-                            msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
-                                    '', ...                                
-                                   'Do you want to continue with the changes to the model construction?'};
-                        else
-                            msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
-                                    '', ...
-                                   'Do you want to continue with the changes to the model construction?'};
-                        end                            
-                        % Change cursor and show message
-                        set(this.Figure, 'pointer', 'arrow');   
-                        drawnow update;                                
-                        response = questdlg(msg,'Overwrite exiting model?','Yes','No','No');
-
-                        % Check if 'cancel, else delete the model object
-                        if strcmp(response,'No')
-                            return;
-                        end
-
-                        % Change status of the model object.
-                        this.tab_ModelConstruction.Table.Data{irow,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
-
-                        % Delete model from calibration table.
-                        modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
-                        modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
-                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
-                        this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
-
-                        % Update row numbers
-                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                     
-
-                        % Delete models from simulations table.
-                        if ~isempty(this.tab_ModelSimulation.Table.Data)
-                            modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);
-                            modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
-                            ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
-                            this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);
-                        end
-
-                        % Update row numbers
-                        nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                        this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                     
-                    end
-                end
-
-                % Apply new model options.
-                this.tab_ModelConstruction.Table.Data{this.tab_ModelConstruction.currentRow,8} = modelOptionsArray;
-                
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update                
-            catch ME
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update                
-                
-                errordlg('The model options could not be applied to the model. Please check the model options are sensible.');
-            end
-
-        end
-        
-        % Get the model options cell array (as a string).
-        function onApplyModelOptions_selectedBores(this, hObject, eventdata)
-            
-            try
-                % Get the current model type.
-                currentModelType = this.tab_ModelConstruction.Table.Data{this.tab_ModelConstruction.currentRow, 7};
-
-                % Get list of selected bores.
-                selectedBores = this.tab_ModelConstruction.Table.Data(:,1);
-
-                % Check some bores have been selected.
-                if ~any(cellfun(@(x) x, selectedBores))
-                    warndlg('No models are selected.', 'Summary of model options applied to bores...');
-                    return;
-                end
-
-                % Get the model options.
-                modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(currentModelType).obj);
-
-                % Change cursor
-                set(this.Figure, 'pointer', 'watch');   
-                drawnow update;                                
-                
-                % Loop  through the list of selected bore and apply the modle
-                % options.
-                nOptionsCopied = 0;
-                response = '';
-                for i=1:length(selectedBores);
-                    if selectedBores{i} && strcmp(this.tab_ModelConstruction.Table.Data{i,7}, currentModelType)
-
-                        % Warn the user if the model is already built and the
-                        % inputs are to change - reuiring the model object to be
-                        % removed.            
-                        if ~isempty(this.tab_ModelConstruction.Table.Data{i,8}) && ...
-                        ~strcmp(modelOptionsArray, this.tab_ModelConstruction.Table.Data{i,8} )
-
-                            % Get original model label
-                            modelLabel = this.tab_ModelConstruction.Table.Data{i,2};                                                      
-                            
-                            % Remove modle if exists                            
-                            modelLabel4FieldNames = HydroSight_GUI.modelLabel2FieldName(modelLabel);            
-                            if any(strcmp(fieldnames(this.models), modelLabel4FieldNames))
-                                if strcmp(response, 'Yes - all models')
-                                    this.models = rmfield(this.models,modelLabel4FieldNames);
-                                else
-
-                                    % Get model
-                                    tmpModel = getModel(this, modelLabel);
-
-                                    % Check if the model is calibrated
-                                    isCalibrated =  isfield(tmpModel.calibrationResults,'isCalibrated') ... 
-                                        & tmpModel.calibrationResults.isCalibrated;
-                         
-                                    % Create warnign message and display
-                                    if isCalibrated
-                                        msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
-                                                '', ...                                
-                                               'Do you want to continue with the changes to the model construction?'};
-                                    else
-                                        msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
-                                                '', ...
-                                               'Do you want to continue with the changes to the model construction?'};
-                                    end                            
-
-                                    response = questdlg(msg,'Overwrite exiting model?','Yes','Yes - all models','No','No');
-
-                                    % Check if 'cancel, else delete the model object
-                                    if strcmp(response,'No')
-                                        continue;
-                                    else
-                                        % Remove built model
-                                        this.models = rmfield(this.models,modelLabel4FieldNames);
-                                    end
-                                end
-
-                                % Delete model from calibration table.
-                                modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
-                                modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
-                                ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
-                                this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
-
-                                % Update row numbers
-                                nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                                this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                   
-
-                                % Delete models from simulations table.
-                                modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);                            
-                                modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
-                                ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
-                                this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);
-
-                                % Update row numbers
-                                nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                                this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                              
-
-                            end
-                        end                    
-
-                        % Apply model option.
-                        this.tab_ModelConstruction.Table.Data{i,8} = modelOptionsArray;            
-                        
-                        % Change status of the model object.
-                        this.tab_ModelConstruction.Table.Data{i,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
-                        
-                        nOptionsCopied = nOptionsCopied + 1;
-                    end
-                end            
-
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                
-                
-                msgbox(['The model options were copied to ',num2str(nOptionsCopied), ' "', currentModelType ,'" models.'], 'Summary of model options applied to bores...');
-            catch ME
-                
-                % Change cursor
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;                
-                
-                errordlg('The model options could not be applied to the selected models. Please check the model options are sensible.');
-            end
-
-        end
-
-        function onAnalyseBores(this, hObject, eventdata)
-           
-            % Hide the results window.
-            this.tab_DataPrep.modelOptions.resultsOptions.box.Heights = [0 0];
-                            
-            % Hide plot icons
-            plotToolbarState(this,'off');            
-            
-            % Get table data
-            data = this.tab_DataPrep.Table.Data;
-            
-            % Get list of selected bores.
-            selectedBores = data(:,1);
-
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update
-
-            % Check that the user wants to save the projct after each
-            % calib.
-            response = questdlg('Do you want to save the project after each bore is analysed?','Auto-save analysis results?','Yes','No','Cancel','Yes');
-            if strcmp(response,'Cancel')
-                return;
-            end
-            if strcmp(response,'Yes') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
-                msgbox('The project has not yet been saved. Please save it and re-run the analysis.','Project not yet saved ...','error');
-                return;
-            end
-            saveModels=false;
-            if strcmp(response,'Yes')
-                saveModels=true;
-            end            
-            
-            
-            % Count the number of models selected
-            nModels=0;
-            for i=1:length(selectedBores);                                
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    nModels = nModels +1;
-                end
-            end
-            
-            % Add simulation bar
-            minModels4Waitbar = 5;
-            iModels=0;
-            if nModels>=minModels4Waitbar
-                h = waitbar(0, ['Analysing ', num2str(nModels), ' bores. Please wait ...']);
-            end    
-            
-            % Loop  through the list of selected bore and apply the modle
-            % options.
-            nBoresAnalysed = 0;
-            nAnalysisFailed = 0;
-            nBoreNotInHeadFile=0;
-            nBoreInputsError = 0;
-            nBoreIDLabelError = 0;
-            for i=1:length(selectedBores);
-                % Check if the model is to be built.
-                if isempty(selectedBores{i}) ||  ~selectedBores{i}
-                    continue;
-                end
-
-                % Update table with progress'
-                this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FFA500">Analysing bore ...</font></html>';
-                this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                    
-                % Update status in GUI
-                drawnow;
-                
-                % Import head data
-                %----------------------------------------------------------
-                % Check the obs. head file is listed
-                if isdir(this.project_fileName)                             
-                    fname = fullfile(this.project_fileName,data{i,2}); 
-                else
-                    fname = fullfile(fileparts(this.project_fileName),data{i,2});  
-                end                
-                if isempty(fname)                    
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error - file name empty.</font></html>';
-                    nAnalysisFailed = nAnalysisFailed + 1;
-                    continue;
-                end
-
-                % Check the bore ID file exists.
-                if exist(fname,'file') ~= 2;                    
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error - file does not exist.</font></html>';
-                    nAnalysisFailed = nAnalysisFailed + 1;
-                    continue;
-                end
-
-                % Read in the observed head file.
-                try
-                    tbl = readtable(fname);
-                catch                    
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error -  read in failed.</font></html>';
-                    nAnalysisFailed = nAnalysisFailed + 1;
-                    continue;
-                end                
-                
-                % Filter for the required bore.
-                boreID = data{i,3};
-                filt =  strcmp(tbl{:,1},boreID);
-                if sum(filt)<=0
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore not found in head data file -  data error.</font></html>';
-                    nBoreNotInHeadFile = nBoreNotInHeadFile +1;
-                    continue
-                end
-                headData = tbl(filt,2:end);
-                headData = headData{:,:};                
-                %----------------------------------------------------------
-                                
-                % Get required inputs for analysis
-                %----------------------------------------------------------
-                boreDepth = inf;
-                dataCol=4;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
-                    boreDepth = this.tab_DataPrep.Table.Data{i, dataCol};
-                end
-                
-                surfaceElevation = inf;
-                dataCol = 5;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end                
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    surfaceElevation = this.tab_DataPrep.Table.Data{i, dataCol};
-                end
-                
-                caseLength = inf;
-                dataCol = 6;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end                
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
-                    caseLength = this.tab_DataPrep.Table.Data{i, dataCol};
-                end
-                
-                constructionDate = datenum(0,0,0);
-                dataCol = 7;
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    try
-                        constructionDate = datenum(this.tab_DataPrep.Table.Data{i, dataCol});
-                        if constructionDate > now()+1 || constructionDate< datenum(1500,1,1)
-                            nBoreInputsError = nBoreInputsError +1;
-                            continue;
-                        end
-                    catch
-                        this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore not found in head data file -  data error.</font></html>';
-                        nBoreInputsError = nBoreInputsError +1;
-                        continue
-                    end
-                end   
-                
-                checkStartDate = false;
-                dataCol = 8;               
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    checkStartDate = this.tab_DataPrep.Table.Data{i, dataCol};
-                end                
-                
-                checkEndDate = false;
-                dataCol = 9;               
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    checkEndDate = this.tab_DataPrep.Table.Data{i, dataCol};
-                end       
-                
-                checkMinHead = false;
-                dataCol = 10;               
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    checkMinHead = this.tab_DataPrep.Table.Data{i, dataCol};
-                end       
-
-                
-                checkMaxHead = false;
-                dataCol = 11;               
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    checkMaxHead = this.tab_DataPrep.Table.Data{i, dataCol};
-                end         
-                
-                
-                rateOfChangeThreshold = inf;
-                dataCol = 12;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end                
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
-                    rateOfChangeThreshold = this.tab_DataPrep.Table.Data{i, dataCol};
-                end      
-                
-                constHeadDuration = inf;
-                dataCol = 13;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end                
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
-                    constHeadDuration = this.tab_DataPrep.Table.Data{i, dataCol};
-                end
-                
-                numNoiseStdDev = inf;
-                dataCol = 14;
-                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
-                    nBoreInputsError = nBoreInputsError +1;
-                    continue
-                end                
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
-                    numNoiseStdDev = this.tab_DataPrep.Table.Data{i, dataCol};
-                end    
-                
-                
-                outlierForwadBackward=true;
-                dataCol = 15;               
-                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
-                    outlierForwadBackward = this.tab_DataPrep.Table.Data{i, dataCol};
-                end                    
-                %----------------------------------------------------------
-                                
-                % Convert boreID string to appropriate field name and test
-                % if is can be converted to a field name
-                boreID = strrep(boreID,' ','_');
-                boreID = strrep(boreID,'-','_');
-                boreID = strrep(boreID,'?','');
-                boreID = strrep(boreID,'\','_');
-                boreID = strrep(boreID,'/','_');
-                try 
-                    tmp.(boreID) = [1 2 3];
-                    clear tmp;
-                catch ME
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore ID label error - must start with letters and have only letters and numbers.</font></html>';
-                    nBoreIDLabelError = nBoreIDLabelError+1;
-                end
-                    
-                % Convert head date/time columns to a vector.
-                switch size(headData,2)-1
-                    case 3
-                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3));
-                    case 4
-                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4), zeros(size(headData,1),1), zeros(size(headData,1),1));
-                    case 5
-                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4),headData(:,5), zeros(size(headData,1),1));
-                    case 6
-                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4),headData(:,5),headData(:,6));
-                    otherwise
-                        error('The input observed head must be 4 to 7 columns with right hand column being the head and the left columns: year; month; day; hour (optional), minute (options), second (optional).');
-                end                
-                
-                % Call analysis function                
-                try
-                    % Do the analysis and add to the object
-                    chechDuplicateDates = true;
-                    this.dataPrep.(boreID) = doDataQualityAnalysis( [dateVec, headData(:,end)], boreDepth, surfaceElevation, caseLength, constructionDate, ...
-                    checkStartDate, checkEndDate, chechDuplicateDates, checkMinHead, checkMaxHead, rateOfChangeThreshold, ...
-                    constHeadDuration, numNoiseStdDev, outlierForwadBackward );                                                       
-                    
-                    % Add summary stats
-                    numErroneouObs = sum(any(table2array(this.dataPrep.(boreID)(:,7:12)),2));
-                    numOutlierObs = sum(table2array(this.dataPrep.(boreID)(:,13)));                    
-                    this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">',num2str(numErroneouObs),'</font></html>'];
-                    this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">',num2str(numOutlierObs),'</font></html>'];
-                                  
-                    nBoresAnalysed = nBoresAnalysed +1;
-                    
-                    if saveModels
-                        this.tab_DataPrep.Table.Data{i,16} = '<html><font color = "#FFA500">Saving project. </font></html>';
-
-                        % Update status in GUI
-                        drawnow;                        
-                        
-                        % Save project.
-                        onSave(this,hObject,eventdata);
-                    end
-                    
-                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#008000">Bore analysed.</font></html>';
-                    
-                catch ME
-                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Analysis failed - ', ME.message,'</font></html>'];   
-                    this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                    this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                    
-                    nAnalysisFailed = nAnalysisFailed +1;
-                    continue                    
-                end
-          
-                % Update status in GUI
-                drawnow;
-                
-                % Update wait bar
-                if nModels>=minModels4Waitbar            
-                    iModels=iModels+1;
-                    waitbar(iModels/nModels);                
-                end
-            end
-            
-            % Close wait bar
-            if nModels>=minModels4Waitbar            
-                close(h);
-            end              
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update
-            
-            % Report Summary
-            msgbox({['The data analysis was successfully for ',num2str(nBoresAnalysed), ' bores.'], ...
-                    '', ...
-                    ['Below is a summary of the failures:'], ...
-                    ['   - Head data file errors: ', num2str(nBoreNotInHeadFile)] ...
-                    ['   - Input table data errors: ', num2str(nBoreInputsError)] ...
-                    ['   - Data analysis algorithm failures: ', num2str(nAnalysisFailed)] ...
-                    ['   - Bore IDs not starting with a letter: ', num2str(nBoreIDLabelError)]}, ...                    
-                    'Summary of data analysis ...');
-                           
-            
-        end
-        
-        function onBuildModels(this, hObject, eventdata)
-
-            % Hide plotting toolbars
-            plotToolbarState(this, 'off');            
-            
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update            
-            
-            % Delete any empty model objects
-            if ~isempty(this.models)
-                modelFieldNames = fieldnames(this.models);
-                for i=1:length(modelFieldNames)
-                    if isempty(this.models.(modelFieldNames{i}))
-                        this.models = rmfield(this.models,modelFieldNames{i});
-                    end
-                end
-            end
-                       
-            % Get table data
-            data = this.tab_ModelConstruction.Table.Data;
-            
-            % Get list of selected bores.
-            selectedBores = data(:,1);
-                        
-            % Count the number of models selected
-            nModels=0;
-            for i=1:length(selectedBores);                                
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    nModels = nModels +1;
-                end
-            end
-            
-            % Add wait bar
-            minModels4Waitbar = 5;
-            iModels=0;
-            if nModels>=minModels4Waitbar
-                h = waitbar(0, ['Building ', num2str(nModels), ' models. Please wait ...']);
-            end               
-            
-            % Loop  through the list of selected bore and apply the modle
-            % options.
-            nModelsBuilt = 0;
-            nModelsBuiltFailed = 0;
-            for i=1:length(selectedBores);
-                % Check if the model is to be built.
-                if isempty(selectedBores{i}) || ~selectedBores{i}
-                    continue;
-                end
-
-                % Update table with progress'
-                this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FFA500">Building model ...</font></html>';
-
-                % Update status in GUI
-                drawnow
-                                
-                % Import head data
-                %----------------------------------------------------------
-                % Check the obs. head file is listed
-                if isdir(this.project_fileName)
-                    fname = fullfile(this.project_fileName,data{i,3});
-                else
-                    fname = fullfile(fileparts(this.project_fileName),data{i,3});
-                end
-                if isempty(fname)                    
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - file name empty.</font></html>';
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    continue;
-                end
-
-                % Check the bore ID file exists.
-                if exist(fname,'file') ~= 2;                    
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - file does not exist.</font></html>';
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    continue;
-                end
-
-                % Read in the observed head file.
-                try
-                    tbl = readtable(fname);
-                catch                    
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error -  read in failed.</font></html>';
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    continue;
-                end                
-                
-                % Filter for the required bore.
-                filt =  strcmp(tbl{:,1},data{i,6});
-                headData = tbl(filt,2:end);
-                headData = headData{:,:};
-                
-                % Check theer is some obs data
-                if size(headData,1)<=1
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - <=1 observation.</font></html>';
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    continue;
-                end
-                
-                %----------------------------------------------------------
-                
-                % Import forcing data
-                %----------------------------------------------------------
-                % Check fname file exists.
-                if isdir(this.project_fileName)
-                    fname = fullfile(this.project_fileName,data{i,4});
-                else
-                    fname = fullfile(fileparts(this.project_fileName),data{i,4});
-                end
-                if exist(fname,'file') ~= 2;                   
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file error - file name empty.</font></html>';
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   continue;
-                end
-
-                % Read in the file.
-                try
-                   forcingData = readtable(fname);
-                catch                   
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file error -  read in failed.</font></html>';
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   continue;
-                end      
-                if isempty(forcingData)
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file is empty or open elsewhere -  read in failed.</font></html>';
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   continue;                    
-                end
-                %----------------------------------------------------------
-                
-                
-                % Import coordintate data
-                %----------------------------------------------------------
-                % Check fname file exists.
-                if isdir(this.project_fileName)
-                    fname = fullfile(this.project_fileName,data{i,5});
-                else
-                    fname = fullfile(fileparts(this.project_fileName),data{i,5});
-                end
-                if exist(fname,'file') ~= 2;                   
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate file error - file name empty.</font></html>';
-                   continue;
-                end
-
-                % Read in the file.
-                try
-                   coordData = readtable(fname);
-                   coordData = table2cell(coordData);
-                catch              
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate file error -  read in failed.</font></html>';
-                   continue;
-                end
-                
-                % Check site names are unique
-                if length(coordData(:,1)) ~= length(unique(coordData(:,1)))
-                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate site IDs not unique.</font></html>';
-                   continue;                    
-                end
-                %----------------------------------------------------------
-
-                % Get model label
-                model_label = data{i,2};
-                
-                % Get bore IDs
-                boreID= data{i,6};
-                
-                % Get model type
-                modelType = data{i,7};
-                
-                % If the modle options are empty, try and add an empty cell
-                % in case the model does not need options.
-                if isempty(data{i,8})
-                    data{i,8} = '{}';
-                end
-                
-                % Get model options
-                try
-                    modelOptions= eval(data{i,8});
-                catch ME
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Syntax error in model options - string not convertable to cell array.</font></html>';
-                    continue;
-                end                
-                
-                % Build model
-                try 
-                    % Build model
-                    maxHeadObsFreq_asDays = 1;
-                    model_labelAsField = HydroSight_GUI.modelLabel2FieldName(model_label);
-                    tmpModel = HydroSightModel(model_label, boreID, modelType , headData, maxHeadObsFreq_asDays, forcingData, coordData, modelOptions);
-                    setModel(this, model_labelAsField, tmpModel);
-                    
-                    % Check if model is listed in calib table. if so get
-                    % index
-                    isModelListed=false;
-                    if size(this.tab_ModelCalibration.Table.Data,1)>0
-                        calibModelLabelsHTML = this.tab_ModelCalibration.Table.Data(:,2);
-                        calibModelLabels = HydroSight_GUI.removeHTMLTags(calibModelLabelsHTML);
-                        isModelListed = cellfun( @(x) strcmp( model_label, x), calibModelLabels);
-                        isModelListed = find(isModelListed);
-                        
-                        % If multiple are listed, then take the top one
-                        if length(isModelListed)>1
-                            filt = true(length(calibModelLabels),1);
-                            filt(isModelListed(2:end))=false;
-                            isModelListed=isModelListed(1);
-                            this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(filt,:);
-                            
-                            % Update row numbers
-                            nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                            this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                        end
-                    end
-
-                    % Get model start and end dates
-                    obshead_start = min(datenum(headData(:,1),headData(:,2),headData(:,3) ));
-                    obshead_end = max(datenum(headData(:,1),headData(:,2),headData(:,3) ));
-                    
-                    % Add the model to the calibration table.
-                    if ~any(isModelListed)
-                        this.tab_ModelCalibration.Table.Data = [this.tab_ModelCalibration.Table.Data; ...
-                        cell(1,13)];
-                        isModelListed = size(this.tab_ModelCalibration.Table.Data,1);
-                        
-                        this.tab_ModelCalibration.Table.Data{isModelListed,2} = ['<html><font color = "#808080">',model_label,'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,6} = datestr(obshead_start,'dd-mmm-yyyy');
-                        this.tab_ModelCalibration.Table.Data{isModelListed,7} = datestr(obshead_end,'dd-mmm-yyyy');
-                        this.tab_ModelCalibration.Table.Data{isModelListed,8} = 'SP-UCI';
-                        
-                        
-                        % Update row numbers
-                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                        
-                        
-                    else
-                        this.tab_ModelCalibration.Table.Data{isModelListed,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{isModelListed,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];                        
-                    end
-                    this.tab_ModelCalibration.Table.Data{isModelListed,9} = '<html><font color = "#FF0000">Not calibrated.</font></html>';                    
-                    this.tab_ModelCalibration.Table.Data{isModelListed,10} = '(NA)';
-                    this.tab_ModelCalibration.Table.Data{isModelListed,11} = '(NA)';
-                    this.tab_ModelCalibration.Table.Data{isModelListed,12} = '(NA)';
-                    this.tab_ModelCalibration.Table.Data{isModelListed,13} = '(NA)';                    
-                    
-                    
-                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#008000">Model built.</font></html>';
-                    nModelsBuilt = nModelsBuilt + 1; 
-                    
-                catch ME
-                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
-                    this.tab_ModelConstruction.Table.Data{i, 9} = ['<html><font color = "#FF0000">Model build failed - ', ME.message,'</font></html>'];
-                end
-                
-                % Update status in GUI
-                drawnow
-
-               % Update wait bar
-                if nModels>=minModels4Waitbar            
-                    iModels=iModels+1;
-                    waitbar(iModels/nModels);                
-                end
-            end
-            
-            % Close wait bar
-            if nModels>=minModels4Waitbar            
-                close(h);
-            end            
-            
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update
-            
-            % Enable file menu items for HDD offloading.
-            if nModelsBuilt>0
-                for i=1:size(this.figure_Menu.Children,1)
-                    if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
-                        set(this.figure_Menu.Children(i),'Enable','on');
-                    elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                    strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                        set(this.figure_Menu.Children(i),'Enable','on');
-                    end       
-                end
-            end
-            
-            % Report Summary
-            msgbox(['The model was successfully built for ',num2str(nModelsBuilt), ' models and failed for ',num2str(nModelsBuiltFailed), ' models.'], 'Summary of model builds ...');
-                        
-        end
-        
-        function onCalibModels(this, hObject, eventdata)
-                  
-            % The project must be saved to a file. Check the project file
-            % is defined.
-            if isempty(this.project_fileName) || isdir(this.project_fileName)
-                warndlg({'The project must be saved to a file before calibration can start.';'Please first save the project.'}, 'Project not saved...')
-                return
-            end            
-            
-            % Get table data
-            data = this.tab_ModelCalibration.Table.Data;            
-            
-            % Get list of selected bores and check.
-            selectedBores = data(:,1);
-            isModelSelected=false;
-            for i=1:length(selectedBores);                
-                % Check if the model is to be calibrated.
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    isModelSelected=true;
-                    break;
-                end                
-            end                        
-            
-            if ~isModelSelected
-                warndlg('No models have been selected for calibration.','Model Calibration Error ...')
-                return;
-            end                                
-            
-            % Delete any empty model objects
-            if ~isempty(this.models)
-                modelFieldNames = fieldnames(this.models);
-                for i=1:length(modelFieldNames)
-                    if isempty(this.models.(modelFieldNames{i}))
-                        this.models = rmfield(this.models,modelFieldNames{i});
-                    end
-                end
-            else
-                warndlg('No models appear to have been built. Please build the models then calibrate them.','Model Calibration Error ...')
-                return;
-            end            
-
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update            
-            
-            % Delete any models listed in the calibration table that are
-            % not listed in the model construction table            
-            if ~isempty(data)
-                constructModelLabels = this.tab_ModelConstruction.Table.Data(:,2);
-                % Get model label
-                calibModelLabelsHTML = data(:,2);
-                
-                % Remove HTML tags
-                calibModelLabels = HydroSight_GUI.removeHTMLTags(calibModelLabelsHTML);
-                
-                deleteCalibRow = false(size(data,1),1);
-                for i=1:size(data,1)
-
-                    % Add index for row if it is to be deleted                    
-                    if ~any(cellfun( @(x) strcmp( calibModelLabels{i}, x), constructModelLabels))
-                        deleteCalibRow(i) = true;
-                        continue;
-                    end
-                    
-                    % Check if there are duplicate bore IDs. If so, delete
-                    % non-calibrated one
-                    ind = find(cellfun( @(x) strcmp( calibModelLabels{i}, x), calibModelLabels))';
-                    if length(ind)>1
-                        nDubplicates2Remove = 0;
-                        for j=ind
-                           if contains(data{j, 10},'(NA)')
-                               deleteCalibRow(j) = true;
-                               nDubplicates2Remove  = nDubplicates2Remove  +1;
-                           end
-                        end
-                        if nDubplicates2Remove == length(ind)
-                            deleteCalibRow(ind(1)) = false;
-                        end
-                    end
-                    
-                end
-                
-                if any(deleteCalibRow)
-
-                    % Change cursor to arrow
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update
-
-                    warndlg('Some models listed in the calibration table are duplicated or not listed in the model construction table and will be deleted. Please re-run the calibration','Unexpected table error...');
-                    this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~deleteCalibRow,:);
-                    
-                    % Update row numbers
-                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
-                    
-                    return;
-                end
-                
-            end            
-            
-            
-            % CREATE CALIBRATION GUI
-            %--------------------------------------------------------------
-            % Open a window and add some menus
-            this.tab_ModelCalibration.GUI = figure( ...
-                'Name', 'HydroSight Model Calibration', ...
-                'NumberTitle', 'off', ...
-                'MenuBar', 'none', ...
-                'HandleVisibility', 'off', ...
-                'Visible','on', ...
-                'Toolbar','none', ...
-                'DockControls','off', ...
-                'WindowStyle','modal' ...
-                );
-                %'CloseRequestFcn',@this.onExit); 
-                        
-            % Set window Size
-            windowHeight = this.tab_ModelCalibration.GUI.Parent.ScreenSize(4);
-            windowWidth = this.tab_ModelCalibration.GUI.Parent.ScreenSize(3);
-            figWidth = 0.6*windowWidth;
-            figHeight = 0.85*windowHeight;            
-            this.tab_ModelCalibration.GUI.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];
-            
-            % Set default panel color
-            warning('off');
-            uiextras.set( this.tab_ModelCalibration.GUI, 'DefaultBoxPanelTitleColor', [0.7 1.0 0.7] );
-            warning('on');
- 
-            outerVbox= uiextras.VBoxFlex('Parent',this.tab_ModelCalibration.GUI,'Padding', 3, 'Spacing', 3);
-            innerVbox_top= uiextras.VBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);
-            innerVbox_bottom= uiextras.VBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);
- 
-            % Add label
-            uicontrol(innerVbox_top,'Style','text','String','Calibration scheme settings: ','HorizontalAlignment','left', 'Units','normalized');
-            
-            %Create Panels for different windows       
-            outerTabsPanel = uiextras.TabPanel( 'Parent', innerVbox_top, 'Padding', 5, 'TabSize',127,'FontSize',8);
-            CMAES_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','CMA-ES tab');            
-            SPUCI_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','SP-UCI tab');
-            DREAM_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','DREAM tab');
-            %MultiModel_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','Multi-Model tab');
-            %outerTabsPanel.TabNames = {'CMA-ES', 'SP-UCI','DREAM', 'Multi-model'};
-            outerTabsPanel.TabNames = {'CMA-ES', 'SP-UCI','DREAM'};
-            outerTabsPanel.SelectedChild = 1;
-                       
-            % Add buttons.
-            outerButtons = uiextras.HButtonBox('Parent',innerVbox_top,'Padding', 3, 'Spacing', 3);             
-            uicontrol('Parent',outerButtons,'String','Start calibration','Callback', @this.startCalibration, 'Interruptible','on','Tag','Start calibration', 'TooltipString', sprintf('Calibrate all of the selected models.') );
-            if ~isdeployed
-                uicontrol('Parent',outerButtons,'String','HPC Offload','Callback', @this.startCalibration,'Tag','Start calibration - useHPC', 'TooltipString', sprintf('BETA version to export selected models for calibration on a High Performance Cluster.') );
-                uicontrol('Parent',outerButtons,'String','HPC Retrieval','Callback', @this.onImportFromHPC, 'TooltipString', sprintf('BETA version to retrieve calibrated models from a High Performance Cluster.') );
-            end            
-            uicontrol('Parent',outerButtons,'String','Quit calibration','Callback', @this.quitCalibration, 'Enable','off','Tag','Quit calibration', 'TooltipString', sprintf('Stop calibrating the models at the end of the current iteration loop.') );
-            outerButtons.ButtonSize(1) = 225;            
-            
-            % Count the number of models selected
-            nModels=0;
-            iModels=0;
-            for i=1:length(selectedBores);                                
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    nModels = nModels +1;
-                end
-            end            
-            
-            % Add progress bar
-            bar_panel = uipanel('Parent',innerVbox_top, 'BorderType','none');
-            ax = axes( 'Parent', bar_panel);            
-            barh(ax, 0,'Tag','Calib_wait_bar');
-            box(ax,'on');
-            xlim(ax,[0,nModels]);
-            ax.YTick = [];
-            ax.XTick = [0:nModels];
-            title(ax,'Model Calibration Progress','FontSize',10,'FontWeight','normal');
-            
-            % Add large box for calib. iterations
-            uicontrol(innerVbox_bottom,'Style','edit','String','(calibration not started)','Tag','calibration command window', ...
-                'HorizontalAlignment','left', 'Units','normalized','BackgroundColor','white');             
-            
-            set(outerVbox, 'Sizes', [-1 -1]);
-            set(innerVbox_top, 'Sizes', [30 -1 30 50]);
-            set(innerVbox_bottom, 'Sizes', [-1]);
-           
-            % Fill in CMA-ES panel               
-            CMAES_tabVbox= uiextras.Grid('Parent',CMAES_tab,'Padding', 6, 'Spacing', 6);
-            uicontrol(CMAES_tabVbox,'Style','text','String','Maximum number of model evaluations (MaxFunEvals):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(CMAES_tabVbox,'Style','text','String','Number of parameter sets searching for the optima (PopSize):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(CMAES_tabVbox,'Style','text','String','Absolute change in the objective function for convergency (TolFun):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(CMAES_tabVbox,'Style','text','String','Largest absolute change in the parameters for convergency (TolX):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(CMAES_tabVbox,'Style','text','String','Number CMA-ES calibration restarts (Restarts):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(CMAES_tabVbox,'Style','text','String','Standard deviation for the initial parameter sampling, as fraction of plausible parameter bounds (insigmaFrac):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(CMAES_tabVbox,'Style','text','String','Random seed number (only for repetetive testing purposes, an empty value uses a different seed per model):','HorizontalAlignment','left', 'Units','normalized');            
-                  
-            uicontrol(CMAES_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','CMAES MaxFunEvals','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','CMAES popsize','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string','1e-12','Max',1, 'Tag','CMAES TolFun','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string','1e-11','Max',1, 'Tag','CMAES TolX','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string','4','Max',1, 'Tag','CMAES Restarts','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string','0.333','Max',1, 'Tag','CMAES insigmaFrac','HorizontalAlignment','right');
-            uicontrol(CMAES_tabVbox,'Style','edit','string',num2str(floor(rand(1)*1e6)),'Max',1, 'Tag','CMAES iseed','HorizontalAlignment','right');
-            
-            set(CMAES_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,6));                        
-            
-            % Fill in SP-UCI panel   
-            SPUCI_tabVbox= uiextras.Grid('Parent',SPUCI_tab,'Padding', 6, 'Spacing', 6);
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Maximum number of model evaluations (maxn):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Number of evolution loops meeting convergence criteria for calbration (kstop):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Percentage change in the objective function allowed in kstop loops before convergency (pcento):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Normalized geometric range of the parameters before convergency (peps):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Number of complexes (ngs) per model parameter:','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Minimum number of total complexes (ngs):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Maximum number of total complexes (ngs):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(SPUCI_tabVbox,'Style','text','String','Random seed number (only for repetetive testing purposes, an empty value uses a different seed per model):','HorizontalAlignment','left', 'Units','normalized');            
-      
-            uicontrol(SPUCI_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','SP-UCI maxn','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','string','10','Max',1, 'Tag','SP-UCI kstop','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','SP-UCI pcento','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','SP-UCI peps','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','string','2','Max',1, 'Tag','SP-UCI ngs','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','String','1','Max',1, 'Tag','SP-UCI ngs min','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','String','inf','Max',1, 'Tag','SP-UCI ngs max','HorizontalAlignment','right');
-            uicontrol(SPUCI_tabVbox,'Style','edit','string',num2str(floor(rand(1)*1e6)),'Max',1, 'Tag','SP-UCI iseed','HorizontalAlignment','right');
-            
-            set(SPUCI_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,8));
-            
-            
-            % Fill in DREAM panel   
-            DREAM_tabVbox= uiextras.Grid('Parent',DREAM_tab ,'Padding', 6, 'Spacing', 6);
-            uicontrol(DREAM_tabVbox,'Style','text','String','Number of Markov chains per model parameter (N_per_param):','HorizontalAlignment','left', 'Units','normalized');                  
-            uicontrol(DREAM_tabVbox,'Style','text','String','Number of 10,000s of model generations per chain (T):','HorizontalAlignment','left', 'Units','normalized');                  
-            uicontrol(DREAM_tabVbox,'Style','text','String','Number of crossover values (nCR):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(DREAM_tabVbox,'Style','text','String','Number chain pairs for proposal (delta):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(DREAM_tabVbox,'Style','text','String','Random error for ergodicity (lambda):','HorizontalAlignment','left', 'Units','normalized');            
-            uicontrol(DREAM_tabVbox,'Style','text','String','Randomization (zeta):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(DREAM_tabVbox,'Style','text','String','Test function name for detecting outlier chains (outlier):','HorizontalAlignment','left', 'Units','normalized');                        
-            uicontrol(DREAM_tabVbox,'Style','text','String','Probability of jumprate of 1 (pJumpRate_one):','HorizontalAlignment','left', 'Units','normalized');            
-            
-            uicontrol(DREAM_tabVbox,'Style','edit','string','1','Max',1, 'Tag','DREAM N','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','5','Max',1, 'Tag','DREAM T','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','3','Max',1, 'Tag','DREAM nCR','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','3','Max',1, 'Tag','DREAM delta','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','0.05','Max',1, 'Tag','DREAM lambda','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','0.05','Max',1, 'Tag','DREAM zeta','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','iqr','Max',1, 'Tag','DREAM outlier','HorizontalAlignment','right');
-            uicontrol(DREAM_tabVbox,'Style','edit','string','0.2','Max',1, 'Tag','DREAM pJumpRate_one','HorizontalAlignment','right');
-            
-            set(DREAM_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,8));            
-            
-%             % Fill in MultiModel panel   
-%             % MODEL STILL IN DEVELOPMENT
-%             MultiModel_tabVbox= uiextras.Grid('Parent',MultiModel_tab ,'Padding', 6, 'Spacing', 6);
-%             
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Parameters to estimate as fixed-effect (component:parameter-model label):','HorizontalAlignment','left', 'Units','normalized');                        
-%             multiModel_leftList = uicontrol(MultiModel_tabVbox,'Style','listbox','String',cell(0,1),'Min',1,'Max',100,'Tag','NLMEFIT paramsLeftList','HorizontalAlignment','left', 'Units','normalized');                        
-%             
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Number of randomly selected initial starts for gradient calibration (nStarts):','HorizontalAlignment','left', 'Units','normalized');                        
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Maximum number of model evaluations (MaxIter):','HorizontalAlignment','left', 'Units','normalized');                        
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Absolute change in the objective function allowed for convergency (TolFun):','HorizontalAlignment','left', 'Units','normalized');            
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Maximum absolute change in the parameters allowed for convergency (TolX):','HorizontalAlignment','left', 'Units','normalized');                        
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Relative difference for the finite difference gradient estimation (DerivStep):','HorizontalAlignment','left', 'Units','normalized');                        
-% 
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
-%             MultiModel_tabButtons = uiextras.VButtonBox('Parent',MultiModel_tabVbox,'Padding', 3, 'Spacing', 3);             
-%             uicontrol('Parent',MultiModel_tabButtons,'String','>','Callback', @this.multimodel_moveRight, 'Tag','NLMEFIT paramsRight', 'TooltipString', 'Move the selected left-hand parameters to the right-hand box.' );
-%             uicontrol('Parent',MultiModel_tabButtons,'String','<','Callback', @this.multimodel_moveLeft, 'Tag','NLMEFIT paramsLeft', 'TooltipString', 'Move the selected right-hand parameters to the left-hand box.' );            
-%             MultiModel_tabButtons.ButtonSize(1) = 225;            
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','')            
-%             
-%             uicontrol(MultiModel_tabVbox,'Style','text','String','Parameters to jointly estimate as random-effect (component:parameter-model label):','HorizontalAlignment','left', 'Units','normalized');                        
-%             multiModel_RightList = uicontrol(MultiModel_tabVbox,'Style','listbox','String',cell(0,1),'Min',1,'Max',100,'Tag','NLMEFIT paramsRightList','HorizontalAlignment','left', 'Units','normalized');                        
-%                         
-%             uicontrol(MultiModel_tabVbox,'Style','edit','string','10','Max',1, 'Tag','NLMEFIT nStarts','HorizontalAlignment','right');
-%             uicontrol(MultiModel_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','NLMEFIT MaxIter','HorizontalAlignment','right');
-%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-8','Max',1, 'Tag','NLMEFIT TolFun','HorizontalAlignment','right');
-%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','NLMEFIT TolX','HorizontalAlignment','right');
-%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-5','Max',1, 'Tag','NLMEFIT DerivStep','HorizontalAlignment','right');
-% 
-%                         
-%             set(MultiModel_tabVbox, 'ColumnSizes', [-1 40 -1], 'RowSizes', [20, -1, 20, 20, 20, 20, 20]);            
-                        
-            % Loop through each model to be calibrated and disable the
-            % calib. tabs that are not required.
-            % Loop  through the list of selected bore and apply the model
-            % options.
-            initialTab = [];
-            showCMAEStab =  false;
-            showSPUCItab = false;
-            showDREAMtab = false;
-            showMultiModeltab = false;
-            selectedBores = data(:,1);
-            for i=1:length(selectedBores)
-                
-                % Check if the model is to be calibrated.
-                if isempty(selectedBores{i}) || ~selectedBores{i}
-                    continue;
-                end
-
-                % Get the selected model calibration method
-                calibMethod = data{i,8};                
-                 
-                switch calibMethod
-                    case 'CMA-ES'
-                        showCMAEStab = true;
-                        if isempty(initialTab)
-                            initialTab=1;
-                        end                    
-                    case 'SP-UCI'
-                        showSPUCItab =  true;
-                        if isempty(initialTab)
-                            initialTab=2;
-                        end                        
-                    case 'DREAM'
-                        showDREAMtab = true;
-                        if isempty(initialTab)
-                            initialTab=3;
-                        end                        
-                    case 'Multi-model'
-                        showMultiModeltab = true;
-                        if isempty(initialTab)
-                            initialTab=4;
-                        end                        
-                    otherwise
-                        error('The input model type cannot be set-up.')
-                end
-                        
-            end
-                    
-            % Show first active tab
-            outerTabsPanel.SelectedChild = initialTab;
-            
-            if ~showCMAEStab
-                outerTabsPanel.TabEnables{1} = 'off';
-            end
-            if ~showSPUCItab
-                outerTabsPanel.TabEnables{2} = 'off';
-            end            
-            if ~showDREAMtab
-                outerTabsPanel.TabEnables{3} = 'off';
-            end            
-%             if ~showMultiModeltab
-%                 outerTabsPanel.TabEnables{4} = 'off';
-%             end                       
-%             
-%             % Loop through each model that is part of the multi-model and
-%             % get the parameters. Add these to the multi-modle list box.
-%             multiModel_leftList.String={};
-%             multiModel_rightList.String={};
-%             if showMultiModeltab
-%                 for i=1:length(selectedBores)
-% 
-%                     % Check if the model is to be calibrated.
-%                     if isempty(selectedBores{i}) || ~selectedBores{i} || ~strcmp(data{i,8},'Multi-model')
-%                         continue;
-%                     end
-% 
-%                     % Get the selected model for simulation
-%                     calibLabel = data{i,2};
-%                     calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);                    
-%                     
-%                     tmpModel = getModel(this, calibLabel);
-%  
-%                     % Get the model parameters and add to the list box
-%                     if ~isempty(tmpModel)
-% 
-%                          [params, paramNames] = getParameters(tmpModel.model);
-%                          paramNames = strcat(paramNames(:,1),':',paramNames(:,2),['-',calibLabel]);
-%                          multiModel_leftList.String = {multiModel_leftList.String{:}, paramNames{:}};
-%                     end
-%                     
-%                 end                
-%                 multiModel_leftList.String = sort(multiModel_leftList.String);
-%             end
-% 
-        end
-        
-        function onSimModels(this, hObject, eventdata)
-           
-            % Get table data
-            data = this.tab_ModelSimulation.Table.Data;
-            
-            % Get list of selected bores.
-            selectedBores = data(:,1);            
-            if length(selectedBores)==0
-                warndlg('No models selected for simulation.');
-            end
-
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update            
-
-            % Count the number of models selected
-            nModels=0;
-            for i=1:length(selectedBores);                                
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    nModels = nModels +1;
-                end
-            end
-            
-            % Add simulation bar
-            minModels4Waitbar = 5;
-            iModels=0;
-            if nModels>=minModels4Waitbar
-                h = waitbar(0, ['Simulating ', num2str(nModels), ' models. Please wait ...']);
-            end            
-            
-            % Loop  through the list of selected bore and apply the model
-            % options.
-            nModelsSim = 0;
-            nModelsSimFailed = 0;
-            for i=1:length(selectedBores);                                
-                
-                % Check if the model is to be simulated.
-                if isempty(selectedBores{i}) || ~selectedBores{i}
-                    continue;
-                end
-                
-                this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FFA500">Simulating ... </font></html>';
-                        
-                % Update status in GUI
-                drawnow
-        
-                % Get model label
-                calibLabel = data{i,2};
-                
-                % Get a copy of the model object. This is only done to
-                % minimise HDD read when the models are off loaded to HDD using
-                % matfile();
-                tmpModel = getModel(this, calibLabel);
-
-                % Exit if model not found or not calibrated.
-                if isempty(tmpModel) || ~tmpModel.calibrationResults.isCalibrated
-                   nModelsSimFailed = nModelsSimFailed +1;
-                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - Model could not be found. Please rebuild and calibrate it.</font></html>'];
-                   continue;
-                end                                  
-                
-                % Get the exact start and end dates of the obs head.
-                obsHead = getObservedHead(tmpModel);
-                
-                % Get the simulation options
-                obsHeadStartDate = min(obsHead(:,1));
-                obsHeadEndDate = max(obsHead(:,1));               
-                simLabel = data{i,6};
-                forcingdata_fname = data{i,7};      
-                simTimeStep = data{i,10};
-                
-                % Check there is a simulation label.
-                if isempty(simLabel)
-                   nModelsSimFailed = nModelsSimFailed +1;
-                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - No simulation label.</font></html>'];
-                   continue;
-                end                      
-                
-                % Get the forcing data.
-                if ~isempty(forcingdata_fname)
-
-                    % Import forcing data
-                    %-----------------------
-                    % Check fname file exists.
-                    if isdir(this.project_fileName)
-                        forcingdata_fname = fullfile(this.project_fileName,forcingdata_fname);
-                    else
-                        forcingdata_fname = fullfile(fileparts(this.project_fileName),forcingdata_fname);
-                    end
-                    
-                    if exist(forcingdata_fname,'file') ~= 2;                   
-                        this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The new forcing date file could not be open.</font></html>';
-                        nModelsSimFailed = nModelsSimFailed +1;
-                        continue;
-                    end
-
-                    % Read in the file.
-                    try
-                       forcingData= readtable(forcingdata_fname);
-                    catch                   
-                        this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The new forcing date file could not be imported.</font></html>';
-                        nModelsSimFailed = nModelsSimFailed +1;
-                        continue;                        
-                    end                    
-                   
-                    % Convert year, month, day to date vector
-                    forcingData_data = table2array(forcingData);
-                    forcingDates = datenum(forcingData_data(:,1), forcingData_data(:,2), forcingData_data(:,3));
-                    forcingData_data = [forcingDates, forcingData_data(:,4:end)];
-                    forcingData_colnames = forcingData.Properties.VariableNames(4:end);
-                    forcingData_colnames = {'time', forcingData_colnames{:}};
-                    forcingData = array2table(forcingData_data,'VariableNames',forcingData_colnames);
-                    clear forcingData_data forcingDates
-                else
-                    % Set forcing used to empty. This will cause the
-                    % calibration data to be used.
-                    forcingData = [];
-                end
-                                    
-               % Get the start and end dates for the simulation.                               
-               if isempty(data{i,8})
-                   simStartDate = obsHeadStartDate;
-               else
-                   simStartDate = datenum( data{i,8},'dd-mmm-yyyy'); 
-               end
-               if isempty( data{i,9})
-                   simEndDate = obsHeadEndDate;
-               else
-                   simEndDate = datenum( data{i,9},'dd-mmm-yyyy') + datenum(0,0,0,23,59,59);                         
-               end
-
-               % Check the date timestep
-               if isempty(simTimeStep) && (simStartDate <obsHeadStartDate || simEndDate > obsHeadEndDate)
-                    this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The time step must be specified when the simulation dates are outside of the observed head period.</font></html>';
-                    nModelsSimFailed = nModelsSimFailed +1;
-                    continue;                        
-               end               
-               
-               % Create a vector of simulation time points 
-               switch  simTimeStep
-                   case 'Daily'
-                       simTimePoints = [simStartDate:1:simEndDate]';
-                   case 'Weekly'
-                       simTimePoints = [simStartDate:7:simEndDate]';
-                   case 'Monthly'
-                       simTimePoints = zeros(0,3);
-                       startYear = year(simStartDate);
-                       startMonth= month(simStartDate);
-                       startDay= 1;
-                       endYear = year(simEndDate);
-                       endMonth = month(simEndDate);
-                       endDay = day(simEndDate);
-                       iyear = startYear;
-                       imonth = startMonth;
-                       iday = startDay;
-                       j=1;
-                       simTimePoints(j,1:3) = [iyear, imonth, iday];
-                       while datenum(iyear,imonth,iday) <= simEndDate
-                          
-                           if imonth == 12
-                               imonth = 1;
-                               iyear = iyear + 1;
-                           else
-                               imonth = imonth + 1;
-                           end
-                           if datenum(iyear, imonth, iday) >= simStartDate && datenum(iyear, imonth, iday) <= simEndDate
-                               j=j+1;
-                               simTimePoints(j,1:3) = [iyear, imonth, iday];
-                           end
-                       end
-                       
-                       simTimePoints = datenum(simTimePoints);
-                   case 'Yearly'
-                       simTimePoints = [simStartDate:365:simEndDate]';
-                   otherwise
-                       % Get the observed head dates.
-                       obsTimePoints = getObservedHead(tmpModel);
-                       obsTimePoints = obsTimePoints(:,1);
-                       
-                       % Filter observed head time points to between the
-                       % simulation dates.
-                       filt = obsTimePoints >= simStartDate & obsTimePoints<=simEndDate;
-                       simTimePoints = obsTimePoints(filt);
-                       
-               end
-               
-               % Get the flag for kriging the residuals.
-               doKrigingOfResiduals = false;
-               if this.tab_ModelSimulation.Table.Data{i,end-1} && isempty(forcingData)
-                    doKrigingOfResiduals = true;
-               elseif this.tab_ModelSimulation.Table.Data{i,end-1} && ~isempty(forcingData)
-                    this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - Kriging of residuals can only be undertaken if new forcing data is not input.</font></html>';
-                    nModelsSimFailed = nModelsSimFailed +1;                    
-                    continue;                        
-               end                 
-               
-               % Undertake the simulation.
-               try 
-                   solveModel(tmpModel, simTimePoints, forcingData, simLabel, doKrigingOfResiduals);                   
-                   setModel(this, calibLabel, tmpModel);        
-
-                   % Update GUI table
-                   this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#008000">Simulated. </font></html>';
-                   
-                   nModelsSim = nModelsSim + 1;
-                                      
-               catch ME
-                   nModelsSimFailed = nModelsSimFailed +1;
-                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - ', ME.message,'</font></html>']; '<html><font color = "#FF0000">Failed. </font></html>';                       
-               end
-               
-               % Update wait bar
-               if nModels>=minModels4Waitbar            
-                    iModels=iModels+1;
-                    waitbar(iModels/nModels);                
-               end
-            end
-            
-            % Close wait bar
-            if nModels>=minModels4Waitbar            
-                close(h);
-            end
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update            
-            
-            % Report Summary
-            msgbox(['The simulations were successful for ',num2str(nModelsSim), ' models and failed for ',num2str(nModelsSimFailed), ' models.'], 'Summary of model simulaions...');
-
-        end
-        
-        
-        function onImportFromHPC(this, hObject, eventdata)
-           % Get SSH details
-            prompts = { 'URL to the cluster:', ...
-                        'User name for cluster:', ...
-                        'Password for cluster:', ...
-                        'Full path to folder for the jobs:', ...
-                        'Temporary local working folder:'};
-            dlg_title = 'Commands for retrieving HPC cluster model calibration.';        
-            num_lines = 1;
-            if isempty(this.HPCoffload)
-                defaults = {'edward.hpc.unimelb.edu.au', ...
-                            '', ...
-                            '', ...
-                            '', ...
-                            ''};
-            else
-                defaults{1}=this.HPCoffload{2};
-                defaults{2}=this.HPCoffload{3};
-                defaults{4}=this.HPCoffload{6};
-                defaults{3}='';
-                if length(this.HPCoffload)>=14
-                    defaults{5}=this.HPCoffload{14};
-                else
-                    defaults{5}='';                    
-                end
-                for i=1:length(defaults)
-                    if isempty(defaults{i})
-                        defaults{i}='';
-                    end
-                end
-            end
-            userData = inputdlg(prompts,dlg_title,num_lines,defaults);
-            if isempty(userData)
-                return;
-            end
-            
-            % Disaggregate user data
-            URL = userData{1};
-            username = userData{2};
-            password = userData{3};
-            folder = userData{4};
-            workingFolder = userData{5};
-            this.HPCoffload{2} = URL;
-            this.HPCoffload{3} = username;
-            this.HPCoffload{5} = folder;
-            this.HPCoffload{14} = workingFolder;
-            
-            % Get project folder
-            if isempty(this.project_fileName)
-                errordlg('The project folder must be set prior to retrieval of results.','Projct folder not set.');
-                return;
-            else
-                % Get project folder and file name (if a dir)
-                if isdir(this.project_fileName)
-                    projectPath = this.project_fileName;
-                else
-                    [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
-                end
-            end            
-
-            % Start the diary and copying of the command window outputs to
-            % the GUI.
-            commandwindowBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','calibration command window');
-            commandwindowBox.Max=Inf;
-            commandwindowBox.Min=0;
-            diaryFilename = strrep(this.project_fileName,'.mat',['_calibOutputs_',strrep(strrep(datestr(now),':','-'),' ','_'),'.txt']);
-            this.tab_ModelCalibration.QuitObj = calibGUI_interface(commandwindowBox,diaryFilename);
-            startDiary(this.tab_ModelCalibration.QuitObj);                  
-            
-            % Display update
-            display('HPC retrieval progress ...');
-            
-            % Update the diary file
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end  
-                        
-            % Check the local workin folder exists. If not, create it.
-            if ~exist(workingFolder,'dir')
-                % Display update
-                display(['   Making local working folder at:',workingFolder]);
-                
-                % Update the diary file
-                if ~isempty(this.tab_ModelCalibration.QuitObj)
-                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-                end
-                
-                mkdir(workingFolder);
-            end
-            cd(workingFolder);
-            
-            % Check that a SSH channel can be opened           
-            display('   Checking SSH connection to cluster ...');
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end            
-            sshChannel = ssh2_config(URL,username,password); 
-            if isempty(sshChannel)
-                errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
-                return;
-            end
-                      
-            % Get list of selected bores.            
-            data = this.tab_ModelCalibration.Table.Data;
-            selectedBores = data(:,1);
-
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update;
-              
-            % Get model indexes
-            imodels=[];
-            for i=1:length(selectedBores);
-                % Check if the model is to be calibrated.
-                if isempty(selectedBores{i}) || ~selectedBores{i}
-                    continue;
-                else
-                    imodels = [imodels,i];
-                end            
-            end
-            nSelectedModels = length(imodels);
-            
-            % Get a list of .mat files on remote cluster
-            display('   Getting list of results files on cluster ...');
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end        
-            try
-                [~,allMatFiles] = ssh2_command(sshChannel,['cd ',folder,'/models ; find -name \*.mat -print']);
-            catch ME
-                errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
-                return;
-            end
-            
-            % Filter out the input data files
-            ind = find(cellfun( @(x) isempty(strfind(x, 'HPCmodel.mat')), allMatFiles));
-            allMatFiles = allMatFiles(ind);
-            
-            % Build list of mat files results to download
-            display('   Building list of results files to retieve ...');
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end            
-            resultsToDownload=cell(0,1);
-            j=0;
-            imodel_filt=[];            
-            nModelsNoResult=0;
-            nModelsNamesToChange=0;
-            SSH_commands = '';
-            for i=imodels
-                % Get the selected model label.
-                calibLabel = data{i,2};                
-                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
-  
-                % Remove special characters from label (as per jobSubmission.m)
-                calibLabel =  regexprep(calibLabel,'\W','_');                             
-                calibLabel =  regexprep(calibLabel,'____','_');                             
-                calibLabel =  regexprep(calibLabel,'___','_');                             
-                calibLabel =  regexprep(calibLabel,'__','_');                      
-                
-                indResultsFileName = find(cellfun( @(x) ~isempty(strfind(x, ['/',calibLabel,'/results.mat'])), allMatFiles));                
-                if ~isempty(indResultsFileName)
-                    changefilename=true;
-                else
-                    indResultsFileName = find(cellfun( @(x) ~isempty(strfind(x, ['/',calibLabel,'/',calibLabel,'.mat'])), allMatFiles));
-                    changefilename=false;
-                end
-                    
-                if ~isempty(indResultsFileName)
-                    % Build SSH command string for chnaging results file
-                    % names form results.mat to bore label
-                    if changefilename
-                        nModelsNamesToChange=nModelsNamesToChange+1;
-                        SSH_commands = strcat(SSH_commands, [' cd ',folder,'/models/',calibLabel '; mv results.mat ', calibLabel,'.mat ;']);
-                    end
-                    
-                    % Add file name to list of files to download
-                    j=j+1;
-                    resultsToDownload{j,1}=['./',calibLabel,'/',calibLabel,'.mat'];
-                    imodel_filt =  [imodel_filt,true];
-                else
-                    imodel_filt =  [imodel_filt,false];
-                    nModelsNoResult=nModelsNoResult+1;
-                end
-            end
-
-            % Change file names from results.mat
-            if length(SSH_commands)>0
-                display(['   Changing file from results.mat to model label .mat at ', num2str(nModelsNamesToChange), ' models...']);
-                if ~isempty(this.tab_ModelCalibration.QuitObj)
-                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-                end                
-                try
-                    [~,status] = ssh2_command(sshChannel,SSH_commands);
-                catch ME
-                    warndlg({'Some results.mat files could not be changed to the model label.' ,'These models will not be imported.'},'SSH file name change failed.');
-                end
-            end
-            
-            % Download .mat files
-            display(['   Downloading ', num2str(length(resultsToDownload)), ' completed models to working folder ...']);
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end
-            imodels = imodels(logical(imodel_filt));            
-            try
-                ssh2_struct = scp_get(sshChannel,resultsToDownload, workingFolder, [folder,'/models/']);
-            catch ME
-                sshChannel  =  ssh2_close(sshChannel);        
-            end
-
-            display('   Closing SSH connection to cluster ...');
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end
-            
-            % Closing connection
-            try
-                sshChannel  =  ssh2_close(sshChannel);        
-            catch
-                % do nothing
-            end            
-            
-            % Loop  through the list of selected bore and apply the model
-            % options.
-            nModels=length(imodels);
-            nModelsResultFileErr=0;
-            nModelsRetieved=0;
-            k=0;
-            for i=imodels
-                k=k+1;
-                display(['   Importing model ',num2str(k),' of ',num2str(nModels) ' into the project ...']);
-                if ~isempty(this.tab_ModelCalibration.QuitObj)
-                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-                end
-
-                % get the original tabel text
-                tableRowData = this.tab_ModelCalibration.Table.Data(i,:); 
-                
-                % Get the selected model label.
-                calibLabel = data{i,2};                
-                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
-  
-                % Remove special characters from label (as per jobSubmission.m)
-                calibLabel = HydroSight_GUI.modelLabel2FieldName(calibLabel);
-                                                        
-                % Load model
-                try
-                    cd(workingFolder);
-                    importedModel = load([calibLabel,'.mat']);
-
-                    % Convert double precision residuals to single
-                    % to reduce RAM
-                    if isfield(importedModel.model.evaluationResults,'data')
-                        importedModel.model.evaluationResults.data.modelledHead_residuals = single(importedModel.model.evaluationResults.data.modelledHead_residuals);
-                    end
-                    importedModel.model.calibrationResults.data.modelledHead_residuals = single(importedModel.model.calibrationResults.data.modelledHead_residuals);
-
-                    % Clear the residuals from the model.model.variables. This is only done to minimise RAM.
-                    try
-                        if isfield(importedModel.model.model.variables,'resid');
-                            importedModel.model.model.variables = rmfield(importedModel.model.model.variables, 'resid');
-                        end
-                    catch ME                                
-                        % do nothing
-                    end
-
-                    % IF an old version of the HydroSight had been used,
-                    % then remove the cell array of variogram
-                    % values and just keep the relevant data. This
-                    % is done only to reduce RAM.
-                    if isfield(importedModel.model.evaluationResults,'performance')                        
-                        if isfield(importedModel.model.evaluationResults.performance.variogram_residual,'model')                        
-                            nvariograms=size(importedModel.model.evaluationResults.performance.variogram_residual.range,1);
-                            nBins  = length(importedModel.model.calibrationResults.performance.variogram_residual.model{1}.h);
-                            deltaTime=zeros(nBins ,nvariograms);
-                            gamma=zeros(nBins ,nvariograms);
-                            gammaHat=zeros(nBins ,nvariograms);
-                            parfor j=1:nvariograms
-                                deltaTime(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.h;
-                                gamma(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.gamma;
-                                gammaHat(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.gammahat;
-                            end
-                            importedModel.model.evaluationResults.performance.variogram_residual.h = deltaTime;
-                            importedModel.model.evaluationResults.performance.variogram_residual.gamma = gamma;
-                            importedModel.model.evaluationResults.performance.variogram_residual.gammaHat = gammaHat;
-                        end
-                    end                              
-                    if isfield(importedModel.model.calibrationResults.performance.variogram_residual,'model')                        
-                        nvariograms=size(importedModel.model.calibrationResults.performance.variogram_residual.range,1);
-                        nBins  = length(importedModel.model.calibrationResults.performance.variogram_residual.model{1}.h);
-                        deltaTime=zeros(nBins ,nvariograms);
-                        gamma=zeros(nBins ,nvariograms);
-                        gammaHat=zeros(nBins ,nvariograms);
-                        parfor j=1:nvariograms
-                            deltaTime(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.h;
-                            gamma(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.gamma;
-                            gammaHat(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.gammahat;
-                        end
-                        importedModel.model.calibrationResults.performance.variogram_residual.h = deltaTime;
-                        importedModel.model.calibrationResults.performance.variogram_residual.gamma = gamma;
-                        importedModel.model.calibrationResults.performance.variogram_residual.gammaHat = gammaHat;
-                    end
-                    % Assign calib status
-                    tableRowData{1,9} = '<html><font color = "#008000">Calibrated. </font></html>';
-
-                    % Get calib status and set into table.
-                    exitFlag = importedModel.model.calibrationResults.exitFlag;
-                    exitStatus = importedModel.model.calibrationResults.exitStatus;                            
-                    if exitFlag ==0 
-                        this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Calib. failed - ', ME.message,'</font></html>'];
-
-                        % Update status in GUI
-                        %drawnow update
-
-                        continue
-
-                    elseif exitFlag ==1
-                        tableRowData{1,9} = ['<html><font color = "#FFA500">Partially calibrated: ',exitStatus,' </font></html>'];
-                    elseif exitFlag ==2
-                        tableRowData{1,9} = ['<html><font color = "#008000">Calibrated: ',exitStatus,' </font></html>'];
-                    end
-
-                    % Update status in GUI
-                    drawnow update;
-
-                    % Recalculate performance stats
-                    %------------------
-                    head_calib_resid = importedModel.model.calibrationResults.data.modelledHead_residuals;
-                    SSE = sum(head_calib_resid.^2);
-                    RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
-                    importedModel.model.calibrationResults.performance.RMSE = RMSE;                        
-
-                    % CoE
-                    obsHead =  importedModel.model.calibrationResults.data.obsHead;
-                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
-                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(:,2));            
-                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
-
-                    if ~isempty(importedModel.model.evaluationResults)
-
-                        head_eval_resid = importedModel.model.evaluationResults.data.modelledHead_residuals;
-                        obsHead =  importedModel.model.evaluationResults.data.obsHead;
-
-                        % Mean error
-                        importedModel.model.evaluationResults.performance.mean_error = mean(head_eval_resid); 
-
-                        %RMSE
-                        SSE = sum(head_eval_resid.^2);
-                        importedModel.model.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
-
-                        % Unbiased CoE
-                        residuals_unbiased = bsxfun(@minus,head_eval_resid, importedModel.model.evaluationResults.performance.mean_error);
-                        SSE = sum(residuals_unbiased.^2);
-                        importedModel.model.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
-                    end                              
-                    %--------------
-
-                    % Set calib performance stats.
-                    calibAICc = median(importedModel.model.calibrationResults.performance.AICc);
-                    calibBIC = median(importedModel.model.calibrationResults.performance.BIC);
-                    calibCoE = median(importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
-                    tableRowData{1,10} = ['<html><font color = "#808080">',num2str(calibCoE),'</font></html>'];
-                    tableRowData{1,12} = ['<html><font color = "#808080">',num2str(calibAICc),'</font></html>'];
-                    tableRowData{1,13} = ['<html><font color = "#808080">',num2str(calibBIC),'</font></html>'];
-
-                    % Set eval performance stats
-                    if isfield(importedModel.model.evaluationResults,'performance')
-                        evalCoE = median(importedModel.model.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
-                        tableRowData{1,11} = ['<html><font color = "#808080">',num2str(evalCoE),'</font></html>'];
-                    else
-                        evalCoE = '(NA)';
-                        tableRowData{1,11} = ['<html><font color = "#808080">',evalCoE,'</font></html>'];
-                    end
-
-                    %vars=whos('-file','tmp2.mat'); for i=1:6;vars2{i}=vars(i).name;end; for i=1:6; loadedVars=load('tmp2.mat',vars2{i}); if i==1; save('tmp4.mat','-Struct','loadedVars');else;save('tmp4.mat','-Struct','loadedVars','-append');end; end
-                    % Update project with imported model.
-                    setModel(this, calibLabel, importedModel.model);
-                    clear importedModel;
-
-                    nModelsRetieved = nModelsRetieved + 1;
-
-                catch ME
-                    nModelsResultFileErr = nModelsResultFileErr +1;
-                end
-                
-                % Update GUI labels
-                this.tab_ModelCalibration.Table.Data(i,:) = tableRowData;
-
-            end                                  
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update;
-            
-            % Output Summary.
-            msgStr = {};
-            msgStr{length(msgStr)+1} = 'Finished Retrieval of Models.';
-            msgStr{length(msgStr)+1} = 'Summary of Retrieval:';
-            msgStr{length(msgStr)+1} = ['   No. models sucessfully imported to project: ',num2str(nModelsRetieved)];            
-            msgStr{length(msgStr)+1} = ['   No. selected models to retrieve: ',num2str(nSelectedModels )];
-            msgStr{length(msgStr)+1} = ['   No. selected models not yet complete: ',num2str(nModelsNoResult) ];
-            msgStr{length(msgStr)+1} = ['   No. selected models complete: ',num2str(nModels) ];                        
-            msgStr{length(msgStr)+1} = ['   No. models files that could not be imported to project: ',num2str(nModelsResultFileErr) ];     
-            
-            for i=1:length(msgStr)
-                display(msgStr{i});
-            end
-            if ~isempty(this.tab_ModelCalibration.QuitObj)
-                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-            end
-                                   
-        end
-
-        
-        function onImportTable(this, hObject, eventdata)
-        
-            
-            % Create the label for each type of inport
-            switch hObject.Tag
-                case 'Data Preparation'
-                    windowString = 'Select a .csv file containing the data for the data preparation table.';                                    
-                case 'Model Construction'
-                    windowString = 'Select a .csv file containing the data for the model construction.';                    
-                case 'Model Calibration'
-                    windowString = 'Select a .csv file containing the data for model calibration settings.';
-                case 'Model Simulation'
-                    windowString = 'Select a .csv file containing the data for the model simulation.';
-                otherwise
-                    warndlg('Unexpected Error: GUI table type unknown.');
-                    return                    
-            end
-                
-            % Get project folder
-            projectPath='';                
-            if isdir(this.project_fileName)
-                projectPath = this.project_fileName;
-            else
-                projectPath = fileparts(this.project_fileName);
-            end
-            
-            % Show open file window
-             if isempty(projectPath)
-                [fName,pName] = uigetfile({'*.csv'},windowString); 
-            else
-                [fName,pName] = uigetfile({'*.csv'},windowString, projectPath); 
-            end            
-            if fName~=0;
-                % Assign file name to date cell array
-                filename = fullfile(pName,fName);
-            else
-                return;
-            end         
-            
-            % Read in the table.
-            try
-                tbl = readtable(filename,'Delimiter',',');
-            catch ME
-                warndlg('The table datafile could not be read in. Please check it is a CSV file with column headings in the first row.');
-                return;
-            end            
-
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update;            
-            
-            % Check the table format and append to the required table
-            switch hObject.Tag
-                case 'Data Preparation'
-                    if size(tbl,2) ~=18
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                        
-                        warndlg('The table datafile must have 18 columns. That is, all columns shown in the model construction table.');
-                        return;
-                    end                    
-                    
-                    % Get column formats
-                    colFormat = this.tab_DataPrep.Table.ColumnFormat;                    
-
-                    % Convert table to cell array
-                    tableAsCell = table2cell(tbl);                    
-                    
-                    % Loop through each row in tbl, check the data and add
-                    % the user data to the columns of the row.
-                    nRowsNotImported= 0;
-                    nImportedRows = 0;
-                    for i=1: size(tbl,1)
-                        
-                        try                                      
-
-                            % Convert any columns of string data to '' if read
-                            % in as NaN.
-                            for j=1:length(colFormat)                       
-                                if ischar(colFormat{j}) && strcmp(colFormat{j},'char') && ...
-                                isnumeric( tableAsCell{i,j} ) && isnan( tableAsCell{i,j} )
-                                    tableAsCell{i,j} = '';
-                                elseif iscell(colFormat{j}) && ~ischar(tableAsCell{i,j}) && isnan( tableAsCell{i,j} )
-                                    tableAsCell{i,j} = '';
-                                elseif isdatetime(tableAsCell{i,j})
-                                    tableAsCell{i,j} = datestr(tableAsCell{i,j});
-                                end
-                            end
-
-                            % Add results text.
-                            tableAsCell{i,16} = '<html><font color = "#FF0000">Bore not analysed.</font></html>'; 
-                            tableAsCell{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                            tableAsCell{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
-                            
-                            % Convert integer 0/1 to logicals
-                            for j=find(strcmp(colFormat,'logical'))
-                               if tableAsCell{i,j}==1;
-                                   tableAsCell{i,j} = true;
-                               else
-                                   tableAsCell{i,j} = false;
-                               end                                       
-                            end
-                            
-                            % Append data
-                            this.tab_DataPrep.Table.Data = [this.tab_DataPrep.Table.Data; tableAsCell(i,:)];
-                            
-                            nImportedRows = nImportedRows + 1;
-                        catch ME
-                            nRowsNotImported = nRowsNotImported + 1;
-                        end
-                        
-                    end
-                    
-
-                    % Update row numbers.
-                    nrows = size(this.tab_DataPrep.Table.Data,1);
-                    this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                           
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                                
-                    
-                    % Output Summary.
-                    msgbox({['Data preparation table data was imported to ',num2str(nImportedRows), ' rows.'], ...
-                            '', ...
-                            ['Number of rows not imported because of data format errors: ',num2str(nRowsNotImported) ]}, 'Summary of data preparation table importing ...');
-                    
-                    
-                case 'Model Construction'
-                    if size(tbl,2) ~=9
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                        
-                        warndlg('The table datafile must have 9 columns. That is, all columns shown in the model construction table.');
-                        return;
-                    end
-                        
-                    % Set the Model Status column to a cells array (if
-                    % empty matlab assumes its NaN)
-                    tbl.Select_Model=false(size(tbl,1));
-                    tbl.Build_Status = cell(size(tbl,1),1);
-                    tbl.Build_Status(:) = {'<html><font color = "#FF0000">Model not built.</font></html>'};
-                    tbl = table(tbl.Select_Model,tbl.Model_Label,tbl.Obs_Head_File,tbl.Forcing_Data_File,tbl.Coordinates_File,tbl.Bore_ID,tbl.Model_Type,tbl.Model_Options,tbl.Build_Status);
-
-                    % Loop through each row in tbl and find the
-                    % corresponding model within the GUI table and the add
-                    % the user data to the columns of the row.
-                    nModelsNotUnique = 0;
-                    nImportedRows = 0;
-                    for i=1: size(tbl,1)
-
-                        % Get model label
-                        modelLabel_src = tbl{i,2};
-
-                        % Check if model label is unique.
-                        if size(this.tab_ModelConstruction.Table.Data,1)>0
-                            ind = find(strcmp(this.tab_ModelConstruction.Table.Data(:,2), modelLabel_src));                        
-
-                            % Check if the model is found
-                            if ~isempty(ind)
-                                nModelsNotUnique = nModelsNotUnique + 1;
-                                continue;
-                            end
-                        end
-
-                        % Append table. Note: the select column is input as a logical 
-                        if tbl{i,1}==1
-                            this.tab_ModelConstruction.Table.Data = [this.tab_ModelConstruction.Table.Data; true, table2cell(tbl(i,2:end))];
-                        else
-                            this.tab_ModelConstruction.Table.Data = [this.tab_ModelConstruction.Table.Data; false, table2cell(tbl(i,2:end))];
-                        end
-                        nImportedRows = nImportedRows + 1;
-                    end
-
-                    % Update row numbers.
-                    nrows = size(this.tab_ModelConstruction.Table.Data,1);
-                    this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                        
-
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                                                    
-                    
-                    % Output Summary.
-                    msgbox({['Construction data was imported to ',num2str(nImportedRows), ' rows.'], ...
-                            '', ...
-                            ['Number of rows not imported because the model label is not unique: ',num2str(nModelsNotUnique) ]}, 'Summary of model calibration importing ...');
-
-                    
-                case 'Model Calibration'
-                    
-                    if size(tbl,2) ~=13
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                        
-                        warndlg('The table datafile must have 13 columns. That is, all columns shown in the table.');
-                        return;
-                    end
-
-                    
-                    % Loop through each row in tbl and find the
-                    % corresponding model within the GUI table and the add
-                    % the user data to the columns of the row.
-                    nModelsNotFound = 0;
-                    nBoresNotMatching = 0;
-                    nCalibBoresDeleted = 0;
-                    nImportedRows = 0;
-                    for i=1: size(tbl,1)
-                                               
-                        % Get model lablel and bore ID
-                        modelLabel_src = tbl{i,2};
-                        boreID_src = tbl{i,3};
-                        
-                        % Find model within the table.
-                        modelLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,2));
-                        ind = find(strcmp(modelLabel_dest, modelLabel_src));                        
-                        
-                        % Check if the model is found
-                        if isempty(ind)
-                            nModelsNotFound = nModelsNotFound + 1;
-                            continue;
-                        end
-                        
-                        % Check the bore IDs are equal.
-                        boreID_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data{ind,3});
-                        if ~strcmp(boreID_dest, boreID_src{1})
-                            nBoresNotMatching = nBoresNotMatching + 1;
-                            continue;                            
-                        end
-                        
-                        % Record if the model is already built. To do this,
-                        % the model is first locasted with this.models.data{}
-                        if ~isempty(this.models)
-                            
-                            tmpModel = getModel(this,modelLabel_src{1});                                                        
-                            
-                            if ~isempty(tmpModel) && ~isempty(tmpModel.calibrationResults)
-                                % remove calibration results.
-                                tmpModel.calibrationResults = [];
-
-                                % Record that the calib results were overwritten
-                                nCalibBoresDeleted = nCalibBoresDeleted + 1;
-                            end
-                            
-                            setModel(this,modelLabel_src{1},tmpModel);                                                        
-                        end                     
-                        
-                        % Add data from columns 1,5-7.
-                        if tbl{i,1}==1
-                            this.tab_ModelCalibration.Table.Data{ind,1} = true;
-                        else
-                            this.tab_ModelCalibration.Table.Data{ind,1} = false;
-                        end
-                        this.tab_ModelCalibration.Table.Data{ind,6} = datestr(tbl{i,6},'dd-mmm-yyyy');
-                        this.tab_ModelCalibration.Table.Data{ind,7} = datestr(tbl{i,7},'dd-mmm-yyyy');
-                        this.tab_ModelCalibration.Table.Data{ind,8} = tbl{i,8}{1};
-                        
-                        % Input the calibration status.
-                        %this.tab_ModelCalibration.Table.Data{ind,9} = '<html><font color = "#FF0000">Not calibrated.</font></html>';
-                        %this.tab_ModelCalibration.Table.Data{ind,10} = [];                        
-                        %this.tab_ModelCalibration.Table.Data{ind,11} = [];
-                        %this.tab_ModelCalibration.Table.Data{ind,12} = [];
-                        %this.tab_ModelCalibration.Table.Data{ind,13} = [];
-                        
-                        nImportedRows = nImportedRows +1;
-                    end
-
-                    % Update row numbers.
-                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
-
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                                
-                                        
-                    % Output Summary.
-                    msgbox({['Calibration data was imported to ',num2str(nImportedRows), ' rows.'], ...
-                            ['   Number of model labels not found in the calibration table: ',num2str(nModelsNotFound) ], ...
-                            ['   Number of rows were the bore IDs did not match: ',num2str(nBoresNotMatching) ], ...
-                            ['   Number of rows were existing calibration results were deleted: ',num2str(nCalibBoresDeleted) ]}, 'Summary of model calibration importing ...');
-                    
-                case 'Model Simulation'
-                    
-                    % Check the number of columns
-                    if size(tbl,2) ~=12
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                        
-                        warndlg('The table datafile must have 12 columns. That is, all columns shown in the table.');
-                        return;
-                    end
-                    
-                    % Get column formats
-                    colFormat = this.tab_ModelSimulation.Table.ColumnFormat;
-
-                    % Convert table to cell array
-                    tableAsCell = table2cell(tbl);                    
-                    tableAsCell(:,12)=repmat({'(empty)'},size(tbl,1),1);
-                    
-                    % Loop through each row in tbl and find the
-                    % corresponding model within the GUI table and the add
-                    % the user data to the columns of the row.
-                    nSimLabelsNotUnique = 0;
-                    nImportedRows = 0;
-                    for i=1: size(tbl,1)
-                                               
-                        % Get model lablel and bore ID
-                        modelLabel_src = tbl{i,2};
-                        simLabel_src = tbl{i,6};
-                        
-                        % Check that the model ID and simulation label are
-                        % unique
-                        modelLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,2));
-                        simLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,6));
-                        ind = find(strcmp(modelLabel_dest, modelLabel_src) & strcmp(simLabel_dest, simLabel_src));
-                        
-                        % Skip if the model and label are not unique
-                        if ~isempty(ind)
-                            nSimLabelsNotUnique = nSimLabelsNotUnique + 1;
-                            continue;                        
-                        end
-
-                        
-                        % Convert any columns of string data to '' if read
-                        % in as NaN.
-                        for j=1:length(colFormat)                       
-                            if ischar(colFormat{j}) && strcmp(colFormat{j},'char')&& ...
-                            isnumeric( tableAsCell{i,j} ) && isnan( tableAsCell{i,j} )
-                                tableAsCell{i,j} = '';
-                            elseif iscell(colFormat{j}) && ~ischar(tableAsCell{i,j}) && isnan( tableAsCell{i,j} )
-                                tableAsCell{i,j} = '';
-                            end
-                        end
-                        
-                        % Append table. Note: the select column is input as a logical 
-                        tableAsCell{i,12} = '<html><font color = "#FF0000">Not simulated.</font></html>';   
-                        if tbl{i,1}==1
-                            rowData = [true, tableAsCell(i,2:end)];
-                        else
-                            rowData = [false, tableAsCell(i,2:end)];
-                        end     
-                        if tbl{i,11}==1
-                            rowData{11} = true;
-                        else
-                            rowData{11} = false;
-                        end                        
-                        if size(this.tab_ModelSimulation.Table.Data,1)==0
-                            this.tab_ModelSimulation.Table.Data = rowData;
-                        else                            
-                            this.tab_ModelSimulation.Table.Data = [this.tab_ModelSimulation.Table.Data; rowData];
-                        end
-
-                        nImportedRows = nImportedRows + 1;
-                    end
-
-                    % Update row numbers.
-                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                    this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
-                                  
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;
-                    
-                    % Output Summary.
-                    msgbox({['Simulation data was imported to ',num2str(nImportedRows), ' rows.'], ...
-                            '', ...
-                            ['Number of rows not imported because the model label is not unique: ',num2str(nSimLabelsNotUnique) ]}, ...
-                            'Summary of model calibration importing ...');
-                                   
-            end
-        end        
-        
-        function onExportTable(this, hObject, eventdata)
-            % Create the label for each type of inport
-            switch hObject.Tag
-                case 'Data Preparation'
-                    windowString = 'Input the .csv file name for exporting the data preparation table.';
-                    tbl = this.tab_DataPrep.Table.Data;
-                    colnames = this.tab_DataPrep.Table.ColumnName;
-                    colFormat = this.tab_DataPrep.Table.ColumnFormat;
-                    
-                case 'Model Construction'
-                    windowString = 'Input the .csv file name for exporting the model construction table.';
-                    tbl = this.tab_ModelConstruction.Table.Data;
-                    colnames = this.tab_ModelConstruction.Table.ColumnName;
-                    colFormat = this.tab_ModelConstruction.Table.ColumnFormat;
-                case 'Model Calibration'
-                    windowString = 'Input the .csv file name for exporting the model calibration table.';
-                    tbl = this.tab_ModelCalibration.Table.Data;
-                    colnames = this.tab_ModelCalibration.Table.ColumnName;
-                    colFormat = this.tab_ModelCalibration.Table.ColumnFormat;
-                case 'Model Simulation'
-                    windowString = 'Input the .csv file name for exporting the model simulation table.';
-                    tbl = this.tab_ModelSimulation.Table.Data;
-                    colnames = this.tab_ModelSimulation.Table.ColumnName;
-                    colFormat = this.tab_ModelSimulation.Table.ColumnFormat;
-                otherwise
-                    warndlg('Unexpected Error: GUI table type unknown.');
-                    return                    
-            end
-
-            % Show open file window
-            [fName,pName] = uiputfile({'*.csv'},windowString); 
-            if fName~=0;
-                % Assign file name to date cell array
-                filename = fullfile(pName,fName);
-            else
-                return;
-            end         
-            
-            % Remove HTML tags from the column names
-            colnames = HydroSight_GUI.removeHTMLTags(colnames);
-            
-            % Replace spaces within column names with "_"
-            colnames = strrep(colnames,' ','_');
-            colnames = strrep(colnames,'.','');
-            colnames = strrep(colnames,'?','');
-            colnames = strrep(colnames,'(','');
-            colnames = strrep(colnames,')','');
-            colnames = strrep(colnames,'/','_');
-            colnames = strrep(colnames,'-','_');
-            colnames = strrep(colnames,'__','_');
-            
-            %If the model constuction table, then remove possible ',' from
-            %the bore ID and model options columns.
-            if strcmp(hObject.Tag,'Model Construction')
-                tbl(:,6) = strrep(tbl(:,6),',',' ');
-                tbl(:,8) = strrep(tbl(:,8),',',' ');   
-            end
-            
-            % Remove HTML tags from each row
-            for i=find(strcmp(colFormat, 'char'))
-                tbl(:,i) = HydroSight_GUI.removeHTMLTags(tbl(:,i));
-            end           
-            
-            % Convert cell array to table            
-            tbl = cell2table(tbl);
-            tbl.Properties.VariableNames = colnames;
-            
-            % Write the table.
-            try
-                writetable(tbl, filename,'Delimiter',',');
-            catch ME
-                warndlg('The table could not be written. Please check you have write permissions to the destination folder.');
-                return;
-            end            
-        end        
-        
-        function onExportResults(this, hObject, eventdata)
-            
-            % Set initial folder to the project folder (if set)
-            if ~isempty(this.project_fileName)                                
-                try    
-                    if isdir(this.project_fileName)
-                        currentProjectFolder = this.project_fileName;
-                    else
-                        currentProjectFolder = fileparts(this.project_fileName);
-                    end 
-
-                    currentProjectFolder = [currentProjectFolder,filesep];
-                    cd(currentProjectFolder);
-                catch
-                    % do nothing
-                end
-            end      
-
-            % Export results
-            switch hObject.Tag                                           
-                case 'Data Preparation'
-
-                    % Check there is data to export.
-                    if isempty(this.dataPrep) || size(this.dataPrep,1)<1;
-                        warndlg('There is no data analysis results to export.','No data.');
-                        return;
-                    end
-                                  
-                     % Check if there are any rows selected for export
-                     if ~any( cellfun(@(x) x==1, this.tab_DataPrep.Table.Data(:,1)))
-                         warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
-                         return;
-                     end                        
-                    
-                    % Ask the user if they want to export all analysis data
-                    % (ie logical data reporting on the analysis
-                    % undertaken) or just the data not assessed an errerous
-                    % or an outlier.
-                    response = questdlg('Do you want to export the analysis results or just the observations not assessed as being erroneous or outliers?','Data to export?','Export Analysis Results','Export non-erroneous obs.','Cancel','Export non-erroneous obs.');
-                    
-                    if strcmp(response,'Cancel')
-                        return;
-                    end
-                    
-                    if strcmp(response, 'Export Analysis Results')
-                        exportAllData = true;
-                    else
-                        exportAllData = false;
-                    end
-                    
-                    % Get output file name
-                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.'); 
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        filename = fullfile(pName,fName);
-                    else
-                        return;
-                    end                     
-                    
-                    % Open file and write headers
-                    fileID = fopen(filename,'w');
-                    if exportAllData
-                        fprintf(fileID,'BoreID,Year,Month,Day,Hour,Minute,Head,Date_Error,Duplicate_Date_Error,Min_Head_Error,Max_Head_Error,Rate_of_Change_Error,Const_Hear_Error,Outlier_Obs \n');
-                    else
-                        fprintf(fileID,'BoreID,Year,Month,Day,Hour,Minute,Obs_Head \n');                    
-                    end                    
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;                    
-                    
-                    % Get a list of bore IDs
-                    boreIDs = fieldnames(this.dataPrep);
-                    
-                    % Setup wait box
-                    h = waitbar(0,'Exporting results. Please wait ...');                        
-                                   
-                    % Export each bore analysed.
-                    nResultsWritten = 0;
-                    nResultsNotWritten = 0;
-                    nResultToExport=0;
-                    for i = 1:size(boreIDs,1)
-
-                        % update wait bar
-                        waitbar(i/size(boreIDs,1));             
-                        
-                        % Find the model bore ID is within the GUI
-                        % table and then check if the row is to be
-                        % exported.
-                        tableInd = cellfun( @(x) strcmp( x,boreIDs{i}),  this.tab_DataPrep.Table.Data(:,3));
-                        if sum(tableInd)==0 || ~this.tab_DataPrep.Table.Data{tableInd,1}
-                            continue
-                        end                        
-                        
-                        % Get the analysis results.
-                        tableData = this.dataPrep.(boreIDs{i});
-
-                        % Check there is some data
-                        if isempty(tableData) || size(tableData,1)==0
-                            nResultsNotWritten = nResultsNotWritten +1;
-                            continue;                                
-                        end
-                        nResultToExport = nResultToExport + 1;
-                        
-                        % Convert to matrix.
-                        tableData = table2array(tableData);
-
-                        % Convert table to real (if previously set to
-                        % single for memeory issues)
-                        tableData = double(tableData);                        
-                        
-                        % Export data
-                        try
-                            if exportAllData                                                   
-    
-                                %Write each row.
-                                for j=1:size(tableData,1)
-                                    fprintf(fileID,'%s,%i,%i,%i,%i,%i,%12.3f,%i,%i,%i,%i,%i,%i,%i \n', boreIDs{i} , tableData(j,:));                                    
-                                end
-                            
-                                nResultsWritten = nResultsWritten + 1;
-                            else
-
-                                isError = any(tableData(:,7:end)==1,2);
-                                if size(tableData,1) > sum(isError)
-    
-                                    % Filter data
-                                    tableData = tableData(~isError,:);
-                                                                        
-                                    %Write each row.
-                                    for j=1:size(tableData,1)
-                                        fprintf(fileID,'%s,%i,%i,%i,%i,%i,%12.3f \n', boreIDs{i} , tableData(j,1:6));
-                                    end
-                                
-                                    nResultsWritten = nResultsWritten + 1;
-                                else
-                                    nResultsNotWritten = nResultsNotWritten +1;
-                                end
-                            end
-                        catch
-                            nResultsNotWritten = nResultsNotWritten +1;
-                        end
-                    end
-                    fclose(fileID);
-                    
-                    % Close wait bar
-                    close(h);                      
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                    
-                    
-                    % Show summary
-                    msgbox({'Export of results finished.','', ...
-                           ['Number of bore results exported =',num2str(nResultsWritten)], ...
-                           ['Number of rows selected for export =',num2str(nResultToExport)], ...
-                           ['Number of bore results not exported =',num2str(nResultsNotWritten)]}, ...
-                           'Export Summary');
-
-                    
-                case 'Model Calibration'
-                    
-                    % Check if there are any rows selected for export
-                    if ~any( cellfun(@(x) x==1, this.tab_ModelCalibration.Table.Data(:,1)))
-                        warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
-                        return;
-                    end                    
-
-                    % Ask the user if they want to export the time-series results or the model parameters or the derived parameters.
-                    response = questdlg('Do you want to export the time-series results, or the model and derived parameters?', ...
-                        'Export options.','Time-series results','Model & derived parameters','Cancel','Time-series results');                    
-
-                    if strcmp(response, 'Cancel')
-                        return;
-                    end
-                    
-                    % Get output file name
-                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.'); 
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        filename = fullfile(pName,fName);
-                    else
-                        return;
-                    end 
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;                    
-                    
-                    % Open file and write headers
-                    fileID = fopen(filename,'w');
-                    if strcmp(response, 'Time-series results')
-                        fprintf(fileID,'Model_Label,BoreID,Year,Month,Day,Hour,Minute,Obs_Head,Is_Calib_Point?,Calib_Head,Eval_Head,Model_Err,Noise_Lower,Noise_Upper \n');
-                    else 
-                        fprintf(fileID,'Model_Label,BoreID,ComponentName,ParameterName,ParameterSetNumber,ParameterSetValue \n');
-                    end
-                    
-                    % Setup wait box
-                    h = waitbar(0,'Exporting results. Please wait ...');                                            
-                    
-                    % Loop through each row of the calibration table and
-                    % export the calibration results (if calibrated)
-                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
-                    nResultsWritten=0;
-                    nModelsToExport=0;
-                    nModelsNotCalib=0;
-                    for i=1:nrows
-
-                        % update wait bar
-                        waitbar(i/nrows);    
-                        
-                        % Skip if not selected
-                        if ~this.tab_ModelCalibration.Table.Data{i,1}
-                            continue
-                        end                        
-                        
-                        % Get model label.
-                        modelLabel = this.tab_ModelCalibration.Table.Data{i,2};
-                        modelLabel = HydroSight_GUI.removeHTMLTags(modelLabel);
-                        modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-                        
-                        % Incrment number of models selected for export
-                        nModelsToExport = nModelsToExport+1;
-                                                
-                        % Skip if not calibrated
-                        if ~this.model_labels{modelLabel,'isCalibrated'}
-                            nModelsNotCalib= nModelsNotCalib+1;
-                            continue
-                        end
-                        
-                        % Get a copy of the model object. This is only done to
-                        % minimise HDD read when the models are off loaded to HDD using
-                        % matfile();
-                        tmpModel = getModel(this, modelLabel);
-
-                        % Exit if model not found.
-                        if isempty(tmpModel)    
-                            continue;
-                        end                        
-                        
-                        % Get model results
-                        if isstruct(tmpModel.calibrationResults) && tmpModel.calibrationResults.isCalibrated                    
-                    
-                            if strcmp(response, 'Time-series results')
-                                % Get the model calibration data.
-                                tableData = tmpModel.calibrationResults.data.obsHead;
-                                tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
-                                    nan(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead_residuals(:,end), ...
-                                    tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];
-                                
-                                % Get evaluation data
-                                if isfield(tmpModel.evaluationResults,'data')
-                                    % Get data
-                                    evalData = tmpModel.evaluationResults.data.obsHead;
-                                    evalData = [evalData, zeros(size(evalData,1),1), nan(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
-                                        tmpModel.evaluationResults.data.modelledHead_residuals(:,end), ...
-                                        tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
-                                    
-                                    % Append to table of calibration data and sort
-                                    % by time.
-                                    tableData = [tableData; evalData];
-                                    tableData = sortrows(tableData, 1);
-                                end
-                                
-                                % Convert table to real (if previously set to
-                                % single for memeory issues)
-                                tableData = double(tableData);
-                                
-                                % Calculate year, month, day etc
-                                tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
-                                
-                                % Build write format string
-                                fmt = '%s,%s,%i,%i,%i,%i,%i,%12.3f';
-                                for j=1:size(tableData,2)-6
-                                    fmt = strcat(fmt,',%12.3f');
-                                end
-                                fmt = strcat(fmt,'  \n');
-
-                                % Get Bore ID
-                                boreID = tmpModel.bore_ID;
-                                
-                                %Write each row.
-                                for j=1:size(tableData,1)
-                                    fprintf(fileID,fmt, modelLabel, boreID, tableData(j,:));
-                                end
-                                                                
-                            else
-                                % get the parameters and derived parameters
-                                [ParamValues, ParamsNames] = getParameters(tmpModel.model);
-                                [derivedParamValues, derivedParamsNames] = getDerivedParameters(tmpModel.model);
-
-                                % Get Bore ID
-                                boreID = tmpModel.bore_ID;
-                                
-                                % Build write format string
-                                fmt = '%s,%s,%s,%s,%i,%12.6f \n';                          
-                                
-                                %Write each parameter and each parameter set (ie if DREAM was used).
-                                for j=1:size(ParamsNames,1)
-                                    for k=1:size(ParamValues,2)
-                                        fprintf(fileID,fmt, modelLabel, boreID, ParamsNames{j,1},ParamsNames{j,2}, k, ParamValues(j,k));
-                                    end
-                                end
-                                
-                                %Write each derived parameter and each derived parameter set (ie if DREAM was used).
-                                for j=1:size(derivedParamsNames,1)
-                                    for k=1:size(derivedParamValues,2)
-                                        fprintf(fileID,fmt, modelLabel, boreID, derivedParamsNames{j,1},derivedParamsNames{j,2}, k, derivedParamValues(j,k));
-                                    end
-                                end                                
-                            end
-                                
-                            % Update counter
-                            nResultsWritten = nResultsWritten + 1;
-                        else
-                            nModelsNotCalib = nModelsNotCalib + 1;
-                        end        
-                    end
-                    fclose(fileID);
-                    
-                    % Close wait bar
-                    close(h);                      
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                    
-                    
-                    % Show summary
-                    msgbox({'Export of results finished.','',['Number of model results exported =',num2str(nResultsWritten)],['Number of models selected for export =',num2str(nModelsToExport)],['Number of selected models not calibrated =',num2str(nModelsNotCalib)]},'Export Summary');
-                    
-                    
-                case 'Model Simulation'
-                    % Check if there are any rows selected for export
-                    if ~any( cellfun(@(x) x==1, this.tab_ModelSimulation.Table.Data(:,1)))
-                        warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
-                        return;
-                    end
-                      
-                    % Ask the user if they want to export one file per bore (with decomposition)
-                    % or all results in one file.
-                    response = questdlg({'Do you want to export all simulations into one file, or as one file per simulation?','','NOTE: The forcing decomposition results will only be exported using the multi-file option.'},'Export options.','One File','Multiple Files','Cancel','One File');
-                    
-                    if strcmp(response,'Cancel')
-                        return;
-                    end
-                    
-                    if strcmp(response, 'Multiple Files')
-                        useMultipleFiles = true;
-                        folderName = uigetdir('' ,'Select where to save the .csv simulation files (one file per simulation).');    
-                        if isempty(folderName)
-                            return;
-                        end
-                    else
-                        useMultipleFiles = false;
-                        fileName = uiputfile({'*.csv','*.*'} ,'Input the file name for the .csv simulation file (all simulations in one file).');    
-                        if isempty(fileName)
-                            return;
-                        end   
-                        
-                        fileID = fopen(fileName,'w');
-                        fprintf(fileID,'Simulation_Label,Model_Label,BoreID,Year,Month,Day,Hour,Minute,Sim_Head \n');
-    
-                    
-                    end
-                                           
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;                   
-                    
-                    % Setup wait box
-                    h = waitbar(0,'Exporting results. Please wait ...');                                            
-                    
-                    % Loop through each row of the simulation table and
-                    % export the calibration results (if calibrated)
-                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
-                    nResultsWritten=0;
-                    nModelsNotFound=0;
-                    nSimsNotUndertaken = 0;
-                    nSimsNotUnique = 0;
-                    nTableConstFailed = 0;
-                    nWritteError = 0;
-                    for i=1:nrows                        
-                        
-                        % update wait bar
-                        waitbar(i/nrows);                   
-                        
-                        % Skip if the model is not sleected for export
-                        if ~this.tab_ModelSimulation.Table.Data{i,1}
-                            continue
-                        end
-                        
-                        % get model label and simulation label.
-                        modelLabel = this.tab_ModelSimulation.Table.Data{i,2};
-                        simLabel = this.tab_ModelSimulation.Table.Data{i,6};
-                        boreID = this.tab_ModelSimulation.Table.Data{i,3};
-
-                        % Get a copy of the model object. This is only done to
-                        % minimise HDD read when the models are off loaded to HDD using
-                        % matfile();
-                        tmpModel = getModel(this, modelLabel);
-
-                        % Check model exists.
-                        if isempty(tmpModel) 
-                            nModelsNotFound = nModelsNotFound +1;
-                            continue;                            
-                        end
-                        
-                        % Check simulations exists.
-                        if isempty(tmpModel.simulationResults)    
-                            nSimsNotUndertaken = nSimsNotUndertaken +1;
-                            continue;
-                        end                        
-                                                                                                
-                        % Find the simulation.    
-                        if isempty(simLabel)
-                            nSimsNotUndertaken = nSimsNotUndertaken +1;
-                            continue;
-                        end
-                        simInd = cellfun(@(x) strcmp(simLabel, x.simulationLabel), tmpModel.simulationResults);
-                        if all(~simInd)    % Exit if model not found.
-                            nSimsNotUndertaken = nSimsNotUndertaken +1;
-                            continue;
-                        end          
-                        simInd = find(simInd);
-            
-                        % Check only one simulation found
-                        if length(simInd)>1
-                            nSimsNotUnique = nSimsNotUnique +1;
-                            continue;
-                        end
-                        
-
-                        % Get the simulation data and create the output
-                        % table.
-                        try
-                            % Get the model simulation data.
-                            if useMultipleFiles
-                                tableData = tmpModel.simulationResults{simInd,1}.head;
-                            else
-                                tableData = tmpModel.simulationResults{simInd,1}.head(:,1:2);
-                            end
-                                                        
-                            % Calculate year, month, day etc
-                            tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
-
-                            % Create column names.                        
-                            if useMultipleFiles
-                                columnName = {'Year','Month','Day','Hour','Minute',tmpModel.simulationResults{simInd,1}.colnames{2:end}};
-                                
-                                % Check if there are any invalid column names
-                                columnName = regexprep(columnName,'\W','_');                            
-                                
-                                % Create table and add variable names
-                                tableData = array2table(tableData);
-                                tableData.Properties.VariableNames = columnName;
-
-                            end
-                            
-                        catch ME
-                            nTableConstFailed = nTableConstFailed + 1;
-                        end
-                        
-                        % write data to the file
-                        if useMultipleFiles
-                            filename_tmp = fullfile(folderName,[modelLabel,'_',simLabel,'.csv']);
-                            
-                            try
-                                writetable(tableData,filename_tmp);          
-                                nResultsWritten = nResultsWritten + 1;
-                            catch
-                                nWritteError = nWritteError + 1;
-                            end
-                            
-                        else
-                            % Build write format string
-                            fmt = '%s,%s,%s,%i,%i,%i,%i,%i,%12.3f \n';
-                            
-                            % Remove HTML from bore ID
-                            boreID = HydroSight_GUI.removeHTMLTags(boreID);
-                            
-                            %Write each row.
-                            try
-                                for j=1:size(tableData,1)
-                                    fprintf(fileID,fmt, simLabel, modelLabel, boreID, tableData(j,:));
-                                end
-                                nResultsWritten = nResultsWritten + 1;
-                            catch ME
-                                nWritteError = nWritteError + 1;
-                            end
-                            
-                        end
-                        
-                                                    
-                    end
-                    
-                    % Close wait bar
-                    close(h);                         
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'arrow');
-                    drawnow update;                    
-                    
-                    % Show summary                
-                    msgbox({'Export of results finished.','', ...
-                           ['Number of simulations exported =',num2str(nResultsWritten)], ...
-                           ['Number of models not found =',num2str(nModelsNotFound)], ...
-                           ['Number of simulations not undertaken =',num2str(nSimsNotUndertaken)], ...
-                           ['Number of simulations labels not unique =',num2str(nSimsNotUnique)], ...
-                           ['Number of simulations where the construction of results table failed=',num2str(nTableConstFailed)], ...
-                           ['Number of simulations where the file could not be written =',num2str(nWritteError)]}, ...
-                           'Export Summary');                    
-
-                case {'Model Calibration - results table export', ...
-                      'Model Calibration - forcing table export', ...
-                      'Model Calibration - parameter table export', ...
-                      'Model Calibration - derived parameter table export', ...
-                      'Model Calibration - derived data table export'}
-                    
-                    % Get model label.
-                    modelLabel = this.tab_ModelCalibration.Table.Data{this.tab_ModelCalibration.currentRow,2};
-                    modelLabel = HydroSight_GUI.removeHTMLTags(modelLabel);
-                    modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-                                    
-                    % Build a default file name.
-                    fName = strrep(hObject.Tag, 'Model Calibration - ','');
-                    fName = strrep(fName, ' export','');
-                    fName = [modelLabel,'_',fName,'.csv'];
-                  
-                    % Get output file name                    
-                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.',fName); 
-                    if fName~=0;
-                        % Assign file name to date cell array
-                        filename = fullfile(pName,fName);
-                    else
-                        return;
-                    end 
-                    
-                    % Change cursor
-                    set(this.Figure, 'pointer', 'watch');
-                    drawnow update;                    
-                    
-                    % Find table object
-                    tablObj = findobj(this.tab_ModelCalibration.resultsTabs,'Tag',strrep(hObject.Tag,' export',''));
-                    
-                    % Convert cell array to table            
-                    if iscell(tablObj.Data)
-                        tbl = cell2table(tablObj.Data);
-                    else
-                        tbl = array2table(tablObj.Data);
-                    end
-                    columnName = tablObj.ColumnName;
-                    columnName = regexprep(columnName,'\W','_');                            
-                    tbl.Properties.VariableNames =  columnName;
-
-                    % Write the table.
-                    try
-                        writetable(tbl, filename,'Delimiter',',');
-                        
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                            
-                    catch ME
-                        % Change cursor
-                        set(this.Figure, 'pointer', 'arrow');
-                        drawnow update;                            
-                        
-                        warndlg('The table could not be written. Please check you have write permissions to the destination folder.');
-                        return;
-                    end
-
-                otherwise
-                    warndlg('Unexpected Error: GUI table type unknown.');
-                    return                    
-            end
-            
-            
-        end
-        
-        function onDocumentation(this, hObject, eventdata)
-            if isempty(hObject.Tag)  % Open the help on the curretn tab
-                switch this.figure_Layout.Selection
-                    case 1
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Project-description','-browser');
-                    case 2
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Date-preparation','-browser');
-                    case 3
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-Construction','-browser');
-                    case 4
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-Calibration','-browser');
-                    case 5
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-simulation','-browser');
-                    otherwise
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Graphical-interface','-browser');
-                end
-            else
-                switch hObject.Tag
-                    case 'doc_Overview'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki','-browser');
-                    case 'doc_GUI'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Graphical-interface','-browser');
-                    case 'doc_Calibration'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/What-is-Calibration%3F','-browser');
-                    case 'doc_Publications'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Publications','-browser');
-                    case 'doc_timeseries_algorithms'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Time-series-models','-browser');
-                    case 'doc_data_req'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Data-Requirements','-browser');
-                    case 'doc_data_req'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Data-Requirements','-browser');
-                    case 'doc_tutes'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Tutorials','-browser');
-                    case 'doc_Support'
-                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Support','-browser');
-                end
-                
-                
-            end
-            
-        end       
-               
-        function onGitHub(this, hObject, eventdata)
-           if strcmp(hObject.Tag,'doc_GitHubUpdate') 
-               web('https://github.com/peterson-tim-j/HydroSight/releases','-browser');
-           elseif strcmp(hObject.Tag,'doc_GitHubIssue') 
-               web('https://github.com/peterson-tim-j/HydroSight/issues','-browser');
-           end            
-                       
-        end
-        
-        function onVersion(this, hObject, eventdata)           
-            msgbox({['This is version ',this.versionNumber, ' of HydroSight GUI.'],'',['It was released on ',this.versionDate]},'HydroSight GUI version ...');
-        end
-        
-        function onLicenseDisclaimer(this, hObject, eventdata)
-           web('https://github.com/peterson-tim-j/HydroSight/wiki/Disclaimer-and-Licenses','-browser');
-        end                       
-        
-        function onPrint(this, hObject, eventdata)
-            switch this.figure_Layout.Selection
-                case {1,3}
-                    errordlg('No plot is displayed within the current tab.');
-                    return;
-                case 2      % Data prep.
-                    f=figure('Visible','off');
-                    copyobj(this.tab_DataPrep.modelOptions.resultsOptions.plots,f);
-                    printpreview(f);
-                    close(f);                    
-                case 4      % Model Calib.
-                    f=figure('Visible','off');
-                    switch this.tab_ModelCalibration.resultsTabs.SelectedChild
-                        case 1
-                            copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children.Children(2),f);
-                        case 2
-                            copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end),f);
-                        case 3
-                            copyobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1).Children,f);                             
-                        case 4
-                            
-                    end
-                    printpreview(f);
-                    close(f);
-                case 5      % Model Simulation.    
-                    f=figure('Visible','off');
-                    nplots = length(this.tab_ModelSimulation.resultsOptions.plots.panel.Children);
-                    copyobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(1:nplots),f);
-                    printpreview(f);
-                    close(f);                    
-                otherwise
-                    return;
-            end            
-        end
-        
-        function onExportPlot(this, hObject, eventdata)
-            set(this.Figure, 'pointer', 'watch');
-            switch this.figure_Layout.Selection
-                case {1,3}
-                    errordlg('No plot is displayed within the current tab.');
-                    return;
-                case 2      % Data prep.
-                    pos = get(this.tab_DataPrep.modelOptions.resultsOptions.box.Children(2),'Position'); 
-                    legendObj = this.tab_DataPrep.modelOptions.resultsOptions.plots.Legend;
-                    
-                    f=figure('Visible','off');
-                    copyobj(this.tab_DataPrep.modelOptions.resultsOptions.plots,f);
-                    
-                    % Format figure                    
-                    set(f, 'Color', 'w');
-                    set(f, 'PaperType', 'A4');
-                    set(f, 'PaperOrientation', 'landscape');
-                    set(f, 'Position', pos);
-                case 4      % Model Calib.
-                    f=figure('Visible','off');
-                    switch this.tab_ModelCalibration.resultsTabs.SelectedChild
-                        case 1
-                            pos = get(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1),'Position');
-                            if length(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1))==1
-                                legendObj = [];
-                                copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children,f);
-                            else
-                                copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children(2),f);
-                                legendObj = this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children(1);
-                            end
-                        case 2
-                            pos = get(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4),'Position');
-                            if length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4)==1)
-                                copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children,f);
-                                legendObj = [];
-                            else
-                                copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end),f);
-                                legendObj = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end-1);                                
-                            end
-                        case 3
-                            pos = get(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1),'Position');
-                            copyobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1).Children,f);                             
-                            legendObj = [];
-                        case 4
-                            pos = get(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel.Children.Children(1),'Position');
-                            copyobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel.Children.Children(1).Children,f);                             
-                            legendObj = [];
-                            
-                        case 5
-                            pos = get(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel.Children.Children(1),'Position');
-                            copyobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel.Children.Children(1).Children,f);                             
-                            legendObj = [];                            
-                    end
-
-                    % Format figure
-                    set(f, 'Color', 'w');
-                    set(f, 'PaperType', 'A4');
-                    set(f, 'PaperOrientation', 'landscape');
-                    set(f, 'Position', pos);
-                    
-                case 5      % Model Simulation.    
-                    f=figure('Visible','off');
-                    nplots = length(this.tab_ModelSimulation.resultsOptions.plots.panel.Children);
-                    pos = get(this.tab_ModelSimulation.resultsOptions.plots.panel,'Position');
-                    copyobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(1:nplots),f);  
-                    legendObj = [];
-                    
-                    % Format each axes
-                    ax =  findall(f,'type','axes');
-                    for i=1:nplots
-                        set(ax(i), 'Color', 'w');
-                    end
-                    
-                    % Format figure
-                    set(f, 'Color', 'w');
-                    set(f, 'PaperType', 'A4');
-                    set(f, 'PaperOrientation', 'landscape');
-                    set(f, 'Position', pos);                    
-                otherwise
-                    return;
-            end
-                        
-            % set current folder to the project folder (if set)
-            set(this.Figure, 'pointer', 'arrow');
-            currentProjectFolder='';
-            if ~isempty(this.project_fileName)                                
-                try    
-                    if isdir(this.project_fileName)
-                        currentProjectFolder = this.project_fileName;
-                    else
-                        currentProjectFolder = fileparts(this.project_fileName);
-                    end 
-                    
-                    currentProjectFolder = [currentProjectFolder,filesep];
-                    cd(currentProjectFolder);
-                catch
-                    % do nothing
-                end
-            end
-            [fName,pName] = uiputfile({'*.png'},'Save plot PNG image as ...','plot.png');    
-            if fName~=0    
-                set(this.Figure, 'pointer', 'watch');
-                
-                % Export image
-                if ~isempty(legendObj)
-                    legend(gca,legendObj.String,'Location',legendObj.Location)
-                end
-                export_fig(f, fName);
-            end
-            close(f);
-            set(this.Figure, 'pointer', 'arrow');
-            
-        end
-        
-        % Show splash 
-        function onAbout(this, hObject, eventdata)
-             % Create opening window while the GUI is being built.
-            %--------------------------------------------------------------
-            % Create figure
-            this.FigureSplash = figure( ...
-                'NumberTitle', 'off', ...
-                'MenuBar', 'none', ...
-                'Toolbar', 'none', ...
-                'HandleVisibility', 'off', ...
-                'Visible','off');
-
-            % Get window size and set figure to middle
-            splashWidth =  1200;
-            splashHeight = splashWidth/3;
-            windowHeight = this.FigureSplash.Parent.ScreenSize(4);
-            windowWidth = this.FigureSplash.Parent.ScreenSize(3);
-            this.FigureSplash.Position = [(windowWidth - splashWidth)/2 (windowHeight - splashHeight)/2 splashWidth splashHeight];
-            this.FigureSplash.Visible = 'on';
-            
-            % Load icons
-            h = axes('Parent',this.FigureSplash, 'Position', [0 0 1 1] );
-            img = imread(fullfile('icons','splash.png'));
-            image(img,'Parent',h);
-            axis(h,'off');
-            axis(h,'image');  
-            axis(h,'tight');
-            drawnow update; 
-        end        
-        
-        % Load example models
-        function onExamples(this, hObject, eventdata)
-            
-            % Check if all of the GUI tables are empty. If not, warn the
-            % user the opening the example will delete the existing data.
-            if ~isempty(this.tab_Project.project_name.String) || ...
-            ~isempty(this.tab_Project.project_description.String) || ...
-            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
-            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
-            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
-                response = questdlg({'Opening an example project will close the current project.','','Do you want to continue?'}, ...
-                 'Close the current project?','Yes','No','No');
-             
-                if strcmp(response,'No')
-                    return;
-                end
-            end
-            
-            % Add message explaing the model examples.
-            h= msgbox({'The example models provide an overview of some of the features of the', ...
-                    'toolbox plus example input data files.', ...
-                    '', ...
-                    'To open an example, you will first be asked to specify a folder in which', ...
-                    'the .csv file are to be generated by the toolbox.', ...
-                    '', ...
-                    'Once the files are created, the toolbox will contain a series of', ...
-                    'calibrated time-series models. You can use these models to undertake', ...
-                    'simulations, or alternatively you can rebuild and calibrate, or edit,', ...
-                    'the models.'},'Opening Example Models ...','help') ;
-            uiwait(h);
-                
-            % Ask user the locations where the input data is to be saved.          
-            if isnumeric(this.project_fileName)
-                this.project_fileName='';
-            end
-            if ~isempty(this.project_fileName)
-                folderName = fileparts(this.project_fileName);
-            else
-                folderName = fileparts(pwd); 
-            end
-            folderName = uigetdir(folderName ,'Select folder in which to save the example .csv files.');    
-            if isempty(folderName)
-                return;
-            end
-            this.project_fileName = folderName;
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');      
-            drawnow update;
-            
-            % Build .csv file names and add to the GUI construction table.
-            display('Saving .csv files ...');
-            forcingFileName = fullfile(folderName,'forcing.csv');
-            coordsFileName = fullfile(folderName,'coordinates.csv');
-            headFileName = fullfile(folderName,'obsHead.csv');
-                        
-            % Open example .mat data file for the required example.
-            display('Opening data files ...');
-            switch eventdata.Source.Tag
-                case 'TFN - LUC'
-                    exampleData = load('BourkesFlat_data.mat');
-                case 'TFN - Pumping'
-                    exampleData = load('Clydebank_data.mat');
-                otherwise
-                    set(this.Figure, 'pointer', 'arrow');   
-                    drawnow update;
-                    warndlg('The requested example model could not be found.','Example Model Error ...');
-                    return;
-            end
-            
-            % Check there is the required data
-            if ~isfield(exampleData,'forcing') || ~isfield(exampleData,'coordinates') || ~isfield(exampleData,'obsHead')
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;
-                warndlg('The example data for the model does not exist. It must contain the following Matlab tables: forcing, coordinates, obsHead.','Example Model Data Error ...');                
-                return;                
-            end
-            
-            % Check that the data is a table. 
-            if ~istable(exampleData.forcing) || ~istable(exampleData.coordinates) || ~istable(exampleData.obsHead) 
-                set(this.Figure, 'pointer', 'arrow');   
-                drawnow update;
-                warndlg('The example models could not be loaded because the source data is not of the correct format. It must be a Matlab table variable.','Example Model Error ...');                
-                return;
-            end
-            
-            % Export the csv file names.
-            display('Saving .csv files ...');
-            writetable(exampleData.forcing,forcingFileName);
-            writetable(exampleData.coordinates,coordsFileName);
-            writetable(exampleData.obsHead,headFileName);
-            
-            
-            % Load the GUI table data and model.
-            display('Opening model files ...');
-            try                
-                switch eventdata.Source.Tag
-                    case 'TFN - LUC'                    
-                        exampleModel = load('BourkesFlat_model.mat');
-                    case 'TFN - Pumping'
-                        exampleModel = load('Clydebank_model.mat');
-                    otherwise
-                        set(this.Figure, 'pointer', 'arrow');             
-                        drawnow update;
-                        warndlg('The requested example model could not be found.','Example Model Error ...');
-                        return;
-                end            
-                
-            catch ME
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update;
-                warndlg('Project file could not be loaded.','File error');                
-                return;
-            end
-            
-            
-            % Assign data to the GUI
-            %------------------------------          
-            display('Updating file names in GUI ...');
-            % Assign loaded data to the tables and models.
-            this.tab_Project.project_name.String = exampleModel.tableData.tab_Project.title;
-            this.tab_Project.project_description.String = exampleModel.tableData.tab_Project.description;
-
-            % Data prep data.
-            this.tab_DataPrep.Table.Data = exampleModel.tableData.tab_DataPrep;
-            nrows = size(this.tab_DataPrep.Table.Data,1);
-            this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                    
-
-            % Model constrcuion data
-            this.tab_ModelConstruction.Table.Data = exampleModel.tableData.tab_ModelConstruction;
-            nrows = size(this.tab_ModelConstruction.Table.Data,1);
-            this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));     
-
-            % Model Calib data
-            if size(exampleModel.tableData.tab_ModelCalibration,2)==14
-                this.tab_ModelCalibration.Table.Data =  exampleModel.tableData.tab_ModelCalibration(:,[1:8,10:14]);
-            else
-                this.tab_ModelCalibration.Table.Data = exampleModel.tableData.tab_ModelCalibration;
-            end
-            nrows = size(this.tab_ModelCalibration.Table.Data,1);
-            this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                      
-
-            % Model Simulation
-            this.tab_ModelSimulation.Table.Data = exampleModel.tableData.tab_ModelSimulation;
-            nrows = size(this.tab_ModelSimulation.Table.Data,1);
-            this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                       
-
-            % Set flag denoting models are on RAM.            
-            this.modelsOnHDD =  '';
-
-            % Load modle objects 
-            %-------------
-            % CLear models in preject.                        
-            this.models=[];
-
-            % Determine number of models
-            filt = strcmp(fieldnames(exampleModel),'dataPrep') | strcmp(fieldnames(exampleModel),'settings') |  strcmp(fieldnames(exampleModel),'tableData');
-            modelLabels = fieldnames(exampleModel);
-            modelLabels = modelLabels(~filt);
-            nModels = length(modelLabels);
-
-            % Create object array
-            %this.models.data(nModels,1) = [HydroSight];
-
-            % Set model
-            for i=1:nModels
-                modelLabeltmp = HydroSight_GUI.modelLabel2FieldName(modelLabels{i});
-                setModel(this, modelLabeltmp , exampleModel.(modelLabeltmp));
-            end
-            %-------------
-            
-            % Assign analysed bores.
-            this.dataPrep = exampleModel.dataPrep;            
-        
-            % Updating project location with title bar
-            set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
-            drawnow update;                            
-            
-            % Disable file menu items
-            for i=1:size(this.figure_Menu.Children,1)
-                if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
-                    set(this.figure_Menu.Children(i),'Enable','off');
-                elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
-                strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
-                    set(this.figure_Menu.Children(i),'Enable','off');
-                    set(this.figure_Menu.Children(i),'Label','Move models from RAM to HDD...');
-                end       
-            end   
-            
-            % Change pointer
-            set(this.Figure, 'pointer', 'arrow');  
-            drawnow update;
-            
-        end
-        
-        
-        function onChangeTableWidth(this, hObject, eventdata, tableTag)
-            
-           % Get current table panel
-           h = findobj(this.figure_Layout, 'Tag',tableTag);
-                      
-           % Get Current width
-           w = get(h,'Widths');
-           
-           % Set the widths abovethe current panel to -1
-           w(2:end) = -1;
-           sumW = sum(w(2:end));
-           
-           % Solve the implict problem defining the relative width of the
-           % current panel.
-           targetWidthRatio = hObject.Value;
-           fun = @(x) x/(x+sumW)-targetWidthRatio;
-           newWidth=fzero(fun,[-1000, -0.01]);
-           set(h,'Widths',[newWidth, w(2:end)]);
-                     
-        end
-        
-        function rowAddDelete(this, hObject, eventdata)
-           
-            % Get the table object from UserData
-            tableObj = eval(eventdata.Source.Parent.UserData);            
-            
-            % Get selected rows
-            if isempty(tableObj.Data)
-                selectedRows = [];
-            else
-                for i=1:size(tableObj.Data,1)
-                   if isempty(tableObj.Data{i,1}) 
-                       tableObj.Data{i,1}=false;
-                   end
-                end
-                selectedRows = cell2mat(tableObj.Data(:,1));
-            end
-
-            % Check if any rows are selected. Note, if not then
-            % rows will be added (for all but the calibration
-            % table).
-            anySelected = any(selectedRows);
-            indSelected = find(selectedRows)';
-            
-            if ~isempty(tableObj.Data)
-                if size(tableObj.Data(:,1),1)>0 &&  ~anySelected && ~strcmp(hObject.Label,'Paste rows')
-                    warndlg('No rows are selected for the requested operation.');
-                    return;
-                elseif size(tableObj.Data(:,1),1)==0 ...
-                &&  (strcmp(hObject.Label, 'Copy selected row') || strcmp(hObject.Label, 'Delete selected rows'))                
-                    return;
-                end               
-            end
-            
-            % Get column widths
-            colWidths = tableObj.ColumnWidth;
-            
-            % Define the input for the status column and default data for
-            % inserting new rows.
-            defaultData = cell(1,size(tableObj.Data,2));
-            switch tableObj.Tag
-                case 'Data Preparation'
-                    modelStatus = '<html><font color = "#FF0000">Bore not analysed.</font></html>';
-                    modelStatus_col = 16;
-                    defaultData = {false, '', '',0, 0, 0, '01/01/1900',true, true, true, true, 10, 120, 4,false, modelStatus, ...
-                    '<html><font color = "#808080">(NA)</font></html>', ...
-                    '<html><font color = "#808080">(NA)</font></html>'};
-                case 'Model Construction'
-                    modelStatus = '<html><font color = "#FF0000">Model not built.</font></html>';
-                    modelStatus_col = 9;
-                case 'Model Calibration'
-                    modelStatus = '<html><font color = "#FF0000">Model not calibrated.</font></html>';
-                    modelStatus_col = 9;
-                case 'Model Simulation'
-                    modelStatus = '<html><font color = "#FF0000">Not simulated.</font></html>';                    
-                    modelStatus_col = 12;
-            end
-            
-            % Change pointer
-            set(this.Figure, 'pointer', 'watch');  
-            
-            % Do the selected action            
-            switch hObject.Label
-                case 'Copy selected row'
-                    this.copiedData.tableName = tableObj.Tag;
-                    this.copiedData.data = tableObj.Data(selectedRows,:);
-                    
-                case 'Paste rows'    
-                    % Check that name of the table is same as that from the
-                    % copied data. 
-                    if ~strcmp(this.copiedData.tableName, tableObj.Tag)
-                        warndlg('The copied row data was sourced from a different table.');
-                        return;
-                    end    
-                    
-                    % Paste data and update model build status
-                    switch this.copiedData.tableName
-                        case 'Data Preparation'
-                            if anySelected
-                                j=1;
-                                for i=indSelected
-                                    
-                                    % Paste data
-                                    tableObj.Data{i,2} = this.copiedData.data{1,2};
-                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
-                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
-                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
-                                    tableObj.Data{i,6} = this.copiedData.data{1,6};
-                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
-                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
-                                    this.copiedData.data{i,modelStatus_col} = modelStatus;
-                                    j=j+1;
-                                end
-                            else
-                                for i=1: size(this.copiedData.data,1)
-                                   
-                                    % Add a row
-                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
-                                
-                                    % Add data to the new row.
-                                    tableObj.Data{irow,modelStatus_col} = modelStatus;                                    
-                                end
-                                % Update row numbers.
-                                nrows = size(tableObj.Data,1);
-                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                
-                            end                        
-                        case 'Model Construction'
-                            if anySelected
-                                j=1;
-                                for i=indSelected
-                                    
-                                    % Get a unique model label. 
-                                    newModelLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,2), this.copiedData.data{1,2}, i);
-                                    
-                                    % Paste data
-                                    tableObj.Data{i,2} = newModelLabel;
-                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
-                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
-                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
-                                    tableObj.Data{i,6} = this.copiedData.data{1,6};
-                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
-                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
-                                    this.copiedData.data{i,9} = modelStatus;
-                                    j=j+1;
-                                end
-                            else
-                                for i=1: size(this.copiedData.data,1)
-                                   
-                                    % Add a row
-                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
-                                
-                                    % Get a unique model label.
-                                    irow = size(tableObj.Data,1);
-                                    newModLabel = this.copiedData.data(i,2);
-                                    newModLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,2), newModLabel, irow);
-
-                                    % Add data to the new row.
-                                    tableObj.Data{irow,2} = newModLabel;
-                                    tableObj.Data{irow,9} = modelStatus;                                    
-                                end
-                                % Update row numbers.
-                                nrows = size(tableObj.Data,1);
-                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                           
-                            end
-                        case 'Model Calibration'
-                            for i=indSelected
-                                tableObj.Data{i,6} = this.copiedData.data{1,6};
-                                tableObj.Data{i,7} = this.copiedData.data{1,7};
-                                tableObj.Data{i,8} = this.copiedData.data{1,8};
-                                tableObj.Data(i,9) =  modelStatus;
-                                tableObj.Data{i,10} = '';
-                                tableObj.Data{i,11} = '';
-                                tableObj.Data{i,12} = '';
-                                tableObj.Data{i,13} = '';
-                            end
-                            % Update row numbers.
-                            nrows = size(tableObj.Data,1);
-                            tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                        
-                        case 'Model Simulation'
-                            if anySelected
-                                j=1;
-                                for i=indSelected
-                                    
-                                    % Get a unique model label.
-                                    newSimLabel = {this.copiedData.data{1,2}, this.copiedData.data{1,6}};
-                                    newSimLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,[2,6]), newSimLabel, i);
-                                    
-                                    tableObj.Data{i,2} = this.copiedData.data{1,2};                                    
-                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
-                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
-                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
-                                    tableObj.Data{i,6} = newSimLabel{2};
-                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
-                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
-                                    tableObj.Data{i,9} = this.copiedData.data{1,9};
-                                    tableObj.Data{i,10} = this.copiedData.data{1,10};
-                                    tableObj.Data{i,11} = this.copiedData.data{1,11};
-                                    this.copiedData.data{i,modelStatus_col} = modelStatus;
-                                    j=j+1;
-                                end
-                            else
-                                for i=1: size(this.copiedData.data,1)
-                                   
-                                    % Add a row
-                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
-                                    
-                                    % Edit simulation label and model
-                                    % status.
-                                    irow = size(tableObj.Data,1);
-                                    newSimLabel = {this.copiedData.data{i,2}, this.copiedData.data{i,6}};
-                                    newSimLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,[2,6]), newSimLabel, irow );
-                                    tableObj.Data{irow,6} = newSimLabel{2};
-                                    tableObj.Data{irow,modelStatus_col} = modelStatus;                                    
-                                end
-                                % Update row numbers.
-                                nrows = size(tableObj.Data,1);
-                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                            
-                            end                            
-                    end
-                    
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                    
-                case 'Insert row above selection'
-                    if isempty(tableObj.Data)
-                        tableObj.Data = defaultData;
-                    else
-                        selectedRows= find(selectedRows);
-                        for i=1:length(selectedRows)
-
-                            ind = max(0,selectedRows(i) + i-1);
-
-                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
-                                             defaultData; ...
-                                             tableObj.Data(ind:end,:)];
-                            tableObj.Data{ind,1} = false; 
-                            
-                            % Update model build status
-                            tableObj.Data{ind,modelStatus_col} = modelStatus;
-
-                        end
-                    end
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                        
-                case 'Insert row below selection'    
-                    if isempty(tableObj.Data)
-                        tableObj.Data = defaultData;
-                    else
-                        selectedRows= find(selectedRows);
-                        for i=1:length(selectedRows)
-
-                            ind = selectedRows(i) + i;
-
-                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
-                                             defaultData; ...
-                                             tableObj.Data(ind:end,:)];
-                            tableObj.Data{ind,1} = false;                                                              
-                            
-                            % Update model build status
-                            tableObj.Data{ind,modelStatus_col} = modelStatus;
-                            
-                        end
-                    end
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-
-                case 'Delete selected rows'    
-                    
-                    % Delete the model objects if within the model
-                    % construction table
-                    if strcmp(tableObj.Tag,'Model Construction')                        
-                        for i=indSelected
-                            % Delete the model object
-                            try
-                                deleteModel(this, this.tab_ModelConstruction.Table.Data{i,2});
-                            catch ME
-                                % do nothing
-                            end
-                        end
-                    elseif strcmp(tableObj.Tag,'Data Preparation')                        
-                        for i=indSelected
-                            
-                            % Get model label
-                            boreID= tableObj.Data{i,3};
-                            
-                            % Remove model object
-                            if isfield(this.dataPrep,boreID)
-                                this.dataPrep = rmfield(this.dataPrep,boreID);
-                            end
-                        end                        
-                    end
-                    
-                    % Delete table data
-                    tableObj.Data = tableObj.Data(~selectedRows,:);
-                    
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                    
-            end
-            
-            % Reset column widths
-            tableObj.ColumnWidth = colWidths;            
-            
-             % Change pointer
-            set(this.Figure, 'pointer', 'arrow');  
-        end
-        
-        function rowSelection(this, hObject, eventdata)
-
-            % Get the table object from UserData
-            tableObj = eval(eventdata.Source.Parent.UserData);            
-                                    
-            % Get selected rows
-            selectedRows = cellfun(@(x) ~isempty(x) && islogical(x) && x, tableObj.Data(:,1));
-            
-            % Do the selected action            
-            switch hObject.Label
-                case 'Select all'
-                    tableObj.Data(:,1) = mat2cell(true(size(selectedRows,1),1),ones(1, size(selectedRows,1)));
-                case 'Select none'
-                    tableObj.Data(:,1) = mat2cell(false(size(selectedRows,1),1),ones(1, size(selectedRows,1)));
-                case 'Invert selection'
-                    tableObj.Data(:,1) = mat2cell(~selectedRows,ones(1, size(selectedRows,1)));
-                case 'Select row range ...'
-                    rowRange = inputdlg( {'First row number:', 'Last row number:'}, 'Input row range for selection.',1,{'1','2'});
-                    if isempty(rowRange)
-                        return;
-                    end
-                    irows = [1:size(tableObj.Data,1)]';
-                    try
-                        startRow=str2num(rowRange{1});
-                        endRow=str2num(rowRange{2});
-                    catch ME
-                        warndlg('The first and last row inputs must be numbers','Input Error')
-                        return;
-                    end
-                    if startRow>= endRow
-                        warndlg('The first row must be less than the last row.','Input Error')
-                        return;                        
-                    end
-                    filt =  (irows >=startRow &  irows <=endRow) | selectedRows;
-                    tableObj.Data(:,1) =  mat2cell(filt,ones(1, size(selectedRows,1)));
-                    
-                case 'Select by col. value ...'
-                    colNames = HydroSight_GUI.removeHTMLTags(tableObj.ColumnName);
-                    
-                    [selectedCol,ok] = listdlg('PromptString', 'Select column:', 'Name', 'Select by col. value ...', ...
-                        'ListString', colNames,'SelectionMode','single');                                        
-
-                    if ok==1
-                        colNames_str = inputdlg('Input the string to be found within the selected column:',  'Select by col. value ...',1);
-                        
-                        if isempty(colNames_str)
-                            return;
-                        end
-                        
-                        % Find rows within table
-                        filt = cellfun(@(x) ~isempty(strfind(upper(x),upper(colNames_str{1}))) , tableObj.Data(:,selectedCol));
-                        filt = filt | selectedRows;
-                        
-                        % Select rows
-                        tableObj.Data(:,1) =  mat2cell(filt,ones(1, size(selectedRows,1)));
-                    end
-
-            end
-                        
-        end
-        
-        
-        function fName = getFileName(this, dialogString)
-           
-            % If project folder is not set, exit
-            if isempty(this.project_fileName)
-                errordlg('The project folder must be set before files can be input.');
-                fName=0;
-                return
-            end                        
-
-            % Get the project path
-            if isdir(this.project_fileName)
-                projectPath = this.project_fileName;
-            else
-                [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
-            end
-
-            % Set the current folder to the project folder
-            try
-                cd(projectPath);
-            catch
-                errordlg({'The project folder could not be opened. Try re-inputting the', ...
-                        'project folder and/or check your network access to the folder.'}, 'Project folder access error.');
-                return;
-            end
-            
-            % Get file name
-            [fName,pName] = uigetfile({'*.*'},dialogString); 
-            if fName~=0;
-                % Check if the file name is the same as the
-                % project path.
-                if isempty(strfind(pName,projectPath))
-                    errordlg('The selected file path must be a within the project folder or a sub-folder of it.')
-                    return;
-                end
-
-                % Get the extra part of the file path not
-                % within the project path.
-                fName = fullfile(pName,fName); 
-                fName = fName(length(projectPath)+2:end);
-            end                        
-        end
-
-        function [model, errmsg] = getModel(this, modelLabel)
-            % Convert model label to a valid field name
-            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-            
-            model = [];
-            if isempty(modelLabel)
-                    errmsg = 'Model label is empty.';
-                    return;
-            end
-            
-            % Get model            
-            if isempty(this.modelsOnHDD) || (islogical(this.modelsOnHDD) && ~this.modelsOnHDD)
-                if isa(this.models.(modelLabel),'HydroSightModel')                    
-                    model = this.models.(modelLabel);
-                else
-                    errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];
-                end
-            elseif ~isempty(this.modelsOnHDD)
-                try                    
-                    % Build file path to model .mat file
-                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
-
-                    % Change file seperator fro OS
-                    filepath = strrep(filepath, '/',filesep);
-                    filepath = strrep(filepath, '\',filesep);
-                                        
-                    % Check the file exists
-                    if exist(filepath, 'file')==0
-                        errmsg = ['The following HydroSight .mat file could not be found:',modelLabel];
-                        return;
-                    end                   
-                           
-                    % Load model and check.
-                    savedData = load(filepath,'model');
-                    model = savedData.model;
-                    if ~isa(model,'HydroSightModel');
-                        errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];    
-                        model=[];
-                    end
-                catch
-                    errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];
-                end
-            else
-                errmsg = ['The following model could not be loaded:',modelLabel];
-            end            
-        end
-        
-        function errmsg = setModel(this, modelLabel, model)
-            % Convert model label to a valid field name
-            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-           
-            isCalibrated=false;
-            
-            % Set model
-            if isempty(this.modelsOnHDD)
-                if isa(model,'HydroSightModel')                    
-                    % Add model object
-                    this.models.(modelLabel) = model;
-                    
-                    % Check if calibrated                    
-                    if isfield(this.models.(modelLabel).calibrationResults,'isCalibrated')
-                        isCalibrated = this.models.(modelLabel).calibrationResults.isCalibrated;
-                    end
-
-                else
-                    errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
-                    return;
-                end
-            elseif ~isempty(this.modelsOnHDD)
-                if isa(model,'HydroSightModel')   
-                    % Build file path to model .mat file
-                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
-
-                    % Change file seperator fro OS
-                    filepath = strrep(filepath, '/',filesep);
-                    filepath = strrep(filepath, '\',filesep);                
-                           
-                    % Delete file it if exists
-                    if exist(filepath,'file')>0
-                        delete(filepath);
-                    end
-                    
-                    % Save model object                          
-                    save(filepath,'model');
-                    
-                    % Update object hold the relative path to the .mat file
-                    this.models.(modelLabel) = fullfile(this.modelsOnHDD, [modelLabel,'.mat']);
-                    
-                    % Check if calibrated                    
-                    if isfield(model.calibrationResults,'isCalibrated')
-                        isCalibrated = model.calibrationResults.isCalibrated;
-                    end
-                elseif ischar(model);
-                    % Build file path to model .mat file
-                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
-                    
-                    % Change file seperator fro OS
-                    filepath = strrep(filepath, '/',filesep);
-                    filepath = strrep(filepath, '\',filesep);
-                    
-                    % Check the file exists
-                    if exist(filepath, 'file')==0
-                        errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
-                        return;
-                    end
-                    
-                    % Load model to assess if calibrated.
-                    savedData= load(filepath);
-                    model = savedData.model;
-                    
-                    % Update object hold the relative path to the .mat file
-                    this.models.(modelLabel) = fullfile(this.modelsOnHDD, [modelLabel,'.mat']);
-                    
-                    % Check if calibrated                    
-                    if isfield(model.calibrationResults,'isCalibrated')
-                        isCalibrated = model.calibrationResults.isCalibrated;
-                    end                    
-                else
-                    errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
-                    return;
-                end
-            else
-                errmsg = ['The following model could not be set:',modelLabel];
-                return;
-            end            
-            
-            % Update or add model lable to list
-            if ~isempty(this.model_labels) && any(strcmp(this.model_labels.Properties.RowNames,modelLabel))
-                this.model_labels{modelLabel,1} = isCalibrated;
-            else           
-                % Add model label to object. This is done to elimnate the need
-                % to open all models, which can be slow if they're on the HDD.
-                modelLabelasCell{1} = modelLabel;
-                tbleRow = table(isCalibrated,'RowNames',modelLabelasCell);
-                if isempty(this.model_labels);
-                    this.model_labels = tbleRow;
-                else
-                    this.model_labels = [this.model_labels;tbleRow];
-                end
-            end
-            
-        end
-        
-        function [model, errmsg] = deleteModel(this, modelLabel)
-            % Convert model label to a valid field name
-            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
-            
-            model = [];
-            if isempty(modelLabel)
-                    errmsg = 'Model label is empty.';
-                    return;
-            end
-            
-            % Delete model            
-            if isempty(this.modelsOnHDD) || ~this.modelsOnHDD
-                if isa(this.models.(modelLabel),'HydroSightModel')                    
-                    this.models = rmfield(this.models, modelLabel);
-                else
-                    errmsg = ['The following model is not a HydroSight object and could not be deleted:',modelLabel];
-                end
-                
-                % Remove from model label list
-                filt = ~strcmp(this.model_labels.Properties.RowNames,modelLabel);
-                this.model_labels = this.model_labels(filt,:);
-                                
-            elseif ~isempty(this.modelsOnHDD)
-                try                    
-                    % Build file path to model .mat file
-                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
-
-                    % Change file seperator fro OS
-                    filepath = strrep(filepath, '/',filesep);
-                    filepath = strrep(filepath, '\',filesep);
-                                        
-                    % Check the file exists
-                    if exist(filepath, 'file')==0
-                        errmsg = ['The following HydroSight .mat file could not be found:',modelLabel];
-                        return;
-                    end                   
-                           
-                    % Delete model file.                    
-                    delete(filepath);
-                    this.models = rmfield(this.models, modelLabel);
-
-                    % Remove from model label list
-                    filt = ~strcmp(this.model_labels.Properties.RowNames,modelLabel);
-                    this.model_labels = this.model_labels(filt,:);                    
-                    
-                catch
-                    errmsg = ['The following model is not a HydroSight object and could not be deleted:',modelLabel];
-                end
-            else
-                errmsg = ['The following model could not be deleted:',modelLabel];
-            end            
-        end        
-        
-    end
-    
-    methods (Access=private)
-        function this = calibGUI(this, calibTable)
-           
-            
-        end
-                
-        
-        function startCalibration(this,hObject,eventdata)
-            
-            % Get table data
-            data = this.tab_ModelCalibration.Table.Data;            
-            
-            % Get list of selected bores and check.
-            selectedBores = data(:,1);
-            isModelSelected=false;
-            for i=1:length(selectedBores);                
-                % Check if the model is to be calibrated.
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    isModelSelected=true;
-                    break;
-                end                
-            end                        
-                                    
-            % Check that the user wants to save the projct after each
-            % calib.
-            saveModels=false;
-            if ~strcmp(hObject.Tag,'Start calibration - useHPC')
-                set(this.Figure, 'pointer', 'arrow');
-                drawnow update
-                
-                response = questdlg('Do you want to save the project after each model is calibrated?','Auto-save models?','Yes','No','Cancel','Yes');
-                if strcmp(response,'Cancel')
-                    return;
-                end
-                if strcmp(response,'Yes') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
-                    msgbox('The project has not yet been saved. Please save it and re-run the calibration.','Project not yet saved ...','error');
-                    return;
-                end
-                saveModels=false;
-                if strcmp(response,'Yes')
-                    saveModels=true;
-                end  
-            end
-
-            % Change label of button to quit
-            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Start calibration');
-            obj.Enable = 'off';
-            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Quit calibration');
-            obj.Enable = 'on';            
-            
-            % Change cursor
-            set(this.Figure, 'pointer', 'watch');
-            drawnow update
-            
-            % Open parrallel engine for calibration
-            if ~strcmp(hObject.Tag,'Start calibration - useHPC')
-                try
-                    %nCores=str2double(getenv('NUMBER_OF_PROCESSORS'));
-                    parpool('local');
-                catch ME
-                    % Do nothing. An error is probably that parpool is already
-                    % open
-                end
-            end
-            
-            % Count the number of models selected
-            nModels=0;
-            iModels=0;
-            for i=1:length(selectedBores);                                
-                if ~isempty(selectedBores{i}) && selectedBores{i}
-                    nModels = nModels +1;
-                end
-            end
-            
-            % Setup wait bar
-            waitBarPlot = findobj(this.tab_ModelCalibration.GUI, 'Tag','Calib_wait_bar');
-            waitBarPlot.YData=0;
-                                    
-            % Setup inputs for the GUI diary file.
-            commandwindowBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','calibration command window');
-            commandwindowBox.Max=Inf;
-            commandwindowBox.Min=0;
-                        
-            %% Start calibration                             
-            % Loop  through the list of selected bore and apply the model
-            % options.
-            nModelsCalib = 0;
-            nModelsCalibFailed = 0;
-            nHPCmodels = 0;
-            for i=1:length(selectedBores)
-                
-                % Check if the model is to be calibrated.
-                if isempty(selectedBores{i}) || ~selectedBores{i}
-                    continue;
-                end
-                
-                % Get the selected model for simulation
-                calibLabel = data{i,2};
-                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
-                
-                % Get start and end date. Note, start date is at the start
-                % of the day and end date is shifted to the end of the day.
-                calibStartDate = datenum( data{i,6},'dd-mmm-yyyy');
-                calibEndDate = datenum( data{i,7},'dd-mmm-yyyy') + datenum(0,0,0,23,59,59);
-                calibMethod = data{i,8};
-
-                % Get a copy of the model object. This is only done to
-                % minimise HDD read when the models are off loaded to HDD using
-                % matfile();                
-                tmpModel = getModel(this, calibLabel);
-
-                % Exit if model is model not found
-                if isempty(tmpModel)
-                    nModelsCalibFailed = nModelsCalibFailed +1;
-                    this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FF0000">Fail-Model appears not to have been built.</font></html>';
-                    continue;
-                end  
-
-                % Start the diary and copying of the command window outputs to
-                % the GUI. Unfortunately a new diary file needs to be
-                % created per model because the updating can be very slow
-                % when multiple models outputs are within one diary file
-                % (eg 12 seconds per update!)
-                diaryFilename = strrep(this.project_fileName,'.mat',['_',calibLabel,'_calibOutputs_',strrep(strrep(datestr(now),':','-'),' ','_'),'.txt']);
-                this.tab_ModelCalibration.QuitObj = calibGUI_interface(commandwindowBox,diaryFilename);
-                startDiary(this.tab_ModelCalibration.QuitObj);   
-
-                obj = this.tab_ModelCalibration.QuitObj;
-                lh = addlistener(this,'quitModelCalibration',@obj.quitCalibrationListener);
-                
-                % Update status to starting calib.
-                this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Calibrating ... </font></html>';
-
-                % Update status in GUI
-                drawnow
-
-                % Collate calibration settings
-                calibMethodSetting=struct();
-                switch calibMethod                    
-                    case 'CMAES'
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES MaxFunEvals');
-                        calibMethodSetting.MaxFunEvals= str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES TolFun');
-                        calibMethodSetting.TolFun= str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES TolX');
-                        calibMethodSetting.TolX= str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES Restarts');
-                        calibMethodSetting.Restarts= str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES insigmaFrac');
-                        calibMethodSetting.insigmaFrac= str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES Seed');
-                        calibMethodSetting.Seed= str2double(obj.String);
-
-                    case 'SP-UCI'
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI maxn');
-                        calibMethodSetting.maxn = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI kstop');
-                        calibMethodSetting.kstop = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI pcento');
-                        calibMethodSetting.pcento = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI peps');
-                        calibMethodSetting.peps = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs');
-                        ngs_per_param = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs min');
-                        ngs_min = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs max');
-                        ngs_max = str2double(obj.String);                                                        
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI iseed');
-                        calibMethodSetting.iseed = str2double(obj.String);
-
-
-                        % Get the number of parameters.
-                        [params, param_names] = getParameters(tmpModel.model);
-                        nparams = size(param_names,1);
-
-                        % Calculate the number of complexes for this model.
-                        calibMethodSetting.ngs = max(ngs_min, min(ngs_max, ngs_per_param*nparams));
-
-                    case 'DREAM'
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM N');
-                        calibMethodSetting.N = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM T');
-                        calibMethodSetting.T = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM nCR');
-                        calibMethodSetting.nCR = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM delta');
-                        calibMethodSetting.delta = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM lambda');
-                        calibMethodSetting.lambda = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM zeta');
-                        calibMethodSetting.zeta = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM outlier');
-                        calibMethodSetting.outlier = str2double(obj.String);
-
-                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM pJumpRate_one');
-                        calibMethodSetting.pJumpRate_one = str2double(obj.String);                            
-                end
-                        
-                % Start calib.
-                try                    
-                    if strcmp(hObject.Tag,'Start calibration - useHPC')
-                        display(['BUILDING OFFLOAD DATA FOR MODEL: ',calibLabel]);
-                        display('  ');
-                        % Update the diary file
-                        if ~isempty(this.tab_ModelCalibration.QuitObj)
-                            updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-                        end
-                        
-                        nHPCmodels = nHPCmodels +1;
-                        HPCmodelData{nHPCmodels,1} = tmpModel;
-                        HPCmodelData{nHPCmodels,2} = calibStartDate;
-                        HPCmodelData{nHPCmodels,3} = calibEndDate;
-                        HPCmodelData{nHPCmodels,4} = calibMethod;
-                        HPCmodelData{nHPCmodels,5} = calibMethodSetting;                        
-                        this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Calib. on HPC... </font></html>';
-                    else
-                        display(['CALIBRATING MODEL: ',calibLabel]);
-                        display( '--------------------------------------------------------------------------------');
-                        % Update the diary file
-                        if ~isempty(this.tab_ModelCalibration.QuitObj)
-                            updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
-                        end   
-                    
-                        calibrateModel( tmpModel, this.tab_ModelCalibration.QuitObj, calibStartDate, calibEndDate, calibMethod,  calibMethodSetting);
-
-                        % Delete CMAES working filescopl                        
-                        switch calibMethod
-                            case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
-                                delete('*.dat');
-                            otherwise
-                                % do nothing
-                        end
-
-                        % Get calib exit status
-                        exitFlag = tmpModel.calibrationResults.exitFlag;
-                        exitStatus = tmpModel.calibrationResults.exitStatus;                          
-
-                        % Set calib performance stats.
-                        calibAICc = median(tmpModel.calibrationResults.performance.AICc);
-                        calibBIC =median( tmpModel.calibrationResults.performance.BIC);
-                        calibCoE = median(tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
-                        this.tab_ModelCalibration.Table.Data{i,10} = ['<html><font color = "#808080">',num2str(calibCoE),'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{i,12} = ['<html><font color = "#808080">',num2str(calibAICc),'</font></html>'];
-                        this.tab_ModelCalibration.Table.Data{i,13} = ['<html><font color = "#808080">',num2str(calibBIC),'</font></html>'];
-
-                        % Set eval performance stats
-                        if isfield(tmpModel.evaluationResults,'performance')
-                            %evalAIC = this.models.data{ind, 1}.evaluationResults.performance.AIC;
-                            evalCoE = mean(tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
-
-                            this.tab_ModelCalibration.Table.Data{i,11} = ['<html><font color = "#808080">',num2str(evalCoE),'</font></html>'];                            
-                        else
-                            evalCoE = '(NA)';
-                            %evalAIC = '(NA)';
-
-                            this.tab_ModelCalibration.Table.Data{i,11} = ['<html><font color = "#808080">',evalCoE,'</font></html>'];                            
-                        end
-                        nModelsCalib = nModelsCalib +1;
-
-                        % Add updated tmpModel back to data structure of
-                        % all models
-                        setModel(this, calibLabel, tmpModel)
-
-                        if saveModels
-                            this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Saving project. </font></html>';
-
-                            % Update status in GUI
-                            drawnow                        
-
-                            % Save project.
-                            onSave(this,hObject,eventdata);
-                        end                        
-
-                        % Update calibr status                          
-                        if exitFlag ==0 
-                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', ME.message,'</font></html>'];
-                        elseif exitFlag ==1
-                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FFA500">Partially calibrated: ',exitStatus,' </font></html>'];
-                        elseif exitFlag ==2
-                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#008000">Calibrated: ',exitStatus,' </font></html>'];
-                        end                      
-                        
-                    end
-
-
-                    % Check if the user quit the calibration.
-                    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(this.tab_ModelCalibration.QuitObj);                        
-                    if doQuit
-                        exitFlag = exitFlagQuit;
-                        exitStatus = exitStatusQuit;
-                        this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', exitStatus,'</font></html>'];                            
-                        break;
-                    end
-
-
-                catch ME
-                    nModelsCalibFailed = nModelsCalibFailed +1;
-                    this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', ME.message,'</font></html>'];
-                end
-                
-                % Update wait bar
-                waitBarPlot.YData = waitBarPlot.YData+1;
-                
-                % Update status in GUI
-                drawnow
-                                
-            end
-            
-            % Close wait bar
-            %if ~strcmp(hObject.Tag,'useHPC') && nModels>=minModels4Waitbar            
-            %    close(h);
-            %end
-            
-            % Change label of button to quit
-            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Start calibration');
-            obj.Enable = 'on';
-            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Quit calibration');
-            obj.Enable = 'off';            
-            
-            % Change cursor to arrow
-            set(this.Figure, 'pointer', 'arrow');
-            drawnow update
-            if strcmp(hObject.Tag,'Start calibration - useHPC')
-               project_fileName = this.project_fileName;
-               if ~isdir(this.project_fileName)
-                   project_fileName = fileparts(project_fileName);
-               end
-               
-               userData = jobSubmission(this.HPCoffload, project_fileName, HPCmodelData, this.tab_ModelCalibration.QuitObj) ; 
-               if ~isempty(userData)
-                   this.HPCoffload = userData;
-               end
-
-                % Update status in GUI
-                drawnow update
-                    
-            else
-                % Report Summary
-                msgbox(['The model was successfully calibrated for ',num2str(nModelsCalib), ' models and failed for ',num2str(nModelsCalibFailed), ' models.'], 'Summary of model calibration ...');
-            end        
-        end
-        
-        function quitCalibration(this,hObject,eventdata)
-            % Notify event that the user is quiting the calibration. The
-            % listener in class "calibGUI_interface" should then be called.
-            notify(this,'quitModelCalibration');
-        end
-        
-
-        function multimodel_moveLeft(this,hObject,eventdata)
-            % Find the RH list object
-            leftlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsLeftList');
-            rightlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsRightList');
-            
-            % Add selected params to LH box
-            leftlistBox.String = {leftlistBox.String{:}, rightlistBox.String{rightlistBox.Value}};
-            leftlistBox.String = sort(leftlistBox.String);
-            
-            % Remove from RH box.
-            ind  = true(size(rightlistBox.String));
-            ind(rightlistBox.Value)=false;
-            rightlistBox.String = rightlistBox.String(ind);
-            rightlistBox.String = sort(rightlistBox.String);
-        
-        end
-        
-        function multimodel_moveRight(this,hObject,eventdata)                    
-            % Find the RH list object
-            leftlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsLeftList');
-            rightlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsRightList');
-            
-            % Add selected params to RH box
-            rightlistBox.String = {rightlistBox.String{:}, leftlistBox.String{leftlistBox.Value}};
-            rightlistBox.String = sort(rightlistBox.String);
-            
-            % Remove from LH box.
-            ind  = true(size(leftlistBox.String));
-            ind(leftlistBox.Value)=false;
-            leftlistBox.String = leftlistBox.String(ind);
-            leftlistBox.String = sort(leftlistBox.String);
-        end        
-    end
-    
-    methods(Static=true)
-       
-        % Remove HTML tags from each model label
-        function str = removeHTMLTags(str)
-            if iscell(str)
-               for i=1:length(str)
-                    if ~isempty(strfind(upper(str{i}),'HTML'))
-                        str{i} = regexp(str{i},'>.*?<','match');
-                        str{i} = strrep(str{i}, '<', '');
-                        str{i} = strrep(str{i}, '>', '');
-                        str{i} = strtrim(strjoin(str{i}));
-                    end
-               end
-            else
-                if ~isempty(strfind(upper(str),'HTML'))
-                    str = regexp(str,'>.*?<','match');
-                    str = strrep(str, '<', '');
-                    str = strrep(str, '>', '');
-                    str = strtrim(strjoin(str));            
-                end
-            end
-        end
-        
-        % Create a unique model or simulation label. This is used in the
-        % copy/paste of table rows to ensure each row is unique
-        function newLabel = createUniqueLabel(allLabels, newLabel, currentRow)
-           
-            % Check if the proposed label is unique.
-            if size(allLabels,2)==1
-                ind = cellfun( @(x,y) strcmp( newLabel, x) , allLabels);
-            elseif size(allLabels,2)==2
-                ind = cellfun( @(x,y) strcmp( newLabel{1}, x) &&  strcmp( newLabel{2}, y) , allLabels(:,1), allLabels(:,2));
-            else
-                error('A maximum of two columns of model / simulation labels can be evaluated for uniqueness.');
-            end
-            ind = find(ind);                        
-            if length(ind)==1 && ind(1) == currentRow % Return if only the current row has the proposed new label.
-                return;
-            end            
-            
-            % If not unique, then add an integer to the label so that it is
-            % unique.
-            origLabel = newLabel;
-            label_extension = 1;      
-            if size(allLabels,2)==1
-
-                if ischar(origLabel)
-                    origLabel_tmp{1} = origLabel;
-                    origLabel = origLabel_tmp;
-                    clear origLabel_tmp
-                end
-                
-                newLabel = [origLabel{1}, ' copy ',num2str(label_extension)];
-                while  any(find(cellfun( @(x,y) strcmp( newLabel, x) , allLabels))~=currentRow)
-                    label_extension = label_extension + 1;
-                    newLabel = [origLabel{1}, ' copy ',num2str(label_extension)];    
-                end
-            elseif size(allLabels,2)==2
-                newLabel = {origLabel{1}, [origLabel{2}, ' copy ',num2str(label_extension)]};
-                while any(find(cellfun( @(x,y) strcmp( newLabel{1}, x) &&  strcmp( newLabel{2}, y) , allLabels(:,1), allLabels(:,2)))~=currentRow)
-                    label_extension = label_extension + 1;
-                    newLabel = {origLabel{1}, [origLabel{2}, ' copy ',num2str(label_extension)]};
-                end                    
-            end
-        end
-        
-        
-        function newLabel = modelLabel2FieldName(inputLabel)
-           
-            % Remove characeters that are not able to be included in a
-            % field name.
-            inputLabel = strrep(inputLabel,' ','_');
-            inputLabel = strrep(inputLabel,'-','_');
-            inputLabel = strrep(inputLabel,'?','');
-            inputLabel = strrep(inputLabel,'\','_');
-            inputLabel = strrep(inputLabel,'/','_');
-            inputLabel = strrep(inputLabel,'___','_');
-            inputLabel = strrep(inputLabel,'__','_');
-                       
-            % Check if any reserved labels are used. The reserved labels
-            % are GUI variables to saved within a project .mat file.
-            if strcmp(inputLabel, {'tableData'; 'dataPrep';'settings'})
-                warndlg('Model label cannot be one of the following reserved labels "tableData", "dataPrep", "settings".','Model label error ...');
-                newLabel='';
-                return;
-            end                
-            
-            % Check if a field name can be created with the model label
-            try 
-                tmp.(inputLabel) = [1 2 3];
-                clear tmp;
-                newLabel = inputLabel;
-            catch ME
-                warndlg('Model label must start with letters and have only letters and numbers.','Model label error ...');
-                newLabel='';
-                return;
-            end
-            
-        end
-    end
-end
-
+classdef HydroSight_GUI < handle  
+    %HydroSight_GUI Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    %class properties - access is private so nothing else can access these
+    %variables. Useful in different sitionations
+    properties
+        % Version number
+        versionNumber = '1.3.5';
+        versionDate= '11 September 2019';
+        
+        % Model types supported
+        %modelTypes = {'model_TFN','model_TFN_LOA', 'ExpSmooth'};
+        modelTypes = {'model_TFN', 'ExpSmooth'};
+        
+        % GUI properies for the overall figure.
+        FigureSplash
+        Figure;        
+        figure_Menu
+        figure_contextMenu
+        figure_examples
+        figure_Help
+        figure_Layout 
+        
+        % GUI properties for the individual tabs.
+        tab_Project
+        tab_DataPrep;
+        tab_ModelConstruction;
+        tab_ModelCalibration;        
+        tab_ModelSimulation;
+        
+        % Store model data
+        models=[];
+        
+        % Store a record of the model lables (as row names in a tavle) and
+        % if the models are calibrated.
+        model_labels=[];
+        
+        % Store the data preparation analysis results;
+        dataPrep
+        
+        % Copies of data
+        copiedData = {};
+        
+        % Setting for off loading calibration to HPC cluster
+        HPCoffload = {};
+        
+        % File name for the current set of models
+        project_fileName = '';
+        
+        % Are the models to be stored on HDD
+        modelsOnHDD = '';
+    end
+    
+    % Events
+    events
+       quitModelCalibration 
+    end
+    
+    methods
+        
+        function this = HydroSight_GUI
+            
+%             % Check the toolbox for GUIs exists
+%             if ~isdeployed && isempty(ver('layout'))
+%                 msgbox({'The following toolbox file must be installed within Matlab to use.', ...
+%                     'HydroSight. Please download and install it and then re-start', ...
+%                     'HydroSight. Also, a web browser will now open at the toolbox site.', ...
+%                     '', ...
+%                     'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox'}, ...
+%                 'Toolbox missing: gui-layout-toolbox', 'error');
+%             
+%                 web( 'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox' ) 
+%                 
+%                 return
+%             end
+
+            
+            %--------------------------------------------------------------
+            % Open a window and add some menus
+            this.Figure = figure( ...
+                'Name', 'HydroSight', ...
+                'NumberTitle', 'off', ...
+                'MenuBar', 'none', ...
+                'HandleVisibility', 'off', ...
+                'Visible','off', ...
+                'Toolbar','figure', ...
+                'CloseRequestFcn',@this.onExit); 
+            
+            % Show splash (if code not deployed)
+            if ~isdeployed
+               onAbout(this, [],[]);
+            end
+            
+            % Set window Size
+            windowHeight = this.Figure.Parent.ScreenSize(4);
+            windowWidth = this.Figure.Parent.ScreenSize(3);
+            figWidth = 0.8*windowWidth;
+            figHeight = 0.6*windowHeight;            
+            this.Figure.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];
+            %this.Figure.Visible = 'off';
+            
+            
+            % Set default panel color
+            %warning('off');
+            %uiextras.set( this.Figure, 'DefaultBoxPanelTitleColor', [0.7 1.0 0.7] );
+            %warning('on');
+            
+            % + File menu
+            this.figure_Menu = uimenu( this.Figure, 'Label', 'File' );
+            uimenu( this.figure_Menu, 'Label', 'New Project', 'Callback', @this.onNew);
+            uimenu( this.figure_Menu, 'Label', 'Set Project Folder ...', 'Callback', @this.onSetProjectFolder);
+            uimenu( this.figure_Menu, 'Label', 'Open Project...', 'Callback', @this.onOpen);
+            uimenu( this.figure_Menu, 'Label', 'Import Model(s) ...', 'Callback', @this.onImportModel);
+            uimenu( this.figure_Menu, 'Label', 'Save Project as ...', 'Callback', @this.onSaveAs );
+            uimenu( this.figure_Menu, 'Label', 'Save Project', 'Callback', @this.onSave,'Enable','off');
+            uimenu( this.figure_Menu, 'Label', 'Move models from RAM to HDD...', 'Callback', @this.onMoveModels,'Separator','on', 'Enable','off');
+            uimenu( this.figure_Menu, 'Label', 'Exit', 'Callback', @this.onExit,'Separator','on' );
+
+            % + Examples menu
+            this.figure_examples = uimenu( this.Figure, 'Label', 'Examples' );
+            uimenu( this.figure_examples, 'Label', 'TFN model - Landuse change', 'Tag','TFN - LUC','Callback', @this.onExamples );
+            uimenu( this.figure_examples, 'Label', 'TFN model - Pumping and climate', 'Tag','TFN - Pumping','Callback', @this.onExamples );
+
+            % + Help menu
+            this.figure_Help = uimenu( this.Figure, 'Label', 'Help' );
+            uimenu(this.figure_Help, 'Label', 'Overview', 'Tag','doc_Overview','Callback', @this.onDocumentation);
+            uimenu(this.figure_Help, 'Label', 'User Interface', 'Tag','doc_GUI','Callback', @this.onDocumentation);
+            uimenu(this.figure_Help, 'Label', 'Data requirements', 'Tag','doc_data_req','Callback', @this.onDocumentation);            
+            uimenu(this.figure_Help, 'Label', 'Time-series algorithms', 'Tag','doc_timeseries_algorithms','Callback', @this.onDocumentation);                
+            uimenu(this.figure_Help, 'Label', 'Tutorials', 'Tag','doc_tutes','Callback', @this.onDocumentation);                            
+            uimenu(this.figure_Help, 'Label', 'Calibration Fundementals','Tag','doc_Calibration','Callback', @this.onDocumentation);            
+            uimenu(this.figure_Help, 'Label', 'Support', 'Tag','doc_Support','Callback', @this.onDocumentation);                            
+            uimenu(this.figure_Help, 'Label', 'Publications', 'Tag','doc_Publications','Callback', @this.onDocumentation);                
+            
+            uimenu(this.figure_Help, 'Label', 'Check for updates at GitHub', 'Tag','doc_GitHubUpdate','Callback', @this.onGitHub,'Separator','on');
+            uimenu(this.figure_Help, 'Label', 'Submit bug report to GitHub', 'Tag','doc_GitHubIssue','Callback', @this.onGitHub);
+            
+            
+            uimenu(this.figure_Help, 'Label', 'License and Disclaimer', 'Tag','doc_Publications','Callback', @this.onLicenseDisclaimer,'Separator','on');
+            uimenu(this.figure_Help, 'Label', 'Version', 'Tag','doc_Version','Callback', @this.onVersion);
+            uimenu(this.figure_Help, 'Label', 'About', 'Callback', @this.onAbout );
+                        
+            % Get toolbar object
+            hToolbar = findall(this.Figure,'tag','FigureToolBar');
+            
+            % Hide toolbar button not used (2018B prior and after)
+            hToolbutton = findall(hToolbar,'tag','Annotation.InsertLegend');            
+            hToolbutton.Visible = 'off';
+            hToolbutton.UserData = 'Never';
+            hToolbutton.Separator = 'off';
+            hToolbutton = findall(hToolbar,'tag','Annotation.InsertColorbar');            
+            hToolbutton.Visible = 'off';
+            hToolbutton.UserData = 'Never';
+            hToolbutton.Separator = 'off';
+            hToolbutton = findall(hToolbar,'tag','DataManager.Linking');            
+            hToolbutton.Visible = 'off';
+            hToolbutton.UserData = 'Never';	
+            hToolbutton.Separator = 'off';      
+            hToolbutton = findall(hToolbar,'tag','Standard.EditPlot');            
+            hToolbutton.Visible = 'off';
+            hToolbutton.UserData = 'Plot';
+            hToolbutton.Separator = 'off';            
+            
+            % Redefine print button
+            hToolbutton = findall(hToolbar,'tag','Standard.PrintFigure');            
+            set(hToolbutton, 'ClickedCallback',@this.onPrint, 'TooltipString','Open the print preview window for the displayed plot ...');
+            hToolbutton.Visible = 'off';
+            hToolbutton.UserData = 'Plot';
+            hToolbutton.Separator = 'off';
+            icon = 'implay_export.gif';
+            [cdata,map] = imread(icon);
+            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
+            cdata = ind2rgb(cdata,map);
+            
+            
+            % Check if version is 2018b or later. From this point the
+            % plot toolbar buttons moved into the plot.
+            v=version();
+            isBefore2018b = str2double(v(1:3))<9.5;           
+            
+            % Hide property inspector
+            try 
+                hToolbutton = findall(hToolbar,'tag','Standard.OpenInspector');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';            
+            catch
+                % do nothing
+            end
+                
+            
+            % Add tool bar          
+            if isBefore2018b
+                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOn');                        
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';	
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOff');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Never';	
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.Brushing');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.DataCursor');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.Rotate');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Never';
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.Pan');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomOut');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomIn');            
+                hToolbutton.Visible = 'off';
+                hToolbutton.UserData = 'Plot';
+                hToolbutton.Separator = 'off';
+                uipushtool(hToolbar,'cdata',cdata, 'tooltip','Export displayed plot to PNG file ...', ...
+                    'ClickedCallback',@this.onExportPlot, ...
+                    'tag','Export.plot', 'Visible','off');
+            end
+
+            hToolbutton = findall(hToolbar,'tag','Standard.NewFigure');            
+            hToolbutton.Visible = 'on';
+            hToolbutton.UserData = 'Always';	                            
+            set(hToolbutton, 'ClickedCallback',@this.onNew, 'TooltipString','Start a new project.');            
+            
+            hToolbutton = findall(hToolbar,'tag','Standard.FileOpen');            
+            hToolbutton.Visible = 'on';
+            hToolbutton.UserData = 'Always';	                            
+            set(hToolbutton, 'ClickedCallback',@this.onOpen, 'TooltipString','Open a new project ...');
+
+            hToolbutton = findall(hToolbar,'tag','Standard.SaveFigure');            
+            hToolbutton.Visible = 'on';
+            hToolbutton.UserData = 'Always';	                            
+            set(hToolbutton, 'ClickedCallback',@this.onSave, 'TooltipString','Save current project ...');            
+            
+            % Get hidden state and show hidden children
+            oldState = get(0,'ShowHiddenHandles');
+            set(0,'ShowHiddenHandles','on')
+            
+            % Add new button for 'st folder and shift new button to the far left.
+            icon = 'foldericon.gif';
+            [cdata,map] = imread(icon);
+            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
+            cdata = ind2rgb(cdata,map);
+            uipushtool(hToolbar,'cdata',cdata, 'tooltip','Set the project folder ...','Tag','Standard.SetFolder','ClickedCallback',@this.onSetProjectFolder);
+            hToolbutton = findall(hToolbar);            
+            set(hToolbar,'Children',hToolbutton([3:length(hToolbutton), 2]));            
+                                    
+            % Add new button for help.
+            icon = 'helpicon.gif';
+            [cdata,map] = imread(icon);
+            map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
+            cdata = ind2rgb(cdata,map);
+            uipushtool(hToolbar,'cdata',cdata, 'tooltip','Open help for the current tab ...', 'ClickedCallback',@this.onDocumentation);
+            
+            % Add separator.
+            if isBefore2018b
+                hToolbar.Children(13).Separator = 'on';
+            end
+            
+            % Reset hidden state
+            set(0,'ShowHiddenHandles',oldState);
+            
+            %Create Panels for different windows       
+            this.figure_Layout = uiextras.TabPanel( 'Parent', this.Figure, 'Padding', ...
+                5, 'TabSize',127,'FontSize',8);
+            this.tab_Project.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ProjectDescription');            
+            this.tab_DataPrep.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','DataPreparation');
+            this.tab_ModelConstruction.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelConstruction');
+            this.tab_ModelCalibration.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelCalibration');
+            this.tab_ModelSimulation.Panel = uiextras.Panel( 'Parent', this.figure_Layout, 'Padding', 5, 'Tag','ModelSimulation');
+            this.figure_Layout.TabNames = {'Project Description', 'Outlier Removal','Model Construction', 'Model Calibration','Model Simulation'};
+            this.figure_Layout.SelectedChild = 1;
+           
+%%          Layout Tab1 - Project description
+            %------------------------------------------------------------------
+            % Project title
+            hbox1t1 = uiextras.VBoxFlex('Parent', this.tab_Project.Panel,'Padding', 3, 'Spacing', 3);
+            uicontrol(hbox1t1,'Style','text','String','Project Title: ','HorizontalAlignment','left', 'Units','normalized');            
+            this.tab_Project.project_name = uicontrol(hbox1t1,'Style','edit','HorizontalAlignment','left', 'Units','normalized',...
+                'TooltipString','Input a project title. This is an optional input to assist project management.');            
+            
+            % Empty row spacer
+            uicontrol(hbox1t1,'Style','text','String','','Units','normalized');                      
+                        
+            % Project description
+            uicontrol(hbox1t1,'Style','text','String','Project Description: ','HorizontalAlignment','left', 'Units','normalized');                      
+            this.tab_Project.project_description = uicontrol(hbox1t1,'Style','edit','HorizontalAlignment','left', 'Units','normalized', ...
+                'Min',1,'Max',100,'TooltipString','Input an extended project description. This is an optional input to assist project management.');            
+            
+            % Set sizes
+            set(hbox1t1, 'Sizes', [20 20 20 20 -1]);            
+            
+            
+
+%%          Layout Tab2 - Data Preparation
+            % -----------------------------------------------------------------
+            % Declare panels        
+            hbox1t2 = uiextras.HBoxFlex('Parent', this.tab_DataPrep.Panel,'Padding', 3, 'Spacing', 3,'Tag','Outlier detection outer hbox');
+            vbox1t2 = uiextras.VBox('Parent',hbox1t2,'Padding', 3, 'Spacing', 3);
+            %hbox3t2 = uiextras.HButtonBox('Parent',vbox1t2,'Padding', 3, 'Spacing', 3);                
+            hbox3t2 = uiextras.HBox('Parent',vbox1t2,'Padding', 3, 'Spacing', 3);
+            hboxBtn1 = uiextras.HButtonBox('Parent',hbox3t2 ,'Padding', 3, 'Spacing', 3);             
+            hboxBtn2 = uiextras.HButtonBox('Parent',hbox3t2 ,'Padding', 3, 'Spacing', 3);              
+            
+            % Create table for model construction
+            cnames1t2 ={'<html><center>Select<br />Bore</center></html>', ...                                                 
+                        '<html><center>Obs. Head<br />File</center></html>', ...   
+                        '<html><center>Bore<br />ID</center></html>', ...   
+                        '<html><center>Bore Depth<br />(Below Surface)</center></html>', ...   
+                        '<html><center>Surface<br />Elevation</center></html>', ...   
+                        '<html><center>Casing Length<br />(above surface)</center></html>', ...   
+                        '<html><center>Construction<br />Date (dd/mm/yy)</center></html>', ...   
+                        '<html><center>Check<br />Start Date?</center></html>', ...                           
+                        '<html><center>Check<br />End Date?</center></html>', ...                                                   
+                        '<html><center>Check Above<br />Bore Depth?</center></html>', ...                      
+                        '<html><center>Check Below <br />Casing?</center></html>', ...                      
+                        '<html><center>Threshold for Max. Daily abs(Head)<br />Change</center></html>', ...
+                        '<html><center>Threshold Duration for<br />Constant Head (days)?</center></html>', ...
+                        '<html><center>Auto-Outlier<br />Num. St. dev?</center></html>', ...                        
+                        '<html><center>Auto-Outlier foward<br />backward analysis?</center></html>', ...                        
+                        '<html><center>Analysis<br />Status</center></html>', ...
+                        '<html><center>No. Erroneous<br />Obs.</center></html>', ...
+                        '<html><center>No. Outlier<br />Obs.</center></html>', ...
+                        };
+            cformats1t2 = {'logical', 'char', 'char','numeric','numeric','numeric','char','logical','logical','logical','logical','numeric','numeric','numeric','logical','char','char','char'};
+            cedit1t2 = logical([1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0]);            
+            rnames1t2 = {[1]};
+            toolTipStr = ['<html>Optional detection and removal<br>' ...
+                  'of erroneous groundwater level observations.</ul>'];
+            
+            % Initialise data.
+            data = {false, '', '',0, 0, 0, '01/01/1900',true, true, true, true, 10, 120, 4, 1,...
+                '<html><font color = "#FF0000">Bore not analysed.</font></html>', ...
+                ['<html><font color = "#808080">','(NA)','</font></html>'], ...
+                ['<html><font color = "#808080">','(NA)','</font></html>']};
+            
+            % Add table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.            
+%             this.tab_DataPrep.Table = createTable(vbox1t2,cnames1t2,data, false, ...
+%                 'ColumnEditable',cedit1t2,'ColumnFormat',cformats1t2,'RowName', rnames1t2, ...
+%                 'CellSelectionCallback', @this.dataPrep_tableSelection,...
+%                 'Tag','Data Preparation', 'TooltipString', toolTipStr);     
+
+            this.tab_DataPrep.Table = uitable(vbox1t2 , 'ColumnName', cnames1t2,'Data', data, ...
+                'ColumnEditable',cedit1t2,'ColumnFormat',cformats1t2,'RowName', rnames1t2, ...
+                'CellSelectionCallback', @this.dataPrep_tableSelection,...
+                'Tag','Data Preparation', 'TooltipString', toolTipStr);
+                        
+%             % Find java sorting object in table
+%             try
+%                 this.figure_Layout.Selection = 2;
+%                 drawnow update;
+%                 jscrollpane = findjobj(this.tab_DataPrep.Table);
+%                 jtable = jscrollpane.getViewport.getView;
+% 
+%                 % Turn the JIDE sorting on
+%                 jtable.setSortable(true);
+%                 jtable.setAutoResort(true);
+%                 jtable.setMultiColumnSortable(true);
+%                 jtable.setPreserveSelectionsAfterSorting(true);            
+%             catch
+%                 warndlg('Creating of the GUI row-sorting module failed for the data preparation table.');
+%             end            
+                        
+            % Add buttons to top left panel               
+            uicontrol('Parent',hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Data Preparation', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Use this feature to efficiently analyse a large number of bores.') );
+            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Data Preparation', 'TooltipString', sprintf('Export a .csv file of the table below.') );
+            uicontrol('Parent',hboxBtn2,'String','Analyse Selected Bores','Callback', @this.onAnalyseBores, 'Tag','Data Preparation', 'TooltipString', sprintf('Use the tick-box below to select the models to analyse then click here. \n After analysing, the status is given in the right most column.') );            
+            uicontrol('Parent',hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Data Preparation', 'TooltipString', sprintf('Export a .csv file of the analyses results. \n After analysing, the .csv file can be used in the time-series modelling.') );            
+            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
+                'Callback', {@this.onChangeTableWidth, 'Outlier detection outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
+            hboxBtn1.ButtonSize(1) = 225;
+            hboxBtn2.ButtonSize(1) = 225;
+            set(hbox3t2,'Sizes',[90 -1])  
+            
+            % Create vbox for the various model options
+            this.tab_DataPrep.modelOptions.vbox = uiextras.VBoxFlex('Parent',hbox1t2,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
+            
+            % Add model options panel for bore IDs
+            dynList = [];
+            vbox4t2 = uiextras.VBox('Parent',this.tab_DataPrep.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
+            uicontrol( 'Parent', vbox4t2,'Style','text','String',sprintf('%s\n%s%s','Please select the Bore ID for the analysis:'), 'Units','normalized');            
+            this.tab_DataPrep.modelOptions.boreIDList = uicontrol('Parent',vbox4t2,'Style','list','BackgroundColor','w', ...
+                'String',dynList(:),'Value',1,'Callback',...
+                @this.dataPrep_optionsSelection, 'Units','normalized');     
+            
+             % Create vbox for showing a table of results and plotting hydrographs
+            this.tab_DataPrep.modelOptions.resultsOptions.box = uiextras.VBoxFlex('Parent', this.tab_DataPrep.modelOptions.vbox,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
+            panelt2 = uipanel('Parent',this.tab_DataPrep.modelOptions.resultsOptions.box);
+            this.tab_DataPrep.modelOptions.resultsOptions.plots = axes( 'Parent', panelt2); 
+
+            % Add table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.            
+%             this.tab_DataPrep.modelOptions.resultsOptions.table = createTable(this.tab_DataPrep.modelOptions.resultsOptions.box, ...
+%                 {'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'}, ... 
+%                 cell(0,13), false, ...
+%                 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','logical','logical','logical','logical','logical','logical'}, ...
+%                 'ColumnEditable', [false(1,6) true(1,7)], ...
+%                 'Tag','Data Preparation - results table', ...
+%                 'CellEditCallback', @this.dataPrep_resultsTableEdit, ...,
+%                 'TooltipString', 'Results data from the bore data analysis for erroneous observations and outliers.');   
+%             
+%             
+            this.tab_DataPrep.modelOptions.resultsOptions.table = uitable(this.tab_DataPrep.modelOptions.resultsOptions.box, ...
+                'ColumnName',{'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'}, ... 
+                'Data',cell(0,13), ...
+                'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','logical','logical','logical','logical','logical','logical'}, ...
+                'ColumnEditable', [false(1,6) true(1,7)], ...
+                'Tag','Data Preparation - results table', ...
+                'CellEditCallback', @this.dataPrep_resultsTableEdit, ...,
+                'TooltipString', 'Results data from the bore data analysis for erroneous observations and outliers.');   
+                        
+            
+            % Resize the panels            
+            set(vbox1t2, 'Sizes', [30 -1]);
+            set(vbox4t2, 'Sizes', [30 -1]);            
+            set(this.tab_DataPrep.modelOptions.resultsOptions.box, 'Sizes',[-1 -1]);
+            set(this.tab_DataPrep.modelOptions.vbox, 'Sizes',[0 0]);
+            
+            
+            % Hide the panel for the analysis opens and results
+            set(this.tab_DataPrep.modelOptions.vbox,'Heights',[0 0]);
+            
+%           Add context menu
+            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
+            
+            % Add items
+            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
+            
+            % Attach menu to the construction table
+            set(this.tab_DataPrep.Table,'UIContextMenu',this.Figure.UIContextMenu);
+                        
+            % Add table name to .UserData
+            set(this.tab_DataPrep.Table.UIContextMenu,'UserData','this.tab_DataPrep.Table');            
+            
+%%          Layout Tab3 - Model Construction
+            %------------------------------------------------------------------
+            % Declare panels        
+            hbox1t3 = uiextras.HBoxFlex('Parent', this.tab_ModelConstruction.Panel,'Padding', 3, 'Spacing', 3, 'Tag', 'Model Construction outer hbox');
+            vbox1t3 = uiextras.VBox('Parent',hbox1t3,'Padding', 3, 'Spacing', 3);
+            hbox1t4 = uiextras.HBox('Parent',vbox1t3,'Padding', 3, 'Spacing', 3);
+            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t4 ,'Padding', 3, 'Spacing', 3);             
+            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t4 ,'Padding', 3, 'Spacing', 3);             
+            
+            % Create table for model construction
+            cnames1t3 ={'<html><center>Select<br />Model</center></html>', ... 
+                        '<html><center>Model<br />Label</center></html>', ...   
+                        '<html><center>Obs. Head<br />File</center></html>', ...   
+                        '<html><center>Forcing Data<br />File</center></html>', ...   
+                        '<html><center>Coordinates<br />File</center></html>', ...   
+                        '<html><center>Bore<br />ID</center></html>', ...   
+                        '<html><center>Model<br />Type</center></html>', ...                           
+                        '<html><center>Model<br />Options</center></html>', ...                           
+                        '<html><center>Build<br />Status</center></html>'};
+            cformats1t3 = {'logical', 'char', 'char','char','char','char',this.modelTypes,'char','char'};
+            cedit1t3 = logical([1 1 1 1 1 1 1 1 0]);            
+            rnames1t3 = {[1]};
+            toolTipStr = 'Define the model label, input data, bore ID and model structure for each model.';
+            
+            
+            % Initialise data
+            data = cell(1,9);
+            data{1,9} = '<html><font color = "#FF0000">Model not built.</font></html>';
+
+            % Add table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.
+%             this.tab_ModelConstruction.Table = createTable(vbox1t3,cnames1t3,data, false, ...
+%                 'ColumnEditable',cedit1t3,'ColumnFormat',cformats1t3,'RowName', rnames1t3, ...
+%                 'CellSelectionCallback', @this.modelConstruction_tableSelection,...
+%                 'CellEditCallback', @this.modelConstruction_tableEdit,...
+%                 'Tag','Model Construction', ...
+%                 'TooltipString', toolTipStr);                        
+%             
+            this.tab_ModelConstruction.Table = uitable(vbox1t3,'ColumnName',cnames1t3,'Data',data,  ...
+                'ColumnEditable',cedit1t3,'ColumnFormat',cformats1t3,'RowName', rnames1t3, ...
+                'CellSelectionCallback', @this.modelConstruction_tableSelection,...
+                'CellEditCallback', @this.modelConstruction_tableEdit,...
+                'Tag','Model Construction', ...
+                'TooltipString', toolTipStr);                        
+                        
+            
+%             % Find java sorting object in table
+%             try
+%                 this.figure_Layout.Selection = 3;
+%                 drawnow update;
+%                 jscrollpane = findjobj(this.tab_ModelConstruction.Table);
+%                 jtable = jscrollpane.getViewport.getView;
+% 
+%                 % Turn the JIDE sorting on
+%                 jtable.setSortable(true);
+%                 jtable.setAutoResort(true);
+%                 jtable.setMultiColumnSortable(true);
+%                 jtable.setPreserveSelectionsAfterSorting(true);            
+%             catch
+%                 warndlg('Creating of the GUI row-sorting module failed for the model construction table.');
+%             end
+                        
+            % Add buttons to top left panel               
+            uicontrol('Parent',hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Model Construction', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Use this feature to efficiently build a large number of models.') );
+            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Construction', 'TooltipString', sprintf('Export a .csv file of the table below.') );
+            uicontrol('Parent',hboxBtn2,'String','Build Selected Models','Callback', @this.onBuildModels, 'Tag','Model Construction', 'TooltipString', sprintf('Use the tick-box below to select the models to build then click here. \n After building, the status is given in the right most column.') );                        
+            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
+                'Callback', {@this.onChangeTableWidth, 'Model Construction outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
+            hboxBtn1.ButtonSize(1) = 225;
+            hboxBtn2.ButtonSize(1) = 225;
+            set(hbox1t4,'Sizes',[60 -1])
+            
+            % Create vbox for the various model options
+            this.tab_ModelConstruction.modelOptions.vbox = uiextras.VBoxFlex('Parent',hbox1t3,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
+            
+            % Add model options panel for bore IDs
+            dynList = [];
+            vbox4t3 = uiextras.VBox('Parent',this.tab_ModelConstruction.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
+            uicontrol( 'Parent', vbox4t3,'Style','text','String',sprintf('%s\n%s%s','Please select the Bore ID for the model:'), 'Units','normalized');            
+            this.tab_ModelConstruction.boreIDList = uicontrol('Parent',vbox4t3,'Style','list','BackgroundColor','w', ...
+                'String',dynList(:),'Value',1,'Callback',...
+                @this.modelConstruction_optionsSelection, 'Units','normalized');            
+
+            % Add model options panel for decriptions of each model type
+            dynList = [];
+            vbox5t3 = uiextras.VBox('Parent',this.tab_ModelConstruction.modelOptions.vbox, 'Padding', 3, 'Spacing', 3, 'Visible','on');
+            uicontrol( 'Parent', vbox5t3,'Style','text','String',sprintf('%s\n%s%s','Below is a decsription of the selected model type:'), 'Units','normalized');            
+            this.tab_ModelConstruction.modelDescriptions = uicontrol( 'Parent', vbox5t3,'Style','text','String','(No model type selected.)', 'HorizontalAlignment','left','Units','normalized');                        
+            set(vbox5t3, 'Sizes', [30 -1]);
+            
+            % Resize the panels
+            set(vbox1t3, 'Sizes', [30 -1]);
+            set(hbox1t3, 'Sizes', [-2 -1]);
+            set(vbox4t3, 'Sizes', [30 -1]);            
+            
+            % Build model options for each model type                
+            includeModelOption = false(length(this.modelTypes),1);
+            for i=1:length(this.modelTypes);
+                switch this.modelTypes{i}
+                    case 'model_TFN'
+                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox = uiextras.HBox('Parent',this.tab_ModelConstruction.modelOptions.vbox,'Padding', 3, 'Spacing', 3);
+                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons = uiextras.VButtonBox('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox,'Padding', 3, 'Spacing', 3);
+                        uicontrol('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons,'String','<','Callback', @this.onApplyModelOptions, 'TooltipString','Copy model option to current model.');
+                        uicontrol('Parent',this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).buttons,'String','<<','Callback', @this.onApplyModelOptions_selectedBores, 'TooltipString','Copy model option to selected models (of the current model type).');
+                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).obj = model_TFN_gui( this.tab_ModelConstruction.modelTypes.model_TFN.hbox);
+                        this.tab_ModelConstruction.modelTypes.(this.modelTypes{i}).hbox.Widths=[40 -1];
+                        includeModelOption(i) = true;
+                    case 'ExpSmooth'  
+                        this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox = uiextras.HBox('Parent',this.tab_ModelConstruction.modelOptions.vbox,'Padding', 3, 'Spacing', 3);                        
+                        uicontrol( 'Parent', this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox,'Style','text', ...
+                        'String','The exponential smoothing model does not have any model option.','HorizontalAlignment','center', 'Units','normalized');            
+                    
+                        % Setup GUI.
+                        this.tab_ModelConstruction.modelTypes.ExpSmooth.obj = ExpSmooth_gui( this.tab_ModelConstruction.modelTypes.ExpSmooth.hbox);
+                        
+                        includeModelOption(i) = true;
+                        
+                    otherwise
+                        warndlg({['The following model type is not integrated into the user interface: ', this.modelTypes{i}], ...
+                            '', 'It could not be included in the user interface.'},'Model type unavailable ...');
+                        includeModelOption(i) = false;
+                end                
+            end
+            
+            % Redefine model options to include only those that are
+            % established in the user interface.
+            this.tab_ModelConstruction.Table.ColumnFormat{7} = this.modelTypes(includeModelOption);
+            
+            % Hide all modle option vboxes 
+            this.tab_ModelConstruction.modelOptions.vbox.Heights = zeros(size(this.tab_ModelConstruction.modelOptions.vbox.Heights));
+
+%           Add context menu
+            % Create menu
+            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
+            
+            % Add items
+            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
+                        
+            % Attach menu to the construction table
+            set(this.tab_ModelConstruction.Table,'UIContextMenu',this.Figure.UIContextMenu);
+                        
+            % Add table name to .UserData
+            set(this.tab_ModelConstruction.Table.UIContextMenu,'UserData','this.tab_ModelConstruction.Table');
+            
+            
+%%          Layout Tab4 - Calibrate models
+            %------------------------------------------------------------------
+            hbox1t4 = uiextras.HBoxFlex('Parent', this.tab_ModelCalibration.Panel,'Padding', 3, 'Spacing', 3,'Tag','Model Calibration outer hbox');
+            vbox1t4 = uiextras.VBox('Parent',hbox1t4,'Padding', 3, 'Spacing', 3);
+            hbox1t5 = uiextras.HBox('Parent',vbox1t4,'Padding', 3, 'Spacing', 3);
+            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t5 ,'Padding', 3, 'Spacing', 3);             
+            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t5 ,'Padding', 3, 'Spacing', 3);             
+            
+            
+            % Add table
+            cnames1t4 = {   '<html><center>Select<br />Model</center></html>', ...   
+                            '<html><center>Model<br />Label</center></html>', ...   
+                            '<html><center>Bore<br />ID</center></html>', ...   
+                            '<html><center>Head<br />Start Date</center></html>', ...
+                            '<html><center>Head<br />End Date</center></html>', ...
+                            '<html><center>Calib.<br />Start Date</center></html>', ...
+                            '<html><center>Calib.<br />End Date</center></html>', ...
+                            '<html><center>Calib.<br />Method</center></html>', ...
+                            '<html><center>Calib.<br />Status</center></html>', ...
+                            '<html><center>Calib.<br />Period CoE</center></html>', ...
+                            '<html><center>Eval. Period<br />Unbiased CoE</center></html>', ...
+                            '<html><center>Calib.<br />Period AICc</center></html>', ...
+                            '<html><center>Calib.<br />Period BIC</center></html>'};
+            data = cell(0,14);            
+            rnames1t4 = {[1]};
+            cedit1t4 = logical([1 0 0 0 0 1 1 1 0 0 0 0 0]);            
+            %cformats1t4 = {'logical', 'char', 'char','char','char','char','char', {'SP-UCI' 'CMA-ES' 'DREAM' 'Multi-model'},'char','char','char','char','char'};
+            cformats1t4 = {'logical', 'char', 'char','char','char','char','char', {'SP-UCI' 'CMA-ES' 'DREAM' },'char','char','char','char','char'};
+      
+            toolTipStr = 'Calibration of models that have been successfully built.';              
+            
+            % Add table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.
+            this.tab_ModelCalibration.resultsOptions.currentTab = [];
+            this.tab_ModelCalibration.resultsOptions.currentPlot = [];                        
+%             this.tab_ModelCalibration.Table = createTable(vbox1t4,cnames1t4, data, false, ...
+%                 'ColumnFormat', cformats1t4, 'ColumnEditable', cedit1t4, ...
+%                 'RowName', rnames1t4, 'Tag','Model Calibration', ...
+%                 'CellSelectionCallback', @this.modelCalibration_tableSelection,...
+%                 'TooltipString', toolTipStr, ...
+%                 'Interruptible','off');              
+            this.tab_ModelCalibration.Table = uitable(vbox1t4,'ColumnName',cnames1t4,'Data',data,  ...
+                'ColumnFormat', cformats1t4, 'ColumnEditable', cedit1t4, ...
+                'RowName', rnames1t4, 'Tag','Model Calibration', ...
+                'CellSelectionCallback', @this.modelCalibration_tableSelection,...
+                'TooltipString', toolTipStr, ...
+                'Interruptible','off');   
+            
+%             % Find java sorting object in table
+%             try
+%                 this.figure_Layout.Selection = 4;
+%                 drawnow update;
+%                 jscrollpane = findjobj(this.tab_ModelCalibration.Table);
+%                 jtable = jscrollpane.getViewport.getView;
+% 
+%                 % Turn the JIDE sorting on
+%                 jtable.setSortable(true);
+%                 jtable.setAutoResort(true);
+%                 jtable.setMultiColumnSortable(true);
+%                 jtable.setPreserveSelectionsAfterSorting(true);            
+%             catch
+%                 warndlg('Creating of the GUI row-sorting module failed for the model calibration table.');
+%             end                
+            
+            % Add button for calibration
+            uicontrol('Parent',hboxBtn2,'String','Import Table Data','Callback', @this.onImportTable, 'Tag','Model Calibration', 'TooltipString', sprintf('Import a .csv file of table data to the table below. \n Only rows with a model label and bore ID matching a row within the table will be imported.') );
+            uicontrol('Parent',hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Calibration', 'TooltipString', sprintf('Export a .csv file of the table below.') );            
+            uicontrol('Parent',hboxBtn2,'String','Calibrate Selected Models','Callback', @this.onCalibModels,'Tag','useLocal', 'TooltipString', sprintf('Use the tick-box below to select the models to calibrate then click here. \n During and after calibration, the status is given in the 9th column.') );            
+            uicontrol('Parent',hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Model Calibration', 'TooltipString', sprintf('Export a .csv file of the calibration results from all models.') );            
+            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
+                'Callback', {@this.onChangeTableWidth, 'Model Calibration outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
+            hboxBtn1.ButtonSize(1) = 225;
+            hboxBtn2.ButtonSize(1) = 225;
+            set(hbox1t5,'Sizes',[90 -1])                 
+            
+            % Size boxe
+            set(vbox1t4, 'Sizes', [30 -1]);
+                        
+            % Create vbox for the various model options            
+            resultsvbox = uiextras.VBoxFlex('Parent',hbox1t4,'Padding', 3, 'Spacing', 3, 'DividerMarkings','off');
+                        
+            % Add tabs for various types of results            
+            this.tab_ModelCalibration.resultsTabs = uiextras.TabPanel( 'Parent',resultsvbox, 'Padding', 5, 'TabSize',127,'FontSize',8);
+            this.tab_ModelCalibration.resultsOptions.calibPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
+                'Tag','CalibrationResultsTab');            
+            this.tab_ModelCalibration.resultsOptions.forcingPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
+                'Tag','ForcingDataTab');          
+            this.tab_ModelCalibration.resultsOptions.paramsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
+                'Tag','ParametersTab');
+            this.tab_ModelCalibration.resultsOptions.derivedParamsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
+                'Tag','DerivedParametersTab');                         
+            this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel = uiextras.Panel( 'Parent', this.tab_ModelCalibration.resultsTabs, 'Padding', 5, ...
+                'Tag','ModelSpecifics');                       
+            this.tab_ModelCalibration.resultsTabs.TabNames = {'Calib. Results','Forcing Data','Parameters', ...
+                'Derived Parameters','Model Specifics'};
+            this.tab_ModelCalibration.resultsTabs.SelectedChild = 1;
+                        
+            % Build calibration results tab            
+            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.calibPanel,'Padding', 3, 'Spacing', 3);
+            resultsvboxTable = uiextras.Grid('Parent', resultsvbox ,'Padding', 3, 'Spacing', 3);            
+            tbl = uitable(resultsvboxTable , 'ColumnName',{'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head','Model Err.','Noise Lower','Noise Upper'}, ... 
+                'Data',cell(0,11), 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','logical','numeric','numeric','numeric','numeric'}, ...
+                'ColumnEditable', true(1,11), 'Tag','Model Calibration - results table', ...
+                'TooltipString',['<html>This table shows the calibration & evaluation results. <br>', ... 
+                'A range of plots can be used to explore aspects of the calibration.']);   
+
+            % Build calibration results table contect menu
+            contextMenu = uicontextmenu(this.Figure,'Visible','on');
+            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - results table export', 'Callback',@this.onExportResults);                 
+            set(tbl,'UIContextMenu',contextMenu);
+            set(tbl.UIContextMenu,'UserData','Model Calibration - results table');
+            
+            resultsvboxDropDown = uiextras.Grid('Parent', resultsvboxTable ,'Padding', 3, 'Spacing', 3);            
+            uicontrol(resultsvboxDropDown,'Style','text','String','Select the plot type:','HorizontalAlignment','left' );
+            uicontrol(resultsvboxDropDown,'Style','popupmenu', ...
+                'String',{  'Time-series of Heads', ...
+                            'Time-series of Residuals', ...
+                            'Histogram of Calib. Residuals', ...
+                            'Histogram of Eval. Residuals', ...
+                            'Obs. Head vs Modelled Head', ...
+                            'Obs. Head vs Residuals', ...
+                            'Variogram of Residuals', ...
+                            '(none)'}, ...
+                'Value',1,'Callback', @this.modelCalibration_onUpdatePlotSetting);                                                
+            set(resultsvboxTable, 'ColumnSizes', -1, 'RowSizes', [-1 20] );
+            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
+                'Tag','Model Calibration - results plot');  
+            set(resultsvbox, 'Sizes', [-1 -1]);
+            
+            % Building forcing data
+            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.forcingPanel,'Padding', 3, 'Spacing', 3);
+            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);
+            uicontrol(resultsvboxOptions,'Style','text','String','Time step:','HorizontalAlignment','left' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'daily','weekly','monthly','quarterly','annual','full-record'}, ...
+                'Value',1, 'Callback', @this.modelCalibration_onUpdateForcingData, ...
+                'TooltipString', ['<html>Select the time-scale for presentation of the forcing data.  <br>', ...
+                'Note, all time-scales >daily are reported as daily mean.'],'HorizontalAlignment','right');    
+            uicontrol(resultsvboxOptions,'Style','text','String','Time step metric:','HorizontalAlignment','left' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', ...
+                'String',{'sum','mean','st. dev.','variance','skew','min','5th %ile','10th %ile','25th %ile','50th %ile','75th %ile','90th %ile','95th %ile', 'max', ...
+                'inter-quantile range', 'No. zero days', 'No. <0 days', 'No. >0 day'}, ...
+                'Value',1, 'Callback', @this.modelCalibration_onUpdateForcingData, ...
+                'TooltipString', ['<html>Select calculation to apply when aggregating the daily data.  <br>', ...
+                'Note, all plots will use the resulting data.'],'HorizontalAlignment','right');    
+
+            uicontrol(resultsvboxOptions,'Style','text','String','Start date:','HorizontalAlignment','left');
+            uicontrol(resultsvboxOptions,'Style','edit','String','01/01/0001','TooltipString','Filter the data and plot to that above a data (as dd/mm/yyyy).', 'Callback', @this.modelCalibration_onUpdateForcingData );
+
+            uicontrol(resultsvboxOptions,'Style','text','String','End date:','HorizontalAlignment','left');
+            uicontrol(resultsvboxOptions,'Style','edit','String','31/12/9999','TooltipString','Filter the data and plot to that below a data (as dd/mm/yyyy).', 'Callback', @this.modelCalibration_onUpdateForcingData );
+                   
+            set(resultsvboxOptions, 'ColumnSizes', [-1 -1 -1 -1 -1 -1 -1 -1], 'RowSizes', [25] );
+            
+            tbl = uitable(resultsvbox, 'ColumnName',{'Year','Month', 'Day'}, ... 
+                'Data',cell(0,3), 'ColumnFormat', {'numeric','numeric','numeric'}, ...
+                'ColumnEditable', true(1,3), 'Tag','Model Calibration - forcing table', ...
+                'TooltipString',['<html>This table allows exploration of the forcing data used for the calibration <br>', ... 
+                     '& evaluation and forcing data derived from the model (e.g. from a soil moisture <br>', ... 
+                     'transformation model). Use the table to explore forcing dynamics at a range of time-steps.']);  
+            contextMenu = uicontextmenu(this.Figure,'Visible','on');
+            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - forcing table export', 'Callback',@this.onExportResults);                 
+            set(tbl,'UIContextMenu',contextMenu);
+            set(tbl.UIContextMenu,'UserData','Model Calibration - forcing table');
+                 
+                 
+            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);
+            uicontrol(resultsvboxOptions,'Style','text','String','Plot type:' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'line','scatter','bar','histogram','cdf','box-plot (daily metric)', ...
+                'box-plot (monthly metric)','box-plot (quarterly metric)','box-plot (annually metric)'}, 'Value',1,'HorizontalAlignment','right', ...
+                'Callback',@this.modelCalibration_onUpdateForcingPlotType);    
+            uicontrol(resultsvboxOptions,'Style','text','String','x-axis:' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'Date', '(none)'}, 'Value',1,'HorizontalAlignment','right');    
+            uicontrol(resultsvboxOptions,'Style','text','String','y-axis:' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'Date', '(none)'}, 'Value',1,'HorizontalAlignment','right');                
+            uicontrol(resultsvboxOptions,'Style','pushbutton','String','Build plot','Callback', @this.modelCalibration_onUpdateForcingPlot, ...
+                'Tag','Model Calibration - forcing plot', 'TooltipString', 'Build the forcing plot.','ForegroundColor','blue');            
+            set(resultsvboxOptions, 'ColumnSizes', [-1 -2 -1 -2 -1 -2 -2], 'RowSizes', [25] );
+            %hbuttons = uiextras.HButtonBox('Parent',resultsvboxOptions,'Padding', 3, 'Spacing', 3);  
+            %uicontrol('Parent',hbuttons,'String','Build plot','Callback', @this.modelCalibration_onUpdatePlotSetting, ...
+            %    'Tag','Model Calibration - forcing plot', 'TooltipString', 'Build the forcing plot.','ForegroundColor','blue');            
+            
+            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1] );             
+            set(resultsvbox, 'Sizes', [30 -1 30 -1]);
+            
+            % set selected tab and plot to 
+            this.tab_ModelCalibration.resultsOptions.currentTab = 1;
+            this.tab_ModelCalibration.resultsOptions.currentPlot = 7;
+            
+            % Build parameters tab
+            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.paramsPanel,'Padding', 3, 'Spacing', 3);  
+            tbl = uitable(resultsvbox, ...
+                'ColumnName',{'Component Name','Parameter Name','Value'}, ... 
+                'ColumnFormat', {'char','char','numeric'}, ...
+                'ColumnEditable', true(1,3), ...
+                'Tag','Model Calibration - parameter table', ...
+                'TooltipString',['<html>This table allows exploration of the calibrated model parameters. <br>', ... 
+                 'Use this table to inform assessment of the validity of the model.']);            
+             
+            contextMenu = uicontextmenu(this.Figure,'Visible','on');
+            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - parameter table export', 'Callback',@this.onExportResults);                 
+            set(tbl,'UIContextMenu',contextMenu);
+            set(tbl.UIContextMenu,'UserData','Model Calibration - parameter table');                         
+
+            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
+                'Tag','Model Calibration - parameter plot');  
+            set(resultsvbox, 'Sizes', [-1 -2]);
+            
+            % Build derived parameters tab
+            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.derivedParamsPanel,'Padding', 3, 'Spacing', 3);  
+            tbl = uitable(resultsvbox, ...
+                'ColumnName',{'Component Name','Parameter Name','Derived Value'}, ... 
+                'ColumnFormat', {'char','char','numeric'}, ...
+                'ColumnEditable', true(1,3), ...
+                'Tag','Model Calibration - derived parameter table', ...
+                'TooltipString',['<html>This table allows exploration of the parameters derived from the calibrated <br>', ... 
+                 'parameters. The parameters shown are dependent upon the model structure. <br>', ...
+                 'For example, TFN models having using, say, the Ferris Knowles weighting function <br>', ...
+                 'will show the transmissivity and storativity.']); 
+             
+            contextMenu = uicontextmenu(this.Figure,'Visible','on');
+            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - derived parameter table export', 'Callback',@this.onExportResults);                 
+            set(tbl,'UIContextMenu',contextMenu);
+            set(tbl.UIContextMenu,'UserData','Model Calibration - derived parameter table');                         
+             
+            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
+                'Tag','Model Calibration - derived parameter plot');  
+            set(resultsvbox, 'Sizes', [-1 -2]);
+                        
+            % Build model specific outputs tab.
+            resultsvbox= uiextras.VBoxFlex('Parent', this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel,'Padding', 3, 'Spacing', 3);                        
+            resultsvboxOptions = uiextras.Grid('Parent', resultsvbox,'Padding', 3, 'Spacing', 3);            
+            uicontrol(resultsvboxOptions,'Style','text','String','Select model specific output:' );
+            uicontrol(resultsvboxOptions,'Style','popupmenu', 'String',{'(none)'},'Value',1, ...
+                'Tag','Model Calibration - derived data dropdown', ...
+                'Callback', @this.modelCalibration_onUpdateDerivedData);                         
+            tbl = uitable(resultsvbox, ...
+                'ColumnName',{'Variabe 1','Variabe 2'}, ... 
+                'ColumnFormat', {'numeric','numeric'}, ...
+                'ColumnEditable', true(1,2), ...
+                'Tag','Model Calibration - derived data table', ...
+                'TooltipString',['<html>This table allows exploration of the data derived from the calibrated. <br>', ... 
+                 'The data shown are very dependent upon the model structure. <br>', ...
+                 'For example, TFN models having using, say, the Pearsons weighting function <br>', ...
+                 'will show the weighting data and a plot.']);     
+            contextMenu = uicontextmenu(this.Figure,'Visible','on');
+            uimenu(contextMenu,'Label','Export table data ...','Tag','Model Calibration - derived data table export', 'Callback',@this.onExportResults);                 
+            set(tbl,'UIContextMenu',contextMenu);
+            set(tbl.UIContextMenu,'UserData','Model Calibration - derived data table export');               
+            uiextras.Panel('Parent', resultsvbox,'BackgroundColor',[1 1 1], ...
+                'Tag','Model Calibration - derived data plot');             
+            set(resultsvbox, 'Sizes', [30 -1 -2]);                                                            
+                        
+%           Add context menu
+            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
+            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
+            
+            % Attach menu to the construction table
+            set(this.tab_ModelCalibration.Table,'UIContextMenu',this.Figure.UIContextMenu);
+                        
+            % Add table name to .UserData
+            set(this.tab_ModelCalibration.Table.UIContextMenu,'UserData','this.tab_ModelCalibration.Table');
+
+            
+
+%%          Layout Tab5 - Model Simulation
+            %------------------------------------------------------------------            
+            hbox1t5 = uiextras.HBoxFlex('Parent', this.tab_ModelSimulation.Panel,'Padding', 3, 'Spacing', 3, 'Tag','Model Simulation outer hbox');
+            vbox1t5 = uiextras.VBox('Parent',hbox1t5,'Padding', 3, 'Spacing', 3);
+            vbox2t5 = uiextras.VBox('Parent',hbox1t5,'Padding', 3, 'Spacing', 3);
+            hbox1t6 = uiextras.HBox('Parent',vbox1t5,'Padding', 3, 'Spacing', 3);
+            hboxBtn1 = uiextras.HButtonBox('Parent',hbox1t6 ,'Padding', 3, 'Spacing', 3);             
+            hboxBtn2 = uiextras.HButtonBox('Parent',hbox1t6 ,'Padding', 3, 'Spacing', 3);                 
+            
+                        
+            % Add button for calibration
+            % Add buttons to top left panel               
+            uicontrol(hboxBtn2,'String','Append Table Data','Callback', @this.onImportTable, 'Tag','Model Simulation', 'TooltipString', sprintf('Append a .csv file of table data to the table below. \n Only rows where the model label is for a model that have been calibrated will be imported.') );
+            uicontrol(hboxBtn2,'String','Export Table Data','Callback', @this.onExportTable, 'Tag','Model Simulation', 'TooltipString', sprintf('Export a .csv file of the table below.') );                        
+            uicontrol(hboxBtn2,'String','Simulate Selected Models','Callback', @this.onSimModels, 'TooltipString', sprintf('Use the tick-box below to select the models to simulate then click here. \n During and after simulation, the status is given in the 9th column.') );            
+            uicontrol(hboxBtn2,'String','Export Selected Results','Callback', @this.onExportResults, 'Tag','Model Simulation', 'TooltipString', sprintf('Export a .csv file of the simulation results from all models.') );            
+            uicontrol('Parent',hboxBtn1,'Style','slider','Min',0.05,'Max',0.95,'Value',0.5,'Tag','WidthofPanelConstruct', ...
+                'Callback', {@this.onChangeTableWidth, 'Model Simulation outer hbox'} , 'TooltipString', 'Adjust table width');                                                     
+            hboxBtn1.ButtonSize(1) = 225;
+            hboxBtn2.ButtonSize(1) = 225;
+            set(hbox1t6,'Sizes',[90 -1])              
+            
+            % Add table
+            cnames1t5 = {   '<html><center>Select<br />Model</center></html>', ...   
+                            '<html><center>Model<br />Label</center></html>', ...                               
+                            '<html><center>Bore<br />ID</center></html>', ...   
+                            '<html><center>Head<br />Start Date</center></html>', ...
+                            '<html><center>Head<br />End Date</center></html>', ...
+                            '<html><center>Simulation<br />Label</center></html>', ...   
+                            '<html><center>Forcing Data<br />File</center></html>', ...
+                            '<html><center>Simulation<br />Start Date</center></html>', ...
+                            '<html><center>Simulation<br />End Date</center></html>', ...
+                            '<html><center>Simulation<br />Time step</center></html>', ...                            
+                            '<html><center>Krig<br />Sim. Residuals?</center></html>', ... 
+                            '<html><center>Simulation<br />Status</center></html>'};
+            data = cell(1,12);            
+            rnames1t5 = {[1]};
+            cedit1t5 = logical([1 1 0 0 0 1 1 1 1 1 1 0]);            
+            cformats1t5 = {'logical', {'(none calibrated)'}', 'char','char','char','char','char','char', 'char',{'Daily' 'Weekly' 'Monthly' 'Yearly'}, 'logical','char' };
+            toolTipStr = 'Run simulations of calibrated models';
+              
+            % Add table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.
+%             this.tab_ModelSimulation.Table = createTable(vbox1t5,cnames1t5, data, false, ...
+%                 'ColumnFormat', cformats1t5, 'ColumnEditable', cedit1t5, ...
+%                 'RowName', rnames1t5, 'Tag','Model Simulation', ...
+%                 'CellSelectionCallback', @this.modelSimulation_tableSelection,...
+%                 'CellEditCallback', @this.modelSimulation_tableEdit,...
+%                 'TooltipString', toolTipStr);   
+
+            this.tab_ModelSimulation.Table = uitable(vbox1t5, ...
+                'ColumnName', cnames1t5, 'Data', data, ...
+                'ColumnFormat', cformats1t5, 'ColumnEditable', cedit1t5, ...
+                'RowName', rnames1t5, 'Tag','Model Simulation', ...
+                'CellSelectionCallback', @this.modelSimulation_tableSelection,...
+                'CellEditCallback', @this.modelSimulation_tableEdit,...
+                'TooltipString', toolTipStr);            
+                                 
+            
+%             % Find java sorting object in table
+%             try
+%                 this.figure_Layout.Selection = 5;
+%                 drawnow update;
+%                 jscrollpane = findjobj(this.tab_ModelSimulation.Table);
+%                 jtable = jscrollpane.getViewport.getView;
+% 
+%                 % Turn the JIDE sorting on
+%                 jtable.setSortable(true);
+%                 jtable.setAutoResort(true);
+%                 jtable.setMultiColumnSortable(true);
+%                 jtable.setPreserveSelectionsAfterSorting(true);            
+%             catch
+%                 warndlg('Creating of the GUI row-sorting module failed for the model simulation table.');
+%             end                         
+                        
+            % Add drop-down for the results box
+            uicontrol(vbox2t5,'Style','text','String','Select simulation results to display:' );
+            this.tab_ModelSimulation.resultsOptions.popup = uicontrol('Parent',vbox2t5,'Style','popupmenu', ...
+                'String',{'Simulation data', 'Simulation & decomposition plots','(none)'}, ...
+                'Value',3,'Callback', @this.modelSimulation_onResultsSelection);         
+            
+            this.tab_ModelSimulation.resultsOptions.dataTable.box = uiextras.Grid('Parent', vbox2t5,'Padding', 3, 'Spacing', 3);
+
+            % Add results table. Importantly, this is done using createTable, not
+            % uitable. This was required to achieve acceptable perforamnce
+            % for large tables.
+%             this.tab_ModelSimulation.resultsOptions.dataTable.table = createTable(this.tab_ModelSimulation.resultsOptions.dataTable.box, ...
+%                 {'Year','Month', 'Day','Hour','Minute', 'Sim. Head','Noise Lower','Noise Upper'}, ...
+%                 cell(0,8), false, ...
+%                 'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','numeric','numeric'}, ...
+%                 'ColumnEditable', true(1,8), ...
+%                 'Tag','Model Simulation - results table', ...
+%                 'TooltipString', 'Results data from the model simulation.');     
+            this.tab_ModelSimulation.resultsOptions.dataTable.table = uitable(this.tab_ModelSimulation.resultsOptions.dataTable.box, ...
+                'ColumnName',{'Year','Month', 'Day','Hour','Minute', 'Sim. Head','Noise Lower','Noise Upper'}, ...
+                'Data',cell(0,8), ...
+                'ColumnFormat', {'numeric','numeric','numeric','numeric', 'numeric','numeric','numeric','numeric'}, ...
+                'ColumnEditable', true(1,8), ...
+                'Tag','Model Simulation - results table', ...
+                'TooltipString', 'Results data from the model simulation.');                                 
+            
+            this.tab_ModelSimulation.resultsOptions.box = vbox2t5;
+            this.tab_ModelSimulation.resultsOptions.plots.panel = uiextras.BoxPanel('Parent', vbox2t5);                                              
+            
+            % Set box sizes
+            set(hbox1t5, 'Sizes', [-2 -1]);
+            set(vbox1t5, 'Sizes', [30 -1]);
+            set(vbox2t5, 'Sizes', [30 20 0 0]);
+                        
+%           Add context menu
+            this.Figure.UIContextMenu = uicontextmenu(this.Figure,'Visible','off');
+            uimenu(this.Figure.UIContextMenu,'Label','Copy selected row','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(this.Figure.UIContextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(this.Figure.UIContextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(this.Figure.UIContextMenu,'Label','Select all','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select none','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Invert selection','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select row range ...','Callback',@this.rowSelection);
+            uimenu(this.Figure.UIContextMenu,'Label','Select by col. value ...','Callback',@this.rowSelection);
+            
+            % Attach menu to the construction table
+            set(this.tab_ModelSimulation.Table,'UIContextMenu',this.Figure.UIContextMenu);
+                        
+            % Add table name to .UserData
+            set(this.tab_ModelSimulation.Table.UIContextMenu,'UserData','this.tab_ModelSimulation.Table');
+            
+%%          Store this.models on HDD
+            %----------------------------------------------------
+            this.modelsOnHDD = '';
+            this.models = [];
+            
+%%          Close the splash window and show the app
+            %----------------------------------------------------
+            if ~isdeployed
+               close(this.FigureSplash);
+            end                        
+            this.figure_Layout.Selection = 1;
+            set(this.Figure,'Visible','on');
+        end
+
+        % Show show plotting icons
+        function plotToolbarState(this,iconState)
+            % Check if version is 2018b or later. From this point the
+            % plot toolbar buttons moved into the plot.
+            v=version();
+            isBefore2018b = str2double(v(1:3))<9.5;
+
+            if isBefore2018b 
+                hToolbar = findall(this.Figure,'tag','FigureToolBar');
+                hToolbutton = findall(hToolbar,'tag','Exploration.Brushing');            
+                hToolbutton.Visible = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.DataCursor');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(hToolbar,'tag','Exploration.Rotate');            
+                hToolbutton.Visible = 'off';
+                hToolbutton = findall(hToolbar,'tag','Exploration.Pan');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomOut');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(hToolbar,'tag','Exploration.ZoomIn');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(hToolbar,'tag','Standard.PrintFigure');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(this.Figure,'tag','Export.plot');            
+                hToolbutton.Visible = iconState;
+                hToolbutton = findall(hToolbar,'tag','Standard.EditPlot');            
+                hToolbutton.Visible = 'off';            
+                hToolbutton = findall(hToolbar,'tag','Plottools.PlottoolsOn');            
+                hToolbutton.Visible = 'off';                          
+            end
+        end        
+        
+        % Set project folder
+        function onSetProjectFolder(this,hObject,eventdata)
+
+            % Initialise project names
+            projectName = '';
+            projectExt = '';
+            
+            % Get project folder
+            if isempty(this.project_fileName)
+                projectPath = uigetdir('Select project folder.');    
+            else
+                % Get project folder and file name (if a dir)
+                if isdir(this.project_fileName)
+                    projectPath = this.project_fileName;
+                else
+                    [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
+                end
+                                
+                if isempty(projectPath)
+                    projectPath = uigetdir('Select project folder.'); 
+                else
+                    projectPath = uigetdir(projectPath, 'Select project folder.'); 
+                end
+                
+            end
+
+            if projectPath~=0;
+
+                % Get the current project folder. If the project folder has
+                % changed then warn the user that all infput file names
+                % must be within the new project folder.
+                if ~isempty(this.project_fileName)
+                    currentProjectFolder = fileparts(this.project_fileName);
+                    
+                    if ~strcmp(currentProjectFolder, projectPath)
+                        warndlg({'The project folder is different to that already set.';''; ...
+                                 'Importantly, all file names in the project are relative'; ...
+                                 'to the project folder and so all input .csv files must be'; ...
+                                 'within the project folder or a sub-folder within it.'},'Input file name validity.','modal');
+                    end
+                end
+                
+                % Update project folder
+                this.project_fileName = projectPath;
+                                
+                % Update GUI title
+                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
+                drawnow update;
+            end            
+            
+        end
+            
+        function onMoveModels(this,hObject,eventdata)
+            
+            % The project must be saved to a file. Check the project file
+            % is defined.
+            if isempty(this.project_fileName) || isdir(this.project_fileName)
+                warndlg({'The project must first be saved to a file.';'Please first save the project.'}, 'Project not saved...')
+                return
+            end
+               
+            % Tell the user what is going to be done.
+            if ~isempty(this.modelsOnHDD)
+                response = questdlg({'Moving the models to the RAM will shift all built, calibrated and '; ...
+                                   'simulated models from the hard-drive of the project file to the RAM.'; ...
+                                   ''; ...
+                                   'This allows significantly fewer models to be analysed within one '; ...
+                                   'project. However, any operation requiring access to the models '; ...
+                                   'may be muxh faster. Such operations include changing settings or '; ...
+                                   'plot and exporting results.'; ...
+                                   ''; ...
+                                   'Additionally, changes made to a model (e.g. rebuilting, calibrating'; ...
+                                   'or simulating) will NOT be automatically saved to the project file.'; ...
+                                   ''; ...
+                                   'To move the models to RAM, click OK.'},'Move the project models to RAM ...','OK','Cancel','Cancel');                
+            else
+                response = questdlg({'Moving the models to the HDD will shift all built, calibrated and '; ...
+                                   'simulated models from the RAM to the project file on the hard disk.'; ...
+                                   ''; ...
+                                   'This allows significantly more models to be analysed within one '; ...
+                                   'project. However, any operation requiring access to the models '; ...
+                                   'may be very slow. Such operations include changing settings or '; ...
+                                   'plot and exporting results. Hence, it is highly advisable to '; ...
+                                   'store the project file on a local hard-drive, and not a network'; ...
+                                   'location.'; ...
+                                   ''; ...
+                                   'Additionally, changes made to a model (e.g. rebuilting, calibrating'; ...
+                                   'or simulating) will be automatically saved to the project file. The'; ...
+                                   'project data tables within each tab will however not be automatically'; ...
+                                   'saved.'; ...
+                                   ''; ...
+                                   'To move the models to the hard-drive, click OK.'},'Move the project models to hard-drive ...','OK','Cancel','Cancel');
+            end
+                           
+             if ~strcmp(response,'OK')
+                 return
+             end
+             
+             % Change cursor
+             set(this.Figure, 'pointer', 'watch');                
+             drawnow update;                    
+
+             % Store original - in case of error
+             modelsOnHDD_orig = this.modelsOnHDD;
+             
+             if ~isempty(this.modelsOnHDD)    
+                 % Move models to RAM. The following loads each model
+                 % object and then assigns si to the project.
+                 try                      
+                     model_labels = fieldnames(this.models);
+                     nModels = length(model_labels);
+                     for i=1:nModels
+                        tmpModels.(model_labels{i}) = getModel(this, model_labels{i});
+                     end
+                     this.models = tmpModels;
+                     this.modelsOnHDD = '';
+                     
+                     % Change file menu label
+                     for i=1:size(this.figure_Menu.Children,1)
+                        if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                        strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                            set(this.figure_Menu.Children(i),'Label','Move models from RAM to HDD...');
+                        end       
+                     end   
+
+                     % Change cursor
+                     set(this.Figure, 'pointer', 'arrow');                
+                     drawnow update;                    
+                                   
+                     msgbox('Models were successfully moved to the RAM.','Successful model relocation','help');                     
+                 catch ME
+                     this.modelsOnHDD = modelsOnHDD_orig;
+                     msgbox({'Models relocation failed!','','Check access to the project file and RAM availability.'},'Relcation of models failed.','error');
+                 end
+             else               % Move models to HDD
+                try 
+                    
+                    % Get the folder for the project files
+                    folderName = uigetdir(fileparts(this.project_fileName) ,'Select folder for the model files.');    
+                    if isempty(folderName)
+                        return;
+                    end                                                        
+                    
+                    % remove project folder from file paths
+                    [ind_start,ind_end]=regexp(folderName ,fileparts(this.project_fileName));
+                    folderName = folderName(ind_end+2:end);
+                    
+                    % Check models are to go into a subfolder.
+                    if isempty(folderName)
+                        msgbox({'Models relocation failed!','','The models must be offloaded to a sub-folder of the project.'},'Relcation of models failed.','error');
+                    
+                        this.modelsOnHDD = modelsOnHDD_orig;
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;   
+                        
+                        return
+                    end
+                    
+                    % Store sub-folder to models in project
+                    this.modelsOnHDD = folderName;
+                    
+                    % Move models to HDD
+                    model_labels = fieldnames(this.models);
+                    nModels = length(model_labels);
+                    for i=1:nModels
+                        model = this.models.(model_labels{i});                        
+                        setModel( this, model_labels{i}, model);
+                    end
+
+                    % Change file menu label
+                    for i=1:size(this.figure_Menu.Children,1)
+                        if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                        strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                            set(this.figure_Menu.Children(i),'Label','Move models from HDD to RAM...');
+                        end       
+                    end                       
+
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');                
+                    drawnow update;                    
+                    
+                    msgbox('Models were successfully moved to the hard-drive.','Successful model relocation','help');
+                catch ME
+                    this.modelsOnHDD = modelsOnHDD_orig;
+                    msgbox({'Models relocation failed!','','Check access to the project file and RAM availability.'},'Relcation of models failed.','error');
+                end
+             end
+             
+             % Change cursor
+             set(this.Figure, 'pointer', 'arrow');                
+             drawnow update;               
+        end
+        
+        % Open saved model
+        function onNew(this,hObject,eventdata)
+
+            % Check if all of the GUI tables are empty. If not, warn the
+            % user the opening the example will delete the existing data.
+            if ~isempty(this.tab_Project.project_name.String) || ...
+            ~isempty(this.tab_Project.project_description.String) || ...
+            (size(this.tab_ModelConstruction.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
+            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
+            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
+                response = questdlg({'Started a new project will close the current project.','','Do you want to continue?'}, ...
+                 'Close the current project?','Yes','No','No');
+             
+                if strcmp(response,'No')
+                    return;
+                end
+            end              
+            
+            % Initialise project data
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update;              
+            this.project_fileName ='';
+            this.model_labels=[];
+            this.models=[];              
+            this.tab_Project.project_name.String = '';
+            this.tab_Project.project_description.String = '';
+            this.tab_DataPrep.Table.Data = {};
+            this.tab_DataPrep.Table.RowName = {}; 
+            this.tab_ModelConstruction.Table.Data = { [],[],[],[],[],[],[],[], '<html><font color = "#FF0000">Model not built.</font></html>'};
+            this.tab_ModelConstruction.Table.RowName = {}; 
+            this.tab_ModelCalibration.Table.Data = {};
+            this.tab_ModelCalibration.Table.RowName = {};
+            this.tab_ModelSimulation.Table.Data = {};
+            this.tab_ModelSimulation.Table.RowName = {};
+            this.dataPrep = [];
+            this.copiedData={};
+            this.HPCoffload={};
+            this.modelsOnHDD='';
+            set(this.Figure,'Name','HydroSight');
+            drawnow update;   
+                
+            % Enable file menu items
+            for i=1:size(this.figure_Menu.Children,1)
+                if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
+                    set(this.figure_Menu.Children(i),'Enable','on');
+                elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                    if this.modelsOnHDD
+                        set(this.figure_Menu.Children(i),'Label', 'Move models from HDD to RAM...');
+                    else
+                        set(this.figure_Menu.Children(i),'Label', 'Move models from RAM to HDD...');
+                    end
+                    set(this.figure_Menu.Children(i),'Enable','on');
+                end       
+            end
+
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;                               
+        end
+        
+        % Open saved model
+        function onOpen(this,hObject,eventdata)
+            
+            % Check if all of the GUI tables are empty. If not, warn the
+            % user the opening the example will delete the existing data.
+            if ~isempty(this.tab_Project.project_name.String) || ...
+            ~isempty(this.tab_Project.project_description.String) || ...
+            (size(this.tab_ModelConstruction.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
+            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
+            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
+                response = questdlg({'Opening a new project will close the current project.','','Do you want to continue?'}, ...
+                 'Close the current project?','Yes','No','No');
+             
+                if strcmp(response,'No')
+                    return;
+                end
+            end            
+            
+            % Set initial folder to the project folder (if set)
+            currentProjectFolder='';
+            if ~isempty(this.project_fileName)                                
+                try    
+                    if isdir(this.project_fileName)
+                        currentProjectFolder = this.project_fileName;
+                    else
+                        currentProjectFolder = fileparts(this.project_fileName);
+                    end 
+                    
+                    currentProjectFolder = [currentProjectFolder,filesep];
+                    cd(currentProjectFolder);
+                catch
+                    % do nothing
+                end
+            end
+            
+            % Show folder selection dialog
+            [fName,pName] = uigetfile({'*.mat'},'Select project to open.');    
+
+            if fName~=0;
+                
+                % Assign the file name 
+                this.project_fileName = fullfile(pName,fName);
+
+                % Ask if the performance stats in the GUI calib table are
+                % to be rebuilt
+                doPerforamnceMeasureRestimation=false;
+                if ~isdeployed
+                    response = questdlg({'Would you like the calibration performance metrics to be re-calculated?','','Note, the calculations can be slow if there are 100''s of models'},'Re-calculate performance metrics?','Yes','No','Cancel','No');
+                    if strcmp(response,'Cancel')
+                        return;
+                    elseif strcmp(response,'Yes')
+                        doPerforamnceMeasureRestimation=true;
+                    end
+                end
+                
+                % Change cursor
+                set(this.Figure, 'pointer', 'watch');                
+                drawnow update;
+                
+                % Analyse the variables in the file
+                %----------------------------------------------------------
+                % Get variables in file
+                try
+                    vars= whos('-file',this.project_fileName);
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    errordlg(['The following project file could not be opened:',this.project_fileName],'Project file read error ...');
+                    return;
+                end
+
+                % Filter out 'models' variable
+                j=0;
+                hasDataVar = false;
+                hasModelsVar = false;
+                for i=1:size(vars)
+                   if ~strcmp(vars(i).name,'data') && ~strcmp(vars(i).name,'label') && ~strcmp(vars(i).name,'models') 
+                       j=j+1;
+                       varNames{j} = vars(i).name;
+                   end
+                   if strcmp(vars(i).name,'data')
+                       hasDataVar = true;
+                   end
+                   if strcmp(vars(i).name,'models')
+                       hasModelsVar = true;
+                   end                   
+                end                
+                
+                % GET MODEL DATA
+                %----------------------------------------------------------                
+                % Add HPC settings
+                savedData = load(this.project_fileName, 'settings','-mat');
+                try
+                    this.HPCoffload = savedData.settings.HPCoffload;
+                catch
+                    this.HPCoffload = {};
+                end
+                
+                % Add model on HDD setting
+                try 
+                    if islogical(savedData.settings.modelsOnHDD)
+                        this.modelsOnHDD = '';
+                    else
+                        this.modelsOnHDD = savedData.settings.modelsOnHDD;
+                    end
+                catch
+                    this.modelsOnHDD = '';
+                end
+
+                % Add model label settings
+                try
+                    savedData = load(this.project_fileName, 'model_labels','-mat');
+                    if isempty(fieldnames(savedData))
+                        this.model_labels=[];
+                    else
+                        this.model_labels=savedData.model_labels;                
+                    end
+                catch ME
+                    this.model_labels=[];
+                end
+                
+                % Clear models in project.                        
+                this.models=[];                                
+                
+                % Assign built models.
+                try
+                    % Load data and labels assuming they are not in a
+                    % field of 'models'.                        
+                    if hasModelsVar
+                        savedData = load(this.project_fileName, 'models','-mat');
+                        savedData = savedData.models;                                                        
+                    else
+                        savedData = load(this.project_fileName, '-mat');                            
+
+                        % Remove GUI data fields
+                        try
+                        	filt = strcmp(fieldnames(savedData),'dataPrep') | strcmp(fieldnames(savedData),'settings') |  strcmp(fieldnames(savedData),'tableData') |  strcmp(fieldnames(savedData),'model_labels');
+                            removedFields = fieldnames(savedData);
+                            removedFields = removedFields(filt);                            
+                            savedData = rmfield(savedData,removedFields);
+                        catch ME
+                            % do nothing
+                        end                            
+                    end
+
+                    if ~isempty(this.modelsOnHDD)
+                        
+                        % Setup matfile link to 'this'. NOTE: for
+                        % offloadeed models setModel() can handle the third input being the 
+                        % file name to the matfile link. 
+                        % Note 2: The models only need to be loaded if
+                        % this.model_labels does not contain calib. status.
+                        model_labels = fieldnames(savedData);
+                        nModels = length(model_labels);
+                        nErr=0;
+                        if isempty(this.model_labels) || size(this.model_labels,1)<nModels
+                            this.model_labels=[];
+                            h = waitbar(0,'Re-building list of calibrated models because of error. Please wait ...');                        
+                            for i=1:nModels             
+                                waitbar(i/nModels);
+                                try                                
+                                    setModel(this, model_labels{i,1}, savedData.(model_labels{i,1}));
+                                catch ME
+                                    nErr = nErr+1;
+                                end                                
+                            end
+                            close(h);
+                        else
+                            % Update object hold the relative path to the .mat file                            
+                            for i=1:nModels   
+                                model_label_tmp = HydroSight_GUI.modelLabel2FieldName(model_labels{i,1});
+                                this.models.(model_label_tmp) = fullfile(this.modelsOnHDD, [model_label_tmp,'.mat']);                                
+                            end
+                        end
+                        
+                        if nErr>0
+                            warndlg(['The HDD stored .mat files could not be loaded for ', num2str(nErr), ' models. Check the .mat files exist for all calibrated models in the project'],'Model load errors...');
+                        end
+                    else
+                        
+                        if iscell(savedData)
+                            nModels = size(savedData,1);
+                            for i=1:nModels                                
+                                setModel(this, savedData{i,1}.model_label, savedData{i,1});
+                            end
+                        else
+                            model_labels = fieldnames(savedData);
+                            nModels = length(model_labels);
+                            for i=1:nModels
+                                setModel(this, savedData.(model_labels{i,1}).model_label, savedData.(model_labels{i,1}));
+                            end
+                        end                 
+                    end
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Loaded models could not be assigned to the user interface.','File model data error');
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;
+                end                  
+                
+                % GET GUI TABLE DATA
+                %----------------------------------------------------------
+                % Load file (except 'model')
+                try
+                    savedData = load(this.project_fileName, varNames{:}, '-mat');
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Project file could not be loaded.','File error');
+                    return;
+                end
+                
+                % Assign loaded data to the tables and models.
+                try
+                    this.tab_Project.project_name.String = savedData.tableData.tab_Project.title;
+                    this.tab_Project.project_description.String = savedData.tableData.tab_Project.description;
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Data could not be assigned to the user interface table: Project Description','File table data error');
+                     set(this.Figure, 'pointer', 'watch');
+                     drawnow update;
+                end
+                try
+                    if size(savedData.tableData.tab_DataPrep,2)==17
+                       savedData.tableData.tab_DataPrep = [ savedData.tableData.tab_DataPrep(:,1:14), ...
+                                                            repmat(false,size(savedData.tableData.tab_DataPrep,1),1), ...
+                                                            savedData.tableData.tab_DataPrep(:,15:17) ];
+                    end
+                    this.tab_DataPrep.Table.Data = savedData.tableData.tab_DataPrep;
+                    
+                    % Update row numbers
+                    nrows = size(this.tab_DataPrep.Table.Data,1);
+                    this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                    
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Data could not be assigned to the user interface table: Data Preparation','File table data error');
+                     set(this.Figure, 'pointer', 'watch');
+                     drawnow update;
+                end               
+                try
+
+                    this.tab_ModelConstruction.Table.Data = savedData.tableData.tab_ModelConstruction;
+                    
+                    % Update row numbers
+                    nrows = size(this.tab_ModelConstruction.Table.Data,1);
+                    this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));     
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Data could not be assigned to the user interface table: Model Construction','File table data error');
+                     set(this.Figure, 'pointer', 'watch');
+                     drawnow update;
+                end     
+                
+                try       
+                    
+                    % Check if the input calibration table has 14 columns.
+                    % If so, then delete the calib settings column.
+                    if size(savedData.tableData.tab_ModelCalibration,2)==14
+                        savedData.tableData.tab_ModelCalibration = savedData.tableData.tab_ModelCalibration(:,[1:8,10:14]);
+                    end
+                    
+                    % The following loop addresses a problem that first
+                    % arose with the MCMC calibration (ie DREAM) whereby
+                    % the calib perforamnce states were shown for posterior all
+                    % parameter sets. The code eblow simple takes the coe
+                    % values from the GUI table and recalculates the mean.
+                    % Obviously if there is only one value listed then
+                    % this code does not chnage the result.
+                    if doPerforamnceMeasureRestimation
+                        % This code allows the recalculation of the model
+                        % performance statistics. Uncomment if required.
+                        h = waitbar(0,'Recalculating performance metrics. Please wait ...');
+                        for i=1:size(savedData.tableData.tab_ModelCalibration,1)
+                            
+                            % update progress bar
+                            waitbar(i/size(savedData.tableData.tab_ModelCalibration,1));
+                            
+                            % Get model lable
+                            model_Label = savedData.tableData.tab_ModelCalibration{i,2};
+                            model_Label = HydroSight_GUI.removeHTMLTags(model_Label); 
+                            model_Label = HydroSight_GUI.modelLabel2FieldName(model_Label);
+                            try
+                                tmpModel = getModel(this,model_Label);                        
+                            catch ME
+                                savedData.tableData.tab_ModelCalibration{i,10} = '<html><font color = "#808080">(NA)</font></html>';
+                                savedData.tableData.tab_ModelCalibration{i,12} = '<html><font color = "#808080">(NA)</font></html>';
+                                savedData.tableData.tab_ModelCalibration{i,13} = '<html><font color = "#808080">(NA)</font></html>';                            
+                                continue;
+                            end
+
+                            if ~isempty(tmpModel.calibrationResults) && tmpModel.calibrationResults.isCalibrated
+                                head_calib_resid = tmpModel.calibrationResults.data.modelledHead_residuals;
+                                SSE = sum(head_calib_resid.^2);
+                                RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
+                                tmpModel.calibrationResults.performance.RMSE = RMSE;                        
+
+                                % CoE
+                                obsHead =  tmpModel.calibrationResults.data.obsHead;
+                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
+                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(:,2));            
+                                tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
+                                                                
+                                CoE_cal = median(tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
+                                AICc = median(tmpModel.calibrationResults.performance.AICc);
+                                BIC = median(tmpModel.calibrationResults.performance.BIC);
+
+                                savedData.tableData.tab_ModelCalibration{i,10} = ['<html><font color = "#808080">',num2str(CoE_cal),'</font></html>'];
+
+                                savedData.tableData.tab_ModelCalibration{i,12} = ['<html><font color = "#808080">',num2str(AICc),'</font></html>'];
+                                savedData.tableData.tab_ModelCalibration{i,13} = ['<html><font color = "#808080">',num2str(BIC),'</font></html>'];
+
+                                if ~isempty(tmpModel.evaluationResults)
+                                    
+                                    head_eval_resid = tmpModel.evaluationResults.data.modelledHead_residuals;
+                                    obsHead =  tmpModel.evaluationResults.data.obsHead;
+                                    
+                                    % Mean error
+                                    tmpModel.evaluationResults.performance.mean_error = mean(head_eval_resid); 
+
+                                    %RMSE
+                                    SSE = sum(head_eval_resid.^2);
+                                    tmpModel.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
+
+                                    % Unbiased CoE
+                                    residuals_unbiased = bsxfun(@minus,head_eval_resid, tmpModel.evaluationResults.performance.mean_error);
+                                    SSE = sum(residuals_unbiased.^2);
+                                    tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
+                                    
+                                    CoE_eval = median(tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
+                                    savedData.tableData.tab_ModelCalibration{i,11} = ['<html><font color = "#808080">',num2str(CoE_eval),'</font></html>'];
+                                else
+                                    savedData.tableData.tab_ModelCalibration{i,11} = '<html><font color = "#808080">(NA)</font></html>';
+                                end  
+                                
+                                % Save model
+                                setModel(this,model_Label, tmpModel);                        
+                            else
+                                savedData.tableData.tab_ModelCalibration{i,10} = '<html><font color = "#808080">(NA)</font></html>';
+                                savedData.tableData.tab_ModelCalibration{i,12} = '<html><font color = "#808080">(NA)</font></html>';
+                                savedData.tableData.tab_ModelCalibration{i,13} = '<html><font color = "#808080">(NA)</font></html>';
+                            end
+                        end
+                       close (h);
+                    else
+                        colInd=[10:13];
+                        for i=colInd                    
+                            performanceStat = HydroSight_GUI.removeHTMLTags(savedData.tableData.tab_ModelCalibration(:,i));
+                            ind = cellfun(@(x) isempty(x) || strcmp(x,'(NA)'), performanceStat);
+                            performanceStat(ind) = repmat({'<html><font color = "#808080">(NA)</font></html>'},sum(ind),1);
+                            performanceStat(~ind) = cellfun( @(x) ['<html><font color = "#808080">',num2str(median(str2num(x))),'</font></html>'],performanceStat(~ind),'UniformOutput',false);
+                            savedData.tableData.tab_ModelCalibration(:,i) = performanceStat;
+                        end
+                    end
+                    this.tab_ModelCalibration.Table.Data = savedData.tableData.tab_ModelCalibration;
+                    
+                    % Update row numbers
+                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));           
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Data could not be assigned to the user interface table: Model Calibration','File table data error');
+                     set(this.Figure, 'pointer', 'watch');
+                     drawnow update;
+                end                
+                try
+                    this.tab_ModelSimulation.Table.Data = savedData.tableData.tab_ModelSimulation;
+                    
+                    % Update row numbers
+                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                    this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                       
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Data could not be assigned to the user interface table: Model Simulation','File table data error');
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;
+                end                              
+                    
+                % Assign analysed bores.
+                try
+                    this.dataPrep = savedData.dataPrep;
+                catch ME
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    warndlg('Loaded data analysis results could not be assigned to the user interface.','File model data error');
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;
+                end          
+                
+                % Update GUI title
+                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
+                drawnow update;   
+                
+                % Enable file menu items
+                for i=1:size(this.figure_Menu.Children,1)
+                    if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
+                        set(this.figure_Menu.Children(i),'Enable','on');
+                    elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                    strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                        if this.modelsOnHDD
+                            set(this.figure_Menu.Children(i),'Label', 'Move models from HDD to RAM...');
+                        else
+                            set(this.figure_Menu.Children(i),'Label', 'Move models from RAM to HDD...');
+                        end
+                        set(this.figure_Menu.Children(i),'Enable','on');
+                    end       
+                end
+
+            end
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;                    
+        end
+
+        function onImportModel(this,hObject,eventdata)
+
+            % Get current project folder
+            projectPath='';                
+            if isdir(this.project_fileName)
+                projectPath = this.project_fileName;
+            else
+                projectPath = fileparts(this.project_fileName);
+            end
+            
+            % Show open file window
+             if isempty(projectPath)
+                [fName,pName] = uigetfile({'*.mat'},'Select project file for importing...'); 
+            else
+                [fName,pName] = uigetfile({'*.mat'},'Select project file for importing...', projectPath); 
+            end            
+            if fName~=0;
+                % Assign file name to date cell array
+                fName = fullfile(pName,fName);
+            else
+                return;
+            end            
+            
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update;   
+            
+            % Check models are not stored off the HDD
+            newProject = load(this.project_fileName, 'settings','-mat');
+            if isfield(newProject.settings,'modelsOnHDD') && ~isempty(newProject.settings.modelsOnHDD)
+                warndlg({'The new project has the models offloaded to the hard-drive..';''; ...
+                         'Importing of such models s not yet supported.'},'Models cannot be imported...','modal');                
+                return
+            end    
+            
+            newProjectGUITables = load(fName, 'tableData','-mat');
+            
+            % Load models
+            newProject = load(fName, '-mat');
+            filt = strcmp(fieldnames(newProject),'dataPrep') | ...
+                   strcmp(fieldnames(newProject),'settings') |  ...
+                   strcmp(fieldnames(newProject),'tableData') |  ...
+                   strcmp(fieldnames(newProject),'model_labels');
+            removedFields = fieldnames(newProject);
+            removedFields = removedFields(filt);                            
+            newProject = rmfield(newProject,removedFields);
+            
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;               
+            
+            % Show list models
+            newModelNames = fieldnames(newProject);
+            [listSelection,ok] = listdlg('PromptString',{'Below is a list of the built models within the selected project.','Select the models(s) to import.'}, ...
+                                    'ListString',newModelNames, ...
+                                    'ListSize',[400,300], ...
+                                    'Name','Select models to import ...');
+            if ok~=1  
+                return
+            end
+                
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update;               
+            
+            % Import selected models  
+            importedModels = cell(0,1);
+            nModels = 0;
+            for i=1:length(listSelection)
+                
+                % Build new model label                
+                newModelLabel = newModelNames{listSelection(i)};
+                ModelLabel = newModelLabel;
+                nModels = nModels +1;
+                importedModels{nModels,1} = newModelLabel;
+                
+                % Check if model exists
+                if isfield(this.models,newModelLabel)
+                    newModelLabel = [newModelLabel,'_imported'];
+                end
+                
+                % Find the model within the GUI tables.
+                filt = strcmp(newProjectGUITables.tableData.tab_ModelConstruction(:,2), ModelLabel);
+                if ~isempty(filt)
+                    newTableData = newProjectGUITables.tableData.tab_ModelConstruction(filt,:);
+                    if size(newTableData,2) ~=size(this.tab_ModelConstruction.Table.Data,2)
+                        importedModels{nModels,1} = [importedModels{nModels,1},': Error - construction table inconsistency'];
+                        continue
+                    end
+                    newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
+                    this.tab_ModelConstruction.Table.Data(end+1,:)=newTableData(1,:);
+                    this.tab_ModelConstruction.Table.RowName{end+1} = num2str(str2num(this.tab_ModelConstruction.Table.RowName{end})+1);
+                end
+                newProjectTableLabels = HydroSight_GUI.removeHTMLTags(newProjectGUITables.tableData.tab_ModelCalibration(:,2));
+                filt = strcmp(newProjectTableLabels, ModelLabel);
+                if ~isempty(filt)
+                    newTableData = newProjectGUITables.tableData.tab_ModelCalibration(filt,:);
+                    if size(newTableData,2) ~=size(this.tab_ModelCalibration.Table.Data,2)
+                        importedModels{nModels,1} = [importedModels{nModels,1},': Error - calib. table inconsistency'];
+                        continue
+                    end
+                    newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
+                    this.tab_ModelCalibration.Table.Data(end+1,:)=newTableData(1,:);
+                    this.tab_ModelCalibration.Table.RowName{end+1} = num2str(str2num(this.tab_ModelCalibration.Table.RowName{end})+1);
+                end
+                newProjectTableLabels = HydroSight_GUI.removeHTMLTags(newProjectGUITables.tableData.tab_ModelSimulation(:,2));
+                filt = strcmp(newProjectTableLabels, ModelLabel);                
+                filt = find(strcmp(newProjectGUITables.tableData.tab_ModelSimulation, ModelLabel));
+                if ~isempty(filt)
+                    for j=filt
+                        newTableData = newProjectGUITables.tableData.tab_ModelSimulation(j,:);
+                        if size(newTableData,2) ~=size(this.tab_ModelSimulation.Table.Data,2)
+                            importedModels{nModels,1} = [importedModels{nModels,1},': Warning - simulation table inconsistency'];
+                            break
+                        end
+                        newTableData{1,2} = strrep(newTableData{1,2}, ModelLabel, newModelLabel);
+                        this.tab_ModelSimulation.Table.Data(end+1,:)=newTableData(1,:);
+                        this.tab_ModelSimulation.Table.RowName{end+1} = num2str(str2num(this.tab_ModelSimulation.Table.RowName{end})+1);
+                    end              
+                end
+                
+                % Add model object and label.
+                try          
+                    setModel(this, newModelLabel, newProject.(ModelLabel));
+                catch ME
+                    importedModels{nModels,1} = [importedModels{nModels,1},': Error - model import failure'];
+                    continue
+                end
+                    
+                % Record model was added
+                importedModels{nModels,1} = [importedModels{nModels,1},': Successfully imported'];
+            end
+                
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;               
+            
+            msgbox({'Below is a summary of the importation:','',importedModels{:}}, ...
+                         'Model import summary...','modal');    
+        end
+        
+        % Save as current model        
+        function onSaveAs(this,hObject,eventdata)
+            
+            % set current folder to the project folder (if set)
+            currentProjectFolder='';
+            if ~isempty(this.project_fileName)                                
+                try    
+                    if isdir(this.project_fileName)
+                        currentProjectFolder = this.project_fileName;
+                    else
+                        currentProjectFolder = fileparts(this.project_fileName);
+                    end 
+                    
+                    currentProjectFolder = [currentProjectFolder,filesep];
+                    cd(currentProjectFolder);
+                catch
+                    % do nothing
+                end
+            end
+            
+            [fName,pName] = uiputfile({'*.mat'},'Save models as ...');    
+            if fName~=0;
+                
+                % Get the current project folder. If the project folder has
+                % changed then warn the user that all infput file names
+                % must be within the new project folder.
+                if ~isempty(currentProjectFolder)                    
+                    if ~strcmp(currentProjectFolder, pName)
+                        warndlg({'The project folder is different to that already set.';''; ...
+                                 'Importantly, all file names in the project are relative'; ...
+                                 'to the project folder and so all input .csv files must be'; ...
+                                 'within the project folder or a sub-folder within it.'},'Input file name invalid.','modal');
+                    end
+                end
+
+                % Change cursor
+                set(this.Figure, 'pointer', 'watch');    
+                drawnow update;
+                
+                % Assign file name to date cell array
+                this.project_fileName = fullfile(pName,fName);
+                
+                % Collate the tables of data to a temp variable.
+                tableData.tab_Project.title = this.tab_Project.project_name.String;
+                tableData.tab_Project.description = this.tab_Project.project_description.String;
+                tableData.tab_DataPrep = this.tab_DataPrep.Table.Data;
+                tableData.tab_ModelConstruction = this.tab_ModelConstruction.Table.Data;
+                tableData.tab_ModelCalibration = this.tab_ModelCalibration.Table.Data;
+                tableData.tab_ModelSimulation = this.tab_ModelSimulation.Table.Data;
+                                
+                % Get the data preparation results
+                dataPrep = this.dataPrep;               
+                
+                % Get settings
+                settings.HPCoffload = this.HPCoffload;
+                settings.modelsOnHDD = this.modelsOnHDD;
+                
+                % Get model labels & calib. status
+                model_labels = this.model_labels;                
+                
+                % Save the GUI tables to the file.
+                save(this.project_fileName, 'tableData', 'dataPrep', 'model_labels','settings', '-v7.3');  
+                
+                % Save models. NOTE: If the models are offloaded to the
+                % HDD, then only the file path to the individual model
+                % needs to be saved.
+                if ~isempty(this.models)
+                    try
+                        tmpModels = this.models;
+                        save(this.project_fileName, '-struct', 'tmpModels', '-append');
+                        clear tmpModels;                                       
+
+                        % Enable file menu items
+                        for i=1:size(this.figure_Menu.Children,1)
+                            if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
+                                set(this.figure_Menu.Children(i),'Enable','on');
+                            elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                            strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                                set(this.figure_Menu.Children(i),'Enable','on');
+                            end       
+                        end
+
+                    catch ME
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;
+                        warndlg('The project could not be saved. Please check you have write access to the directory.','Project not saved ...');
+                        return;
+                    end     
+                end
+                
+                % Update GUI title
+                set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);                                        
+                
+            end
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;
+            
+        end
+        
+        % Save model        
+        function onSave(this,hObject,eventdata)
+        
+            if isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2;
+                onSaveAs(this,hObject,eventdata);
+            else               
+                % Change cursor
+                set(this.Figure, 'pointer', 'watch');   
+                drawnow update;
+                
+                % Collate the tables of data to a temp variable.
+                tableData.tab_Project.title = this.tab_Project.project_name.String;
+                tableData.tab_Project.description = this.tab_Project.project_description.String;
+                tableData.tab_DataPrep = this.tab_DataPrep.Table.Data;
+                tableData.tab_ModelConstruction = this.tab_ModelConstruction.Table.Data;
+                tableData.tab_ModelCalibration = this.tab_ModelCalibration.Table.Data;
+                tableData.tab_ModelSimulation = this.tab_ModelSimulation.Table.Data;
+                                
+                % Get the data preparation results
+                dataPrep = this.dataPrep;
+                
+                % Get settings
+                settings.HPCoffload = this.HPCoffload;
+                settings.modelsOnHDD = this.modelsOnHDD;                               
+                
+                % Get model labels & calib. status
+                model_labels = this.model_labels;
+                
+                % Save the GUI tables to the file.
+                save(this.project_fileName, 'tableData', 'dataPrep', 'model_labels','settings', '-v7.3');  
+                if ~isempty(this.models)
+                    try
+                        % Save each model as a single variable in file. If the
+                        % models have been offloaded to HDD then only the paths
+                        % to the files will be saved.
+                        tmpModels = this.models;
+                        save(this.project_fileName, '-struct', 'tmpModels', '-append');
+                        clear tmpModels;
+                    catch
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;
+                        warndlg('The project models could not be saved. Please check you have write access to the directory.','Project not saved ...');
+                        return;                    
+                    end
+                end
+            end            
+            
+            set(this.Figure, 'pointer', 'arrow');   
+            drawnow update;
+        end    
+        
+        % This function runs when the app is closed        
+        function onExit(this,hObject,eventdata)    
+            
+            ans = questdlg('Do you want to save the project before exiting?','Save project?','Yes','No','Cancel','Yes');
+            
+            % Save project
+            if strcmp(ans,'Yes')
+                onSave(this,hObject,eventdata);
+            end
+            
+            % Check that it was saved (ie if saveas was called from save() )
+            if ~strcmp(ans,'No') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
+                warndlg('HydroSight cannot exit because the project does not appear to have been saved.','Project save error ...');
+                return
+            end
+            
+            % Exit
+            if strcmp(ans,'Yes') || strcmp(ans,'No')
+                delete(this.Figure);
+            end
+        end
+
+        function dataPrep_tableSelection(this, hObject, eventdata)
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);            
+            data=get(hObject,'Data'); % get the data cell array of the table
+            
+            % Fix bug in col 15 bng integer, not logical
+            if ~islogical(data{1,15})
+                if data{1,15}==1
+                    data{1,15}=true;
+                else
+                    data{1,15}=false;
+                end
+                set(hObject,'Data',data);
+            end
+            
+            % Undertake column specific operations.
+            if ~isempty(icol) && ~isempty(irow)
+                
+                % Record the current row and column numbers
+                this.tab_DataPrep.currentRow = irow;
+                this.tab_DataPrep.currentCol = icol;
+            
+                % Remove HTML tags from the column name
+                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
+                
+                switch columnName;
+                    case 'Obs. Head File'
+                        this.tab_DataPrep.modelOptions.vbox.Heights = [0; 0];
+
+                        % Get file name and remove project folder from
+                        % preceeding full path.
+                        fName = getFileName(this, 'Select the Observed Head file.');                                                
+                        if fName~=0;
+                            % Assign file name to date cell array
+                            data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
+                            
+                            % Input file name to the table
+                            set(hObject,'Data',data);
+                        end                        
+                        
+                    case 'Bore ID'
+                         % Check the obs. head file is listed
+                         fname = data{eventdata.Indices(1),2};
+                         if isempty(fname)
+                            warndlg('The observed head file name must be input before selecting the bore ID');
+                            return;
+                         end
+                         
+                         % Construct full file path name
+                         if isdir(this.project_fileName)                             
+                            fname = fullfile(this.project_fileName,fname); 
+                         else
+                            fname = fullfile(fileparts(this.project_fileName),fname);  
+                         end                         
+                         
+                         % Check the bore ID file exists.
+                         if exist(fname,'file') ~= 2;
+                            warndlg('The observed head file does not exist.');
+                            return;
+                         end
+                         
+                         % Read in the observed head file.
+                         try                            
+                            tbl = readtable(fname);
+                         catch
+                            warndlg('The observed head file could not be read in. It must a .csv file of 6 columns');
+                            return;
+                         end
+                        
+                         % Check there are the correct number of columns
+                         if length(tbl.Properties.VariableNames) < 5 || length(tbl.Properties.VariableNames) >8
+                            warndlg({'The observed head file must be in one of the following formats:', ...'
+                                '  -boreID, year, month, day, head', ...
+                                '  -boreID, year, month, day, hour, minute, head', ...
+                                '  -boreID, year, month, day, hour, minute, second, head'});
+                            return;
+                         end
+                             
+                         % Check columns 2 to 6 are numeric.
+                         if any(any(~isnumeric(tbl{:,2:end})))
+                            warndlg('Columns 2 to 6 within the observed head file must contain only numeric data.');
+                            return;
+                         end
+                            
+                         % Find the unique bore IDs   
+                         boreIDs = unique(tbl{:,1});
+                         
+                         % Free up memory
+                         clear tbl;
+                         
+                         % Input the unique bore IDs to the list box.
+                         set(this.tab_DataPrep.modelOptions.boreIDList,'String',boreIDs);  
+                         
+                         % Show the list box.
+                         this.tab_DataPrep.modelOptions.vbox.Heights = [-1; 0];                        
+                    otherwise
+                        % Show the results if the bore has been analysed.
+                        
+                        boreID = data{eventdata.Indices(1),3};
+                        modelStatus = HydroSight_GUI.removeHTMLTags(data{eventdata.Indices(1),16});
+                        
+                        if ~isempty(this.dataPrep) && ~isempty(boreID) && ...
+                        isfield(this.dataPrep,boreID) && ~isempty(this.dataPrep.(boreID)) && ...
+                        strcmp(modelStatus,'Bore analysed.')
+                            this.tab_DataPrep.modelOptions.vbox.Heights = [0; -1];                    
+                            set(this.tab_DataPrep.modelOptions.resultsOptions.box, 'Sizes',[-1 -1]);
+                            
+                            % Get the analysis results.
+                            headData = this.dataPrep.(boreID);
+                            
+                            % Add head data to the uitable                            
+                            nrows = size(headData,1);
+                            ncols = size(headData,2);
+                            this.tab_DataPrep.modelOptions.resultsOptions.table.Data = table2cell(headData);
+
+                            % Convert to a matrix
+                            headData = table2array(headData);                            
+                            
+                            % Create a time vector form head data.
+                            dateVec = datenum(headData(:,1),headData(:,2),headData(:,3),headData(:,4),headData(:,5),zeros(size(headData,1),1));
+                            
+                            % Plot the hydrograph and the errors and outliers
+                            isError = any(headData(:,7:end)==1,2);
+                            h= plot(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(~isError), headData(~isError,6),'b.-');
+                            legendstring{1} = 'Obs. head';
+                            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
+                            
+                            % Date errors
+                            col = 7;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'Date error'};
+                            end
+                            
+                            col = 8;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'Duplicate error'};
+                            end
+
+                            col = 9;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'Min head error'};
+                            end   
+                            
+                            col = 10;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'Max head error'};
+                            end  
+                            
+                            col = 11;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} '|dh/dt| error'};
+                            end                              
+                            
+                            col = 12;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'dh/dt=0 error'};
+                            end                              
+                            
+                            col = 13;
+                            isError = headData(:,col)==1;
+                            if any(isError)
+                                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                                legendstring = {legendstring{:} 'Outlier obs.'};
+                            end  
+                            
+                            % Format plot
+                            %datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x','YY');
+                            datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x');
+                            xlabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Year');
+                            ylabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Head');
+                            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'off');
+                            box(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
+                            axis(this.tab_DataPrep.modelOptions.resultsOptions.plots,'tight');
+                            legend(this.tab_DataPrep.modelOptions.resultsOptions.plots, legendstring,'Location','eastoutside');
+                            
+                            % SHow results
+                            this.tab_DataPrep.modelOptions.resultsOptions.box.Heights = [-1 -1];
+                            
+                            % SHow plot icons
+                            plotToolbarState(this,'on');
+                        else
+                            this.tab_DataPrep.modelOptions.vbox.Heights = [0; 0];
+                        end
+                end
+            end
+        end
+        
+        % Data preparation results table edit.
+        function dataPrep_resultsTableEdit(this, hObject, eventdata)
+
+            % Check the row was found.
+            if ~isfield(this.tab_DataPrep,'currentRow') || isempty(this.tab_DataPrep.currentRow)
+                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
+                return;
+            end            
+            
+            % Get the current row from the main data preparation table.
+            irow = this.tab_DataPrep.currentRow;
+            boreID = this.tab_DataPrep.Table.Data{irow, 3};
+                
+            % Check the row was found.
+            if isempty(boreID)
+                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
+                return;
+            end
+
+            % Get the new table of data.
+            headData=get(hObject,'Data');            
+            
+            % Check the number of rows in the data strcuture equal that of
+            % the results table.
+            if size(this.dataPrep.(boreID),1) ~= size(headData,1)
+                warndlg('An unexpected system error has occured. Please try re-selecting a grid cell from the main to replot the results.','System error ...');
+                return;
+            end
+                        
+            % Update the data analysis data structure.
+            this.dataPrep.(boreID)(:,:) = headData;
+            
+            %Convert to matrix.
+            headData = table2array(this.dataPrep.(boreID)(:,:));
+            
+            % Update table statistics
+            numErroneouObs = sum(any(headData(:,7:12),2));
+            numOutlierObs = sum(headData(:,13));                    
+            this.tab_DataPrep.Table.Data{irow,17} = ['<html><font color = "#808080">',num2str(numErroneouObs),'</font></html>'];
+            this.tab_DataPrep.Table.Data{irow,18} = ['<html><font color = "#808080">',num2str(numOutlierObs),'</font></html>'];            
+            
+            % Redraw the plot.
+            %--------------------------------------------------------------
+
+            
+            % Create a time vector form head data.
+            dateVec = datenum(headData(:,1),headData(:,2),headData(:,3),headData(:,4),headData(:,5),zeros(size(headData,1),1));
+
+            % Plot the hydrograph and the errors and outliers
+            isError = any(headData(:,7:end)==1,2);
+            h= plot(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(~isError), headData(~isError,6),'b.-');
+            legendstring{1} = 'Obs. head';
+            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
+
+            % Date errors
+            col = 7;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'Date error'};
+            end
+
+            col = 8;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'Duplicate error'};
+            end
+
+            col = 9;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'Min head error'};
+            end   
+
+            col = 10;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'Max head error'};
+            end  
+
+            col = 11;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} '|dh/dt| error'};
+            end                              
+
+            col = 12;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'dh/dt=0 error'};
+            end                              
+
+            col = 13;
+            isError = headData(:,col)==1;
+            if any(isError)
+                scatter(this.tab_DataPrep.modelOptions.resultsOptions.plots, dateVec(isError), headData(isError,6),'o');
+                legendstring = {legendstring{:} 'Outlier obs.'};
+            end  
+
+            % Format plot
+            datetick(this.tab_DataPrep.modelOptions.resultsOptions.plots,'x','YY');
+            xlabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Year');
+            ylabel(this.tab_DataPrep.modelOptions.resultsOptions.plots,'Head');
+            hold(this.tab_DataPrep.modelOptions.resultsOptions.plots,'off');
+            box(this.tab_DataPrep.modelOptions.resultsOptions.plots,'on');
+            axis(this.tab_DataPrep.modelOptions.resultsOptions.plots,'tight');
+            legend(this.tab_DataPrep.modelOptions.resultsOptions.plots, legendstring,'Location','eastoutside');            
+            
+            
+            
+        end
+        
+        function dataPrep_optionsSelection(this, hObject, eventdata)
+            try                         
+                switch this.tab_DataPrep.currentCol
+                    case 3 % Bore ID column
+                        
+                         % Get selected bores
+                         listSelection = get(hObject,'Value');
+
+                         % Get data from model construction table
+                         data=get(this.tab_DataPrep.Table,'Data'); 
+
+                         % Get the selected bore row index.
+                         index_selected = this.tab_DataPrep.currentRow;
+
+                         % Check if the nwe bore ID is unique.
+                         newBoreID = hObject.String(listSelection,1);
+                         if any(strcmp( data(:,3) , newBoreID) & index_selected~=[1:size(data,1)]')
+                             warndlg('The bore ID must be unique.','Bore ID error...');
+                             return;
+                         end
+                         
+                         % Check the bore ID is a valid field name.
+                         tmp=struct();
+                         try 
+                             tmp.(newBoreID{1}) = 1;
+                         catch ME
+                             warndlg('The bore ID is of an invalid format. It must not start with a number. Consider appending a non-numeric prefix, eg "Bore_"','Bore ID error...');
+                             return;
+                         end
+                         
+                         
+                         % Add selected bore ID is cell array at the currently
+                         % selected bore.
+                         data(index_selected,3) =  hObject.String(listSelection,1);
+
+                         % Set bore ID 
+                         set(this.tab_DataPrep.Table,'Data', data); 
+                end
+            catch ME
+                return;
+            end        
+        end        
+        
+        %Cell selection response for tab 1 - model construction
+        function modelConstruction_tableSelection(this, hObject, eventdata)
+            
+            % Hide plotting toolbars
+            plotToolbarState(this, 'off');
+            
+            % Get table indexes
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);                        
+            
+            % Undertake column specific operations.
+            if isempty(icol) && isempty(irow)
+                return
+            end
+            
+            % Hide the adjacent panels
+            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0; 0; 0];
+                        
+            % Remove HTML tags from the column name
+            columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
+
+            if ~(strcmp(columnName, 'Obs. Head File') || ...
+            strcmp(columnName, 'Forcing Data File') || ...
+            strcmp(columnName, 'Coordinates File') || ...
+            strcmp(columnName, 'Bore ID') || ...
+            strcmp(columnName, 'Model Type') || ...
+            strcmp(columnName, 'Model Options'))
+                return;
+            end
+            
+            % Record the current row and column numbers
+            this.tab_ModelConstruction.currentRow = irow;
+            this.tab_ModelConstruction.currentCol = icol;
+                        
+            % Get the data cell array of the table            
+            data=get(hObject,'Data'); 
+            
+            switch columnName;
+                case 'Obs. Head File'
+                    % Get file name and remove project folder from
+                    % preceeding full path.                    
+                    fName = getFileName(this, 'Select the Observed Head file.');               
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
+
+                        % Input file name to the table
+                        set(hObject,'Data',data);
+                    end
+
+                     % Hide the panels.
+                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];                        
+
+                case 'Forcing Data File'
+
+                    % Get file name and remove project folder from
+                    % preceeding full path.
+                    fName = getFileName(this, 'Select the Forcing Data file.');                                                
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
+
+                        % Input file name to the table
+                        set(hObject,'Data',data);
+                    end
+                    % Hide the panels.
+                    this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];                        
+
+                case 'Coordinates File'
+                    % Get file name and remove project folder from
+                    % preceeding full path.
+                    fName = getFileName(this, 'Select the Coordinates file.');                                                
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        data{eventdata.Indices(1),eventdata.Indices(2)} = fName;
+
+                        % Input file name to the table
+                        set(hObject,'Data',data);
+                    end
+
+                    % Hide the panels.
+                    this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];
+
+                case 'Bore ID'
+                     % Check the obs. head file is listed
+                     fname = data{eventdata.Indices(1),3};
+                     if isempty(fname)
+                        warndlg('The observed head file name must be input before selecting the bore ID');
+                        return;
+                     end
+
+                     % Construct full file name.
+                     if isdir(this.project_fileName)                             
+                        fname = fullfile(this.project_fileName,fname); 
+                     else
+                        fname = fullfile(fileparts(this.project_fileName),fname);  
+                     end
+
+                     % Check the bore ID file exists.
+                     if exist(fname,'file') ~= 2;
+                        warndlg('The observed head file does not exist.');
+                        return;
+                     end
+
+                     % Read in the observed head file.
+                     try
+                        tbl = readtable(fname);
+                     catch
+                        warndlg('The observed head file could not be read in. It must a .csv file of 6 columns');
+                        return;
+                     end
+
+                     % Check there are the correct number of columns
+                     if length(tbl.Properties.VariableNames) <5 || length(tbl.Properties.VariableNames) >8
+                        warndlg({'The observed head file in one of the following structure:', ...
+                            '- boreID, year, month, day, head', ...
+                            '- boreID, year, month, day, hour, head', ...
+                            '- boreID, year, month, day, hour, minute, head', ...
+                            '- boreID, year, month, day, hour, minute, second, head', ...
+                            });
+                        return;
+                     end
+
+                     % Check columns 2 to 6 are numeric.
+                     if any(any(~isnumeric(tbl{:,2:length(tbl.Properties.VariableNames)})))
+                        warndlg(['Columns 2 to ',num2str(length(tbl.Properties.VariableNames)),' within the observed head file must contain only numeric data.']);
+                        return;
+                     end
+
+                     % Find the unique bore IDs   
+                     boreIDs = unique(tbl{:,1});
+
+                     % Free up memory
+                     clear tbl;
+
+                     % Input the unique bore IDs to the list box.
+                     set(this.tab_ModelConstruction.boreIDList,'String',boreIDs);  
+
+                     % Show the list box.
+                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [-1; 0 ; 0; 0];
+                case 'Model Type'                        
+                     % Get the current model type.
+                     modelType = data{irow,7};
+
+                     % Get description for the current model type
+                     try                         
+                        modelDecription  =eval([modelType,'.description()']);                          
+                     catch
+                        modelDecription = 'No decription is available for the selected model.';
+                     end
+
+                     % Assign model decription to GUI string box                         
+                     this.tab_ModelConstruction.modelDescriptions.String = modelDecription; 
+
+                     % Show the description.
+                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; -1 ; 0; 0];
+
+                case 'Model Options'
+                    % Check the preceeding inputs have been defined.
+                    if  any(cellfun(@(x) isempty(x), data(irow,3:7)))
+                        warndlg('The observed head data file, forcing data file, coordinates file, bore ID and model type must be input before the model option can be set.');
+                        return;
+                    end
+
+                    % Set the forcing data, bore ID, coordinates and options
+                    % for the given model type.
+                    try          
+                        modelType = eventdata.Source.Data{irow,7};
+
+                        if isempty(this.project_fileName)
+                            warningdlg('The project folder must be set before the model options can be viewed or edited.')
+                            return
+                        end
+                        if isdir(this.project_fileName)
+                            dirname = this.project_fileName;
+                        else
+                            dirname = fileparts(this.project_fileName);
+                        end                            
+
+                        fname = fullfile(dirname,data{irow,4}); 
+                        setForcingData(this.tab_ModelConstruction.modelTypes.(modelType).obj, fname);
+                        fname = fullfile(dirname,data{irow,5});
+                        setCoordinatesData(this.tab_ModelConstruction.modelTypes.(modelType).obj, fname);                        
+                        setBoreID(this.tab_ModelConstruction.modelTypes.(modelType).obj, data{irow,6});
+
+                        % If the model options are empty, then add a
+                        % default empty cell, else set the existing
+                        % options into the model type GUI RHS panel.
+                        if isempty(eventdata.Source.Data{irow,8}) || strcmp(eventdata.Source.Data{irow,8},'{}')
+                            %eventdata.Source.Data{irow,8} = [];
+                            data{irow,8} = [];
+                        end
+                        setModelOptions(this.tab_ModelConstruction.modelTypes.(modelType).obj, data{irow,8})
+
+                    catch ME
+                        warndlg('Unknown model type selected or the GUI for the selected model crashed.');
+                        this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0;  0; 0];
+                    end
+
+                     % Show model type options.
+                     switch modelType
+                         case 'model_TFN'
+                            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0;-1; 0];
+                         case 'ExpSmooth'
+                            this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0; 0; 0];
+                         otherwise
+                             this.tab_ModelConstruction.modelOptions.vbox.Heights =[0; 0; 0; 0];
+                     end
+
+                otherwise
+                     % Hide the panels.
+                     this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; 0 ; 0; 0];
+                end
+
+        end
+            
+        function modelConstruction_optionsSelection(this, hObject, eventdata)
+            try                         
+                switch this.tab_ModelConstruction.currentCol
+                    case 6
+                         % Get selected bores
+                         listSelection = get(hObject,'Value');
+
+                         % Get data from model construction table
+                         data=get(this.tab_ModelConstruction.Table,'Data'); 
+
+                         % Get the selected bore row index.
+                         index_selected = this.tab_ModelConstruction.currentRow;
+
+                         % Add selected bore ID is cell array at the currently
+                         % selected bore.
+                         data(index_selected,6) =  hObject.String(listSelection,1);
+
+                         % Set bore ID 
+                         set(this.tab_ModelConstruction.Table,'Data', data); 
+                end
+            catch ME
+                return;
+            end        
+        end
+        
+        function modelConstruction_tableEdit(this, hObject, eventdata)
+            
+            % Hide plotting toolbars
+            plotToolbarState(this, 'off');            
+            
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);                        
+            
+            % Undertake column specific operations.
+            if ~isempty(icol) && ~isempty(irow)
+
+                % Record the current row and column numbers
+                this.tab_ModelConstruction.currentRow = irow;
+                this.tab_ModelConstruction.currentCol = icol;
+            
+                % Remove HTML tags from the column name
+                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});                
+                                
+                % Warn the user if the model is already built and the
+                % inputs are to change - reuiring the model object to be
+                % removed.
+                if ~isempty(this.models) && ~strcmp(eventdata.PreviousData, eventdata.NewData) && icol~=1 && ~isempty(eventdata.PreviousData)
+                    
+                    % Get original model label
+                    if strcmp(columnName, 'Model Label')
+                        modelLabel=eventdata.PreviousData;
+                    else
+                        modelLabel = hObject.Data{irow,2};
+                    end
+                    
+                    
+                    % Convert model label to field label.
+                    model_labelAsField = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+                    
+                    % Check if the model object exists                    
+                    if any(strcmp(fieldnames(this.models), model_labelAsField))
+                       
+                        % Check if the model is calibrated
+                        try
+                            isCalibrated = this.model_labels{model_labelAsField,1};
+                        catch
+                            isCalibrated = false;
+                        end
+                        
+                        % Create warnign message and display
+                        if isCalibrated
+                            msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
+                                    '', ...                                
+                                   'Do you want to continue with the changes to the model construction?'};
+                        else
+                            msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
+                                    '', ...
+                                   'Do you want to continue with the changes to the model construction?'};
+                        end                            
+                               
+                        response = questdlg(msg,'Overwrite exiting model?','Yes','No','No');
+                        
+                        % Check if 'cancel, else delete the model object
+                        if strcmp(response,'No')
+                            return
+                        else
+                            this.models = rmfield(this.models,model_labelAsField);
+                        end
+                                                                        
+                        % Change status of the model object.
+                        hObject.Data{irow,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
+
+                        % Delete model from calibration table.
+                        modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
+                        modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
+                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
+                        this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
+
+                        % Update row numbers
+                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                     
+                        
+                        % Delete models from simulations table.
+                        modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);                            
+                        modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
+                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
+                        this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);                        
+                        
+                        % Update row numbers
+                        nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                        this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                     
+                    end
+                    
+                end
+
+                
+                switch columnName;
+                    
+                    case 'Model Label'                    
+                        % Get current and all modle labels
+                        allLabels = hObject.Data(:,2);
+                        newLabel = eventdata.NewData;
+                        
+                        % Check the model label can be converted to an
+                        % appropriate field name (for saving)                        
+                        if isempty(HydroSight_GUI.modelLabel2FieldName(newLabel))
+                            return;
+                        end
+                        
+                        % Check that the model label is unique.
+                        newLabel = HydroSight_GUI.createUniqueLabel(allLabels, newLabel, irow);                          
+                        
+                        % Report error if required
+                        if ~strcmp(newLabel, hObject.Data{irow,2})
+                            warndlg('The model label must be unique! An extension has been added to the label.','Model label error ...');
+                        end                        
+                        
+                        % Input model label to GUI
+                        hObject.Data{irow,2} = newLabel;
+                        
+                    case 'Model Options'
+                        if any(strcmp(hObject.Data{irow,7},{'model_TFN'}))
+                            modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(hObject.Data{irow,7}).obj);
+                        elseif strcmp(hObject.Data{irow,7},'expSmooth') 
+                            % do nothing
+                        else
+                            error('Model type unmkown.')
+                        end
+                        hObject.Data(irow,icol) = modelOptionsArray;
+
+                    case 'Model Type'                        
+                         % Get the current model type.
+                         modelType = hObject.Data{irow,7};
+                         
+                         % Get description for the current model type
+                         try                         
+                            modelDecription  =eval([modelType,'.description()']);                          
+                         catch
+                         	modelDecription = 'No decription is available for the selected model.';
+                         end
+                             
+                         
+                         % Assign model decription to GUI string b8ox                         
+                         this.tab_ModelConstruction.modelDescriptions.String = modelDecription; 
+                         
+                         % Show the description.
+                         this.tab_ModelConstruction.modelOptions.vbox.Heights = [0; -1 ; 0; 0];                        
+                    otherwise
+                            % Do nothing                                             
+                end
+            end
+        end
+        
+        function modelCalibration_tableSelection(this, hObject, eventdata)
+                        
+            % Get indexes to table data
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);            
+            data=get(hObject,'Data'); % get the data cell array of the table
+            
+            % Reset stored daily forcing data.
+            this.tab_ModelCalibration.resultsOptions.forcingData.data_input = [];
+            this.tab_ModelCalibration.resultsOptions.forcingData.data_derived = [];
+            this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input = {};
+            this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived = {};
+            this.tab_ModelCalibration.resultsOptions.forcingData.filt=[];
+            
+            % Exit if no cell is selected.
+            if isempty(icol) && isempty(irow)
+                return
+            end
+                
+            % Record the current row and column numbers
+            this.tab_ModelCalibration.currentRow = irow;
+            this.tab_ModelCalibration.currentCol = icol;
+
+            % Remove HTML tags from the column name
+            columnName  = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
+
+            % Exit if no results are to be viewed and none of the following are to columns edits
+            if ~(strcmp(columnName, 'Calib. Start Date') || strcmp(columnName, 'Calib. End Date'))                  
+                if isfield(this.tab_ModelCalibration,'resultsOptions')
+                    if isempty(this.tab_ModelCalibration.resultsOptions.currentTab) || ...
+                    isempty(this.tab_ModelCalibration.resultsOptions.currentPlot)
+                        return;                    
+                    end
+                else
+                    return
+                end
+            end
+
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');   
+            drawnow update;   
+
+            switch columnName;
+                case 'Calib. Start Date'
+                    % Get start and end dates of the observed head
+                    % data, remove HTML tags and then convert to a
+                    % date number.
+                    startDate = data{irow,4};
+                    endDate = data{irow,5};
+                    startDate = HydroSight_GUI.removeHTMLTags(startDate);
+                    endDate = HydroSight_GUI.removeHTMLTags(endDate);                        
+                    startDate = datenum(startDate,'dd-mmm-yyyy');
+                    endDate = datenum(endDate,'dd-mmm-yyyy');
+
+                    % Open the calander with the already input date,
+                    % else use the start date of the obs. head.
+                    if isempty(data{irow,icol})
+                        inputDate = startDate;                            
+                    else
+                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
+                    end
+                    selectedDate = uical(inputDate, 'English',startDate, endDate);
+
+                    % Get the calibration end date 
+                    calibEndDate = datenum( data{irow,icol+1},'dd-mmm-yyyy');                        
+
+                    % Check date is between start and end date of obs
+                    % head.
+                    if selectedDate < startDate || selectedDate > endDate    
+                        warndlg('The calibration start date must be within the range of the observed head data.');
+                    elseif calibEndDate<=selectedDate
+                        warndlg('The calibration start date must be less than the calibration end date.');
+                    else
+                        data{eventdata.Indices(1),eventdata.Indices(2)} = datestr(selectedDate,'dd-mmm-yyyy');
+                    end
+                    set(hObject,'Data',data);
+                case 'Calib. End Date'
+                    % Get start and end dates of the observed head
+                    % data, remove HTML tags and then convert to a
+                    % date number.
+                    startDate = data{irow,4};
+                    endDate = data{irow,5};
+                    startDate = HydroSight_GUI.removeHTMLTags(startDate);
+                    endDate = HydroSight_GUI.removeHTMLTags(endDate);
+                    startDate = datenum(startDate,'dd-mmm-yyyy');
+                    endDate = datenum(endDate,'dd-mmm-yyyy');
+
+                    % Open the calander with the already input date,
+                    % else use the start date of the obs. head.
+                    if isempty(data{irow,icol})
+                        inputDate = endDate;                            
+                    else
+                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
+                    end
+                    selectedDate = uical(inputDate, 'English',startDate, endDate);
+
+                    % Get the calibration start date 
+                    calibStartDate = datenum( data{irow,icol-1},'dd-mmm-yyyy');                        
+
+                    % Check date is between start and end date of obs
+                    % head.
+                    if selectedDate < startDate || selectedDate > endDate    
+                        warndlg('The calibration end date must be within the range of the observed head data.');
+                    elseif calibStartDate>=selectedDate
+                        warndlg('The calibration end date must be greater than the calibration start date.');
+                    else
+                        data{eventdata.Indices(1),eventdata.Indices(2)} = datestr(selectedDate,'dd-mmm-yyyy');
+                    end
+                    set(hObject,'Data',data);
+
+                otherwise
+                    % Do nothing
+            end
+            
+            % Check there is any table data
+            if isempty(data)
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                   
+                return;
+            end            
+                         
+                        
+            % Find index to the calibrated model label within the
+            % list of constructed models.
+            if isempty(irow)
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                                   
+                return;
+            end
+            
+            % Find the curretn model label
+            calibLabel = HydroSight_GUI.removeHTMLTags(data{this.tab_ModelCalibration.currentRow,2});             
+            this.tab_ModelCalibration.currentModel = calibLabel;
+            
+            % Get a copy of the model object. This is only done to
+            % minimise HDD read when the models are off loaded to HDD using
+            % matfile();
+            tmpModel = getModel(this, calibLabel);
+            
+            
+            % Update table of data
+            %-------------------------------------------------------------
+            % Display the requested calibration results if the model object
+            % exists and there are calibration results.
+            if ~isempty(tmpModel) ...
+            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
+            && tmpModel.calibrationResults.isCalibrated        
+        
+                    % Show a table of calibration data
+                    %---------------------------------
+                    % Get the model calibration data.
+                    tableData = tmpModel.calibrationResults.data.obsHead;
+                    hasModelledDistn = false;
+                    if size(tmpModel.calibrationResults.data.modelledHead,2)>2
+                        hasModelledDistn = true;
+                    end
+                    if hasModelledDistn
+                        tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
+                            tmpModel.calibrationResults.data.modelledHead(:,3),tmpModel.calibrationResults.data.modelledHead(:,4), ...                                
+                            double(tmpModel.calibrationResults.data.modelledHead_residuals(:,2:4)), ...
+                            tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];                            
+                    else
+                        tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
+                            double(tmpModel.calibrationResults.data.modelledHead_residuals(:,end)), ...
+                            tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];
+                    end
+                    % Get evaluation data
+                    if isfield(tmpModel.evaluationResults,'data')
+                        % Get data
+                        evalData = tmpModel.evaluationResults.data.obsHead;
+                        if hasModelledDistn
+                            evalData = [evalData, zeros(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
+                                tmpModel.evaluationResults.data.modelledHead(:,3), tmpModel.evaluationResults.data.modelledHead(:,4), ...
+                                double(tmpModel.evaluationResults.data.modelledHead_residuals(:,2:4)), ...
+                                tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
+
+                        else
+                            evalData = [evalData, zeros(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
+                                double(tmpModel.evaluationResults.data.modelledHead_residuals(:,end)), ...
+                                tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
+                        end
+                        % Append to table of calibration data and sort
+                        % by time.
+                        tableData = [tableData; evalData];
+                        tableData = sortrows(tableData, 1);
+                    end
+
+                    % Calculate year, month, day etc
+                    tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
+
+                    % COnvert table to a cell array so that the logical
+                    % variables can be displayed.
+                    tableData = [num2cell(tableData(:,1:6)), num2cell( tableData(:,7)==true), num2cell(tableData(:,8:end))];
+
+                    % Add data to the table.
+                    this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Contents(1).Contents(1).Data = tableData;  
+
+                    if hasModelledDistn
+                        this.tab_ModelCalibration.resultsOptions.dataTable.table.ColumnName = {'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head (50th %ile)','Mod. Head (5th %ile)','Mod. Head (95th %ile)','Model Residual (50th %ile)','Model Residual (5th %ile)','Model Residual (95th %ile)','Total Err. (5th %ile)','Total Err. (95th %ile)'};
+                    else
+                        this.tab_ModelCalibration.resultsOptions.dataTable.table.ColumnName = {'Year','Month', 'Day','Hour','Minute', 'Obs. Head','Is Calib. Point?','Mod. Head','Model Residual','Total Err. (5th %ile)','Total Err. (95th %ile)'};
+                    end
+                    
+                    % Add drop-down options for the derived data and update
+                    % plot etc
+                    %---------------------------------            
+                    % Get index to the model specific outpus tab
+                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Model Specifics');
+                    
+                    % Set the model derived drop-down options.
+                    obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data dropdown');                    
+                    derivedData_types = getDerivedDataTypes(tmpModel);  
+                    if isempty(derivedData_types)                        
+                        obj.String = {'(none)'};
+                        obj.Value = 1;
+                        
+                        % Disable tab
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind}='off';                        
+                    else
+                        % Enable tab
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind}='on';
+                        
+                        % Build drop-down data
+                        derivedData_types = strcat(derivedData_types(:,1), ':', derivedData_types(:,2));
+
+                        obj.String = derivedData_types;
+                        if obj.Value>length(obj.String)
+                            obj.Value = min(1,length(obj.String));
+                        end   
+
+                        modelCalibration_onUpdateDerivedData(this, hObject, eventdata)                
+                    end
+    
+                    %---------------------------------
+                    
+                    % Update calibration plot
+                    modelCalibration_onUpdatePlotSetting(this);
+                    
+                    % Show model parameter data
+                    %---------------------------------
+                    %Get parameters and names 
+                    [paramValues, paramsNames] = getParameters(tmpModel.model);  
+
+                    % Add to the table
+                    obj = findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel, 'Tag','Model Calibration - parameter table');
+                    obj.Data = cell(size(paramValues,1),size(paramValues,2)+2);
+                    obj.Data(:,1) = paramsNames(:,1);
+                    obj.Data(:,2) = paramsNames(:,2);
+                    obj.Data(:,3:end) = num2cell(paramValues);
+
+                    nparams=size(paramValues,2);                        
+                    colnames = cell(nparams+2,1);
+                    colnames{1,1}='Component Name';
+                    colnames{2,1}='Parameter Name';
+                    colnames(3:end,1) = strcat(repmat({'Parm. Set '},1,nparams)',num2str([1:nparams]'));
+                    obj.ColumnName = colnames;
+                    %---------------------------------                    
+                                        
+                    % Show derived parameter data
+                    %---------------------------------
+                    % Get table
+                    obj = findobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel, 'Tag','Model Calibration - derived parameter table');
+                    
+                    %Get parameters and names 
+                    [derivedParamValues, derivedParamsNames] = getDerivedParameters(tmpModel);        
+
+                    % Get tab index for the forcing data tab.
+                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Derived Parameters');                                        
+                    
+                    % Add to the table (if not empty)
+                    if isempty(derivedParamValues)
+                        obj.Data = [];
+                        obj.ColumnName = {};
+                        
+                        % Disable tab if there is no data 
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'off';  
+                    else
+                        % Enable tab if there is no data 
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'on';
+                        
+                        obj.Data = cell(size(derivedParamValues,1),size(derivedParamValues,2)+2);
+                        obj.Data(:,1) = derivedParamsNames(:,1);
+                        obj.Data(:,2) = derivedParamsNames(:,2);
+                        obj.Data(:,3:end) = num2cell(derivedParamValues);
+
+                        nderivedParams=size(derivedParamValues,2);                        
+                        colnames = cell(nderivedParams+2,1);
+                        colnames{1,1}='Component Name';
+                        colnames{2,1}='Parameter Name';
+                        colnames(3:end,1) = strcat(repmat({'Parm. Set '},1,nderivedParams)',num2str([1:nderivedParams]'));
+                        obj.ColumnName = colnames;
+                    end
+                    %---------------------------------                               
+                    
+                    % Show model forcing data
+                    %---------------------------------                                        
+                    % Get the input forcing data
+                    [tableData, forcingData_colnames] = getForcingData(tmpModel);
+                    
+                    % Get tab index for the forcing data tab.
+                    tab_ind = strcmp(this.tab_ModelCalibration.resultsTabs.TabTitles,'Forcing Data');                    
+                    
+                    % Get the model derived forcing data (if not empty)
+                    if isempty(tableData)
+                        tableData = [];
+                        tableData_derived = [];
+                        forcingData_colnames = {};
+                        forcingData_colnames_derived= {};
+                        this.tab_ModelCalibration.resultsOptions.forcingData.filt = [];
+                        
+                        % Disable tab if there is no data 
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'off';
+                    else
+                        % Enable tab if there is no data 
+                        this.tab_ModelCalibration.resultsTabs.TabEnables{tab_ind} = 'on';
+                        
+                        
+                        if size(paramValues,2)>1
+                            % Get forcing data from the first parameter set
+                            setParameters(tmpModel.model,paramValues(:,1), paramsNames);  
+                            [tableData_derived_tmp, forcingData_colnames_derived] = getDerivedForcingData(tmpModel,tableData(:,1));                                        
+
+                            % Initialise derived forcing data matrix
+                            tableData_derived = nan(size(tableData_derived_tmp,1),size(tableData_derived_tmp,2), size(paramValues,2));
+                            tableData_derived(:,:,1) = tableData_derived_tmp;
+                            clear tableData_derived_tmp;
+
+                            % Change cursor
+                            set(this.Figure, 'pointer', 'watch');   
+                            drawnow update;                         
+
+                            % Derive forcing using parfor. 
+                            % Note, the use of a waitbar with parfor is adapted
+                            % from http://au.mathworks.com/matlabcentral/newsreader/view_thread/166139                        
+                            poolobj = gcp('nocreate');
+                            matlabpoolsize = max(1,poolobj.NumWorkers);
+                            nparamSets = size(paramValues,2);
+                            nLoops = 10;
+                            nparamSetsPerLoop =  ceil(nparamSets/nLoops);
+                            startInd = 0;
+                            h = waitbar(0, ['Calculating transformed forcing for ', num2str(size(paramValues,2)), ' parameter sets. Please wait ...']);
+                            t = tableData(:,1);                        
+                            % do a for loop that is big enough to do all necessary iterations
+
+                            for n=1:nLoops ;
+                                startInd = min(startInd + (n-1)*nparamSetsPerLoop+1,nparamSets);
+                                endInd = min(startInd +nparamSetsPerLoop,nparamSets);
+                                parfor z = startInd:endInd 
+                                    setParameters(tmpModel.model,paramValues(:,z), paramsNames);  
+                                    tableData_derived(:,:,z) = getDerivedForcingData(tmpModel,t);    
+                                end
+                                % update waitbar each "matlabpoolsize"th iteration
+                                waitbar(n/nLoops);
+                            end
+                            close(h);                                                                    
+
+                            % Change cursor
+                            set(this.Figure, 'pointer', 'watch');   
+                            drawnow update;                         
+
+                            % Reset all parameters
+                            setParameters(tmpModel.model,paramValues, paramsNames);  
+
+                            % Clear model object
+                            clear tmpModel
+
+                        else
+                            [tableData_derived, forcingData_colnames_derived] = getDerivedForcingData(tmpModel,tableData(:,1)); 
+                        end
+
+                        % Calculate year, month, day etc
+                        t = datetime(tableData(:,1), 'ConvertFrom','datenum');
+                        tableData = [year(t), quarter(t), month(t), week(t,'weekofyear'), day(t), tableData(:,2:end)];                    
+                        forcingData_colnames = {'Year','Quarter','Month','Week','Day', forcingData_colnames{2:end}};                        
+                    end
+
+                    
+                    % Store the daily forcing data. This is just done to
+                    % avoid re-loading the model within updateForcingData()
+                    % and updateForcinfPlot().
+                    this.tab_ModelCalibration.resultsOptions.forcingData.data_input = tableData;
+                    this.tab_ModelCalibration.resultsOptions.forcingData.data_derived = tableData_derived;
+                    this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input = forcingData_colnames;
+                    this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived = forcingData_colnames_derived;
+                    this.tab_ModelCalibration.resultsOptions.forcingData.filt=true(size(tableData,1),1);
+                                  
+                    % Free up RAM
+                    clear tableData_derived tableData
+                    
+                    % Update table and plots
+                    if ~isempty(this.tab_ModelCalibration.resultsOptions.forcingData.data_input)
+                        modelCalibration_onUpdateForcingData(this)
+                    end
+                    %---------------------------------
+            end
+                           
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');      
+            drawnow update;
+        end                
+        
+        function modelCalibration_onUpdatePlotSetting(this, hObject, eventdata)
+            
+            % Get selected popup menu item
+            plotID = this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Contents(1).Contents(2).Contents(2).Value;
+            
+            % Get a copy of the model object. This is only done to
+            % minimise HDD read when the models are off loaded to HDD using
+            % matfile();
+            tmpModel = getModel(this, this.tab_ModelCalibration.currentModel);
+
+            % Exit if model not found.
+            if isempty(tmpModel)    
+                % Turn off plot icons
+                plotToolbarState(this,'off');  
+                
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                                                   
+                return;
+            end                                             
+            
+            % Display the requested calibration results if the model object
+            % exists and there are calibration results.
+            if ~isempty(tmpModel) ...
+            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
+            && tmpModel.calibrationResults.isCalibrated        
+                
+                % Plot calibration result.
+                %-----------------------
+                % Create an axis handle for the figure.
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.calibPanel,'Tag','Model Calibration - results plot');
+                delete( findobj(obj ,'type','axes'));
+                delete( findobj(obj ,'type','legend'));     
+                
+                obj = uipanel('Parent', obj);
+                axisHandle = axes( 'Parent', obj);
+                % Show the calibration plots. NOTE: QQ plot type
+                % fails so is skipped
+                if plotID<=4
+                    calibrateModelPlotResults(tmpModel, plotID, axisHandle);
+                else
+                    calibrateModelPlotResults(tmpModel, plotID+1, axisHandle);
+                end                                
+                
+                % Plot parameters
+                %-----------------------
+                [paramValues, paramsNames] = getParameters(tmpModel.model);                          
+                paramsNames  = strrep(paramsNames(:,2), '_',' ');
+
+                % Create an axis handle for the figure.
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel, 'Tag','Model Calibration - parameter plot');
+                delete( findobj(obj ,'type','axes'));
+                %delete( findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2),'type','legend'));     
+                %delete( findobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2),'type','uipanel'));     
+                %h = uipanel('Parent', this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(2));
+                axisHandle = axes( 'Parent',obj);
+
+                if size(paramValues,2)==1
+                    bar(axisHandle,paramValues);
+                    set(axisHandle, 'xTickLabel', paramsNames,'FontSize',8,'xTickLabelRotation',45);
+                    ylabel(axisHandle,'Param. value');
+                else
+                    % A bug seems to occur when the builtin plotmatrix is ran to produce a plot inside a GUI 
+                    % whereby the default fig menu items and icons
+                    % appear. The version of plotmatrix below has a
+                    % a few lines commented out to supress this
+                    % proble,m (see lines 232-236)                            
+                    [~, ax] = plotmatrix(axisHandle,paramValues', '.');      
+                    for i=1:size(ax,1)
+                        ylabel(ax(i,1), paramsNames(i),'FontSize',8);
+                        xlabel(ax(end,i), paramsNames(i),'FontSize',8);
+                    end
+                end                
+                
+                % Plot derived parameters
+                %-----------------------
+                % Create an axis handle for the figure.
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel, 'Tag','Model Calibration - derived parameter plot');
+                delete( findobj(obj ,'type','axes'));
+                axisHandle = axes( 'Parent',obj);
+
+                % Get data and show
+                [paramValues, paramsNames] = getDerivedParameters(tmpModel);                          
+                if ~isempty(paramValues)
+                    
+                    paramsNames = paramsNames(:,2);
+                    ind=strfind(paramsNames,':');
+                    for i=1:length(ind)
+                       if ~isempty(ind{i})
+                           paramsNames{i} = paramsNames{i}(1:max(1,ind{i}-1));
+                       end
+                    end
+                    paramsNames  = strrep(paramsNames, '_',' ');
+
+                    if size(paramValues,2)==1
+                        bar(axisHandle,paramValues);
+                        set(axisHandle, 'xTick', 1:length(paramsNames));
+                        set(axisHandle, 'xTickLabel', paramsNames,'FontSize',8,'xTickLabelRotation',45);
+                        ylabel(axisHandle,'Derived param. value');
+                    else
+                        % A bug seems to occur when the builtin plotmatrix is ran to produce a plot inside a GUI 
+                        % whereby the default fig menu items and icons
+                        % appear. The version of plotmatrix below has a
+                        % a few lines commented out to supress this
+                        % proble,m (see lines 232-236)                            
+                        [~, ax] = plotmatrix(axisHandle,paramValues', '.');      
+                        for i=1:size(ax,1)
+                            ylabel(ax(i,1), paramsNames(i),'FontSize',8);
+                            xlabel(ax(end,i), paramsNames(i),'FontSize',8);
+                        end
+                    end                       
+                    
+                end                                           
+                %-----------------------         
+                                
+                drawnow update;
+            end
+           
+            % Turn on plot icons
+            plotToolbarState(this,'on');            
+
+            % Chane cursor
+            set(this.Figure, 'pointer', 'arrow');   
+            drawnow update;           
+        end
+        
+        function modelCalibration_onUpdateForcingData(this, hObject, eventdata)
+            
+            % Get time step value
+            timestepID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(2).Value;
+            
+            % Get the calculate for the time stepa aggregation
+            calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
+            calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
+            calcString = calcString{calcID};
+            
+            % check the start and end dates
+            sdate = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(6).String;
+            edate = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(8).String;
+            try
+                if isempty(sdate)
+                    sdate = datetime('01/01/0001','InputFormat','dd/MM/yyyy');
+                else
+                    sdate=datetime(sdate,'InputFormat','dd/MM/yyyy');
+                end
+            catch me
+               errordlg('The start date does not appear to be in the correct format of dd/mm/yyy.','Input date error ...');
+               return;
+            end
+            try
+                if isempty(edate)
+                    edate= datetime('31/12/9999','InputFormat','dd/MM/yyyy');
+                else                
+                    edate = datetime(edate,'InputFormat','dd/MM/yyyy');
+                end
+            catch me
+               errordlg('The end date does not appear to be in the correct format of dd/mm/yyy.','Input date error ...');
+               return;
+            end            
+            if sdate > edate
+               errordlg('The end date must be after the start date.','Input date error ...');
+               return;                
+            end            
+                        
+            % Get daily input forcing data.
+            tableData = this.tab_ModelCalibration.resultsOptions.forcingData.data_input;
+            forcingData_colnames = this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input;
+                       
+            % Exit if no data
+            if isempty(tableData)
+                return;
+            end
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');      
+            drawnow update;
+            
+            % Get daily model derived forcing data.
+            tableData_derived = this.tab_ModelCalibration.resultsOptions.forcingData.data_derived;
+            forcingData_colnames_derived = this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived;            
+                        
+            % Filter the table data by the input dates
+            t = datetime(tableData(:,1),tableData(:,3),tableData(:,5));
+            filt = t>=sdate & t<=edate;
+            tableData = tableData(filt,:);
+            if length(size(tableData_derived))==3
+                tableData_derived = tableData_derived(filt,:,:);
+            else
+                tableData_derived = tableData_derived(filt,:);
+            end
+            
+            % Add filter to object for plotting            
+            this.tab_ModelCalibration.resultsOptions.forcingData.filt = filt;                        
+            
+            % Build forcing data at requested time step
+            switch timestepID
+                case 1  %daily
+                    ind = [1:size(tableData,1)]';
+                case 2  % weekly
+                     [~,~,ind] = unique(tableData(:,[1,4]),'rows');                           
+                case 3  % monthly
+                    [~,~,ind] = unique(tableData(:,[1,3]),'rows');                        
+                case 4  % quarterly
+                    [~,~,ind] = unique(tableData(:,[1,2]),'rows');                        
+                case 5  % annually
+                    [~,~,ind] = unique(tableData(:,1),'rows');                        
+                case 6  % all data
+                    ind = ones(size(tableData,1),1);
+                otherwise
+                    error('Unknown forcing data time ste.')
+            end
+
+            % Set function for aggregation equation
+            switch calcID             
+                case 1  % sum
+                    fhandle = @sum;                    
+                case 2
+                    fhandle = @mean;
+                case 3
+                    fhandle = @std;
+                case 4
+                    fhandle = @var;
+                case 5
+                    fhandle = @skewness;
+                case 6
+                    fhandle = @min;
+                case {7,8,9,10,11,12,13}
+                    p = str2num(calcString(1:length(calcString)-7));
+                    fhandle = @(x) prctile(x,p);
+                case 14
+                    fhandle = @max;
+                case 15
+                    fhandle = @iqr;                    
+                case 16
+                    fhandle = @(x) sum(x==0);
+                case 17
+                    fhandle = @(x) sum(x<0);
+                case 18
+                    fhandle = @(x) sum(x>0);
+                otherwise
+                    error('Equation for the aggregation of daily data is unkown.');
+            end
+            
+            % Upscale input data
+            %----------
+            % Only upacale the data if the time step is greater than daily
+            if timestepID>1            
+                tableData_noDates = tableData(:,6:end);
+                [rowidx, colidx] = ndgrid(ind, 1:size(tableData_noDates, 2)) ;
+                upscaledData = accumarray([rowidx(:) colidx(:)], tableData_noDates(:), [], fhandle);                
+            else
+                upscaledData = tableData(:,6:end);
+            end
+            
+            % Upscale derived data and compine with upscaled input data
+            if length(size(tableData_derived))==3
+                % Build colume names without %iles
+                forcingData_colnames_yaxis = {forcingData_colnames{:}, forcingData_colnames_derived{:}};
+                
+                % Upscale derived data
+                for i=1:1:size(tableData_derived, 2)
+                    % Only upscale the data if the time step is greater than daily
+                    if timestepID>1                                
+                        [rowidx, colidx, depthidx] = ndgrid(ind, 1, 1:size(tableData_derived, 3));
+                        upscaledData_derived_prctiles = tableData_derived(:,i,:);
+                        upscaledData_derived_prctiles = accumarray([rowidx(:) colidx(:) depthidx(:)], upscaledData_derived_prctiles(:), [], fhandle);                
+                    else
+                        upscaledData_derived_prctiles = tableData_derived(:,i,:);
+                    end
+                
+                    % Calculate percentiles for the upscaled data
+                    upscaledData_derived_prctiles = prctile( upscaledData_derived_prctiles,[5 10 25 50 75 90 95],3);
+                    upscaledData_derived_prctiles = permute(upscaledData_derived_prctiles,[1 3 2]);
+
+                    % Merge input forcing and percentiles of upsaled derived
+                    % forcing
+                    upscaledData = [upscaledData, upscaledData_derived_prctiles];
+                    
+                    
+                    % Build column names
+                    forcingData_colnames = {forcingData_colnames{:}, ...
+                                            [forcingData_colnames_derived{i},'-05th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-10th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-25th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-50th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-75th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-90th%ile'], ...
+                                            [forcingData_colnames_derived{i},'-95th%ile']};                
+                end
+                                                            
+            else    % Upscale derived data
+                                
+                % Only upscale the data if the time step is greater than daily
+                if timestepID>1                                                
+                    [rowidx, colidx] = ndgrid(ind, 1:size(tableData_derived, 2)) ;
+                    upscaledData_derived = accumarray([rowidx(:) colidx(:)], tableData_derived(:), [], fhandle);                
+                    upscaledData = [upscaledData, upscaledData_derived];
+                else
+                    upscaledData = [upscaledData, tableData_derived];
+                end
+                
+                forcingData_colnames = {forcingData_colnames{:}, forcingData_colnames_derived{:}};
+                forcingData_colnames_yaxis = forcingData_colnames;
+            end
+            
+            % Build date column.
+            tableData_year = accumarray(ind,tableData(:,1),[],@max);
+            tableData_quarter = accumarray(ind,tableData(:,2),[],@max);
+            tableData_month = accumarray(ind,tableData(:,3),[],@max);            
+            tableData_week = accumarray(ind,tableData(:,4),[],@max);                            
+            tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
+            
+            % Build new table
+            tableData = [tableData_year, tableData_quarter, tableData_month, tableData_week, tableData_day, upscaledData];
+            
+            % Add to the table
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).Data = tableData;
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).ColumnName = forcingData_colnames;
+
+            % Get the plotting type
+            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
+            plotType = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
+            plotType = plotType{plotType_val};            
+            
+            % Get the currently selected x and y axis options
+            forcingData_colnames_xaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String;
+            forcingData_colnames_yaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String;
+            forcingData_value_xaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value;
+            forcingData_value_yaxis_prior = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value;            
+                        
+            % Update the drop-down x and y axis options
+            if any(strfind(plotType,'box-plot'))
+                forcingData_colnames_xaxis = {'Date','(none)'};
+            else
+                %ind = strfind(forcingData_colnames,'th%ile');
+                %forcingData_colnames(k>0)=forcingData_colnames(k>0){1:k-3};
+                forcingData_colnames_xaxis = {'Date', forcingData_colnames_yaxis{6:end},'(none)'};
+            end
+            forcingData_colnames_yaxis = {'Date', forcingData_colnames_yaxis{6:end},'(none)'};
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String = forcingData_colnames_xaxis;
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String = forcingData_colnames_yaxis;
+
+            % Update the selected x anbd y axis items. To do this, the
+            % original item is selected, else reset to 1.
+            if ~isempty(forcingData_value_xaxis_prior ) && forcingData_value_xaxis_prior >0
+                ind = find(strcmp(forcingData_colnames_xaxis, forcingData_colnames_xaxis_prior{forcingData_value_xaxis_prior}));
+                if ~isempty(ind)
+                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = ind;    
+                else
+                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
+                end                    
+            else
+                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
+            end
+            if ~isempty(forcingData_value_yaxis_prior ) && forcingData_value_yaxis_prior >0
+                ind = find(strcmp(forcingData_colnames_yaxis, forcingData_colnames_yaxis_prior{forcingData_value_yaxis_prior}));
+                if ~isempty(ind)
+                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = ind;    
+                else
+                    this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = 1;
+                end                    
+            else
+                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value = 1;
+            end            
+            % Update forcing plot
+            modelCalibration_onUpdateForcingPlot(this)
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');      
+            drawnow update;
+            
+        end
+
+        function modelCalibration_onUpdateForcingPlotType(this, hObject, eventdata)
+            
+            % Get the forcing data columns
+            forcingData_colnames = {this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input{:}, ...
+                this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived{:} };            
+            
+           % Remove those with %iles
+           filt = cellfun(@(x) ~isempty(strfind(x,'-05th%ile')) || ...
+                  ~isempty(strfind(x,'-10th%ile')) || ...
+                  ~isempty(strfind(x,'-25th%ile')) || ...                                   
+                  ~isempty(strfind(x,'-50th%ile')) || ...                                   
+                  ~isempty(strfind(x,'-75th%ile')) || ...                                   
+                  ~isempty(strfind(x,'-90th%ile')) || ...                                   
+                  ~isempty(strfind(x,'-95th%ile')) ...                                   
+                  ,forcingData_colnames);       
+            if any(filt)
+                forcingData_colnames_wpcntiles = forcingData_colnames(filt);  
+                forcingData_colnames_wpcntiles = cellfun(@(x) x(1:length(x) - 9),forcingData_colnames_wpcntiles);
+                forcingData_colnames_wpcntiles = unique(forcingData_colnames_wpcntiles );
+                forcingData_colnames = {forcingData_colnames{~filt}, forcingData_colnames_wpcntiles{:}};
+            end
+              
+              
+            % Get the plotting type
+            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
+            plotType = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
+            plotType = plotType{plotType_val};
+            
+            % Update the drop-down x and y axis options
+            if any(strfind(plotType,'box-plot'))
+                this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value = 1;
+                forcingData_colnames_xaxis = {'Date','(none)'};
+            else
+                forcingData_colnames_xaxis = {'Date', forcingData_colnames{6:end},'(none)'};
+            end
+            forcingData_colnames_yaxis = {'Date', forcingData_colnames{6:end},'(none)'};
+            
+            
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String = forcingData_colnames_xaxis ;
+            this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String = forcingData_colnames_yaxis;
+            
+        end
+        
+        function modelCalibration_onUpdateForcingPlot(this, hObject, eventdata)
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');      
+            drawnow update;
+                        
+            % Turn on plot icons
+            plotToolbarState(this,'on');
+            
+            % Get the calculate for the time step aggregation
+            calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
+            calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
+            calcString = calcString{calcID};            
+            
+            % Get the user plotting settings
+            plotType_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).String;
+            plotType_val = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(2).Value;
+            
+            xaxis_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String;
+            xaxis_val = min(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).Value, ...
+                        length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(4).String));
+            
+            yaxis_options = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String;
+            yaxis_val = min(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).Value, ...
+                        length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(3).Contents(6).String));
+                        
+            % Get the data
+            tableData = this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).Data;
+            forcingData_colnames = this.tab_ModelCalibration.resultsOptions.forcingPanel.Children.Children(3).ColumnName;
+            
+            % Calc date
+            if xaxis_val==1 || yaxis_val==1
+                forcingDates = datetime(tableData(:,1), tableData(:,3), tableData(:,5));
+            end
+            
+            %Check if a box plot is to be created
+            plotType_isBoxPlot = false;
+            if plotType_val>=6 && plotType_val<=9
+                plotType_isBoxPlot = true;
+            end
+            
+            % Get axis data
+            xdataHasErrorVals =  false;
+            ydataHasErrorVals =  false;
+            if xaxis_val==1
+                xdata = forcingDates;
+                xdataLabel = 'Date';
+            elseif xaxis_val~=length(xaxis_options)
+                
+               % Get the data type to plot
+               xdataLabel = xaxis_options{xaxis_val};
+                
+               % Find the  data columns with the requsted name and extract
+               % comulns of data.
+               filt = cellfun(@(x) ~isempty(strfind(x,[xdataLabel,'-05th%ile'])) || ...
+                      ~isempty(strfind(x,[xdataLabel,'-10th%ile'])) || ...
+                      ~isempty(strfind(x,[xdataLabel,'-25th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[xdataLabel,'-50th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[xdataLabel,'-75th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[xdataLabel,'-90th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[xdataLabel,'-95th%ile'])) ...                                   
+                      ,forcingData_colnames);   
+               if ~any(filt)
+                    filt =  strcmp(forcingData_colnames,xdataLabel);
+               end                  
+               xdata =  tableData(:,filt);
+               xdataHasErrorVals = sum(filt)>1;
+                               
+               % Build the y-label
+               xSeriesLabel = forcingData_colnames(filt);               
+               xSeriesLabel = strrep(xSeriesLabel,'_',' ');
+               xdataLabel = strrep(xdataLabel,'_',' ');
+               if ~plotType_isBoxPlot 
+                xdataLabel = [calcString,' of ',xaxis_options{xaxis_val}];
+                xdataLabel = strrep(xdataLabel,'_',' ');
+               end
+            else
+               xdata = [];
+               xdataLabel = '(none)';
+            end
+            if yaxis_val==1
+                ydata = forcingDates;
+                ydataLabel = 'Date';
+            elseif yaxis_val~=length(yaxis_options)
+               % Get the data type to plot
+               ydataLabel = yaxis_options{yaxis_val};
+                
+               % Find the  data columns with the requsted name and extract
+               % comulns of data.
+               filt = cellfun(@(x) ~isempty(strfind(x,[ydataLabel,'-05th%ile'])) || ...
+                      ~isempty(strfind(x,[ydataLabel,'-10th%ile'])) || ...
+                      ~isempty(strfind(x,[ydataLabel,'-25th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[ydataLabel,'-50th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[ydataLabel,'-75th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[ydataLabel,'-90th%ile'])) || ...                                   
+                      ~isempty(strfind(x,[ydataLabel,'-95th%ile'])) ...                                   
+                      ,forcingData_colnames);               
+               if ~any(filt)
+                    filt =  strcmp(forcingData_colnames,ydataLabel);
+               end
+               ydata =  tableData(:,filt);
+               ydataHasErrorVals = sum(filt)>1;
+               
+               % Build the y-label and series label
+               ySeriesLabel = forcingData_colnames(filt);               
+               ySeriesLabel = strrep(ySeriesLabel,'_',' ');
+               ydataLabel = strrep(ydataLabel,'_',' ');
+               if ~plotType_isBoxPlot 
+                ydataLabel = [calcString,' of ',ydataLabel];
+               end
+            else
+               ydata = [];
+               ydataLabel = '(none)';                
+            end
+
+            % Create an axis handle for the figure.
+            delete( findobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4),'type','axes'));
+            axisHandle = axes( 'Parent',this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4));            
+            
+            % Exit of no data to plot (and not plotting a distrbution)
+            if isempty(ydata) && plotType_val<4
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');      
+                drawnow update;       
+                
+                return
+            end
+            
+            % Check if using date for either axis            
+            xdata_isdate=false;
+            ydata_isdate=false;
+            if isdatetime(xdata)                        
+                xdata_isdate=true;
+            end
+            if isdatetime(ydata)            
+                ydata_isdate=true;
+            end            
+            
+                        
+            switch plotType_val
+                case {1,2}     
+                    plotSymbol = 'b.-';
+                    if plotType_val==2
+                        plotSymbol = 'b.';
+                    end
+                    if xdataHasErrorVals && ydataHasErrorVals
+                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
+                        hold(axisHandle,'on');
+                        try
+                            errorbar(axisHandle,xdata(:,4), ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)), ...
+                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);                        
+                        catch
+                            errorbar(axisHandle,xdata(:,4), ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)), ...
+                                'linestyle','none','color',[0.6 0.6 0.6]);                                                    
+                        end
+                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
+                        hold(axisHandle,'off');
+                    elseif ~xdataHasErrorVals && xdata_isdate && ydataHasErrorVals
+
+                        if xdata_isdate
+                            xdata = datenum(xdata);
+                        end
+                        XFill = [xdata' fliplr(xdata')];
+                        YFill = [ydata(:,1)', fliplr(ydata(:,7)')];                   
+                        fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
+                        hold(axisHandle,'on');                    
+                        YFill = [ydata(:,2)', fliplr(ydata(:,6)')];                   
+                        fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
+                        hold(axisHandle,'on');
+                        YFill = [ydata(:,3)', fliplr(ydata(:,5)')];                   
+                        fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
+                        hold(axisHandle,'on');
+                        clear XFill YFill     
+
+                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
+                        hold(axisHandle,'off');
+                        
+                        % Date date axis. NOTE, code adopted from dateaxis.m.
+                        % Pre-2016B dateaxis did not allow input of axis
+                        % handle.                        
+                        if xdata_isdate;
+                            dateaxis_local(axisHandle,'x');
+                        end
+                        if ydata_isdate;
+                            dateaxis_local(axisHandle,'y');
+                        end                
+                        legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');
+                    elseif ~xdataHasErrorVals && ~xdata_isdate && ydataHasErrorVals                        
+                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
+                        hold(axisHandle,'on');
+                        errorbar(axisHandle,xdata, ydata(:,4), abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);
+                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
+                        hold(axisHandle,'off');                 
+                    elseif xdataHasErrorVals && ~ydataHasErrorVals            
+                        plot(axisHandle,xdata, ydata(:,4),plotSymbol);
+                        try
+                            hold(axisHandle,'on');
+                            errorbar(axisHandle,xdata(:,4), ydata, ...
+                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)), 'ornt','horizontal','linestyle','none','color',[0.6 0.6 0.6]);                                                
+                        catch
+                            % do nothing
+                        end           
+                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
+                        hold(axisHandle,'off');
+                    else
+                        plot(axisHandle,xdata, ydata,plotSymbol);
+                        axis(axisHandle,'tight');                                                
+                    end
+                    xlabel(axisHandle,xdataLabel);  
+                    ylabel(axisHandle,ydataLabel);             
+                    
+                case 3
+                    if strcmp(xdataLabel,'(none)') || strcmp(ydataLabel,'(none)')
+                        errordlg('A bar plot requires both x-axis and y-axis inputs.','Axis selection error...');
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');      
+                        drawnow update;
+                                                
+                        return;
+                    end
+                    if xdata_isdate
+                        xdata=datenum(xdata);
+                        if ydataHasErrorVals
+                            bar(axisHandle,xdata, ydata(:,4)); 
+                        else
+                            bar(axisHandle,xdata, ydata);
+                        end
+                    end
+                    if ydata_isdate
+                        ydata=datenum(ydata);
+                        if xdataHasErrorVals
+                            barh(axisHandle,ydata, xdata(:,4));
+                        else
+                            barh(axisHandle,ydata, xdata);
+                        end
+                    end                
+                    if ~xdata_isdate && ~ydata_isdate
+                        errordlg('A bar plot requires either the x-axis or y-axis to be "Date".','Axis selection error...');
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');      
+                        drawnow update;                        
+                        
+                        return;
+                    end
+                    
+                    if xdataHasErrorVals && ~xdata_isdate
+                        try
+                            hold(axisHandle,'on');
+                            errorbar(axisHandle,xdata(:,4), ydata, ...
+                            abs(xdata(:,1)-xdata(:,4)), abs(xdata(:,7)-xdata(:,4)), 'ornt','horizontal','linestyle','none','color',[0.6 0.6 0.6]);                                                
+                            legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
+                        catch
+                            % do nothing
+                        end           
+                        hold(axisHandle,'off');
+                    elseif ydataHasErrorVals && ~ydata_isdate
+                        hold(axisHandle,'on');
+                        errorbar(axisHandle,xdata, ydata(:,4), ...
+                        abs(ydata(:,1)-ydata(:,4)), abs(ydata(:,7)-ydata(:,4)),'linestyle','none','color',[0.6 0.6 0.6]);                                                
+                        legend(axisHandle, 'median','5-95th%ile','Location', 'northeastoutside');
+                        hold(axisHandle,'off');
+                    end
+                                                            
+                    xlabel(axisHandle,xdataLabel);  
+                    ylabel(axisHandle,ydataLabel);    
+                    % Date date axis. NOTE, code adaopted from dateaxis.m.
+                    % Pre-2016B dateaxis did not allow input of axis
+                    % handle.
+                    if xdata_isdate;
+                        dateaxis_local(axisHandle,'x');
+                    end
+                    if ydata_isdate;
+                        dateaxis_local(axisHandle,'y');
+                    end
+                    axis(axisHandle,'tight');
+                case 4                    
+                    if strcmp(ydataLabel,'(none)')
+                        if xdataHasErrorVals
+                            xdata=xdata(:,4);
+                        end
+                        histogram(axisHandle,xdata, floor(sqrt(length(xdata))),'Normalization','probability');
+                        ylabel(axisHandle,'Probability');             
+                        xlabel(axisHandle,xdataLabel);     
+                        if xdataHasErrorVals
+                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
+                        end
+                    elseif strcmp(xdataLabel,'(none)')
+                        if ydataHasErrorVals
+                            ydata=ydata(:,4);
+                        end                        
+                        histogram(axisHandle,ydata, floor(sqrt(length(ydata))),'Normalization','probability');
+                        ylabel(axisHandle,'Probability');             
+                        xlabel(axisHandle,ydataLabel);                  
+                        if ydataHasErrorVals
+                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
+                        end                        
+                    elseif ~strcmp(xdataLabel,'(none)') && ~strcmp(xdataLabel,'(none)')
+                        % Plot median value
+                        if xdataHasErrorVals
+                            xdata=xdata(:,4);
+                        end                        
+                        if ydataHasErrorVals
+                            ydata=ydata(:,4);
+                        end                        
+                                                
+                        % Convert date is to be plotted
+                        if xdata_isdate
+                            xdata=datenum(xdata);
+                        end
+                        if ydata_isdate
+                            ydata=datenum(ydata);
+                        end
+                        
+                        % Make bivariate histogram
+                        histogram2(axisHandle,xdata,ydata, floor(sqrt(length(xdata))), 'DisplayStyle','tile','ShowEmptyBins','on','Normalization','probability');
+                        h = colorbar(axisHandle);
+                        xlabel(axisHandle,xdataLabel);  
+                        ylabel(axisHandle,ydataLabel);                  
+                        ylabel(h,'Probability'); 
+                        box(axisHandle,'on');
+                        
+                        % Date date axis. NOTE, code adaopted from dateaxis.m.
+                        % Pre-2016B dateaxis did not allow input of axis
+                        % handle.                        
+                        if xdata_isdate;
+                            dateaxis_local(axisHandle,'x');
+                        end
+                        if ydata_isdate;
+                            dateaxis_local(axisHandle,'y');
+                        end
+                        if xdataHasErrorVals || ydataHasErrorVals
+                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
+                        end                        
+                    end
+                case 5
+                    % Convert date is to be plotted
+                    if xdata_isdate
+                        xdata=datenum(xdata);
+                    end
+                    if ydata_isdate
+                        ydata=datenum(ydata);
+                    end
+
+                    % Make CDF plot
+                    if strcmp(ydataLabel,'(none)')
+                        if xdataHasErrorVals
+                            [f, xtmp] = ecdf(xdata(:,1));                            
+                            plot(axisHandle, xtmp, f,'linestyle',':','color',[0.8 0.8 0.8]);                            
+                            hold(axisHandle,'on');
+                            [f, xtmp] = ecdf(xdata(:,2));                            
+                            plot(axisHandle, xtmp, f,'linestyle','-.','color',[0.6 0.6 0.6]);                            
+                            [f, xtmp] = ecdf(xdata(:,3));                            
+                            plot(axisHandle, xtmp, f,'linestyle','--','color',[0.4 0.4 0.4]);                                                        
+                            [f, xtmp] = ecdf(xdata(:,4));
+                            plot(axisHandle, xtmp, f,'b.-');
+                            [f, xtmp] = ecdf(xdata(:,5));                            
+                            plot(axisHandle, xtmp, f,'linestyle','--','color',[0.4 0.4 0.4]);                            
+                            [f, xtmp] = ecdf(xdata(:,6));                            
+                            plot(axisHandle, xtmp, f,'linestyle','-.','color',[0.6 0.6 0.6]);                                                        
+                            [f, xtmp] = ecdf(xdata(:,7));                            
+                            plot(axisHandle, xtmp, f,'linestyle',':','color',[0.8 0.8 0.8]);                            
+                            legend(axisHandle,' 5th%ile','10th%ile','25th%ile','50th%ile','75th%ile','90th%ile','95th%ile','Location', 'northeastoutside');
+                            hold(axisHandle,'off');
+                        else
+                            [f, xdata] = ecdf(xdata);
+                            stairs(axisHandle, xdata, f,'b.-');
+                        end
+                        ylabel(axisHandle,'Probability');             
+                        xlabel(axisHandle,xdataLabel);                  
+                    elseif strcmp(xdataLabel,'(none)')
+                        [f, xdata] = ecdf(ydata);
+                        stairs(axisHandle, xdata, f,'b.-');
+                        ylabel(axisHandle,'Probability');  
+                        xlabel(axisHandle,ydataLabel);                  
+                    elseif ~strcmp(xdataLabel,'(none)') && ~strcmp(xdataLabel,'(none)')
+                        % Plot median value
+                        if xdataHasErrorVals
+                            xdata=xdata(:,4);
+                        end                        
+                        if ydataHasErrorVals
+                            ydata=ydata(:,4);
+                        end                        
+                                                
+                        histogram2(axisHandle,xdata,ydata, floor(sqrt(length(xdata))), 'DisplayStyle','tile','ShowEmptyBins','on','Normalization','cdf');
+                        h = colorbar(axisHandle);
+                        xlabel(axisHandle,xdataLabel);                  
+                        ylabel(axisHandle,ydataLabel);                  
+                        ylabel(h,'Probability'); 
+                        box(axisHandle,'on');
+                        
+                        if xdataHasErrorVals || ydataHasErrorVals
+                            legend(axisHandle, 'Distribution of median','Location', 'northeastoutside');
+                        end                            
+                    end
+                    
+                    % Date date axis. NOTE, code adaopted from dateaxis.m.
+                    % Pre-2016B dateaxis did not allow input of axis
+                    % handle.                        
+                    if xdata_isdate;
+                        dateaxis_local(axisHandle,'x');
+                    end
+                    if ydata_isdate;
+                        dateaxis_local(axisHandle,'y');
+                    end                       
+                case {6,7,8,9}      % Box plots at daily sum, monthly sum, 1/4 sum, annual sum
+                    
+                    if ydataHasErrorVals || xdataHasErrorVals
+                        errordlg('HydroSight cannot create box plots of ensemble data (ie as derived from DREAM calibation).', 'Feature unavailable ...')
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');      
+                        drawnow update;                        
+                        
+                        return
+                    end
+                    
+                    % Check the x-axis is date
+                    if xdata_isdate                  
+                        xdata=datenum(xdata);
+                    else
+                        errordlg('The x-axis must plot the "Date" for box plots.','Axis selection error...');
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');      
+                        drawnow update;                        
+                        
+                        return;
+                    end
+                    if isdatetime(ydata)
+                        errordlg('Only the x-axis can set to "Date" for box plots.','Axis selection error...');
+
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');      
+                        drawnow update;                        
+                        
+                        return;
+                    end
+
+                    % Get the daily data and apply time filter
+                    tableData = [this.tab_ModelCalibration.resultsOptions.forcingData.data_input, ...
+                                 this.tab_ModelCalibration.resultsOptions.forcingData.data_derived];                    
+                    forcingData_colnames = {this.tab_ModelCalibration.resultsOptions.forcingData.colnames_input{:}, ...
+                                this.tab_ModelCalibration.resultsOptions.forcingData.colnames_derived{:} };            
+
+                    filt  = this.tab_ModelCalibration.resultsOptions.forcingData.filt;
+                    tableData = tableData(filt,:);
+                    
+                    % re-extract ydata
+                    ydata = tableData(:,yaxis_val+4);
+    
+                    % Calculate time steps                    
+                    tableData = [tableData(:,1:5),ydata];                    
+                    forcingData_colnames = {forcingData_colnames{1:5}, ydataLabel};                    
+                 
+                    % Get the calculate for the time stepa aggregation
+                    calcID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).Value;
+                    calcString = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(4).String;
+                    calcString = calcString{calcID};                    
+                    
+                    % Set function for aggregation equation
+                    switch calcID             
+                        case 1  % sum
+                            fhandle = @sum;                    
+                        case 2
+                            fhandle = @mean;
+                        case 3
+                            fhandle = @std;
+                        case 4
+                            fhandle = @var;
+                        case 5
+                            fhandle = @skewness;
+                        case 6
+                            fhandle = @min;
+                        case {7,8,9,10,11,12,13}
+                            p = str2num(calcString(1:length(calcString)-7));
+                            fhandle = @(x) prctile(x,p);
+                        case 14
+                            fhandle = @max;
+                        case 15
+                            fhandle = @iqr;                    
+                        otherwise
+                            error('Equation for the aggregation of daily data is unkown.');
+                    end
+
+                    
+                    % Sum the data to the required sum time step
+                    switch plotType_val
+                        case 6  %daily
+                            ind = [1:size(tableData,1)]';
+                            ydataLabel = [ydataLabel, ' (daily rate)'];
+                        case 7  % monthly
+                            [~,~,ind] = unique(tableData(:,[1,3]),'rows');
+                            ydataLabel = [ydataLabel, ' (monthly ',calcString,')'];
+                        case 8  % quarterly
+                            [~,~,ind] = unique(tableData(:,[1,2]),'rows');   
+                            ydataLabel = [ydataLabel, ' (quarterly ',calcString,')'];
+                        case 9  % annually
+                            [~,~,ind] = unique(tableData(:,1),'rows');                        
+                            ydataLabel = [ydataLabel, ' (annual ',calcString,')'];
+                        otherwise
+                            error('Unknown type of box plot.')
+                    end
+                    tableData_sum = accumarray(ind,tableData(:,end),[],fhandle);
+
+                    % Build date column and new tableData.
+                    tableData_year = accumarray(ind,tableData(:,1),[],@max);
+                    tableData_quarter = accumarray(ind,tableData(:,2),[],@max);
+                    tableData_month = accumarray(ind,tableData(:,3),[],@max);            
+                    tableData_week = accumarray(ind,tableData(:,4),[],@max);                            
+                    tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
+                    tableData = [tableData_year, tableData_quarter, tableData_month, tableData_week, tableData_day, tableData_sum];
+                    
+                    % Get time step value
+                    timestepID = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(1).Contents(2).Value;
+                    
+                    % Group the y-data to the next greatest time step
+                    % Build foring data at requtested time step
+
+                    % Build foring data at requtested time step
+                    switch timestepID
+                        case 1  %daily
+                            ind = [1:size(tableData,1)]';                            
+                        case 2  % weekly
+                             [~,~,ind] = unique(tableData(:,[1,4]),'rows');                         
+                             xdataTickLabels = 'dd/mm/yy'; 
+                             xdataLabel = 'Date';
+                        case 3  % monthly
+                            [~,~,ind] = unique(tableData(:,[1,3]),'rows');                        
+                            xdataTickLabels = 'mmyy';      
+                            xdataLabel = 'Month-Year';
+                        case 4  % quarterly
+                            [~,~,ind] = unique(tableData(:,[1,2]),'rows');                            
+                            xdataTickLabels = 'QQ-YY';
+                            xdataLabel = 'Quarter-Year';
+                        case 5  % annually
+                            [~,~,ind] = unique(tableData(:,1),'rows');                              
+                            xdataTickLabels = 'YY';
+                            xdataLabel = 'Year';
+                        case 6  % all data
+                            ind = ones(size(tableData,1),1);
+                            xdataTickLabels = '';
+                            xdataLabel = '';
+                        otherwise
+                            error('Unknown forcing data time step.')
+                    end                                        
+
+                    % Build box plot
+                    if plotType_val==6
+                        boxplot(axisHandle,tableData(:,end), ind,'notch','on','ExtremeMode','clip','Jitter',0.75,'symbol','.');                    
+                    else
+                        boxplot(axisHandle,tableData(:,end), ind,'notch','off','ExtremeMode','clip','Jitter',0.75,'symbol','.');                    
+                    end
+                    
+                    %Add x tick labels
+                    if timestepID<6
+                        tableData_year = accumarray(ind,tableData(:,1),[],@max);
+                        tableData_month = accumarray(ind,tableData(:,3),[],@max);            
+                        tableData_day = accumarray(ind,tableData(:,5),[],@(x) x(end));
+                        t = unique(datenum(tableData_year, tableData_month, tableData_day));
+                        xdataTickLabels = datestr(t,xdataTickLabels);
+                        set(axisHandle, 'XTickLabel',xdataTickLabels);
+                        xlabel(axisHandle,xdataLabel); 
+                    else
+                        set(axisHandle, 'XTickLabel',xdataTickLabels);
+                        xlabel(axisHandle,xdataLabel);                         
+                    end                   
+                    ylabel(axisHandle,ydataLabel);                  
+                    
+                otherwise
+                    error('Unknown forcing data plot type.')                    
+            end                    
+            
+            box(axisHandle,'on');
+            axis(axisHandle,'tight');            
+            hold(axisHandle,'off');
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');      
+            drawnow update;            
+            
+            function dateaxis_local(ax, tickaxis)
+                % Determine range of data and choose appropriate label format 
+                Lim= get(ax, [tickaxis,'lim']);
+                Cond = Lim(2)-Lim(1); 
+
+                if Cond <= 14 % Range less than 15 days, day of week   
+                    dateform = 7;  
+                elseif Cond > 14 && Cond <= 31 % Range less than 32 days, day of month 
+                    dateform = 6; 
+                elseif Cond > 31 && Cond <= 180 % Range less than 181 days, month/day 
+                    dateform = 5; 
+                elseif Cond > 180 && Cond <= 365  % Range less than 366 days, 3 letter month 
+                    dateform = 3; 
+                elseif Cond > 365 && Cond <= 365*3 % Range less than 3 years, month year  
+                    dateform = 11; 
+                else % Range greater than 3 years, 2 digit year 
+                    dateform = 10; 
+                end 
+
+                % Get axis tick values and add appropriate start date. 
+                xl = get(ax,[tickaxis,'tick'])'; 
+                set(ax,[tickaxis,'tickmode'],'manual',[tickaxis,'limmode'],'manual') 
+                n = length(xl); 
+
+                % Guarantee that the day, month, and year strings have the 
+                % the same number of characters
+                switch dateform
+                  case {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16} 
+                    dstr = datestr(xl,dateform);
+                  case 17 % Year/Month/Day  (ISO format)    
+                    dstr = datestr(xl,25);
+                  otherwise
+                    error(message('finance:calendar:dateAxis'))       
+                end 
+
+                % Set axis tick labels 
+                set(ax,[tickaxis,'ticklabel'],dstr)                         
+            end
+        end       
+        
+        % Get derived data AND add data table and plot.
+        function modelCalibration_onUpdateDerivedData(this, hObject, eventdata)
+       
+            % Record the current row and column numbers
+            irow = this.tab_ModelCalibration.currentRow;
+            icol = this.tab_ModelCalibration.currentCol;
+
+            % Get the calibration table data
+            obj = this.tab_ModelCalibration.Table;
+            data = obj.Data;
+            
+            % Find index to the calibrated model label within the
+            % list of constructed models.
+            if isempty(irow)
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                                   
+                return;
+            end
+            
+            % Find the curretn model label
+            calibLabel = HydroSight_GUI.removeHTMLTags(data{irow,2});             
+            this.tab_ModelCalibration.currentModel = calibLabel;
+            
+            % Get a copy of the model object. This is only done to
+            % minimise HDD read when the models are off loaded to HDD using
+            % matfile();
+            tmpModel = getModel(this, calibLabel);            
+            
+            % Display the requested calibration results if the model object
+            % exists and there are calibration results.
+            if ~isempty(tmpModel) ...
+            && isfield(tmpModel.calibrationResults,'isCalibrated') ...
+            && tmpModel.calibrationResults.isCalibrated  
+            
+                % Get model specific derived data and plot etc
+                %-----------------------             
+                % Create an axis handle for the figure.
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data plot'); 
+                delete( findobj(obj ,'type','axes'));
+                axisHandle = axes( 'Parent',obj);                
+
+                % Get length of forcing data.
+                t = getForcingData(tmpModel);
+                t = [1:max(t(:,1))-min(t(1,1))]';
+
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data dropdown');                    
+                derivedData_type = obj.String;
+                derivedData_type = derivedData_type{obj.Value};
+                ind = strfind(derivedData_type,':');
+                modelComponant = derivedData_type(1:ind(1)-1);
+                derivedData_variable = derivedData_type(ind(1)+1:end);
+                [derivedData, derivedData_names] = getDerivedData(tmpModel, modelComponant, derivedData_variable, t, axisHandle);
+
+                obj = findobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel, 'Tag','Model Calibration - derived data table');                    
+                obj.Data = derivedData;
+                obj.ColumnName = derivedData_names;
+                drawnow update;            
+            else
+                errordlg(['The following selected model must be calibrated to display the results:',calibLabel],'Model not calibrated ...');
+            end            
+        end
+        
+        function modelSimulation_tableEdit(this, hObject, eventdata)
+
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');                
+            drawnow update;            
+            
+            % Get GUI table indexes
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);            
+            data=get(hObject,'Data'); % get the data cell array of the table
+                        
+            % Undertake column specific operations.
+            if ~isempty(icol) && ~isempty(irow)
+                
+                % Record the current row and column numbers
+                this.tab_ModelSimulation.currentRow = irow;
+                this.tab_ModelSimulation.currentCol = icol;
+            
+                % Remove HTML tags from the column name
+                columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});
+                
+                switch columnName;
+                    % Get the selected model.
+                    case 'Model Label'
+                        
+                        % Get the selected model for simulation
+                        calibLabel = eventdata.EditData;
+
+                        % Check if any models are calibrated.
+                        if strcmp(calibLabel,'(none calibrated)')
+                            return;
+                        end
+                        
+                        % Get a copy of the model object. This is only done to
+                        % minimise HDD read when the models are off loaded to HDD using
+                        % matfile();
+                        tmpModel = getModel(this, calibLabel);
+
+                        % Exit if model is model not found
+                        if isempty(tmpModel) || ~tmpModel.calibrationResults.isCalibrated
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                            
+                            return
+                        end                        
+                        
+                        % Assign data from the calbration toable to the simulation
+                        % table.
+                        irow = eventdata.Indices(:,1);     
+                        headData = getObservedHead(tmpModel);
+                        obshead_start = floor(min(headData(:,1)));
+                        obshead_end = max(headData(:,1));
+                        boreID = tmpModel.bore_ID;
+                        if size(hObject.Data,1)<irow
+                            hObject.Data = [hObject.Data; cell(1,size(hObject.Data,2))];
+
+                        end
+                        hObject.Data{irow,1} = false;
+                        hObject.Data{irow,2} = calibLabel;
+                        hObject.Data{irow,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
+                        hObject.Data{irow,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
+                        hObject.Data{irow,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];
+                        hObject.Data{irow,6} = '';
+                        hObject.Data{irow,7} = '';
+                        hObject.Data{irow,8} = '';
+                        hObject.Data{irow,9} = '';
+                        hObject.Data{irow,10} = '';   
+                        hObject.Data{irow,11} = false;   
+                        hObject.Data{irow,12} = '<html><font color = "#FF0000">Not Simulated.</font></html>';
+                        
+                        % Update status in GUI
+                        drawnow update                        
+                        
+                    % Check the input model simulation label is unique for the selected model.    
+                    case 'Simulation Label'                        
+                        %  Check if the new model label is unique and
+                        %  create a new label if not.
+                        allLabels = hObject.Data(:,[2,6]);
+                        newLabel = {hObject.Data{irow, 2}, eventdata.EditData};
+                        newLabel = HydroSight_GUI.createUniqueLabel(allLabels, newLabel, irow);                        
+                        hObject.Data{irow,6} = newLabel{2};
+
+                        % Warn user if the label has chnaged.
+                        if ~strcmp(newLabel, eventdata.EditData)
+                            warndlg('The model and simulation label pair must be unique. An modified label has been input','Error ...');
+                        end                        
+                end
+            end
+            
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;                            
+
+        end        
+        
+        function modelSimulation_tableSelection(this, hObject, eventdata)
+            
+            % Hide plotting toolbar
+            plotToolbarState(this,'off');
+                       
+            % Get GUI table indexes            
+            icol=eventdata.Indices(:,2);
+            irow=eventdata.Indices(:,1);                        
+            
+            % Exit of no cells are selected
+            if isempty(icol) && isempty(irow)
+                return
+            end
+
+            % Remove HTML tags from the column name
+            columnName = HydroSight_GUI.removeHTMLTags(eventdata.Source.ColumnName{icol});            
+            
+            % Exit if no cells edits are to be dealt with and no results
+            % are to viewed.
+            if ~(strcmp(columnName, 'Model Label') ||  strcmp(columnName, 'Forcing Data File') || ...
+            strcmp(columnName, 'Simulation Start Date') || strcmp(columnName, 'Simulation End Date'))
+        
+                if isfield( this.tab_ModelSimulation,'resultsOptions')
+                    if this.tab_ModelSimulation.resultsOptions.popup.Value==3
+                        return
+                    end
+                else
+                    return
+                end
+            end
+
+            % Record the current row and column numbers
+            this.tab_ModelSimulation.currentRow = irow;
+            this.tab_ModelSimulation.currentCol = icol;            
+            
+            % Get table data
+            data=get(hObject,'Data'); % get the data cell array of the table
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');                
+            drawnow update;            
+
+            switch columnName;
+                case 'Model Label'
+                    % Get list of models
+                    model_label = fieldnames(this.models);
+                    
+                    % Get list of those that are calibrated
+                    filt = false(length(model_label),1);
+                    for i=1:length(model_label)                           
+                        
+                        % Convert model label to field label.
+                        model_labelAsField = HydroSight_GUI.modelLabel2FieldName(model_label{i});
+                        
+                        % Check if model is calibrated
+                        filt(i) = this.model_labels{model_labelAsField, 'isCalibrated'};
+                    end                    
+                    model_label = model_label(filt);
+
+                    % Assign calib model labels to drop down
+                    hObject.ColumnFormat{2} = model_label';   
+
+                    % Update status in GUI
+                    drawnow update
+
+                case 'Forcing Data File'
+
+                    % Get file name and remove project folder from
+                    % preceeding full path.
+                    fName = getFileName(this, 'Select the Forcing Data file.');
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        data{irow,icol} = fName;
+
+                        % Input file name to the table
+                        set(hObject,'Data',data);
+                    end
+
+                case {'Simulation Start Date', 'Simulation End Date'}
+                    % Get the selected model for simulation
+                    calibLabel = data{irow,2};
+
+                    % Get calibrated model field name
+                    calibLabel = HydroSight_GUI.modelLabel2FieldName(calibLabel);            
+
+                    % Check the model is calibrated.
+                    if ~this.model_labels{calibLabel, 'isCalibrated'};
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                            
+                        warndlg('A calibrated model must be first selected from the "Model Label" column.', 'Error ...');
+                        return;
+                    end
+
+                    % Get the forcing data for the model.
+                    % If a new forcing data file is given, then open it
+                    % up and get the start and end dates from it.
+                    if isempty( data{irow,7}) || strcmp(data{irow,7},'');                            
+                        % Get a copy of the model object. This is only done to
+                        % minimise HDD read when the models are off loaded to HDD using
+                        % matfile();
+                        tmpModel = getModel(this, calibLabel);
+
+                        % Get forcing data
+                        forcingData = getForcingData(tmpModel);
+
+                        % Get start and end dates of the forcing
+                        % data, remove HTML tags and then convert to a
+                        % date number.
+                        startDate = min(forcingData(:,1));
+                        endDate = max(forcingData(:,1));                            
+                    else
+                        % Import forcing data
+                        % Check fname file exists.
+                        fname = data{irow,7};
+                        if exist(fname,'file') ~= 2;   
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                                  
+                            warndlg('The new forcing date file could not be open for examination of the start and end dates.', 'Error ...');
+                            return;
+                        end
+
+                        % Read in the file.
+                        try
+                           forcingData = readtable(fname);
+                        catch           
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                                  
+                            warndlg('The new forcing date file could not be imported for extraction of the start and end dates. Please check its format.', 'Error ...');
+                            return;
+                        end    
+
+                        % Calculate the start and end dates
+                        try
+                           forcingData_dates = datenum(forcingData{:,1}, forcingData{:,2}, forcingData{:,3});
+                           startDate = min(forcingData_dates);
+                           endDate = max(forcingData_dates);
+                        catch
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                                  
+                            warndlg('The dates from the new forcing data file could not be calculated. Please check its format.', 'Error ...');
+                            return;                                
+                        end
+                    end
+
+                    % Open the calander with the already input date,
+                    % else use the start date of the obs. head.
+                    if isempty(data{irow,icol})
+                        inputDate = startDate;                            
+                    else
+                        inputDate = datenum( data{irow,icol},'dd-mmm-yyyy');
+                    end
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                          
+                    selectedDate = uical(inputDate, 'English',startDate, endDate);
+
+                    % Check if user cancelled uical
+                    if isempty(selectedDate)
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                              
+                        return;
+                    end
+
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;                                                  
+
+                    % Check the selected date
+                    if strcmp(columnName, 'Simulation Start Date')
+                        % Get the end date 
+                        simEndDate=inf;
+                        if ~isempty(data{irow,icol+1})
+                            simEndDate = datenum( data{irow,icol+1},'dd-mmm-yyyy');
+                        end
+
+                        % Check date is between start and end date of obs
+                        % head.
+                        if selectedDate < startDate || selectedDate > endDate 
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                                                          
+                            warndlg('The simulation start date must be within the range of the observed forcing data.');
+                            return;
+                        elseif selectedDate>=simEndDate
+                            set(this.Figure, 'pointer', 'arrow');
+                            drawnow update;                                                          
+                            warndlg('The simulation start date must be less than the simulation end date.');
+                            return;
+                        else
+                            data{irow,icol} = datestr(selectedDate,'dd-mmm-yyyy');
+                            set(hObject,'Data',data);
+                        end
+
+                        set(this.Figure, 'pointer', 'watch');
+                        drawnow update;                               
+
+                    else
+                        % Get the end date 
+                        simStartDate = -inf;
+                        if ~isempty(data{irow,icol-1})
+                            simStartDate = datenum( data{irow,icol-1},'dd-mmm-yyyy');                        
+                        end
+
+                        % Check date is between start and end date of obs
+                        % head.
+                        if selectedDate < startDate || selectedDate > endDate    
+                            warndlg('The simulation end date must be within the range of the observed forcing data.');
+                        elseif selectedDate<=simStartDate
+                            warndlg('The simulation end date must be less than the simulation end date.');
+                        else
+                            data{irow,icol} = datestr(selectedDate,'dd-mmm-yyyy');
+                            set(hObject,'Data',data);
+                        end
+
+                    end
+
+                otherwise
+                    % Do nothing
+            end
+                        
+            % Check a row and column are selected.
+            if isempty(irow) || isempty(icol)
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update;                 
+                return
+            end
+            
+            % Find index to the calibrated model label within the list of calibrated
+            % models.
+            modelLabel = data{irow,2};
+            if isempty(modelLabel)
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update;                 
+                return;
+            end          
+            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);                 
+            
+            % Check if there is a simulation label within the
+            % identified calibrated model.            
+            simLabel = data{irow,6};
+            if isempty(simLabel)
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update;                 
+                return;
+            end
+            
+            % Get a copy of the model object. This is only done to
+            % minimise HDD read when the models are off loaded to HDD using
+            % matfile();
+            tmpModel = getModel(this, modelLabel);
+
+            % Exit if model is model not found or model is empty
+            if isempty(tmpModel) || isempty(tmpModel.simulationResults)
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update;                            
+                return
+            end                        
+            
+            % Find index to the simulation label within the
+            % identified calibrated model.                        
+            simInd = cellfun(@(x) strcmp(simLabel, x.simulationLabel), tmpModel.simulationResults);
+            if all(~simInd)    % Exit if model not found.
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update; 
+                return;
+            end          
+            simInd = find(simInd);
+            
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update; 
+                                
+            % Display the requested simulation results if the model object
+            % exists and there are simulation results.
+            if ~isempty(simInd) && isfield(tmpModel.simulationResults{simInd,1},'head') && ...
+            ~isempty(tmpModel.simulationResults{simInd,1}.head )               
+                
+                % Get pop up menu item for the selection of results to
+                % display.
+                results_item = this.tab_ModelSimulation.resultsOptions.popup.Value;
+                        
+                switch results_item
+                    case 1
+                        % Show a table of calibration data
+                        
+                        % Get the model simulation data.
+                        tableData = tmpModel.simulationResults{simInd,1}.head;                        
+
+                        % Calculate year, month, day etc
+                        tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
+
+                        % Convert to a table data type and add data to the table.
+                        this.tab_ModelSimulation.resultsOptions.dataTable.table.Data = tableData;
+                        
+                        
+                        if size(tmpModel.calibrationResults.parameters.params_final,2)==1
+                            this.tab_ModelSimulation.resultsOptions.dataTable.table.ColumnName = {'Year','Month','Day','Hour','Minute',tmpModel.simulationResults{simInd,1}.colnames{2:end}};                            
+                        else
+                            % Create column names
+                            colnames={};
+                            for i=2:length(tmpModel.simulationResults{simInd,1}.colnames)
+                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-50th %ile']];
+                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-5th %ile']];
+                                colnames = [colnames, [tmpModel.simulationResults{simInd,1}.colnames{i},'-95th %ile']];
+                            end
+                            
+                            % Convert to a table data type and add data to the table.                            
+                            this.tab_ModelSimulation.resultsOptions.dataTable.table.ColumnName = {'Year','Month','Day','Hour','Minute',colnames{:}};                            
+                        end
+                        
+
+                    case 2
+                       % Show plotting toolbar
+                       plotToolbarState(this,'on');
+                        
+                       % Determine the number of plots to create.
+                       if size(tmpModel.calibrationResults.parameters.params_final,2)==1
+                            nsubPlots = size(tmpModel.simulationResults{simInd,1}.head,2) - 1;
+                       else                            
+                            nsubPlots = (size(tmpModel.simulationResults{simInd,1}.head,2)-1)/3 ;
+                       end                        
+                        
+                        % Delete existing panels
+                        delete( findobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children,'type','panel'));
+                        delete(findobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children,'type','uipanel'))
+                                                
+                        % Add uipanel and axes for each plot
+                        for i=1:nsubPlots
+                            h = uipanel('Parent',this.tab_ModelSimulation.resultsOptions.plots.panel, 'Visible','off');
+                            axisHandles{i} = axes( 'Parent',h);
+                        end
+                        
+                        % Edit position and visibility of each panel
+                        for i=1:nsubPlots                            
+                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'BorderType','none');
+                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Units','normalized');
+                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Position',[0.01, 1-i/nsubPlots, 0.99, 1/nsubPlots - 0.01]);
+                            set(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(nsubPlots-i+1), 'Visible','on');
+                        end
+                            
+                        % Plot the simulation data using the axis handles
+                        solveModelPlotResults(tmpModel, simLabel, axisHandles);
+                        
+                    case 3
+                        % do nothing
+                end
+            else
+                this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 0];
+            end            
+            
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;             
+        end        
+        
+        function modelSimulation_onResultsSelection(this, hObject, eventdata)
+            % Get selected popup menu item
+            listSelection = get(hObject,'Value');
+                         
+            switch listSelection
+                case 1 %Data 
+                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 -1 0];
+                case 2 %Summary plots
+                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 -1];
+                case 3 %None
+                    this.tab_ModelSimulation.resultsOptions.box.Heights = [30 20 0 0];
+            end
+        end
+        
+        % Get the model options cell array (as a string).
+        function onApplyModelOptions(this, hObject, eventdata)
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');   
+            drawnow update;    
+            
+            try
+                % get the new model options
+                irow = this.tab_ModelConstruction.currentRow;
+                modelType = this.tab_ModelConstruction.Table.Data{irow,7};
+                modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(modelType).obj);            
+
+                % Warn the user if the model is already built and the
+                % inputs are to change - reuiring the model object to be
+                % removed.                            
+                if ~isempty(this.tab_ModelConstruction.Table.Data{irow,8}) && ...
+                ~strcmp(modelOptionsArray, this.tab_ModelConstruction.Table.Data{irow,8} )
+
+                    % Get original model label
+                    modelLabel = this.tab_ModelConstruction.Table.Data{irow,2};
+
+                    % Check if the model object exists
+                    modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);            
+                    if ~isempty(this.models) && any(strcmp(fieldnames(this.models), modelLabel))
+                        
+                        % Get model
+                        tmpModel = getModel(this, modelLabel);
+                                               
+                        % Check if the model is calibrated
+                        isCalibrated =  isfield(tmpModel.calibrationResults,'isCalibrated') ... 
+                            & tmpModel.calibrationResults.isCalibrated;
+
+                        % Create warnign message and display
+                        if isCalibrated
+                            msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
+                                    '', ...                                
+                                   'Do you want to continue with the changes to the model construction?'};
+                        else
+                            msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
+                                    '', ...
+                                   'Do you want to continue with the changes to the model construction?'};
+                        end                            
+                        % Change cursor and show message
+                        set(this.Figure, 'pointer', 'arrow');   
+                        drawnow update;                                
+                        response = questdlg(msg,'Overwrite exiting model?','Yes','No','No');
+
+                        % Check if 'cancel, else delete the model object
+                        if strcmp(response,'No')
+                            return;
+                        end
+
+                        % Change status of the model object.
+                        this.tab_ModelConstruction.Table.Data{irow,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
+
+                        % Delete model from calibration table.
+                        modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
+                        modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
+                        ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
+                        this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
+
+                        % Update row numbers
+                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                     
+
+                        % Delete models from simulations table.
+                        if ~isempty(this.tab_ModelSimulation.Table.Data)
+                            modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);
+                            modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
+                            ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
+                            this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);
+                        end
+
+                        % Update row numbers
+                        nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                        this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                     
+                    end
+                end
+
+                % Apply new model options.
+                this.tab_ModelConstruction.Table.Data{this.tab_ModelConstruction.currentRow,8} = modelOptionsArray;
+                
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update                
+            catch ME
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update                
+                
+                errordlg('The model options could not be applied to the model. Please check the model options are sensible.');
+            end
+
+        end
+        
+        % Get the model options cell array (as a string).
+        function onApplyModelOptions_selectedBores(this, hObject, eventdata)
+            
+            try
+                % Get the current model type.
+                currentModelType = this.tab_ModelConstruction.Table.Data{this.tab_ModelConstruction.currentRow, 7};
+
+                % Get list of selected bores.
+                selectedBores = this.tab_ModelConstruction.Table.Data(:,1);
+
+                % Check some bores have been selected.
+                if ~any(cellfun(@(x) x, selectedBores))
+                    warndlg('No models are selected.', 'Summary of model options applied to bores...');
+                    return;
+                end
+
+                % Get the model options.
+                modelOptionsArray = getModelOptions(this.tab_ModelConstruction.modelTypes.(currentModelType).obj);
+
+                % Change cursor
+                set(this.Figure, 'pointer', 'watch');   
+                drawnow update;                                
+                
+                % Loop  through the list of selected bore and apply the modle
+                % options.
+                nOptionsCopied = 0;
+                response = '';
+                for i=1:length(selectedBores);
+                    if selectedBores{i} && strcmp(this.tab_ModelConstruction.Table.Data{i,7}, currentModelType)
+
+                        % Warn the user if the model is already built and the
+                        % inputs are to change - reuiring the model object to be
+                        % removed.            
+                        if ~isempty(this.tab_ModelConstruction.Table.Data{i,8}) && ...
+                        ~strcmp(modelOptionsArray, this.tab_ModelConstruction.Table.Data{i,8} )
+
+                            % Get original model label
+                            modelLabel = this.tab_ModelConstruction.Table.Data{i,2};                                                      
+                            
+                            % Remove modle if exists                            
+                            modelLabel4FieldNames = HydroSight_GUI.modelLabel2FieldName(modelLabel);            
+                            if any(strcmp(fieldnames(this.models), modelLabel4FieldNames))
+                                if strcmp(response, 'Yes - all models')
+                                    this.models = rmfield(this.models,modelLabel4FieldNames);
+                                else
+
+                                    % Get model
+                                    tmpModel = getModel(this, modelLabel);
+
+                                    % Check if the model is calibrated
+                                    isCalibrated =  isfield(tmpModel.calibrationResults,'isCalibrated') ... 
+                                        & tmpModel.calibrationResults.isCalibrated;
+                         
+                                    % Create warnign message and display
+                                    if isCalibrated
+                                        msg = {['Model ',modelLabel, ' has already been built and calibrated. If you change the model construction all calibration and simulation results will be deleted.'], ...
+                                                '', ...                                
+                                               'Do you want to continue with the changes to the model construction?'};
+                                    else
+                                        msg = {['Model ',modelLabel, ' has already been built (but not calibrated). If you change the model construction you will need to rebuild the model.'], ...
+                                                '', ...
+                                               'Do you want to continue with the changes to the model construction?'};
+                                    end                            
+
+                                    response = questdlg(msg,'Overwrite exiting model?','Yes','Yes - all models','No','No');
+
+                                    % Check if 'cancel, else delete the model object
+                                    if strcmp(response,'No')
+                                        continue;
+                                    else
+                                        % Remove built model
+                                        this.models = rmfield(this.models,modelLabel4FieldNames);
+                                    end
+                                end
+
+                                % Delete model from calibration table.
+                                modelLabels_calibTable =  this.tab_ModelCalibration.Table.Data(:,2);                            
+                                modelLabels_calibTable = HydroSight_GUI.removeHTMLTags(modelLabels_calibTable);
+                                ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_calibTable);
+                                this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~ind,:);
+
+                                % Update row numbers
+                                nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                                this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                                   
+
+                                % Delete models from simulations table.
+                                modelLabels_simTable =  this.tab_ModelSimulation.Table.Data(:,2);                            
+                                modelLabels_simTable = HydroSight_GUI.removeHTMLTags(modelLabels_simTable);
+                                ind = cellfun( @(x) strcmp(x,modelLabel), modelLabels_simTable);
+                                this.tab_ModelSimulation.Table.Data = this.tab_ModelSimulation.Table.Data(~ind,:);
+
+                                % Update row numbers
+                                nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                                this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                              
+
+                            end
+                        end                    
+
+                        % Apply model option.
+                        this.tab_ModelConstruction.Table.Data{i,8} = modelOptionsArray;            
+                        
+                        % Change status of the model object.
+                        this.tab_ModelConstruction.Table.Data{i,end} = '<html><font color = "#FF0000">Model not built.</font></html>';
+                        
+                        nOptionsCopied = nOptionsCopied + 1;
+                    end
+                end            
+
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                
+                
+                msgbox(['The model options were copied to ',num2str(nOptionsCopied), ' "', currentModelType ,'" models.'], 'Summary of model options applied to bores...');
+            catch ME
+                
+                % Change cursor
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;                
+                
+                errordlg('The model options could not be applied to the selected models. Please check the model options are sensible.');
+            end
+
+        end
+
+        function onAnalyseBores(this, hObject, eventdata)
+           
+            % Hide the results window.
+            this.tab_DataPrep.modelOptions.resultsOptions.box.Heights = [0 0];
+                            
+            % Hide plot icons
+            plotToolbarState(this,'off');            
+            
+            % Get table data
+            data = this.tab_DataPrep.Table.Data;
+            
+            % Get list of selected bores.
+            selectedBores = data(:,1);
+
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update
+
+            % Check that the user wants to save the projct after each
+            % calib.
+            response = questdlg('Do you want to save the project after each bore is analysed?','Auto-save analysis results?','Yes','No','Cancel','Yes');
+            if strcmp(response,'Cancel')
+                return;
+            end
+            if strcmp(response,'Yes') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
+                msgbox('The project has not yet been saved. Please save it and re-run the analysis.','Project not yet saved ...','error');
+                return;
+            end
+            saveModels=false;
+            if strcmp(response,'Yes')
+                saveModels=true;
+            end            
+            
+            
+            % Count the number of models selected
+            nModels=0;
+            for i=1:length(selectedBores);                                
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    nModels = nModels +1;
+                end
+            end
+            
+            % Add simulation bar
+            minModels4Waitbar = 5;
+            iModels=0;
+            if nModels>=minModels4Waitbar
+                h = waitbar(0, ['Analysing ', num2str(nModels), ' bores. Please wait ...']);
+            end    
+            
+            % Loop  through the list of selected bore and apply the modle
+            % options.
+            nBoresAnalysed = 0;
+            nAnalysisFailed = 0;
+            nBoreNotInHeadFile=0;
+            nBoreInputsError = 0;
+            nBoreIDLabelError = 0;
+            for i=1:length(selectedBores);
+                % Check if the model is to be built.
+                if isempty(selectedBores{i}) ||  ~selectedBores{i}
+                    continue;
+                end
+
+                % Update table with progress'
+                this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FFA500">Analysing bore ...</font></html>';
+                this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                    
+                % Update status in GUI
+                drawnow;
+                
+                % Import head data
+                %----------------------------------------------------------
+                % Check the obs. head file is listed
+                if isdir(this.project_fileName)                             
+                    fname = fullfile(this.project_fileName,data{i,2}); 
+                else
+                    fname = fullfile(fileparts(this.project_fileName),data{i,2});  
+                end                
+                if isempty(fname)                    
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error - file name empty.</font></html>';
+                    nAnalysisFailed = nAnalysisFailed + 1;
+                    continue;
+                end
+
+                % Check the bore ID file exists.
+                if exist(fname,'file') ~= 2;                    
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error - file does not exist.</font></html>';
+                    nAnalysisFailed = nAnalysisFailed + 1;
+                    continue;
+                end
+
+                % Read in the observed head file.
+                try
+                    tbl = readtable(fname);
+                catch                    
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Head data file error -  read in failed.</font></html>';
+                    nAnalysisFailed = nAnalysisFailed + 1;
+                    continue;
+                end                
+                
+                % Filter for the required bore.
+                boreID = data{i,3};
+                filt =  strcmp(tbl{:,1},boreID);
+                if sum(filt)<=0
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore not found in head data file -  data error.</font></html>';
+                    nBoreNotInHeadFile = nBoreNotInHeadFile +1;
+                    continue
+                end
+                headData = tbl(filt,2:end);
+                headData = headData{:,:};                
+                %----------------------------------------------------------
+                                
+                % Get required inputs for analysis
+                %----------------------------------------------------------
+                boreDepth = inf;
+                dataCol=4;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
+                    boreDepth = this.tab_DataPrep.Table.Data{i, dataCol};
+                end
+                
+                surfaceElevation = inf;
+                dataCol = 5;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end                
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    surfaceElevation = this.tab_DataPrep.Table.Data{i, dataCol};
+                end
+                
+                caseLength = inf;
+                dataCol = 6;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end                
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
+                    caseLength = this.tab_DataPrep.Table.Data{i, dataCol};
+                end
+                
+                constructionDate = datenum(0,0,0);
+                dataCol = 7;
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    try
+                        constructionDate = datenum(this.tab_DataPrep.Table.Data{i, dataCol});
+                        if constructionDate > now()+1 || constructionDate< datenum(1500,1,1)
+                            nBoreInputsError = nBoreInputsError +1;
+                            continue;
+                        end
+                    catch
+                        this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore not found in head data file -  data error.</font></html>';
+                        nBoreInputsError = nBoreInputsError +1;
+                        continue
+                    end
+                end   
+                
+                checkStartDate = false;
+                dataCol = 8;               
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    checkStartDate = this.tab_DataPrep.Table.Data{i, dataCol};
+                end                
+                
+                checkEndDate = false;
+                dataCol = 9;               
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    checkEndDate = this.tab_DataPrep.Table.Data{i, dataCol};
+                end       
+                
+                checkMinHead = false;
+                dataCol = 10;               
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    checkMinHead = this.tab_DataPrep.Table.Data{i, dataCol};
+                end       
+
+                
+                checkMaxHead = false;
+                dataCol = 11;               
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    checkMaxHead = this.tab_DataPrep.Table.Data{i, dataCol};
+                end         
+                
+                
+                rateOfChangeThreshold = inf;
+                dataCol = 12;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end                
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
+                    rateOfChangeThreshold = this.tab_DataPrep.Table.Data{i, dataCol};
+                end      
+                
+                constHeadDuration = inf;
+                dataCol = 13;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end                
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
+                    constHeadDuration = this.tab_DataPrep.Table.Data{i, dataCol};
+                end
+                
+                numNoiseStdDev = inf;
+                dataCol = 14;
+                if ~isnumeric(this.tab_DataPrep.Table.Data{i, dataCol})
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Input for column ', num2str(dataCol), ' must be a number. </font></html>'];
+                    nBoreInputsError = nBoreInputsError +1;
+                    continue
+                end                
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol}) && this.tab_DataPrep.Table.Data{i, dataCol}>0
+                    numNoiseStdDev = this.tab_DataPrep.Table.Data{i, dataCol};
+                end    
+                
+                
+                outlierForwadBackward=true;
+                dataCol = 15;               
+                if ~isempty(this.tab_DataPrep.Table.Data{i, dataCol})
+                    outlierForwadBackward = this.tab_DataPrep.Table.Data{i, dataCol};
+                end                    
+                %----------------------------------------------------------
+                                
+                % Convert boreID string to appropriate field name and test
+                % if is can be converted to a field name
+                boreID = strrep(boreID,' ','_');
+                boreID = strrep(boreID,'-','_');
+                boreID = strrep(boreID,'?','');
+                boreID = strrep(boreID,'\','_');
+                boreID = strrep(boreID,'/','_');
+                try 
+                    tmp.(boreID) = [1 2 3];
+                    clear tmp;
+                catch ME
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#FF0000">Bore ID label error - must start with letters and have only letters and numbers.</font></html>';
+                    nBoreIDLabelError = nBoreIDLabelError+1;
+                end
+                    
+                % Convert head date/time columns to a vector.
+                switch size(headData,2)-1
+                    case 3
+                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3));
+                    case 4
+                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4), zeros(size(headData,1),1), zeros(size(headData,1),1));
+                    case 5
+                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4),headData(:,5), zeros(size(headData,1),1));
+                    case 6
+                        dateVec = datenum(headData(:,1), headData(:,2), headData(:,3),headData(:,4),headData(:,5),headData(:,6));
+                    otherwise
+                        error('The input observed head must be 4 to 7 columns with right hand column being the head and the left columns: year; month; day; hour (optional), minute (options), second (optional).');
+                end                
+                
+                % Call analysis function                
+                try
+                    % Do the analysis and add to the object
+                    chechDuplicateDates = true;
+                    this.dataPrep.(boreID) = doDataQualityAnalysis( [dateVec, headData(:,end)], boreDepth, surfaceElevation, caseLength, constructionDate, ...
+                    checkStartDate, checkEndDate, chechDuplicateDates, checkMinHead, checkMaxHead, rateOfChangeThreshold, ...
+                    constHeadDuration, numNoiseStdDev, outlierForwadBackward );                                                       
+                    
+                    % Add summary stats
+                    numErroneouObs = sum(any(table2array(this.dataPrep.(boreID)(:,7:12)),2));
+                    numOutlierObs = sum(table2array(this.dataPrep.(boreID)(:,13)));                    
+                    this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">',num2str(numErroneouObs),'</font></html>'];
+                    this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">',num2str(numOutlierObs),'</font></html>'];
+                                  
+                    nBoresAnalysed = nBoresAnalysed +1;
+                    
+                    if saveModels
+                        this.tab_DataPrep.Table.Data{i,16} = '<html><font color = "#FFA500">Saving project. </font></html>';
+
+                        % Update status in GUI
+                        drawnow;                        
+                        
+                        % Save project.
+                        onSave(this,hObject,eventdata);
+                    end
+                    
+                    this.tab_DataPrep.Table.Data{i, 16} = '<html><font color = "#008000">Bore analysed.</font></html>';
+                    
+                catch ME
+                    this.tab_DataPrep.Table.Data{i, 16} = ['<html><font color = "#FF0000">Analysis failed - ', ME.message,'</font></html>'];   
+                    this.tab_DataPrep.Table.Data{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                    this.tab_DataPrep.Table.Data{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                    
+                    nAnalysisFailed = nAnalysisFailed +1;
+                    continue                    
+                end
+          
+                % Update status in GUI
+                drawnow;
+                
+                % Update wait bar
+                if nModels>=minModels4Waitbar            
+                    iModels=iModels+1;
+                    waitbar(iModels/nModels);                
+                end
+            end
+            
+            % Close wait bar
+            if nModels>=minModels4Waitbar            
+                close(h);
+            end              
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update
+            
+            % Report Summary
+            msgbox({['The data analysis was successfully for ',num2str(nBoresAnalysed), ' bores.'], ...
+                    '', ...
+                    ['Below is a summary of the failures:'], ...
+                    ['   - Head data file errors: ', num2str(nBoreNotInHeadFile)] ...
+                    ['   - Input table data errors: ', num2str(nBoreInputsError)] ...
+                    ['   - Data analysis algorithm failures: ', num2str(nAnalysisFailed)] ...
+                    ['   - Bore IDs not starting with a letter: ', num2str(nBoreIDLabelError)]}, ...                    
+                    'Summary of data analysis ...');
+                           
+            
+        end
+        
+        function onBuildModels(this, hObject, eventdata)
+
+            % Hide plotting toolbars
+            plotToolbarState(this, 'off');            
+            
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update            
+            
+            % Delete any empty model objects
+            if ~isempty(this.models)
+                modelFieldNames = fieldnames(this.models);
+                for i=1:length(modelFieldNames)
+                    if isempty(this.models.(modelFieldNames{i}))
+                        this.models = rmfield(this.models,modelFieldNames{i});
+                    end
+                end
+            end
+                       
+            % Get table data
+            data = this.tab_ModelConstruction.Table.Data;
+            
+            % Get list of selected bores.
+            selectedBores = data(:,1);
+                        
+            % Count the number of models selected
+            nModels=0;
+            for i=1:length(selectedBores);                                
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    nModels = nModels +1;
+                end
+            end
+            
+            % Add wait bar
+            minModels4Waitbar = 5;
+            iModels=0;
+            if nModels>=minModels4Waitbar
+                h = waitbar(0, ['Building ', num2str(nModels), ' models. Please wait ...']);
+            end               
+            
+            % Loop  through the list of selected bore and apply the modle
+            % options.
+            nModelsBuilt = 0;
+            nModelsBuiltFailed = 0;
+            for i=1:length(selectedBores);
+                % Check if the model is to be built.
+                if isempty(selectedBores{i}) || ~selectedBores{i}
+                    continue;
+                end
+
+                % Update table with progress'
+                this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FFA500">Building model ...</font></html>';
+
+                % Update status in GUI
+                drawnow
+                                
+                % Import head data
+                %----------------------------------------------------------
+                % Check the obs. head file is listed
+                if isdir(this.project_fileName)
+                    fname = fullfile(this.project_fileName,data{i,3});
+                else
+                    fname = fullfile(fileparts(this.project_fileName),data{i,3});
+                end
+                if isempty(fname)                    
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - file name empty.</font></html>';
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    continue;
+                end
+
+                % Check the bore ID file exists.
+                if exist(fname,'file') ~= 2;                    
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - file does not exist.</font></html>';
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    continue;
+                end
+
+                % Read in the observed head file.
+                try
+                    tbl = readtable(fname);
+                catch                    
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error -  read in failed.</font></html>';
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    continue;
+                end                
+                
+                % Filter for the required bore.
+                filt =  strcmp(tbl{:,1},data{i,6});
+                headData = tbl(filt,2:end);
+                headData = headData{:,:};
+                
+                % Check theer is some obs data
+                if size(headData,1)<=1
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Head data file error - <=1 observation.</font></html>';
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    continue;
+                end
+                
+                %----------------------------------------------------------
+                
+                % Import forcing data
+                %----------------------------------------------------------
+                % Check fname file exists.
+                if isdir(this.project_fileName)
+                    fname = fullfile(this.project_fileName,data{i,4});
+                else
+                    fname = fullfile(fileparts(this.project_fileName),data{i,4});
+                end
+                if exist(fname,'file') ~= 2;                   
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file error - file name empty.</font></html>';
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   continue;
+                end
+
+                % Read in the file.
+                try
+                   forcingData = readtable(fname);
+                catch                   
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file error -  read in failed.</font></html>';
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   continue;
+                end      
+                if isempty(forcingData)
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Forcing file is empty or open elsewhere -  read in failed.</font></html>';
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   continue;                    
+                end
+                %----------------------------------------------------------
+                
+                
+                % Import coordintate data
+                %----------------------------------------------------------
+                % Check fname file exists.
+                if isdir(this.project_fileName)
+                    fname = fullfile(this.project_fileName,data{i,5});
+                else
+                    fname = fullfile(fileparts(this.project_fileName),data{i,5});
+                end
+                if exist(fname,'file') ~= 2;                   
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate file error - file name empty.</font></html>';
+                   continue;
+                end
+
+                % Read in the file.
+                try
+                   coordData = readtable(fname);
+                   coordData = table2cell(coordData);
+                catch              
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate file error -  read in failed.</font></html>';
+                   continue;
+                end
+                
+                % Check site names are unique
+                if length(coordData(:,1)) ~= length(unique(coordData(:,1)))
+                   nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                   this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Coordinate site IDs not unique.</font></html>';
+                   continue;                    
+                end
+                %----------------------------------------------------------
+
+                % Get model label
+                model_label = data{i,2};
+                
+                % Get bore IDs
+                boreID= data{i,6};
+                
+                % Get model type
+                modelType = data{i,7};
+                
+                % If the modle options are empty, try and add an empty cell
+                % in case the model does not need options.
+                if isempty(data{i,8})
+                    data{i,8} = '{}';
+                end
+                
+                % Get model options
+                try
+                    modelOptions= eval(data{i,8});
+                catch ME
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#FF0000">Syntax error in model options - string not convertable to cell array.</font></html>';
+                    continue;
+                end                
+                
+                % Build model
+                try 
+                    % Build model
+                    maxHeadObsFreq_asDays = 1;
+                    model_labelAsField = HydroSight_GUI.modelLabel2FieldName(model_label);
+                    tmpModel = HydroSightModel(model_label, boreID, modelType , headData, maxHeadObsFreq_asDays, forcingData, coordData, modelOptions);
+                    setModel(this, model_labelAsField, tmpModel);
+                    
+                    % Check if model is listed in calib table. if so get
+                    % index
+                    isModelListed=false;
+                    if size(this.tab_ModelCalibration.Table.Data,1)>0
+                        calibModelLabelsHTML = this.tab_ModelCalibration.Table.Data(:,2);
+                        calibModelLabels = HydroSight_GUI.removeHTMLTags(calibModelLabelsHTML);
+                        isModelListed = cellfun( @(x) strcmp( model_label, x), calibModelLabels);
+                        isModelListed = find(isModelListed);
+                        
+                        % If multiple are listed, then take the top one
+                        if length(isModelListed)>1
+                            filt = true(length(calibModelLabels),1);
+                            filt(isModelListed(2:end))=false;
+                            isModelListed=isModelListed(1);
+                            this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(filt,:);
+                            
+                            % Update row numbers
+                            nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                            this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                        end
+                    end
+
+                    % Get model start and end dates
+                    obshead_start = min(datenum(headData(:,1),headData(:,2),headData(:,3) ));
+                    obshead_end = max(datenum(headData(:,1),headData(:,2),headData(:,3) ));
+                    
+                    % Add the model to the calibration table.
+                    if ~any(isModelListed)
+                        this.tab_ModelCalibration.Table.Data = [this.tab_ModelCalibration.Table.Data; ...
+                        cell(1,13)];
+                        isModelListed = size(this.tab_ModelCalibration.Table.Data,1);
+                        
+                        this.tab_ModelCalibration.Table.Data{isModelListed,2} = ['<html><font color = "#808080">',model_label,'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,6} = datestr(obshead_start,'dd-mmm-yyyy');
+                        this.tab_ModelCalibration.Table.Data{isModelListed,7} = datestr(obshead_end,'dd-mmm-yyyy');
+                        this.tab_ModelCalibration.Table.Data{isModelListed,8} = 'SP-UCI';
+                        
+                        
+                        % Update row numbers
+                        nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                        this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                        
+                        
+                    else
+                        this.tab_ModelCalibration.Table.Data{isModelListed,3} = ['<html><font color = "#808080">',boreID,'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,4} = ['<html><font color = "#808080">',datestr(obshead_start,'dd-mmm-yyyy'),'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{isModelListed,5} = ['<html><font color = "#808080">',datestr(obshead_end,'dd-mmm-yyyy'),'</font></html>'];                        
+                    end
+                    this.tab_ModelCalibration.Table.Data{isModelListed,9} = '<html><font color = "#FF0000">Not calibrated.</font></html>';                    
+                    this.tab_ModelCalibration.Table.Data{isModelListed,10} = '(NA)';
+                    this.tab_ModelCalibration.Table.Data{isModelListed,11} = '(NA)';
+                    this.tab_ModelCalibration.Table.Data{isModelListed,12} = '(NA)';
+                    this.tab_ModelCalibration.Table.Data{isModelListed,13} = '(NA)';                    
+                    
+                    
+                    this.tab_ModelConstruction.Table.Data{i, 9} = '<html><font color = "#008000">Model built.</font></html>';
+                    nModelsBuilt = nModelsBuilt + 1; 
+                    
+                catch ME
+                    nModelsBuiltFailed = nModelsBuiltFailed + 1;
+                    this.tab_ModelConstruction.Table.Data{i, 9} = ['<html><font color = "#FF0000">Model build failed - ', ME.message,'</font></html>'];
+                end
+                
+                % Update status in GUI
+                drawnow
+
+               % Update wait bar
+                if nModels>=minModels4Waitbar            
+                    iModels=iModels+1;
+                    waitbar(iModels/nModels);                
+                end
+            end
+            
+            % Close wait bar
+            if nModels>=minModels4Waitbar            
+                close(h);
+            end            
+            
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update
+            
+            % Enable file menu items for HDD offloading.
+            if nModelsBuilt>0
+                for i=1:size(this.figure_Menu.Children,1)
+                    if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
+                        set(this.figure_Menu.Children(i),'Enable','on');
+                    elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                    strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                        set(this.figure_Menu.Children(i),'Enable','on');
+                    end       
+                end
+            end
+            
+            % Report Summary
+            msgbox(['The model was successfully built for ',num2str(nModelsBuilt), ' models and failed for ',num2str(nModelsBuiltFailed), ' models.'], 'Summary of model builds ...');
+                        
+        end
+        
+        function onCalibModels(this, hObject, eventdata)
+                  
+            % The project must be saved to a file. Check the project file
+            % is defined.
+            if isempty(this.project_fileName) || isdir(this.project_fileName)
+                warndlg({'The project must be saved to a file before calibration can start.';'Please first save the project.'}, 'Project not saved...')
+                return
+            end            
+            
+            % Get table data
+            data = this.tab_ModelCalibration.Table.Data;            
+            
+            % Get list of selected bores and check.
+            selectedBores = data(:,1);
+            isModelSelected=false;
+            for i=1:length(selectedBores);                
+                % Check if the model is to be calibrated.
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    isModelSelected=true;
+                    break;
+                end                
+            end                        
+            
+            if ~isModelSelected
+                warndlg('No models have been selected for calibration.','Model Calibration Error ...')
+                return;
+            end                                
+            
+            % Delete any empty model objects
+            if ~isempty(this.models)
+                modelFieldNames = fieldnames(this.models);
+                for i=1:length(modelFieldNames)
+                    if isempty(this.models.(modelFieldNames{i}))
+                        this.models = rmfield(this.models,modelFieldNames{i});
+                    end
+                end
+            else
+                warndlg('No models appear to have been built. Please build the models then calibrate them.','Model Calibration Error ...')
+                return;
+            end            
+
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update            
+            
+            % Delete any models listed in the calibration table that are
+            % not listed in the model construction table            
+            if ~isempty(data)
+                constructModelLabels = this.tab_ModelConstruction.Table.Data(:,2);
+                % Get model label
+                calibModelLabelsHTML = data(:,2);
+                
+                % Remove HTML tags
+                calibModelLabels = HydroSight_GUI.removeHTMLTags(calibModelLabelsHTML);
+                
+                deleteCalibRow = false(size(data,1),1);
+                for i=1:size(data,1)
+
+                    % Add index for row if it is to be deleted                    
+                    if ~any(cellfun( @(x) strcmp( calibModelLabels{i}, x), constructModelLabels))
+                        deleteCalibRow(i) = true;
+                        continue;
+                    end
+                    
+                    % Check if there are duplicate bore IDs. If so, delete
+                    % non-calibrated one
+                    ind = find(cellfun( @(x) strcmp( calibModelLabels{i}, x), calibModelLabels))';
+                    if length(ind)>1
+                        nDubplicates2Remove = 0;
+                        for j=ind
+                           if contains(data{j, 10},'(NA)')
+                               deleteCalibRow(j) = true;
+                               nDubplicates2Remove  = nDubplicates2Remove  +1;
+                           end
+                        end
+                        if nDubplicates2Remove == length(ind)
+                            deleteCalibRow(ind(1)) = false;
+                        end
+                    end
+                    
+                end
+                
+                if any(deleteCalibRow)
+
+                    % Change cursor to arrow
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update
+
+                    warndlg('Some models listed in the calibration table are duplicated or not listed in the model construction table and will be deleted. Please re-run the calibration','Unexpected table error...');
+                    this.tab_ModelCalibration.Table.Data = this.tab_ModelCalibration.Table.Data(~deleteCalibRow,:);
+                    
+                    % Update row numbers
+                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
+                    
+                    return;
+                end
+                
+            end            
+            
+            
+            % CREATE CALIBRATION GUI
+            %--------------------------------------------------------------
+            % Open a window and add some menus
+            this.tab_ModelCalibration.GUI = figure( ...
+                'Name', 'HydroSight Model Calibration', ...
+                'NumberTitle', 'off', ...
+                'MenuBar', 'none', ...
+                'HandleVisibility', 'off', ...
+                'Visible','on', ...
+                'Toolbar','none', ...
+                'DockControls','off', ...
+                'WindowStyle','modal' ...
+                );
+                %'CloseRequestFcn',@this.onExit); 
+                        
+            % Set window Size
+            windowHeight = this.tab_ModelCalibration.GUI.Parent.ScreenSize(4);
+            windowWidth = this.tab_ModelCalibration.GUI.Parent.ScreenSize(3);
+            figWidth = 0.6*windowWidth;
+            figHeight = 0.85*windowHeight;            
+            this.tab_ModelCalibration.GUI.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];
+            
+            % Set default panel color
+            warning('off');
+            uiextras.set( this.tab_ModelCalibration.GUI, 'DefaultBoxPanelTitleColor', [0.7 1.0 0.7] );
+            warning('on');
+ 
+            outerVbox= uiextras.VBoxFlex('Parent',this.tab_ModelCalibration.GUI,'Padding', 3, 'Spacing', 3);
+            innerVbox_top= uiextras.VBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);
+            innerVbox_bottom= uiextras.VBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);
+ 
+            % Add label
+            uicontrol(innerVbox_top,'Style','text','String','Calibration scheme settings: ','HorizontalAlignment','left', 'Units','normalized');
+            
+            %Create Panels for different windows       
+            outerTabsPanel = uiextras.TabPanel( 'Parent', innerVbox_top, 'Padding', 5, 'TabSize',127,'FontSize',8);
+            CMAES_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','CMA-ES tab');            
+            SPUCI_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','SP-UCI tab');
+            DREAM_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','DREAM tab');
+            %MultiModel_tab = uiextras.Panel( 'Parent', outerTabsPanel, 'Padding', 5, 'Tag','Multi-Model tab');
+            %outerTabsPanel.TabNames = {'CMA-ES', 'SP-UCI','DREAM', 'Multi-model'};
+            outerTabsPanel.TabNames = {'CMA-ES', 'SP-UCI','DREAM'};
+            outerTabsPanel.SelectedChild = 1;
+                       
+            % Add buttons.
+            outerButtons = uiextras.HButtonBox('Parent',innerVbox_top,'Padding', 3, 'Spacing', 3);             
+            uicontrol('Parent',outerButtons,'String','Start calibration','Callback', @this.startCalibration, 'Interruptible','on','Tag','Start calibration', 'TooltipString', sprintf('Calibrate all of the selected models.') );
+            if ~isdeployed
+                uicontrol('Parent',outerButtons,'String','HPC Offload','Callback', @this.startCalibration,'Tag','Start calibration - useHPC', 'TooltipString', sprintf('BETA version to export selected models for calibration on a High Performance Cluster.') );
+                uicontrol('Parent',outerButtons,'String','HPC Retrieval','Callback', @this.onImportFromHPC, 'TooltipString', sprintf('BETA version to retrieve calibrated models from a High Performance Cluster.') );
+            end            
+            uicontrol('Parent',outerButtons,'String','Quit calibration','Callback', @this.quitCalibration, 'Enable','off','Tag','Quit calibration', 'TooltipString', sprintf('Stop calibrating the models at the end of the current iteration loop.') );
+            outerButtons.ButtonSize(1) = 225;            
+            
+            % Count the number of models selected
+            nModels=0;
+            iModels=0;
+            for i=1:length(selectedBores);                                
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    nModels = nModels +1;
+                end
+            end            
+            
+            % Add progress bar
+            bar_panel = uipanel('Parent',innerVbox_top, 'BorderType','none');
+            ax = axes( 'Parent', bar_panel);            
+            barh(ax, 0,'Tag','Calib_wait_bar');
+            box(ax,'on');
+            xlim(ax,[0,nModels]);
+            ax.YTick = [];
+            ax.XTick = [0:nModels];
+            title(ax,'Model Calibration Progress','FontSize',10,'FontWeight','normal');
+            
+            % Add large box for calib. iterations
+            uicontrol(innerVbox_bottom,'Style','edit','String','(calibration not started)','Tag','calibration command window', ...
+                'HorizontalAlignment','left', 'Units','normalized','BackgroundColor','white');             
+            
+            set(outerVbox, 'Sizes', [-1 -1]);
+            set(innerVbox_top, 'Sizes', [30 -1 30 50]);
+            set(innerVbox_bottom, 'Sizes', [-1]);
+           
+            % Fill in CMA-ES panel               
+            CMAES_tabVbox= uiextras.Grid('Parent',CMAES_tab,'Padding', 6, 'Spacing', 6);
+            uicontrol(CMAES_tabVbox,'Style','text','String','Maximum number of model evaluations (MaxFunEvals):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(CMAES_tabVbox,'Style','text','String','Number of parameter sets searching for the optima (PopSize):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(CMAES_tabVbox,'Style','text','String','Absolute change in the objective function for convergency (TolFun):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(CMAES_tabVbox,'Style','text','String','Largest absolute change in the parameters for convergency (TolX):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(CMAES_tabVbox,'Style','text','String','Number CMA-ES calibration restarts (Restarts):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(CMAES_tabVbox,'Style','text','String','Standard deviation for the initial parameter sampling, as fraction of plausible parameter bounds (insigmaFrac):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(CMAES_tabVbox,'Style','text','String','Random seed number (only for repetetive testing purposes, an empty value uses a different seed per model):','HorizontalAlignment','left', 'Units','normalized');            
+                  
+            uicontrol(CMAES_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','CMAES MaxFunEvals','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','CMAES popsize','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string','1e-12','Max',1, 'Tag','CMAES TolFun','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string','1e-11','Max',1, 'Tag','CMAES TolX','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string','4','Max',1, 'Tag','CMAES Restarts','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string','0.333','Max',1, 'Tag','CMAES insigmaFrac','HorizontalAlignment','right');
+            uicontrol(CMAES_tabVbox,'Style','edit','string',num2str(floor(rand(1)*1e6)),'Max',1, 'Tag','CMAES iseed','HorizontalAlignment','right');
+            
+            set(CMAES_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,6));                        
+            
+            % Fill in SP-UCI panel   
+            SPUCI_tabVbox= uiextras.Grid('Parent',SPUCI_tab,'Padding', 6, 'Spacing', 6);
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Maximum number of model evaluations (maxn):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Number of evolution loops meeting convergence criteria for calbration (kstop):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Percentage change in the objective function allowed in kstop loops before convergency (pcento):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Normalized geometric range of the parameters before convergency (peps):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Number of complexes (ngs) per model parameter:','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Minimum number of total complexes (ngs):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Maximum number of total complexes (ngs):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(SPUCI_tabVbox,'Style','text','String','Random seed number (only for repetetive testing purposes, an empty value uses a different seed per model):','HorizontalAlignment','left', 'Units','normalized');            
+      
+            uicontrol(SPUCI_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','SP-UCI maxn','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','string','10','Max',1, 'Tag','SP-UCI kstop','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','SP-UCI pcento','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','SP-UCI peps','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','string','2','Max',1, 'Tag','SP-UCI ngs','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','String','1','Max',1, 'Tag','SP-UCI ngs min','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','String','inf','Max',1, 'Tag','SP-UCI ngs max','HorizontalAlignment','right');
+            uicontrol(SPUCI_tabVbox,'Style','edit','string',num2str(floor(rand(1)*1e6)),'Max',1, 'Tag','SP-UCI iseed','HorizontalAlignment','right');
+            
+            set(SPUCI_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,8));
+            
+            
+            % Fill in DREAM panel   
+            DREAM_tabVbox= uiextras.Grid('Parent',DREAM_tab ,'Padding', 6, 'Spacing', 6);
+            uicontrol(DREAM_tabVbox,'Style','text','String','Number of Markov chains per model parameter (N_per_param):','HorizontalAlignment','left', 'Units','normalized');                  
+            uicontrol(DREAM_tabVbox,'Style','text','String','Number of 10,000s of model generations per chain (T):','HorizontalAlignment','left', 'Units','normalized');                  
+            uicontrol(DREAM_tabVbox,'Style','text','String','Number of crossover values (nCR):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(DREAM_tabVbox,'Style','text','String','Number chain pairs for proposal (delta):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(DREAM_tabVbox,'Style','text','String','Random error for ergodicity (lambda):','HorizontalAlignment','left', 'Units','normalized');            
+            uicontrol(DREAM_tabVbox,'Style','text','String','Randomization (zeta):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(DREAM_tabVbox,'Style','text','String','Test function name for detecting outlier chains (outlier):','HorizontalAlignment','left', 'Units','normalized');                        
+            uicontrol(DREAM_tabVbox,'Style','text','String','Probability of jumprate of 1 (pJumpRate_one):','HorizontalAlignment','left', 'Units','normalized');            
+            
+            uicontrol(DREAM_tabVbox,'Style','edit','string','1','Max',1, 'Tag','DREAM N','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','5','Max',1, 'Tag','DREAM T','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','3','Max',1, 'Tag','DREAM nCR','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','3','Max',1, 'Tag','DREAM delta','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','0.05','Max',1, 'Tag','DREAM lambda','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','0.05','Max',1, 'Tag','DREAM zeta','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','iqr','Max',1, 'Tag','DREAM outlier','HorizontalAlignment','right');
+            uicontrol(DREAM_tabVbox,'Style','edit','string','0.2','Max',1, 'Tag','DREAM pJumpRate_one','HorizontalAlignment','right');
+            
+            set(DREAM_tabVbox, 'ColumnSizes', [-1 100], 'RowSizes', repmat(20,1,8));            
+            
+%             % Fill in MultiModel panel   
+%             % MODEL STILL IN DEVELOPMENT
+%             MultiModel_tabVbox= uiextras.Grid('Parent',MultiModel_tab ,'Padding', 6, 'Spacing', 6);
+%             
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Parameters to estimate as fixed-effect (component:parameter-model label):','HorizontalAlignment','left', 'Units','normalized');                        
+%             multiModel_leftList = uicontrol(MultiModel_tabVbox,'Style','listbox','String',cell(0,1),'Min',1,'Max',100,'Tag','NLMEFIT paramsLeftList','HorizontalAlignment','left', 'Units','normalized');                        
+%             
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Number of randomly selected initial starts for gradient calibration (nStarts):','HorizontalAlignment','left', 'Units','normalized');                        
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Maximum number of model evaluations (MaxIter):','HorizontalAlignment','left', 'Units','normalized');                        
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Absolute change in the objective function allowed for convergency (TolFun):','HorizontalAlignment','left', 'Units','normalized');            
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Maximum absolute change in the parameters allowed for convergency (TolX):','HorizontalAlignment','left', 'Units','normalized');                        
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Relative difference for the finite difference gradient estimation (DerivStep):','HorizontalAlignment','left', 'Units','normalized');                        
+% 
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
+%             MultiModel_tabButtons = uiextras.VButtonBox('Parent',MultiModel_tabVbox,'Padding', 3, 'Spacing', 3);             
+%             uicontrol('Parent',MultiModel_tabButtons,'String','>','Callback', @this.multimodel_moveRight, 'Tag','NLMEFIT paramsRight', 'TooltipString', 'Move the selected left-hand parameters to the right-hand box.' );
+%             uicontrol('Parent',MultiModel_tabButtons,'String','<','Callback', @this.multimodel_moveLeft, 'Tag','NLMEFIT paramsLeft', 'TooltipString', 'Move the selected right-hand parameters to the left-hand box.' );            
+%             MultiModel_tabButtons.ButtonSize(1) = 225;            
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','')            
+%             
+%             uicontrol(MultiModel_tabVbox,'Style','text','String','Parameters to jointly estimate as random-effect (component:parameter-model label):','HorizontalAlignment','left', 'Units','normalized');                        
+%             multiModel_RightList = uicontrol(MultiModel_tabVbox,'Style','listbox','String',cell(0,1),'Min',1,'Max',100,'Tag','NLMEFIT paramsRightList','HorizontalAlignment','left', 'Units','normalized');                        
+%                         
+%             uicontrol(MultiModel_tabVbox,'Style','edit','string','10','Max',1, 'Tag','NLMEFIT nStarts','HorizontalAlignment','right');
+%             uicontrol(MultiModel_tabVbox,'Style','edit','string','Inf','Max',1, 'Tag','NLMEFIT MaxIter','HorizontalAlignment','right');
+%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-8','Max',1, 'Tag','NLMEFIT TolFun','HorizontalAlignment','right');
+%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-6','Max',1, 'Tag','NLMEFIT TolX','HorizontalAlignment','right');
+%             uicontrol(MultiModel_tabVbox,'Style','edit','string','1e-5','Max',1, 'Tag','NLMEFIT DerivStep','HorizontalAlignment','right');
+% 
+%                         
+%             set(MultiModel_tabVbox, 'ColumnSizes', [-1 40 -1], 'RowSizes', [20, -1, 20, 20, 20, 20, 20]);            
+                        
+            % Loop through each model to be calibrated and disable the
+            % calib. tabs that are not required.
+            % Loop  through the list of selected bore and apply the model
+            % options.
+            initialTab = [];
+            showCMAEStab =  false;
+            showSPUCItab = false;
+            showDREAMtab = false;
+            showMultiModeltab = false;
+            selectedBores = data(:,1);
+            for i=1:length(selectedBores)
+                
+                % Check if the model is to be calibrated.
+                if isempty(selectedBores{i}) || ~selectedBores{i}
+                    continue;
+                end
+
+                % Get the selected model calibration method
+                calibMethod = data{i,8};                
+                 
+                switch calibMethod
+                    case 'CMA-ES'
+                        showCMAEStab = true;
+                        if isempty(initialTab)
+                            initialTab=1;
+                        end                    
+                    case 'SP-UCI'
+                        showSPUCItab =  true;
+                        if isempty(initialTab)
+                            initialTab=2;
+                        end                        
+                    case 'DREAM'
+                        showDREAMtab = true;
+                        if isempty(initialTab)
+                            initialTab=3;
+                        end                        
+                    case 'Multi-model'
+                        showMultiModeltab = true;
+                        if isempty(initialTab)
+                            initialTab=4;
+                        end                        
+                    otherwise
+                        error('The input model type cannot be set-up.')
+                end
+                        
+            end
+                    
+            % Show first active tab
+            outerTabsPanel.SelectedChild = initialTab;
+            
+            if ~showCMAEStab
+                outerTabsPanel.TabEnables{1} = 'off';
+            end
+            if ~showSPUCItab
+                outerTabsPanel.TabEnables{2} = 'off';
+            end            
+            if ~showDREAMtab
+                outerTabsPanel.TabEnables{3} = 'off';
+            end            
+%             if ~showMultiModeltab
+%                 outerTabsPanel.TabEnables{4} = 'off';
+%             end                       
+%             
+%             % Loop through each model that is part of the multi-model and
+%             % get the parameters. Add these to the multi-modle list box.
+%             multiModel_leftList.String={};
+%             multiModel_rightList.String={};
+%             if showMultiModeltab
+%                 for i=1:length(selectedBores)
+% 
+%                     % Check if the model is to be calibrated.
+%                     if isempty(selectedBores{i}) || ~selectedBores{i} || ~strcmp(data{i,8},'Multi-model')
+%                         continue;
+%                     end
+% 
+%                     % Get the selected model for simulation
+%                     calibLabel = data{i,2};
+%                     calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);                    
+%                     
+%                     tmpModel = getModel(this, calibLabel);
+%  
+%                     % Get the model parameters and add to the list box
+%                     if ~isempty(tmpModel)
+% 
+%                          [params, paramNames] = getParameters(tmpModel.model);
+%                          paramNames = strcat(paramNames(:,1),':',paramNames(:,2),['-',calibLabel]);
+%                          multiModel_leftList.String = {multiModel_leftList.String{:}, paramNames{:}};
+%                     end
+%                     
+%                 end                
+%                 multiModel_leftList.String = sort(multiModel_leftList.String);
+%             end
+% 
+        end
+        
+        function onSimModels(this, hObject, eventdata)
+           
+            % Get table data
+            data = this.tab_ModelSimulation.Table.Data;
+            
+            % Get list of selected bores.
+            selectedBores = data(:,1);            
+            if length(selectedBores)==0
+                warndlg('No models selected for simulation.');
+            end
+
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update            
+
+            % Count the number of models selected
+            nModels=0;
+            for i=1:length(selectedBores);                                
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    nModels = nModels +1;
+                end
+            end
+            
+            % Add simulation bar
+            minModels4Waitbar = 5;
+            iModels=0;
+            if nModels>=minModels4Waitbar
+                h = waitbar(0, ['Simulating ', num2str(nModels), ' models. Please wait ...']);
+            end            
+            
+            % Loop  through the list of selected bore and apply the model
+            % options.
+            nModelsSim = 0;
+            nModelsSimFailed = 0;
+            for i=1:length(selectedBores);                                
+                
+                % Check if the model is to be simulated.
+                if isempty(selectedBores{i}) || ~selectedBores{i}
+                    continue;
+                end
+                
+                this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FFA500">Simulating ... </font></html>';
+                        
+                % Update status in GUI
+                drawnow
+        
+                % Get model label
+                calibLabel = data{i,2};
+                
+                % Get a copy of the model object. This is only done to
+                % minimise HDD read when the models are off loaded to HDD using
+                % matfile();
+                tmpModel = getModel(this, calibLabel);
+
+                % Exit if model not found or not calibrated.
+                if isempty(tmpModel) || ~tmpModel.calibrationResults.isCalibrated
+                   nModelsSimFailed = nModelsSimFailed +1;
+                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - Model could not be found. Please rebuild and calibrate it.</font></html>'];
+                   continue;
+                end                                  
+                
+                % Get the exact start and end dates of the obs head.
+                obsHead = getObservedHead(tmpModel);
+                
+                % Get the simulation options
+                obsHeadStartDate = min(obsHead(:,1));
+                obsHeadEndDate = max(obsHead(:,1));               
+                simLabel = data{i,6};
+                forcingdata_fname = data{i,7};      
+                simTimeStep = data{i,10};
+                
+                % Check there is a simulation label.
+                if isempty(simLabel)
+                   nModelsSimFailed = nModelsSimFailed +1;
+                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - No simulation label.</font></html>'];
+                   continue;
+                end                      
+                
+                % Get the forcing data.
+                if ~isempty(forcingdata_fname)
+
+                    % Import forcing data
+                    %-----------------------
+                    % Check fname file exists.
+                    if isdir(this.project_fileName)
+                        forcingdata_fname = fullfile(this.project_fileName,forcingdata_fname);
+                    else
+                        forcingdata_fname = fullfile(fileparts(this.project_fileName),forcingdata_fname);
+                    end
+                    
+                    if exist(forcingdata_fname,'file') ~= 2;                   
+                        this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The new forcing date file could not be open.</font></html>';
+                        nModelsSimFailed = nModelsSimFailed +1;
+                        continue;
+                    end
+
+                    % Read in the file.
+                    try
+                       forcingData= readtable(forcingdata_fname);
+                    catch                   
+                        this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The new forcing date file could not be imported.</font></html>';
+                        nModelsSimFailed = nModelsSimFailed +1;
+                        continue;                        
+                    end                    
+                   
+                    % Convert year, month, day to date vector
+                    forcingData_data = table2array(forcingData);
+                    forcingDates = datenum(forcingData_data(:,1), forcingData_data(:,2), forcingData_data(:,3));
+                    forcingData_data = [forcingDates, forcingData_data(:,4:end)];
+                    forcingData_colnames = forcingData.Properties.VariableNames(4:end);
+                    forcingData_colnames = {'time', forcingData_colnames{:}};
+                    forcingData = array2table(forcingData_data,'VariableNames',forcingData_colnames);
+                    clear forcingData_data forcingDates
+                else
+                    % Set forcing used to empty. This will cause the
+                    % calibration data to be used.
+                    forcingData = [];
+                end
+                                    
+               % Get the start and end dates for the simulation.                               
+               if isempty(data{i,8})
+                   simStartDate = obsHeadStartDate;
+               else
+                   simStartDate = datenum( data{i,8},'dd-mmm-yyyy'); 
+               end
+               if isempty( data{i,9})
+                   simEndDate = obsHeadEndDate;
+               else
+                   simEndDate = datenum( data{i,9},'dd-mmm-yyyy') + datenum(0,0,0,23,59,59);                         
+               end
+
+               % Check the date timestep
+               if isempty(simTimeStep) && (simStartDate <obsHeadStartDate || simEndDate > obsHeadEndDate)
+                    this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - The time step must be specified when the simulation dates are outside of the observed head period.</font></html>';
+                    nModelsSimFailed = nModelsSimFailed +1;
+                    continue;                        
+               end               
+               
+               % Create a vector of simulation time points 
+               switch  simTimeStep
+                   case 'Daily'
+                       simTimePoints = [simStartDate:1:simEndDate]';
+                   case 'Weekly'
+                       simTimePoints = [simStartDate:7:simEndDate]';
+                   case 'Monthly'
+                       simTimePoints = zeros(0,3);
+                       startYear = year(simStartDate);
+                       startMonth= month(simStartDate);
+                       startDay= 1;
+                       endYear = year(simEndDate);
+                       endMonth = month(simEndDate);
+                       endDay = day(simEndDate);
+                       iyear = startYear;
+                       imonth = startMonth;
+                       iday = startDay;
+                       j=1;
+                       simTimePoints(j,1:3) = [iyear, imonth, iday];
+                       while datenum(iyear,imonth,iday) <= simEndDate
+                          
+                           if imonth == 12
+                               imonth = 1;
+                               iyear = iyear + 1;
+                           else
+                               imonth = imonth + 1;
+                           end
+                           if datenum(iyear, imonth, iday) >= simStartDate && datenum(iyear, imonth, iday) <= simEndDate
+                               j=j+1;
+                               simTimePoints(j,1:3) = [iyear, imonth, iday];
+                           end
+                       end
+                       
+                       simTimePoints = datenum(simTimePoints);
+                   case 'Yearly'
+                       simTimePoints = [simStartDate:365:simEndDate]';
+                   otherwise
+                       % Get the observed head dates.
+                       obsTimePoints = getObservedHead(tmpModel);
+                       obsTimePoints = obsTimePoints(:,1);
+                       
+                       % Filter observed head time points to between the
+                       % simulation dates.
+                       filt = obsTimePoints >= simStartDate & obsTimePoints<=simEndDate;
+                       simTimePoints = obsTimePoints(filt);
+                       
+               end
+               
+               % Get the flag for kriging the residuals.
+               doKrigingOfResiduals = false;
+               if this.tab_ModelSimulation.Table.Data{i,end-1} && isempty(forcingData)
+                    doKrigingOfResiduals = true;
+               elseif this.tab_ModelSimulation.Table.Data{i,end-1} && ~isempty(forcingData)
+                    this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#FF0000">Sim. failed - Kriging of residuals can only be undertaken if new forcing data is not input.</font></html>';
+                    nModelsSimFailed = nModelsSimFailed +1;                    
+                    continue;                        
+               end                 
+               
+               % Undertake the simulation.
+               try 
+                   solveModel(tmpModel, simTimePoints, forcingData, simLabel, doKrigingOfResiduals);                   
+                   setModel(this, calibLabel, tmpModel);        
+
+                   % Update GUI table
+                   this.tab_ModelSimulation.Table.Data{i,end} = '<html><font color = "#008000">Simulated. </font></html>';
+                   
+                   nModelsSim = nModelsSim + 1;
+                                      
+               catch ME
+                   nModelsSimFailed = nModelsSimFailed +1;
+                   this.tab_ModelSimulation.Table.Data{i,end} = ['<html><font color = "#FF0000">Sim. failed - ', ME.message,'</font></html>']; '<html><font color = "#FF0000">Failed. </font></html>';                       
+               end
+               
+               % Update wait bar
+               if nModels>=minModels4Waitbar            
+                    iModels=iModels+1;
+                    waitbar(iModels/nModels);                
+               end
+            end
+            
+            % Close wait bar
+            if nModels>=minModels4Waitbar            
+                close(h);
+            end
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update            
+            
+            % Report Summary
+            msgbox(['The simulations were successful for ',num2str(nModelsSim), ' models and failed for ',num2str(nModelsSimFailed), ' models.'], 'Summary of model simulaions...');
+
+        end
+        
+        
+        function onImportFromHPC(this, hObject, eventdata)
+           % Get SSH details
+            prompts = { 'URL to the cluster:', ...
+                        'User name for cluster:', ...
+                        'Password for cluster:', ...
+                        'Full path to folder for the jobs:', ...
+                        'Temporary local working folder:'};
+            dlg_title = 'Commands for retrieving HPC cluster model calibration.';        
+            num_lines = 1;
+            if isempty(this.HPCoffload)
+                defaults = {'edward.hpc.unimelb.edu.au', ...
+                            '', ...
+                            '', ...
+                            '', ...
+                            ''};
+            else
+                defaults{1}=this.HPCoffload{2};
+                defaults{2}=this.HPCoffload{3};
+                defaults{4}=this.HPCoffload{6};
+                defaults{3}='';
+                if length(this.HPCoffload)>=14
+                    defaults{5}=this.HPCoffload{14};
+                else
+                    defaults{5}='';                    
+                end
+                for i=1:length(defaults)
+                    if isempty(defaults{i})
+                        defaults{i}='';
+                    end
+                end
+            end
+            userData = inputdlg(prompts,dlg_title,num_lines,defaults);
+            if isempty(userData)
+                return;
+            end
+            
+            % Disaggregate user data
+            URL = userData{1};
+            username = userData{2};
+            password = userData{3};
+            folder = userData{4};
+            workingFolder = userData{5};
+            this.HPCoffload{2} = URL;
+            this.HPCoffload{3} = username;
+            this.HPCoffload{5} = folder;
+            this.HPCoffload{14} = workingFolder;
+            
+            % Get project folder
+            if isempty(this.project_fileName)
+                errordlg('The project folder must be set prior to retrieval of results.','Projct folder not set.');
+                return;
+            else
+                % Get project folder and file name (if a dir)
+                if isdir(this.project_fileName)
+                    projectPath = this.project_fileName;
+                else
+                    [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
+                end
+            end            
+
+            % Start the diary and copying of the command window outputs to
+            % the GUI.
+            commandwindowBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','calibration command window');
+            commandwindowBox.Max=Inf;
+            commandwindowBox.Min=0;
+            diaryFilename = strrep(this.project_fileName,'.mat',['_calibOutputs_',strrep(strrep(datestr(now),':','-'),' ','_'),'.txt']);
+            this.tab_ModelCalibration.QuitObj = calibGUI_interface(commandwindowBox,diaryFilename);
+            startDiary(this.tab_ModelCalibration.QuitObj);                  
+            
+            % Display update
+            display('HPC retrieval progress ...');
+            
+            % Update the diary file
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end  
+                        
+            % Check the local workin folder exists. If not, create it.
+            if ~exist(workingFolder,'dir')
+                % Display update
+                display(['   Making local working folder at:',workingFolder]);
+                
+                % Update the diary file
+                if ~isempty(this.tab_ModelCalibration.QuitObj)
+                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+                end
+                
+                mkdir(workingFolder);
+            end
+            cd(workingFolder);
+            
+            % Check that a SSH channel can be opened           
+            display('   Checking SSH connection to cluster ...');
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end            
+            sshChannel = ssh2_config(URL,username,password); 
+            if isempty(sshChannel)
+                errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
+                return;
+            end
+                      
+            % Get list of selected bores.            
+            data = this.tab_ModelCalibration.Table.Data;
+            selectedBores = data(:,1);
+
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update;
+              
+            % Get model indexes
+            imodels=[];
+            for i=1:length(selectedBores);
+                % Check if the model is to be calibrated.
+                if isempty(selectedBores{i}) || ~selectedBores{i}
+                    continue;
+                else
+                    imodels = [imodels,i];
+                end            
+            end
+            nSelectedModels = length(imodels);
+            
+            % Get a list of .mat files on remote cluster
+            display('   Getting list of results files on cluster ...');
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end        
+            try
+                [~,allMatFiles] = ssh2_command(sshChannel,['cd ',folder,'/models ; find -name \*.mat -print']);
+            catch ME
+                errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
+                return;
+            end
+            
+            % Filter out the input data files
+            ind = find(cellfun( @(x) isempty(strfind(x, 'HPCmodel.mat')), allMatFiles));
+            allMatFiles = allMatFiles(ind);
+            
+            % Build list of mat files results to download
+            display('   Building list of results files to retieve ...');
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end            
+            resultsToDownload=cell(0,1);
+            j=0;
+            imodel_filt=[];            
+            nModelsNoResult=0;
+            nModelsNamesToChange=0;
+            SSH_commands = '';
+            for i=imodels
+                % Get the selected model label.
+                calibLabel = data{i,2};                
+                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
+  
+                % Remove special characters from label (as per jobSubmission.m)
+                calibLabel =  regexprep(calibLabel,'\W','_');                             
+                calibLabel =  regexprep(calibLabel,'____','_');                             
+                calibLabel =  regexprep(calibLabel,'___','_');                             
+                calibLabel =  regexprep(calibLabel,'__','_');                      
+                
+                indResultsFileName = find(cellfun( @(x) ~isempty(strfind(x, ['/',calibLabel,'/results.mat'])), allMatFiles));                
+                if ~isempty(indResultsFileName)
+                    changefilename=true;
+                else
+                    indResultsFileName = find(cellfun( @(x) ~isempty(strfind(x, ['/',calibLabel,'/',calibLabel,'.mat'])), allMatFiles));
+                    changefilename=false;
+                end
+                    
+                if ~isempty(indResultsFileName)
+                    % Build SSH command string for chnaging results file
+                    % names form results.mat to bore label
+                    if changefilename
+                        nModelsNamesToChange=nModelsNamesToChange+1;
+                        SSH_commands = strcat(SSH_commands, [' cd ',folder,'/models/',calibLabel '; mv results.mat ', calibLabel,'.mat ;']);
+                    end
+                    
+                    % Add file name to list of files to download
+                    j=j+1;
+                    resultsToDownload{j,1}=['./',calibLabel,'/',calibLabel,'.mat'];
+                    imodel_filt =  [imodel_filt,true];
+                else
+                    imodel_filt =  [imodel_filt,false];
+                    nModelsNoResult=nModelsNoResult+1;
+                end
+            end
+
+            % Change file names from results.mat
+            if length(SSH_commands)>0
+                display(['   Changing file from results.mat to model label .mat at ', num2str(nModelsNamesToChange), ' models...']);
+                if ~isempty(this.tab_ModelCalibration.QuitObj)
+                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+                end                
+                try
+                    [~,status] = ssh2_command(sshChannel,SSH_commands);
+                catch ME
+                    warndlg({'Some results.mat files could not be changed to the model label.' ,'These models will not be imported.'},'SSH file name change failed.');
+                end
+            end
+            
+            % Download .mat files
+            display(['   Downloading ', num2str(length(resultsToDownload)), ' completed models to working folder ...']);
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end
+            imodels = imodels(logical(imodel_filt));            
+            try
+                ssh2_struct = scp_get(sshChannel,resultsToDownload, workingFolder, [folder,'/models/']);
+            catch ME
+                sshChannel  =  ssh2_close(sshChannel);        
+            end
+
+            display('   Closing SSH connection to cluster ...');
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end
+            
+            % Closing connection
+            try
+                sshChannel  =  ssh2_close(sshChannel);        
+            catch
+                % do nothing
+            end            
+            
+            % Loop  through the list of selected bore and apply the model
+            % options.
+            nModels=length(imodels);
+            nModelsResultFileErr=0;
+            nModelsRetieved=0;
+            k=0;
+            for i=imodels
+                k=k+1;
+                display(['   Importing model ',num2str(k),' of ',num2str(nModels) ' into the project ...']);
+                if ~isempty(this.tab_ModelCalibration.QuitObj)
+                    updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+                end
+
+                % get the original tabel text
+                tableRowData = this.tab_ModelCalibration.Table.Data(i,:); 
+                
+                % Get the selected model label.
+                calibLabel = data{i,2};                
+                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
+  
+                % Remove special characters from label (as per jobSubmission.m)
+                calibLabel = HydroSight_GUI.modelLabel2FieldName(calibLabel);
+                                                        
+                % Load model
+                try
+                    cd(workingFolder);
+                    importedModel = load([calibLabel,'.mat']);
+
+                    % Convert double precision residuals to single
+                    % to reduce RAM
+                    if isfield(importedModel.model.evaluationResults,'data')
+                        importedModel.model.evaluationResults.data.modelledHead_residuals = single(importedModel.model.evaluationResults.data.modelledHead_residuals);
+                    end
+                    importedModel.model.calibrationResults.data.modelledHead_residuals = single(importedModel.model.calibrationResults.data.modelledHead_residuals);
+
+                    % Clear the residuals from the model.model.variables. This is only done to minimise RAM.
+                    try
+                        if isfield(importedModel.model.model.variables,'resid');
+                            importedModel.model.model.variables = rmfield(importedModel.model.model.variables, 'resid');
+                        end
+                    catch ME                                
+                        % do nothing
+                    end
+
+                    % IF an old version of the HydroSight had been used,
+                    % then remove the cell array of variogram
+                    % values and just keep the relevant data. This
+                    % is done only to reduce RAM.
+                    if isfield(importedModel.model.evaluationResults,'performance')                        
+                        if isfield(importedModel.model.evaluationResults.performance.variogram_residual,'model')                        
+                            nvariograms=size(importedModel.model.evaluationResults.performance.variogram_residual.range,1);
+                            nBins  = length(importedModel.model.calibrationResults.performance.variogram_residual.model{1}.h);
+                            deltaTime=zeros(nBins ,nvariograms);
+                            gamma=zeros(nBins ,nvariograms);
+                            gammaHat=zeros(nBins ,nvariograms);
+                            parfor j=1:nvariograms
+                                deltaTime(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.h;
+                                gamma(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.gamma;
+                                gammaHat(:,j) = importedModel.model.evaluationResults.performance.variogram_residual.model{j}.gammahat;
+                            end
+                            importedModel.model.evaluationResults.performance.variogram_residual.h = deltaTime;
+                            importedModel.model.evaluationResults.performance.variogram_residual.gamma = gamma;
+                            importedModel.model.evaluationResults.performance.variogram_residual.gammaHat = gammaHat;
+                        end
+                    end                              
+                    if isfield(importedModel.model.calibrationResults.performance.variogram_residual,'model')                        
+                        nvariograms=size(importedModel.model.calibrationResults.performance.variogram_residual.range,1);
+                        nBins  = length(importedModel.model.calibrationResults.performance.variogram_residual.model{1}.h);
+                        deltaTime=zeros(nBins ,nvariograms);
+                        gamma=zeros(nBins ,nvariograms);
+                        gammaHat=zeros(nBins ,nvariograms);
+                        parfor j=1:nvariograms
+                            deltaTime(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.h;
+                            gamma(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.gamma;
+                            gammaHat(:,j) = importedModel.model.calibrationResults.performance.variogram_residual.model{j}.gammahat;
+                        end
+                        importedModel.model.calibrationResults.performance.variogram_residual.h = deltaTime;
+                        importedModel.model.calibrationResults.performance.variogram_residual.gamma = gamma;
+                        importedModel.model.calibrationResults.performance.variogram_residual.gammaHat = gammaHat;
+                    end
+                    % Assign calib status
+                    tableRowData{1,9} = '<html><font color = "#008000">Calibrated. </font></html>';
+
+                    % Get calib status and set into table.
+                    exitFlag = importedModel.model.calibrationResults.exitFlag;
+                    exitStatus = importedModel.model.calibrationResults.exitStatus;                            
+                    if exitFlag ==0 
+                        this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Calib. failed - ', ME.message,'</font></html>'];
+
+                        % Update status in GUI
+                        %drawnow update
+
+                        continue
+
+                    elseif exitFlag ==1
+                        tableRowData{1,9} = ['<html><font color = "#FFA500">Partially calibrated: ',exitStatus,' </font></html>'];
+                    elseif exitFlag ==2
+                        tableRowData{1,9} = ['<html><font color = "#008000">Calibrated: ',exitStatus,' </font></html>'];
+                    end
+
+                    % Update status in GUI
+                    drawnow update;
+
+                    % Recalculate performance stats
+                    %------------------
+                    head_calib_resid = importedModel.model.calibrationResults.data.modelledHead_residuals;
+                    SSE = sum(head_calib_resid.^2);
+                    RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
+                    importedModel.model.calibrationResults.performance.RMSE = RMSE;                        
+
+                    % CoE
+                    obsHead =  importedModel.model.calibrationResults.data.obsHead;
+                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
+                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(:,2));            
+                    importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
+
+                    if ~isempty(importedModel.model.evaluationResults)
+
+                        head_eval_resid = importedModel.model.evaluationResults.data.modelledHead_residuals;
+                        obsHead =  importedModel.model.evaluationResults.data.obsHead;
+
+                        % Mean error
+                        importedModel.model.evaluationResults.performance.mean_error = mean(head_eval_resid); 
+
+                        %RMSE
+                        SSE = sum(head_eval_resid.^2);
+                        importedModel.model.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
+
+                        % Unbiased CoE
+                        residuals_unbiased = bsxfun(@minus,head_eval_resid, importedModel.model.evaluationResults.performance.mean_error);
+                        SSE = sum(residuals_unbiased.^2);
+                        importedModel.model.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE./sum( (obsHead(:,2) - mean(obsHead(:,2)) ).^2);            
+                    end                              
+                    %--------------
+
+                    % Set calib performance stats.
+                    calibAICc = median(importedModel.model.calibrationResults.performance.AICc);
+                    calibBIC = median(importedModel.model.calibrationResults.performance.BIC);
+                    calibCoE = median(importedModel.model.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
+                    tableRowData{1,10} = ['<html><font color = "#808080">',num2str(calibCoE),'</font></html>'];
+                    tableRowData{1,12} = ['<html><font color = "#808080">',num2str(calibAICc),'</font></html>'];
+                    tableRowData{1,13} = ['<html><font color = "#808080">',num2str(calibBIC),'</font></html>'];
+
+                    % Set eval performance stats
+                    if isfield(importedModel.model.evaluationResults,'performance')
+                        evalCoE = median(importedModel.model.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
+                        tableRowData{1,11} = ['<html><font color = "#808080">',num2str(evalCoE),'</font></html>'];
+                    else
+                        evalCoE = '(NA)';
+                        tableRowData{1,11} = ['<html><font color = "#808080">',evalCoE,'</font></html>'];
+                    end
+
+                    %vars=whos('-file','tmp2.mat'); for i=1:6;vars2{i}=vars(i).name;end; for i=1:6; loadedVars=load('tmp2.mat',vars2{i}); if i==1; save('tmp4.mat','-Struct','loadedVars');else;save('tmp4.mat','-Struct','loadedVars','-append');end; end
+                    % Update project with imported model.
+                    setModel(this, calibLabel, importedModel.model);
+                    clear importedModel;
+
+                    nModelsRetieved = nModelsRetieved + 1;
+
+                catch ME
+                    nModelsResultFileErr = nModelsResultFileErr +1;
+                end
+                
+                % Update GUI labels
+                this.tab_ModelCalibration.Table.Data(i,:) = tableRowData;
+
+            end                                  
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update;
+            
+            % Output Summary.
+            msgStr = {};
+            msgStr{length(msgStr)+1} = 'Finished Retrieval of Models.';
+            msgStr{length(msgStr)+1} = 'Summary of Retrieval:';
+            msgStr{length(msgStr)+1} = ['   No. models sucessfully imported to project: ',num2str(nModelsRetieved)];            
+            msgStr{length(msgStr)+1} = ['   No. selected models to retrieve: ',num2str(nSelectedModels )];
+            msgStr{length(msgStr)+1} = ['   No. selected models not yet complete: ',num2str(nModelsNoResult) ];
+            msgStr{length(msgStr)+1} = ['   No. selected models complete: ',num2str(nModels) ];                        
+            msgStr{length(msgStr)+1} = ['   No. models files that could not be imported to project: ',num2str(nModelsResultFileErr) ];     
+            
+            for i=1:length(msgStr)
+                display(msgStr{i});
+            end
+            if ~isempty(this.tab_ModelCalibration.QuitObj)
+                updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+            end
+                                   
+        end
+
+        
+        function onImportTable(this, hObject, eventdata)
+        
+            
+            % Create the label for each type of inport
+            switch hObject.Tag
+                case 'Data Preparation'
+                    windowString = 'Select a .csv file containing the data for the data preparation table.';                                    
+                case 'Model Construction'
+                    windowString = 'Select a .csv file containing the data for the model construction.';                    
+                case 'Model Calibration'
+                    windowString = 'Select a .csv file containing the data for model calibration settings.';
+                case 'Model Simulation'
+                    windowString = 'Select a .csv file containing the data for the model simulation.';
+                otherwise
+                    warndlg('Unexpected Error: GUI table type unknown.');
+                    return                    
+            end
+                
+            % Get project folder
+            projectPath='';                
+            if isdir(this.project_fileName)
+                projectPath = this.project_fileName;
+            else
+                projectPath = fileparts(this.project_fileName);
+            end
+            
+            % Show open file window
+             if isempty(projectPath)
+                [fName,pName] = uigetfile({'*.csv'},windowString); 
+            else
+                [fName,pName] = uigetfile({'*.csv'},windowString, projectPath); 
+            end            
+            if fName~=0;
+                % Assign file name to date cell array
+                filename = fullfile(pName,fName);
+            else
+                return;
+            end         
+            
+            % Read in the table.
+            try
+                tbl = readtable(filename,'Delimiter',',');
+            catch ME
+                warndlg('The table datafile could not be read in. Please check it is a CSV file with column headings in the first row.');
+                return;
+            end            
+
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update;            
+            
+            % Check the table format and append to the required table
+            switch hObject.Tag
+                case 'Data Preparation'
+                    if size(tbl,2) ~=18
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                        
+                        warndlg('The table datafile must have 18 columns. That is, all columns shown in the model construction table.');
+                        return;
+                    end                    
+                    
+                    % Get column formats
+                    colFormat = this.tab_DataPrep.Table.ColumnFormat;                    
+
+                    % Convert table to cell array
+                    tableAsCell = table2cell(tbl);                    
+                    
+                    % Loop through each row in tbl, check the data and add
+                    % the user data to the columns of the row.
+                    nRowsNotImported= 0;
+                    nImportedRows = 0;
+                    for i=1: size(tbl,1)
+                        
+                        try                                      
+
+                            % Convert any columns of string data to '' if read
+                            % in as NaN.
+                            for j=1:length(colFormat)                       
+                                if ischar(colFormat{j}) && strcmp(colFormat{j},'char') && ...
+                                isnumeric( tableAsCell{i,j} ) && isnan( tableAsCell{i,j} )
+                                    tableAsCell{i,j} = '';
+                                elseif iscell(colFormat{j}) && ~ischar(tableAsCell{i,j}) && isnan( tableAsCell{i,j} )
+                                    tableAsCell{i,j} = '';
+                                elseif isdatetime(tableAsCell{i,j})
+                                    tableAsCell{i,j} = datestr(tableAsCell{i,j});
+                                end
+                            end
+
+                            % Add results text.
+                            tableAsCell{i,16} = '<html><font color = "#FF0000">Bore not analysed.</font></html>'; 
+                            tableAsCell{i,17} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                            tableAsCell{i,18} = ['<html><font color = "#808080">','(NA)','</font></html>'];
+                            
+                            % Convert integer 0/1 to logicals
+                            for j=find(strcmp(colFormat,'logical'))
+                               if tableAsCell{i,j}==1;
+                                   tableAsCell{i,j} = true;
+                               else
+                                   tableAsCell{i,j} = false;
+                               end                                       
+                            end
+                            
+                            % Append data
+                            this.tab_DataPrep.Table.Data = [this.tab_DataPrep.Table.Data; tableAsCell(i,:)];
+                            
+                            nImportedRows = nImportedRows + 1;
+                        catch ME
+                            nRowsNotImported = nRowsNotImported + 1;
+                        end
+                        
+                    end
+                    
+
+                    % Update row numbers.
+                    nrows = size(this.tab_DataPrep.Table.Data,1);
+                    this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                           
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                                
+                    
+                    % Output Summary.
+                    msgbox({['Data preparation table data was imported to ',num2str(nImportedRows), ' rows.'], ...
+                            '', ...
+                            ['Number of rows not imported because of data format errors: ',num2str(nRowsNotImported) ]}, 'Summary of data preparation table importing ...');
+                    
+                    
+                case 'Model Construction'
+                    if size(tbl,2) ~=9
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                        
+                        warndlg('The table datafile must have 9 columns. That is, all columns shown in the model construction table.');
+                        return;
+                    end
+                        
+                    % Set the Model Status column to a cells array (if
+                    % empty matlab assumes its NaN)
+                    tbl.Select_Model=false(size(tbl,1));
+                    tbl.Build_Status = cell(size(tbl,1),1);
+                    tbl.Build_Status(:) = {'<html><font color = "#FF0000">Model not built.</font></html>'};
+                    tbl = table(tbl.Select_Model,tbl.Model_Label,tbl.Obs_Head_File,tbl.Forcing_Data_File,tbl.Coordinates_File,tbl.Bore_ID,tbl.Model_Type,tbl.Model_Options,tbl.Build_Status);
+
+                    % Loop through each row in tbl and find the
+                    % corresponding model within the GUI table and the add
+                    % the user data to the columns of the row.
+                    nModelsNotUnique = 0;
+                    nImportedRows = 0;
+                    for i=1: size(tbl,1)
+
+                        % Get model label
+                        modelLabel_src = tbl{i,2};
+
+                        % Check if model label is unique.
+                        if size(this.tab_ModelConstruction.Table.Data,1)>0
+                            ind = find(strcmp(this.tab_ModelConstruction.Table.Data(:,2), modelLabel_src));                        
+
+                            % Check if the model is found
+                            if ~isempty(ind)
+                                nModelsNotUnique = nModelsNotUnique + 1;
+                                continue;
+                            end
+                        end
+
+                        % Append table. Note: the select column is input as a logical 
+                        if tbl{i,1}==1
+                            this.tab_ModelConstruction.Table.Data = [this.tab_ModelConstruction.Table.Data; true, table2cell(tbl(i,2:end))];
+                        else
+                            this.tab_ModelConstruction.Table.Data = [this.tab_ModelConstruction.Table.Data; false, table2cell(tbl(i,2:end))];
+                        end
+                        nImportedRows = nImportedRows + 1;
+                    end
+
+                    % Update row numbers.
+                    nrows = size(this.tab_ModelConstruction.Table.Data,1);
+                    this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                        
+
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                                                    
+                    
+                    % Output Summary.
+                    msgbox({['Construction data was imported to ',num2str(nImportedRows), ' rows.'], ...
+                            '', ...
+                            ['Number of rows not imported because the model label is not unique: ',num2str(nModelsNotUnique) ]}, 'Summary of model calibration importing ...');
+
+                    
+                case 'Model Calibration'
+                    
+                    if size(tbl,2) ~=13
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                        
+                        warndlg('The table datafile must have 13 columns. That is, all columns shown in the table.');
+                        return;
+                    end
+
+                    
+                    % Loop through each row in tbl and find the
+                    % corresponding model within the GUI table and the add
+                    % the user data to the columns of the row.
+                    nModelsNotFound = 0;
+                    nBoresNotMatching = 0;
+                    nCalibBoresDeleted = 0;
+                    nImportedRows = 0;
+                    for i=1: size(tbl,1)
+                                               
+                        % Get model lablel and bore ID
+                        modelLabel_src = tbl{i,2};
+                        boreID_src = tbl{i,3};
+                        
+                        % Find model within the table.
+                        modelLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,2));
+                        ind = find(strcmp(modelLabel_dest, modelLabel_src));                        
+                        
+                        % Check if the model is found
+                        if isempty(ind)
+                            nModelsNotFound = nModelsNotFound + 1;
+                            continue;
+                        end
+                        
+                        % Check the bore IDs are equal.
+                        boreID_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data{ind,3});
+                        if ~strcmp(boreID_dest, boreID_src{1})
+                            nBoresNotMatching = nBoresNotMatching + 1;
+                            continue;                            
+                        end
+                        
+                        % Record if the model is already built. To do this,
+                        % the model is first locasted with this.models.data{}
+                        if ~isempty(this.models)
+                            
+                            tmpModel = getModel(this,modelLabel_src{1});                                                        
+                            
+                            if ~isempty(tmpModel) && ~isempty(tmpModel.calibrationResults)
+                                % remove calibration results.
+                                tmpModel.calibrationResults = [];
+
+                                % Record that the calib results were overwritten
+                                nCalibBoresDeleted = nCalibBoresDeleted + 1;
+                            end
+                            
+                            setModel(this,modelLabel_src{1},tmpModel);                                                        
+                        end                     
+                        
+                        % Add data from columns 1,5-7.
+                        if tbl{i,1}==1
+                            this.tab_ModelCalibration.Table.Data{ind,1} = true;
+                        else
+                            this.tab_ModelCalibration.Table.Data{ind,1} = false;
+                        end
+                        this.tab_ModelCalibration.Table.Data{ind,6} = datestr(tbl{i,6},'dd-mmm-yyyy');
+                        this.tab_ModelCalibration.Table.Data{ind,7} = datestr(tbl{i,7},'dd-mmm-yyyy');
+                        this.tab_ModelCalibration.Table.Data{ind,8} = tbl{i,8}{1};
+                        
+                        % Input the calibration status.
+                        %this.tab_ModelCalibration.Table.Data{ind,9} = '<html><font color = "#FF0000">Not calibrated.</font></html>';
+                        %this.tab_ModelCalibration.Table.Data{ind,10} = [];                        
+                        %this.tab_ModelCalibration.Table.Data{ind,11} = [];
+                        %this.tab_ModelCalibration.Table.Data{ind,12} = [];
+                        %this.tab_ModelCalibration.Table.Data{ind,13} = [];
+                        
+                        nImportedRows = nImportedRows +1;
+                    end
+
+                    % Update row numbers.
+                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                    this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
+
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                                
+                                        
+                    % Output Summary.
+                    msgbox({['Calibration data was imported to ',num2str(nImportedRows), ' rows.'], ...
+                            ['   Number of model labels not found in the calibration table: ',num2str(nModelsNotFound) ], ...
+                            ['   Number of rows were the bore IDs did not match: ',num2str(nBoresNotMatching) ], ...
+                            ['   Number of rows were existing calibration results were deleted: ',num2str(nCalibBoresDeleted) ]}, 'Summary of model calibration importing ...');
+                    
+                case 'Model Simulation'
+                    
+                    % Check the number of columns
+                    if size(tbl,2) ~=12
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                        
+                        warndlg('The table datafile must have 12 columns. That is, all columns shown in the table.');
+                        return;
+                    end
+                    
+                    % Get column formats
+                    colFormat = this.tab_ModelSimulation.Table.ColumnFormat;
+
+                    % Convert table to cell array
+                    tableAsCell = table2cell(tbl);                    
+                    tableAsCell(:,12)=repmat({'(empty)'},size(tbl,1),1);
+                    
+                    % Loop through each row in tbl and find the
+                    % corresponding model within the GUI table and the add
+                    % the user data to the columns of the row.
+                    nSimLabelsNotUnique = 0;
+                    nImportedRows = 0;
+                    for i=1: size(tbl,1)
+                                               
+                        % Get model lablel and bore ID
+                        modelLabel_src = tbl{i,2};
+                        simLabel_src = tbl{i,6};
+                        
+                        % Check that the model ID and simulation label are
+                        % unique
+                        modelLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,2));
+                        simLabel_dest = HydroSight_GUI.removeHTMLTags(this.tab_ModelCalibration.Table.Data(:,6));
+                        ind = find(strcmp(modelLabel_dest, modelLabel_src) & strcmp(simLabel_dest, simLabel_src));
+                        
+                        % Skip if the model and label are not unique
+                        if ~isempty(ind)
+                            nSimLabelsNotUnique = nSimLabelsNotUnique + 1;
+                            continue;                        
+                        end
+
+                        
+                        % Convert any columns of string data to '' if read
+                        % in as NaN.
+                        for j=1:length(colFormat)                       
+                            if ischar(colFormat{j}) && strcmp(colFormat{j},'char')&& ...
+                            isnumeric( tableAsCell{i,j} ) && isnan( tableAsCell{i,j} )
+                                tableAsCell{i,j} = '';
+                            elseif iscell(colFormat{j}) && ~ischar(tableAsCell{i,j}) && isnan( tableAsCell{i,j} )
+                                tableAsCell{i,j} = '';
+                            end
+                        end
+                        
+                        % Append table. Note: the select column is input as a logical 
+                        tableAsCell{i,12} = '<html><font color = "#FF0000">Not simulated.</font></html>';   
+                        if tbl{i,1}==1
+                            rowData = [true, tableAsCell(i,2:end)];
+                        else
+                            rowData = [false, tableAsCell(i,2:end)];
+                        end     
+                        if tbl{i,11}==1
+                            rowData{11} = true;
+                        else
+                            rowData{11} = false;
+                        end                        
+                        if size(this.tab_ModelSimulation.Table.Data,1)==0
+                            this.tab_ModelSimulation.Table.Data = rowData;
+                        else                            
+                            this.tab_ModelSimulation.Table.Data = [this.tab_ModelSimulation.Table.Data; rowData];
+                        end
+
+                        nImportedRows = nImportedRows + 1;
+                    end
+
+                    % Update row numbers.
+                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                    this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                            
+                                  
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;
+                    
+                    % Output Summary.
+                    msgbox({['Simulation data was imported to ',num2str(nImportedRows), ' rows.'], ...
+                            '', ...
+                            ['Number of rows not imported because the model label is not unique: ',num2str(nSimLabelsNotUnique) ]}, ...
+                            'Summary of model calibration importing ...');
+                                   
+            end
+        end        
+        
+        function onExportTable(this, hObject, eventdata)
+            % Create the label for each type of inport
+            switch hObject.Tag
+                case 'Data Preparation'
+                    windowString = 'Input the .csv file name for exporting the data preparation table.';
+                    tbl = this.tab_DataPrep.Table.Data;
+                    colnames = this.tab_DataPrep.Table.ColumnName;
+                    colFormat = this.tab_DataPrep.Table.ColumnFormat;
+                    
+                case 'Model Construction'
+                    windowString = 'Input the .csv file name for exporting the model construction table.';
+                    tbl = this.tab_ModelConstruction.Table.Data;
+                    colnames = this.tab_ModelConstruction.Table.ColumnName;
+                    colFormat = this.tab_ModelConstruction.Table.ColumnFormat;
+                case 'Model Calibration'
+                    windowString = 'Input the .csv file name for exporting the model calibration table.';
+                    tbl = this.tab_ModelCalibration.Table.Data;
+                    colnames = this.tab_ModelCalibration.Table.ColumnName;
+                    colFormat = this.tab_ModelCalibration.Table.ColumnFormat;
+                case 'Model Simulation'
+                    windowString = 'Input the .csv file name for exporting the model simulation table.';
+                    tbl = this.tab_ModelSimulation.Table.Data;
+                    colnames = this.tab_ModelSimulation.Table.ColumnName;
+                    colFormat = this.tab_ModelSimulation.Table.ColumnFormat;
+                otherwise
+                    warndlg('Unexpected Error: GUI table type unknown.');
+                    return                    
+            end
+
+            % Show open file window
+            [fName,pName] = uiputfile({'*.csv'},windowString); 
+            if fName~=0;
+                % Assign file name to date cell array
+                filename = fullfile(pName,fName);
+            else
+                return;
+            end         
+            
+            % Remove HTML tags from the column names
+            colnames = HydroSight_GUI.removeHTMLTags(colnames);
+            
+            % Replace spaces within column names with "_"
+            colnames = strrep(colnames,' ','_');
+            colnames = strrep(colnames,'.','');
+            colnames = strrep(colnames,'?','');
+            colnames = strrep(colnames,'(','');
+            colnames = strrep(colnames,')','');
+            colnames = strrep(colnames,'/','_');
+            colnames = strrep(colnames,'-','_');
+            colnames = strrep(colnames,'__','_');
+            
+            %If the model constuction table, then remove possible ',' from
+            %the bore ID and model options columns.
+            if strcmp(hObject.Tag,'Model Construction')
+                tbl(:,6) = strrep(tbl(:,6),',',' ');
+                tbl(:,8) = strrep(tbl(:,8),',',' ');   
+            end
+            
+            % Remove HTML tags from each row
+            for i=find(strcmp(colFormat, 'char'))
+                tbl(:,i) = HydroSight_GUI.removeHTMLTags(tbl(:,i));
+            end           
+            
+            % Convert cell array to table            
+            tbl = cell2table(tbl);
+            tbl.Properties.VariableNames = colnames;
+            
+            % Write the table.
+            try
+                writetable(tbl, filename,'Delimiter',',');
+            catch ME
+                warndlg('The table could not be written. Please check you have write permissions to the destination folder.');
+                return;
+            end            
+        end        
+        
+        function onExportResults(this, hObject, eventdata)
+            
+            % Set initial folder to the project folder (if set)
+            if ~isempty(this.project_fileName)                                
+                try    
+                    if isdir(this.project_fileName)
+                        currentProjectFolder = this.project_fileName;
+                    else
+                        currentProjectFolder = fileparts(this.project_fileName);
+                    end 
+
+                    currentProjectFolder = [currentProjectFolder,filesep];
+                    cd(currentProjectFolder);
+                catch
+                    % do nothing
+                end
+            end      
+
+            % Export results
+            switch hObject.Tag                                           
+                case 'Data Preparation'
+
+                    % Check there is data to export.
+                    if isempty(this.dataPrep) || size(this.dataPrep,1)<1;
+                        warndlg('There is no data analysis results to export.','No data.');
+                        return;
+                    end
+                                  
+                     % Check if there are any rows selected for export
+                     if ~any( cellfun(@(x) x==1, this.tab_DataPrep.Table.Data(:,1)))
+                         warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
+                         return;
+                     end                        
+                    
+                    % Ask the user if they want to export all analysis data
+                    % (ie logical data reporting on the analysis
+                    % undertaken) or just the data not assessed an errerous
+                    % or an outlier.
+                    response = questdlg('Do you want to export the analysis results or just the observations not assessed as being erroneous or outliers?','Data to export?','Export Analysis Results','Export non-erroneous obs.','Cancel','Export non-erroneous obs.');
+                    
+                    if strcmp(response,'Cancel')
+                        return;
+                    end
+                    
+                    if strcmp(response, 'Export Analysis Results')
+                        exportAllData = true;
+                    else
+                        exportAllData = false;
+                    end
+                    
+                    % Get output file name
+                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.'); 
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        filename = fullfile(pName,fName);
+                    else
+                        return;
+                    end                     
+                    
+                    % Open file and write headers
+                    fileID = fopen(filename,'w');
+                    if exportAllData
+                        fprintf(fileID,'BoreID,Year,Month,Day,Hour,Minute,Head,Date_Error,Duplicate_Date_Error,Min_Head_Error,Max_Head_Error,Rate_of_Change_Error,Const_Hear_Error,Outlier_Obs \n');
+                    else
+                        fprintf(fileID,'BoreID,Year,Month,Day,Hour,Minute,Obs_Head \n');                    
+                    end                    
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;                    
+                    
+                    % Get a list of bore IDs
+                    boreIDs = fieldnames(this.dataPrep);
+                    
+                    % Setup wait box
+                    h = waitbar(0,'Exporting results. Please wait ...');                        
+                                   
+                    % Export each bore analysed.
+                    nResultsWritten = 0;
+                    nResultsNotWritten = 0;
+                    nResultToExport=0;
+                    for i = 1:size(boreIDs,1)
+
+                        % update wait bar
+                        waitbar(i/size(boreIDs,1));             
+                        
+                        % Find the model bore ID is within the GUI
+                        % table and then check if the row is to be
+                        % exported.
+                        tableInd = cellfun( @(x) strcmp( x,boreIDs{i}),  this.tab_DataPrep.Table.Data(:,3));
+                        if sum(tableInd)==0 || ~this.tab_DataPrep.Table.Data{tableInd,1}
+                            continue
+                        end                        
+                        
+                        % Get the analysis results.
+                        tableData = this.dataPrep.(boreIDs{i});
+
+                        % Check there is some data
+                        if isempty(tableData) || size(tableData,1)==0
+                            nResultsNotWritten = nResultsNotWritten +1;
+                            continue;                                
+                        end
+                        nResultToExport = nResultToExport + 1;
+                        
+                        % Convert to matrix.
+                        tableData = table2array(tableData);
+
+                        % Convert table to real (if previously set to
+                        % single for memeory issues)
+                        tableData = double(tableData);                        
+                        
+                        % Export data
+                        try
+                            if exportAllData                                                   
+    
+                                %Write each row.
+                                for j=1:size(tableData,1)
+                                    fprintf(fileID,'%s,%i,%i,%i,%i,%i,%12.3f,%i,%i,%i,%i,%i,%i,%i \n', boreIDs{i} , tableData(j,:));                                    
+                                end
+                            
+                                nResultsWritten = nResultsWritten + 1;
+                            else
+
+                                isError = any(tableData(:,7:end)==1,2);
+                                if size(tableData,1) > sum(isError)
+    
+                                    % Filter data
+                                    tableData = tableData(~isError,:);
+                                                                        
+                                    %Write each row.
+                                    for j=1:size(tableData,1)
+                                        fprintf(fileID,'%s,%i,%i,%i,%i,%i,%12.3f \n', boreIDs{i} , tableData(j,1:6));
+                                    end
+                                
+                                    nResultsWritten = nResultsWritten + 1;
+                                else
+                                    nResultsNotWritten = nResultsNotWritten +1;
+                                end
+                            end
+                        catch
+                            nResultsNotWritten = nResultsNotWritten +1;
+                        end
+                    end
+                    fclose(fileID);
+                    
+                    % Close wait bar
+                    close(h);                      
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                    
+                    
+                    % Show summary
+                    msgbox({'Export of results finished.','', ...
+                           ['Number of bore results exported =',num2str(nResultsWritten)], ...
+                           ['Number of rows selected for export =',num2str(nResultToExport)], ...
+                           ['Number of bore results not exported =',num2str(nResultsNotWritten)]}, ...
+                           'Export Summary');
+
+                    
+                case 'Model Calibration'
+                    
+                    % Check if there are any rows selected for export
+                    if ~any( cellfun(@(x) x==1, this.tab_ModelCalibration.Table.Data(:,1)))
+                        warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
+                        return;
+                    end                    
+
+                    % Ask the user if they want to export the time-series results or the model parameters or the derived parameters.
+                    response = questdlg('Do you want to export the time-series results, or the model and derived parameters?', ...
+                        'Export options.','Time-series results','Model & derived parameters','Cancel','Time-series results');                    
+
+                    if strcmp(response, 'Cancel')
+                        return;
+                    end
+                    
+                    % Get output file name
+                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.'); 
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        filename = fullfile(pName,fName);
+                    else
+                        return;
+                    end 
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;                    
+                    
+                    % Open file and write headers
+                    fileID = fopen(filename,'w');
+                    if strcmp(response, 'Time-series results')
+                        fprintf(fileID,'Model_Label,BoreID,Year,Month,Day,Hour,Minute,Obs_Head,Is_Calib_Point?,Calib_Head,Eval_Head,Model_Err,Noise_Lower,Noise_Upper \n');
+                    else 
+                        fprintf(fileID,'Model_Label,BoreID,ComponentName,ParameterName,ParameterSetNumber,ParameterSetValue \n');
+                    end
+                    
+                    % Setup wait box
+                    h = waitbar(0,'Exporting results. Please wait ...');                                            
+                    
+                    % Loop through each row of the calibration table and
+                    % export the calibration results (if calibrated)
+                    nrows = size(this.tab_ModelCalibration.Table.Data,1);
+                    nResultsWritten=0;
+                    nModelsToExport=0;
+                    nModelsNotCalib=0;
+                    for i=1:nrows
+
+                        % update wait bar
+                        waitbar(i/nrows);    
+                        
+                        % Skip if not selected
+                        if ~this.tab_ModelCalibration.Table.Data{i,1}
+                            continue
+                        end                        
+                        
+                        % Get model label.
+                        modelLabel = this.tab_ModelCalibration.Table.Data{i,2};
+                        modelLabel = HydroSight_GUI.removeHTMLTags(modelLabel);
+                        modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+                        
+                        % Incrment number of models selected for export
+                        nModelsToExport = nModelsToExport+1;
+                                                
+                        % Skip if not calibrated
+                        if ~this.model_labels{modelLabel,'isCalibrated'}
+                            nModelsNotCalib= nModelsNotCalib+1;
+                            continue
+                        end
+                        
+                        % Get a copy of the model object. This is only done to
+                        % minimise HDD read when the models are off loaded to HDD using
+                        % matfile();
+                        tmpModel = getModel(this, modelLabel);
+
+                        % Exit if model not found.
+                        if isempty(tmpModel)    
+                            continue;
+                        end                        
+                        
+                        % Get model results
+                        if isstruct(tmpModel.calibrationResults) && tmpModel.calibrationResults.isCalibrated                    
+                    
+                            if strcmp(response, 'Time-series results')
+                                % Get the model calibration data.
+                                tableData = tmpModel.calibrationResults.data.obsHead;
+                                tableData = [tableData, ones(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead(:,2), ...
+                                    nan(size(tableData,1),1), tmpModel.calibrationResults.data.modelledHead_residuals(:,end), ...
+                                    tmpModel.calibrationResults.data.modelledNoiseBounds(:,end-1:end)];
+                                
+                                % Get evaluation data
+                                if isfield(tmpModel.evaluationResults,'data')
+                                    % Get data
+                                    evalData = tmpModel.evaluationResults.data.obsHead;
+                                    evalData = [evalData, zeros(size(evalData,1),1), nan(size(evalData,1),1), tmpModel.evaluationResults.data.modelledHead(:,2), ...
+                                        tmpModel.evaluationResults.data.modelledHead_residuals(:,end), ...
+                                        tmpModel.evaluationResults.data.modelledNoiseBounds(:,end-1:end)];
+                                    
+                                    % Append to table of calibration data and sort
+                                    % by time.
+                                    tableData = [tableData; evalData];
+                                    tableData = sortrows(tableData, 1);
+                                end
+                                
+                                % Convert table to real (if previously set to
+                                % single for memeory issues)
+                                tableData = double(tableData);
+                                
+                                % Calculate year, month, day etc
+                                tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
+                                
+                                % Build write format string
+                                fmt = '%s,%s,%i,%i,%i,%i,%i,%12.3f';
+                                for j=1:size(tableData,2)-6
+                                    fmt = strcat(fmt,',%12.3f');
+                                end
+                                fmt = strcat(fmt,'  \n');
+
+                                % Get Bore ID
+                                boreID = tmpModel.bore_ID;
+                                
+                                %Write each row.
+                                for j=1:size(tableData,1)
+                                    fprintf(fileID,fmt, modelLabel, boreID, tableData(j,:));
+                                end
+                                                                
+                            else
+                                % get the parameters and derived parameters
+                                [ParamValues, ParamsNames] = getParameters(tmpModel.model);
+                                [derivedParamValues, derivedParamsNames] = getDerivedParameters(tmpModel.model);
+
+                                % Get Bore ID
+                                boreID = tmpModel.bore_ID;
+                                
+                                % Build write format string
+                                fmt = '%s,%s,%s,%s,%i,%12.6f \n';                          
+                                
+                                %Write each parameter and each parameter set (ie if DREAM was used).
+                                for j=1:size(ParamsNames,1)
+                                    for k=1:size(ParamValues,2)
+                                        fprintf(fileID,fmt, modelLabel, boreID, ParamsNames{j,1},ParamsNames{j,2}, k, ParamValues(j,k));
+                                    end
+                                end
+                                
+                                %Write each derived parameter and each derived parameter set (ie if DREAM was used).
+                                for j=1:size(derivedParamsNames,1)
+                                    for k=1:size(derivedParamValues,2)
+                                        fprintf(fileID,fmt, modelLabel, boreID, derivedParamsNames{j,1},derivedParamsNames{j,2}, k, derivedParamValues(j,k));
+                                    end
+                                end                                
+                            end
+                                
+                            % Update counter
+                            nResultsWritten = nResultsWritten + 1;
+                        else
+                            nModelsNotCalib = nModelsNotCalib + 1;
+                        end        
+                    end
+                    fclose(fileID);
+                    
+                    % Close wait bar
+                    close(h);                      
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                    
+                    
+                    % Show summary
+                    msgbox({'Export of results finished.','',['Number of model results exported =',num2str(nResultsWritten)],['Number of models selected for export =',num2str(nModelsToExport)],['Number of selected models not calibrated =',num2str(nModelsNotCalib)]},'Export Summary');
+                    
+                    
+                case 'Model Simulation'
+                    % Check if there are any rows selected for export
+                    if ~any( cellfun(@(x) x==1, this.tab_ModelSimulation.Table.Data(:,1)))
+                        warndlg({'No rows are selected for export.','Please select the models to export using the left-hand tick boxes.'},'No rows selected for export ...')
+                        return;
+                    end
+                      
+                    % Ask the user if they want to export one file per bore (with decomposition)
+                    % or all results in one file.
+                    response = questdlg({'Do you want to export all simulations into one file, or as one file per simulation?','','NOTE: The forcing decomposition results will only be exported using the multi-file option.'},'Export options.','One File','Multiple Files','Cancel','One File');
+                    
+                    if strcmp(response,'Cancel')
+                        return;
+                    end
+                    
+                    if strcmp(response, 'Multiple Files')
+                        useMultipleFiles = true;
+                        folderName = uigetdir('' ,'Select where to save the .csv simulation files (one file per simulation).');    
+                        if isempty(folderName)
+                            return;
+                        end
+                    else
+                        useMultipleFiles = false;
+                        fileName = uiputfile({'*.csv','*.*'} ,'Input the file name for the .csv simulation file (all simulations in one file).');    
+                        if isempty(fileName)
+                            return;
+                        end   
+                        
+                        fileID = fopen(fileName,'w');
+                        fprintf(fileID,'Simulation_Label,Model_Label,BoreID,Year,Month,Day,Hour,Minute,Sim_Head \n');
+    
+                    
+                    end
+                                           
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;                   
+                    
+                    % Setup wait box
+                    h = waitbar(0,'Exporting results. Please wait ...');                                            
+                    
+                    % Loop through each row of the simulation table and
+                    % export the calibration results (if calibrated)
+                    nrows = size(this.tab_ModelSimulation.Table.Data,1);
+                    nResultsWritten=0;
+                    nModelsNotFound=0;
+                    nSimsNotUndertaken = 0;
+                    nSimsNotUnique = 0;
+                    nTableConstFailed = 0;
+                    nWritteError = 0;
+                    for i=1:nrows                        
+                        
+                        % update wait bar
+                        waitbar(i/nrows);                   
+                        
+                        % Skip if the model is not sleected for export
+                        if ~this.tab_ModelSimulation.Table.Data{i,1}
+                            continue
+                        end
+                        
+                        % get model label and simulation label.
+                        modelLabel = this.tab_ModelSimulation.Table.Data{i,2};
+                        simLabel = this.tab_ModelSimulation.Table.Data{i,6};
+                        boreID = this.tab_ModelSimulation.Table.Data{i,3};
+
+                        % Get a copy of the model object. This is only done to
+                        % minimise HDD read when the models are off loaded to HDD using
+                        % matfile();
+                        tmpModel = getModel(this, modelLabel);
+
+                        % Check model exists.
+                        if isempty(tmpModel) 
+                            nModelsNotFound = nModelsNotFound +1;
+                            continue;                            
+                        end
+                        
+                        % Check simulations exists.
+                        if isempty(tmpModel.simulationResults)    
+                            nSimsNotUndertaken = nSimsNotUndertaken +1;
+                            continue;
+                        end                        
+                                                                                                
+                        % Find the simulation.    
+                        if isempty(simLabel)
+                            nSimsNotUndertaken = nSimsNotUndertaken +1;
+                            continue;
+                        end
+                        simInd = cellfun(@(x) strcmp(simLabel, x.simulationLabel), tmpModel.simulationResults);
+                        if all(~simInd)    % Exit if model not found.
+                            nSimsNotUndertaken = nSimsNotUndertaken +1;
+                            continue;
+                        end          
+                        simInd = find(simInd);
+            
+                        % Check only one simulation found
+                        if length(simInd)>1
+                            nSimsNotUnique = nSimsNotUnique +1;
+                            continue;
+                        end
+                        
+
+                        % Get the simulation data and create the output
+                        % table.
+                        try
+                            % Get the model simulation data.
+                            if useMultipleFiles
+                                tableData = tmpModel.simulationResults{simInd,1}.head;
+                            else
+                                tableData = tmpModel.simulationResults{simInd,1}.head(:,1:2);
+                            end
+                                                        
+                            % Calculate year, month, day etc
+                            tableData = [year(tableData(:,1)), month(tableData(:,1)), day(tableData(:,1)), hour(tableData(:,1)), minute(tableData(:,1)), tableData(:,2:end)];
+
+                            % Create column names.                        
+                            if useMultipleFiles
+                                columnName = {'Year','Month','Day','Hour','Minute',tmpModel.simulationResults{simInd,1}.colnames{2:end}};
+                                
+                                % Check if there are any invalid column names
+                                columnName = regexprep(columnName,'\W','_');                            
+                                
+                                % Create table and add variable names
+                                tableData = array2table(tableData);
+                                tableData.Properties.VariableNames = columnName;
+
+                            end
+                            
+                        catch ME
+                            nTableConstFailed = nTableConstFailed + 1;
+                        end
+                        
+                        % write data to the file
+                        if useMultipleFiles
+                            filename_tmp = fullfile(folderName,[modelLabel,'_',simLabel,'.csv']);
+                            
+                            try
+                                writetable(tableData,filename_tmp);          
+                                nResultsWritten = nResultsWritten + 1;
+                            catch
+                                nWritteError = nWritteError + 1;
+                            end
+                            
+                        else
+                            % Build write format string
+                            fmt = '%s,%s,%s,%i,%i,%i,%i,%i,%12.3f \n';
+                            
+                            % Remove HTML from bore ID
+                            boreID = HydroSight_GUI.removeHTMLTags(boreID);
+                            
+                            %Write each row.
+                            try
+                                for j=1:size(tableData,1)
+                                    fprintf(fileID,fmt, simLabel, modelLabel, boreID, tableData(j,:));
+                                end
+                                nResultsWritten = nResultsWritten + 1;
+                            catch ME
+                                nWritteError = nWritteError + 1;
+                            end
+                            
+                        end
+                        
+                                                    
+                    end
+                    
+                    % Close wait bar
+                    close(h);                         
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'arrow');
+                    drawnow update;                    
+                    
+                    % Show summary                
+                    msgbox({'Export of results finished.','', ...
+                           ['Number of simulations exported =',num2str(nResultsWritten)], ...
+                           ['Number of models not found =',num2str(nModelsNotFound)], ...
+                           ['Number of simulations not undertaken =',num2str(nSimsNotUndertaken)], ...
+                           ['Number of simulations labels not unique =',num2str(nSimsNotUnique)], ...
+                           ['Number of simulations where the construction of results table failed=',num2str(nTableConstFailed)], ...
+                           ['Number of simulations where the file could not be written =',num2str(nWritteError)]}, ...
+                           'Export Summary');                    
+
+                case {'Model Calibration - results table export', ...
+                      'Model Calibration - forcing table export', ...
+                      'Model Calibration - parameter table export', ...
+                      'Model Calibration - derived parameter table export', ...
+                      'Model Calibration - derived data table export'}
+                    
+                    % Get model label.
+                    modelLabel = this.tab_ModelCalibration.Table.Data{this.tab_ModelCalibration.currentRow,2};
+                    modelLabel = HydroSight_GUI.removeHTMLTags(modelLabel);
+                    modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+                                    
+                    % Build a default file name.
+                    fName = strrep(hObject.Tag, 'Model Calibration - ','');
+                    fName = strrep(fName, ' export','');
+                    fName = [modelLabel,'_',fName,'.csv'];
+                  
+                    % Get output file name                    
+                    [fName,pName] = uiputfile({'*.csv'},'Input the .csv file name for results file.',fName); 
+                    if fName~=0;
+                        % Assign file name to date cell array
+                        filename = fullfile(pName,fName);
+                    else
+                        return;
+                    end 
+                    
+                    % Change cursor
+                    set(this.Figure, 'pointer', 'watch');
+                    drawnow update;                    
+                    
+                    % Find table object
+                    tablObj = findobj(this.tab_ModelCalibration.resultsTabs,'Tag',strrep(hObject.Tag,' export',''));
+                    
+                    % Convert cell array to table            
+                    if iscell(tablObj.Data)
+                        tbl = cell2table(tablObj.Data);
+                    else
+                        tbl = array2table(tablObj.Data);
+                    end
+                    columnName = tablObj.ColumnName;
+                    columnName = regexprep(columnName,'\W','_');                            
+                    tbl.Properties.VariableNames =  columnName;
+
+                    % Write the table.
+                    try
+                        writetable(tbl, filename,'Delimiter',',');
+                        
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                            
+                    catch ME
+                        % Change cursor
+                        set(this.Figure, 'pointer', 'arrow');
+                        drawnow update;                            
+                        
+                        warndlg('The table could not be written. Please check you have write permissions to the destination folder.');
+                        return;
+                    end
+
+                otherwise
+                    warndlg('Unexpected Error: GUI table type unknown.');
+                    return                    
+            end
+            
+            
+        end
+        
+        function onDocumentation(this, hObject, eventdata)
+            if isempty(hObject.Tag)  % Open the help on the curretn tab
+                switch this.figure_Layout.Selection
+                    case 1
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Project-description','-browser');
+                    case 2
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Date-preparation','-browser');
+                    case 3
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-Construction','-browser');
+                    case 4
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-Calibration','-browser');
+                    case 5
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Model-simulation','-browser');
+                    otherwise
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Graphical-interface','-browser');
+                end
+            else
+                switch hObject.Tag
+                    case 'doc_Overview'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki','-browser');
+                    case 'doc_GUI'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Graphical-interface','-browser');
+                    case 'doc_Calibration'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/What-is-Calibration%3F','-browser');
+                    case 'doc_Publications'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Publications','-browser');
+                    case 'doc_timeseries_algorithms'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Time-series-models','-browser');
+                    case 'doc_data_req'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Data-Requirements','-browser');
+                    case 'doc_data_req'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Data-Requirements','-browser');
+                    case 'doc_tutes'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Tutorials','-browser');
+                    case 'doc_Support'
+                        web('https://github.com/peterson-tim-j/HydroSight/wiki/Support','-browser');
+                end
+                
+                
+            end
+            
+        end       
+               
+        function onGitHub(this, hObject, eventdata)
+           if strcmp(hObject.Tag,'doc_GitHubUpdate') 
+               web('https://github.com/peterson-tim-j/HydroSight/releases','-browser');
+           elseif strcmp(hObject.Tag,'doc_GitHubIssue') 
+               web('https://github.com/peterson-tim-j/HydroSight/issues','-browser');
+           end            
+                       
+        end
+        
+        function onVersion(this, hObject, eventdata)           
+            msgbox({['This is version ',this.versionNumber, ' of HydroSight GUI.'],'',['It was released on ',this.versionDate]},'HydroSight GUI version ...');
+        end
+        
+        function onLicenseDisclaimer(this, hObject, eventdata)
+           web('https://github.com/peterson-tim-j/HydroSight/wiki/Disclaimer-and-Licenses','-browser');
+        end                       
+        
+        function onPrint(this, hObject, eventdata)
+            switch this.figure_Layout.Selection
+                case {1,3}
+                    errordlg('No plot is displayed within the current tab.');
+                    return;
+                case 2      % Data prep.
+                    f=figure('Visible','off');
+                    copyobj(this.tab_DataPrep.modelOptions.resultsOptions.plots,f);
+                    printpreview(f);
+                    close(f);                    
+                case 4      % Model Calib.
+                    f=figure('Visible','off');
+                    switch this.tab_ModelCalibration.resultsTabs.SelectedChild
+                        case 1
+                            copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children.Children(2),f);
+                        case 2
+                            copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end),f);
+                        case 3
+                            copyobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1).Children,f);                             
+                        case 4
+                            
+                    end
+                    printpreview(f);
+                    close(f);
+                case 5      % Model Simulation.    
+                    f=figure('Visible','off');
+                    nplots = length(this.tab_ModelSimulation.resultsOptions.plots.panel.Children);
+                    copyobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(1:nplots),f);
+                    printpreview(f);
+                    close(f);                    
+                otherwise
+                    return;
+            end            
+        end
+        
+        function onExportPlot(this, hObject, eventdata)
+            set(this.Figure, 'pointer', 'watch');
+            switch this.figure_Layout.Selection
+                case {1,3}
+                    errordlg('No plot is displayed within the current tab.');
+                    return;
+                case 2      % Data prep.
+                    pos = get(this.tab_DataPrep.modelOptions.resultsOptions.box.Children(2),'Position'); 
+                    legendObj = this.tab_DataPrep.modelOptions.resultsOptions.plots.Legend;
+                    
+                    f=figure('Visible','off');
+                    copyobj(this.tab_DataPrep.modelOptions.resultsOptions.plots,f);
+                    
+                    % Format figure                    
+                    set(f, 'Color', 'w');
+                    set(f, 'PaperType', 'A4');
+                    set(f, 'PaperOrientation', 'landscape');
+                    set(f, 'Position', pos);
+                case 4      % Model Calib.
+                    f=figure('Visible','off');
+                    switch this.tab_ModelCalibration.resultsTabs.SelectedChild
+                        case 1
+                            pos = get(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1),'Position');
+                            if length(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1))==1
+                                legendObj = [];
+                                copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children,f);
+                            else
+                                copyobj(this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children(2),f);
+                                legendObj = this.tab_ModelCalibration.resultsOptions.calibPanel.Children.Children(1).Children(1).Children(1);
+                            end
+                        case 2
+                            pos = get(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4),'Position');
+                            if length(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4)==1)
+                                copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children,f);
+                                legendObj = [];
+                            else
+                                copyobj(this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end),f);
+                                legendObj = this.tab_ModelCalibration.resultsOptions.forcingPanel.Contents.Contents(4).Children(end-1);                                
+                            end
+                        case 3
+                            pos = get(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1),'Position');
+                            copyobj(this.tab_ModelCalibration.resultsOptions.paramsPanel.Children.Children(1).Children,f);                             
+                            legendObj = [];
+                        case 4
+                            pos = get(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel.Children.Children(1),'Position');
+                            copyobj(this.tab_ModelCalibration.resultsOptions.derivedParamsPanel.Children.Children(1).Children,f);                             
+                            legendObj = [];
+                            
+                        case 5
+                            pos = get(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel.Children.Children(1),'Position');
+                            copyobj(this.tab_ModelCalibration.resultsOptions.modelSpecificsPanel.Children.Children(1).Children,f);                             
+                            legendObj = [];                            
+                    end
+
+                    % Format figure
+                    set(f, 'Color', 'w');
+                    set(f, 'PaperType', 'A4');
+                    set(f, 'PaperOrientation', 'landscape');
+                    set(f, 'Position', pos);
+                    
+                case 5      % Model Simulation.    
+                    f=figure('Visible','off');
+                    nplots = length(this.tab_ModelSimulation.resultsOptions.plots.panel.Children);
+                    pos = get(this.tab_ModelSimulation.resultsOptions.plots.panel,'Position');
+                    copyobj(this.tab_ModelSimulation.resultsOptions.plots.panel.Children(1:nplots),f);  
+                    legendObj = [];
+                    
+                    % Format each axes
+                    ax =  findall(f,'type','axes');
+                    for i=1:nplots
+                        set(ax(i), 'Color', 'w');
+                    end
+                    
+                    % Format figure
+                    set(f, 'Color', 'w');
+                    set(f, 'PaperType', 'A4');
+                    set(f, 'PaperOrientation', 'landscape');
+                    set(f, 'Position', pos);                    
+                otherwise
+                    return;
+            end
+                        
+            % set current folder to the project folder (if set)
+            set(this.Figure, 'pointer', 'arrow');
+            currentProjectFolder='';
+            if ~isempty(this.project_fileName)                                
+                try    
+                    if isdir(this.project_fileName)
+                        currentProjectFolder = this.project_fileName;
+                    else
+                        currentProjectFolder = fileparts(this.project_fileName);
+                    end 
+                    
+                    currentProjectFolder = [currentProjectFolder,filesep];
+                    cd(currentProjectFolder);
+                catch
+                    % do nothing
+                end
+            end
+            [fName,pName] = uiputfile({'*.png'},'Save plot PNG image as ...','plot.png');    
+            if fName~=0    
+                set(this.Figure, 'pointer', 'watch');
+                
+                % Export image
+                if ~isempty(legendObj)
+                    legend(gca,legendObj.String,'Location',legendObj.Location)
+                end
+                export_fig(f, fName);
+            end
+            close(f);
+            set(this.Figure, 'pointer', 'arrow');
+            
+        end
+        
+        % Show splash 
+        function onAbout(this, hObject, eventdata)
+             % Create opening window while the GUI is being built.
+            %--------------------------------------------------------------
+            % Create figure
+            this.FigureSplash = figure( ...
+                'NumberTitle', 'off', ...
+                'MenuBar', 'none', ...
+                'Toolbar', 'none', ...
+                'HandleVisibility', 'off', ...
+                'Visible','off');
+
+            % Get window size and set figure to middle
+            splashWidth =  1200;
+            splashHeight = splashWidth/3;
+            windowHeight = this.FigureSplash.Parent.ScreenSize(4);
+            windowWidth = this.FigureSplash.Parent.ScreenSize(3);
+            this.FigureSplash.Position = [(windowWidth - splashWidth)/2 (windowHeight - splashHeight)/2 splashWidth splashHeight];
+            this.FigureSplash.Visible = 'on';
+            
+            % Load icons
+            h = axes('Parent',this.FigureSplash, 'Position', [0 0 1 1] );
+            img = imread(fullfile('icons','splash.png'));
+            image(img,'Parent',h);
+            axis(h,'off');
+            axis(h,'image');  
+            axis(h,'tight');
+            drawnow update; 
+        end        
+        
+        % Load example models
+        function onExamples(this, hObject, eventdata)
+            
+            % Check if all of the GUI tables are empty. If not, warn the
+            % user the opening the example will delete the existing data.
+            if ~isempty(this.tab_Project.project_name.String) || ...
+            ~isempty(this.tab_Project.project_description.String) || ...
+            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelConstruction.Table.Data(:,1:8))))) || ...
+            (size(this.tab_ModelCalibration.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelCalibration.Table.Data)))) || ...
+            (size(this.tab_ModelSimulation.Table.Data,1)~=0 && any(~any(cellfun( @(x) isempty(x), this.tab_ModelSimulation.Table.Data))))
+                response = questdlg({'Opening an example project will close the current project.','','Do you want to continue?'}, ...
+                 'Close the current project?','Yes','No','No');
+             
+                if strcmp(response,'No')
+                    return;
+                end
+            end
+            
+            % Add message explaing the model examples.
+            h= msgbox({'The example models provide an overview of some of the features of the', ...
+                    'toolbox plus example input data files.', ...
+                    '', ...
+                    'To open an example, you will first be asked to specify a folder in which', ...
+                    'the .csv file are to be generated by the toolbox.', ...
+                    '', ...
+                    'Once the files are created, the toolbox will contain a series of', ...
+                    'calibrated time-series models. You can use these models to undertake', ...
+                    'simulations, or alternatively you can rebuild and calibrate, or edit,', ...
+                    'the models.'},'Opening Example Models ...','help') ;
+            uiwait(h);
+                
+            % Ask user the locations where the input data is to be saved.          
+            if isnumeric(this.project_fileName)
+                this.project_fileName='';
+            end
+            if ~isempty(this.project_fileName)
+                folderName = fileparts(this.project_fileName);
+            else
+                folderName = fileparts(pwd); 
+            end
+            folderName = uigetdir(folderName ,'Select folder in which to save the example .csv files.');    
+            if isempty(folderName)
+                return;
+            end
+            this.project_fileName = folderName;
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');      
+            drawnow update;
+            
+            % Build .csv file names and add to the GUI construction table.
+            display('Saving .csv files ...');
+            forcingFileName = fullfile(folderName,'forcing.csv');
+            coordsFileName = fullfile(folderName,'coordinates.csv');
+            headFileName = fullfile(folderName,'obsHead.csv');
+                        
+            % Open example .mat data file for the required example.
+            display('Opening data files ...');
+            switch eventdata.Source.Tag
+                case 'TFN - LUC'
+                    exampleData = load('BourkesFlat_data.mat');
+                case 'TFN - Pumping'
+                    exampleData = load('Clydebank_data.mat');
+                otherwise
+                    set(this.Figure, 'pointer', 'arrow');   
+                    drawnow update;
+                    warndlg('The requested example model could not be found.','Example Model Error ...');
+                    return;
+            end
+            
+            % Check there is the required data
+            if ~isfield(exampleData,'forcing') || ~isfield(exampleData,'coordinates') || ~isfield(exampleData,'obsHead')
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;
+                warndlg('The example data for the model does not exist. It must contain the following Matlab tables: forcing, coordinates, obsHead.','Example Model Data Error ...');                
+                return;                
+            end
+            
+            % Check that the data is a table. 
+            if ~istable(exampleData.forcing) || ~istable(exampleData.coordinates) || ~istable(exampleData.obsHead) 
+                set(this.Figure, 'pointer', 'arrow');   
+                drawnow update;
+                warndlg('The example models could not be loaded because the source data is not of the correct format. It must be a Matlab table variable.','Example Model Error ...');                
+                return;
+            end
+            
+            % Export the csv file names.
+            display('Saving .csv files ...');
+            writetable(exampleData.forcing,forcingFileName);
+            writetable(exampleData.coordinates,coordsFileName);
+            writetable(exampleData.obsHead,headFileName);
+            
+            
+            % Load the GUI table data and model.
+            display('Opening model files ...');
+            try                
+                switch eventdata.Source.Tag
+                    case 'TFN - LUC'                    
+                        exampleModel = load('BourkesFlat_model.mat');
+                    case 'TFN - Pumping'
+                        exampleModel = load('Clydebank_model.mat');
+                    otherwise
+                        set(this.Figure, 'pointer', 'arrow');             
+                        drawnow update;
+                        warndlg('The requested example model could not be found.','Example Model Error ...');
+                        return;
+                end            
+                
+            catch ME
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update;
+                warndlg('Project file could not be loaded.','File error');                
+                return;
+            end
+            
+            
+            % Assign data to the GUI
+            %------------------------------          
+            display('Updating file names in GUI ...');
+            % Assign loaded data to the tables and models.
+            this.tab_Project.project_name.String = exampleModel.tableData.tab_Project.title;
+            this.tab_Project.project_description.String = exampleModel.tableData.tab_Project.description;
+
+            % Data prep data.
+            this.tab_DataPrep.Table.Data = exampleModel.tableData.tab_DataPrep;
+            nrows = size(this.tab_DataPrep.Table.Data,1);
+            this.tab_DataPrep.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                    
+
+            % Model constrcuion data
+            this.tab_ModelConstruction.Table.Data = exampleModel.tableData.tab_ModelConstruction;
+            nrows = size(this.tab_ModelConstruction.Table.Data,1);
+            this.tab_ModelConstruction.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));     
+
+            % Model Calib data
+            if size(exampleModel.tableData.tab_ModelCalibration,2)==14
+                this.tab_ModelCalibration.Table.Data =  exampleModel.tableData.tab_ModelCalibration(:,[1:8,10:14]);
+            else
+                this.tab_ModelCalibration.Table.Data = exampleModel.tableData.tab_ModelCalibration;
+            end
+            nrows = size(this.tab_ModelCalibration.Table.Data,1);
+            this.tab_ModelCalibration.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                      
+
+            % Model Simulation
+            this.tab_ModelSimulation.Table.Data = exampleModel.tableData.tab_ModelSimulation;
+            nrows = size(this.tab_ModelSimulation.Table.Data,1);
+            this.tab_ModelSimulation.Table.RowName = mat2cell([1:nrows]',ones(1, nrows));                       
+
+            % Set flag denoting models are on RAM.            
+            this.modelsOnHDD =  '';
+
+            % Load modle objects 
+            %-------------
+            % CLear models in preject.                        
+            this.models=[];
+
+            % Determine number of models
+            filt = strcmp(fieldnames(exampleModel),'dataPrep') | strcmp(fieldnames(exampleModel),'settings') |  strcmp(fieldnames(exampleModel),'tableData');
+            modelLabels = fieldnames(exampleModel);
+            modelLabels = modelLabels(~filt);
+            nModels = length(modelLabels);
+
+            % Create object array
+            %this.models.data(nModels,1) = [HydroSight];
+
+            % Set model
+            for i=1:nModels
+                modelLabeltmp = HydroSight_GUI.modelLabel2FieldName(modelLabels{i});
+                setModel(this, modelLabeltmp , exampleModel.(modelLabeltmp));
+            end
+            %-------------
+            
+            % Assign analysed bores.
+            this.dataPrep = exampleModel.dataPrep;            
+        
+            % Updating project location with title bar
+            set(this.Figure,'Name',['HydroSight - ', this.project_fileName]);
+            drawnow update;                            
+            
+            % Disable file menu items
+            for i=1:size(this.figure_Menu.Children,1)
+                if strcmp(get(this.figure_Menu.Children(i),'Label'), 'Save Project')
+                    set(this.figure_Menu.Children(i),'Enable','off');
+                elseif strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from RAM to HDD...') || ...
+                strcmp(get(this.figure_Menu.Children(i),'Label'), 'Move models from HDD to RAM...')
+                    set(this.figure_Menu.Children(i),'Enable','off');
+                    set(this.figure_Menu.Children(i),'Label','Move models from RAM to HDD...');
+                end       
+            end   
+            
+            % Change pointer
+            set(this.Figure, 'pointer', 'arrow');  
+            drawnow update;
+            
+        end
+        
+        
+        function onChangeTableWidth(this, hObject, eventdata, tableTag)
+            
+           % Get current table panel
+           h = findobj(this.figure_Layout, 'Tag',tableTag);
+                      
+           % Get Current width
+           w = get(h,'Widths');
+           
+           % Set the widths abovethe current panel to -1
+           w(2:end) = -1;
+           sumW = sum(w(2:end));
+           
+           % Solve the implict problem defining the relative width of the
+           % current panel.
+           targetWidthRatio = hObject.Value;
+           fun = @(x) x/(x+sumW)-targetWidthRatio;
+           newWidth=fzero(fun,[-1000, -0.01]);
+           set(h,'Widths',[newWidth, w(2:end)]);
+                     
+        end
+        
+        function rowAddDelete(this, hObject, eventdata)
+           
+            % Get the table object from UserData
+            tableObj = eval(eventdata.Source.Parent.UserData);            
+            
+            % Get selected rows
+            if isempty(tableObj.Data)
+                selectedRows = [];
+            else
+                for i=1:size(tableObj.Data,1)
+                   if isempty(tableObj.Data{i,1}) 
+                       tableObj.Data{i,1}=false;
+                   end
+                end
+                selectedRows = cell2mat(tableObj.Data(:,1));
+            end
+
+            % Check if any rows are selected. Note, if not then
+            % rows will be added (for all but the calibration
+            % table).
+            anySelected = any(selectedRows);
+            indSelected = find(selectedRows)';
+            
+            if ~isempty(tableObj.Data)
+                if size(tableObj.Data(:,1),1)>0 &&  ~anySelected && ~strcmp(hObject.Label,'Paste rows')
+                    warndlg('No rows are selected for the requested operation.');
+                    return;
+                elseif size(tableObj.Data(:,1),1)==0 ...
+                &&  (strcmp(hObject.Label, 'Copy selected row') || strcmp(hObject.Label, 'Delete selected rows'))                
+                    return;
+                end               
+            end
+            
+            % Get column widths
+            colWidths = tableObj.ColumnWidth;
+            
+            % Define the input for the status column and default data for
+            % inserting new rows.
+            defaultData = cell(1,size(tableObj.Data,2));
+            switch tableObj.Tag
+                case 'Data Preparation'
+                    modelStatus = '<html><font color = "#FF0000">Bore not analysed.</font></html>';
+                    modelStatus_col = 16;
+                    defaultData = {false, '', '',0, 0, 0, '01/01/1900',true, true, true, true, 10, 120, 4,false, modelStatus, ...
+                    '<html><font color = "#808080">(NA)</font></html>', ...
+                    '<html><font color = "#808080">(NA)</font></html>'};
+                case 'Model Construction'
+                    modelStatus = '<html><font color = "#FF0000">Model not built.</font></html>';
+                    modelStatus_col = 9;
+                case 'Model Calibration'
+                    modelStatus = '<html><font color = "#FF0000">Model not calibrated.</font></html>';
+                    modelStatus_col = 9;
+                case 'Model Simulation'
+                    modelStatus = '<html><font color = "#FF0000">Not simulated.</font></html>';                    
+                    modelStatus_col = 12;
+            end
+            
+            % Change pointer
+            set(this.Figure, 'pointer', 'watch');  
+            
+            % Do the selected action            
+            switch hObject.Label
+                case 'Copy selected row'
+                    this.copiedData.tableName = tableObj.Tag;
+                    this.copiedData.data = tableObj.Data(selectedRows,:);
+                    
+                case 'Paste rows'    
+                    % Check that name of the table is same as that from the
+                    % copied data. 
+                    if ~strcmp(this.copiedData.tableName, tableObj.Tag)
+                        warndlg('The copied row data was sourced from a different table.');
+                        return;
+                    end    
+                    
+                    % Paste data and update model build status
+                    switch this.copiedData.tableName
+                        case 'Data Preparation'
+                            if anySelected
+                                j=1;
+                                for i=indSelected
+                                    
+                                    % Paste data
+                                    tableObj.Data{i,2} = this.copiedData.data{1,2};
+                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
+                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
+                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
+                                    tableObj.Data{i,6} = this.copiedData.data{1,6};
+                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
+                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
+                                    this.copiedData.data{i,modelStatus_col} = modelStatus;
+                                    j=j+1;
+                                end
+                            else
+                                for i=1: size(this.copiedData.data,1)
+                                   
+                                    % Add a row
+                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
+                                
+                                    % Add data to the new row.
+                                    tableObj.Data{irow,modelStatus_col} = modelStatus;                                    
+                                end
+                                % Update row numbers.
+                                nrows = size(tableObj.Data,1);
+                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                
+                            end                        
+                        case 'Model Construction'
+                            if anySelected
+                                j=1;
+                                for i=indSelected
+                                    
+                                    % Get a unique model label. 
+                                    newModelLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,2), this.copiedData.data{1,2}, i);
+                                    
+                                    % Paste data
+                                    tableObj.Data{i,2} = newModelLabel;
+                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
+                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
+                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
+                                    tableObj.Data{i,6} = this.copiedData.data{1,6};
+                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
+                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
+                                    this.copiedData.data{i,9} = modelStatus;
+                                    j=j+1;
+                                end
+                            else
+                                for i=1: size(this.copiedData.data,1)
+                                   
+                                    % Add a row
+                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
+                                
+                                    % Get a unique model label.
+                                    irow = size(tableObj.Data,1);
+                                    newModLabel = this.copiedData.data(i,2);
+                                    newModLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,2), newModLabel, irow);
+
+                                    % Add data to the new row.
+                                    tableObj.Data{irow,2} = newModLabel;
+                                    tableObj.Data{irow,9} = modelStatus;                                    
+                                end
+                                % Update row numbers.
+                                nrows = size(tableObj.Data,1);
+                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                           
+                            end
+                        case 'Model Calibration'
+                            for i=indSelected
+                                tableObj.Data{i,6} = this.copiedData.data{1,6};
+                                tableObj.Data{i,7} = this.copiedData.data{1,7};
+                                tableObj.Data{i,8} = this.copiedData.data{1,8};
+                                tableObj.Data(i,9) =  modelStatus;
+                                tableObj.Data{i,10} = '';
+                                tableObj.Data{i,11} = '';
+                                tableObj.Data{i,12} = '';
+                                tableObj.Data{i,13} = '';
+                            end
+                            % Update row numbers.
+                            nrows = size(tableObj.Data,1);
+                            tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                        
+                        case 'Model Simulation'
+                            if anySelected
+                                j=1;
+                                for i=indSelected
+                                    
+                                    % Get a unique model label.
+                                    newSimLabel = {this.copiedData.data{1,2}, this.copiedData.data{1,6}};
+                                    newSimLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,[2,6]), newSimLabel, i);
+                                    
+                                    tableObj.Data{i,2} = this.copiedData.data{1,2};                                    
+                                    tableObj.Data{i,3} = this.copiedData.data{1,3};
+                                    tableObj.Data{i,4} = this.copiedData.data{1,4};
+                                    tableObj.Data{i,5} = this.copiedData.data{1,5};
+                                    tableObj.Data{i,6} = newSimLabel{2};
+                                    tableObj.Data{i,7} = this.copiedData.data{1,7};
+                                    tableObj.Data{i,8} = this.copiedData.data{1,8};
+                                    tableObj.Data{i,9} = this.copiedData.data{1,9};
+                                    tableObj.Data{i,10} = this.copiedData.data{1,10};
+                                    tableObj.Data{i,11} = this.copiedData.data{1,11};
+                                    this.copiedData.data{i,modelStatus_col} = modelStatus;
+                                    j=j+1;
+                                end
+                            else
+                                for i=1: size(this.copiedData.data,1)
+                                   
+                                    % Add a row
+                                    tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
+                                    
+                                    % Edit simulation label and model
+                                    % status.
+                                    irow = size(tableObj.Data,1);
+                                    newSimLabel = {this.copiedData.data{i,2}, this.copiedData.data{i,6}};
+                                    newSimLabel = HydroSight_GUI.createUniqueLabel(tableObj.Data(:,[2,6]), newSimLabel, irow );
+                                    tableObj.Data{irow,6} = newSimLabel{2};
+                                    tableObj.Data{irow,modelStatus_col} = modelStatus;                                    
+                                end
+                                % Update row numbers.
+                                nrows = size(tableObj.Data,1);
+                                tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));                                            
+                            end                            
+                    end
+                    
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                    
+                case 'Insert row above selection'
+                    if isempty(tableObj.Data)
+                        tableObj.Data = defaultData;
+                    else
+                        selectedRows= find(selectedRows);
+                        for i=1:length(selectedRows)
+
+                            ind = max(0,selectedRows(i) + i-1);
+
+                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
+                                             defaultData; ...
+                                             tableObj.Data(ind:end,:)];
+                            tableObj.Data{ind,1} = false; 
+                            
+                            % Update model build status
+                            tableObj.Data{ind,modelStatus_col} = modelStatus;
+
+                        end
+                    end
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                        
+                case 'Insert row below selection'    
+                    if isempty(tableObj.Data)
+                        tableObj.Data = defaultData;
+                    else
+                        selectedRows= find(selectedRows);
+                        for i=1:length(selectedRows)
+
+                            ind = selectedRows(i) + i;
+
+                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
+                                             defaultData; ...
+                                             tableObj.Data(ind:end,:)];
+                            tableObj.Data{ind,1} = false;                                                              
+                            
+                            % Update model build status
+                            tableObj.Data{ind,modelStatus_col} = modelStatus;
+                            
+                        end
+                    end
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+
+                case 'Delete selected rows'    
+                    
+                    % Delete the model objects if within the model
+                    % construction table
+                    if strcmp(tableObj.Tag,'Model Construction')                        
+                        for i=indSelected
+                            % Delete the model object
+                            try
+                                deleteModel(this, this.tab_ModelConstruction.Table.Data{i,2});
+                            catch ME
+                                % do nothing
+                            end
+                        end
+                    elseif strcmp(tableObj.Tag,'Data Preparation')                        
+                        for i=indSelected
+                            
+                            % Get model label
+                            boreID= tableObj.Data{i,3};
+                            
+                            % Remove model object
+                            if isfield(this.dataPrep,boreID)
+                                this.dataPrep = rmfield(this.dataPrep,boreID);
+                            end
+                        end                        
+                    end
+                    
+                    % Delete table data
+                    tableObj.Data = tableObj.Data(~selectedRows,:);
+                    
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                    
+            end
+            
+            % Reset column widths
+            tableObj.ColumnWidth = colWidths;            
+            
+             % Change pointer
+            set(this.Figure, 'pointer', 'arrow');  
+        end
+        
+        function rowSelection(this, hObject, eventdata)
+
+            % Get the table object from UserData
+            tableObj = eval(eventdata.Source.Parent.UserData);            
+                                    
+            % Get selected rows
+            selectedRows = cellfun(@(x) ~isempty(x) && islogical(x) && x, tableObj.Data(:,1));
+            
+            % Do the selected action            
+            switch hObject.Label
+                case 'Select all'
+                    tableObj.Data(:,1) = mat2cell(true(size(selectedRows,1),1),ones(1, size(selectedRows,1)));
+                case 'Select none'
+                    tableObj.Data(:,1) = mat2cell(false(size(selectedRows,1),1),ones(1, size(selectedRows,1)));
+                case 'Invert selection'
+                    tableObj.Data(:,1) = mat2cell(~selectedRows,ones(1, size(selectedRows,1)));
+                case 'Select row range ...'
+                    rowRange = inputdlg( {'First row number:', 'Last row number:'}, 'Input row range for selection.',1,{'1','2'});
+                    if isempty(rowRange)
+                        return;
+                    end
+                    irows = [1:size(tableObj.Data,1)]';
+                    try
+                        startRow=str2num(rowRange{1});
+                        endRow=str2num(rowRange{2});
+                    catch ME
+                        warndlg('The first and last row inputs must be numbers','Input Error')
+                        return;
+                    end
+                    if startRow>= endRow
+                        warndlg('The first row must be less than the last row.','Input Error')
+                        return;                        
+                    end
+                    filt =  (irows >=startRow &  irows <=endRow) | selectedRows;
+                    tableObj.Data(:,1) =  mat2cell(filt,ones(1, size(selectedRows,1)));
+                    
+                case 'Select by col. value ...'
+                    colNames = HydroSight_GUI.removeHTMLTags(tableObj.ColumnName);
+                    
+                    [selectedCol,ok] = listdlg('PromptString', 'Select column:', 'Name', 'Select by col. value ...', ...
+                        'ListString', colNames,'SelectionMode','single');                                        
+
+                    if ok==1
+                        colNames_str = inputdlg('Input the string to be found within the selected column:',  'Select by col. value ...',1);
+                        
+                        if isempty(colNames_str)
+                            return;
+                        end
+                        
+                        % Find rows within table
+                        filt = cellfun(@(x) ~isempty(strfind(upper(x),upper(colNames_str{1}))) , tableObj.Data(:,selectedCol));
+                        filt = filt | selectedRows;
+                        
+                        % Select rows
+                        tableObj.Data(:,1) =  mat2cell(filt,ones(1, size(selectedRows,1)));
+                    end
+
+            end
+                        
+        end
+        
+        
+        function fName = getFileName(this, dialogString)
+           
+            % If project folder is not set, exit
+            if isempty(this.project_fileName)
+                errordlg('The project folder must be set before files can be input.');
+                fName=0;
+                return
+            end                        
+
+            % Get the project path
+            if isdir(this.project_fileName)
+                projectPath = this.project_fileName;
+            else
+                [projectPath,projectName,projectExt] = fileparts(this.project_fileName);
+            end
+
+            % Set the current folder to the project folder
+            try
+                cd(projectPath);
+            catch
+                errordlg({'The project folder could not be opened. Try re-inputting the', ...
+                        'project folder and/or check your network access to the folder.'}, 'Project folder access error.');
+                return;
+            end
+            
+            % Get file name
+            [fName,pName] = uigetfile({'*.*'},dialogString); 
+            if fName~=0;
+                % Check if the file name is the same as the
+                % project path.
+                if isempty(strfind(pName,projectPath))
+                    errordlg('The selected file path must be a within the project folder or a sub-folder of it.')
+                    return;
+                end
+
+                % Get the extra part of the file path not
+                % within the project path.
+                fName = fullfile(pName,fName); 
+                fName = fName(length(projectPath)+2:end);
+            end                        
+        end
+
+        function [model, errmsg] = getModel(this, modelLabel)
+            % Convert model label to a valid field name
+            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+            
+            model = [];
+            if isempty(modelLabel)
+                    errmsg = 'Model label is empty.';
+                    return;
+            end
+            
+            % Get model            
+            if isempty(this.modelsOnHDD) || (islogical(this.modelsOnHDD) && ~this.modelsOnHDD)
+                if isa(this.models.(modelLabel),'HydroSightModel')                    
+                    model = this.models.(modelLabel);
+                else
+                    errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];
+                end
+            elseif ~isempty(this.modelsOnHDD)
+                try                    
+                    % Build file path to model .mat file
+                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
+
+                    % Change file seperator fro OS
+                    filepath = strrep(filepath, '/',filesep);
+                    filepath = strrep(filepath, '\',filesep);
+                                        
+                    % Check the file exists
+                    if exist(filepath, 'file')==0
+                        errmsg = ['The following HydroSight .mat file could not be found:',modelLabel];
+                        return;
+                    end                   
+                           
+                    % Load model and check.
+                    savedData = load(filepath,'model');
+                    model = savedData.model;
+                    if ~isa(model,'HydroSightModel');
+                        errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];    
+                        model=[];
+                    end
+                catch
+                    errmsg = ['The following model is not a HydroSight object and could not be loaded:',modelLabel];
+                end
+            else
+                errmsg = ['The following model could not be loaded:',modelLabel];
+            end            
+        end
+        
+        function errmsg = setModel(this, modelLabel, model)
+            % Convert model label to a valid field name
+            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+           
+            isCalibrated=false;
+            
+            % Set model
+            if isempty(this.modelsOnHDD)
+                if isa(model,'HydroSightModel')                    
+                    % Add model object
+                    this.models.(modelLabel) = model;
+                    
+                    % Check if calibrated                    
+                    if isfield(this.models.(modelLabel).calibrationResults,'isCalibrated')
+                        isCalibrated = this.models.(modelLabel).calibrationResults.isCalibrated;
+                    end
+
+                else
+                    errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
+                    return;
+                end
+            elseif ~isempty(this.modelsOnHDD)
+                if isa(model,'HydroSightModel')   
+                    % Build file path to model .mat file
+                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
+
+                    % Change file seperator fro OS
+                    filepath = strrep(filepath, '/',filesep);
+                    filepath = strrep(filepath, '\',filesep);                
+                           
+                    % Delete file it if exists
+                    if exist(filepath,'file')>0
+                        delete(filepath);
+                    end
+                    
+                    % Save model object                          
+                    save(filepath,'model');
+                    
+                    % Update object hold the relative path to the .mat file
+                    this.models.(modelLabel) = fullfile(this.modelsOnHDD, [modelLabel,'.mat']);
+                    
+                    % Check if calibrated                    
+                    if isfield(model.calibrationResults,'isCalibrated')
+                        isCalibrated = model.calibrationResults.isCalibrated;
+                    end
+                elseif ischar(model);
+                    % Build file path to model .mat file
+                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
+                    
+                    % Change file seperator fro OS
+                    filepath = strrep(filepath, '/',filesep);
+                    filepath = strrep(filepath, '\',filesep);
+                    
+                    % Check the file exists
+                    if exist(filepath, 'file')==0
+                        errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
+                        return;
+                    end
+                    
+                    % Load model to assess if calibrated.
+                    savedData= load(filepath);
+                    model = savedData.model;
+                    
+                    % Update object hold the relative path to the .mat file
+                    this.models.(modelLabel) = fullfile(this.modelsOnHDD, [modelLabel,'.mat']);
+                    
+                    % Check if calibrated                    
+                    if isfield(model.calibrationResults,'isCalibrated')
+                        isCalibrated = model.calibrationResults.isCalibrated;
+                    end                    
+                else
+                    errmsg = ['The following model is not a HydroSight object and could not be set:',modelLabel];
+                    return;
+                end
+            else
+                errmsg = ['The following model could not be set:',modelLabel];
+                return;
+            end            
+            
+            % Update or add model lable to list
+            if ~isempty(this.model_labels) && any(strcmp(this.model_labels.Properties.RowNames,modelLabel))
+                this.model_labels{modelLabel,1} = isCalibrated;
+            else           
+                % Add model label to object. This is done to elimnate the need
+                % to open all models, which can be slow if they're on the HDD.
+                modelLabelasCell{1} = modelLabel;
+                tbleRow = table(isCalibrated,'RowNames',modelLabelasCell);
+                if isempty(this.model_labels);
+                    this.model_labels = tbleRow;
+                else
+                    this.model_labels = [this.model_labels;tbleRow];
+                end
+            end
+            
+        end
+        
+        function [model, errmsg] = deleteModel(this, modelLabel)
+            % Convert model label to a valid field name
+            modelLabel = HydroSight_GUI.modelLabel2FieldName(modelLabel);
+            
+            model = [];
+            if isempty(modelLabel)
+                    errmsg = 'Model label is empty.';
+                    return;
+            end
+            
+            % Delete model            
+            if isempty(this.modelsOnHDD) || ~this.modelsOnHDD
+                if isa(this.models.(modelLabel),'HydroSightModel')                    
+                    this.models = rmfield(this.models, modelLabel);
+                else
+                    errmsg = ['The following model is not a HydroSight object and could not be deleted:',modelLabel];
+                end
+                
+                % Remove from model label list
+                filt = ~strcmp(this.model_labels.Properties.RowNames,modelLabel);
+                this.model_labels = this.model_labels(filt,:);
+                                
+            elseif ~isempty(this.modelsOnHDD)
+                try                    
+                    % Build file path to model .mat file
+                    filepath = fullfile(fileparts(this.project_fileName), this.modelsOnHDD, [modelLabel,'.mat']);
+
+                    % Change file seperator fro OS
+                    filepath = strrep(filepath, '/',filesep);
+                    filepath = strrep(filepath, '\',filesep);
+                                        
+                    % Check the file exists
+                    if exist(filepath, 'file')==0
+                        errmsg = ['The following HydroSight .mat file could not be found:',modelLabel];
+                        return;
+                    end                   
+                           
+                    % Delete model file.                    
+                    delete(filepath);
+                    this.models = rmfield(this.models, modelLabel);
+
+                    % Remove from model label list
+                    filt = ~strcmp(this.model_labels.Properties.RowNames,modelLabel);
+                    this.model_labels = this.model_labels(filt,:);                    
+                    
+                catch
+                    errmsg = ['The following model is not a HydroSight object and could not be deleted:',modelLabel];
+                end
+            else
+                errmsg = ['The following model could not be deleted:',modelLabel];
+            end            
+        end        
+        
+    end
+    
+    methods (Access=private)
+        function this = calibGUI(this, calibTable)
+           
+            
+        end
+                
+        
+        function startCalibration(this,hObject,eventdata)
+            
+            % Get table data
+            data = this.tab_ModelCalibration.Table.Data;            
+            
+            % Get list of selected bores and check.
+            selectedBores = data(:,1);
+            isModelSelected=false;
+            for i=1:length(selectedBores);                
+                % Check if the model is to be calibrated.
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    isModelSelected=true;
+                    break;
+                end                
+            end                        
+                                    
+            % Check that the user wants to save the projct after each
+            % calib.
+            saveModels=false;
+            if ~strcmp(hObject.Tag,'Start calibration - useHPC')
+                set(this.Figure, 'pointer', 'arrow');
+                drawnow update
+                
+                response = questdlg('Do you want to save the project after each model is calibrated?','Auto-save models?','Yes','No','Cancel','Yes');
+                if strcmp(response,'Cancel')
+                    return;
+                end
+                if strcmp(response,'Yes') && (isempty(this.project_fileName) || exist(this.project_fileName,'file') ~= 2);
+                    msgbox('The project has not yet been saved. Please save it and re-run the calibration.','Project not yet saved ...','error');
+                    return;
+                end
+                saveModels=false;
+                if strcmp(response,'Yes')
+                    saveModels=true;
+                end  
+            end
+
+            % Change label of button to quit
+            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Start calibration');
+            obj.Enable = 'off';
+            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Quit calibration');
+            obj.Enable = 'on';            
+            
+            % Change cursor
+            set(this.Figure, 'pointer', 'watch');
+            drawnow update
+            
+            % Open parrallel engine for calibration
+            if ~strcmp(hObject.Tag,'Start calibration - useHPC')
+                try
+                    %nCores=str2double(getenv('NUMBER_OF_PROCESSORS'));
+                    parpool('local');
+                catch ME
+                    % Do nothing. An error is probably that parpool is already
+                    % open
+                end
+            end
+            
+            % Count the number of models selected
+            nModels=0;
+            iModels=0;
+            for i=1:length(selectedBores);                                
+                if ~isempty(selectedBores{i}) && selectedBores{i}
+                    nModels = nModels +1;
+                end
+            end
+            
+            % Setup wait bar
+            waitBarPlot = findobj(this.tab_ModelCalibration.GUI, 'Tag','Calib_wait_bar');
+            waitBarPlot.YData=0;
+                                    
+            % Setup inputs for the GUI diary file.
+            commandwindowBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','calibration command window');
+            commandwindowBox.Max=Inf;
+            commandwindowBox.Min=0;
+                        
+            %% Start calibration                             
+            % Loop  through the list of selected bore and apply the model
+            % options.
+            nModelsCalib = 0;
+            nModelsCalibFailed = 0;
+            nHPCmodels = 0;
+            for i=1:length(selectedBores)
+                
+                % Check if the model is to be calibrated.
+                if isempty(selectedBores{i}) || ~selectedBores{i}
+                    continue;
+                end
+                
+                % Get the selected model for simulation
+                calibLabel = data{i,2};
+                calibLabel = HydroSight_GUI.removeHTMLTags(calibLabel);
+                
+                % Get start and end date. Note, start date is at the start
+                % of the day and end date is shifted to the end of the day.
+                calibStartDate = datenum( data{i,6},'dd-mmm-yyyy');
+                calibEndDate = datenum( data{i,7},'dd-mmm-yyyy') + datenum(0,0,0,23,59,59);
+                calibMethod = data{i,8};
+
+                % Get a copy of the model object. This is only done to
+                % minimise HDD read when the models are off loaded to HDD using
+                % matfile();                
+                tmpModel = getModel(this, calibLabel);
+
+                % Exit if model is model not found
+                if isempty(tmpModel)
+                    nModelsCalibFailed = nModelsCalibFailed +1;
+                    this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FF0000">Fail-Model appears not to have been built.</font></html>';
+                    continue;
+                end  
+
+                % Start the diary and copying of the command window outputs to
+                % the GUI. Unfortunately a new diary file needs to be
+                % created per model because the updating can be very slow
+                % when multiple models outputs are within one diary file
+                % (eg 12 seconds per update!)
+                diaryFilename = strrep(this.project_fileName,'.mat',['_',calibLabel,'_calibOutputs_',strrep(strrep(datestr(now),':','-'),' ','_'),'.txt']);
+                this.tab_ModelCalibration.QuitObj = calibGUI_interface(commandwindowBox,diaryFilename);
+                startDiary(this.tab_ModelCalibration.QuitObj);   
+
+                obj = this.tab_ModelCalibration.QuitObj;
+                lh = addlistener(this,'quitModelCalibration',@obj.quitCalibrationListener);
+                
+                % Update status to starting calib.
+                this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Calibrating ... </font></html>';
+
+                % Update status in GUI
+                drawnow
+
+                % Collate calibration settings
+                calibMethodSetting=struct();
+                switch calibMethod                    
+                    case 'CMAES'
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES MaxFunEvals');
+                        calibMethodSetting.MaxFunEvals= str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES TolFun');
+                        calibMethodSetting.TolFun= str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES TolX');
+                        calibMethodSetting.TolX= str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES Restarts');
+                        calibMethodSetting.Restarts= str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES insigmaFrac');
+                        calibMethodSetting.insigmaFrac= str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','CMAES Seed');
+                        calibMethodSetting.Seed= str2double(obj.String);
+
+                    case 'SP-UCI'
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI maxn');
+                        calibMethodSetting.maxn = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI kstop');
+                        calibMethodSetting.kstop = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI pcento');
+                        calibMethodSetting.pcento = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI peps');
+                        calibMethodSetting.peps = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs');
+                        ngs_per_param = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs min');
+                        ngs_min = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI ngs max');
+                        ngs_max = str2double(obj.String);                                                        
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','SP-UCI iseed');
+                        calibMethodSetting.iseed = str2double(obj.String);
+
+
+                        % Get the number of parameters.
+                        [params, param_names] = getParameters(tmpModel.model);
+                        nparams = size(param_names,1);
+
+                        % Calculate the number of complexes for this model.
+                        calibMethodSetting.ngs = max(ngs_min, min(ngs_max, ngs_per_param*nparams));
+
+                    case 'DREAM'
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM N');
+                        calibMethodSetting.N = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM T');
+                        calibMethodSetting.T = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM nCR');
+                        calibMethodSetting.nCR = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM delta');
+                        calibMethodSetting.delta = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM lambda');
+                        calibMethodSetting.lambda = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM zeta');
+                        calibMethodSetting.zeta = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM outlier');
+                        calibMethodSetting.outlier = str2double(obj.String);
+
+                        obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','DREAM pJumpRate_one');
+                        calibMethodSetting.pJumpRate_one = str2double(obj.String);                            
+                end
+                        
+                % Start calib.
+                try                    
+                    if strcmp(hObject.Tag,'Start calibration - useHPC')
+                        display(['BUILDING OFFLOAD DATA FOR MODEL: ',calibLabel]);
+                        display('  ');
+                        % Update the diary file
+                        if ~isempty(this.tab_ModelCalibration.QuitObj)
+                            updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+                        end
+                        
+                        nHPCmodels = nHPCmodels +1;
+                        HPCmodelData{nHPCmodels,1} = tmpModel;
+                        HPCmodelData{nHPCmodels,2} = calibStartDate;
+                        HPCmodelData{nHPCmodels,3} = calibEndDate;
+                        HPCmodelData{nHPCmodels,4} = calibMethod;
+                        HPCmodelData{nHPCmodels,5} = calibMethodSetting;                        
+                        this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Calib. on HPC... </font></html>';
+                    else
+                        display(['CALIBRATING MODEL: ',calibLabel]);
+                        display( '--------------------------------------------------------------------------------');
+                        % Update the diary file
+                        if ~isempty(this.tab_ModelCalibration.QuitObj)
+                            updatetextboxFromDiary(this.tab_ModelCalibration.QuitObj);
+                        end   
+                    
+                        calibrateModel( tmpModel, this.tab_ModelCalibration.QuitObj, calibStartDate, calibEndDate, calibMethod,  calibMethodSetting);
+
+                        % Delete CMAES working filescopl                        
+                        switch calibMethod
+                            case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
+                                delete('*.dat');
+                            otherwise
+                                % do nothing
+                        end
+
+                        % Get calib exit status
+                        exitFlag = tmpModel.calibrationResults.exitFlag;
+                        exitStatus = tmpModel.calibrationResults.exitStatus;                          
+
+                        % Set calib performance stats.
+                        calibAICc = median(tmpModel.calibrationResults.performance.AICc);
+                        calibBIC =median( tmpModel.calibrationResults.performance.BIC);
+                        calibCoE = median(tmpModel.calibrationResults.performance.CoeffOfEfficiency_mean.CoE);
+                        this.tab_ModelCalibration.Table.Data{i,10} = ['<html><font color = "#808080">',num2str(calibCoE),'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{i,12} = ['<html><font color = "#808080">',num2str(calibAICc),'</font></html>'];
+                        this.tab_ModelCalibration.Table.Data{i,13} = ['<html><font color = "#808080">',num2str(calibBIC),'</font></html>'];
+
+                        % Set eval performance stats
+                        if isfield(tmpModel.evaluationResults,'performance')
+                            %evalAIC = this.models.data{ind, 1}.evaluationResults.performance.AIC;
+                            evalCoE = mean(tmpModel.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias);
+
+                            this.tab_ModelCalibration.Table.Data{i,11} = ['<html><font color = "#808080">',num2str(evalCoE),'</font></html>'];                            
+                        else
+                            evalCoE = '(NA)';
+                            %evalAIC = '(NA)';
+
+                            this.tab_ModelCalibration.Table.Data{i,11} = ['<html><font color = "#808080">',evalCoE,'</font></html>'];                            
+                        end
+                        nModelsCalib = nModelsCalib +1;
+
+                        % Add updated tmpModel back to data structure of
+                        % all models
+                        setModel(this, calibLabel, tmpModel)
+
+                        if saveModels
+                            this.tab_ModelCalibration.Table.Data{i,9} = '<html><font color = "#FFA500">Saving project. </font></html>';
+
+                            % Update status in GUI
+                            drawnow                        
+
+                            % Save project.
+                            onSave(this,hObject,eventdata);
+                        end                        
+
+                        % Update calibr status                          
+                        if exitFlag ==0 
+                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', ME.message,'</font></html>'];
+                        elseif exitFlag ==1
+                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FFA500">Partially calibrated: ',exitStatus,' </font></html>'];
+                        elseif exitFlag ==2
+                            this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#008000">Calibrated: ',exitStatus,' </font></html>'];
+                        end                      
+                        
+                    end
+
+
+                    % Check if the user quit the calibration.
+                    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(this.tab_ModelCalibration.QuitObj);                        
+                    if doQuit
+                        exitFlag = exitFlagQuit;
+                        exitStatus = exitStatusQuit;
+                        this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', exitStatus,'</font></html>'];                            
+                        break;
+                    end
+
+
+                catch ME
+                    nModelsCalibFailed = nModelsCalibFailed +1;
+                    this.tab_ModelCalibration.Table.Data{i,9} = ['<html><font color = "#FF0000">Fail-', ME.message,'</font></html>'];
+                end
+                
+                % Update wait bar
+                waitBarPlot.YData = waitBarPlot.YData+1;
+                
+                % Update status in GUI
+                drawnow
+                                
+            end
+            
+            % Close wait bar
+            %if ~strcmp(hObject.Tag,'useHPC') && nModels>=minModels4Waitbar            
+            %    close(h);
+            %end
+            
+            % Change label of button to quit
+            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Start calibration');
+            obj.Enable = 'on';
+            obj = findobj(this.tab_ModelCalibration.GUI, 'Tag','Quit calibration');
+            obj.Enable = 'off';            
+            
+            % Change cursor to arrow
+            set(this.Figure, 'pointer', 'arrow');
+            drawnow update
+            if strcmp(hObject.Tag,'Start calibration - useHPC')
+               project_fileName = this.project_fileName;
+               if ~isdir(this.project_fileName)
+                   project_fileName = fileparts(project_fileName);
+               end
+               
+               userData = jobSubmission(this.HPCoffload, project_fileName, HPCmodelData, this.tab_ModelCalibration.QuitObj) ; 
+               if ~isempty(userData)
+                   this.HPCoffload = userData;
+               end
+
+                % Update status in GUI
+                drawnow update
+                    
+            else
+                % Report Summary
+                msgbox(['The model was successfully calibrated for ',num2str(nModelsCalib), ' models and failed for ',num2str(nModelsCalibFailed), ' models.'], 'Summary of model calibration ...');
+            end        
+        end
+        
+        function quitCalibration(this,hObject,eventdata)
+            % Notify event that the user is quiting the calibration. The
+            % listener in class "calibGUI_interface" should then be called.
+            notify(this,'quitModelCalibration');
+        end
+        
+
+        function multimodel_moveLeft(this,hObject,eventdata)
+            % Find the RH list object
+            leftlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsLeftList');
+            rightlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsRightList');
+            
+            % Add selected params to LH box
+            leftlistBox.String = {leftlistBox.String{:}, rightlistBox.String{rightlistBox.Value}};
+            leftlistBox.String = sort(leftlistBox.String);
+            
+            % Remove from RH box.
+            ind  = true(size(rightlistBox.String));
+            ind(rightlistBox.Value)=false;
+            rightlistBox.String = rightlistBox.String(ind);
+            rightlistBox.String = sort(rightlistBox.String);
+        
+        end
+        
+        function multimodel_moveRight(this,hObject,eventdata)                    
+            % Find the RH list object
+            leftlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsLeftList');
+            rightlistBox = findobj(this.tab_ModelCalibration.GUI, 'Tag','NLMEFIT paramsRightList');
+            
+            % Add selected params to RH box
+            rightlistBox.String = {rightlistBox.String{:}, leftlistBox.String{leftlistBox.Value}};
+            rightlistBox.String = sort(rightlistBox.String);
+            
+            % Remove from LH box.
+            ind  = true(size(leftlistBox.String));
+            ind(leftlistBox.Value)=false;
+            leftlistBox.String = leftlistBox.String(ind);
+            leftlistBox.String = sort(leftlistBox.String);
+        end        
+    end
+    
+    methods(Static=true)
+       
+        % Remove HTML tags from each model label
+        function str = removeHTMLTags(str)
+            if iscell(str)
+               for i=1:length(str)
+                    if ~isempty(strfind(upper(str{i}),'HTML'))
+                        str{i} = regexp(str{i},'>.*?<','match');
+                        str{i} = strrep(str{i}, '<', '');
+                        str{i} = strrep(str{i}, '>', '');
+                        str{i} = strtrim(strjoin(str{i}));
+                    end
+               end
+            else
+                if ~isempty(strfind(upper(str),'HTML'))
+                    str = regexp(str,'>.*?<','match');
+                    str = strrep(str, '<', '');
+                    str = strrep(str, '>', '');
+                    str = strtrim(strjoin(str));            
+                end
+            end
+        end
+        
+        % Create a unique model or simulation label. This is used in the
+        % copy/paste of table rows to ensure each row is unique
+        function newLabel = createUniqueLabel(allLabels, newLabel, currentRow)
+           
+            % Check if the proposed label is unique.
+            if size(allLabels,2)==1
+                ind = cellfun( @(x,y) strcmp( newLabel, x) , allLabels);
+            elseif size(allLabels,2)==2
+                ind = cellfun( @(x,y) strcmp( newLabel{1}, x) &&  strcmp( newLabel{2}, y) , allLabels(:,1), allLabels(:,2));
+            else
+                error('A maximum of two columns of model / simulation labels can be evaluated for uniqueness.');
+            end
+            ind = find(ind);                        
+            if length(ind)==1 && ind(1) == currentRow % Return if only the current row has the proposed new label.
+                return;
+            end            
+            
+            % If not unique, then add an integer to the label so that it is
+            % unique.
+            origLabel = newLabel;
+            label_extension = 1;      
+            if size(allLabels,2)==1
+
+                if ischar(origLabel)
+                    origLabel_tmp{1} = origLabel;
+                    origLabel = origLabel_tmp;
+                    clear origLabel_tmp
+                end
+                
+                newLabel = [origLabel{1}, ' copy ',num2str(label_extension)];
+                while  any(find(cellfun( @(x,y) strcmp( newLabel, x) , allLabels))~=currentRow)
+                    label_extension = label_extension + 1;
+                    newLabel = [origLabel{1}, ' copy ',num2str(label_extension)];    
+                end
+            elseif size(allLabels,2)==2
+                newLabel = {origLabel{1}, [origLabel{2}, ' copy ',num2str(label_extension)]};
+                while any(find(cellfun( @(x,y) strcmp( newLabel{1}, x) &&  strcmp( newLabel{2}, y) , allLabels(:,1), allLabels(:,2)))~=currentRow)
+                    label_extension = label_extension + 1;
+                    newLabel = {origLabel{1}, [origLabel{2}, ' copy ',num2str(label_extension)]};
+                end                    
+            end
+        end
+        
+        
+        function newLabel = modelLabel2FieldName(inputLabel)
+           
+            % Remove characeters that are not able to be included in a
+            % field name.
+            inputLabel = strrep(inputLabel,' ','_');
+            inputLabel = strrep(inputLabel,'-','_');
+            inputLabel = strrep(inputLabel,'?','');
+            inputLabel = strrep(inputLabel,'\','_');
+            inputLabel = strrep(inputLabel,'/','_');
+            inputLabel = strrep(inputLabel,'___','_');
+            inputLabel = strrep(inputLabel,'__','_');
+                       
+            % Check if any reserved labels are used. The reserved labels
+            % are GUI variables to saved within a project .mat file.
+            if strcmp(inputLabel, {'tableData'; 'dataPrep';'settings'})
+                warndlg('Model label cannot be one of the following reserved labels "tableData", "dataPrep", "settings".','Model label error ...');
+                newLabel='';
+                return;
+            end                
+            
+            % Check if a field name can be created with the model label
+            try 
+                tmp.(inputLabel) = [1 2 3];
+                clear tmp;
+                newLabel = inputLabel;
+            catch ME
+                warndlg('Model label must start with letters and have only letters and numbers.','Model label error ...');
+                newLabel='';
+                return;
+            end
+            
+        end
+    end
+end
+
diff --git a/GUI/export_fig/LICENSE b/GUI/export_fig/LICENSE
index 1a05830..d643cee 100644
--- a/GUI/export_fig/LICENSE
+++ b/GUI/export_fig/LICENSE
@@ -1,27 +1,27 @@
-Copyright (c) 2014, Oliver J. Woodford, Yair M. Altman
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
-  list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
-  this list of conditions and the following disclaimer in the documentation
-  and/or other materials provided with the distribution.
-
-* Neither the name of the {organization} nor the names of its
-  contributors may be used to endorse or promote products derived from
-  this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2014, Oliver J. Woodford, Yair M. Altman
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the {organization} nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/GUI/export_fig/README.md b/GUI/export_fig/README.md
index 2bb510f..2a165db 100644
--- a/GUI/export_fig/README.md
+++ b/GUI/export_fig/README.md
@@ -1,270 +1,270 @@
-export_fig
-==========
-
-A toolbox for exporting figures from MATLAB to standard image and document formats nicely.
-
-### Overview
-Exporting a figure from MATLAB the way you want it (hopefully the way it looks on screen), can be a real headache for the unitiated, thanks to all the settings that are required, and also due to some eccentricities (a.k.a. features and bugs) of functions such as `print`. The first goal of export_fig is to make transferring a plot from screen to document, just the way you expect (again, assuming that's as it appears on screen), a doddle.
-  
-The second goal is to make the output media suitable for publication, allowing you to publish your results in the full glory that you originally intended. This includes embedding fonts, setting image compression levels (including lossless), anti-aliasing, cropping, setting the colourspace, alpha-blending and getting the right resolution.
-
-Perhaps the best way to demonstrate what export_fig can do is with some examples.
-  
-### Examples
-**Visual accuracy** - MATLAB's exporting functions, namely `saveas` and `print`, change many visual properties of a figure, such as size, axes limits and ticks, and background colour, in unexpected and unintended ways. Export_fig aims to faithfully reproduce the figure as it appears on screen. For example:  
-```Matlab
-plot(cos(linspace(0, 7, 1000)));
-set(gcf, 'Position', [100 100 150 150]);
-saveas(gcf, 'test.png');
-export_fig test2.png
-```
-generates the following:
-
-| Figure: | test.png: | test2.png: |
-|:-------:|:---------:|:----------:|
-|![](https://farm6.staticflickr.com/5616/15589249291_16e485c29a_o_d.png)|![](https://farm4.staticflickr.com/3944/15406302850_4d2e1c7afa_o_d.png)|![](https://farm6.staticflickr.com/5607/15568225476_8ce9bd5f6b_o_d.png)|
-
-Note that the size and background colour of test2.png (the output of export_fig) are the same as those of the on screen figure, in contrast to test.png. Of course, if you want the figure background to be white (or any other colour) in the exported file then you can set this prior to exporting using:
-```Matlab
-set(gcf, 'Color', 'w');
-```
-  
-Notice also that export_fig crops and anti-aliases (smooths, for bitmaps only) the output by default. However, these options can be disabled; see the Tips section below for details.
-  
-**Resolution** - by default, export_fig exports bitmaps at screen resolution. However, you may wish to save them at a different resolution. You can do this using either of two options: `-m<val>`, where <val> is a positive real number, magnifies the figure by the factor <val> for export, e.g. `-m2` produces an image double the size (in pixels) of the on screen figure; `-r<val>`, again where <val> is a positive real number, specifies the output bitmap to have <val> pixels per inch, the dimensions of the figure (in inches) being those of the on screen figure. For example, using:  
-```Matlab
-export_fig test.png -m2.5
-```
-on the figure from the example above generates:
-
-![](https://farm4.staticflickr.com/3937/15591910915_dc7040c477_o_d.png)
-
-Sometimes you might have a figure with an image in. For example:
-```Matlab
-imshow(imread('cameraman.tif'))
-hold on
-plot(0:255, sin(linspace(0, 10, 256))*127+128);
-set(gcf, 'Position', [100 100 150 150]);
-```
-generates this figure:
-
-![](https://farm4.staticflickr.com/3942/15589249581_ff87a56a3f_o_d.png)
-  
-Here the image is displayed in the figure at resolution lower than its native resolution. However, you might want to export the figure at a resolution such that the image is output at its native (i.e. original) size (in pixels). Ordinarily this would require some non-trivial computation to work out what that resolution should be, but export_fig has an option to do this for you. Using:
-```Matlab
-export_fig test.png -native
-```
-produces:
-
-![](https://farm6.staticflickr.com/5604/15589249591_da2b2652e4_o_d.png)
-
-with the image being the size (in pixels) of the original image. Note that if you want an image to be a particular size, in pixels, in the output (other than its original size) then you can resize it to this size and use the `-native` option to achieve this.
-
-All resolution options (`-m<val>`, `-q<val>` and `-native`) correctly set the resolution information in PNG and TIFF files, as if the image were the dimensions of the on screen figure.
-
-**Shrinking dots & dashes** - when exporting figures with dashed or dotted lines using either the ZBuffer or OpenGL (default for bitmaps) renderers, the dots and dashes can appear much shorter, even non-existent, in the output file, especially if the lines are thick and/or the resolution is high. For example:  
-```Matlab
-plot(sin(linspace(0, 10, 1000)), 'b:', 'LineWidth', 4);
-hold on
-plot(cos(linspace(0, 7, 1000)), 'r--', 'LineWidth', 3);
-grid on
-export_fig test.png
-```
-generates:
-
-![](https://farm4.staticflickr.com/3956/15592747732_f943d4aa0a_o_d.png)
-
-This problem can be overcome by using the painters renderer. For example:
-```Matlab
-export_fig test.png -painters
-```
-used on the same figure generates:
-
-![](https://farm4.staticflickr.com/3945/14971168504_77692f11f5_o_d.png)
-
-Note that not only are the plot lines correct, but the grid lines are too.
-
-**Transparency** - sometimes you might want a figure and axes' backgrounds to be transparent, so that you can see through them to a document (for example a presentation slide, with coloured or textured background) that the exported figure is placed in. To achieve this, first (optionally) set the axes' colour to 'none' prior to exporting, using:  
-```Matlab
-set(gca, 'Color', 'none'); % Sets axes background
-```
-    
-then use export_fig's `-transparent` option when exporting:
-```Matlab
-export_fig test.png -transparent
-```
-
-This will make the background transparent in PDF, EPS and PNG outputs. You can additionally save fully alpha-blended semi-transparent patch objects to the PNG format. For example:
-
-```Matlab
-logo;
-alpha(0.5);
-```
-
-generates a figure like this:
-
-![](https://farm4.staticflickr.com/3933/15405290339_b08de33528_o_d.png)
-
-If you then export this to PNG using the `-transparent` option you can then put the resulting image into, for example, a presentation slide with fancy, textured background, like so:
-
-![](https://farm6.staticflickr.com/5599/15406302920_59beaefff1_o_d.png)
-
-and the image blends seamlessly with the background.
-
-**Image quality** - when publishing images of your results, you want them to look as good as possible. By default, when outputting to lossy file formats (PDF, EPS and JPEG), export_fig uses a high quality setting, i.e. low compression, for images, so little information is lost. This is in contrast to MATLAB's print and saveas functions, whose default quality settings are poor. For example:
-```Matlab
-A = im2double(imread('peppers.png'));
-B = randn(ceil(size(A, 1)/6), ceil(size(A, 2)/6), 3) * 0.1;
-B = cat(3, kron(B(:,:,1), ones(6)), kron(B(:,:,2), ones(6)), kron(B(:,:,3), ones(6)));
-B = A + B(1:size(A, 1),1:size(A, 2),:);
-imshow(B);
-print -dpdf test.pdf
-```
-generates a PDF file, a sub-window of which looks (when zoomed in) like this:
-
-![](https://farm6.staticflickr.com/5613/15405290309_881b2774d6_o_d.png)
-
-while the command
-
-```Matlab
-export_fig test.pdf
-```
-on the same figure produces this:
-
-![](https://farm4.staticflickr.com/3947/14971168174_687473133f_o_d.png)
-
-While much better, the image still contains some compression artifacts (see the low level noise around the edge of the pepper). You may prefer to export with no artifacts at all, i.e. lossless compression. Alternatively, you might need a smaller file, and be willing to accept more compression. Either way, export_fig has an option that can suit your needs: `-q<val>`, where <val> is a number from 0-100, will set the level of lossy image compression (again in PDF, EPS and JPEG outputs only; other formats are lossless), from high compression (0) to low compression/high quality (100). If you want lossless compression in any of those formats then specify a <val> greater than 100. For example:
-```Matlab
-export_fig test.pdf -q101
-```
-again on the same figure, produces this:
-
-![](https://farm6.staticflickr.com/5608/15405803908_934512c1fe_o_d.png)
-
-Notice that all the noise has gone.
-
-### Tips
-**Anti-aliasing** - the anti-aliasing which export_fig applies to bitmap outputs by default makes the images look nice, but it can also blur images and increase exporting time and memory requirements, so you might not always want it. You can set the level of anti-aliasing by using the `-a<val>` option, where <val> is 1 (no anti-aliasing), 2, 3 (default) or 4 (maximum anti-aliasing).  
-  
-**Cropping** - by default, export_fig crops its output to minimize the amount of empty space around the figure. If you'd prefer the figure to be uncropped, and instead have the same appearance (in terms of border width) as the on screen figure, then use the `-nocrop` option.  
-  
-**Colourspace** - by default, export_fig generates files in the RGB [colourspace](https://en.wikipedia.org/wiki/Color_space). However, you can also export in greyscale or the CMYK colourspace, using the `-grey` (or `-gray`) and `-cmyk` options respectively. The CMYK option is useful for publishers who require documents in this colourspace, but the option is only supported for PDF, EPS and TIFF files.
-
-**Specifying a target directory** - you can get export_fig to save output files to any directory (for which you have write permission), simply by specifying the full or relative path in the filename. For example:
-```Matlab
-export_fig ../subdir/fig.png;
-export_fig('C:/Users/Me/Documents/figures/myfig', '-pdf', '-png');
-```
-
-**Variable file names** - often you might want to save a series of figures in a for loop, each with a different name. For this you can use the functional form of input arguments, i.e. `export_fig(arg1, arg2)`,  and construct the filename string in a variable. Here's an example of this:  
-```Matlab
-for a = 1:5
-    plot(rand(5, 2));
-    export_fig(sprintf('plot%d.png', a));
-end
-```
-When using the functional form like this, be sure to put string variables in quotes:
-```Matlab
-export_fig(sprintf('plot%d', a), '-a1', '-pdf', '-png');
-```
-
-**Specifying the figure/axes** - if you have multiple figures open you can specify which figure to export using its handle:  
-```Matlab
-export_fig(figure_handle, filename);
-```
-Equally, if your figure contains several subplots then you can export just one of them by giving export_fig the handle to the relevant axes:
-```Matlab
-export_fig(axes_handle, filename);
-```
-
-**Multiple formats** - save time by exporting to multiple formats simultaneously. E.g.: 
-```Matlab
-export_fig filename -pdf -eps -png -jpg -tiff
-```
-
-**Other file formats** - if you'd like to save your figure to a bitmap format that is not supported by export_fig, e.g. animated GIF, PPM file or a frame in a movie, then you can use export_fig to output the image, and optionally an alpha-matte, to the workspace. E.g.:  
-```Matlab
-frame = export_fig;
-```
-or
-```Matlab
-[frame, alpha] = export_fig;
-```
-These variables can then be saved to other image formats using other functions, such as imwrite.
-
-**Appending to a file** - you can use the `-append` option to append the figure to the end of an image/document, if it already exists. This is supported for PDF and TIFF files only. Note that if you wish to append a lot of figures consecutively to a PDF, it can be more efficient to save all the figures to PDF separately then append them all in one go at the end (e.g. using [append_pdfs](http://www.mathworks.com/matlabcentral/fileexchange/31215-appendpdfs)).  
-  
-**Output to clipboard** - you can use the `-clipboard` option to copy the specified figure or axes to the system clipboard, for easy paste into other documents (e.g., Word or PowerPoint). Note that the image is copied in bitmap (not vector) format.  
-  
-**Font size** - if you want to place an exported figure in a document with the font a particular size then you need to set the font to that size in the figure, and not resize the output of export_fig in the document. To avoid resizing, simply make sure that the on screen figure is the size you want the output to be in the document before exporting.  
-  
-**Renderers** - MATLAB has three renderers for displaying and exporting figures: painters, OpenGL and ZBuffer. The different renderers have different [features](http://www.mathworks.com/access/helpdesk/help/techdoc/creating_plots/f3-84337.html#f3-102410), so if you aren't happy with the result from one renderer try another. By default, vector formats (i.e. PDF and EPS outputs) use the painters renderer, while other formats use the OpenGL renderer. Non-default renderers can be selected by using one of these three export_fig input options: `-painters`, `-opengl`, `-zbuffer`:  
-```Matlab
-export_fig test.png -painters
-```
-  
-**Artifacts** - sometimes the output that you get from export_fig is not what you expected. If an output file contains artifacts that aren't in the on screen figure then make sure that the renderer used for rendering the figure on screen is the same as that used for exporting. To set the renderer used to display the figure, use:  
-```Matlab
-set(figure_handle, 'Renderer', 'opengl');
-```
-After matching the two renderers, if the artifact appears in the on screen figure then you'll need to fix that before exporting. Alternatively you can try changing the renderer used by export_fig. Finally check that it isn't one of the known issues mentioned in the section below.
-
-**Smoothed/interpolated images in output PDF** - if you produce a PDF using export_fig and images in the PDF look overly smoothed or interpolated, this is because the software you are using to view the PDF is smoothing or interpolating the image data. The image is not smoothed in the PDF file itself. If the software has an option to disable this feature, you should select it. Alternatively, use another PDF viewer that doesn't exhibit this problem.  
-  
-**Locating Ghostscript/pdftops** - You may find a dialogue box appears when using export_fig, asking you to locate either [Ghostscript](http://www.ghostscript.com) or [pdftops (part of the Xpdf package)](http://www.xpdfreader.com). These are separate applications which export_fig requires to perform certain functions. If such a dialogue appears it is because export_fig can't find the application automatically. This is because you either haven't installed it, or it isn't in the normal place. Make sure you install the applications correctly first. They can be downloaded from the following places:  
- 1. Ghostscript:     [www.ghostscript.com](http://www.ghostscript.com)
- 2. pdftops (install the Xpdf package): [www.xpdfreader.com](http://www.xpdfreader.com)
-
-If you choose to install them in a non-default location then point export_fig
-to this location using the dialogue box.
-
-**Undefined function errors** - If you download and run export_fig and get an error similar to this:  
-```
-??? Undefined function or method 'print2array' for input arguments of type 'double'.
-```
-then you are missing one or more of the files that come in the export_fig package. Make sure that you click the "Get from GitHub" button at the top-right of the download [page](http://www.mathworks.co.uk/matlabcentral/fileexchange/23629-exportfig), then extract all the files in the zip file to the same directory. You should then have all the necessary files.
-  
-### Known issues
-There are lots of problems with MATLAB's exporting functions, especially `print`. Export_fig is simply a glorified wrapper for MATLAB's `print` function, and doesn't solve all of its bugs (yet?). Some of the problems I know about are:
-  
-**Fonts** - when using the painters renderer, MATLAB can only export a small number of fonts, details of which can be found [here](http://www.mathworks.com/help/releases/R2014a/matlab/creating_plots/choosing-a-printer-driver.html#f3-96545). Export_fig attempts to correct font names in the resulting EPS file (up to a maximum of 11 different fonts in one figure), but this is not always guaranteed to work. In particular, the text positions will be affected. It also does not work for text blocks where the 'Interpreter' property is set to 'latex'.
-
-Also, when using the painters renderer, ghostscript will sometimes throw an error such as `Error: /undefined in /findfont`. This suggests that ghostscript could not find a definition file for one of your fonts. One possible fix for this is to make sure the file `EXPORT_FIG_PATH/.ignore/gs_font_path.txt` exists and contains a list of paths to the folder(s) containing the necessary font definitions (make sure that they are TrueType definitions!), separated by a semicolon.
-
-**RGB color data not yet supported in Painter's mode** - you will see this as a warning if you try to export a figure which contains patch objects whose face or vertex colors are specified as an RGB colour, rather than an index into the colormap, using the painters renderer (the default renderer for vector output). This problem can arise if you use `pcolor`, for example. This is a problem with MATLAB's painters renderer, which also affects `print`; there is currently no fix available in export_fig (other than to export to bitmap). The suggested workaround is to avoid colouring patches using RGB. First, try to use colours in the figure's colourmap (instructions [here](http://www.mathworks.co.uk/support/solutions/en/data/1-6OTPQE/)) - change the colourmap, if necessary. If you are using `pcolor`, try using [uimagesc](http://www.mathworks.com/matlabcentral/fileexchange/11368) (on the file exchange) instead.  
-
-**Dashed contour lines appear solid** - when using the painters renderer, MATLAB cannot generate dashed lines using the `contour` function (either on screen or in exported PDF and EPS files). Details can be found [here](http://www.mathworks.com/support/solutions/en/data/1-14PPHB/?solution=1-14PPHB).  
-  
-**Text size** - when using the OpenGL or ZBuffer renderers, large text can be resized relative to the figure when exporting at non-screen-resolution (including using anti-alising at screen resolution). This is a feature of MATLAB's `print `function. In this case, try using the `-painters` option.  
-  
-**Lighting and transparency** - when using the painters renderer, transparency and lighting effects are not supported. Sorry, but this is an inherent feature of MATLAB's painters renderer. To find out more about the capabilities of each rendering method, see [here](http://www.mathworks.com/access/helpdesk/help/techdoc/creating_plots/f3-84337.html#f3-102410). You can still export transparent objects to vector format (SVG) using the excellent [plot2svg](http://www.mathworks.com/matlabcentral/fileexchange/7401) package, then convert this to PDF, for example using [Inkscape](http://inkscape.org/). However, it can't handle lighting.  
-  
-**Lines in patch objects** - when exporting patch objects to PDF using the painters renderer (default), sometimes the output can appear to have lines across the middle of rectangular patches; these lines are the colour of the background, as if there is a crack in the patch, allowing you to see through. This appears to be due to bugs in MATLAB's internal vector rendering code. These lines can often be removed from the PDF using software such as [InkScape](https://inkscape.org). Sometimes disabling anti-aliasing in the PDF-reader software can get rid of the lines ([discussion](https://github.com/altmany/export_fig/issues/44)).  
-  
-**Out of memory** - if you run into memory issues when using export_fig, some ways to get round this are:  
- 1. Reduce the level of anti-aliasing.
- 2. Reduce the size of the figure.
- 3. Reduce the export resolution (dpi). 
- 4. Change the renderer to painters or ZBuffer.  
-  
-**Errors** - the other common type of errors people get with export_fig are OpenGL errors. This isn't a fault of export_fig, but either a bug in MATLAB's `print`, or your graphics driver getting itself into a state. Always make sure your graphics driver is up-to-date. If it still doesn't work, try using the ZBuffer renderer.  
-  
-### Raising issues
-If you think you have found a genuine error or issue with export_fig **that is not listed above**, first ensure that the figure looks correct on screen when rendered using the renderer that export_fig is set to use (e.g. if exporting to PDF or EPS, does the figure look correct on screen using the painters renderer, or if exporting to bitmap, does the figure look correct on screen using the OpenGL renderer?). If it looks wrong then the problem is there, and I cannot help (other than to suggest you try exporting using a different renderer).
-
-Secondly, if exporting to bitmap, do try all the renderers (i.e. try the options `-opengl`, `-zbuffer` and `-painters` separately), to see if one of them does produce an acceptable output, and if so, use that.
-
-If this still does not help, then ensure that you are using the latest version of export_fig, which is available [here](https://github.com/altmany/export_fig/archive/master.zip).  
- 
-If the figure looks correct on screen, but an error exists in the exported output (which cannot be solved using a different renderer) then please feel free to raise an [issue](https://github.com/altmany/export_fig/issues). Please be sure to include the .fig file, the export_fig command you use, the output you get, and a description of what you expected. I can't promise anything, but if it's easy to fix I may indeed do it. Often I will find that the error is due to a bug in MATLAB's `print` function, in which case I will suggest you submit it as a bug to TheMathWorks, and inform me of any fix they suggest. Also, if there's a feature you'd like that isn't supported please tell me what it is and I'll consider implementing it.
-
-### And finally...
-
-![](https://farm4.staticflickr.com/3956/15591911455_b9008bd77e_o_d.jpg)
-
-If you've ever wondered what's going on in the logo on the export_fig download page (reproduced here), then this explanantion is for you. The logo is designed to demonstrate as many of export_fig's features as possible: 
- 
-Given a figure containing a translucent mesh (top right), export_fig can export to pdf (bottom centre), which allows the figure to be zoomed-in without losing quality (because it's a vector graphic), but isn't able to reproduce the translucency. Also, depending on the PDF viewer program, small gaps appear between the patches, which are seen here as thin white lines. 
- 
-By contrast, when exporting to png (top left), translucency is preserved (see how the graphic below shows through), and the figure is anti-aliased. However, zooming-in does not reveal more detail since png is a bitmap format. Also, lines appear less sharp than in the pdf output.
-
+export_fig
+==========
+
+A toolbox for exporting figures from MATLAB to standard image and document formats nicely.
+
+### Overview
+Exporting a figure from MATLAB the way you want it (hopefully the way it looks on screen), can be a real headache for the unitiated, thanks to all the settings that are required, and also due to some eccentricities (a.k.a. features and bugs) of functions such as `print`. The first goal of export_fig is to make transferring a plot from screen to document, just the way you expect (again, assuming that's as it appears on screen), a doddle.
+  
+The second goal is to make the output media suitable for publication, allowing you to publish your results in the full glory that you originally intended. This includes embedding fonts, setting image compression levels (including lossless), anti-aliasing, cropping, setting the colourspace, alpha-blending and getting the right resolution.
+
+Perhaps the best way to demonstrate what export_fig can do is with some examples.
+  
+### Examples
+**Visual accuracy** - MATLAB's exporting functions, namely `saveas` and `print`, change many visual properties of a figure, such as size, axes limits and ticks, and background colour, in unexpected and unintended ways. Export_fig aims to faithfully reproduce the figure as it appears on screen. For example:  
+```Matlab
+plot(cos(linspace(0, 7, 1000)));
+set(gcf, 'Position', [100 100 150 150]);
+saveas(gcf, 'test.png');
+export_fig test2.png
+```
+generates the following:
+
+| Figure: | test.png: | test2.png: |
+|:-------:|:---------:|:----------:|
+|![](https://farm6.staticflickr.com/5616/15589249291_16e485c29a_o_d.png)|![](https://farm4.staticflickr.com/3944/15406302850_4d2e1c7afa_o_d.png)|![](https://farm6.staticflickr.com/5607/15568225476_8ce9bd5f6b_o_d.png)|
+
+Note that the size and background colour of test2.png (the output of export_fig) are the same as those of the on screen figure, in contrast to test.png. Of course, if you want the figure background to be white (or any other colour) in the exported file then you can set this prior to exporting using:
+```Matlab
+set(gcf, 'Color', 'w');
+```
+  
+Notice also that export_fig crops and anti-aliases (smooths, for bitmaps only) the output by default. However, these options can be disabled; see the Tips section below for details.
+  
+**Resolution** - by default, export_fig exports bitmaps at screen resolution. However, you may wish to save them at a different resolution. You can do this using either of two options: `-m<val>`, where <val> is a positive real number, magnifies the figure by the factor <val> for export, e.g. `-m2` produces an image double the size (in pixels) of the on screen figure; `-r<val>`, again where <val> is a positive real number, specifies the output bitmap to have <val> pixels per inch, the dimensions of the figure (in inches) being those of the on screen figure. For example, using:  
+```Matlab
+export_fig test.png -m2.5
+```
+on the figure from the example above generates:
+
+![](https://farm4.staticflickr.com/3937/15591910915_dc7040c477_o_d.png)
+
+Sometimes you might have a figure with an image in. For example:
+```Matlab
+imshow(imread('cameraman.tif'))
+hold on
+plot(0:255, sin(linspace(0, 10, 256))*127+128);
+set(gcf, 'Position', [100 100 150 150]);
+```
+generates this figure:
+
+![](https://farm4.staticflickr.com/3942/15589249581_ff87a56a3f_o_d.png)
+  
+Here the image is displayed in the figure at resolution lower than its native resolution. However, you might want to export the figure at a resolution such that the image is output at its native (i.e. original) size (in pixels). Ordinarily this would require some non-trivial computation to work out what that resolution should be, but export_fig has an option to do this for you. Using:
+```Matlab
+export_fig test.png -native
+```
+produces:
+
+![](https://farm6.staticflickr.com/5604/15589249591_da2b2652e4_o_d.png)
+
+with the image being the size (in pixels) of the original image. Note that if you want an image to be a particular size, in pixels, in the output (other than its original size) then you can resize it to this size and use the `-native` option to achieve this.
+
+All resolution options (`-m<val>`, `-q<val>` and `-native`) correctly set the resolution information in PNG and TIFF files, as if the image were the dimensions of the on screen figure.
+
+**Shrinking dots & dashes** - when exporting figures with dashed or dotted lines using either the ZBuffer or OpenGL (default for bitmaps) renderers, the dots and dashes can appear much shorter, even non-existent, in the output file, especially if the lines are thick and/or the resolution is high. For example:  
+```Matlab
+plot(sin(linspace(0, 10, 1000)), 'b:', 'LineWidth', 4);
+hold on
+plot(cos(linspace(0, 7, 1000)), 'r--', 'LineWidth', 3);
+grid on
+export_fig test.png
+```
+generates:
+
+![](https://farm4.staticflickr.com/3956/15592747732_f943d4aa0a_o_d.png)
+
+This problem can be overcome by using the painters renderer. For example:
+```Matlab
+export_fig test.png -painters
+```
+used on the same figure generates:
+
+![](https://farm4.staticflickr.com/3945/14971168504_77692f11f5_o_d.png)
+
+Note that not only are the plot lines correct, but the grid lines are too.
+
+**Transparency** - sometimes you might want a figure and axes' backgrounds to be transparent, so that you can see through them to a document (for example a presentation slide, with coloured or textured background) that the exported figure is placed in. To achieve this, first (optionally) set the axes' colour to 'none' prior to exporting, using:  
+```Matlab
+set(gca, 'Color', 'none'); % Sets axes background
+```
+    
+then use export_fig's `-transparent` option when exporting:
+```Matlab
+export_fig test.png -transparent
+```
+
+This will make the background transparent in PDF, EPS and PNG outputs. You can additionally save fully alpha-blended semi-transparent patch objects to the PNG format. For example:
+
+```Matlab
+logo;
+alpha(0.5);
+```
+
+generates a figure like this:
+
+![](https://farm4.staticflickr.com/3933/15405290339_b08de33528_o_d.png)
+
+If you then export this to PNG using the `-transparent` option you can then put the resulting image into, for example, a presentation slide with fancy, textured background, like so:
+
+![](https://farm6.staticflickr.com/5599/15406302920_59beaefff1_o_d.png)
+
+and the image blends seamlessly with the background.
+
+**Image quality** - when publishing images of your results, you want them to look as good as possible. By default, when outputting to lossy file formats (PDF, EPS and JPEG), export_fig uses a high quality setting, i.e. low compression, for images, so little information is lost. This is in contrast to MATLAB's print and saveas functions, whose default quality settings are poor. For example:
+```Matlab
+A = im2double(imread('peppers.png'));
+B = randn(ceil(size(A, 1)/6), ceil(size(A, 2)/6), 3) * 0.1;
+B = cat(3, kron(B(:,:,1), ones(6)), kron(B(:,:,2), ones(6)), kron(B(:,:,3), ones(6)));
+B = A + B(1:size(A, 1),1:size(A, 2),:);
+imshow(B);
+print -dpdf test.pdf
+```
+generates a PDF file, a sub-window of which looks (when zoomed in) like this:
+
+![](https://farm6.staticflickr.com/5613/15405290309_881b2774d6_o_d.png)
+
+while the command
+
+```Matlab
+export_fig test.pdf
+```
+on the same figure produces this:
+
+![](https://farm4.staticflickr.com/3947/14971168174_687473133f_o_d.png)
+
+While much better, the image still contains some compression artifacts (see the low level noise around the edge of the pepper). You may prefer to export with no artifacts at all, i.e. lossless compression. Alternatively, you might need a smaller file, and be willing to accept more compression. Either way, export_fig has an option that can suit your needs: `-q<val>`, where <val> is a number from 0-100, will set the level of lossy image compression (again in PDF, EPS and JPEG outputs only; other formats are lossless), from high compression (0) to low compression/high quality (100). If you want lossless compression in any of those formats then specify a <val> greater than 100. For example:
+```Matlab
+export_fig test.pdf -q101
+```
+again on the same figure, produces this:
+
+![](https://farm6.staticflickr.com/5608/15405803908_934512c1fe_o_d.png)
+
+Notice that all the noise has gone.
+
+### Tips
+**Anti-aliasing** - the anti-aliasing which export_fig applies to bitmap outputs by default makes the images look nice, but it can also blur images and increase exporting time and memory requirements, so you might not always want it. You can set the level of anti-aliasing by using the `-a<val>` option, where <val> is 1 (no anti-aliasing), 2, 3 (default) or 4 (maximum anti-aliasing).  
+  
+**Cropping** - by default, export_fig crops its output to minimize the amount of empty space around the figure. If you'd prefer the figure to be uncropped, and instead have the same appearance (in terms of border width) as the on screen figure, then use the `-nocrop` option.  
+  
+**Colourspace** - by default, export_fig generates files in the RGB [colourspace](https://en.wikipedia.org/wiki/Color_space). However, you can also export in greyscale or the CMYK colourspace, using the `-grey` (or `-gray`) and `-cmyk` options respectively. The CMYK option is useful for publishers who require documents in this colourspace, but the option is only supported for PDF, EPS and TIFF files.
+
+**Specifying a target directory** - you can get export_fig to save output files to any directory (for which you have write permission), simply by specifying the full or relative path in the filename. For example:
+```Matlab
+export_fig ../subdir/fig.png;
+export_fig('C:/Users/Me/Documents/figures/myfig', '-pdf', '-png');
+```
+
+**Variable file names** - often you might want to save a series of figures in a for loop, each with a different name. For this you can use the functional form of input arguments, i.e. `export_fig(arg1, arg2)`,  and construct the filename string in a variable. Here's an example of this:  
+```Matlab
+for a = 1:5
+    plot(rand(5, 2));
+    export_fig(sprintf('plot%d.png', a));
+end
+```
+When using the functional form like this, be sure to put string variables in quotes:
+```Matlab
+export_fig(sprintf('plot%d', a), '-a1', '-pdf', '-png');
+```
+
+**Specifying the figure/axes** - if you have multiple figures open you can specify which figure to export using its handle:  
+```Matlab
+export_fig(figure_handle, filename);
+```
+Equally, if your figure contains several subplots then you can export just one of them by giving export_fig the handle to the relevant axes:
+```Matlab
+export_fig(axes_handle, filename);
+```
+
+**Multiple formats** - save time by exporting to multiple formats simultaneously. E.g.: 
+```Matlab
+export_fig filename -pdf -eps -png -jpg -tiff
+```
+
+**Other file formats** - if you'd like to save your figure to a bitmap format that is not supported by export_fig, e.g. animated GIF, PPM file or a frame in a movie, then you can use export_fig to output the image, and optionally an alpha-matte, to the workspace. E.g.:  
+```Matlab
+frame = export_fig;
+```
+or
+```Matlab
+[frame, alpha] = export_fig;
+```
+These variables can then be saved to other image formats using other functions, such as imwrite.
+
+**Appending to a file** - you can use the `-append` option to append the figure to the end of an image/document, if it already exists. This is supported for PDF and TIFF files only. Note that if you wish to append a lot of figures consecutively to a PDF, it can be more efficient to save all the figures to PDF separately then append them all in one go at the end (e.g. using [append_pdfs](http://www.mathworks.com/matlabcentral/fileexchange/31215-appendpdfs)).  
+  
+**Output to clipboard** - you can use the `-clipboard` option to copy the specified figure or axes to the system clipboard, for easy paste into other documents (e.g., Word or PowerPoint). Note that the image is copied in bitmap (not vector) format.  
+  
+**Font size** - if you want to place an exported figure in a document with the font a particular size then you need to set the font to that size in the figure, and not resize the output of export_fig in the document. To avoid resizing, simply make sure that the on screen figure is the size you want the output to be in the document before exporting.  
+  
+**Renderers** - MATLAB has three renderers for displaying and exporting figures: painters, OpenGL and ZBuffer. The different renderers have different [features](http://www.mathworks.com/access/helpdesk/help/techdoc/creating_plots/f3-84337.html#f3-102410), so if you aren't happy with the result from one renderer try another. By default, vector formats (i.e. PDF and EPS outputs) use the painters renderer, while other formats use the OpenGL renderer. Non-default renderers can be selected by using one of these three export_fig input options: `-painters`, `-opengl`, `-zbuffer`:  
+```Matlab
+export_fig test.png -painters
+```
+  
+**Artifacts** - sometimes the output that you get from export_fig is not what you expected. If an output file contains artifacts that aren't in the on screen figure then make sure that the renderer used for rendering the figure on screen is the same as that used for exporting. To set the renderer used to display the figure, use:  
+```Matlab
+set(figure_handle, 'Renderer', 'opengl');
+```
+After matching the two renderers, if the artifact appears in the on screen figure then you'll need to fix that before exporting. Alternatively you can try changing the renderer used by export_fig. Finally check that it isn't one of the known issues mentioned in the section below.
+
+**Smoothed/interpolated images in output PDF** - if you produce a PDF using export_fig and images in the PDF look overly smoothed or interpolated, this is because the software you are using to view the PDF is smoothing or interpolating the image data. The image is not smoothed in the PDF file itself. If the software has an option to disable this feature, you should select it. Alternatively, use another PDF viewer that doesn't exhibit this problem.  
+  
+**Locating Ghostscript/pdftops** - You may find a dialogue box appears when using export_fig, asking you to locate either [Ghostscript](http://www.ghostscript.com) or [pdftops (part of the Xpdf package)](http://www.xpdfreader.com). These are separate applications which export_fig requires to perform certain functions. If such a dialogue appears it is because export_fig can't find the application automatically. This is because you either haven't installed it, or it isn't in the normal place. Make sure you install the applications correctly first. They can be downloaded from the following places:  
+ 1. Ghostscript:     [www.ghostscript.com](http://www.ghostscript.com)
+ 2. pdftops (install the Xpdf package): [www.xpdfreader.com](http://www.xpdfreader.com)
+
+If you choose to install them in a non-default location then point export_fig
+to this location using the dialogue box.
+
+**Undefined function errors** - If you download and run export_fig and get an error similar to this:  
+```
+??? Undefined function or method 'print2array' for input arguments of type 'double'.
+```
+then you are missing one or more of the files that come in the export_fig package. Make sure that you click the "Get from GitHub" button at the top-right of the download [page](http://www.mathworks.co.uk/matlabcentral/fileexchange/23629-exportfig), then extract all the files in the zip file to the same directory. You should then have all the necessary files.
+  
+### Known issues
+There are lots of problems with MATLAB's exporting functions, especially `print`. Export_fig is simply a glorified wrapper for MATLAB's `print` function, and doesn't solve all of its bugs (yet?). Some of the problems I know about are:
+  
+**Fonts** - when using the painters renderer, MATLAB can only export a small number of fonts, details of which can be found [here](http://www.mathworks.com/help/releases/R2014a/matlab/creating_plots/choosing-a-printer-driver.html#f3-96545). Export_fig attempts to correct font names in the resulting EPS file (up to a maximum of 11 different fonts in one figure), but this is not always guaranteed to work. In particular, the text positions will be affected. It also does not work for text blocks where the 'Interpreter' property is set to 'latex'.
+
+Also, when using the painters renderer, ghostscript will sometimes throw an error such as `Error: /undefined in /findfont`. This suggests that ghostscript could not find a definition file for one of your fonts. One possible fix for this is to make sure the file `EXPORT_FIG_PATH/.ignore/gs_font_path.txt` exists and contains a list of paths to the folder(s) containing the necessary font definitions (make sure that they are TrueType definitions!), separated by a semicolon.
+
+**RGB color data not yet supported in Painter's mode** - you will see this as a warning if you try to export a figure which contains patch objects whose face or vertex colors are specified as an RGB colour, rather than an index into the colormap, using the painters renderer (the default renderer for vector output). This problem can arise if you use `pcolor`, for example. This is a problem with MATLAB's painters renderer, which also affects `print`; there is currently no fix available in export_fig (other than to export to bitmap). The suggested workaround is to avoid colouring patches using RGB. First, try to use colours in the figure's colourmap (instructions [here](http://www.mathworks.co.uk/support/solutions/en/data/1-6OTPQE/)) - change the colourmap, if necessary. If you are using `pcolor`, try using [uimagesc](http://www.mathworks.com/matlabcentral/fileexchange/11368) (on the file exchange) instead.  
+
+**Dashed contour lines appear solid** - when using the painters renderer, MATLAB cannot generate dashed lines using the `contour` function (either on screen or in exported PDF and EPS files). Details can be found [here](http://www.mathworks.com/support/solutions/en/data/1-14PPHB/?solution=1-14PPHB).  
+  
+**Text size** - when using the OpenGL or ZBuffer renderers, large text can be resized relative to the figure when exporting at non-screen-resolution (including using anti-alising at screen resolution). This is a feature of MATLAB's `print `function. In this case, try using the `-painters` option.  
+  
+**Lighting and transparency** - when using the painters renderer, transparency and lighting effects are not supported. Sorry, but this is an inherent feature of MATLAB's painters renderer. To find out more about the capabilities of each rendering method, see [here](http://www.mathworks.com/access/helpdesk/help/techdoc/creating_plots/f3-84337.html#f3-102410). You can still export transparent objects to vector format (SVG) using the excellent [plot2svg](http://www.mathworks.com/matlabcentral/fileexchange/7401) package, then convert this to PDF, for example using [Inkscape](http://inkscape.org/). However, it can't handle lighting.  
+  
+**Lines in patch objects** - when exporting patch objects to PDF using the painters renderer (default), sometimes the output can appear to have lines across the middle of rectangular patches; these lines are the colour of the background, as if there is a crack in the patch, allowing you to see through. This appears to be due to bugs in MATLAB's internal vector rendering code. These lines can often be removed from the PDF using software such as [InkScape](https://inkscape.org). Sometimes disabling anti-aliasing in the PDF-reader software can get rid of the lines ([discussion](https://github.com/altmany/export_fig/issues/44)).  
+  
+**Out of memory** - if you run into memory issues when using export_fig, some ways to get round this are:  
+ 1. Reduce the level of anti-aliasing.
+ 2. Reduce the size of the figure.
+ 3. Reduce the export resolution (dpi). 
+ 4. Change the renderer to painters or ZBuffer.  
+  
+**Errors** - the other common type of errors people get with export_fig are OpenGL errors. This isn't a fault of export_fig, but either a bug in MATLAB's `print`, or your graphics driver getting itself into a state. Always make sure your graphics driver is up-to-date. If it still doesn't work, try using the ZBuffer renderer.  
+  
+### Raising issues
+If you think you have found a genuine error or issue with export_fig **that is not listed above**, first ensure that the figure looks correct on screen when rendered using the renderer that export_fig is set to use (e.g. if exporting to PDF or EPS, does the figure look correct on screen using the painters renderer, or if exporting to bitmap, does the figure look correct on screen using the OpenGL renderer?). If it looks wrong then the problem is there, and I cannot help (other than to suggest you try exporting using a different renderer).
+
+Secondly, if exporting to bitmap, do try all the renderers (i.e. try the options `-opengl`, `-zbuffer` and `-painters` separately), to see if one of them does produce an acceptable output, and if so, use that.
+
+If this still does not help, then ensure that you are using the latest version of export_fig, which is available [here](https://github.com/altmany/export_fig/archive/master.zip).  
+ 
+If the figure looks correct on screen, but an error exists in the exported output (which cannot be solved using a different renderer) then please feel free to raise an [issue](https://github.com/altmany/export_fig/issues). Please be sure to include the .fig file, the export_fig command you use, the output you get, and a description of what you expected. I can't promise anything, but if it's easy to fix I may indeed do it. Often I will find that the error is due to a bug in MATLAB's `print` function, in which case I will suggest you submit it as a bug to TheMathWorks, and inform me of any fix they suggest. Also, if there's a feature you'd like that isn't supported please tell me what it is and I'll consider implementing it.
+
+### And finally...
+
+![](https://farm4.staticflickr.com/3956/15591911455_b9008bd77e_o_d.jpg)
+
+If you've ever wondered what's going on in the logo on the export_fig download page (reproduced here), then this explanantion is for you. The logo is designed to demonstrate as many of export_fig's features as possible: 
+ 
+Given a figure containing a translucent mesh (top right), export_fig can export to pdf (bottom centre), which allows the figure to be zoomed-in without losing quality (because it's a vector graphic), but isn't able to reproduce the translucency. Also, depending on the PDF viewer program, small gaps appear between the patches, which are seen here as thin white lines. 
+ 
+By contrast, when exporting to png (top left), translucency is preserved (see how the graphic below shows through), and the figure is anti-aliased. However, zooming-in does not reveal more detail since png is a bitmap format. Also, lines appear less sharp than in the pdf output.
+
diff --git a/GUI/export_fig/user_string.m b/GUI/export_fig/user_string.m
index 48d1131..1cb3282 100644
--- a/GUI/export_fig/user_string.m
+++ b/GUI/export_fig/user_string.m
@@ -1,111 +1,111 @@
-function string = user_string(string_name, string)
-%USER_STRING  Get/set a user specific string
-%
-% Examples:
-%   string  = user_string(string_name)
-%   isSaved = user_string(string_name, new_string)
-%
-% Function to get and set a string in a system or user specific file. This
-% enables, for example, system specific paths to binaries to be saved.
-%
-% The specified string will be saved in a file named <string_name>.txt,
-% either in a subfolder named .ignore under this file's folder, or in the
-% user's prefdir folder (in case this file's folder is non-writable).
-%
-% IN:
-%   string_name - String containing the name of the string required, which
-%                 sets the filename storing the string: <string_name>.txt
-%   new_string  - The new string to be saved in the <string_name>.txt file
-%
-% OUT:
-%   string  - The currently saved string. Default: ''
-%   isSaved - Boolean indicating whether the save was succesful
-
-% Copyright (C) Oliver Woodford 2011-2014, Yair Altman 2015-
-
-% This method of saving paths avoids changing .m files which might be in a
-% version control system. Instead it saves the user dependent paths in
-% separate files with a .txt extension, which need not be checked in to
-% the version control system. Thank you to Jonas Dorn for suggesting this
-% approach.
-
-% 10/01/2013 - Access files in text, not binary mode, as latter can cause
-%              errors. Thanks to Christian for pointing this out.
-% 29/05/2015 - Save file in prefdir if current folder is non-writable (issue #74)
-% 09/01/2018 - Fix issue #232: if the string looks like a file/folder path, ensure it actually exists
-
-    if ~ischar(string_name)
-        error('string_name must be a string.');
-    end
-    % Create the full filename
-    fname = [string_name '.txt'];
-    dname = fullfile(fileparts(mfilename('fullpath')), '.ignore');
-    file_name = fullfile(dname, fname);
-    if nargin > 1
-        % Set string
-        if ~ischar(string)
-            error('new_string must be a string.');
-        end
-        % Make sure the save directory exists
-        %dname = fileparts(file_name);
-        if ~exist(dname, 'dir')
-            % Create the directory
-            try
-                if ~mkdir(dname)
-                    string = false;
-                    return
-                end
-            catch
-                string = false;
-                return
-            end
-            % Make it hidden
-            try
-                fileattrib(dname, '+h');
-            catch
-            end
-        end
-        % Write the file
-        fid = fopen(file_name, 'wt');
-        if fid == -1
-            % file cannot be created/updated - use prefdir if file does not already exist
-            % (if file exists but is simply not writable, don't create a duplicate in prefdir)
-            if ~exist(file_name,'file')
-                file_name = fullfile(prefdir, fname);
-                fid = fopen(file_name, 'wt');
-            end
-            if fid == -1
-                string = false;
-                return;
-            end
-        end
-        try
-            fprintf(fid, '%s', string);
-        catch
-            fclose(fid);
-            string = false;
-            return
-        end
-        fclose(fid);
-        string = true;
-    else
-        % Get string
-        fid = fopen(file_name, 'rt');
-        if fid == -1
-            % file cannot be read, try to read the file in prefdir
-            file_name = fullfile(prefdir, fname);
-            fid = fopen(file_name, 'rt');
-            if fid == -1
-                string = '';
-                return
-            end
-        end
-        string = fgetl(fid);
-        fclose(fid);
-
-        % Fix issue #232: if the string looks like a file/folder path, ensure it actually exists
-        if ~isempty(string) && any(string=='\' | string=='/') && ~exist(string) %#ok<EXIST>
-            string = '';
-        end
-    end
-end
+function string = user_string(string_name, string)
+%USER_STRING  Get/set a user specific string
+%
+% Examples:
+%   string  = user_string(string_name)
+%   isSaved = user_string(string_name, new_string)
+%
+% Function to get and set a string in a system or user specific file. This
+% enables, for example, system specific paths to binaries to be saved.
+%
+% The specified string will be saved in a file named <string_name>.txt,
+% either in a subfolder named .ignore under this file's folder, or in the
+% user's prefdir folder (in case this file's folder is non-writable).
+%
+% IN:
+%   string_name - String containing the name of the string required, which
+%                 sets the filename storing the string: <string_name>.txt
+%   new_string  - The new string to be saved in the <string_name>.txt file
+%
+% OUT:
+%   string  - The currently saved string. Default: ''
+%   isSaved - Boolean indicating whether the save was succesful
+
+% Copyright (C) Oliver Woodford 2011-2014, Yair Altman 2015-
+
+% This method of saving paths avoids changing .m files which might be in a
+% version control system. Instead it saves the user dependent paths in
+% separate files with a .txt extension, which need not be checked in to
+% the version control system. Thank you to Jonas Dorn for suggesting this
+% approach.
+
+% 10/01/2013 - Access files in text, not binary mode, as latter can cause
+%              errors. Thanks to Christian for pointing this out.
+% 29/05/2015 - Save file in prefdir if current folder is non-writable (issue #74)
+% 09/01/2018 - Fix issue #232: if the string looks like a file/folder path, ensure it actually exists
+
+    if ~ischar(string_name)
+        error('string_name must be a string.');
+    end
+    % Create the full filename
+    fname = [string_name '.txt'];
+    dname = fullfile(fileparts(mfilename('fullpath')), '.ignore');
+    file_name = fullfile(dname, fname);
+    if nargin > 1
+        % Set string
+        if ~ischar(string)
+            error('new_string must be a string.');
+        end
+        % Make sure the save directory exists
+        %dname = fileparts(file_name);
+        if ~exist(dname, 'dir')
+            % Create the directory
+            try
+                if ~mkdir(dname)
+                    string = false;
+                    return
+                end
+            catch
+                string = false;
+                return
+            end
+            % Make it hidden
+            try
+                fileattrib(dname, '+h');
+            catch
+            end
+        end
+        % Write the file
+        fid = fopen(file_name, 'wt');
+        if fid == -1
+            % file cannot be created/updated - use prefdir if file does not already exist
+            % (if file exists but is simply not writable, don't create a duplicate in prefdir)
+            if ~exist(file_name,'file')
+                file_name = fullfile(prefdir, fname);
+                fid = fopen(file_name, 'wt');
+            end
+            if fid == -1
+                string = false;
+                return;
+            end
+        end
+        try
+            fprintf(fid, '%s', string);
+        catch
+            fclose(fid);
+            string = false;
+            return
+        end
+        fclose(fid);
+        string = true;
+    else
+        % Get string
+        fid = fopen(file_name, 'rt');
+        if fid == -1
+            % file cannot be read, try to read the file in prefdir
+            file_name = fullfile(prefdir, fname);
+            fid = fopen(file_name, 'rt');
+            if fid == -1
+                string = '';
+                return
+            end
+        end
+        string = fgetl(fid);
+        fclose(fid);
+
+        % Fix issue #232: if the string looks like a file/folder path, ensure it actually exists
+        if ~isempty(string) && any(string=='\' | string=='/') && ~exist(string) %#ok<EXIST>
+            string = '';
+        end
+    end
+end
diff --git a/GUI/findjobj/license.txt b/GUI/findjobj/license.txt
index cba4567..ba2ad26 100644
--- a/GUI/findjobj/license.txt
+++ b/GUI/findjobj/license.txt
@@ -1,24 +1,24 @@
-Copyright (c) 2017, Yair Altman
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2017, Yair Altman
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/GUI/icons/webpage_icons.svg b/GUI/icons/webpage_icons.svg
index aba807a..ade390f 100644
--- a/GUI/icons/webpage_icons.svg
+++ b/GUI/icons/webpage_icons.svg
@@ -1,15558 +1,15558 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!-- Created with Inkscape (http://www.inkscape.org/) -->
-
-<svg
-   xmlns:dc="http://purl.org/dc/elements/1.1/"
-   xmlns:cc="http://creativecommons.org/ns#"
-   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
-   xmlns:svg="http://www.w3.org/2000/svg"
-   xmlns="http://www.w3.org/2000/svg"
-   xmlns:xlink="http://www.w3.org/1999/xlink"
-   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
-   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
-   width="708.66138"
-   height="177.16534"
-   id="svg2"
-   version="1.1"
-   inkscape:version="0.48.4 r9939"
-   sodipodi:docname="webpage_icons.svg">
-  <defs
-     id="defs4" />
-  <sodipodi:namedview
-     id="base"
-     pagecolor="#ffffff"
-     bordercolor="#666666"
-     borderopacity="1.0"
-     inkscape:pageopacity="0"
-     inkscape:pageshadow="2"
-     inkscape:zoom="0.98994949"
-     inkscape:cx="259.38732"
-     inkscape:cy="-342.65798"
-     inkscape:document-units="cm"
-     inkscape:current-layer="layer1"
-     showgrid="false"
-     units="cm"
-     inkscape:window-width="1247"
-     inkscape:window-height="1388"
-     inkscape:window-x="65"
-     inkscape:window-y="24"
-     inkscape:window-maximized="0" />
-  <metadata
-     id="metadata7">
-    <rdf:RDF>
-      <cc:Work
-         rdf:about="">
-        <dc:format>image/svg+xml</dc:format>
-        <dc:type
-           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
-        <dc:title />
-      </cc:Work>
-    </rdf:RDF>
-  </metadata>
-  <g
-     inkscape:label="Layer 1"
-     inkscape:groupmode="layer"
-     id="layer1"
-     transform="translate(0,-875.19551)">
-    <image
-       y="929.55945"
-       x="393.14276"
-       id="image3026"
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diff --git a/GUI/models/model_TFN_gui.m b/GUI/models/model_TFN_gui.m
index 4d30aeb..c984ec5 100644
--- a/GUI/models/model_TFN_gui.m
+++ b/GUI/models/model_TFN_gui.m
@@ -1,2234 +1,2234 @@
-classdef model_TFN_gui < model_gui_abstract
-    %MODEL_TFN_GUI Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-        % NOTE: Inputs from the parent table are define in abstract
-        %  
-        % Model specific GUI properties.
-        forcingTranforms
-        weightingFunctions
-        derivedForcingTranforms
-        derivedWeightingFunctions
-        modelComponants
-        modelOptions 
-        
-        % Current active main table, row, col
-        currentSelection;
-
-        % Copied table rows
-        copiedData;
-    end
-    
-    methods
-        %% Build the GUI for this model type.
-        function this = model_TFN_gui(parent_handle)
-            
-            % Initialise properties not initialised below
-            this.boreID = [];
-            this.siteData = [];
-            this.forcingData = [];  
-            this.currentSelection.table = '';            
-            this.currentSelection.row = 0;
-            this.currentSelection.col = 0;
-            
-            % Get the available modle options
-            %--------------------------------------------------------------
-            % Get the types of weighting function and derived weighting function
-            quickload = true;
-            if ~isdeployed && ~quickload
-                warning('off');
-                forcingFunctions = findClassDefsUsingAbstractName( 'forcingTransform_abstract', 'model_TFN');
-                derivedForcingFunctions = findClassDefsUsingAbstractName( 'derivedForcingTransform_abstract', 'model_TFN');
-
-                % Get the types of weighting function and derived weighting function
-                weightFunctions = findClassDefsUsingAbstractName( 'responseFunction_abstract', 'model_TFN');
-                derivedWeightFunctions = findClassDefsUsingAbstractName( 'derivedResponseFunction_abstract', 'model_TFN');
-                warning('on');
-            else
-                % Hard code in function names if the code is deployed. This
-                % is required because depfun does not dunction in deployed
-                % code.                
-                forcingFunctions = {        'climateTransform_soilMoistureModels', ...                                            
-                                            'climateTransform_soilMoistureModels_v2', ...
-                                            'climateTransform_soilMoistureModels_2layer', ...
-                                            'climateTransform_soilMoistureModels_2layer_v2', ...
-                                            'pumpingRate_SAestimation'};                
-                derivedForcingFunctions = { 'derivedForcing_linearUnconstrainedScaling', ...
-                                            'derivedForcing_linearUnconstrainedScaling_dup', ...
-                                            'derivedForcing_logisticScaling'};                
-                derivedWeightFunctions = {  'derivedResponseFunction_abstract', ...
-                                            'derivedweighting_UnconstrainedRescaled', ...
-                                            'derivedweighting_PearsonsNegativeRescaled', ...
-                                            'derivedweighting_PearsonsPositiveRescaled'};
-                weightFunctions = {         'responseFunction_abstract', ...
-                                            'responseFunction_Bruggeman', ...
-                                            'responseFunction_FerrisKnowles', ...
-                                            'responseFunction_FerrisKnowlesJacobs', ...
-                                            'responseFunction_Hantush', ...
-                                            'responseFunction_JacobsCorrection', ...
-                                            'responseFunction_Pearsons', ...
-                                            'responseFunction_PearsonsNegative'};
-            end
-                
-
-            % Forcing function column headings etc
-            cnames_forcing = {'Select','Forcing Transform Function','Input Data', 'Options'};
-            cformats_forcing = {'logical',forcingFunctions,'char','char'};
-            cedit_forcing = logical([1 1 1 1]);
-            rnames_forcing = {'1'};
-            cdata_forcing = cell(1,4);
-            toolTip_forcing = ['<html>This table (optional) allows the transformation of the input forcing data (e.g. rainfall to recharge). <br>', ...
-                          'To build a transformation function, you''ll need to select a function and define the input forcing data <br>', ...                 
-                          'that is to be transformed. </html>'];                          
-            
-            % Derived forcing function column headings etc
-            cnames_forcingDerived = {'Select','Forcing Transform Function','Source Forcing Function','Input Data', 'Options'};
-            cformats_forcingDerived = {'logical',derivedForcingFunctions,{'(No functions available)'},'char','char'};    
-            cedit_forcingDerived = logical([1 1 1 1 1]);
-            rnames_forcingDerived = {'1'};
-            cdata_forcingDerived = cell(1,5);
-            toolTip_forcingDerived = ['<html>This table (optional) allows the transformation of derived forcing data (e.g. recharge) <br>', ...
-                          'and can be used to estimate the impacts from, say, land use change on the head.  To build such a transformation <br>', ...
-                          'function, you''ll need to first build a transformation function (step 1)</html>'];                          
-            
-            % Weighting function column headings etc
-            cnames_weighting = {'Select','Component Name','Weighting Function','Input Data', 'Options'};
-            cformats_weighting = {'logical','char', weightFunctions,'char','char'};
-            cedit_weighting = logical([1 1 1 1 1]);
-            rnames_weighting = {'1'};
-            cdata_weighting = cell(1,5);
-            toolTip_weighting = ['<html>This table (required) allows the weighting of forcing data (e.g. rainfall, recharge or pumping). <br>', ...
-                          'into a groundwater head change. To build a weighting function, you''ll need to define the name of the model <br>', ...
-                          'componant, select a function type and then define the data to be weighted.</html>'];                          
-
-            
-            % Derived Weighting function column headings etc
-            cnames_weightingDerived = {'Select','Component Name','Weighting Function','Source Component','Input Data', 'Options'};
-            cformats_weightingDerived = {'logical','char', derivedWeightFunctions,'char','char','char'};    
-            cedit_weightingDerived = logical([1 1 1 1 1 1]);
-            rnames_weightingDerived = {'1'};
-            cdata_weightingDerived = cell(1,6);
-            toolTip_weightingDerived = ['<html>This table (optional) allows weighting of forcing data (e.g. rainfall, recharge <br>', ...
-                                               'or pumping) using a previously defined weighting function. </html>'];                          
-            
-            % Create the GUI elements
-            %--------------------------------------------------------------
-            % Create grid the model settings            
-            %this.Figure = uiextras.HBoxFlex('Parent',parent_handle,'Padding', 3, 'Spacing', 3);
-            this.Figure = uiextras.VBoxFlex('Parent',parent_handle,'Padding', 0, 'Spacing', 0);
-            
-            % Add box for the four model settings sub-boxes
-            this.modelComponants = uiextras.GridFlex('Parent', this.Figure,'Padding', 3, 'Spacing', 3);                                    
-            
-            % Build the forcing transformation settings items
-            this.forcingTranforms.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
-            this.forcingTranforms.lbl = uicontrol( 'Parent', this.forcingTranforms.vbox,'Style','text','String','1. Forcing Transform Function (optional)','Visible','on');
-            this.forcingTranforms.tbl = uitable(this.forcingTranforms.vbox,'ColumnName',cnames_forcing,...
-                'ColumnEditable',cedit_forcing,'ColumnFormat',cformats_forcing,'RowName',...
-                rnames_forcing ,'Data',cdata_forcing, 'Visible','on', 'Units','normalized', ...
-                'CellSelectionCallback', @this.tableSelection, ...
-                'CellEditCallback', @this.tableEdit, ...
-                'Tag','Forcing Transform', ...
-                'TooltipString',toolTip_forcing);
-            
-            set( this.forcingTranforms.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-
-%             % Find java sorting object in table
-%             jscrollpane = findjobj(this.forcingTranforms.tbl);
-%             jtable = jscrollpane.getViewport.getView;
-% 
-%             % Turn the JIDE sorting on
-%             jtable.setSortable(true);
-%             jtable.setAutoResort(true);
-%             jtable.setMultiColumnSortable(true);
-%             jtable.setPreserveSelectionsAfterSorting(true);            
-                                    
-            % Build the derived  forcing transformation settings items
-            this.derivedForcingTranforms.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
-            this.derivedForcingTranforms.lbl = uicontrol( 'Parent', this.derivedForcingTranforms.vbox,'Style','text','String','3. Derived Forcing Transform Function (optional)','Visible','on');
-            this.derivedForcingTranforms.tbl = uitable(this.derivedForcingTranforms.vbox,'ColumnName',cnames_forcingDerived, ...
-                'ColumnEditable',cedit_forcingDerived,'ColumnFormat',cformats_forcingDerived,'RowName',...
-                rnames_forcingDerived ,'Data',cdata_forcingDerived, 'Visible','on', 'Units','normalized', ...
-                'CellSelectionCallback', @this.tableSelection, ...
-                'CellEditCallback', @this.tableEdit, ...
-                'Tag','Derived Forcing Transform', ...
-                'TooltipString', toolTip_forcingDerived);
-            
-            set( this.derivedForcingTranforms.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
-
-%             % Find java sorting object in table
-%             jscrollpane = findjobj(this.derivedForcingTranforms.tbl);
-%             jtable = jscrollpane.getViewport.getView;
-% 
-%             % Turn the JIDE sorting on
-%             jtable.setSortable(true);
-%             jtable.setAutoResort(true);
-%             jtable.setMultiColumnSortable(true);
-%             jtable.setPreserveSelectionsAfterSorting(true);            
-                        
-            % Build the weighting function settings items
-            this.weightingFunctions.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
-            this.weightingFunctions.lbl = uicontrol( 'Parent', this.weightingFunctions.vbox,'Style','text','String','2. Weighting Functions (required)','Visible','on');
-            this.weightingFunctions.tbl = uitable(this.weightingFunctions.vbox,'ColumnName',cnames_weighting,...
-                'ColumnEditable',cedit_weighting,'ColumnFormat',cformats_weighting,'RowName',...
-                rnames_weighting ,'Data',cdata_weighting, 'Visible','on', 'Units','normalized', ...
-                'CellSelectionCallback', @this.tableSelection, ...
-                'CellEditCallback', @this.tableEdit, ...
-                'Tag','Weighting Functions', ...
-                'TooltipString', toolTip_weighting);
-            
-            set( this.weightingFunctions.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );    
-
-%             % Find java sorting object in table
-%             drawnow();
-%             jscrollpane = findjobj(this.weightingFunctions.tbl);
-%             jtable = jscrollpane.getViewport.getView;
-% 
-%             % Turn the JIDE sorting on
-%             jtable.setSortable(true);
-%             jtable.setAutoResort(true);
-%             jtable.setMultiColumnSortable(true);
-%             jtable.setPreserveSelectionsAfterSorting(true);            
-                        
-            % Build the derived weighting functions
-            this.derivedWeightingFunctions.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
-            this.derivedWeightingFunctions.lbl = uicontrol( 'Parent', this.derivedWeightingFunctions.vbox,'Style','text','String','4. Derived Weighting Functions (optional)','Visible','on');
-            this.derivedWeightingFunctions.tbl = uitable(this.derivedWeightingFunctions.vbox,'ColumnName',cnames_weightingDerived,...
-                'ColumnEditable',cedit_weightingDerived,'ColumnFormat',cformats_weightingDerived,'RowName',...
-                rnames_weightingDerived ,'Data',cdata_weightingDerived, 'Visible','on', 'Units','normalized', ...
-                'CellSelectionCallback', @this.tableSelection, ...
-                'CellEditCallback', @this.tableEdit, ...
-                'Tag','Derived Weighting Functions', ...
-                'TooltipString', toolTip_weightingDerived);
-            
-            set( this.derivedWeightingFunctions.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-            
-%             % Find java sorting object in table
-%             jscrollpane = findjobj(this.derivedWeightingFunctions.tbl);
-%             jtable = jscrollpane.getViewport.getView;
-% 
-%             % Turn the JIDE sorting on
-%             jtable.setSortable(true);
-%             jtable.setAutoResort(true);
-%             jtable.setMultiColumnSortable(true);
-%             jtable.setPreserveSelectionsAfterSorting(true);            
-
-            % Build the forcing transformation and weighting function options
-            %----------------------------------------
-            % Create box for the sub-boxes
-            this.modelOptions.grid = uiextras.Grid('Parent',this.Figure,'Padding', 3, 'Spacing', 3);
-            
-            % Add list box for selecting the input forcing data
-            %-------------------
-            cnames = {'Required Model Data', 'Input Forcing Data'};
-            cedit = logical([0 1]);
-            rnames = {'1'};
-            cdata = cell(1,2);
-            cformats = {'char', 'char'};
-                      
-            this.modelOptions.options{1,1}.ParentName = 'forcingTranforms';
-            this.modelOptions.options{1,1}.ParentSettingName = 'inputForcing';
-            this.modelOptions.options{1,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{1,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{1,1}.box,'Style','text','String','1. Forcing Transform - Input Data','Visible','on');     
-            this.modelOptions.options{1,1}.tbl =  uitable(this.modelOptions.options{1,1}.box,'ColumnName',cnames,...
-                                                'ColumnEditable',cedit,'ColumnFormat',cformats,'RowName',...
-                                                rnames,'Data',cdata, 'Visible','on', 'Units','normalized', ...
-                                                'CellEditCallback', @this.optionsSelection, ...                                                
-                                                'Tag','Forcing Transform - Input Data');
-            set(this.modelOptions.options{1,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-            
-            % Add context menu for Wizard.
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy all rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste all rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Wizard ...','Callback',@this.wizard,'Separator','on'); 
-            uimenu(contextMenu,'Label','Push "Required Data" to component','Callback',@this.push2component);
-                           
-            set(this.modelOptions.options{1,1}.tbl,'UIContextMenu',contextMenu);
-            set(this.modelOptions.options{1,1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{1,1}.tbl');
-            
-            % Add table for defining the transformation options eg soil
-            % moisture model parameters for calibration.
-            %-------------------
-            data = [];
-            this.modelOptions.options{2,1}.ParentName = 'forcingTranforms';
-            this.modelOptions.options{2,1}.ParentSettingName = 'options';            
-            this.modelOptions.options{2,1}.box = uiextras.Grid('Parent',  this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{2,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{2,1}.box,'Style','text','String','1. Forcing Transform - Model Settings','Visible','on');     
-            this.modelOptions.options{2,1}.tbl = uitable(this.modelOptions.options{2,1}.box,'ColumnName',{'Parameter','(none set)'}, ...
-                'ColumnEditable',true,'Data',[], ...
-                'CellEditCallback', @this.optionsSelection, ...    
-                'Tag','Forcing Transform - Model Settings', 'Visible','on');
-            set(this.modelOptions.options{2,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-               
-            % Add context menu for Wizard.
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy all rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste all rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(contextMenu,'Label','Wizard ...','Callback',@this.wizard);                
-            set(this.modelOptions.options{2,1}.tbl,'UIContextMenu',contextMenu);
-            set(this.modelOptions.options{2,1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2,1}.tbl');            
-            
-            % Add list box for selecting the weighting functions input
-            % data.
-            % NOTE: Multiple selection of input forcing data is allowed.
-            % This is defined in tableSelection().
-            %-------------------
-            this.modelOptions.options{3,1}.ParentName = 'weightingFunctions';
-            this.modelOptions.options{3,1}.ParentSettingName = 'inputForcing';                     
-            this.modelOptions.options{3,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{3,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{3,1}.box,'Style','text','String','2. Weighting Functions - Input Data','Visible','on');     
-            this.modelOptions.options{3,1}.lst = uicontrol('Parent',this.modelOptions.options{3,1}.box,'Style','list', 'BackgroundColor','w', ...
-                'String',{},'Value',1,'Tag','Weighting Functions - Input Data','Callback', @this.optionsSelection, 'Visible','on');
-            set(this.modelOptions.options{3,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-
-            % Add table for selecting the weighting functions options
-            %-------------------
-            this.modelOptions.options{4,1}.ParentName = 'weightingFunctions';
-            this.modelOptions.options{4,1}.ParentSettingName = 'options';                     
-            this.modelOptions.options{4,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{4,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{4,1}.box,'Style','text','String','2. Weighting Functions - Model Settings','Visible','on');     
-            this.modelOptions.options{4,1}.tabs = uiextras.TabPanel( 'Parent', this.modelOptions.options{4,1}.box, 'Padding', 5, 'TabSize',127,'FontSize',8, 'Tag','Weighting Functions - Options');
-            
-            tab1 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 1');            
-            tab2 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 2');
-            tab3 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 3');            
-            tab4 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 4');            
-            tab5 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 5');            
-            
-            uitable( tab1,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 1 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            uitable( tab2,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 2 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            uitable( tab3,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 3 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            uitable( tab4,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 4 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            uitable( tab5,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 5 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            set(this.modelOptions.options{4,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
-            this.modelOptions.options{4,1}.tabs.TabNames = {'Option 1', 'Option 2','Option 3', 'Option 4','Option 5'};
-            this.modelOptions.options{4,1}.tabs.SelectedChild = 1;            
-            
-            % Add table for defining the transformation options eg soil
-            % moisture model parameters for calibration.            
-            %-------------------
-            this.modelOptions.options{5,1}.ParentName = 'DerivedForcingTransformation';
-            this.modelOptions.options{5,1}.ParentSettingName = 'inputForcing';                     
-            this.modelOptions.options{5,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{5,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{5,1}.box,'Style','text','String','3. Derived Forcing Transform - Input Data','Visible','on');     
-            this.modelOptions.options{5,1}.lst = uicontrol('Parent',this.modelOptions.options{5,1}.box,'Style','list', 'BackgroundColor','w', ...
-                'String',{},'Value',1, ...
-                'Tag','Derived Forcing Functions - Source Function', ...
-                'Callback', @this.optionsSelection, 'Visible','on');
-            set(this.modelOptions.options{5,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
-
-            % Add table for derived forcing inut data options
-            %-------------------
-            this.modelOptions.options{6,1}.ParentName = 'DerivedForcingTransformation';           
-            this.modelOptions.options{6,1}.ParentSettingName = 'inputForcing';
-            this.modelOptions.options{6,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{6,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{6,1}.box, ...
-                'Style','text', ...
-                'String','3. Derived Forcing Transform - Input Data','Visible','on');     
-            this.modelOptions.options{6,1}.tbl =  uitable(this.modelOptions.options{6,1}.box,'ColumnName',cnames,...
-                'ColumnEditable',cedit,'ColumnFormat',cformats,'RowName',...
-                rnames,'Data',cdata, 'Visible','on', 'Units','normalized', ...
-                'CellEditCallback', @this.optionsSelection, ...                                                
-                'Tag','Derived Forcing Functions - Input Data');
-            set(this.modelOptions.options{6,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-                        
-            % Add table for derived forcing options
-            %-------------------
-            this.modelOptions.options{7,1}.ParentName = 'DerivedForcingTransformation';
-            this.modelOptions.options{7,1}.ParentSettingName = 'options';            
-            this.modelOptions.options{7,1}.box = uiextras.Grid('Parent',  this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{7,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{7,1}.box,'Style','text','String','3. Derived Forcing Transform - Model Settings','Visible','on');     
-            this.modelOptions.options{7,1}.tbl = uitable(this.modelOptions.options{7,1}.box,'ColumnName',{'Parameter','(none set)'}, ...
-                'ColumnEditable',true,'Data',[], ...
-                'CellEditCallback', @this.optionsSelection, ...    
-                'Tag','Derived Forcing Transform - Model Settings', 'Visible','on');
-            set(this.modelOptions.options{7,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-
-            % Add list box for selecting the derived weighting functions input
-            % data.
-            % NOTE: Multiple selection of input forcing data is allowed.
-            % This is defined in tableSelection().
-            %-------------------
-            this.modelOptions.options{8,1}.ParentName = 'derivedWeightingFunctions';
-            this.modelOptions.options{8,1}.ParentSettingName = 'inputForcing';                     
-            this.modelOptions.options{8,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{8,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{8,1}.box,'Style','text','String','4. Derived Weighting Functions - Input Data','Visible','on');     
-            this.modelOptions.options{8,1}.lst = uicontrol('Parent',this.modelOptions.options{8,1}.box,'Style','list', 'BackgroundColor','w', ...
-                'String',{},'Value',1,'Tag','Derived Weighting Functions - Input Data','Callback', @this.optionsSelection, 'Visible','on');
-            set(this.modelOptions.options{8,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
-
-            % Add table for selecting the derived weighting functions options
-            %-------------------
-            this.modelOptions.options{9,1}.ParentName = 'derivedWeightingFunctions';
-            this.modelOptions.options{9,1}.ParentSettingName = 'options';                     
-            this.modelOptions.options{9,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{9,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{9,1}.box,'Style','text','String','4. Derived Weighting Functions - Model Settings','Visible','on');     
-            this.modelOptions.options{9,1}.tbl = uitable(this.modelOptions.options{9,1}.box,'ColumnName',{'(none)'}, ...
-                'ColumnEditable',true,'Data',[], 'Tag','Derived Weighting Functions - Model Settings', ...
-                'CellEditCallback', @this.optionsSelection, 'Visible','on');
-            set(this.modelOptions.options{9,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );                                   
-            
-            % Add label for general communications to user eg to state that
-            % a weighting fnction has no options available.
-            %-------------------
-            this.modelOptions.options{10,1}.ParentName = 'general';
-            this.modelOptions.options{10,1}.ParentSettingName = 'general';                     
-            this.modelOptions.options{10,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
-            this.modelOptions.options{10,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{10,1}.box,'Style','text','String','(empty)','Visible','on');                 
-            %----------------------------------------
-
-            % Add context menu for adding /deleting rows
-            % NOTE: UIContextMenu.UserData is used to store the table name
-            % for the required operation.
-            %----------------------------------------
-            % Create menu for forcing transforms
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);                
-            set(this.forcingTranforms.tbl,'UIContextMenu',contextMenu);
-            set(this.forcingTranforms.tbl.UIContextMenu,'UserData', 'this.forcingTranforms.tbl');
-            
-            % Create menu for weighting functions
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-            set(this.weightingFunctions.tbl,'UIContextMenu',contextMenu);
-            set(this.weightingFunctions.tbl.UIContextMenu,'UserData', 'this.weightingFunctions.tbl');
-            
-            % Create menu for derived forcing transforms
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-            set(this.derivedForcingTranforms.tbl,'UIContextMenu',contextMenu);
-            set(this.derivedForcingTranforms.tbl.UIContextMenu,'UserData', 'this.derivedForcingTranforms.tbl');
-
-            % Create menu for derived weighting functions
-            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-            set(this.derivedWeightingFunctions.tbl,'UIContextMenu',contextMenu);
-            set(this.derivedWeightingFunctions.tbl.UIContextMenu,'UserData', 'this.derivedWeightingFunctions.tbl');
-            
-            %----------------------------------------            
-            % Set dimensions for the grid     
-            set( this.modelComponants, 'ColumnSizes', [-1 -1], 'RowSizes', [-1 -1] );      
-            this.Figure.Heights = [-1 -1];
-            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Heights));
-        end
-        
-        function initialise(this)
-            
-        end
-        
-        function setForcingData(this, fname)
-
-             % Check fname file exists.
-             if exist(fname,'file') ~= 2;
-                warndlg(['The following forcing data file does not exist:', fname]);
-                return;
-             end
-
-             % Read in the file.
-             try
-                tbl = readtable(fname);
-             catch
-                warndlg(['The following forcing data file could not be read in. It must a .csv file of at least 4 columns (year, month, day, value):',fname]);
-                clear tbl;
-                return;
-             end
-
-             % Check there are sufficient number of columns
-             if length(tbl.Properties.VariableNames) <4
-                warndlg(['The following forcing data file must contain at least 4 columns (year, month, day, value):',fname]);
-                clear tbl;
-                return;
-             end
-
-             % Check all columns are numeric.
-             for i=1:size(tbl,2)
-                if any(~isnumeric(tbl{:,i}))
-                    warndlg(['Column ',num2str(i), ' within the following forcing data file contains non-numeric data:',fname]);
-                    clear tbl;
-                    return;
-                end                    
-             end
-
-             % Set the column names.
-             this.forcingData.colnames = tbl.Properties.VariableNames;
-             this.forcingData.data = tbl;
-             
-             % Clear tbl
-             clear tbl;
-            
-        end
-        
-        function setCoordinatesData(this, fname)
-             % Check fname file exists.
-             if exist(fname,'file') ~= 2;
-                warndlg(['The following site coordinates file does not exist:', fname]);
-                return;
-             end
-
-             % Read in the file.
-             try
-                tbl = readtable(fname);
-             catch
-                warndlg(['The following site coordinates file could not be read in. It must a .csv file of 3 columns (site ID, easting, northing):',fname]);
-                return;
-             end
-
-             % Check there are sufficient number of columns
-             if length(tbl.Properties.VariableNames) ~=3
-                warndlg(['The following site coordinates file must contain 3 columns  (site ID, easting, northing):',fname]);
-                return;
-             end
-
-             % Check all columns are numeric.
-             if any(any(~isnumeric(tbl{:,2:3})))
-                warndlg(['Columns 2 and 3 within the following site coordinates file must contain only numeric data:',fname]);
-                return;
-             end
-
-             % Check if the Bore ID is listed in the coordinates file
-             if ~isempty(this.boreID) && ~any(strcmp(tbl{:,1},this.boreID))
-                 warndlg(['The following bore is nost listed in the coordinates files: ',this.boreID]);
-             end
-             
-             % Set the site data.
-             this.siteData = tbl;
-             
-             % Clear tbl
-             clear tbl;
-            
-        end
-        
-        function setBoreID(this, boreID)
-            this.boreID = boreID;
-        end
-        
-        function setModelOptions(this, modelOptionsStr)
-            
-            % Convert model options string to a cell array
-            if isempty(modelOptionsStr)
-                modelOptions = cell(0,3);
-            else
-                modelOptions = eval(modelOptionsStr);                  
-            end
-            
-            % Get the name of all model components
-            componentNames = unique( modelOptions(:,1));
-            
-            % Initialise some variables            
-            transformFunctionName = [];
-            transformForcingdata = [];
-            forcingDataforWeighting = [];
-            transformOptions = [];
-            transformInputcomponent = [];
-            
-            % Clear GUI tables
-            this.weightingFunctions.tbl.Data = cell(0, 5);
-            this.derivedWeightingFunctions.tbl.Data = cell(0, 6);
-            this.forcingTranforms.tbl.Data = cell(0, 4);
-            this.derivedForcingTranforms.tbl.Data = cell(0, 5);
-            
-            % Loop through each weighting function and add the data to the
-            % GUI table. Also, get the cell arrays for the transformation
-            % functions.
-            for i=1:size(componentNames,1)
-                
-                % Find the cell options for the current weighting function
-                compentFilter = cellfun( @(x) strcmp(x, componentNames{i}), modelOptions(:,1));
-                
-                % Get model options relating to the current component.
-                modelOption_tmp = modelOptions(compentFilter,:);
-                
-                % initilise variables
-                weightingFunctionName = '';
-                inputcomponentName = '';
-                forcingdataName = '';
-                optionsName = '';
-                transformFunctionName = '';
-                transformForcingdata = '';
-                forcingDataforWeighting = '';
-                transformInputcomponent = '';
-                transformOptions = '';
-                
-                % Get each term for the component
-                weightingFunctionName_filt = cellfun( @(x) strcmp(x, 'weightingfunction'), modelOption_tmp(:,2));
-                if any(weightingFunctionName_filt)
-                    weightingFunctionName = modelOption_tmp{weightingFunctionName_filt,3};
-                end
-                
-                inputcomponentName_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), modelOption_tmp(:,2));
-                if any(inputcomponentName_filt)
-                    inputcomponentName = modelOption_tmp{inputcomponentName_filt,3};
-                end
-                
-                forcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), modelOption_tmp(:,2));
-                if any(forcingdata_filt)
-                    forcingdataName = modelOption_tmp{forcingdata_filt,3};
-                end
-                
-                options_filt = cellfun( @(x) strcmp(x, 'options'), modelOption_tmp(:,2));
-                if any(options_filt)
-                    optionsName = modelOption_tmp{options_filt,3}; 
-                    for k=1:length(optionsName)
-                        if ~isempty(optionsName{k}) && iscell(optionsName{k})
-                            optionsName{k} = model_TFN_gui.cell2string(optionsName{k}, []);
-                        else
-                            optionsName{k}='{}';
-                        end                        
-                    end
-                    optionsName = ['{',strjoin(optionsName),'}'];
-                end
-                
-                % Check if the forcing data uses a transformation function.
-                % If so, extract the data and get the output required for
-                % the weighting function. Else if the forcing data is not
-                % empty, then assign the required input data.
-                hasTransformFunction = false;
-                isFullTransformationFunction = false;
-                if ~isempty(forcingdataName) && iscell(forcingdataName) && ...
-                (size(forcingdataName,2)==2 || (size(forcingdataName,2)==1 && size(forcingdataName{1},2)==2))
-                    hasTransformFunction = true;
-                    
-                    % If there are multiple input forcing data (eg
-                    % multiple groundwater pumps) then find the one that
-                    % lists all of the required inputs.
-                    forcingDataforWeighting={};
-                    if (size(forcingdataName,2)==1 && size(forcingdataName{1},2)==2)                    
-                        for k=1:size(forcingdataName,1)
-                            forcingdataNameTmp = forcingdataName{k};
-                            transformFunctionName_filt = cellfun( @(x) strcmp(x, 'transformfunction'), forcingdataNameTmp(:,1));
-                            if any(transformFunctionName_filt)
-                                transformFunctionName = forcingdataNameTmp{transformFunctionName_filt,2};
-                            end
-
-                            transformForcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), forcingdataNameTmp(:,1));
-                            if any(transformForcingdata_filt)
-                                transformForcingdata = forcingdataNameTmp{transformForcingdata_filt,2};
-                            end
-
-                            forcingDataforWeighting_filt = cellfun( @(x) strcmp(x, 'outputdata'), forcingdataNameTmp(:,1));
-                            if any(forcingDataforWeighting_filt )
-                                if isempty(forcingDataforWeighting)
-                                    forcingDataforWeighting{1,1} = forcingdataNameTmp{forcingDataforWeighting_filt,2};
-                                else
-                                    forcingDataforWeighting{k,1} = forcingdataNameTmp{forcingDataforWeighting_filt,2};
-                                end
-                            end
-
-                            transformOptions_filt = cellfun( @(x) strcmp(x, 'options'), forcingdataNameTmp(:,1));
-                            if any(transformOptions_filt)
-                                transformOptions = forcingdataNameTmp{transformOptions_filt,2};
-                            end
-
-                            transformInputcomponent_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), forcingdataNameTmp(:,1));
-                            if any(transformInputcomponent_filt)
-                                transformInputcomponent = forcingdataNameTmp{transformInputcomponent_filt,2};                    
-                            end                           
-  
-                        end
-                        
-                    else
-                        transformFunctionName_filt = cellfun( @(x) strcmp(x, 'transformfunction'), forcingdataName(:,1));
-                        if any(transformFunctionName_filt)
-                            transformFunctionName = forcingdataName{transformFunctionName_filt,2};
-                        end
-
-                        transformForcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), forcingdataName(:,1));
-                        if any(transformForcingdata_filt)
-                            transformForcingdata = forcingdataName{transformForcingdata_filt,2};
-                        end
-
-                        forcingDataforWeighting_filt = cellfun( @(x) strcmp(x, 'outputdata'), forcingdataName(:,1));
-                        if any(forcingDataforWeighting_filt )
-                            forcingDataforWeighting = forcingdataName{forcingDataforWeighting_filt,2};
-                        end
-
-                        transformOptions_filt = cellfun( @(x) strcmp(x, 'options'), forcingdataName(:,1));
-                        if any(transformOptions_filt)
-                            transformOptions = forcingdataName{transformOptions_filt,2};
-                        end
-
-                        transformInputcomponent_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), forcingdataName(:,1));
-                        if any(transformInputcomponent_filt)
-                            transformInputcomponent = forcingdataName{transformInputcomponent_filt,2};                    
-                        end                        
-                    end
-                    
-                    % Check if the full transformation model is defined or
-                    % is it just the output data for a tranformation model
-                    % modle defined for another weighting function.
-                    isFullTransformationFunction = true;
-                    if ~isempty(transformFunctionName) && ~isempty(forcingDataforWeighting) && ...
-                       isempty(transformForcingdata) && isempty(transformInputcomponent) && isempty(transformOptions)
-                        isFullTransformationFunction = false;
-                    end
-                elseif ~isempty(forcingdataName)
-                    % Data is assumed to be name of an input data column.
-                    forcingDataforWeighting = forcingdataName;                    
-                end
-                
-                % Add the weight function data to the GUI table. In doing
-                % so, check if it is to be input to the weighting function
-                % or derived weighting function.
-                if isempty(inputcomponentName)
-                   ind = size(this.weightingFunctions.tbl.Data,1)+1;
-                   if ind==1
-                        this.weightingFunctions.tbl.Data = cell(1,size(this.weightingFunctions.tbl.Data,2)); 
-                   else
-                       this.weightingFunctions.tbl.Data = [this.weightingFunctions.tbl.Data; cell(1,size(this.weightingFunctions.tbl.Data,2))]; 
-                   end
-                   this.weightingFunctions.tbl.Data{ind,2} = componentNames{i};
-                   this.weightingFunctions.tbl.Data{ind,3} = weightingFunctionName;
-                   this.weightingFunctions.tbl.Data{ind,5} = optionsName;
-                   this.weightingFunctions.tbl.RowName = [1:size(this.weightingFunctions.tbl.Data,1)];
-
-                   % Check if the input comes from the output of a
-                   % transformation function.                   
-                   if hasTransformFunction
-                       if iscell(forcingDataforWeighting)
-                           
-                            nForcingInputs = size(forcingDataforWeighting,1);
-                            LHS = cellstr(repmat([transformFunctionName, ' :'],nForcingInputs,1));
-                            RHS = cell(nForcingInputs,1);
-                            RHS(:) = {' '};
-                            LHS = strcat(LHS,RHS);
-                            
-%                             switch nForcingInputs
-%                                 case 1;
-%                                     LHS  = strcat(LHS, {' '});
-%                                 case 2;
-%                                     LHS  = strcat(LHS, {' ';' '});
-%                                 case 3;
-%                                     LHS  = strcat(LHS, {' ';' ';' '});
-%                                 case 4;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' '});
-%                                 case 5;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' '});
-%                                 case 6;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' '});
-%                                 case 7;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' '});
-%                                 case 8;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 case 9;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 case 10;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 otherwise
-%                                     warndlg('A maximum of 10 forcing data inputs can be assigned to a weighting function')
-%                                     nForcingInputs = 10;
-%                             end
-
-                            forcingDataforWeighting = strcat( LHS, forcingDataforWeighting(1:nForcingInputs));
-                            forcingDataforWeighting = model_TFN_gui.cell2string(forcingDataforWeighting,'');
-                            this.weightingFunctions.tbl.Data{ind,4} = forcingDataforWeighting;
-                            
-                                                                                    
-                       else
-                            this.weightingFunctions.tbl.Data{ind,4} = [transformFunctionName, ' : ', forcingDataforWeighting];
-                       end
-                   else
-                       if iscell(forcingDataforWeighting)
-                           
-                            nForcingInputs = size(forcingDataforWeighting,1);
-                            LHS = cellstr(repmat('Input Data : ',nForcingInputs,1));
-                            RHS = cell(nForcingInputs,1);
-                            RHS(:) = {' '};
-                            LHS = strcat(LHS,RHS);
-%                             switch nForcingInputs
-%                                 case 1;
-%                                     LHS  = strcat(LHS, {' '});
-%                                 case 2;
-%                                     LHS  = strcat(LHS, {' ';' '});
-%                                 case 3;
-%                                     LHS  = strcat(LHS, {' ';' ';' '});
-%                                 case 4;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' '});
-%                                 case 5;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' '});
-%                                 case 6;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' '});
-%                                 case 7;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' '});
-%                                 case 8;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 case 9;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 case 10;
-%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' ';' '});
-%                                 otherwise
-%                                     warndlg('A maximum of 10 forcing data inputs can be assigned to a weighting function')
-%                                     nForcingInputs = 10;
-%                             end
-                            forcingDataforWeighting = strcat( LHS, forcingDataforWeighting(1:nForcingInputs));
-                            forcingDataforWeighting = model_TFN_gui.cell2string(forcingDataforWeighting,'');
-                            this.weightingFunctions.tbl.Data{ind,4} = forcingDataforWeighting;
-                            
-                       else
-                           this.weightingFunctions.tbl.Data{ind,4} = ['Input Data : ', forcingDataforWeighting];
-                       end
-                   end
-                    
-                   
-                    
-                else
-                   ind = size(this.derivedWeightingFunctions.tbl.Data,1)+1;
-                   if ind==1
-                        this.derivedWeightingFunctions.tbl.Data = cell(1,size(this.derivedWeightingFunctions.tbl.Data,2)); 
-                   else
-                       this.derivedWeightingFunctions.tbl.Data = [this.derivedWeightingFunctions.tbl.Data; cell(1,size(this.derivedWeightingFunctions.tbl.Data,2))]; 
-                   end                   
-                   this.derivedWeightingFunctions.tbl.Data{ind,2} = componentNames{i};
-                   this.derivedWeightingFunctions.tbl.Data{ind,3} = weightingFunctionName;
-                   this.derivedWeightingFunctions.tbl.Data{ind,4} = inputcomponentName; 
-                   this.derivedWeightingFunctions.tbl.Data{ind,6} = optionsName; 
-                   this.derivedWeightingFunctions.tbl.RowName = [1:size(this.derivedWeightingFunctions.tbl.Data,1)];
-                   
-                   % Check if the input comes from the output of a
-                   % transformation function.
-                   if hasTransformFunction
-                       if iscell(forcingDataforWeighting)
-                            this.derivedWeightingFunctions.tbl.Data{ind,5} = strcat( [transformFunctionName, ' : '], forcingDataforWeighting);
-                       else
-                            this.derivedWeightingFunctions.tbl.Data{ind,5} = [transformFunctionName, ' : ', forcingDataforWeighting];
-                       end
-                   else
-                       if iscell(forcingDataforWeighting)
-                            this.derivedWeightingFunctions.tbl.Data{ind,5} = strcat( ['Input Data : ', forcingDataforWeighting]);
-                       else
-                           this.derivedWeightingFunctions.tbl.Data{ind,5} = ['Input Data : ', forcingDataforWeighting];
-                       end
-                   end
-                    
-                                      
-                end
-                
-                % Add the forcing transforation data to the GUI tables
-                if hasTransformFunction && isFullTransformationFunction
-                   
-                    if isempty(transformInputcomponent)
-                        ind = size(this.forcingTranforms.tbl.Data,1)+1;
-                        if ind==1
-                            this.forcingTranforms.tbl.Data = cell(1,size(this.forcingTranforms.tbl.Data,2)); 
-                        else
-                            this.forcingTranforms.tbl.Data = [this.forcingTranforms.tbl.Data; cell(1,size(this.forcingTranforms.tbl.Data,2))]; 
-                        end                             
-                        this.forcingTranforms.tbl.Data{ind,2} = transformFunctionName;
-                        
-                        if ~isempty(transformForcingdata) && iscell(transformForcingdata)
-                            transformForcingdata = model_TFN_gui.cell2string(transformForcingdata, []);
-                        end
-                        this.forcingTranforms.tbl.Data{ind,3} = transformForcingdata;
-
-                        if ~isempty(transformOptions) && iscell(transformOptions)
-                            transformOptions = model_TFN_gui.cell2string(transformOptions, []);
-                        end 
-                        this.forcingTranforms.tbl.Data{ind,4} = transformOptions;
-                        this.forcingTranforms.tbl.RowName = [1:size(this.forcingTranforms.tbl.Data,1)];
-                    else
-                        ind = size(this.derivedForcingTranforms.tbl.Data,1)+1;
-                        if ind==1
-                            this.derivedForcingTranforms.tbl.Data = cell(1,size(this.derivedForcingTranforms.tbl.Data,2)); 
-                        else
-                            this.derivedForcingTranforms.tbl.Data = [this.derivedForcingTranforms.tbl.Data; cell(1,size(this.derivedForcingTranforms.tbl.Data,2))]; 
-                        end                            
-                        this.derivedForcingTranforms.tbl.Data{ind,2} = transformFunctionName;
-                        this.derivedForcingTranforms.tbl.Data{ind,3} = transformInputcomponent;                        
-                        
-                        if ~isempty(transformForcingdata) && iscell(transformForcingdata)
-                            transformForcingdata = model_TFN_gui.cell2string(transformForcingdata, []);
-                        end
-                        this.derivedForcingTranforms.tbl.Data{ind,4} = transformForcingdata;
-
-                        if ~isempty(transformOptions) && iscell(transformOptions)
-                            transformOptions = model_TFN_gui.cell2string(transformOptions, []);
-                        end 
-                        this.derivedForcingTranforms.tbl.Data{ind,5} = transformOptions;                        
-                        this.derivedForcingTranforms.tbl.RowName = [1:size(this.derivedForcingTranforms.tbl.Data,1)];
-                    end
-                    
-                    
-                end     
-            end            
-            
-            
-        end
-        
-        function modelOptionsArray = getModelOptions(this)
-            % Convert forcing tranformation and derived forcing tranformation functions to strings.
-            for k=1:2
-                if k==1
-                    cellData  = this.forcingTranforms.tbl.Data;                    
-                else
-                    cellData  = this.derivedForcingTranforms.tbl.Data;
-                end
-                
-                % Goto next loop if all of cell data is empty
-                if size(cellData ,1)==1 && all(cellfun(@(x) isempty(x),cellData))
-                    continue;
-                end
-                
-                % Loop though each row of cell data
-                for j=1:size(cellData ,1);
-                   stringCell = '{';           
-                   stringCell = strcat(stringCell, sprintf(' ''transformfunction'',  ''%s'';',cellData{j,2} ));
-                   if k==2
-                       stringCell = strcat(stringCell, sprintf(' ''inputcomponent'',  ''%s'';',cellData{j,3} ));               
-                   end
-                   stringCell = strcat(stringCell, sprintf(' ''forcingdata'', ''%s'';',cellData{j,2+k} )); 
-                   if ~isempty(cellData{j,3+k})
-                        stringCell = strcat(stringCell, sprintf(' ''options'', ''%s'';',cellData{j,3+k} )); 
-                   end
-                   stringCell = strcat(stringCell, '}'); 
-                   forcingString.(cellData{j,2}) = stringCell;
-
-                   % Initialise logical variable denoting if its already been
-                   % inserted.
-                   forcingStringInserted.(cellData{j,2}) = false;
-                end
-            end
-                       
-            % Convert weighting functions to string.
-            modelOptionsArray =  '{';
-            
-            for k=1:2
-                if k==1
-                    cellData  = this.weightingFunctions.tbl.Data;
-                else
-                    cellData  = this.derivedWeightingFunctions.tbl.Data;
-                end
-                    
-                % Goto next loop if all of cell data is empty
-                if size(cellData ,1)==1 && all(cellfun(@(x) isempty(x),cellData))
-                    continue;
-                end
-                
-                % Loop through each row of weighting table data
-                for i=1:size(cellData ,1);        
-                    
-                   % Add weighting function name. 
-                   modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''weightingfunction'',  ''%s'';',cellData{i,2},cellData{i,3} ));
-
-                   % Add source componant name. 
-                   if k==2
-                        modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''inputcomponent'',  ''%s'';',cellData{i,2},cellData{i,4} ));
-                   end
-
-                   % Add model options 
-                   if ~isempty(cellData{i,4+k})
-                        modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''options'', %s;',cellData{i,2},cellData{i,4+k} )); 
-                   end                   
-                                      
-                   % Convert forcing data to a cell array
-                   try
-                       forcingColNames =  eval(cellData{i,3+k});
-                   catch
-                       forcingColNames =  cellData(i,3+k);
-                   end
-
-                   %Check if there is any focring data.
-                   if isempty(forcingColNames{1})
-                       continue;
-                   end
-                   
-                   % Loop through each forcing data input.
-                   for j=1:length(forcingColNames)
-
-                       % Insert input data.                     
-                       if ~isempty(strfind(forcingColNames{j}, 'Input Data : '))
-                           [ind_start, ind_end] = regexp(forcingColNames{j}, 'Input Data : ');
-                           forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);
-                           ind_start = regexp(forcingColNames_tmp, '''');
-                           if ~isempty(ind_start)
-                                forcingColNames_tmp =  forcingColNames_tmp(1:ind_start-1);
-                           end
-                       else
-                           % Find function name
-                           forcingFunc_Ind = regexp(forcingColNames{j}, ' : ');  
-                           forcingFuncName = forcingColNames{j}(1:forcingFunc_Ind-1);
-
-                           % Insert cell array string for required function name.
-                           % If the forcing function has already been inserted then
-                           % just build a string for the forcing to be extracted
-                           % from it.
-                           if ~forcingStringInserted.(forcingFuncName) 
-                                % Get just output name from string
-                               [ind_start, ind_end] = regexp(forcingColNames{j}, [forcingFuncName,' : ']);
-                               forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);
-
-                                % Add required output from weighting function to forcing function cell array
-                                forcingString.(forcingFuncName) = forcingString.(forcingFuncName)(1:end-1);
-                                forcingString.(forcingFuncName) = strcat(forcingString.(forcingFuncName), ...
-                                     sprintf(' ''outputdata'' , ''%s'' };', forcingColNames_tmp ) );
-                                forcingColNames_tmp = forcingString.(forcingFuncName);
-                                forcingStringInserted.(forcingFuncName) = true;
-                           else
-
-                                % Get just output name from string
-                               [ind_start, ind_end] = regexp(forcingColNames{j}, [forcingFuncName,' : ']);
-                               forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);                           
-
-                               % Create cell array for an already created
-                               % forcing transform function but only declare
-                               % the forcing to be taken from the function.
-                               forcingColNames_tmp = { 'transformfunction', forcingFuncName; 'outputdata', forcingColNames_tmp};
-
-                               % Convert to a string
-                               className = metaclass(this);
-                               className = className.Name;
-                               colnames = {'component','property','value'};
-                               forcingColNames_tmp =  eval([className,'.cell2string(forcingColNames_tmp, colnames)']);                           
-                           end
-
-                       end
-
-                       % Add forcing string to the model options string
-                       if length(forcingColNames)==1
-                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''forcingdata'', ''%s'';',cellData{i,2}, forcingColNames_tmp ));                            
-                       elseif length(forcingColNames)>1 && j==1
-                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''forcingdata'', { ''%s'';',cellData{i,2}, forcingColNames_tmp ));                            
-                       elseif length(forcingColNames)>1 && j==length(forcingColNames)
-                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'' };',forcingColNames_tmp ));                                                    
-                       else                           
-                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'';',forcingColNames_tmp ));
-                       end                       
-                   end
-
-                end
-            end
-
-            modelOptionsArray= strcat(modelOptionsArray, '}'); 
-
-            % Remove any ' symbols for cell arrays internal to the larger
-            % string.
-            while ~isempty(strfind(modelOptionsArray, '}''' ))
-                ind = strfind(modelOptionsArray, '}''' );
-                ind = ind(1);
-                modelOptionsArray = [modelOptionsArray(1:ind),  modelOptionsArray(ind+2:end)];
-            end
-            while ~isempty(strfind(modelOptionsArray, '''{' ))
-                ind = strfind(modelOptionsArray, '''{' );
-                ind = ind(1);
-                modelOptionsArray = [modelOptionsArray(1:ind-1),  modelOptionsArray(ind+1:end)];
-            end            
-            while ~isempty(strfind(modelOptionsArray, ';'';' ))
-                ind = strfind(modelOptionsArray, ';'';' );
-                ind = ind(1);
-                modelOptionsArray = [modelOptionsArray(1:ind),  modelOptionsArray(ind+3:end)];
-            end             
-        end
-        
-        
-        function tableSelection(this, hObject, eventdata)
-            icol=[];
-            irow=[];
-            if isprop(eventdata, 'Indices')
-                if ~isempty(eventdata.Indices)
-                    icol=eventdata.Indices(:,2);
-                    irow=eventdata.Indices(:,1);  
-                end
-            end
-                            
-            % Record the current table, row, col if the inputs are
-            % not empty. Else, extract the exiting values from
-            % this.currentSelection                    
-            if ~isempty(irow) && ~isempty(icol)
-                this.currentSelection.row = irow;
-                this.currentSelection.col= icol;
-                this.currentSelection.table = eventdata.Source.Tag;
-            else
-                irow = this.currentSelection.row;
-                icol = this.currentSelection.col;                                                
-            end
-
-            % Undertake table/list specific operations.
-            switch eventdata.Source.Tag;
-                case 'Forcing Transform'
-                    
-                    % Add a row if the table is empty
-                    if size(get(hObject,'Data'),1)==0
-                        this.forcingTranforms.tbl.Data = cell(1,size(this.forcingTranforms.tbl.Data,2));
-                    end
-
-                    % Get the forcing function name.
-                    if irow > size(this.forcingTranforms.tbl.Data,1)
-                        return;
-                    end
-                    funName = this.forcingTranforms.tbl.Data{irow, 2};
-
-                    switch eventdata.Source.ColumnName{icol};
-                        case 'Forcing Transform Function'
-                        
-                            % Get description of the function.
-                            functionName = this.forcingTranforms.tbl.Data{this.currentSelection.row, 2};
-                            if isempty(functionName)
-                                return;
-                            end
-                            modelDescription = eval([functionName,'.modelDescription']);
-                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
-                            this.modelOptions.options{10,1}.lbl.String = {'1. Forcing Transform - Function description','',modelDescription{:}};
-                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];   
-
-                        case 'Input Data'
-                           % Define the drop down options for the input
-                           % data
-                           dropdownOptions = sort(this.forcingData.colnames(4:end));
-                           this.modelOptions.options{1, 1}.tbl.ColumnFormat = {'char',{'(none)' dropdownOptions{:}} }; 
-                            
-                           % Call function method giving required
-                           % variable name.
-                           requiredVariables = feval(strcat(funName,'.inputForcingData_required'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-
-                           % Add row for each required variable
-                           if isempty(this.forcingTranforms.tbl.Data{this.currentSelection.row ,3})
-                               this.modelOptions.options{1, 1}.tbl.Data = cell(size(requiredVariables,1),2);
-                               this.modelOptions.options{1, 1}.tbl.RowName=cell(1,length(requiredVariables)); 
-                               for i=1:length(requiredVariables)                                   
-                                    this.modelOptions.options{1, 1}.tbl.Data{i,1}= requiredVariables{i};
-                                    this.modelOptions.options{1, 1}.tbl.RowName{i}= num2str(i);
-                               end
-                           else
-                               try
-                                   this.modelOptions.options{1, 1}.tbl.Data = cell(0,2);
-                                   this.modelOptions.options{1, 1}.tbl.RowName = cell(size(this.modelOptions.options{1, 1}.tbl.Data,1),1);
-                                   this.modelOptions.options{1, 1}.tbl.Data = eval(this.forcingTranforms.tbl.Data{irow,3});
-                                   for i=1:size(this.modelOptions.options{1, 1}.tbl.Data,1)                                                                           
-                                        this.modelOptions.options{1, 1}.tbl.RowName{i}= num2str(i);
-                                   end
-                               catch
-                                   warndlg('The input string appears to have a syntax error. It should be an Nx2 cell array.');                                       
-                               end
-                           end
-
-                           % Display table
-                           this.modelOptions.grid.Widths = [-1 0 0 0 0 0 0 0 0 0];
-
-                        case 'Options' 
-
-                           % Get the model options.
-                           [modelSettings, colNames, colFormats, colEdits, tooltips] = feval(strcat(funName,'.modelOptions'));
-
-                           % Check if options are available.
-                           if isempty(colNames)                          
-                                this.modelOptions.options{2,1}.lbl.String = {'1. Forcing Transform - Model Settings',['(No options are available for the following weighting function: ',funName,')']};
-                                this.modelOptions.grid.Widths = [0 -1 0 0 0 0 0 0 0 0];
-                           else
-
-                               % Assign model properties and data
-                               this.modelOptions.options{2,1}.tbl.ColumnName = colNames;
-                               this.modelOptions.options{2,1}.tbl.ColumnEditable = colEdits;
-                               this.modelOptions.options{2,1}.tbl.ColumnFormat = colFormats;                               
-                               this.modelOptions.options{2,1}.tbl.TooltipString = tooltips;                               
-                               this.modelOptions.options{2,1}.tbl.Tag = 'Forcing Transform - Model Settings';                                                              
-
-                               if isempty(this.forcingTranforms.tbl.Data{this.currentSelection.row ,4})
-                                   this.forcingTranforms.tbl.Data{this.currentSelection.row ,4} = model_TFN_gui.cell2string(modelSettings,colNames);
-                                   this.modelOptions.options{2,1}.tbl.Data = modelSettings;
-                               else
-                                   try                                       
-                                       data = eval(this.forcingTranforms.tbl.Data{irow,4});
-                                       if strcmpi(colNames(1),'Select')
-                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))),  data];
-                                       end
-
-                                       this.modelOptions.options{2, 1}.tbl.Data = data;
-                                   catch ME
-                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');                                       
-                                   end
-                               end
-
-                               % Assign context menu if the first column is
-                               % named 'Select' and is a tick box.
-                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
-                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-                                    set(this.modelOptions.options{2, 1}.tbl,'UIContextMenu',contextMenu);  
-                                    set(this.modelOptions.options{2, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2, 1}.tbl');
-                               else
-                                   set(this.modelOptions.options{2, 1}.tbl,'UIContextMenu',[]);
-                               end                               
-                               
-                               % Show table
-                               this.modelOptions.grid.Widths = [0 -1 0 0 0 0 0 0 0 0];  
-                           end
-                        otherwise
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                    end
-                case 'Weighting Functions'
-
-
-                    % Add a row if the table is empty
-                    if size(get(hObject,'Data'),1)==0
-                        this.weightingFunctions.tbl.Data = cell(1,size(this.weightingFunctions.tbl.Data,2));
-                    end               
-                    
-                    switch eventdata.Source.ColumnName{icol};
-                        case 'Input Data'
-                           % Get the list of input forcing data.
-                           lstOptions = reshape(this.forcingData.colnames(4:end),[length(this.forcingData.colnames(4:end)),1]);
-
-                           % Add source name
-                           lstOptions = strcat('Input Data :',{' '}, lstOptions);
-
-                           % Loop through each forcing function and add
-                           % the possible outputs.
-                           for i=1: size(this.forcingTranforms.tbl.Data,1)
-                               if isempty(this.forcingTranforms.tbl.Data{i,2})
-                                   continue
-                               end
-
-                               % Get output options.
-                               %outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.forcingData.colnames);
-                               outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-
-                               % Transpose if required
-                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
-                                   outputOptions = outputOptions';
-                               end
-                               
-                               % Add output options from the function
-                               % to the list of available options
-                               lstOptions = [lstOptions; strcat(this.forcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
-                           end
-
-
-                           % Loop through each derived forcing function and add
-                           % the possible outputs.
-                           for i=1: size(this.derivedForcingTranforms.tbl.Data,1)
-                               if isempty(this.derivedForcingTranforms.tbl.Data{i,2})
-                                   continue
-                               end
-
-                               % Get output options.
-                               outputOptions = feval(strcat(this.derivedForcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-
-                               % Transpose if required
-                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
-                                   outputOptions = outputOptions';
-                               end
-                               
-                               % Add output options from the function
-                               % to the list of available options
-                               lstOptions = [lstOptions; strcat(this.derivedForcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
-                           end                                                      
-                           
-                           % Assign the list of options to the list box                               
-                           this.modelOptions.options{3,1}.lst.String = lstOptions;
-                           
-                           % Allow multipple-selection.
-                           this.modelOptions.options{3,1}.lst.Min = 1;
-                           this.modelOptions.options{3,1}.lst.Max = length(lstOptions);
-                           
-                           % Highlight the previosuly selected option. If
-                           % it looks like a string expression for a
-                           % cell, evaluate it.
-                           userSelections = this.weightingFunctions.tbl.Data{this.currentSelection.row, 4};
-                           if ~isempty(userSelections) && strcmp(userSelections(1) ,'{') && strcmp(userSelections(end) ,'}')
-                                userSelections = eval(this.weightingFunctions.tbl.Data{this.currentSelection.row, 4});
-                           end
-                           rowInd= [];
-                           if ~iscell(userSelections)
-                               userSelections_tmp{1} =userSelections;
-                               userSelections = userSelections_tmp;
-                               clear userSelections_tmp
-                           end
-                           for i=1:size(userSelections,1)
-                                ind = find(cellfun( @(x) strcmp(x, userSelections{i}), lstOptions ));
-                                rowInd = [rowInd; ind];
-                           end
-                           this.modelOptions.options{3,1}.lst.Value = rowInd;
-                           
-                           % Show the list box
-                           this.modelOptions.grid.Widths = [0 0 -1 0 0 0 0 0 0 0];
-
-                        case 'Weighting Function'   
-                            % Get description of the function.
-                            functionName = this.weightingFunctions.tbl.Data{this.currentSelection.row, 3};
-                            if ~isempty(functionName)
-                                modelDescription = eval([functionName,'.modelDescription']);
-                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
-                                this.modelOptions.options{10,1}.lbl.String = {'2. Weighting Functions - Function description','',modelDescription{:}};
-                            end
-                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];                           
-                        case 'Options'
-                            % Check that the weigthing function and forcing
-                            % data have been defined
-                            if any(isempty(this.weightingFunctions.tbl.Data(this.currentSelection.row,2:4)))
-                                warndlg('The component name, weighting function ands input data must be specified before setting the options.');
-                                return;
-                            end
-                            
-                            % Get the input forcing data options.
-                            inputDataNames = this.weightingFunctions.tbl.Data{this.currentSelection.row, 4};
-                            
-                            % Convert input data to a cell array if it is a
-                            % list of multiple inputs.
-                            try
-                                inputDataNames = eval(inputDataNames);
-                            catch
-                                % do nothing
-                            end
-                            
-                           % Remove source name for input data.     
-                           if ischar(inputDataNames)
-                               inputDataNames_tmp{1}=inputDataNames;
-                               inputDataNames = inputDataNames_tmp;
-                               clear inputDataNames_tmp
-                           end
-                           for j=1: size(inputDataNames,1)
-                               if ~isempty(strfind(inputDataNames{j}, 'Input Data : '))
-                                   [~,ind] = regexp(inputDataNames{j}, 'Input Data : ');
-                               else
-                                   [~,ind] = regexp(inputDataNames{j}, ' : ');  
-                               end                                                   
-                               %inputDataNames{j} =  inputDataNames{j}(ind+3:end);
-                               inputDataNames{j} =  inputDataNames{j}(ind+1:end);
-                           end
-                           
-                           if isempty(inputDataNames) || (iscell(inputDataNames) && isempty(inputDataNames{1,1}))
-                               warndlg('The input data does not appear to have been input for this model compnenent.','Input data error ...');
-                               return;
-                           end
-                           
-                           % Get the list of input forcing data.
-                           funName = this.weightingFunctions.tbl.Data{this.currentSelection.row, 3};
-
-                           % Get the weighting function options.
-%                            [modelSettings, colNames, colFormats, colEdits, tooltip] = feval(strcat(funName,'.modelOptions'), ...
-%                            this.boreID, inputDataNames, this.siteData);
-                           modelSettings= feval(strcat(funName,'.modelOptions'), this.boreID, inputDataNames, this.siteData);
-
-                           % If the function has any options the
-                           % display the options else display a message
-                           % in box stating no options are available.                           
-                           if isempty(modelSettings)  
-                               set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','center');
-                                this.modelOptions.options{10,1}.lbl.String = {'2. Weighting Functions - Options',['(No options are available for the following weighting function: ',funName,')']};                                    
-                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
-                           else
-                               this.modelOptions.options{4,1}.lbl.String = '2. Weighting Functions - Options';
-
-                               % Loop through each model option
-                               if ~iscell(modelSettings)
-                                   modelSettingsTmp = cell(1,1);
-                                   modelSettingsTmp{1} = modelSettings; 
-                                   modelSettings = modelSettingsTmp; 
-                                   clear modelSettingsTmp 
-                               end
-                               
-                               % Disable all options tabs
-                               for i=1:length(this.modelOptions.options{4, 1}.tabs.TabEnables)
-                                  this.modelOptions.options{4, 1}.tabs.TabEnables{i}='off'; 
-                                  drawnow();   
-                               end
-                               
-                               % Get existing options data and convert from
-                               % a string.
-                               haveInputData = false(size(modelSettings));
-                               if ~isempty(this.weightingFunctions.tbl.Data{this.currentSelection.row ,5})
-                                   try
-                                       data = eval(this.weightingFunctions.tbl.Data{this.currentSelection.row ,5});
-                                       
-                                       % Check 'data' and the model options
-                                       % are the same length
-                                       if length(data) == length(modelSettings)
-                                           for i=1:length(data)
-                                               if strcmpi(modelSettings{i}.colNames(1),'Select')
-                                                   data{i} = [ mat2cell(false(size(data{i},1),1),ones(1,size(data{i},1))) , data{i}];
-                                               end
-                                           end 
-                                           haveInputData(i) =  true;
-                                       else
-                                           warndlg('There are an inconsistent number of input and expected options. The inputs data is being ignored.');
-                                       end
-                                   catch ME
-                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');
-                                       %this.modelOptions.options{4, 1}.tbl.Data = '';
-                                   end
-                                   
-                               end
-                               % Assign model properties and data                               
-                               for i=1:length(modelSettings)
-                                   
-                                   this.modelOptions.options{4, 1}.tabs.TabTitles{i} =  modelSettings{i}.label;
-                                   this.modelOptions.options{4, 1}.tabs.TabEnables{i} =  'on';
-                                   
-                                   optionsTab = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i)]);
-                                   optionsTable = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i),' table']);
-                                   %optionsTab.Title =  modelSettings{i}.label;
-                                   optionsTable.ColumnName = modelSettings{i}.colNames;
-                                   optionsTable.ColumnEditable = modelSettings{i}.colEdits;
-                                   optionsTable.ColumnFormat = modelSettings{i}.colFormats;          
-                                   optionsTable.TooltipString = modelSettings{i}.TooltipString;
-
-                                   % Input the existing data or else the
-                                   % default settings.
-                                   if haveInputData(i)
-                                       this.modelOptions.options{4, 1}.tbl.Data = data{i};
-                                   else                                       
-                                       if isempty(modelSettings{i}.options)
-                                           optionsTable.Data = cell(1,length(modelSettings{i}.colNames));
-                                       else
-                                           optionsTable.Data = modelSettings{i}.options;
-                                       end
-                                   end    
-                                   
-                                   % Assign context menu if the first column is
-                                   % named 'Select' and is a tick box.
-                                   if strcmp(modelSettings{i}.colNames{1},'Select') && strcmp(modelSettings{i}.colFormats{1},'logical')
-                                        contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-                                        uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-                                        uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-                                        uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-                                        uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-                                        uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-                                        set(optionsTable,'UIContextMenu',contextMenu);  
-                                        set(optionsTable.UIContextMenu,'UserData', 'this.modelOptions.options{4, 1}.tbl');
-                                   else
-                                       set(optionsTable,'UIContextMenu',[]);
-                                   end
-                               end
-                               % Show table
-                               this.modelOptions.grid.Widths = [0 0 0 -1 0 0 0 0 0 0];
-                           end
-
-                        otherwise
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                    end
-
-                case 'Derived Forcing Transform'                
-                    
-                    % Add a row if the table is empty
-                    if size(get(hObject,'Data'),1)==0
-                        this.derivedForcingTranforms.tbl.Data = cell(1,size(this.derivedForcingTranforms.tbl.Data,2));
-                        return;
-                    end                    
-                    
-                    % Get the derived forcing function name.
-                    funName = this.derivedForcingTranforms.tbl.Data{irow, 2};
-
-                    % Get the source forcing function name.
-                    sourceFunName = this.derivedForcingTranforms.tbl.Data{irow, 3};
-
-                    switch eventdata.Source.ColumnName{icol};
-                        case 'Forcing Transform Function'
-                        
-                            % Get description of the function.
-                            functionName = this.derivedForcingTranforms.tbl.Data{this.currentSelection.row, 2};
-                            if isempty(functionName)
-                                return;
-                            end
-                            modelDescription = eval([functionName,'.modelDescription']);
-                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
-                            this.modelOptions.options{10,1}.lbl.String = {'3. Derived Forcing Transform - Function description','',modelDescription{:}};
-                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];          
-                            
-                        case 'Source Forcing Function' 
-                            derivedForcingFunctionsListed = this.forcingTranforms.tbl.Data(:,2);
-                            this.derivedForcingTranforms.tbl.ColumnFormat{3} = derivedForcingFunctionsListed;
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                        case 'Input Data'
-                           % Call function method giving required
-                           % variable name.
-                           requiredVariables = feval(strcat(funName,'.inputForcingData_required'));
-
-                           % Add row for each required variable
-                           if isempty(this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,4})
-                               for i=1:length(requiredVariables)                                   
-                                    this.modelOptions.options{6, 1}.tbl.Data{i,1}= requiredVariables{i};
-                                    this.modelOptions.options{6, 1}.tbl.RowName{i}= num2str(i);
-                               end
-                           else
-                               try
-                                   this.modelOptions.options{6, 1}.tbl.Data = eval(this.derivedForcingTranforms.tbl.Data{irow,4});
-                                   for i=1:length(requiredVariables)                                                                           
-                                        this.modelOptions.options{6, 1}.tbl.RowName{i}= num2str(i);
-                                   end
-                               catch
-                                   warndlg('The input string appears to have a syntax error. It should be an Nx2 cell array.');                                       
-                               end
-                           end
-                           
-                           % Get the list of input forcing data and add source name
-                           lstOptions = reshape(this.forcingData.colnames(4:end),[1, length(this.forcingData.colnames(4:end))]);
-
-                           % Add input list to drop down
-                           this.modelOptions.options{6, 1}.tbl.ColumnFormat = {'char',lstOptions};
-
-                           % Display table
-                           this.modelOptions.grid.Widths = [0 0 0 0 0 -1 0 0 0 0];
-                           
-                        case 'Options'    
-                           % Get the model options.
-                           [modelSettings, colNames, colFormats, colEdits, tooltips] = feval(strcat(funName,'.modelOptions'), sourceFunName );
-
-                           % Check if options are available.
-                           if isempty(colNames)                          
-                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
-                                this.modelOptions.options{10,1}.lbl.String = {'3. Derived Forcing Transform - Model Settings','',['(No options are available for the following function: ',funName,')']};
-                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
-                           else
-
-                               % Assign model properties and data
-                               this.modelOptions.options{7,1}.tbl.ColumnName = colNames;
-                               this.modelOptions.options{7,1}.tbl.ColumnEditable = colEdits;
-                               this.modelOptions.options{7,1}.tbl.ColumnFormat = colFormats;                               
-                               this.modelOptions.options{7,1}.tbl.TooltipString = tooltips;                               
-                               this.modelOptions.options{7,1}.tbl.Tag = 'Derived Forcing Transform - Model Settings';                                                              
-
-                               if isempty(this.derivedForcingTranforms.tbl.Data{irow ,5})
-                                    this.modelOptions.options{7,1}.tbl.Data = modelSettings;
-                               else
-                                   try                                       
-                                       data = eval(this.derivedForcingTranforms.tbl.Data{irow,5});
-                                       if strcmpi(colNames(1),'Select')
-                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))),  data];
-                                       end
-
-                                       this.modelOptions.options{7, 1}.tbl.Data = data;
-                                   catch
-                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');                                       
-                                   end
-                               end
-
-                               % Assign context menu if the first column is
-                               % named 'Select' and is a tick box.
-                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
-                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-                                    set(this.modelOptions.options{7, 1}.tbl,'UIContextMenu',contextMenu);  
-                                    set(this.modelOptions.options{7, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2, 1}.tbl');
-                               else
-                                   set(this.modelOptions.options{7, 1}.tbl,'UIContextMenu',[]);
-                               end                               
-
-                               % Display table
-                               this.modelOptions.grid.Widths = [0 0 0 0 0 0 -1 0 0 0];                
-                           end
-                        otherwise
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                    end
-
-                case 'Derived Weighting Functions'
-
-                    % Add a row if the table is empty
-                    if size(get(hObject,'Data'),1)==0
-                        this.derivedWeightingFunctions.tbl.Data = cell(1,size(this.derivedWeightingFunctions.tbl.Data,2));
-                        return;
-                    end                    
-                    
-                    
-                    switch eventdata.Source.ColumnName{icol};
-                        case 'Weighting Function'   
-                            % Get description of the function.
-                            functionName = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 3};
-                            if isempty(functionName)
-                                return;
-                            end
-                            modelDescription = eval([functionName,'.modelDescription']);
-                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
-                            this.modelOptions.options{10,1}.lbl.String = {'4. Derived Weighting Functions - Function description','',modelDescription{:}};
-                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];                              
-                        case 'Source Component'
-                            derivedWeightingFunctionsListed = this.weightingFunctions.tbl.Data(:,2);
-                            this.derivedWeightingFunctions.tbl.ColumnFormat{4} = derivedWeightingFunctionsListed';
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                        case 'Input Data'
-                            
-                           % Get the list of input forcing data.
-                           lstOptions = reshape(this.forcingData.colnames(4:end),[length(this.forcingData.colnames(4:end)),1]);
-
-                           % Add source name
-                           lstOptions = strcat('Input Data :',{' '}, lstOptions);
-
-                           % Loop through each forcing function and add
-                           % the possible outputs.
-                           for i=1: size(this.forcingTranforms.tbl.Data,1)
-                               if isempty(this.forcingTranforms.tbl.Data{i,2})
-                                   continue
-                               end
-
-                               % Get output options.
-                               outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-
-                               % Transpose if required
-                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
-                                   outputOptions = outputOptions';
-                               end
-                               
-                               % Add output options from the function
-                               % to the list of available options
-                               lstOptions = [lstOptions; strcat(this.forcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
-                           end
-
-
-                           % Loop through each derived forcing function and add
-                           % the possible outputs.
-                           for i=1: size(this.derivedForcingTranforms.tbl.Data,1)
-                               if isempty(this.derivedForcingTranforms.tbl.Data{i,2})
-                                   continue
-                               end
-
-                               % Get output options.
-                               outputOptions = feval(strcat(this.derivedForcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-
-                               % Transpose if required
-                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
-                                   outputOptions = outputOptions';
-                               end                               
-                               
-                               % Add output options from the function
-                               % to the list of available options
-                               lstOptions = [lstOptions; strcat(this.derivedForcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
-                           end                                                      
-                           
-                           % Assign the list of options to the list box                               
-                           this.modelOptions.options{8,1}.lst.String = lstOptions;
-                           
-                           % Allow multipple-selection.
-                           this.modelOptions.options{8,1}.lst.Min = 1;
-                           this.modelOptions.options{8,1}.lst.Max = length(lstOptions);
-
-                           % Highlight the previosuly selected option. If
-                           % it looks like a string expression for a
-                           % cell, evaluate it.
-                           userSelections = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5};
-                           if ~isempty(userSelections) && strcmp(userSelections(1) ,'{') && strcmp(userSelections(end) ,'}')
-                                userSelections = eval(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5});
-                           end
-                           
-                           rowInd= [];
-                           if ~iscell(userSelections)
-                               userSelections_tmp{1} =userSelections;
-                               userSelections = userSelections_tmp;
-                               clear userSelections_tmp
-                           end
-                           for i=1:size(userSelections,1)
-                                ind = find(cellfun( @(x) strcmp(x, userSelections{i}), lstOptions ));
-                                rowInd = [rowInd; ind];
-                           end
-                           this.modelOptions.options{8,1}.lst.Value = rowInd;                           
-                           
-                           % Show list box 
-                           this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 -1 0 0];
-                           
-                        case 'Options'
-                            % Check that the weigthing function and forcing
-                            % data have been defined
-                            if any(isempty(this.derivedWeightingFunctions.tbl.Data(this.currentSelection.row,2:5)))
-                                warndlg('The component name, derived weighting function, source function and input data must be specified before setting the options.');
-                                return;
-                            end
-                            
-                            % Get the input forcing data options.
-                            inputDataNames = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5};
-                            
-                            % Convert input data to a cell array if it is a
-                            % list of multiple inputs.
-                            try
-                                inputDataNames = eval(inputDataNames)';
-                            catch
-                                % do nothing
-                            end
-                            
-                           % Remove source name for input data.     
-                           if ischar(inputDataNames)
-                               inputDataNames_tmp{1}=inputDataNames;
-                               inputDataNames = inputDataNames_tmp;
-                               clear inputDataNames_tmp
-                           end
-                           for j=1: size(inputDataNames,1)
-                               if ~isempty(strfind(inputDataNames{j}, 'Input Data : '))
-                                   ind = regexp(inputDataNames{j}, 'Input Data : ');
-                               else
-                                   ind = regexp(inputDataNames{j}, ' : ');  
-                               end                                                   
-                               inputDataNames{j} =  inputDataNames{j}(ind+3:end);
-                           end                            
-                            
-                            % Get the list of input forcing data.
-                            funName = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 3};
-
-                           % Get the weighting function options.
-                           [modelSettings, colNames, colFormats, colEdits, tooltip] = feval(strcat(funName,'.modelOptions'), ...
-                           this.boreID, inputDataNames, this.siteData);
-
-                           % If the function has any options the
-                           % display the options else display a message
-                           % in box stating no options are available.
-                           if isempty(colNames)                          
-                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','center');
-                                this.modelOptions.options{10,1}.lbl.String = {'4. Derived Weighting Functions - Options','',['(No options are available for the following weighting function: ',funName,')']};                                    
-                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
-                           else
-                               this.modelOptions.options{9,1}.lbl.String = '4. Derived Weighting Functions - Options';
-
-                               % Assign model properties and data
-                               this.modelOptions.options{9,1}.tbl.ColumnName = colNames;
-                               this.modelOptions.options{9,1}.tbl.ColumnEditable = colEdits;
-                               this.modelOptions.options{9,1}.tbl.ColumnFormat = colFormats;                               
-                               this.modelOptions.options{9,1}.tbl.TooltipString = tooltip;
-                               % Input the existing data or else the
-                               % default settings.
-                               if isempty(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6})
-                                   if isempty(modelSettings)
-                                       this.modelOptions.options{9,1}.tbl.Data = cell(1,length(colNames));
-                                   else
-                                       this.modelOptions.options{9,1}.tbl.Data = modelSettings;
-                                   end
-                               else
-                                   try
-                                       data = eval(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6});
-                                       if strcmpi(colNames(1),'Select')
-                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))) , data];
-                                       end
-
-                                       this.modelOptions.options{9, 1}.tbl.Data = data;
-                                   catch
-                                       warndlg('The function options string appears to have a sytax error.');                                       
-                                   end
-                               end                                 
-                               % Assign context menu if the first column is
-                               % named 'Select' and is a tick box.
-                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
-                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
-                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
-                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
-                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
-                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
-                                    set(this.modelOptions.options{9, 1}.tbl,'UIContextMenu',contextMenu);  
-                                    set(this.modelOptions.options{9, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{9, 1}.tbl');
-                               else
-                                   set(this.modelOptions.options{9, 1}.tbl,'UIContextMenu',[]);
-                               end
-                               
-                               % Show table
-                               this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 -1 0];
-                           end
-                        otherwise
-                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                    end   
-            end            
-        end
-        
-        function optionsSelection(this, hObject, eventdata)
-            try
-                data=get(hObject,'Data'); % get the data cell array of the table
-            catch
-                data=[];
-            end
-
-            % Get class name (for calling the abstract)
-            className = metaclass(this);
-            className = className.Name;
-                        
-            % Undertake table/list specific operations.
-            switch eventdata.Source.Tag;
-               
-                case 'Forcing Transform - Input Data'                        
-                    colnames = hObject.ColumnName;
-                    this.forcingTranforms.tbl.Data{this.currentSelection.row ,3} = eval([className,'.cell2string(data, colnames)']);
-
-                case 'Forcing Transform - Model Settings'
-                    colnames = hObject.ColumnName;
-                    this.forcingTranforms.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
-
-                case 'Weighting Functions - Input Data'       
-
-                    % Get selected input option
-                    listSelection = get(hObject,'Value');
- 
-                    % Get cell array of selected strings.
-                    data = hObject.String(listSelection);
-                    
-                    % Convert to string.
-                    colnames = 'NA';                    
-                    this.weightingFunctions.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
-
-                case {'Weighting Functions - Options Tab 1 table','Weighting Functions - Options Tab 2 table', ...
-                     'Weighting Functions - Options Tab 3 table','Weighting Functions - Options Tab 4 table', ...
-                     'Weighting Functions - Options Tab 5 table'}
-                 
-                    optionsString = '{';
-                    for i=1:length(this.modelOptions.options{4, 1}.tabs.TabEnables)
-                        if strcmp(this.modelOptions.options{4, 1}.tabs.TabEnables{i},'on')
-                            optionsTable = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i),' table']);
-                            data = get(optionsTable,'Data');
-                            if any(any(cellfun(@(x) ~isempty(x), data)))
-                                colnames = get(optionsTable,'ColumnName');                                
-                                optionsString  = [optionsString , eval([className,'.cell2string(data, colnames)']),', '];
-                            else
-                                optionsString  = [optionsString , '{}, '];
-                            end                            
-                        end                        
-                    end
-                    if ~isempty(optionsString)
-                       optionsString = optionsString(1:end-2); 
-                    end
-                    optionsString  = [optionsString , '} '];
-                    this.weightingFunctions.tbl.Data{this.currentSelection.row ,5} = optionsString;
-
-                case 'Derived Forcing Functions - Source Function'                      
-                    % Get selected input option
-                    listSelection = get(hObject,'Value');
- 
-                    % Get cell array of selected strings.
-                    data = hObject.String(listSelection);
-
-                    % Assign to the table
-                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,3} = data{1};
-                    
-                    
-                case 'Derived Forcing Functions - Input Data'                      
-                    
-                    colnames = hObject.ColumnName;
-                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
-                    
-                case 'Derived Forcing Transform - Model Settings'
-                    
-                    colnames = hObject.ColumnName;
-                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,5} = eval([className,'.cell2string(data, colnames)']);
-                    
-                    
-                case 'Derived Weighting Functions - Input Data'      
-                    % Get selected input option
-                    listSelection = get(hObject,'Value');
- 
-                    % Get cell array of selected strings.
-                    data = hObject.String(listSelection);
-                    
-                    % Convert to string.
-                    colnames = 'NA';                    
-                    this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,5} = eval([className,'.cell2string(data, colnames)']);
-
-                case 'Derived Weighting Functions - Model Settings'       
-                    colnames = hObject.ColumnName;
-                    this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6} = eval([className,'.cell2string(data, colnames)']);
-                    
-                otherwise
-                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths)); 
-            end
-        end
-        
-    %end
-    
-    %methods(Access=private)  
-        
-        function tableEdit(this, hObject, eventdata)
-            icol=[];
-            irow=[];
-            if isprop(eventdata, 'Indices')
-                if ~isempty(eventdata.Indices)
-                    icol=eventdata.Indices(:,2);
-                    irow=eventdata.Indices(:,1);  
-                end
-            end
-            if size(get(hObject,'Data'),1)==0
-                return
-            end
-            
-            % Return if the select column is the corrent column
-            if icol==1
-                return;
-            end
-           
-            % Undertake table/list specific operations.
-            switch eventdata.Source.Tag;
-                case 'Forcing Transform'
-                   % Check the function name is unique
-                   nrows  = size(this.forcingTranforms.tbl.Data,1);                   
-                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
-                   if nrows>1          
-                       otherFunctionNames = this.forcingTranforms.tbl.Data(otherFunctionNames_ind ,3);
-                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
-                           this.forcingTranforms.tbl.Data{irow ,2} = eventdata.PreviousData;
-                           warndlg('Each function name must be unique within the model - i.e you can use it only once.');
-                           return;
-                       end
-                   end
-                   
-                   % Reset other fields of the model name changes
-                   if icol==2 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
-                        this.forcingTranforms.tbl.Data{irow ,3} = '';
-                        this.forcingTranforms.tbl.Data{irow,4} = '';
-                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
-                        
-                   end
-                   
-                case 'Weighting Functions'
-                   
-                   % Check the function name is unique
-                   nrows  = size(this.weightingFunctions.tbl.Data,1);                   
-                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
-                   if nrows>1          
-                       otherFunctionNames = this.weightingFunctions.tbl.Data(otherFunctionNames_ind ,2);
-                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
-                           this.weightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
-                           warndlg('Each component name must be unique within the model.');
-                           return;
-                       end
-                   end  
-                   
-                   % Check that the compent name is a valid fiewd name
-                   try
-                       componentName = eventdata.NewData;               
-                       a.(componentName) = 1;
-                       clear a
-                   catch ME
-                       this.weightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
-                       warndlg('The component name is invalid. It must contain only letters, numbers and under-scores and cannot start with a number.');
-                       return;
-                   end
-                    
-                   % Reset other fields of the model name changes
-                   if icol==3 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
-                        this.weightingFunctions.tbl.Data{irow ,4} = '';
-                        this.weightingFunctions.tbl.Data{irow ,5} = '';
-                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
-                   end                   
-                case 'Derived Forcing Transform'
-                    
-                   % Check the function name is unique
-                   nrows  = size(this.derivedForcingTranforms.tbl.Data,1);                   
-                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
-                   if nrows>1          
-                       otherFunctionNames = this.derivedForcingTranforms.tbl.Data(otherFunctionNames_ind ,2);
-                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
-                           this.derivedForcingTranforms.tbl.Data{irow ,2} = eventdata.PreviousData;
-                           warndlg('Each function name must be unique within the model - i.e you can use it only once.');
-                           return;
-                       end
-                   end
-                   
-                   % Reset other fields of the model name changes
-                   if icol==2 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
-                        this.derivedForcingTranforms.tbl.Data{irow ,3} = '';
-                        this.derivedForcingTranforms.tbl.Data{irow ,4} = '';
-                        this.derivedForcingTranforms.tbl.Data{irow ,5} = '';
-                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
-                   end                   
-                case 'Derived Weighting Functions'
-                    
-                   % Check the function name is unique
-                   nrows  = size(this.derivedWeightingFunctions.tbl.Data,1);                   
-                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
-                   if nrows>1          
-                       otherFunctionNames = this.derivedWeightingFunctions.tbl.Data(otherFunctionNames_ind ,3);
-                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
-                           this.derivedWeightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
-                           warndlg('Each component name must be unique within the model.');
-                           return;
-                       end
-                   end      
-                   
-                   % Check that the compent name is a valid fiewd name
-                   try
-                       componentName = eventdata.NewData;               
-                       a.(componentName) = 1;
-                       clear a
-                   catch
-                       this.derivedWeightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
-                       warndlg('The component name is invalid. It must contain only letters, numbers and under-scores and cannot start with a number.');
-                       return;
-                   end                   
-                   
-                   % Reset other fields of the model name changes
-                   if icol==3 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
-                        this.derivedWeightingFunctions.tbl.Data{irow ,4} = '';
-                        this.derivedWeightingFunctions.tbl.Data{irow ,5} = '';
-                        this.derivedWeightingFunctions.tbl.Data{irow ,6} = '';
-                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
-                   end                      
-                    
-            end
-        end
-        
-        function rowAddDelete(this, hObject, eventdata)
-
-           
-            % Get the table object from UserData
-            tableObj = eval(eventdata.Source.Parent.UserData);
-            
-            % Get selected rows
-            selectedRow = false(size(tableObj.Data(:,1),1),1);
-            for i=1:size(tableObj.Data(:,1),1)
-                if tableObj.Data{i,1}
-                    selectedRow(i) = true;
-                end
-            end
-      
-            % Check if any rows are selected. Note, if not then
-            % rows will be added (for all but the calibration
-            % table).
-            anySelected = any(selectedRow);
-            indSelected = find(selectedRow)';
-            
-            
-            if ~strcmp(hObject.Label,'Paste rows') && size(tableObj.Data(:,1),1)>0 &&  sum(selectedRow) == 0                             
-                warndlg('No rows are selected for the requested operation.');
-                return;
-            elseif size(tableObj.Data(:,1),1)==0 ...
-            &&  (strcmp(hObject.Label, 'Copy selected rows') || strcmp(hObject.Label, 'Delete selected rows'))                
-                return;
-            end            
-            
-            % Do the selected action            
-            switch hObject.Label
-                case 'Copy selected rows'
-                    this.copiedData.tableName = tableObj.Tag;
-                    this.copiedData.data = tableObj.Data(selectedRow,:);
-                    
-                case 'Paste rows'    
-                    % Check that name of the table is same as that from the
-                    % copied data. If so copy the data.
-                    if strcmp(this.copiedData.tableName, tableObj.Tag)
-                        if anySelected
-                            for i=indSelected
-                                tableObj.Data{i,:} = this.copiedData.data(1,:);
-                            end
-                       else
-                          for i=1: size(this.copiedData.data,1)
-                            tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
-                          end
- 
-                        end                        
-                    
-                        % Update row numbers.
-                        nrows = size(tableObj.Data,1);
-                        tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                    else
-                        warndlg('The copied row data was sourced froma different table.');
-                        return;
-                    end    
-                    
-                case 'Insert row above selection'
-                    if size(tableObj.Data,1)==0
-                        tableObj.Data = cell(1,size(tableObj.Data,2));
-                    else
-                        selectedRow= find(selectedRow);
-                        for i=1:length(selectedRow)
-
-                            ind = max(0,selectedRow(i) + i-1);
-
-                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
-                                        cell(1,size(tableObj.Data,2)); ...
-                                        tableObj.Data(ind:end,:)];
-                            tableObj.Data{ind,1} = false;                                                              
-                        end
-                    end
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-                        
-                case 'Insert row below selection'    
-                    if size(tableObj.Data,1)==0
-                        tableObj.Data = cell(1,size(tableObj.Data,2));
-                    else
-                        selectedRow= find(selectedRow);
-                        for i=1:length(selectedRow)
-
-                            ind = selectedRow(i) + i;
-
-                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
-                                        cell(1,size(tableObj.Data,2)); ...
-                                        tableObj.Data(ind:end,:)];
-
-                            tableObj.Data{ind,1} = false;                                                              
-                        end
-                    end
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-
-                case 'Delete selected rows'    
-                    tableObj.Data = tableObj.Data(~selectedRow,:);
-                    
-                    % Update row numbers.
-                    nrows = size(tableObj.Data,1);
-                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
-            end
-        end
-        
-         function wizard(this, hObject, eventdata)
-             
-            % Get the selelcted table, row and col.
-            irow = this.currentSelection.row;
-            icol = this.currentSelection.col;
-            currentTable = this.currentSelection.table;
-            
-            % Get the name of the function within the table     
-            switch currentTable
-                case 'Forcing Transform'
-                    className = this.forcingTranforms.tbl.Data{irow, 2};
-                    
-                    if icol==3
-                        methodName = '.dataWizard';
-                    elseif icol==4
-                        methodName = '.optionsWizard';
-                    end
-                    
-                otherwise
-                    msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')     
-                    return
-            end
-            
-            
-            % Call the wizard for the current table.
-            try
-               wizardResults= feval(strcat(className,methodName),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
-            catch ME
-               msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')
-               return
-            end
-
-           % Input the wizard data into the current table.
-           switch currentTable
-                case 'Forcing Transform'
-                    switch methodName
-                        case '.dataWizard'
-                           this.modelOptions.options{1, 1}.tbl.Data = cell(0,2);
-                           this.modelOptions.options{1, 1}.tbl.Data = cell(size(wizardResults,1),2);
-                           this.modelOptions.options{1, 1}.tbl.RowName=cell(1,size(wizardResults,1)); 
-                           for i=1:size(wizardResults,1)                                   
-                                this.modelOptions.options{1, 1}.tbl.Data{i,1}= wizardResults{i,1};
-                                this.modelOptions.options{1, 1}.tbl.Data{i,2}= wizardResults{i,2};
-                           end     
-                           this.modelOptions.options{1, 1}.tbl.RowName = [1:size(this.modelOptions.options{1, 1}.tbl.Data,1)];
-                           
-                           % In case the user does not input/chnage
-                           % the data, then apply it to the forcing
-                           % data field.
-                           className= 'model_TFN_gui';
-                           colnames = {'Required Model Data';'Input Forcing Data'};
-                           data=this.modelOptions.options{1, 1}.tbl.Data;
-                           this.forcingTranforms.tbl.Data{irow ,3} = eval([className,'.cell2string(data, colnames)']);
-
-                        case '.optionsWizard'
-                            
-                    end
-           end
-
-           drawnow();   
-         end
-         
-         function push2component(this, hObject, eventdata)
-             
-            dlg_title = 'Push required model data to compnant input data ...';
-            prompt = 'Select the componant:';      
-            ListString = this.weightingFunctions.tbl.Data(:,2);
-           
-            if length(ListString)<1
-                errordlg('The weighting function component names must first be defined.')
-                return
-            end
-            
-            [ind, OK]= listdlg('Name',dlg_title, 'PromptString',prompt, 'ListString',ListString, 'SelectionMode','single');
-            if OK==1
-               
-                
-                % Get the selelcted table, row and col.
-                irow = this.currentSelection.row;
-                icol = this.currentSelection.col;
-                currentTable = this.currentSelection.table;
-                
-                % Get the name of the function within the table
-                switch currentTable
-                    case 'Forcing Transform'
-                        className = this.forcingTranforms.tbl.Data{irow, 2};                                               
-                    otherwise
-                        msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')
-                        return
-                end
-
-                % Build filter for rows where the obs data is not "(none)"
-                filt = ~strcmp(this.modelOptions.options{1, 1}.tbl.Data(:,2),'(none)');
-                
-                % get input data names                
-                data = this.modelOptions.options{1, 1}.tbl.Data(filt,1);
-                
-                LHS = [className,'...'];
-                data = strcat( LHS, data);
-                data = strrep(data,'...',' : ');
-                               
-                % Get class name (for calling the abstract)
-                className = metaclass(this);
-                className = className.Name;
-                
-                % Convert to string.
-                colnames = 'NA';                    
-                this.weightingFunctions.tbl.Data{ind,4} = eval([className,'.cell2string(data, colnames)']);
-                    
-                
-                
-            end
-         end
-    end
-    
-    methods(Static, Access=private)  
-        function stringCell = cell2string(cellData, colnames) 
-                % Ignore first column of it is for row selection
-                if isempty(colnames)
-                    startCol = 1; 
-                elseif strcmpi(colnames(1),'Select')
-                    startCol = 2;                    
-                else
-                    startCol = 1;
-                end
-            
-                % Check the format of each column.
-                % All rows of a column must be numeric to be
-                % deemed numeric.
-                isNumericColumn = all(cellfun(@(x) isnumeric(x) || (~isempty(str2double(x)) && ~isnan(str2double(x))), cellData),1);
-                
-                % Loop through each column, then row.
-                stringCell= '{';
-                for i=1:size(cellData,1)
-                    for j=startCol:size(cellData,2)
-                        
-                        if isNumericColumn(j)
-                            if ischar(cellData{i,j})
-                                stringCell = strcat(stringCell, sprintf(' %f,',str2double(cellData{i,j}) ));
-                            else
-                                stringCell = strcat(stringCell, sprintf(' %f,',cellData{i,j} ));
-                            end
-                        else
-                            stringCell = strcat(stringCell, sprintf(' ''%s'',',cellData{i,j} ));
-                        end
-                    end
-                    % remove end ,
-                    stringCell = stringCell(1:end-1);
-                    % add ;
-                    stringCell = strcat(stringCell, ' ; ');
-                end
-                stringCell = strcat(stringCell,'}');            
-        end        
-    end
-    
-end
-
+classdef model_TFN_gui < model_gui_abstract
+    %MODEL_TFN_GUI Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+        % NOTE: Inputs from the parent table are define in abstract
+        %  
+        % Model specific GUI properties.
+        forcingTranforms
+        weightingFunctions
+        derivedForcingTranforms
+        derivedWeightingFunctions
+        modelComponants
+        modelOptions 
+        
+        % Current active main table, row, col
+        currentSelection;
+
+        % Copied table rows
+        copiedData;
+    end
+    
+    methods
+        %% Build the GUI for this model type.
+        function this = model_TFN_gui(parent_handle)
+            
+            % Initialise properties not initialised below
+            this.boreID = [];
+            this.siteData = [];
+            this.forcingData = [];  
+            this.currentSelection.table = '';            
+            this.currentSelection.row = 0;
+            this.currentSelection.col = 0;
+            
+            % Get the available modle options
+            %--------------------------------------------------------------
+            % Get the types of weighting function and derived weighting function
+            quickload = true;
+            if ~isdeployed && ~quickload
+                warning('off');
+                forcingFunctions = findClassDefsUsingAbstractName( 'forcingTransform_abstract', 'model_TFN');
+                derivedForcingFunctions = findClassDefsUsingAbstractName( 'derivedForcingTransform_abstract', 'model_TFN');
+
+                % Get the types of weighting function and derived weighting function
+                weightFunctions = findClassDefsUsingAbstractName( 'responseFunction_abstract', 'model_TFN');
+                derivedWeightFunctions = findClassDefsUsingAbstractName( 'derivedResponseFunction_abstract', 'model_TFN');
+                warning('on');
+            else
+                % Hard code in function names if the code is deployed. This
+                % is required because depfun does not dunction in deployed
+                % code.                
+                forcingFunctions = {        'climateTransform_soilMoistureModels', ...                                            
+                                            'climateTransform_soilMoistureModels_v2', ...
+                                            'climateTransform_soilMoistureModels_2layer', ...
+                                            'climateTransform_soilMoistureModels_2layer_v2', ...
+                                            'pumpingRate_SAestimation'};                
+                derivedForcingFunctions = { 'derivedForcing_linearUnconstrainedScaling', ...
+                                            'derivedForcing_linearUnconstrainedScaling_dup', ...
+                                            'derivedForcing_logisticScaling'};                
+                derivedWeightFunctions = {  'derivedResponseFunction_abstract', ...
+                                            'derivedweighting_UnconstrainedRescaled', ...
+                                            'derivedweighting_PearsonsNegativeRescaled', ...
+                                            'derivedweighting_PearsonsPositiveRescaled'};
+                weightFunctions = {         'responseFunction_abstract', ...
+                                            'responseFunction_Bruggeman', ...
+                                            'responseFunction_FerrisKnowles', ...
+                                            'responseFunction_FerrisKnowlesJacobs', ...
+                                            'responseFunction_Hantush', ...
+                                            'responseFunction_JacobsCorrection', ...
+                                            'responseFunction_Pearsons', ...
+                                            'responseFunction_PearsonsNegative'};
+            end
+                
+
+            % Forcing function column headings etc
+            cnames_forcing = {'Select','Forcing Transform Function','Input Data', 'Options'};
+            cformats_forcing = {'logical',forcingFunctions,'char','char'};
+            cedit_forcing = logical([1 1 1 1]);
+            rnames_forcing = {'1'};
+            cdata_forcing = cell(1,4);
+            toolTip_forcing = ['<html>This table (optional) allows the transformation of the input forcing data (e.g. rainfall to recharge). <br>', ...
+                          'To build a transformation function, you''ll need to select a function and define the input forcing data <br>', ...                 
+                          'that is to be transformed. </html>'];                          
+            
+            % Derived forcing function column headings etc
+            cnames_forcingDerived = {'Select','Forcing Transform Function','Source Forcing Function','Input Data', 'Options'};
+            cformats_forcingDerived = {'logical',derivedForcingFunctions,{'(No functions available)'},'char','char'};    
+            cedit_forcingDerived = logical([1 1 1 1 1]);
+            rnames_forcingDerived = {'1'};
+            cdata_forcingDerived = cell(1,5);
+            toolTip_forcingDerived = ['<html>This table (optional) allows the transformation of derived forcing data (e.g. recharge) <br>', ...
+                          'and can be used to estimate the impacts from, say, land use change on the head.  To build such a transformation <br>', ...
+                          'function, you''ll need to first build a transformation function (step 1)</html>'];                          
+            
+            % Weighting function column headings etc
+            cnames_weighting = {'Select','Component Name','Weighting Function','Input Data', 'Options'};
+            cformats_weighting = {'logical','char', weightFunctions,'char','char'};
+            cedit_weighting = logical([1 1 1 1 1]);
+            rnames_weighting = {'1'};
+            cdata_weighting = cell(1,5);
+            toolTip_weighting = ['<html>This table (required) allows the weighting of forcing data (e.g. rainfall, recharge or pumping). <br>', ...
+                          'into a groundwater head change. To build a weighting function, you''ll need to define the name of the model <br>', ...
+                          'componant, select a function type and then define the data to be weighted.</html>'];                          
+
+            
+            % Derived Weighting function column headings etc
+            cnames_weightingDerived = {'Select','Component Name','Weighting Function','Source Component','Input Data', 'Options'};
+            cformats_weightingDerived = {'logical','char', derivedWeightFunctions,'char','char','char'};    
+            cedit_weightingDerived = logical([1 1 1 1 1 1]);
+            rnames_weightingDerived = {'1'};
+            cdata_weightingDerived = cell(1,6);
+            toolTip_weightingDerived = ['<html>This table (optional) allows weighting of forcing data (e.g. rainfall, recharge <br>', ...
+                                               'or pumping) using a previously defined weighting function. </html>'];                          
+            
+            % Create the GUI elements
+            %--------------------------------------------------------------
+            % Create grid the model settings            
+            %this.Figure = uiextras.HBoxFlex('Parent',parent_handle,'Padding', 3, 'Spacing', 3);
+            this.Figure = uiextras.VBoxFlex('Parent',parent_handle,'Padding', 0, 'Spacing', 0);
+            
+            % Add box for the four model settings sub-boxes
+            this.modelComponants = uiextras.GridFlex('Parent', this.Figure,'Padding', 3, 'Spacing', 3);                                    
+            
+            % Build the forcing transformation settings items
+            this.forcingTranforms.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
+            this.forcingTranforms.lbl = uicontrol( 'Parent', this.forcingTranforms.vbox,'Style','text','String','1. Forcing Transform Function (optional)','Visible','on');
+            this.forcingTranforms.tbl = uitable(this.forcingTranforms.vbox,'ColumnName',cnames_forcing,...
+                'ColumnEditable',cedit_forcing,'ColumnFormat',cformats_forcing,'RowName',...
+                rnames_forcing ,'Data',cdata_forcing, 'Visible','on', 'Units','normalized', ...
+                'CellSelectionCallback', @this.tableSelection, ...
+                'CellEditCallback', @this.tableEdit, ...
+                'Tag','Forcing Transform', ...
+                'TooltipString',toolTip_forcing);
+            
+            set( this.forcingTranforms.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+
+%             % Find java sorting object in table
+%             jscrollpane = findjobj(this.forcingTranforms.tbl);
+%             jtable = jscrollpane.getViewport.getView;
+% 
+%             % Turn the JIDE sorting on
+%             jtable.setSortable(true);
+%             jtable.setAutoResort(true);
+%             jtable.setMultiColumnSortable(true);
+%             jtable.setPreserveSelectionsAfterSorting(true);            
+                                    
+            % Build the derived  forcing transformation settings items
+            this.derivedForcingTranforms.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
+            this.derivedForcingTranforms.lbl = uicontrol( 'Parent', this.derivedForcingTranforms.vbox,'Style','text','String','3. Derived Forcing Transform Function (optional)','Visible','on');
+            this.derivedForcingTranforms.tbl = uitable(this.derivedForcingTranforms.vbox,'ColumnName',cnames_forcingDerived, ...
+                'ColumnEditable',cedit_forcingDerived,'ColumnFormat',cformats_forcingDerived,'RowName',...
+                rnames_forcingDerived ,'Data',cdata_forcingDerived, 'Visible','on', 'Units','normalized', ...
+                'CellSelectionCallback', @this.tableSelection, ...
+                'CellEditCallback', @this.tableEdit, ...
+                'Tag','Derived Forcing Transform', ...
+                'TooltipString', toolTip_forcingDerived);
+            
+            set( this.derivedForcingTranforms.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
+
+%             % Find java sorting object in table
+%             jscrollpane = findjobj(this.derivedForcingTranforms.tbl);
+%             jtable = jscrollpane.getViewport.getView;
+% 
+%             % Turn the JIDE sorting on
+%             jtable.setSortable(true);
+%             jtable.setAutoResort(true);
+%             jtable.setMultiColumnSortable(true);
+%             jtable.setPreserveSelectionsAfterSorting(true);            
+                        
+            % Build the weighting function settings items
+            this.weightingFunctions.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
+            this.weightingFunctions.lbl = uicontrol( 'Parent', this.weightingFunctions.vbox,'Style','text','String','2. Weighting Functions (required)','Visible','on');
+            this.weightingFunctions.tbl = uitable(this.weightingFunctions.vbox,'ColumnName',cnames_weighting,...
+                'ColumnEditable',cedit_weighting,'ColumnFormat',cformats_weighting,'RowName',...
+                rnames_weighting ,'Data',cdata_weighting, 'Visible','on', 'Units','normalized', ...
+                'CellSelectionCallback', @this.tableSelection, ...
+                'CellEditCallback', @this.tableEdit, ...
+                'Tag','Weighting Functions', ...
+                'TooltipString', toolTip_weighting);
+            
+            set( this.weightingFunctions.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );    
+
+%             % Find java sorting object in table
+%             drawnow();
+%             jscrollpane = findjobj(this.weightingFunctions.tbl);
+%             jtable = jscrollpane.getViewport.getView;
+% 
+%             % Turn the JIDE sorting on
+%             jtable.setSortable(true);
+%             jtable.setAutoResort(true);
+%             jtable.setMultiColumnSortable(true);
+%             jtable.setPreserveSelectionsAfterSorting(true);            
+                        
+            % Build the derived weighting functions
+            this.derivedWeightingFunctions.vbox = uiextras.Grid('Parent', this.modelComponants,'Padding', 3, 'Spacing', 3);
+            this.derivedWeightingFunctions.lbl = uicontrol( 'Parent', this.derivedWeightingFunctions.vbox,'Style','text','String','4. Derived Weighting Functions (optional)','Visible','on');
+            this.derivedWeightingFunctions.tbl = uitable(this.derivedWeightingFunctions.vbox,'ColumnName',cnames_weightingDerived,...
+                'ColumnEditable',cedit_weightingDerived,'ColumnFormat',cformats_weightingDerived,'RowName',...
+                rnames_weightingDerived ,'Data',cdata_weightingDerived, 'Visible','on', 'Units','normalized', ...
+                'CellSelectionCallback', @this.tableSelection, ...
+                'CellEditCallback', @this.tableEdit, ...
+                'Tag','Derived Weighting Functions', ...
+                'TooltipString', toolTip_weightingDerived);
+            
+            set( this.derivedWeightingFunctions.vbox, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+            
+%             % Find java sorting object in table
+%             jscrollpane = findjobj(this.derivedWeightingFunctions.tbl);
+%             jtable = jscrollpane.getViewport.getView;
+% 
+%             % Turn the JIDE sorting on
+%             jtable.setSortable(true);
+%             jtable.setAutoResort(true);
+%             jtable.setMultiColumnSortable(true);
+%             jtable.setPreserveSelectionsAfterSorting(true);            
+
+            % Build the forcing transformation and weighting function options
+            %----------------------------------------
+            % Create box for the sub-boxes
+            this.modelOptions.grid = uiextras.Grid('Parent',this.Figure,'Padding', 3, 'Spacing', 3);
+            
+            % Add list box for selecting the input forcing data
+            %-------------------
+            cnames = {'Required Model Data', 'Input Forcing Data'};
+            cedit = logical([0 1]);
+            rnames = {'1'};
+            cdata = cell(1,2);
+            cformats = {'char', 'char'};
+                      
+            this.modelOptions.options{1,1}.ParentName = 'forcingTranforms';
+            this.modelOptions.options{1,1}.ParentSettingName = 'inputForcing';
+            this.modelOptions.options{1,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{1,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{1,1}.box,'Style','text','String','1. Forcing Transform - Input Data','Visible','on');     
+            this.modelOptions.options{1,1}.tbl =  uitable(this.modelOptions.options{1,1}.box,'ColumnName',cnames,...
+                                                'ColumnEditable',cedit,'ColumnFormat',cformats,'RowName',...
+                                                rnames,'Data',cdata, 'Visible','on', 'Units','normalized', ...
+                                                'CellEditCallback', @this.optionsSelection, ...                                                
+                                                'Tag','Forcing Transform - Input Data');
+            set(this.modelOptions.options{1,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+            
+            % Add context menu for Wizard.
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy all rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste all rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Wizard ...','Callback',@this.wizard,'Separator','on'); 
+            uimenu(contextMenu,'Label','Push "Required Data" to component','Callback',@this.push2component);
+                           
+            set(this.modelOptions.options{1,1}.tbl,'UIContextMenu',contextMenu);
+            set(this.modelOptions.options{1,1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{1,1}.tbl');
+            
+            % Add table for defining the transformation options eg soil
+            % moisture model parameters for calibration.
+            %-------------------
+            data = [];
+            this.modelOptions.options{2,1}.ParentName = 'forcingTranforms';
+            this.modelOptions.options{2,1}.ParentSettingName = 'options';            
+            this.modelOptions.options{2,1}.box = uiextras.Grid('Parent',  this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{2,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{2,1}.box,'Style','text','String','1. Forcing Transform - Model Settings','Visible','on');     
+            this.modelOptions.options{2,1}.tbl = uitable(this.modelOptions.options{2,1}.box,'ColumnName',{'Parameter','(none set)'}, ...
+                'ColumnEditable',true,'Data',[], ...
+                'CellEditCallback', @this.optionsSelection, ...    
+                'Tag','Forcing Transform - Model Settings', 'Visible','on');
+            set(this.modelOptions.options{2,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+               
+            % Add context menu for Wizard.
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy all rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste all rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(contextMenu,'Label','Wizard ...','Callback',@this.wizard);                
+            set(this.modelOptions.options{2,1}.tbl,'UIContextMenu',contextMenu);
+            set(this.modelOptions.options{2,1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2,1}.tbl');            
+            
+            % Add list box for selecting the weighting functions input
+            % data.
+            % NOTE: Multiple selection of input forcing data is allowed.
+            % This is defined in tableSelection().
+            %-------------------
+            this.modelOptions.options{3,1}.ParentName = 'weightingFunctions';
+            this.modelOptions.options{3,1}.ParentSettingName = 'inputForcing';                     
+            this.modelOptions.options{3,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{3,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{3,1}.box,'Style','text','String','2. Weighting Functions - Input Data','Visible','on');     
+            this.modelOptions.options{3,1}.lst = uicontrol('Parent',this.modelOptions.options{3,1}.box,'Style','list', 'BackgroundColor','w', ...
+                'String',{},'Value',1,'Tag','Weighting Functions - Input Data','Callback', @this.optionsSelection, 'Visible','on');
+            set(this.modelOptions.options{3,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+
+            % Add table for selecting the weighting functions options
+            %-------------------
+            this.modelOptions.options{4,1}.ParentName = 'weightingFunctions';
+            this.modelOptions.options{4,1}.ParentSettingName = 'options';                     
+            this.modelOptions.options{4,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{4,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{4,1}.box,'Style','text','String','2. Weighting Functions - Model Settings','Visible','on');     
+            this.modelOptions.options{4,1}.tabs = uiextras.TabPanel( 'Parent', this.modelOptions.options{4,1}.box, 'Padding', 5, 'TabSize',127,'FontSize',8, 'Tag','Weighting Functions - Options');
+            
+            tab1 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 1');            
+            tab2 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 2');
+            tab3 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 3');            
+            tab4 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 4');            
+            tab5 = uiextras.Panel( 'Parent', this.modelOptions.options{4,1}.tabs , 'Padding', 5, 'Tag','Weighting Functions - Options Tab 5');            
+            
+            uitable( tab1,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 1 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            uitable( tab2,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 2 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            uitable( tab3,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 3 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            uitable( tab4,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 4 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            uitable( tab5,'ColumnName',{'(none)'}, 'ColumnEditable',true,'Data',[], 'Tag','Weighting Functions - Options Tab 5 table', 'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            set(this.modelOptions.options{4,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
+            this.modelOptions.options{4,1}.tabs.TabNames = {'Option 1', 'Option 2','Option 3', 'Option 4','Option 5'};
+            this.modelOptions.options{4,1}.tabs.SelectedChild = 1;            
+            
+            % Add table for defining the transformation options eg soil
+            % moisture model parameters for calibration.            
+            %-------------------
+            this.modelOptions.options{5,1}.ParentName = 'DerivedForcingTransformation';
+            this.modelOptions.options{5,1}.ParentSettingName = 'inputForcing';                     
+            this.modelOptions.options{5,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{5,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{5,1}.box,'Style','text','String','3. Derived Forcing Transform - Input Data','Visible','on');     
+            this.modelOptions.options{5,1}.lst = uicontrol('Parent',this.modelOptions.options{5,1}.box,'Style','list', 'BackgroundColor','w', ...
+                'String',{},'Value',1, ...
+                'Tag','Derived Forcing Functions - Source Function', ...
+                'Callback', @this.optionsSelection, 'Visible','on');
+            set(this.modelOptions.options{5,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );            
+
+            % Add table for derived forcing inut data options
+            %-------------------
+            this.modelOptions.options{6,1}.ParentName = 'DerivedForcingTransformation';           
+            this.modelOptions.options{6,1}.ParentSettingName = 'inputForcing';
+            this.modelOptions.options{6,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{6,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{6,1}.box, ...
+                'Style','text', ...
+                'String','3. Derived Forcing Transform - Input Data','Visible','on');     
+            this.modelOptions.options{6,1}.tbl =  uitable(this.modelOptions.options{6,1}.box,'ColumnName',cnames,...
+                'ColumnEditable',cedit,'ColumnFormat',cformats,'RowName',...
+                rnames,'Data',cdata, 'Visible','on', 'Units','normalized', ...
+                'CellEditCallback', @this.optionsSelection, ...                                                
+                'Tag','Derived Forcing Functions - Input Data');
+            set(this.modelOptions.options{6,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+                        
+            % Add table for derived forcing options
+            %-------------------
+            this.modelOptions.options{7,1}.ParentName = 'DerivedForcingTransformation';
+            this.modelOptions.options{7,1}.ParentSettingName = 'options';            
+            this.modelOptions.options{7,1}.box = uiextras.Grid('Parent',  this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{7,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{7,1}.box,'Style','text','String','3. Derived Forcing Transform - Model Settings','Visible','on');     
+            this.modelOptions.options{7,1}.tbl = uitable(this.modelOptions.options{7,1}.box,'ColumnName',{'Parameter','(none set)'}, ...
+                'ColumnEditable',true,'Data',[], ...
+                'CellEditCallback', @this.optionsSelection, ...    
+                'Tag','Derived Forcing Transform - Model Settings', 'Visible','on');
+            set(this.modelOptions.options{7,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+
+            % Add list box for selecting the derived weighting functions input
+            % data.
+            % NOTE: Multiple selection of input forcing data is allowed.
+            % This is defined in tableSelection().
+            %-------------------
+            this.modelOptions.options{8,1}.ParentName = 'derivedWeightingFunctions';
+            this.modelOptions.options{8,1}.ParentSettingName = 'inputForcing';                     
+            this.modelOptions.options{8,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{8,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{8,1}.box,'Style','text','String','4. Derived Weighting Functions - Input Data','Visible','on');     
+            this.modelOptions.options{8,1}.lst = uicontrol('Parent',this.modelOptions.options{8,1}.box,'Style','list', 'BackgroundColor','w', ...
+                'String',{},'Value',1,'Tag','Derived Weighting Functions - Input Data','Callback', @this.optionsSelection, 'Visible','on');
+            set(this.modelOptions.options{8,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );
+
+            % Add table for selecting the derived weighting functions options
+            %-------------------
+            this.modelOptions.options{9,1}.ParentName = 'derivedWeightingFunctions';
+            this.modelOptions.options{9,1}.ParentSettingName = 'options';                     
+            this.modelOptions.options{9,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{9,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{9,1}.box,'Style','text','String','4. Derived Weighting Functions - Model Settings','Visible','on');     
+            this.modelOptions.options{9,1}.tbl = uitable(this.modelOptions.options{9,1}.box,'ColumnName',{'(none)'}, ...
+                'ColumnEditable',true,'Data',[], 'Tag','Derived Weighting Functions - Model Settings', ...
+                'CellEditCallback', @this.optionsSelection, 'Visible','on');
+            set(this.modelOptions.options{9,1}.box, 'ColumnSizes', -1, 'RowSizes', [20 -1] );                                   
+            
+            % Add label for general communications to user eg to state that
+            % a weighting fnction has no options available.
+            %-------------------
+            this.modelOptions.options{10,1}.ParentName = 'general';
+            this.modelOptions.options{10,1}.ParentSettingName = 'general';                     
+            this.modelOptions.options{10,1}.box = uiextras.Grid('Parent', this.modelOptions.grid,'Padding', 3, 'Spacing', 3);                        
+            this.modelOptions.options{10,1}.lbl = uicontrol( 'Parent', this.modelOptions.options{10,1}.box,'Style','text','String','(empty)','Visible','on');                 
+            %----------------------------------------
+
+            % Add context menu for adding /deleting rows
+            % NOTE: UIContextMenu.UserData is used to store the table name
+            % for the required operation.
+            %----------------------------------------
+            % Create menu for forcing transforms
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);                
+            set(this.forcingTranforms.tbl,'UIContextMenu',contextMenu);
+            set(this.forcingTranforms.tbl.UIContextMenu,'UserData', 'this.forcingTranforms.tbl');
+            
+            % Create menu for weighting functions
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+            set(this.weightingFunctions.tbl,'UIContextMenu',contextMenu);
+            set(this.weightingFunctions.tbl.UIContextMenu,'UserData', 'this.weightingFunctions.tbl');
+            
+            % Create menu for derived forcing transforms
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+            set(this.derivedForcingTranforms.tbl,'UIContextMenu',contextMenu);
+            set(this.derivedForcingTranforms.tbl.UIContextMenu,'UserData', 'this.derivedForcingTranforms.tbl');
+
+            % Create menu for derived weighting functions
+            contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+            uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+            uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+            uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+            uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+            set(this.derivedWeightingFunctions.tbl,'UIContextMenu',contextMenu);
+            set(this.derivedWeightingFunctions.tbl.UIContextMenu,'UserData', 'this.derivedWeightingFunctions.tbl');
+            
+            %----------------------------------------            
+            % Set dimensions for the grid     
+            set( this.modelComponants, 'ColumnSizes', [-1 -1], 'RowSizes', [-1 -1] );      
+            this.Figure.Heights = [-1 -1];
+            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Heights));
+        end
+        
+        function initialise(this)
+            
+        end
+        
+        function setForcingData(this, fname)
+
+             % Check fname file exists.
+             if exist(fname,'file') ~= 2;
+                warndlg(['The following forcing data file does not exist:', fname]);
+                return;
+             end
+
+             % Read in the file.
+             try
+                tbl = readtable(fname);
+             catch
+                warndlg(['The following forcing data file could not be read in. It must a .csv file of at least 4 columns (year, month, day, value):',fname]);
+                clear tbl;
+                return;
+             end
+
+             % Check there are sufficient number of columns
+             if length(tbl.Properties.VariableNames) <4
+                warndlg(['The following forcing data file must contain at least 4 columns (year, month, day, value):',fname]);
+                clear tbl;
+                return;
+             end
+
+             % Check all columns are numeric.
+             for i=1:size(tbl,2)
+                if any(~isnumeric(tbl{:,i}))
+                    warndlg(['Column ',num2str(i), ' within the following forcing data file contains non-numeric data:',fname]);
+                    clear tbl;
+                    return;
+                end                    
+             end
+
+             % Set the column names.
+             this.forcingData.colnames = tbl.Properties.VariableNames;
+             this.forcingData.data = tbl;
+             
+             % Clear tbl
+             clear tbl;
+            
+        end
+        
+        function setCoordinatesData(this, fname)
+             % Check fname file exists.
+             if exist(fname,'file') ~= 2;
+                warndlg(['The following site coordinates file does not exist:', fname]);
+                return;
+             end
+
+             % Read in the file.
+             try
+                tbl = readtable(fname);
+             catch
+                warndlg(['The following site coordinates file could not be read in. It must a .csv file of 3 columns (site ID, easting, northing):',fname]);
+                return;
+             end
+
+             % Check there are sufficient number of columns
+             if length(tbl.Properties.VariableNames) ~=3
+                warndlg(['The following site coordinates file must contain 3 columns  (site ID, easting, northing):',fname]);
+                return;
+             end
+
+             % Check all columns are numeric.
+             if any(any(~isnumeric(tbl{:,2:3})))
+                warndlg(['Columns 2 and 3 within the following site coordinates file must contain only numeric data:',fname]);
+                return;
+             end
+
+             % Check if the Bore ID is listed in the coordinates file
+             if ~isempty(this.boreID) && ~any(strcmp(tbl{:,1},this.boreID))
+                 warndlg(['The following bore is nost listed in the coordinates files: ',this.boreID]);
+             end
+             
+             % Set the site data.
+             this.siteData = tbl;
+             
+             % Clear tbl
+             clear tbl;
+            
+        end
+        
+        function setBoreID(this, boreID)
+            this.boreID = boreID;
+        end
+        
+        function setModelOptions(this, modelOptionsStr)
+            
+            % Convert model options string to a cell array
+            if isempty(modelOptionsStr)
+                modelOptions = cell(0,3);
+            else
+                modelOptions = eval(modelOptionsStr);                  
+            end
+            
+            % Get the name of all model components
+            componentNames = unique( modelOptions(:,1));
+            
+            % Initialise some variables            
+            transformFunctionName = [];
+            transformForcingdata = [];
+            forcingDataforWeighting = [];
+            transformOptions = [];
+            transformInputcomponent = [];
+            
+            % Clear GUI tables
+            this.weightingFunctions.tbl.Data = cell(0, 5);
+            this.derivedWeightingFunctions.tbl.Data = cell(0, 6);
+            this.forcingTranforms.tbl.Data = cell(0, 4);
+            this.derivedForcingTranforms.tbl.Data = cell(0, 5);
+            
+            % Loop through each weighting function and add the data to the
+            % GUI table. Also, get the cell arrays for the transformation
+            % functions.
+            for i=1:size(componentNames,1)
+                
+                % Find the cell options for the current weighting function
+                compentFilter = cellfun( @(x) strcmp(x, componentNames{i}), modelOptions(:,1));
+                
+                % Get model options relating to the current component.
+                modelOption_tmp = modelOptions(compentFilter,:);
+                
+                % initilise variables
+                weightingFunctionName = '';
+                inputcomponentName = '';
+                forcingdataName = '';
+                optionsName = '';
+                transformFunctionName = '';
+                transformForcingdata = '';
+                forcingDataforWeighting = '';
+                transformInputcomponent = '';
+                transformOptions = '';
+                
+                % Get each term for the component
+                weightingFunctionName_filt = cellfun( @(x) strcmp(x, 'weightingfunction'), modelOption_tmp(:,2));
+                if any(weightingFunctionName_filt)
+                    weightingFunctionName = modelOption_tmp{weightingFunctionName_filt,3};
+                end
+                
+                inputcomponentName_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), modelOption_tmp(:,2));
+                if any(inputcomponentName_filt)
+                    inputcomponentName = modelOption_tmp{inputcomponentName_filt,3};
+                end
+                
+                forcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), modelOption_tmp(:,2));
+                if any(forcingdata_filt)
+                    forcingdataName = modelOption_tmp{forcingdata_filt,3};
+                end
+                
+                options_filt = cellfun( @(x) strcmp(x, 'options'), modelOption_tmp(:,2));
+                if any(options_filt)
+                    optionsName = modelOption_tmp{options_filt,3}; 
+                    for k=1:length(optionsName)
+                        if ~isempty(optionsName{k}) && iscell(optionsName{k})
+                            optionsName{k} = model_TFN_gui.cell2string(optionsName{k}, []);
+                        else
+                            optionsName{k}='{}';
+                        end                        
+                    end
+                    optionsName = ['{',strjoin(optionsName),'}'];
+                end
+                
+                % Check if the forcing data uses a transformation function.
+                % If so, extract the data and get the output required for
+                % the weighting function. Else if the forcing data is not
+                % empty, then assign the required input data.
+                hasTransformFunction = false;
+                isFullTransformationFunction = false;
+                if ~isempty(forcingdataName) && iscell(forcingdataName) && ...
+                (size(forcingdataName,2)==2 || (size(forcingdataName,2)==1 && size(forcingdataName{1},2)==2))
+                    hasTransformFunction = true;
+                    
+                    % If there are multiple input forcing data (eg
+                    % multiple groundwater pumps) then find the one that
+                    % lists all of the required inputs.
+                    forcingDataforWeighting={};
+                    if (size(forcingdataName,2)==1 && size(forcingdataName{1},2)==2)                    
+                        for k=1:size(forcingdataName,1)
+                            forcingdataNameTmp = forcingdataName{k};
+                            transformFunctionName_filt = cellfun( @(x) strcmp(x, 'transformfunction'), forcingdataNameTmp(:,1));
+                            if any(transformFunctionName_filt)
+                                transformFunctionName = forcingdataNameTmp{transformFunctionName_filt,2};
+                            end
+
+                            transformForcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), forcingdataNameTmp(:,1));
+                            if any(transformForcingdata_filt)
+                                transformForcingdata = forcingdataNameTmp{transformForcingdata_filt,2};
+                            end
+
+                            forcingDataforWeighting_filt = cellfun( @(x) strcmp(x, 'outputdata'), forcingdataNameTmp(:,1));
+                            if any(forcingDataforWeighting_filt )
+                                if isempty(forcingDataforWeighting)
+                                    forcingDataforWeighting{1,1} = forcingdataNameTmp{forcingDataforWeighting_filt,2};
+                                else
+                                    forcingDataforWeighting{k,1} = forcingdataNameTmp{forcingDataforWeighting_filt,2};
+                                end
+                            end
+
+                            transformOptions_filt = cellfun( @(x) strcmp(x, 'options'), forcingdataNameTmp(:,1));
+                            if any(transformOptions_filt)
+                                transformOptions = forcingdataNameTmp{transformOptions_filt,2};
+                            end
+
+                            transformInputcomponent_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), forcingdataNameTmp(:,1));
+                            if any(transformInputcomponent_filt)
+                                transformInputcomponent = forcingdataNameTmp{transformInputcomponent_filt,2};                    
+                            end                           
+  
+                        end
+                        
+                    else
+                        transformFunctionName_filt = cellfun( @(x) strcmp(x, 'transformfunction'), forcingdataName(:,1));
+                        if any(transformFunctionName_filt)
+                            transformFunctionName = forcingdataName{transformFunctionName_filt,2};
+                        end
+
+                        transformForcingdata_filt = cellfun( @(x) strcmp(x, 'forcingdata'), forcingdataName(:,1));
+                        if any(transformForcingdata_filt)
+                            transformForcingdata = forcingdataName{transformForcingdata_filt,2};
+                        end
+
+                        forcingDataforWeighting_filt = cellfun( @(x) strcmp(x, 'outputdata'), forcingdataName(:,1));
+                        if any(forcingDataforWeighting_filt )
+                            forcingDataforWeighting = forcingdataName{forcingDataforWeighting_filt,2};
+                        end
+
+                        transformOptions_filt = cellfun( @(x) strcmp(x, 'options'), forcingdataName(:,1));
+                        if any(transformOptions_filt)
+                            transformOptions = forcingdataName{transformOptions_filt,2};
+                        end
+
+                        transformInputcomponent_filt = cellfun( @(x) strcmp(x, 'inputcomponent'), forcingdataName(:,1));
+                        if any(transformInputcomponent_filt)
+                            transformInputcomponent = forcingdataName{transformInputcomponent_filt,2};                    
+                        end                        
+                    end
+                    
+                    % Check if the full transformation model is defined or
+                    % is it just the output data for a tranformation model
+                    % modle defined for another weighting function.
+                    isFullTransformationFunction = true;
+                    if ~isempty(transformFunctionName) && ~isempty(forcingDataforWeighting) && ...
+                       isempty(transformForcingdata) && isempty(transformInputcomponent) && isempty(transformOptions)
+                        isFullTransformationFunction = false;
+                    end
+                elseif ~isempty(forcingdataName)
+                    % Data is assumed to be name of an input data column.
+                    forcingDataforWeighting = forcingdataName;                    
+                end
+                
+                % Add the weight function data to the GUI table. In doing
+                % so, check if it is to be input to the weighting function
+                % or derived weighting function.
+                if isempty(inputcomponentName)
+                   ind = size(this.weightingFunctions.tbl.Data,1)+1;
+                   if ind==1
+                        this.weightingFunctions.tbl.Data = cell(1,size(this.weightingFunctions.tbl.Data,2)); 
+                   else
+                       this.weightingFunctions.tbl.Data = [this.weightingFunctions.tbl.Data; cell(1,size(this.weightingFunctions.tbl.Data,2))]; 
+                   end
+                   this.weightingFunctions.tbl.Data{ind,2} = componentNames{i};
+                   this.weightingFunctions.tbl.Data{ind,3} = weightingFunctionName;
+                   this.weightingFunctions.tbl.Data{ind,5} = optionsName;
+                   this.weightingFunctions.tbl.RowName = [1:size(this.weightingFunctions.tbl.Data,1)];
+
+                   % Check if the input comes from the output of a
+                   % transformation function.                   
+                   if hasTransformFunction
+                       if iscell(forcingDataforWeighting)
+                           
+                            nForcingInputs = size(forcingDataforWeighting,1);
+                            LHS = cellstr(repmat([transformFunctionName, ' :'],nForcingInputs,1));
+                            RHS = cell(nForcingInputs,1);
+                            RHS(:) = {' '};
+                            LHS = strcat(LHS,RHS);
+                            
+%                             switch nForcingInputs
+%                                 case 1;
+%                                     LHS  = strcat(LHS, {' '});
+%                                 case 2;
+%                                     LHS  = strcat(LHS, {' ';' '});
+%                                 case 3;
+%                                     LHS  = strcat(LHS, {' ';' ';' '});
+%                                 case 4;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' '});
+%                                 case 5;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' '});
+%                                 case 6;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' '});
+%                                 case 7;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' '});
+%                                 case 8;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 case 9;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 case 10;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 otherwise
+%                                     warndlg('A maximum of 10 forcing data inputs can be assigned to a weighting function')
+%                                     nForcingInputs = 10;
+%                             end
+
+                            forcingDataforWeighting = strcat( LHS, forcingDataforWeighting(1:nForcingInputs));
+                            forcingDataforWeighting = model_TFN_gui.cell2string(forcingDataforWeighting,'');
+                            this.weightingFunctions.tbl.Data{ind,4} = forcingDataforWeighting;
+                            
+                                                                                    
+                       else
+                            this.weightingFunctions.tbl.Data{ind,4} = [transformFunctionName, ' : ', forcingDataforWeighting];
+                       end
+                   else
+                       if iscell(forcingDataforWeighting)
+                           
+                            nForcingInputs = size(forcingDataforWeighting,1);
+                            LHS = cellstr(repmat('Input Data : ',nForcingInputs,1));
+                            RHS = cell(nForcingInputs,1);
+                            RHS(:) = {' '};
+                            LHS = strcat(LHS,RHS);
+%                             switch nForcingInputs
+%                                 case 1;
+%                                     LHS  = strcat(LHS, {' '});
+%                                 case 2;
+%                                     LHS  = strcat(LHS, {' ';' '});
+%                                 case 3;
+%                                     LHS  = strcat(LHS, {' ';' ';' '});
+%                                 case 4;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' '});
+%                                 case 5;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' '});
+%                                 case 6;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' '});
+%                                 case 7;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' '});
+%                                 case 8;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 case 9;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 case 10;
+%                                     LHS  = strcat(LHS, {' ';' ';' ';' ';' ';' ';' ';' ';' ';' '});
+%                                 otherwise
+%                                     warndlg('A maximum of 10 forcing data inputs can be assigned to a weighting function')
+%                                     nForcingInputs = 10;
+%                             end
+                            forcingDataforWeighting = strcat( LHS, forcingDataforWeighting(1:nForcingInputs));
+                            forcingDataforWeighting = model_TFN_gui.cell2string(forcingDataforWeighting,'');
+                            this.weightingFunctions.tbl.Data{ind,4} = forcingDataforWeighting;
+                            
+                       else
+                           this.weightingFunctions.tbl.Data{ind,4} = ['Input Data : ', forcingDataforWeighting];
+                       end
+                   end
+                    
+                   
+                    
+                else
+                   ind = size(this.derivedWeightingFunctions.tbl.Data,1)+1;
+                   if ind==1
+                        this.derivedWeightingFunctions.tbl.Data = cell(1,size(this.derivedWeightingFunctions.tbl.Data,2)); 
+                   else
+                       this.derivedWeightingFunctions.tbl.Data = [this.derivedWeightingFunctions.tbl.Data; cell(1,size(this.derivedWeightingFunctions.tbl.Data,2))]; 
+                   end                   
+                   this.derivedWeightingFunctions.tbl.Data{ind,2} = componentNames{i};
+                   this.derivedWeightingFunctions.tbl.Data{ind,3} = weightingFunctionName;
+                   this.derivedWeightingFunctions.tbl.Data{ind,4} = inputcomponentName; 
+                   this.derivedWeightingFunctions.tbl.Data{ind,6} = optionsName; 
+                   this.derivedWeightingFunctions.tbl.RowName = [1:size(this.derivedWeightingFunctions.tbl.Data,1)];
+                   
+                   % Check if the input comes from the output of a
+                   % transformation function.
+                   if hasTransformFunction
+                       if iscell(forcingDataforWeighting)
+                            this.derivedWeightingFunctions.tbl.Data{ind,5} = strcat( [transformFunctionName, ' : '], forcingDataforWeighting);
+                       else
+                            this.derivedWeightingFunctions.tbl.Data{ind,5} = [transformFunctionName, ' : ', forcingDataforWeighting];
+                       end
+                   else
+                       if iscell(forcingDataforWeighting)
+                            this.derivedWeightingFunctions.tbl.Data{ind,5} = strcat( ['Input Data : ', forcingDataforWeighting]);
+                       else
+                           this.derivedWeightingFunctions.tbl.Data{ind,5} = ['Input Data : ', forcingDataforWeighting];
+                       end
+                   end
+                    
+                                      
+                end
+                
+                % Add the forcing transforation data to the GUI tables
+                if hasTransformFunction && isFullTransformationFunction
+                   
+                    if isempty(transformInputcomponent)
+                        ind = size(this.forcingTranforms.tbl.Data,1)+1;
+                        if ind==1
+                            this.forcingTranforms.tbl.Data = cell(1,size(this.forcingTranforms.tbl.Data,2)); 
+                        else
+                            this.forcingTranforms.tbl.Data = [this.forcingTranforms.tbl.Data; cell(1,size(this.forcingTranforms.tbl.Data,2))]; 
+                        end                             
+                        this.forcingTranforms.tbl.Data{ind,2} = transformFunctionName;
+                        
+                        if ~isempty(transformForcingdata) && iscell(transformForcingdata)
+                            transformForcingdata = model_TFN_gui.cell2string(transformForcingdata, []);
+                        end
+                        this.forcingTranforms.tbl.Data{ind,3} = transformForcingdata;
+
+                        if ~isempty(transformOptions) && iscell(transformOptions)
+                            transformOptions = model_TFN_gui.cell2string(transformOptions, []);
+                        end 
+                        this.forcingTranforms.tbl.Data{ind,4} = transformOptions;
+                        this.forcingTranforms.tbl.RowName = [1:size(this.forcingTranforms.tbl.Data,1)];
+                    else
+                        ind = size(this.derivedForcingTranforms.tbl.Data,1)+1;
+                        if ind==1
+                            this.derivedForcingTranforms.tbl.Data = cell(1,size(this.derivedForcingTranforms.tbl.Data,2)); 
+                        else
+                            this.derivedForcingTranforms.tbl.Data = [this.derivedForcingTranforms.tbl.Data; cell(1,size(this.derivedForcingTranforms.tbl.Data,2))]; 
+                        end                            
+                        this.derivedForcingTranforms.tbl.Data{ind,2} = transformFunctionName;
+                        this.derivedForcingTranforms.tbl.Data{ind,3} = transformInputcomponent;                        
+                        
+                        if ~isempty(transformForcingdata) && iscell(transformForcingdata)
+                            transformForcingdata = model_TFN_gui.cell2string(transformForcingdata, []);
+                        end
+                        this.derivedForcingTranforms.tbl.Data{ind,4} = transformForcingdata;
+
+                        if ~isempty(transformOptions) && iscell(transformOptions)
+                            transformOptions = model_TFN_gui.cell2string(transformOptions, []);
+                        end 
+                        this.derivedForcingTranforms.tbl.Data{ind,5} = transformOptions;                        
+                        this.derivedForcingTranforms.tbl.RowName = [1:size(this.derivedForcingTranforms.tbl.Data,1)];
+                    end
+                    
+                    
+                end     
+            end            
+            
+            
+        end
+        
+        function modelOptionsArray = getModelOptions(this)
+            % Convert forcing tranformation and derived forcing tranformation functions to strings.
+            for k=1:2
+                if k==1
+                    cellData  = this.forcingTranforms.tbl.Data;                    
+                else
+                    cellData  = this.derivedForcingTranforms.tbl.Data;
+                end
+                
+                % Goto next loop if all of cell data is empty
+                if size(cellData ,1)==1 && all(cellfun(@(x) isempty(x),cellData))
+                    continue;
+                end
+                
+                % Loop though each row of cell data
+                for j=1:size(cellData ,1);
+                   stringCell = '{';           
+                   stringCell = strcat(stringCell, sprintf(' ''transformfunction'',  ''%s'';',cellData{j,2} ));
+                   if k==2
+                       stringCell = strcat(stringCell, sprintf(' ''inputcomponent'',  ''%s'';',cellData{j,3} ));               
+                   end
+                   stringCell = strcat(stringCell, sprintf(' ''forcingdata'', ''%s'';',cellData{j,2+k} )); 
+                   if ~isempty(cellData{j,3+k})
+                        stringCell = strcat(stringCell, sprintf(' ''options'', ''%s'';',cellData{j,3+k} )); 
+                   end
+                   stringCell = strcat(stringCell, '}'); 
+                   forcingString.(cellData{j,2}) = stringCell;
+
+                   % Initialise logical variable denoting if its already been
+                   % inserted.
+                   forcingStringInserted.(cellData{j,2}) = false;
+                end
+            end
+                       
+            % Convert weighting functions to string.
+            modelOptionsArray =  '{';
+            
+            for k=1:2
+                if k==1
+                    cellData  = this.weightingFunctions.tbl.Data;
+                else
+                    cellData  = this.derivedWeightingFunctions.tbl.Data;
+                end
+                    
+                % Goto next loop if all of cell data is empty
+                if size(cellData ,1)==1 && all(cellfun(@(x) isempty(x),cellData))
+                    continue;
+                end
+                
+                % Loop through each row of weighting table data
+                for i=1:size(cellData ,1);        
+                    
+                   % Add weighting function name. 
+                   modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''weightingfunction'',  ''%s'';',cellData{i,2},cellData{i,3} ));
+
+                   % Add source componant name. 
+                   if k==2
+                        modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''inputcomponent'',  ''%s'';',cellData{i,2},cellData{i,4} ));
+                   end
+
+                   % Add model options 
+                   if ~isempty(cellData{i,4+k})
+                        modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''options'', %s;',cellData{i,2},cellData{i,4+k} )); 
+                   end                   
+                                      
+                   % Convert forcing data to a cell array
+                   try
+                       forcingColNames =  eval(cellData{i,3+k});
+                   catch
+                       forcingColNames =  cellData(i,3+k);
+                   end
+
+                   %Check if there is any focring data.
+                   if isempty(forcingColNames{1})
+                       continue;
+                   end
+                   
+                   % Loop through each forcing data input.
+                   for j=1:length(forcingColNames)
+
+                       % Insert input data.                     
+                       if ~isempty(strfind(forcingColNames{j}, 'Input Data : '))
+                           [ind_start, ind_end] = regexp(forcingColNames{j}, 'Input Data : ');
+                           forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);
+                           ind_start = regexp(forcingColNames_tmp, '''');
+                           if ~isempty(ind_start)
+                                forcingColNames_tmp =  forcingColNames_tmp(1:ind_start-1);
+                           end
+                       else
+                           % Find function name
+                           forcingFunc_Ind = regexp(forcingColNames{j}, ' : ');  
+                           forcingFuncName = forcingColNames{j}(1:forcingFunc_Ind-1);
+
+                           % Insert cell array string for required function name.
+                           % If the forcing function has already been inserted then
+                           % just build a string for the forcing to be extracted
+                           % from it.
+                           if ~forcingStringInserted.(forcingFuncName) 
+                                % Get just output name from string
+                               [ind_start, ind_end] = regexp(forcingColNames{j}, [forcingFuncName,' : ']);
+                               forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);
+
+                                % Add required output from weighting function to forcing function cell array
+                                forcingString.(forcingFuncName) = forcingString.(forcingFuncName)(1:end-1);
+                                forcingString.(forcingFuncName) = strcat(forcingString.(forcingFuncName), ...
+                                     sprintf(' ''outputdata'' , ''%s'' };', forcingColNames_tmp ) );
+                                forcingColNames_tmp = forcingString.(forcingFuncName);
+                                forcingStringInserted.(forcingFuncName) = true;
+                           else
+
+                                % Get just output name from string
+                               [ind_start, ind_end] = regexp(forcingColNames{j}, [forcingFuncName,' : ']);
+                               forcingColNames_tmp =  forcingColNames{j}(ind_end+1:end);                           
+
+                               % Create cell array for an already created
+                               % forcing transform function but only declare
+                               % the forcing to be taken from the function.
+                               forcingColNames_tmp = { 'transformfunction', forcingFuncName; 'outputdata', forcingColNames_tmp};
+
+                               % Convert to a string
+                               className = metaclass(this);
+                               className = className.Name;
+                               colnames = {'component','property','value'};
+                               forcingColNames_tmp =  eval([className,'.cell2string(forcingColNames_tmp, colnames)']);                           
+                           end
+
+                       end
+
+                       % Add forcing string to the model options string
+                       if length(forcingColNames)==1
+                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''forcingdata'', ''%s'';',cellData{i,2}, forcingColNames_tmp ));                            
+                       elseif length(forcingColNames)>1 && j==1
+                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'', ''forcingdata'', { ''%s'';',cellData{i,2}, forcingColNames_tmp ));                            
+                       elseif length(forcingColNames)>1 && j==length(forcingColNames)
+                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'' };',forcingColNames_tmp ));                                                    
+                       else                           
+                            modelOptionsArray = strcat(modelOptionsArray, sprintf(' ''%s'';',forcingColNames_tmp ));
+                       end                       
+                   end
+
+                end
+            end
+
+            modelOptionsArray= strcat(modelOptionsArray, '}'); 
+
+            % Remove any ' symbols for cell arrays internal to the larger
+            % string.
+            while ~isempty(strfind(modelOptionsArray, '}''' ))
+                ind = strfind(modelOptionsArray, '}''' );
+                ind = ind(1);
+                modelOptionsArray = [modelOptionsArray(1:ind),  modelOptionsArray(ind+2:end)];
+            end
+            while ~isempty(strfind(modelOptionsArray, '''{' ))
+                ind = strfind(modelOptionsArray, '''{' );
+                ind = ind(1);
+                modelOptionsArray = [modelOptionsArray(1:ind-1),  modelOptionsArray(ind+1:end)];
+            end            
+            while ~isempty(strfind(modelOptionsArray, ';'';' ))
+                ind = strfind(modelOptionsArray, ';'';' );
+                ind = ind(1);
+                modelOptionsArray = [modelOptionsArray(1:ind),  modelOptionsArray(ind+3:end)];
+            end             
+        end
+        
+        
+        function tableSelection(this, hObject, eventdata)
+            icol=[];
+            irow=[];
+            if isprop(eventdata, 'Indices')
+                if ~isempty(eventdata.Indices)
+                    icol=eventdata.Indices(:,2);
+                    irow=eventdata.Indices(:,1);  
+                end
+            end
+                            
+            % Record the current table, row, col if the inputs are
+            % not empty. Else, extract the exiting values from
+            % this.currentSelection                    
+            if ~isempty(irow) && ~isempty(icol)
+                this.currentSelection.row = irow;
+                this.currentSelection.col= icol;
+                this.currentSelection.table = eventdata.Source.Tag;
+            else
+                irow = this.currentSelection.row;
+                icol = this.currentSelection.col;                                                
+            end
+
+            % Undertake table/list specific operations.
+            switch eventdata.Source.Tag;
+                case 'Forcing Transform'
+                    
+                    % Add a row if the table is empty
+                    if size(get(hObject,'Data'),1)==0
+                        this.forcingTranforms.tbl.Data = cell(1,size(this.forcingTranforms.tbl.Data,2));
+                    end
+
+                    % Get the forcing function name.
+                    if irow > size(this.forcingTranforms.tbl.Data,1)
+                        return;
+                    end
+                    funName = this.forcingTranforms.tbl.Data{irow, 2};
+
+                    switch eventdata.Source.ColumnName{icol};
+                        case 'Forcing Transform Function'
+                        
+                            % Get description of the function.
+                            functionName = this.forcingTranforms.tbl.Data{this.currentSelection.row, 2};
+                            if isempty(functionName)
+                                return;
+                            end
+                            modelDescription = eval([functionName,'.modelDescription']);
+                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
+                            this.modelOptions.options{10,1}.lbl.String = {'1. Forcing Transform - Function description','',modelDescription{:}};
+                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];   
+
+                        case 'Input Data'
+                           % Define the drop down options for the input
+                           % data
+                           dropdownOptions = sort(this.forcingData.colnames(4:end));
+                           this.modelOptions.options{1, 1}.tbl.ColumnFormat = {'char',{'(none)' dropdownOptions{:}} }; 
+                            
+                           % Call function method giving required
+                           % variable name.
+                           requiredVariables = feval(strcat(funName,'.inputForcingData_required'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+
+                           % Add row for each required variable
+                           if isempty(this.forcingTranforms.tbl.Data{this.currentSelection.row ,3})
+                               this.modelOptions.options{1, 1}.tbl.Data = cell(size(requiredVariables,1),2);
+                               this.modelOptions.options{1, 1}.tbl.RowName=cell(1,length(requiredVariables)); 
+                               for i=1:length(requiredVariables)                                   
+                                    this.modelOptions.options{1, 1}.tbl.Data{i,1}= requiredVariables{i};
+                                    this.modelOptions.options{1, 1}.tbl.RowName{i}= num2str(i);
+                               end
+                           else
+                               try
+                                   this.modelOptions.options{1, 1}.tbl.Data = cell(0,2);
+                                   this.modelOptions.options{1, 1}.tbl.RowName = cell(size(this.modelOptions.options{1, 1}.tbl.Data,1),1);
+                                   this.modelOptions.options{1, 1}.tbl.Data = eval(this.forcingTranforms.tbl.Data{irow,3});
+                                   for i=1:size(this.modelOptions.options{1, 1}.tbl.Data,1)                                                                           
+                                        this.modelOptions.options{1, 1}.tbl.RowName{i}= num2str(i);
+                                   end
+                               catch
+                                   warndlg('The input string appears to have a syntax error. It should be an Nx2 cell array.');                                       
+                               end
+                           end
+
+                           % Display table
+                           this.modelOptions.grid.Widths = [-1 0 0 0 0 0 0 0 0 0];
+
+                        case 'Options' 
+
+                           % Get the model options.
+                           [modelSettings, colNames, colFormats, colEdits, tooltips] = feval(strcat(funName,'.modelOptions'));
+
+                           % Check if options are available.
+                           if isempty(colNames)                          
+                                this.modelOptions.options{2,1}.lbl.String = {'1. Forcing Transform - Model Settings',['(No options are available for the following weighting function: ',funName,')']};
+                                this.modelOptions.grid.Widths = [0 -1 0 0 0 0 0 0 0 0];
+                           else
+
+                               % Assign model properties and data
+                               this.modelOptions.options{2,1}.tbl.ColumnName = colNames;
+                               this.modelOptions.options{2,1}.tbl.ColumnEditable = colEdits;
+                               this.modelOptions.options{2,1}.tbl.ColumnFormat = colFormats;                               
+                               this.modelOptions.options{2,1}.tbl.TooltipString = tooltips;                               
+                               this.modelOptions.options{2,1}.tbl.Tag = 'Forcing Transform - Model Settings';                                                              
+
+                               if isempty(this.forcingTranforms.tbl.Data{this.currentSelection.row ,4})
+                                   this.forcingTranforms.tbl.Data{this.currentSelection.row ,4} = model_TFN_gui.cell2string(modelSettings,colNames);
+                                   this.modelOptions.options{2,1}.tbl.Data = modelSettings;
+                               else
+                                   try                                       
+                                       data = eval(this.forcingTranforms.tbl.Data{irow,4});
+                                       if strcmpi(colNames(1),'Select')
+                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))),  data];
+                                       end
+
+                                       this.modelOptions.options{2, 1}.tbl.Data = data;
+                                   catch ME
+                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');                                       
+                                   end
+                               end
+
+                               % Assign context menu if the first column is
+                               % named 'Select' and is a tick box.
+                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
+                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+                                    set(this.modelOptions.options{2, 1}.tbl,'UIContextMenu',contextMenu);  
+                                    set(this.modelOptions.options{2, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2, 1}.tbl');
+                               else
+                                   set(this.modelOptions.options{2, 1}.tbl,'UIContextMenu',[]);
+                               end                               
+                               
+                               % Show table
+                               this.modelOptions.grid.Widths = [0 -1 0 0 0 0 0 0 0 0];  
+                           end
+                        otherwise
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                    end
+                case 'Weighting Functions'
+
+
+                    % Add a row if the table is empty
+                    if size(get(hObject,'Data'),1)==0
+                        this.weightingFunctions.tbl.Data = cell(1,size(this.weightingFunctions.tbl.Data,2));
+                    end               
+                    
+                    switch eventdata.Source.ColumnName{icol};
+                        case 'Input Data'
+                           % Get the list of input forcing data.
+                           lstOptions = reshape(this.forcingData.colnames(4:end),[length(this.forcingData.colnames(4:end)),1]);
+
+                           % Add source name
+                           lstOptions = strcat('Input Data :',{' '}, lstOptions);
+
+                           % Loop through each forcing function and add
+                           % the possible outputs.
+                           for i=1: size(this.forcingTranforms.tbl.Data,1)
+                               if isempty(this.forcingTranforms.tbl.Data{i,2})
+                                   continue
+                               end
+
+                               % Get output options.
+                               %outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.forcingData.colnames);
+                               outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+
+                               % Transpose if required
+                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
+                                   outputOptions = outputOptions';
+                               end
+                               
+                               % Add output options from the function
+                               % to the list of available options
+                               lstOptions = [lstOptions; strcat(this.forcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
+                           end
+
+
+                           % Loop through each derived forcing function and add
+                           % the possible outputs.
+                           for i=1: size(this.derivedForcingTranforms.tbl.Data,1)
+                               if isempty(this.derivedForcingTranforms.tbl.Data{i,2})
+                                   continue
+                               end
+
+                               % Get output options.
+                               outputOptions = feval(strcat(this.derivedForcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+
+                               % Transpose if required
+                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
+                                   outputOptions = outputOptions';
+                               end
+                               
+                               % Add output options from the function
+                               % to the list of available options
+                               lstOptions = [lstOptions; strcat(this.derivedForcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
+                           end                                                      
+                           
+                           % Assign the list of options to the list box                               
+                           this.modelOptions.options{3,1}.lst.String = lstOptions;
+                           
+                           % Allow multipple-selection.
+                           this.modelOptions.options{3,1}.lst.Min = 1;
+                           this.modelOptions.options{3,1}.lst.Max = length(lstOptions);
+                           
+                           % Highlight the previosuly selected option. If
+                           % it looks like a string expression for a
+                           % cell, evaluate it.
+                           userSelections = this.weightingFunctions.tbl.Data{this.currentSelection.row, 4};
+                           if ~isempty(userSelections) && strcmp(userSelections(1) ,'{') && strcmp(userSelections(end) ,'}')
+                                userSelections = eval(this.weightingFunctions.tbl.Data{this.currentSelection.row, 4});
+                           end
+                           rowInd= [];
+                           if ~iscell(userSelections)
+                               userSelections_tmp{1} =userSelections;
+                               userSelections = userSelections_tmp;
+                               clear userSelections_tmp
+                           end
+                           for i=1:size(userSelections,1)
+                                ind = find(cellfun( @(x) strcmp(x, userSelections{i}), lstOptions ));
+                                rowInd = [rowInd; ind];
+                           end
+                           this.modelOptions.options{3,1}.lst.Value = rowInd;
+                           
+                           % Show the list box
+                           this.modelOptions.grid.Widths = [0 0 -1 0 0 0 0 0 0 0];
+
+                        case 'Weighting Function'   
+                            % Get description of the function.
+                            functionName = this.weightingFunctions.tbl.Data{this.currentSelection.row, 3};
+                            if ~isempty(functionName)
+                                modelDescription = eval([functionName,'.modelDescription']);
+                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
+                                this.modelOptions.options{10,1}.lbl.String = {'2. Weighting Functions - Function description','',modelDescription{:}};
+                            end
+                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];                           
+                        case 'Options'
+                            % Check that the weigthing function and forcing
+                            % data have been defined
+                            if any(isempty(this.weightingFunctions.tbl.Data(this.currentSelection.row,2:4)))
+                                warndlg('The component name, weighting function ands input data must be specified before setting the options.');
+                                return;
+                            end
+                            
+                            % Get the input forcing data options.
+                            inputDataNames = this.weightingFunctions.tbl.Data{this.currentSelection.row, 4};
+                            
+                            % Convert input data to a cell array if it is a
+                            % list of multiple inputs.
+                            try
+                                inputDataNames = eval(inputDataNames);
+                            catch
+                                % do nothing
+                            end
+                            
+                           % Remove source name for input data.     
+                           if ischar(inputDataNames)
+                               inputDataNames_tmp{1}=inputDataNames;
+                               inputDataNames = inputDataNames_tmp;
+                               clear inputDataNames_tmp
+                           end
+                           for j=1: size(inputDataNames,1)
+                               if ~isempty(strfind(inputDataNames{j}, 'Input Data : '))
+                                   [~,ind] = regexp(inputDataNames{j}, 'Input Data : ');
+                               else
+                                   [~,ind] = regexp(inputDataNames{j}, ' : ');  
+                               end                                                   
+                               %inputDataNames{j} =  inputDataNames{j}(ind+3:end);
+                               inputDataNames{j} =  inputDataNames{j}(ind+1:end);
+                           end
+                           
+                           if isempty(inputDataNames) || (iscell(inputDataNames) && isempty(inputDataNames{1,1}))
+                               warndlg('The input data does not appear to have been input for this model compnenent.','Input data error ...');
+                               return;
+                           end
+                           
+                           % Get the list of input forcing data.
+                           funName = this.weightingFunctions.tbl.Data{this.currentSelection.row, 3};
+
+                           % Get the weighting function options.
+%                            [modelSettings, colNames, colFormats, colEdits, tooltip] = feval(strcat(funName,'.modelOptions'), ...
+%                            this.boreID, inputDataNames, this.siteData);
+                           modelSettings= feval(strcat(funName,'.modelOptions'), this.boreID, inputDataNames, this.siteData);
+
+                           % If the function has any options the
+                           % display the options else display a message
+                           % in box stating no options are available.                           
+                           if isempty(modelSettings)  
+                               set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','center');
+                                this.modelOptions.options{10,1}.lbl.String = {'2. Weighting Functions - Options',['(No options are available for the following weighting function: ',funName,')']};                                    
+                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
+                           else
+                               this.modelOptions.options{4,1}.lbl.String = '2. Weighting Functions - Options';
+
+                               % Loop through each model option
+                               if ~iscell(modelSettings)
+                                   modelSettingsTmp = cell(1,1);
+                                   modelSettingsTmp{1} = modelSettings; 
+                                   modelSettings = modelSettingsTmp; 
+                                   clear modelSettingsTmp 
+                               end
+                               
+                               % Disable all options tabs
+                               for i=1:length(this.modelOptions.options{4, 1}.tabs.TabEnables)
+                                  this.modelOptions.options{4, 1}.tabs.TabEnables{i}='off'; 
+                                  drawnow();   
+                               end
+                               
+                               % Get existing options data and convert from
+                               % a string.
+                               haveInputData = false(size(modelSettings));
+                               if ~isempty(this.weightingFunctions.tbl.Data{this.currentSelection.row ,5})
+                                   try
+                                       data = eval(this.weightingFunctions.tbl.Data{this.currentSelection.row ,5});
+                                       
+                                       % Check 'data' and the model options
+                                       % are the same length
+                                       if length(data) == length(modelSettings)
+                                           for i=1:length(data)
+                                               if strcmpi(modelSettings{i}.colNames(1),'Select')
+                                                   data{i} = [ mat2cell(false(size(data{i},1),1),ones(1,size(data{i},1))) , data{i}];
+                                               end
+                                           end 
+                                           haveInputData(i) =  true;
+                                       else
+                                           warndlg('There are an inconsistent number of input and expected options. The inputs data is being ignored.');
+                                       end
+                                   catch ME
+                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');
+                                       %this.modelOptions.options{4, 1}.tbl.Data = '';
+                                   end
+                                   
+                               end
+                               % Assign model properties and data                               
+                               for i=1:length(modelSettings)
+                                   
+                                   this.modelOptions.options{4, 1}.tabs.TabTitles{i} =  modelSettings{i}.label;
+                                   this.modelOptions.options{4, 1}.tabs.TabEnables{i} =  'on';
+                                   
+                                   optionsTab = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i)]);
+                                   optionsTable = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i),' table']);
+                                   %optionsTab.Title =  modelSettings{i}.label;
+                                   optionsTable.ColumnName = modelSettings{i}.colNames;
+                                   optionsTable.ColumnEditable = modelSettings{i}.colEdits;
+                                   optionsTable.ColumnFormat = modelSettings{i}.colFormats;          
+                                   optionsTable.TooltipString = modelSettings{i}.TooltipString;
+
+                                   % Input the existing data or else the
+                                   % default settings.
+                                   if haveInputData(i)
+                                       this.modelOptions.options{4, 1}.tbl.Data = data{i};
+                                   else                                       
+                                       if isempty(modelSettings{i}.options)
+                                           optionsTable.Data = cell(1,length(modelSettings{i}.colNames));
+                                       else
+                                           optionsTable.Data = modelSettings{i}.options;
+                                       end
+                                   end    
+                                   
+                                   % Assign context menu if the first column is
+                                   % named 'Select' and is a tick box.
+                                   if strcmp(modelSettings{i}.colNames{1},'Select') && strcmp(modelSettings{i}.colFormats{1},'logical')
+                                        contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+                                        uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+                                        uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+                                        uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+                                        uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+                                        uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+                                        set(optionsTable,'UIContextMenu',contextMenu);  
+                                        set(optionsTable.UIContextMenu,'UserData', 'this.modelOptions.options{4, 1}.tbl');
+                                   else
+                                       set(optionsTable,'UIContextMenu',[]);
+                                   end
+                               end
+                               % Show table
+                               this.modelOptions.grid.Widths = [0 0 0 -1 0 0 0 0 0 0];
+                           end
+
+                        otherwise
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                    end
+
+                case 'Derived Forcing Transform'                
+                    
+                    % Add a row if the table is empty
+                    if size(get(hObject,'Data'),1)==0
+                        this.derivedForcingTranforms.tbl.Data = cell(1,size(this.derivedForcingTranforms.tbl.Data,2));
+                        return;
+                    end                    
+                    
+                    % Get the derived forcing function name.
+                    funName = this.derivedForcingTranforms.tbl.Data{irow, 2};
+
+                    % Get the source forcing function name.
+                    sourceFunName = this.derivedForcingTranforms.tbl.Data{irow, 3};
+
+                    switch eventdata.Source.ColumnName{icol};
+                        case 'Forcing Transform Function'
+                        
+                            % Get description of the function.
+                            functionName = this.derivedForcingTranforms.tbl.Data{this.currentSelection.row, 2};
+                            if isempty(functionName)
+                                return;
+                            end
+                            modelDescription = eval([functionName,'.modelDescription']);
+                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
+                            this.modelOptions.options{10,1}.lbl.String = {'3. Derived Forcing Transform - Function description','',modelDescription{:}};
+                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];          
+                            
+                        case 'Source Forcing Function' 
+                            derivedForcingFunctionsListed = this.forcingTranforms.tbl.Data(:,2);
+                            this.derivedForcingTranforms.tbl.ColumnFormat{3} = derivedForcingFunctionsListed;
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                        case 'Input Data'
+                           % Call function method giving required
+                           % variable name.
+                           requiredVariables = feval(strcat(funName,'.inputForcingData_required'));
+
+                           % Add row for each required variable
+                           if isempty(this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,4})
+                               for i=1:length(requiredVariables)                                   
+                                    this.modelOptions.options{6, 1}.tbl.Data{i,1}= requiredVariables{i};
+                                    this.modelOptions.options{6, 1}.tbl.RowName{i}= num2str(i);
+                               end
+                           else
+                               try
+                                   this.modelOptions.options{6, 1}.tbl.Data = eval(this.derivedForcingTranforms.tbl.Data{irow,4});
+                                   for i=1:length(requiredVariables)                                                                           
+                                        this.modelOptions.options{6, 1}.tbl.RowName{i}= num2str(i);
+                                   end
+                               catch
+                                   warndlg('The input string appears to have a syntax error. It should be an Nx2 cell array.');                                       
+                               end
+                           end
+                           
+                           % Get the list of input forcing data and add source name
+                           lstOptions = reshape(this.forcingData.colnames(4:end),[1, length(this.forcingData.colnames(4:end))]);
+
+                           % Add input list to drop down
+                           this.modelOptions.options{6, 1}.tbl.ColumnFormat = {'char',lstOptions};
+
+                           % Display table
+                           this.modelOptions.grid.Widths = [0 0 0 0 0 -1 0 0 0 0];
+                           
+                        case 'Options'    
+                           % Get the model options.
+                           [modelSettings, colNames, colFormats, colEdits, tooltips] = feval(strcat(funName,'.modelOptions'), sourceFunName );
+
+                           % Check if options are available.
+                           if isempty(colNames)                          
+                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
+                                this.modelOptions.options{10,1}.lbl.String = {'3. Derived Forcing Transform - Model Settings','',['(No options are available for the following function: ',funName,')']};
+                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
+                           else
+
+                               % Assign model properties and data
+                               this.modelOptions.options{7,1}.tbl.ColumnName = colNames;
+                               this.modelOptions.options{7,1}.tbl.ColumnEditable = colEdits;
+                               this.modelOptions.options{7,1}.tbl.ColumnFormat = colFormats;                               
+                               this.modelOptions.options{7,1}.tbl.TooltipString = tooltips;                               
+                               this.modelOptions.options{7,1}.tbl.Tag = 'Derived Forcing Transform - Model Settings';                                                              
+
+                               if isempty(this.derivedForcingTranforms.tbl.Data{irow ,5})
+                                    this.modelOptions.options{7,1}.tbl.Data = modelSettings;
+                               else
+                                   try                                       
+                                       data = eval(this.derivedForcingTranforms.tbl.Data{irow,5});
+                                       if strcmpi(colNames(1),'Select')
+                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))),  data];
+                                       end
+
+                                       this.modelOptions.options{7, 1}.tbl.Data = data;
+                                   catch
+                                       warndlg('The function options string appears to have a sytax error. It should be an Nx4 cell array.');                                       
+                                   end
+                               end
+
+                               % Assign context menu if the first column is
+                               % named 'Select' and is a tick box.
+                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
+                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+                                    set(this.modelOptions.options{7, 1}.tbl,'UIContextMenu',contextMenu);  
+                                    set(this.modelOptions.options{7, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{2, 1}.tbl');
+                               else
+                                   set(this.modelOptions.options{7, 1}.tbl,'UIContextMenu',[]);
+                               end                               
+
+                               % Display table
+                               this.modelOptions.grid.Widths = [0 0 0 0 0 0 -1 0 0 0];                
+                           end
+                        otherwise
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                    end
+
+                case 'Derived Weighting Functions'
+
+                    % Add a row if the table is empty
+                    if size(get(hObject,'Data'),1)==0
+                        this.derivedWeightingFunctions.tbl.Data = cell(1,size(this.derivedWeightingFunctions.tbl.Data,2));
+                        return;
+                    end                    
+                    
+                    
+                    switch eventdata.Source.ColumnName{icol};
+                        case 'Weighting Function'   
+                            % Get description of the function.
+                            functionName = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 3};
+                            if isempty(functionName)
+                                return;
+                            end
+                            modelDescription = eval([functionName,'.modelDescription']);
+                            set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','left');
+                            this.modelOptions.options{10,1}.lbl.String = {'4. Derived Weighting Functions - Function description','',modelDescription{:}};
+                            this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];                              
+                        case 'Source Component'
+                            derivedWeightingFunctionsListed = this.weightingFunctions.tbl.Data(:,2);
+                            this.derivedWeightingFunctions.tbl.ColumnFormat{4} = derivedWeightingFunctionsListed';
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                        case 'Input Data'
+                            
+                           % Get the list of input forcing data.
+                           lstOptions = reshape(this.forcingData.colnames(4:end),[length(this.forcingData.colnames(4:end)),1]);
+
+                           % Add source name
+                           lstOptions = strcat('Input Data :',{' '}, lstOptions);
+
+                           % Loop through each forcing function and add
+                           % the possible outputs.
+                           for i=1: size(this.forcingTranforms.tbl.Data,1)
+                               if isempty(this.forcingTranforms.tbl.Data{i,2})
+                                   continue
+                               end
+
+                               % Get output options.
+                               outputOptions = feval(strcat(this.forcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+
+                               % Transpose if required
+                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
+                                   outputOptions = outputOptions';
+                               end
+                               
+                               % Add output options from the function
+                               % to the list of available options
+                               lstOptions = [lstOptions; strcat(this.forcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
+                           end
+
+
+                           % Loop through each derived forcing function and add
+                           % the possible outputs.
+                           for i=1: size(this.derivedForcingTranforms.tbl.Data,1)
+                               if isempty(this.derivedForcingTranforms.tbl.Data{i,2})
+                                   continue
+                               end
+
+                               % Get output options.
+                               outputOptions = feval(strcat(this.derivedForcingTranforms.tbl.Data{i,2},'.outputForcingdata_options'),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+
+                               % Transpose if required
+                               if size(outputOptions,1)==1 && size(outputOptions,2)>1
+                                   outputOptions = outputOptions';
+                               end                               
+                               
+                               % Add output options from the function
+                               % to the list of available options
+                               lstOptions = [lstOptions; strcat(this.derivedForcingTranforms.tbl.Data{i,2},{' : '}, outputOptions)];
+                           end                                                      
+                           
+                           % Assign the list of options to the list box                               
+                           this.modelOptions.options{8,1}.lst.String = lstOptions;
+                           
+                           % Allow multipple-selection.
+                           this.modelOptions.options{8,1}.lst.Min = 1;
+                           this.modelOptions.options{8,1}.lst.Max = length(lstOptions);
+
+                           % Highlight the previosuly selected option. If
+                           % it looks like a string expression for a
+                           % cell, evaluate it.
+                           userSelections = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5};
+                           if ~isempty(userSelections) && strcmp(userSelections(1) ,'{') && strcmp(userSelections(end) ,'}')
+                                userSelections = eval(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5});
+                           end
+                           
+                           rowInd= [];
+                           if ~iscell(userSelections)
+                               userSelections_tmp{1} =userSelections;
+                               userSelections = userSelections_tmp;
+                               clear userSelections_tmp
+                           end
+                           for i=1:size(userSelections,1)
+                                ind = find(cellfun( @(x) strcmp(x, userSelections{i}), lstOptions ));
+                                rowInd = [rowInd; ind];
+                           end
+                           this.modelOptions.options{8,1}.lst.Value = rowInd;                           
+                           
+                           % Show list box 
+                           this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 -1 0 0];
+                           
+                        case 'Options'
+                            % Check that the weigthing function and forcing
+                            % data have been defined
+                            if any(isempty(this.derivedWeightingFunctions.tbl.Data(this.currentSelection.row,2:5)))
+                                warndlg('The component name, derived weighting function, source function and input data must be specified before setting the options.');
+                                return;
+                            end
+                            
+                            % Get the input forcing data options.
+                            inputDataNames = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 5};
+                            
+                            % Convert input data to a cell array if it is a
+                            % list of multiple inputs.
+                            try
+                                inputDataNames = eval(inputDataNames)';
+                            catch
+                                % do nothing
+                            end
+                            
+                           % Remove source name for input data.     
+                           if ischar(inputDataNames)
+                               inputDataNames_tmp{1}=inputDataNames;
+                               inputDataNames = inputDataNames_tmp;
+                               clear inputDataNames_tmp
+                           end
+                           for j=1: size(inputDataNames,1)
+                               if ~isempty(strfind(inputDataNames{j}, 'Input Data : '))
+                                   ind = regexp(inputDataNames{j}, 'Input Data : ');
+                               else
+                                   ind = regexp(inputDataNames{j}, ' : ');  
+                               end                                                   
+                               inputDataNames{j} =  inputDataNames{j}(ind+3:end);
+                           end                            
+                            
+                            % Get the list of input forcing data.
+                            funName = this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row, 3};
+
+                           % Get the weighting function options.
+                           [modelSettings, colNames, colFormats, colEdits, tooltip] = feval(strcat(funName,'.modelOptions'), ...
+                           this.boreID, inputDataNames, this.siteData);
+
+                           % If the function has any options the
+                           % display the options else display a message
+                           % in box stating no options are available.
+                           if isempty(colNames)                          
+                                set(this.modelOptions.options{10,1}.lbl,'HorizontalAlignment','center');
+                                this.modelOptions.options{10,1}.lbl.String = {'4. Derived Weighting Functions - Options','',['(No options are available for the following weighting function: ',funName,')']};                                    
+                                this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 0 -1];
+                           else
+                               this.modelOptions.options{9,1}.lbl.String = '4. Derived Weighting Functions - Options';
+
+                               % Assign model properties and data
+                               this.modelOptions.options{9,1}.tbl.ColumnName = colNames;
+                               this.modelOptions.options{9,1}.tbl.ColumnEditable = colEdits;
+                               this.modelOptions.options{9,1}.tbl.ColumnFormat = colFormats;                               
+                               this.modelOptions.options{9,1}.tbl.TooltipString = tooltip;
+                               % Input the existing data or else the
+                               % default settings.
+                               if isempty(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6})
+                                   if isempty(modelSettings)
+                                       this.modelOptions.options{9,1}.tbl.Data = cell(1,length(colNames));
+                                   else
+                                       this.modelOptions.options{9,1}.tbl.Data = modelSettings;
+                                   end
+                               else
+                                   try
+                                       data = eval(this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6});
+                                       if strcmpi(colNames(1),'Select')
+                                           data = [ mat2cell(false(size(data,1),1),ones(1,size(data,1))) , data];
+                                       end
+
+                                       this.modelOptions.options{9, 1}.tbl.Data = data;
+                                   catch
+                                       warndlg('The function options string appears to have a sytax error.');                                       
+                                   end
+                               end                                 
+                               % Assign context menu if the first column is
+                               % named 'Select' and is a tick box.
+                               if strcmp(colNames{1},'Select') && strcmp(colFormats{1},'logical')
+                                    contextMenu = uicontextmenu(this.Figure.Parent.Parent.Parent.Parent.Parent,'Visible','on');
+                                    uimenu(contextMenu,'Label','Copy selected rows','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Paste rows','Callback',@this.rowAddDelete,'Separator','on');
+                                    uimenu(contextMenu,'Label','Insert row above selection','Callback',@this.rowAddDelete);
+                                    uimenu(contextMenu,'Label','Insert row below selection','Callback',@this.rowAddDelete);            
+                                    uimenu(contextMenu,'Label','Delete selected rows','Callback',@this.rowAddDelete);            
+                                    set(this.modelOptions.options{9, 1}.tbl,'UIContextMenu',contextMenu);  
+                                    set(this.modelOptions.options{9, 1}.tbl.UIContextMenu,'UserData', 'this.modelOptions.options{9, 1}.tbl');
+                               else
+                                   set(this.modelOptions.options{9, 1}.tbl,'UIContextMenu',[]);
+                               end
+                               
+                               % Show table
+                               this.modelOptions.grid.Widths = [0 0 0 0 0 0 0 0 -1 0];
+                           end
+                        otherwise
+                            this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                    end   
+            end            
+        end
+        
+        function optionsSelection(this, hObject, eventdata)
+            try
+                data=get(hObject,'Data'); % get the data cell array of the table
+            catch
+                data=[];
+            end
+
+            % Get class name (for calling the abstract)
+            className = metaclass(this);
+            className = className.Name;
+                        
+            % Undertake table/list specific operations.
+            switch eventdata.Source.Tag;
+               
+                case 'Forcing Transform - Input Data'                        
+                    colnames = hObject.ColumnName;
+                    this.forcingTranforms.tbl.Data{this.currentSelection.row ,3} = eval([className,'.cell2string(data, colnames)']);
+
+                case 'Forcing Transform - Model Settings'
+                    colnames = hObject.ColumnName;
+                    this.forcingTranforms.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
+
+                case 'Weighting Functions - Input Data'       
+
+                    % Get selected input option
+                    listSelection = get(hObject,'Value');
+ 
+                    % Get cell array of selected strings.
+                    data = hObject.String(listSelection);
+                    
+                    % Convert to string.
+                    colnames = 'NA';                    
+                    this.weightingFunctions.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
+
+                case {'Weighting Functions - Options Tab 1 table','Weighting Functions - Options Tab 2 table', ...
+                     'Weighting Functions - Options Tab 3 table','Weighting Functions - Options Tab 4 table', ...
+                     'Weighting Functions - Options Tab 5 table'}
+                 
+                    optionsString = '{';
+                    for i=1:length(this.modelOptions.options{4, 1}.tabs.TabEnables)
+                        if strcmp(this.modelOptions.options{4, 1}.tabs.TabEnables{i},'on')
+                            optionsTable = findobj(this.modelOptions.options{4,1}.tabs,'Tag',['Weighting Functions - Options Tab ',num2str(i),' table']);
+                            data = get(optionsTable,'Data');
+                            if any(any(cellfun(@(x) ~isempty(x), data)))
+                                colnames = get(optionsTable,'ColumnName');                                
+                                optionsString  = [optionsString , eval([className,'.cell2string(data, colnames)']),', '];
+                            else
+                                optionsString  = [optionsString , '{}, '];
+                            end                            
+                        end                        
+                    end
+                    if ~isempty(optionsString)
+                       optionsString = optionsString(1:end-2); 
+                    end
+                    optionsString  = [optionsString , '} '];
+                    this.weightingFunctions.tbl.Data{this.currentSelection.row ,5} = optionsString;
+
+                case 'Derived Forcing Functions - Source Function'                      
+                    % Get selected input option
+                    listSelection = get(hObject,'Value');
+ 
+                    % Get cell array of selected strings.
+                    data = hObject.String(listSelection);
+
+                    % Assign to the table
+                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,3} = data{1};
+                    
+                    
+                case 'Derived Forcing Functions - Input Data'                      
+                    
+                    colnames = hObject.ColumnName;
+                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,4} = eval([className,'.cell2string(data, colnames)']);
+                    
+                case 'Derived Forcing Transform - Model Settings'
+                    
+                    colnames = hObject.ColumnName;
+                    this.derivedForcingTranforms.tbl.Data{this.currentSelection.row ,5} = eval([className,'.cell2string(data, colnames)']);
+                    
+                    
+                case 'Derived Weighting Functions - Input Data'      
+                    % Get selected input option
+                    listSelection = get(hObject,'Value');
+ 
+                    % Get cell array of selected strings.
+                    data = hObject.String(listSelection);
+                    
+                    % Convert to string.
+                    colnames = 'NA';                    
+                    this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,5} = eval([className,'.cell2string(data, colnames)']);
+
+                case 'Derived Weighting Functions - Model Settings'       
+                    colnames = hObject.ColumnName;
+                    this.derivedWeightingFunctions.tbl.Data{this.currentSelection.row ,6} = eval([className,'.cell2string(data, colnames)']);
+                    
+                otherwise
+                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths)); 
+            end
+        end
+        
+    %end
+    
+    %methods(Access=private)  
+        
+        function tableEdit(this, hObject, eventdata)
+            icol=[];
+            irow=[];
+            if isprop(eventdata, 'Indices')
+                if ~isempty(eventdata.Indices)
+                    icol=eventdata.Indices(:,2);
+                    irow=eventdata.Indices(:,1);  
+                end
+            end
+            if size(get(hObject,'Data'),1)==0
+                return
+            end
+            
+            % Return if the select column is the corrent column
+            if icol==1
+                return;
+            end
+           
+            % Undertake table/list specific operations.
+            switch eventdata.Source.Tag;
+                case 'Forcing Transform'
+                   % Check the function name is unique
+                   nrows  = size(this.forcingTranforms.tbl.Data,1);                   
+                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
+                   if nrows>1          
+                       otherFunctionNames = this.forcingTranforms.tbl.Data(otherFunctionNames_ind ,3);
+                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
+                           this.forcingTranforms.tbl.Data{irow ,2} = eventdata.PreviousData;
+                           warndlg('Each function name must be unique within the model - i.e you can use it only once.');
+                           return;
+                       end
+                   end
+                   
+                   % Reset other fields of the model name changes
+                   if icol==2 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
+                        this.forcingTranforms.tbl.Data{irow ,3} = '';
+                        this.forcingTranforms.tbl.Data{irow,4} = '';
+                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));
+                        
+                   end
+                   
+                case 'Weighting Functions'
+                   
+                   % Check the function name is unique
+                   nrows  = size(this.weightingFunctions.tbl.Data,1);                   
+                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
+                   if nrows>1          
+                       otherFunctionNames = this.weightingFunctions.tbl.Data(otherFunctionNames_ind ,2);
+                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
+                           this.weightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
+                           warndlg('Each component name must be unique within the model.');
+                           return;
+                       end
+                   end  
+                   
+                   % Check that the compent name is a valid fiewd name
+                   try
+                       componentName = eventdata.NewData;               
+                       a.(componentName) = 1;
+                       clear a
+                   catch ME
+                       this.weightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
+                       warndlg('The component name is invalid. It must contain only letters, numbers and under-scores and cannot start with a number.');
+                       return;
+                   end
+                    
+                   % Reset other fields of the model name changes
+                   if icol==3 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
+                        this.weightingFunctions.tbl.Data{irow ,4} = '';
+                        this.weightingFunctions.tbl.Data{irow ,5} = '';
+                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
+                   end                   
+                case 'Derived Forcing Transform'
+                    
+                   % Check the function name is unique
+                   nrows  = size(this.derivedForcingTranforms.tbl.Data,1);                   
+                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
+                   if nrows>1          
+                       otherFunctionNames = this.derivedForcingTranforms.tbl.Data(otherFunctionNames_ind ,2);
+                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
+                           this.derivedForcingTranforms.tbl.Data{irow ,2} = eventdata.PreviousData;
+                           warndlg('Each function name must be unique within the model - i.e you can use it only once.');
+                           return;
+                       end
+                   end
+                   
+                   % Reset other fields of the model name changes
+                   if icol==2 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
+                        this.derivedForcingTranforms.tbl.Data{irow ,3} = '';
+                        this.derivedForcingTranforms.tbl.Data{irow ,4} = '';
+                        this.derivedForcingTranforms.tbl.Data{irow ,5} = '';
+                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
+                   end                   
+                case 'Derived Weighting Functions'
+                    
+                   % Check the function name is unique
+                   nrows  = size(this.derivedWeightingFunctions.tbl.Data,1);                   
+                   otherFunctionNames_ind = [1:irow-1, irow+1:nrows];
+                   if nrows>1          
+                       otherFunctionNames = this.derivedWeightingFunctions.tbl.Data(otherFunctionNames_ind ,3);
+                       if any(cellfun(@(x) strcmp(eventdata.NewData,x), otherFunctionNames))                           
+                           this.derivedWeightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
+                           warndlg('Each component name must be unique within the model.');
+                           return;
+                       end
+                   end      
+                   
+                   % Check that the compent name is a valid fiewd name
+                   try
+                       componentName = eventdata.NewData;               
+                       a.(componentName) = 1;
+                       clear a
+                   catch
+                       this.derivedWeightingFunctions.tbl.Data{irow ,2} = eventdata.PreviousData;
+                       warndlg('The component name is invalid. It must contain only letters, numbers and under-scores and cannot start with a number.');
+                       return;
+                   end                   
+                   
+                   % Reset other fields of the model name changes
+                   if icol==3 && ~isempty(eventdata.PreviousData) && ~strcmp(eventdata.PreviousData, eventdata.NewData)
+                        this.derivedWeightingFunctions.tbl.Data{irow ,4} = '';
+                        this.derivedWeightingFunctions.tbl.Data{irow ,5} = '';
+                        this.derivedWeightingFunctions.tbl.Data{irow ,6} = '';
+                        this.modelOptions.grid.Widths = zeros(size(this.modelOptions.grid.Widths));                        
+                   end                      
+                    
+            end
+        end
+        
+        function rowAddDelete(this, hObject, eventdata)
+
+           
+            % Get the table object from UserData
+            tableObj = eval(eventdata.Source.Parent.UserData);
+            
+            % Get selected rows
+            selectedRow = false(size(tableObj.Data(:,1),1),1);
+            for i=1:size(tableObj.Data(:,1),1)
+                if tableObj.Data{i,1}
+                    selectedRow(i) = true;
+                end
+            end
+      
+            % Check if any rows are selected. Note, if not then
+            % rows will be added (for all but the calibration
+            % table).
+            anySelected = any(selectedRow);
+            indSelected = find(selectedRow)';
+            
+            
+            if ~strcmp(hObject.Label,'Paste rows') && size(tableObj.Data(:,1),1)>0 &&  sum(selectedRow) == 0                             
+                warndlg('No rows are selected for the requested operation.');
+                return;
+            elseif size(tableObj.Data(:,1),1)==0 ...
+            &&  (strcmp(hObject.Label, 'Copy selected rows') || strcmp(hObject.Label, 'Delete selected rows'))                
+                return;
+            end            
+            
+            % Do the selected action            
+            switch hObject.Label
+                case 'Copy selected rows'
+                    this.copiedData.tableName = tableObj.Tag;
+                    this.copiedData.data = tableObj.Data(selectedRow,:);
+                    
+                case 'Paste rows'    
+                    % Check that name of the table is same as that from the
+                    % copied data. If so copy the data.
+                    if strcmp(this.copiedData.tableName, tableObj.Tag)
+                        if anySelected
+                            for i=indSelected
+                                tableObj.Data{i,:} = this.copiedData.data(1,:);
+                            end
+                       else
+                          for i=1: size(this.copiedData.data,1)
+                            tableObj.Data = [tableObj.Data; this.copiedData.data(i,:)];
+                          end
+ 
+                        end                        
+                    
+                        % Update row numbers.
+                        nrows = size(tableObj.Data,1);
+                        tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                    else
+                        warndlg('The copied row data was sourced froma different table.');
+                        return;
+                    end    
+                    
+                case 'Insert row above selection'
+                    if size(tableObj.Data,1)==0
+                        tableObj.Data = cell(1,size(tableObj.Data,2));
+                    else
+                        selectedRow= find(selectedRow);
+                        for i=1:length(selectedRow)
+
+                            ind = max(0,selectedRow(i) + i-1);
+
+                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
+                                        cell(1,size(tableObj.Data,2)); ...
+                                        tableObj.Data(ind:end,:)];
+                            tableObj.Data{ind,1} = false;                                                              
+                        end
+                    end
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+                        
+                case 'Insert row below selection'    
+                    if size(tableObj.Data,1)==0
+                        tableObj.Data = cell(1,size(tableObj.Data,2));
+                    else
+                        selectedRow= find(selectedRow);
+                        for i=1:length(selectedRow)
+
+                            ind = selectedRow(i) + i;
+
+                            tableObj.Data = [tableObj.Data(1:ind-1,:); ...
+                                        cell(1,size(tableObj.Data,2)); ...
+                                        tableObj.Data(ind:end,:)];
+
+                            tableObj.Data{ind,1} = false;                                                              
+                        end
+                    end
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+
+                case 'Delete selected rows'    
+                    tableObj.Data = tableObj.Data(~selectedRow,:);
+                    
+                    % Update row numbers.
+                    nrows = size(tableObj.Data,1);
+                    tableObj.RowName = mat2cell([1:nrows]',ones(1, nrows));
+            end
+        end
+        
+         function wizard(this, hObject, eventdata)
+             
+            % Get the selelcted table, row and col.
+            irow = this.currentSelection.row;
+            icol = this.currentSelection.col;
+            currentTable = this.currentSelection.table;
+            
+            % Get the name of the function within the table     
+            switch currentTable
+                case 'Forcing Transform'
+                    className = this.forcingTranforms.tbl.Data{irow, 2};
+                    
+                    if icol==3
+                        methodName = '.dataWizard';
+                    elseif icol==4
+                        methodName = '.optionsWizard';
+                    end
+                    
+                otherwise
+                    msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')     
+                    return
+            end
+            
+            
+            % Call the wizard for the current table.
+            try
+               wizardResults= feval(strcat(className,methodName),this.boreID, this.forcingData.data,  this.forcingData.colnames, this.siteData);
+            catch ME
+               msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')
+               return
+            end
+
+           % Input the wizard data into the current table.
+           switch currentTable
+                case 'Forcing Transform'
+                    switch methodName
+                        case '.dataWizard'
+                           this.modelOptions.options{1, 1}.tbl.Data = cell(0,2);
+                           this.modelOptions.options{1, 1}.tbl.Data = cell(size(wizardResults,1),2);
+                           this.modelOptions.options{1, 1}.tbl.RowName=cell(1,size(wizardResults,1)); 
+                           for i=1:size(wizardResults,1)                                   
+                                this.modelOptions.options{1, 1}.tbl.Data{i,1}= wizardResults{i,1};
+                                this.modelOptions.options{1, 1}.tbl.Data{i,2}= wizardResults{i,2};
+                           end     
+                           this.modelOptions.options{1, 1}.tbl.RowName = [1:size(this.modelOptions.options{1, 1}.tbl.Data,1)];
+                           
+                           % In case the user does not input/chnage
+                           % the data, then apply it to the forcing
+                           % data field.
+                           className= 'model_TFN_gui';
+                           colnames = {'Required Model Data';'Input Forcing Data'};
+                           data=this.modelOptions.options{1, 1}.tbl.Data;
+                           this.forcingTranforms.tbl.Data{irow ,3} = eval([className,'.cell2string(data, colnames)']);
+
+                        case '.optionsWizard'
+                            
+                    end
+           end
+
+           drawnow();   
+         end
+         
+         function push2component(this, hObject, eventdata)
+             
+            dlg_title = 'Push required model data to compnant input data ...';
+            prompt = 'Select the componant:';      
+            ListString = this.weightingFunctions.tbl.Data(:,2);
+           
+            if length(ListString)<1
+                errordlg('The weighting function component names must first be defined.')
+                return
+            end
+            
+            [ind, OK]= listdlg('Name',dlg_title, 'PromptString',prompt, 'ListString',ListString, 'SelectionMode','single');
+            if OK==1
+               
+                
+                % Get the selelcted table, row and col.
+                irow = this.currentSelection.row;
+                icol = this.currentSelection.col;
+                currentTable = this.currentSelection.table;
+                
+                % Get the name of the function within the table
+                switch currentTable
+                    case 'Forcing Transform'
+                        className = this.forcingTranforms.tbl.Data{irow, 2};                                               
+                    otherwise
+                        msgbox('This model componant does not have a data wizard.', 'No data wizard ...','help')
+                        return
+                end
+
+                % Build filter for rows where the obs data is not "(none)"
+                filt = ~strcmp(this.modelOptions.options{1, 1}.tbl.Data(:,2),'(none)');
+                
+                % get input data names                
+                data = this.modelOptions.options{1, 1}.tbl.Data(filt,1);
+                
+                LHS = [className,'...'];
+                data = strcat( LHS, data);
+                data = strrep(data,'...',' : ');
+                               
+                % Get class name (for calling the abstract)
+                className = metaclass(this);
+                className = className.Name;
+                
+                % Convert to string.
+                colnames = 'NA';                    
+                this.weightingFunctions.tbl.Data{ind,4} = eval([className,'.cell2string(data, colnames)']);
+                    
+                
+                
+            end
+         end
+    end
+    
+    methods(Static, Access=private)  
+        function stringCell = cell2string(cellData, colnames) 
+                % Ignore first column of it is for row selection
+                if isempty(colnames)
+                    startCol = 1; 
+                elseif strcmpi(colnames(1),'Select')
+                    startCol = 2;                    
+                else
+                    startCol = 1;
+                end
+            
+                % Check the format of each column.
+                % All rows of a column must be numeric to be
+                % deemed numeric.
+                isNumericColumn = all(cellfun(@(x) isnumeric(x) || (~isempty(str2double(x)) && ~isnan(str2double(x))), cellData),1);
+                
+                % Loop through each column, then row.
+                stringCell= '{';
+                for i=1:size(cellData,1)
+                    for j=startCol:size(cellData,2)
+                        
+                        if isNumericColumn(j)
+                            if ischar(cellData{i,j})
+                                stringCell = strcat(stringCell, sprintf(' %f,',str2double(cellData{i,j}) ));
+                            else
+                                stringCell = strcat(stringCell, sprintf(' %f,',cellData{i,j} ));
+                            end
+                        else
+                            stringCell = strcat(stringCell, sprintf(' ''%s'',',cellData{i,j} ));
+                        end
+                    end
+                    % remove end ,
+                    stringCell = stringCell(1:end-1);
+                    % add ;
+                    stringCell = strcat(stringCell, ' ; ');
+                end
+                stringCell = strcat(stringCell,'}');            
+        end        
+    end
+    
+end
+
diff --git a/GUI/models/model_gui_abstract.m b/GUI/models/model_gui_abstract.m
index 7610537..95811fa 100644
--- a/GUI/models/model_gui_abstract.m
+++ b/GUI/models/model_gui_abstract.m
@@ -1,29 +1,29 @@
-classdef model_gui_abstract < handle
-    %MODEL_GUI_ABSTRACT Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-        Figure;
-        boreID
-        siteData
-        forcingData
-    end
-    
-    methods (Abstract)
-        
-        initialise(this);
-        
-        setForcingData(this, fname);
-        
-        setCoordinatesData(this, fname);
-        
-        setBoreID(this, fname);
-        
-        setModelOptions(this, modelOptionsStr);
-        
-        modelOptionsArray = getModelOptions(this);
-
-    end
-    
-end
-
+classdef model_gui_abstract < handle
+    %MODEL_GUI_ABSTRACT Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+        Figure;
+        boreID
+        siteData
+        forcingData
+    end
+    
+    methods (Abstract)
+        
+        initialise(this);
+        
+        setForcingData(this, fname);
+        
+        setCoordinatesData(this, fname);
+        
+        setBoreID(this, fname);
+        
+        setModelOptions(this, modelOptionsStr);
+        
+        modelOptionsArray = getModelOptions(this);
+
+    end
+    
+end
+
diff --git a/GUI/rdir/license.txt b/GUI/rdir/license.txt
index a2bbbdf..1129580 100644
--- a/GUI/rdir/license.txt
+++ b/GUI/rdir/license.txt
@@ -1,24 +1,24 @@
-Copyright (c) 2009, Gus Brown
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2009, Gus Brown
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/GUI/uical/license.txt b/GUI/uical/license.txt
index 4c590fb..2866c3b 100644
--- a/GUI/uical/license.txt
+++ b/GUI/uical/license.txt
@@ -1,27 +1,27 @@
-Copyright (c) 2009, Thomas Montagnon
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-    * Neither the name of the The MathWorks, Inc. nor the names
-      of its contributors may be used to endorse or promote products derived
-      from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2009, Thomas Montagnon
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+    * Neither the name of the The MathWorks, Inc. nor the names
+      of its contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/GUI/uiinspect/license.txt b/GUI/uiinspect/license.txt
index 2d72f00..3860b78 100644
--- a/GUI/uiinspect/license.txt
+++ b/GUI/uiinspect/license.txt
@@ -1,24 +1,24 @@
-Copyright (c) 2014, Yair Altman
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2014, Yair Altman
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/HydroSight.m b/HydroSight.m
index 7eb68c9..ae13fb2 100644
--- a/HydroSight.m
+++ b/HydroSight.m
@@ -1,57 +1,57 @@
-function HydroSight()
-
-    % Add Paths
-%    addpath(pwd);
-    addpath(genpath([pwd, filesep, 'algorithms']));
-    addpath(genpath([pwd, filesep, 'dataPreparationAnalysis']));
-    addpath(genpath([pwd, filesep, 'Examples']));
-    addpath(genpath([pwd, filesep, 'GUI']));
-    
-%     addpath(genpath(pwd));
-%     
-%     % Remove paths to .git folders
-%     if ~isdeployed
-%         rmpath(genpath( fullfile( pwd, '.git')));
-%     end toat 
-%     
-    % Load GUI
-    try
-        % Use GUI Layout Toolbox if it exists.
-        if ~isdeployed && ~isempty(ver('layout')) && ispc          
-            rmpath(genpath(fullfile( pwd, 'GUI','GUI Layout Toolbox 2.3.4')));
-        end
-        HydroSight_GUI();
-    catch ME
-        % Check the toolbox for GUIs exists
-        if ~isdeployed && isempty(ver('layout')) 
-            msgbox({'The GUI cannot be opened. This may be because the GUI Layout Toolbox is ', ...
-                    'not installed within Matlab. Please download it using the URL below and', ...
-                    'then install it and re-start HydroSight.', ...
-                    '', ...
-                    'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox'}, ...
-                    'Toolbox missing: gui-layout-toolbox', 'error');
-
-        else
-            if size(ME.stack,1)>0
-                functionName = ME.stack(1,1).name;
-                functionLine = num2str(ME.stack(1,1).line);
-            else
-                functionName='';
-                functionLine ='';
-            end
-            msgbox({'An unexpected error occured. To help with fixing the issue please', ...
-                    'copy the error message below and submit a bug report to:', ...
-                    'https://github.com/peterson-tim-j/HydroSight/issues', ...
-                    '', ...
-                    ['Message:', ME.message], ...
-                    '', ...
-                    ['Function:', functionName], ...
-                    '', ...
-                    ['Line Number:', functionLine]}, ...
-                    'Unknown error', 'error');
-            
-        end
-    end
-
-
+function HydroSight()
+
+    % Add Paths
+%    addpath(pwd);
+    addpath(genpath([pwd, filesep, 'algorithms']));
+    addpath(genpath([pwd, filesep, 'dataPreparationAnalysis']));
+    addpath(genpath([pwd, filesep, 'Examples']));
+    addpath(genpath([pwd, filesep, 'GUI']));
+    
+%     addpath(genpath(pwd));
+%     
+%     % Remove paths to .git folders
+%     if ~isdeployed
+%         rmpath(genpath( fullfile( pwd, '.git')));
+%     end toat 
+%     
+    % Load GUI
+    try
+        % Use GUI Layout Toolbox if it exists.
+        if ~isdeployed && ~isempty(ver('layout')) && ispc          
+            rmpath(genpath(fullfile( pwd, 'GUI','GUI Layout Toolbox 2.3.4')));
+        end
+        HydroSight_GUI();
+    catch ME
+        % Check the toolbox for GUIs exists
+        if ~isdeployed && isempty(ver('layout')) 
+            msgbox({'The GUI cannot be opened. This may be because the GUI Layout Toolbox is ', ...
+                    'not installed within Matlab. Please download it using the URL below and', ...
+                    'then install it and re-start HydroSight.', ...
+                    '', ...
+                    'https://www.mathworks.com/matlabcentral/fileexchange/47982-gui-layout-toolbox'}, ...
+                    'Toolbox missing: gui-layout-toolbox', 'error');
+
+        else
+            if size(ME.stack,1)>0
+                functionName = ME.stack(1,1).name;
+                functionLine = num2str(ME.stack(1,1).line);
+            else
+                functionName='';
+                functionLine ='';
+            end
+            msgbox({'An unexpected error occured. To help with fixing the issue please', ...
+                    'copy the error message below and submit a bug report to:', ...
+                    'https://github.com/peterson-tim-j/HydroSight/issues', ...
+                    '', ...
+                    ['Message:', ME.message], ...
+                    '', ...
+                    ['Function:', functionName], ...
+                    '', ...
+                    ['Line Number:', functionLine]}, ...
+                    'Unknown error', 'error');
+            
+        end
+    end
+
+
 end
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index ef7e7ef..efe40f6 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,674 @@
-GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
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-authors of previous versions.
-
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-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
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-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
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-
-Also add information on how to contact you by electronic and paper mail.
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-  If the program does terminal interaction, make it output a short
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-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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-The hypothetical commands `show w' and `show c' should show the appropriate
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-  You should also get your employer (if you work as a programmer) or school,
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-For more information on this, and how to apply and follow the GNU GPL, see
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-
-  The GNU General Public License does not permit incorporating your program
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-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
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+  The licenses for most software and other practical works are designed
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+  The precise terms and conditions for copying, distribution and
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+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
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+  A "covered work" means either the unmodified Program or a work based
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+  To "propagate" a work means to do anything with it that, without
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+  An interactive user interface displays "Appropriate Legal Notices"
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diff --git a/README.md b/README.md
index f8e2d9e..af560de 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
-HydroSight - _A toolbox for data-driven hydrogeological insights_
-==============================
-
-HydroSight is a highly flexible statistical toolbox for deriving quantitative insights value from groundwater monitoring data. It includes a powerful graphical interface for analying 100s of groundwater observation bores, allowing data-driven:
-
-* decomposition of a groundwater hydrograph into individual drivers, such as climate and pumping or climate ([Shapoori et al., 2015a](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015A.pdf)) and landuse change ([Peterson and Western, 2014](https://doi.org/10.1029/2017WR021838)).
-* estimation of aquifer hydraulic properties from the hydrograph ([Shapoori et al., 2015c](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015C.pdf), [Peterson et al., in-press](https://doi.org/10.1111/gwat.12946))
-* statistical identification of the major groundwater processes ([Shapoori et al., 2015b](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015B.pdf)).
-* interpolation or extrapolation of the observed hydrographs to a regular time step ([Peterson & Western, 2018](https://doi.org/10.1029/2017WR021838).
-* simulation of groundwater head under different climate or, say, pumping scenarios.
-* numerical identification of hydrograph monitoring errors and outliers ([Peterson et al., 2018](https://doi.org/10.1007/s10040-017-1660-7)).
-
-For futher details visit http://peterson-tim-j.github.io/HydroSight/ or the [user documentation](https://github.com/peterson-tim-j/HydroSight/wiki).
+HydroSight - _A toolbox for data-driven hydrogeological insights_
+==============================
+
+HydroSight is a highly flexible statistical toolbox for deriving quantitative insights value from groundwater monitoring data. It includes a powerful graphical interface for analying 100s of groundwater observation bores, allowing data-driven:
+
+* decomposition of a groundwater hydrograph into individual drivers, such as climate and pumping or climate ([Shapoori et al., 2015a](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015A.pdf)) and landuse change ([Peterson and Western, 2014](https://doi.org/10.1029/2017WR021838)).
+* estimation of aquifer hydraulic properties from the hydrograph ([Shapoori et al., 2015c](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015C.pdf), [Peterson et al., in-press](https://doi.org/10.1111/gwat.12946))
+* statistical identification of the major groundwater processes ([Shapoori et al., 2015b](https://github.com/peterson-tim-j/HydroSight/blob/master/documentation/html/papers/Shapoori_2015B.pdf)).
+* interpolation or extrapolation of the observed hydrographs to a regular time step ([Peterson & Western, 2018](https://doi.org/10.1029/2017WR021838).
+* simulation of groundwater head under different climate or, say, pumping scenarios.
+* numerical identification of hydrograph monitoring errors and outliers ([Peterson et al., 2018](https://doi.org/10.1007/s10040-017-1660-7)).
+
+For futher details visit http://peterson-tim-j.github.io/HydroSight/ or the [user documentation](https://github.com/peterson-tim-j/HydroSight/wiki).
diff --git a/algorithms/GroundwaterStatisticsToolbox.m b/algorithms/GroundwaterStatisticsToolbox.m
index 7f20bc7..7fb22e8 100644
--- a/algorithms/GroundwaterStatisticsToolbox.m
+++ b/algorithms/GroundwaterStatisticsToolbox.m
@@ -1,16 +1,16 @@
-classdef GroundwaterStatisticsToolbox < HydroSightModel
-    %GROUNDWATERSTATISICALTOOLBOX Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-    end
-    
-    methods
-        function obj = GroundwaterStatisticsToolbox(model_label, bore_ID, model_class_name, obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin)
-            % Call constructor for hydroSight model,
-            obj@HydroSightModel(model_label, bore_ID, model_class_name, obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin{1})            
-        end
-    end
-    
-end
-
+classdef GroundwaterStatisticsToolbox < HydroSightModel
+    %GROUNDWATERSTATISICALTOOLBOX Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+    end
+    
+    methods
+        function obj = GroundwaterStatisticsToolbox(model_label, bore_ID, model_class_name, obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin)
+            % Call constructor for hydroSight model,
+            obj@HydroSightModel(model_label, bore_ID, model_class_name, obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin{1})            
+        end
+    end
+    
+end
+
diff --git a/algorithms/HPCoffload/jobSubmission.m b/algorithms/HPCoffload/jobSubmission.m
index 20ef466..0f95faa 100644
--- a/algorithms/HPCoffload/jobSubmission.m
+++ b/algorithms/HPCoffload/jobSubmission.m
@@ -1,458 +1,458 @@
-function [ userData, calibLabel ] = jobSubmission(userData, projectPath, HPCmodelData, calibGUI_interface_obj)
-% jobSubmission Steps for job submission to qsub
-
-
-    if isempty(HPCmodelData)
-        msgbox({'No models are selected for offload to a cluster.','Please select models and re-run.'}, 'No models selected','error');
-        return;
-    end        
-
-    % Determine the number of models to calibrate and their labels
-    %hasModelName = cellfun(@(x) ~isempty(x), calibLabel);
-    %calibLabel = calibLabel(hasModelName);
-    nModels = size(HPCmodelData,1);    
-    
-    % Get the following inputs: cluster URL, user name, folder name, max cores, qsub command (queue, wall time, matlab command)
-    prompts = getPrompts();
-    
-    inputFormat = {{'PBS','SLURM'},'char','char','char','char','char','char','char','char','numeric','numeric','char','char'};
-    dlg_title = 'HydroSight Calibration: Offload calibration to cluster';        
-    num_lines = 1;
-    if isempty(userData) || length(userData)~=13
-        defaults = {2, ...
-                    'spartan.hpc.unimelb.edu.au', ...
-                    '', ...
-                    '', ...
-                    '', ...                    
-                    '~/HydroSight', ...
-                    'HydroSight-MPI-ARRAY', ...
-                    '', ...
-                    '', ...
-                    num2str(nModels), ...
-                    '1', ...
-                    '24:00:00', ...
-                    'module load GCC; module load MATLAB; source /usr/local/Edward/local/intel/2013.1/composer_xe_2013_sp1.2.144/bin/compilervars.sh intel64 '};
-    else
-        defaults=userData;
-    end
-    
-    % Build the figure
-    calibOffloadGUI = figure( ...
-        'Name', dlg_title , ...
-        'NumberTitle', 'off', ...
-        'MenuBar', 'none', ...
-        'HandleVisibility', 'off', ...
-        'Visible','on', ...
-        'Toolbar','none', ...
-        'DockControls','off', ...        
-        'WindowStyle','modal');
-    
-    % Set window Size
-    windowHeight = calibOffloadGUI.Parent.ScreenSize(4);
-    windowWidth = calibOffloadGUI.Parent.ScreenSize(3);
-    figWidth = 600;
-    figHeight = 0.6*windowHeight;            
-    calibOffloadGUI.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];    
-    
-    % Add outer most panel
-    outerVbox= uiextras.VBox('Parent',calibOffloadGUI,'Padding', 3, 'Spacing', 3);    
-    
-    % Add intro text
-    uicontrol(outerVbox, 'Style','text','String', ...
-              { '', ...
-                'This feature allows calibration of all of the selected models  on a high performance cluster. To use this feature you must have an account on a cluster and know the URL to the cluser.', ...
-                '', ...
-                'Importantly, cluster calibration and retrieval has only beed tested on a PBS and SLURM queuing cluster using "mpiexec". Use on any other type of cluster will require modifications to the submission settings.', ...
-                '', ...
-                'Additionally, the provided pre-job submission settings provided are for the University of Melbourne cluster. They specify loading Matlab and initiating the Xeon Phi coprocessors (requires Intel ICC >=2013). Please edit these settings for your cluster.',''}, ... 
-                'HorizontalAlignment','left', 'Units','normalized');                
-    
-    % Fill in the elements of the GUI.
-    vbox= uiextras.Grid('Parent',outerVbox,'Padding', 6, 'Spacing', 6);
-    for i=1:length(prompts)
-        uicontrol(vbox,'Style','text','String',prompts{i},'HorizontalAlignment','left', 'Units','normalized');                                
-    end
-    for i=1:length(prompts)
-        if iscell(inputFormat{i})
-            uicontrol(vbox,'Style','popup','string',inputFormat{i},'Max',1,'HorizontalAlignment','right','Tag',prompts{i});
-        else
-            uicontrol(vbox,'Style','edit','string',defaults{i},'HorizontalAlignment','right','Tag',prompts{i});
-        end
-    end
-    set(vbox, 'ColumnSizes', [300 -1], 'RowSizes', repmat(20,1,length(prompts)));                        
-    
-    % Build cell array to hold input data. This is input 'start' button so
-    % that the callback can access the modle data.
-    inputData = {projectPath, HPCmodelData, calibGUI_interface_obj};
-    
-    % Add buttons
-    buttonsBox = uiextras.HButtonBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);             
-    uicontrol('Parent',buttonsBox,'String','Start offload calibration', 'TooltipString', sprintf('Start calibration on the cluster.'),'Callback', @startOffloadCalibration, ...
-        'UserData',inputData);
-    uicontrol('Parent',buttonsBox,'String','Close', 'TooltipString', sprintf('Exit set-up the cluster calibration.'),'Callback', @cancelOffloadCalibration );
-    buttonsBox.ButtonSize(1) = 225;       
-    
-    % Set outte box size.
-    set(outerVbox, 'Sizes', [225 -1 30]);
-   
-    % Wait until the figure closes.
-    uiwait(calibOffloadGUI);
-    
-    function GUIprompts= getPrompts()
-    GUIprompts = { 'Type of cluster system:', ...
-                    'URL to the cluster:', ...
-                    'User name for cluster:', ...
-                    'Password for cluster:', ...
-                    'Email address for updates (optional):', ...
-                    'Full path to folder for the jobs:', ...
-                    'Job name:', ...
-                    'Queue name (optional):', ...
-                    'Node name (optional) :', ...
-                    ['Max. MPI jobs (ie total number of nodes):'], ...
-                    'CPUs per node per model:', ...
-                    'Max. runtime per model:', ...                
-                    'Command for pre-job submission  (optional):'};
-    end
-
-    function [clusterType, URL, username, password, email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands] = getUserInputData(fig)
-
-        % get GUI prompts. These are used for finding the GUI input objects.
-        prompts= getPrompts();
-
-        % Disaggregate user data
-        clusterType = findobj(fig, 'Tag',prompts{1});
-        clusterTypeValue = clusterType.Value;
-        clusterType = clusterType.String{clusterType.Value};
-
-        URL = findobj(fig, 'Tag',prompts{2});
-        URL = URL.String;
-
-        username = findobj(fig, 'Tag',prompts{3});
-        username = username.String;
-
-        password = findobj(fig, 'Tag',prompts{4});
-        password = password.String;
-
-        email = findobj(fig, 'Tag',prompts{5});
-        email = email.String;
-
-        folder = findobj(fig, 'Tag',prompts{6});
-        folder = folder.String;
-
-        jobName = findobj(fig, 'Tag',prompts{7});
-        jobName = jobName.String;
-
-        queue = findobj(fig, 'Tag',prompts{8});
-        queue = queue.String;
-
-        nodeName = findobj(fig, 'Tag',prompts{9});
-        nodeName = nodeName.String;
-
-        nJobs = findobj(fig, 'Tag',prompts{10});
-        nJobs = nJobs.String;
-
-        nCPUs= findobj(fig, 'Tag',prompts{11});
-        nCPUs = nCPUs.String;
-
-        walltime= findobj(fig, 'Tag',prompts{12});
-        walltime = walltime.String;
-
-        preCommands = findobj(fig, 'Tag',prompts{13});
-        preCommands = preCommands.String;
-
-        userData = {clusterTypeValue, URL, username, '', email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands};
-
-    end
-
-    function cancelOffloadCalibration(this,hObject,eventdata)
-        close(hObject.Source.Parent.Parent.Parent)
-    end
-
-    function startOffloadCalibration(this,hObject,eventdata)
-
-        set(hObject.Source.Parent.Parent.Parent,'Visible','off');
-
-        % Get the input data
-        projectPath = this.UserData{1};
-        HPCmodelData = this.UserData{2};
-        calibGUI_interface_obj = this.UserData{3};
-
-        % Get GUI data
-        [clusterType, URL, username, password, email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands] = getUserInputData(hObject.Source.Parent.Parent);
-
-        if isempty(URL) || isempty(username) || isempty(password) || ...
-        isempty(folder) ||  isempty(jobName) ||  isempty(nCPUs) ||    ...
-        isempty(walltime)
-            msgbox({'All non-optional inputs are required.','Please re-run with the required inputs.'}, 'Insufficient inputs','error');
-            return;
-        end    
-
-        %CD to project path 
-        cd(fileparts(projectPath));    
-
-        %Load Java SSH module
-        %sshfrommatlabinstall();
-        %sshfrommatlabinstall(1);
-
-        % Check that a SSH channel can be opened and set to auto-reconnect
-        sshChannel = ssh2_config(URL,username,password);    
-        if isempty(sshChannel)
-
-            errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
-            return;
-        end
-        sshChannel.autoreconnect = 1;
-
-        % Check if the foilder exists
-        [sshChannel,SSHresult] = ssh2_command(sshChannel,['if test -d ',folder,' ; then echo "exist"; else echo "not exists"; fi']);
-        if strcmp(SSHresult,'exist')
-
-
-            ans = questdlg({'The input full path to folder for the jobs already exists.','All existing jobs within this folder will be deleted.','Do you want to continue?'},'Delete existing folders?','Continue','Cancel','Cancel');
-            if strcmp(ans,'Cancel')
-                return;
-            end
-
-            % Delete existing folders
-            [sshChannel,SSHresult] = ssh2_command(sshChannel,['rm -r ',folder]);
-        end
-
-        % Update progress
-        display('HPC offload progress ...');
-        if ~isempty(calibGUI_interface_obj)
-            updatetextboxFromDiary(calibGUI_interface_obj);
-        end 
-
-        try
-
-            % Create project folder for latter copy to HPC        
-            display('   Making project folder on cluster...');
-            if ~isempty(calibGUI_interface_obj)
-                updatetextboxFromDiary(calibGUI_interface_obj);
-            end 
-
-            [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir -p ',folder]);
-            [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir -p ',folder,'/models']);
-
-            % Loop through each model to be calibrated, create a folder, save a text file for the
-            % calib options and the model object    
-            nModelsUploadFailed=0;
-            nModels = size(HPCmodelData,1);
-            for i=1:nModels
-
-               display(['   Uploading ',num2str(i),' of ',num2str(nModels),' model data files ...']);           
-                if ~isempty(calibGUI_interface_obj)
-                    updatetextboxFromDiary(calibGUI_interface_obj);
-                end 
-
-               model = HPCmodelData{i,1};
-               calibLabel = model.model_label;           
-               calibStartDate = HPCmodelData{i,2};
-               calibEndDate = HPCmodelData{i,3};
-               calibMethod = HPCmodelData{i,4};
-               calibMethodSetting = HPCmodelData{i,5};
-
-               % Create model folder
-               calibLabel_orig = calibLabel;
-               calibLabelFolder{i} =  regexprep(calibLabel,'\W','_');                             
-               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'____','_');                             
-               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'___','_');                             
-               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'__','_');                     
-               try
-                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir ',folder,'/models/',calibLabelFolder{i}]);
-
-                   % Move into model folder
-                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd models/',calibLabelFolder{i}]);
-
-                   % Save calib options text file   
-                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',datestr(calibStartDate),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
-                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',datestr(calibEndDate),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
-                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',calibMethod,' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
-                   settingNames = fieldnames(calibMethodSetting);
-                   for j=1:length(settingNames)
-                    if isnumeric(calibMethodSetting.(settingNames{j}))
-                        % Set random seed using clock time.
-                        if strcmpi(settingNames{j},'iseed') && ~isfinite(calibMethodSetting.(settingNames{j}))                        
-                            calibMethodSetting.(settingNames{j}) = floor(mod(datenum(now),1)*1000000);
-                        end
-
-                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',settingNames{j},':',num2str(calibMethodSetting.(settingNames{j})),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
-                    else
-                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',settingNames{j},':',calibMethodSetting.(settingNames{j}),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
-                    end
-                   end
-
-
-                   % Save model object to .mat file
-                   save(fullfile(projectPath,'HPCmodel.mat'), 'model');
-
-                   % Copy .mat to cluster
-                   scp_put(sshChannel, 'HPCmodel.mat', [folder,'/models/',calibLabelFolder{i}], projectPath,'HPCmodel.mat');
-
-               catch ME
-                   sshChannel  =  ssh2_close(sshChannel);
-                   sshChannel = ssh2_config(URL,username,password); 
-                   nModelsUploadFailed = nModelsUploadFailed +1;
-                   display(['   Upload of model ',calibLabelFolder{i},' failed.']);           
-                   display(['   Error message is:',ME.message]);           
-                   continue;
-               end
-
-               % Delete file
-               delete(fullfile(projectPath,'HPCmodel.mat'));
-
-            end
-
-            %Copy .m algorithms folder to HPC
-            display('   Zipping and uploading HydroSight algorithms ...');
-            if ~isempty(calibGUI_interface_obj)
-                updatetextboxFromDiary(calibGUI_interface_obj);
-            end
-            if ~isdeployed
-                % Find path to alogorithsm folder
-                algorithmsPath = fileparts(mfilename('fullpath'));
-                algorithmsPath = algorithmsPath(1:end-11);
-
-                % Zip algorithms folder (save nto project folder)
-
-                zip(fullfile(projectPath,'algorithms.zip'),algorithmsPath);
-
-                % Copy zipped file to cluster            
-                scp_put(sshChannel, 'algorithms.zip', folder, projectPath, 'algorithms.zip');
-
-                % Unzip the file
-                [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ', folder,'; unzip algorithms.zip']);        
-
-                % remove local zip file
-                delete(fullfile(projectPath,'algorithms.zip'));
-            else
-                errordlg('HPC offloading for a deployed (i.e. standalone) application is not yet implemented')
-                return;
-            end
-
-            % Set the number of MPI jobs
-            if isempty(nJobs)
-                nJobs = nModels;
-            else
-                nJobs=str2num(nJobs);
-            end            
-
-            % Calculate the number of models per job, and if theer are to be an 
-            % unequal number per job then add blank model IDs
-            if floor(nModels/nJobs) ~= nModels/nJobs            
-                nModelsPerJob = ceil(nModels/nJobs);
-                nBlankModels = nModelsPerJob*nJobs - nModels;
-                blankModels = cellstr(repmat(' ',nBlankModels,1))';
-                calibLabel = {calibLabel{:}, blankModels{:}};
-            else
-                nModelsPerJob = nModels/nJobs;
-            end            
-
-            % Reshape list of model names to allocate models to a job.
-            calibLabelFolder = reshape(calibLabelFolder, nJobs, nModelsPerJob);
-
-            % Write file of model names. Each 'mpirun' jobs will open this file and find the requred model name(s) it has 
-            % been allocated to calibrate. If more than one model name is
-            % listed in a row then the MPI job sequentially runs multiple
-            % models. 
-            display('   Writing file of model names ...');
-            if ~isempty(calibGUI_interface_obj)
-                updatetextboxFromDiary(calibGUI_interface_obj);
-            end 
-            for i=1:nJobs
-               % Save calib options text file   
-               [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',strjoin(calibLabelFolder(i,:),','),' \n" >> ',folder,'/ModelNames.csv']);
-            end
-
-            display('   Writing mpiexec submission file ...');
-            if ~isempty(calibGUI_interface_obj)
-                updatetextboxFromDiary(calibGUI_interface_obj);
-            end 
-
-            switch clusterType
-                case 'PBS'
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#! /bin/bash \n" >> ',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -N ',jobName,' \n" >> ',folder,'/SubmitJobs.in']);
-                    if ~isempty(queue)
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -q ',queue,' \n" >> ',folder,'/SubmitJobs.in']);
-                    end
-                    if isempty(nodeName)
-                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l nodes=1:ppn=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
-                    else
-                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l nodes=',nodeName,':ppn=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
-                    end
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l walltime=',walltime,' \n" >> ',folder,'/SubmitJobs.in']);        
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -t 1-',num2str(nJobs),' \n" >> ',folder,'/SubmitJobs.in']);
-                    if ~isempty(email)
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -m bea \n" >> ',folder,'/SubmitJobs.in']);
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -M ',email,' \n" >> ',folder,'/SubmitJobs.in']);
-                    end
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',preCommands,' \n" >>',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd ',folder,' \n" >>',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd algorithms/calibration/Utilities \n" >>',folder,'/SubmitJobs.in']);        
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "mpiexec.hydra -np 1 matlab -nodesktop -nosplash -r ''iModel=''\${PBS_ARRAYID}\$'';calibrateOnCluster;'' \n" >>',folder,'/SubmitJobs.in']);
-
-                    % Submit qsub to call script of tasks.
-                    display('   Submitting job to the HPC queue...');
-                    if ~isempty(calibGUI_interface_obj)
-                        updatetextboxFromDiary(calibGUI_interface_obj);
-                    end 
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ',folder, '&& qsub SubmitJobs.in']);
-                case 'SLURM'
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#!/bin/bash \n" >> ',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --job-name=',jobName,' \n" >> ',folder,'/SubmitJobs.in']);
-                    if ~isempty(queue)
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -p ',queue,' \n" >> ',folder,'/SubmitJobs.in']);
-                    end
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --cpus-per-task=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --time=',walltime,' \n" >> ',folder,'/SubmitJobs.in']);        
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --array=1-',num2str(nJobs),' \n" >> ',folder,'/SubmitJobs.in']);
-                    if ~isempty(email)
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -mail-type=ALL \n" >> ',folder,'/SubmitJobs.in']);
-                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -mail-user=',email,' \n" >> ',folder,'/SubmitJobs.in']);
-                    end
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',preCommands,' \n" >>',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd ',folder,' \n" >>',folder,'/SubmitJobs.in']);
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd algorithms/calibration/Utilities \n" >>',folder,'/SubmitJobs.in']);        
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "matlab -nodesktop -nosplash -r ''iModel=''\${SLURM_ARRAY_TASK_ID}\$'';calibrateOnCluster;'' \n" >>',folder,'/SubmitJobs.in']);
-
-                    % Submit qsub to call script of tasks.
-                    display('   Submitting job to the HPC queue...');
-                    if ~isempty(calibGUI_interface_obj)
-                        updatetextboxFromDiary(calibGUI_interface_obj);
-                    end 
-                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ',folder, '&& sbatch SubmitJobs.in']);                
-            end
-
-            % Closing connection
-            sshChannel  =  ssh2_close(sshChannel);
-
-        catch ME
-            display({'   HPC offload failed with the following message:',['     ', ME.message]});
-            if ~isempty(calibGUI_interface_obj)
-                updatetextboxFromDiary(calibGUI_interface_obj);
-            end 
-
-            set(hObject.Source.Parent.Parent.Parent, 'pointer', 'arrow'); 
-            drawnow;
-
-            return;        
-        end
-
-        display('   HPC offload finished successfully.');  
-        if nModelsUploadFailed>0
-            display(['   WARNING: Offload failed for ',num2str(nModelsUploadFailed),' models.']);
-        end
-        if ~isempty(calibGUI_interface_obj)
-            updatetextboxFromDiary(calibGUI_interface_obj);
-        end 
-
-        set(hObject.Source.Parent.Parent.Parent, 'pointer', 'arrow'); 
-        drawnow;
-
-        userData = {clusterType, URL, username, '', email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands};
-        
-        close (hObject.Source.Parent.Parent.Parent);
-    end
-
+function [ userData, calibLabel ] = jobSubmission(userData, projectPath, HPCmodelData, calibGUI_interface_obj)
+% jobSubmission Steps for job submission to qsub
+
+
+    if isempty(HPCmodelData)
+        msgbox({'No models are selected for offload to a cluster.','Please select models and re-run.'}, 'No models selected','error');
+        return;
+    end        
+
+    % Determine the number of models to calibrate and their labels
+    %hasModelName = cellfun(@(x) ~isempty(x), calibLabel);
+    %calibLabel = calibLabel(hasModelName);
+    nModels = size(HPCmodelData,1);    
+    
+    % Get the following inputs: cluster URL, user name, folder name, max cores, qsub command (queue, wall time, matlab command)
+    prompts = getPrompts();
+    
+    inputFormat = {{'PBS','SLURM'},'char','char','char','char','char','char','char','char','numeric','numeric','char','char'};
+    dlg_title = 'HydroSight Calibration: Offload calibration to cluster';        
+    num_lines = 1;
+    if isempty(userData) || length(userData)~=13
+        defaults = {2, ...
+                    'spartan.hpc.unimelb.edu.au', ...
+                    '', ...
+                    '', ...
+                    '', ...                    
+                    '~/HydroSight', ...
+                    'HydroSight-MPI-ARRAY', ...
+                    '', ...
+                    '', ...
+                    num2str(nModels), ...
+                    '1', ...
+                    '24:00:00', ...
+                    'module load GCC; module load MATLAB; source /usr/local/Edward/local/intel/2013.1/composer_xe_2013_sp1.2.144/bin/compilervars.sh intel64 '};
+    else
+        defaults=userData;
+    end
+    
+    % Build the figure
+    calibOffloadGUI = figure( ...
+        'Name', dlg_title , ...
+        'NumberTitle', 'off', ...
+        'MenuBar', 'none', ...
+        'HandleVisibility', 'off', ...
+        'Visible','on', ...
+        'Toolbar','none', ...
+        'DockControls','off', ...        
+        'WindowStyle','modal');
+    
+    % Set window Size
+    windowHeight = calibOffloadGUI.Parent.ScreenSize(4);
+    windowWidth = calibOffloadGUI.Parent.ScreenSize(3);
+    figWidth = 600;
+    figHeight = 0.6*windowHeight;            
+    calibOffloadGUI.Position = [(windowWidth - figWidth)/2 (windowHeight - figHeight)/2 figWidth figHeight];    
+    
+    % Add outer most panel
+    outerVbox= uiextras.VBox('Parent',calibOffloadGUI,'Padding', 3, 'Spacing', 3);    
+    
+    % Add intro text
+    uicontrol(outerVbox, 'Style','text','String', ...
+              { '', ...
+                'This feature allows calibration of all of the selected models  on a high performance cluster. To use this feature you must have an account on a cluster and know the URL to the cluser.', ...
+                '', ...
+                'Importantly, cluster calibration and retrieval has only beed tested on a PBS and SLURM queuing cluster using "mpiexec". Use on any other type of cluster will require modifications to the submission settings.', ...
+                '', ...
+                'Additionally, the provided pre-job submission settings provided are for the University of Melbourne cluster. They specify loading Matlab and initiating the Xeon Phi coprocessors (requires Intel ICC >=2013). Please edit these settings for your cluster.',''}, ... 
+                'HorizontalAlignment','left', 'Units','normalized');                
+    
+    % Fill in the elements of the GUI.
+    vbox= uiextras.Grid('Parent',outerVbox,'Padding', 6, 'Spacing', 6);
+    for i=1:length(prompts)
+        uicontrol(vbox,'Style','text','String',prompts{i},'HorizontalAlignment','left', 'Units','normalized');                                
+    end
+    for i=1:length(prompts)
+        if iscell(inputFormat{i})
+            uicontrol(vbox,'Style','popup','string',inputFormat{i},'Max',1,'HorizontalAlignment','right','Tag',prompts{i});
+        else
+            uicontrol(vbox,'Style','edit','string',defaults{i},'HorizontalAlignment','right','Tag',prompts{i});
+        end
+    end
+    set(vbox, 'ColumnSizes', [300 -1], 'RowSizes', repmat(20,1,length(prompts)));                        
+    
+    % Build cell array to hold input data. This is input 'start' button so
+    % that the callback can access the modle data.
+    inputData = {projectPath, HPCmodelData, calibGUI_interface_obj};
+    
+    % Add buttons
+    buttonsBox = uiextras.HButtonBox('Parent',outerVbox,'Padding', 3, 'Spacing', 3);             
+    uicontrol('Parent',buttonsBox,'String','Start offload calibration', 'TooltipString', sprintf('Start calibration on the cluster.'),'Callback', @startOffloadCalibration, ...
+        'UserData',inputData);
+    uicontrol('Parent',buttonsBox,'String','Close', 'TooltipString', sprintf('Exit set-up the cluster calibration.'),'Callback', @cancelOffloadCalibration );
+    buttonsBox.ButtonSize(1) = 225;       
+    
+    % Set outte box size.
+    set(outerVbox, 'Sizes', [225 -1 30]);
+   
+    % Wait until the figure closes.
+    uiwait(calibOffloadGUI);
+    
+    function GUIprompts= getPrompts()
+    GUIprompts = { 'Type of cluster system:', ...
+                    'URL to the cluster:', ...
+                    'User name for cluster:', ...
+                    'Password for cluster:', ...
+                    'Email address for updates (optional):', ...
+                    'Full path to folder for the jobs:', ...
+                    'Job name:', ...
+                    'Queue name (optional):', ...
+                    'Node name (optional) :', ...
+                    ['Max. MPI jobs (ie total number of nodes):'], ...
+                    'CPUs per node per model:', ...
+                    'Max. runtime per model:', ...                
+                    'Command for pre-job submission  (optional):'};
+    end
+
+    function [clusterType, URL, username, password, email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands] = getUserInputData(fig)
+
+        % get GUI prompts. These are used for finding the GUI input objects.
+        prompts= getPrompts();
+
+        % Disaggregate user data
+        clusterType = findobj(fig, 'Tag',prompts{1});
+        clusterTypeValue = clusterType.Value;
+        clusterType = clusterType.String{clusterType.Value};
+
+        URL = findobj(fig, 'Tag',prompts{2});
+        URL = URL.String;
+
+        username = findobj(fig, 'Tag',prompts{3});
+        username = username.String;
+
+        password = findobj(fig, 'Tag',prompts{4});
+        password = password.String;
+
+        email = findobj(fig, 'Tag',prompts{5});
+        email = email.String;
+
+        folder = findobj(fig, 'Tag',prompts{6});
+        folder = folder.String;
+
+        jobName = findobj(fig, 'Tag',prompts{7});
+        jobName = jobName.String;
+
+        queue = findobj(fig, 'Tag',prompts{8});
+        queue = queue.String;
+
+        nodeName = findobj(fig, 'Tag',prompts{9});
+        nodeName = nodeName.String;
+
+        nJobs = findobj(fig, 'Tag',prompts{10});
+        nJobs = nJobs.String;
+
+        nCPUs= findobj(fig, 'Tag',prompts{11});
+        nCPUs = nCPUs.String;
+
+        walltime= findobj(fig, 'Tag',prompts{12});
+        walltime = walltime.String;
+
+        preCommands = findobj(fig, 'Tag',prompts{13});
+        preCommands = preCommands.String;
+
+        userData = {clusterTypeValue, URL, username, '', email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands};
+
+    end
+
+    function cancelOffloadCalibration(this,hObject,eventdata)
+        close(hObject.Source.Parent.Parent.Parent)
+    end
+
+    function startOffloadCalibration(this,hObject,eventdata)
+
+        set(hObject.Source.Parent.Parent.Parent,'Visible','off');
+
+        % Get the input data
+        projectPath = this.UserData{1};
+        HPCmodelData = this.UserData{2};
+        calibGUI_interface_obj = this.UserData{3};
+
+        % Get GUI data
+        [clusterType, URL, username, password, email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands] = getUserInputData(hObject.Source.Parent.Parent);
+
+        if isempty(URL) || isempty(username) || isempty(password) || ...
+        isempty(folder) ||  isempty(jobName) ||  isempty(nCPUs) ||    ...
+        isempty(walltime)
+            msgbox({'All non-optional inputs are required.','Please re-run with the required inputs.'}, 'Insufficient inputs','error');
+            return;
+        end    
+
+        %CD to project path 
+        cd(fileparts(projectPath));    
+
+        %Load Java SSH module
+        %sshfrommatlabinstall();
+        %sshfrommatlabinstall(1);
+
+        % Check that a SSH channel can be opened and set to auto-reconnect
+        sshChannel = ssh2_config(URL,username,password);    
+        if isempty(sshChannel)
+
+            errordlg({'An SSH connection to the cluster could not be established.','Please check the input URL, username and passord.'},'SSH connection failed.');
+            return;
+        end
+        sshChannel.autoreconnect = 1;
+
+        % Check if the foilder exists
+        [sshChannel,SSHresult] = ssh2_command(sshChannel,['if test -d ',folder,' ; then echo "exist"; else echo "not exists"; fi']);
+        if strcmp(SSHresult,'exist')
+
+
+            ans = questdlg({'The input full path to folder for the jobs already exists.','All existing jobs within this folder will be deleted.','Do you want to continue?'},'Delete existing folders?','Continue','Cancel','Cancel');
+            if strcmp(ans,'Cancel')
+                return;
+            end
+
+            % Delete existing folders
+            [sshChannel,SSHresult] = ssh2_command(sshChannel,['rm -r ',folder]);
+        end
+
+        % Update progress
+        display('HPC offload progress ...');
+        if ~isempty(calibGUI_interface_obj)
+            updatetextboxFromDiary(calibGUI_interface_obj);
+        end 
+
+        try
+
+            % Create project folder for latter copy to HPC        
+            display('   Making project folder on cluster...');
+            if ~isempty(calibGUI_interface_obj)
+                updatetextboxFromDiary(calibGUI_interface_obj);
+            end 
+
+            [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir -p ',folder]);
+            [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir -p ',folder,'/models']);
+
+            % Loop through each model to be calibrated, create a folder, save a text file for the
+            % calib options and the model object    
+            nModelsUploadFailed=0;
+            nModels = size(HPCmodelData,1);
+            for i=1:nModels
+
+               display(['   Uploading ',num2str(i),' of ',num2str(nModels),' model data files ...']);           
+                if ~isempty(calibGUI_interface_obj)
+                    updatetextboxFromDiary(calibGUI_interface_obj);
+                end 
+
+               model = HPCmodelData{i,1};
+               calibLabel = model.model_label;           
+               calibStartDate = HPCmodelData{i,2};
+               calibEndDate = HPCmodelData{i,3};
+               calibMethod = HPCmodelData{i,4};
+               calibMethodSetting = HPCmodelData{i,5};
+
+               % Create model folder
+               calibLabel_orig = calibLabel;
+               calibLabelFolder{i} =  regexprep(calibLabel,'\W','_');                             
+               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'____','_');                             
+               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'___','_');                             
+               calibLabelFolder{i} =  regexprep(calibLabelFolder{i},'__','_');                     
+               try
+                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['mkdir ',folder,'/models/',calibLabelFolder{i}]);
+
+                   % Move into model folder
+                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd models/',calibLabelFolder{i}]);
+
+                   % Save calib options text file   
+                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',datestr(calibStartDate),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
+                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',datestr(calibEndDate),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
+                   [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',calibMethod,' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
+                   settingNames = fieldnames(calibMethodSetting);
+                   for j=1:length(settingNames)
+                    if isnumeric(calibMethodSetting.(settingNames{j}))
+                        % Set random seed using clock time.
+                        if strcmpi(settingNames{j},'iseed') && ~isfinite(calibMethodSetting.(settingNames{j}))                        
+                            calibMethodSetting.(settingNames{j}) = floor(mod(datenum(now),1)*1000000);
+                        end
+
+                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',settingNames{j},':',num2str(calibMethodSetting.(settingNames{j})),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
+                    else
+                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',settingNames{j},':',calibMethodSetting.(settingNames{j}),' \n" >> ',folder,'/models/',calibLabelFolder{i},'/options.txt']);
+                    end
+                   end
+
+
+                   % Save model object to .mat file
+                   save(fullfile(projectPath,'HPCmodel.mat'), 'model');
+
+                   % Copy .mat to cluster
+                   scp_put(sshChannel, 'HPCmodel.mat', [folder,'/models/',calibLabelFolder{i}], projectPath,'HPCmodel.mat');
+
+               catch ME
+                   sshChannel  =  ssh2_close(sshChannel);
+                   sshChannel = ssh2_config(URL,username,password); 
+                   nModelsUploadFailed = nModelsUploadFailed +1;
+                   display(['   Upload of model ',calibLabelFolder{i},' failed.']);           
+                   display(['   Error message is:',ME.message]);           
+                   continue;
+               end
+
+               % Delete file
+               delete(fullfile(projectPath,'HPCmodel.mat'));
+
+            end
+
+            %Copy .m algorithms folder to HPC
+            display('   Zipping and uploading HydroSight algorithms ...');
+            if ~isempty(calibGUI_interface_obj)
+                updatetextboxFromDiary(calibGUI_interface_obj);
+            end
+            if ~isdeployed
+                % Find path to alogorithsm folder
+                algorithmsPath = fileparts(mfilename('fullpath'));
+                algorithmsPath = algorithmsPath(1:end-11);
+
+                % Zip algorithms folder (save nto project folder)
+
+                zip(fullfile(projectPath,'algorithms.zip'),algorithmsPath);
+
+                % Copy zipped file to cluster            
+                scp_put(sshChannel, 'algorithms.zip', folder, projectPath, 'algorithms.zip');
+
+                % Unzip the file
+                [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ', folder,'; unzip algorithms.zip']);        
+
+                % remove local zip file
+                delete(fullfile(projectPath,'algorithms.zip'));
+            else
+                errordlg('HPC offloading for a deployed (i.e. standalone) application is not yet implemented')
+                return;
+            end
+
+            % Set the number of MPI jobs
+            if isempty(nJobs)
+                nJobs = nModels;
+            else
+                nJobs=str2num(nJobs);
+            end            
+
+            % Calculate the number of models per job, and if theer are to be an 
+            % unequal number per job then add blank model IDs
+            if floor(nModels/nJobs) ~= nModels/nJobs            
+                nModelsPerJob = ceil(nModels/nJobs);
+                nBlankModels = nModelsPerJob*nJobs - nModels;
+                blankModels = cellstr(repmat(' ',nBlankModels,1))';
+                calibLabel = {calibLabel{:}, blankModels{:}};
+            else
+                nModelsPerJob = nModels/nJobs;
+            end            
+
+            % Reshape list of model names to allocate models to a job.
+            calibLabelFolder = reshape(calibLabelFolder, nJobs, nModelsPerJob);
+
+            % Write file of model names. Each 'mpirun' jobs will open this file and find the requred model name(s) it has 
+            % been allocated to calibrate. If more than one model name is
+            % listed in a row then the MPI job sequentially runs multiple
+            % models. 
+            display('   Writing file of model names ...');
+            if ~isempty(calibGUI_interface_obj)
+                updatetextboxFromDiary(calibGUI_interface_obj);
+            end 
+            for i=1:nJobs
+               % Save calib options text file   
+               [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',strjoin(calibLabelFolder(i,:),','),' \n" >> ',folder,'/ModelNames.csv']);
+            end
+
+            display('   Writing mpiexec submission file ...');
+            if ~isempty(calibGUI_interface_obj)
+                updatetextboxFromDiary(calibGUI_interface_obj);
+            end 
+
+            switch clusterType
+                case 'PBS'
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#! /bin/bash \n" >> ',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -N ',jobName,' \n" >> ',folder,'/SubmitJobs.in']);
+                    if ~isempty(queue)
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -q ',queue,' \n" >> ',folder,'/SubmitJobs.in']);
+                    end
+                    if isempty(nodeName)
+                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l nodes=1:ppn=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
+                    else
+                        [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l nodes=',nodeName,':ppn=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
+                    end
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -l walltime=',walltime,' \n" >> ',folder,'/SubmitJobs.in']);        
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -t 1-',num2str(nJobs),' \n" >> ',folder,'/SubmitJobs.in']);
+                    if ~isempty(email)
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -m bea \n" >> ',folder,'/SubmitJobs.in']);
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#PBS -M ',email,' \n" >> ',folder,'/SubmitJobs.in']);
+                    end
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',preCommands,' \n" >>',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd ',folder,' \n" >>',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd algorithms/calibration/Utilities \n" >>',folder,'/SubmitJobs.in']);        
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "mpiexec.hydra -np 1 matlab -nodesktop -nosplash -r ''iModel=''\${PBS_ARRAYID}\$'';calibrateOnCluster;'' \n" >>',folder,'/SubmitJobs.in']);
+
+                    % Submit qsub to call script of tasks.
+                    display('   Submitting job to the HPC queue...');
+                    if ~isempty(calibGUI_interface_obj)
+                        updatetextboxFromDiary(calibGUI_interface_obj);
+                    end 
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ',folder, '&& qsub SubmitJobs.in']);
+                case 'SLURM'
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#!/bin/bash \n" >> ',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --job-name=',jobName,' \n" >> ',folder,'/SubmitJobs.in']);
+                    if ~isempty(queue)
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -p ',queue,' \n" >> ',folder,'/SubmitJobs.in']);
+                    end
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --cpus-per-task=',num2str(nCPUs),' \n" >> ',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --time=',walltime,' \n" >> ',folder,'/SubmitJobs.in']);        
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH --array=1-',num2str(nJobs),' \n" >> ',folder,'/SubmitJobs.in']);
+                    if ~isempty(email)
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -mail-type=ALL \n" >> ',folder,'/SubmitJobs.in']);
+                       [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "#SBATCH -mail-user=',email,' \n" >> ',folder,'/SubmitJobs.in']);
+                    end
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "',preCommands,' \n" >>',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd ',folder,' \n" >>',folder,'/SubmitJobs.in']);
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "cd algorithms/calibration/Utilities \n" >>',folder,'/SubmitJobs.in']);        
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['printf "matlab -nodesktop -nosplash -r ''iModel=''\${SLURM_ARRAY_TASK_ID}\$'';calibrateOnCluster;'' \n" >>',folder,'/SubmitJobs.in']);
+
+                    % Submit qsub to call script of tasks.
+                    display('   Submitting job to the HPC queue...');
+                    if ~isempty(calibGUI_interface_obj)
+                        updatetextboxFromDiary(calibGUI_interface_obj);
+                    end 
+                    [sshChannel,SSHresult] = ssh2_command(sshChannel,['cd ',folder, '&& sbatch SubmitJobs.in']);                
+            end
+
+            % Closing connection
+            sshChannel  =  ssh2_close(sshChannel);
+
+        catch ME
+            display({'   HPC offload failed with the following message:',['     ', ME.message]});
+            if ~isempty(calibGUI_interface_obj)
+                updatetextboxFromDiary(calibGUI_interface_obj);
+            end 
+
+            set(hObject.Source.Parent.Parent.Parent, 'pointer', 'arrow'); 
+            drawnow;
+
+            return;        
+        end
+
+        display('   HPC offload finished successfully.');  
+        if nModelsUploadFailed>0
+            display(['   WARNING: Offload failed for ',num2str(nModelsUploadFailed),' models.']);
+        end
+        if ~isempty(calibGUI_interface_obj)
+            updatetextboxFromDiary(calibGUI_interface_obj);
+        end 
+
+        set(hObject.Source.Parent.Parent.Parent, 'pointer', 'arrow'); 
+        drawnow;
+
+        userData = {clusterType, URL, username, '', email, folder, jobName, queue, nodeName, nJobs, nCPUs, walltime, preCommands};
+        
+        close (hObject.Source.Parent.Parent.Parent);
+    end
+
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/license.txt b/algorithms/HPCoffload/ssh2_v2_m1_r6/license.txt
index 0ad0b59..b524c75 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/license.txt
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/license.txt
@@ -1,24 +1,24 @@
-Copyright (c) 2013, David S. Freedman
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2013, David S. Freedman
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp.m
index 1cfad74..e44da97 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp.m
@@ -1,23 +1,23 @@
-function ssh2_struct = scp(ssh2_struct)
-% SCP   Main SCP interface for SSH2. The input struct provides all 
-%       the parameters for the SSH2 function to perform a scp copy.
-%  
-%   SCP(SSH2_CONN) enables a ssh2 connection for scp and runs
-%   it through the ssh2 engine where a previous configured scp command
-%   will be executed.
-% 
-%   SCP returns the SSH2_CONN for future use.
-%
-%see also scp_get, scp_put, scp_simple_get, scp_simple_put
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin == 1
-    ssh2_struct.scp = 1;
-    ssh2_struct = ssh2(ssh2_struct);
-else
-    ssh2_struct = [];
-    help scp
+function ssh2_struct = scp(ssh2_struct)
+% SCP   Main SCP interface for SSH2. The input struct provides all 
+%       the parameters for the SSH2 function to perform a scp copy.
+%  
+%   SCP(SSH2_CONN) enables a ssh2 connection for scp and runs
+%   it through the ssh2 engine where a previous configured scp command
+%   will be executed.
+% 
+%   SCP returns the SSH2_CONN for future use.
+%
+%see also scp_get, scp_put, scp_simple_get, scp_simple_put
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin == 1
+    ssh2_struct.scp = 1;
+    ssh2_struct = ssh2(ssh2_struct);
+else
+    ssh2_struct = [];
+    help scp
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_get.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_get.m
index d4c61f5..ef5cd73 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_get.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_get.m
@@ -1,51 +1,51 @@
-function ssh2_struct = scp_get(ssh2_struct, remoteFilename, localPath, remotePath)
-% SCP_GET   Reuse configured ssh2_connection to SCP remote files to local host
-%
-%   SCP_GET(SSH2_CONN,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
-%   uses a ssh2_connection and downloads the REMOTEFILENAME from the remote
-%   host using SCP. SSH2_CONN must already be confgured using 
-%   ssh2_config or ssh2_config_publickey.
-%
-%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
-%   If REMOTEFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   LOCALPATH specifies the folder to download the remote file to.
-%   Otherwise the working directory is used.
-%   REMOTEPATH specifies a specific path on the remote host to look for the 
-%   file to download. Otherwise, the default (home) folder is used.
-%
-%   SCP_GET returns the SSH2_CONN for future use.
-%
-%see also scp_get, scp_simple_get, scp_simple_put, scp
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 2
-    if nargin == 0
-        ssh2_struct = [];
-    end
-    help scp_get
-else
-    if nargin < 3
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();        
-    end
-    if nargin < 4
-        remotePath = '';          
-    end
-
-    ssh2_struct.getfiles = 1;
-    ssh2_struct.remote_file = remoteFilename;
-    ssh2_struct.local_target_direcory = localPath;
-    ssh2_struct.remote_target_direcory = remotePath;
-    %ssh2_struct.local_file = localFilename; unused in SCP, filename will
-    %be taken from remoteFilename
-
-    ssh2_struct = scp(ssh2_struct);
+function ssh2_struct = scp_get(ssh2_struct, remoteFilename, localPath, remotePath)
+% SCP_GET   Reuse configured ssh2_connection to SCP remote files to local host
+%
+%   SCP_GET(SSH2_CONN,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
+%   uses a ssh2_connection and downloads the REMOTEFILENAME from the remote
+%   host using SCP. SSH2_CONN must already be confgured using 
+%   ssh2_config or ssh2_config_publickey.
+%
+%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
+%   If REMOTEFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   LOCALPATH specifies the folder to download the remote file to.
+%   Otherwise the working directory is used.
+%   REMOTEPATH specifies a specific path on the remote host to look for the 
+%   file to download. Otherwise, the default (home) folder is used.
+%
+%   SCP_GET returns the SSH2_CONN for future use.
+%
+%see also scp_get, scp_simple_get, scp_simple_put, scp
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 2
+    if nargin == 0
+        ssh2_struct = [];
+    end
+    help scp_get
+else
+    if nargin < 3
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();        
+    end
+    if nargin < 4
+        remotePath = '';          
+    end
+
+    ssh2_struct.getfiles = 1;
+    ssh2_struct.remote_file = remoteFilename;
+    ssh2_struct.local_target_direcory = localPath;
+    ssh2_struct.remote_target_direcory = remotePath;
+    %ssh2_struct.local_file = localFilename; unused in SCP, filename will
+    %be taken from remoteFilename
+
+    ssh2_struct = scp(ssh2_struct);
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_put.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_put.m
index 48f8b3d..baf1b0e 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_put.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_put.m
@@ -1,61 +1,61 @@
-function ssh2_struct = scp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename)
-% SCP_PUT   Reuse configured ssh2_connection to SCP local files to remote host.
-%
-%   SCP_PUT(SSH2_CONN,LOCALFILENAME,[REMOTEPATH],[LOCALPATH],[REMOTEFILENAME])
-%   uses a ssh2_connection and uploads the LOCALFILENAME to the remote
-%   host using SCP. SSH2_CONN must already be confgured using 
-%   ssh2_config or ssh2_config_publickey.
-%
-%   LOCALFILENAME can be either a single string, or a cell array of strings. 
-%   If LOCALFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
-%   the default (home) folder is used.
-%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
-%   is outside the working directory.
-%   REMOTEFILENAME can be specified to rename the file on the remote host.
-%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
-%
-%   SCP_PUT returns the SSH2_CONN for future use.
-%
-%see also scp_get, scp_simple_get, scp_simple_put, scp
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 2
-    if nargin == 0
-        ssh2_struct = [];
-    end
-    help scp_put
-else
-    if nargin < 3
-        remotePath = '';
-    end
-    
-    if nargin < 4
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-
-    if nargin >= 5
-        ssh2_struct.remote_file_new_name = remoteFilename;
-    else 
-        remoteFilename = [];
-    end
-    
-    ssh2_struct.sendfiles = 1;
-    ssh2_struct.local_file = localFilename;
-    ssh2_struct.local_target_direcory = localPath;
-    ssh2_struct.remote_target_direcory = remotePath;
-
-    ssh2_struct = scp(ssh2_struct);
-end
-
-
-    
+function ssh2_struct = scp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename)
+% SCP_PUT   Reuse configured ssh2_connection to SCP local files to remote host.
+%
+%   SCP_PUT(SSH2_CONN,LOCALFILENAME,[REMOTEPATH],[LOCALPATH],[REMOTEFILENAME])
+%   uses a ssh2_connection and uploads the LOCALFILENAME to the remote
+%   host using SCP. SSH2_CONN must already be confgured using 
+%   ssh2_config or ssh2_config_publickey.
+%
+%   LOCALFILENAME can be either a single string, or a cell array of strings. 
+%   If LOCALFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
+%   the default (home) folder is used.
+%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
+%   is outside the working directory.
+%   REMOTEFILENAME can be specified to rename the file on the remote host.
+%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
+%
+%   SCP_PUT returns the SSH2_CONN for future use.
+%
+%see also scp_get, scp_simple_get, scp_simple_put, scp
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 2
+    if nargin == 0
+        ssh2_struct = [];
+    end
+    help scp_put
+else
+    if nargin < 3
+        remotePath = '';
+    end
+    
+    if nargin < 4
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+
+    if nargin >= 5
+        ssh2_struct.remote_file_new_name = remoteFilename;
+    else 
+        remoteFilename = [];
+    end
+    
+    ssh2_struct.sendfiles = 1;
+    ssh2_struct.local_file = localFilename;
+    ssh2_struct.local_target_direcory = localPath;
+    ssh2_struct.remote_target_direcory = remotePath;
+
+    ssh2_struct = scp(ssh2_struct);
+end
+
+
+    
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_get.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_get.m
index 9a2e65e..a3df5a9 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_get.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_get.m
@@ -1,46 +1,46 @@
-function ssh2_struct = scp_simple_get(hostname, username, password, remoteFilename, localPath, remotePath)
-% SCP_SIMPLE_GET   creates a simple SSH2 connection and downloads a file
-%
-%   SCP_SIMPLE_GET(HOSTNAME,USERNAME,PASSWORD,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
-%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
-%   PASSWORD. Once connected the REMOTEFILENAME is downloaded from the
-%   remote host using SCP. The connection is then closed.
-%
-%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
-%   If REMOTEFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   LOCALPATH specifies the folder to download the remote file to.
-%   Otherwise the working directory is used.
-%   REMOTEPATH specifies a specific path on the remote host to look for the 
-%   file to download. Otherwise, the default (home) folder is used.
-% 
-%   SCP_SIMPLE_GET returns the SSH2_CONN for future use.
-%
-%see also scp_get, scp_put, scp, ssh2, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 4
-    ssh2_struct = [];
-    help scp_simple_get
-else
-    if nargin < 5
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-    
-    if nargin < 6
-        remotePath = '';          
-    end
-
-
-    ssh2_struct = ssh2_config(hostname, username, password);
-    ssh2_struct.close_connection = 1; %close connection use
-    ssh2_struct = scp_get(ssh2_struct, remoteFilename, localPath, remotePath);
+function ssh2_struct = scp_simple_get(hostname, username, password, remoteFilename, localPath, remotePath)
+% SCP_SIMPLE_GET   creates a simple SSH2 connection and downloads a file
+%
+%   SCP_SIMPLE_GET(HOSTNAME,USERNAME,PASSWORD,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
+%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
+%   PASSWORD. Once connected the REMOTEFILENAME is downloaded from the
+%   remote host using SCP. The connection is then closed.
+%
+%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
+%   If REMOTEFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   LOCALPATH specifies the folder to download the remote file to.
+%   Otherwise the working directory is used.
+%   REMOTEPATH specifies a specific path on the remote host to look for the 
+%   file to download. Otherwise, the default (home) folder is used.
+% 
+%   SCP_SIMPLE_GET returns the SSH2_CONN for future use.
+%
+%see also scp_get, scp_put, scp, ssh2, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 4
+    ssh2_struct = [];
+    help scp_simple_get
+else
+    if nargin < 5
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+    
+    if nargin < 6
+        remotePath = '';          
+    end
+
+
+    ssh2_struct = ssh2_config(hostname, username, password);
+    ssh2_struct.close_connection = 1; %close connection use
+    ssh2_struct = scp_get(ssh2_struct, remoteFilename, localPath, remotePath);
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_put.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_put.m
index 0a93f01..f2c1005 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_put.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/scp_simple_put.m
@@ -1,59 +1,59 @@
-function ssh2_struct = scp_simple_put(hostname, username, password, localFilename, remotePath, localPath, remoteFilename)
-% SCP_SIMPLE_PUT   creates a simple SSH2 connection and send a remote file
-%
-%   SCP_SIMPLE_PUT(HOSTNAME,USERNAME,PASSWORD,LOCALFILENAME,[REMOTEPATH][LOCALPATH],REMOTEFILENAME)
-%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
-%   PASSWORD. Once connected the LOCALFILENAME is uploaded from the
-%   remote host using SCP. The connection is then closed.
-%
-%   LOCALFILENAME can be either a single string, or a cell array of strings. 
-%   If LOCALFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
-%   the default (home) folder is used.
-%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
-%   is outside the working directory.
-%   REMOTEFILENAME can be specified to rename the file on the remote host.
-%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
-% 
-%   SCP_SIMPLE_PUT returns the SSH2_CONN for future use.
-%
-%see also scp_get, scp_put, scp, ssh2, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 4
-    ssh2_struct = [];
-    help scp_simple_put
-else
-    if nargin < 5
-        remotePath = '';
-    end
-    
-    if nargin < 6
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-
-    ssh2_struct = ssh2_config(hostname, username, password);
-
-    if nargin >= 7
-        ssh2_struct.remote_file_new_name = remoteFilename;
-    else 
-        remoteFilename = [];
-    end
-    
-    
-    ssh2_struct = ssh2_config(hostname, username, password);
-    ssh2_struct.close_connection = 1; %close connection use
-    ssh2_struct = scp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename);
-end
-
-
-    
+function ssh2_struct = scp_simple_put(hostname, username, password, localFilename, remotePath, localPath, remoteFilename)
+% SCP_SIMPLE_PUT   creates a simple SSH2 connection and send a remote file
+%
+%   SCP_SIMPLE_PUT(HOSTNAME,USERNAME,PASSWORD,LOCALFILENAME,[REMOTEPATH][LOCALPATH],REMOTEFILENAME)
+%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
+%   PASSWORD. Once connected the LOCALFILENAME is uploaded from the
+%   remote host using SCP. The connection is then closed.
+%
+%   LOCALFILENAME can be either a single string, or a cell array of strings. 
+%   If LOCALFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
+%   the default (home) folder is used.
+%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
+%   is outside the working directory.
+%   REMOTEFILENAME can be specified to rename the file on the remote host.
+%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
+% 
+%   SCP_SIMPLE_PUT returns the SSH2_CONN for future use.
+%
+%see also scp_get, scp_put, scp, ssh2, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 4
+    ssh2_struct = [];
+    help scp_simple_put
+else
+    if nargin < 5
+        remotePath = '';
+    end
+    
+    if nargin < 6
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+
+    ssh2_struct = ssh2_config(hostname, username, password);
+
+    if nargin >= 7
+        ssh2_struct.remote_file_new_name = remoteFilename;
+    else 
+        remoteFilename = [];
+    end
+    
+    
+    ssh2_struct = ssh2_config(hostname, username, password);
+    ssh2_struct.close_connection = 1; %close connection use
+    ssh2_struct = scp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename);
+end
+
+
+    
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp.m
index 3dc517b..5595ced 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp.m
@@ -1,23 +1,23 @@
-function ssh2_struct = sftp(ssh2_struct)
-% SFTP   Main SFTP interface for SSH2. The input struct provides all 
-%        the parameters for the SSH2 function to perform a sftp command.
-%
-%   SFTP(SSH2_CONN) enables a ssh2 connection for sftp and runs
-%   it through the ssh2 engine where a previous configured scp command
-%   will be executed.
-% 
-%   SFTP returns the SSH2_CONN for future use.
-%
-%see also sftp_get, sftp_put, sftp_simple_get, sftp_simple_put
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin == 1
-    ssh2_struct.sftp = 1;
-    ssh2_struct = ssh2(ssh2_struct);
-else
-    ssh2_struct = [];
-    help sftp
-end
+function ssh2_struct = sftp(ssh2_struct)
+% SFTP   Main SFTP interface for SSH2. The input struct provides all 
+%        the parameters for the SSH2 function to perform a sftp command.
+%
+%   SFTP(SSH2_CONN) enables a ssh2 connection for sftp and runs
+%   it through the ssh2 engine where a previous configured scp command
+%   will be executed.
+% 
+%   SFTP returns the SSH2_CONN for future use.
+%
+%see also sftp_get, sftp_put, sftp_simple_get, sftp_simple_put
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin == 1
+    ssh2_struct.sftp = 1;
+    ssh2_struct = ssh2(ssh2_struct);
+else
+    ssh2_struct = [];
+    help sftp
+end
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_get.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_get.m
index ff6d966..1c5a37b 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_get.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_get.m
@@ -1,57 +1,57 @@
-function ssh2_struct = sftp_get(ssh2_struct, remoteFilename, localPath, remotePath)
-%  SFTP_GET   creates a simple SSH2 connection and retrieves a remote file
-%             NOTE: BECAUSE OF THE WAY MATLAB WORKS WITH JAVA BYTE ARRAYS
-%             GARYMED-SSH2 is CUMBERSOME TO USE WITH SFTP.
-%             SCP IS USED (INPLACE) INSTEAD
-% SFTP_GET   Reuse configured ssh2_connection to SFTP remote files to local host
-%
-%   SFTP_GET(SSH2_CONN,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
-%   uses a ssh2_connection and downloads the REMOTEFILENAME from the remote
-%   host using SFTP. SSH2_CONN must already be confgured using 
-%   ssh2_config or ssh2_config_publickey.
-%
-%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
-%   If REMOTEFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   LOCALPATH specifies the folder to download the remote file to.
-%   Otherwise the working directory is used.
-%   REMOTEPATH specifies a specific path on the remote host to look for the 
-%   file to download. Otherwise, the default (home) folder is used.
-%
-%   SFTP_GET returns the SSH2_CONN for future use.
-%
-%see also sftp_put, sftp_simple_get, sftp_simple_put, sftp
-%
-% (c)2013 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 4.0
-
-if nargin < 2
-    if nargin == 0
-        ssh2_struct = [];
-    end
-    help sftp_get
-else
-    if nargin < 3
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-    
-    if nargin < 4
-        remotePath = '';         
-    end
-    
-    ssh2_struct.getfiles = 1;
-    ssh2_struct.remote_file = remoteFilename;
-    ssh2_struct.local_target_direcory = localPath;
-    ssh2_struct.remote_target_direcory = remotePath;
-    %ssh2_struct.local_file = localFilename; unused in SFTP, filename will
-    %be taken from remoteFilename
-
-    ssh2_struct = sftp(ssh2_struct);
-end
-
+function ssh2_struct = sftp_get(ssh2_struct, remoteFilename, localPath, remotePath)
+%  SFTP_GET   creates a simple SSH2 connection and retrieves a remote file
+%             NOTE: BECAUSE OF THE WAY MATLAB WORKS WITH JAVA BYTE ARRAYS
+%             GARYMED-SSH2 is CUMBERSOME TO USE WITH SFTP.
+%             SCP IS USED (INPLACE) INSTEAD
+% SFTP_GET   Reuse configured ssh2_connection to SFTP remote files to local host
+%
+%   SFTP_GET(SSH2_CONN,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
+%   uses a ssh2_connection and downloads the REMOTEFILENAME from the remote
+%   host using SFTP. SSH2_CONN must already be confgured using 
+%   ssh2_config or ssh2_config_publickey.
+%
+%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
+%   If REMOTEFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   LOCALPATH specifies the folder to download the remote file to.
+%   Otherwise the working directory is used.
+%   REMOTEPATH specifies a specific path on the remote host to look for the 
+%   file to download. Otherwise, the default (home) folder is used.
+%
+%   SFTP_GET returns the SSH2_CONN for future use.
+%
+%see also sftp_put, sftp_simple_get, sftp_simple_put, sftp
+%
+% (c)2013 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 4.0
+
+if nargin < 2
+    if nargin == 0
+        ssh2_struct = [];
+    end
+    help sftp_get
+else
+    if nargin < 3
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+    
+    if nargin < 4
+        remotePath = '';         
+    end
+    
+    ssh2_struct.getfiles = 1;
+    ssh2_struct.remote_file = remoteFilename;
+    ssh2_struct.local_target_direcory = localPath;
+    ssh2_struct.remote_target_direcory = remotePath;
+    %ssh2_struct.local_file = localFilename; unused in SFTP, filename will
+    %be taken from remoteFilename
+
+    ssh2_struct = sftp(ssh2_struct);
+end
+
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_put.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_put.m
index 8e0e9a1..609b1d9 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_put.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_put.m
@@ -1,59 +1,59 @@
-function ssh2_struct = sftp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename)
-%  SFTP_PUT   Reuse configured ssh2_connection to SFP local files to remote
-%             host.
-%
-%   SFTP_PUT(SSH2_CONN,LOCALFILENAME,[REMOTEPATH],[LOCALPATH],[REMOTEFILENAME])
-%   uses a ssh2_connection and uploads the LOCALFILENAME to the remote
-%   host using SFTP. SSH2_CONN must already be confgured using 
-%   ssh2_config or ssh2_config_publickey.
-%
-%   LOCALFILENAME can be either a single string, or a cell array of strings. 
-%   If LOCALFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
-%   the default (home) folder is used.
-%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
-%   is outside the working directory.
-%   REMOTEFILENAME can be specified to rename the file on the remote host.
-%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
-%
-%   SFTP_PUT returns the SSH2_CONN for future use.
-%
-%see also sftp_get, sftp_simple_get, sftp_simple_put, sftp
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 2
-    if nargin == 0
-        ssh2_struct = [];
-    end
-    help sftp_put
-else
-    if nargin < 3
-        remotePath = '';
-    end
-    
-    if nargin < 4
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-    
-    if nargin >= 5
-        ssh2_struct.remote_file_new_name = remoteFilename;
-    else 
-        remoteFilename = [];
-    end
-    
-    ssh2_struct.sendfiles = 1;
-    ssh2_struct.local_file = localFilename;
-    ssh2_struct.local_target_direcory = localPath;
-    ssh2_struct.remote_target_direcory = remotePath;
-
-    ssh2_struct = sftp(ssh2_struct);
+function ssh2_struct = sftp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename)
+%  SFTP_PUT   Reuse configured ssh2_connection to SFP local files to remote
+%             host.
+%
+%   SFTP_PUT(SSH2_CONN,LOCALFILENAME,[REMOTEPATH],[LOCALPATH],[REMOTEFILENAME])
+%   uses a ssh2_connection and uploads the LOCALFILENAME to the remote
+%   host using SFTP. SSH2_CONN must already be confgured using 
+%   ssh2_config or ssh2_config_publickey.
+%
+%   LOCALFILENAME can be either a single string, or a cell array of strings. 
+%   If LOCALFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
+%   the default (home) folder is used.
+%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
+%   is outside the working directory.
+%   REMOTEFILENAME can be specified to rename the file on the remote host.
+%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
+%
+%   SFTP_PUT returns the SSH2_CONN for future use.
+%
+%see also sftp_get, sftp_simple_get, sftp_simple_put, sftp
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 2
+    if nargin == 0
+        ssh2_struct = [];
+    end
+    help sftp_put
+else
+    if nargin < 3
+        remotePath = '';
+    end
+    
+    if nargin < 4
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+    
+    if nargin >= 5
+        ssh2_struct.remote_file_new_name = remoteFilename;
+    else 
+        remoteFilename = [];
+    end
+    
+    ssh2_struct.sendfiles = 1;
+    ssh2_struct.local_file = localFilename;
+    ssh2_struct.local_target_direcory = localPath;
+    ssh2_struct.remote_target_direcory = remotePath;
+
+    ssh2_struct = sftp(ssh2_struct);
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_get.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_get.m
index e0b151e..c373ed7 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_get.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_get.m
@@ -1,50 +1,50 @@
-function ssh2_struct = sftp_simple_get(hostname, username, password, remoteFilename, localPath, remotePath)
-% SFTP_SIMPLE_GET   creates a simple SSH2 connection and retrieves a remote file
-%                   NOTE: BECAUSE OF THE WAY MATLAB WORKS WITH JAVA BYTE ARRAYS
-%                   GARYMED-SSH2 is CUMBERSOME TO USE WITH SFTP GETS.
-%                   SCP IS USED INSTEAD
-%
-%   SFTP_SIMPLE_GET(HOSTNAME,USERNAME,PASSWORD,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
-%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
-%   PASSWORD. Once connected the REMOTEFILENAME is downloaded from the
-%   remote host using SFTP. The connection is then closed.
-%
-%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
-%   If REMOTEFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   LOCALPATH specifies the folder to download the remote file to.
-%   Otherwise the working directory is used.
-%   REMOTEPATH specifies a specific path on the remote host to look for the 
-%   file to download. Otherwise, the default (home) folder is used.
-% 
-%   SFTP_SIMPLE_GET returns the SSH2_CONN for future use.
-%
-%see also sftp_simple_put, sftp, sftp_get, sftp_put, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 4
-    ssh2_struct = [];
-    help sftp_simple_get
-else
-    if nargin < 5
-        localPath = pwd();
-    elseif isempty(localPath)
-        localPath = pwd();   
-    end    
-    
-    if nargin < 6
-        remotePath = '';         
-    end
-    
-
-    ssh2_struct = ssh2_config(hostname, username, password);
-    ssh2_struct.close_connection = 1; %close connection use
-    ssh2_struct = sftp_get(ssh2_struct, remoteFilename, localPath, remotePath);
-end
-
+function ssh2_struct = sftp_simple_get(hostname, username, password, remoteFilename, localPath, remotePath)
+% SFTP_SIMPLE_GET   creates a simple SSH2 connection and retrieves a remote file
+%                   NOTE: BECAUSE OF THE WAY MATLAB WORKS WITH JAVA BYTE ARRAYS
+%                   GARYMED-SSH2 is CUMBERSOME TO USE WITH SFTP GETS.
+%                   SCP IS USED INSTEAD
+%
+%   SFTP_SIMPLE_GET(HOSTNAME,USERNAME,PASSWORD,REMOTEFILENAME,[LOCALPATH],[REMOTEPATH])
+%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
+%   PASSWORD. Once connected the REMOTEFILENAME is downloaded from the
+%   remote host using SFTP. The connection is then closed.
+%
+%   REMOTEFILENAME can be either a single string, or a cell array of strings. 
+%   If REMOTEFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   LOCALPATH specifies the folder to download the remote file to.
+%   Otherwise the working directory is used.
+%   REMOTEPATH specifies a specific path on the remote host to look for the 
+%   file to download. Otherwise, the default (home) folder is used.
+% 
+%   SFTP_SIMPLE_GET returns the SSH2_CONN for future use.
+%
+%see also sftp_simple_put, sftp, sftp_get, sftp_put, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 4
+    ssh2_struct = [];
+    help sftp_simple_get
+else
+    if nargin < 5
+        localPath = pwd();
+    elseif isempty(localPath)
+        localPath = pwd();   
+    end    
+    
+    if nargin < 6
+        remotePath = '';         
+    end
+    
+
+    ssh2_struct = ssh2_config(hostname, username, password);
+    ssh2_struct.close_connection = 1; %close connection use
+    ssh2_struct = sftp_get(ssh2_struct, remoteFilename, localPath, remotePath);
+end
+
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_put.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_put.m
index 9c385cb..1846a2e 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_put.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/sftp_simple_put.m
@@ -1,51 +1,51 @@
-function ssh2_struct = sftp_simple_put(hostname, username, password, localFilename, remotePath, localPath, remoteFilename)
-% SFTP_SIMPLE_PUT   creates a simple SSH2 connection and send a remote file
-%
-%   SFTP_SIMPLE_PUT(HOSTNAME,USERNAME,PASSWORD,LOCALFILENAME,[REMOTEPATH][LOCALPATH],REMOTEFILENAME)
-%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
-%   PASSWORD. Once connected the LOCALFILENAME is uploaded from the
-%   remote host using SFTP. The connection is then closed.
-%
-%   LOCALFILENAME can be either a single string, or a cell array of strings. 
-%   If LOCALFILENAME is a cell array, all files will be downloaded
-%   sequentially.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
-%   the default (home) folder is used.
-%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
-%   is outside the working directory.
-%   REMOTEFILENAME can be specified to rename the file on the remote host.
-%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
-% 
-%   SFTP_SIMPLE_PUT returns the SSH2_CONN for future use.
-%
-%see also sftp_simple_get, sftp, sftp_get, sftp_put, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 4
-    ssh2_struct = [];
-    help sftp_simple_put
-else
-    if nargin < 5
-        remotePath = '';
-    end
-    
-    if nargin < 6
-        localPath = '';
-    end
-    
-    if nargin >= 7
-        ssh2_struct.remote_file_new_name = remoteFilename;
-    else 
-        remoteFilename = [];
-    end
-    
-    ssh2_struct = ssh2_config(hostname, username, password);
-    ssh2_struct.close_connection = 1; %close connection use
-    ssh2_struct = sftp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename);
+function ssh2_struct = sftp_simple_put(hostname, username, password, localFilename, remotePath, localPath, remoteFilename)
+% SFTP_SIMPLE_PUT   creates a simple SSH2 connection and send a remote file
+%
+%   SFTP_SIMPLE_PUT(HOSTNAME,USERNAME,PASSWORD,LOCALFILENAME,[REMOTEPATH][LOCALPATH],REMOTEFILENAME)
+%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
+%   PASSWORD. Once connected the LOCALFILENAME is uploaded from the
+%   remote host using SFTP. The connection is then closed.
+%
+%   LOCALFILENAME can be either a single string, or a cell array of strings. 
+%   If LOCALFILENAME is a cell array, all files will be downloaded
+%   sequentially.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   REMOTEPATH specifies a specific path to upload the file to. Otherwise, 
+%   the default (home) folder is used.
+%   LOCALPATH specifies the folder to find the LOCALFILENAME in the file
+%   is outside the working directory.
+%   REMOTEFILENAME can be specified to rename the file on the remote host.
+%   If LOCALFILENAME is a cell array, REMOTEFILENAME must be too.
+% 
+%   SFTP_SIMPLE_PUT returns the SSH2_CONN for future use.
+%
+%see also sftp_simple_get, sftp, sftp_get, sftp_put, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 4
+    ssh2_struct = [];
+    help sftp_simple_put
+else
+    if nargin < 5
+        remotePath = '';
+    end
+    
+    if nargin < 6
+        localPath = '';
+    end
+    
+    if nargin >= 7
+        ssh2_struct.remote_file_new_name = remoteFilename;
+    else 
+        remoteFilename = [];
+    end
+    
+    ssh2_struct = ssh2_config(hostname, username, password);
+    ssh2_struct.close_connection = 1; %close connection use
+    ssh2_struct = sftp_put(ssh2_struct, localFilename, remotePath, localPath, remoteFilename);
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2.m
index 96e60b0..2d0086b 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2.m
@@ -1,85 +1,85 @@
-function ssh2_struct = ssh2(ssh2_struct, arg2, arg3, arg4, arg5)
-% SSH2   main control program for interfacing with Ganymed Java SSH-2 Library
-%
-% SSH2(SSH2_CONN) uses the SSH2_CONN to perform a SSH2 function, e.g.
-% issue a command or transfer a file using SCP or SFTP. All options for the
-% SSH2 connection are specified in the SSH2_CONN struct. This struct can be
-% configued manually, or by using any of the SSH2 support functions
-% available. 
-%
-% The SSH2_CONN is returned, but can be reused. Typically, the commands and
-% scp/sftp options are cleared after each function call. If you don't
-% desire this behaviour, supply a copy of the SSH2_CONN into the SSH2
-% function.
-%
-% The connection is not closed be default. To close a connection, use
-% SSH2_CLOSE, which will setup SSH2_CONN to close and then call this
-% function. The exception to this are the SIMPLE functions. They all close
-% the connection be default.
-%
-% For convience, SSH2 also provides a front-end to the SSH2_SIMPLE_COMMAND. 
-% [SSH2_STRUCT] = SSH2(HOSTNAME,USERNAME,PASSWORD,COMMAND,[ENABLEPRINTTOSCREEN])
-%
-%   SSH2 returns the SSH2_CONN for future use.
-%
-%see also ssh2_config, ssh2_config_publickey, ssh2_command, scp_get, scp_put, sftp,get, sftp_put, ssh2_close
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-%% BEGIN CODE
-
-input_check = 0; % have not passed the input check
-if nargin < 1
-    ssh2_struct = [];
-else
-    if (isstruct(ssh2_struct))
-        if (isfield(ssh2_struct,'verified_config') || ...
-           isfield(ssh2_struct,'hostname') && ...
-           isfield(ssh2_struct,'username') && ...
-           isfield(ssh2_struct,'password') ) % min required inputs
-                input_check = 1;
-                ssh2_struct.verified_config = 1;
-                if (nargin == 2)
-                    ssh2_struct.command = arg2;
-                end
-        else
-
-        end
-    else
-        if nargin >= 4
-            input_check = 2; %alternate use of ssh2
-        end
-    end
-end
-
-% do we have enough to proceed?
-if (input_check == 0)
-    help ssh2.m;
-    %fprintf('\n\n!!Incorrect input supplied to ssh2, please try again!!\n');
-    if nargout == 0
-        clear ssh2_struct
-    end
-elseif (input_check == 2) %allow ssh2 to be interface to ssh_simpleCommand
-    hostname = ssh2_struct;
-    username = arg2;
-    password = arg3;
-    command = arg4;
-    if nargin < 5
-        if nargout == 0
-            enableprint = 1;
-        else
-            enableprint = 0;
-        end
-    else
-        enableprint = arg4;
-    end
-    if nargout == 0
-        ssh2_simple_command(hostname, username, password, command, enableprint);
-    else
-        ssh2_struct = ssh2_simple_command(hostname, username, password, command, enableprint);
-    end
-else
-    ssh2_struct = ssh2_main(ssh2_struct);    
+function ssh2_struct = ssh2(ssh2_struct, arg2, arg3, arg4, arg5)
+% SSH2   main control program for interfacing with Ganymed Java SSH-2 Library
+%
+% SSH2(SSH2_CONN) uses the SSH2_CONN to perform a SSH2 function, e.g.
+% issue a command or transfer a file using SCP or SFTP. All options for the
+% SSH2 connection are specified in the SSH2_CONN struct. This struct can be
+% configued manually, or by using any of the SSH2 support functions
+% available. 
+%
+% The SSH2_CONN is returned, but can be reused. Typically, the commands and
+% scp/sftp options are cleared after each function call. If you don't
+% desire this behaviour, supply a copy of the SSH2_CONN into the SSH2
+% function.
+%
+% The connection is not closed be default. To close a connection, use
+% SSH2_CLOSE, which will setup SSH2_CONN to close and then call this
+% function. The exception to this are the SIMPLE functions. They all close
+% the connection be default.
+%
+% For convience, SSH2 also provides a front-end to the SSH2_SIMPLE_COMMAND. 
+% [SSH2_STRUCT] = SSH2(HOSTNAME,USERNAME,PASSWORD,COMMAND,[ENABLEPRINTTOSCREEN])
+%
+%   SSH2 returns the SSH2_CONN for future use.
+%
+%see also ssh2_config, ssh2_config_publickey, ssh2_command, scp_get, scp_put, sftp,get, sftp_put, ssh2_close
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+%% BEGIN CODE
+
+input_check = 0; % have not passed the input check
+if nargin < 1
+    ssh2_struct = [];
+else
+    if (isstruct(ssh2_struct))
+        if (isfield(ssh2_struct,'verified_config') || ...
+           isfield(ssh2_struct,'hostname') && ...
+           isfield(ssh2_struct,'username') && ...
+           isfield(ssh2_struct,'password') ) % min required inputs
+                input_check = 1;
+                ssh2_struct.verified_config = 1;
+                if (nargin == 2)
+                    ssh2_struct.command = arg2;
+                end
+        else
+
+        end
+    else
+        if nargin >= 4
+            input_check = 2; %alternate use of ssh2
+        end
+    end
+end
+
+% do we have enough to proceed?
+if (input_check == 0)
+    help ssh2.m;
+    %fprintf('\n\n!!Incorrect input supplied to ssh2, please try again!!\n');
+    if nargout == 0
+        clear ssh2_struct
+    end
+elseif (input_check == 2) %allow ssh2 to be interface to ssh_simpleCommand
+    hostname = ssh2_struct;
+    username = arg2;
+    password = arg3;
+    command = arg4;
+    if nargin < 5
+        if nargout == 0
+            enableprint = 1;
+        else
+            enableprint = 0;
+        end
+    else
+        enableprint = arg4;
+    end
+    if nargout == 0
+        ssh2_simple_command(hostname, username, password, command, enableprint);
+    else
+        ssh2_struct = ssh2_simple_command(hostname, username, password, command, enableprint);
+    end
+else
+    ssh2_struct = ssh2_main(ssh2_struct);    
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_close.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_close.m
index 38b1b7a..e892e81 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_close.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_close.m
@@ -1,22 +1,22 @@
-function ssh2_struct = ssh2_close(ssh2_struct)
-% SSH2_CLOSE   closes an existing SSH2 connection. 
-%              This function can be used as the last SSH2 commmand
-%              because the connection won't be closed until the very end.
-%   SSH2_CLOSE(SSH2_CONN) takes a ssh2_connection, sets it close, and runs
-%   it through the ssh2 engine where it is closed at the end. 
-% 
-%   SSH2_CLOSE returns the SSH2_CONN for future use.
-%
-%see also ssh2, ssh2_setup, ssh2_config, ssh2_config_publickey, scp, sftp
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin == 1
-    ssh2_struct.close_connection = 1;%;
-    ssh2_struct = ssh2(ssh2_struct); %close the connection
-else
-    ssh2_struct = [];
-    help ssh2_close
+function ssh2_struct = ssh2_close(ssh2_struct)
+% SSH2_CLOSE   closes an existing SSH2 connection. 
+%              This function can be used as the last SSH2 commmand
+%              because the connection won't be closed until the very end.
+%   SSH2_CLOSE(SSH2_CONN) takes a ssh2_connection, sets it close, and runs
+%   it through the ssh2 engine where it is closed at the end. 
+% 
+%   SSH2_CLOSE returns the SSH2_CONN for future use.
+%
+%see also ssh2, ssh2_setup, ssh2_config, ssh2_config_publickey, scp, sftp
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin == 1
+    ssh2_struct.close_connection = 1;%;
+    ssh2_struct = ssh2(ssh2_struct); %close the connection
+else
+    ssh2_struct = [];
+    help ssh2_close
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command.m
index 8e6cac5..25d0c38 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command.m
@@ -1,45 +1,45 @@
-function [ssh2_struct, command_result] = ssh2_command(ssh2_struct, command, enableprint)
-% SSH2_COMMAND   Reuses a SSH2 connection and issues the specified command
-%
-%   [SSH2_CONN, [COMMAND_RESULT]] = SSH2_COMMAND(SSH2_CONN,COMMAND,[ENABLEPRINTTOSCREEN])
-%   Connects to the SSH2 host with a configured SSH2_CONN. Once connected 
-%   the COMMAND is issues. The output from the remote host is returned.
-%   The connection to the remote host is still open the function completes.
-%   When finished, close the connection with SSH2_CLOSE(SSH2_CONN)
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   ENABLEPRINTTOSCREEN  force printing the remote host output on screen.
-%
-% 
-%   SSH2_COMMAND returns the SSH2_CONN for future use and the 
-%   cell array containing the host response.
-%
-%see also ssh2, ssh2_config, ssh2_config_publickey, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargin < 3
-    enableprint = 0;
-end
-
-ssh2_struct.command = command;
-
-%% SSH TO HOST
-ssh2_struct = ssh2(ssh2_struct);
-
-%% OUTPUT RESPONSE FROM HOST
-if (nargout < 2 || enableprint)
-    for i = 1:numel(ssh2_struct.command_result)
-       fprintf('%s\n', ssh2_struct.command_result{i});
-    end
-end
-
-if nargout > 1
-    command_result = ssh2_struct.command_result;
-end
-if nargout == 0
-    clear ssh2_struct;
-end
+function [ssh2_struct, command_result] = ssh2_command(ssh2_struct, command, enableprint)
+% SSH2_COMMAND   Reuses a SSH2 connection and issues the specified command
+%
+%   [SSH2_CONN, [COMMAND_RESULT]] = SSH2_COMMAND(SSH2_CONN,COMMAND,[ENABLEPRINTTOSCREEN])
+%   Connects to the SSH2 host with a configured SSH2_CONN. Once connected 
+%   the COMMAND is issues. The output from the remote host is returned.
+%   The connection to the remote host is still open the function completes.
+%   When finished, close the connection with SSH2_CLOSE(SSH2_CONN)
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   ENABLEPRINTTOSCREEN  force printing the remote host output on screen.
+%
+% 
+%   SSH2_COMMAND returns the SSH2_CONN for future use and the 
+%   cell array containing the host response.
+%
+%see also ssh2, ssh2_config, ssh2_config_publickey, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargin < 3
+    enableprint = 0;
+end
+
+ssh2_struct.command = command;
+
+%% SSH TO HOST
+ssh2_struct = ssh2(ssh2_struct);
+
+%% OUTPUT RESPONSE FROM HOST
+if (nargout < 2 || enableprint)
+    for i = 1:numel(ssh2_struct.command_result)
+       fprintf('%s\n', ssh2_struct.command_result{i});
+    end
+end
+
+if nargout > 1
+    command_result = ssh2_struct.command_result;
+end
+if nargout == 0
+    clear ssh2_struct;
+end
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command_response.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command_response.m
index f64fb00..586c47a 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command_response.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_command_response.m
@@ -1,28 +1,28 @@
-function [command_result] = ssh2_command_response(ssh2_struct)
-% SSH2_COMMAND_RESPONSE   Returns the host response from the last SSH2 command
-%
-%   SSH2_COMMAND(SSH2_CONN)
-%   Is a quick method to retrieve the command response from the last SSH2
-%   command from the remote host.
-% 
-%   SSH2_COMMAND returns the COMMAND RESPONSE from the last remote command.
-%
-%see also ssh2, ssh2_commmand, ssh2_simple_command
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-if nargout == 1
-    if nargin == 1
-        command_result = ssh2_struct.command_result;
-    else
-        command_result = 0;
-    end
-else
-    if (nargout < 2 || enableprint)
-        for i = 1:numel(ssh2_struct.command_result)
-           fprintf('%s\n', ssh2_struct.command_result{i});
-        end
-    end
-end
+function [command_result] = ssh2_command_response(ssh2_struct)
+% SSH2_COMMAND_RESPONSE   Returns the host response from the last SSH2 command
+%
+%   SSH2_COMMAND(SSH2_CONN)
+%   Is a quick method to retrieve the command response from the last SSH2
+%   command from the remote host.
+% 
+%   SSH2_COMMAND returns the COMMAND RESPONSE from the last remote command.
+%
+%see also ssh2, ssh2_commmand, ssh2_simple_command
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+if nargout == 1
+    if nargin == 1
+        command_result = ssh2_struct.command_result;
+    else
+        command_result = 0;
+    end
+else
+    if (nargout < 2 || enableprint)
+        for i = 1:numel(ssh2_struct.command_result)
+           fprintf('%s\n', ssh2_struct.command_result{i});
+        end
+    end
+end
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config.m
index 13f9388..d8d1f5e 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config.m
@@ -1,33 +1,33 @@
-function ssh2_struct = ssh2_config(hostname, username, password, port)
-% SSH2_CONFIG   creates a simple SSH2 connection with the
-%               specified hostname, username, and password
-%
-%   SSH2_CONFIG(HOSTNAME,USERNAME,PASSWORD, [PORT])
-%   Configures a connection to the host, HOSTNAME with user USERNAME and
-%   password, PASSWORD. The returned SSH2_CONN can be used by SSH2_COMMAND 
-%   or other SSH2 file transfer commands.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   PORT  to specify a non-standard SSH TCP/IP port. Default is 22.
-%
-% 
-%   SSH2_CONFIG returns the SSH2_CONN for future use
-%
-%see also ssh2_config_publickey, ssh2, ssh2_command, scp_get, scp_put, sftp_get, sftp_put
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-ssh2_struct = ssh2_setup(); %default config
-if (nargin >= 3)
-    ssh2_struct.hostname = hostname;
-    ssh2_struct.username = username;
-    ssh2_struct.password = password;
-    if nargin >= 4
-        ssh2_struct.port = port;
-    end
-else
-    help ssh2_config
+function ssh2_struct = ssh2_config(hostname, username, password, port)
+% SSH2_CONFIG   creates a simple SSH2 connection with the
+%               specified hostname, username, and password
+%
+%   SSH2_CONFIG(HOSTNAME,USERNAME,PASSWORD, [PORT])
+%   Configures a connection to the host, HOSTNAME with user USERNAME and
+%   password, PASSWORD. The returned SSH2_CONN can be used by SSH2_COMMAND 
+%   or other SSH2 file transfer commands.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   PORT  to specify a non-standard SSH TCP/IP port. Default is 22.
+%
+% 
+%   SSH2_CONFIG returns the SSH2_CONN for future use
+%
+%see also ssh2_config_publickey, ssh2, ssh2_command, scp_get, scp_put, sftp_get, sftp_put
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+ssh2_struct = ssh2_setup(); %default config
+if (nargin >= 3)
+    ssh2_struct.hostname = hostname;
+    ssh2_struct.username = username;
+    ssh2_struct.password = password;
+    if nargin >= 4
+        ssh2_struct.port = port;
+    end
+else
+    help ssh2_config
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config_publickey.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config_publickey.m
index 21a9e9e..de428be 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config_publickey.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_config_publickey.m
@@ -1,44 +1,44 @@
-function ssh2_struct = ssh2_config_publickey(hostname, username, pemFile, pemFilePassword, port)
-% SSH2_CONFIG_PUBLICKEY   setups a  SSH2 connection with the
-%                         specified hostname, username, and public key
-%
-%   SSH2_CONFIG_PUBLICKEY(HOSTNAME,USERNAME,PRIVATE_KEY,PRIVATE_KEY_PASSWORD, [PORT])
-%   Configures a connection to the host, HOSTNAME with user USERNAME. The
-%   private key can be a path to a private key file or a private key string
-%   since this is unlikey they can be confused. The password to unencrypt
-%   the private key us supplied as PRIVATE_KEY_PASSWORD. The returned
-%   SSH2_CONN can be used by SSH2_COMMAND or other SSH2 file transfer
-%   commands.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   PORT  to specify a non-standard SSH TCP/IP port. Default is 22.
-%
-% 
-%   SSH2_CONFIG_PUBLICKEY returns the SSH2_CONN for future use
-%
-%see also ssh2_config, ssh2, ssh2_command, scp_get, scp_put, sftp_get, sftp_put
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-ssh2_struct = ssh2_setup(); %default config
-
-if (nargin >= 4)
-    ssh2_struct.hostname = hostname;
-    ssh2_struct.username = username;
-    if (exist(pemFile,'file'))
-        ssh2_struct.pem_file = pemFile;
-    else
-        ssh2_struct.pem_private_key = pemFile;
-    end
-
-    ssh2_struct.pem_private_key_password = pemFilePassword;
-
-    if nargin >= 5
-        ssh2_struct.port = port;
-    end
-else
-    help ssh2_config_publickey
+function ssh2_struct = ssh2_config_publickey(hostname, username, pemFile, pemFilePassword, port)
+% SSH2_CONFIG_PUBLICKEY   setups a  SSH2 connection with the
+%                         specified hostname, username, and public key
+%
+%   SSH2_CONFIG_PUBLICKEY(HOSTNAME,USERNAME,PRIVATE_KEY,PRIVATE_KEY_PASSWORD, [PORT])
+%   Configures a connection to the host, HOSTNAME with user USERNAME. The
+%   private key can be a path to a private key file or a private key string
+%   since this is unlikey they can be confused. The password to unencrypt
+%   the private key us supplied as PRIVATE_KEY_PASSWORD. The returned
+%   SSH2_CONN can be used by SSH2_COMMAND or other SSH2 file transfer
+%   commands.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   PORT  to specify a non-standard SSH TCP/IP port. Default is 22.
+%
+% 
+%   SSH2_CONFIG_PUBLICKEY returns the SSH2_CONN for future use
+%
+%see also ssh2_config, ssh2, ssh2_command, scp_get, scp_put, sftp_get, sftp_put
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+ssh2_struct = ssh2_setup(); %default config
+
+if (nargin >= 4)
+    ssh2_struct.hostname = hostname;
+    ssh2_struct.username = username;
+    if (exist(pemFile,'file'))
+        ssh2_struct.pem_file = pemFile;
+    else
+        ssh2_struct.pem_private_key = pemFile;
+    end
+
+    ssh2_struct.pem_private_key_password = pemFilePassword;
+
+    if nargin >= 5
+        ssh2_struct.port = port;
+    end
+else
+    help ssh2_config_publickey
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_main.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_main.m
index 3c0a93e..417cf83 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_main.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_main.m
@@ -1,381 +1,381 @@
-function ssh2_struct = ssh2_main(ssh2_struct)
-% SSH2_MAIN   main control program for interfacing with Ganymed Java SSH-2 Library
-%             SHOULD NOT BE CALLED DIRECTLY, but through ssh2.m
-%
-%see also ssh2_config, ssh2_config_publickey, ssh2_command, scp_get, scp_put, sftp,get, sftp_put, ssh2_close
-%
-% (c)2013 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 4.0
-
-%% LOAD JAVA LIB
-%
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.io.File;
-
-
-import ch.ethz.ssh2.*;
-import ch.ethz.ssh2.Connection;
-import ch.ethz.ssh2.Session;
-import ch.ethz.ssh2.StreamGobbler;   
-import ch.ethz.ssh2.SCPClient;
-import ch.ethz.ssh2.SFTPv3Client;
-import ch.ethz.ssh2.SFTPv3FileHandle;
-
-%% ASSUME SSH2.M HAS CHECKED SETUP
-%
-
-%% INPUTS are OK, PROCEED WITH MAIN FUNCTION
-%   will check for lib and connection first, then connect and perform task
-
-% SETUP SSH2 LIBRARY
-if (ssh2_struct.ssh2_java_library_loaded == 0)
-    error('Error: SSH2 could not load the Ganymed SSH2 java package!'); 
-end
-
-%% SETUP CONNECTION
-
-%% AUTO-RECONNECT IF ENABLED
-if (ssh2_struct.autoreconnect == 1 && ssh2_struct.authenticated == 1) % assume we're already connected
-    try
-        fprintf('Checking Connection...\n');
-        tmp_command_session  =  ssh2_struct.connection.openSession();
-        tmp_command_session.close();
-    catch 
-        fprintf('Reconnecting...\n');
-        ssh2_struct.authenticated = 0;
-    end
-
-end
-
-%% AUTHENTICATE (PASSWORD OR PRIVATE KEY)
-if (ssh2_struct.authenticated == 1) % assume we're already connected
-
-else
-    try %% MAKE A CONNECTION HERE
-        ssh2_struct.connection = Connection(ssh2_struct.hostname,...
-                                            ssh2_struct.port);
-        ssh2_struct.connection.connect();
-
-        if (~isempty(ssh2_struct.pem_private_key))
-                           ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPublicKey(...
-            ssh2_struct.username,ssh2_struct.pem_private_key,ssh2_struct.pem_private_key_password);
-        elseif (~isempty(ssh2_struct.pem_file))
-             try
-                 private_key_file_handle = java.io.File(ssh2_struct.pem_file);
-             catch 
-                 error(['Error: SSH2 could not open private key file'...
-                    ' %s ...'],...
-                    private_key);
-             end
-            ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPublicKey(...
-            ssh2_struct.username,private_key_file_handle,ssh2_struct.pem_private_key_password);
-        else
-            ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPassword(...
-            ssh2_struct.username,ssh2_struct.password);
-        end
-
-
-        if(~ssh2_struct.authenticated)
-            error('Error: SSH2 could not authenticate the connection!');
-        end
-    catch ME
-        error('Error: SSH2 could not connect to the ssh2 host - "%s"!',...
-                ssh2_struct.hostname);
-    end
-end
-
-
-%% SCP FILE TRANSFER
-if (ssh2_struct.scp > 0) 
-    ssh2_struct.scp = 0; % clear for next time
-    getFile = 0;
-    putFile = 0;
-    strArrayGet = [];
-    strArrayPut = [];
-    strArrayPutRename = [];
-    
-    scp1 = SCPClient(ssh2_struct.connection);
-    if (ssh2_struct.getfiles)
-        ssh2_struct.getfiles = 0; %setup for next time
-        getFile = 1;
-        if (iscell(ssh2_struct.remote_file))
-            num_of_files = numel(ssh2_struct.remote_file);
-            strArrayGet = javaArray('java.lang.String', num_of_files);
-            for remote_file_index = 1:num_of_files
-                strArrayGet(remote_file_index) = java.lang.String( ...
-                   ssh2_remotePathString(...
-                        ssh2_struct.remote_file{remote_file_index},...
-                        ssh2_struct.remote_target_direcory) );
-            end
-        else %assume a string
-            strArrayGet = ssh2_remotePathString(...
-                                ssh2_struct.remote_file,...
-                                ssh2_struct.remote_target_direcory);
-        end
-        ssh2_struct.remote_file = []; % clear out for next time
-    end
-    if (ssh2_struct.sendfiles)
-        ssh2_struct.sendfiles = 0; %setup for next time
-        putFile = 1;
-        if (iscell(ssh2_struct.local_file))
-            num_of_files = numel(ssh2_struct.local_file);
-            strArrayPut = javaArray('java.lang.String', num_of_files);
-            for local_file_index = 1:num_of_files
-                strArrayPut(local_file_index) = java.lang.String(...
-                   ssh2_localPathString(...
-                            ssh2_struct.local_file{local_file_index},...
-                            ssh2_struct.local_target_direcory) );
-            end
-        else %assume a string
-            strArrayPut = ssh2_localPathString(...
-                            ssh2_struct.local_file,...
-                            ssh2_struct.local_target_direcory);
-        end
-        ssh2_struct.local_file = []; % clear out for next time
-        if (~isempty(ssh2_struct.remote_file_new_name))
-            if (iscell(ssh2_struct.remote_file_new_name))
-                num_of_files = numel(ssh2_struct.remote_file_new_name);
-                strArrayPutRename = javaArray('java.lang.String', num_of_files);
-                for remoteRename_file_index = 1:num_of_files
-                    strArrayPutRename(remoteRename_file_index) = java.lang.String(...
-                                ssh2_struct.remote_file_new_name{remoteRename_file_index});
-                end
-            else %assume a string
-                strArrayPutRename = ssh2_struct.remote_file_new_name;
-            end
-        end
-        ssh2_struct.remote_file_new_name = []; % clear out for next time
-    end
-    
-    try
-        if (getFile > 0)
-            scp1.get(strArrayGet,ssh2_struct.local_target_direcory);
-        end
-        if (putFile > 0)
-            if (~isempty(strArrayPutRename)) %rename remote files
-                scp1.put(strArrayPut,strArrayPutRename,...
-                        ssh2_struct.remote_target_direcory, ...
-                        sprintf('%04d',ssh2_struct.remote_file_mode));
-            else %use regular file names
-                scp1.put(strArrayPut,ssh2_struct.remote_target_direcory, ...
-                        sprintf('%04d',ssh2_struct.remote_file_mode));
-            end
-        end
-    catch err
-        error('\nError Transferring File\nSEE JAVA ERROR BELOW\n\n%s',err.message);
-    end
-    clear scp1;
-end
-
-%% SFTP FILE TRANSFER
-if (ssh2_struct.sftp > 0) 
-    ssh2_struct.sftp = 0; % clear for next time
-    getFile = 0;
-    putFile = 0;
-    strArrayGet = [];
-    strArrayPut = [];
-    strArrayPutRename = [];
-
-    if (ssh2_struct.getfiles)
-        ssh2_struct.getfiles = 0; %setup for next time
-        getFile = 1;
-        if (iscell(ssh2_struct.remote_file))
-            strArrayGet = ssh2_struct.remote_file;
-        else %assume a string
-            strArrayGet{1} = ssh2_struct.remote_file;
-        end
-        ssh2_struct.remote_file = []; % clear out for next time
-    end
-    if (ssh2_struct.sendfiles)
-        ssh2_struct.sendfiles = 0; %setup for next time
-        putFile = 1;
-        if (iscell(ssh2_struct.local_file))
-            strArrayPut = ssh2_struct.local_file;
-        else %assume a string
-            strArrayPut{1} = ssh2_struct.local_file;
-        end
-        ssh2_struct.local_file = []; % clear out for next time
-        if (~isempty(ssh2_struct.remote_file_new_name))
-            if (iscell(ssh2_struct.remote_file_new_name))
-                strArrayPutRename = ssh2_struct.remote_file_new_name;
-            else %assume a string
-                strArrayPutRename{1} = ssh2_struct.remote_file_new_name;
-            end
-        end
-        ssh2_struct.remote_file_new_name = []; % clear out for next time
-    end
-
-    try
-        if (getFile > 0)
-            %% A custom ganymed-ssh2 (ganymed-ssh2-m1, not ganymed-ssh2-build250)
-            %% file enables this functionaly. Its' not efficient to check
-            %% whether the custom library is being used because the script will error out
-            %% anyways. Instead, a better error message is added to the end.
-            
-            %% NETWORK TRANSFERS ARE SLOWER, USE SCP IF POSSIBLE
-            
-            %Open SFTP Connection
-            sftp1 = SFTPv3Client(ssh2_struct.connection);
-            for getIndex = 1:numel(strArrayGet)
-                %create files
-
-                remoteFilePath = ssh2_remotePathString(...
-                        strArrayGet{getIndex},...
-                        ssh2_struct.remote_target_direcory);
-                localFilePath = ssh2_localPathString(...
-                        ssh2_remotePathFilenameString(strArrayGet{getIndex}),...
-                        ssh2_struct.local_target_direcory);
-
-                remotef=sftp1.openFileRO(remoteFilePath); %read only
-
-                %transfer file byte by byte in 1kB chunks
-                count=0;
-                readcnt = 0; 
-                bufsize = 1024;
-                Jbuf = javaArray('java.lang.Byte', bufsize);
-                local_fileid = fopen(localFilePath,'w');
-                
-                
-                try
-                    while(readcnt~=-1)
-                        Jbuf = sftp1.readMatlab(remotef,uint32(count), uint8(zeros(1,bufsize)),uint16(0),uint16(bufsize));
-                        readcnt = sftp1.getReadLen();
-                        count = count + readcnt;
-                        % from Handling Data Returned from Java Methods,
-                        % byte arrays will return as signed int8
-                        fwrite(local_fileid, Jbuf(1:readcnt),'int8');
-                    end   
-                catch 
-                    extraErrStr = '';
-                    if (~strcmp(ssh2_struct.ganymed_java_library,'ganymed-ssh2-m1'))
-                        extraErrStr = sprintf('Possible JAVA/MATLAB incompatibility\nCannot use SFTP to retrieve files!\n');
-                        extraErrStr = [extraErrStr sprintf(' !! Please ensure the included CUSTOM ganymed-ssh2 library is being used!!\n')];
-                        extraErrStr = [extraErrStr sprintf(' see ssh2_setup.m for more information... or use SCP instead!\n')];
-                        extraErrStr = [extraErrStr sprintf('See http://www.mathworks.com/matlabcentral/newsreader/view_thread/71084 and\n')];
-                        extraErrStr = [extraErrStr sprintf(' http://www.mathworks.com/help/matlab/matlab_external/handling-data-returned-from-a-java-method.html\n for more info.\n\n')];
-                    end
-                     error(['%sError: SFTP could not write to the file: %s, '...
-                        ' on local machine!'],extraErrStr,localFilePath);
-                end
-                sftp1.closeFile(remotef); %all done!
-                fclose(local_fileid); %close the file
-            end
-            sftp1.close(); %close the connection
-        end
-        if (putFile > 0)
-            %Open SFTP Connection
-            sftp1 = SFTPv3Client(ssh2_struct.connection);
-            for putIndex = 1:numel(strArrayPut)
-                %create files
-                if (~isempty(strArrayPutRename)) %rename remote files
-                    remoteFilePath = ssh2_remotePathString(...
-                        strArrayPutRename{putIndex},...
-                        ssh2_struct.remote_target_direcory);
-                else
-                    remoteFilePath = ssh2_remotePathString(...
-                        strArrayPut{putIndex},...
-                        ssh2_struct.remote_target_direcory);
-                end
-                localFilePath = ssh2_localPathString(...
-                        strArrayPut{putIndex},...
-                        ssh2_struct.local_target_direcory);
-
-                localf=sftp1.createFile(remoteFilePath); %create the file
-                remotef=sftp1.openFileRW(remoteFilePath); %get ready to write
-
-               %transfer file byte by byte in 1kB chunks
-                count=0; buf = zeros(1,1024);
-                local_fileid = fopen(localFilePath,'r');
-                [buf, bufsize] = fread(local_fileid, 16*1024);
-                try 
-                    while(bufsize~=0)
-                        sftp1.write(remotef,count,buf,0,bufsize);
-                        count=count+bufsize;
-                        [buf, bufsize] = fread(local_fileid, 16*1024);
-                    end   
-                catch
-                    error(['Error: SFTP could not write to the file: %s, '...
-                        ' on remote machine - "%s"!'],remoteFilePath,ssh2_struct.hostName);
-                end
-                sftp1.closeFile(remotef); %all done!
-                fclose(local_fileid); %close the file
-            end
-            sftp1.close(); %close the connection
-        end
-    catch err
-
-        error('\nSFTP: Error Transferring File\nSEE JAVA ERROR BELOW\n\n%s',err.message);
-    end
-end
-
-%% ISSUE COMMANDS
-ssh2_struct.command_result = {''}; % clear this out
-if (ischar(ssh2_struct.command)) % we should send a command then.
-    % open session and send commands
-    ssh2_struct.command_session  =  ssh2_struct.connection.openSession();
-    ssh2_struct.command_session.execCommand(ssh2_struct.command);
-
-
-    if (~ssh2_struct.command_ignore_response) %get the response from the host
-        stdout = StreamGobbler(ssh2_struct.command_session.getStdout());
-        br = BufferedReader(InputStreamReader(stdout));
-        while(true)
-            line = br.readLine();
-            if(isempty(line))
-                break
-            else
-                if(isempty(ssh2_struct.command_result{1}))
-                    ssh2_struct.command_result{1}  =  char(line);
-                else
-                    ssh2_struct.command_result{end+1}  =  char(line);
-                end
-            end
-        end
-        ssh2_struct.command_result = ssh2_struct.command_result';
-    else
-
-    end
-    ssh2_struct.command_session.close();
-    ssh2_struct.command = []; % clear out the previous command
-end
-
-%% CLOSE THE CONNECTION
-if(ssh2_struct.close_connection > 0)
-    ssh2_struct.connection.close();
-    ssh2_struct.authenticated = 0;
-end
-    
-
-%% HELPER FUNCTIONS FOR REMOTE DIRECTORY PARSING
-function [str] = ssh2_remotePathFilenameString(fileStr)
-% for SFTP to get the correct path when downloading from a unix server to
-% windows client
-[pathstr, name, ext] = fileparts(fileStr);
-str = [name ext];
-
-
-function [str] = ssh2_remotePathString(fileStr,pathStr)
-% assume that we're uploading to a unix server, filesep = '/';
-filesepstr = '/';
-if (~isempty(pathStr) && (pathStr(end) ~= filesepstr)) %no pathsep included
-    str = [pathStr filesepstr fileStr];
-elseif (~isempty(pathStr)) %pathsep is included
-    str = [pathStr fileStr];
-else
-    str = [fileStr];
-end
-
-function [str] = ssh2_localPathString(fileStr,pathStr)
-% use the encoding scheme of local host
-filesepstr = filesep();
-if (~isempty(pathStr) && (pathStr(end) ~= filesepstr)) %no pathsep included
-    str = [pathStr filesepstr fileStr];
-elseif (~isempty(pathStr)) %pathsep is included
-    str = [pathStr fileStr];
-else
-    str = [fileStr];
+function ssh2_struct = ssh2_main(ssh2_struct)
+% SSH2_MAIN   main control program for interfacing with Ganymed Java SSH-2 Library
+%             SHOULD NOT BE CALLED DIRECTLY, but through ssh2.m
+%
+%see also ssh2_config, ssh2_config_publickey, ssh2_command, scp_get, scp_put, sftp,get, sftp_put, ssh2_close
+%
+% (c)2013 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 4.0
+
+%% LOAD JAVA LIB
+%
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.io.File;
+
+
+import ch.ethz.ssh2.*;
+import ch.ethz.ssh2.Connection;
+import ch.ethz.ssh2.Session;
+import ch.ethz.ssh2.StreamGobbler;   
+import ch.ethz.ssh2.SCPClient;
+import ch.ethz.ssh2.SFTPv3Client;
+import ch.ethz.ssh2.SFTPv3FileHandle;
+
+%% ASSUME SSH2.M HAS CHECKED SETUP
+%
+
+%% INPUTS are OK, PROCEED WITH MAIN FUNCTION
+%   will check for lib and connection first, then connect and perform task
+
+% SETUP SSH2 LIBRARY
+if (ssh2_struct.ssh2_java_library_loaded == 0)
+    error('Error: SSH2 could not load the Ganymed SSH2 java package!'); 
+end
+
+%% SETUP CONNECTION
+
+%% AUTO-RECONNECT IF ENABLED
+if (ssh2_struct.autoreconnect == 1 && ssh2_struct.authenticated == 1) % assume we're already connected
+    try
+        fprintf('Checking Connection...\n');
+        tmp_command_session  =  ssh2_struct.connection.openSession();
+        tmp_command_session.close();
+    catch 
+        fprintf('Reconnecting...\n');
+        ssh2_struct.authenticated = 0;
+    end
+
+end
+
+%% AUTHENTICATE (PASSWORD OR PRIVATE KEY)
+if (ssh2_struct.authenticated == 1) % assume we're already connected
+
+else
+    try %% MAKE A CONNECTION HERE
+        ssh2_struct.connection = Connection(ssh2_struct.hostname,...
+                                            ssh2_struct.port);
+        ssh2_struct.connection.connect();
+
+        if (~isempty(ssh2_struct.pem_private_key))
+                           ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPublicKey(...
+            ssh2_struct.username,ssh2_struct.pem_private_key,ssh2_struct.pem_private_key_password);
+        elseif (~isempty(ssh2_struct.pem_file))
+             try
+                 private_key_file_handle = java.io.File(ssh2_struct.pem_file);
+             catch 
+                 error(['Error: SSH2 could not open private key file'...
+                    ' %s ...'],...
+                    private_key);
+             end
+            ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPublicKey(...
+            ssh2_struct.username,private_key_file_handle,ssh2_struct.pem_private_key_password);
+        else
+            ssh2_struct.authenticated = ssh2_struct.connection.authenticateWithPassword(...
+            ssh2_struct.username,ssh2_struct.password);
+        end
+
+
+        if(~ssh2_struct.authenticated)
+            error('Error: SSH2 could not authenticate the connection!');
+        end
+    catch ME
+        error('Error: SSH2 could not connect to the ssh2 host - "%s"!',...
+                ssh2_struct.hostname);
+    end
+end
+
+
+%% SCP FILE TRANSFER
+if (ssh2_struct.scp > 0) 
+    ssh2_struct.scp = 0; % clear for next time
+    getFile = 0;
+    putFile = 0;
+    strArrayGet = [];
+    strArrayPut = [];
+    strArrayPutRename = [];
+    
+    scp1 = SCPClient(ssh2_struct.connection);
+    if (ssh2_struct.getfiles)
+        ssh2_struct.getfiles = 0; %setup for next time
+        getFile = 1;
+        if (iscell(ssh2_struct.remote_file))
+            num_of_files = numel(ssh2_struct.remote_file);
+            strArrayGet = javaArray('java.lang.String', num_of_files);
+            for remote_file_index = 1:num_of_files
+                strArrayGet(remote_file_index) = java.lang.String( ...
+                   ssh2_remotePathString(...
+                        ssh2_struct.remote_file{remote_file_index},...
+                        ssh2_struct.remote_target_direcory) );
+            end
+        else %assume a string
+            strArrayGet = ssh2_remotePathString(...
+                                ssh2_struct.remote_file,...
+                                ssh2_struct.remote_target_direcory);
+        end
+        ssh2_struct.remote_file = []; % clear out for next time
+    end
+    if (ssh2_struct.sendfiles)
+        ssh2_struct.sendfiles = 0; %setup for next time
+        putFile = 1;
+        if (iscell(ssh2_struct.local_file))
+            num_of_files = numel(ssh2_struct.local_file);
+            strArrayPut = javaArray('java.lang.String', num_of_files);
+            for local_file_index = 1:num_of_files
+                strArrayPut(local_file_index) = java.lang.String(...
+                   ssh2_localPathString(...
+                            ssh2_struct.local_file{local_file_index},...
+                            ssh2_struct.local_target_direcory) );
+            end
+        else %assume a string
+            strArrayPut = ssh2_localPathString(...
+                            ssh2_struct.local_file,...
+                            ssh2_struct.local_target_direcory);
+        end
+        ssh2_struct.local_file = []; % clear out for next time
+        if (~isempty(ssh2_struct.remote_file_new_name))
+            if (iscell(ssh2_struct.remote_file_new_name))
+                num_of_files = numel(ssh2_struct.remote_file_new_name);
+                strArrayPutRename = javaArray('java.lang.String', num_of_files);
+                for remoteRename_file_index = 1:num_of_files
+                    strArrayPutRename(remoteRename_file_index) = java.lang.String(...
+                                ssh2_struct.remote_file_new_name{remoteRename_file_index});
+                end
+            else %assume a string
+                strArrayPutRename = ssh2_struct.remote_file_new_name;
+            end
+        end
+        ssh2_struct.remote_file_new_name = []; % clear out for next time
+    end
+    
+    try
+        if (getFile > 0)
+            scp1.get(strArrayGet,ssh2_struct.local_target_direcory);
+        end
+        if (putFile > 0)
+            if (~isempty(strArrayPutRename)) %rename remote files
+                scp1.put(strArrayPut,strArrayPutRename,...
+                        ssh2_struct.remote_target_direcory, ...
+                        sprintf('%04d',ssh2_struct.remote_file_mode));
+            else %use regular file names
+                scp1.put(strArrayPut,ssh2_struct.remote_target_direcory, ...
+                        sprintf('%04d',ssh2_struct.remote_file_mode));
+            end
+        end
+    catch err
+        error('\nError Transferring File\nSEE JAVA ERROR BELOW\n\n%s',err.message);
+    end
+    clear scp1;
+end
+
+%% SFTP FILE TRANSFER
+if (ssh2_struct.sftp > 0) 
+    ssh2_struct.sftp = 0; % clear for next time
+    getFile = 0;
+    putFile = 0;
+    strArrayGet = [];
+    strArrayPut = [];
+    strArrayPutRename = [];
+
+    if (ssh2_struct.getfiles)
+        ssh2_struct.getfiles = 0; %setup for next time
+        getFile = 1;
+        if (iscell(ssh2_struct.remote_file))
+            strArrayGet = ssh2_struct.remote_file;
+        else %assume a string
+            strArrayGet{1} = ssh2_struct.remote_file;
+        end
+        ssh2_struct.remote_file = []; % clear out for next time
+    end
+    if (ssh2_struct.sendfiles)
+        ssh2_struct.sendfiles = 0; %setup for next time
+        putFile = 1;
+        if (iscell(ssh2_struct.local_file))
+            strArrayPut = ssh2_struct.local_file;
+        else %assume a string
+            strArrayPut{1} = ssh2_struct.local_file;
+        end
+        ssh2_struct.local_file = []; % clear out for next time
+        if (~isempty(ssh2_struct.remote_file_new_name))
+            if (iscell(ssh2_struct.remote_file_new_name))
+                strArrayPutRename = ssh2_struct.remote_file_new_name;
+            else %assume a string
+                strArrayPutRename{1} = ssh2_struct.remote_file_new_name;
+            end
+        end
+        ssh2_struct.remote_file_new_name = []; % clear out for next time
+    end
+
+    try
+        if (getFile > 0)
+            %% A custom ganymed-ssh2 (ganymed-ssh2-m1, not ganymed-ssh2-build250)
+            %% file enables this functionaly. Its' not efficient to check
+            %% whether the custom library is being used because the script will error out
+            %% anyways. Instead, a better error message is added to the end.
+            
+            %% NETWORK TRANSFERS ARE SLOWER, USE SCP IF POSSIBLE
+            
+            %Open SFTP Connection
+            sftp1 = SFTPv3Client(ssh2_struct.connection);
+            for getIndex = 1:numel(strArrayGet)
+                %create files
+
+                remoteFilePath = ssh2_remotePathString(...
+                        strArrayGet{getIndex},...
+                        ssh2_struct.remote_target_direcory);
+                localFilePath = ssh2_localPathString(...
+                        ssh2_remotePathFilenameString(strArrayGet{getIndex}),...
+                        ssh2_struct.local_target_direcory);
+
+                remotef=sftp1.openFileRO(remoteFilePath); %read only
+
+                %transfer file byte by byte in 1kB chunks
+                count=0;
+                readcnt = 0; 
+                bufsize = 1024;
+                Jbuf = javaArray('java.lang.Byte', bufsize);
+                local_fileid = fopen(localFilePath,'w');
+                
+                
+                try
+                    while(readcnt~=-1)
+                        Jbuf = sftp1.readMatlab(remotef,uint32(count), uint8(zeros(1,bufsize)),uint16(0),uint16(bufsize));
+                        readcnt = sftp1.getReadLen();
+                        count = count + readcnt;
+                        % from Handling Data Returned from Java Methods,
+                        % byte arrays will return as signed int8
+                        fwrite(local_fileid, Jbuf(1:readcnt),'int8');
+                    end   
+                catch 
+                    extraErrStr = '';
+                    if (~strcmp(ssh2_struct.ganymed_java_library,'ganymed-ssh2-m1'))
+                        extraErrStr = sprintf('Possible JAVA/MATLAB incompatibility\nCannot use SFTP to retrieve files!\n');
+                        extraErrStr = [extraErrStr sprintf(' !! Please ensure the included CUSTOM ganymed-ssh2 library is being used!!\n')];
+                        extraErrStr = [extraErrStr sprintf(' see ssh2_setup.m for more information... or use SCP instead!\n')];
+                        extraErrStr = [extraErrStr sprintf('See http://www.mathworks.com/matlabcentral/newsreader/view_thread/71084 and\n')];
+                        extraErrStr = [extraErrStr sprintf(' http://www.mathworks.com/help/matlab/matlab_external/handling-data-returned-from-a-java-method.html\n for more info.\n\n')];
+                    end
+                     error(['%sError: SFTP could not write to the file: %s, '...
+                        ' on local machine!'],extraErrStr,localFilePath);
+                end
+                sftp1.closeFile(remotef); %all done!
+                fclose(local_fileid); %close the file
+            end
+            sftp1.close(); %close the connection
+        end
+        if (putFile > 0)
+            %Open SFTP Connection
+            sftp1 = SFTPv3Client(ssh2_struct.connection);
+            for putIndex = 1:numel(strArrayPut)
+                %create files
+                if (~isempty(strArrayPutRename)) %rename remote files
+                    remoteFilePath = ssh2_remotePathString(...
+                        strArrayPutRename{putIndex},...
+                        ssh2_struct.remote_target_direcory);
+                else
+                    remoteFilePath = ssh2_remotePathString(...
+                        strArrayPut{putIndex},...
+                        ssh2_struct.remote_target_direcory);
+                end
+                localFilePath = ssh2_localPathString(...
+                        strArrayPut{putIndex},...
+                        ssh2_struct.local_target_direcory);
+
+                localf=sftp1.createFile(remoteFilePath); %create the file
+                remotef=sftp1.openFileRW(remoteFilePath); %get ready to write
+
+               %transfer file byte by byte in 1kB chunks
+                count=0; buf = zeros(1,1024);
+                local_fileid = fopen(localFilePath,'r');
+                [buf, bufsize] = fread(local_fileid, 16*1024);
+                try 
+                    while(bufsize~=0)
+                        sftp1.write(remotef,count,buf,0,bufsize);
+                        count=count+bufsize;
+                        [buf, bufsize] = fread(local_fileid, 16*1024);
+                    end   
+                catch
+                    error(['Error: SFTP could not write to the file: %s, '...
+                        ' on remote machine - "%s"!'],remoteFilePath,ssh2_struct.hostName);
+                end
+                sftp1.closeFile(remotef); %all done!
+                fclose(local_fileid); %close the file
+            end
+            sftp1.close(); %close the connection
+        end
+    catch err
+
+        error('\nSFTP: Error Transferring File\nSEE JAVA ERROR BELOW\n\n%s',err.message);
+    end
+end
+
+%% ISSUE COMMANDS
+ssh2_struct.command_result = {''}; % clear this out
+if (ischar(ssh2_struct.command)) % we should send a command then.
+    % open session and send commands
+    ssh2_struct.command_session  =  ssh2_struct.connection.openSession();
+    ssh2_struct.command_session.execCommand(ssh2_struct.command);
+
+
+    if (~ssh2_struct.command_ignore_response) %get the response from the host
+        stdout = StreamGobbler(ssh2_struct.command_session.getStdout());
+        br = BufferedReader(InputStreamReader(stdout));
+        while(true)
+            line = br.readLine();
+            if(isempty(line))
+                break
+            else
+                if(isempty(ssh2_struct.command_result{1}))
+                    ssh2_struct.command_result{1}  =  char(line);
+                else
+                    ssh2_struct.command_result{end+1}  =  char(line);
+                end
+            end
+        end
+        ssh2_struct.command_result = ssh2_struct.command_result';
+    else
+
+    end
+    ssh2_struct.command_session.close();
+    ssh2_struct.command = []; % clear out the previous command
+end
+
+%% CLOSE THE CONNECTION
+if(ssh2_struct.close_connection > 0)
+    ssh2_struct.connection.close();
+    ssh2_struct.authenticated = 0;
+end
+    
+
+%% HELPER FUNCTIONS FOR REMOTE DIRECTORY PARSING
+function [str] = ssh2_remotePathFilenameString(fileStr)
+% for SFTP to get the correct path when downloading from a unix server to
+% windows client
+[pathstr, name, ext] = fileparts(fileStr);
+str = [name ext];
+
+
+function [str] = ssh2_remotePathString(fileStr,pathStr)
+% assume that we're uploading to a unix server, filesep = '/';
+filesepstr = '/';
+if (~isempty(pathStr) && (pathStr(end) ~= filesepstr)) %no pathsep included
+    str = [pathStr filesepstr fileStr];
+elseif (~isempty(pathStr)) %pathsep is included
+    str = [pathStr fileStr];
+else
+    str = [fileStr];
+end
+
+function [str] = ssh2_localPathString(fileStr,pathStr)
+% use the encoding scheme of local host
+filesepstr = filesep();
+if (~isempty(pathStr) && (pathStr(end) ~= filesepstr)) %no pathsep included
+    str = [pathStr filesepstr fileStr];
+elseif (~isempty(pathStr)) %pathsep is included
+    str = [pathStr fileStr];
+else
+    str = [fileStr];
 end
\ No newline at end of file
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_setup.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_setup.m
index 0b7177e..ed15c33 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_setup.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_setup.m
@@ -1,277 +1,277 @@
-function ssh2_struct = ssh2_setup(ssh2_struct)
-% SSH2_SETUP   Create configuration structure, SSH2_CONN,
-%               for use with ssh2, scp, sftp and supporting functions. 
-%               The file must be created, either directly or indirectly,
-%               in order to create a ssh connection. 
-%
-%               SSH2_CONFIG or SSH2_SIMPLE commands will 
-%               automatically create this file for you.
-%
-% SSH2_SETUP([SSH2_CONN])  normally, no inputs are provided.
-%
-% In addition to creating (or checking) a default SSH2_CONN config to use, 
-% this file will also perform the following steps to ensure the ganymed-ssh2
-% java library is loaded in MATLAB.
-% 
-% If you would like to use SFTP-GET, it is recommended that you change 
-% the variable USE_CUSTOM_GANYMED_LIB = 1 %% (line 107)
-% In this case, an included custom ganymed-ssh2 library
-% will be used that supports this function. The default value of 
-% USE_CUSTOM_GANYMED_LIB is 0 in which 1) is performed below
-% -----------------------------------------------------------------------
-%
-% 1.  Download and unzip the ganymed-ssh2 jar file from (if necessary)
-%                       http://www.cleondris.ch/opensource/ssh2/
-% 2.  Add the jar file to the Matlab dynamic or static Java path
-%                       e.g.  to add as a dynamic library 
-%                             issue the command
-%
-%                             javaaddpath('ganymed-ssh2-build250.jar')
-%
-%                             assuming the SSH-2 Java library is in your
-%                             working directory and named
-%                             ganymed-ssh2-build250.jar
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   SSH2_CONN  when an input is supplied, the SSH2_CONN struct will be 
-%              examined to verify all necessary fields are present.
-%
-%
-% SSH2_CONN Fields:
-% ------------------------------------------------------------------------
-%
-%   CONNECTION VALUES
-%   .hostname  - network name of the SSH2 host
-%   .username  - username to login to host as
-%   .password  - password associated with username on remote host
-%   .port  - SSH2 remote TCP/IP port, default is 22
-%
-%   PUBLIC/PRIVATE KEY VALUES
-%   .pem_file  - file location of the private key
-%   .pem_private_key  - private key as a string
-%   .pem_private_key_password  - password for the private key
-%
-%   REMOTE COMMAND PARAMETERS AND OPTIONS
-%   .command  - command line string to issue to remote host
-%   .command_session  - session object for the ssh2 command line 
-%   .command_ignore_response  - set to 1 to ignore response from host
-%   .command_result  - cell array containing the response from the host
-%
-%   GANYMED JAVA OBJECT
-%   .connection  - java connection object
-%   .ssh2_java_library_loaded  - flag to determine whether ganymed lib is loaded
-%
-%   CONNECTION FLAGS AND OPTIONS
-%   .authenticated  - flag to determine whether we've authenticated
-%   .autoreconnect  - flag to check whether or not to reconnect if disconnected
-%   .close_connection  - flag to close connection at the end of ssh2.m
-%
-%   SCP and SFTP FLAGS
-%   .scp  - set to 1 to enable an SCP transfer
-%   .sftp  - set to 1 to enable an SFTP transfer
-%   .sendfiles  - set to 1 to send a file to the remote host
-%   .getfiles  - set to 1 to download a file from the remote host
-%
-%   FILE AND PATHS, LOCAL AND REMOTE
-%   .remote_file  - string or cell array of remote files to up/download
-%   .local_target_direcory  - string of path to local files to up/download
-%   .local_file  - string or cell array of local files to up/download
-%   .remote_target_direcory  - string of path to remote files to up/download
-%   .remote_file_new_name  - string or cell array of new names of remote files
-%   .remote_file_mode  - Integer specifying new uploaded files permissions, default is 0600
-%
-%   GANYMED VALUES
-%   .ganymed_java_library  - name of the current ganymed java library
-%   .ganymed_java_library_zip  - filename of ganymed lib to download
-%   .ganymed_java_library_jar  - filename of ganymed java lib
-%   .ganymed_java_library_jar_path  - path to ganymed Jar file
-%
-%   UNUSED
-%   .verified_config  - Unused variable to bypass config check in ssh2.m
-%
-%         
-%   SSH2_SETUP returns the SSH2_CONN for future use.
-%
-%see also ssh2_config, ssh2_config_publickey, ssh2, ssh2_simple_command
-%
-% (c)2013 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 4.0
-
-
-%% SETUP SSH2 CONNECTION STRUCT
-% now includes custom ganymed library, auto download code is still default
-
-% 0 for standard lib, 1 for custom (SFTP-GET support) ganymed-ssh2 library
-USE_CUSTOM_GANYMED_LIB = 0; 
-% CHANGING THE VARIABLE USE_CUSTOM_GANYMED_LIB REQUIRES RESTARTING MATLAB OR 
-% CLEARING previous java DYNAMIC PATH if SSH2 has already been run this
-% session.
-% NETWORK TRANSFERS ARE SLOWER, USE SCP IF POSSIBLE
-
-
-if (USE_CUSTOM_GANYMED_LIB > 0)
-    ganymed_java_library = 'ganymed-ssh2-m1'; %included custom ganymed library
-     ganymed_java_library_zip = [ganymed_java_library '.zip'];
-    ganymed_java_library_http = '';
-else
-    % PICK EITHER CLEONDRIS or GOOGLECODE, NOT BOTH
-    % FROM CLEONDRIS
-    ganymed_java_library = 'ganymed-ssh2-build210'; 
-    ganymed_java_library_zip = [ganymed_java_library '.zip'];
-    
-    ganymed_java_library_http = sprintf('http://www.ganymed.ethz.ch/ssh2/%s',ganymed_java_library_zip);
-    %ganymed_java_library_http = sprintf('http://www.cleondrisch/ssh2/%s',ganymed_java_library_zip);
-    
-    % FROM GOOGLECODE
-    %ganymed_java_library = 'ganymed-ssh2-build251beta1'; % official download from cleondris.ch
-    %ganymed_java_library_zip = [ganymed_java_library '.zip'];
-    %ganymed_java_library_http = sprintf('http://ganymed-ssh-2.googlecode.com/files/%s',ganymed_java_library_zip);
-end
-
-
-ganymed_java_library_jar = [ganymed_java_library '.jar'];
-ganymed_java_library_jar_path = [ganymed_java_library ...
-                                    filesep() ganymed_java_library_jar];
-error_message = 0;
-if nargin == 0 %SETUP THE DEFAULT CONFIG
-    ssh2_struct.ganymed_java_library = ganymed_java_library;
-    ssh2_struct.ganymed_java_library_zip = ganymed_java_library_zip;
-    ssh2_struct.ganymed_java_library_jar = ganymed_java_library_jar;
-    ssh2_struct.ganymed_java_library_jar_path = ganymed_java_library_jar_path;
-    
-    ssh2_struct.hostname = [];
-    ssh2_struct.username = [];
-    ssh2_struct.password = [];
-    ssh2_struct.port = 22;
-
-    ssh2_struct.connection = [];
-    ssh2_struct.authenticated = 0;
-    ssh2_struct.autoreconnect = 0;
-    ssh2_struct.close_connection = 0;
-    
-    ssh2_struct.pem_file = [];
-    ssh2_struct.pem_private_key = [];
-    ssh2_struct.pem_private_key_password = [];
-    
-    ssh2_struct.command = [];
-    ssh2_struct.command_session = [];
-    ssh2_struct.command_ignore_response = 0;
-    ssh2_struct.command_result = [];
-    
-    ssh2_struct.sftp = 0;
-    ssh2_struct.scp = 0;
-    ssh2_struct.sendfiles = 0;
-    ssh2_struct.getfiles = 0;
-    
-    ssh2_struct.remote_file = [];
-    ssh2_struct.local_target_direcory = [];
-    ssh2_struct.local_file = [];
-    ssh2_struct.remote_target_direcory = [];
-    ssh2_struct.remote_file_new_name = [];
-    ssh2_struct.remote_file_mode = 0600; %0600 is default
-    
-    ssh2_struct.verified_config = 0;
-    ssh2_struct.ssh2_java_library_loaded = 0;
-else
-    error_message = 1;
-    if (isstruct(ssh2_struct))
-        if (isfield(ssh2_struct,'ganymed_java_library') && ...
-            isfield(ssh2_struct,'ganymed_java_library_zip') && ...
-            isfield(ssh2_struct,'ganymed_java_library_jar') && ...
-            isfield(ssh2_struct,'ganymed_java_library_jar_path') && ...
-            isfield(ssh2_struct,'hostname') && ...
-            isfield(ssh2_struct,'username') && ...
-            isfield(ssh2_struct,'password') && ...
-            isfield(ssh2_struct,'port') && ...
-            isfield(ssh2_struct,'connection') && ...
-            isfield(ssh2_struct,'authenticated') && ...
-            isfield(ssh2_struct,'autoreconnect') && ...
-            isfield(ssh2_struct,'close_connection') && ...
-            isfield(ssh2_struct,'pem_file') && ...
-            isfield(ssh2_struct,'pem_private_key') && ...
-            isfield(ssh2_struct,'pem_private_key_password') && ...
-            isfield(ssh2_struct,'command') && ...
-            isfield(ssh2_struct,'command_session') && ...
-            isfield(ssh2_struct,'command_ignore_response') && ...
-            isfield(ssh2_struct,'command_result') && ...
-            isfield(ssh2_struct,'sftp') && ...
-            isfield(ssh2_struct,'scp') && ...
-            isfield(ssh2_struct,'sendfiles') && ...
-            isfield(ssh2_struct,'getfiles') && ...
-            isfield(ssh2_struct,'remote_file') && ...
-            isfield(ssh2_struct,'local_target_direcory') && ...
-            isfield(ssh2_struct,'local_file') && ...
-            isfield(ssh2_struct,'remote_target_direcory') && ...
-            isfield(ssh2_struct,'remote_file_new_name') && ...
-            isfield(ssh2_struct,'remote_file_mode') && ...
-            isfield(ssh2_struct,'verified_config') && ...
-            isfield(ssh2_struct,'ssh2_java_library_loaded') )
-            error_message = 0;
-        end
-    end
-    if (error_message == 1)
-        warning('SSH2_SETUP: Invalid input provided!');
-        help ssh2_setup
-    end        
-end
-
-%% LOAD GANYMED LIBRARY IF NECESSARY
-%   will automatically download the library if it's not available
-if (error_message == 0 && ~ssh2_struct.ssh2_java_library_loaded)
-    [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct);
-    if (~ssh2_struct.ssh2_java_library_loaded)
-        if (exist(ganymed_java_library_jar_path,'file'))
-            fprintf('Adding Ganymed-ssh2 to the java path by running\njavaaddpath(''%s'')\n',ganymed_java_library_jar_path);
-            javaaddpath(ganymed_java_library_jar_path);
-            fprintf('\nJust added Ganymed-ssh2 to Matlab''s dynamic java Classpath.\n');
-        else
-            if (USE_CUSTOM_GANYMED_LIB == 0) %try to download official (non-custom) version 
-                if (exist(ganymed_java_library_zip))
-                    % FILE IS INCLUDED NOW BECAUSE IT IS NO LONGER AVAILABE FROM www.cleondris.ch 
-                else
-                    fprintf('Downloading %s\nFrom %s\n',ganymed_java_library_zip,ganymed_java_library_http);
-                    [fname, status] = urlwrite(ganymed_java_library_http, ganymed_java_library_zip);
-                    % Tim Peterson 2016
-                    if status==0
-                        error(['Error downloading ',ganymed_java_library_http]);
-                        return;
-                    end
-                end
-                fprintf('Unzipping %s\n',ganymed_java_library_zip);
-                unzip(ganymed_java_library_zip);
-                if (exist(ganymed_java_library_jar_path,'file'))
-                    fprintf('Adding Ganymed-ssh2 to the java path by running\njavaaddpath(''%s'')\n',ganymed_java_library_jar_path);
-                    javaaddpath(ganymed_java_library_jar_path);
-                    fprintf('\nJust added Ganymed-ssh2 to Matlab''s dynamic java Classpath.\n');
-                end
-            else
-                fprintf('A custom ganymed-ssh2 (%s) library should be used and wasn''t, it should be included with the ssh2 matlab files.\n',ganymed_java_library);
-                fprintf('Otherwise, change the variable USE_CUSTOM_GANYMED_LIB to zero to automatically download the offical ganymed-ssh2 version');
-                fprintf(', which doesn''t support SFTP-get files.');
-            end
-        end
-        [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct);
-    end
-else
-    ssh2_struct = [];
-end
-
-
-function [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct)
-jcp = javaclasspath('-all');
-gany_found = 0;
-for jcp_index = 1:length(jcp)
-    if size(strfind(jcp{jcp_index},ssh2_struct.ganymed_java_library_jar),1)
-        gany_found = gany_found + 1;
-    end
-end
-if (gany_found > 0)
-    ssh2_struct.ssh2_java_library_loaded = 1; % lib is loaded
-else
-    ssh2_struct.ssh2_java_library_loaded = 0; % lib is NOT loaded
-end
-
-
-
+function ssh2_struct = ssh2_setup(ssh2_struct)
+% SSH2_SETUP   Create configuration structure, SSH2_CONN,
+%               for use with ssh2, scp, sftp and supporting functions. 
+%               The file must be created, either directly or indirectly,
+%               in order to create a ssh connection. 
+%
+%               SSH2_CONFIG or SSH2_SIMPLE commands will 
+%               automatically create this file for you.
+%
+% SSH2_SETUP([SSH2_CONN])  normally, no inputs are provided.
+%
+% In addition to creating (or checking) a default SSH2_CONN config to use, 
+% this file will also perform the following steps to ensure the ganymed-ssh2
+% java library is loaded in MATLAB.
+% 
+% If you would like to use SFTP-GET, it is recommended that you change 
+% the variable USE_CUSTOM_GANYMED_LIB = 1 %% (line 107)
+% In this case, an included custom ganymed-ssh2 library
+% will be used that supports this function. The default value of 
+% USE_CUSTOM_GANYMED_LIB is 0 in which 1) is performed below
+% -----------------------------------------------------------------------
+%
+% 1.  Download and unzip the ganymed-ssh2 jar file from (if necessary)
+%                       http://www.cleondris.ch/opensource/ssh2/
+% 2.  Add the jar file to the Matlab dynamic or static Java path
+%                       e.g.  to add as a dynamic library 
+%                             issue the command
+%
+%                             javaaddpath('ganymed-ssh2-build250.jar')
+%
+%                             assuming the SSH-2 Java library is in your
+%                             working directory and named
+%                             ganymed-ssh2-build250.jar
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   SSH2_CONN  when an input is supplied, the SSH2_CONN struct will be 
+%              examined to verify all necessary fields are present.
+%
+%
+% SSH2_CONN Fields:
+% ------------------------------------------------------------------------
+%
+%   CONNECTION VALUES
+%   .hostname  - network name of the SSH2 host
+%   .username  - username to login to host as
+%   .password  - password associated with username on remote host
+%   .port  - SSH2 remote TCP/IP port, default is 22
+%
+%   PUBLIC/PRIVATE KEY VALUES
+%   .pem_file  - file location of the private key
+%   .pem_private_key  - private key as a string
+%   .pem_private_key_password  - password for the private key
+%
+%   REMOTE COMMAND PARAMETERS AND OPTIONS
+%   .command  - command line string to issue to remote host
+%   .command_session  - session object for the ssh2 command line 
+%   .command_ignore_response  - set to 1 to ignore response from host
+%   .command_result  - cell array containing the response from the host
+%
+%   GANYMED JAVA OBJECT
+%   .connection  - java connection object
+%   .ssh2_java_library_loaded  - flag to determine whether ganymed lib is loaded
+%
+%   CONNECTION FLAGS AND OPTIONS
+%   .authenticated  - flag to determine whether we've authenticated
+%   .autoreconnect  - flag to check whether or not to reconnect if disconnected
+%   .close_connection  - flag to close connection at the end of ssh2.m
+%
+%   SCP and SFTP FLAGS
+%   .scp  - set to 1 to enable an SCP transfer
+%   .sftp  - set to 1 to enable an SFTP transfer
+%   .sendfiles  - set to 1 to send a file to the remote host
+%   .getfiles  - set to 1 to download a file from the remote host
+%
+%   FILE AND PATHS, LOCAL AND REMOTE
+%   .remote_file  - string or cell array of remote files to up/download
+%   .local_target_direcory  - string of path to local files to up/download
+%   .local_file  - string or cell array of local files to up/download
+%   .remote_target_direcory  - string of path to remote files to up/download
+%   .remote_file_new_name  - string or cell array of new names of remote files
+%   .remote_file_mode  - Integer specifying new uploaded files permissions, default is 0600
+%
+%   GANYMED VALUES
+%   .ganymed_java_library  - name of the current ganymed java library
+%   .ganymed_java_library_zip  - filename of ganymed lib to download
+%   .ganymed_java_library_jar  - filename of ganymed java lib
+%   .ganymed_java_library_jar_path  - path to ganymed Jar file
+%
+%   UNUSED
+%   .verified_config  - Unused variable to bypass config check in ssh2.m
+%
+%         
+%   SSH2_SETUP returns the SSH2_CONN for future use.
+%
+%see also ssh2_config, ssh2_config_publickey, ssh2, ssh2_simple_command
+%
+% (c)2013 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 4.0
+
+
+%% SETUP SSH2 CONNECTION STRUCT
+% now includes custom ganymed library, auto download code is still default
+
+% 0 for standard lib, 1 for custom (SFTP-GET support) ganymed-ssh2 library
+USE_CUSTOM_GANYMED_LIB = 0; 
+% CHANGING THE VARIABLE USE_CUSTOM_GANYMED_LIB REQUIRES RESTARTING MATLAB OR 
+% CLEARING previous java DYNAMIC PATH if SSH2 has already been run this
+% session.
+% NETWORK TRANSFERS ARE SLOWER, USE SCP IF POSSIBLE
+
+
+if (USE_CUSTOM_GANYMED_LIB > 0)
+    ganymed_java_library = 'ganymed-ssh2-m1'; %included custom ganymed library
+     ganymed_java_library_zip = [ganymed_java_library '.zip'];
+    ganymed_java_library_http = '';
+else
+    % PICK EITHER CLEONDRIS or GOOGLECODE, NOT BOTH
+    % FROM CLEONDRIS
+    ganymed_java_library = 'ganymed-ssh2-build210'; 
+    ganymed_java_library_zip = [ganymed_java_library '.zip'];
+    
+    ganymed_java_library_http = sprintf('http://www.ganymed.ethz.ch/ssh2/%s',ganymed_java_library_zip);
+    %ganymed_java_library_http = sprintf('http://www.cleondrisch/ssh2/%s',ganymed_java_library_zip);
+    
+    % FROM GOOGLECODE
+    %ganymed_java_library = 'ganymed-ssh2-build251beta1'; % official download from cleondris.ch
+    %ganymed_java_library_zip = [ganymed_java_library '.zip'];
+    %ganymed_java_library_http = sprintf('http://ganymed-ssh-2.googlecode.com/files/%s',ganymed_java_library_zip);
+end
+
+
+ganymed_java_library_jar = [ganymed_java_library '.jar'];
+ganymed_java_library_jar_path = [ganymed_java_library ...
+                                    filesep() ganymed_java_library_jar];
+error_message = 0;
+if nargin == 0 %SETUP THE DEFAULT CONFIG
+    ssh2_struct.ganymed_java_library = ganymed_java_library;
+    ssh2_struct.ganymed_java_library_zip = ganymed_java_library_zip;
+    ssh2_struct.ganymed_java_library_jar = ganymed_java_library_jar;
+    ssh2_struct.ganymed_java_library_jar_path = ganymed_java_library_jar_path;
+    
+    ssh2_struct.hostname = [];
+    ssh2_struct.username = [];
+    ssh2_struct.password = [];
+    ssh2_struct.port = 22;
+
+    ssh2_struct.connection = [];
+    ssh2_struct.authenticated = 0;
+    ssh2_struct.autoreconnect = 0;
+    ssh2_struct.close_connection = 0;
+    
+    ssh2_struct.pem_file = [];
+    ssh2_struct.pem_private_key = [];
+    ssh2_struct.pem_private_key_password = [];
+    
+    ssh2_struct.command = [];
+    ssh2_struct.command_session = [];
+    ssh2_struct.command_ignore_response = 0;
+    ssh2_struct.command_result = [];
+    
+    ssh2_struct.sftp = 0;
+    ssh2_struct.scp = 0;
+    ssh2_struct.sendfiles = 0;
+    ssh2_struct.getfiles = 0;
+    
+    ssh2_struct.remote_file = [];
+    ssh2_struct.local_target_direcory = [];
+    ssh2_struct.local_file = [];
+    ssh2_struct.remote_target_direcory = [];
+    ssh2_struct.remote_file_new_name = [];
+    ssh2_struct.remote_file_mode = 0600; %0600 is default
+    
+    ssh2_struct.verified_config = 0;
+    ssh2_struct.ssh2_java_library_loaded = 0;
+else
+    error_message = 1;
+    if (isstruct(ssh2_struct))
+        if (isfield(ssh2_struct,'ganymed_java_library') && ...
+            isfield(ssh2_struct,'ganymed_java_library_zip') && ...
+            isfield(ssh2_struct,'ganymed_java_library_jar') && ...
+            isfield(ssh2_struct,'ganymed_java_library_jar_path') && ...
+            isfield(ssh2_struct,'hostname') && ...
+            isfield(ssh2_struct,'username') && ...
+            isfield(ssh2_struct,'password') && ...
+            isfield(ssh2_struct,'port') && ...
+            isfield(ssh2_struct,'connection') && ...
+            isfield(ssh2_struct,'authenticated') && ...
+            isfield(ssh2_struct,'autoreconnect') && ...
+            isfield(ssh2_struct,'close_connection') && ...
+            isfield(ssh2_struct,'pem_file') && ...
+            isfield(ssh2_struct,'pem_private_key') && ...
+            isfield(ssh2_struct,'pem_private_key_password') && ...
+            isfield(ssh2_struct,'command') && ...
+            isfield(ssh2_struct,'command_session') && ...
+            isfield(ssh2_struct,'command_ignore_response') && ...
+            isfield(ssh2_struct,'command_result') && ...
+            isfield(ssh2_struct,'sftp') && ...
+            isfield(ssh2_struct,'scp') && ...
+            isfield(ssh2_struct,'sendfiles') && ...
+            isfield(ssh2_struct,'getfiles') && ...
+            isfield(ssh2_struct,'remote_file') && ...
+            isfield(ssh2_struct,'local_target_direcory') && ...
+            isfield(ssh2_struct,'local_file') && ...
+            isfield(ssh2_struct,'remote_target_direcory') && ...
+            isfield(ssh2_struct,'remote_file_new_name') && ...
+            isfield(ssh2_struct,'remote_file_mode') && ...
+            isfield(ssh2_struct,'verified_config') && ...
+            isfield(ssh2_struct,'ssh2_java_library_loaded') )
+            error_message = 0;
+        end
+    end
+    if (error_message == 1)
+        warning('SSH2_SETUP: Invalid input provided!');
+        help ssh2_setup
+    end        
+end
+
+%% LOAD GANYMED LIBRARY IF NECESSARY
+%   will automatically download the library if it's not available
+if (error_message == 0 && ~ssh2_struct.ssh2_java_library_loaded)
+    [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct);
+    if (~ssh2_struct.ssh2_java_library_loaded)
+        if (exist(ganymed_java_library_jar_path,'file'))
+            fprintf('Adding Ganymed-ssh2 to the java path by running\njavaaddpath(''%s'')\n',ganymed_java_library_jar_path);
+            javaaddpath(ganymed_java_library_jar_path);
+            fprintf('\nJust added Ganymed-ssh2 to Matlab''s dynamic java Classpath.\n');
+        else
+            if (USE_CUSTOM_GANYMED_LIB == 0) %try to download official (non-custom) version 
+                if (exist(ganymed_java_library_zip))
+                    % FILE IS INCLUDED NOW BECAUSE IT IS NO LONGER AVAILABE FROM www.cleondris.ch 
+                else
+                    fprintf('Downloading %s\nFrom %s\n',ganymed_java_library_zip,ganymed_java_library_http);
+                    [fname, status] = urlwrite(ganymed_java_library_http, ganymed_java_library_zip);
+                    % Tim Peterson 2016
+                    if status==0
+                        error(['Error downloading ',ganymed_java_library_http]);
+                        return;
+                    end
+                end
+                fprintf('Unzipping %s\n',ganymed_java_library_zip);
+                unzip(ganymed_java_library_zip);
+                if (exist(ganymed_java_library_jar_path,'file'))
+                    fprintf('Adding Ganymed-ssh2 to the java path by running\njavaaddpath(''%s'')\n',ganymed_java_library_jar_path);
+                    javaaddpath(ganymed_java_library_jar_path);
+                    fprintf('\nJust added Ganymed-ssh2 to Matlab''s dynamic java Classpath.\n');
+                end
+            else
+                fprintf('A custom ganymed-ssh2 (%s) library should be used and wasn''t, it should be included with the ssh2 matlab files.\n',ganymed_java_library);
+                fprintf('Otherwise, change the variable USE_CUSTOM_GANYMED_LIB to zero to automatically download the offical ganymed-ssh2 version');
+                fprintf(', which doesn''t support SFTP-get files.');
+            end
+        end
+        [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct);
+    end
+else
+    ssh2_struct = [];
+end
+
+
+function [ssh2_struct] = ssh2_config_check_for_java_lib(ssh2_struct)
+jcp = javaclasspath('-all');
+gany_found = 0;
+for jcp_index = 1:length(jcp)
+    if size(strfind(jcp{jcp_index},ssh2_struct.ganymed_java_library_jar),1)
+        gany_found = gany_found + 1;
+    end
+end
+if (gany_found > 0)
+    ssh2_struct.ssh2_java_library_loaded = 1; % lib is loaded
+else
+    ssh2_struct.ssh2_java_library_loaded = 0; % lib is NOT loaded
+end
+
+
+
diff --git a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_simple_command.m b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_simple_command.m
index 7036472..655b3d9 100644
--- a/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_simple_command.m
+++ b/algorithms/HPCoffload/ssh2_v2_m1_r6/ssh2_simple_command.m
@@ -1,42 +1,42 @@
-function command_result = ssh2_simple_command(hostname, username, password, command, enableprint)
-% SSH2_SIMPLE_COMMAND   connects to host using given username and password.
-%                       A command can be given and the output will be
-%                       displayed unless supressed.
-%
-%   SSH2_SIMPLE_COMMAND(HOSTNAME,USERNAME,PASSWORD,COMMAND,[ENABLEPRINTTOSCREEN])
-%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
-%   PASSWORD. Once connected the COMMAND is issues. The output from the 
-%   remote host is returned and the connection is closed.
-%
-%   OPTIONAL INPUTS:
-%   -----------------------------------------------------------------------
-%   ENABLEPRINTTOSCREEN  force printing the remote host output on screen.
-%
-% 
-%   SSH2_SIMPLE_COMMAND returns a cell array containing the host response.
-%
-%see also ssh2_command, ssh2, scp_simple_get, scp_simple_put, sftp_simple_get, sftp_simple_put
-%
-% (c)2011 Boston University - ECE
-%    David Scott Freedman (dfreedma@bu.edu)
-%    Version 2.0
-
-command_result = [];
-
-if nargin < 4
-    ssh2_struct = [];
-    help ssh2_simple_command
-else
-    if nargin < 5
-        enableprint = 0;
-    end
-
-    ssh2_struct = ssh2_config(hostname, username, password);
-    ssh2_struct.close_connection = 1; %close connection use
-
-    if nargout == 0
-        ssh2_struct = ssh2_command(ssh2_struct, command, enableprint);
-    else
-        [ssh2_struct, command_result] = ssh2_command(ssh2_struct, command, enableprint);
-    end
-end
+function command_result = ssh2_simple_command(hostname, username, password, command, enableprint)
+% SSH2_SIMPLE_COMMAND   connects to host using given username and password.
+%                       A command can be given and the output will be
+%                       displayed unless supressed.
+%
+%   SSH2_SIMPLE_COMMAND(HOSTNAME,USERNAME,PASSWORD,COMMAND,[ENABLEPRINTTOSCREEN])
+%   Connects to the SSH2 host, HOSTNAME with supplied USERNAME and
+%   PASSWORD. Once connected the COMMAND is issues. The output from the 
+%   remote host is returned and the connection is closed.
+%
+%   OPTIONAL INPUTS:
+%   -----------------------------------------------------------------------
+%   ENABLEPRINTTOSCREEN  force printing the remote host output on screen.
+%
+% 
+%   SSH2_SIMPLE_COMMAND returns a cell array containing the host response.
+%
+%see also ssh2_command, ssh2, scp_simple_get, scp_simple_put, sftp_simple_get, sftp_simple_put
+%
+% (c)2011 Boston University - ECE
+%    David Scott Freedman (dfreedma@bu.edu)
+%    Version 2.0
+
+command_result = [];
+
+if nargin < 4
+    ssh2_struct = [];
+    help ssh2_simple_command
+else
+    if nargin < 5
+        enableprint = 0;
+    end
+
+    ssh2_struct = ssh2_config(hostname, username, password);
+    ssh2_struct.close_connection = 1; %close connection use
+
+    if nargout == 0
+        ssh2_struct = ssh2_command(ssh2_struct, command, enableprint);
+    else
+        [ssh2_struct, command_result] = ssh2_command(ssh2_struct, command, enableprint);
+    end
+end
diff --git a/algorithms/HydroSightModel.m b/algorithms/HydroSightModel.m
index 5b15fb2..b4e0598 100644
--- a/algorithms/HydroSightModel.m
+++ b/algorithms/HydroSightModel.m
@@ -1,2911 +1,2917 @@
-classdef HydroSightModel < handle
-% Class definition for building a time-series model of groundwater head.
-%
-% Description:
-%   This class allows the building, calibration, simulation and interpolation
-%   of a dynamic type of groundwater head time-series model. The model type 
-%   is defined within a seperate class object and must adhere to the structure
-%   as defined within the abstract class 'model_abstract.m'. Currently, the
-%   following models have been developed: 
-%`
-%   * 'model_TFN': Transfer Function Noise model of Peterson and
-%     Western (2014) and von Asmuth et al. (2005). To run this model,
-%     the following MEX .c source-code file may need to be compiled:
-%     * 'doIRFconvolution.c'
-%     * 'forcingTransform_soilMoisture.c'
-%   * 'ExpSmooth': A double exponential smoothing filter where the 'double'
-%     allows a trend to be accounted for in the smoothing.
-% 
-%   Below are links to the details of the public methods of this class. 
-%   The example presented uses the 'model_TFN' model. 
-% 
-%   To open an example model, add the folder and sub-folders of 
-%   'Model_Algorithms' to the MatLab path and then enter the following
-%   command: 
-%   open example_TFN_model()
-%   
-% See also
-%   HydroSightModel: model_construction;
-%   calibrateModel: model_calibration;
-%   solveModel: solve_the_model;
-%   model_TFN: - constructor_for_transfer_fuction_noise_models;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%   variogram.m
-%   variogramfit.m
-%   lsqcurvefit.m
-%   IPDM.m
-%   SPUCI.m
-%   cmaes.m
-%
-% References:
-%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
-%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure 
-%   Engineering, The University of Melbourne, Australia
-%
-% Date:
-%   2 Mar 2017
-%
-%
-% License:
-%   GNU GPL3 or later.
-%
-    properties
-
-        % Model Label
-        model_label
-        
-        % Bore ID (string). Most model types require the bore ID to be listed within the coordinates file. 
-        bore_ID;
-        
-        % Model class object.       
-        model;
-        
-        % Calibration results structure.
-        calibrationResults;
-        % Evaluation results structure.
-        evaluationResults;  
-        % Simulation results structure.        
-        simulationResults;
-    end
-    
-
-%%  STATIC METHODS        
-% Static methods used by the Graphical User Interface to inform the
-% user of the available model types. Any new models must be listed here
-% in order to be accessable within the GUI.
-    methods(Static)
-        function [types,recommendedType] = model_types()
-            types = {'model_TFN','Transfer function noise model with nonlinear transformation of climate forcing' ; ...
-                    'model_HARTT','Linear regression model usinf cumulative rainfall residual'};
-            recommendedType = 1;    
-        end
-    end    
-
-%%  PUBLIC METHODS     
-    methods
-%% Construct the model
-        function obj = HydroSightModel(model_label, bore_ID, model_class_name, obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin)
-% Model construction.
-%
-% Syntax:
-%   model = HydroSightModel(model_label, bore_ID, model_class_name , obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, modelOptions)
-%
-% Description:
-%   Builds the model object, declares initial parameters and sets the
-%   observationc,coordinates and forcing data. This is the method that must be
-%   called to build a new model. The form of the model is entirely defined
-%   by the input 'model_class_name'. This is a highly flexible model
-%   structure and allows the efficient inclusion of new model types.
-%
-%   For details of how to build a linear and nonlinear transfer function
-%   noise model, see the documentation for model_TFN.m. To open an example 
-%   model, add the folder and sub-folders of 'models' and 'calibration' to the
-%   MatLab path and then enter the following command: 
-%   open example_TFN_model()
-%
-% Input:
-%   model_label - label for describing the model. This is only used to
-%   inform the user of the model.
-%   
-%   bore_ID - string identified for the model, eg 'Bore_ID_1234'. This bore
-%   ID must be listed within the cell array of 'siteCoordinates'.
-%
-%   model_class_name - string of the model type to be built. A class
-%   definition must already have been written for the model type. To date
-%   the following inputs are allows:
-%       - 'model_TFN'
-%
-%   obsHead - Nx5 matrix of observed groundwater elevation data. The
-%   matrix must comprise of the following columns: year, month, day, hour,
-%   water level elevation. The data can be of a sub-daily frequency and
-%   of an non-uniform frequency. However, the method will aggregate
-%   sub-daily observations to daily observations by taling the last
-%   observation of the day.
-%
-%   obsHead_maxObsFreq - scalar for the maximum frequency of observation
-%   data. This input allows the upscaling of daily head data to, say,
-%   monthly freqency data. This feature was included to overcome large
-%   computational demands when calibrating the model to daily data.
-%
-%   forcingData - a variable data-type input containing the forcing data (e.g.
-%   daily precipitation, potential evapo-transpiration, pumping at each site)
-%   and column headings. The input can be of the following form:
-%      - structure data-type with field named 'colnames' and 'data' where 
-%        'data' contains only numeric data;
-%      - cell array with the first row being the column names and lower
-%        rows being numerical data.
-%      - or a table data-type with the heading being the column names
-%        (NOTE: this input form is only available for Matlab 2013b or later). 
-%
-%   The forcing data must comprise of the following columns: year, month,
-%   day, observation data. The observation data must include precipitation
-%   but may include other data as defined within the input model options. 
-%   The record must be continuous (no skipped days) and cannot contain blank
-%   observations and start prior to the first groundwater level
-%   observation (ideally, it should start many years prior). Each forcing
-%   data column name must be listed within the cell array 'siteCoordinates'
-%
-%   siteCoordinates - Mx3 cell matrix containing the following columns :
-%   site ID, projected easting, projected northing. 
-%
-%   Importantly, the input 'bore_ID' must be listed and all columns within
-%   the forcing data (excluding the year, month,day). Additionally, sites
-%   (and coordinates) not listed in the input 'forcingData' can be input.
-%   This provides a means for image wells to be input.
-%   
-%   modelOptions - cell matrix defining the model componants, ie how the
-%   model should be constructed. The structure of the model options are
-%   entirely dependent upon the model type. See the construction
-%   documentation for the relevant model.
-%
-% Output:
-%   model - HydroSightModel class object 
-%
-% Example: 
-%   Load the input head and forcing data:
-%   >> load '124705_boreData.mat'
-%   >> load '124705_forcingData.mat'
-%
-%   Create a cell matrix of the model options for model_TFN:
-%   >> modelOptions_TFN = {'precip','weightingfunction','responseFunction_Pearsons' ;
-%                          'precip','forcingdata',4}
-%
-%   Create bore coordinates cell array. NOTE: the coordinates below are
-%   arbitrary. NOTE, The bote is at Longitude/Latitude of 142.924, -37.145 
-%   (GDA94).
-%   siteCoordinates = {'Bore_124705', 670870.5 , 5887258.8 ; ...
-%                      'precip', 670870.5 , 5887258.8 ; ...
-%                      'APET', 670870.5 , 5887258.8 ; ...
-%                      'LandRevegFraction', 670870.5 , 5887258.8 };
-%
-%   Convert the forcing data to a table data type:
-%   >> forcingData=array2table(forcingData,'VariableNames', ...
-%                     {'Year','Month','Day','precip','APET','LandRevegFraction'});
-%   
-%   Build the model with a maximum frequency of observation data of 7 days:
-%   >> model_124705 = HydroSightModel('Example TFN model', 'Bore_124705',  ...
-%                     'model_TFN', boreDataWL, 7, forcingData, ...
-%                     siteCoordinates, modelOptions_TFN)
-%
-%                   
-%   Inspect model:
-%    >> open model_124705
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModel: model_calibration;
-%   calibrateModelPlotResults: plot_model_calibration_results;
-%   solveModel: run_model_simulations;
-%   solveModelPlotResults: plot_model_simulation_results;
-%   model_TFN: - constructor_for_transfer_fuction_noise_models;
-%   ExpSmooth: - constructor_for_esponential_smoothing_models;
-%   climateTransform_soilMoistureModels: - constructor_for_soil_moisture_models;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%   trialData.mat
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-%    
-
-            % Check constructor inputs.
-            if nargin < 7
-                error('All seven inputs are required!');
-            end            
-            if size(obsHead,2) <4
-                error('The input observed head must be at least four columns: year; month; day; head; which is assumed to be the last column.');
-            end            
-            if ~iscell(siteCoordinates)
-                error('The input "siteCoordinates" must be a cell data type of >2 rows and following three columns: site ID, projected easting, projected northing');
-            end
-            
-            % Check the form of the forcing data input
-            if isnumeric(forcingData)
-                error('The input forcing data cannot be a numerical array. It must be a structure data type, a cell array or a table object. Please see the model documentation.');
-            elseif isstruct(forcingData)
-                if isfield(forcingData,'colnames') && isfield(forcingData,'data')
-                    forcingData_data = forcingData.data;
-                    forcingData_colnames = forcingData.colnames;
-                else
-                    error('When the input forcing data is a structure data type the fields must be "colnames" and "data".');
-                end                   
-            elseif iscell(forcingData)
-                try
-                    forcingData_data = cell2mat(forcingData(2:end,:));
-                    forcingData_colnames = forcingData(1,:);
-                catch ME
-                    error('An error occured when extracting the forcing data from the input cell array. The first row must be the column headings and all of the following rows the numeric data.');
-                end
-            elseif strcmp(class(forcingData),'table')
-                % Assume the datatype is a table object
-                forcingData_colnames = forcingData.Properties.VariableNames;
-                forcingData_data = forcingData{:,:};
-            else
-                error('The must be a structure data type, a cell array or a table object. Please see the model documentation.');
-            end
-            % Check the size of the forcing data
-            if size(forcingData_data,2) <4
-                error('The input forcing data must be at least four columns: year, month, day, Precip');
-            end
-            % Check the first three columns are named year,month,day.
-            forcingData_colnames(1:3) = lower(forcingData_colnames(1:3));
-            if ~strcmp(forcingData_colnames(1),'year') || ~strcmp(forcingData_colnames(2),'month') || ~strcmp(forcingData_colnames(3),'day')
-               error('The three left most forcing data column names must be the following and in the following order: "year,"month",day"');
-            end
-            
-            % Derive columns of year, month, day etc to matlab date value
-            forcingDates = datenum(forcingData_data(:,1), forcingData_data(:,2), forcingData_data(:,3));
-            switch size(obsHead,2)-1
-                case 3
-                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3));
-                case 4
-                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4), zeros(size(obsHead,1),1), zeros(size(obsHead,1),1));
-                case 5
-                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4),obsHead(:,5), zeros(size(obsHead,1),1));
-                case 6
-                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4),obsHead(:,5),obsHead(:,6));
-                otherwise
-                    error('The input observed head must be 4 to 7 columns with right hand column being the head and the left columns: year; month; day; hour (optional), minute (options), second (optional).');
-            end
-            
-            % Check date limits of obs head and forcing data
-            if max(diff(forcingDates)) > 1 || min(diff(forcingDates)) < 1
-                error('The input forcing data must of a daily time step with no gaps!');
-            end
-            if strcmp(model_class_name,'')
-                error('The model class name cannot be empty.');
-            end           
-            if floor(max(obsDates)) > max(forcingDates) || floor(min(obsDates)) < min(forcingDates) 
-                error('The observed head records extend prior to and or after the forcing observations');
-            end
-                  
-            % Check the form of the coordinate data and check that the bore
-            % ID and all forcing date columns are listed within it.
-            if iscell(siteCoordinates) 
-               % Check the cell has 3 columsn and atleast 2 rows.
-               if size(siteCoordinates,1) < 2 || size(siteCoordinates,2) ~= 3
-                   error('The input "siteCoordinates" must be a cell array of three columns (site name, easting, northing) and atleast two rows (for bore ID and precipitation).');
-               end
-               
-               % Check if the column names contain '/' or '\'
-               for i=1:size(siteCoordinates,1)
-                  if ~isvarname(siteCoordinates{i,1})
-                    error(['The site ',siteCoordinates{i,1}, ' is an invalid name. Use only letters, numbers, dash and underscores.']);                
-                  end
-               end
-               % Check the bore ID coordinates are input.
-               hasBoreIDCoordinates=false;
-               for i=1:size(siteCoordinates,1)
-                  if strcmp(siteCoordinates(i,1), bore_ID)
-                      hasBoreIDCoordinates=true;
-                      break;
-                  end
-               end
-               if ~hasBoreIDCoordinates
-                   error('The site coordinate cell array must list the coordinate for the input bore ID. Note, the site names are case-sensitive.');
-               end               
-               
-               % Loop through each forcing data column.
-               hasForcingCoordinate = false(1,length(forcingData_colnames)-3);
-               for i=4:length(forcingData_colnames)
-                   for j=1:size(siteCoordinates,1);                   
-                      if strcmp(siteCoordinates(j,1), forcingData_colnames(i))
-                          hasForcingCoordinate(i-3) = true;
-                          break;
-                      end
-                   end
-               end
-               if any(~hasForcingCoordinate)
-                   error('The site coordinate cell array must list the coordinates for all columns of the forcing data (excluding "year","month",day"). Note, the site names are case-sensitive.');
-               end                              
-            else
-               error('The input "siteCoordinates" must be a cell array of three columns (site name, easting, northing) and atleast two rows (for bore ID and precipitation).'); 
-            end
-            
-            % Ammend column 1-3 from year, month, day  to time.
-            forcingData_data = [forcingDates, forcingData_data(:,4:end)];
-            forcingData_colnames = {'time', forcingData_colnames{4:end}};
-            
-            % Thin out observed heads to a user defined maximum frequency.
-            % This feature is included to overcome the considerable
-            % computational burdon of calibrating the model to daily head
-            % observations using 50+ years of prior daily climate data.
-            if ~isempty(obsHead_maxObsFreq) && obsHead_maxObsFreq >0
-                
-                % Aggregate sub-daily observed groundwater to daily steps            
-                j=1;
-                ii=1;
-                while ii  <= size(obsDates,1)
-                    % Find last observation for the day
-                    [iirow, junk] = find( obsDates == obsDates(ii,1), 1, 'last');
-
-                    % Derive the date and time at the end of the day
-                    date_time = datenum(obsHead(iirow,1), obsHead(iirow,2), obsHead(iirow,3), 23, 59, 59 );
-
-                    % Add date and head to new matrix.
-                    obsHead(j,1:2) = [ date_time , obsHead(iirow,end)];
-                    j=j+1;
-                    ii = iirow+1;
-                end
-                obsHead = obsHead(1:j-1,1:2);                
-                
-                % Now thin out to requested freq.
-                obsHead_orig = obsHead;
-                obsHead = zeros(size(obsHead));
-                obsHead(1,:) = obsHead_orig(1,:);
-                j=1;
-                for ii = 2:size(obsHead_orig,1)
-                    % Accept obs only when duration to prior accepted obs
-                    % is >=  obsHead_maxObsFreq.
-                    if obsHead_orig(ii,1) - obsHead(j,1) >= obsHead_maxObsFreq
-                        j=j+1;
-                        obsHead(j,:) = obsHead_orig(ii,:);
-                    end
-                end
-                
-                % Trim rows of zero value.
-                obsHead = obsHead(1:j,:);
-                
-                clear obsHead_orig;
-            else
-                % Use all input data
-                obsHead = [obsDates, obsHead(:,end)];
-                
-            end
-            % Warn the user of the obs. date was aggregated.
-            if size(obsHead,1) < size(obsHead,1)
-                display([char(13), 'Warning: some input head observations were of a sub-daily frequency.', char(13), ...
-                    'These have been aggregeated to a daily frequency by taking the last obseration of the day.', char(13), ...
-                    'The aggregation process assumed that the input data was sorted by date and time.']);
-            end
-            
-            % Add data to object.
-            obj.model_label = model_label;            
-            obj.bore_ID = bore_ID;            
-            obj.model = feval(model_class_name, bore_ID, obsHead, forcingData_data, forcingData_colnames, siteCoordinates, varargin{1} );
-            
-            % Add flag to denote the calibration has not been undertaken.
-            obj.calibrationResults.isCalibrated = false;
-        end
-
-%% Solve the model        
-        function h = solveModel(obj, time_points, forcingData, simulationLabel, doKrigingOnResiduals)
-% Run simulations using a calibrated model.
-%
-% Syntax:
-%   h = solveModel(obj, time_points)
-%   h = solveModel(obj, time_points, forcingData)
-%   h = solveModel(obj, time_points, forcingData, simulationLabel)
-%   h = solveModel(obj, time_points, forcingData, simulationLabel, doKrigingOnResiduals)
-%
-% Description:
-%   Solves the model using the calibrated parameters and, depending upon
-%   the method's inputs, decomposes the groundwater head into the
-%   contribution from various periods of climate forcing and plots the
-%   results. The simulation results can also be adjusted to honour head
-%   observations. This can only be done if the same forcing data is used in
-%   the simulation as was used in the model calibration. After the
-%   simulation, use 'solveModelPlotResults' to graph the results.
-%
-% Input:
-%   obj -  model object
-%
-%   time_points - column vector of the time points to be simulated. This
-%   can be prior to or after the first or last head observation. However,
-%   forcing data must exist for each time point to be simulated.
-%
-%   forcingData - optional input for undertaking simulations with different
-%   forcing data to that used in the model calibration. It must have
-%   at least the same columns (and names) as used in the calibration but
-%   can be of a different duration and values. Also, it can be of the 
-%   following form:
-%      - structure data-type with field named 'colnames' and 'data' where 
-%        'data' contains only numeric data;
-%      - cell array with the first row being the column names and lower
-%        rows being numerical data.
-%      - or a table data-type with the heading being the column names
-%        (NOTE: this input form is only available for Matlab 2013b or later). 
-%
-%   The forcing data must comprise of the following columns: year, month,
-%   day, observation data. The observation data must include precipitation
-%   but may include other data as defined within the input model options. 
-%   The record must be continuous (no skipped days) and cannot contain blank
-%   observations and start prior to the first time point to be simulated
-%   (ideally, it should start many years prior). Each forcing
-%   data column name must be listed within the cell array 'siteCoordinates'
-%
-%   simulationLabel - optional string to label the simulation. This allows
-%   multiple simulations to be stored within the model object. If the
-%   provided string already exists then it is overwritten with the new
-%   simulation results. If no string is provided, then the simulation is
-%   labeled '(No label)'.
-%
-%   doKrigingOnResiduals - logical scaler or scaler integer>0 to krige the 
-%   model residuals so that observaed head values equal obsevations. If
-%   true then the kriging in undertaken. If an integer >0 then the integer
-%   is used to set the maximum number of data points used in the kriging. For 
-%   more details of the interpolation see 'interpolateData'.
-%
-% Output:
-%   h - matrix of simulated head formatted as the following columns:
-%   date/time; simulated head; contribution from model componant 1 to n
-%   Note:  simulation results are output to obj.simulationResults{}.
-%
-% Example:
-%   Define the start date for simulation:
-%   >> start_date = datenum(1995, 1, 1);
-%
-%   Define the end date for simulation:
-%   >> end_date = datenum(2002, 1, 1);
-%
-%   Define the time points for simulation as from start_date to end_date at 
-%   steps of 28 days:
-%   >> time_points = [start_date : 28 : end_date]';
-%
-%   Solve the model object 'model_124705', with the climate decomposition.
-%   >> solveModel(model_124705, time_points);
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModel: model_calibration;
-%   solveModelPlotResults: plot_model_simulation_results;
-%   interpolateData: time-series_interpolation_algorithm.
-%
-% Dependencies
-%   model_TFN.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-
-           % Find out if the simulation label already exists. If not, add a new simulation. Else, replace it.
-           switch nargin
-               case 2
-                   forcingData = [];
-                   simulationLabel = '(No label)';
-                   doKrigingOnResiduals = false;
-               case 3
-                   simulationLabel = '(No label)';
-                   doKrigingOnResiduals = false;
-               case 4                   
-                   doKrigingOnResiduals = false;
-               case 5
-                   % do nothing                   
-               otherwise
-                   error('Incorrect number of inputs for the model simulation.');   
-           end
-           
-           if iscell(obj.simulationResults)
-               
-               % Find simulation label
-               simInd = cellfun( @(x) strcmp(x.simulationLabel , simulationLabel), obj.simulationResults);               
-               
-               % Check there is only one simulation with the given label,
-               % If the simulations does not exist then add a new
-               % simulation. 
-               if sum(simInd)>1
-                   error('The input simulation label is not unique. Multiple simulations exist with this label.');                   
-               elseif sum(simInd)==0                   
-                   if isempty(obj.simulationResults)
-                       simInd =  1;
-                   else
-                       simInd =  size(obj.simulationResults,1)+1;
-                   end                   
-               end                   
-           else
-                obj.simulationResults = cell(1,1);
-                simInd =  1;
-           end
-
-           % Add the forcing data.
-           if ~isempty(forcingData)
-               % Get the forcing data used to build the model.
-               [forcingData_modConstruc, forcingData_colnames_modConstruc] = getForcingData(obj);
-               
-               % Extract the forcing data and column names.
-                if isstruct(forcingData)
-                    if isfield(forcingData,'colnames') && isfield(forcingData,'data')
-                        forcingData_data = forcingData.data;
-                        forcingData_colnames = forcingData.colnames;
-                    else
-                        error('When the input forcing data is a structure data type the fields must be "colnames" and "data".');
-                    end                   
-                elseif iscell(forcingData)
-                    try
-                        forcingData_data = cell2mat(forcingData(2:end,:));
-                        forcingData_colnames = forcingData(1,:);
-                    catch ME
-                        error('An error occured when extracting the forcing data from the input cell array. The first row must be the column headings and all of the following rows the numeric data.');
-                    end
-                elseif strcmp(class(forcingData),'table')
-                    % Assume the datatype is a table object
-                    forcingData_colnames = forcingData.Properties.VariableNames;
-                    forcingData_data = forcingData{:,:}; 
-                else 
-                    error('The input forcing data must be a table, structure ot cell array variable. It cannot be double array.');
-                    return;
-                end
-
-               % Check the column names for the new forcing data are identical
-               for i=1:length(forcingData_colnames)
-                  if ~any(cellfun( @(x) strcmp(x, forcingData_colnames{i}), forcingData_colnames_modConstruc))
-                      error('New forcing dat must have identical column names to the original data.');
-                  end
-               end
-               
-               % Assign the new forcing data and keep a copy in the
-               % simulation structure.
-               setForcingData(obj, forcingData_data, forcingData_colnames);                  
-           end
-           
-           % Add input data for the simulation to the structure.
-           obj.simulationResults{simInd,1}.simulationLabel = simulationLabel;
-           if ~isempty(forcingData)
-                 obj.simulationResults{simInd,1}.forcingData = forcingData_data;
-                 obj.simulationResults{simInd,1}.forcingData_colnames = forcingData_colnames;
-           end
-           
-           
-           % Undertake the simulation of the head                      
-           try
-              [obj.simulationResults{simInd,1}.head, obj.simulationResults{simInd,1}.colnames, obj.simulationResults{simInd,1}.noise] = solve(obj.model, time_points);
-           catch ME
-                         
-              % Add the original forcing back into the model.
-              if ~isempty(forcingData)
-                  setForcingData(obj, forcingData_modConstruc, forcingData_colnames_modConstruc);
-              end  
-               
-              error(ME.message); 
-           end
-              
-           
-           % Krige the residuals so that the simulation honours observation
-           % points.
-           krigingVariance=[];
-           if (doKrigingOnResiduals || doKrigingOnResiduals>0) && isempty(forcingData)           
-               nparams = size(obj.simulationResults{simInd,1}.head,3);
-               head_estimates = zeros( size(obj.simulationResults{simInd,1}.head,1), 3, nparams);   
-               modelResults = cell( size(head_estimates,3),1);
-               for i=1:nparams
-                  modelResults{i,1}.head_estimates = [obj.simulationResults{simInd,1}.head(:,1:2,i), ...
-                      obj.simulationResults{simInd,1}.head(:,2,i) - obj.simulationResults{simInd,1}.noise(:,2,i),  ...
-                      obj.simulationResults{simInd,1}.head(:,2,i) + obj.simulationResults{simInd,1}.noise(:,3,i)];
-                  modelResults{i,1}.krigingData = [obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead_residuals(:,i)];
-                  modelResults{i,1}.range = obj.calibrationResults.performance.variogram_residual.range(i);
-                  modelResults{i,1}.sill = obj.calibrationResults.performance.variogram_residual.sill(i);
-                  modelResults{i,1}.nugget = obj.calibrationResults.performance.variogram_residual.nugget(i);
-                  
-                  
-                  if iscell(modelResults{i,1}.range)
-                      modelResults{i,1}.range = modelResults{i,1}.range{1};
-                  end
-                  if iscell(modelResults{i,1}.sill)
-                      modelResults{i,1}.sill = modelResults{i,1}.sill{1};
-                  end
-                  if iscell(modelResults{i,1}.nugget)
-                      modelResults{i,1}.nugget = modelResults{i,1}.nugget{1};
-                  end          
-               end
-               
-              % Set constant for the kriging
-              maxKrigingObs = 24;
-              if isnumeric(doKrigingOnResiduals) && ceil(doKrigingOnResiduals)>0
-                maxKrigingObs = ceil(doKrigingOnResiduals);
-              end
-              maxKrigingObs = min(maxKrigingObs,length(getObservedHead(obj)));
-              useModel = true;
-              
-              % Remove the simulation results. This is done to minimise the
-              % size of obj in the following parfor loop, which if nparams
-              % >>1 then the RAM requirements can be huge.
-              simulationResults = obj.simulationResults;
-              obj.simulationResults=[];
-              
-              % Call model interpolation
-              parfor i=1:nparams
-                  head_estimates(:,:,i) = interpolateData(obj, time_points, maxKrigingObs, useModel,modelResults{i});                 
-              end
-
-              % Add simulation results back onto the object.
-              obj.simulationResults = simulationResults;
-              clear simulationResults;
-              
-              % Calculate the contribution from interpolation
-              kriging_contribution = head_estimates(:,2,:) - obj.simulationResults{simInd,1}.head(:,2,:);
-
-              % Add interpolated head into the simulation results.
-              obj.simulationResults{simInd,1}.head(:,2,:) = head_estimates(:,2,:);
-
-              % Store the kriging variance.
-              krigingVariance = head_estimates(:,end,:);              
-               
-           end
-           
-           % Add columns to output for the noise componant.
-           if ~isempty(obj.simulationResults{simInd,1}.noise)
-               h = [obj.simulationResults{simInd,1}.head(:,1:2,:), obj.simulationResults{simInd,1}.head(:,2,:) - obj.simulationResults{simInd,1}.noise(:,2,:), obj.simulationResults{simInd,1}.head(:,2,:) + obj.simulationResults{simInd,1}.noise(:,3,:)];
-           else
-               h = [obj.simulationResults{simInd,1}.head(:,1:2), zeros(size(obj.simulationResults{simInd,1}.head(:,2),1),2)];
-           end
-
-           % Add contribution from kriging plus the variance
-           if ~isempty(krigingVariance);               
-               obj.simulationResults{simInd,1}.head = [obj.simulationResults{simInd,1}.head, kriging_contribution, krigingVariance];
-               obj.simulationResults{simInd,1}.colnames = {obj.simulationResults{simInd,1}.colnames{:}, 'Kriging Adjustment','Kriging Variance'};
-               h = obj.simulationResults{simInd,1}.head;
-           end           
-           
-           % Post-proess the stored simulation results to just percentiles
-           if size(obj.simulationResults{simInd,1}.head,3)>1                
-               head_tmp=obj.simulationResults{simInd,1}.head(:,1,1);
-                for i=2:size(obj.simulationResults{simInd,1}.head,2)
-                     head_tmp = [head_tmp, permute(prctile( obj.simulationResults{simInd,1}.head(:, i,:),[50 5 95],3),[1,3,2])];
-                end
-                obj.simulationResults{simInd,1}.head = head_tmp;
-                obj.simulationResults{simInd,1}.noise = [obj.simulationResults{simInd,1}.head(:,1,1), prctile( obj.simulationResults{simInd,1}.noise(:, 2,:),5,3), prctile( obj.simulationResults{simInd,1}.noise(:, 2,:),95,3)];
-           end
-           
-           % Add the original forcing back into the model.
-           if ~isempty(forcingData)
-               setForcingData(obj, forcingData_modConstruc, forcingData_colnames_modConstruc);
-           end
-           
-           % TO DO: shift code for temporal decomposition to model_TFN.m
-           % Calculate contribution from past climate
-           %---------------------
-%            try
-%                if doClimateLagCalcuations 
-%                    % Set the year past for which the focing contribution is to be
-%                    % derived.
-%                    obj.simulationResults.tor_min = [0; 1; 2; 5;  10;  20; 50; 75;  100];
-%                    obj.simulationResults.tor_max = [1; 2; 5; 10; 20; 50; 75;  100; inf];
-% 
-%                    % Limit the maximum climate lag to be <= the length of the
-%                    % climate record.
-%                    forcingData = getForcingData(obj);
-%                    filt = forcingData( : ,1) < ceil(time_points(end));
-%                    tor_max =  max(time_points(1)  - forcingData( filt ,1));
-%                    filt = obj.simulationResults.tor_max*365 < tor_max;
-%                    obj.simulationResults.tor_min = obj.simulationResults.tor_min(filt);
-%                    obj.simulationResults.tor_max = obj.simulationResults.tor_max(filt);
-% 
-%                    % Calc the head forcing for each period.
-%                    for ii=1: size(obj.simulationResults.tor_min,1)                    
-%                         head_lag_temp = solve(obj.model, time_points, ...
-%                             obj.simulationResults.tor_min(ii)*365.25, obj.simulationResults.tor_max(ii)*365.25 );
-% 
-%                         obj.simulationResults.head_lag(:,ii) = head_lag_temp(:,2);                    
-%                    end               
-% 
-%                    % Add time column.
-%                    obj.simulationResults.head_lag = [time_points, obj.simulationResults.head_lag];                              
-%                else
-%                    obj.simulationResults.tor_min = [];
-%                    obj.simulationResults.tor_max = [];
-%                    obj.simulationResults.head_lag = [];               
-%                end
-%            catch
-               obj.simulationResults{simInd,1}.tor_min = [];
-               obj.simulationResults{simInd,1}.tor_max = [];
-               obj.simulationResults{simInd,1}.head_lag = [];         
-           %end
-        end
-    
-        function solveModelPlotResults(obj, simulationLabel, axisHandle)        
-% Plot the simulation results
-%
-% Syntax:
-%   solveModelPlotResults(obj, simulationLabel)
-%   solveModelPlotResults(obj, simulationLabel, handle)
-%
-% Description:
-%   Creates a summary plot of the simulation results. The plot presents
-%   the fit with the observed data and, if the model types 
-%   provides an appropriate output, a decomposition of the head to 
-%   individual drivers and over various time lags.
-%
-% Input:
-%   obj -  model object
-%
-%   simulationLabel - string for the model simulation labelto plot.
-%
-%   handle - Matlab figure handle to a pre-existing figure window in which
-%   the plot is to be created (optional).
-%
-% Output:  
-%   (none)
-%
-% Example:
-%
-%   % Plot the simulation results for object 'model_124705':
-%   >> solveModelPlotResults(model_124705);
-%
-% See also:
-%   HydroSightModel: class_description;
-%   solveModel: model_simulations;
-%
-% Dependencies
-%   model_TFN.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   7 May 2012
-%
-            
-           % Create the figure. If varargin is empty then a new figure
-           % window is created. If varagin equals a figure handle, h, then
-           % the calibration results are plotted into 'h'.            
-           if nargin==2 || isempty(axisHandle)
-               % Create new figure window.
-               figHandle = figure('Name',['Soln. ',strrep(obj.bore_ID,'_',' ')]);
-           elseif ~iscell(axisHandle)    
-                error('Input handle is not a valid figure handle.');
-           else
-                h = axisHandle;                
-           end
-            
-           % Find simulation label
-           if isempty(simulationLabel)
-               error('The simulation label must be specified.');
-           end
-           simInd = cellfun( @(x) strcmp(x.simulationLabel , simulationLabel), obj.simulationResults);               
-
-            % Assess if the data is from a population of simlations (eg
-            % from DREAM calibration)
-            hasModelledDistn = false;
-            if size(obj.calibrationResults.data.modelledHead,2)>2   
-                hasModelledDistn = true;                
-            end
-            
-            hasNoiseComponant=false;
-            if ~isempty(obj.simulationResults{simInd,1}.noise)
-                hasNoiseComponant=true;
-            end           
-           
-           % Get number of model componant (ie forcing types).
-           if hasModelledDistn
-                nModelComponants = (size(obj.simulationResults{simInd,1}.head,2)-1)/3 - 1;    
-           else
-                nModelComponants = size(obj.simulationResults{simInd,1}.head,2)-2;
-           end
-
-           % Check if there is data for the climate lags
-           doClimateLagCalcuations = false;
-           if isfield(obj.simulationResults,'head_lag')
-               doClimateLagCalcuations = true;
-           end
-           
-           % Plot observed and modelled time series.
-           %-------
-           if ~isempty(axisHandle)
-              h = axisHandle{1}; 
-           elseif nModelComponants>0
-              h = subplot(2+nModelComponants+doClimateLagCalcuations,1,1:2, 'Parent',figHandle);  
-              h_legend = [];
-           else
-              h = gca();
-           end
-            
-            % Plot time series of heads.                        
-            %-------
-            % Plot bounds for noise component.
-            if hasNoiseComponant
-               XFill = [obj.simulationResults{simInd,1}.head(:,1)' fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
-               YFill = [[obj.simulationResults{simInd,1}.head(:,2) + obj.simulationResults{simInd,1}.noise(:,3)]', fliplr([obj.simulationResults{simInd,1}.head(:,2) - obj.simulationResults{simInd,1}.noise(:,2)]')];
-
-               fill(XFill, YFill,[0.8 0.8 0.8],'Parent',h);
-               clear XFill YFill               
-               hold(h,'on');
-            end
-
-            if hasModelledDistn
-
-               XFill = [obj.simulationResults{simInd,1}.head(:,1)' ...
-                        fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
-               YFill = [obj.simulationResults{simInd,1}.head(:,4)', ...
-                        fliplr(obj.simulationResults{simInd,1}.head(:,3)')];
-                    
-               fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
-               clear XFill YFill               
-               hold(h,'on');                    
-            end
-            
-           % Plot modelled deterministic componant.
-           plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,2),'-b' );
-           hold(h,'on');         
-           
-           % Plot observed head
-           head = getObservedHead(obj);
-           plot(h, head(:,1), head(:,2),'.-k' );
-           
-           % Calculate and set the date limits from the simulation period
-           dateLimits = [floor(min(obj.simulationResults{simInd,1}.head(:,1))), ceil(max(obj.simulationResults{simInd,1}.head(:,1)))];
-           dateRange = dateLimits(2) - dateLimits(1);
-           dateLimits(1) = floor(dateLimits(1) - (dateRange * 0.05));
-           dateLimits(2) = ceil(dateLimits(2) + (dateRange * 0.05));
-           
-           % Set axis labels,  title and x axis limits
-           datetick(h, 'x','yy');
-           ylabel(h, 'Head (m)');           
-           title(h, ['Bore ', strrep(obj.bore_ID,'_',' ') , ' - Simulated head']); 
-           xlim(h,dateLimits);
-           
-            % Create legend strings  
-            i=1;
-            if hasNoiseComponant
-                if hasModelledDistn
-                    legendstr{i}='Total Err. (5th-95th)';
-                else
-                    legendstr{i}='Total Err.';
-                end
-                i=i+1;
-            end
-            if hasModelledDistn
-                legendstr{i}='Param. Err. (5th-95th)';            
-                i=i+1;
-            end
-            if hasModelledDistn
-                legendstr{i}='Sim. (median)';
-            else
-                legendstr{i}='Sim.';
-            end            
-            i=i+1;                                
-            legendstr{i}='Observed';
-            i=i+1;            
-            legend(h,legendstr, 'Location','NorthWest' );
-           
-
-           hold(h,'off');
-           %-------
-            
-           % Plot contributions to head
-           %-------
-           if nModelComponants>0
-               for ii=1:nModelComponants
-                   if ~isempty(axisHandle)
-                       h = axisHandle{ii+1}; 
-                   else
-                        h = subplot(2+nModelComponants+doClimateLagCalcuations,1, 2+ii, 'Parent',figHandle );
-                   end
-                   if hasModelledDistn
-                       
-                       XFill = [obj.simulationResults{simInd,1}.head(:,1)' ...
-                                fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
-                       YFill = [obj.simulationResults{simInd,1}.head(:,3*(ii+1)+1)', ...
-                                fliplr(obj.simulationResults{simInd,1}.head(:, 3*(ii+1))')];
-
-                       fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
-                       clear XFill YFill               
-                       hold(h,'on');                    
-                       plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,3*(ii+1)-1),'-b' );
-                       
-                   else
-                       plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,ii+2),'-b' );
-                   end
-                        
-                   
-                   % Set axis labels and title
-                   datetick(h, 'x','yy');                   
-                   ylabel(h, 'Head rise(m)');
-                   title(h, ['Head contribution from: ', strrep(obj.simulationResults{simInd,1}.colnames{ii+2},'_',' ') ]);
-                   xlim(h,dateLimits);                   
-                   
-               end
-           end       
-        end
-
-%% Interpolate and extrapolate observation data        
-        function head_estimates = interpolateData(obj, targetDates, maxKrigingObs, useModel, modelResults)
-% Interpolate and extrapolate observation data.
-%
-% Syntax:
-%   interpolateData(obj, targetDates, maxKrigingObs, useModel)
-%
-% Description:
-%   Interpolates or extrapolates observed heads of a calibrated model and
-%   provides a linear estimate of the prediction uncertainty to a user set
-%   probability.
-%
-%   Importantly, if interpolation is to a date very close to an observed
-%   value then the uncertainty should be less than that estimated by the 
-%   linear prediction error. To achieve this, this method implements
-%   univeral kriging on the simulation residuals to weight the linear prediction
-%   errror.
-%
-% Input:
-%   obj -  calibrated model object.
-%
-%   time_points - column vector of the time points to be interpolated 
-%   and or extrapolated.
-%
-%   maxKrigingObs - scalar integer for the maximum number of observation
-%   points to use in the kriging. The default is inf.
-%   
-%   useModel - logical scalar indicating if the kriging is to be
-%   undertaken using the model residuals or the observed data. The default is
-%   true.
-%
-% Output:
-%   head_estimates - matrix of simulated head formatted as the following columns:
-%   date/time; simulated head; uncertainity estimate.
-%
-% Example:
-%   Define the start date for simulation:
-%   >> start_date = datenum(2000, 1, 1);
-%
-%   Define the end date for simulation:
-%   >> end_date = datenum(2010, 1, 1);
-%
-%   Define the time points for simulation as from start_date to end_date at 
-%   steps of 365 days:
-%   >> time_points = [start_date : 365: end_date]';
-%
-%   Interpolate the model object 'model_124705' to time_points with the 
-%   error estimate defined for a 95% prediction interval:
-%   >> head_estimates = interpolateData(model_124705, time_points, 20, true)
-%
-% See also:
-%   HydroSightModel: class_description;
-%   solveModel: solve_the_model;
-%   calibrateModel: model_calibration;
-%
-% Dependencies
-%   model_TFN.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-           
-            % Check format of target dates
-            if ~isnumeric(targetDates)
-               error('The taget date(s) for interpolation must be an array with the following columns: year, month, day, hour (optional), minute (options)');
-            end
-            if size(targetDates,2) ~= 1 && size(targetDates,2) ~= 3 && size(targetDates,2) ~= 6
-                error('The taget date(s) must be either a matlab date number or have the following columns: year, month, day, hour (optional), minute (options)');
-            end
-            
-            % Assign defaults for 3rd and 4th inputs. 
-            if nargin==2
-                maxKrigingObs = inf;
-                useModel = true;
-            end
-                
-            % Check if any target dates are beyond the end of the climate
-            % record.
-            forcingData = getForcingData(obj);
-            if useModel && ~isempty(forcingData) && (max(forcingData(:,1)) < max(targetDates))
-                error('The maximum date for interpolation is greater than the maximum date of the input forcing data');
-            end
-            clear forcingData
-            
-            % Convert target dates
-            targetDates = sort(datenum(targetDates), 'ascend');                                    
-            
-            % Call model at target date and remove the first column
-            % containing the date (it is added back later)
-            if useModel   
-                if nargin==4 || isempty(modelResults) 
-                    head_estimates= solveModel(obj, targetDates, [], '', false);
-                    krigingData = obj.calibrationResults.data.modelledHead_residuals;
-                    
-                    range = obj.calibrationResults.performance.variogram_residual.range;
-                    sill = obj.calibrationResults.performance.variogram_residual.sill;
-                    nugget = obj.calibrationResults.performance.variogram_residual.nugget;                
-
-                else 
-                    head_estimates = modelResults.head_estimates;
-                    krigingData = modelResults.krigingData;
-                    range = modelResults.range;
-                    sill = modelResults.sill;
-                    nugget = modelResults.nugget;
-                    clear modelResults;
-                end
-                %head_estimates = head_estimates(:,2:4);
-                %krigingData = obj.calibrationResults.data.modelledHead_residuals;
-
-            else
-                head_estimates = zeros(length(targetDates),5);
-                krigingData = getObservedHead(obj.model);
-                
-                % Set variogram fitting calibration options.
-                variogramOptions = optimset('fminsearch');
-                variogramOptions.MaxIter = 1200;
-                variogramOptions.MaxFunEvals = 1000000;
-                
-                % Derive model variogram from the observed data.
-                expVariogram = variogram([krigingData(:,1), zeros( size(krigingData(:,1))) ] ...
-                , krigingData(:,2) , 'maxdist', min(365*10, krigingData(end,1) - krigingData(1,1) ), 'nrbins', 10);
-            
-                [range, sill, nugget, variogram_model] ...
-                    = variogramfit(expVariogram.distance, expVariogram.val, 365/4, 0.75.*var( krigingData(:,2)), expVariogram.num, variogramOptions,...
-                    'model', 'exponential', 'nugget', 0.25.*var( krigingData(:,2)) ,'plotit',false );          
-                
-            end
-            
-            % Convert kriging data to double (if saved as single)
-            if isa(krigingData,'single')
-                krigingData = double(krigingData);
-            end
-            
-            % Undertake universal kriging. 
-            % If 'useModel'==true then the krigin is undertaken on the
-            % simulation residuals. Else, it is undertaken using entire 
-            % record of the the observed head.
-            % Adapted from :
-            % Martin H. Trauth, Robin Gebbers, Norbert Marwan, MATLAB
-            % recipes for earth sciences.
-            %----------------------------
-            
-            % Calculate distance (1-D in units of days) between all obs.
-            dist_allObs = ipdm( krigingData(:,1));
-                        
-            warning off;
-            for ii=1: length(targetDates)
-
-                % Get pre-calc distance (1-D in units of days) between the closest
-                % 'maxObs'
-                dist_to_target =  krigingData(:,1) - targetDates(ii);
-
-                % Find the closest observations. This is somewhat convoluted. The
-                % closest 1/4 of obs on either side of the target date are
-                % first found. The remaining 1/2 of the max obs are then
-                % selected by finding the remaining obs and finding the
-                % closest. This was required to ensure the kriging
-                % trends are smooth when there the data is very
-                % irregularly sampled. The approch is very similar to the
-                % max-octant search of spatial kriging.
-                indNegDuration = find(dist_to_target<0, maxKrigingObs, 'last');
-                indPosDuration = find(dist_to_target>=0, maxKrigingObs, 'first');
-                indNegClosestQuaterObObs = 1:length(indNegDuration)>length(indNegDuration) - ceil(maxKrigingObs/4);
-                indPosClosestQuaterObObs = 1:length(indPosDuration)<=ceil(maxKrigingObs/4);
-                ind_closestHalf = [indNegDuration(indNegClosestQuaterObObs) ; indPosDuration(indPosClosestQuaterObObs)]; ...
-                dist_furthestHalf = [dist_to_target(indNegDuration(~indNegClosestQuaterObObs)); ...
-                                     dist_to_target(indPosDuration(~indPosClosestQuaterObObs))];
-                ind_furthestHalf = [indNegDuration(~indNegClosestQuaterObObs); ...
-                                    indPosDuration(~indPosClosestQuaterObObs)];                                 
-                [~,ind_furthestHalf_sorted] = sort(abs(dist_furthestHalf));
-                ind_furthestHalf = ind_furthestHalf(ind_furthestHalf_sorted);
-                ind = [ind_closestHalf; ind_furthestHalf];                        
-                ind = ind(1: min(length(ind), maxKrigingObs));    
-                
-                % Get the distance to the selected obs.
-                dist = dist_allObs(ind, ind);
-                dist_to_target = dist_to_target(ind);
-                nobs = length(ind);
-                
-                % Create LHS matrix with fixes 0/1
-                G_mod = zeros(nobs+2, nobs+2); 
-                G_mod(: , nobs+1) = 1;
-                G_mod(nobs+1, :) = 1;
-                %G_mod(nobs+1:end, nobs+1:end) = 0;
-                G_target = zeros(nobs+2,1);
-                                
-                % Calculate kriging matrix for obs data from avriogram, then
-                % expand g_mod matrix for kriging and finally invert.               
-                %G_mod(1:nobs,1:nobs) = nugget + sill*(1-exp(-abs(dist)./range));
-                %G_mod( logical(eye(nobs+2)) ) = 0; 
-                G_mod(1:nobs,1:nobs) = sill*exp(-3.*abs(dist)./range);
-                G_mod( logical(eye(nobs+2)) ) = nugget + sill; 
-                G_mod(nobs+1:end, nobs+1:end) = 0;
-                G_mod(nobs+2 , 1:nobs) = krigingData(ind,1)-targetDates(ii);
-                G_mod(1:nobs,nobs+2) = krigingData(ind,1)-targetDates(ii);
-                
-                % Calculate the distance from the cloest maxObs to the
-                % target obs.                
-                %G_target(1:nobs) =nugget + sill*(1-exp(-abs(dist_to_target)./range));
-                G_target(1:nobs) =sill*exp(-3.*abs(dist_to_target)./range);
-                G_target(dist_to_target==0) = nugget + sill;
-                G_target(nobs+1) = 1;
-                G_target(nobs+2) = 0;
-                kriging_weights = G_mod \ G_target;
-                
-                % Estimate residual at target date
-                head_estimates(ii,5) = sum( kriging_weights(1:nobs,1) .* krigingData(ind,2));                    
-                
-                % Estimate kriging variance of the residual at target date.
-                head_estimates(ii,6) = nugget + sill - sum( kriging_weights(1:nobs,1) .* G_target(1:nobs,1)) - sum(kriging_weights(nobs+1:end,1).*G_target(nobs+1:end,1));
-            end
-            warning on;                   
-            %----------------------------
-
-           % Adjust head estimate by kriging residual (ie the bias in the
-           % estimate).
-           head_estimates(:,5) = head_estimates(:,2) + head_estimates(:,5);
-            
-           % Normalise kriging weights to between zero and one.
-           head_estimates(:,6) = head_estimates(:,6)./ (sill + nugget);
-           
-           % Estimate prediction error with weighting by normalised kriging
-           % variance.
-           if useModel
-                head_estimates(:,6) = head_estimates(:,6) .* 0.5.*(head_estimates(:,4) - head_estimates(:,3));
-           end
-
-           % Remove the columns of working data and return prediction and
-           % the error estimate.
-           head_estimates = [targetDates, head_estimates(:,5), head_estimates(:,6)];
-            
-        end
-        
-%% Calibrate the model        
-        function calibSchemeSettings = calibrateModel(obj, diaryObj, t_start, t_end, calibrationSchemeName, SchemeSetting , params_upperBound, params_lowerBound)
-
-% Calibrate the model
-%
-% Syntax:
-%   calibrateModel(obj, t_start, t_end, calibrationSchemeName, SchemeSetting)
-%   calibrateModel(obj, t_start, t_end, calibrationSchemeName, SchemeSetting, params_upperBound, params_lowerBound)
-%
-% Description:
-%   Global calibration of the time-series model and derivation of the following: 
-%       1) simulation of the groundwater heads; 
-%       2) evalution of the calibration to the remaining observation data;
-%       3) calculation of various performance statistics and and a variogram
-%          of the residuals.
-% 
-%   Importantly, the time-series models can be very challenging to
-%   calibrate to the global optima, particularly for the nonlinear TFN models.
-%   To assist in identifying the global optima, a range of calibration schemes
-%   are available (see below for details of the schemes). This allows
-%   the reliability of a calibration result to be assessed by re-running
-%   the calibration with a different scheme and comparing the results. If
-%   each scheme gives near identical results then this supports the
-%   conclusion that the gloabl optima has been idenified. Additionally, the 
-%   reliability of the calibration solution can be assessed by re-running
-%   the same calibration scheme multiple times, ideally with increasingly
-%   stringent settings. If the very best solution is repeatedly obtained
-%   then it is good evidence that the global optima has been located.
-%   
-%   1.  Covariance Matrix Adaptation Evolution Strategy (CMA-ES) (Hansen, 2006)
-%   from https://www.lri.fr/~hansen/cmaes_inmatlab.html#matlab. Note, the 
-%   code was modified to account for complex parameter
-%   boundaries and efficient sampling of parameter sets within the
-%   boundaries.
-%
-%   2. Shuffled complex evolution with principal components analysis–University 
-%   of California at Irvine (SP-UCI) method is a global optimization algorithm 
-%   designed for high-dimensional and complex problems. It is based on the 
-%   Shuffled Complex Evolution (SCE-UA) Method (Qingyun Duan et al.), but solves
-%   a serious problem in searching over high-dimensional spaces - population
-%   degeneration. The population degeneration problem refers to the phenomenon
-%   that, when searching over the high-dimensional parameter spaces, the 
-%   population of the searching particles is very likely to collapse into a
-%   subspace of the parameter space, therefore losing the capability of 
-%   exploring the entire parameter space. In addition, the SP-UCI method also
-%   combines the strength of shuffled complex, the Nelder-Mead simplex, and
-%   mutinormal resampling to achieve efficient and effective high-dimensional 
-%   optimization. (Code and above description from http://www.mathworks.com
-%   /matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method)
-%   Note, the SP-CUI code has been edited by Tim Peterson to allow the inclusion of
-%   parameter constraints and parrallel calculation of each complex. For
-%   details of the algorithm see Chu et al. (2010).
-%
-% Input:
-%   obj -  model object
-%
-%   t_start - scalar start time, eg datenum(1995,1,1);
-%
-%   t_end - scalar end time, eg datenum(2005,1,1);
-%
-%   calibrationSchemeName - string for the name of the calibration scheme to 
-%   use. The options are 'SP-UCI' and 'CMA-ES'.
-%
-%   SchemeSetting - scalar rational integer defining the rigor of the input
-%   calibration scheme. For 'SP-UCI' it defines the number of complexes per
-%   model parameter and must be >=1. For 'CAM-ES' it defines the number of
-%   re-runs undertaken with each re-run having double the population size as
-%   the previous run and must be >=0;
-%
-%   params_upperBound - column vector of the upper bound to the parameters.
-%   Care must be taken to ensure the parameter bound is of the same
-%   dimesnions as the parameter vector passed to the optimisation method
-%   and in the same order. If this vector is not input, a defualt upper
-%   bound will be assigned. This default bound is a scalar multiplier of 
-%   each parameter value. 
-%
-%   params_lowerBound - column vector of the lower bound to the parameters.
-%   See additional notes for 'params_upperBound'. 
-%
-% Output:  
-%   (none, the calibration and simulation results are output to 
-%   obj.calibrationResults, obj.evaluationResults and obj.simulationResults
-%   respectively)
-%
-% Example:
-%   Define the start date for simulation:
-%   >> start_date = datenum(1995, 1, 1);
-%
-%   Define the end date for simulation:
-%   >> end_date = datenum(2002, 1, 1);
-%
-%   Calibrate the model object 'model_124705' with 20 iterations and
-%   between 2 and 10 clusters:
-%   >> calibrateModel(model_124705, start_date, end_date, 'SPUCI', 2);
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModelPlotResults: plot_model_calibration_results;
-%   SPUCI: SP-UCI_calibration_algorithm;
-%   cmaes: CMA-ES_calibration_algorithm;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%   SPUCI.m
-%   cmaes.m
-%   variogram.m
-%   variogramfit.m
-%   lsqcurvefit.m
-%   IPDM.m
-%
-% References:
-%   Chu, W., X. Gao, and S. Sorooshian (2010), Improving the shuffled complex 
-%   evolution scheme for optimization of complex nonlinear hydrological systems:
-%   Application to the calibration of the Sacramento soil-moisture accounting 
-%   model, Water Resour. Res., 46, W09530, doi:10.1029/2010WR009224.
-%
-%   Hansen (2006). The CMA Evolution Strategy: A Comparing Review. In 
-%   J.A. Lozano, P. Larrañaga, I. Inza and E. Bengoetxea (Eds.). Towards a 
-%   new evolutionary computation. Advances in estimation of distribution 
-%   algorithms. Springer, pp. 75-102.
-%
-%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
-%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   7 May 2012
-%
-
-            % reset matlab random number generator
-            rng('default');
-
-            % Set up the calibration options including the random seed.
-            calibrationSchemeName = upper(calibrationSchemeName);
-            switch calibrationSchemeName
-                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
-                    params = getParameters(obj.model);  
-                    nparams = size(params,1); 
-                    MaxFunEvals = Inf;
-                    PopSize = (4 + floor(3*log(nparams)));  
-                    TolX = 1e-11;
-                    TolFun = 1e-12;                    
-                    Restarts = 4;
-                    insigmaFrac = 1/3;
-                    Seed = floor(mod(datenum(now),1)*1000000);                   
-                    
-                     if isnumeric(SchemeSetting) 
-                        if SchemeSetting<0 || floor(SchemeSetting)~=ceil(SchemeSetting) 
-                            error('The CMAES calibration scheme requires, at a minimum, an input scalar integer >=0 for the number of restart iterations.');
-                        end
-                        Restarts = SchemeSetting;
-                     elseif isstruct(SchemeSetting)
-                        if isfield(SchemeSetting,'MaxFunEvals') && isfinite(SchemeSetting.MaxFunEvals)
-                            MaxFunEvals = SchemeSetting.MaxFunEvals;
-                        end
-                        if isfield(SchemeSetting,'PopSize') && isfinite(SchemeSetting.PopSize)
-                            PopSize = SchemeSetting.PopSize;
-                        end                        
-                        if isfield(SchemeSetting,'TolX') && isfinite(SchemeSetting.TolX)
-                            TolX = SchemeSetting.TolX;
-                        end                        
-                        if isfield(SchemeSetting,'tolFun') && isfinite(SchemeSetting.tolFun)
-                            TolFun = SchemeSetting.TolFun;
-                        end
-                        if isfield(SchemeSetting,'restarts') && isfinite(SchemeSetting.restarts)
-                            Restarts = SchemeSetting.Restarts;
-                        end                         
-                        if isfield(SchemeSetting,'insigmaFrac') && isfinite(SchemeSetting.insigmaFrac)
-                            insigmaFrac = SchemeSetting.insigmaFrac;
-                        end                         
-                        if isfield(SchemeSetting,'Seed') && isfinite(SchemeSetting.Seed)
-                            Seed = SchemeSetting.Seed;
-                        end                         
-                        
-                     else
-                         error('The input for the CMAES calibration scheme must be an input scalar integer >=0 or a structure variable.');    
-                     end
-                     
-                     % Build the complete calibSchemeSettings structural
-                     % variable.
-                     calibSchemeSettings.MaxFunEvals = MaxFunEvals;
-                     calibSchemeSettings.PopSize = PopSize;
-                     calibSchemeSettings.TolX = TolX;
-                     calibSchemeSettings.TolFun = TolFun;
-                     calibSchemeSettings.Restarts = Restarts;
-                     calibSchemeSettings.insigmaFrac = insigmaFrac;
-                     calibSchemeSettings.Seed = Seed;
-                     
-                     % Set the random seed
-                     rng(calibSchemeSettings.Seed);
-                     
-                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}                    
-                    
-                    params = getParameters(obj.model);  
-                    nparams = size(params,1);                                         
-                    
-                    % Set default options
-                    maxn = inf;
-                    kstop = 10;    
-                    pcento = 1e-6;    
-                    peps = 1e-6;
-                    ngs = 2 * nparams;
-                    iseed = floor(mod(datenum(now),1)*1000000);                    
-                    
-                    if isnumeric(SchemeSetting) 
-                        if SchemeSetting<1 || floor(SchemeSetting)~=ceil(SchemeSetting) 
-                            error('The SP-UCI calibration scheme requires, at a minimum, an input scalar integer >=1 for the number of complexes.');
-                        end
-                        ngs = SchemeSetting;
-                        
-                    elseif isstruct(SchemeSetting)
-                        if isfield(SchemeSetting,'maxn') && isfinite(SchemeSetting.maxn)
-                            maxn = SchemeSetting.maxn;
-                        end
-                        if isfield(SchemeSetting,'kstop') && isfinite(SchemeSetting.kstop)
-                            kstop = SchemeSetting.kstop;
-                        end
-                        if isfield(SchemeSetting,'pcento') && isfinite(SchemeSetting.pcento)
-                            pcento = SchemeSetting.pcento;
-                        end
-                        if isfield(SchemeSetting,'peps') && isfinite(SchemeSetting.peps)
-                            peps = SchemeSetting.peps;
-                        end
-                        if isfield(SchemeSetting,'ngs') && isfinite(SchemeSetting.ngs)
-                            ngs = SchemeSetting.ngs;
-                        end
-                        if isfield(SchemeSetting,'iseed') && isfinite(SchemeSetting.iseed)
-                            iseed = SchemeSetting.iseed;
-                        end                 
-                    else
-                        error('The input for the SP-UCI calibration scheme must be an input scalar integer >=1 or a structure variable.');    
-                    end                    
-                    
-                    % Build the complete calibSchemeSettings structural
-                    % variable.
-                    calibSchemeSettings.maxn = maxn;
-                    calibSchemeSettings.kstop = kstop;
-                    calibSchemeSettings.pcento = pcento;
-                    calibSchemeSettings.peps = peps;
-                    calibSchemeSettings.ngs = ngs;
-                    calibSchemeSettings.iseed = iseed;
-                                    
-                     % Set the random seed
-                     rng(calibSchemeSettings.iseed);                    
-                    
-                case 'DREAM'
-                    % Set default options
-                    N_per_param = 1;
-                    T = 5;
-                    nCR = 3;    
-                    delta = 3;
-                    lambda = 0.05;
-                    zeta = 0.05;
-                    outlier = 'iqr';
-                    pJumpRate_one = 0.2;
-                    
-                    if isnumeric(SchemeSetting) 
-                        if SchemeSetting<1 || floor(SchemeSetting)~=ceil(SchemeSetting) 
-                            error('The DREAM calibration scheme requires, at a minimum, an input scalar integer >=1 for the number 10,000s of model generations per Markov chain.');
-                        end
-                        N_per_param = SchemeSetting;
-                        
-                    elseif isstruct(SchemeSetting)
-                        if isfield(SchemeSetting,'N_per_param') && isfinite(SchemeSetting.N_per_param)
-                            N_per_param = SchemeSetting.N_per_param;
-                        end
-                        if isfield(SchemeSetting,'T') && isfinite(SchemeSetting.T)
-                            T = SchemeSetting.T;
-                        end                        
-                        if isfield(SchemeSetting,'nCR') && isfinite(SchemeSetting.nCR)
-                            nCR = SchemeSetting.nCR;
-                        end
-                        if isfield(SchemeSetting,'delta') && isfinite(SchemeSetting.delta)
-                            delta = SchemeSetting.delta;
-                        end
-                        if isfield(SchemeSetting,'lambda') && isfinite(SchemeSetting.lambda)
-                            lambda = SchemeSetting.lambda; 
-                        end
-                        if isfield(SchemeSetting,'zeta') && isfinite(SchemeSetting.zeta)
-                            zeta = SchemeSetting.zeta;
-                        end
-                        if isfield(SchemeSetting,'outlier') && isfinite(SchemeSetting.outlier)
-                            outlier = SchemeSetting.outlier;
-                        end
-                        if isfield(SchemeSetting,'pJumpRate_one') && isfinite(SchemeSetting.pJumpRate_one)
-                            pJumpRate_one = SchemeSetting.pJumpRate_one;
-                        end                        
-                    end
-                    
-                    % Build the complete calibSchemeSettings structural
-                    % variable.
-                    calibSchemeSettings.N_per_param = N_per_param;
-                    calibSchemeSettings.T = T;
-                    calibSchemeSettings.nCR = nCR;
-                    calibSchemeSettings.delta = delta;
-                    calibSchemeSettings.lambda = lambda;
-                    calibSchemeSettings.zeta = zeta;
-                    calibSchemeSettings.outlier = outlier;
-                    calibSchemeSettings.pJumpRate_one = pJumpRate_one;
-                                                                              
-                otherwise
-                    error('The requested calibration scheme is unknown.');
-            end
-            
-            
-            % Check if the model uses stochastic derived forcing. If so,
-            % then check SP-UCI is used            
-            hasStochDerivedForcing = false;
-            if ~isempty(getStochForcingData(obj))
-                hasStochDerivedForcing=true;
-            end
-            if hasStochDerivedForcing && ~any(strcmp({'SP UCI','SP_UCI','SPUCI','SP-UCI'}, calibrationSchemeName))
-                error('SP-UCI must be used for stochastic derived forcing models (e.g. pumpingRate_SAestimation()).');
-            end
-                
-            
-            % Set general constants.
-            obj.calibrationResults = [];
-            obj.evaluationResults = [];            
-
-            % Add flag to denote the calibration is not complete.
-            obj.calibrationResults.isCalibrated = false;
-            
-            % Check the number of inputs.            
-            if nargin < 6
-                error('Calibration of the model requires input of at least the following: model object, start date and end date, calibration scheme name, calibration setting');
-            end                
-            
-            % Check the input options
-            if max(size(t_start))~=1 || max(size(t_end))~=1 || t_start >= t_end
-                error('The input start date must be less than the input end data and both must be scalar integers!');
-            end
-            
-            % Initialsise model calibration and evaluation outputs.
-            t_filt = obj.model.inputData.head(:,1) >=t_start  & obj.model.inputData.head(:,1) <= t_end;  
-            time_points = obj.model.inputData.head(t_filt,1);
-            obj.calibrationResults.time_start =  t_start;
-            obj.calibrationResults.time_end =  t_end;
-            obj.calibrationResults.data.obsHead =  obj.model.inputData.head(t_filt , :);
-            
-            % If evaluation is to be undertakes, check atleast 2 obs remain
-            % for the evaluation.
-            if size(obj.model.inputData.head(~t_filt,1),1)>0 && size(obj.model.inputData.head(~t_filt,1),1)<2
-                error(['The input calibration dates are such that observations remain for model evaluation.', char(13) ...
-                    'However, model evaluation requires >=2 observations!', char(13), ...
-                    'Please modify the dates to either:', char(13), ...
-                    '  (i) extent the calibration dates to eliminate any observation data for evaluation; or', char(13), ...
-                    '  (ii) contact the calibration dates to increase the observation data for evaluation.']);
-            end                            
-            
-            if obj.model.inputData.head(~t_filt,1)>0
-                % Add data to evaluation structure
-                obj.evaluationResults.time_lessThan =  t_start;
-                obj.evaluationResults.time_greaterThan =  t_end;
-                obj.evaluationResults.data.obsHead =  obj.model.inputData.head(~t_filt , :);
-            elseif isfield(obj,'evaluationResults')
-                obj = rmfield(obj,'evaluationResults');                
-            end
-            
-            % Initialsie model for calibration and check params
-            [params, time_points] = calibration_initialise(obj.model, t_start, t_end);       
-            params =  median(params,2);
-            if any(isnan(params))
-                error('At least one model parameter value equals NaN. Please input a correct value to the model');
-            end
-            nparams = size(params,1); 
-                            
-            % Construct and output parameter boundaries.
-            %------------
-            if nargin ==8 ...
-            && (size(params_upperBound,1) ~= size(params,1) || size(params_lowerBound,1) ~= size(params,1))
-                error(['The column vectors of parameter bounds "params_upperBound" and "params_lowerBound" ', char(13), ...
-                      'must be of the same size as the parameter vector, that is 1 column and ', num2str(nparams), ' rows.']);            
-            end            
-            [params_upperPhysBound, params_lowerPhysBound] = getParameters_physicalLimit(obj.model);
-            if any(isnan(params_upperPhysBound)) || any(isnan(params_lowerPhysBound)) || ...
-               any(~isreal(params_upperPhysBound)) || any(~isreal(params_lowerPhysBound))
-                error('The physical parameter boundaries must be a real number between (and including) -inf and inf.')
-            end                      
-            
-            if nargin ==6
-
-                [params_upperBound, params_lowerBound] = getParameters_plausibleLimit(obj.model);
-                if any(isnan(params_upperBound)) || any(isnan(params_upperBound)) || ...
-                any(~isreal(params_lowerBound)) || any(~isreal(params_lowerBound))
-                    error('The plausible parameter boundaries must be a real number between (and including) -inf and inf.')
-                end                
-            else
-                params_upperBound = min(params_upperBound,  params_upperPhysBound);
-                params_lowerBound = max(params_lowerBound,  params_lowerPhysBound);
-            end         
-            
-            % Check the plausible boundaries are consistant with the
-            % physical boundaries.
-            if any( params_upperBound > params_upperPhysBound) 
-                params_upperBound = min(params_upperBound, params_upperPhysBound);    
-                display('WARNING: At least one upper parameter boundary exceeds the upper lower physical boundary.');
-                display('         They have been shifted to equal the upper physical boundary.');                
-                display( char(13) );            
-            end
-            if any( params_lowerBound < params_lowerPhysBound)               
-                params_lowerBound = max(params_lowerBound, params_lowerPhysBound);                
-                display('WARNING: At least one lower parameter boundary exceeds the lower physical boundary.');
-                display('         They have been shifted to equal the lower physical boundary.');
-                display( char(13) );            
-            end             
-            
-            % Check the upper bound is greater than the lower parameter
-            % bound. 
-            if any(params_lowerBound >= params_upperBound - sqrt(eps()) )
-                disp(sprintf('          %s \t %s \t  %s \t %s \t \t %s \t \t %s', 'Model','Param.','Lower', 'Upper'));
-                disp(sprintf('          %s \t %s \t \t %s \t %s \t %s \t %s', 'Componant','Name'));            
-                for ii=1:nparams
-                    if length(obj.model.variables.param_names{ii,1}) < 5
-                        params_str = sprintf('          %s \t \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii) );
-                    else
-                        params_str = sprintf('          %s \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii) );
-                    end
-                    disp([params_str]);    
-                end 
-
-                error('The parameter lower bounds must be less than the parameter upper bounds and the difference must be greater than sqrt(eps()).');
-            end    
-            
-            % Check the initial parameter values are within the upper and 
-            % lower parameter boundaries. If not adjust the violating
-            % parameter to the closest boundary.
-            if any(params < params_lowerBound)
-                display(  'WARNING: Some of the initial parameter values have been shifted to the lower');
-                display(  '         boundary because they violoate the lower parameter boundary.');
-                display( char(13) );
-                params = max(params, params_lowerBound);
-            elseif any(params > params_upperBound)
-                display(  'WARNING: Some of the initial parameter values have been shifted to the upper');
-                display(  '         boundary because they violoate the upper parameter boundary.');
-                display( char(13) );
-                params = min(params, params_upperBound);                
-            end            
-            %------------
-            
-            % Output user set options.
-            display( char(13) );           
-            display('Global calibration scheme is to be undertaken using the following settings');
-             switch calibrationSchemeName
-                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}                    
-                    display( '      - Calibration scheme: Covariance Matrix Adaptation Evolution Strategy (CMA-ES)');
-                    display(['      - Number of initial CMA-ES parameter sets  = ',num2str(PopSize)]);                      
-                    display(['      - Maximum number of model evaluations (maxFunEvals) = ',num2str(MaxFunEvals)]);
-                    display(['      - Absolute change in the objective function for convergency (tolFun) = ',num2str(TolFun)]);
-                    display(['      - Largest absolute change in the parameters for convergency (tolX) = ',num2str(TolX)]);
-                    display(['      - Number CMA-ES calibration restarts (Restarts) = ',num2str(Restarts)]);
-                    display(['      - Standard deviation for the initial parameter sampling, as fraction of plausible parameter bounds (insigmaFrac) = ',num2str(insigmaFrac)]);
-                    display(['      - Random seed number (only for repetetive testing purposes) = ',num2str(Seed)]);
-                                        
-                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}
-                    display( '      - Calibration scheme: Shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI)');                    
-                    display(['      - Max. number of model evaluations (maxn)= ',num2str(maxn)]);  
-                    display(['      - No. of evolution loops meeting convergence criteria (kstop) = ',num2str(kstop)]);  
-                    display(['      - % change in the objective function allowed in kstop loops before convergence (pcento)= ',num2str(pcento)]);  
-                    display(['      - Normalized geometric range of the parameters before convergence (peps)= ',num2str(peps)]);  
-                    display(['      - No. of complexes = ',num2str(ngs)]);  
-                    display(['      - Random seed = ',num2str(iseed)]);  
-                    
-                case 'DREAM'
-                    display( '      - Calibration scheme: DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)');                    
-                    display(['      - Number of generations per chain (T) = ',num2str(T*10000)]);                      
-                    display(['      - Number of Markov chains per model parameter (N_per_param) = ',num2str(N_per_param)]);  
-                    display(['      - Number of crossover values (nCR) = ',num2str(nCR)]);  
-                    display(['      - Number chain pairs for proposal (delta) = ',num2str(delta)]);  
-                    display(['      - Random error for ergodicity (lambda) = ',num2str(lambda)]);  
-                    display(['      - Randomization (zeta) = ',num2str(zeta)]);  
-                    display(['      - Test function name for detecting outlier chains (outlier) = ',outlier]);  
-                    display(['      - Probability of jumprate of 1 (pJumpRate_one) = ',num2str(pJumpRate_one)]);  
-                                        
-             end             
-            display( '      - Summary of parameters for calibration and their bounds: ');
-            display( '        Param. componant and name, lower and upper boundary value: ');
-            disp(sprintf('          %s \t %s \t  %s \t %s \t \t %s \t \t %s', 'Model','Param.','Lower', 'Upper', 'Lower', 'Upper'));
-            disp(sprintf('          %s \t %s \t \t %s \t %s \t %s \t %s', 'Componant','Name','(Plausible)', '(Plausible)','(Physical)', '(Physical)'));            
-            for ii=1:nparams
-                if length(obj.model.variables.param_names{ii,1}) < 5
-                    params_str = sprintf('          %s \t \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii), params_lowerPhysBound(ii), params_upperPhysBound(ii) );
-                else
-                    params_str = sprintf('          %s \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii), params_lowerPhysBound(ii), params_upperPhysBound(ii) );
-                end
-                disp([params_str]);    
-            end 
-            
-            % Update the diary file
-            if ~isempty(diaryObj)
-                updatetextboxFromDiary(diaryObj);
-            end
-            
-            %--------------------------------------------------------------
-            % Do SCE calibration using the objective function SSE.m
-            log_L=[];
-            switch upper(calibrationSchemeName)
-                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
-                    
-                    calibSchemeSettings.LBounds = params_lowerPhysBound;
-                    calibSchemeSettings.UBounds = params_upperPhysBound;
-                    calibSchemeSettings.LogFilenamePrefix = ['CMAES_',obj.bore_ID];
-                    calibSchemeSettings.LogPlot = 'off';
-                    calibSchemeSettings.CMA.active = 1;
-                    
-                    calibSchemeSettings.EvalParallel = 'yes';     % Undertake parrallel function evaluation
-                    calibSchemeSettings.SaveVariables = 'off';    % Do not save .mat file of results.
-                    
-                    useLikelihood = false;
-                    
-                    % Define bounds and initial standard dev of params
-                    insigma = insigmaFrac*(params_upperBound - params_lowerBound);
-                    params_start = params_lowerBound + 1/2.*(params_upperBound - params_lowerBound);
-                    params_start = [mat2str(params_start) '+ insigma .* (2 * rand(',num2str(nparams),',1) -1)'];                    
-                    
-                    % Do calibration
-                    doParamTranspose = false;
-                    [params_finalEvol, fmin_finalEvol, numFunctionEvals, exitflag, evolutions, params_bestever] ...            
-                     = cmaes( 'calibrationObjectiveFunction', params_start, insigma, calibSchemeSettings, diaryObj, obj, time_points, doParamTranspose, useLikelihood );
-
-                    % Assign best every solution to params variable
-                    params = params_bestever.x;
-                    fmin = params_bestever.f;          
-                    
-                    % Extract exit flag message.
-                    if iscell(exitflag)
-                        exitflag = exitflag{1};
-                    end                    
-                    
-                    % Store exit status
-                    if any( strcmp(exitflag, 'maxfunevals'))
-                        exitFlag = 1;
-                        existStatus = 'Insufficient maximum number of model evaluations for convergence.';
-                    elseif any( strcmp(exitflag, 'maxiter'))
-                        exitFlag = 1;
-                        existStatus = 'Insufficient maximum number of iterations for convergence.';
-                    elseif any( strcmp(exitflag, 'maxiter'))
-                        exitFlag = 1;
-                        existStatus = 'Insufficient maximum number of iterations for convergence.';                    
-                    elseif any( strcmp(exitflag, 'tolx')) && ~any( strcmp(exitflag, 'tolfun'))
-                        exitFlag=1;
-                        exitStatus = ['Only parameter convergence (not obj. function convergence) achieved in ', num2str(numFunctionEvals),' function evaluations.'];
-                    elseif ~any( strcmp(exitflag, 'tolx')) && any( strcmp(exitflag, 'tolfun'))
-                        exitFlag=1;
-                        exitStatus = ['Only objective function convergence achieved (not param. convergence) in ', num2str(numFunctionEvals),' function evaluations.'];
-                    elseif  any( strcmp(exitflag, 'maxfunevals')) || ...
-                    any( strcmp(exitflag, 'maxiter')) || ...    
-                    any( strcmp(exitflag, 'stoptoresume')) || ...    
-                    any( strcmp(exitflag, 'manual')) || ...    
-                    any( strcmp(exitflag, 'stagnation')) || ...    
-                    any( strcmp(exitflag, 'warnconditioncov')) || ...    
-                    any( strcmp(exitflag, 'warnnoeffectcoord')) || ...    
-                    any( strcmp(exitflag, 'warnnoeffectaxis')) || ...    
-                    any( strcmp(exitflag, 'warnequalfunvals')) || ...    
-                    any( strcmp(exitflag, 'warnequalfunvalhist')) ...
-                    any( strcmp(exitflag, 'bug'))
-                                                
-                        exitStatus = ['Calibration warning encountered:', exitflag,' See CMA-ES scheme documentation for details.'];                        
-                        exitFlag=1;
-                    elseif any( strcmp(exitflag, 'tolx')) && any( strcmp(exitflag, 'tolfun'))
-                        exitFlag=2;
-                        exitStatus = ['Parameter and objective function convergence achieved in ', num2str(numFunctionEvals),' function evaluations.'];
-                    else
-                        exitFlag=0;
-                        exitStatus = ['Unhandled non-convergence issues. Total model evaluations undertaken = ', num2str(numFunctionEvals)];
-                        
-                        obj.calibrationResults.exitFlag = exitFlag;
-                        obj.calibrationResults.exitStatus = exitStatus;
-                        
-                        ME = MException('HydroSightModel:CalibrationFailure',exitStatus);
-                        throw(ME);                        
-                    end                        
-                        
-                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}
-                    maxn = calibSchemeSettings.maxn;
-                    kstop = calibSchemeSettings.kstop; 
-                    pcento = calibSchemeSettings.pcento; 
-                    peps = calibSchemeSettings.peps;
-                    ngs = calibSchemeSettings.ngs;
-                    iseed = calibSchemeSettings.iseed;
-                    iniflg =  1;                
-                    useLikelihood=false;
-
-                    %if hasStochDerivedForcing
-                    %    kstop = kstop*2;
-                    %end
-                    
-                    % Do calibration                                        
-                    doParamTranspose = false;
-                    [params, fmin,numFunctionEvals, exitFlag, exitStatus] = SPUCI(@calibrationObjectiveFunction, @calibrationValidParameters, ...
-                        params', params_lowerBound', params_upperBound', params_lowerPhysBound', params_upperPhysBound', maxn, ...
-                        kstop, pcento, peps, ngs, iseed, iniflg, diaryObj, obj, time_points, doParamTranspose, useLikelihood); 
-                        params = params';
-                    
-                    if exitFlag==0
-                        obj.calibrationResults.exitFlag = exitFlag;
-                        obj.calibrationResults.exitStatus = exitStatus;
-                        
-                        ME = MException('HydroSightModel:CalibrationFailure',exitStatus);
-                        throw(ME);
-                    end
-
-                    
-                case 'DREAM'
-              
-                    % Application specific settings.
-                    % -------------------------------------------------------------------------
-                    % Set the approx. number of samples required for
-                    % reliable inferance. Col 1: the number of parameters,
-                    % Col 2: number of samples. (source Vrugt, 2016, http://dx.doi.org/10.1016/j.envsoft.2015.08.013 p 293).                    
-                    reqMinParamSamples = [ 1 500; 2 1000; 5 5000; 10 10000; 25 50000; 50 200000; 100 1000000; 250 5000000];
-                    
-                    %Interpolate above matrix to number of parameters for
-                    %this model.
-                    reqMinParamSamples = floor(interp1( reqMinParamSamples(:,1), reqMinParamSamples(:,2), nparams, 'linear'));
-                    
-                    % -------------------------------------------------------------------------
-                    %                           DEFAULT VALUES
-                    % -------------------------------------------------------------------------
-                    calibSchemeSettings.pCR = 'yes';             % Adaptive tuning crossover values
-                    calibSchemeSettings.thinning = 1;            % Each Tth sample is stored         
-                    % -------------------------------------------------------------------------
-                    %                           MODEL SPECIFIC VALUES
-                    % -------------------------------------------------------------------------                    
-                    calibSchemeSettings.d = nparams;             % Dimensionality target distribution
-                    calibSchemeSettings.N = max(calibSchemeSettings.N_per_param * nparams, 2*calibSchemeSettings.delta+1);   % Number of Markov chains                    
-                    calibSchemeSettings.T = 10000*calibSchemeSettings.T;  % Number of generations
-                    calibSchemeSettings.lik=2;                   % Choice of likelihood function
-                    useLikelihood = true;
-                    calibSchemeSettings.restart = 'no';
-                    calibSchemeSettings.modout='no';
-                    calibSchemeSettings.save='no';
-                                        
-                    % -------------------------------------------------------------------------
-                    %                      OPTIONAL (DEFAULT = 'no'  / not used )
-                    % -------------------------------------------------------------------------
-                    % Multi-core computation chains? Turn on if there are
-                    % >1 cores available.
-                    if feature('numCores')>1
-                        calibSchemeSettings.parallel = 'yes';        
-                    else
-                        calibSchemeSettings.parallel = 'no';
-                    end
-                    % Set parameter bounds
-                    Par_info.prior ='latin';
-                    Par_info.min = params_lowerBound'; % If 'latin', min parameter values
-                    Par_info.max = params_upperBound'; % If 'latin', max parameter values                    
-                    Par_info.boundhandling ='reflect';% Explicit boundary handling
-
-                    % Do calibration 
-                    doParamTranspose = true;
-                    [params,output,fx,log_L] = DREAM(@calibrationObjectiveFunction,calibSchemeSettings,Par_info,[], diaryObj, obj, time_points, doParamTranspose,useLikelihood);
-                              
-                    % Extract the R_statistic values and dilter for those
-                    % where R_statistic<1.2 for all parameters in the set.
-                    r_stat_threshold = 1.2;
-                    r_stat_acceptable = all(output.R_stat(1: end, 2: calibSchemeSettings.d + 1)<r_stat_threshold,2);
-                    
-                    % Find the threshold generations where R-stat criteria is first met.
-                    convergedParamSamplesThreshold = min(output.R_stat(r_stat_acceptable,1));
-                    
-                    % Find the total number of samples
-                    nParamSamples = output.R_stat(end, 1);
-                    
-                    % Find the number of viable parameter sets                    
-                    convergedParamSamples = max(1,nParamSamples - convergedParamSamplesThreshold);
-
-                    % To minimise RAM, only save a maximum of
-                    % 2*convergedGenerations parameter sets.
-                    if convergedParamSamples>=  2*reqMinParamSamples                        
-                        convergedParamSamples = 2*reqMinParamSamples;
-                        convergedParamSamplesThreshold = nParamSamples - convergedParamSamples;
-                    end
-                    
-                    if isempty(convergedParamSamples) || convergedParamSamples < 0.1*reqMinParamSamples
-                        if isempty(convergedParamSamples)
-                            convergedParamSamples = 0;
-                        end                    
-                        exitFlag=1;
-                        exitStatus = ['Insufficient DREAM generations for reliable calibration-selected lesser of last 10,000 samples of 10% of no. samples. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
-                            ' and recommended is at least ',num2str(reqMinParamSamples),'. Increase method number to greater than ',num2str(SchemeSetting)];
-                        
-                        obj.calibrationResults.exitFlag = exitFlag;
-                        obj.calibrationResults.exitStatus = exitStatus;
-                        
-                        convergedParamSamples = min(floor(nParamSamples*0.1), 10000);
-                        convergedParamSamplesThreshold = nParamSamples - convergedParamSamples;
-                        
-                    elseif convergedParamSamples < reqMinParamSamples
-                        exitFlag=1;
-                        exitStatus = ['Possible insufficient DREAM generations. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
-                            ' and recommended is at least ',num2str(reqMinParamSamples),'. Increase method number to greater than ',num2str(SchemeSetting)];
-                    else
-                        exitFlag=2;
-                        exitStatus = ['Sufficient DREAM iterations. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
-                            ' and recommended is at least ',num2str(reqMinParamSamples)];                        
-                    end
-
-                    convergedParamSamplesThreshold = floor(convergedParamSamplesThreshold/calibSchemeSettings.N);
-                    params = params(convergedParamSamplesThreshold:end,:,:);
-                    params = genparset(params);
-                    paramsTmp = params(:,1:nparams)';
-                    log_L = params(:,end)';
-                    params=paramsTmp;    
-                    
-                    % Filt out any inf likiloof values (just in case)
-                    filt = ~isinf(log_L);
-                    if any(~filt)
-                        exitStatus = [exitStatus, ' WARNING: ', num2str(sum(~filt)), ' parameter sets with liklihood value of inf were removed.'];
-                        params = params(:,filt);                    
-                        log_L = log_L(filt);
-                    end;                    
-                    
-                    clear paramsTmp
-                otherwise
-                    error('The requested calibration scheme is unknown.');
-            end            
-            
-            display('--------------------------------------------');             
-
-            %--------------------------------------------------------------
-           
-            % Finalise model for calibration
-            %--------------------------------------------------------------            
-            % Get likelihood est (for latter AICc estimate).
-            useLikelihood=true;
-            if isempty(log_L)
-                log_L = objectiveFunction(params, time_points, obj.model, useLikelihood);
-            end    
-            
-            % Get observed head during calib. periods            
-            obsHead = obj.model.inputData.head;            
-            
-            % Call model objects to finalise calibration.
-            calibration_initialise(obj.model, t_start, t_end);
-            calibration_finalise(obj.model, params, false );            
-                       
-            % Add final parameters
-            [obj.calibrationResults.parameters.params_final, ...
-                obj.calibrationResults.parameters.params_name] = getParameters(obj.model);  
-
-            % Add exist status
-            obj.calibrationResults.exitFlag = exitFlag;
-            obj.calibrationResults.exitStatus = exitStatus;            
-            
-            % Calculate calibration and evaluation heads
-            %--------------------------------------------------------------            
-            try                
-                head_est = solveModel(obj, obsHead(:,1), [], '', false );
-            catch
-                head_est = solveModel(obj, obsHead(:,1));
-            end
-                
-            % Convert to real (just n case errors in pram est arose)
-            head_est = real(head_est);
-
-            % Eval. residuals.            
-            neval = sum(~t_filt);        
-            if neval>0
-                
-                if size(head_est,3)>1
-                    obj.evaluationResults.data.modelledHead = [head_est(~t_filt,1,1), permute(prctile( head_est(~t_filt, 2,:),[50 5 95],3),[1,3,2])];
-                    obj.evaluationResults.data.modelledNoiseBounds = [head_est(~t_filt,1,1), prctile( head_est(~t_filt, 3,:),5,3), prctile( head_est(~t_filt, 4,:),95,3)];
-                    obj.evaluationResults.data.modelledHead_residuals = permute(bsxfun(@minus, obsHead(~t_filt,2), head_est(~t_filt, 2,:)),[ 1 3 2]);
-                    head_eval_resid = obj.evaluationResults.data.modelledHead_residuals;
-                else
-                    obj.evaluationResults.data.modelledHead = head_est(~t_filt,1:2);
-                    obj.evaluationResults.data.modelledNoiseBounds = head_est(~t_filt,[1,3,4]);
-                    obj.evaluationResults.data.modelledHead_residuals = obsHead(~t_filt,2) -obj.evaluationResults.data.modelledHead(:,2);
-                    head_eval_resid = obj.evaluationResults.data.modelledHead_residuals;
-                end
-                if size(head_est,3)>1
-                    obj.evaluationResults.data.modelledHead_residuals = single(obj.evaluationResults.data.modelledHead_residuals);
-                end
-            else
-                obj.evaluationResults =[];
-            end
-            
-            % Add calib. obs data and residuals
-            if size(head_est,3)>1
-                obj.calibrationResults.data.modelledHead = [head_est(t_filt,1,1), permute(prctile( head_est(t_filt, 2,:),[50 5 95],3),[1,3,2])];                
-                obj.calibrationResults.data.modelledNoiseBounds = [head_est(t_filt,1,1), prctile( head_est(t_filt, 3,:),5,3), prctile( head_est(t_filt, 4,:),95,3)];
-                obj.calibrationResults.data.modelledHead_residuals = permute( bsxfun(@minus, obsHead(t_filt,2), head_est(t_filt, 2,:)),[1 3 2]);
-                head_calib_resid = obj.calibrationResults.data.modelledHead_residuals;
-            else
-                obj.calibrationResults.data.modelledHead = head_est(t_filt,1:2);
-                obj.calibrationResults.data.modelledNoiseBounds = head_est(t_filt,[1,3,4]);
-                obj.calibrationResults.data.modelledHead_residuals = obsHead(t_filt,2) -obj.calibrationResults.data.modelledHead(:,2);
-                head_calib_resid = obj.calibrationResults.data.modelledHead_residuals;
-            end
-            if size(head_est,3)>1
-                obj.calibrationResults.data.modelledHead_residuals = single(obj.calibrationResults.data.modelledHead_residuals);  % to reduce RAM from DREAM runs
-            end
-                                                
-            nparams = size(params,1);
-            nobs = size(obj.calibrationResults.data.modelledHead,1);            
-            %--------------------------------------------------------------
-            
-                        
-            % Calc. various performance measures including the coefficient of efficiency using the mean observed head
-            %------------------
-            % Mean error
-            obj.calibrationResults.performance.mean_error =  mean(head_calib_resid);
-            
-            %RMSE
-            SSE = sum(head_calib_resid.^2);
-            RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
-            obj.calibrationResults.performance.RMSE = RMSE;                        
-            
-            % CoE
-            obj.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
-            obj.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(t_filt,2));            
-            obj.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(t_filt,2) - mean(obsHead(t_filt,2)) ).^2);            
-            
-            % Unbiased CoE
-            residuals_unbiased = bsxfun(@minus,head_calib_resid, obj.calibrationResults.performance.mean_error);
-            SSE_unbiased = sum(residuals_unbiased.^2);
-            obj.calibrationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE_unbiased./sum( (obsHead(t_filt,2) - mean(obsHead(t_filt,2)) ).^2);            
-            
-            if neval > 0;                                   
-                % Mean error
-                obj.evaluationResults.performance.mean_error = mean(head_eval_resid); 
-                
-                %RMSE
-                SSE = sum(head_eval_resid.^2);
-                obj.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
-                
-                % CoE
-                obj.evaluationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
-                obj.evaluationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(~t_filt,2));            
-                obj.evaluationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(~t_filt,2) - mean(obsHead(~t_filt,2)) ).^2);            
-
-                % Unbiased CoE
-                residuals_unbiased = bsxfun(@minus,head_eval_resid, obj.evaluationResults.performance.mean_error);
-                SSE_unbiased = sum(residuals_unbiased.^2);
-                obj.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE_unbiased./sum( (obsHead(~t_filt,2) - mean(obsHead(~t_filt,2)) ).^2);            
-            end
-            %------------------
-                                    
-            % Calc. F-test and P(F>F_critical)  
-            if size(head_est,3)>1
-                meanObsHead = mean(obj.calibrationResults.data.obsHead(:,2));
-                for i=1:size(head_est,3)
-                    RSS(:,i) = norm( head_est(t_filt, 2,i) - meanObsHead).^2;
-                    s2(:,i) = (norm(head_calib_resid(:,i))/sqrt(nobs - nparams)).^2;
-                end
-            else
-                RSS = norm( obj.calibrationResults.data.modelledHead(:,2)  - mean(obj.calibrationResults.data.obsHead(:,2))).^2;            
-                s2 = (norm(head_calib_resid)/sqrt(nobs - nparams)).^2;
-            end                        
-            obj.calibrationResults.performance.F_test = (RSS/(nparams-1))./s2;      % F statistic for regression
-            if size(head_est,3)>1
-                for i=1:size(head_est,3)
-                    obj.calibrationResults.performance.F_prob(:,i) = fcdf(1./obj.calibrationResults.performance.F_test(:,i),nobs - nparams, nparams-1); % Significance probability for regression
-                end
-            else
-                obj.calibrationResults.performance.F_prob = fcdf(1./obj.calibrationResults.performance.F_test,nobs - nparams, nparams-1); % Significance probability for regression
-            end
-            
-            if neval > 0;   
-                if size(head_est,3)>1
-                    meanObsHead = mean(obj.evaluationResults.data.obsHead(:,2));
-                    for i=1:size(head_est,3)
-                        RSS(:,i) = norm( head_est(~t_filt, 2,i) - meanObsHead).^2;
-                        s2(:,i) = (norm(head_eval_resid(:,i))/sqrt(neval - nparams)).^2;
-                    end
-                else
-                    RSS = norm( obj.evaluationResults.data.modelledHead(:,2)  - mean(obj.evaluationResults.data.obsHead(:,2))).^2;            
-                    s2 = (norm(head_eval_resid)/sqrt(neval - nparams)).^2;
-                end                        
-                obj.evaluationResults.performance.F_test = (RSS/(nparams-1))./s2;      % F statistic for regression
-                if size(head_est,3)>1
-                    for i=1:size(head_est,3)
-                        obj.evaluationResults.performance.F_prob(:,i) = fcdf(1./obj.evaluationResults.performance.F_test(:,i),neval - nparams, nparams-1); % Significance probability for regression
-                    end
-                else
-                    obj.evaluationResults.performance.F_prob = fcdf(1./obj.evaluationResults.performance.F_test,neval - nparams, nparams-1); % Significance probability for regression
-                end
-            end
-            
-            % Add BIC and Akaike information criterion                                 
-            obj.calibrationResults.performance.AICc = 2*nparams - 2*log_L;
-            obj.calibrationResults.performance.AICc = obj.calibrationResults.performance.AICc + 2*nparams*(nparams+1)/(nobs-nparams-1);
-            obj.calibrationResults.performance.BIC = nparams*log(nobs) -2*log_L;
-            
-            % Calculate experimental variogram of residuals and fit an
-            % exponential model
-            for i=1:size(head_est,3)   
-                try
-                    resid = [obsHead(t_filt,1), obsHead(t_filt,2) - head_est(t_filt, 2,i)];
-                    calib_var = variogram([resid(:,1), zeros( size(resid(:,1))) ] ...
-                        , resid(:,2) , 'maxdist', min(365,max(resid(:,1))-min(resid(:,1))), 'nrbins', min(size(resid,1),12));
-
-                    [obj.calibrationResults.performance.variogram_residual.range(i,1), obj.calibrationResults.performance.variogram_residual.sill(i,1), ...
-                        obj.calibrationResults.performance.variogram_residual.nugget(i,1), varmodel] ...
-                        = variogramfit(calib_var.distance, ...                
-                        calib_var.val, 365/4, 0.75.*var( resid(:,2)), calib_var.num, [], ...
-                        'model', 'exponential', 'nugget', 0.25.*var( resid(:,2)) ,'plotit',false );
-
-                    obj.calibrationResults.performance.variogram_residual.h(:,i) = varmodel.h;
-                    obj.calibrationResults.performance.variogram_residual.gamma(:,i) = varmodel.gamma;
-                    obj.calibrationResults.performance.variogram_residual.gammahat(:,i) = varmodel.gammahat;
-                catch ME
-                    continue;
-                end
-                    
-            end 
-            if neval > 0;                
-                for i=1:size(head_est,3)      
-                    try
-                        resid = [obsHead(~t_filt,1), obsHead(~t_filt,2) - head_est(~t_filt, 2,i)];
-                        eval_var = variogram([resid(:,1), zeros( size(resid(:,1))) ] ...
-                            , resid(:,2) , 'maxdist', min(365,max(resid(:,1))-min(resid(:,1))), 'nrbins', min(size(resid,1),12));
-
-                        [obj.evaluationResults.performance.variogram_residual.range(i,1), obj.evaluationResults.performance.variogram_residual.sill(i,1), ...
-                            obj.evaluationResults.performance.variogram_residual.nugget(i,1), varmodel] ...
-                            = variogramfit(eval_var.distance, ...                
-                            eval_var.val, 365/4, 0.75.*var( resid(:,2)), eval_var.num, [], ...
-                            'model', 'exponential', 'nugget', 0.25.*var( resid(:,2)) ,'plotit',false );
-
-                        obj.evaluationResults.performance.variogram_residual.h(:,i) = varmodel.h;
-                        obj.evaluationResults.performance.variogram_residual.gamma(:,i) = varmodel.gamma;
-                        obj.evaluationResults.performance.variogram_residual.gammahat(:,i) = varmodel.gammahat;
-                    catch ME
-                        continue;
-                    end                    
-                end                                 
-            end                        
-                                   
-            % Update flag to denote the calibration completed successfully.
-            obj.calibrationResults.isCalibrated = true;            
-        end
-
-        function handle = calibrateModelPlotResults(obj, plotNumber, figHandle)
-% Plot the calibration results
-%
-% Syntax:
-%   calibrateModelPlotResults(obj)
-%   calibrateModelPlotResults(obj, handle)
-%
-% Description:
-%   Creates a summary plot of the calibration results. The plot presents
-%   the fit with the observed data and the model noise estimate; time
-%   series of the residuals and various diagnotic plots.
-%
-% Input:
-%   obj -  model object
-%
-%   handle - Matlab figure handle to a pre-existing figure window in which
-%   the plot is to be created.
-%
-% Output:  
-%   (none)
-%
-% Example:
-%
-%   % Plot the calibrate results for object 'model_124705':
-%   >> calibrateModelPlotResults(model_124705);
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModel: model_calibration;
-%
-% Dependencies
-%   model_TFN.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   7 May 2012
-%
-
-            % Create the figure. If varargin is empty then a new figure
-            % window is created. If varagin equals a figure handle, h, then
-            % the calibration results are plotted into 'h'.            
-            if nargin<3 || isempty(figHandle)
-                % Create new figure window.
-                figHandle = figure('Name',['Calib. ',obj.bore_ID]);
-                h=axes(figHandle);
-            elseif ~ishandle(figHandle)    
-                error('Input handle is not a valid figure handle.');
-            else
-                h = figHandle;
-            end
-            
-            % Define the dimensions of the subplots
-            if isempty(plotNumber) || nargin==1
-                ncol_plots = 3;
-                nrow_plots = 4;
-            end
-            
-            % Calc. number of evaluation points.
-            neval=0;
-            if isfield(obj.evaluationResults,'data')
-                if isfield(obj.evaluationResults.data,'modelledHead')
-                    neval = size(obj.evaluationResults.data.modelledHead,1);
-                end
-            end
-                    
-            % Assess if theer is data on the noise componant.
-            hasNoiseComponant = false;
-            if max(max(abs(obj.calibrationResults.data.modelledNoiseBounds)))>0   
-                hasNoiseComponant = true;
-            end
-            
-            % Assess if the data is from a population of simlations (eg
-            % from DREAM calibration)
-            hasModelledDistn = false;
-            if size(obj.calibrationResults.data.modelledHead,2)>2   
-                hasModelledDistn = true;
-                %hasNoiseComponant = false;
-            end
-            
-            % Plot time series of heads.                        
-            %-------
-            if isempty(plotNumber)
-                iplot = 1;
-                nplots = ncol_plots;
-                h=subplot(nrow_plots,ncol_plots , 1 :ncol_plots,'Parent',figHandle);
-                iplot = iplot + nplots;
-            end
-            
-            % Plot bounds for noise component.
-            if isempty(plotNumber) || plotNumber==1 
-                if hasNoiseComponant
-                   if neval > 0; 
-                       XFill = [obj.calibrationResults.data.modelledNoiseBounds(:,1)' ...
-                                obj.evaluationResults.data.modelledNoiseBounds(:,1)' ...
-                                fliplr([obj.calibrationResults.data.modelledNoiseBounds(:,1); ...
-                                        obj.evaluationResults.data.modelledNoiseBounds(:,1)]')];
-                       YFill = [obj.calibrationResults.data.modelledNoiseBounds(:,3)', ...
-                                obj.evaluationResults.data.modelledNoiseBounds(:,3)', ...
-                                fliplr([obj.calibrationResults.data.modelledNoiseBounds(:,2); ...
-                                obj.evaluationResults.data.modelledNoiseBounds(:,2)]')];
-                   else
-                       XFill = [obj.calibrationResults.data.modelledNoiseBounds(:,1)' ...
-                                fliplr(obj.calibrationResults.data.modelledHead(:,1)')];
-                       YFill = [obj.calibrationResults.data.modelledNoiseBounds(:,3)', ...
-                                fliplr(obj.calibrationResults.data.modelledNoiseBounds(:,2)')];
-                   end
-                   fill(XFill, YFill,[0.8 0.8 0.8],'Parent',h);
-                   clear XFill YFill               
-                   hold(h,'on');
-                end
-
-                if hasModelledDistn
-                   if neval > 0; 
-                       XFill = [obj.calibrationResults.data.modelledHead(:,1)' ...
-                                obj.evaluationResults.data.modelledHead(:,1)' ...
-                                fliplr([obj.calibrationResults.data.modelledHead(:,1); ...
-                                        obj.evaluationResults.data.modelledHead(:,1)]')];
-                       YFill = [obj.calibrationResults.data.modelledHead(:,4)', ...
-                                obj.evaluationResults.data.modelledHead(:,4)', ...
-                                fliplr([obj.calibrationResults.data.modelledHead(:,3); ...
-                                obj.evaluationResults.data.modelledHead(:,3)]')];
-                   else
-                       XFill = [obj.calibrationResults.data.modelledHead(:,1)' ...
-                                fliplr(obj.calibrationResults.data.modelledHead(:,1)')];
-                       YFill = [obj.calibrationResults.data.modelledHead(:,4)', ...
-                                fliplr(obj.calibrationResults.data.modelledHead(:,3)')];
-                   end
-                   fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
-                   clear XFill YFill               
-                   hold(h,'on');                    
-                end
-                % Plot the observed head
-                plot(h,obj.model.inputData.head(:,1), obj.model.inputData.head(:,2),'.-k');
-                hold(h,'on');
-
-                % Plot model results
-                h_plot = plot(h,obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead(:,2),'.-b' );
-                if neval > 0;
-                    plot(h,obj.evaluationResults.data.modelledHead(:,1), obj.evaluationResults.data.modelledHead(:,2),'.-r' );
-                end
-                datetick(h, 'x','yy');
-                xlabel(h, 'Date');
-                ylabel(h, 'Head (m)');           
-                if isempty(plotNumber)
-                    title(h, ['Bore ', obj.bore_ID, ' - Observed and modelled head']);
-                else
-                    title(h, 'Observed and modelled head');
-                end
-
-                % Create legend strings  
-                i=1;
-                if hasNoiseComponant
-                    if hasModelledDistn
-                        legendstr{i}='Total Err. (5th-95th)';
-                    else
-                        legendstr{i}='Total Err.';
-                    end
-                    i=i+1;
-                end
-                if hasModelledDistn
-                    legendstr{i}='Param. Err. (5th-95th)';
-                    i=i+1;
-                end
-                legendstr{i}='Observed';
-                i=i+1;
-                if hasModelledDistn
-                    legendstr{i}='Calib. (median)';
-                else
-                    legendstr{i}='Calib.';
-                end
-                i=i+1;                                
-                if neval > 0;
-                    if hasModelledDistn
-                        legendstr{i}='Eval. (median)';
-                    else
-                        legendstr{i}='Eval.';
-                    end
-                end                
-
-                % Finish the first plot!
-                legend(h, legendstr,'Location','northeastoutside');
-                box(h,'on');
-                hold(h,'off');               
-            end
-            
-            % Time series of residuals  
-            %-------
-            if isempty(plotNumber)
-                nplots = ncol_plots;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots-1,'Parent',figHandle);
-                iplot = iplot + nplots;
-            end
-            if isempty(plotNumber) || plotNumber==2
-                if hasModelledDistn
-                    residuals = [obj.calibrationResults.data.modelledHead(:,1), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
-                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
-                    set(ax,'Color',[0.6 0.6 0.6]);
-                    set(ax,'MarkerEdgeColor','b');
-                else
-                    scatter(h, obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead_residuals, '.b' );
-                end
-                hold(h,'on');
-                if neval > 0;
-                    if hasModelledDistn
-                        residuals = [obj.evaluationResults.data.modelledHead(:,1), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
-                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
-                        set(ax,'Color',[0.6 0.6 0.6]);
-                        set(ax,'MarkerEdgeColor','r');
-                    else
-                        scatter( h, obj.evaluationResults.data.modelledHead(:,1),  obj.evaluationResults.data.modelledHead_residuals, '.r'  );                
-                    end
-                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
-                end
-                xlabel(h, 'Date');
-                ylabel(h, 'Residuals (obs-est) (m)');
-                if hasModelledDistn
-                    title(h, 'Time series of residuals with parameter uncertainty (5th-95th) bars.');
-                else
-                    title(h, 'Time series of residuals');
-                end
-                datetick(h, 'x','yy');
-                box(h,'on');
-            end
-            
-            % Histograms of calibration data
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;                        
-            end
-            if isempty(plotNumber) || plotNumber==3
-                residuals = [obj.calibrationResults.data.modelledHead(:,1), prctile( obj.calibrationResults.data.modelledHead_residuals,50,2)];
-                hist(h, residuals(:,2) , 0.5*size(residuals,1) );            
-                ylabel(h, 'Freq.');
-                xlabel(h, 'Calib. residuals (obs-est) (m)');
-                axis(h, 'tight');
-                title(h, 'Histogram of calib. residuals');
-                box(h,'on');
-            end
-            
-            % Histograms of evaluation data
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;                        
-            end
-            if isempty(plotNumber) || plotNumber==4
-                if neval > 0;            
-                    residuals = [obj.evaluationResults.data.modelledHead(:,1), prctile( obj.evaluationResults.data.modelledHead_residuals,50,2)];
-                    hist(h, residuals(:,2) , 0.5*size(residuals,1) );                                
-                end           
-                ylabel(h, 'Freq.');
-                xlabel(h, 'Eval. residuals (obs-est) (m)');
-                axis(h, 'tight');
-                title(h, 'Histogram of eval. residuals');      
-                box(h,'on');
-            end
-            
-            % QQ plot
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;
-            end
-            if isempty(plotNumber) || plotNumber==5
-                residuals_cal = prctile( obj.calibrationResults.data.modelledHead_residuals,50,2);
-                if neval > 0;                              
-                    residuals_eval = prctile( obj.evaluationResults.data.modelledHead_residuals,50,2);
-                    QQdata = NaN(size(residuals_cal,1),2);
-                    QQdata(:,1) = residuals_cal;
-                    QQdata(1:neval,2) = residuals_eval;
-                    qqplot(QQdata);
-                    legend('Calibration','Evaluation','Location','northeastoutside');                
-                else
-                    qqplot(residuals_cal );
-                end            
-                title(h, 'Quantile-quantile plot of residuals');
-                box(h,'on');
-            end
-            
-            % Scatter plot of obs versus modelled
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;
-            end
-            if isempty(plotNumber) || plotNumber==6
-                if hasModelledDistn                    
-                    residuals = [obj.calibrationResults.data.obsHead(:,2), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
-                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
-                    set(ax,'Color',[0.6 0.6 0.6]);
-                    set(ax,'MarkerEdgeColor','b');
-                else
-                    scatter(h, obj.calibrationResults.data.obsHead(:,2),  obj.calibrationResults.data.modelledHead(:,2),'.b');
-                end
-                hold(h,'on');
-                if neval > 0;    
-                    if hasModelledDistn                        
-                        residuals = [obj.evaluationResults.data.obsHead(:,2), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
-                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
-                        set(ax,'Color',[0.6 0.6 0.6]);
-                        set(ax,'MarkerEdgeColor','r');
-                    else
-                        scatter(h, obj.evaluationResults.data.obsHead(:,2),  obj.evaluationResults.data.modelledHead(:,2),'.r');
-                    end
-                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
-                    head_min = min([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2); obj.evaluationResults.data.modelledHead(:,2)] );
-                    head_max = max([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2); obj.evaluationResults.data.modelledHead(:,2)] );
-                else
-                    head_min = min([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2)] );
-                    head_max = max([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2)] );
-                end            
-                xlabel(h, 'Obs. head (m)');
-                ylabel(h, 'Modelled. head (m)');
-                if hasModelledDistn
-                    title(h, 'Observed vs. modelled heads with with parameter uncertainty (5th-95th) bars.');
-                else
-                    title(h, 'Observed vs. modelled heads');
-                end
-                xlim(h, [head_min , head_max] );
-                ylim(h, [head_min , head_max] );
-                plot(h, [head_min, head_max] , [head_min, head_max],'--k');
-                box(h,'on');
-                hold(h,'off');
-            end
-            
-            % Scatter plot of residuals versus observed head
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;
-            end
-            if isempty(plotNumber) || plotNumber==7
-                if hasModelledDistn
-                    residuals = [obj.calibrationResults.data.obsHead(:,2), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
-                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
-                    set(ax,'Color',[0.6 0.6 0.6]);
-                    set(ax,'MarkerEdgeColor','b');
-                else
-                    scatter(h, obj.calibrationResults.data.obsHead(:,2), obj.calibrationResults.data.modelledHead_residuals,'.b');
-                end
-                hold(h,'on');
-                if neval > 0;                
-                    if hasModelledDistn
-                        residuals = [obj.evaluationResults.data.obsHead(:,2), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
-                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
-                        set(ax,'Color',[0.6 0.6 0.6]);
-                        set(ax,'MarkerEdgeColor','r');
-                    else                    
-                        scatter(h, obj.evaluationResults.data.obsHead(:,2),  obj.evaluationResults.data.modelledHead_residuals,'.r');
-                    end
-                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
-                end            
-                xlabel(h, 'Obs. head (m)');
-                ylabel(h, 'Residuals (obs-est) (m)'); 
-                if hasModelledDistn
-                    title(h, 'Observed vs. residuals with parameter uncertainty (5th-95th) bars.');
-                else                
-                    title(h, 'Observed vs. residuals');
-                end
-                box(h,'on');
-                hold(h,'off');        
-            end
-            
-            % Semi-variogram of residuals
-            if isempty(plotNumber)
-                nplots = 0;
-                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
-                iplot = iplot + nplots + 1;
-            end
-            if isempty(plotNumber) || plotNumber==8
-                if hasModelledDistn
-                    % Extract data from cell arrays
-                    deltaTime=[];
-                    gamma=[];
-                    gammaHat=[];
-                    if isfield(obj.calibrationResults.performance.variogram_residual,'h')                        
-                        deltaTime = obj.calibrationResults.performance.variogram_residual.h;
-                        gamma = obj.calibrationResults.performance.variogram_residual.gamma;
-                        gammaHat = obj.calibrationResults.performance.variogram_residual.gammahat;                        
-                    else
-                        for i=1:size(obj.calibrationResults.performance.variogram_residual.range,1)
-                            deltaTime = [deltaTime,obj.calibrationResults.performance.variogram_residual.model{i}.h];
-                            gamma = [gamma,obj.calibrationResults.performance.variogram_residual.model{i}.gamma];
-                            gammaHat = [gammaHat,obj.calibrationResults.performance.variogram_residual.model{i}.gammahat];
-                        end
-                    end
-                    
-                    %Plot data
-                    ax = errorbar(h, median(deltaTime,2) , median(gamma,2), median(gamma,2)-prctile(gamma,5 ,2),prctile(gamma,95 ,2)-median(gamma,2), '.b');
-                    set(ax,'Color',[0.6 0.6 0.6]);
-                    set(ax,'MarkerEdgeColor','b');
-                    hold(h,'on');
-                    plot(h, median(deltaTime,2) , median(gammaHat,2), '-b');
-                    plot(h, prctile(deltaTime,5,2) , prctile(gammaHat,5,2), '--b');
-                    plot(h, prctile(deltaTime,95,2) , prctile(gammaHat,95,2), '--b');                    
-                    
-                    if neval > 0;                                
-                        % Extract data from cell arrays
-                        deltaTime=[];
-                        gamma=[];
-                        gammaHat=[];
-                        if isfield(obj.evaluationResults.performance.variogram_residual,'h')                        
-                            deltaTime = obj.evaluationResults.performance.variogram_residual.h;
-                            gamma = obj.evaluationResults.performance.variogram_residual.gamma;
-                            gammaHat = obj.evaluationResults.performance.variogram_residual.gammahat;                        
-                        else
-                            for i=1:size(obj.evaluationResults.performance.variogram_residual.range,1)
-                                deltaTime = [deltaTime,obj.evaluationResults.performance.variogram_residual.model{i}.h];
-                                gamma = [gamma,obj.evaluationResults.performance.variogram_residual.model{i}.gamma];
-                                gammaHat = [gammaHat,obj.evaluationResults.performance.variogram_residual.model{i}.gammahat];
-                            end
-                        end                        
-
-                        %Plot data
-                        ax = errorbar(h, median(deltaTime,2) , median(gamma,2), median(gamma,2)-prctile(gamma,5 ,2),prctile(gamma,95 ,2)-median(gamma,2), '.r');
-                        set(ax,'Color',[0.6 0.6 0.6]);
-                        set(ax,'MarkerEdgeColor','r');                                        
-                        plot(h, median(deltaTime,2) , median(gammaHat,2), '-r');
-                        plot(h, prctile(deltaTime,5,2) , prctile(gammaHat,5,2), '--r');
-                        plot(h, prctile(deltaTime,95,2) , prctile(gammaHat,95,2), '--r');                    
-                    
-                        legend(h, 'Calib.(5-50-95th %ile)', 'Calib. model-50th %ile','Calib. model-5th %ile','Calib. model-95th %ile', ...
-                            'Eval.(5-50-95th %ile)','Eval. model-50th %ile','Eval. model-5th %ile','Eval. model-95th %ile','Location','northeastoutside');                
-                    else
-                        legend(h, 'Calib.(5-50-95th %ile)','Calib. model-50th %ile','Calib. model-5th %ile','Calib. model-95th %ile','Location','northeastoutside');                        
-                    end
-                else
-                    if isfield(obj.calibrationResults.performance.variogram_residual,'h')
-                        deltaTime = obj.calibrationResults.performance.variogram_residual.h;
-                        gamma = obj.calibrationResults.performance.variogram_residual.gamma;
-                        gammaHat = obj.calibrationResults.performance.variogram_residual.gammahat;                          
-                    else
-                        deltaTime=[];
-                        gamma=[];
-                        gammaHat=[];
-                        for i=1:size(obj.calibrationResults.performance.variogram_residual.range,1)
-                            deltaTime = [deltaTime,obj.calibrationResults.performance.variogram_residual.model{i}.h];
-                            gamma = [gamma,obj.calibrationResults.performance.variogram_residual.model{i}.gamma];
-                            gammaHat = [gammaHat,obj.calibrationResults.performance.variogram_residual.model{i}.gammahat];
-                        end                        
-                    end
-                    scatter(h, deltaTime, gamma, 'ob');            
-                    hold(h,'on');
-                    plot(h, deltaTime, gammaHat, '-b');                        
-                    if neval > 0;     
-                        if isfield(obj.evaluationResults.performance.variogram_residual,'h')
-                            deltaTime = obj.evaluationResults.performance.variogram_residual.h;
-                            gamma = obj.evaluationResults.performance.variogram_residual.gamma;
-                            gammaHat = obj.evaluationResults.performance.variogram_residual.gammahat;                          
-                        else
-                            deltaTime=[];
-                            gamma=[];
-                            gammaHat=[];
-                            for i=1:size(obj.evaluationResults.performance.variogram_residual.range,1)
-                                deltaTime = [deltaTime,obj.evaluationResults.performance.variogram_residual.model{i}.h];
-                                gamma = [gamma,obj.evaluationResults.performance.variogram_residual.model{i}.gamma];
-                                gammaHat = [gammaHat,obj.evaluationResults.performance.variogram_residual.model{i}.gammahat];
-                            end                                                    
-                        end                        
-                        
-                        scatter(h, deltaTime, gamma, 'or');
-                        plot(h, deltaTime,  gammaHat, '-r');                
-                        legend(h, 'Calib.','Calib model','Eval. experimental','Eval. model','Location','northeastoutside');                
-                    else
-                        legend(h, 'Calib.','Calib model','Location','northeastoutside');                
-                    end
-                end
-                xlabel(h, 'Separation distance (days)' );
-                ylabel(h, 'Semi-variance (m^2)' );
-                title(h, 'Semi-variogram of residuals');
-                box(h,'on');
-                hold(h,'off');               
-            end
-        end
-
-        %% Calculate the sum of squared errors and model residuals for CAM-ES.
-        function objectiveFunctionValue = calibrationObjectiveFunction(params, obj, time_points, doParamTranspose, getLikelihood)
-% Calculate the model objective function value for the input parameter set
-%
-% Syntax:
-%   objectiveFunctionValue = calibrationObjectiveFunction(params, obj, time_points)
-%
-% Description:
-%   The objective function value is calculated for each parameter set by
-%   calling the model's own method called objectiveFunction(). For each
-%   parameter set a scaler number is returned. It is intended that this 
-%   function is only be called from the calibration scheme via
-%   calibrateModel(). Importantly, the calculation of the objective
-%   function for each parameter set is undertaken in parallel to reduce the
-%   calibration time.
-%
-% Input:
-%   params - nxm numerical array of m parameter sets where each parameter
-%   set contains n parameters.
-%
-%   obj -  model object.
-%
-%   time_points - column vector of the time points over which the model is 
-%   to be calibrated.
-%
-% Output:  
-%   objectiveFunctionValue - 1xm numerical vector of the obecjtive function
-%   values.
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModel: model_calibration;
-%   calibrationValidParameters: model_parameter_set_validity_check
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%   calibrationValidParameters.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   30 June 2015
-%
-            if doParamTranspose
-                params = params';
-            end
-
-            if size(params,2)==1
-                try
-                    % Calcule sume of squared errors
-                    objectiveFunctionValue =  objectiveFunction( params, time_points, obj.model, getLikelihood );
-                catch ME
-                    objectiveFunctionValue = inf;
-                end
-            else
-                objectiveFunctionValue = inf(1,size(params,2));
-                parfor i=1: size(params,2)
-                    %Calculate the residuals
-                    try
-                        % Calcule sume of squared errors
-                        objectiveFunctionValue(i) =  objectiveFunction( params(:,i), time_points, obj.model, getLikelihood );
-                    catch ME
-                        objectiveFunctionValue(i) = inf;
-                    end
-                end
-            end
-        end        
-        
-        %% Check validity of parameter set.
-        function validParams = calibrationValidParameters(params, obj, time_points,  doParamTranspose, varargin)
-% Checks if the parameter set is valid
-%
-% Syntax:
-%   function validParams = calibrationValidParameters(params, obj, time_points)
-%
-% Description:
-%   Each parameter is assessed to dermine if it is valid. That is, if it is
-%   within a valid region of the possible parameter space. For each
-%   parameter set a scaler logical value is returned. It is intended that this 
-%   function is only be called from the calibration scheme via
-%   calibrateModel() and the assessment of the parameters is undertaken
-%   within the model's method getParameterValidity().
-%
-% Input:
-%   params - nxm numerical array of m parameter sets where each parameter
-%   set contains n parameters.
-%
-%   obj -  model object.
-%
-%   time_points - column vector of the time points over which the model is 
-%   to be calibrated.
-%
-% Output:  
-%   validParams - 1xm logical vector denoting if the parameter set is valid
-%   (true) or invalid (false).
-%
-% See also:
-%   HydroSightModel: class_description;
-%   calibrateModel: model_calibration;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   30 June 2015
-%           
-    
-            if doParamTranspose
-                params = params';
-            end
-            
-            try
-                validParams = getParameterValidity(obj.model, params, time_points);
-            catch ME
-                validParams = false;
-            end
-        end        
-        
-        %% Get observed Head.
-        function head = getObservedHead(obj)
-% Gets the observed head data used to build the model
-%
-% Syntax:
-%   head = getObservedHead(obj)
-%
-% Description:
-%   Returns the observed head data input when the model was constructed.
-%
-% Input:
-%   obj -  model object.
-%
-% Output:  
-%   head - nx2 numerical array of n rows of observed head values. The
-%   columns are date, head where the date is as a matlab date number.
-%
-% See also:
-%   HydroSightModel: class_description;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   30 June 2015
-%              
-            head = getObservedHead(obj.model);
-        end
-        
-        %% Get the forcing data from the model
-        function [forcingData, forcingData_colnames] = getForcingData(obj)
-% Gets the forcing data for the model.
-%
-% Syntax:
-%   [forcingData, forcingData_colnames] = getForcingData(obj)
-%
-% Description:
-%   Returns the input forcing data input to the model and the column names.
-%
-% Input:
-%   obj -  model object.
-%
-% Output:  
-%   forcingData - nxm numerical array of n rows of forcing data. The
-%   columns are the date followed by the forcing values for each type of forcing.
-%
-%   forcingData_colnames - cells array of column name string associated with the
-%   focring data.
-%
-% See also:
-%   HydroSightModel: class_description;
-%   setForcingData: assign_forcing_data;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   30 June 2015
-%             
-            [forcingData, forcingData_colnames] = getForcingData(obj.model);
-        end
-
-        function derivedData_types = getDerivedDataTypes(obj)
-            if any(strcmp(methods(obj.model),'getDerivedDataTypes'))
-                derivedData_types = getDerivedDataTypes(obj.model);
-            else
-                derivedData_types = {};
-            end            
-        end        
-        
-        function [params, param_names] = getDerivedParameters(obj)
-            if any(strcmp(methods(obj.model),'getStochForcingData'))
-                [params, param_names] = getDerivedParameters(obj.model);
-            else
-                params = [];
-                param_names = {};
-            end            
-        end
-        
-        function [derivedData, derivedData_names] = getDerivedData(obj, modelComponant, derivedData_variable, t, plot_axes)
-            if any(strcmp(methods(obj.model),'getDerivedData'))
-                [derivedData, derivedData_names] = getDerivedData(obj.model, modelComponant, derivedData_variable, t, plot_axes);
-            else
-                derivedData = [];
-                derivedData_names = {};
-            end                    
-        end
-        
-        function [forcingData, forcingData_colnames] = getDerivedForcingData(obj, t)
-            if any(strcmp(methods(obj.model),'getDerivedForcingData'))
-                [forcingData, forcingData_colnames] = getDerivedForcingData(obj.model, t);
-            else
-                forcingData = [];
-                forcingData_colnames = {};
-            end            
-        end
-            
-        
-        function forcingData = getStochForcingData(obj)
-           
-            if any(strcmp(methods(obj.model),'getStochForcingData'))
-                forcingData = getStochForcingData(obj.model);
-            else
-                forcingData = [];
-            end
-            
-        end
-        
-        function finishedStochForcing = updateStochForcingData(obj, forcingData, refineStochForcingMethod)
-            finishedStochForcing = false;
-            if any(strcmp(methods(obj.model),'updateStochForcingData'))           
-                if nargin==1
-                    updateStochForcingData(obj.model);  
-                elseif nargin==2
-                    updateStochForcingData(obj.model, forcingData);
-                elseif nargin==3
-                    finishedStochForcing = updateStochForcingData(obj.model, forcingData, refineStochForcingMethod);
-                end
-            end            
-        end  
-        
-        function params = updateStochForcingParameters(obj, forcingData, params)
-            if any(strcmp(methods(obj.model),'updateStochForcingParameters'))           
-                for i=1:size(params,2)
-                    params(:,i) = updateStochForcingParameters(obj.model, forcingData, params(:,i));
-                end
-            end            
-        end               
-      
-        
-        %% Get the forcing data from the model
-        function setForcingData(obj, forcingData, forcingData_colnames)
-% Sets the forcing data for the model.
-%
-% Syntax:
-%   setForcingData(obj, forcingData, forcingData_colnames)
-%
-% Description:
-%   Returns the input forcing data input to the model and the column names.
-%
-% Input:
-%   obj -  model object.
-%
-%   forcingData - nxm numerical array of n rows of forcing data. The
-%   columns are the date followed by the forcing values for each type of forcing.
-%
-%   forcingData_colnames - cells array of column name string associated with the
-%   focring data.
-%
-% Output:  
-%   (none)
-%
-% See also:
-%   HydroSightModel: class_description;
-%   getForcingData: get_model_forcing_data;
-%
-% Dependencies
-%   model_TFN.m
-%   ExpSmooth.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   30 June 2015
-%             
-            setForcingData(obj.model, forcingData, forcingData_colnames);
-        end
-       
-        function delete(obj)
-            % Clear object properties
-            obj.model_label=[];
-            obj.bore_ID=[];
-            obj.calibrationResults=[];
-            obj.evaluationResults=[];  
-            obj.simulationResults=[];
-
-            % Delete model object.       
-            if ~isempty(obj.model)
-                delete(obj.model);
-            end
-
-        end        
-        
-    end    
-end
-
+classdef HydroSightModel < handle
+% Class definition for building a time-series model of groundwater head.
+%
+% Description:
+%   This class allows the building, calibration, simulation and interpolation
+%   of a dynamic type of groundwater head time-series model. The model type 
+%   is defined within a seperate class object and must adhere to the structure
+%   as defined within the abstract class 'model_abstract.m'. Currently, the
+%   following models have been developed: 
+%`
+%   * 'model_TFN': Transfer Function Noise model of Peterson and
+%     Western (2014) and von Asmuth et al. (2005). To run this model,
+%     the following MEX .c source-code file may need to be compiled:
+%     * 'doIRFconvolution.c'
+%     * 'forcingTransform_soilMoisture.c'
+%   * 'ExpSmooth': A double exponential smoothing filter where the 'double'
+%     allows a trend to be accounted for in the smoothing.
+% 
+%   Below are links to the details of the public methods of this class. 
+%   The example presented uses the 'model_TFN' model. 
+% 
+%   To open an example model, add the folder and sub-folders of 
+%   'Model_Algorithms' to the MatLab path and then enter the following
+%   command: 
+%   open example_TFN_model()
+%   
+% See also
+%   HydroSightModel: model_construction;
+%   calibrateModel: model_calibration;
+%   solveModel: solve_the_model;
+%   model_TFN: - constructor_for_transfer_fuction_noise_models;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%   variogram.m
+%   variogramfit.m
+%   lsqcurvefit.m
+%   IPDM.m
+%   SPUCI.m
+%   cmaes.m
+%
+% References:
+%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
+%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure 
+%   Engineering, The University of Melbourne, Australia
+%
+% Date:
+%   2 Mar 2017
+%
+%
+% License:
+%   GNU GPL3 or later.
+%
+    properties
+
+        % Model Label
+        model_label
+        
+        % Bore ID (string). Most model types require the bore ID to be listed within the coordinates file. 
+        bore_ID;
+        
+        % Model class object.       
+        model;
+        
+        % Calibration results structure.
+        calibrationResults;
+        % Evaluation results structure.
+        evaluationResults;  
+        % Simulation results structure.        
+        simulationResults;
+    end
+    
+
+%%  STATIC METHODS        
+% Static methods used by the Graphical User Interface to inform the
+% user of the available model types. Any new models must be listed here
+% in order to be accessable within the GUI.
+    methods(Static)
+        function [types,recommendedType] = model_types()
+            types = {'model_TFN','Transfer function noise model with nonlinear transformation of climate forcing' ; ...
+                    'model_HARTT','Linear regression model usinf cumulative rainfall residual'};
+            recommendedType = 1;    
+        end
+    end    
+
+%%  PUBLIC METHODS     
+    methods
+%% Construct the model
+        function obj = HydroSightModel(model_label, bore_ID, model_class_name, obsHead, obsDataFlow, obsHead_maxObsFreq, forcingData, siteCoordinates, varargin)
+% Model construction.
+%
+% Syntax:
+%   model = HydroSightModel(model_label, bore_ID, model_class_name , obsHead, obsHead_maxObsFreq, forcingData, siteCoordinates, modelOptions)
+%
+% Description:
+%   Builds the model object, declares initial parameters and sets the
+%   observationc,coordinates and forcing data. This is the method that must be
+%   called to build a new model. The form of the model is entirely defined
+%   by the input 'model_class_name'. This is a highly flexible model
+%   structure and allows the efficient inclusion of new model types.
+%
+%   For details of how to build a linear and nonlinear transfer function
+%   noise model, see the documentation for model_TFN.m. To open an example 
+%   model, add the folder and sub-folders of 'models' and 'calibration' to the
+%   MatLab path and then enter the following command: 
+%   open example_TFN_model()
+%
+% Input:
+%   model_label - label for describing the model. This is only used to
+%   inform the user of the model.
+%   
+%   bore_ID - string identified for the model, eg 'Bore_ID_1234'. This bore
+%   ID must be listed within the cell array of 'siteCoordinates'.
+%
+%   model_class_name - string of the model type to be built. A class
+%   definition must already have been written for the model type. To date
+%   the following inputs are allows:
+%       - 'model_TFN'
+%
+%   obsHead - Nx5 matrix of observed groundwater elevation data. The
+%   matrix must comprise of the following columns: year, month, day, hour,
+%   water level elevation. The data can be of a sub-daily frequency and
+%   of an non-uniform frequency. However, the method will aggregate
+%   sub-daily observations to daily observations by taling the last
+%   observation of the day.
+%
+%   obsHead_maxObsFreq - scalar for the maximum frequency of observation
+%   data. This input allows the upscaling of daily head data to, say,
+%   monthly freqency data. This feature was included to overcome large
+%   computational demands when calibrating the model to daily data.
+%
+%   forcingData - a variable data-type input containing the forcing data (e.g.
+%   daily precipitation, potential evapo-transpiration, pumping at each site)
+%   and column headings. The input can be of the following form:
+%      - structure data-type with field named 'colnames' and 'data' where 
+%        'data' contains only numeric data;
+%      - cell array with the first row being the column names and lower
+%        rows being numerical data.
+%      - or a table data-type with the heading being the column names
+%        (NOTE: this input form is only available for Matlab 2013b or later). 
+%
+%   The forcing data must comprise of the following columns: year, month,
+%   day, observation data. The observation data must include precipitation
+%   but may include other data as defined within the input model options. 
+%   The record must be continuous (no skipped days) and cannot contain blank
+%   observations and start prior to the first groundwater level
+%   observation (ideally, it should start many years prior). Each forcing
+%   data column name must be listed within the cell array 'siteCoordinates'
+%
+%   siteCoordinates - Mx3 cell matrix containing the following columns :
+%   site ID, projected easting, projected northing. 
+%
+%   Importantly, the input 'bore_ID' must be listed and all columns within
+%   the forcing data (excluding the year, month,day). Additionally, sites
+%   (and coordinates) not listed in the input 'forcingData' can be input.
+%   This provides a means for image wells to be input.
+%   
+%   modelOptions - cell matrix defining the model componants, ie how the
+%   model should be constructed. The structure of the model options are
+%   entirely dependent upon the model type. See the construction
+%   documentation for the relevant model.
+%
+% Output:
+%   model - HydroSightModel class object 
+%
+% Example: 
+%   Load the input head and forcing data:
+%   >> load '124705_boreData.mat'
+%   >> load '124705_forcingData.mat'
+%
+%   Create a cell matrix of the model options for model_TFN:
+%   >> modelOptions_TFN = {'precip','weightingfunction','responseFunction_Pearsons' ;
+%                          'precip','forcingdata',4}
+%
+%   Create bore coordinates cell array. NOTE: the coordinates below are
+%   arbitrary. NOTE, The bote is at Longitude/Latitude of 142.924, -37.145 
+%   (GDA94).
+%   siteCoordinates = {'Bore_124705', 670870.5 , 5887258.8 ; ...
+%                      'precip', 670870.5 , 5887258.8 ; ...
+%                      'APET', 670870.5 , 5887258.8 ; ...
+%                      'LandRevegFraction', 670870.5 , 5887258.8 };
+%
+%   Convert the forcing data to a table data type:
+%   >> forcingData=array2table(forcingData,'VariableNames', ...
+%                     {'Year','Month','Day','precip','APET','LandRevegFraction'});
+%   
+%   Build the model with a maximum frequency of observation data of 7 days:
+%   >> model_124705 = HydroSightModel('Example TFN model', 'Bore_124705',  ...
+%                     'model_TFN', boreDataWL, 7, forcingData, ...
+%                     siteCoordinates, modelOptions_TFN)
+%
+%                   
+%   Inspect model:
+%    >> open model_124705
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModel: model_calibration;
+%   calibrateModelPlotResults: plot_model_calibration_results;
+%   solveModel: run_model_simulations;
+%   solveModelPlotResults: plot_model_simulation_results;
+%   model_TFN: - constructor_for_transfer_fuction_noise_models;
+%   ExpSmooth: - constructor_for_esponential_smoothing_models;
+%   climateTransform_soilMoistureModels: - constructor_for_soil_moisture_models;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%   trialData.mat
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+%    
+
+            % Check constructor inputs.
+            if nargin < 7
+                error('All seven inputs are required!');
+            end            
+            if size(obsHead,2) <4
+                error('The input observed head must be at least four columns: year; month; day; head; which is assumed to be the last column.');
+            end            
+            if ~iscell(siteCoordinates)
+                error('The input "siteCoordinates" must be a cell data type of >2 rows and following three columns: site ID, projected easting, projected northing');
+            end
+            
+            % Check the form of the forcing data input
+            if isnumeric(forcingData)
+                error('The input forcing data cannot be a numerical array. It must be a structure data type, a cell array or a table object. Please see the model documentation.');
+            elseif isstruct(forcingData)
+                if isfield(forcingData,'colnames') && isfield(forcingData,'data')
+                    forcingData_data = forcingData.data;
+                    forcingData_colnames = forcingData.colnames;
+                else
+                    error('When the input forcing data is a structure data type the fields must be "colnames" and "data".');
+                end                   
+            elseif iscell(forcingData)
+                try
+                    forcingData_data = cell2mat(forcingData(2:end,:));
+                    forcingData_colnames = forcingData(1,:);
+                catch ME
+                    error('An error occured when extracting the forcing data from the input cell array. The first row must be the column headings and all of the following rows the numeric data.');
+                end
+            elseif strcmp(class(forcingData),'table')
+                % Assume the datatype is a table object
+                forcingData_colnames = forcingData.Properties.VariableNames;
+                forcingData_data = forcingData{:,:};
+            else
+                error('The must be a structure data type, a cell array or a table object. Please see the model documentation.');
+            end
+            % Check the size of the forcing data
+            if size(forcingData_data,2) <4
+                error('The input forcing data must be at least four columns: year, month, day, Precip');
+            end
+            % Check the first three columns are named year,month,day.
+            forcingData_colnames(1:3) = lower(forcingData_colnames(1:3));
+            if ~strcmp(forcingData_colnames(1),'year') || ~strcmp(forcingData_colnames(2),'month') || ~strcmp(forcingData_colnames(3),'day')
+               error('The three left most forcing data column names must be the following and in the following order: "year,"month",day"');
+            end
+            
+            % Derive columns of year, month, day etc to matlab date value
+            forcingDates = datenum(forcingData_data(:,1), forcingData_data(:,2), forcingData_data(:,3));
+            switch size(obsHead,2)-1
+                case 3
+                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3));
+                case 4
+                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4), zeros(size(obsHead,1),1), zeros(size(obsHead,1),1));
+                case 5
+                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4),obsHead(:,5), zeros(size(obsHead,1),1));
+                case 6
+                    obsDates = datenum(obsHead(:,1), obsHead(:,2), obsHead(:,3),obsHead(:,4),obsHead(:,5),obsHead(:,6));
+                otherwise
+                    error('The input observed head must be 4 to 7 columns with right hand column being the head and the left columns: year; month; day; hour (optional), minute (options), second (optional).');
+            end
+            
+            % Check date limits of obs head and forcing data
+            if max(diff(forcingDates)) > 1 || min(diff(forcingDates)) < 1
+                error('The input forcing data must of a daily time step with no gaps!');
+            end
+            if strcmp(model_class_name,'')
+                error('The model class name cannot be empty.');
+            end           
+            if floor(max(obsDates)) > max(forcingDates) || floor(min(obsDates)) < min(forcingDates) 
+                error('The observed head records extend prior to and or after the forcing observations');
+            end
+                  
+            % Check the form of the coordinate data and check that the bore
+            % ID and all forcing date columns are listed within it.
+            if iscell(siteCoordinates) 
+               % Check the cell has 3 columsn and atleast 2 rows.
+               if size(siteCoordinates,1) < 2 || size(siteCoordinates,2) ~= 3
+                   error('The input "siteCoordinates" must be a cell array of three columns (site name, easting, northing) and atleast two rows (for bore ID and precipitation).');
+               end
+               
+               % Check if the column names contain '/' or '\'
+               for i=1:size(siteCoordinates,1)
+                  if ~isvarname(siteCoordinates{i,1})
+                    error(['The site ',siteCoordinates{i,1}, ' is an invalid name. Use only letters, numbers, dash and underscores.']);                
+                  end
+               end
+               % Check the bore ID coordinates are input.
+               hasBoreIDCoordinates=false;
+               for i=1:size(siteCoordinates,1)
+                  if strcmp(siteCoordinates(i,1), bore_ID)
+                      hasBoreIDCoordinates=true;
+                      break;
+                  end
+               end
+               if ~hasBoreIDCoordinates
+                   error('The site coordinate cell array must list the coordinate for the input bore ID. Note, the site names are case-sensitive.');
+               end               
+               
+               % Loop through each forcing data column.
+               hasForcingCoordinate = false(1,length(forcingData_colnames)-3);
+               for i=4:length(forcingData_colnames)
+                   for j=1:size(siteCoordinates,1);                   
+                      if strcmp(siteCoordinates(j,1), forcingData_colnames(i))
+                          hasForcingCoordinate(i-3) = true;
+                          break;
+                      end
+                   end
+               end
+               if any(~hasForcingCoordinate)
+                   error('The site coordinate cell array must list the coordinates for all columns of the forcing data (excluding "year","month",day"). Note, the site names are case-sensitive.');
+               end                              
+            else
+               error('The input "siteCoordinates" must be a cell array of three columns (site name, easting, northing) and atleast two rows (for bore ID and precipitation).'); 
+            end
+            
+            % Ammend column 1-3 from year, month, day  to time.
+            forcingData_data = [forcingDates, forcingData_data(:,4:end)];
+            forcingData_colnames = {'time', forcingData_colnames{4:end}};
+            
+            % Thin out observed heads to a user defined maximum frequency.
+            % This feature is included to overcome the considerable
+            % computational burdon of calibrating the model to daily head
+            % observations using 50+ years of prior daily climate data.
+            if ~isempty(obsHead_maxObsFreq) && obsHead_maxObsFreq >0
+                
+                % Aggregate sub-daily observed groundwater to daily steps            
+                j=1;
+                ii=1;
+                while ii  <= size(obsDates,1)
+                    % Find last observation for the day
+                    [iirow, junk] = find( obsDates == obsDates(ii,1), 1, 'last');
+
+                    % Derive the date and time at the end of the day
+                    date_time = datenum(obsHead(iirow,1), obsHead(iirow,2), obsHead(iirow,3), 23, 59, 59 );
+
+                    % Add date and head to new matrix.
+                    obsHead(j,1:2) = [ date_time , obsHead(iirow,end)];
+                    j=j+1;
+                    ii = iirow+1;
+                end
+                obsHead = obsHead(1:j-1,1:2);                
+                
+                % Now thin out to requested freq.
+                obsHead_orig = obsHead;
+                obsHead = zeros(size(obsHead));
+                obsHead(1,:) = obsHead_orig(1,:);
+                j=1;
+                for ii = 2:size(obsHead_orig,1)
+                    % Accept obs only when duration to prior accepted obs
+                    % is >=  obsHead_maxObsFreq.
+                    if obsHead_orig(ii,1) - obsHead(j,1) >= obsHead_maxObsFreq
+                        j=j+1;
+                        obsHead(j,:) = obsHead_orig(ii,:);
+                    end
+                end
+                
+                % Trim rows of zero value.
+                obsHead = obsHead(1:j,:);
+                
+                clear obsHead_orig;
+            else
+                % Use all input data
+                obsHead = [obsDates, obsHead(:,end)];
+                
+            end
+            % Warn the user of the obs. date was aggregated.
+            if size(obsHead,1) < size(obsHead,1)
+                display([char(13), 'Warning: some input head observations were of a sub-daily frequency.', char(13), ...
+                    'These have been aggregeated to a daily frequency by taking the last obseration of the day.', char(13), ...
+                    'The aggregation process assumed that the input data was sorted by date and time.']);
+            end
+            
+            % Add data to object.
+            obj.model_label = model_label;            
+            obj.bore_ID = bore_ID;            
+            
+            %  obj.model = feval(model_class_name, bore_ID, obsHead, forcingData_data, forcingData_colnames, siteCoordinates, varargin{1} );
+            if contains(model_class_name,'model_TFN_SW_GW')
+                obj.model = feval(model_class_name, model_label, bore_ID, obsHead, obsDataFlow, forcingData_data, forcingData_colnames, siteCoordinates, varargin{1} );
+            else
+                obj.model = feval(model_class_name, bore_ID, obsHead, forcingData_data, forcingData_colnames, siteCoordinates, varargin{1} );
+            end 
+            
+            % Add flag to denote the calibration has not been undertaken.
+            obj.calibrationResults.isCalibrated = false;
+        end
+
+%% Solve the model        
+        function h = solveModel(obj, time_points, forcingData, simulationLabel, doKrigingOnResiduals)
+% Run simulations using a calibrated model.
+%
+% Syntax:
+%   h = solveModel(obj, time_points)
+%   h = solveModel(obj, time_points, forcingData)
+%   h = solveModel(obj, time_points, forcingData, simulationLabel)
+%   h = solveModel(obj, time_points, forcingData, simulationLabel, doKrigingOnResiduals)
+%
+% Description:
+%   Solves the model using the calibrated parameters and, depending upon
+%   the method's inputs, decomposes the groundwater head into the
+%   contribution from various periods of climate forcing and plots the
+%   results. The simulation results can also be adjusted to honour head
+%   observations. This can only be done if the same forcing data is used in
+%   the simulation as was used in the model calibration. After the
+%   simulation, use 'solveModelPlotResults' to graph the results.
+%
+% Input:
+%   obj -  model object
+%
+%   time_points - column vector of the time points to be simulated. This
+%   can be prior to or after the first or last head observation. However,
+%   forcing data must exist for each time point to be simulated.
+%
+%   forcingData - optional input for undertaking simulations with different
+%   forcing data to that used in the model calibration. It must have
+%   at least the same columns (and names) as used in the calibration but
+%   can be of a different duration and values. Also, it can be of the 
+%   following form:
+%      - structure data-type with field named 'colnames' and 'data' where 
+%        'data' contains only numeric data;
+%      - cell array with the first row being the column names and lower
+%        rows being numerical data.
+%      - or a table data-type with the heading being the column names
+%        (NOTE: this input form is only available for Matlab 2013b or later). 
+%
+%   The forcing data must comprise of the following columns: year, month,
+%   day, observation data. The observation data must include precipitation
+%   but may include other data as defined within the input model options. 
+%   The record must be continuous (no skipped days) and cannot contain blank
+%   observations and start prior to the first time point to be simulated
+%   (ideally, it should start many years prior). Each forcing
+%   data column name must be listed within the cell array 'siteCoordinates'
+%
+%   simulationLabel - optional string to label the simulation. This allows
+%   multiple simulations to be stored within the model object. If the
+%   provided string already exists then it is overwritten with the new
+%   simulation results. If no string is provided, then the simulation is
+%   labeled '(No label)'.
+%
+%   doKrigingOnResiduals - logical scaler or scaler integer>0 to krige the 
+%   model residuals so that observaed head values equal obsevations. If
+%   true then the kriging in undertaken. If an integer >0 then the integer
+%   is used to set the maximum number of data points used in the kriging. For 
+%   more details of the interpolation see 'interpolateData'.
+%
+% Output:
+%   h - matrix of simulated head formatted as the following columns:
+%   date/time; simulated head; contribution from model componant 1 to n
+%   Note:  simulation results are output to obj.simulationResults{}.
+%
+% Example:
+%   Define the start date for simulation:
+%   >> start_date = datenum(1995, 1, 1);
+%
+%   Define the end date for simulation:
+%   >> end_date = datenum(2002, 1, 1);
+%
+%   Define the time points for simulation as from start_date to end_date at 
+%   steps of 28 days:
+%   >> time_points = [start_date : 28 : end_date]';
+%
+%   Solve the model object 'model_124705', with the climate decomposition.
+%   >> solveModel(model_124705, time_points);
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModel: model_calibration;
+%   solveModelPlotResults: plot_model_simulation_results;
+%   interpolateData: time-series_interpolation_algorithm.
+%
+% Dependencies
+%   model_TFN.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+
+           % Find out if the simulation label already exists. If not, add a new simulation. Else, replace it.
+           switch nargin
+               case 2
+                   forcingData = [];
+                   simulationLabel = '(No label)';
+                   doKrigingOnResiduals = false;
+               case 3
+                   simulationLabel = '(No label)';
+                   doKrigingOnResiduals = false;
+               case 4                   
+                   doKrigingOnResiduals = false;
+               case 5
+                   % do nothing                   
+               otherwise
+                   error('Incorrect number of inputs for the model simulation.');   
+           end
+           
+           if iscell(obj.simulationResults)
+               
+               % Find simulation label
+               simInd = cellfun( @(x) strcmp(x.simulationLabel , simulationLabel), obj.simulationResults);               
+               
+               % Check there is only one simulation with the given label,
+               % If the simulations does not exist then add a new
+               % simulation. 
+               if sum(simInd)>1
+                   error('The input simulation label is not unique. Multiple simulations exist with this label.');                   
+               elseif sum(simInd)==0                   
+                   if isempty(obj.simulationResults)
+                       simInd =  1;
+                   else
+                       simInd =  size(obj.simulationResults,1)+1;
+                   end                   
+               end                   
+           else
+                obj.simulationResults = cell(1,1);
+                simInd =  1;
+           end
+
+           % Add the forcing data.
+           if ~isempty(forcingData)
+               % Get the forcing data used to build the model.
+               [forcingData_modConstruc, forcingData_colnames_modConstruc] = getForcingData(obj);
+               
+               % Extract the forcing data and column names.
+                if isstruct(forcingData)
+                    if isfield(forcingData,'colnames') && isfield(forcingData,'data')
+                        forcingData_data = forcingData.data;
+                        forcingData_colnames = forcingData.colnames;
+                    else
+                        error('When the input forcing data is a structure data type the fields must be "colnames" and "data".');
+                    end                   
+                elseif iscell(forcingData)
+                    try
+                        forcingData_data = cell2mat(forcingData(2:end,:));
+                        forcingData_colnames = forcingData(1,:);
+                    catch ME
+                        error('An error occured when extracting the forcing data from the input cell array. The first row must be the column headings and all of the following rows the numeric data.');
+                    end
+                elseif strcmp(class(forcingData),'table')
+                    % Assume the datatype is a table object
+                    forcingData_colnames = forcingData.Properties.VariableNames;
+                    forcingData_data = forcingData{:,:}; 
+                else 
+                    error('The input forcing data must be a table, structure ot cell array variable. It cannot be double array.');
+                    return;
+                end
+
+               % Check the column names for the new forcing data are identical
+               for i=1:length(forcingData_colnames)
+                  if ~any(cellfun( @(x) strcmp(x, forcingData_colnames{i}), forcingData_colnames_modConstruc))
+                      error('New forcing dat must have identical column names to the original data.');
+                  end
+               end
+               
+               % Assign the new forcing data and keep a copy in the
+               % simulation structure.
+               setForcingData(obj, forcingData_data, forcingData_colnames);                  
+           end
+           
+           % Add input data for the simulation to the structure.
+           obj.simulationResults{simInd,1}.simulationLabel = simulationLabel;
+           if ~isempty(forcingData)
+                 obj.simulationResults{simInd,1}.forcingData = forcingData_data;
+                 obj.simulationResults{simInd,1}.forcingData_colnames = forcingData_colnames;
+           end
+           
+           
+           % Undertake the simulation of the head                      
+           try
+              [obj.simulationResults{simInd,1}.head, obj.simulationResults{simInd,1}.colnames, obj.simulationResults{simInd,1}.noise] = solve(obj.model, time_points);
+           catch ME
+                         
+              % Add the original forcing back into the model.
+              if ~isempty(forcingData)
+                  setForcingData(obj, forcingData_modConstruc, forcingData_colnames_modConstruc);
+              end  
+               
+              error(ME.message); 
+           end
+              
+           
+           % Krige the residuals so that the simulation honours observation
+           % points.
+           krigingVariance=[];
+           if (doKrigingOnResiduals || doKrigingOnResiduals>0) && isempty(forcingData)           
+               nparams = size(obj.simulationResults{simInd,1}.head,3);
+               head_estimates = zeros( size(obj.simulationResults{simInd,1}.head,1), 3, nparams);   
+               modelResults = cell( size(head_estimates,3),1);
+               for i=1:nparams
+                  modelResults{i,1}.head_estimates = [obj.simulationResults{simInd,1}.head(:,1:2,i), ...
+                      obj.simulationResults{simInd,1}.head(:,2,i) - obj.simulationResults{simInd,1}.noise(:,2,i),  ...
+                      obj.simulationResults{simInd,1}.head(:,2,i) + obj.simulationResults{simInd,1}.noise(:,3,i)];
+                  modelResults{i,1}.krigingData = [obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead_residuals(:,i)];
+                  modelResults{i,1}.range = obj.calibrationResults.performance.variogram_residual.range(i);
+                  modelResults{i,1}.sill = obj.calibrationResults.performance.variogram_residual.sill(i);
+                  modelResults{i,1}.nugget = obj.calibrationResults.performance.variogram_residual.nugget(i);
+                  
+                  
+                  if iscell(modelResults{i,1}.range)
+                      modelResults{i,1}.range = modelResults{i,1}.range{1};
+                  end
+                  if iscell(modelResults{i,1}.sill)
+                      modelResults{i,1}.sill = modelResults{i,1}.sill{1};
+                  end
+                  if iscell(modelResults{i,1}.nugget)
+                      modelResults{i,1}.nugget = modelResults{i,1}.nugget{1};
+                  end          
+               end
+               
+              % Set constant for the kriging
+              maxKrigingObs = 24;
+              if isnumeric(doKrigingOnResiduals) && ceil(doKrigingOnResiduals)>0
+                maxKrigingObs = ceil(doKrigingOnResiduals);
+              end
+              maxKrigingObs = min(maxKrigingObs,length(getObservedHead(obj)));
+              useModel = true;
+              
+              % Remove the simulation results. This is done to minimise the
+              % size of obj in the following parfor loop, which if nparams
+              % >>1 then the RAM requirements can be huge.
+              simulationResults = obj.simulationResults;
+              obj.simulationResults=[];
+              
+              % Call model interpolation
+              parfor i=1:nparams
+                  head_estimates(:,:,i) = interpolateData(obj, time_points, maxKrigingObs, useModel,modelResults{i});                 
+              end
+
+              % Add simulation results back onto the object.
+              obj.simulationResults = simulationResults;
+              clear simulationResults;
+              
+              % Calculate the contribution from interpolation
+              kriging_contribution = head_estimates(:,2,:) - obj.simulationResults{simInd,1}.head(:,2,:);
+
+              % Add interpolated head into the simulation results.
+              obj.simulationResults{simInd,1}.head(:,2,:) = head_estimates(:,2,:);
+
+              % Store the kriging variance.
+              krigingVariance = head_estimates(:,end,:);              
+               
+           end
+           
+           % Add columns to output for the noise componant.
+           if ~isempty(obj.simulationResults{simInd,1}.noise)
+               h = [obj.simulationResults{simInd,1}.head(:,1:2,:), obj.simulationResults{simInd,1}.head(:,2,:) - obj.simulationResults{simInd,1}.noise(:,2,:), obj.simulationResults{simInd,1}.head(:,2,:) + obj.simulationResults{simInd,1}.noise(:,3,:)];
+           else
+               h = [obj.simulationResults{simInd,1}.head(:,1:2), zeros(size(obj.simulationResults{simInd,1}.head(:,2),1),2)];
+           end
+
+           % Add contribution from kriging plus the variance
+           if ~isempty(krigingVariance);               
+               obj.simulationResults{simInd,1}.head = [obj.simulationResults{simInd,1}.head, kriging_contribution, krigingVariance];
+               obj.simulationResults{simInd,1}.colnames = {obj.simulationResults{simInd,1}.colnames{:}, 'Kriging Adjustment','Kriging Variance'};
+               h = obj.simulationResults{simInd,1}.head;
+           end           
+           
+           % Post-proess the stored simulation results to just percentiles
+           if size(obj.simulationResults{simInd,1}.head,3)>1                
+               head_tmp=obj.simulationResults{simInd,1}.head(:,1,1);
+                for i=2:size(obj.simulationResults{simInd,1}.head,2)
+                     head_tmp = [head_tmp, permute(prctile( obj.simulationResults{simInd,1}.head(:, i,:),[50 5 95],3),[1,3,2])];
+                end
+                obj.simulationResults{simInd,1}.head = head_tmp;
+                obj.simulationResults{simInd,1}.noise = [obj.simulationResults{simInd,1}.head(:,1,1), prctile( obj.simulationResults{simInd,1}.noise(:, 2,:),5,3), prctile( obj.simulationResults{simInd,1}.noise(:, 2,:),95,3)];
+           end
+           
+           % Add the original forcing back into the model.
+           if ~isempty(forcingData)
+               setForcingData(obj, forcingData_modConstruc, forcingData_colnames_modConstruc);
+           end
+           
+           % TO DO: shift code for temporal decomposition to model_TFN.m
+           % Calculate contribution from past climate
+           %---------------------
+%            try
+%                if doClimateLagCalcuations 
+%                    % Set the year past for which the focing contribution is to be
+%                    % derived.
+%                    obj.simulationResults.tor_min = [0; 1; 2; 5;  10;  20; 50; 75;  100];
+%                    obj.simulationResults.tor_max = [1; 2; 5; 10; 20; 50; 75;  100; inf];
+% 
+%                    % Limit the maximum climate lag to be <= the length of the
+%                    % climate record.
+%                    forcingData = getForcingData(obj);
+%                    filt = forcingData( : ,1) < ceil(time_points(end));
+%                    tor_max =  max(time_points(1)  - forcingData( filt ,1));
+%                    filt = obj.simulationResults.tor_max*365 < tor_max;
+%                    obj.simulationResults.tor_min = obj.simulationResults.tor_min(filt);
+%                    obj.simulationResults.tor_max = obj.simulationResults.tor_max(filt);
+% 
+%                    % Calc the head forcing for each period.
+%                    for ii=1: size(obj.simulationResults.tor_min,1)                    
+%                         head_lag_temp = solve(obj.model, time_points, ...
+%                             obj.simulationResults.tor_min(ii)*365.25, obj.simulationResults.tor_max(ii)*365.25 );
+% 
+%                         obj.simulationResults.head_lag(:,ii) = head_lag_temp(:,2);                    
+%                    end               
+% 
+%                    % Add time column.
+%                    obj.simulationResults.head_lag = [time_points, obj.simulationResults.head_lag];                              
+%                else
+%                    obj.simulationResults.tor_min = [];
+%                    obj.simulationResults.tor_max = [];
+%                    obj.simulationResults.head_lag = [];               
+%                end
+%            catch
+               obj.simulationResults{simInd,1}.tor_min = [];
+               obj.simulationResults{simInd,1}.tor_max = [];
+               obj.simulationResults{simInd,1}.head_lag = [];         
+           %end
+        end
+    
+        function solveModelPlotResults(obj, simulationLabel, axisHandle)        
+% Plot the simulation results
+%
+% Syntax:
+%   solveModelPlotResults(obj, simulationLabel)
+%   solveModelPlotResults(obj, simulationLabel, handle)
+%
+% Description:
+%   Creates a summary plot of the simulation results. The plot presents
+%   the fit with the observed data and, if the model types 
+%   provides an appropriate output, a decomposition of the head to 
+%   individual drivers and over various time lags.
+%
+% Input:
+%   obj -  model object
+%
+%   simulationLabel - string for the model simulation labelto plot.
+%
+%   handle - Matlab figure handle to a pre-existing figure window in which
+%   the plot is to be created (optional).
+%
+% Output:  
+%   (none)
+%
+% Example:
+%
+%   % Plot the simulation results for object 'model_124705':
+%   >> solveModelPlotResults(model_124705);
+%
+% See also:
+%   HydroSightModel: class_description;
+%   solveModel: model_simulations;
+%
+% Dependencies
+%   model_TFN.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   7 May 2012
+%
+            
+           % Create the figure. If varargin is empty then a new figure
+           % window is created. If varagin equals a figure handle, h, then
+           % the calibration results are plotted into 'h'.            
+           if nargin==2 || isempty(axisHandle)
+               % Create new figure window.
+               figHandle = figure('Name',['Soln. ',strrep(obj.bore_ID,'_',' ')]);
+           elseif ~iscell(axisHandle)    
+                error('Input handle is not a valid figure handle.');
+           else
+                h = axisHandle;                
+           end
+            
+           % Find simulation label
+           if isempty(simulationLabel)
+               error('The simulation label must be specified.');
+           end
+           simInd = cellfun( @(x) strcmp(x.simulationLabel , simulationLabel), obj.simulationResults);               
+
+            % Assess if the data is from a population of simlations (eg
+            % from DREAM calibration)
+            hasModelledDistn = false;
+            if size(obj.calibrationResults.data.modelledHead,2)>2   
+                hasModelledDistn = true;                
+            end
+            
+            hasNoiseComponant=false;
+            if ~isempty(obj.simulationResults{simInd,1}.noise)
+                hasNoiseComponant=true;
+            end           
+           
+           % Get number of model componant (ie forcing types).
+           if hasModelledDistn
+                nModelComponants = (size(obj.simulationResults{simInd,1}.head,2)-1)/3 - 1;    
+           else
+                nModelComponants = size(obj.simulationResults{simInd,1}.head,2)-2;
+           end
+
+           % Check if there is data for the climate lags
+           doClimateLagCalcuations = false;
+           if isfield(obj.simulationResults,'head_lag')
+               doClimateLagCalcuations = true;
+           end
+           
+           % Plot observed and modelled time series.
+           %-------
+           if ~isempty(axisHandle)
+              h = axisHandle{1}; 
+           elseif nModelComponants>0
+              h = subplot(2+nModelComponants+doClimateLagCalcuations,1,1:2, 'Parent',figHandle);  
+              h_legend = [];
+           else
+              h = gca();
+           end
+            
+            % Plot time series of heads.                        
+            %-------
+            % Plot bounds for noise component.
+            if hasNoiseComponant
+               XFill = [obj.simulationResults{simInd,1}.head(:,1)' fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
+               YFill = [[obj.simulationResults{simInd,1}.head(:,2) + obj.simulationResults{simInd,1}.noise(:,3)]', fliplr([obj.simulationResults{simInd,1}.head(:,2) - obj.simulationResults{simInd,1}.noise(:,2)]')];
+
+               fill(XFill, YFill,[0.8 0.8 0.8],'Parent',h);
+               clear XFill YFill               
+               hold(h,'on');
+            end
+
+            if hasModelledDistn
+
+               XFill = [obj.simulationResults{simInd,1}.head(:,1)' ...
+                        fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
+               YFill = [obj.simulationResults{simInd,1}.head(:,4)', ...
+                        fliplr(obj.simulationResults{simInd,1}.head(:,3)')];
+                    
+               fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
+               clear XFill YFill               
+               hold(h,'on');                    
+            end
+            
+           % Plot modelled deterministic componant.
+           plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,2),'-b' );
+           hold(h,'on');         
+           
+           % Plot observed head
+           head = getObservedHead(obj);
+           plot(h, head(:,1), head(:,2),'.-k' );
+           
+           % Calculate and set the date limits from the simulation period
+           dateLimits = [floor(min(obj.simulationResults{simInd,1}.head(:,1))), ceil(max(obj.simulationResults{simInd,1}.head(:,1)))];
+           dateRange = dateLimits(2) - dateLimits(1);
+           dateLimits(1) = floor(dateLimits(1) - (dateRange * 0.05));
+           dateLimits(2) = ceil(dateLimits(2) + (dateRange * 0.05));
+           
+           % Set axis labels,  title and x axis limits
+           datetick(h, 'x','yy');
+           ylabel(h, 'Head (m)');           
+           title(h, ['Bore ', strrep(obj.bore_ID,'_',' ') , ' - Simulated head']); 
+           xlim(h,dateLimits);
+           
+            % Create legend strings  
+            i=1;
+            if hasNoiseComponant
+                if hasModelledDistn
+                    legendstr{i}='Total Err. (5th-95th)';
+                else
+                    legendstr{i}='Total Err.';
+                end
+                i=i+1;
+            end
+            if hasModelledDistn
+                legendstr{i}='Param. Err. (5th-95th)';            
+                i=i+1;
+            end
+            if hasModelledDistn
+                legendstr{i}='Sim. (median)';
+            else
+                legendstr{i}='Sim.';
+            end            
+            i=i+1;                                
+            legendstr{i}='Observed';
+            i=i+1;            
+            legend(h,legendstr, 'Location','NorthWest' );
+           
+
+           hold(h,'off');
+           %-------
+            
+           % Plot contributions to head
+           %-------
+           if nModelComponants>0
+               for ii=1:nModelComponants
+                   if ~isempty(axisHandle)
+                       h = axisHandle{ii+1}; 
+                   else
+                        h = subplot(2+nModelComponants+doClimateLagCalcuations,1, 2+ii, 'Parent',figHandle );
+                   end
+                   if hasModelledDistn
+                       
+                       XFill = [obj.simulationResults{simInd,1}.head(:,1)' ...
+                                fliplr(obj.simulationResults{simInd,1}.head(:,1)')];
+                       YFill = [obj.simulationResults{simInd,1}.head(:,3*(ii+1)+1)', ...
+                                fliplr(obj.simulationResults{simInd,1}.head(:, 3*(ii+1))')];
+
+                       fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
+                       clear XFill YFill               
+                       hold(h,'on');                    
+                       plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,3*(ii+1)-1),'-b' );
+                       
+                   else
+                       plot(h, obj.simulationResults{simInd,1}.head(:,1), obj.simulationResults{simInd,1}.head(:,ii+2),'-b' );
+                   end
+                        
+                   
+                   % Set axis labels and title
+                   datetick(h, 'x','yy');                   
+                   ylabel(h, 'Head rise(m)');
+                   title(h, ['Head contribution from: ', strrep(obj.simulationResults{simInd,1}.colnames{ii+2},'_',' ') ]);
+                   xlim(h,dateLimits);                   
+                   
+               end
+           end       
+        end
+
+%% Interpolate and extrapolate observation data        
+        function head_estimates = interpolateData(obj, targetDates, maxKrigingObs, useModel, modelResults)
+% Interpolate and extrapolate observation data.
+%
+% Syntax:
+%   interpolateData(obj, targetDates, maxKrigingObs, useModel)
+%
+% Description:
+%   Interpolates or extrapolates observed heads of a calibrated model and
+%   provides a linear estimate of the prediction uncertainty to a user set
+%   probability.
+%
+%   Importantly, if interpolation is to a date very close to an observed
+%   value then the uncertainty should be less than that estimated by the 
+%   linear prediction error. To achieve this, this method implements
+%   univeral kriging on the simulation residuals to weight the linear prediction
+%   errror.
+%
+% Input:
+%   obj -  calibrated model object.
+%
+%   time_points - column vector of the time points to be interpolated 
+%   and or extrapolated.
+%
+%   maxKrigingObs - scalar integer for the maximum number of observation
+%   points to use in the kriging. The default is inf.
+%   
+%   useModel - logical scalar indicating if the kriging is to be
+%   undertaken using the model residuals or the observed data. The default is
+%   true.
+%
+% Output:
+%   head_estimates - matrix of simulated head formatted as the following columns:
+%   date/time; simulated head; uncertainity estimate.
+%
+% Example:
+%   Define the start date for simulation:
+%   >> start_date = datenum(2000, 1, 1);
+%
+%   Define the end date for simulation:
+%   >> end_date = datenum(2010, 1, 1);
+%
+%   Define the time points for simulation as from start_date to end_date at 
+%   steps of 365 days:
+%   >> time_points = [start_date : 365: end_date]';
+%
+%   Interpolate the model object 'model_124705' to time_points with the 
+%   error estimate defined for a 95% prediction interval:
+%   >> head_estimates = interpolateData(model_124705, time_points, 20, true)
+%
+% See also:
+%   HydroSightModel: class_description;
+%   solveModel: solve_the_model;
+%   calibrateModel: model_calibration;
+%
+% Dependencies
+%   model_TFN.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+           
+            % Check format of target dates
+            if ~isnumeric(targetDates)
+               error('The taget date(s) for interpolation must be an array with the following columns: year, month, day, hour (optional), minute (options)');
+            end
+            if size(targetDates,2) ~= 1 && size(targetDates,2) ~= 3 && size(targetDates,2) ~= 6
+                error('The taget date(s) must be either a matlab date number or have the following columns: year, month, day, hour (optional), minute (options)');
+            end
+            
+            % Assign defaults for 3rd and 4th inputs. 
+            if nargin==2
+                maxKrigingObs = inf;
+                useModel = true;
+            end
+                
+            % Check if any target dates are beyond the end of the climate
+            % record.
+            forcingData = getForcingData(obj);
+            if useModel && ~isempty(forcingData) && (max(forcingData(:,1)) < max(targetDates))
+                error('The maximum date for interpolation is greater than the maximum date of the input forcing data');
+            end
+            clear forcingData
+            
+            % Convert target dates
+            targetDates = sort(datenum(targetDates), 'ascend');                                    
+            
+            % Call model at target date and remove the first column
+            % containing the date (it is added back later)
+            if useModel   
+                if nargin==4 || isempty(modelResults) 
+                    head_estimates= solveModel(obj, targetDates, [], '', false);
+                    krigingData = obj.calibrationResults.data.modelledHead_residuals;
+                    
+                    range = obj.calibrationResults.performance.variogram_residual.range;
+                    sill = obj.calibrationResults.performance.variogram_residual.sill;
+                    nugget = obj.calibrationResults.performance.variogram_residual.nugget;                
+
+                else 
+                    head_estimates = modelResults.head_estimates;
+                    krigingData = modelResults.krigingData;
+                    range = modelResults.range;
+                    sill = modelResults.sill;
+                    nugget = modelResults.nugget;
+                    clear modelResults;
+                end
+                %head_estimates = head_estimates(:,2:4);
+                %krigingData = obj.calibrationResults.data.modelledHead_residuals;
+
+            else
+                head_estimates = zeros(length(targetDates),5);
+                krigingData = getObservedHead(obj.model);
+                
+                % Set variogram fitting calibration options.
+                variogramOptions = optimset('fminsearch');
+                variogramOptions.MaxIter = 1200;
+                variogramOptions.MaxFunEvals = 1000000;
+                
+                % Derive model variogram from the observed data.
+                expVariogram = variogram([krigingData(:,1), zeros( size(krigingData(:,1))) ] ...
+                , krigingData(:,2) , 'maxdist', min(365*10, krigingData(end,1) - krigingData(1,1) ), 'nrbins', 10);
+            
+                [range, sill, nugget, variogram_model] ...
+                    = variogramfit(expVariogram.distance, expVariogram.val, 365/4, 0.75.*var( krigingData(:,2)), expVariogram.num, variogramOptions,...
+                    'model', 'exponential', 'nugget', 0.25.*var( krigingData(:,2)) ,'plotit',false );          
+                
+            end
+            
+            % Convert kriging data to double (if saved as single)
+            if isa(krigingData,'single')
+                krigingData = double(krigingData);
+            end
+            
+            % Undertake universal kriging. 
+            % If 'useModel'==true then the krigin is undertaken on the
+            % simulation residuals. Else, it is undertaken using entire 
+            % record of the the observed head.
+            % Adapted from :
+            % Martin H. Trauth, Robin Gebbers, Norbert Marwan, MATLAB
+            % recipes for earth sciences.
+            %----------------------------
+            
+            % Calculate distance (1-D in units of days) between all obs.
+            dist_allObs = ipdm( krigingData(:,1));
+                        
+            warning off;
+            for ii=1: length(targetDates)
+
+                % Get pre-calc distance (1-D in units of days) between the closest
+                % 'maxObs'
+                dist_to_target =  krigingData(:,1) - targetDates(ii);
+
+                % Find the closest observations. This is somewhat convoluted. The
+                % closest 1/4 of obs on either side of the target date are
+                % first found. The remaining 1/2 of the max obs are then
+                % selected by finding the remaining obs and finding the
+                % closest. This was required to ensure the kriging
+                % trends are smooth when there the data is very
+                % irregularly sampled. The approch is very similar to the
+                % max-octant search of spatial kriging.
+                indNegDuration = find(dist_to_target<0, maxKrigingObs, 'last');
+                indPosDuration = find(dist_to_target>=0, maxKrigingObs, 'first');
+                indNegClosestQuaterObObs = 1:length(indNegDuration)>length(indNegDuration) - ceil(maxKrigingObs/4);
+                indPosClosestQuaterObObs = 1:length(indPosDuration)<=ceil(maxKrigingObs/4);
+                ind_closestHalf = [indNegDuration(indNegClosestQuaterObObs) ; indPosDuration(indPosClosestQuaterObObs)]; ...
+                dist_furthestHalf = [dist_to_target(indNegDuration(~indNegClosestQuaterObObs)); ...
+                                     dist_to_target(indPosDuration(~indPosClosestQuaterObObs))];
+                ind_furthestHalf = [indNegDuration(~indNegClosestQuaterObObs); ...
+                                    indPosDuration(~indPosClosestQuaterObObs)];                                 
+                [~,ind_furthestHalf_sorted] = sort(abs(dist_furthestHalf));
+                ind_furthestHalf = ind_furthestHalf(ind_furthestHalf_sorted);
+                ind = [ind_closestHalf; ind_furthestHalf];                        
+                ind = ind(1: min(length(ind), maxKrigingObs));    
+                
+                % Get the distance to the selected obs.
+                dist = dist_allObs(ind, ind);
+                dist_to_target = dist_to_target(ind);
+                nobs = length(ind);
+                
+                % Create LHS matrix with fixes 0/1
+                G_mod = zeros(nobs+2, nobs+2); 
+                G_mod(: , nobs+1) = 1;
+                G_mod(nobs+1, :) = 1;
+                %G_mod(nobs+1:end, nobs+1:end) = 0;
+                G_target = zeros(nobs+2,1);
+                                
+                % Calculate kriging matrix for obs data from avriogram, then
+                % expand g_mod matrix for kriging and finally invert.               
+                %G_mod(1:nobs,1:nobs) = nugget + sill*(1-exp(-abs(dist)./range));
+                %G_mod( logical(eye(nobs+2)) ) = 0; 
+                G_mod(1:nobs,1:nobs) = sill*exp(-3.*abs(dist)./range);
+                G_mod( logical(eye(nobs+2)) ) = nugget + sill; 
+                G_mod(nobs+1:end, nobs+1:end) = 0;
+                G_mod(nobs+2 , 1:nobs) = krigingData(ind,1)-targetDates(ii);
+                G_mod(1:nobs,nobs+2) = krigingData(ind,1)-targetDates(ii);
+                
+                % Calculate the distance from the cloest maxObs to the
+                % target obs.                
+                %G_target(1:nobs) =nugget + sill*(1-exp(-abs(dist_to_target)./range));
+                G_target(1:nobs) =sill*exp(-3.*abs(dist_to_target)./range);
+                G_target(dist_to_target==0) = nugget + sill;
+                G_target(nobs+1) = 1;
+                G_target(nobs+2) = 0;
+                kriging_weights = G_mod \ G_target;
+                
+                % Estimate residual at target date
+                head_estimates(ii,5) = sum( kriging_weights(1:nobs,1) .* krigingData(ind,2));                    
+                
+                % Estimate kriging variance of the residual at target date.
+                head_estimates(ii,6) = nugget + sill - sum( kriging_weights(1:nobs,1) .* G_target(1:nobs,1)) - sum(kriging_weights(nobs+1:end,1).*G_target(nobs+1:end,1));
+            end
+            warning on;                   
+            %----------------------------
+
+           % Adjust head estimate by kriging residual (ie the bias in the
+           % estimate).
+           head_estimates(:,5) = head_estimates(:,2) + head_estimates(:,5);
+            
+           % Normalise kriging weights to between zero and one.
+           head_estimates(:,6) = head_estimates(:,6)./ (sill + nugget);
+           
+           % Estimate prediction error with weighting by normalised kriging
+           % variance.
+           if useModel
+                head_estimates(:,6) = head_estimates(:,6) .* 0.5.*(head_estimates(:,4) - head_estimates(:,3));
+           end
+
+           % Remove the columns of working data and return prediction and
+           % the error estimate.
+           head_estimates = [targetDates, head_estimates(:,5), head_estimates(:,6)];
+            
+        end
+        
+%% Calibrate the model        
+        function calibSchemeSettings = calibrateModel(obj, diaryObj, t_start, t_end, calibrationSchemeName, SchemeSetting , params_upperBound, params_lowerBound)
+
+% Calibrate the model
+%
+% Syntax:
+%   calibrateModel(obj, t_start, t_end, calibrationSchemeName, SchemeSetting)
+%   calibrateModel(obj, t_start, t_end, calibrationSchemeName, SchemeSetting, params_upperBound, params_lowerBound)
+%
+% Description:
+%   Global calibration of the time-series model and derivation of the following: 
+%       1) simulation of the groundwater heads; 
+%       2) evalution of the calibration to the remaining observation data;
+%       3) calculation of various performance statistics and and a variogram
+%          of the residuals.
+% 
+%   Importantly, the time-series models can be very challenging to
+%   calibrate to the global optima, particularly for the nonlinear TFN models.
+%   To assist in identifying the global optima, a range of calibration schemes
+%   are available (see below for details of the schemes). This allows
+%   the reliability of a calibration result to be assessed by re-running
+%   the calibration with a different scheme and comparing the results. If
+%   each scheme gives near identical results then this supports the
+%   conclusion that the gloabl optima has been idenified. Additionally, the 
+%   reliability of the calibration solution can be assessed by re-running
+%   the same calibration scheme multiple times, ideally with increasingly
+%   stringent settings. If the very best solution is repeatedly obtained
+%   then it is good evidence that the global optima has been located.
+%   
+%   1.  Covariance Matrix Adaptation Evolution Strategy (CMA-ES) (Hansen, 2006)
+%   from https://www.lri.fr/~hansen/cmaes_inmatlab.html#matlab. Note, the 
+%   code was modified to account for complex parameter
+%   boundaries and efficient sampling of parameter sets within the
+%   boundaries.
+%
+%   2. Shuffled complex evolution with principal components analysis–University 
+%   of California at Irvine (SP-UCI) method is a global optimization algorithm 
+%   designed for high-dimensional and complex problems. It is based on the 
+%   Shuffled Complex Evolution (SCE-UA) Method (Qingyun Duan et al.), but solves
+%   a serious problem in searching over high-dimensional spaces - population
+%   degeneration. The population degeneration problem refers to the phenomenon
+%   that, when searching over the high-dimensional parameter spaces, the 
+%   population of the searching particles is very likely to collapse into a
+%   subspace of the parameter space, therefore losing the capability of 
+%   exploring the entire parameter space. In addition, the SP-UCI method also
+%   combines the strength of shuffled complex, the Nelder-Mead simplex, and
+%   mutinormal resampling to achieve efficient and effective high-dimensional 
+%   optimization. (Code and above description from http://www.mathworks.com
+%   /matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method)
+%   Note, the SP-CUI code has been edited by Tim Peterson to allow the inclusion of
+%   parameter constraints and parrallel calculation of each complex. For
+%   details of the algorithm see Chu et al. (2010).
+%
+% Input:
+%   obj -  model object
+%
+%   t_start - scalar start time, eg datenum(1995,1,1);
+%
+%   t_end - scalar end time, eg datenum(2005,1,1);
+%
+%   calibrationSchemeName - string for the name of the calibration scheme to 
+%   use. The options are 'SP-UCI' and 'CMA-ES'.
+%
+%   SchemeSetting - scalar rational integer defining the rigor of the input
+%   calibration scheme. For 'SP-UCI' it defines the number of complexes per
+%   model parameter and must be >=1. For 'CAM-ES' it defines the number of
+%   re-runs undertaken with each re-run having double the population size as
+%   the previous run and must be >=0;
+%
+%   params_upperBound - column vector of the upper bound to the parameters.
+%   Care must be taken to ensure the parameter bound is of the same
+%   dimesnions as the parameter vector passed to the optimisation method
+%   and in the same order. If this vector is not input, a defualt upper
+%   bound will be assigned. This default bound is a scalar multiplier of 
+%   each parameter value. 
+%
+%   params_lowerBound - column vector of the lower bound to the parameters.
+%   See additional notes for 'params_upperBound'. 
+%
+% Output:  
+%   (none, the calibration and simulation results are output to 
+%   obj.calibrationResults, obj.evaluationResults and obj.simulationResults
+%   respectively)
+%
+% Example:
+%   Define the start date for simulation:
+%   >> start_date = datenum(1995, 1, 1);
+%
+%   Define the end date for simulation:
+%   >> end_date = datenum(2002, 1, 1);
+%
+%   Calibrate the model object 'model_124705' with 20 iterations and
+%   between 2 and 10 clusters:
+%   >> calibrateModel(model_124705, start_date, end_date, 'SPUCI', 2);
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModelPlotResults: plot_model_calibration_results;
+%   SPUCI: SP-UCI_calibration_algorithm;
+%   cmaes: CMA-ES_calibration_algorithm;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%   SPUCI.m
+%   cmaes.m
+%   variogram.m
+%   variogramfit.m
+%   lsqcurvefit.m
+%   IPDM.m
+%
+% References:
+%   Chu, W., X. Gao, and S. Sorooshian (2010), Improving the shuffled complex 
+%   evolution scheme for optimization of complex nonlinear hydrological systems:
+%   Application to the calibration of the Sacramento soil-moisture accounting 
+%   model, Water Resour. Res., 46, W09530, doi:10.1029/2010WR009224.
+%
+%   Hansen (2006). The CMA Evolution Strategy: A Comparing Review. In 
+%   J.A. Lozano, P. Larrañaga, I. Inza and E. Bengoetxea (Eds.). Towards a 
+%   new evolutionary computation. Advances in estimation of distribution 
+%   algorithms. Springer, pp. 75-102.
+%
+%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
+%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   7 May 2012
+%
+
+            % reset matlab random number generator
+            rng('default');
+
+            % Set up the calibration options including the random seed.
+            calibrationSchemeName = upper(calibrationSchemeName);
+            switch calibrationSchemeName
+                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
+                    params = getParameters(obj.model);  
+                    nparams = size(params,1); 
+                    MaxFunEvals = Inf;
+                    PopSize = (4 + floor(3*log(nparams)));  
+                    TolX = 1e-11;
+                    TolFun = 1e-12;                    
+                    Restarts = 4;
+                    insigmaFrac = 1/3;
+                    Seed = floor(mod(datenum(now),1)*1000000);                   
+                    
+                     if isnumeric(SchemeSetting) 
+                        if SchemeSetting<0 || floor(SchemeSetting)~=ceil(SchemeSetting) 
+                            error('The CMAES calibration scheme requires, at a minimum, an input scalar integer >=0 for the number of restart iterations.');
+                        end
+                        Restarts = SchemeSetting;
+                     elseif isstruct(SchemeSetting)
+                        if isfield(SchemeSetting,'MaxFunEvals') && isfinite(SchemeSetting.MaxFunEvals)
+                            MaxFunEvals = SchemeSetting.MaxFunEvals;
+                        end
+                        if isfield(SchemeSetting,'PopSize') && isfinite(SchemeSetting.PopSize)
+                            PopSize = SchemeSetting.PopSize;
+                        end                        
+                        if isfield(SchemeSetting,'TolX') && isfinite(SchemeSetting.TolX)
+                            TolX = SchemeSetting.TolX;
+                        end                        
+                        if isfield(SchemeSetting,'tolFun') && isfinite(SchemeSetting.tolFun)
+                            TolFun = SchemeSetting.TolFun;
+                        end
+                        if isfield(SchemeSetting,'restarts') && isfinite(SchemeSetting.restarts)
+                            Restarts = SchemeSetting.Restarts;
+                        end                         
+                        if isfield(SchemeSetting,'insigmaFrac') && isfinite(SchemeSetting.insigmaFrac)
+                            insigmaFrac = SchemeSetting.insigmaFrac;
+                        end                         
+                        if isfield(SchemeSetting,'Seed') && isfinite(SchemeSetting.Seed)
+                            Seed = SchemeSetting.Seed;
+                        end                         
+                        
+                     else
+                         error('The input for the CMAES calibration scheme must be an input scalar integer >=0 or a structure variable.');    
+                     end
+                     
+                     % Build the complete calibSchemeSettings structural
+                     % variable.
+                     calibSchemeSettings.MaxFunEvals = MaxFunEvals;
+                     calibSchemeSettings.PopSize = PopSize;
+                     calibSchemeSettings.TolX = TolX;
+                     calibSchemeSettings.TolFun = TolFun;
+                     calibSchemeSettings.Restarts = Restarts;
+                     calibSchemeSettings.insigmaFrac = insigmaFrac;
+                     calibSchemeSettings.Seed = Seed;
+                     
+                     % Set the random seed
+                     rng(calibSchemeSettings.Seed);
+                     
+                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}                    
+                    
+                    params = getParameters(obj.model);  
+                    nparams = size(params,1);                                         
+                    
+                    % Set default options
+                    maxn = inf;
+                    kstop = 10;    
+                    pcento = 1e-6;    
+                    peps = 1e-6;
+                    ngs = 2 * nparams;
+                    iseed = floor(mod(datenum(now),1)*1000000);                    
+                    
+                    if isnumeric(SchemeSetting) 
+                        if SchemeSetting<1 || floor(SchemeSetting)~=ceil(SchemeSetting) 
+                            error('The SP-UCI calibration scheme requires, at a minimum, an input scalar integer >=1 for the number of complexes.');
+                        end
+                        ngs = SchemeSetting;
+                        
+                    elseif isstruct(SchemeSetting)
+                        if isfield(SchemeSetting,'maxn') && isfinite(SchemeSetting.maxn)
+                            maxn = SchemeSetting.maxn;
+                        end
+                        if isfield(SchemeSetting,'kstop') && isfinite(SchemeSetting.kstop)
+                            kstop = SchemeSetting.kstop;
+                        end
+                        if isfield(SchemeSetting,'pcento') && isfinite(SchemeSetting.pcento)
+                            pcento = SchemeSetting.pcento;
+                        end
+                        if isfield(SchemeSetting,'peps') && isfinite(SchemeSetting.peps)
+                            peps = SchemeSetting.peps;
+                        end
+                        if isfield(SchemeSetting,'ngs') && isfinite(SchemeSetting.ngs)
+                            ngs = SchemeSetting.ngs;
+                        end
+                        if isfield(SchemeSetting,'iseed') && isfinite(SchemeSetting.iseed)
+                            iseed = SchemeSetting.iseed;
+                        end                 
+                    else
+                        error('The input for the SP-UCI calibration scheme must be an input scalar integer >=1 or a structure variable.');    
+                    end                    
+                    
+                    % Build the complete calibSchemeSettings structural
+                    % variable.
+                    calibSchemeSettings.maxn = maxn;
+                    calibSchemeSettings.kstop = kstop;
+                    calibSchemeSettings.pcento = pcento;
+                    calibSchemeSettings.peps = peps;
+                    calibSchemeSettings.ngs = ngs;
+                    calibSchemeSettings.iseed = iseed;
+                                    
+                     % Set the random seed
+                     rng(calibSchemeSettings.iseed);                    
+                    
+                case 'DREAM'
+                    % Set default options
+                    N_per_param = 1;
+                    T = 5;
+                    nCR = 3;    
+                    delta = 3;
+                    lambda = 0.05;
+                    zeta = 0.05;
+                    outlier = 'iqr';
+                    pJumpRate_one = 0.2;
+                    
+                    if isnumeric(SchemeSetting) 
+                        if SchemeSetting<1 || floor(SchemeSetting)~=ceil(SchemeSetting) 
+                            error('The DREAM calibration scheme requires, at a minimum, an input scalar integer >=1 for the number 10,000s of model generations per Markov chain.');
+                        end
+                        N_per_param = SchemeSetting;
+                        
+                    elseif isstruct(SchemeSetting)
+                        if isfield(SchemeSetting,'N_per_param') && isfinite(SchemeSetting.N_per_param)
+                            N_per_param = SchemeSetting.N_per_param;
+                        end
+                        if isfield(SchemeSetting,'T') && isfinite(SchemeSetting.T)
+                            T = SchemeSetting.T;
+                        end                        
+                        if isfield(SchemeSetting,'nCR') && isfinite(SchemeSetting.nCR)
+                            nCR = SchemeSetting.nCR;
+                        end
+                        if isfield(SchemeSetting,'delta') && isfinite(SchemeSetting.delta)
+                            delta = SchemeSetting.delta;
+                        end
+                        if isfield(SchemeSetting,'lambda') && isfinite(SchemeSetting.lambda)
+                            lambda = SchemeSetting.lambda; 
+                        end
+                        if isfield(SchemeSetting,'zeta') && isfinite(SchemeSetting.zeta)
+                            zeta = SchemeSetting.zeta;
+                        end
+                        if isfield(SchemeSetting,'outlier') && isfinite(SchemeSetting.outlier)
+                            outlier = SchemeSetting.outlier;
+                        end
+                        if isfield(SchemeSetting,'pJumpRate_one') && isfinite(SchemeSetting.pJumpRate_one)
+                            pJumpRate_one = SchemeSetting.pJumpRate_one;
+                        end                        
+                    end
+                    
+                    % Build the complete calibSchemeSettings structural
+                    % variable.
+                    calibSchemeSettings.N_per_param = N_per_param;
+                    calibSchemeSettings.T = T;
+                    calibSchemeSettings.nCR = nCR;
+                    calibSchemeSettings.delta = delta;
+                    calibSchemeSettings.lambda = lambda;
+                    calibSchemeSettings.zeta = zeta;
+                    calibSchemeSettings.outlier = outlier;
+                    calibSchemeSettings.pJumpRate_one = pJumpRate_one;
+                                                                              
+                otherwise
+                    error('The requested calibration scheme is unknown.');
+            end
+            
+            
+            % Check if the model uses stochastic derived forcing. If so,
+            % then check SP-UCI is used            
+            hasStochDerivedForcing = false;
+            if ~isempty(getStochForcingData(obj))
+                hasStochDerivedForcing=true;
+            end
+            if hasStochDerivedForcing && ~any(strcmp({'SP UCI','SP_UCI','SPUCI','SP-UCI'}, calibrationSchemeName))
+                error('SP-UCI must be used for stochastic derived forcing models (e.g. pumpingRate_SAestimation()).');
+            end
+                
+            
+            % Set general constants.
+            obj.calibrationResults = [];
+            obj.evaluationResults = [];            
+
+            % Add flag to denote the calibration is not complete.
+            obj.calibrationResults.isCalibrated = false;
+            
+            % Check the number of inputs.            
+            if nargin < 6
+                error('Calibration of the model requires input of at least the following: model object, start date and end date, calibration scheme name, calibration setting');
+            end                
+            
+            % Check the input options
+            if max(size(t_start))~=1 || max(size(t_end))~=1 || t_start >= t_end
+                error('The input start date must be less than the input end data and both must be scalar integers!');
+            end
+            
+            % Initialsise model calibration and evaluation outputs.
+            t_filt = obj.model.inputData.head(:,1) >=t_start  & obj.model.inputData.head(:,1) <= t_end;  
+            time_points = obj.model.inputData.head(t_filt,1);
+            obj.calibrationResults.time_start =  t_start;
+            obj.calibrationResults.time_end =  t_end;
+            obj.calibrationResults.data.obsHead =  obj.model.inputData.head(t_filt , :);
+            
+            % If evaluation is to be undertakes, check atleast 2 obs remain
+            % for the evaluation.
+            if size(obj.model.inputData.head(~t_filt,1),1)>0 && size(obj.model.inputData.head(~t_filt,1),1)<2
+                error(['The input calibration dates are such that observations remain for model evaluation.', char(13) ...
+                    'However, model evaluation requires >=2 observations!', char(13), ...
+                    'Please modify the dates to either:', char(13), ...
+                    '  (i) extent the calibration dates to eliminate any observation data for evaluation; or', char(13), ...
+                    '  (ii) contact the calibration dates to increase the observation data for evaluation.']);
+            end                            
+            
+            if obj.model.inputData.head(~t_filt,1)>0
+                % Add data to evaluation structure
+                obj.evaluationResults.time_lessThan =  t_start;
+                obj.evaluationResults.time_greaterThan =  t_end;
+                obj.evaluationResults.data.obsHead =  obj.model.inputData.head(~t_filt , :);
+            elseif isfield(obj,'evaluationResults')
+                obj = rmfield(obj,'evaluationResults');                
+            end
+            
+            % Initialsie model for calibration and check params
+            [params, time_points] = calibration_initialise(obj.model, t_start, t_end);       
+            params =  median(params,2);
+            if any(isnan(params))
+                error('At least one model parameter value equals NaN. Please input a correct value to the model');
+            end
+            nparams = size(params,1); 
+                            
+            % Construct and output parameter boundaries.
+            %------------
+            if nargin ==8 ...
+            && (size(params_upperBound,1) ~= size(params,1) || size(params_lowerBound,1) ~= size(params,1))
+                error(['The column vectors of parameter bounds "params_upperBound" and "params_lowerBound" ', char(13), ...
+                      'must be of the same size as the parameter vector, that is 1 column and ', num2str(nparams), ' rows.']);            
+            end            
+            [params_upperPhysBound, params_lowerPhysBound] = getParameters_physicalLimit(obj.model);
+            if any(isnan(params_upperPhysBound)) || any(isnan(params_lowerPhysBound)) || ...
+               any(~isreal(params_upperPhysBound)) || any(~isreal(params_lowerPhysBound))
+                error('The physical parameter boundaries must be a real number between (and including) -inf and inf.')
+            end                      
+            
+            if nargin ==6
+
+                [params_upperBound, params_lowerBound] = getParameters_plausibleLimit(obj.model);
+                if any(isnan(params_upperBound)) || any(isnan(params_upperBound)) || ...
+                any(~isreal(params_lowerBound)) || any(~isreal(params_lowerBound))
+                    error('The plausible parameter boundaries must be a real number between (and including) -inf and inf.')
+                end                
+            else
+                params_upperBound = min(params_upperBound,  params_upperPhysBound);
+                params_lowerBound = max(params_lowerBound,  params_lowerPhysBound);
+            end         
+            
+            % Check the plausible boundaries are consistant with the
+            % physical boundaries.
+            if any( params_upperBound > params_upperPhysBound) 
+                params_upperBound = min(params_upperBound, params_upperPhysBound);    
+                display('WARNING: At least one upper parameter boundary exceeds the upper lower physical boundary.');
+                display('         They have been shifted to equal the upper physical boundary.');                
+                display( char(13) );            
+            end
+            if any( params_lowerBound < params_lowerPhysBound)               
+                params_lowerBound = max(params_lowerBound, params_lowerPhysBound);                
+                display('WARNING: At least one lower parameter boundary exceeds the lower physical boundary.');
+                display('         They have been shifted to equal the lower physical boundary.');
+                display( char(13) );            
+            end             
+            
+            % Check the upper bound is greater than the lower parameter
+            % bound. 
+            if any(params_lowerBound >= params_upperBound - sqrt(eps()) )
+                disp(sprintf('          %s \t %s \t  %s \t %s \t \t %s \t \t %s', 'Model','Param.','Lower', 'Upper'));
+                disp(sprintf('          %s \t %s \t \t %s \t %s \t %s \t %s', 'Componant','Name'));            
+                for ii=1:nparams
+                    if length(obj.model.variables.param_names{ii,1}) < 5
+                        params_str = sprintf('          %s \t \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii) );
+                    else
+                        params_str = sprintf('          %s \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii) );
+                    end
+                    disp([params_str]);    
+                end 
+
+                error('The parameter lower bounds must be less than the parameter upper bounds and the difference must be greater than sqrt(eps()).');
+            end    
+            
+            % Check the initial parameter values are within the upper and 
+            % lower parameter boundaries. If not adjust the violating
+            % parameter to the closest boundary.
+            if any(params < params_lowerBound)
+                display(  'WARNING: Some of the initial parameter values have been shifted to the lower');
+                display(  '         boundary because they violoate the lower parameter boundary.');
+                display( char(13) );
+                params = max(params, params_lowerBound);
+            elseif any(params > params_upperBound)
+                display(  'WARNING: Some of the initial parameter values have been shifted to the upper');
+                display(  '         boundary because they violoate the upper parameter boundary.');
+                display( char(13) );
+                params = min(params, params_upperBound);                
+            end            
+            %------------
+            
+            % Output user set options.
+            display( char(13) );           
+            display('Global calibration scheme is to be undertaken using the following settings');
+             switch calibrationSchemeName
+                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}                    
+                    display( '      - Calibration scheme: Covariance Matrix Adaptation Evolution Strategy (CMA-ES)');
+                    display(['      - Number of initial CMA-ES parameter sets  = ',num2str(PopSize)]);                      
+                    display(['      - Maximum number of model evaluations (maxFunEvals) = ',num2str(MaxFunEvals)]);
+                    display(['      - Absolute change in the objective function for convergency (tolFun) = ',num2str(TolFun)]);
+                    display(['      - Largest absolute change in the parameters for convergency (tolX) = ',num2str(TolX)]);
+                    display(['      - Number CMA-ES calibration restarts (Restarts) = ',num2str(Restarts)]);
+                    display(['      - Standard deviation for the initial parameter sampling, as fraction of plausible parameter bounds (insigmaFrac) = ',num2str(insigmaFrac)]);
+                    display(['      - Random seed number (only for repetetive testing purposes) = ',num2str(Seed)]);
+                                        
+                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}
+                    display( '      - Calibration scheme: Shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI)');                    
+                    display(['      - Max. number of model evaluations (maxn)= ',num2str(maxn)]);  
+                    display(['      - No. of evolution loops meeting convergence criteria (kstop) = ',num2str(kstop)]);  
+                    display(['      - % change in the objective function allowed in kstop loops before convergence (pcento)= ',num2str(pcento)]);  
+                    display(['      - Normalized geometric range of the parameters before convergence (peps)= ',num2str(peps)]);  
+                    display(['      - No. of complexes = ',num2str(ngs)]);  
+                    display(['      - Random seed = ',num2str(iseed)]);  
+                    
+                case 'DREAM'
+                    display( '      - Calibration scheme: DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)');                    
+                    display(['      - Number of generations per chain (T) = ',num2str(T*10000)]);                      
+                    display(['      - Number of Markov chains per model parameter (N_per_param) = ',num2str(N_per_param)]);  
+                    display(['      - Number of crossover values (nCR) = ',num2str(nCR)]);  
+                    display(['      - Number chain pairs for proposal (delta) = ',num2str(delta)]);  
+                    display(['      - Random error for ergodicity (lambda) = ',num2str(lambda)]);  
+                    display(['      - Randomization (zeta) = ',num2str(zeta)]);  
+                    display(['      - Test function name for detecting outlier chains (outlier) = ',outlier]);  
+                    display(['      - Probability of jumprate of 1 (pJumpRate_one) = ',num2str(pJumpRate_one)]);  
+                                        
+             end             
+            display( '      - Summary of parameters for calibration and their bounds: ');
+            display( '        Param. componant and name, lower and upper boundary value: ');
+            disp(sprintf('          %s \t %s \t  %s \t %s \t \t %s \t \t %s', 'Model','Param.','Lower', 'Upper', 'Lower', 'Upper'));
+            disp(sprintf('          %s \t %s \t \t %s \t %s \t %s \t %s', 'Componant','Name','(Plausible)', '(Plausible)','(Physical)', '(Physical)'));            
+            for ii=1:nparams
+                if length(obj.model.variables.param_names{ii,1}) < 5
+                    params_str = sprintf('          %s \t \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii), params_lowerPhysBound(ii), params_upperPhysBound(ii) );
+                else
+                    params_str = sprintf('          %s \t %s \t \t %8.4g \t %8.4g \t %8.4g \t %8.4g', obj.model.variables.param_names{ii,1}, obj.model.variables.param_names{ii,2}, params_lowerBound(ii), params_upperBound(ii), params_lowerPhysBound(ii), params_upperPhysBound(ii) );
+                end
+                disp([params_str]);    
+            end 
+            
+            % Update the diary file
+            if ~isempty(diaryObj)
+                updatetextboxFromDiary(diaryObj);
+            end
+            
+            %--------------------------------------------------------------
+            % Do SCE calibration using the objective function SSE.m
+            log_L=[];
+            switch upper(calibrationSchemeName)
+                case {'CMA ES','CMA_ES','CMAES','CMA-ES'}
+                    
+                    calibSchemeSettings.LBounds = params_lowerPhysBound;
+                    calibSchemeSettings.UBounds = params_upperPhysBound;
+                    calibSchemeSettings.LogFilenamePrefix = ['CMAES_',obj.bore_ID];
+                    calibSchemeSettings.LogPlot = 'off';
+                    calibSchemeSettings.CMA.active = 1;
+                    
+                    calibSchemeSettings.EvalParallel = 'yes';     % Undertake parrallel function evaluation
+                    calibSchemeSettings.SaveVariables = 'off';    % Do not save .mat file of results.
+                    
+                    useLikelihood = false;
+                    
+                    % Define bounds and initial standard dev of params
+                    insigma = insigmaFrac*(params_upperBound - params_lowerBound);
+                    params_start = params_lowerBound + 1/2.*(params_upperBound - params_lowerBound);
+                    params_start = [mat2str(params_start) '+ insigma .* (2 * rand(',num2str(nparams),',1) -1)'];                    
+                    
+                    % Do calibration
+                    doParamTranspose = false;
+                    [params_finalEvol, fmin_finalEvol, numFunctionEvals, exitflag, evolutions, params_bestever] ...            
+                     = cmaes( 'calibrationObjectiveFunction', params_start, insigma, calibSchemeSettings, diaryObj, obj, time_points, doParamTranspose, useLikelihood );
+
+                    % Assign best every solution to params variable
+                    params = params_bestever.x;
+                    fmin = params_bestever.f;          
+                    
+                    % Extract exit flag message.
+                    if iscell(exitflag)
+                        exitflag = exitflag{1};
+                    end                    
+                    
+                    % Store exit status
+                    if any( strcmp(exitflag, 'maxfunevals'))
+                        exitFlag = 1;
+                        existStatus = 'Insufficient maximum number of model evaluations for convergence.';
+                    elseif any( strcmp(exitflag, 'maxiter'))
+                        exitFlag = 1;
+                        existStatus = 'Insufficient maximum number of iterations for convergence.';
+                    elseif any( strcmp(exitflag, 'maxiter'))
+                        exitFlag = 1;
+                        existStatus = 'Insufficient maximum number of iterations for convergence.';                    
+                    elseif any( strcmp(exitflag, 'tolx')) && ~any( strcmp(exitflag, 'tolfun'))
+                        exitFlag=1;
+                        exitStatus = ['Only parameter convergence (not obj. function convergence) achieved in ', num2str(numFunctionEvals),' function evaluations.'];
+                    elseif ~any( strcmp(exitflag, 'tolx')) && any( strcmp(exitflag, 'tolfun'))
+                        exitFlag=1;
+                        exitStatus = ['Only objective function convergence achieved (not param. convergence) in ', num2str(numFunctionEvals),' function evaluations.'];
+                    elseif  any( strcmp(exitflag, 'maxfunevals')) || ...
+                    any( strcmp(exitflag, 'maxiter')) || ...    
+                    any( strcmp(exitflag, 'stoptoresume')) || ...    
+                    any( strcmp(exitflag, 'manual')) || ...    
+                    any( strcmp(exitflag, 'stagnation')) || ...    
+                    any( strcmp(exitflag, 'warnconditioncov')) || ...    
+                    any( strcmp(exitflag, 'warnnoeffectcoord')) || ...    
+                    any( strcmp(exitflag, 'warnnoeffectaxis')) || ...    
+                    any( strcmp(exitflag, 'warnequalfunvals')) || ...    
+                    any( strcmp(exitflag, 'warnequalfunvalhist')) ...
+                    any( strcmp(exitflag, 'bug'))
+                                                
+                        exitStatus = ['Calibration warning encountered:', exitflag,' See CMA-ES scheme documentation for details.'];                        
+                        exitFlag=1;
+                    elseif any( strcmp(exitflag, 'tolx')) && any( strcmp(exitflag, 'tolfun'))
+                        exitFlag=2;
+                        exitStatus = ['Parameter and objective function convergence achieved in ', num2str(numFunctionEvals),' function evaluations.'];
+                    else
+                        exitFlag=0;
+                        exitStatus = ['Unhandled non-convergence issues. Total model evaluations undertaken = ', num2str(numFunctionEvals)];
+                        
+                        obj.calibrationResults.exitFlag = exitFlag;
+                        obj.calibrationResults.exitStatus = exitStatus;
+                        
+                        ME = MException('HydroSightModel:CalibrationFailure',exitStatus);
+                        throw(ME);                        
+                    end                        
+                        
+                case {'SP UCI','SP_UCI','SPUCI','SP-UCI'}
+                    maxn = calibSchemeSettings.maxn;
+                    kstop = calibSchemeSettings.kstop; 
+                    pcento = calibSchemeSettings.pcento; 
+                    peps = calibSchemeSettings.peps;
+                    ngs = calibSchemeSettings.ngs;
+                    iseed = calibSchemeSettings.iseed;
+                    iniflg =  1;                
+                    useLikelihood=false;
+
+                    %if hasStochDerivedForcing
+                    %    kstop = kstop*2;
+                    %end
+                    
+                    % Do calibration                                        
+                    doParamTranspose = false;
+                    [params, fmin,numFunctionEvals, exitFlag, exitStatus] = SPUCI(@calibrationObjectiveFunction, @calibrationValidParameters, ...
+                        params', params_lowerBound', params_upperBound', params_lowerPhysBound', params_upperPhysBound', maxn, ...
+                        kstop, pcento, peps, ngs, iseed, iniflg, diaryObj, obj, time_points, doParamTranspose, useLikelihood); 
+                        params = params';
+                    
+                    if exitFlag==0
+                        obj.calibrationResults.exitFlag = exitFlag;
+                        obj.calibrationResults.exitStatus = exitStatus;
+                        
+                        ME = MException('HydroSightModel:CalibrationFailure',exitStatus);
+                        throw(ME);
+                    end
+
+                    
+                case 'DREAM'
+              
+                    % Application specific settings.
+                    % -------------------------------------------------------------------------
+                    % Set the approx. number of samples required for
+                    % reliable inferance. Col 1: the number of parameters,
+                    % Col 2: number of samples. (source Vrugt, 2016, http://dx.doi.org/10.1016/j.envsoft.2015.08.013 p 293).                    
+                    reqMinParamSamples = [ 1 500; 2 1000; 5 5000; 10 10000; 25 50000; 50 200000; 100 1000000; 250 5000000];
+                    
+                    %Interpolate above matrix to number of parameters for
+                    %this model.
+                    reqMinParamSamples = floor(interp1( reqMinParamSamples(:,1), reqMinParamSamples(:,2), nparams, 'linear'));
+                    
+                    % -------------------------------------------------------------------------
+                    %                           DEFAULT VALUES
+                    % -------------------------------------------------------------------------
+                    calibSchemeSettings.pCR = 'yes';             % Adaptive tuning crossover values
+                    calibSchemeSettings.thinning = 1;            % Each Tth sample is stored         
+                    % -------------------------------------------------------------------------
+                    %                           MODEL SPECIFIC VALUES
+                    % -------------------------------------------------------------------------                    
+                    calibSchemeSettings.d = nparams;             % Dimensionality target distribution
+                    calibSchemeSettings.N = max(calibSchemeSettings.N_per_param * nparams, 2*calibSchemeSettings.delta+1);   % Number of Markov chains                    
+                    calibSchemeSettings.T = 10000*calibSchemeSettings.T;  % Number of generations
+                    calibSchemeSettings.lik=2;                   % Choice of likelihood function
+                    useLikelihood = true;
+                    calibSchemeSettings.restart = 'no';
+                    calibSchemeSettings.modout='no';
+                    calibSchemeSettings.save='no';
+                                        
+                    % -------------------------------------------------------------------------
+                    %                      OPTIONAL (DEFAULT = 'no'  / not used )
+                    % -------------------------------------------------------------------------
+                    % Multi-core computation chains? Turn on if there are
+                    % >1 cores available.
+                    if feature('numCores')>1
+                        calibSchemeSettings.parallel = 'yes';        
+                    else
+                        calibSchemeSettings.parallel = 'no';
+                    end
+                    % Set parameter bounds
+                    Par_info.prior ='latin';
+                    Par_info.min = params_lowerBound'; % If 'latin', min parameter values
+                    Par_info.max = params_upperBound'; % If 'latin', max parameter values                    
+                    Par_info.boundhandling ='reflect';% Explicit boundary handling
+
+                    % Do calibration 
+                    doParamTranspose = true;
+                    [params,output,fx,log_L] = DREAM(@calibrationObjectiveFunction,calibSchemeSettings,Par_info,[], diaryObj, obj, time_points, doParamTranspose,useLikelihood);
+                              
+                    % Extract the R_statistic values and dilter for those
+                    % where R_statistic<1.2 for all parameters in the set.
+                    r_stat_threshold = 1.2;
+                    r_stat_acceptable = all(output.R_stat(1: end, 2: calibSchemeSettings.d + 1)<r_stat_threshold,2);
+                    
+                    % Find the threshold generations where R-stat criteria is first met.
+                    convergedParamSamplesThreshold = min(output.R_stat(r_stat_acceptable,1));
+                    
+                    % Find the total number of samples
+                    nParamSamples = output.R_stat(end, 1);
+                    
+                    % Find the number of viable parameter sets                    
+                    convergedParamSamples = max(1,nParamSamples - convergedParamSamplesThreshold);
+
+                    % To minimise RAM, only save a maximum of
+                    % 2*convergedGenerations parameter sets.
+                    if convergedParamSamples>=  2*reqMinParamSamples                        
+                        convergedParamSamples = 2*reqMinParamSamples;
+                        convergedParamSamplesThreshold = nParamSamples - convergedParamSamples;
+                    end
+                    
+                    if isempty(convergedParamSamples) || convergedParamSamples < 0.1*reqMinParamSamples
+                        if isempty(convergedParamSamples)
+                            convergedParamSamples = 0;
+                        end                    
+                        exitFlag=1;
+                        exitStatus = ['Insufficient DREAM generations for reliable calibration-selected lesser of last 10,000 samples of 10% of no. samples. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
+                            ' and recommended is at least ',num2str(reqMinParamSamples),'. Increase method number to greater than ',num2str(SchemeSetting)];
+                        
+                        obj.calibrationResults.exitFlag = exitFlag;
+                        obj.calibrationResults.exitStatus = exitStatus;
+                        
+                        convergedParamSamples = min(floor(nParamSamples*0.1), 10000);
+                        convergedParamSamplesThreshold = nParamSamples - convergedParamSamples;
+                        
+                    elseif convergedParamSamples < reqMinParamSamples
+                        exitFlag=1;
+                        exitStatus = ['Possible insufficient DREAM generations. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
+                            ' and recommended is at least ',num2str(reqMinParamSamples),'. Increase method number to greater than ',num2str(SchemeSetting)];
+                    else
+                        exitFlag=2;
+                        exitStatus = ['Sufficient DREAM iterations. Number of reliable param. sets is ', num2str(convergedParamSamples), ...
+                            ' and recommended is at least ',num2str(reqMinParamSamples)];                        
+                    end
+
+                    convergedParamSamplesThreshold = floor(convergedParamSamplesThreshold/calibSchemeSettings.N);
+                    params = params(convergedParamSamplesThreshold:end,:,:);
+                    params = genparset(params);
+                    paramsTmp = params(:,1:nparams)';
+                    log_L = params(:,end)';
+                    params=paramsTmp;    
+                    
+                    % Filt out any inf likiloof values (just in case)
+                    filt = ~isinf(log_L);
+                    if any(~filt)
+                        exitStatus = [exitStatus, ' WARNING: ', num2str(sum(~filt)), ' parameter sets with liklihood value of inf were removed.'];
+                        params = params(:,filt);                    
+                        log_L = log_L(filt);
+                    end;                    
+                    
+                    clear paramsTmp
+                otherwise
+                    error('The requested calibration scheme is unknown.');
+            end            
+            
+            display('--------------------------------------------');             
+
+            %--------------------------------------------------------------
+           
+            % Finalise model for calibration
+            %--------------------------------------------------------------            
+            % Get likelihood est (for latter AICc estimate).
+            useLikelihood=true;
+            if isempty(log_L)
+                log_L = objectiveFunction(params, time_points, obj.model, useLikelihood);
+            end    
+            
+            % Get observed head during calib. periods            
+            obsHead = obj.model.inputData.head;            
+            
+            % Call model objects to finalise calibration.
+            calibration_initialise(obj.model, t_start, t_end);
+            calibration_finalise(obj.model, params, false );            
+                       
+            % Add final parameters
+            [obj.calibrationResults.parameters.params_final, ...
+                obj.calibrationResults.parameters.params_name] = getParameters(obj.model);  
+
+            % Add exist status
+            obj.calibrationResults.exitFlag = exitFlag;
+            obj.calibrationResults.exitStatus = exitStatus;            
+            
+            % Calculate calibration and evaluation heads
+            %--------------------------------------------------------------            
+            try                
+                head_est = solveModel(obj, obsHead(:,1), [], '', false );
+            catch
+                head_est = solveModel(obj, obsHead(:,1));
+            end
+                
+            % Convert to real (just n case errors in pram est arose)
+            head_est = real(head_est);
+
+            % Eval. residuals.            
+            neval = sum(~t_filt);        
+            if neval>0
+                
+                if size(head_est,3)>1
+                    obj.evaluationResults.data.modelledHead = [head_est(~t_filt,1,1), permute(prctile( head_est(~t_filt, 2,:),[50 5 95],3),[1,3,2])];
+                    obj.evaluationResults.data.modelledNoiseBounds = [head_est(~t_filt,1,1), prctile( head_est(~t_filt, 3,:),5,3), prctile( head_est(~t_filt, 4,:),95,3)];
+                    obj.evaluationResults.data.modelledHead_residuals = permute(bsxfun(@minus, obsHead(~t_filt,2), head_est(~t_filt, 2,:)),[ 1 3 2]);
+                    head_eval_resid = obj.evaluationResults.data.modelledHead_residuals;
+                else
+                    obj.evaluationResults.data.modelledHead = head_est(~t_filt,1:2);
+                    obj.evaluationResults.data.modelledNoiseBounds = head_est(~t_filt,[1,3,4]);
+                    obj.evaluationResults.data.modelledHead_residuals = obsHead(~t_filt,2) -obj.evaluationResults.data.modelledHead(:,2);
+                    head_eval_resid = obj.evaluationResults.data.modelledHead_residuals;
+                end
+                if size(head_est,3)>1
+                    obj.evaluationResults.data.modelledHead_residuals = single(obj.evaluationResults.data.modelledHead_residuals);
+                end
+            else
+                obj.evaluationResults =[];
+            end
+            
+            % Add calib. obs data and residuals
+            if size(head_est,3)>1
+                obj.calibrationResults.data.modelledHead = [head_est(t_filt,1,1), permute(prctile( head_est(t_filt, 2,:),[50 5 95],3),[1,3,2])];                
+                obj.calibrationResults.data.modelledNoiseBounds = [head_est(t_filt,1,1), prctile( head_est(t_filt, 3,:),5,3), prctile( head_est(t_filt, 4,:),95,3)];
+                obj.calibrationResults.data.modelledHead_residuals = permute( bsxfun(@minus, obsHead(t_filt,2), head_est(t_filt, 2,:)),[1 3 2]);
+                head_calib_resid = obj.calibrationResults.data.modelledHead_residuals;
+            else
+                obj.calibrationResults.data.modelledHead = head_est(t_filt,1:2);
+                obj.calibrationResults.data.modelledNoiseBounds = head_est(t_filt,[1,3,4]);
+                obj.calibrationResults.data.modelledHead_residuals = obsHead(t_filt,2) -obj.calibrationResults.data.modelledHead(:,2);
+                head_calib_resid = obj.calibrationResults.data.modelledHead_residuals;
+            end
+            if size(head_est,3)>1
+                obj.calibrationResults.data.modelledHead_residuals = single(obj.calibrationResults.data.modelledHead_residuals);  % to reduce RAM from DREAM runs
+            end
+                                                
+            nparams = size(params,1);
+            nobs = size(obj.calibrationResults.data.modelledHead,1);            
+            %--------------------------------------------------------------
+            
+                        
+            % Calc. various performance measures including the coefficient of efficiency using the mean observed head
+            %------------------
+            % Mean error
+            obj.calibrationResults.performance.mean_error =  mean(head_calib_resid);
+            
+            %RMSE
+            SSE = sum(head_calib_resid.^2);
+            RMSE = sqrt( 1/size(head_calib_resid,1) * SSE);
+            obj.calibrationResults.performance.RMSE = RMSE;                        
+            
+            % CoE
+            obj.calibrationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
+            obj.calibrationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(t_filt,2));            
+            obj.calibrationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(t_filt,2) - mean(obsHead(t_filt,2)) ).^2);            
+            
+            % Unbiased CoE
+            residuals_unbiased = bsxfun(@minus,head_calib_resid, obj.calibrationResults.performance.mean_error);
+            SSE_unbiased = sum(residuals_unbiased.^2);
+            obj.calibrationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE_unbiased./sum( (obsHead(t_filt,2) - mean(obsHead(t_filt,2)) ).^2);            
+            
+            if neval > 0;                                   
+                % Mean error
+                obj.evaluationResults.performance.mean_error = mean(head_eval_resid); 
+                
+                %RMSE
+                SSE = sum(head_eval_resid.^2);
+                obj.evaluationResults.performance.RMSE = sqrt( 1/size(head_eval_resid,1) * SSE);                
+                
+                % CoE
+                obj.evaluationResults.performance.CoeffOfEfficiency_mean.description = 'Coefficient of Efficiency (CoE) calculated using a base model of the mean observed head. If the CoE > 0 then the model produces an estimate better than the mean head.';
+                obj.evaluationResults.performance.CoeffOfEfficiency_mean.base_estimate = mean(obsHead(~t_filt,2));            
+                obj.evaluationResults.performance.CoeffOfEfficiency_mean.CoE  = 1 - SSE./sum( (obsHead(~t_filt,2) - mean(obsHead(~t_filt,2)) ).^2);            
+
+                % Unbiased CoE
+                residuals_unbiased = bsxfun(@minus,head_eval_resid, obj.evaluationResults.performance.mean_error);
+                SSE_unbiased = sum(residuals_unbiased.^2);
+                obj.evaluationResults.performance.CoeffOfEfficiency_mean.CoE_unbias  = 1 - SSE_unbiased./sum( (obsHead(~t_filt,2) - mean(obsHead(~t_filt,2)) ).^2);            
+            end
+            %------------------
+                                    
+            % Calc. F-test and P(F>F_critical)  
+            if size(head_est,3)>1
+                meanObsHead = mean(obj.calibrationResults.data.obsHead(:,2));
+                for i=1:size(head_est,3)
+                    RSS(:,i) = norm( head_est(t_filt, 2,i) - meanObsHead).^2;
+                    s2(:,i) = (norm(head_calib_resid(:,i))/sqrt(nobs - nparams)).^2;
+                end
+            else
+                RSS = norm( obj.calibrationResults.data.modelledHead(:,2)  - mean(obj.calibrationResults.data.obsHead(:,2))).^2;            
+                s2 = (norm(head_calib_resid)/sqrt(nobs - nparams)).^2;
+            end                        
+            obj.calibrationResults.performance.F_test = (RSS/(nparams-1))./s2;      % F statistic for regression
+            if size(head_est,3)>1
+                for i=1:size(head_est,3)
+                    obj.calibrationResults.performance.F_prob(:,i) = fcdf(1./obj.calibrationResults.performance.F_test(:,i),nobs - nparams, nparams-1); % Significance probability for regression
+                end
+            else
+                obj.calibrationResults.performance.F_prob = fcdf(1./obj.calibrationResults.performance.F_test,nobs - nparams, nparams-1); % Significance probability for regression
+            end
+            
+            if neval > 0;   
+                if size(head_est,3)>1
+                    meanObsHead = mean(obj.evaluationResults.data.obsHead(:,2));
+                    for i=1:size(head_est,3)
+                        RSS(:,i) = norm( head_est(~t_filt, 2,i) - meanObsHead).^2;
+                        s2(:,i) = (norm(head_eval_resid(:,i))/sqrt(neval - nparams)).^2;
+                    end
+                else
+                    RSS = norm( obj.evaluationResults.data.modelledHead(:,2)  - mean(obj.evaluationResults.data.obsHead(:,2))).^2;            
+                    s2 = (norm(head_eval_resid)/sqrt(neval - nparams)).^2;
+                end                        
+                obj.evaluationResults.performance.F_test = (RSS/(nparams-1))./s2;      % F statistic for regression
+                if size(head_est,3)>1
+                    for i=1:size(head_est,3)
+                        obj.evaluationResults.performance.F_prob(:,i) = fcdf(1./obj.evaluationResults.performance.F_test(:,i),neval - nparams, nparams-1); % Significance probability for regression
+                    end
+                else
+                    obj.evaluationResults.performance.F_prob = fcdf(1./obj.evaluationResults.performance.F_test,neval - nparams, nparams-1); % Significance probability for regression
+                end
+            end
+            
+            % Add BIC and Akaike information criterion                                 
+            obj.calibrationResults.performance.AICc = 2*nparams - 2*log_L;
+            obj.calibrationResults.performance.AICc = obj.calibrationResults.performance.AICc + 2*nparams*(nparams+1)/(nobs-nparams-1);
+            obj.calibrationResults.performance.BIC = nparams*log(nobs) -2*log_L;
+            
+            % Calculate experimental variogram of residuals and fit an
+            % exponential model
+            for i=1:size(head_est,3)   
+                try
+                    resid = [obsHead(t_filt,1), obsHead(t_filt,2) - head_est(t_filt, 2,i)];
+                    calib_var = variogram([resid(:,1), zeros( size(resid(:,1))) ] ...
+                        , resid(:,2) , 'maxdist', min(365,max(resid(:,1))-min(resid(:,1))), 'nrbins', min(size(resid,1),12));
+
+                    [obj.calibrationResults.performance.variogram_residual.range(i,1), obj.calibrationResults.performance.variogram_residual.sill(i,1), ...
+                        obj.calibrationResults.performance.variogram_residual.nugget(i,1), varmodel] ...
+                        = variogramfit(calib_var.distance, ...                
+                        calib_var.val, 365/4, 0.75.*var( resid(:,2)), calib_var.num, [], ...
+                        'model', 'exponential', 'nugget', 0.25.*var( resid(:,2)) ,'plotit',false );
+
+                    obj.calibrationResults.performance.variogram_residual.h(:,i) = varmodel.h;
+                    obj.calibrationResults.performance.variogram_residual.gamma(:,i) = varmodel.gamma;
+                    obj.calibrationResults.performance.variogram_residual.gammahat(:,i) = varmodel.gammahat;
+                catch ME
+                    continue;
+                end
+                    
+            end 
+            if neval > 0;                
+                for i=1:size(head_est,3)      
+                    try
+                        resid = [obsHead(~t_filt,1), obsHead(~t_filt,2) - head_est(~t_filt, 2,i)];
+                        eval_var = variogram([resid(:,1), zeros( size(resid(:,1))) ] ...
+                            , resid(:,2) , 'maxdist', min(365,max(resid(:,1))-min(resid(:,1))), 'nrbins', min(size(resid,1),12));
+
+                        [obj.evaluationResults.performance.variogram_residual.range(i,1), obj.evaluationResults.performance.variogram_residual.sill(i,1), ...
+                            obj.evaluationResults.performance.variogram_residual.nugget(i,1), varmodel] ...
+                            = variogramfit(eval_var.distance, ...                
+                            eval_var.val, 365/4, 0.75.*var( resid(:,2)), eval_var.num, [], ...
+                            'model', 'exponential', 'nugget', 0.25.*var( resid(:,2)) ,'plotit',false );
+
+                        obj.evaluationResults.performance.variogram_residual.h(:,i) = varmodel.h;
+                        obj.evaluationResults.performance.variogram_residual.gamma(:,i) = varmodel.gamma;
+                        obj.evaluationResults.performance.variogram_residual.gammahat(:,i) = varmodel.gammahat;
+                    catch ME
+                        continue;
+                    end                    
+                end                                 
+            end                        
+                                   
+            % Update flag to denote the calibration completed successfully.
+            obj.calibrationResults.isCalibrated = true;            
+        end
+
+        function handle = calibrateModelPlotResults(obj, plotNumber, figHandle)
+% Plot the calibration results
+%
+% Syntax:
+%   calibrateModelPlotResults(obj)
+%   calibrateModelPlotResults(obj, handle)
+%
+% Description:
+%   Creates a summary plot of the calibration results. The plot presents
+%   the fit with the observed data and the model noise estimate; time
+%   series of the residuals and various diagnotic plots.
+%
+% Input:
+%   obj -  model object
+%
+%   handle - Matlab figure handle to a pre-existing figure window in which
+%   the plot is to be created.
+%
+% Output:  
+%   (none)
+%
+% Example:
+%
+%   % Plot the calibrate results for object 'model_124705':
+%   >> calibrateModelPlotResults(model_124705);
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModel: model_calibration;
+%
+% Dependencies
+%   model_TFN.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   7 May 2012
+%
+
+            % Create the figure. If varargin is empty then a new figure
+            % window is created. If varagin equals a figure handle, h, then
+            % the calibration results are plotted into 'h'.            
+            if nargin<3 || isempty(figHandle)
+                % Create new figure window.
+                figHandle = figure('Name',['Calib. ',obj.bore_ID]);
+                h=axes(figHandle);
+            elseif ~ishandle(figHandle)    
+                error('Input handle is not a valid figure handle.');
+            else
+                h = figHandle;
+            end
+            
+            % Define the dimensions of the subplots
+            if isempty(plotNumber) || nargin==1
+                ncol_plots = 3;
+                nrow_plots = 4;
+            end
+            
+            % Calc. number of evaluation points.
+            neval=0;
+            if isfield(obj.evaluationResults,'data')
+                if isfield(obj.evaluationResults.data,'modelledHead')
+                    neval = size(obj.evaluationResults.data.modelledHead,1);
+                end
+            end
+                    
+            % Assess if theer is data on the noise componant.
+            hasNoiseComponant = false;
+            if max(max(abs(obj.calibrationResults.data.modelledNoiseBounds)))>0   
+                hasNoiseComponant = true;
+            end
+            
+            % Assess if the data is from a population of simlations (eg
+            % from DREAM calibration)
+            hasModelledDistn = false;
+            if size(obj.calibrationResults.data.modelledHead,2)>2   
+                hasModelledDistn = true;
+                %hasNoiseComponant = false;
+            end
+            
+            % Plot time series of heads.                        
+            %-------
+            if isempty(plotNumber)
+                iplot = 1;
+                nplots = ncol_plots;
+                h=subplot(nrow_plots,ncol_plots , 1 :ncol_plots,'Parent',figHandle);
+                iplot = iplot + nplots;
+            end
+            
+            % Plot bounds for noise component.
+            if isempty(plotNumber) || plotNumber==1 
+                if hasNoiseComponant
+                   if neval > 0; 
+                       XFill = [obj.calibrationResults.data.modelledNoiseBounds(:,1)' ...
+                                obj.evaluationResults.data.modelledNoiseBounds(:,1)' ...
+                                fliplr([obj.calibrationResults.data.modelledNoiseBounds(:,1); ...
+                                        obj.evaluationResults.data.modelledNoiseBounds(:,1)]')];
+                       YFill = [obj.calibrationResults.data.modelledNoiseBounds(:,3)', ...
+                                obj.evaluationResults.data.modelledNoiseBounds(:,3)', ...
+                                fliplr([obj.calibrationResults.data.modelledNoiseBounds(:,2); ...
+                                obj.evaluationResults.data.modelledNoiseBounds(:,2)]')];
+                   else
+                       XFill = [obj.calibrationResults.data.modelledNoiseBounds(:,1)' ...
+                                fliplr(obj.calibrationResults.data.modelledHead(:,1)')];
+                       YFill = [obj.calibrationResults.data.modelledNoiseBounds(:,3)', ...
+                                fliplr(obj.calibrationResults.data.modelledNoiseBounds(:,2)')];
+                   end
+                   fill(XFill, YFill,[0.8 0.8 0.8],'Parent',h);
+                   clear XFill YFill               
+                   hold(h,'on');
+                end
+
+                if hasModelledDistn
+                   if neval > 0; 
+                       XFill = [obj.calibrationResults.data.modelledHead(:,1)' ...
+                                obj.evaluationResults.data.modelledHead(:,1)' ...
+                                fliplr([obj.calibrationResults.data.modelledHead(:,1); ...
+                                        obj.evaluationResults.data.modelledHead(:,1)]')];
+                       YFill = [obj.calibrationResults.data.modelledHead(:,4)', ...
+                                obj.evaluationResults.data.modelledHead(:,4)', ...
+                                fliplr([obj.calibrationResults.data.modelledHead(:,3); ...
+                                obj.evaluationResults.data.modelledHead(:,3)]')];
+                   else
+                       XFill = [obj.calibrationResults.data.modelledHead(:,1)' ...
+                                fliplr(obj.calibrationResults.data.modelledHead(:,1)')];
+                       YFill = [obj.calibrationResults.data.modelledHead(:,4)', ...
+                                fliplr(obj.calibrationResults.data.modelledHead(:,3)')];
+                   end
+                   fill(XFill, YFill,[0.6 0.6 0.6],'Parent',h);
+                   clear XFill YFill               
+                   hold(h,'on');                    
+                end
+                % Plot the observed head
+                plot(h,obj.model.inputData.head(:,1), obj.model.inputData.head(:,2),'.-k');
+                hold(h,'on');
+
+                % Plot model results
+                h_plot = plot(h,obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead(:,2),'.-b' );
+                if neval > 0;
+                    plot(h,obj.evaluationResults.data.modelledHead(:,1), obj.evaluationResults.data.modelledHead(:,2),'.-r' );
+                end
+                datetick(h, 'x','yy');
+                xlabel(h, 'Date');
+                ylabel(h, 'Head (m)');           
+                if isempty(plotNumber)
+                    title(h, ['Bore ', obj.bore_ID, ' - Observed and modelled head']);
+                else
+                    title(h, 'Observed and modelled head');
+                end
+
+                % Create legend strings  
+                i=1;
+                if hasNoiseComponant
+                    if hasModelledDistn
+                        legendstr{i}='Total Err. (5th-95th)';
+                    else
+                        legendstr{i}='Total Err.';
+                    end
+                    i=i+1;
+                end
+                if hasModelledDistn
+                    legendstr{i}='Param. Err. (5th-95th)';
+                    i=i+1;
+                end
+                legendstr{i}='Observed';
+                i=i+1;
+                if hasModelledDistn
+                    legendstr{i}='Calib. (median)';
+                else
+                    legendstr{i}='Calib.';
+                end
+                i=i+1;                                
+                if neval > 0;
+                    if hasModelledDistn
+                        legendstr{i}='Eval. (median)';
+                    else
+                        legendstr{i}='Eval.';
+                    end
+                end                
+
+                % Finish the first plot!
+                legend(h, legendstr,'Location','northeastoutside');
+                box(h,'on');
+                hold(h,'off');               
+            end
+            
+            % Time series of residuals  
+            %-------
+            if isempty(plotNumber)
+                nplots = ncol_plots;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots-1,'Parent',figHandle);
+                iplot = iplot + nplots;
+            end
+            if isempty(plotNumber) || plotNumber==2
+                if hasModelledDistn
+                    residuals = [obj.calibrationResults.data.modelledHead(:,1), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
+                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
+                    set(ax,'Color',[0.6 0.6 0.6]);
+                    set(ax,'MarkerEdgeColor','b');
+                else
+                    scatter(h, obj.calibrationResults.data.modelledHead(:,1), obj.calibrationResults.data.modelledHead_residuals, '.b' );
+                end
+                hold(h,'on');
+                if neval > 0;
+                    if hasModelledDistn
+                        residuals = [obj.evaluationResults.data.modelledHead(:,1), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
+                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
+                        set(ax,'Color',[0.6 0.6 0.6]);
+                        set(ax,'MarkerEdgeColor','r');
+                    else
+                        scatter( h, obj.evaluationResults.data.modelledHead(:,1),  obj.evaluationResults.data.modelledHead_residuals, '.r'  );                
+                    end
+                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
+                end
+                xlabel(h, 'Date');
+                ylabel(h, 'Residuals (obs-est) (m)');
+                if hasModelledDistn
+                    title(h, 'Time series of residuals with parameter uncertainty (5th-95th) bars.');
+                else
+                    title(h, 'Time series of residuals');
+                end
+                datetick(h, 'x','yy');
+                box(h,'on');
+            end
+            
+            % Histograms of calibration data
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;                        
+            end
+            if isempty(plotNumber) || plotNumber==3
+                residuals = [obj.calibrationResults.data.modelledHead(:,1), prctile( obj.calibrationResults.data.modelledHead_residuals,50,2)];
+                hist(h, residuals(:,2) , 0.5*size(residuals,1) );            
+                ylabel(h, 'Freq.');
+                xlabel(h, 'Calib. residuals (obs-est) (m)');
+                axis(h, 'tight');
+                title(h, 'Histogram of calib. residuals');
+                box(h,'on');
+            end
+            
+            % Histograms of evaluation data
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;                        
+            end
+            if isempty(plotNumber) || plotNumber==4
+                if neval > 0;            
+                    residuals = [obj.evaluationResults.data.modelledHead(:,1), prctile( obj.evaluationResults.data.modelledHead_residuals,50,2)];
+                    hist(h, residuals(:,2) , 0.5*size(residuals,1) );                                
+                end           
+                ylabel(h, 'Freq.');
+                xlabel(h, 'Eval. residuals (obs-est) (m)');
+                axis(h, 'tight');
+                title(h, 'Histogram of eval. residuals');      
+                box(h,'on');
+            end
+            
+            % QQ plot
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;
+            end
+            if isempty(plotNumber) || plotNumber==5
+                residuals_cal = prctile( obj.calibrationResults.data.modelledHead_residuals,50,2);
+                if neval > 0;                              
+                    residuals_eval = prctile( obj.evaluationResults.data.modelledHead_residuals,50,2);
+                    QQdata = NaN(size(residuals_cal,1),2);
+                    QQdata(:,1) = residuals_cal;
+                    QQdata(1:neval,2) = residuals_eval;
+                    qqplot(QQdata);
+                    legend('Calibration','Evaluation','Location','northeastoutside');                
+                else
+                    qqplot(residuals_cal );
+                end            
+                title(h, 'Quantile-quantile plot of residuals');
+                box(h,'on');
+            end
+            
+            % Scatter plot of obs versus modelled
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;
+            end
+            if isempty(plotNumber) || plotNumber==6
+                if hasModelledDistn                    
+                    residuals = [obj.calibrationResults.data.obsHead(:,2), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
+                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
+                    set(ax,'Color',[0.6 0.6 0.6]);
+                    set(ax,'MarkerEdgeColor','b');
+                else
+                    scatter(h, obj.calibrationResults.data.obsHead(:,2),  obj.calibrationResults.data.modelledHead(:,2),'.b');
+                end
+                hold(h,'on');
+                if neval > 0;    
+                    if hasModelledDistn                        
+                        residuals = [obj.evaluationResults.data.obsHead(:,2), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
+                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
+                        set(ax,'Color',[0.6 0.6 0.6]);
+                        set(ax,'MarkerEdgeColor','r');
+                    else
+                        scatter(h, obj.evaluationResults.data.obsHead(:,2),  obj.evaluationResults.data.modelledHead(:,2),'.r');
+                    end
+                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
+                    head_min = min([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2); obj.evaluationResults.data.modelledHead(:,2)] );
+                    head_max = max([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2); obj.evaluationResults.data.modelledHead(:,2)] );
+                else
+                    head_min = min([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2)] );
+                    head_max = max([obj.model.inputData.head(:,2);  obj.calibrationResults.data.modelledHead(:,2)] );
+                end            
+                xlabel(h, 'Obs. head (m)');
+                ylabel(h, 'Modelled. head (m)');
+                if hasModelledDistn
+                    title(h, 'Observed vs. modelled heads with with parameter uncertainty (5th-95th) bars.');
+                else
+                    title(h, 'Observed vs. modelled heads');
+                end
+                xlim(h, [head_min , head_max] );
+                ylim(h, [head_min , head_max] );
+                plot(h, [head_min, head_max] , [head_min, head_max],'--k');
+                box(h,'on');
+                hold(h,'off');
+            end
+            
+            % Scatter plot of residuals versus observed head
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;
+            end
+            if isempty(plotNumber) || plotNumber==7
+                if hasModelledDistn
+                    residuals = [obj.calibrationResults.data.obsHead(:,2), prctile( obj.calibrationResults.data.modelledHead_residuals,[50 5 95],2)];
+                    ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.b' );
+                    set(ax,'Color',[0.6 0.6 0.6]);
+                    set(ax,'MarkerEdgeColor','b');
+                else
+                    scatter(h, obj.calibrationResults.data.obsHead(:,2), obj.calibrationResults.data.modelledHead_residuals,'.b');
+                end
+                hold(h,'on');
+                if neval > 0;                
+                    if hasModelledDistn
+                        residuals = [obj.evaluationResults.data.obsHead(:,2), prctile( obj.evaluationResults.data.modelledHead_residuals,[50 5 95],2)];
+                        ax=errorbar(h, residuals(:,1), residuals(:,2), residuals(:,2)-residuals(:,3), residuals(:,4)-residuals(:,2), '.r' );
+                        set(ax,'Color',[0.6 0.6 0.6]);
+                        set(ax,'MarkerEdgeColor','r');
+                    else                    
+                        scatter(h, obj.evaluationResults.data.obsHead(:,2),  obj.evaluationResults.data.modelledHead_residuals,'.r');
+                    end
+                    legend(h,'Calibration','Evaluation','Location','northeastoutside');                
+                end            
+                xlabel(h, 'Obs. head (m)');
+                ylabel(h, 'Residuals (obs-est) (m)'); 
+                if hasModelledDistn
+                    title(h, 'Observed vs. residuals with parameter uncertainty (5th-95th) bars.');
+                else                
+                    title(h, 'Observed vs. residuals');
+                end
+                box(h,'on');
+                hold(h,'off');        
+            end
+            
+            % Semi-variogram of residuals
+            if isempty(plotNumber)
+                nplots = 0;
+                h = subplot(nrow_plots,ncol_plots,iplot:iplot+nplots,'Parent',figHandle);
+                iplot = iplot + nplots + 1;
+            end
+            if isempty(plotNumber) || plotNumber==8
+                if hasModelledDistn
+                    % Extract data from cell arrays
+                    deltaTime=[];
+                    gamma=[];
+                    gammaHat=[];
+                    if isfield(obj.calibrationResults.performance.variogram_residual,'h')                        
+                        deltaTime = obj.calibrationResults.performance.variogram_residual.h;
+                        gamma = obj.calibrationResults.performance.variogram_residual.gamma;
+                        gammaHat = obj.calibrationResults.performance.variogram_residual.gammahat;                        
+                    else
+                        for i=1:size(obj.calibrationResults.performance.variogram_residual.range,1)
+                            deltaTime = [deltaTime,obj.calibrationResults.performance.variogram_residual.model{i}.h];
+                            gamma = [gamma,obj.calibrationResults.performance.variogram_residual.model{i}.gamma];
+                            gammaHat = [gammaHat,obj.calibrationResults.performance.variogram_residual.model{i}.gammahat];
+                        end
+                    end
+                    
+                    %Plot data
+                    ax = errorbar(h, median(deltaTime,2) , median(gamma,2), median(gamma,2)-prctile(gamma,5 ,2),prctile(gamma,95 ,2)-median(gamma,2), '.b');
+                    set(ax,'Color',[0.6 0.6 0.6]);
+                    set(ax,'MarkerEdgeColor','b');
+                    hold(h,'on');
+                    plot(h, median(deltaTime,2) , median(gammaHat,2), '-b');
+                    plot(h, prctile(deltaTime,5,2) , prctile(gammaHat,5,2), '--b');
+                    plot(h, prctile(deltaTime,95,2) , prctile(gammaHat,95,2), '--b');                    
+                    
+                    if neval > 0;                                
+                        % Extract data from cell arrays
+                        deltaTime=[];
+                        gamma=[];
+                        gammaHat=[];
+                        if isfield(obj.evaluationResults.performance.variogram_residual,'h')                        
+                            deltaTime = obj.evaluationResults.performance.variogram_residual.h;
+                            gamma = obj.evaluationResults.performance.variogram_residual.gamma;
+                            gammaHat = obj.evaluationResults.performance.variogram_residual.gammahat;                        
+                        else
+                            for i=1:size(obj.evaluationResults.performance.variogram_residual.range,1)
+                                deltaTime = [deltaTime,obj.evaluationResults.performance.variogram_residual.model{i}.h];
+                                gamma = [gamma,obj.evaluationResults.performance.variogram_residual.model{i}.gamma];
+                                gammaHat = [gammaHat,obj.evaluationResults.performance.variogram_residual.model{i}.gammahat];
+                            end
+                        end                        
+
+                        %Plot data
+                        ax = errorbar(h, median(deltaTime,2) , median(gamma,2), median(gamma,2)-prctile(gamma,5 ,2),prctile(gamma,95 ,2)-median(gamma,2), '.r');
+                        set(ax,'Color',[0.6 0.6 0.6]);
+                        set(ax,'MarkerEdgeColor','r');                                        
+                        plot(h, median(deltaTime,2) , median(gammaHat,2), '-r');
+                        plot(h, prctile(deltaTime,5,2) , prctile(gammaHat,5,2), '--r');
+                        plot(h, prctile(deltaTime,95,2) , prctile(gammaHat,95,2), '--r');                    
+                    
+                        legend(h, 'Calib.(5-50-95th %ile)', 'Calib. model-50th %ile','Calib. model-5th %ile','Calib. model-95th %ile', ...
+                            'Eval.(5-50-95th %ile)','Eval. model-50th %ile','Eval. model-5th %ile','Eval. model-95th %ile','Location','northeastoutside');                
+                    else
+                        legend(h, 'Calib.(5-50-95th %ile)','Calib. model-50th %ile','Calib. model-5th %ile','Calib. model-95th %ile','Location','northeastoutside');                        
+                    end
+                else
+                    if isfield(obj.calibrationResults.performance.variogram_residual,'h')
+                        deltaTime = obj.calibrationResults.performance.variogram_residual.h;
+                        gamma = obj.calibrationResults.performance.variogram_residual.gamma;
+                        gammaHat = obj.calibrationResults.performance.variogram_residual.gammahat;                          
+                    else
+                        deltaTime=[];
+                        gamma=[];
+                        gammaHat=[];
+                        for i=1:size(obj.calibrationResults.performance.variogram_residual.range,1)
+                            deltaTime = [deltaTime,obj.calibrationResults.performance.variogram_residual.model{i}.h];
+                            gamma = [gamma,obj.calibrationResults.performance.variogram_residual.model{i}.gamma];
+                            gammaHat = [gammaHat,obj.calibrationResults.performance.variogram_residual.model{i}.gammahat];
+                        end                        
+                    end
+                    scatter(h, deltaTime, gamma, 'ob');            
+                    hold(h,'on');
+                    plot(h, deltaTime, gammaHat, '-b');                        
+                    if neval > 0;     
+                        if isfield(obj.evaluationResults.performance.variogram_residual,'h')
+                            deltaTime = obj.evaluationResults.performance.variogram_residual.h;
+                            gamma = obj.evaluationResults.performance.variogram_residual.gamma;
+                            gammaHat = obj.evaluationResults.performance.variogram_residual.gammahat;                          
+                        else
+                            deltaTime=[];
+                            gamma=[];
+                            gammaHat=[];
+                            for i=1:size(obj.evaluationResults.performance.variogram_residual.range,1)
+                                deltaTime = [deltaTime,obj.evaluationResults.performance.variogram_residual.model{i}.h];
+                                gamma = [gamma,obj.evaluationResults.performance.variogram_residual.model{i}.gamma];
+                                gammaHat = [gammaHat,obj.evaluationResults.performance.variogram_residual.model{i}.gammahat];
+                            end                                                    
+                        end                        
+                        
+                        scatter(h, deltaTime, gamma, 'or');
+                        plot(h, deltaTime,  gammaHat, '-r');                
+                        legend(h, 'Calib.','Calib model','Eval. experimental','Eval. model','Location','northeastoutside');                
+                    else
+                        legend(h, 'Calib.','Calib model','Location','northeastoutside');                
+                    end
+                end
+                xlabel(h, 'Separation distance (days)' );
+                ylabel(h, 'Semi-variance (m^2)' );
+                title(h, 'Semi-variogram of residuals');
+                box(h,'on');
+                hold(h,'off');               
+            end
+        end
+
+        %% Calculate the sum of squared errors and model residuals for CAM-ES.
+        function objectiveFunctionValue = calibrationObjectiveFunction(params, obj, time_points, doParamTranspose, getLikelihood)
+% Calculate the model objective function value for the input parameter set
+%
+% Syntax:
+%   objectiveFunctionValue = calibrationObjectiveFunction(params, obj, time_points)
+%
+% Description:
+%   The objective function value is calculated for each parameter set by
+%   calling the model's own method called objectiveFunction(). For each
+%   parameter set a scaler number is returned. It is intended that this 
+%   function is only be called from the calibration scheme via
+%   calibrateModel(). Importantly, the calculation of the objective
+%   function for each parameter set is undertaken in parallel to reduce the
+%   calibration time.
+%
+% Input:
+%   params - nxm numerical array of m parameter sets where each parameter
+%   set contains n parameters.
+%
+%   obj -  model object.
+%
+%   time_points - column vector of the time points over which the model is 
+%   to be calibrated.
+%
+% Output:  
+%   objectiveFunctionValue - 1xm numerical vector of the obecjtive function
+%   values.
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModel: model_calibration;
+%   calibrationValidParameters: model_parameter_set_validity_check
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%   calibrationValidParameters.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   30 June 2015
+%
+            if doParamTranspose
+                params = params';
+            end
+
+            if size(params,2)==1
+                try
+                    % Calcule sume of squared errors
+                    objectiveFunctionValue =  objectiveFunction( params, time_points, obj.model, getLikelihood );
+                catch ME
+                    objectiveFunctionValue = inf;
+                end
+            else
+                objectiveFunctionValue = inf(1,size(params,2));
+                parfor i=1: size(params,2)
+                    %Calculate the residuals
+                    try
+                        % Calcule sume of squared errors
+                        objectiveFunctionValue(i) =  objectiveFunction( params(:,i), time_points, obj.model, getLikelihood );
+                    catch ME
+                        objectiveFunctionValue(i) = inf;
+                    end
+                end
+            end
+        end        
+        
+        %% Check validity of parameter set.
+        function validParams = calibrationValidParameters(params, obj, time_points,  doParamTranspose, varargin)
+% Checks if the parameter set is valid
+%
+% Syntax:
+%   function validParams = calibrationValidParameters(params, obj, time_points)
+%
+% Description:
+%   Each parameter is assessed to dermine if it is valid. That is, if it is
+%   within a valid region of the possible parameter space. For each
+%   parameter set a scaler logical value is returned. It is intended that this 
+%   function is only be called from the calibration scheme via
+%   calibrateModel() and the assessment of the parameters is undertaken
+%   within the model's method getParameterValidity().
+%
+% Input:
+%   params - nxm numerical array of m parameter sets where each parameter
+%   set contains n parameters.
+%
+%   obj -  model object.
+%
+%   time_points - column vector of the time points over which the model is 
+%   to be calibrated.
+%
+% Output:  
+%   validParams - 1xm logical vector denoting if the parameter set is valid
+%   (true) or invalid (false).
+%
+% See also:
+%   HydroSightModel: class_description;
+%   calibrateModel: model_calibration;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   30 June 2015
+%           
+    
+            if doParamTranspose
+                params = params';
+            end
+            
+            try
+                validParams = getParameterValidity(obj.model, params, time_points);
+            catch ME
+                validParams = false;
+            end
+        end        
+        
+        %% Get observed Head.
+        function head = getObservedHead(obj)
+% Gets the observed head data used to build the model
+%
+% Syntax:
+%   head = getObservedHead(obj)
+%
+% Description:
+%   Returns the observed head data input when the model was constructed.
+%
+% Input:
+%   obj -  model object.
+%
+% Output:  
+%   head - nx2 numerical array of n rows of observed head values. The
+%   columns are date, head where the date is as a matlab date number.
+%
+% See also:
+%   HydroSightModel: class_description;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   30 June 2015
+%              
+            head = getObservedHead(obj.model);
+        end
+        
+        %% Get the forcing data from the model
+        function [forcingData, forcingData_colnames] = getForcingData(obj)
+% Gets the forcing data for the model.
+%
+% Syntax:
+%   [forcingData, forcingData_colnames] = getForcingData(obj)
+%
+% Description:
+%   Returns the input forcing data input to the model and the column names.
+%
+% Input:
+%   obj -  model object.
+%
+% Output:  
+%   forcingData - nxm numerical array of n rows of forcing data. The
+%   columns are the date followed by the forcing values for each type of forcing.
+%
+%   forcingData_colnames - cells array of column name string associated with the
+%   focring data.
+%
+% See also:
+%   HydroSightModel: class_description;
+%   setForcingData: assign_forcing_data;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   30 June 2015
+%             
+            [forcingData, forcingData_colnames] = getForcingData(obj.model);
+        end
+
+        function derivedData_types = getDerivedDataTypes(obj)
+            if any(strcmp(methods(obj.model),'getDerivedDataTypes'))
+                derivedData_types = getDerivedDataTypes(obj.model);
+            else
+                derivedData_types = {};
+            end            
+        end        
+        
+        function [params, param_names] = getDerivedParameters(obj)
+            if any(strcmp(methods(obj.model),'getStochForcingData'))
+                [params, param_names] = getDerivedParameters(obj.model);
+            else
+                params = [];
+                param_names = {};
+            end            
+        end
+        
+        function [derivedData, derivedData_names] = getDerivedData(obj, modelComponant, derivedData_variable, t, plot_axes)
+            if any(strcmp(methods(obj.model),'getDerivedData'))
+                [derivedData, derivedData_names] = getDerivedData(obj.model, modelComponant, derivedData_variable, t, plot_axes);
+            else
+                derivedData = [];
+                derivedData_names = {};
+            end                    
+        end
+        
+        function [forcingData, forcingData_colnames] = getDerivedForcingData(obj, t)
+            if any(strcmp(methods(obj.model),'getDerivedForcingData'))
+                [forcingData, forcingData_colnames] = getDerivedForcingData(obj.model, t);
+            else
+                forcingData = [];
+                forcingData_colnames = {};
+            end            
+        end
+            
+        
+        function forcingData = getStochForcingData(obj)
+           
+            if any(strcmp(methods(obj.model),'getStochForcingData'))
+                forcingData = getStochForcingData(obj.model);
+            else
+                forcingData = [];
+            end
+            
+        end
+        
+        function finishedStochForcing = updateStochForcingData(obj, forcingData, refineStochForcingMethod)
+            finishedStochForcing = false;
+            if any(strcmp(methods(obj.model),'updateStochForcingData'))           
+                if nargin==1
+                    updateStochForcingData(obj.model);  
+                elseif nargin==2
+                    updateStochForcingData(obj.model, forcingData);
+                elseif nargin==3
+                    finishedStochForcing = updateStochForcingData(obj.model, forcingData, refineStochForcingMethod);
+                end
+            end            
+        end  
+        
+        function params = updateStochForcingParameters(obj, forcingData, params)
+            if any(strcmp(methods(obj.model),'updateStochForcingParameters'))           
+                for i=1:size(params,2)
+                    params(:,i) = updateStochForcingParameters(obj.model, forcingData, params(:,i));
+                end
+            end            
+        end               
+      
+        
+        %% Get the forcing data from the model
+        function setForcingData(obj, forcingData, forcingData_colnames)
+% Sets the forcing data for the model.
+%
+% Syntax:
+%   setForcingData(obj, forcingData, forcingData_colnames)
+%
+% Description:
+%   Returns the input forcing data input to the model and the column names.
+%
+% Input:
+%   obj -  model object.
+%
+%   forcingData - nxm numerical array of n rows of forcing data. The
+%   columns are the date followed by the forcing values for each type of forcing.
+%
+%   forcingData_colnames - cells array of column name string associated with the
+%   focring data.
+%
+% Output:  
+%   (none)
+%
+% See also:
+%   HydroSightModel: class_description;
+%   getForcingData: get_model_forcing_data;
+%
+% Dependencies
+%   model_TFN.m
+%   ExpSmooth.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   30 June 2015
+%             
+            setForcingData(obj.model, forcingData, forcingData_colnames);
+        end
+       
+        function delete(obj)
+            % Clear object properties
+            obj.model_label=[];
+            obj.bore_ID=[];
+            obj.calibrationResults=[];
+            obj.evaluationResults=[];  
+            obj.simulationResults=[];
+
+            % Delete model object.       
+            if ~isempty(obj.model)
+                delete(obj.model);
+            end
+
+        end        
+        
+    end    
+end
+
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/AMALGAM.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/AMALGAM.m
new file mode 100644
index 0000000..119591a
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/AMALGAM.m
@@ -0,0 +1,232 @@
+function [output,ParGen,ObjVals,ParSet,allOriginalObjVals_Flow] = AMALGAM(AMALGAMPar,ModelName,ParRange,Measurement,Extra,Fpareto,model_object);
+% --------------------------------------------------------------------------------------------- %
+%                                                                                               %
+%        AA      MM       MM     AA      LL            GGGGGGGGG       AA      MM       MM      %
+%        AA      MM       MM     AA      LL           GG               AA      MM       MM      %
+%        AA      MM       MM     AA      LL           GG               AA      MM       MM      %
+%       AAAA     MM       MM    AAAA     LL           GG              AAAA     MM       MM      %
+%       AAAA     MM       MM    AAAA     LL           GG              AAAA     MM       MM      %
+%      AA  AA    MMM     MMM   AA  AA    LL           GG             AA  AA    MMM     MMM      %
+%      AA  AA    MMMM   MMMM   AA  AA    LL           GG   GGGGGG    AA  AA    MMMM   MMMM      %
+%      AAAAAA    MM MM MM MM   AAAAAA    LL           GG   GGGGGG    AAAAAA    MM MM MM MM      %
+%     AA    AA   MM  MMM  MM  AA    AA   LL           GG       GG   AA    AA   MM  MMM  MM      %
+%     AA    AA   MM       MM  AA    AA   LL           GG       GG   AA    AA   MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GG       GG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GG       GG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GGGGGGGGGGG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LLLLLLLLLLL   GGGGGGGGG   AA      AA  MM       MM      %
+%                                                                                               %
+% --------------------------------------------------------------------------------------------- %
+
+% ------------------ The AMALGAM multiobjective optimization algorithm ------------------------ % 
+%                                                                                               %
+% This general purpose MATLAB code is designed to find a set of parameter values that defines   %
+% the Pareto trade-off surface corresponding to a vector of different objective functions. In   %
+% principle, each Pareto solution is a different weighting of the objectives used. Therefore,   %
+% one could use multiple trials with a single objective optimization algorithms using diferent  %
+% values of the weights to find different Pareto solutions. However, various contributions to   %
+% the optimization literature have demonstrated that this approach is rather inefficient. The   %
+% AMALGAM code developed herein is designed to find an approximation of the Pareto solution set %
+% within a single optimization run. The AMALGAM method combines two new concepts,               %
+% simultaneous multimethod search, and self-adaptive offspring creation, to ensure a fast,      %
+% reliable, and computationally efficient solution to multiobjective optimization problems. 	%
+% This method is called a multi-algorithm, genetically adaptive multiobjective, or AMALGAM, 	%
+% method, to evoke the image of a procedure that blends the attributes of the best available 	%
+% individual optimization algorithms.                                                           %
+%                                                                                               %
+% This algorithm has been described in:                                                         %
+%                                                                                               %
+% J.A. Vrugt, and B.A. Robinson, Improved evolutionary optimization from genetically adaptive   %
+%    multimethod search, Proceedings of the National Academy of Sciences of the United States   %
+%    of America, 104, 708 - 711, doi:10.1073/pnas.0610471104, 2007.                             %
+%                                                                                               %
+% J.A. Vrugt, B.A. Robinson, and J.M. Hyman, Self-adaptive multimethod search for global        %
+%    optimization in real-parameter spaces, IEEE Transactions on Evolutionary Computation,      %
+%    13(2), 243-259, doi:10.1109/TEVC.2008.924428, 2009.					%
+%                                                                                               %
+% For more information please read:                                                             %
+%                                                                                               %
+% J.A. Vrugt, H.V. Gupta, L.A. Bastidas, W. Bouten, and S. Sorooshian, Effective and efficient  %
+%    algorithm for multi-objective optimization of hydrologic models, Water Resources Research, %
+%    39(8), art. No. 1214, doi:10.1029/2002WR001746, 2003.                                      %
+%                                                                                               %
+% G.H. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W.Wallender, Multi-objective        %
+%    optimization of a regional spatially-distributed subsurface water flow model, Journal      %
+%    of Hydrology, 20 - 48, 311(1-4), doi:10.1016/j.jhydrol.2005.01.001, 2005.                  %
+%                                                                                               %
+% J.A. Vrugt, P.H. Stauffer, T. Wöhling, B.A. Robinson, and V.V. Vesselinov, Inverse modeling   %
+%    of subsurface flow and transport properties: A review with new developments, Vadose Zone   %
+%    Journal, 7(2), 843 - 864, doi:10.2136/vzj2007.0078, 2008.                                  %
+%                                                                                               %
+% T. Wöhling, J.A. Vrugt, and G.F. Barkle, Comparison of three multiobjective optimization      %
+%    algorithms for inverse modeling of vadose zone hydraulic properties, Soil Science Society  %
+%    of America Journal, 72, 305 - 319, doi:10.2136/sssaj2007.0176, 2008.                       %
+%                                                                                               %
+% T. Wöhling, and J.A. Vrugt, Combining multi-objective optimization and Bayesian model         %
+%    averaging to calibrate forecast ensembles of soil hydraulic models, Water Resources        %
+%    Research, 44, W12432, doi:10.1029/2008WR007154, 2008.                                      %
+%                                                                                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                               %
+%     Copyright (C) 2011-2012  the authors                                                      %
+%                                                                                               %
+%     This program is free software: you can modify it under the terms of the GNU General       %
+%     Public License as published by the Free Software Foundation, either version 3 of the      %
+%     License, or (at your option) any later version.                                           %
+%                                                                                               %
+%     This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; %
+%     without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. %
+%     See the GNU General Public License for more details.                                      %
+%                                                                                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                               %
+% AMALGAM code developed by Jasper A. Vrugt, University of California Irvine: jasper@uci.edu    %
+%                                                                                               %
+% Version 0.5:   June 2006                                                                      %
+% Version 1.0:   January 2009    Cleaned up source code and implemented 4 test example problems %
+% Version 1.1:   January 2010    Population size flexible and does not divide by # algorithms   %
+% Version 1.2:   August 2010     Sampling from prior distribution                               %
+%                                                                                               %
+% --------------------------------------------------------------------------------------------- %
+
+% Initialize algorithmic variables and other properties
+[AMALGAMPar,Extra,output,Bounds,ParSet,V] = InitVariables(AMALGAMPar,Extra);
+
+% Sample AMALGAMPar.N points in the parameter space
+ParGen = LHSU(ParRange.minn,ParRange.maxn,AMALGAMPar.N);
+
+if strcmp(Extra.InitPopulation,'LHS'),
+    % Latin hypercube sampling when indicated
+    [ParGen] = LHSU(ParRange.minn,ParRange.maxn,AMALGAMPar.N);
+elseif strcmp(Extra.InitPopulation,'PRIOR');
+    % Loop over each parameter and create MCMCPar.seq number of samples from respective distribution
+    for qq = 1:AMALGAMPar.n,
+        for zz = 1:AMALGAMPar.N,
+            ParGen(zz,qq) = eval(Extra.prior(qq,1:end));
+        end;
+    end;
+end;
+
+% Calculate objective function values for each of the AMALGAMPar.N points
+% [ObjVals,Cg] = CompOF(ParGen,AMALGAMPar,Measurement,ModelName,Extra);
+
+ObjVals = repmat(inf, AMALGAMPar.N, 2); % initialize to speed up the parfor loop with the correct matrix of ObjVals
+Par_allOriginalObjVals_Flow = repmat(inf, AMALGAMPar.N, 6); % initialize to speed up the parfor loop with the correct matrix of all original ObjVals for flow
+Cg = repmat(0, AMALGAMPar.N, 1); % Initialize/Define the contstraint violation
+
+
+
+    parfor ii = 1:AMALGAMPar.N % computing the Obj-functions using parallel computing
+%     for ii = 1:AMALGAMPar.N % computing the Obj-functions using parallel computing
+
+        [ObjVals_prime, ~, ~, objFn_flow_NSE, objFn_flow_NNSE, objFn_flow_RMSE, objFn_flow_SSE, objFn_flow_bias, objFn_flow_KGE, ~, ~,~] = objectiveFunction_joint(ParGen(ii,:)', Measurement.time_points_head, Measurement.time_points_streamflow, model_object,{}); % using time points from calibration_initialise to avoid mismatch of dimensions in line 2803 of model_TFN
+        
+        % Store the objective function values for each point that are minimized in AMALGAM
+        ObjVals(ii,:) = ObjVals_prime;
+
+        % Store the all original objective function values for flow for each point
+        Par_allOriginalObjVals_Flow(ii,:) = [objFn_flow_NSE objFn_flow_NNSE objFn_flow_RMSE objFn_flow_SSE objFn_flow_bias objFn_flow_KGE];
+
+        Cg(ii,1) = 0; % Define the constraint violation
+
+    end
+    
+% ParGen a matrix of parameter sets
+% AMALGAMPar is a struct variable of number of parameters etc
+% Measurements I this can be empty
+% ModelName is 'ScenarioName'
+% Extra will be the model object eg model_7params.model
+
+% Ranking and CrowdDistance Calculation
+[Ranking,CrowdDist] = CalcRank(ObjVals,Bounds,Cg);
+
+% Define the current iteration value
+Iter = AMALGAMPar.N;
+
+% Compute convergence diagnostics -- distance to Pareeto optimal front (only values for synthetic problems!)
+[Gamma,Delta,Hvol] = CompConv(AMALGAMPar,Fpareto,ObjVals);
+
+% Store the convergence statistics in output.R
+output.R(1,1:4) = [Iter Gamma Delta Hvol];
+
+% Store current population in ParSet
+ParSet(1:AMALGAMPar.N,1:AMALGAMPar.n + AMALGAMPar.nobj + 1) = [ParGen Cg ObjVals]; 
+
+% Define counter
+counter = 2;
+
+% ADDED Initialize matrix to store all the Obj-Functions for flow
+% that are calculated at each iteration of AMALGAM
+allOriginalObjVals_Flow = Par_allOriginalObjVals_Flow;
+
+% Now iterate
+while (Iter < AMALGAMPar.ndraw),
+
+    % Step 1: Now determine Pbest and Nbest for Particle Swarm Optimization
+    [pBest,nBest] = SelBest(Ranking,ParSet(1:Iter,1:end),AMALGAMPar,Extra);
+
+    % Step 2: Generate offspring
+    [NewGen,V,Itot] = GenChild(ParGen,ObjVals,Ranking,CrowdDist,Cg,V,pBest,nBest,AMALGAMPar,ParRange,Extra);
+
+    % Step 2b: Check whether parameters are in bound
+    [NewGen] = CheckPars(NewGen,ParRange,Extra.BoundHandling);
+
+    % Step 3: Compute Objective Function values offspring
+%     [ChildObjVals,ChildCg] = objectiveFunction_joint4AMALGAM(NewGen,AMALGAMPar,Measurement,ModelName,Extra);
+
+    ChildObjVals = repmat(inf, AMALGAMPar.N, 2); % initialize to speed up the parfor loop with the correct matrix of ObjVals
+    Child_allOriginalObjVals_Flow = repmat(inf, AMALGAMPar.N, 6); % initialize to speed up the parfor loop with the correct matrix of all original ObjVals for flow
+    ChildCg = repmat(inf, AMALGAMPar.N, 1); % initialize physical constrains matrix
+
+    parfor ii = 1:AMALGAMPar.N % computing the Obj-functions using parallel computing
+
+         [ObjVals_prime, ~, ~, objFn_flow_NSE, objFn_flow_NNSE, objFn_flow_RMSE, objFn_flow_SSE, objFn_flow_bias, objFn_flow_KGE, ~, ~,~] = objectiveFunction_joint(NewGen(ii,:)', Measurement.time_points_head, Measurement.time_points_streamflow, model_object,{}); % using time points from calibration_initialise to avoid mismatch of dimensions in line 2803 of model_TFN
+         
+         % Store the objective function values for each point
+         ChildObjVals(ii,:) = ObjVals_prime;
+         
+         % Store the all original objective function values for flow for each point
+         Child_allOriginalObjVals_Flow(ii,:) = [objFn_flow_NSE objFn_flow_NNSE objFn_flow_RMSE objFn_flow_SSE objFn_flow_bias objFn_flow_KGE];
+         
+         % Define the contstraint violation
+         ChildCg(ii,1) = 0;
+         
+     end
+          
+
+    % Step 4: Now merge parent and child populations and generate new one
+    %[ParGen,ObjVals,Ranking,CrowdDist,Iout,Cg] = CreateNewPop(ParGen,NewGen,ObjVals,ChildObjVals,Itot,Cg,ChildCg,ParRange,Bounds); 
+    [ParGen,ObjVals,Ranking,CrowdDist,Iout,Cg,Par_allOriginalObjVals_Flow] = CreateNewPop(ParGen,NewGen,ObjVals,ChildObjVals,Itot,Cg,ChildCg,ParRange,Bounds, Par_allOriginalObjVals_Flow,Child_allOriginalObjVals_Flow); 
+
+    % Step 5: Determine the new number of offspring points for individual algorithms
+    [AMALGAMPar] = DetN(Iout,AMALGAMPar);
+
+    % Step 6: Append the new points to ParSet
+    ParSet(Iter+1:Iter+AMALGAMPar.N,1:end) = [ParGen Cg ObjVals];
+    
+    
+    % Step 6b: Append the new points to allOriginalObjVals_Flow
+    allOriginalObjVals_Flow(Iter+1:Iter+AMALGAMPar.N,1:end) = Par_allOriginalObjVals_Flow;
+    
+
+    % Step 7: Compute convergence statistics -- this can only be done for synthetic problems
+    [Gamma,Delta,Hvol] = CompConv(AMALGAMPar,Fpareto,ObjVals);
+
+    % Step 8: Update Iteration
+    Iter = Iter + AMALGAMPar.N;
+
+    % Step 9a: Save AMALGAMPar.m
+    output.algN(counter,1:AMALGAMPar.q + 1) = [Iter AMALGAMPar.m];
+
+    % Step 9b: Store the convergence statistics in output.R
+    output.R(counter,1:4) = [Iter Gamma Delta Hvol];
+
+    % Step 10: Update counter
+    counter = counter + 1;
+
+    % Write Iter to screen -- to show progress
+    Iter
+    
+
+    
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcOF.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcOF.m
new file mode 100644
index 0000000..5980aa3
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcOF.m
@@ -0,0 +1,33 @@
+function OF = CalcOF(ModPred,Measurement,Extra);
+% Calculate the objective function
+
+% Zitzler and Thiele function 4
+if (Extra.example == 1),
+    OF(1) = ModPred.f; OF(2) = ModPred.g * ModPred.h;
+end;
+
+% Zitzler and Thiele function 6
+if (Extra.example == 2),
+    OF(1) = ModPred.f; OF(2) = ModPred.g * ModPred.h;
+end;
+
+% Rotated problem
+if (Extra.example == 3),
+    OF(1) = ModPred.y(1); OF(2) = ModPred.g * exp(-ModPred.y(1)./ModPred.g);
+    % Put in this check
+    if abs(ModPred.y(1)) > 0.3,
+        OF(1) = OF(1) + 1000; OF(2) = OF(2) + 1000;
+    end;
+end;
+
+% HYMOD rainfall - runoff model
+if (Extra.example == 4),
+    % Find nondriven part of hydrograph
+    ND = find(Extra.Precip(65:Extra.MaxT,1) == 0);
+    % Find driven part of hydrograph
+    D = find(Extra.Precip(65:Extra.MaxT,1) > 0);
+    % Calculate the RMSE for ND part
+    OF(1) = sqrt(sum((ModPred(ND) - Measurement.MeasData(ND)).^2)/size(ND,1));
+    % Calculate the RMSE for D part
+    OF(2) = sqrt(sum((ModPred(D) - Measurement.MeasData(D)).^2)/size(D,1));
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcRank.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcRank.m
new file mode 100644
index 0000000..00e264d
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CalcRank.m
@@ -0,0 +1,26 @@
+function [Ranking,CrowdDist] = CalcRank(ObjVals,Bounds,Cg);
+% Ranking based on Pareto rank and constraint violation
+
+% First find the individuals with no constraint violation
+[idx] = find(Cg == 0); Ntot = length(idx);
+if Ntot > 0,
+    % Rank these individuals
+    [Ranking(idx,:),CrowdDist(idx,:)] = ParetoRanking(ObjVals(idx,:),Bounds,Cg(idx,1),0);
+end;
+
+if Ntot > 0,
+    % Now derive maximum Rank and NmbOfFront of this one
+    maxRank = max(Ranking); 
+else
+    maxRank = 0; maxFront = 0;
+end;
+
+% Now find the individuals with constraint violation
+[idx] = find(Cg < 0); Ntot = length(idx);
+
+% Now do the ranking of these individuals
+if Ntot > 0,
+    [Rank,CrowdDist(idx,:)] = ParetoRanking(ObjVals(idx,:),Bounds,Cg(idx,1),1);
+    % Now update the Rank and the NmbOfFront
+    Ranking(idx,:) = Rank + maxRank; 
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckBounds.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckBounds.m
new file mode 100644
index 0000000..2a972e8
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckBounds.m
@@ -0,0 +1,13 @@
+function [new] = CheckBounds(new,old,ParRange);
+% Checks the bounds of the parameters
+
+minn = ParRange.minn; maxn = ParRange.maxn;
+
+% First determine the size of new
+[NrComb,Dim] = size(new);
+
+% Loop over each individual element
+for qq = 1:NrComb,
+    [ii] = find(new(qq,:)<minn); if isempty(ii) == 0, new(qq,ii) = minn(ii); end; 
+    [ii] = find(new(qq,:)>maxn); if isempty(ii) == 0, new(qq,ii) = maxn(ii); end; 
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckPars.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckPars.m
new file mode 100644
index 0000000..dde959e
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CheckPars.m
@@ -0,0 +1,16 @@
+function [NewGen] = CheckPars(NewGen,ParRange,BoundHandling);
+% Check whether parameters are in bound 
+    
+% Do boundary handling -- what to do when points fall outside bound
+if strcmp(BoundHandling,'Reflect');
+    [NewGen] = ReflectBounds(NewGen,ParRange);
+end;
+if strcmp(BoundHandling,'Bound');
+    [NewGen] = SetToBounds(NewGen,ParRange);
+end;
+if strcmp(BoundHandling,'Fold');
+    [NewGen] = FoldBounds(NewGen,ParRange);
+end;
+if strcmp(BoundHandling,'None');
+    % Do nothing
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CompConv.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompConv.m
new file mode 100644
index 0000000..8c988a6
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompConv.m
@@ -0,0 +1,28 @@
+function [Gamma,Delta,Hvol] = CompConv(AMALGAMPar,Fpareto,Falg);
+% ---------- Computes the gamma convergence metric ---------------
+
+% Return empty values if true Pareto front does not exist
+if isempty(Fpareto) == 1,
+    % Define some numbers
+    Gamma = NaN; Delta = NaN; Hvol = NaN;
+end;
+
+% Return values if true Pareto front does exist
+if isempty(Fpareto) == 0,
+    
+    % First sort the data in Falg and Fpareto
+    Falg = sortrows(Falg,[1]); Fpareto = sortrows(Fpareto,[1]);
+    
+    % Now compute the length of both arrays
+    Npareto = size(Fpareto,[1]); Nalg = size(Falg,[1]);
+    
+    % Now compute Gamma convergence measures
+    [Gamma] = CompGamma(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+    
+    % Now compute the delta convergence metric 
+    [Delta] = CompDelta(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+    
+    % Now compute hypervolume
+    [Hvol] = CompHvol(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+    
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CompDelta.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompDelta.m
new file mode 100644
index 0000000..c1e4cfb
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompDelta.m
@@ -0,0 +1,44 @@
+function [Delta] = CompDelta(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+% Now compute the delta convergence metric
+
+for qq = 2:Nalg,
+    d(qq-1,1) = sqrt(sum((Falg(qq,1:AMALGAMPar.nobj)-Falg(qq-1,1:AMALGAMPar.nobj)).^2));
+end
+% Remove points with large distance -> indicates a discontinuous Pareto set
+[i] = find(d < 10*mean(d)); d = d(i,:);
+% Now compute average Euclidean distance between members nondominated set
+dmean = mean(d);
+% Now compute dl (qq=1) and df (qq=2)
+for qq = 1:2,
+    if qq == 1,
+        % determine df
+        temp = repmat(Fpareto(1,1:AMALGAMPar.nobj),Nalg,1);
+    else
+        % determine dl
+        temp = repmat(Fpareto(end,1:AMALGAMPar.nobj),Nalg,1);
+    end;
+    % Compute distance between extreme Falg solutions and Fpareto solutions
+    Dist = sqrt(sum((temp - Falg).^2,[2]));
+    % Find minimum distance
+    idx = find(Dist==min(Dist)); idx = idx(1);
+    % Find dx and dy between
+    if qq == 1,
+        % Check whether algorithm found better point than end of Pareto set
+        if sum(abs(Fpareto(1,1:AMALGAMPar.nobj)-Falg(idx,1:AMALGAMPar.nobj))) == AMALGAMPar.nobj,
+            df = 0;
+        else
+            df = Dist(idx);
+        end;
+    end;
+    if qq == 2,
+        % Check whether algorithm found better point than end of Pareto set
+        if sum(abs(Fpareto(end,1:AMALGAMPar.nobj)-Falg(idx,1:AMALGAMPar.nobj))) == AMALGAMPar.nobj,
+            dl = 0;
+        else
+            dl = Dist(idx);
+        end;
+    end;
+end;
+
+D = ((df + dl ) + sum(abs(d-dmean)))./((df + dl) + ((Nalg-1)*dmean));
+Delta = [mean(D)];
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CompGamma.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompGamma.m
new file mode 100644
index 0000000..7198181
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompGamma.m
@@ -0,0 +1,14 @@
+function  [Gamma] = CompGamma(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+% Computes Gamma convergence measure
+
+for qq = 1:Nalg,
+    % Fill array with alg values of length Npareto
+    A = Falg(qq,1:AMALGAMPar.nobj);
+    temp = A(ones(Npareto,1),:);
+    % Compute Euclidean distance
+    Dist = sqrt(sum((temp - Fpareto).^2,[2]));
+    idx = find(Dist==min(Dist));
+    D(qq,1) = Dist(idx(1),1);
+end
+% Now compute Gamma from average distance
+Gamma = mean(D);
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CompHvol.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompHvol.m
new file mode 100644
index 0000000..becd6ce
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompHvol.m
@@ -0,0 +1,77 @@
+function [Hvol] = CompHvol(Falg,Fpareto,Nalg,Npareto,AMALGAMPar);
+% Compute hypervolume
+
+% Start to compute maximum and minimum of objective values
+minF1 = min(Fpareto(:,1)); maxF1 = max(Fpareto(:,1)); minF2 = min(Fpareto(:,2)); maxF2 = max(Fpareto(:,2));
+
+% First start with the 2-D estimation problem
+ideal = 100; Sideal = (maxF1-minF1) * (ideal-minF2); 
+
+% Compute hypervolume of true Pareto front
+Strue = 0; 
+for qq = 2:Npareto,
+    
+    if AMALGAMPar.nobj == 2,
+        % Volume is x size multiplied with the y value
+        dx = (Fpareto(qq,1) - Fpareto(qq-1,1)); dy = (ideal - Fpareto(qq-1,2));
+    end
+
+    if AMALGAMPar.nobj == 3,
+        dx = sqrt(sum((Fpareto(qq,1:2)-Fpareto(qq-1,1:2)).^2)); dy = (ideal-Fpareto(qq-1,3));
+    end;
+    
+    % Compute Strue and normalize according to ideal
+    Strue = Strue + abs(dx * dy);
+end;
+
+Sfront = 0; 
+% Then compute hypervolume using approximation from algorithm
+for qq = 1:Nalg,
+    
+    % First  check whether extreme solutions algorithms are more extreme than the true set
+    if (Falg(qq,1) < minF1) & (Falg(qq,2) > maxF2),
+        Falg(qq,1) = minF1; Falg(qq,2) = maxF2;
+    end;
+    
+    if (Falg(qq,1) > maxF1) & (Falg(qq,2) < minF2),
+        Falg(qq,1) = maxF1; Falg(qq,2) = minF2;
+    end;
+    
+    % Now check whether algorithm found better nondominated solutions than true set
+    if (Falg(qq,2) < minF2) & (Falg(qq,1) < maxF1),
+        Falg(qq,1:2) = Fpareto(end,1:2);
+    end;
+end;
+
+Falg = sortrows(Falg,[1]);
+
+% Now compute hypervolume
+for qq = 2:Nalg,
+    
+    % First do 2-D case
+    if AMALGAMPar.nobj == 2,
+        % Now Calculate Hypervolume
+        if (Falg(qq-1,1) >= minF1) & (Falg(qq,1) <= maxF1)
+            % Now compute distance
+            dx = (Falg(qq,1) - Falg(qq-1,1)); dy = (ideal - Falg(qq-1,2));
+        else
+            dx = 0; dy = 0;
+        end;
+    end;
+    
+    % Now do 3-D case
+    if AMALGAMPar.nobj == 3,
+        if (Falg(qq-1,1) >= minF1) & (Falg(qq,1) <= maxF1) & (Falg(qq-1,2) >= minF2) & (Falg(qq,2) <= maxF2)
+            % Now compute distance
+            dx = sqrt(sum((Falg(qq,1:2)-Falg(qq-1,1:2)).^2)); dy = (ideal-Falg(qq-1,3));
+        else
+            dx = 0; dy = 0;
+        end;
+    end;
+
+    % Compute Strue and normalize according to ideal
+    Sfront = Sfront + abs(dx * dy);
+end;
+
+% Check whether front is positive or negative
+Hvol = 1 - Sfront/Strue;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CompOF.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompOF.m
new file mode 100644
index 0000000..bec68ee
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CompOF.m
@@ -0,0 +1,19 @@
+function [newOF,newCg] = CompOF(x,AMALGAMPar,Measurement,ModelName,Extra)
+% This function computes the objective function for each x value
+
+% Evaluate each parameter combination and compute the objective functions
+for ii = 1:AMALGAMPar.N,
+
+    % Call model to generate simulated data
+    evalstr = ['ModPred = ',ModelName,'(x(ii,:),Extra);']; eval(evalstr);
+
+    % Calculate the objective functions by comparing model predictions with
+    OF = CalcOF(ModPred,Measurement,Extra);
+    
+    % Store the objective function values for each point
+    newOF(ii,1:AMALGAMPar.nobj) = OF;
+
+    % Define the contstraint violation
+    newCg(ii,1) = 0;
+    
+end;
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/CreateNewPop.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/CreateNewPop.m
new file mode 100644
index 0000000..3345d97
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/CreateNewPop.m
@@ -0,0 +1,64 @@
+function [ParGen,ObjVals,Ranking,CrowdDist,Iout,Cg, Par_Flow_ObjFuns] = CreateNewPop(ParGen,ChildGen,ObjVals,ChildObjVals,Itot,Cg,ChildCg,ParRange,Bounds, Par_Flow_ObjFuns,Child_Flow_ObjFuns);
+% Function selects new population based on current offspring and parents
+
+% Save ParGen in memory
+oldParGen = ParGen; counter = 0; rk = 1;
+
+% Determine the number of individuals in population
+[nmbOfIndivs] = size(ParGen,1); 
+
+% Append children to parents
+Par_ChildGen = [ParGen;ChildGen]; ObjVals = [ObjVals;ChildObjVals]; Cg = [Cg;ChildCg];
+
+% ADDED THIS: Append children to parents in terms of all original flow Objective
+Par_Child_FlowObjFuns = [Par_Flow_ObjFuns; Child_Flow_ObjFuns];
+
+% Rank children and parents together
+[Ranking,CrowdDist] = CalcRank(ObjVals,Bounds,Cg);
+
+% Now generate new population
+for rk = 1:max(Ranking),
+    % Check how many members in next front
+    [ii] = length(find(Ranking == rk));
+    % Now save this information
+    N(rk) = ii;
+end;
+
+% Check whether population is full, otherwise select from first rank based on crowding distance
+Ntot = cumsum(N);
+if Ntot(1) >= nmbOfIndivs,
+    ii = find(Ranking==1);
+    [Cdist sortidx] = sortrows(-CrowdDist(ii)); 
+    % Select from first rank based on crowding distance
+    ii = ii(sortidx); ii = ii(1:nmbOfIndivs);
+end;
+if Ntot(1) < nmbOfIndivs,
+    % Now select which ranks are within the size of the population
+    idx = find(Ntot>nmbOfIndivs); rk = idx(1) - 1;
+    if Ntot(rk) ~= nmbOfIndivs,
+        % Fill population by selecting from next population
+        ii = find(Ranking==rk+1);
+        % How many points?
+        T = nmbOfIndivs - Ntot(rk);
+        % Now select based on Crowding Distance
+        [Cdist sortidx] = sortrows(-CrowdDist(ii));
+        %
+        ii = ii(sortidx); ii = ii(1:T);
+    else
+        ii = [];
+    end;
+    % Now fill with all the other ranks
+    ii = [ii;find(Ranking<=rk)]; 
+end;
+
+% Generate new population
+ParGen = Par_ChildGen(ii,:); ObjVals = ObjVals(ii,:); Cg = Cg(ii,:);
+
+% ADDED THIS: Generate new generation of all original flow Obj-func
+Par_Flow_ObjFuns = Par_Child_FlowObjFuns(ii,:);
+
+% Select appropriate Rank and Distance Operator
+Ranking = Ranking(ii,:); CrowdDist = CrowdDist(ii,:);
+
+% Update Itot
+Iout = Itot(ii,:);
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/DE_ChildGen.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/DE_ChildGen.m
new file mode 100644
index 0000000..3b36da0
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/DE_ChildGen.m
@@ -0,0 +1,17 @@
+function [ChildGen] = DE_ChildGen(ParGen,NmbOfFront,pBest,nBest,AMALGAMPar);
+% Performs differential evolution
+
+% F = 0.8; K = 0.4 -- in Previous papers
+for tt = 1:AMALGAMPar.N,
+    % Randomly generate F 
+    F = unifrnd(0.6,1.0); 
+    % Randomly generate K
+    K = unifrnd(0.2,0.6); 
+    % Now randomly select 3 Points from SCEMPar.s
+    [ii] = randperm(AMALGAMPar.N); ii = ii(1:3);
+    % Define the various r values
+    r1 = ii(1); r2 = ii(2); r3 = ii(3);
+    % Generate children using differential evolution
+    ChildGen(tt,1:AMALGAMPar.n) = ParGen(tt,1:AMALGAMPar.n) + K.*(ParGen(r3,1:AMALGAMPar.n) - ParGen(tt,1:AMALGAMPar.n)) + ...
+        F.*(ParGen(r1,1:AMALGAMPar.n) - ParGen(r2,1:AMALGAMPar.n));
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/DetN.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/DetN.m
new file mode 100644
index 0000000..e90be83
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/DetN.m
@@ -0,0 +1,36 @@
+function [AMALGAMPar] = DetN(Iout,AMALGAMPar);
+% Determines the new number of points to generate with individual algorithms
+
+% Define minN
+minN = 5;
+
+% First determine the number of new points generated by the total set of algorithms
+T = sum(Iout > 0);
+
+% Then calculate the percentage of individual algorithms
+for qq = 1:AMALGAMPar.q,
+    % Calc percentage
+    perc(1,qq) = sum(Iout == qq)/AMALGAMPar.m(qq);
+    % Calculate new size M
+    M(1,qq) = perc(1,qq);
+end;
+
+% Determine the sum of M
+Mnew = sum(M);
+
+% Now do check whether new points have been added to ParGen
+if Mnew == 0,
+    M = (AMALGAMPar.N/AMALGAMPar.q) * ones(1,AMALGAMPar.q);
+else
+    % Define scaling parameter
+    Scaling = 1/Mnew;
+    % Do scaling so adds up to 100 percent
+    M = max(floor(Scaling * M * AMALGAMPar.N),minN);
+    % Now check whether everything is fine
+    Mnew = sum(M); Diff = (AMALGAMPar.N - Mnew);
+    % Now adjust the one with the most points
+    [i] = find(M==max(M)); M(i(1)) = M(i(1)) + Diff;
+end;
+
+% Update AMALGAMPar
+AMALGAMPar.m = M;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/FoldBounds.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/FoldBounds.m
new file mode 100644
index 0000000..6240ec1
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/FoldBounds.m
@@ -0,0 +1,25 @@
+function [y] = FoldBounds(new,ParRange);
+% Checks the bounds of the parameters
+
+% ------- New approach that maintains detailed balance ----------
+
+% First determine the size of new
+[nmbOfIndivs,Dim] = size(new);
+
+% Now replicate minn and maxn
+minn = repmat(ParRange.minn,nmbOfIndivs,1); maxn = repmat(ParRange.maxn,nmbOfIndivs,1);
+
+% Define y
+y = new;
+
+% Now check whether points are within bound
+[ii] = find(y < minn); y(ii) = maxn(ii) - (minn(ii) - y(ii));
+% Do upper bound
+[ii] = find(y > maxn); y(ii) = minn(ii) + (y(ii) - maxn(ii));
+
+% ----- End New approach that maintains detailed balance --------
+
+% Just in case if still outside bound (should not happen)
+[ii] = find(y < minn); y(ii) = minn(ii) + rand * (maxn(ii) - minn(ii));
+% Do upper bound
+[ii] = find(y > maxn); y(ii) = minn(ii) + rand * (maxn(ii) - minn(ii));
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/GenChild.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/GenChild.m
new file mode 100644
index 0000000..9d20765
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/GenChild.m
@@ -0,0 +1,68 @@
+function [NewGen,V,Itot] = GenChild(ParGen,ObjVals,Ranking,CrowdDist,Cviols,V,pBest,nBest,AMALGAMPar,ParRange,Extra);
+% Test function generates new offspring
+
+% First initialize ChildGen and other variables
+NewGen = []; Itot = zeros(length(ParGen),1);
+% Define Cons 
+Cons = [ParRange.minn' ParRange.maxn']; 
+% Define size of ParGen
+[nmbOfIndivs nmbOfVars] = size(ParGen);
+
+% ------------------- NSGA-II GENETIC ALGORITHM ----------------------
+[NrGenetic] = find(strcmp(Extra.Alg,'GA')==1); Nc = length(NrGenetic);
+for qq = 1:Nc,
+    % Define number first
+    NrPoints = AMALGAMPar.m(NrGenetic(qq));
+    % Define algorithmic settings
+    pC = 0.9; pM = 1/AMALGAMPar.n; etaC = 20; etaM = 20;    
+    % Create children using Genetic rules
+    [ChildGen] = NSGA_ChildGen(ParGen,ObjVals,Cviols,CrowdDist,ParRange,AMALGAMPar,pC,etaC,pM,etaM);
+    % Now randomly select NrPoints from AMALGAMPar.N
+    [ii] = randperm(AMALGAMPar.N); ii = ii(1:NrPoints)';
+    % Now add ChildGen
+    NewGen = [NewGen ; ChildGen(ii,1:AMALGAMPar.n)]; Itot = [Itot;NrGenetic(qq)*ones(NrPoints,1)];
+end;
+
+% --------------------------------------------------------------------
+
+% ---------------- METROPOLIS HASTINGS ALGORITHM ---------------------
+[NrMetro] = find(strcmp(Extra.Alg,'AMS')==1); Nc = length(NrMetro);
+for qq = 1:Nc,
+    % Define number first
+    NrPoints = AMALGAMPar.m(NrMetro(qq));
+    % Create children using Metropolis rules
+    [ChildGen] = Metro_ChildGen(ParGen,Ranking,AMALGAMPar,Extra);
+    % Now randomly select NrPoints from AMALGAMPar.N
+    [ii] = randperm(AMALGAMPar.N); ii = ii(1:NrPoints)';
+    % Now add ChildGen
+    NewGen = [NewGen ; ChildGen(ii,1:AMALGAMPar.n)]; Itot = [Itot;NrMetro(qq)*ones(NrPoints,1)];
+end;
+% --------------------------------------------------------------------
+
+% ----------------- PARTICLE SWARM OPTIMIZATION ----------------------
+[NrSwarm] = find(strcmp(Extra.Alg,'PSO')==1); Nc = length(NrSwarm);
+for qq = 1:Nc,
+    % Define number first
+    NrPoints = AMALGAMPar.m(NrSwarm(qq));
+    % Create children using Particle Swam Optimization
+    [ChildGen,V(:,:,NrSwarm(qq))] = Swarm_ChildGen(ParGen,V(:,:,NrSwarm(qq)),pBest,nBest,AMALGAMPar,Extra);
+    % Now randomly select NrPoints from AMALGAMPar.N
+    [ii] = randperm(AMALGAMPar.N); ii = ii(1:NrPoints)';
+    % Now add ChildGen
+    NewGen = [NewGen ; ChildGen(ii,1:AMALGAMPar.n)]; Itot = [Itot;NrSwarm(qq)*ones(NrPoints,1)];
+end;
+% --------------------------------------------------------------------
+
+% ------------------ DIFFERENTIAL EVOLUTION --------------------------
+[NrRand] = find(strcmp(Extra.Alg,'DE')==1); Nc = length(NrRand);
+for qq = 1:Nc,
+    % Define number first
+    NrPoints = AMALGAMPar.m(NrRand(qq));
+    % Create children using Particle Swam Optimization
+    [ChildGen] = DE_ChildGen(ParGen,Ranking,pBest,nBest,AMALGAMPar);
+    % Now randomly select NrPoints from AMALGAMPar.N
+    [ii] = randperm(AMALGAMPar.N); ii = ii(1:NrPoints)';
+    % Now add ChildGen
+    NewGen = [NewGen ; ChildGen(ii,1:AMALGAMPar.n)]; Itot = [Itot;NrRand(qq)*ones(NrPoints,1)];
+end;
+% --------------------------------------------------------------------
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVariables.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVariables.m
new file mode 100644
index 0000000..da17469
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVariables.m
@@ -0,0 +1,39 @@
+function [AMALGAMPar,Extra,output,Bounds,ParSet,V] = InitVariables(AMALGAMPar,Extra);
+% Initialize algorithmic variables and other properties
+
+% Define the number of offspring points each algorithm is initially creating
+AMALGAMPar.m = ones(1,AMALGAMPar.q) * floor(AMALGAMPar.N/AMALGAMPar.q);
+
+% Make sure that offspring is of similze size as AMALGAMPar.N
+Delta = AMALGAMPar.N - sum(AMALGAMPar.m);
+
+% Change algorithm with max contribution of points; probably least sensitive
+t = randperm(AMALGAMPar.q); AMALGAMPar.m(t(1)) = AMALGAMPar.m(t(1)) + Delta;
+
+% Define the minimum number of points each algorithm should contribute
+AMALGAMPar.min_m = 5 * ones(1,AMALGAMPar.q);
+
+% Initialize matrix with AMALGAMPar.m as function of Iter
+output.algN = zeros(round(AMALGAMPar.ndraw/AMALGAMPar.N),AMALGAMPar.q + 1);
+
+% Save initial contribution of different algorithms
+output.algN(1,1:AMALGAMPar.q + 1) = [AMALGAMPar.N AMALGAMPar.m];
+
+% Initialize matrix with convergence diagnostics of AMALGAM 
+output.R = zeros(round(AMALGAMPar.ndraw/AMALGAMPar.N),4);
+
+% Define the bounds on the objective functions
+Bounds = [-1000*ones(AMALGAMPar.nobj,1) 1000*ones(AMALGAMPar.nobj,1)];
+% Bounds = [-1000*ones(AMALGAMPar.nobj,1) inf*ones(AMALGAMPar.nobj,1)]; %%
+% TO DO: maybe we need to increase the upper bound depending on the
+% obj-function. Currently using SSE so the value can be very high and
+% require inf. upper bound..
+
+% Define algorithmic parameters
+Extra.Jump = (2.4/sqrt(AMALGAMPar.n)).^2; Extra.m = AMALGAMPar.n;
+
+% Allocate memory for ParSet and initialize this matrix with zeros
+ParSet = zeros(AMALGAMPar.ndraw,AMALGAMPar.n + AMALGAMPar.nobj + 1);
+
+% Initialize the velocity of the particles for PSO
+[V] = InitVel(AMALGAMPar,Extra);
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVel.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVel.m
new file mode 100644
index 0000000..ffabeaa
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/InitVel.m
@@ -0,0 +1,13 @@
+function [V] = InitVel(AMALGAMPar,Extra);
+% Initializes PSO velocity
+V = [];
+
+% Determine the number of complexes that do particle swarm optimization
+NrSwarm = find(strcmp(Extra.Alg,'PSO')==1); Nc = length(NrSwarm);
+if Nc > 0,
+    % First initialize with zeros
+    V = zeros(AMALGAMPar.N,AMALGAMPar.n,AMALGAMPar.q);  
+    for qq = 1:Nc,
+        V(:,:,NrSwarm(qq)) = rand(AMALGAMPar.N,AMALGAMPar.n);
+    end;
+end
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/LHSU.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/LHSU.m
new file mode 100644
index 0000000..3321a08
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/LHSU.m
@@ -0,0 +1,18 @@
+function x = LHSU(xmin,xmax,nsample)
+% Latin Hypercube sampling
+
+% Define the size of xmin
+nvar = length(xmin);
+% Initialize array ran with random numbers
+ran = rand(nsample,nvar);
+% Initialize array s with zeros
+x = zeros(nsample,nvar);
+% Now fill s
+for j= 1:nvar,
+    % Random permutation
+    idx = randperm(nsample);
+    %
+    P =(idx'-ran(:,j))/nsample;
+    %
+    x(:,j) = xmin(j) + P.* (xmax(j)-xmin(j));
+end
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/Metro_ChildGen.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/Metro_ChildGen.m
new file mode 100644
index 0000000..d7d0102
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/Metro_ChildGen.m
@@ -0,0 +1,25 @@
+function [ChildGen] = Metro_ChildGen(ParGen,Ranking,SCEMPar,Extra);
+% Does multi-objective offspring generation
+
+% First determine array size of Parset
+[nmbOfIndivs nmbOfVars] = size(ParGen);
+% Now find which points jave rank 1
+idx = find(Ranking==1);  
+% Then set JumpRate
+JumpRate = Extra.Jump;
+% Calculate the covariance structure
+[TT,pp] = chol(cov(JumpRate * ParGen(idx,1:SCEMPar.n)));
+if pp > 0,
+    % Random point
+    ChildGen = LHSU(min(ParGen(:,1:SCEMPar.n)),max(ParGen(:,1:SCEMPar.n)),nmbOfIndivs);
+end;
+if pp <= 0,
+    % No do Metropolis offspring creation
+    for tt = 1:nmbOfIndivs,
+        muC = ParGen(tt,1:SCEMPar.n);
+        % Generate draw from multi-normal distribution
+        ru = normrnd(0,1,SCEMPar.n,1)';
+        % Now generate new candidate point
+        ChildGen(tt,1:SCEMPar.n) = real(muC+(ru*TT));
+    end;
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/Mutate.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/Mutate.m
new file mode 100644
index 0000000..b0368ea
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/Mutate.m
@@ -0,0 +1,38 @@
+function [ChildGen] = Mutate(ChildGen,AMALGAMPar,ParRange,pM,etaM);
+% This function performs Mutation of Children
+
+% First determine the dimension of the Children
+[nmbOfIndivs,dim] = size(ChildGen);
+% Now loop over individuals
+for qq = 1:nmbOfIndivs,
+    % Loop over individual elements of each individual
+    for jj = 1:AMALGAMPar.n,
+        % Check whether this number if larger than mutation factpr
+        if (rand <= pM),
+            y = ChildGen(qq,jj);
+            % Define boundaries 
+            yl = ParRange.minn(jj); yu = ParRange.maxn(jj);
+            % Normalize range
+            delta1 = (y-yl)/(yu-yl); delta2 = (yu-y)/(yu-yl);
+            % Again draw random number
+            rnd = rand;
+            % Compute mutation power
+            mut_pow = 1/(etaM+1);
+            if rnd <= 0.5,
+                xy = 1-delta1;
+                val = 2.0*rnd+(1.0-2.0*rnd)*(xy^(etaM+1.0));
+                deltaq = val^mut_pow - 1;
+            else
+                xy = 1.0-delta2;
+                val = 2.0*(1.0-rnd)+2.0*(rnd-0.5)*(xy^(etaM+1.0));
+                deltaq = 1.0 - val^mut_pow;
+            end;
+            % Check found bounds
+            y = y + deltaq*(yu-yl);
+            if (y<yl), y = yl; end;
+            if (y>yu), y = yu; end;
+            % Now update ChildGen
+            ChildGen(qq,jj) = y;
+        end;
+    end;
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/NSGA_ChildGen.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/NSGA_ChildGen.m
new file mode 100644
index 0000000..72edbf1
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/NSGA_ChildGen.m
@@ -0,0 +1,8 @@
+function [ChildGen] = NSGA_ChildGen(ParGen,ObjVals,Cviols,CrowdDist,ParRange,AMALGAMPar,pC,etaC,pM,etaM);
+% Written by Jasper Vrugt based on C-code supplied by Deb
+
+% First do selection
+[ChildGen] = Selection(ParGen,Cviols,ObjVals,CrowdDist,ParRange,AMALGAMPar,pC,etaC);
+
+% Do the normal mutation
+[ChildGen] = Mutate(ChildGen,AMALGAMPar,ParRange,pM,etaM);
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ParetoRanking.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/ParetoRanking.m
new file mode 100644
index 0000000..880f2a4
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ParetoRanking.m
@@ -0,0 +1,94 @@
+function [NmbOfFront, CrowdDist] = ParetoRanking(ObjVals,Bounds,Cg,flag);
+% This function ranks the individuals according to:
+% 1. Their degree of constraint violation,
+% 2. The number of their Pareto-optimal front, 
+% 3. The degree to which they are `crowded' by other individuals
+
+% Do normal ranking based on objective function values
+if flag == 0,
+    % First compute size of individual arrays    
+    [nmbOfIndivs nmbOfObjs] = size(ObjVals);
+    % Pareto-optimal fronts
+    Front = {[]};
+    % number of Pareto-optimal front for each individual; 2nd highest priority sorting key
+    NmbOfFront = zeros( nmbOfIndivs, 1);
+    % set of individuals a particular individual dominates
+    Dominated = cell( nmbOfIndivs, 1);
+    % number of individuals by which a particular individual is dominated
+    NmbOfDominating = zeros( nmbOfIndivs, 1);
+
+    for p = 1:nmbOfIndivs,
+        % First replicate current point
+        Ptemp = ObjVals(p,1:nmbOfObjs); Pobj = Ptemp(ones(nmbOfIndivs,1),:);
+        % Then find set of Dominated points
+        [idx] = find((sum(Pobj <= ObjVals,[2]) == nmbOfObjs) & (sum(Pobj < ObjVals,[2]) > 0)); Nidx = length(idx);
+        if Nidx > 0,
+            Dominated{ p} = idx';
+        end
+        % Now find set of Nondominated points
+        [idx] = find((sum(ObjVals <= Pobj,[2]) == nmbOfObjs) & (sum(ObjVals < Pobj,[2]) > 0)); Nidx = length(idx);
+        if Nidx > 0,
+            NmbOfDominating( p) = NmbOfDominating( p) + Nidx;
+        end;
+
+        if NmbOfDominating( p) == 0
+            NmbOfFront( p) = 1;
+            Front{ 1}(end + 1) = p;
+        end
+    end
+    i = 1;
+
+    while ~isempty( Front{ i})
+        NextFront = [];
+        for k = 1:length( Front{ i})
+            p = Front{ i}( k);
+            % Instead of loop try direct implementation
+            q = Dominated{p}; NmbOfDominating( q) = NmbOfDominating( q) - 1;
+            % Now do second line
+            idx = (NmbOfDominating( q)==0); NmbOfFront(q(idx)) = i + 1;
+            % Updat the front
+            NextFront = [NextFront q(idx)];
+        end
+        i = i + 1;
+        Front{ end + 1} = NextFront;
+    end
+end;
+
+% Rank point based on their constraint violation
+if flag == 1,
+    % First compute size of individual arrays  
+    [nmbOfIndivs nmbOfObjs] = size(Cg);
+    % Sort the vector
+    [CgSorted SortIdx] = sort(-Cg);
+    % Generate initial rank
+    [Rank] = [1:nmbOfIndivs]';
+    % Generate final rank
+    NmbOfFront(SortIdx,1) = Rank;
+end;
+
+% Crowding distance assignment
+maxF = max(NmbOfFront);
+% Loop over the individual fronts
+for j = 1:maxF,
+    % First find the particular rank points
+    [SelectIdx] = find(NmbOfFront==j); Ntot = length(SelectIdx);
+    % Select the appropriate points
+    ObjValsSelected = ObjVals(SelectIdx,1:nmbOfObjs);
+    % Re-initialize the crowding distance
+    CrowdDist = zeros(Ntot, 1);
+    % Now loop over the individual objectives
+    for i = 1:nmbOfObjs
+        % First sort the objective function in ascending order
+        [ObjValsSorted SortIdx] = sort( ObjValsSelected( :, i));
+        % Then assign boundary solutions extreme value
+        CrowdDist(SortIdx(1),1) = CrowdDist(SortIdx(1),1) + inf; CrowdDist(SortIdx(Ntot),1) = CrowdDist(SortIdx(Ntot),1) + inf;
+        % Now determine the crowding distance of the other individuals
+        for z = 2:(Ntot - 1),
+            % Now compute the distance of the other solutions and normalize
+            CrowdDist(SortIdx(z),1) = CrowdDist(SortIdx(z),1) + (ObjValsSorted( z + 1) - ObjValsSorted( z - 1));
+        end
+    end
+    Crowd(SelectIdx,1) = CrowdDist;
+end;
+% Assign crowding distance
+CrowdDist = Crowd;
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT.m
new file mode 100644
index 0000000..14a8431
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT.m
@@ -0,0 +1,14 @@
+function [ModPred] = ROT(x,Extra);
+% Rotated problem
+
+% Test function: Rotated problem
+y = Extra.R * x';
+% Now compute g
+g = 1 + 10 * (Extra.m - 1);
+% Update g
+for qq = 2:Extra.m,
+    g = g + y(qq)^2 - 10*cos(4 * pi * y(qq));
+end;
+
+% Define the model output
+ModPred.y = y; ModPred.g = g;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT_front.txt b/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT_front.txt
new file mode 100644
index 0000000..324b561
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ROT_front.txt
@@ -0,0 +1,498 @@
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+  1.1210533e-001  8.9395009e-001
+  1.1338769e-001  8.9280446e-001
+  1.1467078e-001  8.9165965e-001
+  1.1595460e-001  8.9051565e-001
+  1.1723915e-001  8.8937248e-001
+  1.1852442e-001  8.8823012e-001
+  1.1981043e-001  8.8708859e-001
+  1.2109716e-001  8.8594787e-001
+  1.2238463e-001  8.8480798e-001
+  1.2367282e-001  8.8366891e-001
+  1.2496174e-001  8.8253067e-001
+  1.2625139e-001  8.8139325e-001
+  1.2754176e-001  8.8025665e-001
+  1.2883286e-001  8.7912089e-001
+  1.3012469e-001  8.7798594e-001
+  1.3141725e-001  8.7685183e-001
+  1.3271053e-001  8.7571855e-001
+  1.3400454e-001  8.7458609e-001
+  1.3529928e-001  8.7345447e-001
+  1.3659474e-001  8.7232367e-001
+  1.3789093e-001  8.7119371e-001
+  1.3918784e-001  8.7006458e-001
+  1.4048548e-001  8.6893629e-001
+  1.4178384e-001  8.6780882e-001
+  1.4308292e-001  8.6668220e-001
+  1.4438274e-001  8.6555641e-001
+  1.4568327e-001  8.6443145e-001
+  1.4698453e-001  8.6330733e-001
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+  1.5480727e-001  8.5658025e-001
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+  1.5742062e-001  8.5434463e-001
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+  1.6527799e-001  8.4765803e-001
+  1.6659007e-001  8.4654657e-001
+  1.6790287e-001  8.4543595e-001
+  1.6921639e-001  8.4432618e-001
+  1.7053063e-001  8.4321726e-001
+  1.7184558e-001  8.4210920e-001
+  1.7316125e-001  8.4100199e-001
+  1.7447765e-001  8.3989563e-001
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+  1.7711258e-001  8.3768547e-001
+  1.7843112e-001  8.3658168e-001
+  1.7975038e-001  8.3547874e-001
+  1.8107035e-001  8.3437666e-001
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+  1.8503456e-001  8.3107556e-001
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+  1.8768094e-001  8.2887912e-001
+  1.8900520e-001  8.2778220e-001
+  1.9033018e-001  8.2668613e-001
+  1.9165587e-001  8.2559093e-001
+  1.9298227e-001  8.2449659e-001
+  1.9430938e-001  8.2340312e-001
+  1.9563720e-001  8.2231051e-001
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+  1.9829499e-001  8.2012789e-001
+  1.9962495e-001  8.1903788e-001
+  2.0095562e-001  8.1794873e-001
+  2.0228700e-001  8.1686046e-001
+  2.0361909e-001  8.1577305e-001
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+  2.0628539e-001  8.1360085e-001
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+  2.1296355e-001  8.0818559e-001
+  2.1430131e-001  8.0710516e-001
+  2.1563976e-001  8.0602561e-001
+  2.1697893e-001  8.0494693e-001
+  2.1831880e-001  8.0386913e-001
+  2.1965938e-001  8.0279220e-001
+  2.2100066e-001  8.0171615e-001
+  2.2234264e-001  8.0064098e-001
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+  2.2502873e-001  7.9849328e-001
+  2.2637282e-001  7.9742075e-001
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+  2.2906312e-001  7.9527833e-001
+  2.3040932e-001  7.9420845e-001
+  2.3175623e-001  7.9313945e-001
+  2.3310383e-001  7.9207133e-001
+  2.3445214e-001  7.9100409e-001
+  2.3580114e-001  7.8993774e-001
+  2.3715085e-001  7.8887228e-001
+  2.3850125e-001  7.8780770e-001
+  2.3985235e-001  7.8674401e-001
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+  2.6565545e-001  7.6670325e-001
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+  2.8345380e-001  7.5317792e-001
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+  2.8620227e-001  7.5111067e-001
+  2.8757754e-001  7.5007841e-001
+  2.8895348e-001  7.4904705e-001
+  2.9033010e-001  7.4801660e-001
+  2.9170741e-001  7.4698707e-001
+  2.9308539e-001  7.4595844e-001
+  2.9446405e-001  7.4493072e-001
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+  2.9722342e-001  7.4287802e-001
+  2.9995892e-001  7.4084866e-001
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ReflectBounds.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/ReflectBounds.m
new file mode 100644
index 0000000..0d0d555
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ReflectBounds.m
@@ -0,0 +1,16 @@
+function [y] = ReflectBounds(new,ParRange);
+% Checks the bounds of the parameters
+
+% First determine the size of new
+[nmbOfIndivs,Dim] = size(new);
+% Now replicate minn and maxn
+minn = repmat(ParRange.minn,nmbOfIndivs,1); maxn = repmat(ParRange.maxn,nmbOfIndivs,1);
+% Define y
+y = new;
+% Now check whether points are within bond
+[ii] = find(y<minn); y(ii)= 2 * minn(ii) - y(ii); % reflect in minn
+[ii] = find(y>maxn); y(ii)= 2 * maxn(ii) - y(ii); % reflect in maxn
+
+% Now double check if all elements are within bounds
+[ii] = find(y<minn); y(ii) = minn(ii) + rand*(maxn(ii)-minn(ii));
+[ii] = find(y>maxn); y(ii) = minn(ii) + rand*(maxn(ii)-minn(ii));
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/SelBest.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/SelBest.m
new file mode 100644
index 0000000..029306a
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/SelBest.m
@@ -0,0 +1,27 @@
+function [pBest,nBest] = SelBest(NmbOfFront,ParSet,AMALGAMPar,Extra);
+% Selects Pbest and Nbest for the Particle Swarm Optimization
+
+% First remove the solutions that violate constraints
+Cg = ParSet(:,AMALGAMPar.n+1);
+% and select the appropriate ones
+[idx] = find(Cg==0); ParSet = ParSet(idx,:);
+
+% Now compute pBest and nBest;
+pBest = []; nBest = [];
+% Determine the number of complexes that do particle swarm optimization
+Complex = find(strcmp(Extra.Alg,'PSO')==1); Nc = length(Complex);
+% Other approach -- first find minimum of individual objectives
+minF = min(ParSet(:,AMALGAMPar.n+2:end)); minF = minF(ones(length(ParSet),1),:);
+% Now determine Euclidean distance from these optimal solutions
+[T] = sqrt(sum(((ParSet(:,AMALGAMPar.n+2:end)-minF).^2),[2]));
+% Sort T and determine idx
+[dummy,idx] = sort(T);
+% Now sort ParSet
+P = ParSet(idx,1:AMALGAMPar.n);
+
+if Nc > 0,
+    % Initialize pBest with values
+    pBest = repmat(P(1,1:AMALGAMPar.n),AMALGAMPar.N,1);
+    % Now initialize nBest with values
+    nBest = P(1:AMALGAMPar.N,1:AMALGAMPar.n);  
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/Selection.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/Selection.m
new file mode 100644
index 0000000..6c23254
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/Selection.m
@@ -0,0 +1,190 @@
+function [ChildGen] = Selection(ParGen,Cviols,ObjVals,CrowdDist,ParRange,AMALGAMPar,pC,etaC);
+% Function performs genetic selection and crossover
+
+% First determine the dimension of the Children
+[nmbOfIndivs,dim] = size(ParGen);
+
+
+% Define a1 and a2
+a1 = randperm(nmbOfIndivs); a2 = randperm(nmbOfIndivs);
+
+% Initialize the new Children Generation
+ChildGen = [];
+% Now do tournament selection and crossover
+for qq = 0:4:nmbOfIndivs-4,
+    % Select parents
+    a_1 = a1(qq+1); a_2 = a1(qq+2); a_3 = a1(qq+3); a_4 = a1(qq+4);
+
+    parent1 = Tournament(ParGen(a_1,:),ParGen(a_2,:),Cviols(a_1,1),Cviols(a_2,1),ObjVals(a_1,:),ObjVals(a_2,:),...
+        CrowdDist(a_1,1),CrowdDist(a_2,1),AMALGAMPar);
+    parent2 = Tournament(ParGen(a_3,:),ParGen(a_4,:),Cviols(a_3,1),Cviols(a_4,1),ObjVals(a_3,:),ObjVals(a_4,:),...
+        CrowdDist(a_3,1),CrowdDist(a_4,1),AMALGAMPar);
+    % Now do crossover
+    [child1,child2] = Crossover(parent1,parent2,AMALGAMPar,ParRange,pC,etaC); 
+    % Append the children to the offspring 
+    ChildGen = [ChildGen;child1;child2];
+    
+    % Select parents
+    a_1 = a2(qq+1); a_2 = a2(qq+2); a_3 = a2(qq+3); a_4 = a2(qq+4);
+    
+    parent1 = Tournament(ParGen(a_1,:),ParGen(a_2,:),Cviols(a_1,1),Cviols(a_2,1),ObjVals(a_1,:),ObjVals(a_2,:),...
+        CrowdDist(a_1,1),CrowdDist(a_2,1),AMALGAMPar);
+    parent2 = Tournament(ParGen(a_3,:),ParGen(a_4,:),Cviols(a_3,1),Cviols(a_4,1),ObjVals(a_3,:),ObjVals(a_4,:),...
+        CrowdDist(a_3,1),CrowdDist(a_4,1),AMALGAMPar);
+    % Now do crossover
+    [child1,child2] = Crossover(parent1,parent2,AMALGAMPar,ParRange,pC,etaC); 
+    % Append the children to the offspring
+    ChildGen = [ChildGen;child1;child2];
+    
+end;
+
+% Check whether size of 
+if (size(ChildGen,1) < nmbOfIndivs),
+    % Possible to miss 2 children if population size does not divide by 4 
+    a_1 = a1(end-1); a_2 = a1(end); a_3 = a1(round(0.5*nmbOfIndivs)); a_4 = a1(round(0.5*nmbOfIndivs)); 
+    % Select two parents
+    parent1 = Tournament(ParGen(a_1,:),ParGen(a_2,:),Cviols(a_1,1),Cviols(a_2,1),ObjVals(a_1,:),ObjVals(a_2,:),...
+        CrowdDist(a_1,1),CrowdDist(a_2,1),AMALGAMPar);
+     parent2 = Tournament(ParGen(a_3,:),ParGen(a_4,:),Cviols(a_3,1),Cviols(a_4,1),ObjVals(a_3,:),ObjVals(a_4,:),...
+        CrowdDist(a_3,1),CrowdDist(a_4,1),AMALGAMPar);
+    % Now do crossover
+    [child1,child2] = Crossover(parent1,parent2,AMALGAMPar,ParRange,pC,etaC); ChildGen = [ChildGen;child1;child2];
+end; 
+    
+% ------------------------------------------------------------------------
+function [parent] = Tournament(a,b,cviola,cviolb,obja,objb,CrowdDa,CrowdDb,AMALGAMPar);
+% Function performs tournament selection
+
+% First draw random number
+rnd = rand; done = 0;
+% First check dominance
+
+flagout = Check_dominance(cviola,cviolb,obja,objb,AMALGAMPar);
+% Now check various flags
+if flagout == 1,
+    parent = a; done = 1;
+end
+if flagout == -1,
+    parent = b; done = 1;
+end;
+if (CrowdDa > CrowdDb) & (done == 0),
+    parent = a; done = 1;
+end;
+if (CrowdDa < CrowdDb) & (done == 0),
+    parent = b; done = 1;
+end;
+if (rnd <= 0.5) & (done == 0),
+    parent = a; 
+end;
+if (rnd > 0.5) & (done == 0),
+    parent = b;
+end;
+
+% ------------------------------------------------------------------------
+function [flagout] = Check_dominance(cviola,cviolb,obja,objb,AMALGAMPar);
+% Function checks for dominance
+
+% First initialize some important variables
+flag1 = 0; flag2 = 0;
+% Now start loop to determine flag
+if (cviola < 0) & (cviolb < 0),
+    % If overall contraint violation a < b -> select individual a
+    if cviola > cviolb,
+        flagout = 1,
+    end;
+    % If overall contraint violation a > b -> select individual b
+    if cviola < cviolb,
+        flagout = -1,
+    end
+    if cviola == cviolb,
+        flagout = 0;
+    end;
+end;
+% If contraint violation a = 0 and b < 0 -> select individual a
+if (cviola == 0) & (cviolb < 0),
+    flagout = 1;
+end;
+% If contraint violation a < 0 and b = 0 -> select individual b
+if (cviola < 0) & (cviolb == 0),
+    flagout = -1;
+end;
+% If there is no contraint violation, do objective function analysis
+if (cviola == 0) & (cviolb == 0),
+    for qq = 1:AMALGAMPar.nobj,
+        if obja(qq) < objb(qq),
+            flag1 = 1;
+        end;
+        if obja(qq) > objb(qq),
+            flag2 = 1;
+        end;
+    end;
+    if (flag1==1) & (flag2==0),
+        flagout = 1;
+    end
+    if (flag1==0) & (flag2==1),
+        flagout = -1;
+    end;
+    if (flag1==1) & (flag2==1),
+        flagout = 0;
+    end;
+    if (flag1==0) & (flag2==0),
+        flagout = 0;
+    end;
+end;
+
+% ------------------------------------------------------------------------
+function [child1,child2] = Crossover(parent1,parent2,AMALGAMPar,ParRange,pC,etaC);
+% Function performs crossover of two individuals
+
+newgen = []; EPS = 1e-14; 
+% Now do if loop
+if (rand <= pC),
+    for qq = 1:AMALGAMPar.n,
+        if (rand <= 0.5),
+            if abs(parent1(qq)-parent2(qq)) > EPS,
+                if parent1(qq) < parent2(qq),
+                    y1 = parent1(qq); y2 = parent2(qq);
+                else
+                    y1 = parent2(qq); y2 = parent1(qq);
+                end
+                yl = ParRange.minn(qq); yu = ParRange.maxn(qq);
+                rnd = rand;
+                beta = 1.0 + (2.0*(y1-yl)/(y2-y1)); alpha = 2.0 - beta^(-(etaC+1.0));
+
+                if (rnd <= (1.0/alpha)),
+                    betaq = (rnd*alpha)^(1.0/(etaC+1.0));
+                else
+                    betaq = (1.0/(2.0 - rnd*alpha))^(1.0/(etaC+1.0));
+                end;
+
+                c1 = 0.5*((y1+y2)-betaq*(y2-y1));
+                beta = 1.0 + (2.0*(yu-y2)/(y2-y1));
+                alpha = 2.0 - beta^(-(etaC+1.0));
+
+                if (rnd <= (1.0/alpha)),
+                    betaq = (rnd*alpha)^(1.0/(etaC+1.0));
+                else
+                    betaq = (1.0/(2.0 - rnd*alpha))^(1.0/(etaC+1.0));
+                end;
+
+                c2 = 0.5*((y1+y2)+betaq*(y2-y1));
+                if (c1<yl), c1=yl; end;
+                if (c2<yl), c2=yl; end;
+                if (c1>yu), c1=yu; end;
+                if (c2>yu), c2=yu; end;
+
+                if (rand<=0.5),
+                    child1(qq) = c2; child2(qq) = c1;
+                else
+                    child1(qq) = c1; child2(qq) = c2;
+                end;
+            else
+                child1(qq) = parent1(qq); child2(qq) = parent2(qq);
+            end;
+        else
+            child1(qq) = parent1(qq); child2(qq) = parent2(qq);
+        end;
+    end;
+else
+    child1 = parent1; child2 = parent2;
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/SetToBounds.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/SetToBounds.m
new file mode 100644
index 0000000..47ee3d0
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/SetToBounds.m
@@ -0,0 +1,12 @@
+function [y] = SetToBounds(new,ParRange);
+% Checks the bounds of the parameters
+
+% First determine the size of new
+[nmbOfIndivs,Dim] = size(new);
+% Now replicate minn and maxn
+minn = repmat(ParRange.minn,nmbOfIndivs,1); maxn = repmat(ParRange.maxn,nmbOfIndivs,1);
+% Define y
+y = new;
+% Now check whether points are within bond
+[ii] = find(y<minn); y(ii)= minn(ii); % set to bound value
+[ii] = find(y>maxn); y(ii)= maxn(ii); % set to bound value
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/Swarm_ChildGen.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/Swarm_ChildGen.m
new file mode 100644
index 0000000..c730f87
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/Swarm_ChildGen.m
@@ -0,0 +1,20 @@
+function [ChildGen,Vnew] = Swarm_ChildGen(ParGen,Vold,pBest,nBest,SCEMPar,Extra);
+% Performs particle swarm optimization
+
+% Determine the size of the Swarm
+[SwarmSize, Dim] = size(ParGen);
+% Determine the value of intertia and weights
+w =  0.5 + (rand/2); c1 = 1.5; c2 = c1; Chi = 1; % => Did all my tests like this
+%w = 0.5 + (rand/2); c1 = 0.5; c2 = c1; Chi = 1;
+% Generate Random Numbers
+R1 = rand(SwarmSize, Dim); R2 = rand(SwarmSize, Dim);
+% Calculate Velocity
+Vnew = w*Vold + c1*R1.*(pBest-ParGen) + c2*R2.*(nBest-ParGen);
+% Update the position of the particles
+ChildGen = ParGen + Chi * Vnew;
+% Now do random change
+U = unifrnd(-1,1,SwarmSize,1);
+% Do loop
+for qq = 1:SwarmSize,
+    ChildGen(qq,1:SCEMPar.n) = ChildGen(qq,1:SCEMPar.n) + U(qq,1).*ChildGen(qq,1:SCEMPar.n);
+end;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4.m
new file mode 100644
index 0000000..f8d5878
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4.m
@@ -0,0 +1,16 @@
+function [ModPred] = ZT_4(x,Extra);
+% Zitzler and Thiele function 4: Multimodal Pareto optimal front with 21^9 local Pareto fronts
+
+% Define f
+f = x(1);
+% Define g
+g = 1 + 10 * (Extra.m - 1);
+% Update g
+for qq = 2:Extra.m,
+    g = g + x(qq)^2 - 10*cos(4*pi*x(qq));
+end;
+% Define h
+h = 1 - sqrt(f/g);
+
+% Define the model output
+ModPred.f = f; ModPred.g = g; ModPred.h = h;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4_front.txt b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4_front.txt
new file mode 100644
index 0000000..0a0788e
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_4_front.txt
@@ -0,0 +1,498 @@
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diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6.m
new file mode 100644
index 0000000..8c9a514
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6.m
@@ -0,0 +1,10 @@
+function [ModPred] = ZT_6(x,Extra);
+% Zitzler and Thiele function 6: Pareto-optimal front consists of several noncontiguous convex parts
+
+% Calculate f, g and h
+f = 1 - exp(-4 * x(1)) * sin((6 * pi * x(1))).^6;
+g = 1 + 9*(sum(x(2:end)./(Extra.m - 1)))^(1/4);
+h = 1 - (f / g)^2;
+
+% Define the model output
+ModPred.f = f; ModPred.g = g; ModPred.h = h;
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6_front.txt b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6_front.txt
new file mode 100644
index 0000000..fe844d0
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/ZT_6_front.txt
@@ -0,0 +1,498 @@
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+  9.9478336e-001  1.0405363e-002
+  9.9582866e-001  8.3252789e-003
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diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/binary.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/binary.m
new file mode 100644
index 0000000..25955fd
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/binary.m
@@ -0,0 +1,23 @@
+function b = binary(x)
+
+% BINARY  Binary representation of decimal integers.
+%	B=BINARY(X) Returns matrx B with rows
+%	representing binary form of each element of
+%	vector X.
+
+%  Kirill K. Pankratov, kirill@plume.mit.edu
+%  03/02/95
+
+x = x(:);
+
+m2 = nextpow2(max(x));
+v2 = 2.^(0:m2);
+b = zeros(length(x),m2);
+af = x-floor(x);
+
+for jj = m2:-1:1
+    a = x>=v2(jj);
+    x = x-a*v2(jj);
+    b(:,m2-jj+1) = a+1/2*(af>1/v2(jj));
+end
+
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/bound.txt b/algorithms/calibration/AMALGAM-Sequential-V1.2/bound.txt
new file mode 100644
index 0000000..076d621
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/bound.txt
@@ -0,0 +1,3717 @@
+  2.8000000e+001  7.0000000e+000  1.9520000e+003  2.3503000e+000  6.7965000e+000  0.0000000e+000  2.6534000e+000  1.3484000e+000  1.3220700e+001
+  2.9000000e+001  7.0000000e+000  1.9520000e+003  2.3786000e+000  5.1718000e+000  1.0490000e-001  2.1550000e-001  1.5552000e+000  4.6142000e+000
+  3.0000000e+001  7.0000000e+000  1.9520000e+003  2.7184000e+000  4.2819000e+000  0.0000000e+000  1.3675000e+000  0.0000000e+000  1.8233000e+000
+  3.1000000e+001  7.0000000e+000  1.9520000e+003  3.0299000e+000  4.8400000e+000  0.0000000e+000  0.0000000e+000  2.4527000e+000  3.3000000e-003
+  1.0000000e+000  8.0000000e+000  1.9520000e+003  3.8228000e+000  2.5549000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.0000000e+000  8.0000000e+000  1.9520000e+003  3.4264000e+000  4.3532000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  3.0000000e+000  8.0000000e+000  1.9520000e+003  2.8317000e+000  4.1424000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  4.0000000e+000  8.0000000e+000  1.9520000e+003  2.6052000e+000  5.5749000e+000  0.0000000e+000  0.0000000e+000  4.6587000e+000  1.0640000e-001
+  5.0000000e+000  8.0000000e+000  1.9520000e+003  2.4636000e+000  6.1330000e+000  0.0000000e+000  2.1160000e-001  1.0863600e+001  0.0000000e+000
+  6.0000000e+000  8.0000000e+000  1.9520000e+003  3.2282000e+000  4.8834000e+000  8.4000000e-003  2.5494000e+000  6.9890000e+000  0.0000000e+000
+  7.0000000e+000  8.0000000e+000  1.9520000e+003  6.4563000e+000  5.1811000e+000  2.3000000e-003  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  8.0000000e+000  8.0000000e+000  1.9520000e+003  4.0210000e+000  5.1439000e+000  0.0000000e+000  6.9620000e-001  2.0509000e+000  5.1270000e-001
+  9.0000000e+000  8.0000000e+000  1.9520000e+003  3.7095000e+000  2.4929000e+000  6.5000000e-003  3.1697000e+000  0.0000000e+000  0.0000000e+000
+  1.0000000e+001  8.0000000e+000  1.9520000e+003  3.9644000e+000  4.1734000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.1000000e+001  8.0000000e+000  1.9520000e+003  3.3981000e+000  4.0401000e+000  0.0000000e+000  1.6793000e+000  0.0000000e+000  0.0000000e+000
+  1.2000000e+001  8.0000000e+000  1.9520000e+003  2.9450000e+000  2.7874000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.3000000e+001  8.0000000e+000  1.9520000e+003  2.7751000e+000  3.3207000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.4000000e+001  8.0000000e+000  1.9520000e+003  2.7468000e+000  5.2214000e+000  0.0000000e+000  1.2763000e+000  0.0000000e+000  0.0000000e+000
+  1.5000000e+001  8.0000000e+000  1.9520000e+003  2.6901000e+000  4.9144000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.6000000e+001  8.0000000e+000  1.9520000e+003  2.4353000e+000  5.3795000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.7000000e+001  8.0000000e+000  1.9520000e+003  2.3220000e+000  6.6291000e+000  0.0000000e+000  4.0300000e-001  3.0000000e-004  6.2950000e-001
+  1.8000000e+001  8.0000000e+000  1.9520000e+003  2.2937000e+000  4.9578000e+000  0.0000000e+000  0.0000000e+000  3.4919600e+001  7.8657000e+000
+  1.9000000e+001  8.0000000e+000  1.9520000e+003  3.0016000e+000  4.2354000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.0000000e+001  8.0000000e+000  1.9520000e+003  5.9466000e+000  4.6261000e+000  0.0000000e+000  3.0228000e+000  0.0000000e+000  0.0000000e+000
+  2.1000000e+001  8.0000000e+000  1.9520000e+003  3.7945000e+000  3.2184000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.2000000e+001  8.0000000e+000  1.9520000e+003  3.8228000e+000  5.2989000e+000  0.0000000e+000  4.9037000e+000  1.9637000e+000  0.0000000e+000
+  2.3000000e+001  8.0000000e+000  1.9520000e+003  3.3131000e+000  3.4572000e+000  0.0000000e+000  1.0372000e+000  8.0700000e-002  0.0000000e+000
+  2.4000000e+001  8.0000000e+000  1.9520000e+003  3.4264000e+000  3.0014000e+000  0.0000000e+000  0.0000000e+000  6.0918000e+000  0.0000000e+000
+  2.5000000e+001  8.0000000e+000  1.9520000e+003  3.2282000e+000  2.6665000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
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+  3.0000000e+000  8.0000000e+000  1.9620000e+003  2.8600300e+001  2.7135000e+000  0.0000000e+000  1.4817000e+000  5.5742000e+000  3.1010000e-001
+  4.0000000e+000  8.0000000e+000  1.9620000e+003  1.9935300e+001  4.2626000e+000  0.0000000e+000  2.0570000e-001  0.0000000e+000  0.0000000e+000
+  5.0000000e+000  8.0000000e+000  1.9620000e+003  1.0250800e+001  4.7894000e+000  0.0000000e+000  0.0000000e+000  2.6911000e+000  0.0000000e+000
+  6.0000000e+000  8.0000000e+000  1.9620000e+003  7.5890000e+000  6.8810000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  7.0000000e+000  8.0000000e+000  1.9620000e+003  6.7961000e+000  6.9755000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  8.0000000e+000  8.0000000e+000  1.9620000e+003  6.2015000e+000  7.4478000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  9.0000000e+000  8.0000000e+000  1.9620000e+003  5.6634000e+000  8.2877000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.0000000e+001  8.0000000e+000  1.9620000e+003  5.0971000e+000  7.1434000e+000  0.0000000e+000  2.6870000e-001  0.0000000e+000  0.0000000e+000
+  1.1000000e+001  8.0000000e+000  1.9620000e+003  4.8139000e+000  7.7474000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.2000000e+001  8.0000000e+000  1.9620000e+003  4.5307000e+000  7.7331000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.3000000e+001  8.0000000e+000  1.9620000e+003  4.4175000e+000  5.1989000e+000  0.0000000e+000  0.0000000e+000  6.7170000e-001  0.0000000e+000
+  1.4000000e+001  8.0000000e+000  1.9620000e+003  4.8705000e+000  5.1412000e+000  0.0000000e+000  0.0000000e+000  1.2133000e+000  0.0000000e+000
+  1.5000000e+001  8.0000000e+000  1.9620000e+003  5.0404000e+000  3.8617000e+000  0.0000000e+000  4.2730000e-001  1.1081600e+001  1.1281000e+000
+  1.6000000e+001  8.0000000e+000  1.9620000e+003  4.8139000e+000  3.2539000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.7000000e+001  8.0000000e+000  1.9620000e+003  4.6723000e+000  6.4451000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  4.0640000e+000
+  1.8000000e+001  8.0000000e+000  1.9620000e+003  4.6723000e+000  6.3019000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.9000000e+001  8.0000000e+000  1.9620000e+003  4.5307000e+000  6.2271000e+000  0.0000000e+000  0.0000000e+000  1.3015000e+000  0.0000000e+000
+  2.0000000e+001  8.0000000e+000  1.9620000e+003  4.5307000e+000  5.2461000e+000  0.0000000e+000  0.0000000e+000  2.0990000e-001  0.0000000e+000
+  2.1000000e+001  8.0000000e+000  1.9620000e+003  4.3892000e+000  3.5281000e+000  0.0000000e+000  0.0000000e+000  1.1755000e+000  0.0000000e+000
+  2.2000000e+001  8.0000000e+000  1.9620000e+003  4.3892000e+000  3.8390000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.3000000e+001  8.0000000e+000  1.9620000e+003  4.2476000e+000  4.5813000e+000  0.0000000e+000  0.0000000e+000  1.1810000e-001  1.0860000e-001
+  2.4000000e+001  8.0000000e+000  1.9620000e+003  4.2476000e+000  3.7488000e+000  3.5602000e+000  0.0000000e+000  0.0000000e+000  1.0500000e-001
+  2.5000000e+001  8.0000000e+000  1.9620000e+003  7.0793000e+000  1.7946000e+000  1.1965000e+000  0.0000000e+000  2.0150000e-001  0.0000000e+000
+  2.6000000e+001  8.0000000e+000  1.9620000e+003  9.3447000e+000  3.3100000e+000  0.0000000e+000  0.0000000e+000  1.5492000e+000  0.0000000e+000
+  2.7000000e+001  8.0000000e+000  1.9620000e+003  7.0793000e+000  5.5941000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.8000000e+001  8.0000000e+000  1.9620000e+003  5.6634000e+000  5.5198000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.9000000e+001  8.0000000e+000  1.9620000e+003  4.8139000e+000  3.0373000e+000  0.0000000e+000  0.0000000e+000  1.8515000e+000  0.0000000e+000
+  3.0000000e+001  8.0000000e+000  1.9620000e+003  4.5307000e+000  4.9055000e+000  0.0000000e+000  0.0000000e+000  9.6600000e-002  0.0000000e+000
+  3.1000000e+001  8.0000000e+000  1.9620000e+003  4.2476000e+000  4.5308000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.0000000e+000  9.0000000e+000  1.9620000e+003  3.9644000e+000  4.9824000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.0000000e+000  9.0000000e+000  1.9620000e+003  3.8228000e+000  5.0549000e+000  0.0000000e+000  0.0000000e+000  8.3970000e-001  0.0000000e+000
+  3.0000000e+000  9.0000000e+000  1.9620000e+003  3.6812000e+000  5.3439000e+000  0.0000000e+000  0.0000000e+000  8.4000000e-002  0.0000000e+000
+  4.0000000e+000  9.0000000e+000  1.9620000e+003  3.6812000e+000  5.5833000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  2.0000000e-004
+  5.0000000e+000  9.0000000e+000  1.9620000e+003  3.6529000e+000  3.1917000e+000  4.1960000e-001  5.0429000e+000  1.6513800e+001  1.8230000e-001
+  6.0000000e+000  9.0000000e+000  1.9620000e+003  5.0404000e+000  3.5939000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  7.0000000e+000  9.0000000e+000  1.9620000e+003  5.6917000e+000  2.3464000e+000  2.1410000e-001  2.5190000e-001  0.0000000e+000  0.0000000e+000
+  8.0000000e+000  9.0000000e+000  1.9620000e+003  4.4175000e+000  2.2992000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  9.0000000e+000  9.0000000e+000  1.9620000e+003  4.3608000e+000  3.2793000e+000  0.0000000e+000  0.0000000e+000  1.1450000e-001  7.6860000e+000
+  1.0000000e+001  9.0000000e+000  1.9620000e+003  7.5890000e+000  3.7643000e+000  0.0000000e+000  0.0000000e+000  2.6282000e+000  0.0000000e+000
+  1.1000000e+001  9.0000000e+000  1.9620000e+003  5.0404000e+000  4.7772000e+000  0.0000000e+000  0.0000000e+000  8.5856000e+000  0.0000000e+000
+  1.2000000e+001  9.0000000e+000  1.9620000e+003  4.6157000e+000  4.8511000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.3000000e+001  9.0000000e+000  1.9620000e+003  4.4741000e+000  5.1530000e+000  0.0000000e+000  2.0150000e-001  0.0000000e+000  0.0000000e+000
+  1.4000000e+001  9.0000000e+000  1.9620000e+003  4.0777000e+000  3.7513000e+000  0.0000000e+000  4.0630000e-001  4.2652600e+001  1.7400000e-002
+  1.5000000e+001  9.0000000e+000  1.9620000e+003  1.1156900e+001  2.2361000e+000  0.0000000e+000  2.9250000e-001  0.0000000e+000  2.1000000e-002
+  1.6000000e+001  9.0000000e+000  1.9620000e+003  6.8244000e+000  2.9261000e+000  0.0000000e+000  0.0000000e+000  1.5954000e+000  9.6600000e-002
+  1.7000000e+001  9.0000000e+000  1.9620000e+003  5.3519000e+000  3.4230000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.8000000e+001  9.0000000e+000  1.9620000e+003  4.3608000e+000  4.1149000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  1.9000000e+001  9.0000000e+000  1.9620000e+003  3.9644000e+000  5.3350000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.0000000e+001  9.0000000e+000  1.9620000e+003  3.7662000e+000  5.4795000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.1000000e+001  9.0000000e+000  1.9620000e+003  3.5680000e+000  5.7278000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.2000000e+001  9.0000000e+000  1.9620000e+003  3.4547000e+000  4.7755000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.3000000e+001  9.0000000e+000  1.9620000e+003  3.4547000e+000  5.0611000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.4000000e+001  9.0000000e+000  1.9620000e+003  3.3981000e+000  4.1354000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.5000000e+001  9.0000000e+000  1.9620000e+003  3.3697000e+000  3.7895000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.6000000e+001  9.0000000e+000  1.9620000e+003  3.3697000e+000  9.8620000e-001  0.0000000e+000  1.2600000e-001  0.0000000e+000  0.0000000e+000
+  2.7000000e+001  9.0000000e+000  1.9620000e+003  3.3131000e+000  4.6725000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.8000000e+001  9.0000000e+000  1.9620000e+003  3.2565000e+000  4.4010000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  2.9000000e+001  9.0000000e+000  1.9620000e+003  3.2282000e+000  4.9763000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
+  3.0000000e+001  9.0000000e+000  1.9620000e+003  3.1715000e+000  4.9871000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000  0.0000000e+000
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/combin.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/combin.m
new file mode 100644
index 0000000..30e9cae
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/combin.m
@@ -0,0 +1,42 @@
+function  C = combin(n,m)
+% COMBIN  Combinations of N choose M.
+%	C=COMBIN(M,N) where N>=M, M and N are
+%	positive integers returns a matrix C of the
+%	size N!/(M!*(N-M)!) by N with rows containing
+%	all possible combinations of N choose M.
+
+%  Kirill K. Pankratov,  kirill@plume.mit.edu
+%  03/19/95
+
+% Handle input ..........................
+if nargin<2,
+    error('  Not enough input arguments.')
+end
+m = fix(m(1));
+n = fix(n(1));
+if n<0 | m<0
+    error(' In COMBIN(N,M) N and M must be positive integers')
+end
+if m>n
+    error(' In COMBIN(N,M) N must be greater than M')
+end
+
+% Take care of simple cases .............
+if m==0,   C = zeros(1,m); return, end
+if m==n,   C = ones(1,m);  return, end
+if m==1,   C = eye(n);     return, end
+if m==n-1, C = ~eye(n);    return, end
+
+% Calculate sizes and limits ............
+n2 = 2^n-1;
+m2 = 2^m-1;
+mn2 = 2^(m-n)-1;
+
+% Binary representation .................
+C = binary(m2:n2-mn2);
+
+% Now choose only those with sum equal m
+s = sum(C');
+C = C(find(s==m),:);
+
+
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/excess.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/excess.m
new file mode 100644
index 0000000..ef7c0bf
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/excess.m
@@ -0,0 +1,13 @@
+function [UT1,UT2,xn] = excess(x_loss,cmax,bexp,Pval,PETval);
+% this function calculates excess precipitation and evaporation
+
+xn_prev = x_loss;
+ct_prev = cmax*(1-power((1-((bexp+1)*(xn_prev)/cmax)),(1/(bexp+1))));
+UT1 = max((Pval-cmax+ct_prev),0.0);
+Pval = Pval-UT1;
+dummy = min(((ct_prev+Pval)/cmax),1);
+xn = (cmax/(bexp+1))*(1-power((1-dummy),(bexp+1)));
+UT2 = max(Pval-(xn-xn_prev),0);
+evap = min(xn,PETval);
+xn = xn-evap;
+
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/hymod.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/hymod.m
new file mode 100644
index 0000000..7478f0e
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/hymod.m
@@ -0,0 +1,35 @@
+function [SimRR] = hymod(x,Extra); 
+% Runs the HYMOD model
+
+% Define the rainfall
+PET = Extra.PET; Precip = Extra.Precip; MaxT = Extra.MaxT;
+% Define the parameters
+cmax = x(1); bexp = x(2); alpha = x(3); Rs = x(4); Rq = x(5);
+% HYMOD PROGRAM IS SIMPLE RAINFALL RUNOFF MODEL
+x_loss = 0.0;
+% Initialize slow tank state
+x_slow = 2.3503/(Rs*22.5);
+% Initialize state(s) of quick tank(s)
+x_quick(1:3,1) = 0; t=1; outflow = [];
+% START PROGRAMMING LOOP WITH DETERMINING RAINFALL - RUNOFF AMOUNTS
+while t < MaxT+1,
+   Pval = Precip(t,1); PETval = PET(t,1);
+   % Compute excess precipitation and evaporation
+   [UT1,UT2,x_loss] = excess(x_loss,cmax,bexp,Pval,PETval);
+   % Partition UT1 and UT2 into quick and slow flow component
+   UQ = alpha*UT2 + UT1; US = (1-alpha)*UT2;
+   % Route slow flow component with single linear reservoir
+   inflow = US; [x_slow,outflow] = linres(x_slow,inflow,outflow,Rs); QS = outflow;
+   % Route quick flow component with linear reservoirs
+   inflow = UQ; k = 1; 
+   while k < 4,
+      [x_quick(k),outflow] = linres(x_quick(k),inflow,outflow,Rq); inflow = outflow; 
+      k = k+1;
+   end;
+   % Compute total flow for timestep
+   output(t,1) = (QS + outflow)*22.5;
+   % Update the time
+   t = t+1;   
+end;
+% Define the output using spin up period of 65 days
+SimRR = output(65:MaxT,1);
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/linres.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/linres.m
new file mode 100644
index 0000000..7d5d24a
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/linres.m
@@ -0,0 +1,4 @@
+function [x_slow,outflow] = linres(x_slow,inflow,outflow,Rs);
+% Linear reservoir
+x_slow = (1-Rs)*x_slow + (1-Rs)*inflow;
+outflow = (Rs/(1-Rs))*x_slow;
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/rotmat.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/rotmat.m
new file mode 100644
index 0000000..a9041e1
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/rotmat.m
@@ -0,0 +1,24 @@
+function  R = rotmat(d,th);
+% Rotational (unitary) matrix.
+
+% Component pairs ..........................
+C = combin(d,2);
+n = size(C,1);
+[i1,i2] = find(C');
+i1 = fliplr(reshape(i1,2,n));
+
+% Angles ....................
+thr = ones(n,1) * th;
+thr(1:length(th)) = th;
+
+c = cos(thr);
+s = sin(thr);
+
+R = eye(d);
+for jj = 1:n
+    ii = i1(:,jj);
+    cc = c(jj); ss = s(jj);
+    A = eye(d);
+    A(ii,ii) = [cc ss; -ss cc];
+    R = R*A;
+end
\ No newline at end of file
diff --git a/algorithms/calibration/AMALGAM-Sequential-V1.2/runAMALGAM.m b/algorithms/calibration/AMALGAM-Sequential-V1.2/runAMALGAM.m
new file mode 100644
index 0000000..1ae6d67
--- /dev/null
+++ b/algorithms/calibration/AMALGAM-Sequential-V1.2/runAMALGAM.m
@@ -0,0 +1,248 @@
+% --------------------------------------------------------------------------------------------- %
+%                                                                                               %
+%        AA      MM       MM     AA      LL            GGGGGGGGG       AA      MM       MM      %
+%        AA      MM       MM     AA      LL           GG               AA      MM       MM      %
+%        AA      MM       MM     AA      LL           GG               AA      MM       MM      %
+%       AAAA     MM       MM    AAAA     LL           GG              AAAA     MM       MM      %
+%       AAAA     MM       MM    AAAA     LL           GG              AAAA     MM       MM      %
+%      AA  AA    MMM     MMM   AA  AA    LL           GG             AA  AA    MMM     MMM      %
+%      AA  AA    MMMM   MMMM   AA  AA    LL           GG   GGGGGG    AA  AA    MMMM   MMMM      %
+%      AAAAAA    MM MM MM MM   AAAAAA    LL           GG   GGGGGG    AAAAAA    MM MM MM MM      %
+%     AA    AA   MM  MMM  MM  AA    AA   LL           GG       GG   AA    AA   MM  MMM  MM      %
+%     AA    AA   MM       MM  AA    AA   LL           GG       GG   AA    AA   MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GG       GG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GG       GG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LL           GGGGGGGGGGG  AA      AA  MM       MM      %
+%    AA      AA  MM       MM AA      AA  LLLLLLLLLLL   GGGGGGGGG   AA      AA  MM       MM      %
+%                                                                                               %
+% --------------------------------------------------------------------------------------------- %
+
+% ------------------ The AMALGAM multiobjective optimization algorithm ------------------------ % 
+%                                                                                               %
+% This general purpose MATLAB code is designed to find a set of parameter values that defines   %
+% the Pareto trade-off surface corresponding to a vector of different objective functions. In   %
+% principle, each Pareto solution is a different weighting of the objectives used. Therefore,   %
+% one could use multiple trials with a single objective optimization algorithms using diferent  %
+% values of the weights to find different Pareto solutions. However, various contributions to   %
+% the optimization literature have demonstrated that this approach is rather inefficient. The   %
+% AMALGAM code developed herein is designed to find an approximation of the Pareto solution set %
+% within a single optimization run. The AMALGAM method combines two new concepts,               %
+% simultaneous multimethod search, and self-adaptive offspring creation, to ensure a fast,      %
+% reliable, and computationally efficient solution to multiobjective optimization problems. 	%
+% This method is called a multi-algorithm, genetically adaptive multiobjective, or AMALGAM, 	%
+% method, to evoke the image of a procedure that blends the attributes of the best available 	%
+% individual optimization algorithms.                                                           %
+%                                                                                               %
+% This algorithm has been described in:                                                         %
+%                                                                                               %
+% J.A. Vrugt, and B.A. Robinson, Improved evolutionary optimization from genetically adaptive   %
+%    multimethod search, Proceedings of the National Academy of Sciences of the United States   %
+%    of America, 104, 708 - 711, doi:10.1073/pnas.0610471104, 2007.                             %
+%                                                                                               %
+% J.A. Vrugt, B.A. Robinson, and J.M. Hyman, Self-adaptive multimethod search for global        %
+%    optimization in real-parameter spaces, IEEE Transactions on Evolutionary Computation,      %
+%    13(2), 243-259, doi:10.1109/TEVC.2008.924428, 2009.					%
+%                                                                                               %
+% For more information please read:                                                             %
+%                                                                                               %
+% J.A. Vrugt, H.V. Gupta, L.A. Bastidas, W. Bouten, and S. Sorooshian, Effective and efficient  %
+%    algorithm for multi-objective optimization of hydrologic models, Water Resources Research, %
+%    39(8), art. No. 1214, doi:10.1029/2002WR001746, 2003.                                      %
+%                                                                                               %
+% G.H. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W.Wallender, Multi-objective        %
+%    optimization of a regional spatially-distributed subsurface water flow model, Journal      %
+%    of Hydrology, 20 - 48, 311(1-4), doi:10.1016/j.jhydrol.2005.01.001, 2005.                  %
+%                                                                                               %
+% J.A. Vrugt, P.H. Stauffer, T. Wöhling, B.A. Robinson, and V.V. Vesselinov, Inverse modeling   %
+%    of subsurface flow and transport properties: A review with new developments, Vadose Zone   %
+%    Journal, 7(2), 843 - 864, doi:10.2136/vzj2007.0078, 2008.                                  %
+%                                                                                               %
+% T. Wöhling, J.A. Vrugt, and G.F. Barkle, Comparison of three multiobjective optimization      %
+%    algorithms for inverse modeling of vadose zone hydraulic properties, Soil Science Society  %
+%    of America Journal, 72, 305 - 319, doi:10.2136/sssaj2007.0176, 2008.                       %
+%                                                                                               %
+% T. Wöhling, and J.A. Vrugt, Combining multi-objective optimization and Bayesian model         %
+%    averaging to calibrate forecast ensembles of soil hydraulic models, Water Resources        %
+%    Research, 44, W12432, doi:10.1029/2008WR007154, 2008.                                      %
+%                                                                                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                               %
+%     Copyright (C) 2011-2012  the authors                                                      %
+%                                                                                               %
+%     This program is free software: you can modify it under the terms of the GNU General       %
+%     Public License as published by the Free Software Foundation, either version 3 of the      %
+%     License, or (at your option) any later version.                                           %
+%                                                                                               %
+%     This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; %
+%     without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. %
+%     See the GNU General Public License for more details.                                      %
+%                                                                                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                               %
+% AMALGAM code developed by Jasper A. Vrugt, University of California Irvine: jasper@uci.edu    %
+%                                                                                               %
+% Version 0.5:   June 2006                                                                      %
+% Version 1.0:   January 2009    Cleaned up source code and implemented 4 test example problems %
+% Version 1.1:   January 2010    Population size flexible and does not divide by # algorithms   %
+% Version 1.2:   August 2010     Sampling from prior distribution                               %
+%                                                                                               %
+% --------------------------------------------------------------------------------------------- %
+
+% --------------------------------------------------------------------------------------------- %
+%                                                                                               %
+%           Note: PRIOR INFORMATION IN AMALGAM CAN BE SPECIFIED AS FOLLOWS                      %
+%                                                                                               %
+%       Set the following: Extra.InitPopulation = 'PRIOR' instead of 'LHS'                      %
+%       Then for each parameter specify the prior distribution using MATLAB language            %
+%       Example: Three parameters: (1) weibull, (2) lognormal, and (3) normal distribution      %
+%                                                                                               %
+%       Extra.prior = [ 'wblrnd(9,3)' ; 'lognrnd(0,2)' ; 'normrnd(-2,3)' ];                     %
+%                                                                                               %
+%       Weibull:   scale = 9; shape = 3;                                                        %
+%       Lognormal: mean = 0; sigma = 2;                                                         %
+%       normal:    mean = -2; sigma = 3;                                                        %
+%                                                                                               %
+% --------------------------------------------------------------------------------------------- %
+
+% Different test examples (1 - 3 are synthetic problems, 4 is real world problem)
+
+% example 1: multimodal Pareto optimal front with 21^9 local Pareto fronts
+% example 2: Pareto-optimal front consists of several noncontiguous convex parts
+% example 3: rotated problem
+% example 4: real-world example using streamflow forecasting with hymod model
+
+
+% Define which algorithms to use in AMALGAM
+Extra.Alg = {'GA','PSO','AMS','DE'};
+
+% Define the number of algorithms
+AMALGAMPar.q = size(Extra.Alg,2);
+
+% Which example to run?
+example = 4;
+
+if example == 1,    % Zitzler and Thiele function 4: Multimodal Pareto optimal front with 21^9 local Pareto fronts
+
+    AMALGAMPar.n = 10;                      % Dimension of the problem
+    AMALGAMPar.N = 100;                     % Size of the population
+    AMALGAMPar.nobj = 2;                    % Number of objectives
+    AMALGAMPar.ndraw = 10000;               % Maximum number of function evaluations
+
+    % Define the parameter ranges (minimum and maximum values)
+    ParRange.minn = [0 -5*ones(1,AMALGAMPar.n-1)]; ParRange.maxn = [1  5*ones(1,AMALGAMPar.n-1)];
+
+    % How is the initial sample created -- Latin Hypercube sampling
+    Extra.InitPopulation = 'LHS';
+   
+    % Define the measured streamflow data
+    Measurement.MeasData = []; Measurement.Sigma = []; Measurement.N = size(Measurement.MeasData,1);
+
+    % Define ModelName
+    ModelName = 'ZT_4';
+    
+    % Define the boundary handling
+    Extra.BoundHandling = 'Bound';
+    
+    % Load the true Pareto optimal front (derived with other script)
+    load ZT_4_front.txt; Fpareto = ZT_4_front;
+
+end;
+
+if example == 2,    % Zitzler and Thiele function 6: Pareto-optimal front consists of several noncontiguous convex parts
+
+    AMALGAMPar.n = 10;                      % Dimension of the problem
+    AMALGAMPar.N = 100;                     % Size of the population
+    AMALGAMPar.nobj = 2;                    % Number of objectives
+    AMALGAMPar.ndraw = 10000;               % Maximum number of function evaluations
+
+    % Define the parameter ranges (minimum and maximum values)
+    ParRange.minn = zeros(1,AMALGAMPar.n); ParRange.maxn = ones(1,AMALGAMPar.n);
+
+    % How is the initial sample created -- Latin Hypercube sampling
+    Extra.InitPopulation = 'LHS';
+
+    % Define the measured streamflow data
+    Measurement.MeasData = []; Measurement.Sigma = []; Measurement.N = size(Measurement.MeasData,1);
+
+    % Define ModelName
+    ModelName = 'ZT_6';
+
+    % Define the boundary handling
+    Extra.BoundHandling = 'Bound';
+
+    % Load the true Pareto optimal front (derived with other script)
+    load ZT_6_front.txt; Fpareto = ZT_6_front;
+
+end;
+
+if example == 3,    % Rotated problem
+
+    AMALGAMPar.n = 10;                      % Dimension of the problem
+    AMALGAMPar.N = 100;                     % Size of the population
+    AMALGAMPar.nobj = 2;                    % Number of objectives
+    AMALGAMPar.ndraw = 25000;               % Maximum number of function evaluations
+
+    % Define the parameter ranges (minimum and maximum values)
+    ParRange.minn = -0.3*ones(1,AMALGAMPar.n); ParRange.maxn = 0.3*ones(1,AMALGAMPar.n);
+
+    % How is the initial sample created -- Latin Hypercube sampling
+    Extra.InitPopulation = 'LHS';
+
+    % Do 45 degrees problem
+    [Extra.R] = rotmat(AMALGAMPar.n,(pi/4));
+
+    % Define the measured streamflow data
+    Measurement.MeasData = []; Measurement.Sigma = []; Measurement.N = size(Measurement.MeasData,1);
+
+    % Define ModelName
+    ModelName = 'ROT'; 
+    
+    % Define the boundary handling
+    Extra.BoundHandling = 'Bound';
+
+    % Load the true Pareto optimal front (derived with other script)
+    load ROT_front.txt; Fpareto = ROT_front;
+    
+end;
+
+if example == 4,    % HYMOD rainfall - runoff model
+
+    AMALGAMPar.n = 5;                       % Dimension of the problem
+    AMALGAMPar.N = 100;                     % Size of the population
+    AMALGAMPar.nobj = 2;                    % Number of objectives
+    AMALGAMPar.ndraw = 10000;               % Maximum number of function evaluations
+
+    % Define the parameter ranges (minimum and maximum values)
+    ParRange.minn = [1.0 0.10 0.10 1e-5 0.10]; ParRange.maxn = [500 2.00 0.99 0.10 0.99];
+
+    % How is the initial sample created -- Latin Hypercube sampling
+    Extra.InitPopulation = 'LHS';
+
+    % Load the Leaf River data
+    load bound.txt;
+
+    % Then read the boundary conditions -- only do two years
+    Extra.MaxT = 795;
+
+    % Define the PET, Measured Streamflow and Precipitation.
+    Extra.PET = bound(1:Extra.MaxT,5); Extra.Precip = sum(bound(1:Extra.MaxT,6:9),2);
+
+    % Define the measured streamflow data
+    Measurement.MeasData = bound(65:Extra.MaxT,4); Measurement.Sigma = []; Measurement.N = size(Measurement.MeasData,1);
+
+    % Define ModelName
+    ModelName = 'hymod';
+       
+    % Define the boundary handling
+    Extra.BoundHandling = 'Bound';
+
+    % True Pareto front is not available -- real world problem
+    Fpareto = [];
+
+end;
+
+% Store example number in structure Extra
+Extra.example = example; Extra.m = AMALGAMPar.n;
+
+% Run the AMALGAM code and obtain non-dominated solution set
+[output,ParGen,ObjVals,ParSet] = AMALGAM(AMALGAMPar,ModelName,ParRange,Measurement,Extra,Fpareto);
\ No newline at end of file
diff --git a/algorithms/calibration/CMA_ES/cmaes.m b/algorithms/calibration/CMA_ES/cmaes.m
index 0d84eeb..a61d780 100644
--- a/algorithms/calibration/CMA_ES/cmaes.m
+++ b/algorithms/calibration/CMA_ES/cmaes.m
@@ -1,3253 +1,3253 @@
-function [xmin, ...      % minimum search point of last iteration
-	  fmin, ...      % function value of xmin
-	  counteval, ... % number of function evaluations done
-	  stopflag, ...  % stop criterion reached
-	  out, ...     % struct with various histories and solutions
-	  bestever ... % struct containing overall best solution (for convenience)
-	 ] = cmaes( ...
-    fitfun, ...    % name of objective/fitness function
-    xstart, ...    % objective variables initial point, determines N
-    insigma, ...   % initial coordinate wise standard deviation(s)
-    inopts, ...    % options struct, see defopts below
-    calibGUI_interface_obj, ...  % Object for interfacing with calibratoin GUI
-    varargin )     % arguments passed to objective function 
-% cmaes.m, Version 3.61.beta, last change: April, 2012 
-% CMAES implements an Evolution Strategy with Covariance Matrix
-% Adaptation (CMA-ES) for nonlinear function minimization.  For
-% introductory comments and copyright (GPL) see end of file (type 
-% 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and, 
-% without data logging and plotting, it should run under Octave 
-% (Linux, package octave-forge is needed).
-%
-% OPTS = CMAES returns default options. 
-% OPTS = CMAES('defaults') returns default options quietly.
-% OPTS = CMAES('displayoptions') displays options. 
-% OPTS = CMAES('defaults', OPTS) supplements options OPTS with default 
-% options. 
-%
-% XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates an approximate minimum 
-% XMIN of function FUN starting from column vector X0 with the initial
-% coordinate wise search standard deviation SIGMA.
-%
-% Input arguments: 
-%
-%  FUN is a string function name like 'frosen'. FUN takes as argument
-%     a column vector of size of X0 and returns a scalar. An easy way to
-%     implement a hard non-linear constraint is to return NaN. Then,
-%     this function evaluation is not counted and a newly sampled
-%     point is tried immediately.
-%
-%   X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
-%     mean(X0, 2) is taken as initial point. If X0 is a string like
-%     '2*rand(10,1)-1', the string is evaluated first.
-%
-%   SIGMA is a scalar, or a column vector of size(X0,1), or a string
-%     that can be evaluated into one of these. SIGMA determines the
-%     initial coordinate wise standard deviations for the search.
-%     Setting SIGMA one third of the initial search region is
-%     appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
-%     means cmaes('myfun', 3*rand(10,1), 2).  If SIGMA is missing and
-%     size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
-%     used as a sample for estimating initial mean and variance of the
-%     search distribution.
-%
-%   OPTS (an optional argument) is a struct holding additional input
-%     options. Valid field names and a short documentation can be
-%     discovered by looking at the default options (type 'cmaes'
-%     without arguments, see above). Empty or missing fields in OPTS
-%     invoke the default value, i.e. OPTS needs not to have all valid
-%     field names.  Capitalization does not matter and unambiguous
-%     abbreviations can be used for the field names. If a string is
-%     given where a numerical value is needed, the string is evaluated
-%     by eval, where 'N' expands to the problem dimension
-%     (==size(X0,1)) and 'popsize' to the population size. 
-%
-% [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
-%    CMAES(FITFUN, X0, SIGMA)
-% returns the best (minimal) point XMIN (found in the last
-% generation); function value FMIN of XMIN; the number of needed
-% function evaluations COUNTEVAL; a STOPFLAG value as cell array,
-% where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
-% 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
-% 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
-% 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
-% e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
-% 'tolx')) for further processing); a record struct OUT with some
-% more output, where the struct SOLUTIONS.BESTEVER contains the overall
-% best evaluated point X with function value F evaluated at evaluation
-% count EVALS. The last output argument BESTEVER equals 
-% OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and 
-% parameters is written to files according to the Log-options. 
-%
-% A regular manual stop can be achieved via the file signals.par. The
-% program is terminated if the first two non-white sequences in any
-% line of this file are 'stop' and the value of the LogFilenamePrefix
-% option (by default 'outcmaes'). Also a run can be skipped.
-% Given, for example, 'skip outcmaes run 2', skips the second run
-% if option Restarts is at least 2, and another run will be started.
-% 
-% To run the code completely silently set Disp, Save, and Log options
-% to 0.  With OPTS.LogModulo > 0 (1 by default) the most important
-% data are written to ASCII files permitting to investigate the
-% results (e.g. plot with function plotcmaesdat) even while CMAES is
-% still running (which can be quite useful on expensive objective
-% functions). When OPTS.SaveVariables==1 (default) everything is saved
-% in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
-% resume the search afterwards by using the resume option.
-%
-% To find the best ever evaluated point load the variables typing
-% "es=load('variablescmaes')" and investigate the variable
-% es.out.solutions.bestever. 
-%
-% In case of a noisy objective function (uncertainties) set
-% OPTS.Noise.on = 1. This option interferes presumably with some 
-% termination criteria, because the step-size sigma will presumably
-% not converge to zero anymore. If CMAES was provided with a 
-% fifth argument (P1 in the below example, which is passed to the 
-% objective function FUN), this argument is multiplied with the 
-% factor given in option Noise.alphaevals, each time the detected 
-% noise exceeds a threshold. This argument can be used within 
-% FUN, for example, as averaging number to reduce the noise level.  
-%
-% OPTS.DiagonalOnly > 1 defines the number of initial iterations,
-% where the covariance matrix remains diagonal and the algorithm has
-% internally linear time complexity. OPTS.DiagonalOnly = 1 means
-% keeping the covariance matrix always diagonal and this setting
-% also exhibits linear space complexity. This can be particularly
-% useful for dimension > 100. The default is OPTS.DiagonalOnly = 0. 
-% 
-% OPTS.CMA.active = 1 turns on "active CMA" with a negative update 
-% of the covariance matrix and checks for positive definiteness. 
-% OPTS.CMA.active = 2 does not check for pos. def. and is numerically
-% faster. Active CMA usually speeds up the adaptation and might 
-% become a default in near future. 
-%
-% The primary strategy parameter to play with is OPTS.PopSize, which
-% can be increased from its default value.  Increasing the population
-% size (by default linked to increasing parent number OPTS.ParentNumber)
-% improves global search properties in exchange to speed. Speed
-% decreases, as a rule, at most linearely with increasing population
-% size. It is advisable to begin with the default small population
-% size. The options Restarts and IncPopSize can be used for an
-% automated multistart where the population size is increased by the
-% factor IncPopSize (two by default) before each restart. X0 (given as
-% string) is reevaluated for each restart. Stopping options
-% StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
-% program, all others including MaxIter invoke another restart, where
-% the iteration counter is reset to zero.
-%
-% Examples: 
-%
-%     XMIN = cmaes('myfun', 5*ones(10,1), 1.5); 
-%
-%   starts the search at 10D-point 5 and initially searches mainly 
-%   between 5-3 and 5+3 (+- two standard deviations), but this is not 
-%   a strict bound. 'myfun' is a name of a function that returns a 
-%   scalar from a 10D column vector.
-%
-%     opts.LBounds = 0; opts.UBounds = 10; 
-%     opts.Restarts = 3;  % doubles the popsize for each restart
-%     X = cmaes('myfun', 10*rand(10,1), 5, opts);
-%
-%   searches within lower bound of 0 and upper bound of 10. Bounds can
-%   also be given as column vectors. If the optimum is not located
-%   on the boundary, use rather a penalty approach to handle bounds. 
-%
-%     opts=cmaes; 
-%     opts.StopFitness=1e-10;
-%     X=cmaes('myfun', rand(5,1), 0.5, opts); 
-%
-%   stops the search, if the function value is smaller than 1e-10.
-%   
-%     [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2); 
-%
-%   passes two additional parameters to the function MYFUN2.
-%
-% See also FMINSEARCH, FMINUNC, FMINBND.
-
-
-% TODO: 
-%       write dispcmaesdat for Matlab (and Octave)
-%       control savemodulo and plotmodulo via signals.par 
-
-
-cmaVersion = '3.62.beta'; 
-
-% ----------- Set Defaults for Input Parameters and Options -------------
-% These defaults may be edited for convenience
-
-% Input Defaults (obsolete, these are obligatory now)
-definput.fitfun = 'felli'; % frosen; fcigar; see end of file for more
-definput.xstart = rand(10,1); % 0.50*ones(10,1);
-definput.sigma = 0.3;
-
-% Options defaults: Stopping criteria % (value of stop flag)
-defopts.StopFitness  = '-Inf % stop if f(xmin) < stopfitness, minimization';
-defopts.MaxFunEvals  = 'Inf  % maximal number of fevals';
-defopts.MaxIter      = '1e3*(N+5)^2/sqrt(popsize) % maximal number of iterations';
-defopts.StopFunEvals = 'Inf  % stop after resp. evaluation, possibly resume later';
-defopts.StopIter     = 'Inf  % stop after resp. iteration, possibly resume later';
-defopts.TolX         = '1e-11*max(insigma) % stop if x-change smaller TolX';
-defopts.TolUpX       = '1e3*max(insigma) % stop if x-changes larger TolUpX';
-defopts.TolFun       = '1e-12 % stop if fun-changes smaller TolFun';
-defopts.TolHistFun   = '1e-13 % stop if back fun-changes smaller TolHistFun';
-defopts.StopOnStagnation = 'on  % stop when fitness stagnates for a long time';
-% TODO: stagnation has four parameters for the period: min = 120, const = 30N/lam, rel = 0.2, max = 2e5
-% defopts.StopOnStagnation = '[120 30*N/popsize 0.2 2e5]  % [min const rel_iter max] measuring period';
-defopts.StopOnWarnings = 'yes  % ''no''==''off''==0, ''on''==''yes''==1 ';
-defopts.StopOnEqualFunctionValues = '2 + N/3  % number of iterations';  
-
-% Options defaults: Other
-defopts.DiffMaxChange = 'Inf  % maximal variable change(s), can be Nx1-vector';
-defopts.DiffMinChange = '0    % minimal variable change(s), can be Nx1-vector';
-defopts.WarnOnEqualFunctionValues = ...
-    'yes  % ''no''==''off''==0, ''on''==''yes''==1 ';
-defopts.LBounds = '-Inf % lower bounds, scalar or Nx1-vector'; 
-defopts.UBounds = 'Inf  % upper bounds, scalar or Nx1-vector'; 
-defopts.EvalParallel = 'no   % objective function FUN accepts NxM matrix, with M>1?';
-defopts.EvalInitialX = 'yes  % evaluation of initial solution';
-defopts.Restarts     = '0    % number of restarts ';
-defopts.IncPopSize   = '2    % multiplier for population size before each restart';
-
-defopts.PopSize      = '(4 + floor(3*log(N)))  % population size, lambda'; 
-defopts.ParentNumber = 'floor(popsize/2)       % AKA mu, popsize equals lambda';
-defopts.RecombinationWeights = 'superlinear decrease % or linear, or equal';
-defopts.DiagonalOnly = '0*(1+100*N/sqrt(popsize))+(N>=1000)  % C is diagonal for given iterations, 1==always'; 
-defopts.Noise.on = '0  % uncertainty handling is off by default'; 
-defopts.Noise.reevals  = '1*ceil(0.05*lambda)  % nb. of re-evaluated for uncertainty measurement';
-defopts.Noise.theta = '0.5  % threshold to invoke uncertainty treatment'; % smaller: more likely to diverge
-defopts.Noise.cum = '0.3  % cumulation constant for uncertainty'; 
-defopts.Noise.cutoff = '2*lambda/3  % rank change cutoff for summation';
-defopts.Noise.alphasigma  = '1+2/(N+10)  % factor for increasing sigma'; % smaller: slower adaptation
-defopts.Noise.epsilon  = '1e-7  % additional relative perturbation before reevaluation';
-defopts.Noise.minmaxevals = '[1 inf]  % min and max value of 2nd arg to fitfun, start value is 5th arg to cmaes'; 
-defopts.Noise.alphaevals  = '1+2/(N+10)  % factor for increasing 2nd arg to fitfun'; 
-defopts.Noise.callback = '[]  % callback function when uncertainty threshold is exceeded';
-% defopts.TPA = 0; 
-defopts.CMA.cs = '(mueff+2)/(N+mueff+3)  % cumulation constant for step-size'; 
-   %qqq defopts.CMA.cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
-defopts.CMA.damps = '1 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
-% defopts.CMA.ccum = '4/(N+4)  % cumulation constant for covariance matrix'; 
-defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N)  % cumulation constant for pc';
-defopts.CMA.ccov1 = '2 / ((N+1.3)^2+mueff)  % learning rate for rank-one update'; 
-defopts.CMA.ccovmu = '2 * (mueff-2+1/mueff) / ((N+2)^2+mueff) % learning rate for rank-mu update'; 
-defopts.CMA.on     = 'yes'; 
-defopts.CMA.active = '0  % active CMA, 1: neg. updates with pos. def. check, 2: neg. updates'; 
-
-flg_future_setting = 0;  % testing for possible future variant(s)
-if flg_future_setting    
-  disp('in the future')
-
-  % damps setting from Brockhoff et al 2010
-  %   this damps diverges with popsize 400:
-  %     defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs  % damping for step-size'; 
-  %   cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
-  % how about:
-  % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
-  defopts.CMA.damps = '0.5 + 0.5*min(1, (0.27*lambda/mueff-1)^2) + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
-
-  if 11 < 3 
-      defopts.CMA.damps = '0.5 + 0.5*min(1,(lam_mirr/(0.159*lambda)-1)^2) + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
-
-      defopts.mirrored_offspring = 'floor(0.5 + 0.159 * lambda)'; 
-    % TODO: this should also depend on diagonal option!?  
-    defopts.CMA.active = 'floor(int8(lam_mirr>0))  % active CMA 1: neg. updates with pos. def. check, 2: neg. updates'; 
-  end
-  
-  % ccum adjusted for large mueff, better on schefelmult? 
-  % TODO: this should also depend on diagonal option!?  
-  defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N)  % cumulation constant for pc';
-
-  defopts.CMA.active = '1  % active CMA 1: neg. updates with pos. def. check, 2: neg. updates'; 
-end
-  
-defopts.Resume   = 'no   % resume former run from SaveFile'; 
-defopts.Science  = 'on  % off==do some additional (minor) problem capturing, NOT IN USE'; 
-defopts.ReadSignals = 'on  % from file signals.par for termination, yet a stumb';
-defopts.Seed = 'sum(100*clock)  % evaluated if it is a string';
-defopts.DispFinal  = 'on   % display messages like initial and final message';
-defopts.DispModulo = '100  % [0:Inf], disp messages after every i-th iteration';
-defopts.SaveVariables = 'on   % [on|final|off][-v6] save variables to .mat file';
-defopts.SaveFilename = 'variablescmaes.mat  % save all variables, see SaveVariables'; 
-defopts.LogModulo = '1    % [0:Inf] if >1 record data less frequently after gen=100';
-defopts.LogTime   = '25   % [0:100] max. percentage of time for recording data';
-defopts.LogFilenamePrefix = 'outcmaes  % files for output data'; 
-defopts.LogPlot = 'off    % plot while running using output data files';
-
-%qqqkkk 
-%defopts.varopt1 = ''; % 'for temporary and hacking purposes'; 
-%defopts.varopt2 = ''; % 'for temporary and hacking purposes'; 
-defopts.UserData = 'for saving data/comments associated with the run';
-defopts.UserDat2 = ''; 'for saving data/comments associated with the run';
-
-% ---------------------- Handling Input Parameters ----------------------
-
-if nargin < 1 || isequal(fitfun, 'defaults') % pass default options
-  if nargin < 1
-    disp('Default options returned (type "help cmaes" for help).');
-  end
-  xmin = defopts;
-  if nargin > 1 % supplement second argument with default options
-    xmin = getoptions(xstart, defopts);
-  end
-  return;
-end
-
-if isequal(fitfun, 'displayoptions')
- names = fieldnames(defopts); 
- for name = names'
-   disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']); 
- end
- return; 
-end
-
-input.fitfun = fitfun; % record used input
-if isempty(fitfun)
-  % fitfun = definput.fitfun; 
-  % warning(['Objective function not determined, ''' fitfun ''' used']);
-  error(['Objective function not determined']);
-end
-if ~ischar(fitfun)
-  error('first argument FUN must be a string');
-end
-
-
-if nargin < 2 
-  xstart = [];
-end
-
-input.xstart = xstart;
-if isempty(xstart)
-  % xstart = definput.xstart;  % objective variables initial point
-  % warning('Initial search point, and problem dimension, not determined');
-  error('Initial search point, and problem dimension, not determined');
-end
-
-if nargin < 3 
-  insigma = [];
-end
-if isa(insigma, 'struct')
-  error(['Third argument SIGMA must be (or eval to) a scalar '...
-	   'or a column vector of size(X0,1)']);
-end
-input.sigma = insigma;
-if isempty(insigma)
-  if all(size(myeval(xstart)) > 1)
-    insigma = std(xstart, 0, 2); 
-    if any(insigma == 0)
-      error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
-    end
-  else
-    % will be captured later
-    % error(['Initial step sizes (SIGMA) not determined']);
-  end
-end
-
-% Compose options opts
-if nargin < 4 || isempty(inopts) % no input options available
-  inopts = []; 
-  opts = defopts;
-else
-  opts = getoptions(inopts, defopts);
-end
-i = strfind(opts.SaveFilename, '%'); % remove everything after comment
-if ~isempty(i)
-  opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
-end
-opts.SaveFilename = deblank(opts.SaveFilename); % remove trailing white spaces
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
-counteval = 0; countevalNaN = 0; 
-irun = 0;
-while irun <= myeval(opts.Restarts) % for-loop does not work with resume
-  irun = irun + 1; 
-
-% ------------------------ Initialization -------------------------------
-
-% Handle resuming of old run
-flgresume = myevalbool(opts.Resume);
-xmean = myeval(xstart); 
-if all(size(xmean) > 1)
-   xmean = mean(xmean, 2); % in case if xstart is a population
-elseif size(xmean, 2) > 1
-  xmean = xmean';
-end 
-if ~flgresume % not resuming a former run
-  % Assign settings from input parameters and options for myeval...
-  N = size(xmean, 1); numberofvariables = N; 
-  lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1)); 
-  % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2)); 
-  popsize = lambda0;
-  lambda = lambda0;
-  insigma = myeval(insigma);
-  if all(size(insigma) == [N 2]) 
-    insigma = 0.5 * (insigma(:,2) - insigma(:,1));
-  end
-else % flgresume is true, do resume former run
-  tmp = whos('-file', opts.SaveFilename);
-  for i = 1:length(tmp)
-    if strcmp(tmp(i).name, 'localopts');
-      error('Saved variables include variable "localopts", please remove');
-    end
-  end
-  local.opts = opts; % keep stopping and display options
-  local.varargin = varargin;
-  load(opts.SaveFilename); 
-  varargin = local.varargin;
-  flgresume = 1;
-
-  % Overwrite old stopping and display options
-  opts.StopFitness = local.opts.StopFitness; 
-  %%opts.MaxFunEvals = local.opts.MaxFunEvals;
-  %%opts.MaxIter = local.opts.MaxIter; 
-  opts.StopFunEvals = local.opts.StopFunEvals; 
-  opts.StopIter = local.opts.StopIter;  
-  opts.TolX = local.opts.TolX;
-  opts.TolUpX = local.opts.TolUpX;
-  opts.TolFun = local.opts.TolFun;
-  opts.TolHistFun = local.opts.TolHistFun;
-  opts.StopOnStagnation = local.opts.StopOnStagnation; 
-  opts.StopOnWarnings = local.opts.StopOnWarnings; 
-  opts.ReadSignals = local.opts.ReadSignals; 
-  opts.DispFinal = local.opts.DispFinal;
-  opts.LogPlot = local.opts.LogPlot;
-  opts.DispModulo = local.opts.DispModulo;
-  opts.SaveVariables = local.opts.SaveVariables;
-  opts.LogModulo = local.opts.LogModulo;
-  opts.LogTime = local.opts.LogTime;
-  clear local; % otherwise local would be overwritten during load
-end
-  
-%--------------------------------------------------------------
-% Evaluate options
-stopFitness = myeval(opts.StopFitness); 
-stopMaxFunEvals = myeval(opts.MaxFunEvals);  
-stopMaxIter = myeval(opts.MaxIter);  
-stopFunEvals = myeval(opts.StopFunEvals);  
-stopIter = myeval(opts.StopIter); 
-if flgresume
-    stopIter = stopIter + countiter
-end
-stopTolX = myeval(opts.TolX);
-stopTolUpX = myeval(opts.TolUpX);
-stopTolFun = myeval(opts.TolFun);
-stopTolHistFun = myeval(opts.TolHistFun);
-stopOnStagnation = myevalbool(opts.StopOnStagnation); 
-stopOnWarnings = myevalbool(opts.StopOnWarnings);
-flgreadsignals = myevalbool(opts.ReadSignals);
-flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
-flgEvalParallel = myevalbool(opts.EvalParallel);
-stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
-arrEqualFunvals = zeros(1, 10+N);
-flgDiagonalOnly = myeval(opts.DiagonalOnly); 
-flgActiveCMA = myeval(opts.CMA.active); 
-noiseHandling = myevalbool(opts.Noise.on);
-noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
-noiseAlphaEvals = myeval(opts.Noise.alphaevals);
-noiseCallback = myeval(opts.Noise.callback); 
-flgdisplay = myevalbool(opts.DispFinal);
-flgplotting = myevalbool(opts.LogPlot);
-verbosemodulo = myeval(opts.DispModulo);
-flgscience = myevalbool(opts.Science);
-flgsaving = [];
-strsaving = [];
-if strfind(opts.SaveVariables, '-v6') 
-  i = strfind(opts.SaveVariables, '%');
-  if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
-    strsaving = '-v6';
-    flgsaving = 1;
-    flgsavingfinal = 1;
-  end
-end
-if strncmp('final', opts.SaveVariables, 5)
-  flgsaving = 0;
-  flgsavingfinal = 1;
-end
-if isempty(flgsaving)
-  flgsaving = myevalbool(opts.SaveVariables);
-  flgsavingfinal = flgsaving;
-end
-savemodulo = myeval(opts.LogModulo);
-savetime = myeval(opts.LogTime);
-
-i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
-if ~isempty(i)
-  opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
-end
-
-% TODO here silent option? set disp, save and log options to 0 
-
-%--------------------------------------------------------------
-
-if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
-  warning('To resume later the saving option needs to be set');
-end
-
-
-% Do more checking and initialization 
-if flgresume % resume is on
-  time.t0 = clock;
-  if flgdisplay
-    disp(['  resumed from ' opts.SaveFilename ]); 
-  end
-  if counteval >= stopMaxFunEvals 
-    error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
-	  'criterion before resume']);
-  end
-  if countiter >= stopMaxIter 
-    error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
-	  'before resume']);
-  end
-  
-else % flgresume
-  % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
-  maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
-  mindx = myeval(opts.DiffMinChange); % minimal sensible variable change 
-				      % can both also be defined as Nx1 vectors
-  lbounds = myeval(opts.LBounds);		     
-  ubounds = myeval(opts.UBounds);
-  if length(lbounds) == 1
-    lbounds = repmat(lbounds, N, 1);
-  end
-  if length(ubounds) == 1
-    ubounds = repmat(ubounds, N, 1);
-  end
-  if isempty(insigma) % last chance to set insigma
-    if all(lbounds > -Inf) && all(ubounds < Inf)
-      if any(lbounds>=ubounds)
-	error('upper bound must be greater than lower bound');
-      end
-      insigma = 0.3*(ubounds-lbounds);
-      stopTolX = myeval(opts.TolX);  % reevaluate these
-      stopTolUpX = myeval(opts.TolUpX);
-    else
-      error(['Initial step sizes (SIGMA) not determined']);
-    end
-  end
-
-  % Check all vector sizes
-  if size(xmean, 2) > 1 || size(xmean,1) ~= N
-    error(['intial search point should be a column vector of size ' ...
-	   num2str(N)]);
-  elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
-    error(['input parameter SIGMA should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
-    error(['option TolX should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
-    error(['option TolUpX should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
-    error(['option DiffMaxChange should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
-    error(['option DiffMinChange should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
-    error(['option lbounds should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
-    error(['option ubounds should be (or eval to) a scalar '...
-	   'or a column vector of size ' num2str(N)] );
-  end
-  
-  % Initialize dynamic internal state parameters
-  if any(insigma <= 0) 
-    error(['Initial search volume (SIGMA) must be greater than zero']);
-  end
-  if max(insigma)/min(insigma) > 1e6
-    error(['Initial search volume (SIGMA) badly conditioned']);
-  end
-  sigma = max(insigma);              % overall standard deviation
-  pc = zeros(N,1); ps = zeros(N,1);  % evolution paths for C and sigma
-
-  if length(insigma) == 1
-    insigma = insigma * ones(N,1) ;
-  end
-  diagD = insigma/max(insigma);      % diagonal matrix D defines the scaling
-  diagC = diagD.^2; 
-  if flgDiagonalOnly ~= 1            % use at some point full covariance matrix
-    B = eye(N,N);                      % B defines the coordinate system
-    BD = B.*repmat(diagD',N,1);        % B*D for speed up only
-    C = diag(diagC);                   % covariance matrix == BD*(BD)'
-  end
-  if flgDiagonalOnly
-    B = 1; 
-  end
-
-  fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
-  fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
-  fitness.histbest=[]; % history of fitness values
-  fitness.histmedian=[]; % history of fitness values
-
-  % Initialize boundary handling
-  bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf); 
-  if bnd.isactive
-    if any(lbounds>ubounds)
-      error('lower bound found to be greater than upper bound');
-    end
-    [xmean ti] = xintobounds(xmean, lbounds, ubounds); % just in case
-    if any(ti)
-      warning('Initial point was out of bounds, corrected');
-    end
-    bnd.weights = zeros(N,1);         % weights for bound penalty
-    % scaling is better in axis-parallel case, worse in rotated
-    bnd.flgscale = 0; % scaling will be omitted if zero 
-    if bnd.flgscale ~= 0 
-      bnd.scale = diagC/mean(diagC);
-    else
-      bnd.scale = ones(N,1);
-    end
-    
-    idx = (lbounds > -Inf) | (ubounds < Inf);
-    if length(idx) == 1
-      idx = idx * ones(N,1);
-    end
-    bnd.isbounded = zeros(N,1);
-    bnd.isbounded(find(idx)) = 1; 
-    maxdx = min(maxdx, (ubounds - lbounds)/2);
-    if any(sigma*sqrt(diagC) > maxdx)
-      fac = min(maxdx ./ sqrt(diagC))/sigma;
-      sigma = min(maxdx ./ sqrt(diagC));
-      warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
-	       ', because it was larger than half' ...
-	       ' of one of the boundary intervals']);
-    end
-    idx = (lbounds > -Inf) & (ubounds < Inf);
-    dd = diagC;
-    if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
-      warning(['Initial SIGMA is, in at least one coordinate, ' ...
-	       'much smaller than the '...
-	       'given boundary intervals. For reasonable ' ...
-	       'global search performance SIGMA should be ' ...
-	       'between 0.2 and 0.5 of the bounded interval in ' ...
-	       'each coordinate. If all coordinates have ' ... 
-	       'lower and upper bounds SIGMA can be empty']);
-    end
-    bnd.dfithist = 1;              % delta fit for setting weights
-    bnd.aridxpoints = [];          % remember complete outside points
-    bnd.arfitness = [];            % and their fitness
-    bnd.validfitval = 0;
-    bnd.iniphase = 1;
-  end
-
-  % Update the diary file
-  if ~isempty(calibGUI_interface_obj)
-     updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end     
-  end  
-  
-  % ooo initial feval, for output only
-  if irun == 1 
-    out.solutions.bestever.x = xmean;
-    out.solutions.bestever.f = Inf;  % for simpler comparison below
-    out.solutions.bestever.evals = counteval;
-    bestever = out.solutions.bestever;
-  end
-  if myevalbool(opts.EvalInitialX)
-    fitness.hist(1)=feval(fitfun, xmean, varargin{:}); 
-    fitness.histsel(1)=fitness.hist(1);
-    counteval = counteval + 1;
-    if fitness.hist(1) < out.solutions.bestever.f 
-	out.solutions.bestever.x = xmean;
-	out.solutions.bestever.f = fitness.hist(1);
-	out.solutions.bestever.evals = counteval;
-	bestever = out.solutions.bestever;
-    end
-  else
-    fitness.hist(1)=NaN; 
-    fitness.histsel(1)=NaN; 
-  end
-    
-  % initialize random number generator
-  if ischar(opts.Seed)
-    randn('state', eval(opts.Seed));     % random number generator state
-  else
-    randn('state', opts.Seed);
-  end
-  %qqq
-%  load(opts.SaveFilename, 'startseed');
-%  randn('state', startseed);
-%  disp(['SEED RELOADED FROM ' opts.SaveFilename]);
-  startseed = randn('state');         % for retrieving in saved variables
-
-  % Initialize further constants
-  chiN=N^0.5*(1-1/(4*N)+1/(21*N^2));  % expectation of 
-				      %   ||N(0,I)|| == norm(randn(N,1))
-  
-  countiter = 0;
-  % Initialize records and output
-  if irun == 1
-    time.t0 = clock;
-    
-    % TODO: keep also median solution? 
-    out.evals = counteval;  % should be first entry
-    out.stopflag = {};
-
-    outiter = 0;
-
-    % Write headers to output data files 
-    filenameprefix = opts.LogFilenamePrefix; 
-    if savemodulo && savetime
-      filenames = {};
-      filenames(end+1) = {'axlen'};
-      filenames(end+1) = {'fit'};
-      filenames(end+1) = {'stddev'};
-      filenames(end+1) = {'xmean'};
-      filenames(end+1) = {'xrecentbest'};
-      str = [' (startseed=' num2str(startseed(2)) ...
-             ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
-      for namecell = filenames(:)'
-        name = namecell{:};
-
-	[fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
-	if fid < 1 % err ~= 0 
-	  warning(['could not open ' filenameprefix name '.dat']);
-	  filenames(find(strcmp(filenames,name))) = [];
-	else
-%	  fprintf(fid, '%s\n', ...
-%	      ['<CMAES-OUTPUT version="' cmaVersion '">']);
-%	  fprintf(fid, ['  <NAME>' name '</NAME>\n']);
-%	  fprintf(fid, ['  <DATE>' date() '</DATE>\n']);
-%	  fprintf(fid, '  <PARAMETERS>\n');
-%	  fprintf(fid, ['    dimension=' num2str(N) '\n']);
-%	  fprintf(fid, '  </PARAMETERS>\n');
-	  % different cases for DATA columns annotations here
-%	  fprintf(fid, '  <DATA');
-	  if strcmp(name, 'axlen')
-	     fprintf(fid, ['%%  columns="iteration, evaluation, sigma, ' ...
-		 'max axis length, min axis length, ' ...
-		 'all principal axes lengths (sorted square roots ' ...
-                  'of eigenvalues of C)"' str]);
-	  elseif strcmp(name, 'fit')
-	    fprintf(fid, ['%%  columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
-		' best, median, worst fitness function value,' ...
-		' further objective values of best"' str]);
-	  elseif strcmp(name, 'stddev')
-	    fprintf(fid, ['%%  columns=["iteration, evaluation, sigma, void, void, ' ...
-		'stds==sigma*sqrt(diag(C))"' str]);
-	  elseif strcmp(name, 'xmean')
-	    fprintf(fid, ['%%  columns="iteration, evaluation, void, ' ...
-                          'void, void, xmean"' str]);
-	  elseif strcmp(name, 'xrecentbest')
-	    fprintf(fid, ['%%  columns="iteration, evaluation, fitness, ' ...
-                          'void, void, xrecentbest"' str]);
-	  end
-	  fprintf(fid, '\n'); % DATA
-	  if strcmp(name, 'xmean')
-	    fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval); 
-	    % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean); 
-%qqq	    fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n'); 
-	    fprintf(fid, '%e ', xmean);
-            fprintf(fid, '\n'); 
-	  end
-	  fclose(fid); 
-          clear fid; % preventing 
-	end
-      end % for files
-    end % savemodulo
-  end % irun == 1
-  
-end % else flgresume 
-
-% Update the diary file
-if ~isempty(calibGUI_interface_obj)
-    updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end 
-end  
-
-
-% -------------------- Generation Loop --------------------------------
-stopflag = {};
-while isempty(stopflag)
-  % set internal parameters
-  if countiter == 0 || lambda ~= lambda_last
-    if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
-          && flgdisplay
-      disp(['  lambda = ' num2str(lambda)]);
-      lambda_hist(:,end+1) = [countiter+1; lambda];
-    else
-      lambda_hist = [countiter+1; lambda]; 
-    end
-    lambda_last = lambda;
-    % Strategy internal parameter setting: Selection  
-    mu = myeval(opts.ParentNumber); % number of parents/points for recombination
-    if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
-      weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
-    elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
-      weights = mu+0.5-(1:mu)'; 
-    elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
-      % use (lambda+1)/2 as reference if mu < lambda/2
-      weights = log(max(mu, lambda/2) + 1/2)-log(1:mu)'; % muXone array for weighted recombination
-    else
-      error(['Recombination weights to be "' opts.RecombinationWeights ...
-             '" is not implemented']);
-    end
-    mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
-    weights = weights/sum(weights);     % normalize recombination weights array
-    if mueff == lambda
-      error(['Combination of values for PopSize, ParentNumber and ' ...
-             ' and RecombinationWeights is not reasonable']);
-    end
-    
-    % Strategy internal parameter setting: Adaptation
-    cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
-    cs = myeval(opts.CMA.cs); 
-
-    % old way TODO: remove this at some point
-    % mucov = mueff;   % size of mu used for calculating learning rate ccov
-    % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
-    %        + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov)); 
-
-    % new way
-    if myevalbool(opts.CMA.on) 
-      ccov1 = myeval(opts.CMA.ccov1); 
-      ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
-    else
-      ccov1 = 0;
-      ccovmu = 0;
-    end
-    
-    % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
-    % 0 : C will never be diagonal anymore
-    % 1 : C will always be diagonal
-    % >1: C is diagonal for first iterations, set to 0 afterwards
-    if flgDiagonalOnly < 1
-      flgDiagonalOnly = 0; 
-    end
-    if flgDiagonalOnly
-      ccov1_sep = min(1, ccov1 * (N+1.5) / 3); 
-      ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3); 
-    elseif N > 98 && flgdisplay && countiter == 0
-      disp('consider option DiagonalOnly for high-dimensional problems');
-    end
-
-    % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
-    %damps = ... % damping for step size control, usually close to one 
-    %    (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
-    %    * max(0.3, ... % reduce damps, if max. iteration number is small
-    %          1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs; 
-    damps = myeval(opts.CMA.damps); 
-    if noiseHandling
-      noiseReevals = min(myeval(opts.Noise.reevals), lambda); 
-      noiseAlpha = myeval(opts.Noise.alphasigma); 
-      noiseEpsilon = myeval(opts.Noise.epsilon); 
-      noiseTheta = myeval(opts.Noise.theta); 
-      noisecum = myeval(opts.Noise.cum);
-      noiseCutOff = myeval(opts.Noise.cutoff);  % arguably of minor relevance
-    else
-      noiseReevals = 0; % more convenient in later coding
-    end
-
-    %qqq hacking of a different parameter setting, e.g. for ccov or damps,
-    %  can be done here, but is not necessary anymore, see opts.CMA. 
-    % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
-    % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
-    % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
-    % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
-
-  end    
-
-  % Display initial message
-  if countiter == 0 && flgdisplay 
-    if mu == 1
-      strw = '100';
-    elseif mu < 8
-      strw = [sprintf('%.0f', 100*weights(1)) ... 
-              sprintf(' %.0f', 100*weights(2:end)')];
-    else
-      strw = [sprintf('%.2g ', 100*weights(1:2)') ...
-              sprintf('%.2g', 100*weights(3)') '...' ...
-              sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];      
-    end
-    if irun > 1
-      strrun = [', run ' num2str(irun)];
-    else
-      strrun = '';
-    end
-    disp(['  n=' num2str(N) ': (' num2str(mu) ',' ...
-          num2str(lambda) ')-CMA-ES(w=[' ...
-          strw ']%, ' ...
-          'mu_eff=' num2str(mueff,'%.1f') ...
-          ') on function ' ...
-          (fitfun) strrun]);
-    if flgDiagonalOnly == 1
-      disp('    C is diagonal');
-    elseif flgDiagonalOnly
-      disp(['    C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
-    end
-  end
-
-  flush;
-
-  countiter = countiter + 1; 
-
-  % Generate and evaluate lambda offspring
- 
-  fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
-
-  % parallel evaluation
-  if flgEvalParallel
-      arz = randn(N,lambda);
-
-      if ~flgDiagonalOnly
-        arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
-      else
-        arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz; 
-      end
-
-      if noiseHandling 
-        if noiseEpsilon == 0
-          arx = [arx arx(:,1:noiseReevals)]; 
-        elseif flgDiagonalOnly
-          arx = [arx arx(:,1:noiseReevals) + ...
-                 repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
-                 .* randn(N,noiseReevals)]; 
-        else 
-          arx = [arx arx(:,1:noiseReevals) + ...
-                 noiseEpsilon * sigma * ...
-                 (BD * randn(N,noiseReevals))]; 
-        end
-      end
-
-      % You may handle constraints here. You may either resample
-      % arz(:,k) and/or multiply it with a factor between -1 and 1
-      % (the latter will decrease the overall step size) and
-      % recalculate arx accordingly. Do not change arx or arz in any
-      % other way.
-      % The code below was written for the Groundwater Time Series
-      % mdoelling developed by Peterson& Western [2014].
-      invalidParamSets = find(any(~feval('calibrationValidParameters', arx, varargin{:}),1));
-      invalidParamSets_final = false(1,size(arx,2));
-      if ~isempty(invalidParamSets)>0
-          arz_tmp = zeros(size(arz,1), length(invalidParamSets));
-          arx_tmp = zeros(size(arx,1), length(invalidParamSets));
-          invalidParamSets_final_tmp = false(1, length(invalidParamSets));
-          if ~exist('noiseEpsilon','var');
-              noiseEpsilon = [];
-          end
-          parfor k=1:length(invalidParamSets)
-              nResamples=0;
-              maxResamples=1000;
-              while (1)
-                % Resample parameter set, k            
-                if invalidParamSets(k) <= lambda  % regular samples (not the re-evaluation-samples)
-                    arz_tmp(:,k) = randn(N,1); % (re)sample
-
-                    if flgDiagonalOnly  
-                        arx_tmp(:,k) = xmean + sigma * diagD .* arz_tmp(:,k);              % Eq. (1)
-                    else
-                        arx_tmp(:,k) = xmean + sigma * (BD * arz_tmp(:,k));                % Eq. (1)
-                    end
-                else % re-evaluation solution with index > lambda
-                    if flgDiagonalOnly  
-                        arx_tmp(:,k) = arx(:,invalidParamSets(k)-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
-                    else
-                        arx_tmp(:,k) = arx(:,invalidParamSets(k)-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
-                    end
-                end
-                nResamples = nResamples + 1;   
-
-                % Assess if parameter set k is valid
-                isValidParameter = feval('calibrationValidParameters', arx_tmp(:,k), varargin{:});
-                if ~any(~isValidParameter)
-                    invalidParamSets_final_tmp(k)=false;
-                    break;
-                elseif nResamples>maxResamples
-                    break;
-                else
-                    invalidParamSets_final_tmp(k)=true;
-                end            
-              end
-          end
-          
-          for k=1:length(invalidParamSets)
-              arz(:,invalidParamSets(k)) = arz_tmp(:,k);
-              arx(:,invalidParamSets(k)) = arx_tmp(:,k);
-              invalidParamSets_final(invalidParamSets(k)) = invalidParamSets_final_tmp(k);
-          end
-          clear arz_tmp arx_tmp invalidParamSets_final_tmp
-      end
-  
-      if ~bnd.isactive
-        arxvalid = arx;
-      else
-        arxvalid = xintobounds(arx, lbounds, ubounds);
-      end
-      % You may handle constraints here.  You may copy and alter
-      % (columns of) arxvalid(:,k) only for the evaluation of the
-      % fitness function. arx and arxvalid should not be changed.
-      if any(~invalidParamSets_final)
-        fitness.raw(~invalidParamSets_final) = feval(fitfun, arxvalid(:,~invalidParamSets_final), varargin{:}); 
-      end
-      
-      countevalNaN = countevalNaN + sum(isnan(fitness.raw));
-      counteval = counteval + sum(~isnan(fitness.raw)); 
-  end
-
-  % non-parallel evaluation and remaining NaN-values
-  % set also the reevaluated solution to NaN
-  fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;  
-  for k=find(isnan(fitness.raw)), 
-      
-    % fitness.raw(k) = NaN; 
-    tries = flgEvalParallel;  % in parallel case this is the first re-trial
-    % Resample, until fitness is not NaN
-    % NOTE: Vectorised sampling of parameter sets and the parrallel
-    % evaluation of viable parameter sets was added by Tim Peterson.
-    % These modiciations were for the Groundwater Time Series
-    % mdoelling developed by Peterson& Western [2014].    
-    while isnan(fitness.raw(k))
-        
-        % The code below was written for the Groundwater Time Series
-        % mdoelling developed by Peterson& Western [2014].        
-        
-        nResamples=1000;
-        nModelReevaluations = 10;
-        % Resample parameter set, k            
-        if k <= lambda  % regular samples (not the re-evaluation-samples)
-            arz_tmp = randn(N,nResamples); % (re)sample
-
-            if flgDiagonalOnly  
-                arx_tmp = bsxfun(@plus, xmean , bsxfun(@times, sigma * diagD, arz_tmp));              % Eq. (1)
-            else
-                arx_tmp = zeros(size(arz_tmp));
-                parfor el=1:nResamples
-                    arx_tmp(:,el) = xmean + sigma * (BD * arz_tmp(:,el));                % Eq. (1)
-                end
-                
-            end
-        else % re-evaluation solution with index > lambda
-            if flgDiagonalOnly  
-                arx_tmp = bsxfun(@plus, arx(:,k-lambda) , bsxfun(@times, (noiseEpsilon * sigma) * diagD , randn(N,nResamples)));
-            else                
-                arx_tmp = zeros(size(arz_tmp));
-                parfor el=1:nResamples
-                    arx_tmp(:,el) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
-                end                
-            end
-        end
-
-        % Assess if any parameter set is valid. if there is at least one valid
-        % parameter set, then get the first such set.
-        isValidParameter = feval('calibrationValidParameters', arx_tmp, varargin{:});        
-        isValidParameterSet = ~any(~isValidParameter,1);
-        if any(isValidParameterSet)
-            invalidParamSets_final_tmp=false;
-            iResamples = find(isValidParameterSet,nModelReevaluations,'first');
-            arx_tmp = arx_tmp(:,iResamples);
-            if k <= lambda
-                arz_tmp = arz_tmp(:,iResamples);
-            end
-        else
-            invalidParamSets_final_tmp=true;
-        end                    
-
-        
-%       if k <= lambda  % regular samples (not the re-evaluation-samples)
-%         arz(:,k) = randn(N,1); % (re)sample
-% 
-%         if flgDiagonalOnly  
-%           arx(:,k) = xmean + sigma * diagD .* arz(:,k);              % Eq. (1)
-%         else
-%           arx(:,k) = xmean + sigma * (BD * arz(:,k));                % Eq. (1)
-%         end
-%       else % re-evaluation solution with index > lambda
-%         if flgDiagonalOnly  
-%           arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
-%         else
-%           arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
-%         end
-%       end
-%      
-%       % You may handle constraints here. You may either resample
-%       % arz(:,k) and/or multiply it with a factor between -1 and 1
-%       % (the latter will decrease the overall step size) and
-%       % recalculate arx accordingly. Do not change arx or arz in any
-%       % other way.
-%       if ~bnd.isactive
-%         arxvalid(:,k) = arx(:,k);
-%       else
-%         arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
-%       end
-
-      % You may handle constraints here. You may either resample
-      % arz(:,k) and/or multiply it with a factor between -1 and 1
-      % (the latter will decrease the overall step size) and
-      % recalculate arx accordingly. Do not change arx or arz in any
-      % other way.
-      if ~bnd.isactive
-        arxvalid_tmp = arx_tmp;
-      else
-        arxvalid_tmp = xintobounds(arx_tmp, lbounds, ubounds);
-      end
-      
-      % You may handle constraints here.  You may copy and alter
-      % (columns of) arxvalid(:,k) only for the evaluation of the
-      % fitness function. arx should not be changed.
-      % If check for valid parameters inserted and use of temporary variabes
-      % by Tim Peterson
-      if ~invalidParamSets_final_tmp
-        fitness_tmp = feval(fitfun, arxvalid_tmp, varargin{:}); 
-      else
-          fitness_tmp =[];
-      end
-      tries = tries + 1;
-      if all(isnan(fitness_tmp))
-        countevalNaN = countevalNaN + 1;
-      else
-        % Following by Tim Peterson.
-        % Find the minimum evaluation.   
-        iResamples = find(fitness_tmp==min(fitness_tmp),1,'first');
-        
-        % Assign parameter sets etc to non-temp variables.
-        arx(:,k) = arx_tmp(:,iResamples);
-        if k <= lambda
-            arz(:,k) = arz_tmp(:,iResamples);
-        end
-        arxvalid(:,k) = arxvalid_tmp(:,iResamples);
-        fitness.raw(k) = fitness_tmp(iResamples);          
-      end
-      
-      if mod(tries, 100) == 0
-        warning([num2str(tries) ...
-        ' NaN objective function values at evaluation ' ...
-        num2str(counteval)]);
-      end
-    end
-    counteval = counteval + 1; % retries due to NaN are not counted
-  end
-
-  fitness.sel = fitness.raw; 
-
-  % ----- handle boundaries -----
-  if 1 < 3 && bnd.isactive
-    % Get delta fitness values
-    val = myprctile(fitness.raw, [25 75]);
-    % more precise would be exp(mean(log(diagC)))
-    val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
-    %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
-    %    / N / mean(diagC) / sigma^2;
-    % Catch non-sensible values 
-    if ~isfinite(val)
-      warning('Non-finite fitness range');
-      val = max(bnd.dfithist);  
-    elseif val == 0 % happens if all points are out of bounds
-      val = min(bnd.dfithist(bnd.dfithist>0));  % seems not to make sense, given all solutions are out of bounds
-    elseif bnd.validfitval == 0 % flag that first sensible val was found
-      bnd.dfithist = [];
-      bnd.validfitval = 1;
-    end
-
-    % Store delta fitness values
-    if length(bnd.dfithist) < 20+(3*N)/lambda
-      bnd.dfithist = [bnd.dfithist val];
-    else
-      bnd.dfithist = [bnd.dfithist(2:end) val];
-    end
-
-    [tx ti]  = xintobounds(xmean, lbounds, ubounds);
-
-    % Set initial weights
-    if bnd.iniphase 
-      if any(ti) 
-        bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
-	if bnd.flgscale == 0 % scale only initial weights then
-	  dd = diagC; 
-	  idx = find(bnd.isbounded); 
-	  dd = dd(idx) / mean(dd); %  remove mean scaling
-	  bnd.weights(idx) = bnd.weights(idx) ./ dd; 
-	end
-	if bnd.validfitval && countiter > 2
-          bnd.iniphase = 0;
-	end
-      end
-    end
-
-    % Increase weights
-    if  1 < 3 && any(ti) % any coordinate of xmean out of bounds
-      % judge distance of xmean to boundary
-      tx = xmean - tx;
-      idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ... 
-	     * sigma*sqrt(diagC)) ;
-      % only increase if xmean is moving away
-      idx = idx & (sign(tx) == sign(xmean - xold));
-      if ~isempty(idx) % increase
-        % the factor became 1.2 instead of 1.1, because
-        % changed from max to min in version 3.52
-        bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx); 
-      end
-    end
-
-    % Calculate scaling biased to unity, product is one
-    if bnd.flgscale ~= 0 
-      bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC)))); 
-    end
-
-    % Assigned penalized fitness
-    bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2; 
-
-    fitness.sel = fitness.raw + bnd.arpenalty;
-
-  end % handle boundaries
-  % ----- end handle boundaries -----
-  
-  % compute noise measurement and reduce fitness arrays to size lambda
-  if noiseHandling 
-    [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
-                                      fitness.sel(lambda+(1:noiseReevals)), ...
-                                      noiseReevals, noiseTheta, noiseCutOff); 
-    if countiter == 1 % TODO: improve this very rude way of initialization
-      noiseSS = 0;
-      noiseN = 0;  % counter for mean
-    end
-    noiseSS = noiseSS + noisecum * (noiseS - noiseSS); 
-
-    % noise-handling could be done here, but the original sigma is still needed
-    % disp([noiseS noiseSS noisecum])
-
-    fitness.rawar12 = fitness.raw; % just documentary
-    fitness.selar12 = fitness.sel; % just documentary
-    % qqq refine fitness based on both values
-    if 11 < 3  % TODO: in case of outliers this mean is counterproductive 
-               % median out of three would be ok 
-      fitness.raw(1:noiseReevals) = ... % not so raw anymore
-          (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2; 
-      fitness.sel(1:noiseReevals) = ... 
-          (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2; 
-    end      
-    fitness.raw = fitness.raw(1:lambda); 
-    fitness.sel = fitness.sel(1:lambda); 
-  end
-  
-  % Sort by fitness 
-  [fitness.raw, fitness.idx] = sort(fitness.raw); 
-  [fitness.sel, fitness.idxsel] = sort(fitness.sel);  % minimization
-  fitness.hist(2:end) = fitness.hist(1:end-1);    % record short history of
-  fitness.hist(1) = fitness.raw(1);               % best fitness values
-  if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
-       (mod(countiter, 5) == 0  && length(fitness.histbest) < 2e4)  % 20 percent of 1e5 gen.
-    fitness.histbest = [fitness.raw(1) fitness.histbest];          % best fitness values
-    fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
-  else
-    fitness.histbest(2:end) = fitness.histbest(1:end-1); 
-    fitness.histmedian(2:end) = fitness.histmedian(1:end-1); 
-    fitness.histbest(1) = fitness.raw(1);           % best fitness values
-    fitness.histmedian(1) = median(fitness.raw);    % median fitness values
-  end
-  fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
-  fitness.histsel(1) = fitness.sel(1);               % best sel fitness values
-
-  % Calculate new xmean, this is selection and recombination 
-  xold = xmean; % for speed up of Eq. (2) and (3)
-  cmean = 1;  % 1/min(max((lambda-1*N)/2, 1), N);  % == 1/kappa
-  xmean = (1-cmean) * xold + cmean * arx(:,fitness.idxsel(1:mu))*weights; 
-  zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
-  if mu == 1
-    fmean = fitness.sel(1);
-  else
-    fmean = NaN; % [] does not work in the latter assignment
-    % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
-    % counteval = counteval + 1;
-  end
-  
-  % Cumulation: update evolution paths
-  ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean);          % Eq. (4)
-  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
-  if flg_future_setting
-    hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
-  end
-%  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
-%  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
-%  hsig = norm(ps) < 1.5 * sqrt(N);
-%  hsig = 1;
-
-  pc = (1-cc)*pc ...
-        + hsig*(sqrt(cc*(2-cc)*mueff)/sigma/cmean) * (xmean-xold);     % Eq. (2)
-  if hsig == 0
-    % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
-  end
-
-  % Adapt covariance matrix
-  neg.ccov = 0;  % TODO: move parameter setting upwards at some point
-  if ccov1 + ccovmu > 0                                                    % Eq. (3)
-    if flgDiagonalOnly % internal linear(?) complexity
-      diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix 
-          + ccov1_sep * pc.^2 ...               % plus rank one update
-          + ccovmu_sep ...                      % plus rank mu update
-            * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
-%             * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
-      diagD = sqrt(diagC); % replaces eig(C)
-    else
-      arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
-      % "active" CMA update: negative update, in case controlling pos. definiteness 
-      if flgActiveCMA > 0
-        % set parameters
-        neg.mu = mu;  
-        neg.mueff = mueff;
-        if flgActiveCMA > 10  % flat weights with mu=lambda/2
-          neg.mu = floor(lambda/2);  
-          neg.mueff = neg.mu;
-        end
-        % neg.mu = ceil(min([N, lambda/4, mueff]));  neg.mueff = mu; % i.e. neg.mu <= N 
-        % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a 
-        % three times larger neg.ccov does not work. 
-        %   increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
-        %   in 30-D to looks fine
-
-        neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
-        neg.minresidualvariance = 0.66;  % keep at least 0.66 in all directions, small popsize are most critical
-        neg.alphaold = 0.5;  % where to make up for the variance loss, 0.5 means no idea what to do
-                             % 1 is slightly more robust and gives a better "guaranty" for pos. def., 
-                             % but does it make sense from the learning perspective for large ccovmu? 
-
-        neg.ccovfinal = neg.ccov;
-
-        % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
-        arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
-        % i-th longest becomes i-th shortest
-        % TODO: this is not in compliance with the paper Hansen&Ros2010, 
-        %       where simply arnorms = arnorms(end:-1:1) ? 
-        [arnorms idxnorms] = sort(sqrt(sum(arzneg.^2, 1))); 
-        [ignore idxnorms] = sort(idxnorms);  % inverse index 
-        arnormfacs = arnorms(end:-1:1) ./ arnorms; 
-        % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
-        arnorms = arnorms(end:-1:1); % for the record
-        if flgActiveCMA < 20
-          arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1);  % E x*x' is N
-          % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1);  % E x*x' is N
-        end
-        if flgActiveCMA < 10 && neg.mu == mu  % weighted sum
-          if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below) 
-            Ccheck = arzneg * diag(weights) * arzneg';  % in order to check the largest EV
-          end
-          artmp = BD * arzneg;
-          Cneg = artmp * diag(weights) * artmp';
-        else  % simple sum
-          if mod(flgActiveCMA, 10) == 1
-            Ccheck = (1/neg.mu) * arzneg*arzneg';  % in order to check largest EV
-          end
-          artmp = BD * arzneg;
-          Cneg = (1/neg.mu) * artmp*artmp';
-
-        end
-
-        % check pos.def. and set learning rate neg.ccov accordingly, 
-        % this check makes the original choice of neg.ccov extremly failsafe 
-        % still assuming C == BD*BD', which is only approxim. correct 
-        if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
-          % TODO: the simple and cheap way would be to set
-          %    fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
-          %    neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
-          % this is the more sophisticated way: 
-          % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts);  % not faster
-          maxeigenval = max(eig(Ccheck));  % norm is much slower, because (norm()==max(svd())
-          %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
-           %          neg.ccov * arnorms(idxnorms).^2 * weights])
-          % pause
-          % remove less than ??34*(1-cmu)%?? of variance in any direction
-          %     1-ccovmu is the variance left from the old C
-          neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval); 
-                                        % -ccov1 removed to avoid error message??
-          if neg.ccovfinal < neg.ccov
-            disp(['active CMA at iteration ' num2str(countiter) ...
-                 ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
-          end
-        end
-        % xmean = xold;  % the distribution does not degenerate!? 
-        % update C
-        C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix 
-            + ccov1 * pc*pc' ...     % plus rank one update
-            + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ...  % plus rank mu update
-              * arpos * (repmat(weights,1,N) .* arpos') ...
-              - neg.ccovfinal * Cneg;                        % minus rank mu update
-      else  % no active (negative) update
-        C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix 
-            + ccov1 * pc*pc' ...     % plus rank one update
-            + ccovmu ...             % plus rank mu update
-              * arpos * (repmat(weights,1,N) .* arpos');
-        % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
-        %   for mu=30*N it is now 10 times faster, overall 3 times faster
-      end
-      diagC = diag(C);
-    end
-  end
-  
-  % the following is de-preciated and will be removed in future
-  % better setting for cc makes this hack obsolete
-  if 11 < 2 && ~flgscience  
-    % remove momentum in ps, if ps is large and fitness is getting worse.
-    % this should rarely happen. 
-    % this might very well be counterproductive in dynamic environments
-    if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
-        fitness.histsel(1) > max(fitness.histsel(2:3))
-      ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
-      if flgdisplay
-        disp(['Momentum in ps removed at [niter neval]=' ...
-              num2str([countiter counteval]) ']']);
-      end
-    end
-  end
-
-  % Adapt sigma
-  if flg_future_setting  % according to a suggestion from Dirk Arnold (2000)
-    % exp(1) is still not reasonably small enough, maybe 2/3?
-    sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps));            % Eq. (5)
-  else
-    % exp(1) is still not reasonably small enough
-    sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps));             % Eq. (5)
-  end
-  % disp([countiter norm(ps)/chiN]);
-  
-  if 11 < 3   % testing with optimal step-size
-      if countiter == 1
-          disp('*********** sigma set to const * ||x|| ******************');
-      end
-      sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
-      sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
-                                                     %      75e3 with def (1.5)
-                                                     %      35e3 with damps=0.25
-  end
-  if 11 < 3 
-          if countiter == 1
-              disp('*********** xmean set to const ******************');
-          end
-      xmean = ones(N,1);
-  end
-  
-  % Update B and D from C
-
-  if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
-    C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
-    [B,tmp] = eig(C);     % eigen decomposition, B==normalized eigenvectors
-                          % effort: approx. 15*N matrix-vector multiplications
-    diagD = diag(tmp); 
-
-    if any(~isfinite(diagD))
-      clear idx; % prevents error under octave 
-      save(['tmp' opts.SaveFilename]);
-      error(['function eig returned non-finited eigenvalues, cond(C)=' ...
-	     num2str(cond(C)) ]);
-    end
-    if any(any(~isfinite(B)))
-      clear idx; % prevents error under octave
-      save(['tmp' opts.SaveFilename]);
-      error(['function eig returned non-finited eigenvectors, cond(C)=' ...
-	     num2str(cond(C)) ]);
-    end
-
-    % limit condition of C to 1e14 + 1
-    if min(diagD) <= 0
-	if stopOnWarnings
-	  stopflag(end+1) = {'warnconditioncov'};
-	else
-	  warning(['Iteration ' num2str(countiter) ...
-		   ': Eigenvalue (smaller) zero']);
-	  diagD(diagD<0) = 0;
-	  tmp = max(diagD)/1e14;
-	  C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1); 
-	end
-    end
-    if max(diagD) > 1e14*min(diagD) 
-	if stopOnWarnings
-	  stopflag(end+1) = {'warnconditioncov'};
-	else
-	  warning(['Iteration ' num2str(countiter) ': condition of C ' ...
-		   'at upper limit' ]);
-	  tmp = max(diagD)/1e14 - min(diagD);
-	  C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1); 
-	end
-    end
-
-    diagC = diag(C); 
-    diagD = sqrt(diagD); % D contains standard deviations now
-    % diagD = diagD / prod(diagD)^(1/N);  C = C / prod(diagD)^(2/N);
-    BD = B.*repmat(diagD',N,1); % O(n^2)
-  end % if mod
-
-  % Align/rescale order of magnitude of scales of sigma and C for nicer output
-  % TODO: interference with sigmafacup: replace 1e10 with 2*sigmafacup
-  % not a very usual case
-  if 1 < 2 && sigma > 1e10*max(diagD) && sigma > 8e14 * max(insigma)
-    fac = sigma; % / max(diagD);
-    sigma = sigma/fac;
-    pc = fac * pc;
-    diagD = fac * diagD; 
-    if ~flgDiagonalOnly
-      C = fac^2 * C; % disp(fac);
-      BD = B .* repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
-    end
-    diagC = fac^2 * diagC; 
-  end
-
-  if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly 
-    % full covariance matrix from now on 
-    flgDiagonalOnly = 0; 
-    B = eye(N,N);
-    BD = diag(diagD);
-    C = diag(diagC); % is better, because correlations are spurious anyway
-  end
-
-  if noiseHandling 
-   if countiter == 1  % assign firstvarargin for noise treatment e.g. as #reevaluations
-     if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
-       if irun == 1
-         firstvarargin = varargin{1};
-       else
-         varargin{1} =  firstvarargin;  % reset varargin{1}
-       end
-     else
-       firstvarargin = 0;
-     end
-   end
-   if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
-     varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4));  % still experimental
-   elseif noiseSS > 0
-    if ~isempty(noiseCallback)  % to be removed? 
-      res = feval(noiseCallback); % should also work without output argument!?
-      if ~isempty(res) && res > 1 % TODO: decide for interface of callback
-                                  %       also a dynamic popsize could be done here 
-        sigma = sigma * noiseAlpha;
-      end
-    else
-      if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
-        varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
-      end
-    
-      sigma = sigma * noiseAlpha; 
-      % lambda = ceil(0.1 * sqrt(lambda) + lambda);
-      % TODO: find smallest increase of lambda with log-linear
-      %       convergence in iterations
-    end
-    % qqq experimental: take a mean to estimate the true optimum
-    noiseN = noiseN + 1;
-    if noiseN == 1
-      noiseX = xmean; 
-    else
-      noiseX = noiseX + (3/noiseN) * (xmean - noiseX); 
-    end
-   end
-  end
-
-  % ----- numerical error management -----
-  % Adjust maximal coordinate axis deviations
-  if any(sigma*sqrt(diagC) > maxdx)
-    sigma = min(maxdx ./ sqrt(diagC));
-    %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
-    %         'deviation at upper limit of ' num2str(maxdx)]);
-    % stopflag(end+1) = {'maxcoorddev'};
-  end
-  % Adjust minimal coordinate axis deviations
-  if any(sigma*sqrt(diagC) < mindx)
-    sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps); 
-    %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
-    %         'deviation at lower limit of ' num2str(mindx)]);
-    % stopflag(end+1) = {'mincoorddev'};;
-  end
-  % Adjust too low coordinate axis deviations
-  if any(xmean == xmean + 0.2*sigma*sqrt(diagC)) 
-    if stopOnWarnings
-      stopflag(end+1) = {'warnnoeffectcoord'};
-    else
-      warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
-                'deviation too low' ]);
-      if flgDiagonalOnly
-        diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
-                                                  (xmean == xmean + 0.2*sigma*sqrt(diagC)));
-      else
-        C = C + (ccov1+ccovmu) * diag(diagC .* ...
-                                      (xmean == xmean + 0.2*sigma*sqrt(diagC)));
-      end
-      sigma = sigma * exp(0.05+cs/damps); 
-    end
-  end
-  % Adjust step size in case of (numerical) precision problem 
-  if flgDiagonalOnly
-    tmp = 0.1*sigma*diagD; 
-  else
-    tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
-  end
-  if all(xmean == xmean + tmp)
-    i = 1+floor(mod(countiter,N));
-    if stopOnWarnings
-	stopflag(end+1) = {'warnnoeffectaxis'};
-    else
-      warning(['Iteration ' num2str(countiter) ...
-	       ': main axis standard deviation ' ...
-	       num2str(sigma*diagD(i)) ' has no effect' ]);
-      sigma = sigma * exp(0.2+cs/damps); 
-    end
-  end
-  % Adjust step size in case of equal function values (flat fitness)
-  % isequalfuncvalues = 0; 
-  if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
-    % isequalfuncvalues = 1; 
-    if stopOnEqualFunctionValues
-      arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
-      % stop if this happens in more than 33%
-      if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
-        stopflag(end+1) = {'equalfunvals'}; 
-      end
-    else
-      if flgWarnOnEqualFunctionValues
-        warning(['Iteration ' num2str(countiter) ...
-		 ': equal function values f=' num2str(fitness.sel(1)) ...
-		 ' at maximal main axis sigma ' ...
-        num2str(sigma*max(diagD))]);
-      end
-      sigma = sigma * exp(0.2+cs/damps); 
-    end
-  end
-  % Adjust step size in case of equal function values
-  if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0  
-    if stopOnWarnings
-	stopflag(end+1) = {'warnequalfunvalhist'};
-    else
-      warning(['Iteration ' num2str(countiter) ...
-	       ': equal function values in history at maximal main ' ...
-	       'axis sigma ' num2str(sigma*max(diagD))]);
-      sigma = sigma * exp(0.2+cs/damps); 
-    end
-  end
-    
-  % ----- end numerical error management -----
-  
-  % Keep overall best solution
-  out.evals = counteval;
-  out.solutions.evals = counteval;
-  out.solutions.mean.x = xmean;
-  out.solutions.mean.f = fmean;
-  out.solutions.mean.evals = counteval;
-  out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
-  out.solutions.recentbest.f = fitness.raw(1);
-  out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
-  out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
-  out.solutions.recentworst.f = fitness.raw(end);
-  out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
-  if fitness.hist(1) < out.solutions.bestever.f
-    out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
-    out.solutions.bestever.f = fitness.hist(1);
-    out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
-    bestever = out.solutions.bestever;
-  end
-
-  % Set stop flag
-  if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
-  if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
-  if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
-  if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX) 
-    stopflag(end+1) = {'tolx'};
-  end
-  if any(sigma*sqrt(diagC) > stopTolUpX) 
-    stopflag(end+1) = {'tolupx'};
-  end
-  if sigma*max(diagD) == 0  % should never happen
-    stopflag(end+1) = {'bug'};
-  end
-  if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun 
-    stopflag(end+1) = {'tolfun'};
-  end
-  if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun 
-    stopflag(end+1) = {'tolhistfun'};
-  end
-  l = floor(length(fitness.histbest)/3);
-  if 1 < 2 && stopOnStagnation && ...  % leads sometimes early stop on ftablet, fcigtab
-      countiter > N * (5+100/lambda) && ...  
-      length(fitness.histbest) > 100 && ... 
-      median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
-      median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
-    stopflag(end+1) = {'stagnation'};
-  end
-
-  if counteval >= stopFunEvals || countiter >= stopIter
-    stopflag(end+1) = {'stoptoresume'};
-    if length(stopflag) == 1 && flgsaving == 0
-      error('To resume later the saving option needs to be set');
-    end
-  end
-  % read stopping message from file signals.par 
-  if flgreadsignals
-    fid = fopen('./signals.par', 'rt');  % can be performance critical 
-    while fid > 0
-      strline = fgetl(fid); %fgets(fid, 300);
-      if strline < 0 % fgets and fgetl returns -1 at end of file
-        break;
-      end
-      % 'stop filename' sets stopflag to manual
-      str = sscanf(strline, ' %s %s', 2);
-      if strcmp(str, ['stop' opts.LogFilenamePrefix]) 
-        stopflag(end+1) = {'manual'};
-        break;
-      end
-      % 'skip filename run 3' skips a run, but not the last
-      str = sscanf(strline, ' %s %s %s', 3);
-      if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
-        i = strfind(strline, 'run');
-        if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
-          stopflag(end+1) = {'skipped'};
-        end      
-      end
-    end % while, break 
-    if fid > 0
-      fclose(fid);
-      clear fid; % prevents strange error under octave
-    end
-  end
-  
-  out.stopflag = stopflag;
-
-  % ----- output generation -----
-  if verbosemodulo > 0 && isfinite(verbosemodulo)
-    if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1 
-      disp(['Iterat, #Fevals:   Function Value    (median,worst) ' ...
-	    '|Axis Ratio|' ...
-	    'idx:Min SD idx:Max SD']); 
-    end
-    if mod(countiter, verbosemodulo) < 1 ...
-	  || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
-	      (countiter < 3 || ~isempty(stopflag)))
-      [minstd minstdidx] = min(sigma*sqrt(diagC));
-      [maxstd maxstdidx] = max(sigma*sqrt(diagC));
-      % format display nicely
-      disp([repmat(' ',1,4-floor(log10(countiter))) ...
-	    num2str(countiter) ' , ' ...
-	    repmat(' ',1,5-floor(log10(counteval))) ...
-	    num2str(counteval) ' : ' ...
-            num2str(fitness.hist(1), '%.13e') ...
-	    ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
-	    ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
-	    ') | ' ...
-	    num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
-	    repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
-	    num2str(minstd, ' %.1e') ' ' ...
-	    repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
-	    num2str(maxstd, ' %.1e')]);
-    end
-  end
-
-  % measure time for recording data
-  if countiter < 3 
-    time.c = 0.05;
-    time.nonoutput = 0;
-    time.recording = 0;
-    time.saving  = 0.15; % first saving after 3 seconds of 100 iterations
-    time.plotting = 0;
-  elseif countiter > 300
-    % set backward horizon, must be long enough to cover infrequent plotting etc
-    % time.c = min(1, time.nonoutput/3 + 1e-9); 
-    time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
-  end
-  % get average time per iteration
-  time.t1 = clock;
-  time.act = max(0,etime(time.t1, time.t0));
-  time.nonoutput = (1-time.c) * time.nonoutput ...
-      + time.c * time.act; 
-
-  time.recording = (1-time.c) * time.recording;  % per iteration
-  time.saving  = (1-time.c) * time.saving;
-  time.plotting = (1-time.c) * time.plotting;
-  
-  % record output data, concerning time issues
-  if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
-	countiter >= outiter + savemodulo)
-    outiter = countiter; 
-      % Save output data to files  
-      for namecell = filenames(:)'
-        name = namecell{:};
-
-	[fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
-	if fid < 1 % err ~= 0 
-	  warning(['could not open ' filenameprefix name '.dat']);
-	else
-	  if strcmp(name, 'axlen')
-	    fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
-		max(diagD), min(diagD)); 
-            fprintf(fid, '%e ', sort(diagD)); 
-            fprintf(fid, '\n');
-	  elseif strcmp(name, 'disp') % TODO
-	  elseif strcmp(name, 'fit')
-	    fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
-                    countiter, counteval, sigma, max(diagD)/min(diagD), ...
-                    out.solutions.bestever.f, ...
-                    fitness.raw(1), median(fitness.raw), fitness.raw(end)); 
-            if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
-              fprintf(fid, ' %f', varargin{1});
-            end                    
-	    fprintf(fid, '\n');
-	  elseif strcmp(name, 'stddev')
-	    fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma); 
-	    fprintf(fid, '%e ', sigma*sqrt(diagC)); 
-            fprintf(fid, '\n');
-	  elseif strcmp(name, 'xmean')
-	    if isnan(fmean)
-	      fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval); 
-	    else
-	      fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean); 
-	    end
-	    fprintf(fid, '%e ', xmean); 
-            fprintf(fid, '\n');
-	  elseif strcmp(name, 'xrecentbest')
-            % TODO: fitness is inconsistent with x-value
-	    fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1)); 
-	    fprintf(fid, '%e ', arx(:,fitness.idx(1))); 
-            fprintf(fid, '\n');
-	  end
-	  fclose(fid); 
-	end
-  end
-
-    % get average time for recording data
-    time.t2 = clock;
-    time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1)); 
-    
-    % plot
-    if flgplotting && countiter > 1
-      if countiter == 2
-        iterplotted = 0;
-      end
-      if ~isempty(stopflag) || ... 
-        ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
-          time.plotting < 0.05 * (time.nonoutput+time.recording))
-        local_plotcmaesdat(324, filenameprefix);
-        iterplotted = countiter; 
-        %  outplot(out); % outplot defined below
-        if time.plotting == 0  % disregard opening of the window
-          time.plotting = time.nonoutput+time.recording;
-        else
-          time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2)); 
-        end
-      end
-    end
-    if countiter > 100 + 20 && savemodulo && ...
-          time.recording * countiter > 0.1 && ...  % absolute time larger 0.1 second
-	  time.recording > savetime * (time.nonoutput+time.recording) / 100 
-      savemodulo = floor(1.02 * savemodulo) + 1;
-      % disp(['++savemodulo == ' num2str(savemodulo) ' at ' num2str(countiter)]); %qqq
-    end
-  end % if output
-
-  % save everything
-  time.t3 = clock;
-  if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
-    xmin = arxvalid(:, fitness.idx(1));
-    fmin = fitness.raw(1);
-    if flgsaving && countiter > 2
-      clear idx; % prevents error under octave
-      % -v6 : non-compressed non-unicode for version 6 and earlier
-      if ~isempty(strsaving) && ~isoctave
-	save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart	
-      else 
-	save('-mat', opts.SaveFilename); % for inspection and possible restart
-      end
-      time.saving = time.saving + time.c * max(0,etime(clock, time.t3)); 
-    end
-  end
-  time.t0 = clock;
-
-  % Update the diary file
-  if ~isempty(calibGUI_interface_obj)
-     updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end     
-  end   
-  
-  % ----- end output generation -----
-
-end % while, end generation loop
-
-% -------------------- Final Procedures -------------------------------
-
-% Evaluate xmean and return best recent point in xmin
-fmin = fitness.raw(1);
-xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
-if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
-  out.solutions.mean.f = ...
-      feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
-  counteval = counteval + 1;
-  out.solutions.mean.evals = counteval;
-  if out.solutions.mean.f < fitness.raw(1)
-    fmin = out.solutions.mean.f;
-    xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
-  end
-  if out.solutions.mean.f < out.solutions.bestever.f
-    out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
-    out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds); 
-    bestever = out.solutions.bestever;
-  end
-end
-
-% Save everything and display final message
-if flgsavingfinal
-  clear idx; % prevents error under octave
-  if ~isempty(strsaving) && ~isoctave
-    save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart	
-  else 
-    save('-mat', opts.SaveFilename);    % for inspection and possible restart
-  end
-  message = [' (saved to ' opts.SaveFilename ')'];
-else
-  message = [];
-end
-
-if flgdisplay
-  disp(['#Fevals:   f(returned x)   |    bestever.f     | stopflag' ...
-        message]);
-  if isoctave
-    strstop = stopflag(:); 
-  else
-      strcat(stopflag(:), '.');
-  end
-  strstop = stopflag(:); %strcat(stopflag(:), '.');
-  disp([repmat(' ',1,6-floor(log10(counteval))) ...
-        num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
-        num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
-	strstop{1:end}]);
-  if N < 102
-     disp(['mean solution:' sprintf(' %+.1e', xmean)]);
-     disp(['std deviation:' sprintf('  %.1e', sigma*sqrt(diagC))]);
-     disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
-  end
-  if exist('sfile', 'var') 
-    disp(['Results saved in ' sfile]); 
-  end
-end
-
-  out.arstopflags{irun} = stopflag;
-  if any(strcmp(stopflag, 'fitness')) ...
-	|| any(strcmp(stopflag, 'maxfunevals')) ...
-	|| any(strcmp(stopflag, 'stoptoresume')) ...
-	|| any(strcmp(stopflag, 'manual'))
-    break; 
-  end
-  
-  % Update the diary file
-  if ~isempty(calibGUI_interface_obj)
-     updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end     
-  end  
-  
-end % while irun <= Restarts
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function [x, idx] = xintobounds(x, lbounds, ubounds)
-%
-% x can be a column vector or a matrix consisting of column vectors
-%
-  if ~isempty(lbounds)
-    if length(lbounds) == 1
-      idx = x < lbounds;
-      x(idx) = lbounds;
-    else
-      arbounds = repmat(lbounds, 1, size(x,2));
-      idx = x < arbounds;
-      x(idx) = arbounds(idx);
-    end
-  else
-    idx = 0;
-  end
-  if ~isempty(ubounds)
-    if length(ubounds) == 1
-      idx2 = x > ubounds;
-      x(idx2) = ubounds;
-    else
-      arbounds = repmat(ubounds, 1, size(x,2));
-      idx2 = x > arbounds;
-      x(idx2) = arbounds(idx2);
-    end
-  else
-    idx2 = 0;
-  end
-  idx = idx2-idx; 
-  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function opts=getoptions(inopts, defopts)
-% OPTS = GETOPTIONS(INOPTS, DEFOPTS) handles an arbitrary number of
-% optional arguments to a function. The given arguments are collected
-% in the struct INOPTS.  GETOPTIONS matches INOPTS with a default
-% options struct DEFOPTS and returns the merge OPTS.  Empty or missing
-% fields in INOPTS invoke the default value.  Fieldnames in INOPTS can
-% be abbreviated.
-%
-% The returned struct OPTS is first assigned to DEFOPTS. Then any
-% field value in OPTS is replaced by the respective field value of
-% INOPTS if (1) the field unambiguously (case-insensitive) matches
-% with the fieldname in INOPTS (cut down to the length of the INOPTS
-% fieldname) and (2) the field is not empty.
-%
-% Example:
-%   In the source-code of the function that needs optional
-%   arguments, the last argument is the struct of optional
-%   arguments:
-%
-%   function results = myfunction(mandatory_arg, inopts)
-%     % Define four default options
-%     defopts.PopulationSize = 200;
-%     defopts.ParentNumber = 50;
-%     defopts.MaxIterations = 1e6;
-%     defopts.MaxSigma = 1;
-%  
-%     % merge default options with input options
-%     opts = getoptions(inopts, defopts);
-%
-%     % Thats it! From now on the values in opts can be used
-%     for i = 1:opts.PopulationSize
-%       % do whatever
-%       if sigma > opts.MaxSigma
-%         % do whatever
-%       end
-%     end
-%   
-%   For calling the function myfunction with default options:
-%   myfunction(argument1, []);
-%   For calling the function myfunction with modified options:
-%   opt.pop = 100; % redefine PopulationSize option
-%   opt.PAR = 10;  % redefine ParentNumber option
-%   opt.maxiter = 2; % opt.max=2 is ambiguous and would result in an error 
-%   myfunction(argument1, opt);
-
-%
-% 04/07/19: Entries can be structs itself leading to a recursive
-%           call to getoptions. 
-%
-
-if nargin < 2 || isempty(defopts) % no default options available
-  opts=inopts;
-  return;
-elseif isempty(inopts) % empty inopts invoke default options
-  opts = defopts;
-  return;
-elseif ~isstruct(defopts) % handle a single option value
-  if isempty(inopts) 
-    opts = defopts;
-  elseif ~isstruct(inopts)
-    opts = inopts;
-  else
-    error('Input options are a struct, while default options are not');
-  end
-  return;
-elseif ~isstruct(inopts) % no valid input options
-  error('The options need to be a struct or empty');
-end
-
-  opts = defopts; % start from defopts 
-  % if necessary overwrite opts fields by inopts values
-  defnames = fieldnames(defopts);
-  idxmatched = []; % indices of defopts that already matched
-  for name = fieldnames(inopts)'
-    name = name{1}; % name of i-th inopts-field
-    if isoctave
-      for i = 1:size(defnames, 1)
-        idx(i) = strncmpi(defnames(i), name, length(name));
-      end
-    else
-	idx = strncmpi(defnames, name, length(name));
-    end
-    if sum(idx) > 1
-      error(['option "' name '" is not an unambigous abbreviation. ' ...
-	     'Use opts=RMFIELD(opts, ''' name, ...
-	     ''') to remove the field from the struct.']);
-    end
-    if sum(idx) == 1
-      defname  = defnames{find(idx)}; 
-      if ismember(find(idx), idxmatched)
-	error(['input options match more than ones with "' ...
-	       defname '". ' ...
-	       'Use opts=RMFIELD(opts, ''' name, ...
-	       ''') to remove the field from the struct.']);
-      end
-      idxmatched = [idxmatched find(idx)];
-      val = getfield(inopts, name);
-      % next line can replace previous line from MATLAB version 6.5.0 on and in octave
-      % val = inopts.(name);
-      if isstruct(val) % valid syntax only from version 6.5.0
-	opts = setfield(opts, defname, ...
-	    getoptions(val, getfield(defopts, defname))); 
-      elseif isstruct(getfield(defopts, defname)) 
-      % next three lines can replace previous three lines from MATLAB 
-      % version 6.5.0 on
-      %   opts.(defname) = ...
-      %      getoptions(val, defopts.(defname)); 
-      % elseif isstruct(defopts.(defname)) 
-	warning(['option "' name '" disregarded (must be struct)']); 
-      elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
-	opts = setfield(opts, defnames{find(idx)}, val);
-	% next line can replace previous line from MATLAB version 6.5.0 on
-	% opts.(defname) = inopts.(name); 
-      end
-    else
-      warning(['option "' name '" disregarded (unknown field name)']);
-    end
-  end
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function res=myeval(s)
-  if ischar(s)
-    res = evalin('caller', s);
-  else
-    res = s;
-  end
-  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function res=myevalbool(s)
-  if ~ischar(s) % s may not and cannot be empty
-    res = s;
-  else % evaluation string s
-    if strncmpi(s, 'yes', 3) || strncmpi(s, 'on', 2) ...
-	  || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
-      res = 1;
-    elseif strncmpi(s, 'no', 2) || strncmpi(s, 'off', 3) ...
-	  || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
-      res = 0;
-    else
-      try res = evalin('caller', s); catch
-	error(['String value "' s '" cannot be evaluated']);
-      end
-      try res ~= 0; catch
-	error(['String value "' s '" cannot be evaluated reasonably']);
-      end
-    end
-  end
-  
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function res = isoctave
-% any hack to find out whether we are running octave
-  s = version;
-  res = 0;
-  if exist('fflush', 'builtin') && eval(s(1)) < 7
-    res = 1;
-  end
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function flush
-  if isoctave
-    feval('fflush', stdout);
-  end
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% ----- replacements for statistic toolbox functions ------------
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function res=myrange(x)
-  res = max(x) - min(x);
-  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-function res = myprctile(inar, perc, idx)
-%
-% Computes the percentiles in vector perc from vector inar
-% returns vector with length(res)==length(perc)
-% idx: optional index-array indicating sorted order
-%
-
-N = length(inar);
-flgtranspose = 0;
-
-% sizes 
-if size(perc,1) > 1
-  perc = perc';
-  flgtranspose = 1;
-  if size(perc,1) > 1
-    error('perc must not be a matrix');
-  end
-end
-if size(inar, 1) > 1 && size(inar,2) > 1
-  error('data inar must not be a matrix');
-end
- 
-% sort inar
-if nargin < 3 || isempty(idx)
-  [sar idx] = sort(inar);
-else
-  sar = inar(idx);
-end
-
-res = [];
-for p = perc
-  if p <= 100*(0.5/N)
-    res(end+1) = sar(1);
-  elseif p >= 100*((N-0.5)/N)
-    res(end+1) = sar(N);
-  else
-    % find largest index smaller than required percentile
-    availablepercentiles = 100*((1:N)-0.5)/N;
-    i = max(find(p > availablepercentiles));
-    % interpolate linearly
-    res(end+1) = sar(i) ...
-	+ (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
-	/ (availablepercentiles(i+1) - availablepercentiles(i));
-
-  end
-end
-
-if flgtranspose
-  res = res';
-end
-
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-
-
-
-function [s ranks rankDelta] = local_noisemeasurement(arf1, arf2, lamreev, theta, cutlimit)
-% function [s ranks rankDelta] = noisemeasurement(arf1, arf2, lamreev, theta)
-%
-% Input: 
-%   arf1, arf2 : two arrays of function values. arf1 is of size 1xlambda, 
-%       arf2 may be of size 1xlamreev or 1xlambda. The first lamreev values 
-%       in arf2 are (re-)evaluations of the respective solutions, i.e. 
-%       arf1(1) and arf2(1) are two evaluations of "the first" solution.
-%    lamreev: number of reevaluated individuals in arf2 
-%    theta : parameter theta for the rank change limit, between 0 and 1, 
-%       typically between 0.2 and 0.7. 
-%    cutlimit (optional): output s is computed as a mean of rankchange minus 
-%       threshold, where rankchange is <=2*(lambda-1). cutlimit limits 
-%       abs(rankchange minus threshold) in this calculation to cutlimit. 
-%       cutlimit=1 evaluates basically the sign only. cutlimit=2 could be 
-%       the rank change with one solution (both evaluations of it). 
-% 
-% Output: 
-%   s : noise measurement, s>0 means the noise measure is above the
-%       acceptance threshold
-%   ranks : 2xlambda array, corresponding to [arf1; arf2], of ranks 
-%       of arf1 and arf2 in the set [arf1 arf2], values are in [1:2*lambda]
-%   rankDelta: 1xlambda array of rank movements of arf2 compared to
-%       arf1.  rankDelta(i) agrees with the number of values from
-%       the set [arf1 arf2] that lie between arf1(i) and arf2(i).
-%
-% Note: equal function values might lead to somewhat spurious results.
-%       For this case a revision is advisable. 
-
-%%% verify input argument sizes
-if size(arf1,1) ~= 1
-  error('arf1 must be an 1xlambda array');
-elseif size(arf2,1) ~= 1
-  error('arf2 must be an 1xsomething array');
-elseif size(arf1,2) < size(arf2,2)  % not really necessary, but saver
-  error('arf2 must not be smaller than arf1 in length');
-end
-lam = size(arf1, 2);
-if size(arf1,2) ~= size(arf2,2)
-   arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
-end
-if nargin < 5
-  cutlimit = inf;
-end
-
-%%% capture unusual values
-if any(diff(arf1) == 0)
-  % this will presumably interpreted as rank change, because
-  % sort(ones(...)) returns 1,2,3,...
-  warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
-end
-
-%%% compute rank changes into rankDelta
-% compute ranks
-[ignore, idx] = sort([arf1 arf2]);
-[ignore, ranks] = sort(idx);
-ranks = reshape(ranks, lam, 2)';
-
-rankDelta = ranks(1,:) - ranks(2,:) - sign(ranks(1,:) - ranks(2,:));
-
-%%% compute rank change limits using both ranks(1,...) and ranks(2,...)
-for i = 1:lamreev
-  sumlim(i) = ...
-      0.5 * (...
-          myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
-                    theta*50) ...      
-          + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
-                      theta*50)); 
-end
-
-%%% compute measurement
-%s = abs(rankDelta(1:lamreev)) - sumlim; % lives roughly in 0..2*lambda
-
-%                               max: 1 rankchange in 2*lambda is always fine
-s = abs(rankDelta(1:lamreev)) - max(1, sumlim); % lives roughly in 0..2*lambda
-
-% cut-off limit
-idx = abs(s) > cutlimit; 
-s(idx) = sign(s(idx)) * cutlimit;
-s = mean(s);
-
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% ---------------------------------------------------------------  
-% just a "local" copy of plotcmaesdat.m, with manual_mode set to zero
-function local_plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarname)
-% PLOTCMAESDAT;
-% PLOTCMAES(FIGURENUMBER_iBEGIN_iEND, FILENAMEPREFIX, FILENAMEEXTENSION, OBJECTVARNAME);
-%   plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy... 
-%   mod(figNb,100)==1 plots versus iterations. 
-%
-% PLOTCMAES([101 300]) plots versus iteration, from iteration 300. 
-% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800. 
-%
-% Upper left subplot: blue/red: function value of the best solution in the 
-%   recent population, cyan: same function value minus best
-%   ever seen function value, green: sigma, red: ratio between 
-%   longest and shortest principal axis length which is equivalent
-%   to sqrt(cond(C)). 
-% Upper right plot: time evolution of the distribution mean (default) or
-%   the recent best solution vector. 
-% Lower left: principal axes lengths of the distribution ellipsoid, 
-%   equivalent with the sqrt(eig(C)) square root eigenvalues of C. 
-% Lower right: magenta: minimal and maximal "true" standard deviation 
-%   (with sigma included) in the coordinates, other colors: sqrt(diag(C)) 
-%   of all diagonal elements of C, if C is diagonal they equal to the
-%   lower left. 
-%
-% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where 
-%   name = axlen, OBJECTVARNAME (xmean|xrecentbest), fit, or stddev.
-%
-
-manual_mode = 0;
-
-  if nargin < 1 || isempty(figNb)
-    figNb = 325;
-  end
-  if nargin < 2 || isempty(filenameprefix)
-    filenameprefix = 'outcmaes';
-  end
-  if nargin < 3 || isempty(filenameextension)
-    filenameextension = '.dat';
-  end
-  if nargin < 4 || isempty(objectvarname)
-    objectvarname = 'xmean';    
-    objectvarname = 'xrecentbest';
-  end
-  % load data
-  d.x = load([filenameprefix objectvarname filenameextension]); 
-  % d.x = load([filenameprefix 'xmean' filenameextension]); 
-  % d.x = load([filenameprefix 'xrecentbest' filenameextension]); 
-  d.f = load([filenameprefix 'fit' filenameextension]); 
-  d.std = load([filenameprefix 'stddev' filenameextension]);
-  d.D = load([filenameprefix 'axlen' filenameextension]);
-
-  % interpret entries in figNb for cutting out some data
-  if length(figNb) > 1
-    iend = inf;
-    istart = figNb(2);
-    if length(figNb) > 2
-      iend = figNb(3);
-    end
-    figNb = figNb(1);
-    d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
-    d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
-    d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
-    d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
-  end
-
-  % decide for x-axis
-  iabscissa = 2; % 1== versus iterations, 2==versus fevals
-  if mod(figNb,100) == 1
-    iabscissa = 1; % a short hack
-  end
-  if iabscissa == 1
-    xlab ='iterations'; 
-  elseif iabscissa == 2
-    xlab = 'function evaluations'; 
-  end
-
-  if size(d.x, 2) < 1000
-    minxend = 1.03*d.x(end, iabscissa);
-  else
-    minxend = 0;
-  end
-
-  % set up figure window
-  if manual_mode
-    figure(figNb);  % just create and raise the figure window
-  else
-    if 1 < 3 && evalin('caller', 'iterplotted') == 0 && evalin('caller', 'irun') == 1 
-      figure(figNb);  % incomment this, if figure raise in the beginning is not desired
-    elseif ismember(figNb, findobj('Type', 'figure'))
-      set(0, 'CurrentFigure', figNb);  % prevents raise of existing figure window
-    else
-      figure(figNb);
-    end
-  end
-
-  % plot fitness etc
-  foffset = 1e-99;
-  dfit = d.f(:,6)-min(d.f(:,6)); 
-  [ignore idxbest] = min(dfit);
-  dfit(dfit<1e-98) = NaN;
-  subplot(2,2,1); hold off; 
-  dd = abs(d.f(:,7:8)) + foffset; 
-  dd(d.f(:,7:8)==0) = NaN;
-  semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
-  % additional fitness data, for example constraints values
-  if size(d.f,2) > 8
-    dd = abs(d.f(:,9:end)) + 10*foffset;  % a hack
-    % dd(d.f(:,9:end)==0) = NaN; 
-    semilogy(d.f(:,iabscissa), dd, '-m'); hold on; 
-    if size(d.f,2) > 12
-      semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
-    end
-  end
-
-  idx = find(d.f(:,6)>1e-98);  % positive values
-  if ~isempty(idx)  % otherwise non-log plot gets hold
-    semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on; 
-  end
-  idx = find(d.f(:,6) < -1e-98);  % negative values
-  if ~isempty(idx)
-    semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on; 
-  end
-  semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
-  semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
-  semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
-  semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
-                      min(d.std(:,6:end)')'], '-m'); % max,min std
-  maxval = max(d.std(end,6:end));
-  minval = min(d.std(end,6:end));
-  text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
-  text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
-
-  semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
-  % plot best f
-  semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
-  semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
-
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  
-  yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3)))); 
-  text(ax(1), yannote, ...
-       [ 'f=' num2str(d.f(end,6), '%.15g') ]);
-
-  title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
-  grid on; 
-
-  subplot(2,2,2); hold off; 
-  plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax); 
-
-  % add some annotation lines
-  [ignore idx] = sort(d.x(end,6:end));
-  % choose no more than 25 indices 
-  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
-  yy = repmat(NaN, 2, size(d.x,2)-5);
-  yy(1,:) = d.x(end, 6:end);
-  yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
-  plot([d.x(end,iabscissa) ax(2)], yy, '-'); 
-  plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
-  for i = idx(idxs)
-    text(ax(2), yy(2,i), ...
-         ['x(' num2str(i) ')=' num2str(yy(1,i))]);
-  end
-  
-  lam = 'NA'; 
-  if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
-    lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
-  end
-  title(['Object Variables (' num2str(size(d.x, 2)-5) ...
-            '-D, popsize~' lam ')']);grid on;
-
-  subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  title('Principal Axes Lengths');grid on;
-  xlabel(xlab); 
-
-  subplot(2,2,4); hold off; 
-  % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on; 
-  % remove sigma from stds
-  d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
-  semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on; 
-  if 11 < 3  % max and min std
-    semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end)')' ...
-                        d.std(:,3).*min(d.std(:,6:end)')'], '-m', 'linewidth', 2);
-    maxval = max(d.std(end,6:end));
-    minval = min(d.std(end,6:end));
-    text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
-    text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
-  end
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  % add some annotation lines
-  [ignore idx] = sort(d.std(end,6:end));
-  % choose no more than 25 indices 
-  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
-  yy = repmat(NaN, 2, size(d.std,2)-5);
-  yy(1,:) = d.std(end, 6:end);
-  yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
-  semilogy([d.std(end,iabscissa) ax(2)], yy, '-'); 
-  semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
-  for i = idx(idxs)
-    text(ax(2), yy(2,i), [' ' num2str(i)]);
-  end
-  title('Standard Deviations in Coordinates divided by sigma');grid on;
-  xlabel(xlab);
-
-  if figNb ~= 324
-    % zoom on;  % does not work in Octave
-  end
-  drawnow;
-
-% ---------------------------------------------------------------  
-% --------------- TEST OBJECTIVE FUNCTIONS ----------------------  
-% ---------------------------------------------------------------  
-
-%%% Unimodal functions
-
-function f=fjens1(x)
-%
-% use population size about 2*N
-%
-  f = sum((x>0) .* x.^1, 1);
-  if any(any(x<0))
-    idx = sum(x < 0, 1) > 0;
-    f(idx) = 1e3;
-%    f = f + 1e3 * sum(x<0, 1);
-%    f = f + 10 * sum((x<0) .* x.^2, 1);
-    f(idx) = f(idx) + 1e-3*abs(randn(1,sum(idx)));
-%    f(idx) = NaN;
-  end
-
-function f=fsphere(x)
-  f = sum(x.^2,1);
-
-function f=fmax(x)
-  f = max(abs(x), [], 1);
-
-function f=fssphere(x)
-  f=sqrt(sum(x.^2, 1));
-
-%  lb = -0.512; ub = 512; 
-%  xfeas = x; 
-%  xfeas(x<lb) = lb;
-%  xfeas(x>ub) = ub; 
-%  f=sum(xfeas.^2, 1);
-%  f = f + 1e-9 * sum((xfeas-x).^2); 
-  
-function f=fspherenoise(x, Nevals)
-  if nargin < 2 || isempty(Nevals)
-    Nevals = 1;
-  end
-  [N,popsi] = size(x);
-%  x = x .* (1 +  0.3e-0 * randn(N, popsi)/(2*N)); % actuator noise
-  fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2; 
-%  f = 0*rand(1,1) ...
-%      + fsum ...
-%      + fsum .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)) ...
-%      + 1*fsum.^0.9 .* 2*randn(1,popsi) / (2*N); % 
-
-%  f = fsum .* exp(0.1*randn(1,popsi));
-  f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
-%  f = fsum .* (1 + (0.1/N)*randn(1,popsi)./randn(1,popsi).^1);
-
-  idx = rand(1,popsi) < 0.0;
-  if sum(idx) > 0
-    f(idx) = f(idx) + 1e3*exp(randn(1,sum(idx)));
-  end
-  
-function f=fmixranks(x)
-  N = size(x,1);
-  f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
-  if size(x, 2) > 1 % compute ranks, if it is a population 
-    [ignore, idx] = sort(f);
-    [ignore, ranks] = sort(idx);
-    k = 9; % number of solutions randomly permuted, lambda/2-1
-           % works still quite well (two time slower)
-    for i = k+1:k-0:size(x,2)
-      idx(i-k+(1:k)) = idx(i-k+randperm(k)); 
-    end
-    %disp([ranks' f'])
-    [ignore, ranks] = sort(idx);
-    %disp([ranks' f'])
-    %pause
-    f = ranks+1e-9*randn(1,1);
-  end
-  
-function f = fsphereoneax(x)
-  f = x(1)^2;
-  f = mean(x)^2;
-  
-function f=frandsphere(x)
-  N = size(x,1);
-  idx = ceil(N*rand(7,1));
-  f=sum(x(idx).^2);
-
-function f=fspherelb0(x, M) % lbound at zero for 1:M needed
-  if nargin < 2 M = 0; end
-  N = size(x,1);
-  % M active bounds, f_i = 1 for x = 0
-  f = -M + sum((x(1:M) + 1).^2);
-  f = f + sum(x(M+1:N).^2);
-  
-function f=fspherehull(x)
-  % Patton, Dexter, Goodman, Punch
-  % in -500..500
-  % spherical ridge through zeros(N,1)
-  % worst case start point seems x = 2*100*sqrt(N)
-  % and small step size
-  N = size(x,1);
-  f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
-  
-function f=fellilb0(x, idxM, scal) % lbound at zero for 1:M needed
-  N = size(x,1);
-  if nargin < 3 || isempty(scal)
-    scal = 100;
-  end
-  scale=scal.^((0:N-1)/(N-1));
-  if nargin < 2 || isempty(idxM)
-    idxM = 1:N;
-  end
-  %scale(N) = 1e0;
-  % M active bounds
-  xopt = 0.1;
-  x(idxM) = x(idxM) + xopt;
-  f = scale.^2*x.^2;
-  f = f - sum((xopt*scale(idxM)).^2); 
-%  f = exp(f) - 1;
-%  f = log10(f+1e-19) + 19;
-
-  f = f + 1e-19;
-  
-function f=fcornersphere(x)
-  w = ones(size(x,1));
-  w(1) = 2.5; w(2)=2.5;
-  idx = x < 0;
-  f = sum(x(idx).^2);
-  idx = x > 0;
-  f = f + 2^2*sum(w(idx).*x(idx).^2);
-  
-function f=fsectorsphere(x, scal)
-%
-% This is deceptive for cumulative sigma control CSA in large dimension:
-% The strategy (initially) diverges for N=50 and popsize = 150.  (Even
-% for cs==1 this can be observed for larger settings of N and
-% popsize.) The reason is obvious from the function topology. 
-% Divergence can be avoided by setting boundaries or adding a
-% penalty for large ||x||. Then, convergence can be observed again. 
-% Conclusion: for popsize>N cumulative sigma control is not completely
-% reasonable, but I do not know better alternatives. In particular:
-% TPA takes longer to converge than CSA when the latter still works. 
-%
-  if nargin < 2 || isempty (scal)
-    scal = 1e3;
-  end
-  f=sum(x.^2,1);
-  idx = x<0;
-  f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
-  if 11 < 3
-    idxpen = find(f>1e9);
-    if ~isempty(idxpen)
-      f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
-    end
-  end
-  
-function f=fstepsphere(x, scal)
-  if nargin < 2 || isempty (scal)
-    scal = 1e0;
-  end
-  N = size(x,1);
-  f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
-  f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
-%  f=1e-11+sum(floor(x+0.5).^2);
-
-function f=fstep(x)
-  % in -5.12..5.12 (bounded)
-  N = size(x,1);
-  f=1e-11+6*N+sum(floor(x));
-
-function f=flnorm(x, scal, e)
-if nargin < 2 || isempty(scal)
-  scal = 1;
-end
-if nargin < 3 || isempty(e)
-  e = 1;
-end
-if e==inf
-  f = max(abs(x));
-else
-  N = size(x,1);
-  scale = scal.^((0:N-1)/(N-1))';
-  f=sum(abs(scale.*x).^e);
-end
-
-function f=fneumaier3(x) 
-  % in -n^2..n^2
-  % x^*-i = i(n+1-i)
-  N = size(x,1);
-%  f = N*(N+4)*(N-1)/6 + sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
-  f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
-
-function f = fmaxmindist(y)
-  % y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
-  % points best
-  %   5    1.4142
-  %   8    1.03527618 
-  %  10    0.842535997 
-  %  20    0.5997   
-  pop = size(y,2);
-  N = size(y,1)/2;
-  f = []; 
-  for ipop = 1:pop
-    if any(abs(y(:,ipop)) > 1)
-      f(ipop) = NaN; 
-    else
-      x = reshape(y(:,ipop), [2, N]);
-      f(ipop) = inf;
-      for i = 1:N
-        f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
-      end
-    end
-  end
-  f = -f;
-
-function f=fchangingsphere(x)
-  N = size(x,1);
-  global scale_G; global count_G; if isempty(count_G) count_G=-1; end
-  count_G = count_G+1;
-  if mod(count_G,10) == 0
-    scale_G = 10.^(2*rand(1,N));
-  end
-  %disp(scale(1));
-  f = scale_G*x.^2;
-  
-function f= flogsphere(x)
- f = 1-exp(-sum(x.^2));
-  
-function f= fexpsphere(x)
- f = exp(sum(x.^2)) - 1;
-  
-function f=fbaluja(x)
-  % in [-0.16 0.16]
-  y = x(1);
-  for i = 2:length(x)
-    y(i) = x(i) + y(i-1);
-  end
-  f = 1e5 - 1/(1e-5 + sum(abs(y)));
-
-function f=fschwefel(x)
-  f = 0;
-  for i = 1:size(x,1),
-    f = f+sum(x(1:i))^2;
-  end
-
-function f=fcigar(x, ar)
-  if nargin < 2 || isempty(ar)
-    ar = 1e3;
-  end
-  f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
-  
-function f=fcigtab(x)
-  f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
-  
-function f=ftablet(x)
-  f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
-
-function f=felli(x, lgscal, expon, expon2)
-  % lgscal: log10(axis ratio)
-  % expon: x_i^expon, sphere==2
-  N = size(x,1); if N < 2 error('dimension must be greater one'); end
-
-%  x = x - repmat(-0.5+(1:N)',1,size(x,2)); % optimum in 1:N
-  if nargin < 2 || isempty(lgscal), lgscal = 3; end
-  if nargin < 3 || isempty(expon), expon = 2; end
-  if nargin < 4 || isempty(expon2), expon2 = 1; end
-
-  f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
-%  if rand(1,1) > 0.015
-%    f = NaN;
-%  end
-%  f = f + randn(size(f));
-
-function f=fellitest(x)
-  beta = 0.9;
-  N = size(x,1);
-  f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta; 
-
-  
-function f=fellii(x, scal)
-  N = size(x,1); if N < 2 error('dimension must be greater one'); end
-  if nargin < 2
-    scal = 1;
-  end
-  f= (scal*(1:N)).^2 * (x).^2;
-
-function f=fellirot(x)
-  N = size(x,1);
-  global ORTHOGONALCOORSYSTEM_G
-  if isempty(ORTHOGONALCOORSYSTEM_G) ...
-	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
-	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
-    coordinatesystem(N);
-  end
-  f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
-  
-function f=frot(x, fun, varargin)
-  N = size(x,1);
-  global ORTHOGONALCOORSYSTEM_G
-  if isempty(ORTHOGONALCOORSYSTEM_G) ...
-	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
-	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
-    coordinatesystem(N);
-  end
-  f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
-  
-function coordinatesystem(N)
-  if nargin < 1 || isempty(N)
-    arN = 2:30;
-  else
-    arN = N;
-  end
-  global ORTHOGONALCOORSYSTEM_G
-  ORTHOGONALCOORSYSTEM_G{1} = 1; 
-  for N = arN
-    ar = randn(N,N);
-    for i = 1:N 
-      for j = 1:i-1
-	ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
-      end
-      ar(:,i) = ar(:,i) / norm(ar(:,i));
-    end
-    ORTHOGONALCOORSYSTEM_G{N} = ar; 
-  end
-
-function f=fplane(x)
-  f=x(1);
-
-function f=ftwoaxes(x)
-  f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
-
-function f=fparabR(x)
-  f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
-
-function f=fsharpR(x)
-  f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
-  
-function f=frosen(x)
-  if size(x,1) < 2 error('dimension must be greater one'); end
-  N = size(x,1); 
-  popsi = size(x,2); 
-  f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
-  % f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)); 
-
-function f=frosenlin(x)
-  if size(x,1) < 2 error('dimension must be greater one'); end
-
-  x_org = x;
-  x(x>30) = 30;
-  x(x<-30) = -30;
-
-  f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
-      sum((x(1:end-1,:)-1).^2,1);
-
-  f = f + sum((x-x_org).^2,1);
-%  f(any(abs(x)>30,1)) = NaN; 
-
-function f=frosenrot(x)
-  N = size(x,1);
-  global ORTHOGONALCOORSYSTEM_G
-  if isempty(ORTHOGONALCOORSYSTEM_G) ...
-	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
-	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
-    coordinatesystem(N);
-  end
-  f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
-
-function f=frosenmodif(x)
-  f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
-      - 400*exp(-sum((x+1).^2)/2/0.05);
-  
-function f=fschwefelrosen1(x)
-  % in [-10 10] 
-  f=sum((x.^2-x(1)).^2 + (x-1).^2);
-  
-function f=fschwefelrosen2(x)
-  % in [-10 10] 
-  f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
-
-function f=fdiffpow(x)
-  [N popsi] = size(x); if N < 2 error('dimension must be greater one'); end
-
-  f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
-  f = sqrt(f); 
-
-function f=fabsprod(x)
-  f = sum(abs(x),1) + prod(abs(x),1);
-
-function f=ffloor(x)
-  f = sum(floor(x+0.5).^2,1); 
-
-function f=fmaxx(x)
-  f = max(abs(x), [], 1);
-
-%%% Multimodal functions 
-
-function f=fbirastrigin(x)
-% todo: the volume needs to be a constant 
-  N = size(x,1); 
-  idx = (sum(x, 1) < 0.5*N); % global optimum
-  f = zeros(1,size(x,2));
-  f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1); 
-  idx = ~idx;
-  f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1); 
-
-function f=fackley(x)
-  % -32.768..32.768
-  % Adding a penalty outside the interval is recommended,  
-  % because for large step sizes, fackley imposes like frand
-  % 
-  N = size(x,1); 
-  f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N)); 
-  f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
-  % add penalty outside the search interval
-  f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
-  
-function f = fbohachevsky(x)
- % -15..15
-  f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
-	  - 0.4 * cos(4*pi*x(2:end)) + 0.7);
-  
-function f=fconcentric(x)
-  % in  +-600
-  s = sum(x.^2);
-  f = s^0.25 * (sin(50*s^0.1)^2 + 1);
-
-function f=fgriewank(x)
-  % in [-600 600]
-  [N, P] = size(x);
-  f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
-  scale = repmat(sqrt(1:N)', 1, P);
-  f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
-  % f = f + 1e4*sum(x(abs(x)>5).^2);
-  % if sum(x(abs(x)>5).^2) > 0
-  %   f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
-  % end
-
-function f=fgriewrosen(x)
-% F13 or F8F2
-  [N, P] = size(x);
-  scale = repmat(sqrt(1:N)', 1, P);
-  y = [x(2:end,:); x(1,:)];
-  x = 100 * (x.^2 - y) + (x - 1).^2;  % Rosenbrock part
-  f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
-  f = sum(1 - cos(x) + x.^2/4e3, 1);
-
-function f=fspallpseudorastrigin(x, scal, skewfac, skewstart, amplitude)
-% by default multi-modal about between -30 and 30
-  if nargin < 5 || isempty(amplitude)
-    amplitude = 40;
-  end
-  if nargin < 4 || isempty(skewstart)
-    skewstart = 0;
-  end
-  if nargin < 3 || isempty(skewfac)
-    skewfac = 1;
-  end
-  if nargin < 2 || isempty(scal)
-    scal = 1;
-  end
-  N = size(x,1); 
-  scale = 1;
-  if N > 1
-    scale=scal.^((0:N-1)'/(N-1)); 
-  end
-  % simple version: 
-  % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
-  % skew version: 
-  y = repmat(scale, 1, size(x,2)) .* x;
-  idx = find(x > skewstart);
-  if ~isempty(idx)
-    y(idx) =  skewfac*y(idx);
-  end
-  f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
-      + randn(1,1);
-
-function f=frastrigin(x, scal, skewfac, skewstart, amplitude)
-% by default multi-modal about between -30 and 30
-  if nargin < 5 || isempty(amplitude)
-    amplitude = 10;
-  end
-  if nargin < 4 || isempty(skewstart)
-    skewstart = 0;
-  end
-  if nargin < 3 || isempty(skewfac)
-    skewfac = 1;
-  end
-  if nargin < 2 || isempty(scal)
-    scal = 1;
-  end
-  N = size(x,1); 
-  scale = 1;
-  if N > 1
-    scale=scal.^((0:N-1)'/(N-1)); 
-  end
-  % simple version: 
-  % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
-  % skew version: 
-  y = repmat(scale, 1, size(x,2)) .* x;
-  idx = find(x > skewstart);
-  % idx = intersect(idx, 2:2:10); 
-  if ~isempty(idx)
-    y(idx) =  skewfac*y(idx);
-  end
-  f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
-  
-function f=frastriginmax(x)
-  N = size(x,1);
-  f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
-  f(any(abs(x) > 5.12)) = 1e2*N;
-
-function f = fschaffer(x)
- % -100..100
-  N = size(x,1);
-  s = x(1:N-1,:).^2 + x(2:N,:).^2;
-  f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
-
-function f=fschwefelmult(x)
-  % -500..500
-  % 
-  N = size(x,1); 
-  f = - sum(x.*sin(sqrt(abs(x))), 1);
-  f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
-  % penalty term 
-  f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
-  
-function f=ftwomax(x)
-  % Boundaries at +/-5
-  N = size(x,1); 
-  f = -abs(sum(x)) + 5*N;
-
-function f=ftwomaxtwo(x)
-  % Boundaries at +/-10
-  N = size(x,1); 
-  f = abs(sum(x));
-  if f > 30
-    f = f - 30;
-  end
-  f = -f;
-  
-function f=frand(x)
-  f=1./(1-rand(1, size(x,2))) - 1;
-
-% CHANGES
-% 12/04/28: (3.61) stopIter is relative to countiter after resume (thanks to Tom Holden)
-% 12/04/28: (3.61) some syncing from 3.32.integer branch (cmean introduced, ...)
-% 12/02/19: "future" setting of ccum, correcting for large mueff, is default now
-% 11/11/15: bug-fix: max value for ccovmu_sep setting corrected
-% 10/11/11: (3.52.beta) boundary handling: replace max with min in change 
-%           rate formula. Active CMA: check of pos.def. improved. 
-%           Plotting: value of lambda appears in the title. 
-% 10/04/03: (3.51.beta) active CMA cleaned up. Equal fitness detection
-%           looks into history now. 
-% 10/03/08: (3.50.beta) "active CMA" revised and bug-fix of ambiguous
-%           option Noise.alpha -> Noise.alphasigma. 
-% 09/10/12: (3.40.beta) a slightly modified version of "active CMA", 
-%           that is a negative covariance matrix update, use option
-%           CMA.active. In 10;30;90-D the gain on ftablet is a factor 
-%           of 1.6;2.5;4.4 (the scaling improves by sqrt(N)). On 
-%           Rosenbrock the gain is about 25%. On sharp ridge the 
-%           behavior is improved. Cigar is unchanged. 
-% 09/08/10: local plotcmaesdat remains in backround  
-% 09/08/10: bug-fix in time management for data writing, logtime was not 
-%        considered properly (usually not at all). 
-% 09/07/05: V3.24: stagnation termination added 
-% 08/09/27: V3.23: momentum alignment is out-commented and de-preciated
-% 08/09/25: V3.22: re-alignment of sigma and C was buggy
-% 08/07/15: V3.20, CMA-parameters are options now. ccov and mucov were replaced
-%        by ccov1 \approx ccov/mucov and ccovmu \approx (1-1/mucov)*ccov
-% 08/06/30: file name xrecent was change to xrecentbest (compatible with other 
-%        versions)
-% 08/06/29: time stamp added to output files 
-% 08/06/28: bug fixed with resume option, commentary did not work 
-% 08/06/28: V3.10, uncertainty (noise) handling added (re-implemented), according
-%        to reference "A Method for Handling Uncertainty..." from below.
-% 08/06/28: bug fix: file xrecent was empty 
-% 08/06/01: diagonalonly clean up. >1 means some iterations. 
-% 08/05/05: output is written to file preventing an increasing data
-%        array and ease long runs. 
-% 08/03/27: DiagonalOnly<0 learns for -DiagonalOnly iterations only the 
-%        diagonal with a larger learning rate. 
-% 08/03 (2.60): option DiagonalOnly>=1 invokes a time- and space-linear  
-%        variant with only diagonal elements of the covariance matrix 
-%        updating.  This can be useful for large dimensions, say > 100. 
-% 08/02: diag(weights) * ... replaced with repmat(weights,1,N) .* ...
-%        in C update, implies O(mu*N^2) instead of O(mu^2*N + mu*N^2). 
-% 07/09: tolhistfun as termination criterion added, "<" changed to
-%        "<=" also for TolFun to allow for stopping on zero difference. 
-%        Name tolfunhist clashes with option tolfun. 
-% 07/07: hsig threshold made slighly smaller for large dimension, 
-%        useful for lambda < lambda_default. 
-% 07/06: boundary handling: scaling in the boundary handling
-%        is omitted now, see bnd.flgscale. This seems not to
-%        have a big impact. Using the scaling is worse on rotated
-%        functions, but better on separable ones. 
-% 07/05: boundary handling: weight i is not incremented anymore
-%        if xmean(i) moves towards the feasible space. Increment
-%        factor changed to 1.2 instead of 1.1. 
-% 07/05: boundary handling code simplified not changing the algorithm
-% 07/04: bug removed for saving in octave
-% 06/11/10: more testing of outcome of eig, fixed max(D) to max(diag(D))
-% 06/10/21: conclusive final bestever assignment in the end 
-% 06/10/21: restart and incpopsize option implemented for restarts
-%        with increasing population size, version 2.50. 
-% 06/09/16: output argument bestever inserted again for convenience and
-%        backward compatibility
-% 06/08: output argument out and struct out reorganized. 
-% 06/01: Possible parallel evaluation included as option EvalParallel
-% 05/11: Compatibility to octave implemented, package octave-forge
-%   is needed. 
-% 05/09: Raise of figure and waiting for first plots improved
-% 05/01: Function coordinatesystem cleaned up. 
-% 05/01: Function prctile, which requires the statistics toolbox,
-%        replaced by myprctile. 
-% 05/01: Option warnonequalfunctionvalues included. 
-% 04/12: Decrease of sigma removed. Problems on fsectorsphere can 
-%        be addressed better by adding search space boundaries. 
-% 04/12: Boundary handling simpyfied. 
-% 04/12: Bug when stopping criteria tolx or tolupx are vectors. 
-% 04/11: Three input parameters are obligatory now. 
-% 04/11: Bug in boundary handling removed: Boundary weights can decrease now. 
-% 04/11: Normalization for boundary weights scale changed. 
-% 04/11: VerboseModulo option bug removed. Documentation improved. 
-% 04/11: Condition for increasing boundary weights changed.
-% 04/10: Decrease of sigma when fitness is getting consistenly
-%        worse. Addresses the problems appearing on fsectorsphere for
-%        large population size.
-% 04/10: VerboseModulo option included. 
-% 04/10: Bug for condition for increasing boundary weights removed.
-% 04/07: tolx depends on initial sigma to achieve scale invariance
-%        for this stopping criterion. 
-% 04/06: Objective function value NaN is not counted as function
-%        evaluation and invokes resampling of the search point. 
-% 04/06: Error handling for eigenvalue beeing zero (never happens
-%        with default parameter setting)
-% 04/05: damps further tuned for large mueff 
-%      o Details for stall of pc-adaptation added (variable hsig 
-%        introduced). 
-% 04/05: Bug in boundary handling removed: A large initial SIGMA was
-%        corrected not until *after* the first iteration, which could
-%        lead to a complete failure.
-% 04/05: Call of function range (works with stats toolbox only) 
-%        changed to myrange. 
-% 04/04: Parameter cs depends on mueff now and damps \propto sqrt(mueff)
-%        instead of \propto mueff. 
-%      o Initial stall to adapt C (flginiphase) is removed and
-%        adaptation of pc is stalled for large norm(ps) instead.
-%      o Returned default options include documentation. 
-%      o Resume part reorganized.
-% 04/03: Stopflag becomes cell-array. 
-
-% ---------------------------------------------------------------
-% CMA-ES: Evolution Strategy with Covariance Matrix Adaptation for
-% nonlinear function minimization. To be used under the terms of the
-% GNU General Public License (http://www.gnu.org/copyleft/gpl.html).
-% Author (copyright): Nikolaus Hansen, 2001-2008. 
-% e-mail: nikolaus.hansen AT inria.fr
-% URL:http://www.bionik.tu-berlin.de/user/niko
-% References: See below. 
-% ---------------------------------------------------------------
-%
-% GENERAL PURPOSE: The CMA-ES (Evolution Strategy with Covariance
-% Matrix Adaptation) is a robust search method which should be
-% applied, if derivative based methods, e.g. quasi-Newton BFGS or
-% conjucate gradient, (supposably) fail due to a rugged search
-% landscape (e.g. noise, local optima, outlier, etc.). On smooth
-% landscapes CMA-ES is roughly ten times slower than BFGS. For up to
-% N=10 variables even the simplex direct search method (Nelder & Mead)
-% is often faster, but far less robust than CMA-ES.  To see the
-% advantage of the CMA, it will usually take at least 30*N and up to
-% 300*N function evaluations, where N is the search problem dimension.
-% On considerably hard problems the complete search (a single run) is
-% expected to take at least 30*N^2 and up to 300*N^2 function
-% evaluations.
-%
-% SOME MORE COMMENTS: 
-% The adaptation of the covariance matrix (e.g. by the CMA) is
-% equivalent to a general linear transformation of the problem
-% coding. Nevertheless every problem specific knowlegde about the best
-% linear transformation should be exploited before starting the
-% search. That is, an appropriate a priori transformation should be
-% applied to the problem. This also makes the identity matrix as
-% initial covariance matrix the best choice.
-%
-% The strategy parameter lambda (population size, opts.PopSize) is the
-% preferred strategy parameter to play with.  If results with the
-% default strategy are not satisfactory, increase the population
-% size. (Remark that the crucial parameter mu (opts.ParentNumber) is
-% increased proportionally to lambda). This will improve the
-% strategies capability of handling noise and local minima. We
-% recomment successively increasing lambda by a factor of about three,
-% starting with initial values between 5 and 20. Casually, population
-% sizes even beyond 1000+100*N can be sensible.
-%
-%
-% ---------------------------------------------------------------
-%%% REFERENCES
-%
-% The equation numbers refer to 
-% Hansen, N. and S. Kern (2004). Evaluating the CMA Evolution
-% Strategy on Multimodal Test Functions.  Eighth International
-% Conference on Parallel Problem Solving from Nature PPSN VIII,
-% Proceedings, pp. 282-291, Berlin: Springer. 
-% (http://www.bionik.tu-berlin.de/user/niko/ppsn2004hansenkern.pdf)
-% 
-% Further references:
-% Hansen, N. and A. Ostermeier (2001). Completely Derandomized
-% Self-Adaptation in Evolution Strategies. Evolutionary Computation,
-% 9(2), pp. 159-195.
-% (http://www.bionik.tu-berlin.de/user/niko/cmaartic.pdf).
-%
-% Hansen, N., S.D. Mueller and P. Koumoutsakos (2003). Reducing the
-% Time Complexity of the Derandomized Evolution Strategy with
-% Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation,
-% 11(1).  (http://mitpress.mit.edu/journals/pdf/evco_11_1_1_0.pdf).
-%
-% Ros, R. and N. Hansen (2008). A Simple Modification in CMA-ES
-% Achieving Linear Time and Space Complexity. To appear in Tenth
-% International Conference on Parallel Problem Solving from Nature
-% PPSN X, Proceedings, Berlin: Springer.
-%
-% Hansen, N., A.S.P. Niederberger, L. Guzzella and P. Koumoutsakos
-% (2009?). A Method for Handling Uncertainty in Evolutionary
-% Optimization with an Application to Feedback Control of
-% Combustion. To appear in IEEE Transactions on Evolutionary
-% Computation.
-
-
-
-
+function [xmin, ...      % minimum search point of last iteration
+	  fmin, ...      % function value of xmin
+	  counteval, ... % number of function evaluations done
+	  stopflag, ...  % stop criterion reached
+	  out, ...     % struct with various histories and solutions
+	  bestever ... % struct containing overall best solution (for convenience)
+	 ] = cmaes( ...
+    fitfun, ...    % name of objective/fitness function
+    xstart, ...    % objective variables initial point, determines N
+    insigma, ...   % initial coordinate wise standard deviation(s)
+    inopts, ...    % options struct, see defopts below
+    calibGUI_interface_obj, ...  % Object for interfacing with calibratoin GUI
+    varargin )     % arguments passed to objective function 
+% cmaes.m, Version 3.61.beta, last change: April, 2012 
+% CMAES implements an Evolution Strategy with Covariance Matrix
+% Adaptation (CMA-ES) for nonlinear function minimization.  For
+% introductory comments and copyright (GPL) see end of file (type 
+% 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and, 
+% without data logging and plotting, it should run under Octave 
+% (Linux, package octave-forge is needed).
+%
+% OPTS = CMAES returns default options. 
+% OPTS = CMAES('defaults') returns default options quietly.
+% OPTS = CMAES('displayoptions') displays options. 
+% OPTS = CMAES('defaults', OPTS) supplements options OPTS with default 
+% options. 
+%
+% XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates an approximate minimum 
+% XMIN of function FUN starting from column vector X0 with the initial
+% coordinate wise search standard deviation SIGMA.
+%
+% Input arguments: 
+%
+%  FUN is a string function name like 'frosen'. FUN takes as argument
+%     a column vector of size of X0 and returns a scalar. An easy way to
+%     implement a hard non-linear constraint is to return NaN. Then,
+%     this function evaluation is not counted and a newly sampled
+%     point is tried immediately.
+%
+%   X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
+%     mean(X0, 2) is taken as initial point. If X0 is a string like
+%     '2*rand(10,1)-1', the string is evaluated first.
+%
+%   SIGMA is a scalar, or a column vector of size(X0,1), or a string
+%     that can be evaluated into one of these. SIGMA determines the
+%     initial coordinate wise standard deviations for the search.
+%     Setting SIGMA one third of the initial search region is
+%     appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
+%     means cmaes('myfun', 3*rand(10,1), 2).  If SIGMA is missing and
+%     size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
+%     used as a sample for estimating initial mean and variance of the
+%     search distribution.
+%
+%   OPTS (an optional argument) is a struct holding additional input
+%     options. Valid field names and a short documentation can be
+%     discovered by looking at the default options (type 'cmaes'
+%     without arguments, see above). Empty or missing fields in OPTS
+%     invoke the default value, i.e. OPTS needs not to have all valid
+%     field names.  Capitalization does not matter and unambiguous
+%     abbreviations can be used for the field names. If a string is
+%     given where a numerical value is needed, the string is evaluated
+%     by eval, where 'N' expands to the problem dimension
+%     (==size(X0,1)) and 'popsize' to the population size. 
+%
+% [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
+%    CMAES(FITFUN, X0, SIGMA)
+% returns the best (minimal) point XMIN (found in the last
+% generation); function value FMIN of XMIN; the number of needed
+% function evaluations COUNTEVAL; a STOPFLAG value as cell array,
+% where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
+% 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
+% 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
+% 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
+% e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
+% 'tolx')) for further processing); a record struct OUT with some
+% more output, where the struct SOLUTIONS.BESTEVER contains the overall
+% best evaluated point X with function value F evaluated at evaluation
+% count EVALS. The last output argument BESTEVER equals 
+% OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and 
+% parameters is written to files according to the Log-options. 
+%
+% A regular manual stop can be achieved via the file signals.par. The
+% program is terminated if the first two non-white sequences in any
+% line of this file are 'stop' and the value of the LogFilenamePrefix
+% option (by default 'outcmaes'). Also a run can be skipped.
+% Given, for example, 'skip outcmaes run 2', skips the second run
+% if option Restarts is at least 2, and another run will be started.
+% 
+% To run the code completely silently set Disp, Save, and Log options
+% to 0.  With OPTS.LogModulo > 0 (1 by default) the most important
+% data are written to ASCII files permitting to investigate the
+% results (e.g. plot with function plotcmaesdat) even while CMAES is
+% still running (which can be quite useful on expensive objective
+% functions). When OPTS.SaveVariables==1 (default) everything is saved
+% in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
+% resume the search afterwards by using the resume option.
+%
+% To find the best ever evaluated point load the variables typing
+% "es=load('variablescmaes')" and investigate the variable
+% es.out.solutions.bestever. 
+%
+% In case of a noisy objective function (uncertainties) set
+% OPTS.Noise.on = 1. This option interferes presumably with some 
+% termination criteria, because the step-size sigma will presumably
+% not converge to zero anymore. If CMAES was provided with a 
+% fifth argument (P1 in the below example, which is passed to the 
+% objective function FUN), this argument is multiplied with the 
+% factor given in option Noise.alphaevals, each time the detected 
+% noise exceeds a threshold. This argument can be used within 
+% FUN, for example, as averaging number to reduce the noise level.  
+%
+% OPTS.DiagonalOnly > 1 defines the number of initial iterations,
+% where the covariance matrix remains diagonal and the algorithm has
+% internally linear time complexity. OPTS.DiagonalOnly = 1 means
+% keeping the covariance matrix always diagonal and this setting
+% also exhibits linear space complexity. This can be particularly
+% useful for dimension > 100. The default is OPTS.DiagonalOnly = 0. 
+% 
+% OPTS.CMA.active = 1 turns on "active CMA" with a negative update 
+% of the covariance matrix and checks for positive definiteness. 
+% OPTS.CMA.active = 2 does not check for pos. def. and is numerically
+% faster. Active CMA usually speeds up the adaptation and might 
+% become a default in near future. 
+%
+% The primary strategy parameter to play with is OPTS.PopSize, which
+% can be increased from its default value.  Increasing the population
+% size (by default linked to increasing parent number OPTS.ParentNumber)
+% improves global search properties in exchange to speed. Speed
+% decreases, as a rule, at most linearely with increasing population
+% size. It is advisable to begin with the default small population
+% size. The options Restarts and IncPopSize can be used for an
+% automated multistart where the population size is increased by the
+% factor IncPopSize (two by default) before each restart. X0 (given as
+% string) is reevaluated for each restart. Stopping options
+% StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
+% program, all others including MaxIter invoke another restart, where
+% the iteration counter is reset to zero.
+%
+% Examples: 
+%
+%     XMIN = cmaes('myfun', 5*ones(10,1), 1.5); 
+%
+%   starts the search at 10D-point 5 and initially searches mainly 
+%   between 5-3 and 5+3 (+- two standard deviations), but this is not 
+%   a strict bound. 'myfun' is a name of a function that returns a 
+%   scalar from a 10D column vector.
+%
+%     opts.LBounds = 0; opts.UBounds = 10; 
+%     opts.Restarts = 3;  % doubles the popsize for each restart
+%     X = cmaes('myfun', 10*rand(10,1), 5, opts);
+%
+%   searches within lower bound of 0 and upper bound of 10. Bounds can
+%   also be given as column vectors. If the optimum is not located
+%   on the boundary, use rather a penalty approach to handle bounds. 
+%
+%     opts=cmaes; 
+%     opts.StopFitness=1e-10;
+%     X=cmaes('myfun', rand(5,1), 0.5, opts); 
+%
+%   stops the search, if the function value is smaller than 1e-10.
+%   
+%     [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2); 
+%
+%   passes two additional parameters to the function MYFUN2.
+%
+% See also FMINSEARCH, FMINUNC, FMINBND.
+
+
+% TODO: 
+%       write dispcmaesdat for Matlab (and Octave)
+%       control savemodulo and plotmodulo via signals.par 
+
+
+cmaVersion = '3.62.beta'; 
+
+% ----------- Set Defaults for Input Parameters and Options -------------
+% These defaults may be edited for convenience
+
+% Input Defaults (obsolete, these are obligatory now)
+definput.fitfun = 'felli'; % frosen; fcigar; see end of file for more
+definput.xstart = rand(10,1); % 0.50*ones(10,1);
+definput.sigma = 0.3;
+
+% Options defaults: Stopping criteria % (value of stop flag)
+defopts.StopFitness  = '-Inf % stop if f(xmin) < stopfitness, minimization';
+defopts.MaxFunEvals  = 'Inf  % maximal number of fevals';
+defopts.MaxIter      = '1e3*(N+5)^2/sqrt(popsize) % maximal number of iterations';
+defopts.StopFunEvals = 'Inf  % stop after resp. evaluation, possibly resume later';
+defopts.StopIter     = 'Inf  % stop after resp. iteration, possibly resume later';
+defopts.TolX         = '1e-11*max(insigma) % stop if x-change smaller TolX';
+defopts.TolUpX       = '1e3*max(insigma) % stop if x-changes larger TolUpX';
+defopts.TolFun       = '1e-12 % stop if fun-changes smaller TolFun';
+defopts.TolHistFun   = '1e-13 % stop if back fun-changes smaller TolHistFun';
+defopts.StopOnStagnation = 'on  % stop when fitness stagnates for a long time';
+% TODO: stagnation has four parameters for the period: min = 120, const = 30N/lam, rel = 0.2, max = 2e5
+% defopts.StopOnStagnation = '[120 30*N/popsize 0.2 2e5]  % [min const rel_iter max] measuring period';
+defopts.StopOnWarnings = 'yes  % ''no''==''off''==0, ''on''==''yes''==1 ';
+defopts.StopOnEqualFunctionValues = '2 + N/3  % number of iterations';  
+
+% Options defaults: Other
+defopts.DiffMaxChange = 'Inf  % maximal variable change(s), can be Nx1-vector';
+defopts.DiffMinChange = '0    % minimal variable change(s), can be Nx1-vector';
+defopts.WarnOnEqualFunctionValues = ...
+    'yes  % ''no''==''off''==0, ''on''==''yes''==1 ';
+defopts.LBounds = '-Inf % lower bounds, scalar or Nx1-vector'; 
+defopts.UBounds = 'Inf  % upper bounds, scalar or Nx1-vector'; 
+defopts.EvalParallel = 'no   % objective function FUN accepts NxM matrix, with M>1?';
+defopts.EvalInitialX = 'yes  % evaluation of initial solution';
+defopts.Restarts     = '0    % number of restarts ';
+defopts.IncPopSize   = '2    % multiplier for population size before each restart';
+
+defopts.PopSize      = '(4 + floor(3*log(N)))  % population size, lambda'; 
+defopts.ParentNumber = 'floor(popsize/2)       % AKA mu, popsize equals lambda';
+defopts.RecombinationWeights = 'superlinear decrease % or linear, or equal';
+defopts.DiagonalOnly = '0*(1+100*N/sqrt(popsize))+(N>=1000)  % C is diagonal for given iterations, 1==always'; 
+defopts.Noise.on = '0  % uncertainty handling is off by default'; 
+defopts.Noise.reevals  = '1*ceil(0.05*lambda)  % nb. of re-evaluated for uncertainty measurement';
+defopts.Noise.theta = '0.5  % threshold to invoke uncertainty treatment'; % smaller: more likely to diverge
+defopts.Noise.cum = '0.3  % cumulation constant for uncertainty'; 
+defopts.Noise.cutoff = '2*lambda/3  % rank change cutoff for summation';
+defopts.Noise.alphasigma  = '1+2/(N+10)  % factor for increasing sigma'; % smaller: slower adaptation
+defopts.Noise.epsilon  = '1e-7  % additional relative perturbation before reevaluation';
+defopts.Noise.minmaxevals = '[1 inf]  % min and max value of 2nd arg to fitfun, start value is 5th arg to cmaes'; 
+defopts.Noise.alphaevals  = '1+2/(N+10)  % factor for increasing 2nd arg to fitfun'; 
+defopts.Noise.callback = '[]  % callback function when uncertainty threshold is exceeded';
+% defopts.TPA = 0; 
+defopts.CMA.cs = '(mueff+2)/(N+mueff+3)  % cumulation constant for step-size'; 
+   %qqq defopts.CMA.cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
+defopts.CMA.damps = '1 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
+% defopts.CMA.ccum = '4/(N+4)  % cumulation constant for covariance matrix'; 
+defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N)  % cumulation constant for pc';
+defopts.CMA.ccov1 = '2 / ((N+1.3)^2+mueff)  % learning rate for rank-one update'; 
+defopts.CMA.ccovmu = '2 * (mueff-2+1/mueff) / ((N+2)^2+mueff) % learning rate for rank-mu update'; 
+defopts.CMA.on     = 'yes'; 
+defopts.CMA.active = '0  % active CMA, 1: neg. updates with pos. def. check, 2: neg. updates'; 
+
+flg_future_setting = 0;  % testing for possible future variant(s)
+if flg_future_setting    
+  disp('in the future')
+
+  % damps setting from Brockhoff et al 2010
+  %   this damps diverges with popsize 400:
+  %     defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs  % damping for step-size'; 
+  %   cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
+  % how about:
+  % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
+  defopts.CMA.damps = '0.5 + 0.5*min(1, (0.27*lambda/mueff-1)^2) + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
+
+  if 11 < 3 
+      defopts.CMA.damps = '0.5 + 0.5*min(1,(lam_mirr/(0.159*lambda)-1)^2) + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs  % damping for step-size';
+
+      defopts.mirrored_offspring = 'floor(0.5 + 0.159 * lambda)'; 
+    % TODO: this should also depend on diagonal option!?  
+    defopts.CMA.active = 'floor(int8(lam_mirr>0))  % active CMA 1: neg. updates with pos. def. check, 2: neg. updates'; 
+  end
+  
+  % ccum adjusted for large mueff, better on schefelmult? 
+  % TODO: this should also depend on diagonal option!?  
+  defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N)  % cumulation constant for pc';
+
+  defopts.CMA.active = '1  % active CMA 1: neg. updates with pos. def. check, 2: neg. updates'; 
+end
+  
+defopts.Resume   = 'no   % resume former run from SaveFile'; 
+defopts.Science  = 'on  % off==do some additional (minor) problem capturing, NOT IN USE'; 
+defopts.ReadSignals = 'on  % from file signals.par for termination, yet a stumb';
+defopts.Seed = 'sum(100*clock)  % evaluated if it is a string';
+defopts.DispFinal  = 'on   % display messages like initial and final message';
+defopts.DispModulo = '100  % [0:Inf], disp messages after every i-th iteration';
+defopts.SaveVariables = 'on   % [on|final|off][-v6] save variables to .mat file';
+defopts.SaveFilename = 'variablescmaes.mat  % save all variables, see SaveVariables'; 
+defopts.LogModulo = '1    % [0:Inf] if >1 record data less frequently after gen=100';
+defopts.LogTime   = '25   % [0:100] max. percentage of time for recording data';
+defopts.LogFilenamePrefix = 'outcmaes  % files for output data'; 
+defopts.LogPlot = 'off    % plot while running using output data files';
+
+%qqqkkk 
+%defopts.varopt1 = ''; % 'for temporary and hacking purposes'; 
+%defopts.varopt2 = ''; % 'for temporary and hacking purposes'; 
+defopts.UserData = 'for saving data/comments associated with the run';
+defopts.UserDat2 = ''; 'for saving data/comments associated with the run';
+
+% ---------------------- Handling Input Parameters ----------------------
+
+if nargin < 1 || isequal(fitfun, 'defaults') % pass default options
+  if nargin < 1
+    disp('Default options returned (type "help cmaes" for help).');
+  end
+  xmin = defopts;
+  if nargin > 1 % supplement second argument with default options
+    xmin = getoptions(xstart, defopts);
+  end
+  return;
+end
+
+if isequal(fitfun, 'displayoptions')
+ names = fieldnames(defopts); 
+ for name = names'
+   disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']); 
+ end
+ return; 
+end
+
+input.fitfun = fitfun; % record used input
+if isempty(fitfun)
+  % fitfun = definput.fitfun; 
+  % warning(['Objective function not determined, ''' fitfun ''' used']);
+  error(['Objective function not determined']);
+end
+if ~ischar(fitfun)
+  error('first argument FUN must be a string');
+end
+
+
+if nargin < 2 
+  xstart = [];
+end
+
+input.xstart = xstart;
+if isempty(xstart)
+  % xstart = definput.xstart;  % objective variables initial point
+  % warning('Initial search point, and problem dimension, not determined');
+  error('Initial search point, and problem dimension, not determined');
+end
+
+if nargin < 3 
+  insigma = [];
+end
+if isa(insigma, 'struct')
+  error(['Third argument SIGMA must be (or eval to) a scalar '...
+	   'or a column vector of size(X0,1)']);
+end
+input.sigma = insigma;
+if isempty(insigma)
+  if all(size(myeval(xstart)) > 1)
+    insigma = std(xstart, 0, 2); 
+    if any(insigma == 0)
+      error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+    end
+  else
+    % will be captured later
+    % error(['Initial step sizes (SIGMA) not determined']);
+  end
+end
+
+% Compose options opts
+if nargin < 4 || isempty(inopts) % no input options available
+  inopts = []; 
+  opts = defopts;
+else
+  opts = getoptions(inopts, defopts);
+end
+i = strfind(opts.SaveFilename, '%'); % remove everything after comment
+if ~isempty(i)
+  opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
+end
+opts.SaveFilename = deblank(opts.SaveFilename); % remove trailing white spaces
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
+counteval = 0; countevalNaN = 0; 
+irun = 0;
+while irun <= myeval(opts.Restarts) % for-loop does not work with resume
+  irun = irun + 1; 
+
+% ------------------------ Initialization -------------------------------
+
+% Handle resuming of old run
+flgresume = myevalbool(opts.Resume);
+xmean = myeval(xstart); 
+if all(size(xmean) > 1)
+   xmean = mean(xmean, 2); % in case if xstart is a population
+elseif size(xmean, 2) > 1
+  xmean = xmean';
+end 
+if ~flgresume % not resuming a former run
+  % Assign settings from input parameters and options for myeval...
+  N = size(xmean, 1); numberofvariables = N; 
+  lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1)); 
+  % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2)); 
+  popsize = lambda0;
+  lambda = lambda0;
+  insigma = myeval(insigma);
+  if all(size(insigma) == [N 2]) 
+    insigma = 0.5 * (insigma(:,2) - insigma(:,1));
+  end
+else % flgresume is true, do resume former run
+  tmp = whos('-file', opts.SaveFilename);
+  for i = 1:length(tmp)
+    if strcmp(tmp(i).name, 'localopts');
+      error('Saved variables include variable "localopts", please remove');
+    end
+  end
+  local.opts = opts; % keep stopping and display options
+  local.varargin = varargin;
+  load(opts.SaveFilename); 
+  varargin = local.varargin;
+  flgresume = 1;
+
+  % Overwrite old stopping and display options
+  opts.StopFitness = local.opts.StopFitness; 
+  %%opts.MaxFunEvals = local.opts.MaxFunEvals;
+  %%opts.MaxIter = local.opts.MaxIter; 
+  opts.StopFunEvals = local.opts.StopFunEvals; 
+  opts.StopIter = local.opts.StopIter;  
+  opts.TolX = local.opts.TolX;
+  opts.TolUpX = local.opts.TolUpX;
+  opts.TolFun = local.opts.TolFun;
+  opts.TolHistFun = local.opts.TolHistFun;
+  opts.StopOnStagnation = local.opts.StopOnStagnation; 
+  opts.StopOnWarnings = local.opts.StopOnWarnings; 
+  opts.ReadSignals = local.opts.ReadSignals; 
+  opts.DispFinal = local.opts.DispFinal;
+  opts.LogPlot = local.opts.LogPlot;
+  opts.DispModulo = local.opts.DispModulo;
+  opts.SaveVariables = local.opts.SaveVariables;
+  opts.LogModulo = local.opts.LogModulo;
+  opts.LogTime = local.opts.LogTime;
+  clear local; % otherwise local would be overwritten during load
+end
+  
+%--------------------------------------------------------------
+% Evaluate options
+stopFitness = myeval(opts.StopFitness); 
+stopMaxFunEvals = myeval(opts.MaxFunEvals);  
+stopMaxIter = myeval(opts.MaxIter);  
+stopFunEvals = myeval(opts.StopFunEvals);  
+stopIter = myeval(opts.StopIter); 
+if flgresume
+    stopIter = stopIter + countiter
+end
+stopTolX = myeval(opts.TolX);
+stopTolUpX = myeval(opts.TolUpX);
+stopTolFun = myeval(opts.TolFun);
+stopTolHistFun = myeval(opts.TolHistFun);
+stopOnStagnation = myevalbool(opts.StopOnStagnation); 
+stopOnWarnings = myevalbool(opts.StopOnWarnings);
+flgreadsignals = myevalbool(opts.ReadSignals);
+flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
+flgEvalParallel = myevalbool(opts.EvalParallel);
+stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
+arrEqualFunvals = zeros(1, 10+N);
+flgDiagonalOnly = myeval(opts.DiagonalOnly); 
+flgActiveCMA = myeval(opts.CMA.active); 
+noiseHandling = myevalbool(opts.Noise.on);
+noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
+noiseAlphaEvals = myeval(opts.Noise.alphaevals);
+noiseCallback = myeval(opts.Noise.callback); 
+flgdisplay = myevalbool(opts.DispFinal);
+flgplotting = myevalbool(opts.LogPlot);
+verbosemodulo = myeval(opts.DispModulo);
+flgscience = myevalbool(opts.Science);
+flgsaving = [];
+strsaving = [];
+if strfind(opts.SaveVariables, '-v6') 
+  i = strfind(opts.SaveVariables, '%');
+  if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
+    strsaving = '-v6';
+    flgsaving = 1;
+    flgsavingfinal = 1;
+  end
+end
+if strncmp('final', opts.SaveVariables, 5)
+  flgsaving = 0;
+  flgsavingfinal = 1;
+end
+if isempty(flgsaving)
+  flgsaving = myevalbool(opts.SaveVariables);
+  flgsavingfinal = flgsaving;
+end
+savemodulo = myeval(opts.LogModulo);
+savetime = myeval(opts.LogTime);
+
+i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
+if ~isempty(i)
+  opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
+end
+
+% TODO here silent option? set disp, save and log options to 0 
+
+%--------------------------------------------------------------
+
+if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
+  warning('To resume later the saving option needs to be set');
+end
+
+
+% Do more checking and initialization 
+if flgresume % resume is on
+  time.t0 = clock;
+  if flgdisplay
+    disp(['  resumed from ' opts.SaveFilename ]); 
+  end
+  if counteval >= stopMaxFunEvals 
+    error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
+	  'criterion before resume']);
+  end
+  if countiter >= stopMaxIter 
+    error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
+	  'before resume']);
+  end
+  
+else % flgresume
+  % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
+  maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
+  mindx = myeval(opts.DiffMinChange); % minimal sensible variable change 
+				      % can both also be defined as Nx1 vectors
+  lbounds = myeval(opts.LBounds);		     
+  ubounds = myeval(opts.UBounds);
+  if length(lbounds) == 1
+    lbounds = repmat(lbounds, N, 1);
+  end
+  if length(ubounds) == 1
+    ubounds = repmat(ubounds, N, 1);
+  end
+  if isempty(insigma) % last chance to set insigma
+    if all(lbounds > -Inf) && all(ubounds < Inf)
+      if any(lbounds>=ubounds)
+	error('upper bound must be greater than lower bound');
+      end
+      insigma = 0.3*(ubounds-lbounds);
+      stopTolX = myeval(opts.TolX);  % reevaluate these
+      stopTolUpX = myeval(opts.TolUpX);
+    else
+      error(['Initial step sizes (SIGMA) not determined']);
+    end
+  end
+
+  % Check all vector sizes
+  if size(xmean, 2) > 1 || size(xmean,1) ~= N
+    error(['intial search point should be a column vector of size ' ...
+	   num2str(N)]);
+  elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
+    error(['input parameter SIGMA should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
+    error(['option TolX should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
+    error(['option TolUpX should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
+    error(['option DiffMaxChange should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
+    error(['option DiffMinChange should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
+    error(['option lbounds should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
+    error(['option ubounds should be (or eval to) a scalar '...
+	   'or a column vector of size ' num2str(N)] );
+  end
+  
+  % Initialize dynamic internal state parameters
+  if any(insigma <= 0) 
+    error(['Initial search volume (SIGMA) must be greater than zero']);
+  end
+  if max(insigma)/min(insigma) > 1e6
+    error(['Initial search volume (SIGMA) badly conditioned']);
+  end
+  sigma = max(insigma);              % overall standard deviation
+  pc = zeros(N,1); ps = zeros(N,1);  % evolution paths for C and sigma
+
+  if length(insigma) == 1
+    insigma = insigma * ones(N,1) ;
+  end
+  diagD = insigma/max(insigma);      % diagonal matrix D defines the scaling
+  diagC = diagD.^2; 
+  if flgDiagonalOnly ~= 1            % use at some point full covariance matrix
+    B = eye(N,N);                      % B defines the coordinate system
+    BD = B.*repmat(diagD',N,1);        % B*D for speed up only
+    C = diag(diagC);                   % covariance matrix == BD*(BD)'
+  end
+  if flgDiagonalOnly
+    B = 1; 
+  end
+
+  fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+  fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+  fitness.histbest=[]; % history of fitness values
+  fitness.histmedian=[]; % history of fitness values
+
+  % Initialize boundary handling
+  bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf); 
+  if bnd.isactive
+    if any(lbounds>ubounds)
+      error('lower bound found to be greater than upper bound');
+    end
+    [xmean ti] = xintobounds(xmean, lbounds, ubounds); % just in case
+    if any(ti)
+      warning('Initial point was out of bounds, corrected');
+    end
+    bnd.weights = zeros(N,1);         % weights for bound penalty
+    % scaling is better in axis-parallel case, worse in rotated
+    bnd.flgscale = 0; % scaling will be omitted if zero 
+    if bnd.flgscale ~= 0 
+      bnd.scale = diagC/mean(diagC);
+    else
+      bnd.scale = ones(N,1);
+    end
+    
+    idx = (lbounds > -Inf) | (ubounds < Inf);
+    if length(idx) == 1
+      idx = idx * ones(N,1);
+    end
+    bnd.isbounded = zeros(N,1);
+    bnd.isbounded(find(idx)) = 1; 
+    maxdx = min(maxdx, (ubounds - lbounds)/2);
+    if any(sigma*sqrt(diagC) > maxdx)
+      fac = min(maxdx ./ sqrt(diagC))/sigma;
+      sigma = min(maxdx ./ sqrt(diagC));
+      warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
+	       ', because it was larger than half' ...
+	       ' of one of the boundary intervals']);
+    end
+    idx = (lbounds > -Inf) & (ubounds < Inf);
+    dd = diagC;
+    if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
+      warning(['Initial SIGMA is, in at least one coordinate, ' ...
+	       'much smaller than the '...
+	       'given boundary intervals. For reasonable ' ...
+	       'global search performance SIGMA should be ' ...
+	       'between 0.2 and 0.5 of the bounded interval in ' ...
+	       'each coordinate. If all coordinates have ' ... 
+	       'lower and upper bounds SIGMA can be empty']);
+    end
+    bnd.dfithist = 1;              % delta fit for setting weights
+    bnd.aridxpoints = [];          % remember complete outside points
+    bnd.arfitness = [];            % and their fitness
+    bnd.validfitval = 0;
+    bnd.iniphase = 1;
+  end
+
+  % Update the diary file
+  if ~isempty(calibGUI_interface_obj)
+     updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end     
+  end  
+  
+  % ooo initial feval, for output only
+  if irun == 1 
+    out.solutions.bestever.x = xmean;
+    out.solutions.bestever.f = Inf;  % for simpler comparison below
+    out.solutions.bestever.evals = counteval;
+    bestever = out.solutions.bestever;
+  end
+  if myevalbool(opts.EvalInitialX)
+    fitness.hist(1)=feval(fitfun, xmean, varargin{:}); 
+    fitness.histsel(1)=fitness.hist(1);
+    counteval = counteval + 1;
+    if fitness.hist(1) < out.solutions.bestever.f 
+	out.solutions.bestever.x = xmean;
+	out.solutions.bestever.f = fitness.hist(1);
+	out.solutions.bestever.evals = counteval;
+	bestever = out.solutions.bestever;
+    end
+  else
+    fitness.hist(1)=NaN; 
+    fitness.histsel(1)=NaN; 
+  end
+    
+  % initialize random number generator
+  if ischar(opts.Seed)
+    randn('state', eval(opts.Seed));     % random number generator state
+  else
+    randn('state', opts.Seed);
+  end
+  %qqq
+%  load(opts.SaveFilename, 'startseed');
+%  randn('state', startseed);
+%  disp(['SEED RELOADED FROM ' opts.SaveFilename]);
+  startseed = randn('state');         % for retrieving in saved variables
+
+  % Initialize further constants
+  chiN=N^0.5*(1-1/(4*N)+1/(21*N^2));  % expectation of 
+				      %   ||N(0,I)|| == norm(randn(N,1))
+  
+  countiter = 0;
+  % Initialize records and output
+  if irun == 1
+    time.t0 = clock;
+    
+    % TODO: keep also median solution? 
+    out.evals = counteval;  % should be first entry
+    out.stopflag = {};
+
+    outiter = 0;
+
+    % Write headers to output data files 
+    filenameprefix = opts.LogFilenamePrefix; 
+    if savemodulo && savetime
+      filenames = {};
+      filenames(end+1) = {'axlen'};
+      filenames(end+1) = {'fit'};
+      filenames(end+1) = {'stddev'};
+      filenames(end+1) = {'xmean'};
+      filenames(end+1) = {'xrecentbest'};
+      str = [' (startseed=' num2str(startseed(2)) ...
+             ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
+      for namecell = filenames(:)'
+        name = namecell{:};
+
+	[fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
+	if fid < 1 % err ~= 0 
+	  warning(['could not open ' filenameprefix name '.dat']);
+	  filenames(find(strcmp(filenames,name))) = [];
+	else
+%	  fprintf(fid, '%s\n', ...
+%	      ['<CMAES-OUTPUT version="' cmaVersion '">']);
+%	  fprintf(fid, ['  <NAME>' name '</NAME>\n']);
+%	  fprintf(fid, ['  <DATE>' date() '</DATE>\n']);
+%	  fprintf(fid, '  <PARAMETERS>\n');
+%	  fprintf(fid, ['    dimension=' num2str(N) '\n']);
+%	  fprintf(fid, '  </PARAMETERS>\n');
+	  % different cases for DATA columns annotations here
+%	  fprintf(fid, '  <DATA');
+	  if strcmp(name, 'axlen')
+	     fprintf(fid, ['%%  columns="iteration, evaluation, sigma, ' ...
+		 'max axis length, min axis length, ' ...
+		 'all principal axes lengths (sorted square roots ' ...
+                  'of eigenvalues of C)"' str]);
+	  elseif strcmp(name, 'fit')
+	    fprintf(fid, ['%%  columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
+		' best, median, worst fitness function value,' ...
+		' further objective values of best"' str]);
+	  elseif strcmp(name, 'stddev')
+	    fprintf(fid, ['%%  columns=["iteration, evaluation, sigma, void, void, ' ...
+		'stds==sigma*sqrt(diag(C))"' str]);
+	  elseif strcmp(name, 'xmean')
+	    fprintf(fid, ['%%  columns="iteration, evaluation, void, ' ...
+                          'void, void, xmean"' str]);
+	  elseif strcmp(name, 'xrecentbest')
+	    fprintf(fid, ['%%  columns="iteration, evaluation, fitness, ' ...
+                          'void, void, xrecentbest"' str]);
+	  end
+	  fprintf(fid, '\n'); % DATA
+	  if strcmp(name, 'xmean')
+	    fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval); 
+	    % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean); 
+%qqq	    fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n'); 
+	    fprintf(fid, '%e ', xmean);
+            fprintf(fid, '\n'); 
+	  end
+	  fclose(fid); 
+          clear fid; % preventing 
+	end
+      end % for files
+    end % savemodulo
+  end % irun == 1
+  
+end % else flgresume 
+
+% Update the diary file
+if ~isempty(calibGUI_interface_obj)
+    updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end 
+end  
+
+
+% -------------------- Generation Loop --------------------------------
+stopflag = {};
+while isempty(stopflag)
+  % set internal parameters
+  if countiter == 0 || lambda ~= lambda_last
+    if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
+          && flgdisplay
+      disp(['  lambda = ' num2str(lambda)]);
+      lambda_hist(:,end+1) = [countiter+1; lambda];
+    else
+      lambda_hist = [countiter+1; lambda]; 
+    end
+    lambda_last = lambda;
+    % Strategy internal parameter setting: Selection  
+    mu = myeval(opts.ParentNumber); % number of parents/points for recombination
+    if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
+      weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
+    elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
+      weights = mu+0.5-(1:mu)'; 
+    elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
+      % use (lambda+1)/2 as reference if mu < lambda/2
+      weights = log(max(mu, lambda/2) + 1/2)-log(1:mu)'; % muXone array for weighted recombination
+    else
+      error(['Recombination weights to be "' opts.RecombinationWeights ...
+             '" is not implemented']);
+    end
+    mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
+    weights = weights/sum(weights);     % normalize recombination weights array
+    if mueff == lambda
+      error(['Combination of values for PopSize, ParentNumber and ' ...
+             ' and RecombinationWeights is not reasonable']);
+    end
+    
+    % Strategy internal parameter setting: Adaptation
+    cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
+    cs = myeval(opts.CMA.cs); 
+
+    % old way TODO: remove this at some point
+    % mucov = mueff;   % size of mu used for calculating learning rate ccov
+    % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
+    %        + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov)); 
+
+    % new way
+    if myevalbool(opts.CMA.on) 
+      ccov1 = myeval(opts.CMA.ccov1); 
+      ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
+    else
+      ccov1 = 0;
+      ccovmu = 0;
+    end
+    
+    % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
+    % 0 : C will never be diagonal anymore
+    % 1 : C will always be diagonal
+    % >1: C is diagonal for first iterations, set to 0 afterwards
+    if flgDiagonalOnly < 1
+      flgDiagonalOnly = 0; 
+    end
+    if flgDiagonalOnly
+      ccov1_sep = min(1, ccov1 * (N+1.5) / 3); 
+      ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3); 
+    elseif N > 98 && flgdisplay && countiter == 0
+      disp('consider option DiagonalOnly for high-dimensional problems');
+    end
+
+    % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
+    %damps = ... % damping for step size control, usually close to one 
+    %    (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
+    %    * max(0.3, ... % reduce damps, if max. iteration number is small
+    %          1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs; 
+    damps = myeval(opts.CMA.damps); 
+    if noiseHandling
+      noiseReevals = min(myeval(opts.Noise.reevals), lambda); 
+      noiseAlpha = myeval(opts.Noise.alphasigma); 
+      noiseEpsilon = myeval(opts.Noise.epsilon); 
+      noiseTheta = myeval(opts.Noise.theta); 
+      noisecum = myeval(opts.Noise.cum);
+      noiseCutOff = myeval(opts.Noise.cutoff);  % arguably of minor relevance
+    else
+      noiseReevals = 0; % more convenient in later coding
+    end
+
+    %qqq hacking of a different parameter setting, e.g. for ccov or damps,
+    %  can be done here, but is not necessary anymore, see opts.CMA. 
+    % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
+    % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
+    % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
+    % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
+
+  end    
+
+  % Display initial message
+  if countiter == 0 && flgdisplay 
+    if mu == 1
+      strw = '100';
+    elseif mu < 8
+      strw = [sprintf('%.0f', 100*weights(1)) ... 
+              sprintf(' %.0f', 100*weights(2:end)')];
+    else
+      strw = [sprintf('%.2g ', 100*weights(1:2)') ...
+              sprintf('%.2g', 100*weights(3)') '...' ...
+              sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];      
+    end
+    if irun > 1
+      strrun = [', run ' num2str(irun)];
+    else
+      strrun = '';
+    end
+    disp(['  n=' num2str(N) ': (' num2str(mu) ',' ...
+          num2str(lambda) ')-CMA-ES(w=[' ...
+          strw ']%, ' ...
+          'mu_eff=' num2str(mueff,'%.1f') ...
+          ') on function ' ...
+          (fitfun) strrun]);
+    if flgDiagonalOnly == 1
+      disp('    C is diagonal');
+    elseif flgDiagonalOnly
+      disp(['    C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
+    end
+  end
+
+  flush;
+
+  countiter = countiter + 1; 
+
+  % Generate and evaluate lambda offspring
+ 
+  fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
+
+  % parallel evaluation
+  if flgEvalParallel
+      arz = randn(N,lambda);
+
+      if ~flgDiagonalOnly
+        arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
+      else
+        arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz; 
+      end
+
+      if noiseHandling 
+        if noiseEpsilon == 0
+          arx = [arx arx(:,1:noiseReevals)]; 
+        elseif flgDiagonalOnly
+          arx = [arx arx(:,1:noiseReevals) + ...
+                 repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
+                 .* randn(N,noiseReevals)]; 
+        else 
+          arx = [arx arx(:,1:noiseReevals) + ...
+                 noiseEpsilon * sigma * ...
+                 (BD * randn(N,noiseReevals))]; 
+        end
+      end
+
+      % You may handle constraints here. You may either resample
+      % arz(:,k) and/or multiply it with a factor between -1 and 1
+      % (the latter will decrease the overall step size) and
+      % recalculate arx accordingly. Do not change arx or arz in any
+      % other way.
+      % The code below was written for the Groundwater Time Series
+      % mdoelling developed by Peterson& Western [2014].
+      invalidParamSets = find(any(~feval('calibrationValidParameters', arx, varargin{:}),1));
+      invalidParamSets_final = false(1,size(arx,2));
+      if ~isempty(invalidParamSets)>0
+          arz_tmp = zeros(size(arz,1), length(invalidParamSets));
+          arx_tmp = zeros(size(arx,1), length(invalidParamSets));
+          invalidParamSets_final_tmp = false(1, length(invalidParamSets));
+          if ~exist('noiseEpsilon','var');
+              noiseEpsilon = [];
+          end
+          parfor k=1:length(invalidParamSets)
+              nResamples=0;
+              maxResamples=1000;
+              while (1)
+                % Resample parameter set, k            
+                if invalidParamSets(k) <= lambda  % regular samples (not the re-evaluation-samples)
+                    arz_tmp(:,k) = randn(N,1); % (re)sample
+
+                    if flgDiagonalOnly  
+                        arx_tmp(:,k) = xmean + sigma * diagD .* arz_tmp(:,k);              % Eq. (1)
+                    else
+                        arx_tmp(:,k) = xmean + sigma * (BD * arz_tmp(:,k));                % Eq. (1)
+                    end
+                else % re-evaluation solution with index > lambda
+                    if flgDiagonalOnly  
+                        arx_tmp(:,k) = arx(:,invalidParamSets(k)-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
+                    else
+                        arx_tmp(:,k) = arx(:,invalidParamSets(k)-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+                    end
+                end
+                nResamples = nResamples + 1;   
+
+                % Assess if parameter set k is valid
+                isValidParameter = feval('calibrationValidParameters', arx_tmp(:,k), varargin{:});
+                if ~any(~isValidParameter)
+                    invalidParamSets_final_tmp(k)=false;
+                    break;
+                elseif nResamples>maxResamples
+                    break;
+                else
+                    invalidParamSets_final_tmp(k)=true;
+                end            
+              end
+          end
+          
+          for k=1:length(invalidParamSets)
+              arz(:,invalidParamSets(k)) = arz_tmp(:,k);
+              arx(:,invalidParamSets(k)) = arx_tmp(:,k);
+              invalidParamSets_final(invalidParamSets(k)) = invalidParamSets_final_tmp(k);
+          end
+          clear arz_tmp arx_tmp invalidParamSets_final_tmp
+      end
+  
+      if ~bnd.isactive
+        arxvalid = arx;
+      else
+        arxvalid = xintobounds(arx, lbounds, ubounds);
+      end
+      % You may handle constraints here.  You may copy and alter
+      % (columns of) arxvalid(:,k) only for the evaluation of the
+      % fitness function. arx and arxvalid should not be changed.
+      if any(~invalidParamSets_final)
+        fitness.raw(~invalidParamSets_final) = feval(fitfun, arxvalid(:,~invalidParamSets_final), varargin{:}); 
+      end
+      
+      countevalNaN = countevalNaN + sum(isnan(fitness.raw));
+      counteval = counteval + sum(~isnan(fitness.raw)); 
+  end
+
+  % non-parallel evaluation and remaining NaN-values
+  % set also the reevaluated solution to NaN
+  fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;  
+  for k=find(isnan(fitness.raw)), 
+      
+    % fitness.raw(k) = NaN; 
+    tries = flgEvalParallel;  % in parallel case this is the first re-trial
+    % Resample, until fitness is not NaN
+    % NOTE: Vectorised sampling of parameter sets and the parrallel
+    % evaluation of viable parameter sets was added by Tim Peterson.
+    % These modiciations were for the Groundwater Time Series
+    % mdoelling developed by Peterson& Western [2014].    
+    while isnan(fitness.raw(k))
+        
+        % The code below was written for the Groundwater Time Series
+        % mdoelling developed by Peterson& Western [2014].        
+        
+        nResamples=1000;
+        nModelReevaluations = 10;
+        % Resample parameter set, k            
+        if k <= lambda  % regular samples (not the re-evaluation-samples)
+            arz_tmp = randn(N,nResamples); % (re)sample
+
+            if flgDiagonalOnly  
+                arx_tmp = bsxfun(@plus, xmean , bsxfun(@times, sigma * diagD, arz_tmp));              % Eq. (1)
+            else
+                arx_tmp = zeros(size(arz_tmp));
+                parfor el=1:nResamples
+                    arx_tmp(:,el) = xmean + sigma * (BD * arz_tmp(:,el));                % Eq. (1)
+                end
+                
+            end
+        else % re-evaluation solution with index > lambda
+            if flgDiagonalOnly  
+                arx_tmp = bsxfun(@plus, arx(:,k-lambda) , bsxfun(@times, (noiseEpsilon * sigma) * diagD , randn(N,nResamples)));
+            else                
+                arx_tmp = zeros(size(arz_tmp));
+                parfor el=1:nResamples
+                    arx_tmp(:,el) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+                end                
+            end
+        end
+
+        % Assess if any parameter set is valid. if there is at least one valid
+        % parameter set, then get the first such set.
+        isValidParameter = feval('calibrationValidParameters', arx_tmp, varargin{:});        
+        isValidParameterSet = ~any(~isValidParameter,1);
+        if any(isValidParameterSet)
+            invalidParamSets_final_tmp=false;
+            iResamples = find(isValidParameterSet,nModelReevaluations,'first');
+            arx_tmp = arx_tmp(:,iResamples);
+            if k <= lambda
+                arz_tmp = arz_tmp(:,iResamples);
+            end
+        else
+            invalidParamSets_final_tmp=true;
+        end                    
+
+        
+%       if k <= lambda  % regular samples (not the re-evaluation-samples)
+%         arz(:,k) = randn(N,1); % (re)sample
+% 
+%         if flgDiagonalOnly  
+%           arx(:,k) = xmean + sigma * diagD .* arz(:,k);              % Eq. (1)
+%         else
+%           arx(:,k) = xmean + sigma * (BD * arz(:,k));                % Eq. (1)
+%         end
+%       else % re-evaluation solution with index > lambda
+%         if flgDiagonalOnly  
+%           arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
+%         else
+%           arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+%         end
+%       end
+%      
+%       % You may handle constraints here. You may either resample
+%       % arz(:,k) and/or multiply it with a factor between -1 and 1
+%       % (the latter will decrease the overall step size) and
+%       % recalculate arx accordingly. Do not change arx or arz in any
+%       % other way.
+%       if ~bnd.isactive
+%         arxvalid(:,k) = arx(:,k);
+%       else
+%         arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
+%       end
+
+      % You may handle constraints here. You may either resample
+      % arz(:,k) and/or multiply it with a factor between -1 and 1
+      % (the latter will decrease the overall step size) and
+      % recalculate arx accordingly. Do not change arx or arz in any
+      % other way.
+      if ~bnd.isactive
+        arxvalid_tmp = arx_tmp;
+      else
+        arxvalid_tmp = xintobounds(arx_tmp, lbounds, ubounds);
+      end
+      
+      % You may handle constraints here.  You may copy and alter
+      % (columns of) arxvalid(:,k) only for the evaluation of the
+      % fitness function. arx should not be changed.
+      % If check for valid parameters inserted and use of temporary variabes
+      % by Tim Peterson
+      if ~invalidParamSets_final_tmp
+        fitness_tmp = feval(fitfun, arxvalid_tmp, varargin{:}); 
+      else
+          fitness_tmp =[];
+      end
+      tries = tries + 1;
+      if all(isnan(fitness_tmp))
+        countevalNaN = countevalNaN + 1;
+      else
+        % Following by Tim Peterson.
+        % Find the minimum evaluation.   
+        iResamples = find(fitness_tmp==min(fitness_tmp),1,'first');
+        
+        % Assign parameter sets etc to non-temp variables.
+        arx(:,k) = arx_tmp(:,iResamples);
+        if k <= lambda
+            arz(:,k) = arz_tmp(:,iResamples);
+        end
+        arxvalid(:,k) = arxvalid_tmp(:,iResamples);
+        fitness.raw(k) = fitness_tmp(iResamples);          
+      end
+      
+      if mod(tries, 100) == 0
+        warning([num2str(tries) ...
+        ' NaN objective function values at evaluation ' ...
+        num2str(counteval)]);
+      end
+    end
+    counteval = counteval + 1; % retries due to NaN are not counted
+  end
+
+  fitness.sel = fitness.raw; 
+
+  % ----- handle boundaries -----
+  if 1 < 3 && bnd.isactive
+    % Get delta fitness values
+    val = myprctile(fitness.raw, [25 75]);
+    % more precise would be exp(mean(log(diagC)))
+    val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
+    %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
+    %    / N / mean(diagC) / sigma^2;
+    % Catch non-sensible values 
+    if ~isfinite(val)
+      warning('Non-finite fitness range');
+      val = max(bnd.dfithist);  
+    elseif val == 0 % happens if all points are out of bounds
+      val = min(bnd.dfithist(bnd.dfithist>0));  % seems not to make sense, given all solutions are out of bounds
+    elseif bnd.validfitval == 0 % flag that first sensible val was found
+      bnd.dfithist = [];
+      bnd.validfitval = 1;
+    end
+
+    % Store delta fitness values
+    if length(bnd.dfithist) < 20+(3*N)/lambda
+      bnd.dfithist = [bnd.dfithist val];
+    else
+      bnd.dfithist = [bnd.dfithist(2:end) val];
+    end
+
+    [tx ti]  = xintobounds(xmean, lbounds, ubounds);
+
+    % Set initial weights
+    if bnd.iniphase 
+      if any(ti) 
+        bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
+	if bnd.flgscale == 0 % scale only initial weights then
+	  dd = diagC; 
+	  idx = find(bnd.isbounded); 
+	  dd = dd(idx) / mean(dd); %  remove mean scaling
+	  bnd.weights(idx) = bnd.weights(idx) ./ dd; 
+	end
+	if bnd.validfitval && countiter > 2
+          bnd.iniphase = 0;
+	end
+      end
+    end
+
+    % Increase weights
+    if  1 < 3 && any(ti) % any coordinate of xmean out of bounds
+      % judge distance of xmean to boundary
+      tx = xmean - tx;
+      idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ... 
+	     * sigma*sqrt(diagC)) ;
+      % only increase if xmean is moving away
+      idx = idx & (sign(tx) == sign(xmean - xold));
+      if ~isempty(idx) % increase
+        % the factor became 1.2 instead of 1.1, because
+        % changed from max to min in version 3.52
+        bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx); 
+      end
+    end
+
+    % Calculate scaling biased to unity, product is one
+    if bnd.flgscale ~= 0 
+      bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC)))); 
+    end
+
+    % Assigned penalized fitness
+    bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2; 
+
+    fitness.sel = fitness.raw + bnd.arpenalty;
+
+  end % handle boundaries
+  % ----- end handle boundaries -----
+  
+  % compute noise measurement and reduce fitness arrays to size lambda
+  if noiseHandling 
+    [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
+                                      fitness.sel(lambda+(1:noiseReevals)), ...
+                                      noiseReevals, noiseTheta, noiseCutOff); 
+    if countiter == 1 % TODO: improve this very rude way of initialization
+      noiseSS = 0;
+      noiseN = 0;  % counter for mean
+    end
+    noiseSS = noiseSS + noisecum * (noiseS - noiseSS); 
+
+    % noise-handling could be done here, but the original sigma is still needed
+    % disp([noiseS noiseSS noisecum])
+
+    fitness.rawar12 = fitness.raw; % just documentary
+    fitness.selar12 = fitness.sel; % just documentary
+    % qqq refine fitness based on both values
+    if 11 < 3  % TODO: in case of outliers this mean is counterproductive 
+               % median out of three would be ok 
+      fitness.raw(1:noiseReevals) = ... % not so raw anymore
+          (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2; 
+      fitness.sel(1:noiseReevals) = ... 
+          (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2; 
+    end      
+    fitness.raw = fitness.raw(1:lambda); 
+    fitness.sel = fitness.sel(1:lambda); 
+  end
+  
+  % Sort by fitness 
+  [fitness.raw, fitness.idx] = sort(fitness.raw); 
+  [fitness.sel, fitness.idxsel] = sort(fitness.sel);  % minimization
+  fitness.hist(2:end) = fitness.hist(1:end-1);    % record short history of
+  fitness.hist(1) = fitness.raw(1);               % best fitness values
+  if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
+       (mod(countiter, 5) == 0  && length(fitness.histbest) < 2e4)  % 20 percent of 1e5 gen.
+    fitness.histbest = [fitness.raw(1) fitness.histbest];          % best fitness values
+    fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
+  else
+    fitness.histbest(2:end) = fitness.histbest(1:end-1); 
+    fitness.histmedian(2:end) = fitness.histmedian(1:end-1); 
+    fitness.histbest(1) = fitness.raw(1);           % best fitness values
+    fitness.histmedian(1) = median(fitness.raw);    % median fitness values
+  end
+  fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
+  fitness.histsel(1) = fitness.sel(1);               % best sel fitness values
+
+  % Calculate new xmean, this is selection and recombination 
+  xold = xmean; % for speed up of Eq. (2) and (3)
+  cmean = 1;  % 1/min(max((lambda-1*N)/2, 1), N);  % == 1/kappa
+  xmean = (1-cmean) * xold + cmean * arx(:,fitness.idxsel(1:mu))*weights; 
+  zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
+  if mu == 1
+    fmean = fitness.sel(1);
+  else
+    fmean = NaN; % [] does not work in the latter assignment
+    % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+    % counteval = counteval + 1;
+  end
+  
+  % Cumulation: update evolution paths
+  ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean);          % Eq. (4)
+  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+  if flg_future_setting
+    hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
+  end
+%  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+%  hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
+%  hsig = norm(ps) < 1.5 * sqrt(N);
+%  hsig = 1;
+
+  pc = (1-cc)*pc ...
+        + hsig*(sqrt(cc*(2-cc)*mueff)/sigma/cmean) * (xmean-xold);     % Eq. (2)
+  if hsig == 0
+    % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
+  end
+
+  % Adapt covariance matrix
+  neg.ccov = 0;  % TODO: move parameter setting upwards at some point
+  if ccov1 + ccovmu > 0                                                    % Eq. (3)
+    if flgDiagonalOnly % internal linear(?) complexity
+      diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix 
+          + ccov1_sep * pc.^2 ...               % plus rank one update
+          + ccovmu_sep ...                      % plus rank mu update
+            * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
+%             * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
+      diagD = sqrt(diagC); % replaces eig(C)
+    else
+      arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
+      % "active" CMA update: negative update, in case controlling pos. definiteness 
+      if flgActiveCMA > 0
+        % set parameters
+        neg.mu = mu;  
+        neg.mueff = mueff;
+        if flgActiveCMA > 10  % flat weights with mu=lambda/2
+          neg.mu = floor(lambda/2);  
+          neg.mueff = neg.mu;
+        end
+        % neg.mu = ceil(min([N, lambda/4, mueff]));  neg.mueff = mu; % i.e. neg.mu <= N 
+        % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a 
+        % three times larger neg.ccov does not work. 
+        %   increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
+        %   in 30-D to looks fine
+
+        neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
+        neg.minresidualvariance = 0.66;  % keep at least 0.66 in all directions, small popsize are most critical
+        neg.alphaold = 0.5;  % where to make up for the variance loss, 0.5 means no idea what to do
+                             % 1 is slightly more robust and gives a better "guaranty" for pos. def., 
+                             % but does it make sense from the learning perspective for large ccovmu? 
+
+        neg.ccovfinal = neg.ccov;
+
+        % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
+        arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
+        % i-th longest becomes i-th shortest
+        % TODO: this is not in compliance with the paper Hansen&Ros2010, 
+        %       where simply arnorms = arnorms(end:-1:1) ? 
+        [arnorms idxnorms] = sort(sqrt(sum(arzneg.^2, 1))); 
+        [ignore idxnorms] = sort(idxnorms);  % inverse index 
+        arnormfacs = arnorms(end:-1:1) ./ arnorms; 
+        % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
+        arnorms = arnorms(end:-1:1); % for the record
+        if flgActiveCMA < 20
+          arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1);  % E x*x' is N
+          % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1);  % E x*x' is N
+        end
+        if flgActiveCMA < 10 && neg.mu == mu  % weighted sum
+          if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below) 
+            Ccheck = arzneg * diag(weights) * arzneg';  % in order to check the largest EV
+          end
+          artmp = BD * arzneg;
+          Cneg = artmp * diag(weights) * artmp';
+        else  % simple sum
+          if mod(flgActiveCMA, 10) == 1
+            Ccheck = (1/neg.mu) * arzneg*arzneg';  % in order to check largest EV
+          end
+          artmp = BD * arzneg;
+          Cneg = (1/neg.mu) * artmp*artmp';
+
+        end
+
+        % check pos.def. and set learning rate neg.ccov accordingly, 
+        % this check makes the original choice of neg.ccov extremly failsafe 
+        % still assuming C == BD*BD', which is only approxim. correct 
+        if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
+          % TODO: the simple and cheap way would be to set
+          %    fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
+          %    neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
+          % this is the more sophisticated way: 
+          % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts);  % not faster
+          maxeigenval = max(eig(Ccheck));  % norm is much slower, because (norm()==max(svd())
+          %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
+           %          neg.ccov * arnorms(idxnorms).^2 * weights])
+          % pause
+          % remove less than ??34*(1-cmu)%?? of variance in any direction
+          %     1-ccovmu is the variance left from the old C
+          neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval); 
+                                        % -ccov1 removed to avoid error message??
+          if neg.ccovfinal < neg.ccov
+            disp(['active CMA at iteration ' num2str(countiter) ...
+                 ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
+          end
+        end
+        % xmean = xold;  % the distribution does not degenerate!? 
+        % update C
+        C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix 
+            + ccov1 * pc*pc' ...     % plus rank one update
+            + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ...  % plus rank mu update
+              * arpos * (repmat(weights,1,N) .* arpos') ...
+              - neg.ccovfinal * Cneg;                        % minus rank mu update
+      else  % no active (negative) update
+        C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix 
+            + ccov1 * pc*pc' ...     % plus rank one update
+            + ccovmu ...             % plus rank mu update
+              * arpos * (repmat(weights,1,N) .* arpos');
+        % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
+        %   for mu=30*N it is now 10 times faster, overall 3 times faster
+      end
+      diagC = diag(C);
+    end
+  end
+  
+  % the following is de-preciated and will be removed in future
+  % better setting for cc makes this hack obsolete
+  if 11 < 2 && ~flgscience  
+    % remove momentum in ps, if ps is large and fitness is getting worse.
+    % this should rarely happen. 
+    % this might very well be counterproductive in dynamic environments
+    if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
+        fitness.histsel(1) > max(fitness.histsel(2:3))
+      ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
+      if flgdisplay
+        disp(['Momentum in ps removed at [niter neval]=' ...
+              num2str([countiter counteval]) ']']);
+      end
+    end
+  end
+
+  % Adapt sigma
+  if flg_future_setting  % according to a suggestion from Dirk Arnold (2000)
+    % exp(1) is still not reasonably small enough, maybe 2/3?
+    sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps));            % Eq. (5)
+  else
+    % exp(1) is still not reasonably small enough
+    sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps));             % Eq. (5)
+  end
+  % disp([countiter norm(ps)/chiN]);
+  
+  if 11 < 3   % testing with optimal step-size
+      if countiter == 1
+          disp('*********** sigma set to const * ||x|| ******************');
+      end
+      sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
+      sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
+                                                     %      75e3 with def (1.5)
+                                                     %      35e3 with damps=0.25
+  end
+  if 11 < 3 
+          if countiter == 1
+              disp('*********** xmean set to const ******************');
+          end
+      xmean = ones(N,1);
+  end
+  
+  % Update B and D from C
+
+  if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
+    C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
+    [B,tmp] = eig(C);     % eigen decomposition, B==normalized eigenvectors
+                          % effort: approx. 15*N matrix-vector multiplications
+    diagD = diag(tmp); 
+
+    if any(~isfinite(diagD))
+      clear idx; % prevents error under octave 
+      save(['tmp' opts.SaveFilename]);
+      error(['function eig returned non-finited eigenvalues, cond(C)=' ...
+	     num2str(cond(C)) ]);
+    end
+    if any(any(~isfinite(B)))
+      clear idx; % prevents error under octave
+      save(['tmp' opts.SaveFilename]);
+      error(['function eig returned non-finited eigenvectors, cond(C)=' ...
+	     num2str(cond(C)) ]);
+    end
+
+    % limit condition of C to 1e14 + 1
+    if min(diagD) <= 0
+	if stopOnWarnings
+	  stopflag(end+1) = {'warnconditioncov'};
+	else
+	  warning(['Iteration ' num2str(countiter) ...
+		   ': Eigenvalue (smaller) zero']);
+	  diagD(diagD<0) = 0;
+	  tmp = max(diagD)/1e14;
+	  C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1); 
+	end
+    end
+    if max(diagD) > 1e14*min(diagD) 
+	if stopOnWarnings
+	  stopflag(end+1) = {'warnconditioncov'};
+	else
+	  warning(['Iteration ' num2str(countiter) ': condition of C ' ...
+		   'at upper limit' ]);
+	  tmp = max(diagD)/1e14 - min(diagD);
+	  C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1); 
+	end
+    end
+
+    diagC = diag(C); 
+    diagD = sqrt(diagD); % D contains standard deviations now
+    % diagD = diagD / prod(diagD)^(1/N);  C = C / prod(diagD)^(2/N);
+    BD = B.*repmat(diagD',N,1); % O(n^2)
+  end % if mod
+
+  % Align/rescale order of magnitude of scales of sigma and C for nicer output
+  % TODO: interference with sigmafacup: replace 1e10 with 2*sigmafacup
+  % not a very usual case
+  if 1 < 2 && sigma > 1e10*max(diagD) && sigma > 8e14 * max(insigma)
+    fac = sigma; % / max(diagD);
+    sigma = sigma/fac;
+    pc = fac * pc;
+    diagD = fac * diagD; 
+    if ~flgDiagonalOnly
+      C = fac^2 * C; % disp(fac);
+      BD = B .* repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
+    end
+    diagC = fac^2 * diagC; 
+  end
+
+  if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly 
+    % full covariance matrix from now on 
+    flgDiagonalOnly = 0; 
+    B = eye(N,N);
+    BD = diag(diagD);
+    C = diag(diagC); % is better, because correlations are spurious anyway
+  end
+
+  if noiseHandling 
+   if countiter == 1  % assign firstvarargin for noise treatment e.g. as #reevaluations
+     if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
+       if irun == 1
+         firstvarargin = varargin{1};
+       else
+         varargin{1} =  firstvarargin;  % reset varargin{1}
+       end
+     else
+       firstvarargin = 0;
+     end
+   end
+   if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+     varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4));  % still experimental
+   elseif noiseSS > 0
+    if ~isempty(noiseCallback)  % to be removed? 
+      res = feval(noiseCallback); % should also work without output argument!?
+      if ~isempty(res) && res > 1 % TODO: decide for interface of callback
+                                  %       also a dynamic popsize could be done here 
+        sigma = sigma * noiseAlpha;
+      end
+    else
+      if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+        varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
+      end
+    
+      sigma = sigma * noiseAlpha; 
+      % lambda = ceil(0.1 * sqrt(lambda) + lambda);
+      % TODO: find smallest increase of lambda with log-linear
+      %       convergence in iterations
+    end
+    % qqq experimental: take a mean to estimate the true optimum
+    noiseN = noiseN + 1;
+    if noiseN == 1
+      noiseX = xmean; 
+    else
+      noiseX = noiseX + (3/noiseN) * (xmean - noiseX); 
+    end
+   end
+  end
+
+  % ----- numerical error management -----
+  % Adjust maximal coordinate axis deviations
+  if any(sigma*sqrt(diagC) > maxdx)
+    sigma = min(maxdx ./ sqrt(diagC));
+    %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+    %         'deviation at upper limit of ' num2str(maxdx)]);
+    % stopflag(end+1) = {'maxcoorddev'};
+  end
+  % Adjust minimal coordinate axis deviations
+  if any(sigma*sqrt(diagC) < mindx)
+    sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps); 
+    %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+    %         'deviation at lower limit of ' num2str(mindx)]);
+    % stopflag(end+1) = {'mincoorddev'};;
+  end
+  % Adjust too low coordinate axis deviations
+  if any(xmean == xmean + 0.2*sigma*sqrt(diagC)) 
+    if stopOnWarnings
+      stopflag(end+1) = {'warnnoeffectcoord'};
+    else
+      warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+                'deviation too low' ]);
+      if flgDiagonalOnly
+        diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
+                                                  (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+      else
+        C = C + (ccov1+ccovmu) * diag(diagC .* ...
+                                      (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+      end
+      sigma = sigma * exp(0.05+cs/damps); 
+    end
+  end
+  % Adjust step size in case of (numerical) precision problem 
+  if flgDiagonalOnly
+    tmp = 0.1*sigma*diagD; 
+  else
+    tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
+  end
+  if all(xmean == xmean + tmp)
+    i = 1+floor(mod(countiter,N));
+    if stopOnWarnings
+	stopflag(end+1) = {'warnnoeffectaxis'};
+    else
+      warning(['Iteration ' num2str(countiter) ...
+	       ': main axis standard deviation ' ...
+	       num2str(sigma*diagD(i)) ' has no effect' ]);
+      sigma = sigma * exp(0.2+cs/damps); 
+    end
+  end
+  % Adjust step size in case of equal function values (flat fitness)
+  % isequalfuncvalues = 0; 
+  if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
+    % isequalfuncvalues = 1; 
+    if stopOnEqualFunctionValues
+      arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
+      % stop if this happens in more than 33%
+      if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
+        stopflag(end+1) = {'equalfunvals'}; 
+      end
+    else
+      if flgWarnOnEqualFunctionValues
+        warning(['Iteration ' num2str(countiter) ...
+		 ': equal function values f=' num2str(fitness.sel(1)) ...
+		 ' at maximal main axis sigma ' ...
+        num2str(sigma*max(diagD))]);
+      end
+      sigma = sigma * exp(0.2+cs/damps); 
+    end
+  end
+  % Adjust step size in case of equal function values
+  if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0  
+    if stopOnWarnings
+	stopflag(end+1) = {'warnequalfunvalhist'};
+    else
+      warning(['Iteration ' num2str(countiter) ...
+	       ': equal function values in history at maximal main ' ...
+	       'axis sigma ' num2str(sigma*max(diagD))]);
+      sigma = sigma * exp(0.2+cs/damps); 
+    end
+  end
+    
+  % ----- end numerical error management -----
+  
+  % Keep overall best solution
+  out.evals = counteval;
+  out.solutions.evals = counteval;
+  out.solutions.mean.x = xmean;
+  out.solutions.mean.f = fmean;
+  out.solutions.mean.evals = counteval;
+  out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
+  out.solutions.recentbest.f = fitness.raw(1);
+  out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
+  out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
+  out.solutions.recentworst.f = fitness.raw(end);
+  out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
+  if fitness.hist(1) < out.solutions.bestever.f
+    out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
+    out.solutions.bestever.f = fitness.hist(1);
+    out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
+    bestever = out.solutions.bestever;
+  end
+
+  % Set stop flag
+  if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
+  if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
+  if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
+  if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX) 
+    stopflag(end+1) = {'tolx'};
+  end
+  if any(sigma*sqrt(diagC) > stopTolUpX) 
+    stopflag(end+1) = {'tolupx'};
+  end
+  if sigma*max(diagD) == 0  % should never happen
+    stopflag(end+1) = {'bug'};
+  end
+  if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun 
+    stopflag(end+1) = {'tolfun'};
+  end
+  if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun 
+    stopflag(end+1) = {'tolhistfun'};
+  end
+  l = floor(length(fitness.histbest)/3);
+  if 1 < 2 && stopOnStagnation && ...  % leads sometimes early stop on ftablet, fcigtab
+      countiter > N * (5+100/lambda) && ...  
+      length(fitness.histbest) > 100 && ... 
+      median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
+      median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
+    stopflag(end+1) = {'stagnation'};
+  end
+
+  if counteval >= stopFunEvals || countiter >= stopIter
+    stopflag(end+1) = {'stoptoresume'};
+    if length(stopflag) == 1 && flgsaving == 0
+      error('To resume later the saving option needs to be set');
+    end
+  end
+  % read stopping message from file signals.par 
+  if flgreadsignals
+    fid = fopen('./signals.par', 'rt');  % can be performance critical 
+    while fid > 0
+      strline = fgetl(fid); %fgets(fid, 300);
+      if strline < 0 % fgets and fgetl returns -1 at end of file
+        break;
+      end
+      % 'stop filename' sets stopflag to manual
+      str = sscanf(strline, ' %s %s', 2);
+      if strcmp(str, ['stop' opts.LogFilenamePrefix]) 
+        stopflag(end+1) = {'manual'};
+        break;
+      end
+      % 'skip filename run 3' skips a run, but not the last
+      str = sscanf(strline, ' %s %s %s', 3);
+      if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
+        i = strfind(strline, 'run');
+        if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
+          stopflag(end+1) = {'skipped'};
+        end      
+      end
+    end % while, break 
+    if fid > 0
+      fclose(fid);
+      clear fid; % prevents strange error under octave
+    end
+  end
+  
+  out.stopflag = stopflag;
+
+  % ----- output generation -----
+  if verbosemodulo > 0 && isfinite(verbosemodulo)
+    if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1 
+      disp(['Iterat, #Fevals:   Function Value    (median,worst) ' ...
+	    '|Axis Ratio|' ...
+	    'idx:Min SD idx:Max SD']); 
+    end
+    if mod(countiter, verbosemodulo) < 1 ...
+	  || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
+	      (countiter < 3 || ~isempty(stopflag)))
+      [minstd minstdidx] = min(sigma*sqrt(diagC));
+      [maxstd maxstdidx] = max(sigma*sqrt(diagC));
+      % format display nicely
+      disp([repmat(' ',1,4-floor(log10(countiter))) ...
+	    num2str(countiter) ' , ' ...
+	    repmat(' ',1,5-floor(log10(counteval))) ...
+	    num2str(counteval) ' : ' ...
+            num2str(fitness.hist(1), '%.13e') ...
+	    ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
+	    ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
+	    ') | ' ...
+	    num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
+	    repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
+	    num2str(minstd, ' %.1e') ' ' ...
+	    repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
+	    num2str(maxstd, ' %.1e')]);
+    end
+  end
+
+  % measure time for recording data
+  if countiter < 3 
+    time.c = 0.05;
+    time.nonoutput = 0;
+    time.recording = 0;
+    time.saving  = 0.15; % first saving after 3 seconds of 100 iterations
+    time.plotting = 0;
+  elseif countiter > 300
+    % set backward horizon, must be long enough to cover infrequent plotting etc
+    % time.c = min(1, time.nonoutput/3 + 1e-9); 
+    time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
+  end
+  % get average time per iteration
+  time.t1 = clock;
+  time.act = max(0,etime(time.t1, time.t0));
+  time.nonoutput = (1-time.c) * time.nonoutput ...
+      + time.c * time.act; 
+
+  time.recording = (1-time.c) * time.recording;  % per iteration
+  time.saving  = (1-time.c) * time.saving;
+  time.plotting = (1-time.c) * time.plotting;
+  
+  % record output data, concerning time issues
+  if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
+	countiter >= outiter + savemodulo)
+    outiter = countiter; 
+      % Save output data to files  
+      for namecell = filenames(:)'
+        name = namecell{:};
+
+	[fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
+	if fid < 1 % err ~= 0 
+	  warning(['could not open ' filenameprefix name '.dat']);
+	else
+	  if strcmp(name, 'axlen')
+	    fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
+		max(diagD), min(diagD)); 
+            fprintf(fid, '%e ', sort(diagD)); 
+            fprintf(fid, '\n');
+	  elseif strcmp(name, 'disp') % TODO
+	  elseif strcmp(name, 'fit')
+	    fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
+                    countiter, counteval, sigma, max(diagD)/min(diagD), ...
+                    out.solutions.bestever.f, ...
+                    fitness.raw(1), median(fitness.raw), fitness.raw(end)); 
+            if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
+              fprintf(fid, ' %f', varargin{1});
+            end                    
+	    fprintf(fid, '\n');
+	  elseif strcmp(name, 'stddev')
+	    fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma); 
+	    fprintf(fid, '%e ', sigma*sqrt(diagC)); 
+            fprintf(fid, '\n');
+	  elseif strcmp(name, 'xmean')
+	    if isnan(fmean)
+	      fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval); 
+	    else
+	      fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean); 
+	    end
+	    fprintf(fid, '%e ', xmean); 
+            fprintf(fid, '\n');
+	  elseif strcmp(name, 'xrecentbest')
+            % TODO: fitness is inconsistent with x-value
+	    fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1)); 
+	    fprintf(fid, '%e ', arx(:,fitness.idx(1))); 
+            fprintf(fid, '\n');
+	  end
+	  fclose(fid); 
+	end
+  end
+
+    % get average time for recording data
+    time.t2 = clock;
+    time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1)); 
+    
+    % plot
+    if flgplotting && countiter > 1
+      if countiter == 2
+        iterplotted = 0;
+      end
+      if ~isempty(stopflag) || ... 
+        ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
+          time.plotting < 0.05 * (time.nonoutput+time.recording))
+        local_plotcmaesdat(324, filenameprefix);
+        iterplotted = countiter; 
+        %  outplot(out); % outplot defined below
+        if time.plotting == 0  % disregard opening of the window
+          time.plotting = time.nonoutput+time.recording;
+        else
+          time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2)); 
+        end
+      end
+    end
+    if countiter > 100 + 20 && savemodulo && ...
+          time.recording * countiter > 0.1 && ...  % absolute time larger 0.1 second
+	  time.recording > savetime * (time.nonoutput+time.recording) / 100 
+      savemodulo = floor(1.02 * savemodulo) + 1;
+      % disp(['++savemodulo == ' num2str(savemodulo) ' at ' num2str(countiter)]); %qqq
+    end
+  end % if output
+
+  % save everything
+  time.t3 = clock;
+  if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
+    xmin = arxvalid(:, fitness.idx(1));
+    fmin = fitness.raw(1);
+    if flgsaving && countiter > 2
+      clear idx; % prevents error under octave
+      % -v6 : non-compressed non-unicode for version 6 and earlier
+      if ~isempty(strsaving) && ~isoctave
+	save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart	
+      else 
+	save('-mat', opts.SaveFilename); % for inspection and possible restart
+      end
+      time.saving = time.saving + time.c * max(0,etime(clock, time.t3)); 
+    end
+  end
+  time.t0 = clock;
+
+  % Update the diary file
+  if ~isempty(calibGUI_interface_obj)
+     updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end     
+  end   
+  
+  % ----- end output generation -----
+
+end % while, end generation loop
+
+% -------------------- Final Procedures -------------------------------
+
+% Evaluate xmean and return best recent point in xmin
+fmin = fitness.raw(1);
+xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
+if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
+  out.solutions.mean.f = ...
+      feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+  counteval = counteval + 1;
+  out.solutions.mean.evals = counteval;
+  if out.solutions.mean.f < fitness.raw(1)
+    fmin = out.solutions.mean.f;
+    xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
+  end
+  if out.solutions.mean.f < out.solutions.bestever.f
+    out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
+    out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds); 
+    bestever = out.solutions.bestever;
+  end
+end
+
+% Save everything and display final message
+if flgsavingfinal
+  clear idx; % prevents error under octave
+  if ~isempty(strsaving) && ~isoctave
+    save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart	
+  else 
+    save('-mat', opts.SaveFilename);    % for inspection and possible restart
+  end
+  message = [' (saved to ' opts.SaveFilename ')'];
+else
+  message = [];
+end
+
+if flgdisplay
+  disp(['#Fevals:   f(returned x)   |    bestever.f     | stopflag' ...
+        message]);
+  if isoctave
+    strstop = stopflag(:); 
+  else
+      strcat(stopflag(:), '.');
+  end
+  strstop = stopflag(:); %strcat(stopflag(:), '.');
+  disp([repmat(' ',1,6-floor(log10(counteval))) ...
+        num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
+        num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
+	strstop{1:end}]);
+  if N < 102
+     disp(['mean solution:' sprintf(' %+.1e', xmean)]);
+     disp(['std deviation:' sprintf('  %.1e', sigma*sqrt(diagC))]);
+     disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
+  end
+  if exist('sfile', 'var') 
+    disp(['Results saved in ' sfile]); 
+  end
+end
+
+  out.arstopflags{irun} = stopflag;
+  if any(strcmp(stopflag, 'fitness')) ...
+	|| any(strcmp(stopflag, 'maxfunevals')) ...
+	|| any(strcmp(stopflag, 'stoptoresume')) ...
+	|| any(strcmp(stopflag, 'manual'))
+    break; 
+  end
+  
+  % Update the diary file
+  if ~isempty(calibGUI_interface_obj)
+     updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end     
+  end  
+  
+end % while irun <= Restarts
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function [x, idx] = xintobounds(x, lbounds, ubounds)
+%
+% x can be a column vector or a matrix consisting of column vectors
+%
+  if ~isempty(lbounds)
+    if length(lbounds) == 1
+      idx = x < lbounds;
+      x(idx) = lbounds;
+    else
+      arbounds = repmat(lbounds, 1, size(x,2));
+      idx = x < arbounds;
+      x(idx) = arbounds(idx);
+    end
+  else
+    idx = 0;
+  end
+  if ~isempty(ubounds)
+    if length(ubounds) == 1
+      idx2 = x > ubounds;
+      x(idx2) = ubounds;
+    else
+      arbounds = repmat(ubounds, 1, size(x,2));
+      idx2 = x > arbounds;
+      x(idx2) = arbounds(idx2);
+    end
+  else
+    idx2 = 0;
+  end
+  idx = idx2-idx; 
+  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function opts=getoptions(inopts, defopts)
+% OPTS = GETOPTIONS(INOPTS, DEFOPTS) handles an arbitrary number of
+% optional arguments to a function. The given arguments are collected
+% in the struct INOPTS.  GETOPTIONS matches INOPTS with a default
+% options struct DEFOPTS and returns the merge OPTS.  Empty or missing
+% fields in INOPTS invoke the default value.  Fieldnames in INOPTS can
+% be abbreviated.
+%
+% The returned struct OPTS is first assigned to DEFOPTS. Then any
+% field value in OPTS is replaced by the respective field value of
+% INOPTS if (1) the field unambiguously (case-insensitive) matches
+% with the fieldname in INOPTS (cut down to the length of the INOPTS
+% fieldname) and (2) the field is not empty.
+%
+% Example:
+%   In the source-code of the function that needs optional
+%   arguments, the last argument is the struct of optional
+%   arguments:
+%
+%   function results = myfunction(mandatory_arg, inopts)
+%     % Define four default options
+%     defopts.PopulationSize = 200;
+%     defopts.ParentNumber = 50;
+%     defopts.MaxIterations = 1e6;
+%     defopts.MaxSigma = 1;
+%  
+%     % merge default options with input options
+%     opts = getoptions(inopts, defopts);
+%
+%     % Thats it! From now on the values in opts can be used
+%     for i = 1:opts.PopulationSize
+%       % do whatever
+%       if sigma > opts.MaxSigma
+%         % do whatever
+%       end
+%     end
+%   
+%   For calling the function myfunction with default options:
+%   myfunction(argument1, []);
+%   For calling the function myfunction with modified options:
+%   opt.pop = 100; % redefine PopulationSize option
+%   opt.PAR = 10;  % redefine ParentNumber option
+%   opt.maxiter = 2; % opt.max=2 is ambiguous and would result in an error 
+%   myfunction(argument1, opt);
+
+%
+% 04/07/19: Entries can be structs itself leading to a recursive
+%           call to getoptions. 
+%
+
+if nargin < 2 || isempty(defopts) % no default options available
+  opts=inopts;
+  return;
+elseif isempty(inopts) % empty inopts invoke default options
+  opts = defopts;
+  return;
+elseif ~isstruct(defopts) % handle a single option value
+  if isempty(inopts) 
+    opts = defopts;
+  elseif ~isstruct(inopts)
+    opts = inopts;
+  else
+    error('Input options are a struct, while default options are not');
+  end
+  return;
+elseif ~isstruct(inopts) % no valid input options
+  error('The options need to be a struct or empty');
+end
+
+  opts = defopts; % start from defopts 
+  % if necessary overwrite opts fields by inopts values
+  defnames = fieldnames(defopts);
+  idxmatched = []; % indices of defopts that already matched
+  for name = fieldnames(inopts)'
+    name = name{1}; % name of i-th inopts-field
+    if isoctave
+      for i = 1:size(defnames, 1)
+        idx(i) = strncmpi(defnames(i), name, length(name));
+      end
+    else
+	idx = strncmpi(defnames, name, length(name));
+    end
+    if sum(idx) > 1
+      error(['option "' name '" is not an unambigous abbreviation. ' ...
+	     'Use opts=RMFIELD(opts, ''' name, ...
+	     ''') to remove the field from the struct.']);
+    end
+    if sum(idx) == 1
+      defname  = defnames{find(idx)}; 
+      if ismember(find(idx), idxmatched)
+	error(['input options match more than ones with "' ...
+	       defname '". ' ...
+	       'Use opts=RMFIELD(opts, ''' name, ...
+	       ''') to remove the field from the struct.']);
+      end
+      idxmatched = [idxmatched find(idx)];
+      val = getfield(inopts, name);
+      % next line can replace previous line from MATLAB version 6.5.0 on and in octave
+      % val = inopts.(name);
+      if isstruct(val) % valid syntax only from version 6.5.0
+	opts = setfield(opts, defname, ...
+	    getoptions(val, getfield(defopts, defname))); 
+      elseif isstruct(getfield(defopts, defname)) 
+      % next three lines can replace previous three lines from MATLAB 
+      % version 6.5.0 on
+      %   opts.(defname) = ...
+      %      getoptions(val, defopts.(defname)); 
+      % elseif isstruct(defopts.(defname)) 
+	warning(['option "' name '" disregarded (must be struct)']); 
+      elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
+	opts = setfield(opts, defnames{find(idx)}, val);
+	% next line can replace previous line from MATLAB version 6.5.0 on
+	% opts.(defname) = inopts.(name); 
+      end
+    else
+      warning(['option "' name '" disregarded (unknown field name)']);
+    end
+  end
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function res=myeval(s)
+  if ischar(s)
+    res = evalin('caller', s);
+  else
+    res = s;
+  end
+  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function res=myevalbool(s)
+  if ~ischar(s) % s may not and cannot be empty
+    res = s;
+  else % evaluation string s
+    if strncmpi(s, 'yes', 3) || strncmpi(s, 'on', 2) ...
+	  || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
+      res = 1;
+    elseif strncmpi(s, 'no', 2) || strncmpi(s, 'off', 3) ...
+	  || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
+      res = 0;
+    else
+      try res = evalin('caller', s); catch
+	error(['String value "' s '" cannot be evaluated']);
+      end
+      try res ~= 0; catch
+	error(['String value "' s '" cannot be evaluated reasonably']);
+      end
+    end
+  end
+  
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function res = isoctave
+% any hack to find out whether we are running octave
+  s = version;
+  res = 0;
+  if exist('fflush', 'builtin') && eval(s(1)) < 7
+    res = 1;
+  end
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function flush
+  if isoctave
+    feval('fflush', stdout);
+  end
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% ----- replacements for statistic toolbox functions ------------
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function res=myrange(x)
+  res = max(x) - min(x);
+  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+function res = myprctile(inar, perc, idx)
+%
+% Computes the percentiles in vector perc from vector inar
+% returns vector with length(res)==length(perc)
+% idx: optional index-array indicating sorted order
+%
+
+N = length(inar);
+flgtranspose = 0;
+
+% sizes 
+if size(perc,1) > 1
+  perc = perc';
+  flgtranspose = 1;
+  if size(perc,1) > 1
+    error('perc must not be a matrix');
+  end
+end
+if size(inar, 1) > 1 && size(inar,2) > 1
+  error('data inar must not be a matrix');
+end
+ 
+% sort inar
+if nargin < 3 || isempty(idx)
+  [sar idx] = sort(inar);
+else
+  sar = inar(idx);
+end
+
+res = [];
+for p = perc
+  if p <= 100*(0.5/N)
+    res(end+1) = sar(1);
+  elseif p >= 100*((N-0.5)/N)
+    res(end+1) = sar(N);
+  else
+    % find largest index smaller than required percentile
+    availablepercentiles = 100*((1:N)-0.5)/N;
+    i = max(find(p > availablepercentiles));
+    % interpolate linearly
+    res(end+1) = sar(i) ...
+	+ (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
+	/ (availablepercentiles(i+1) - availablepercentiles(i));
+
+  end
+end
+
+if flgtranspose
+  res = res';
+end
+
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+
+
+
+function [s ranks rankDelta] = local_noisemeasurement(arf1, arf2, lamreev, theta, cutlimit)
+% function [s ranks rankDelta] = noisemeasurement(arf1, arf2, lamreev, theta)
+%
+% Input: 
+%   arf1, arf2 : two arrays of function values. arf1 is of size 1xlambda, 
+%       arf2 may be of size 1xlamreev or 1xlambda. The first lamreev values 
+%       in arf2 are (re-)evaluations of the respective solutions, i.e. 
+%       arf1(1) and arf2(1) are two evaluations of "the first" solution.
+%    lamreev: number of reevaluated individuals in arf2 
+%    theta : parameter theta for the rank change limit, between 0 and 1, 
+%       typically between 0.2 and 0.7. 
+%    cutlimit (optional): output s is computed as a mean of rankchange minus 
+%       threshold, where rankchange is <=2*(lambda-1). cutlimit limits 
+%       abs(rankchange minus threshold) in this calculation to cutlimit. 
+%       cutlimit=1 evaluates basically the sign only. cutlimit=2 could be 
+%       the rank change with one solution (both evaluations of it). 
+% 
+% Output: 
+%   s : noise measurement, s>0 means the noise measure is above the
+%       acceptance threshold
+%   ranks : 2xlambda array, corresponding to [arf1; arf2], of ranks 
+%       of arf1 and arf2 in the set [arf1 arf2], values are in [1:2*lambda]
+%   rankDelta: 1xlambda array of rank movements of arf2 compared to
+%       arf1.  rankDelta(i) agrees with the number of values from
+%       the set [arf1 arf2] that lie between arf1(i) and arf2(i).
+%
+% Note: equal function values might lead to somewhat spurious results.
+%       For this case a revision is advisable. 
+
+%%% verify input argument sizes
+if size(arf1,1) ~= 1
+  error('arf1 must be an 1xlambda array');
+elseif size(arf2,1) ~= 1
+  error('arf2 must be an 1xsomething array');
+elseif size(arf1,2) < size(arf2,2)  % not really necessary, but saver
+  error('arf2 must not be smaller than arf1 in length');
+end
+lam = size(arf1, 2);
+if size(arf1,2) ~= size(arf2,2)
+   arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
+end
+if nargin < 5
+  cutlimit = inf;
+end
+
+%%% capture unusual values
+if any(diff(arf1) == 0)
+  % this will presumably interpreted as rank change, because
+  % sort(ones(...)) returns 1,2,3,...
+  warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
+end
+
+%%% compute rank changes into rankDelta
+% compute ranks
+[ignore, idx] = sort([arf1 arf2]);
+[ignore, ranks] = sort(idx);
+ranks = reshape(ranks, lam, 2)';
+
+rankDelta = ranks(1,:) - ranks(2,:) - sign(ranks(1,:) - ranks(2,:));
+
+%%% compute rank change limits using both ranks(1,...) and ranks(2,...)
+for i = 1:lamreev
+  sumlim(i) = ...
+      0.5 * (...
+          myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
+                    theta*50) ...      
+          + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
+                      theta*50)); 
+end
+
+%%% compute measurement
+%s = abs(rankDelta(1:lamreev)) - sumlim; % lives roughly in 0..2*lambda
+
+%                               max: 1 rankchange in 2*lambda is always fine
+s = abs(rankDelta(1:lamreev)) - max(1, sumlim); % lives roughly in 0..2*lambda
+
+% cut-off limit
+idx = abs(s) > cutlimit; 
+s(idx) = sign(s(idx)) * cutlimit;
+s = mean(s);
+
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% ---------------------------------------------------------------  
+% just a "local" copy of plotcmaesdat.m, with manual_mode set to zero
+function local_plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarname)
+% PLOTCMAESDAT;
+% PLOTCMAES(FIGURENUMBER_iBEGIN_iEND, FILENAMEPREFIX, FILENAMEEXTENSION, OBJECTVARNAME);
+%   plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy... 
+%   mod(figNb,100)==1 plots versus iterations. 
+%
+% PLOTCMAES([101 300]) plots versus iteration, from iteration 300. 
+% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800. 
+%
+% Upper left subplot: blue/red: function value of the best solution in the 
+%   recent population, cyan: same function value minus best
+%   ever seen function value, green: sigma, red: ratio between 
+%   longest and shortest principal axis length which is equivalent
+%   to sqrt(cond(C)). 
+% Upper right plot: time evolution of the distribution mean (default) or
+%   the recent best solution vector. 
+% Lower left: principal axes lengths of the distribution ellipsoid, 
+%   equivalent with the sqrt(eig(C)) square root eigenvalues of C. 
+% Lower right: magenta: minimal and maximal "true" standard deviation 
+%   (with sigma included) in the coordinates, other colors: sqrt(diag(C)) 
+%   of all diagonal elements of C, if C is diagonal they equal to the
+%   lower left. 
+%
+% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where 
+%   name = axlen, OBJECTVARNAME (xmean|xrecentbest), fit, or stddev.
+%
+
+manual_mode = 0;
+
+  if nargin < 1 || isempty(figNb)
+    figNb = 325;
+  end
+  if nargin < 2 || isempty(filenameprefix)
+    filenameprefix = 'outcmaes';
+  end
+  if nargin < 3 || isempty(filenameextension)
+    filenameextension = '.dat';
+  end
+  if nargin < 4 || isempty(objectvarname)
+    objectvarname = 'xmean';    
+    objectvarname = 'xrecentbest';
+  end
+  % load data
+  d.x = load([filenameprefix objectvarname filenameextension]); 
+  % d.x = load([filenameprefix 'xmean' filenameextension]); 
+  % d.x = load([filenameprefix 'xrecentbest' filenameextension]); 
+  d.f = load([filenameprefix 'fit' filenameextension]); 
+  d.std = load([filenameprefix 'stddev' filenameextension]);
+  d.D = load([filenameprefix 'axlen' filenameextension]);
+
+  % interpret entries in figNb for cutting out some data
+  if length(figNb) > 1
+    iend = inf;
+    istart = figNb(2);
+    if length(figNb) > 2
+      iend = figNb(3);
+    end
+    figNb = figNb(1);
+    d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
+    d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
+    d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
+    d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
+  end
+
+  % decide for x-axis
+  iabscissa = 2; % 1== versus iterations, 2==versus fevals
+  if mod(figNb,100) == 1
+    iabscissa = 1; % a short hack
+  end
+  if iabscissa == 1
+    xlab ='iterations'; 
+  elseif iabscissa == 2
+    xlab = 'function evaluations'; 
+  end
+
+  if size(d.x, 2) < 1000
+    minxend = 1.03*d.x(end, iabscissa);
+  else
+    minxend = 0;
+  end
+
+  % set up figure window
+  if manual_mode
+    figure(figNb);  % just create and raise the figure window
+  else
+    if 1 < 3 && evalin('caller', 'iterplotted') == 0 && evalin('caller', 'irun') == 1 
+      figure(figNb);  % incomment this, if figure raise in the beginning is not desired
+    elseif ismember(figNb, findobj('Type', 'figure'))
+      set(0, 'CurrentFigure', figNb);  % prevents raise of existing figure window
+    else
+      figure(figNb);
+    end
+  end
+
+  % plot fitness etc
+  foffset = 1e-99;
+  dfit = d.f(:,6)-min(d.f(:,6)); 
+  [ignore idxbest] = min(dfit);
+  dfit(dfit<1e-98) = NaN;
+  subplot(2,2,1); hold off; 
+  dd = abs(d.f(:,7:8)) + foffset; 
+  dd(d.f(:,7:8)==0) = NaN;
+  semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
+  % additional fitness data, for example constraints values
+  if size(d.f,2) > 8
+    dd = abs(d.f(:,9:end)) + 10*foffset;  % a hack
+    % dd(d.f(:,9:end)==0) = NaN; 
+    semilogy(d.f(:,iabscissa), dd, '-m'); hold on; 
+    if size(d.f,2) > 12
+      semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
+    end
+  end
+
+  idx = find(d.f(:,6)>1e-98);  % positive values
+  if ~isempty(idx)  % otherwise non-log plot gets hold
+    semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on; 
+  end
+  idx = find(d.f(:,6) < -1e-98);  % negative values
+  if ~isempty(idx)
+    semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on; 
+  end
+  semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
+  semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
+  semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
+  semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
+                      min(d.std(:,6:end)')'], '-m'); % max,min std
+  maxval = max(d.std(end,6:end));
+  minval = min(d.std(end,6:end));
+  text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
+  text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
+
+  semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
+  % plot best f
+  semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
+  semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
+
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  
+  yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3)))); 
+  text(ax(1), yannote, ...
+       [ 'f=' num2str(d.f(end,6), '%.15g') ]);
+
+  title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
+  grid on; 
+
+  subplot(2,2,2); hold off; 
+  plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax); 
+
+  % add some annotation lines
+  [ignore idx] = sort(d.x(end,6:end));
+  % choose no more than 25 indices 
+  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
+  yy = repmat(NaN, 2, size(d.x,2)-5);
+  yy(1,:) = d.x(end, 6:end);
+  yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
+  plot([d.x(end,iabscissa) ax(2)], yy, '-'); 
+  plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+  for i = idx(idxs)
+    text(ax(2), yy(2,i), ...
+         ['x(' num2str(i) ')=' num2str(yy(1,i))]);
+  end
+  
+  lam = 'NA'; 
+  if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
+    lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
+  end
+  title(['Object Variables (' num2str(size(d.x, 2)-5) ...
+            '-D, popsize~' lam ')']);grid on;
+
+  subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  title('Principal Axes Lengths');grid on;
+  xlabel(xlab); 
+
+  subplot(2,2,4); hold off; 
+  % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on; 
+  % remove sigma from stds
+  d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
+  semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on; 
+  if 11 < 3  % max and min std
+    semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end)')' ...
+                        d.std(:,3).*min(d.std(:,6:end)')'], '-m', 'linewidth', 2);
+    maxval = max(d.std(end,6:end));
+    minval = min(d.std(end,6:end));
+    text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
+    text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
+  end
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  % add some annotation lines
+  [ignore idx] = sort(d.std(end,6:end));
+  % choose no more than 25 indices 
+  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
+  yy = repmat(NaN, 2, size(d.std,2)-5);
+  yy(1,:) = d.std(end, 6:end);
+  yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
+  semilogy([d.std(end,iabscissa) ax(2)], yy, '-'); 
+  semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+  for i = idx(idxs)
+    text(ax(2), yy(2,i), [' ' num2str(i)]);
+  end
+  title('Standard Deviations in Coordinates divided by sigma');grid on;
+  xlabel(xlab);
+
+  if figNb ~= 324
+    % zoom on;  % does not work in Octave
+  end
+  drawnow;
+
+% ---------------------------------------------------------------  
+% --------------- TEST OBJECTIVE FUNCTIONS ----------------------  
+% ---------------------------------------------------------------  
+
+%%% Unimodal functions
+
+function f=fjens1(x)
+%
+% use population size about 2*N
+%
+  f = sum((x>0) .* x.^1, 1);
+  if any(any(x<0))
+    idx = sum(x < 0, 1) > 0;
+    f(idx) = 1e3;
+%    f = f + 1e3 * sum(x<0, 1);
+%    f = f + 10 * sum((x<0) .* x.^2, 1);
+    f(idx) = f(idx) + 1e-3*abs(randn(1,sum(idx)));
+%    f(idx) = NaN;
+  end
+
+function f=fsphere(x)
+  f = sum(x.^2,1);
+
+function f=fmax(x)
+  f = max(abs(x), [], 1);
+
+function f=fssphere(x)
+  f=sqrt(sum(x.^2, 1));
+
+%  lb = -0.512; ub = 512; 
+%  xfeas = x; 
+%  xfeas(x<lb) = lb;
+%  xfeas(x>ub) = ub; 
+%  f=sum(xfeas.^2, 1);
+%  f = f + 1e-9 * sum((xfeas-x).^2); 
+  
+function f=fspherenoise(x, Nevals)
+  if nargin < 2 || isempty(Nevals)
+    Nevals = 1;
+  end
+  [N,popsi] = size(x);
+%  x = x .* (1 +  0.3e-0 * randn(N, popsi)/(2*N)); % actuator noise
+  fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2; 
+%  f = 0*rand(1,1) ...
+%      + fsum ...
+%      + fsum .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)) ...
+%      + 1*fsum.^0.9 .* 2*randn(1,popsi) / (2*N); % 
+
+%  f = fsum .* exp(0.1*randn(1,popsi));
+  f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
+%  f = fsum .* (1 + (0.1/N)*randn(1,popsi)./randn(1,popsi).^1);
+
+  idx = rand(1,popsi) < 0.0;
+  if sum(idx) > 0
+    f(idx) = f(idx) + 1e3*exp(randn(1,sum(idx)));
+  end
+  
+function f=fmixranks(x)
+  N = size(x,1);
+  f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
+  if size(x, 2) > 1 % compute ranks, if it is a population 
+    [ignore, idx] = sort(f);
+    [ignore, ranks] = sort(idx);
+    k = 9; % number of solutions randomly permuted, lambda/2-1
+           % works still quite well (two time slower)
+    for i = k+1:k-0:size(x,2)
+      idx(i-k+(1:k)) = idx(i-k+randperm(k)); 
+    end
+    %disp([ranks' f'])
+    [ignore, ranks] = sort(idx);
+    %disp([ranks' f'])
+    %pause
+    f = ranks+1e-9*randn(1,1);
+  end
+  
+function f = fsphereoneax(x)
+  f = x(1)^2;
+  f = mean(x)^2;
+  
+function f=frandsphere(x)
+  N = size(x,1);
+  idx = ceil(N*rand(7,1));
+  f=sum(x(idx).^2);
+
+function f=fspherelb0(x, M) % lbound at zero for 1:M needed
+  if nargin < 2 M = 0; end
+  N = size(x,1);
+  % M active bounds, f_i = 1 for x = 0
+  f = -M + sum((x(1:M) + 1).^2);
+  f = f + sum(x(M+1:N).^2);
+  
+function f=fspherehull(x)
+  % Patton, Dexter, Goodman, Punch
+  % in -500..500
+  % spherical ridge through zeros(N,1)
+  % worst case start point seems x = 2*100*sqrt(N)
+  % and small step size
+  N = size(x,1);
+  f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
+  
+function f=fellilb0(x, idxM, scal) % lbound at zero for 1:M needed
+  N = size(x,1);
+  if nargin < 3 || isempty(scal)
+    scal = 100;
+  end
+  scale=scal.^((0:N-1)/(N-1));
+  if nargin < 2 || isempty(idxM)
+    idxM = 1:N;
+  end
+  %scale(N) = 1e0;
+  % M active bounds
+  xopt = 0.1;
+  x(idxM) = x(idxM) + xopt;
+  f = scale.^2*x.^2;
+  f = f - sum((xopt*scale(idxM)).^2); 
+%  f = exp(f) - 1;
+%  f = log10(f+1e-19) + 19;
+
+  f = f + 1e-19;
+  
+function f=fcornersphere(x)
+  w = ones(size(x,1));
+  w(1) = 2.5; w(2)=2.5;
+  idx = x < 0;
+  f = sum(x(idx).^2);
+  idx = x > 0;
+  f = f + 2^2*sum(w(idx).*x(idx).^2);
+  
+function f=fsectorsphere(x, scal)
+%
+% This is deceptive for cumulative sigma control CSA in large dimension:
+% The strategy (initially) diverges for N=50 and popsize = 150.  (Even
+% for cs==1 this can be observed for larger settings of N and
+% popsize.) The reason is obvious from the function topology. 
+% Divergence can be avoided by setting boundaries or adding a
+% penalty for large ||x||. Then, convergence can be observed again. 
+% Conclusion: for popsize>N cumulative sigma control is not completely
+% reasonable, but I do not know better alternatives. In particular:
+% TPA takes longer to converge than CSA when the latter still works. 
+%
+  if nargin < 2 || isempty (scal)
+    scal = 1e3;
+  end
+  f=sum(x.^2,1);
+  idx = x<0;
+  f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
+  if 11 < 3
+    idxpen = find(f>1e9);
+    if ~isempty(idxpen)
+      f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
+    end
+  end
+  
+function f=fstepsphere(x, scal)
+  if nargin < 2 || isempty (scal)
+    scal = 1e0;
+  end
+  N = size(x,1);
+  f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
+  f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
+%  f=1e-11+sum(floor(x+0.5).^2);
+
+function f=fstep(x)
+  % in -5.12..5.12 (bounded)
+  N = size(x,1);
+  f=1e-11+6*N+sum(floor(x));
+
+function f=flnorm(x, scal, e)
+if nargin < 2 || isempty(scal)
+  scal = 1;
+end
+if nargin < 3 || isempty(e)
+  e = 1;
+end
+if e==inf
+  f = max(abs(x));
+else
+  N = size(x,1);
+  scale = scal.^((0:N-1)/(N-1))';
+  f=sum(abs(scale.*x).^e);
+end
+
+function f=fneumaier3(x) 
+  % in -n^2..n^2
+  % x^*-i = i(n+1-i)
+  N = size(x,1);
+%  f = N*(N+4)*(N-1)/6 + sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
+  f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
+
+function f = fmaxmindist(y)
+  % y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
+  % points best
+  %   5    1.4142
+  %   8    1.03527618 
+  %  10    0.842535997 
+  %  20    0.5997   
+  pop = size(y,2);
+  N = size(y,1)/2;
+  f = []; 
+  for ipop = 1:pop
+    if any(abs(y(:,ipop)) > 1)
+      f(ipop) = NaN; 
+    else
+      x = reshape(y(:,ipop), [2, N]);
+      f(ipop) = inf;
+      for i = 1:N
+        f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
+      end
+    end
+  end
+  f = -f;
+
+function f=fchangingsphere(x)
+  N = size(x,1);
+  global scale_G; global count_G; if isempty(count_G) count_G=-1; end
+  count_G = count_G+1;
+  if mod(count_G,10) == 0
+    scale_G = 10.^(2*rand(1,N));
+  end
+  %disp(scale(1));
+  f = scale_G*x.^2;
+  
+function f= flogsphere(x)
+ f = 1-exp(-sum(x.^2));
+  
+function f= fexpsphere(x)
+ f = exp(sum(x.^2)) - 1;
+  
+function f=fbaluja(x)
+  % in [-0.16 0.16]
+  y = x(1);
+  for i = 2:length(x)
+    y(i) = x(i) + y(i-1);
+  end
+  f = 1e5 - 1/(1e-5 + sum(abs(y)));
+
+function f=fschwefel(x)
+  f = 0;
+  for i = 1:size(x,1),
+    f = f+sum(x(1:i))^2;
+  end
+
+function f=fcigar(x, ar)
+  if nargin < 2 || isempty(ar)
+    ar = 1e3;
+  end
+  f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
+  
+function f=fcigtab(x)
+  f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
+  
+function f=ftablet(x)
+  f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
+
+function f=felli(x, lgscal, expon, expon2)
+  % lgscal: log10(axis ratio)
+  % expon: x_i^expon, sphere==2
+  N = size(x,1); if N < 2 error('dimension must be greater one'); end
+
+%  x = x - repmat(-0.5+(1:N)',1,size(x,2)); % optimum in 1:N
+  if nargin < 2 || isempty(lgscal), lgscal = 3; end
+  if nargin < 3 || isempty(expon), expon = 2; end
+  if nargin < 4 || isempty(expon2), expon2 = 1; end
+
+  f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
+%  if rand(1,1) > 0.015
+%    f = NaN;
+%  end
+%  f = f + randn(size(f));
+
+function f=fellitest(x)
+  beta = 0.9;
+  N = size(x,1);
+  f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta; 
+
+  
+function f=fellii(x, scal)
+  N = size(x,1); if N < 2 error('dimension must be greater one'); end
+  if nargin < 2
+    scal = 1;
+  end
+  f= (scal*(1:N)).^2 * (x).^2;
+
+function f=fellirot(x)
+  N = size(x,1);
+  global ORTHOGONALCOORSYSTEM_G
+  if isempty(ORTHOGONALCOORSYSTEM_G) ...
+	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
+	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
+    coordinatesystem(N);
+  end
+  f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
+  
+function f=frot(x, fun, varargin)
+  N = size(x,1);
+  global ORTHOGONALCOORSYSTEM_G
+  if isempty(ORTHOGONALCOORSYSTEM_G) ...
+	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
+	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
+    coordinatesystem(N);
+  end
+  f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
+  
+function coordinatesystem(N)
+  if nargin < 1 || isempty(N)
+    arN = 2:30;
+  else
+    arN = N;
+  end
+  global ORTHOGONALCOORSYSTEM_G
+  ORTHOGONALCOORSYSTEM_G{1} = 1; 
+  for N = arN
+    ar = randn(N,N);
+    for i = 1:N 
+      for j = 1:i-1
+	ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
+      end
+      ar(:,i) = ar(:,i) / norm(ar(:,i));
+    end
+    ORTHOGONALCOORSYSTEM_G{N} = ar; 
+  end
+
+function f=fplane(x)
+  f=x(1);
+
+function f=ftwoaxes(x)
+  f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
+
+function f=fparabR(x)
+  f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
+
+function f=fsharpR(x)
+  f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
+  
+function f=frosen(x)
+  if size(x,1) < 2 error('dimension must be greater one'); end
+  N = size(x,1); 
+  popsi = size(x,2); 
+  f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
+  % f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)); 
+
+function f=frosenlin(x)
+  if size(x,1) < 2 error('dimension must be greater one'); end
+
+  x_org = x;
+  x(x>30) = 30;
+  x(x<-30) = -30;
+
+  f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
+      sum((x(1:end-1,:)-1).^2,1);
+
+  f = f + sum((x-x_org).^2,1);
+%  f(any(abs(x)>30,1)) = NaN; 
+
+function f=frosenrot(x)
+  N = size(x,1);
+  global ORTHOGONALCOORSYSTEM_G
+  if isempty(ORTHOGONALCOORSYSTEM_G) ...
+	|| length(ORTHOGONALCOORSYSTEM_G) < N ...
+	|| isempty(ORTHOGONALCOORSYSTEM_G{N})
+    coordinatesystem(N);
+  end
+  f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
+
+function f=frosenmodif(x)
+  f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
+      - 400*exp(-sum((x+1).^2)/2/0.05);
+  
+function f=fschwefelrosen1(x)
+  % in [-10 10] 
+  f=sum((x.^2-x(1)).^2 + (x-1).^2);
+  
+function f=fschwefelrosen2(x)
+  % in [-10 10] 
+  f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
+
+function f=fdiffpow(x)
+  [N popsi] = size(x); if N < 2 error('dimension must be greater one'); end
+
+  f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
+  f = sqrt(f); 
+
+function f=fabsprod(x)
+  f = sum(abs(x),1) + prod(abs(x),1);
+
+function f=ffloor(x)
+  f = sum(floor(x+0.5).^2,1); 
+
+function f=fmaxx(x)
+  f = max(abs(x), [], 1);
+
+%%% Multimodal functions 
+
+function f=fbirastrigin(x)
+% todo: the volume needs to be a constant 
+  N = size(x,1); 
+  idx = (sum(x, 1) < 0.5*N); % global optimum
+  f = zeros(1,size(x,2));
+  f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1); 
+  idx = ~idx;
+  f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1); 
+
+function f=fackley(x)
+  % -32.768..32.768
+  % Adding a penalty outside the interval is recommended,  
+  % because for large step sizes, fackley imposes like frand
+  % 
+  N = size(x,1); 
+  f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N)); 
+  f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
+  % add penalty outside the search interval
+  f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
+  
+function f = fbohachevsky(x)
+ % -15..15
+  f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
+	  - 0.4 * cos(4*pi*x(2:end)) + 0.7);
+  
+function f=fconcentric(x)
+  % in  +-600
+  s = sum(x.^2);
+  f = s^0.25 * (sin(50*s^0.1)^2 + 1);
+
+function f=fgriewank(x)
+  % in [-600 600]
+  [N, P] = size(x);
+  f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
+  scale = repmat(sqrt(1:N)', 1, P);
+  f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+  % f = f + 1e4*sum(x(abs(x)>5).^2);
+  % if sum(x(abs(x)>5).^2) > 0
+  %   f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
+  % end
+
+function f=fgriewrosen(x)
+% F13 or F8F2
+  [N, P] = size(x);
+  scale = repmat(sqrt(1:N)', 1, P);
+  y = [x(2:end,:); x(1,:)];
+  x = 100 * (x.^2 - y) + (x - 1).^2;  % Rosenbrock part
+  f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+  f = sum(1 - cos(x) + x.^2/4e3, 1);
+
+function f=fspallpseudorastrigin(x, scal, skewfac, skewstart, amplitude)
+% by default multi-modal about between -30 and 30
+  if nargin < 5 || isempty(amplitude)
+    amplitude = 40;
+  end
+  if nargin < 4 || isempty(skewstart)
+    skewstart = 0;
+  end
+  if nargin < 3 || isempty(skewfac)
+    skewfac = 1;
+  end
+  if nargin < 2 || isempty(scal)
+    scal = 1;
+  end
+  N = size(x,1); 
+  scale = 1;
+  if N > 1
+    scale=scal.^((0:N-1)'/(N-1)); 
+  end
+  % simple version: 
+  % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+  % skew version: 
+  y = repmat(scale, 1, size(x,2)) .* x;
+  idx = find(x > skewstart);
+  if ~isempty(idx)
+    y(idx) =  skewfac*y(idx);
+  end
+  f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
+      + randn(1,1);
+
+function f=frastrigin(x, scal, skewfac, skewstart, amplitude)
+% by default multi-modal about between -30 and 30
+  if nargin < 5 || isempty(amplitude)
+    amplitude = 10;
+  end
+  if nargin < 4 || isempty(skewstart)
+    skewstart = 0;
+  end
+  if nargin < 3 || isempty(skewfac)
+    skewfac = 1;
+  end
+  if nargin < 2 || isempty(scal)
+    scal = 1;
+  end
+  N = size(x,1); 
+  scale = 1;
+  if N > 1
+    scale=scal.^((0:N-1)'/(N-1)); 
+  end
+  % simple version: 
+  % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+  % skew version: 
+  y = repmat(scale, 1, size(x,2)) .* x;
+  idx = find(x > skewstart);
+  % idx = intersect(idx, 2:2:10); 
+  if ~isempty(idx)
+    y(idx) =  skewfac*y(idx);
+  end
+  f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
+  
+function f=frastriginmax(x)
+  N = size(x,1);
+  f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
+  f(any(abs(x) > 5.12)) = 1e2*N;
+
+function f = fschaffer(x)
+ % -100..100
+  N = size(x,1);
+  s = x(1:N-1,:).^2 + x(2:N,:).^2;
+  f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
+
+function f=fschwefelmult(x)
+  % -500..500
+  % 
+  N = size(x,1); 
+  f = - sum(x.*sin(sqrt(abs(x))), 1);
+  f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
+  % penalty term 
+  f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
+  
+function f=ftwomax(x)
+  % Boundaries at +/-5
+  N = size(x,1); 
+  f = -abs(sum(x)) + 5*N;
+
+function f=ftwomaxtwo(x)
+  % Boundaries at +/-10
+  N = size(x,1); 
+  f = abs(sum(x));
+  if f > 30
+    f = f - 30;
+  end
+  f = -f;
+  
+function f=frand(x)
+  f=1./(1-rand(1, size(x,2))) - 1;
+
+% CHANGES
+% 12/04/28: (3.61) stopIter is relative to countiter after resume (thanks to Tom Holden)
+% 12/04/28: (3.61) some syncing from 3.32.integer branch (cmean introduced, ...)
+% 12/02/19: "future" setting of ccum, correcting for large mueff, is default now
+% 11/11/15: bug-fix: max value for ccovmu_sep setting corrected
+% 10/11/11: (3.52.beta) boundary handling: replace max with min in change 
+%           rate formula. Active CMA: check of pos.def. improved. 
+%           Plotting: value of lambda appears in the title. 
+% 10/04/03: (3.51.beta) active CMA cleaned up. Equal fitness detection
+%           looks into history now. 
+% 10/03/08: (3.50.beta) "active CMA" revised and bug-fix of ambiguous
+%           option Noise.alpha -> Noise.alphasigma. 
+% 09/10/12: (3.40.beta) a slightly modified version of "active CMA", 
+%           that is a negative covariance matrix update, use option
+%           CMA.active. In 10;30;90-D the gain on ftablet is a factor 
+%           of 1.6;2.5;4.4 (the scaling improves by sqrt(N)). On 
+%           Rosenbrock the gain is about 25%. On sharp ridge the 
+%           behavior is improved. Cigar is unchanged. 
+% 09/08/10: local plotcmaesdat remains in backround  
+% 09/08/10: bug-fix in time management for data writing, logtime was not 
+%        considered properly (usually not at all). 
+% 09/07/05: V3.24: stagnation termination added 
+% 08/09/27: V3.23: momentum alignment is out-commented and de-preciated
+% 08/09/25: V3.22: re-alignment of sigma and C was buggy
+% 08/07/15: V3.20, CMA-parameters are options now. ccov and mucov were replaced
+%        by ccov1 \approx ccov/mucov and ccovmu \approx (1-1/mucov)*ccov
+% 08/06/30: file name xrecent was change to xrecentbest (compatible with other 
+%        versions)
+% 08/06/29: time stamp added to output files 
+% 08/06/28: bug fixed with resume option, commentary did not work 
+% 08/06/28: V3.10, uncertainty (noise) handling added (re-implemented), according
+%        to reference "A Method for Handling Uncertainty..." from below.
+% 08/06/28: bug fix: file xrecent was empty 
+% 08/06/01: diagonalonly clean up. >1 means some iterations. 
+% 08/05/05: output is written to file preventing an increasing data
+%        array and ease long runs. 
+% 08/03/27: DiagonalOnly<0 learns for -DiagonalOnly iterations only the 
+%        diagonal with a larger learning rate. 
+% 08/03 (2.60): option DiagonalOnly>=1 invokes a time- and space-linear  
+%        variant with only diagonal elements of the covariance matrix 
+%        updating.  This can be useful for large dimensions, say > 100. 
+% 08/02: diag(weights) * ... replaced with repmat(weights,1,N) .* ...
+%        in C update, implies O(mu*N^2) instead of O(mu^2*N + mu*N^2). 
+% 07/09: tolhistfun as termination criterion added, "<" changed to
+%        "<=" also for TolFun to allow for stopping on zero difference. 
+%        Name tolfunhist clashes with option tolfun. 
+% 07/07: hsig threshold made slighly smaller for large dimension, 
+%        useful for lambda < lambda_default. 
+% 07/06: boundary handling: scaling in the boundary handling
+%        is omitted now, see bnd.flgscale. This seems not to
+%        have a big impact. Using the scaling is worse on rotated
+%        functions, but better on separable ones. 
+% 07/05: boundary handling: weight i is not incremented anymore
+%        if xmean(i) moves towards the feasible space. Increment
+%        factor changed to 1.2 instead of 1.1. 
+% 07/05: boundary handling code simplified not changing the algorithm
+% 07/04: bug removed for saving in octave
+% 06/11/10: more testing of outcome of eig, fixed max(D) to max(diag(D))
+% 06/10/21: conclusive final bestever assignment in the end 
+% 06/10/21: restart and incpopsize option implemented for restarts
+%        with increasing population size, version 2.50. 
+% 06/09/16: output argument bestever inserted again for convenience and
+%        backward compatibility
+% 06/08: output argument out and struct out reorganized. 
+% 06/01: Possible parallel evaluation included as option EvalParallel
+% 05/11: Compatibility to octave implemented, package octave-forge
+%   is needed. 
+% 05/09: Raise of figure and waiting for first plots improved
+% 05/01: Function coordinatesystem cleaned up. 
+% 05/01: Function prctile, which requires the statistics toolbox,
+%        replaced by myprctile. 
+% 05/01: Option warnonequalfunctionvalues included. 
+% 04/12: Decrease of sigma removed. Problems on fsectorsphere can 
+%        be addressed better by adding search space boundaries. 
+% 04/12: Boundary handling simpyfied. 
+% 04/12: Bug when stopping criteria tolx or tolupx are vectors. 
+% 04/11: Three input parameters are obligatory now. 
+% 04/11: Bug in boundary handling removed: Boundary weights can decrease now. 
+% 04/11: Normalization for boundary weights scale changed. 
+% 04/11: VerboseModulo option bug removed. Documentation improved. 
+% 04/11: Condition for increasing boundary weights changed.
+% 04/10: Decrease of sigma when fitness is getting consistenly
+%        worse. Addresses the problems appearing on fsectorsphere for
+%        large population size.
+% 04/10: VerboseModulo option included. 
+% 04/10: Bug for condition for increasing boundary weights removed.
+% 04/07: tolx depends on initial sigma to achieve scale invariance
+%        for this stopping criterion. 
+% 04/06: Objective function value NaN is not counted as function
+%        evaluation and invokes resampling of the search point. 
+% 04/06: Error handling for eigenvalue beeing zero (never happens
+%        with default parameter setting)
+% 04/05: damps further tuned for large mueff 
+%      o Details for stall of pc-adaptation added (variable hsig 
+%        introduced). 
+% 04/05: Bug in boundary handling removed: A large initial SIGMA was
+%        corrected not until *after* the first iteration, which could
+%        lead to a complete failure.
+% 04/05: Call of function range (works with stats toolbox only) 
+%        changed to myrange. 
+% 04/04: Parameter cs depends on mueff now and damps \propto sqrt(mueff)
+%        instead of \propto mueff. 
+%      o Initial stall to adapt C (flginiphase) is removed and
+%        adaptation of pc is stalled for large norm(ps) instead.
+%      o Returned default options include documentation. 
+%      o Resume part reorganized.
+% 04/03: Stopflag becomes cell-array. 
+
+% ---------------------------------------------------------------
+% CMA-ES: Evolution Strategy with Covariance Matrix Adaptation for
+% nonlinear function minimization. To be used under the terms of the
+% GNU General Public License (http://www.gnu.org/copyleft/gpl.html).
+% Author (copyright): Nikolaus Hansen, 2001-2008. 
+% e-mail: nikolaus.hansen AT inria.fr
+% URL:http://www.bionik.tu-berlin.de/user/niko
+% References: See below. 
+% ---------------------------------------------------------------
+%
+% GENERAL PURPOSE: The CMA-ES (Evolution Strategy with Covariance
+% Matrix Adaptation) is a robust search method which should be
+% applied, if derivative based methods, e.g. quasi-Newton BFGS or
+% conjucate gradient, (supposably) fail due to a rugged search
+% landscape (e.g. noise, local optima, outlier, etc.). On smooth
+% landscapes CMA-ES is roughly ten times slower than BFGS. For up to
+% N=10 variables even the simplex direct search method (Nelder & Mead)
+% is often faster, but far less robust than CMA-ES.  To see the
+% advantage of the CMA, it will usually take at least 30*N and up to
+% 300*N function evaluations, where N is the search problem dimension.
+% On considerably hard problems the complete search (a single run) is
+% expected to take at least 30*N^2 and up to 300*N^2 function
+% evaluations.
+%
+% SOME MORE COMMENTS: 
+% The adaptation of the covariance matrix (e.g. by the CMA) is
+% equivalent to a general linear transformation of the problem
+% coding. Nevertheless every problem specific knowlegde about the best
+% linear transformation should be exploited before starting the
+% search. That is, an appropriate a priori transformation should be
+% applied to the problem. This also makes the identity matrix as
+% initial covariance matrix the best choice.
+%
+% The strategy parameter lambda (population size, opts.PopSize) is the
+% preferred strategy parameter to play with.  If results with the
+% default strategy are not satisfactory, increase the population
+% size. (Remark that the crucial parameter mu (opts.ParentNumber) is
+% increased proportionally to lambda). This will improve the
+% strategies capability of handling noise and local minima. We
+% recomment successively increasing lambda by a factor of about three,
+% starting with initial values between 5 and 20. Casually, population
+% sizes even beyond 1000+100*N can be sensible.
+%
+%
+% ---------------------------------------------------------------
+%%% REFERENCES
+%
+% The equation numbers refer to 
+% Hansen, N. and S. Kern (2004). Evaluating the CMA Evolution
+% Strategy on Multimodal Test Functions.  Eighth International
+% Conference on Parallel Problem Solving from Nature PPSN VIII,
+% Proceedings, pp. 282-291, Berlin: Springer. 
+% (http://www.bionik.tu-berlin.de/user/niko/ppsn2004hansenkern.pdf)
+% 
+% Further references:
+% Hansen, N. and A. Ostermeier (2001). Completely Derandomized
+% Self-Adaptation in Evolution Strategies. Evolutionary Computation,
+% 9(2), pp. 159-195.
+% (http://www.bionik.tu-berlin.de/user/niko/cmaartic.pdf).
+%
+% Hansen, N., S.D. Mueller and P. Koumoutsakos (2003). Reducing the
+% Time Complexity of the Derandomized Evolution Strategy with
+% Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation,
+% 11(1).  (http://mitpress.mit.edu/journals/pdf/evco_11_1_1_0.pdf).
+%
+% Ros, R. and N. Hansen (2008). A Simple Modification in CMA-ES
+% Achieving Linear Time and Space Complexity. To appear in Tenth
+% International Conference on Parallel Problem Solving from Nature
+% PPSN X, Proceedings, Berlin: Springer.
+%
+% Hansen, N., A.S.P. Niederberger, L. Guzzella and P. Koumoutsakos
+% (2009?). A Method for Handling Uncertainty in Evolutionary
+% Optimization with an Application to Feedback Control of
+% Combustion. To appear in IEEE Transactions on Evolutionary
+% Computation.
+
+
+
+
diff --git a/algorithms/calibration/CMA_ES/plotcmaesdat.m b/algorithms/calibration/CMA_ES/plotcmaesdat.m
index 808405b..e8b8f58 100644
--- a/algorithms/calibration/CMA_ES/plotcmaesdat.m
+++ b/algorithms/calibration/CMA_ES/plotcmaesdat.m
@@ -1,223 +1,223 @@
-function plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarname)
-% PLOTCMAESDAT;
-% PLOTCMAES(FIGURENUMBER_iBEGIN_iEND, FILENAMEPREFIX, FILENAMEEXTENSION, OBJECTVARNAME);
-%   plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy... 
-%   mod(figNb,100)==1 plots versus iterations. 
-%
-% PLOTCMAES([101 300]) plots versus iteration, from iteration 300. 
-% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800. 
-%
-% Upper left subplot: blue/red: function value of the best solution in the 
-%   recent population, cyan: same function value minus best
-%   ever seen function value, green: sigma, red: ratio between 
-%   longest and shortest principle axis length which is equivalent
-%   to sqrt(cond(C)). 
-% Upper right plot: time evolution of the distribution mean (default) or
-%   the recent best solution vector. 
-% Lower left: principle axes lengths of the distribution ellipsoid, 
-%   equivalent with the sqrt(eig(C)) square root eigenvalues of C. 
-% Lower right: magenta: minimal and maximal "true" standard deviation 
-%   (with sigma included) in the coordinates, other colors: sqrt(diag(C)) 
-%   of all diagonal elements of C, if C is diagonal they equal to the
-%   lower left. 
-%
-% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where 
-%   name = axlen, OBJECTVARNAME (xmean|xrecentbest), fit, or stddev.
-%
-manual_mode = 1;
-
-  if nargin < 1 || isempty(figNb)
-    figNb = 325;
-  end
-  if nargin < 2 || isempty(filenameprefix)
-    filenameprefix = 'outcmaes';
-  end
-  if nargin < 3 || isempty(filenameextension)
-    filenameextension = '.dat';
-  end
-  if nargin < 4 || isempty(objectvarname)
-    objectvarname = 'xmean';    
-    objectvarname = 'xrecentbest';
-  end
-
-  % load data
-  d.x = load([filenameprefix objectvarname filenameextension]); 
-  % d.x = load([filenameprefix 'xmean' filenameextension]); 
-  % d.x = load([filenameprefix 'xrecentbest' filenameextension]); 
-  d.f = load([filenameprefix 'fit' filenameextension]); 
-  d.std = load([filenameprefix 'stddev' filenameextension]);
-  d.D = load([filenameprefix 'axlen' filenameextension]);
-
-  % interpret entries in figNb for cutting out some data
-  if length(figNb) > 1
-    iend = inf;
-    istart = figNb(2);
-    if length(figNb) > 2
-      iend = figNb(3);
-    end
-    figNb = figNb(1);
-    d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
-    d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
-    d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
-    d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
-  end
-
-  % set up figure window
-  if manual_mode
-    figure(figNb);  % just create and raise the figure window
-  else  % try to prevent raise
-    if evalin('caller', 'countiter') <= 2 && evalin('caller', 'irun') == 1 
-      figure(324);
-    elseif gcf ~= 324 
-      if ismember(324, findobj('Type', 'figure'))
-        set(0, 'CurrentFigure', 324);  % prevents raise of figure window
-      else
-        figure(324);
-      end
-    end
-  end
-
-  % decide for x-axis
-  iabscissa = 2; % 1== versus iterations, 2==versus fevals
-  if mod(figNb,100) == 1
-    iabscissa = 1; % a short hack
-  end
-  if iabscissa == 1
-    xlab ='iterations'; 
-  elseif iabscissa == 2
-    xlab = 'function evaluations'; 
-  end
-
-  if size(d.x, 2) < 1000
-    minxend = 1.03*d.x(end, iabscissa);
-  else
-    minxend = 0;
-  end
-
-  % plot fitness etc
-  foffset = 1e-99;
-  dfit = d.f(:,6)-min(d.f(:,6)); 
-  [ignore idxbest] = min(dfit);
-  dfit(dfit<1e-98) = NaN;
-  subplot(2,2,1); hold off; 
-  dd = abs(d.f(:,7:8)) + foffset; 
-  dd(d.f(:,7:8)==0) = NaN;
-  semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
-  % additional fitness data, for example constraints values
-  if size(d.f,2) > 8
-    dd = abs(d.f(:,9:end)) + 10*foffset;  % a hack
-    % dd(d.f(:,9:end)==0) = NaN; 
-    semilogy(d.f(:,iabscissa), dd, '-m'); hold on; 
-    if size(d.f,2) > 12
-      semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
-    end
-  end
-
-  idx = find(d.f(:,6)>1e-98);  % positive values
-  if ~isempty(idx)  % otherwise non-log plot gets hold
-    semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on; 
-  end
-  idx = find(d.f(:,6) < -1e-98);  % negative values
-  if ~isempty(idx)
-    semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on; 
-  end
-  semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
-  semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
-  semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
-  semilogy(d.std(:,iabscissa), [max(d.std(:,6:end), [], 2) ...
-                      min(d.std(:,6:end), [], 2)], '-m', 'linewidth', 2); % max,min std
-  [maxval, imax] = max(d.std(end,6:end));
-  [minval, imin] = min(d.std(end,6:end));
-  text(d.std(end,iabscissa), maxval, sprintf('%d:%.0e', imax, maxval));
-  text(d.std(end,iabscissa), minval, sprintf('%d:%.0e', imin, minval));
-
-  semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
-  % plot best f
-  semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
-  semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
-
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  
-  yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3)))); 
-  text(ax(1), yannote, ...
-       [ 'f=' num2str(d.f(end,6), '%.15g') ]);
-
-  title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
-  grid on; 
-%  ax([3,4]) = [1e-9, 1e5];
-%  axis(ax);
-
-  % plot x-values
-  subplot(2,2,2); hold off; 
-  plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax); 
-
-  % add some annotation lines
-  [ignore idx] = sort(d.x(end,6:end));
-  % choose no more than 25 indices 
-  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
-  yy = repmat(NaN, 2, size(d.x,2)-5);
-  yy(1,:) = d.x(end, 6:end);
-  yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
-  plot([d.x(end,iabscissa) ax(2)], yy, '-'); 
-  plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
-  for i = idx(idxs)
-    text(ax(2), yy(2,i), ...
-         ['x(' num2str(i) ')=' num2str(yy(1,i), '%.3g')]);
-  end
-  
-  lam = 'NA'; 
-  if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
-    lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
-  end
-  title(['Object Variables, ' objectvarname(2:min(end,7)) ' (' num2str(size(d.x, 2)-5) ...
-            '-D, popsize~' lam ')']);grid on;
-
-  subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  title('Principle Axes Lengths');grid on;
-  xlabel(xlab); 
-
-  subplot(2,2,4); hold off; 
-  % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on; 
-  % remove sigma from stds
-  d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
-  semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on; 
-  if 11 < 3  % max and min std
-    semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end), [], 2) ...
-                        d.std(:,3).*min(d.std(:,6:end), [], 2)], '-m', 'linewidth', 2);
-    maxval = max(d.std(end,6:end));
-    minval = min(d.std(end,6:end));
-    text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
-    text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
-  end
-  ax = axis;
-  ax(2) = max(minxend, ax(2)); 
-  axis(ax);
-  % add some annotation lines
-  [ignore idx] = sort(d.std(end,6:end));
-  % choose no more than 25 indices 
-  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
-  yy = repmat(NaN, 2, size(d.std,2)-5);
-  yy(1,:) = d.std(end, 6:end);
-  yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
-  semilogy([d.std(end,iabscissa) ax(2)], yy, '-'); 
-  semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
-  for i = idx(idxs)
-    text(ax(2), yy(2,i), [' ' num2str(i)]);
-  end
-  title('Standard Deviations in Coordinates divided by sigma');grid on;
-  xlabel(xlab);
-
-  if figNb ~= 324
-    % zoom on;  % does not work in Octave
-  end
-  drawnow;
-
-
+function plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarname)
+% PLOTCMAESDAT;
+% PLOTCMAES(FIGURENUMBER_iBEGIN_iEND, FILENAMEPREFIX, FILENAMEEXTENSION, OBJECTVARNAME);
+%   plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy... 
+%   mod(figNb,100)==1 plots versus iterations. 
+%
+% PLOTCMAES([101 300]) plots versus iteration, from iteration 300. 
+% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800. 
+%
+% Upper left subplot: blue/red: function value of the best solution in the 
+%   recent population, cyan: same function value minus best
+%   ever seen function value, green: sigma, red: ratio between 
+%   longest and shortest principle axis length which is equivalent
+%   to sqrt(cond(C)). 
+% Upper right plot: time evolution of the distribution mean (default) or
+%   the recent best solution vector. 
+% Lower left: principle axes lengths of the distribution ellipsoid, 
+%   equivalent with the sqrt(eig(C)) square root eigenvalues of C. 
+% Lower right: magenta: minimal and maximal "true" standard deviation 
+%   (with sigma included) in the coordinates, other colors: sqrt(diag(C)) 
+%   of all diagonal elements of C, if C is diagonal they equal to the
+%   lower left. 
+%
+% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where 
+%   name = axlen, OBJECTVARNAME (xmean|xrecentbest), fit, or stddev.
+%
+manual_mode = 1;
+
+  if nargin < 1 || isempty(figNb)
+    figNb = 325;
+  end
+  if nargin < 2 || isempty(filenameprefix)
+    filenameprefix = 'outcmaes';
+  end
+  if nargin < 3 || isempty(filenameextension)
+    filenameextension = '.dat';
+  end
+  if nargin < 4 || isempty(objectvarname)
+    objectvarname = 'xmean';    
+    objectvarname = 'xrecentbest';
+  end
+
+  % load data
+  d.x = load([filenameprefix objectvarname filenameextension]); 
+  % d.x = load([filenameprefix 'xmean' filenameextension]); 
+  % d.x = load([filenameprefix 'xrecentbest' filenameextension]); 
+  d.f = load([filenameprefix 'fit' filenameextension]); 
+  d.std = load([filenameprefix 'stddev' filenameextension]);
+  d.D = load([filenameprefix 'axlen' filenameextension]);
+
+  % interpret entries in figNb for cutting out some data
+  if length(figNb) > 1
+    iend = inf;
+    istart = figNb(2);
+    if length(figNb) > 2
+      iend = figNb(3);
+    end
+    figNb = figNb(1);
+    d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
+    d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
+    d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
+    d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
+  end
+
+  % set up figure window
+  if manual_mode
+    figure(figNb);  % just create and raise the figure window
+  else  % try to prevent raise
+    if evalin('caller', 'countiter') <= 2 && evalin('caller', 'irun') == 1 
+      figure(324);
+    elseif gcf ~= 324 
+      if ismember(324, findobj('Type', 'figure'))
+        set(0, 'CurrentFigure', 324);  % prevents raise of figure window
+      else
+        figure(324);
+      end
+    end
+  end
+
+  % decide for x-axis
+  iabscissa = 2; % 1== versus iterations, 2==versus fevals
+  if mod(figNb,100) == 1
+    iabscissa = 1; % a short hack
+  end
+  if iabscissa == 1
+    xlab ='iterations'; 
+  elseif iabscissa == 2
+    xlab = 'function evaluations'; 
+  end
+
+  if size(d.x, 2) < 1000
+    minxend = 1.03*d.x(end, iabscissa);
+  else
+    minxend = 0;
+  end
+
+  % plot fitness etc
+  foffset = 1e-99;
+  dfit = d.f(:,6)-min(d.f(:,6)); 
+  [ignore idxbest] = min(dfit);
+  dfit(dfit<1e-98) = NaN;
+  subplot(2,2,1); hold off; 
+  dd = abs(d.f(:,7:8)) + foffset; 
+  dd(d.f(:,7:8)==0) = NaN;
+  semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
+  % additional fitness data, for example constraints values
+  if size(d.f,2) > 8
+    dd = abs(d.f(:,9:end)) + 10*foffset;  % a hack
+    % dd(d.f(:,9:end)==0) = NaN; 
+    semilogy(d.f(:,iabscissa), dd, '-m'); hold on; 
+    if size(d.f,2) > 12
+      semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
+    end
+  end
+
+  idx = find(d.f(:,6)>1e-98);  % positive values
+  if ~isempty(idx)  % otherwise non-log plot gets hold
+    semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on; 
+  end
+  idx = find(d.f(:,6) < -1e-98);  % negative values
+  if ~isempty(idx)
+    semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on; 
+  end
+  semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
+  semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
+  semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
+  semilogy(d.std(:,iabscissa), [max(d.std(:,6:end), [], 2) ...
+                      min(d.std(:,6:end), [], 2)], '-m', 'linewidth', 2); % max,min std
+  [maxval, imax] = max(d.std(end,6:end));
+  [minval, imin] = min(d.std(end,6:end));
+  text(d.std(end,iabscissa), maxval, sprintf('%d:%.0e', imax, maxval));
+  text(d.std(end,iabscissa), minval, sprintf('%d:%.0e', imin, minval));
+
+  semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
+  % plot best f
+  semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
+  semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
+
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  
+  yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3)))); 
+  text(ax(1), yannote, ...
+       [ 'f=' num2str(d.f(end,6), '%.15g') ]);
+
+  title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
+  grid on; 
+%  ax([3,4]) = [1e-9, 1e5];
+%  axis(ax);
+
+  % plot x-values
+  subplot(2,2,2); hold off; 
+  plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax); 
+
+  % add some annotation lines
+  [ignore idx] = sort(d.x(end,6:end));
+  % choose no more than 25 indices 
+  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
+  yy = repmat(NaN, 2, size(d.x,2)-5);
+  yy(1,:) = d.x(end, 6:end);
+  yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
+  plot([d.x(end,iabscissa) ax(2)], yy, '-'); 
+  plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+  for i = idx(idxs)
+    text(ax(2), yy(2,i), ...
+         ['x(' num2str(i) ')=' num2str(yy(1,i), '%.3g')]);
+  end
+  
+  lam = 'NA'; 
+  if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
+    lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
+  end
+  title(['Object Variables, ' objectvarname(2:min(end,7)) ' (' num2str(size(d.x, 2)-5) ...
+            '-D, popsize~' lam ')']);grid on;
+
+  subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  title('Principle Axes Lengths');grid on;
+  xlabel(xlab); 
+
+  subplot(2,2,4); hold off; 
+  % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on; 
+  % remove sigma from stds
+  d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
+  semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on; 
+  if 11 < 3  % max and min std
+    semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end), [], 2) ...
+                        d.std(:,3).*min(d.std(:,6:end), [], 2)], '-m', 'linewidth', 2);
+    maxval = max(d.std(end,6:end));
+    minval = min(d.std(end,6:end));
+    text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
+    text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
+  end
+  ax = axis;
+  ax(2) = max(minxend, ax(2)); 
+  axis(ax);
+  % add some annotation lines
+  [ignore idx] = sort(d.std(end,6:end));
+  % choose no more than 25 indices 
+  idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25))); 
+  yy = repmat(NaN, 2, size(d.std,2)-5);
+  yy(1,:) = d.std(end, 6:end);
+  yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
+  semilogy([d.std(end,iabscissa) ax(2)], yy, '-'); 
+  semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+  for i = idx(idxs)
+    text(ax(2), yy(2,i), [' ' num2str(i)]);
+  end
+  title('Standard Deviations in Coordinates divided by sigma');grid on;
+  xlabel(xlab);
+
+  if figNb ~= 324
+    % zoom on;  % does not work in Octave
+  end
+  drawnow;
+
+
diff --git a/algorithms/calibration/DREAM/GenParSet.m b/algorithms/calibration/DREAM/GenParSet.m
index 7e3753b..b29e595 100644
--- a/algorithms/calibration/DREAM/GenParSet.m
+++ b/algorithms/calibration/DREAM/GenParSet.m
@@ -1,19 +1,19 @@
-function ParSet = GenParSet(chain);
-% Generates a 2D matrix ParSet from 3D array chain
-
-% Determine how many elements in chain
-[T,d,N] = size(chain); 
-
-% Initalize ParSet
-ParSet = [];
-
-% If save in memory -> No -- ParSet is empty
-if (T == 0),
-    % Do nothing
-else
-    % ParSet derived from all chain
-    for qq = 1:N,
-        ParSet = [ParSet; chain(:,:,qq) (1:size(chain(:,:,qq),1))'];
-    end;
-    ParSet = sortrows(ParSet,[d+1]); ParSet = ParSet(:,1:d);
+function ParSet = GenParSet(chain);
+% Generates a 2D matrix ParSet from 3D array chain
+
+% Determine how many elements in chain
+[T,d,N] = size(chain); 
+
+% Initalize ParSet
+ParSet = [];
+
+% If save in memory -> No -- ParSet is empty
+if (T == 0),
+    % Do nothing
+else
+    % ParSet derived from all chain
+    for qq = 1:N,
+        ParSet = [ParSet; chain(:,:,qq) (1:size(chain(:,:,qq),1))'];
+    end;
+    ParSet = sortrows(ParSet,[d+1]); ParSet = ParSet(:,1:d);
 end;
\ No newline at end of file
diff --git a/algorithms/calibration/DREAM/postprocessing/acf.m b/algorithms/calibration/DREAM/postprocessing/acf.m
index 698387d..0e42568 100644
--- a/algorithms/calibration/DREAM/postprocessing/acf.m
+++ b/algorithms/calibration/DREAM/postprocessing/acf.m
@@ -1,22 +1,22 @@
-function y=acf(x,lagmax)
-%ACF Autocorrelation function
-% ACF(X,maxlag)
-% default maxlag is floor(10*log10(length(x)))
-
-% There is also acf.mex version which is much faster
-% it is used if Matlab finds it
-
-% Marko Laine <marko.laine@fmi.fi>
-% $Revision: 1.5 $  $Date: 2012/09/27 11:47:33 $
-
-x = x(:)'-mean(x);
-n = length(x);
-if nargin<2
-  lagmax = floor(10*log10(n));
-  lagmax = min(lagmax, n-1);
-end
-y = filter(x(n:-1:1),1,x);
-%y  = conv(flipud(x),x);
-y = y(n:-1:1)/n;
-y = y/y(1);
-y = y(1:lagmax+1);
+function y=acf(x,lagmax)
+%ACF Autocorrelation function
+% ACF(X,maxlag)
+% default maxlag is floor(10*log10(length(x)))
+
+% There is also acf.mex version which is much faster
+% it is used if Matlab finds it
+
+% Marko Laine <marko.laine@fmi.fi>
+% $Revision: 1.5 $  $Date: 2012/09/27 11:47:33 $
+
+x = x(:)'-mean(x);
+n = length(x);
+if nargin<2
+  lagmax = floor(10*log10(n));
+  lagmax = min(lagmax, n-1);
+end
+y = filter(x(n:-1:1),1,x);
+%y  = conv(flipud(x),x);
+y = y(n:-1:1)/n;
+y = y/y(1);
+y = y(1:lagmax+1);
diff --git a/algorithms/calibration/DREAM/postprocessing/density.m b/algorithms/calibration/DREAM/postprocessing/density.m
index 41b163a..29bdd3d 100644
--- a/algorithms/calibration/DREAM/postprocessing/density.m
+++ b/algorithms/calibration/DREAM/postprocessing/density.m
@@ -1,64 +1,64 @@
-function [y,xo]=density(x,xout,ss,gaus)
-%DENSITY  Density estimator using Gaussian kernel
-% Y = DENSITY(X,XOUT,S)
-% X is the vector of data values.
-% The density estimator is evaluated at XOUT points.
-% S is a scale factor for the default kernel bandwidth,
-% default S = 1.
-% Without output arguments the density is plotted.
-
-% Marko Laine <marko.laine@fmi.fi>
-% $Revision: 1.9 $  $Date: 2012/09/27 11:47:35 $
-
-if nargin<3
-  ss=1;
-end
-if nargin<4
-  gaus=1;
-end
-
-if nargin<2 | isempty(xout)
-  xmin=min(x); xmax=max(x); xrange=xmax-xmin;
-  if length(x) > 200
-    xout=linspace(xmin-0.08*xrange,xmax+0.08*xrange);
-  else
-    xout=linspace(mean(x)-4*std(x),mean(x)+4*std(x));
-  end
-end
-y  = zeros(size(xout));
-n  = length(xout);
-nx = length(x);
-
-%%% see MASS 2nd ed page 181.
-if iqrange(x)<=0
-  s=1.06*std(x)*nx^(-1/5);
-else
-  s=1.06*min(std(x),iqrange(x)/1.34)*nx^(-1/5);
-end
-%  s=1.144*std(x)*nx^(-1/5);
-if ss>0
-  s=ss*s;
-elseif ss<0
-  s = abs(ss);
-end
-if gaus
-  % Gaussian kernel
-  for i=1:n
-    y(i) = 1/nx*sum(norpf((xout(i)-x)/s))./s;
-  end
-else
-  % triangular kernel
-  s=s*1.2113;
-  for i=1:n
-    y(i) = 1/nx*sum(max(0,1-abs(xout(i)-x)/s))./s;
-  end
-end
-
-if nargout>1
-  xo=xout;
-end
-
-if nargout==0
-  plot(xout,y)
-  clear y % no output
-end
+function [y,xo]=density(x,xout,ss,gaus)
+%DENSITY  Density estimator using Gaussian kernel
+% Y = DENSITY(X,XOUT,S)
+% X is the vector of data values.
+% The density estimator is evaluated at XOUT points.
+% S is a scale factor for the default kernel bandwidth,
+% default S = 1.
+% Without output arguments the density is plotted.
+
+% Marko Laine <marko.laine@fmi.fi>
+% $Revision: 1.9 $  $Date: 2012/09/27 11:47:35 $
+
+if nargin<3
+  ss=1;
+end
+if nargin<4
+  gaus=1;
+end
+
+if nargin<2 | isempty(xout)
+  xmin=min(x); xmax=max(x); xrange=xmax-xmin;
+  if length(x) > 200
+    xout=linspace(xmin-0.08*xrange,xmax+0.08*xrange);
+  else
+    xout=linspace(mean(x)-4*std(x),mean(x)+4*std(x));
+  end
+end
+y  = zeros(size(xout));
+n  = length(xout);
+nx = length(x);
+
+%%% see MASS 2nd ed page 181.
+if iqrange(x)<=0
+  s=1.06*std(x)*nx^(-1/5);
+else
+  s=1.06*min(std(x),iqrange(x)/1.34)*nx^(-1/5);
+end
+%  s=1.144*std(x)*nx^(-1/5);
+if ss>0
+  s=ss*s;
+elseif ss<0
+  s = abs(ss);
+end
+if gaus
+  % Gaussian kernel
+  for i=1:n
+    y(i) = 1/nx*sum(norpf((xout(i)-x)/s))./s;
+  end
+else
+  % triangular kernel
+  s=s*1.2113;
+  for i=1:n
+    y(i) = 1/nx*sum(max(0,1-abs(xout(i)-x)/s))./s;
+  end
+end
+
+if nargout>1
+  xo=xout;
+end
+
+if nargout==0
+  plot(xout,y)
+  clear y % no output
+end
diff --git a/algorithms/calibration/DREAM/postprocessing/genparset.m b/algorithms/calibration/DREAM/postprocessing/genparset.m
index 7e3753b..b29e595 100644
--- a/algorithms/calibration/DREAM/postprocessing/genparset.m
+++ b/algorithms/calibration/DREAM/postprocessing/genparset.m
@@ -1,19 +1,19 @@
-function ParSet = GenParSet(chain);
-% Generates a 2D matrix ParSet from 3D array chain
-
-% Determine how many elements in chain
-[T,d,N] = size(chain); 
-
-% Initalize ParSet
-ParSet = [];
-
-% If save in memory -> No -- ParSet is empty
-if (T == 0),
-    % Do nothing
-else
-    % ParSet derived from all chain
-    for qq = 1:N,
-        ParSet = [ParSet; chain(:,:,qq) (1:size(chain(:,:,qq),1))'];
-    end;
-    ParSet = sortrows(ParSet,[d+1]); ParSet = ParSet(:,1:d);
+function ParSet = GenParSet(chain);
+% Generates a 2D matrix ParSet from 3D array chain
+
+% Determine how many elements in chain
+[T,d,N] = size(chain); 
+
+% Initalize ParSet
+ParSet = [];
+
+% If save in memory -> No -- ParSet is empty
+if (T == 0),
+    % Do nothing
+else
+    % ParSet derived from all chain
+    for qq = 1:N,
+        ParSet = [ParSet; chain(:,:,qq) (1:size(chain(:,:,qq),1))'];
+    end;
+    ParSet = sortrows(ParSet,[d+1]); ParSet = ParSet(:,1:d);
 end;
\ No newline at end of file
diff --git a/algorithms/calibration/DREAM/postprocessing/iqrange.m b/algorithms/calibration/DREAM/postprocessing/iqrange.m
index 170db90..72d4c06 100644
--- a/algorithms/calibration/DREAM/postprocessing/iqrange.m
+++ b/algorithms/calibration/DREAM/postprocessing/iqrange.m
@@ -1,24 +1,24 @@
-function y=iqrange(x)
-% Interquantile range of each column of x
-
-% ML 2000
-
-% Marko Laine <marko.laine@fmi.fi>
-% $Revision: 1.3 $  $Date: 2012/09/27 11:47:37 $
-
-[n,m]=size(x);
-if n==1
-  x=x';
-  n = m;
-  m = 1;
-end
-
-x  = sort(x);
-% n  = length(x);
-i1 = floor((n+1)/4); 
-i3 = floor(3/4*(n+1));
-f1 = (n+1)/4-i1; 
-f3 = 3/4*(n+1)-i3;
-q1 = (1-f1).*x(i1,:)+f1.*x(i1+1,:);
-q3 = (1-f3).*x(i3,:)+f3.*x(i3+1,:);
-y  = q3-q1;
+function y=iqrange(x)
+% Interquantile range of each column of x
+
+% ML 2000
+
+% Marko Laine <marko.laine@fmi.fi>
+% $Revision: 1.3 $  $Date: 2012/09/27 11:47:37 $
+
+[n,m]=size(x);
+if n==1
+  x=x';
+  n = m;
+  m = 1;
+end
+
+x  = sort(x);
+% n  = length(x);
+i1 = floor((n+1)/4); 
+i3 = floor(3/4*(n+1));
+f1 = (n+1)/4-i1; 
+f3 = 3/4*(n+1)-i3;
+q1 = (1-f1).*x(i1,:)+f1.*x(i1+1,:);
+q3 = (1-f3).*x(i3,:)+f3.*x(i3+1,:);
+y  = q3-q1;
diff --git a/algorithms/calibration/DREAM/postprocessing/norpf.m b/algorithms/calibration/DREAM/postprocessing/norpf.m
index bf5857d..7461c48 100644
--- a/algorithms/calibration/DREAM/postprocessing/norpf.m
+++ b/algorithms/calibration/DREAM/postprocessing/norpf.m
@@ -1,9 +1,9 @@
-function y=norpf(x,mu,sigma2)
-% NORPF(x,mu,sigma2)  Normal (Gaussian) density function
-
-% Marko Laine <marko.laine@fmi.fi>
-% $Revision: 1.4 $  $Date: 2012/09/27 11:47:38 $
-
-if nargin < 2, mu=0; end
-if nargin < 3, sigma2=1; end
-y=1./sqrt(2*pi*sigma2).*exp(-0.5*(x-mu).^2 ./sigma2);
+function y=norpf(x,mu,sigma2)
+% NORPF(x,mu,sigma2)  Normal (Gaussian) density function
+
+% Marko Laine <marko.laine@fmi.fi>
+% $Revision: 1.4 $  $Date: 2012/09/27 11:47:38 $
+
+if nargin < 2, mu=0; end
+if nargin < 3, sigma2=1; end
+y=1./sqrt(2*pi*sigma2).*exp(-0.5*(x-mu).^2 ./sigma2);
diff --git a/algorithms/calibration/SP_UCI/SPUCI.m b/algorithms/calibration/SP_UCI/SPUCI.m
index 1a83071..6d6708d 100644
--- a/algorithms/calibration/SP_UCI/SPUCI.m
+++ b/algorithms/calibration/SP_UCI/SPUCI.m
@@ -1,640 +1,640 @@
-function [bestx,bestf,icall, exitFlag, exitStatus] = SPUCI(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, bl_phys, bu_phys, ... 
-                                                            maxn, kstop,pcento,peps,ngs,iseed,iniflg, calibGUI_interface_obj, varargin) 
-% This is the Matlab code implementing the SP-UCI algorithm,written by Dr.
-% Wei Chu, 08/2012, based on the SCE Matlab codes written by Dr. Q. Duan
-% 9/2004.
-% 
-% If you have any questions or comments,Please contact Dr. Wei Chu at
-% wchu2@uci.edu
-% 
-% This algorithm has been published in Infomration Sciences and shoud be
-% cited as:
-%
-%   Wei	Chu,	X. Gao and S. Sorooshian,(2011), A new evolutionary search
-%   strategy for global optimization of high-dimensional Problems,
-%   Information Sciences, doi:10.1016/j.ins. 2011.06.024.
-%
-%
-% Definition of input variables:
-%  x0 = the initial parameter array at the start;
-%     = the optimized parameter array at the end
-
-%  f0 = the objective function value corresponding to the initial parameters
-%     = the objective function value corresponding to the optimized parameters
-%  bl = the lower bound of the parameters
-%  bu = the upper bound of the parameters
-%  iseed = the random seed number (for repetetive testing purpose)
-%  iniflg = flag for initial parameter array (=1, included it in initial
-%           population; otherwise, not included)
-%  ngs = number of complexes (sub-populations)
-%  maxn = maximum number of function evaluations allowed during optimization
-%  kstop = maximum number of evolution loops before convergency
-%  percento = the percentage change allowed in kstop loops before
-%  convergency
-%
-% Definition of outputs
-%  bestx = best parameter values at the end of the search.
-%  besft = best objective function value at the end of the search.
-%
-% Additional outputs (edits by Tim Peterson 2016)
-%  exitFlag = 0: did not converge, 1=partial convergence, 2 = converged
-%  exitStatus = statement of why the scheme ended.
-%
-% Additional modifications (Tim Peterson 2017)
-%  - Numerous additions were made to SPUCI.m and MCCE.m to handle derived
-%  forcing data (specifically down-scaled pumping forcing data derived from
-%  a simulated annealing approach). 
-%  - Construction of complexes changed from using randperm() to the
-%  standard method of SCE-UA. This was undertaken to allow the assignment
-%  of the best derived forcing time series to the top 'ngs' solutions. That
-%  is, the derived forcing could be extracted for the best ngs parameter
-%  sets and then the derived forcing applied to each of the other parameter
-%  sets within the complex.
-%  - GauSamp() was commented out. It increased the number of model
-%  evaluations by 'npt' for little identifiable improvement in the
-%  solution.
-%
-
-% Check if derived forcing is to be handled
-stochDerivedForcingData_prior = getStochForcingData(varargin{1});
-useDerivedForcing = false;
-if any(~isempty(stochDerivedForcingData_prior))
-    useDerivedForcing = true;
-end
-
-% Initialization of agorithmic 
-%iseed= 123456
-nopt=length(x0); %dimentions of the problem
-npg=2*nopt+1;%  npg = number of members is a complex
-nps=nopt+1;%  nps = number of members in a simplex
-nspl=nps;%  nspl = number of evolution steps for each complex before shuffling
-if useDerivedForcing
-    nspl=2*nps;%  nspl = number of evolution steps for each complex before shuffling
-end
-npt=npg*ngs;%npt = the total number of members (the population size)
-
-bound = bu_plausible - bl_plausible;% boundary of the feasible space.
-
-% Initialise exist outputs. Tim Peterson 2016
-exitFlag = 0;
-exitStatus = 'Calibration scheme did not start.';
-bestf_ever = inf;
-
-% Initialization of the populaton and calculate their objective function value. 
-[x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, iniflg, npt, nopt, 0, varargin{:});
-
-% Check if the population degeneration occured
-[x, xf, icall]=DimRest(funcHandle, funcHangle_validParams, x,xf,bl_phys,bu_phys,icall, varargin{:});
-
-% Get the initial derived forcing data
-stochDerivedForcingData_best = getStochForcingData(varargin{1});
-
-
-% Sort the population again and record the best and worst points
-[xf,idx]=sort(xf);
-x=x(idx,:);
-bestx=x(1,:); bestf=xf(1);
-worstx=x(npt,:); worstf=xf(npt);
-
-% Computes the normalized geometric range of the parameters
-gnrng=exp(mean(log((max(x)-min(x))./bound)));
-
-disp('The Initial Loop: 0');
-disp(['BESTF  : ' num2str(bestf)]);
-disp(['BESTX  : ' num2str(bestx)]);
-disp(['WORSTF : ' num2str(worstf)]);
-disp(['WORSTX : ' num2str(worstx)]);
-disp(' ');
-
-% Check for convergency;
-if icall >= maxn;
-    disp('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT');
-    disp('ON THE MAXIMUM NUMBER OF TRIALS ');
-    disp(maxn);
-    disp('HAS BEEN EXCEEDED.  SEARCH WAS STOPPED AT TRIAL NUMBER:');
-    disp(icall);
-    disp('OF THE INITIAL LOOP!');
-end;
-
-if gnrng < peps;
-    disp('THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE');
-end;
-
-% Update the diary file
-if ~isempty(calibGUI_interface_obj)
-    updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end    
-end
-
-% Update exist status output. Tim Peterson 2016
-exitStatus = 'Calibration scheme did started.';
-
-% Begin evolution loops:
-criter=[];
-criter_change=1e+5;
-xigs = [];
-bestf_ever=bestf;
-bestx_ever=bestx;
-nloop=0;
-minloops = 2*kstop;
-%isbestf_ever_inkstop = false;
-while nloop<=minloops || (icall<maxn && gnrng>peps && criter_change>pcento)
-    nloop=nloop+1;
-    %%%%%%%%%%%%%%%%%%%%%%%%
-    %idexx=randperm(npt);
-    %%%%%%%%%%%%%%%%%%%%%%%%
-
-    % Create indexes for each complex
-    %or igs = 1: ngs;
-    %    ind{igs} = idexx(npg*(igs-1)+1:npg*igs);
-    %end    
-    
-    % NOTE: SPU-UCI originally used randperm for distribution of the
-    % points to complexes. It is unclear why this was adopted over the
-    % determinsitic partitioning approach of SCE-UA. TIm Peterson.
-    for igs = 1: ngs        
-        k1=1:npg;
-        ind{igs}=(k1-1)*ngs+igs;
-    end
-    
-    % Get the stochastic derived forcing data
-    stochDerivedForcingData_prior = getStochForcingData(varargin{1});
-        
-    % Check if derived forcing is to be handled
-    useDerivedForcing = false;
-    if any(~isempty(stochDerivedForcingData_prior))
-        useDerivedForcing = true;
-    end    
-    
-    % Sort derived forcings by new index complexes    
-    if useDerivedForcing && nloop>1
-        stochDerivedForcingData_new = cell(ngs,1);
-        for igs = 1: ngs;
-            % Get the appropriate derived forcing data for each complex. 
-            stochDerivedForcingData_new{igs} = stochDerivedForcingData{xigs(igs)};
-        end
-        stochDerivedForcingData = stochDerivedForcingData_new;
-        clear stochDerivedForcingData_new
-        
-    end
-
-    %-----------------
-    % Importantly, with the derived forcing changing then objective
-    % function value for each parameter set is most likely to change. 
-    % Therefore, all parameter sets are re-evaluated. Also, the stochastic 
-    % forcing parameters are perturbed. This is done to avoid the stoachstic
-    % forcing data from converging to a local miniumum.  
-    if useDerivedForcing                
-        if ~isempty(xigs) && iscell(stochDerivedForcingData) && length(stochDerivedForcingData)==ngs
-            model = varargin{1};
-            stochDerivedForcingData_sliced = stochDerivedForcingData(xigs);
-            
-            parfor i=1:npt
-                % Assign derived forcing using xigs
-                updateStochForcingData(model, stochDerivedForcingData_sliced{i});
-
-                % Update/perturb the stochastic forcing parameters.
-                %xtmp = updateStochForcingParameters(model, stochDerivedForcingData_sliced{i}, x(i,:)');
-                %x(i,:) = xtmp';
-
-                % Get the new objective function value.
-                xf(i) = feval(funcHandle, x(i,:)', varargin{:});
-            end
-            clear stochDerivedForcingData_sliced;
-        end
-        icall = icall + npt;
-    end       
-    %-----------------
-    
-    % Initialise cell array for cx and cf
-    cx = cell(ngs,1);
-    cf = cell(ngs,1);
-    cicall = zeros(ngs,1);
-    if nloop==1
-     stochDerivedForcingData = cell(ngs,1);
-    end
-    model = varargin{1};
-        
-    % Loop on complexes (sub-populations);
-    parfor igs = 1: ngs
-
-        % Assign derived forcing using xigs
-        if useDerivedForcing && nloop>1
-            updateStochForcingData(model, stochDerivedForcingData{igs});
-        end
-        
-        % This is the major computionation load. It was shifted into a
-        % stand alone function by Tim Peterson to allow parrellisation.
-        [cx{igs}, cf{igs}, cicall(igs), stochDerivedForcingData{igs}] = doComplexEvolution(funcHandle, funcHangle_validParams, ind{igs}, x, xf, ...
-        bl_plausible, bu_plausible, bl_phys,bu_phys, nspl, nps, npg, useDerivedForcing , varargin{:});
-        
-    % End of Loop on Complex Evolution;
-    end
-    %display(['... parfor run time =',num2str(toc)]);
-        
-    % Check if derived forcing is to be handled
-    if any(cellfun(@(x) ~isempty(x), stochDerivedForcingData))
-        useDerivedForcing = true;
-    end
-
-    % Put cx and cf back into the population;
-    for igs = 1: ngs;
-        x(ind{igs},:)=cx{igs};
-        xf(ind{igs})=cf{igs};
-    end
-
-    % Record the igs number for each parameter. 
-    for igs = 1: ngs;
-        xigs(ind{igs})=igs;
-    end
-    
-    % Sum the function calls per complex and add to icall
-    icall = icall + sum(cicall);            
-
-    % Sort the complexes, and record the best and worst points;
-    [xf,idx] = sort(xf); 
-    x=x(idx,:);
-    bestx=x(1,:); 
-    bestf=xf(1);
-    worstx=x(npt,:); 
-    worstf=xf(npt); 
-
-    % Sort xigs numbers using xf order
-    xigs=xigs(idx);
-    
-    % Computes the normalized geometric range of the parameters
-    gnrng=exp(mean(log((max(x)-min(x))./bound)));
-
-    % Update the objective function IF derived forcing is being used. 
-    %-----------------
-    if useDerivedForcing                
-        % Importantly, the best solution from each complex may not be the
-        % actual best (or may further decline) because it may be been
-        % derived using a stochastic forcing time-series from ealier within
-        % the evolutionary loop, rather than the stocastic forcing from the
-        % end of the loop. Therefore, the best ngs*2 points agre re-derived
-        % below (note, only ngs*2 are derived rather than all simply to
-        % reduce the computational load).
-        model = varargin{1};
-        stochDerivedForcingData_sliced = stochDerivedForcingData(xigs);
-        parfor i=1:(ngs*2)
-            % Assign derived forcing using xigs
-            updateStochForcingData(model, stochDerivedForcingData_sliced{i});
-            xf(i) = feval(funcHandle, x(i,:)', varargin{:});
-        end
-        clear stochDerivedForcingData_sliced;
-        icall = icall + npt;
-        
-        % Re-sort the complexes, and re-record the best and worst points;
-        [xf,idx] = sort(xf);
-        x=x(idx,:);
-        bestx=x(1,:);
-        bestf=xf(1);
-        worstx=x(npt,:);
-        worstf=xf(npt);
-
-        % Re-sort xigs numbers using xf order
-        xigs=xigs(idx);
-
-        % Add the best ever solution to the model, or else if this loop produced the best 
-        % ever solution, then update the best ever data.
-        if bestf < bestf_ever %|| abs(bestf-bestf_ever)<=pcento
-            
-            % Set the best derived forcing from this evolution to the model.
-            updateStochForcingData(varargin{1}, stochDerivedForcingData{xigs(1)});                
-                        
-            bestf_ever = bestf;
-            bestx_ever = bestx;
-            stochDerivedForcingData_best = stochDerivedForcingData{xigs(1)};                                             
-        else               
-            
-            % Insert the best ever forcing series into a random complex
-            % (except the best complex)
-            ind_rand_ngs = xigs(1);
-            while ind_rand_ngs == xigs(1)
-                ind_rand_ngs = randi(ngs);
-            end
-            stochDerivedForcingData{ind_rand_ngs}  = stochDerivedForcingData_best;
-            
-            % Build an index to only the parameter sets within complex 'ind_rand'.
-            ind_rand_ngs = find(xigs==ind_rand_ngs);
-            ind_rand_ngs = sort(ind_rand_ngs);
-            
-            % Set the derived forcing from the best ever evolution to the model.
-            updateStochForcingData(varargin{1},stochDerivedForcingData_best);
-            
-            % Recalculate the xf for ONLY complex 'ind_rand'.
-            x_tmp = x(ind_rand_ngs,:);
-            xf_tmp = inf(1,length(ind_rand_ngs));
-            parfor i=1:length(ind_rand_ngs)
-                xf_tmp(i) = feval(funcHandle, x_tmp(i,:)', varargin{:});
-            end
-            xf(ind_rand_ngs) = xf_tmp;
-            icall = icall + length(ind_rand_ngs);
-            
-            
-            % Again, re-sort the complexes, and re-record the best and worst points;
-            [xf,idx] = sort(xf);
-            x=x(idx,:);
-            bestx=x(1,:);
-            bestf=xf(1);
-            worstx=x(npt,:);
-            worstf=xf(npt);
-            
-            % Again, re-sort xigs numbers using xf order
-            xigs=xigs(idx);
-            
-            % Check if there is a new bestf.
-            if bestf < bestf_ever
-                bestf_ever = bestf;
-                bestx_ever = bestx;
-            end
-        end
-    else 
-        if bestf < bestf_ever
-            bestf_ever = bestf;
-            bestx_ever = bestx;
-        end
-    end       
-%-----------------
-
-
-    criter_change = inf;
-    criter=[criter;bestf];        
-%     if useDerivedForcing
-%         isbestf_ever_inkstop = false;
-%     else
-%         isbestf_ever_inkstop = true;
-%     end
-    if (nloop >= kstop)
-        criter_change=abs(criter(nloop)-criter(nloop-kstop+1))*100;
-        criter_change=criter_change/mean(abs(criter(nloop-kstop+1:nloop)));
-        
-%         %Check if bestf_ever is within the last kstop iterations.
-%         if any(criter(nloop-kstop+1:nloop)==bestf_ever)
-%           isbestf_ever_inkstop = true;
-%         end
-    end
-    
-    disp(['LOOP       : ' num2str(nloop) '  - Trial - ' num2str(icall)]);
-    if useDerivedForcing   
-        disp(['BESTF loop : ' num2str(bestf,8)]);
-        disp(['BESTF ever : ' num2str(bestf_ever,8)]);
-    else
-        disp(['BESTF      : ' num2str(bestf,8)]);
-    end
-    disp(['BESTX      : ' num2str(bestx)]);
-    disp(['WORSTF     : ' num2str(worstf,8)]);
-    disp(['WORSTX     : ' num2str(worstx)]);
-    disp(['F convergence val.: ',num2str(criter_change,8)]);
-    disp(['X convergence val.: ',num2str(gnrng,8)]);
-    disp(' ');    
-    
-    % Check for convergency;
-    exitCalib = false;
-    if icall >= maxn;
-        disp('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT');
-        disp(['ON THE MAXIMUM NUMBER OF TRIALS ' num2str(maxn) ' HAS BEEN EXCEEDED!']);        
-    end;
-    
-    % If stochastic derived forcing is used, then assess if the method
-    % should be refined (ie for pumpingRate_SAestimation() the
-    % time-step reduced). This feature was added to allow the
-    % downscaling to operate an increasingly fine temporal scales. 
-    if useDerivedForcing && ((criter_change < pcento) || gnrng < peps)
-        refineStochForcingMethod = true;
-        finishedStochForcing = updateStochForcingData(varargin{1},stochDerivedForcingData_best, refineStochForcingMethod);
-        
-        % Get the stochastic forcing in case it has been updated in the
-        % prior step.
-        stochDerivedForcingData_best = getStochForcingData(varargin{1});
-        
-        % If the stochastic forcing has been refined, then repeat the
-        % calibration using the refined apprach eg a finer downscaling
-        % timestep. Considering that the parameter sets are probably very
-        % clustered, then if the stochastic forcing has not finished re-run
-        % but with randomised parameters AND the best solution yet.
-        if ~finishedStochForcing 
-            disp('');
-            disp('NOTE: The derived stochastic forcing is being refined. The calibration is being re-run.');
-            
-            % Put the nest stoch forcing into each cell of the cell array of stoch
-            % forcing for each complex.
-            for igs = 1: ngs
-                % Get the appropriate derived forcing data for each complex. 
-                stochDerivedForcingData{igs} = stochDerivedForcingData_best;
-            end            
-            
-            % Initialization of the populaton and calculate their objective
-            % function value AND add the best parameter set so far.
-            %bestf_ever = inf;
-            [x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, bestx_ever,bl_plausible, bu_plausible, true, npt, nopt, icall, varargin{:});            
-
-            % Check if the population degeneration occured
-            [x, xf, icall]=DimRest(funcHandle, funcHangle_validParams, x,xf,bl_phys,bu_phys,icall, varargin{:});
-            
-            % Initialise convergence measures.
-            criter_change =  inf;
-            gnrng = inf;
-            criter = criter(end);
-            nloop=1;
-            %doReEval_DerivedForcing=false;
-        else
-            disp('NOTE: The derived stochastic forcing has reached the user set resolution.');
-        end
-    end    
-        
-
-    if criter_change < pcento
-        disp(['THE BEST POINT HAS IMPROVED IN LAST ' num2str(kstop) ' LOOPS BY ', ...
-            'LESS THAN THE THRESHOLD ' num2str(pcento) '%']);
-        disp('CONVERGENCY HAS ACHIEVED BASED ON OBJECTIVE FUNCTION CRITERIA!!!')
-    end;
-    
-    if gnrng < peps;
-        disp('THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE');
-    end;
-    
-    % Update the diary file
-    if ~isempty(calibGUI_interface_obj)
-        updatetextboxFromDiary(calibGUI_interface_obj);
-        [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-        if doQuit
-            exitFlag = exitFlagQuit;
-            exitStatus = exitStatusQuit;
-            return;
-        end
-    end    
-    
-    % End of the Outer Loops
-end;
-
-% Apply best model settings
-model = varargin{1};
-if useDerivedForcing                
-    updateStochForcingData(model, stochDerivedForcingData_best);
-end
-bestf = feval(funcHandle, bestx_ever', varargin{:});
-
-disp(['SEARCH WAS STOPPED AT TRIAL NUMBER: ' num2str(icall)]);
-disp(['NORMALIZED GEOMETRIC RANGE = ' num2str(gnrng)]);
-disp(['THE BEST POINT HAS IMPROVED IN LAST ' num2str(kstop) ' LOOPS BY ', ...
-    num2str(criter_change) '%']);
-
-% Update exist status output. Tim Peterson 2016
-if icall>=maxn 
-    exitFlag=1;
-    exitStatus = 'Insufficient maximum number of model evaluations for convergence.';
-elseif gnrng<peps && criter_change>pcento;
-    exitFlag=1;
-    exitStatus = ['Only parameter convergence (obj func. convergence = ',num2str(criter_change),' & threshold = ',num2str(pcento),') achieved in ', num2str(icall),' function evaluations.'];
-elseif gnrng>peps && criter_change<pcento;
-    exitFlag=1;
-    exitStatus = ['Only objective function convergence achieved (param. convergence = ',num2str(gnrng),' & threshold = ',num2str(peps),') in ', num2str(icall),' function evaluations.'];
-elseif gnrng<peps && criter_change<pcento;
-    exitFlag=2;
-    exitStatus = ['Parameter and objective function convergence achieved in ', num2str(icall),' function evaluations.'];
-end
-
-% Update the diary file
-if ~isempty(calibGUI_interface_obj)
-    updatetextboxFromDiary(calibGUI_interface_obj);
-    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
-    if doQuit
-        exitFlag = exitFlagQuit;
-        exitStatus = exitStatusQuit;
-        return;
-    end    
-end
-
-end
-
-function [cx, cf, icall, forcingData] = doComplexEvolution(funcHandle, funcHangle_validParams, ind, x, xf, bl_plausible, bu_plausible, bl, bu, nspl, nps, npg, useDerivedForcing, varargin)
-
-        % initialse count of local function calls
-        icall = 0;
-                
-        % Partition the population into complexes (sub-populations);
-        cx=x(ind,:);
-        cf=xf(ind);                 
-        
-        % Sort population
-        [cf,idd]=sort(cf);
-        cx=cx(idd,:);
-                
-        % Check if the population degeneration occured
-        [cx, cf, icall]=DimRest(funcHandle, funcHangle_validParams, cx,cf,bl,bu,icall, varargin{:});
-
-        % Get initial derived forcing.        
-        if useDerivedForcing
-            forcingData_tmp = getStochForcingData(varargin{1});    
-            forcingData = forcingData_tmp;
-        else
-            forcingData={};
-        end
-                        
-        % Evolve sub-population igs for nspl steps:
-        for loop=1:nspl
-            % Select simplex by sampling the complex according to a linear
-            % probability distribution            
-            lcs(1) = 1;
-            for k3=2:nps
-                for iter=1:1e9
-                    lpos = 1 + floor(npg+0.5-sqrt((npg+0.5)^2 - npg*(npg+1)*rand));
-                    idx=find(lcs(1:k3-1)==lpos); 
-                    if isempty(idx)&&lpos<npg+1 
-                        break; 
-                    end 
-                end
-                lcs(k3) = lpos;
-            end
-            lcs=sort(lcs);
-
-            % Construct the simplex:
-            s=cx(lcs,:); 
-            sf = cf(lcs);
-
-            %[s,sf,icall, forcingData]=MCCE(funcHandle, funcHangle_validParams, s,sf,bl,bu,icall, useDerivedForcing,  forcingData, varargin{:});
-            [s,sf,icall]=MCCE(funcHandle, funcHangle_validParams, s,sf,bl,bu,icall, varargin{:});
-  
-            % Replace the simplex into the complex;
-            cx(lcs,:) = s;
-            cf(lcs) = sf;
-
-            % Sort the complex;
-            [cf,idx] = sort(cf); cx=cx(idx,:);
-            clear lcs
-
-            % End of Inner Loop for Competitive Evolution of Simplexes
-        end
-
-        % Get final derived forcing
-        if useDerivedForcing
-            forcingData= getStochForcingData(varargin{1});
-        end        
-        
-        % Conduct Gaussian Resampling 
-        %[cx, cf, icall]=GauSamp(funcHandle, funcHangle_validParams, cx,cf,bl_plausible, bu_plausible,icall,  forcingData, varargin{:});
-
-%         % Evaluate each parameter set with the final derived forcing
-%         if useDerivedForcing
-%             for loop=1:size(cx,1)
-%                 updateStochForcingData(varargin{1}, forcingData);
-%                 cf(loop)= feval(funcHandle,cx(loop,:)', varargin{:});                                
-%                 icall = icall + 1;
-%             end            
-%         end
-        
-end
-
-function [x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, iniflg, npt, nopt, icall, varargin)
-
-    bound = bu_plausible - bl_plausible;
-    x=zeros(npt,nopt);
-    parfor i=1:npt
-        while 1
-            x(i,:)=bl_plausible+rand(1,nopt).*bound;
-
-            % Check if the parameters are valid
-            isValid = feval(funcHangle_validParams, x(i,:)', varargin{:});
-
-            if all(isValid)
-                break;
-            end
-        end            
-    end
-
-    if iniflg
-        x(1,:)=x0; 
-    end
-    
-    xf=10000*ones(1,npt);
-    stochDerivedForcingData = getStochForcingData(varargin{1}); 
-    
-    % Evaluate the model without parallelisation to (1) check it works and
-    % (2) set any possible model variables (eg phi cards used for get_h_star
-    xf(1) = feval(funcHandle, x(1,:)', varargin{:});
-        
-    %tic;
-    parfor i=2:npt
-        xf(i) = feval(funcHandle, x(i,:)', varargin{:});
-        icall = icall + 1;
-    end
-    %display(['... parfor run time =',num2str(toc)]);
-
-    % Sort the population in order of increasing function values;
-    [xf,idx]=sort(xf);
-    x=x(idx,:);
-
-    % Reset the input derived forcing
-    if any(~isempty(stochDerivedForcingData))
-        updateStochForcingData(varargin{1}, stochDerivedForcingData);
-    end       
-    
-    
-end
+function [bestx,bestf,icall, exitFlag, exitStatus] = SPUCI(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, bl_phys, bu_phys, ... 
+                                                            maxn, kstop,pcento,peps,ngs,iseed,iniflg, calibGUI_interface_obj, varargin) 
+% This is the Matlab code implementing the SP-UCI algorithm,written by Dr.
+% Wei Chu, 08/2012, based on the SCE Matlab codes written by Dr. Q. Duan
+% 9/2004.
+% 
+% If you have any questions or comments,Please contact Dr. Wei Chu at
+% wchu2@uci.edu
+% 
+% This algorithm has been published in Infomration Sciences and shoud be
+% cited as:
+%
+%   Wei	Chu,	X. Gao and S. Sorooshian,(2011), A new evolutionary search
+%   strategy for global optimization of high-dimensional Problems,
+%   Information Sciences, doi:10.1016/j.ins. 2011.06.024.
+%
+%
+% Definition of input variables:
+%  x0 = the initial parameter array at the start;
+%     = the optimized parameter array at the end
+
+%  f0 = the objective function value corresponding to the initial parameters
+%     = the objective function value corresponding to the optimized parameters
+%  bl = the lower bound of the parameters
+%  bu = the upper bound of the parameters
+%  iseed = the random seed number (for repetetive testing purpose)
+%  iniflg = flag for initial parameter array (=1, included it in initial
+%           population; otherwise, not included)
+%  ngs = number of complexes (sub-populations)
+%  maxn = maximum number of function evaluations allowed during optimization
+%  kstop = maximum number of evolution loops before convergency
+%  percento = the percentage change allowed in kstop loops before
+%  convergency
+%
+% Definition of outputs
+%  bestx = best parameter values at the end of the search.
+%  besft = best objective function value at the end of the search.
+%
+% Additional outputs (edits by Tim Peterson 2016)
+%  exitFlag = 0: did not converge, 1=partial convergence, 2 = converged
+%  exitStatus = statement of why the scheme ended.
+%
+% Additional modifications (Tim Peterson 2017)
+%  - Numerous additions were made to SPUCI.m and MCCE.m to handle derived
+%  forcing data (specifically down-scaled pumping forcing data derived from
+%  a simulated annealing approach). 
+%  - Construction of complexes changed from using randperm() to the
+%  standard method of SCE-UA. This was undertaken to allow the assignment
+%  of the best derived forcing time series to the top 'ngs' solutions. That
+%  is, the derived forcing could be extracted for the best ngs parameter
+%  sets and then the derived forcing applied to each of the other parameter
+%  sets within the complex.
+%  - GauSamp() was commented out. It increased the number of model
+%  evaluations by 'npt' for little identifiable improvement in the
+%  solution.
+%
+
+% Check if derived forcing is to be handled
+stochDerivedForcingData_prior = getStochForcingData(varargin{1});
+useDerivedForcing = false;
+if any(~isempty(stochDerivedForcingData_prior))
+    useDerivedForcing = true;
+end
+
+% Initialization of agorithmic 
+%iseed= 123456
+nopt=length(x0); %dimentions of the problem
+npg=2*nopt+1;%  npg = number of members is a complex
+nps=nopt+1;%  nps = number of members in a simplex
+nspl=nps;%  nspl = number of evolution steps for each complex before shuffling
+if useDerivedForcing
+    nspl=2*nps;%  nspl = number of evolution steps for each complex before shuffling
+end
+npt=npg*ngs;%npt = the total number of members (the population size)
+
+bound = bu_plausible - bl_plausible;% boundary of the feasible space.
+
+% Initialise exist outputs. Tim Peterson 2016
+exitFlag = 0;
+exitStatus = 'Calibration scheme did not start.';
+bestf_ever = inf;
+
+% Initialization of the populaton and calculate their objective function value. 
+[x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, iniflg, npt, nopt, 0, varargin{:});
+
+% Check if the population degeneration occured
+[x, xf, icall]=DimRest(funcHandle, funcHangle_validParams, x,xf,bl_phys,bu_phys,icall, varargin{:});
+
+% Get the initial derived forcing data
+stochDerivedForcingData_best = getStochForcingData(varargin{1});
+
+
+% Sort the population again and record the best and worst points
+[xf,idx]=sort(xf);
+x=x(idx,:);
+bestx=x(1,:); bestf=xf(1);
+worstx=x(npt,:); worstf=xf(npt);
+
+% Computes the normalized geometric range of the parameters
+gnrng=exp(mean(log((max(x)-min(x))./bound)));
+
+disp('The Initial Loop: 0');
+disp(['BESTF  : ' num2str(bestf)]);
+disp(['BESTX  : ' num2str(bestx)]);
+disp(['WORSTF : ' num2str(worstf)]);
+disp(['WORSTX : ' num2str(worstx)]);
+disp(' ');
+
+% Check for convergency;
+if icall >= maxn;
+    disp('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT');
+    disp('ON THE MAXIMUM NUMBER OF TRIALS ');
+    disp(maxn);
+    disp('HAS BEEN EXCEEDED.  SEARCH WAS STOPPED AT TRIAL NUMBER:');
+    disp(icall);
+    disp('OF THE INITIAL LOOP!');
+end;
+
+if gnrng < peps;
+    disp('THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE');
+end;
+
+% Update the diary file
+if ~isempty(calibGUI_interface_obj)
+    updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end    
+end
+
+% Update exist status output. Tim Peterson 2016
+exitStatus = 'Calibration scheme did started.';
+
+% Begin evolution loops:
+criter=[];
+criter_change=1e+5;
+xigs = [];
+bestf_ever=bestf;
+bestx_ever=bestx;
+nloop=0;
+minloops = 2*kstop;
+%isbestf_ever_inkstop = false;
+while nloop<=minloops || (icall<maxn && gnrng>peps && criter_change>pcento)
+    nloop=nloop+1;
+    %%%%%%%%%%%%%%%%%%%%%%%%
+    %idexx=randperm(npt);
+    %%%%%%%%%%%%%%%%%%%%%%%%
+
+    % Create indexes for each complex
+    %or igs = 1: ngs;
+    %    ind{igs} = idexx(npg*(igs-1)+1:npg*igs);
+    %end    
+    
+    % NOTE: SPU-UCI originally used randperm for distribution of the
+    % points to complexes. It is unclear why this was adopted over the
+    % determinsitic partitioning approach of SCE-UA. TIm Peterson.
+    for igs = 1: ngs        
+        k1=1:npg;
+        ind{igs}=(k1-1)*ngs+igs;
+    end
+    
+    % Get the stochastic derived forcing data
+    stochDerivedForcingData_prior = getStochForcingData(varargin{1});
+        
+    % Check if derived forcing is to be handled
+    useDerivedForcing = false;
+    if any(~isempty(stochDerivedForcingData_prior))
+        useDerivedForcing = true;
+    end    
+    
+    % Sort derived forcings by new index complexes    
+    if useDerivedForcing && nloop>1
+        stochDerivedForcingData_new = cell(ngs,1);
+        for igs = 1: ngs;
+            % Get the appropriate derived forcing data for each complex. 
+            stochDerivedForcingData_new{igs} = stochDerivedForcingData{xigs(igs)};
+        end
+        stochDerivedForcingData = stochDerivedForcingData_new;
+        clear stochDerivedForcingData_new
+        
+    end
+
+    %-----------------
+    % Importantly, with the derived forcing changing then objective
+    % function value for each parameter set is most likely to change. 
+    % Therefore, all parameter sets are re-evaluated. Also, the stochastic 
+    % forcing parameters are perturbed. This is done to avoid the stoachstic
+    % forcing data from converging to a local miniumum.  
+    if useDerivedForcing                
+        if ~isempty(xigs) && iscell(stochDerivedForcingData) && length(stochDerivedForcingData)==ngs
+            model = varargin{1};
+            stochDerivedForcingData_sliced = stochDerivedForcingData(xigs);
+            
+            parfor i=1:npt
+                % Assign derived forcing using xigs
+                updateStochForcingData(model, stochDerivedForcingData_sliced{i});
+
+                % Update/perturb the stochastic forcing parameters.
+                %xtmp = updateStochForcingParameters(model, stochDerivedForcingData_sliced{i}, x(i,:)');
+                %x(i,:) = xtmp';
+
+                % Get the new objective function value.
+                xf(i) = feval(funcHandle, x(i,:)', varargin{:});
+            end
+            clear stochDerivedForcingData_sliced;
+        end
+        icall = icall + npt;
+    end       
+    %-----------------
+    
+    % Initialise cell array for cx and cf
+    cx = cell(ngs,1);
+    cf = cell(ngs,1);
+    cicall = zeros(ngs,1);
+    if nloop==1
+     stochDerivedForcingData = cell(ngs,1);
+    end
+    model = varargin{1};
+        
+    % Loop on complexes (sub-populations);
+    parfor igs = 1: ngs
+
+        % Assign derived forcing using xigs
+        if useDerivedForcing && nloop>1
+            updateStochForcingData(model, stochDerivedForcingData{igs});
+        end
+        
+        % This is the major computionation load. It was shifted into a
+        % stand alone function by Tim Peterson to allow parrellisation.
+        [cx{igs}, cf{igs}, cicall(igs), stochDerivedForcingData{igs}] = doComplexEvolution(funcHandle, funcHangle_validParams, ind{igs}, x, xf, ...
+        bl_plausible, bu_plausible, bl_phys,bu_phys, nspl, nps, npg, useDerivedForcing , varargin{:});
+        
+    % End of Loop on Complex Evolution;
+    end
+    %display(['... parfor run time =',num2str(toc)]);
+        
+    % Check if derived forcing is to be handled
+    if any(cellfun(@(x) ~isempty(x), stochDerivedForcingData))
+        useDerivedForcing = true;
+    end
+
+    % Put cx and cf back into the population;
+    for igs = 1: ngs;
+        x(ind{igs},:)=cx{igs};
+        xf(ind{igs})=cf{igs};
+    end
+
+    % Record the igs number for each parameter. 
+    for igs = 1: ngs;
+        xigs(ind{igs})=igs;
+    end
+    
+    % Sum the function calls per complex and add to icall
+    icall = icall + sum(cicall);            
+
+    % Sort the complexes, and record the best and worst points;
+    [xf,idx] = sort(xf); 
+    x=x(idx,:);
+    bestx=x(1,:); 
+    bestf=xf(1);
+    worstx=x(npt,:); 
+    worstf=xf(npt); 
+
+    % Sort xigs numbers using xf order
+    xigs=xigs(idx);
+    
+    % Computes the normalized geometric range of the parameters
+    gnrng=exp(mean(log((max(x)-min(x))./bound)));
+
+    % Update the objective function IF derived forcing is being used. 
+    %-----------------
+    if useDerivedForcing                
+        % Importantly, the best solution from each complex may not be the
+        % actual best (or may further decline) because it may be been
+        % derived using a stochastic forcing time-series from ealier within
+        % the evolutionary loop, rather than the stocastic forcing from the
+        % end of the loop. Therefore, the best ngs*2 points agre re-derived
+        % below (note, only ngs*2 are derived rather than all simply to
+        % reduce the computational load).
+        model = varargin{1};
+        stochDerivedForcingData_sliced = stochDerivedForcingData(xigs);
+        parfor i=1:(ngs*2)
+            % Assign derived forcing using xigs
+            updateStochForcingData(model, stochDerivedForcingData_sliced{i});
+            xf(i) = feval(funcHandle, x(i,:)', varargin{:});
+        end
+        clear stochDerivedForcingData_sliced;
+        icall = icall + npt;
+        
+        % Re-sort the complexes, and re-record the best and worst points;
+        [xf,idx] = sort(xf);
+        x=x(idx,:);
+        bestx=x(1,:);
+        bestf=xf(1);
+        worstx=x(npt,:);
+        worstf=xf(npt);
+
+        % Re-sort xigs numbers using xf order
+        xigs=xigs(idx);
+
+        % Add the best ever solution to the model, or else if this loop produced the best 
+        % ever solution, then update the best ever data.
+        if bestf < bestf_ever %|| abs(bestf-bestf_ever)<=pcento
+            
+            % Set the best derived forcing from this evolution to the model.
+            updateStochForcingData(varargin{1}, stochDerivedForcingData{xigs(1)});                
+                        
+            bestf_ever = bestf;
+            bestx_ever = bestx;
+            stochDerivedForcingData_best = stochDerivedForcingData{xigs(1)};                                             
+        else               
+            
+            % Insert the best ever forcing series into a random complex
+            % (except the best complex)
+            ind_rand_ngs = xigs(1);
+            while ind_rand_ngs == xigs(1)
+                ind_rand_ngs = randi(ngs);
+            end
+            stochDerivedForcingData{ind_rand_ngs}  = stochDerivedForcingData_best;
+            
+            % Build an index to only the parameter sets within complex 'ind_rand'.
+            ind_rand_ngs = find(xigs==ind_rand_ngs);
+            ind_rand_ngs = sort(ind_rand_ngs);
+            
+            % Set the derived forcing from the best ever evolution to the model.
+            updateStochForcingData(varargin{1},stochDerivedForcingData_best);
+            
+            % Recalculate the xf for ONLY complex 'ind_rand'.
+            x_tmp = x(ind_rand_ngs,:);
+            xf_tmp = inf(1,length(ind_rand_ngs));
+            parfor i=1:length(ind_rand_ngs)
+                xf_tmp(i) = feval(funcHandle, x_tmp(i,:)', varargin{:});
+            end
+            xf(ind_rand_ngs) = xf_tmp;
+            icall = icall + length(ind_rand_ngs);
+            
+            
+            % Again, re-sort the complexes, and re-record the best and worst points;
+            [xf,idx] = sort(xf);
+            x=x(idx,:);
+            bestx=x(1,:);
+            bestf=xf(1);
+            worstx=x(npt,:);
+            worstf=xf(npt);
+            
+            % Again, re-sort xigs numbers using xf order
+            xigs=xigs(idx);
+            
+            % Check if there is a new bestf.
+            if bestf < bestf_ever
+                bestf_ever = bestf;
+                bestx_ever = bestx;
+            end
+        end
+    else 
+        if bestf < bestf_ever
+            bestf_ever = bestf;
+            bestx_ever = bestx;
+        end
+    end       
+%-----------------
+
+
+    criter_change = inf;
+    criter=[criter;bestf];        
+%     if useDerivedForcing
+%         isbestf_ever_inkstop = false;
+%     else
+%         isbestf_ever_inkstop = true;
+%     end
+    if (nloop >= kstop)
+        criter_change=abs(criter(nloop)-criter(nloop-kstop+1))*100;
+        criter_change=criter_change/mean(abs(criter(nloop-kstop+1:nloop)));
+        
+%         %Check if bestf_ever is within the last kstop iterations.
+%         if any(criter(nloop-kstop+1:nloop)==bestf_ever)
+%           isbestf_ever_inkstop = true;
+%         end
+    end
+    
+    disp(['LOOP       : ' num2str(nloop) '  - Trial - ' num2str(icall)]);
+    if useDerivedForcing   
+        disp(['BESTF loop : ' num2str(bestf,8)]);
+        disp(['BESTF ever : ' num2str(bestf_ever,8)]);
+    else
+        disp(['BESTF      : ' num2str(bestf,8)]);
+    end
+    disp(['BESTX      : ' num2str(bestx)]);
+    disp(['WORSTF     : ' num2str(worstf,8)]);
+    disp(['WORSTX     : ' num2str(worstx)]);
+    disp(['F convergence val.: ',num2str(criter_change,8)]);
+    disp(['X convergence val.: ',num2str(gnrng,8)]);
+    disp(' ');    
+    
+    % Check for convergency;
+    exitCalib = false;
+    if icall >= maxn;
+        disp('*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT');
+        disp(['ON THE MAXIMUM NUMBER OF TRIALS ' num2str(maxn) ' HAS BEEN EXCEEDED!']);        
+    end;
+    
+    % If stochastic derived forcing is used, then assess if the method
+    % should be refined (ie for pumpingRate_SAestimation() the
+    % time-step reduced). This feature was added to allow the
+    % downscaling to operate an increasingly fine temporal scales. 
+    if useDerivedForcing && ((criter_change < pcento) || gnrng < peps)
+        refineStochForcingMethod = true;
+        finishedStochForcing = updateStochForcingData(varargin{1},stochDerivedForcingData_best, refineStochForcingMethod);
+        
+        % Get the stochastic forcing in case it has been updated in the
+        % prior step.
+        stochDerivedForcingData_best = getStochForcingData(varargin{1});
+        
+        % If the stochastic forcing has been refined, then repeat the
+        % calibration using the refined apprach eg a finer downscaling
+        % timestep. Considering that the parameter sets are probably very
+        % clustered, then if the stochastic forcing has not finished re-run
+        % but with randomised parameters AND the best solution yet.
+        if ~finishedStochForcing 
+            disp('');
+            disp('NOTE: The derived stochastic forcing is being refined. The calibration is being re-run.');
+            
+            % Put the nest stoch forcing into each cell of the cell array of stoch
+            % forcing for each complex.
+            for igs = 1: ngs
+                % Get the appropriate derived forcing data for each complex. 
+                stochDerivedForcingData{igs} = stochDerivedForcingData_best;
+            end            
+            
+            % Initialization of the populaton and calculate their objective
+            % function value AND add the best parameter set so far.
+            %bestf_ever = inf;
+            [x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, bestx_ever,bl_plausible, bu_plausible, true, npt, nopt, icall, varargin{:});            
+
+            % Check if the population degeneration occured
+            [x, xf, icall]=DimRest(funcHandle, funcHangle_validParams, x,xf,bl_phys,bu_phys,icall, varargin{:});
+            
+            % Initialise convergence measures.
+            criter_change =  inf;
+            gnrng = inf;
+            criter = criter(end);
+            nloop=1;
+            %doReEval_DerivedForcing=false;
+        else
+            disp('NOTE: The derived stochastic forcing has reached the user set resolution.');
+        end
+    end    
+        
+
+    if criter_change < pcento
+        disp(['THE BEST POINT HAS IMPROVED IN LAST ' num2str(kstop) ' LOOPS BY ', ...
+            'LESS THAN THE THRESHOLD ' num2str(pcento) '%']);
+        disp('CONVERGENCY HAS ACHIEVED BASED ON OBJECTIVE FUNCTION CRITERIA!!!')
+    end;
+    
+    if gnrng < peps;
+        disp('THE POPULATION HAS CONVERGED TO A PRESPECIFIED SMALL PARAMETER SPACE');
+    end;
+    
+    % Update the diary file
+    if ~isempty(calibGUI_interface_obj)
+        updatetextboxFromDiary(calibGUI_interface_obj);
+        [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+        if doQuit
+            exitFlag = exitFlagQuit;
+            exitStatus = exitStatusQuit;
+            return;
+        end
+    end    
+    
+    % End of the Outer Loops
+end;
+
+% Apply best model settings
+model = varargin{1};
+if useDerivedForcing                
+    updateStochForcingData(model, stochDerivedForcingData_best);
+end
+bestf = feval(funcHandle, bestx_ever', varargin{:});
+
+disp(['SEARCH WAS STOPPED AT TRIAL NUMBER: ' num2str(icall)]);
+disp(['NORMALIZED GEOMETRIC RANGE = ' num2str(gnrng)]);
+disp(['THE BEST POINT HAS IMPROVED IN LAST ' num2str(kstop) ' LOOPS BY ', ...
+    num2str(criter_change) '%']);
+
+% Update exist status output. Tim Peterson 2016
+if icall>=maxn 
+    exitFlag=1;
+    exitStatus = 'Insufficient maximum number of model evaluations for convergence.';
+elseif gnrng<peps && criter_change>pcento;
+    exitFlag=1;
+    exitStatus = ['Only parameter convergence (obj func. convergence = ',num2str(criter_change),' & threshold = ',num2str(pcento),') achieved in ', num2str(icall),' function evaluations.'];
+elseif gnrng>peps && criter_change<pcento;
+    exitFlag=1;
+    exitStatus = ['Only objective function convergence achieved (param. convergence = ',num2str(gnrng),' & threshold = ',num2str(peps),') in ', num2str(icall),' function evaluations.'];
+elseif gnrng<peps && criter_change<pcento;
+    exitFlag=2;
+    exitStatus = ['Parameter and objective function convergence achieved in ', num2str(icall),' function evaluations.'];
+end
+
+% Update the diary file
+if ~isempty(calibGUI_interface_obj)
+    updatetextboxFromDiary(calibGUI_interface_obj);
+    [doQuit, exitFlagQuit, exitStatusQuit] = getCalibrationQuitState(calibGUI_interface_obj);
+    if doQuit
+        exitFlag = exitFlagQuit;
+        exitStatus = exitStatusQuit;
+        return;
+    end    
+end
+
+end
+
+function [cx, cf, icall, forcingData] = doComplexEvolution(funcHandle, funcHangle_validParams, ind, x, xf, bl_plausible, bu_plausible, bl, bu, nspl, nps, npg, useDerivedForcing, varargin)
+
+        % initialse count of local function calls
+        icall = 0;
+                
+        % Partition the population into complexes (sub-populations);
+        cx=x(ind,:);
+        cf=xf(ind);                 
+        
+        % Sort population
+        [cf,idd]=sort(cf);
+        cx=cx(idd,:);
+                
+        % Check if the population degeneration occured
+        [cx, cf, icall]=DimRest(funcHandle, funcHangle_validParams, cx,cf,bl,bu,icall, varargin{:});
+
+        % Get initial derived forcing.        
+        if useDerivedForcing
+            forcingData_tmp = getStochForcingData(varargin{1});    
+            forcingData = forcingData_tmp;
+        else
+            forcingData={};
+        end
+                        
+        % Evolve sub-population igs for nspl steps:
+        for loop=1:nspl
+            % Select simplex by sampling the complex according to a linear
+            % probability distribution            
+            lcs(1) = 1;
+            for k3=2:nps
+                for iter=1:1e9
+                    lpos = 1 + floor(npg+0.5-sqrt((npg+0.5)^2 - npg*(npg+1)*rand));
+                    idx=find(lcs(1:k3-1)==lpos); 
+                    if isempty(idx)&&lpos<npg+1 
+                        break; 
+                    end 
+                end
+                lcs(k3) = lpos;
+            end
+            lcs=sort(lcs);
+
+            % Construct the simplex:
+            s=cx(lcs,:); 
+            sf = cf(lcs);
+
+            %[s,sf,icall, forcingData]=MCCE(funcHandle, funcHangle_validParams, s,sf,bl,bu,icall, useDerivedForcing,  forcingData, varargin{:});
+            [s,sf,icall]=MCCE(funcHandle, funcHangle_validParams, s,sf,bl,bu,icall, varargin{:});
+  
+            % Replace the simplex into the complex;
+            cx(lcs,:) = s;
+            cf(lcs) = sf;
+
+            % Sort the complex;
+            [cf,idx] = sort(cf); cx=cx(idx,:);
+            clear lcs
+
+            % End of Inner Loop for Competitive Evolution of Simplexes
+        end
+
+        % Get final derived forcing
+        if useDerivedForcing
+            forcingData= getStochForcingData(varargin{1});
+        end        
+        
+        % Conduct Gaussian Resampling 
+        %[cx, cf, icall]=GauSamp(funcHandle, funcHangle_validParams, cx,cf,bl_plausible, bu_plausible,icall,  forcingData, varargin{:});
+
+%         % Evaluate each parameter set with the final derived forcing
+%         if useDerivedForcing
+%             for loop=1:size(cx,1)
+%                 updateStochForcingData(varargin{1}, forcingData);
+%                 cf(loop)= feval(funcHandle,cx(loop,:)', varargin{:});                                
+%                 icall = icall + 1;
+%             end            
+%         end
+        
+end
+
+function [x,xf, icall] = initialisePopulation(funcHandle, funcHangle_validParams, x0,bl_plausible, bu_plausible, iniflg, npt, nopt, icall, varargin)
+
+    bound = bu_plausible - bl_plausible;
+    x=zeros(npt,nopt);
+    parfor i=1:npt
+        while 1
+            x(i,:)=bl_plausible+rand(1,nopt).*bound;
+
+            % Check if the parameters are valid
+            isValid = feval(funcHangle_validParams, x(i,:)', varargin{:});
+
+            if all(isValid)
+                break;
+            end
+        end            
+    end
+
+    if iniflg
+        x(1,:)=x0; 
+    end
+    
+    xf=10000*ones(1,npt);
+    stochDerivedForcingData = getStochForcingData(varargin{1}); 
+    
+    % Evaluate the model without parallelisation to (1) check it works and
+    % (2) set any possible model variables (eg phi cards used for get_h_star
+    xf(1) = feval(funcHandle, x(1,:)', varargin{:});
+        
+    %tic;
+    parfor i=2:npt
+        xf(i) = feval(funcHandle, x(i,:)', varargin{:});
+        icall = icall + 1;
+    end
+    %display(['... parfor run time =',num2str(toc)]);
+
+    % Sort the population in order of increasing function values;
+    [xf,idx]=sort(xf);
+    x=x(idx,:);
+
+    % Reset the input derived forcing
+    if any(~isempty(stochDerivedForcingData))
+        updateStochForcingData(varargin{1}, stochDerivedForcingData);
+    end       
+    
+    
+end
diff --git a/algorithms/calibration/Utilities/InterPointDistanceMatrix/demo_ipdm.m b/algorithms/calibration/Utilities/InterPointDistanceMatrix/demo_ipdm.m
index a324bc3..230a498 100644
--- a/algorithms/calibration/Utilities/InterPointDistanceMatrix/demo_ipdm.m
+++ b/algorithms/calibration/Utilities/InterPointDistanceMatrix/demo_ipdm.m
@@ -1,149 +1,149 @@
-%{
-
-Demo for ipdm.m (Inter-Point Distance Matrix)
-
-John D'Errico
-e-mail: woodchips@rochester.rr.com
-
-%}
-
-%% A complete (internal) inter-point distance matrix.
-
-% Each point is taken as one row of the input, so each column is a
-% dimension. There will be a 5x5 array of interpoint distances between
-% the points in a 5x2 set of data. Note that the diagonal elements in
-% this matrix will be zero, since they describe the distance from a
-% point to itself.
-A = randn(5,2);
-d = ipdm(A)
-
-%% Distances may be in any number of dimensions, even 1-d.
-A = rand(6,1);
-d = ipdm(A)
-
-%% Or in very many dimensions.
-A = rand(5,1000);
-d = ipdm(A)
-
-%% The default metric used to compute the distance is the 2-norm, or Euclidean norm.
-A = rand(3,2);
-d = ipdm(A)
-
-%% The 1-norm is also available as an option.
-
-% The 1-norm is sometimes known as the city block norm. Of course,
-% the 1-norm is just the sum of absolute values, so it is the total
-% distance one would travel if constrained to move only along
-% "streets" parallel to the x and y axes.
-
-% Options are passed into ipdm using property/value pairs
-d = ipdm(A,'metric',1)
-
-%% The infinity norm is an option too.
-
-% It is the maximum difference in any dimension. We can think
-% of the infinity norm as the limit of a p-norm as p --> inf
-d = ipdm(A,'metric',inf)
-
-%% The 0-norm is not really a valid norm, but I've included it anyway.
-
-% Its the smallest difference in any dimension. Why is it not a valid
-% norm? You can have two widely distinct points with a "0-norm" of 0,
-% as long as they exactly match in any one dimension. You can also
-% look at the 0-norm as the limit of a p-norm, as p --> 0 from above.
-
-% Properties can be shortened, and capitalization is ignored.
-d = ipdm(A,'Met',0)
-
-%% Inter-point distances may between two sets of points.
-
-% Of course, the diagonal elements will no longer be expected to be zero.
-A = randn(10,2);
-B = randn(3,2);
-d = ipdm(A,B)
-
-%% You may only want some subset of the distances. The nearest neighbor is a common choice.
-
-% Note that the result is a sparse matrix, to allow you to compute
-% interpoint distance matrices between very large sets of points.
-% When an array is returned, if that array is likely to be a sparse
-% one, I've chosen to generate the array in a sparse format.
-A = rand(7,3);
-B = rand(5,3);
-d = ipdm(A,B,'Subset','nearest')
-
-%% You can return the result as a structure, or a 2-d array
-d = ipdm(A,B,'Subset','nearest','result','struct')
-
-% A structure as the output can sometimes be useful, if that is how you
-% will be using the results anyway.
-[d.rowindex,d.columnindex,d.distance]
-
-%% You can find the single largest distance.
-A = randn(2000,2);
-B = randn(1000,2);
-
-% Logically, the result should be a sparse matrix.
-d = ipdm(A,B,'Subset','largestfew','limit',1)
-
-%% or the k largest distances (here, k = 3)
-
-% find the k = 3 largest distances
-d = ipdm(A,B,'Subset','largestfew','limit',3)
-
-%% Or the k smallest distances (here, k == 5)
-d = ipdm(A,B,'Subset','smallestfew','limit',5)
-
-%% You can find only those distances above a specific limit.
-A = sort(rand(7,1));
-% If an array is returned, then I fill those below the limit with -inf
-d = ipdm(A,'Subset','Minimum','limit',0.5)
-
-% If a structure is returned, then only the pairs beyond the specified
-% limit are included.
-d = ipdm(A,'Subset','Minimum','limit',0.5,'result','struct')
-
-%% You can also limit the maximum distance found.
-A = randn(10,2);
-B = randn(4,2);
-% Here the other elements are filled with +inf
-d = ipdm(A,B,'Subset','Max','limit',1.5,'metric',inf)
-
-%% Compute only the 1000 smallest distances between a large pair of arrays
-% When the arrays are too large, computing the entire array may not fit
-% entirely into memory. ipdm is smart enough to break the problem up to
-% accomplish the task anyway. In this example, the complete interpoint
-% distance matrix would have required roughly 800 megabytes of RAM to
-% store. This would have exceeded the RAM that I have available on this
-% computer, yet I only wanted the 1000 smallest elements in the end.
-A = rand(10000,2);
-B = rand(10000,2);
-d = ipdm(A,B,'sub','smallestfew','lim',1000,'res','a');
-spy(d)
-
-%% Nearest neighbour is quite efficient in one dimension
-% You don't want to compute the entire interpoint distance matrix,
-% if you only need the nearest neighbors.
-A = rand(100000,1);
-tic,d = ipdm(A,'subset','nearest','result','struct');toc
-d
-
-%% ipdm uses bsxfun where that is possible.
-
-% Older releases of Matlab did not have bsxfun, so I check first to see
-% if this function exists. If it does exist in your release, ipdm can
-% run faster and be more efficient in its use of memory.
-
-% The ipdm code also attempts to use memory in an efficient way, so that
-% very large distance matrices are processed in chunks. I try to minimize
-% the creation of huge intermediate arrays when only a smaller subset of
-% the full distance matrix is desired. The user can control the size of
-% the chunks with the ChunkSize property.
-A = rand(5000,1);
-
-% The default ChunkSize is 2^25
-tic,d = ipdm(A,'Subset','min','limit',0.99,'result','struct');toc
-
-% Here, only 1 megabyte chunks will be processed at any time.
-tic,d = ipdm(A,'Subset','min','limit',0.99,'result','struct','chunksize',2^20);toc
-
+%{
+
+Demo for ipdm.m (Inter-Point Distance Matrix)
+
+John D'Errico
+e-mail: woodchips@rochester.rr.com
+
+%}
+
+%% A complete (internal) inter-point distance matrix.
+
+% Each point is taken as one row of the input, so each column is a
+% dimension. There will be a 5x5 array of interpoint distances between
+% the points in a 5x2 set of data. Note that the diagonal elements in
+% this matrix will be zero, since they describe the distance from a
+% point to itself.
+A = randn(5,2);
+d = ipdm(A)
+
+%% Distances may be in any number of dimensions, even 1-d.
+A = rand(6,1);
+d = ipdm(A)
+
+%% Or in very many dimensions.
+A = rand(5,1000);
+d = ipdm(A)
+
+%% The default metric used to compute the distance is the 2-norm, or Euclidean norm.
+A = rand(3,2);
+d = ipdm(A)
+
+%% The 1-norm is also available as an option.
+
+% The 1-norm is sometimes known as the city block norm. Of course,
+% the 1-norm is just the sum of absolute values, so it is the total
+% distance one would travel if constrained to move only along
+% "streets" parallel to the x and y axes.
+
+% Options are passed into ipdm using property/value pairs
+d = ipdm(A,'metric',1)
+
+%% The infinity norm is an option too.
+
+% It is the maximum difference in any dimension. We can think
+% of the infinity norm as the limit of a p-norm as p --> inf
+d = ipdm(A,'metric',inf)
+
+%% The 0-norm is not really a valid norm, but I've included it anyway.
+
+% Its the smallest difference in any dimension. Why is it not a valid
+% norm? You can have two widely distinct points with a "0-norm" of 0,
+% as long as they exactly match in any one dimension. You can also
+% look at the 0-norm as the limit of a p-norm, as p --> 0 from above.
+
+% Properties can be shortened, and capitalization is ignored.
+d = ipdm(A,'Met',0)
+
+%% Inter-point distances may between two sets of points.
+
+% Of course, the diagonal elements will no longer be expected to be zero.
+A = randn(10,2);
+B = randn(3,2);
+d = ipdm(A,B)
+
+%% You may only want some subset of the distances. The nearest neighbor is a common choice.
+
+% Note that the result is a sparse matrix, to allow you to compute
+% interpoint distance matrices between very large sets of points.
+% When an array is returned, if that array is likely to be a sparse
+% one, I've chosen to generate the array in a sparse format.
+A = rand(7,3);
+B = rand(5,3);
+d = ipdm(A,B,'Subset','nearest')
+
+%% You can return the result as a structure, or a 2-d array
+d = ipdm(A,B,'Subset','nearest','result','struct')
+
+% A structure as the output can sometimes be useful, if that is how you
+% will be using the results anyway.
+[d.rowindex,d.columnindex,d.distance]
+
+%% You can find the single largest distance.
+A = randn(2000,2);
+B = randn(1000,2);
+
+% Logically, the result should be a sparse matrix.
+d = ipdm(A,B,'Subset','largestfew','limit',1)
+
+%% or the k largest distances (here, k = 3)
+
+% find the k = 3 largest distances
+d = ipdm(A,B,'Subset','largestfew','limit',3)
+
+%% Or the k smallest distances (here, k == 5)
+d = ipdm(A,B,'Subset','smallestfew','limit',5)
+
+%% You can find only those distances above a specific limit.
+A = sort(rand(7,1));
+% If an array is returned, then I fill those below the limit with -inf
+d = ipdm(A,'Subset','Minimum','limit',0.5)
+
+% If a structure is returned, then only the pairs beyond the specified
+% limit are included.
+d = ipdm(A,'Subset','Minimum','limit',0.5,'result','struct')
+
+%% You can also limit the maximum distance found.
+A = randn(10,2);
+B = randn(4,2);
+% Here the other elements are filled with +inf
+d = ipdm(A,B,'Subset','Max','limit',1.5,'metric',inf)
+
+%% Compute only the 1000 smallest distances between a large pair of arrays
+% When the arrays are too large, computing the entire array may not fit
+% entirely into memory. ipdm is smart enough to break the problem up to
+% accomplish the task anyway. In this example, the complete interpoint
+% distance matrix would have required roughly 800 megabytes of RAM to
+% store. This would have exceeded the RAM that I have available on this
+% computer, yet I only wanted the 1000 smallest elements in the end.
+A = rand(10000,2);
+B = rand(10000,2);
+d = ipdm(A,B,'sub','smallestfew','lim',1000,'res','a');
+spy(d)
+
+%% Nearest neighbour is quite efficient in one dimension
+% You don't want to compute the entire interpoint distance matrix,
+% if you only need the nearest neighbors.
+A = rand(100000,1);
+tic,d = ipdm(A,'subset','nearest','result','struct');toc
+d
+
+%% ipdm uses bsxfun where that is possible.
+
+% Older releases of Matlab did not have bsxfun, so I check first to see
+% if this function exists. If it does exist in your release, ipdm can
+% run faster and be more efficient in its use of memory.
+
+% The ipdm code also attempts to use memory in an efficient way, so that
+% very large distance matrices are processed in chunks. I try to minimize
+% the creation of huge intermediate arrays when only a smaller subset of
+% the full distance matrix is desired. The user can control the size of
+% the chunks with the ChunkSize property.
+A = rand(5000,1);
+
+% The default ChunkSize is 2^25
+tic,d = ipdm(A,'Subset','min','limit',0.99,'result','struct');toc
+
+% Here, only 1 megabyte chunks will be processed at any time.
+tic,d = ipdm(A,'Subset','min','limit',0.99,'result','struct','chunksize',2^20);toc
+
diff --git a/algorithms/calibration/Utilities/InterPointDistanceMatrix/ipdm.m b/algorithms/calibration/Utilities/InterPointDistanceMatrix/ipdm.m
index 450acf3..d227b94 100644
--- a/algorithms/calibration/Utilities/InterPointDistanceMatrix/ipdm.m
+++ b/algorithms/calibration/Utilities/InterPointDistanceMatrix/ipdm.m
@@ -1,1155 +1,1155 @@
-function d = ipdm(data1,varargin)
-% ipdm: Inter-Point Distance Matrix
-% usage: d = ipdm(data1)
-% usage: d = ipdm(data1,data2)
-% usage: d = ipdm(data1,prop,value)
-% usage: d = ipdm(data1,data2,prop,value)
-%
-% Arguments: (input)
-%  data1 - array of data points, each point is one row. p dimensional
-%          data will be represented by matrix with p columns.
-%          If only data1 is provided, then the distance matrix
-%          is computed between all pairs of rows of data1.
-%
-%          If your data is one dimensional, it MUST form a column
-%          vector. A row vector of length n will be interpreted as
-%          an n-dimensional data set.
-%
-%  data2 - second array, supplied only if distances are to be computed
-%          between two sets of points.
-%
-%
-% Class support: data1 and data2 are assumed to be either
-% single or double precision. I have not tested this code to
-% verify its success on integer data of any class.
-%
-%
-% Additional parameters are expected to be property/value pairs.
-% Property/value pairs are pairs of arguments, the first of which
-% (properties) must always be a character string. These strings
-% may be shortened as long as the shortening is unambiguous.
-% Capitalization is ignored. Valid properties for ipdm are:
-%
-%  'Metric', 'Subset', 'Limit', 'Result'
-%
-%  'Metric' - numeric flag - defines the distance metric used
-%          metric = 2 --> (DEFAULT) Euclidean distance = 2-norm
-%                         The standard distance metric.
-%
-%          metric = 1 --> 1-norm = sum of absolute differences
-%                         Also sometimes known as the "city block
-%                         metric", since this is the sum of the
-%                         differences in each dimension.
-%
-%          metric = inf --> infinity-norm = maximum difference
-%                         over all dimensions. The name refers
-%                         to the limit of the p-norm, as p
-%                         approaches infinity.
-%
-%          metric = 0 --> minimum difference over all dimensions.
-%                         This is not really a useful norm in
-%                         practice.
-%
-%          Note: while other distance metrics exist, IMHO, these
-%          seemed to be the common ones.
-%
-%
-%  'Result' - A string variable that denotes the style of returned
-%          result. Valid result types are 'Array', 'Structure'.
-%          Capitalization is ignored, and the string may be
-%          shortened if you wish.
-%
-%          result = 'Array' --> (DEFAULT) A matrix of all
-%                         interpoint distances will be generated.
-%                         This array may be large. If this option
-%                         is specified along with a minimum or
-%                         maximum value, then those elements above
-%                         or below the limiting values will be
-%                         set as -inf or +inf, as appropriate.
-%
-%                         When any of 'LargestFew', 'SmallestFew',
-%                         or 'NearestNeighbor' are set, then the
-%                         resulting array will be a sparse matrix
-%                         if 'array' is specified as the result.
-%
-%          result = 'Structure' --> A list of all computed distances,
-%                         defined as a structure. This structure
-%                         will have fields named 'rowindex',
-%                         'columnindex', and 'distance'.
-%
-%                         This option will be useful when a subset
-%                         criterion for the distances has been
-%                         specified, since then the distance matrix
-%                         may be very sparsely populated. Distances
-%                         for pairs outside of the criterion will
-%                         not be returned.
-%
-%
-%  'Subset' - Character string, any of:
-%
-%          'All', 'Maximum', 'Minimum', 'LargestFew', 'SmallestFew',
-%          'NearestNeighbor', 'FarthestNeighbor', or empty
-%
-%          Like properties, capitalization is ignored here, and
-%          any unambiguous shortening of the word is acceptable.
-%
-%          DEFAULT = 'All'
-%
-%          Some interpoint distance matrices can be huge. Often
-%          these matrices are too large to be fully retained in
-%          memory, yet only the pair of points with the largest
-%          or smallest distance may be needed. When only some
-%          subset of the complete set of distances is of interest,
-%          these options allow you to specify which distances will
-%          be returned.
-%
-%          If 'result' is defined to be an array, then a sparse
-%          matrix will be returned for the 'LargestFew', 'SmallestFew',
-%          'NearestNeighbor', and 'FarthestNeighbor' subset classes.
-%          'Minimum' and 'Maximum' will yield full matrices by
-%          default. If a structure is specified, then only those
-%          elements which have been identified will be returned.
-%
-%          Where a subset is specified, its limiting value is
-%          specified by the 'Limit' property. Call that value k.
-%
-%
-%          'All' -->     (DEFAULT) Return all interpoint distances
-%
-%          'Minimum' --> Only look for those distances above
-%                        the cutoff k. All other distances will
-%                        be returned as -inf.
-%
-%          'Maximum' --> Only look for those distances below
-%                        the cutoff k. All other distances will
-%                        be returned as +inf.
-%
-%          'SmallestFew' --> Only return the subset of the k
-%                        smallest distances. Where only one data
-%                        set is provided, only the upper triangle
-%                        of the inter-point distance matrix will
-%                        be generated since that matrix is symmetric.
-%
-%          'LargestFew' --> Only return the subset of the k
-%                        largest distances. Where only one data
-%                        set is provided, only the upper triangle
-%                        of the inter-point distance matrix will
-%                        be generated since that matrix is symmetric.
-%
-%          'NearestNeighbor' --> Only return the single nearest
-%                        neighbor in data2 to each point in data1.
-%                        No limiting value is required for this
-%                        option. If multiple points have the same
-%                        nearest distance, then return the first
-%                        such point found. With only one input set,
-%                        a point will not be its own nearest
-%                        neighbor.
-%
-%                        Note that exact replicates in a single set
-%                        will cause problems, since a sparse matrix
-%                        is returned by default. Since they will have
-%                        a zero distance, they will not show up in
-%                        the sparse matrix. A structure return will
-%                        show those points as having a zero distance
-%                        though.
-%
-%          'FarthestNeighbor' --> Only return the single farthest
-%                        neighbor to each point. No limiting value
-%                        is required for this option. If multiple
-%                        points have the same farthest distance,
-%                        then return the first such point found.
-%
-%
-%  'Limit' - scalar numeric value or []. Used only when some
-%           Subset is specified.
-%
-%          DEFAULT = []
-%
-%
-%  'ChunkSize' - allows a user with lower RAM limits
-%          to force the code to only grab smaller chunks of RAM
-%          at a time (where possible). This parameter is specified
-%          in bytes of RAM. The default is 32 megabytes, or 2^22
-%          elements in any piece of the distance matrix. Only some
-%          options will break the problem into chunks, thus as long
-%          as a full matrix is expected to be returned, there seems
-%          no reason to break the problem up into pieces.
-%
-%          DEFAULT = 2^25
-%
-%
-% Arguments: (output)
-%  d     - array of interpoint distances, or a struct wth the
-%          fields {'rowindex', 'columnindex', 'distance'}.
-%
-%          d(i,j) represents the distance between point i
-%          (from data1) and point j (from data2).
-%
-%          If only one (n1 x p) array is supplied, then d will
-%          be an array of size == [n1,n1].
-%
-%          If two arrays (of sizes n1 x p and n2 x p) then d
-%          will be an array of size == [n1,n2].
-%
-%
-% Efficiency considerations:
-%  Where possible, this code will use bsxfun to compute its
-%  distances.
-%
-%
-% Example:
-%  Compute the interpoint distances between all pairs of points
-%  in a list of 5 points, in 2 dimensions and using Euclidean
-%  distance as the distance metric.
-%
-%  A = randn(5,2);
-%  d = ipdm(A,'metric',2)
-%  d =
-%            0       2.3295       3.2263       2.0263       2.8244
-%       2.3295            0       1.1485      0.31798       1.0086
-%       3.2263       1.1485            0       1.4318       1.8479
-%       2.0263      0.31798       1.4318            0       1.0716
-%       2.8244       1.0086       1.8479       1.0716            0
-%
-% (see the demo file for many other examples)
-%
-% See also: pdist
-%
-% Author: John D'Errico
-% e-mail: woodchips@rochester.rr.com
-% Release: 1.0
-% Release date: 2/26/08
-
-% Default property values
-params.Metric = 2;
-params.Result = 'array';
-params.Subset = 'all';
-params.Limit = [];
-params.ChunkSize = 2^25;
-
-% untangle the arguments
-if nargin<1
-  % if called with no arguments, then the user probably
-  % needs help. Give it to them.
-  help ipdm
-  return
-end
-
-% were two sets of data provided?
-pvpairs = {};
-if nargin==1
-  % only 1 set of data provided
-  dataflag = 1;
-  data2 = [];
-else
-  if ischar(varargin{1})
-    dataflag = 1;
-    data2 = [];
-    pvpairs = varargin;
-  else
-    dataflag = 2;
-    data2 = varargin{1};
-    if nargin>2
-      pvpairs = varargin(2:end);
-    end
-  end
-end
-
-% get data sizes for later
-[n1,dim] = size(data1);
-if dataflag == 2
-  n2 = size(data2,1);
-end
-
-% Test the class of the input variables
-if ~(isa(data1,'double') || isa(data1,'single')) || ...
-    ((dataflag == 2) && ~(isa(data2,'double') || isa(data2,'single')))
-  error('data points must be either single or double precision variables.')
-end
-
-% do we need to process any property/value pairs?
-if nargin>2
-  params = parse_pv_pairs(params,pvpairs);
-  
-  % check for problems in the properties
-  
-  % was a legal Subset provided?
-  if ~isempty(params.Subset) && ~ischar(params.Subset)
-    error('If provided, ''Subset'' must be character')
-  elseif isempty(params.Subset)
-    params.Subset = 'all';
-  end
-  valid = {'all','maximum','minimum','largestfew','smallestfew', ...
-    'nearestneighbor','farthestneighbor'};
-  ind = find(strncmpi(params.Subset,valid,length(params.Subset)));
-  if (length(ind)==1)
-    params.Subset = valid{ind};
-  else
-    error(['Invalid Subset: ',params.Subset])
-  end
-  
-  % was a limit provided?
-  if ~ismember(params.Subset,{'all','nearestneighbor','farthestneighbor'}) && ...
-      isempty(params.Limit)
-    error('No limit provided, but a Subset that requires a limit value was specified')
-  end
-  % check the limit values for validity
-  if length(params.Limit)>1
-    error('Limit must be scalar or empty')
-  end
-  
-  switch params.Subset
-    case {'largestfew', 'smallestfew'}
-      % must be at least 1, and an integer
-      if (params.Limit<1) || (round(params.Limit)~=params.Limit)
-        error('Limit must be a positive integer for LargestFew or NearestFew')
-      end
-  end
-  
-  % was a legal Result provided?
-  if isempty(params.Result)
-    params.result = 'Array';
-  elseif ~ischar(params.Result)
-    error('If provided, ''Result'' must be character or empty')
-  end
-  valid = {'array','structure'};
-  ind = find(strncmpi(params.Result,valid,length(params.Result)));
-  if (length(ind)==1)
-    params.Result = valid{ind};
-  else
-    error(['Invalid Result: ',params.Subset])
-  end
-
-  % check for the metric
-  if isempty(params.Metric)
-    params.Metric = 2;
-  elseif (length(params.Metric)~=1) || ~ismember(params.Metric,[0 1 2 inf])
-    error('If supplied, ''Metric'' must be a scalar, and one of [0 1 2 inf]')
-  end
-end % if nargin>2
-  
-% If Metric was given as 2, but the dimension is only 1, then it will
-% be slightly faster (and equivalent) to use the 1-norm Metric.
-if (dim == 1) && (params.Metric == 2)
-  params.Metric = 1;
-end
-
-% Can we use bsxfun to compute the interpoint distances?
-% Older Matlab releases will not have bsxfun, but if it is
-% around, it will ne both faster and less of a memory hog.
-params.usebsxfun = (5==exist('bsxfun','builtin'));
-
-% check for dimension mismatch if 2 sets
-if (dataflag==2) && (size(data2,2)~=dim)
-  error('If 2 point sets provided, then both must have the same number of columns')
-end
-
-% Total number of distances to compute, in case I must do it in batches
-if dataflag==1
-  n2 = n1;
-end
-ntotal = n1*n2;
-
-% FINALLY!!! Compute inter-point distances
-switch params.Subset
-  case 'all'
-    % The complete set of interpoint distances. There is no need
-    % to break this into chunks, since we must return all distances.
-    % If that is too much to compute in memory, then it will fail
-    % anyway when we try to store the result. bsxfun will at least
-    % do the computation efficiently.
-    
-    % One set or two?
-    if dataflag == 1
-      d = distcomp(data1,data1,params);
-    else
-        d = distcomp(data1,data2,params);
-    end
-    
-    % Must we return it as a struct?
-    if params.Result(1) == 's'
-      [rind,cind] = ndgrid(1:size(d,1),1:size(d,2));
-      ds.rowindex = rind(:);
-      ds.columnindex = cind(:);
-      ds.distance = d(:);
-      d = ds;
-    end
-    
-  case {'minimum' 'maximum'}
-    % There is no reason to break this into pieces if the result
-    % sill be filled in the end with +/- inf. Only break it up
-    % if the final result is a struct.
-    if ((ntotal*8)<=params.ChunkSize) || (params.Result(1) == 'a')
-      % its small enough to do it all at once
-      
-      % One set or two?
-      if dataflag == 1
-        d = distcomp(data1,data1,params);
-      else
-          d = distcomp(data1,data2,params);
-      end
-      
-      % Must we return it as a struct?
-      if params.Result(1) == 'a'
-        % its an array, fill the unwanted distances with +/- inf
-        if params.Subset(2) == 'i'
-          % minimum
-          d(d<=params.Limit) = -inf;
-        else
-          % maximum
-          d(d>=params.Limit) = +inf;
-        end
-      else
-        % a struct will be returned
-        if params.Subset(2) == 'i'
-          % minimum
-          [dist.rowindex,dist.columnindex] = find(d>=params.Limit);
-        else
-          % maximum
-          [dist.rowindex,dist.columnindex] = find(d<=params.Limit);
-        end
-        dist.distance = d(dist.rowindex + n1*(dist.columnindex-1));
-        d = dist;
-      end
-      
-    else
-      % we need to break this into chunks. This branch
-      % will always return a struct.
-      
-      % this is the number of rows of data1 that we will
-      % process at a time.
-      bs = floor(params.ChunkSize/(8*n2));
-      bs = min(n1,max(1,bs));
-      
-      % Accumulate the result into a cell array. Do it this
-      % way because we don't know in advance how many elements
-      % that we will find satisfying the minimum or maximum
-      % limit specified.
-      accum = cell(0,1);
-      
-      % now loop over the chunks
-      batch = 1:bs;
-      while ~isempty(batch)
-        
-        % One set or two?
-        if dataflag == 1
-          dist = distcomp(data1(batch,:),data1,params);
-        else
-          dist = distcomp(data1(batch,:),data2,params);
-        end
-        
-        % big or small as requested
-        if ('i'==params.Subset(2))
-          % minimum value specified
-          [I,J,V] = find(dist>=params.Limit);
-        else
-          % maximum limit
-          [I,J] = find(dist<=params.Limit);
-          I = I(:);
-          J = J(:);
-          V = dist(I + (J-1)*length(batch));
-          I = I + (batch(1)-1);
-        end
-
-        % and stuff them into the cell structure
-        if ~isempty(V)
-          accum{end+1,1} = [I,J,V(:)]; %#ok
-        end
-
-        % increment the batch
-        batch = batch + bs;
-        if batch(end)>n1
-          batch(batch>n1) = [];
-        end
-
-      end
-
-      % convert the cells into one flat array
-      accum = cell2mat(accum);
-
-      if isempty(accum)
-        d.rowindex = [];
-        d.columnindex = [];
-        d.distance = [];
-      else
-        % we found something
-
-        % sort on the second column, to put them in a reasonable order
-        accum = sortrows(accum,[2 1]);
-
-        d.rowindex = accum(:,1);
-        d.columnindex = accum(:,2);
-        d.distance = accum(:,3);
-      end
-
-    end
-
-  case {'smallestfew' 'largestfew'}
-    % find the k smallest/largest distances. k is
-    % given by params.Limit
-
-    % if only 1 set, params.Limit must be less than n*(n-1)/2
-    if dataflag == 1
-      params.Limit = min(params.Limit,n1*(n1-1)/2);
-    end
-
-    % is this a large problem?
-    if ((ntotal*8) <= params.ChunkSize)
-      % small potatoes
-
-      % One set or two?
-      if dataflag == 1
-        dist = distcomp(data1,data1,params);
-        % if only one data set, set the diagonal and
-        % below that to +/- inf so we don't find it.
-        temp = find(tril(ones(n1,n1),0));
-        if params.Subset(1) == 's'
-          dist(temp) = inf;
-        else
-          dist(temp) = -inf;
-        end
-      else
-        dist = distcomp(data1,data2,params);
-      end
-
-      % sort the distances to find those we need
-      if ('s'==params.Subset(1))
-        % smallestfew
-        [val,tags] = sort(dist(:),'ascend');
-      else
-        % largestfew
-        [val,tags] = sort(dist(:),'descend');
-      end
-      val = val(1:params.Limit);
-      tags = tags(1:params.Limit);
-
-      % recover the row and column index from the linear
-      % index returned by sort in tags.
-      [d.rowindex,d.columnindex] = ind2sub([n1,size(dist,2)],tags);
-
-      % create the matrix as a sparse one or a struct?
-      if params.Result(1)=='a'
-        % its an array, so make the array sparse.
-        d = sparse(d.rowindex,d.columnindex,val,n1,size(dist,2));
-      else
-        % a structure
-        d.distance = val;
-      end
-
-    else
-      % chunks
-
-      % this is the number of rows of data1 that we will
-      % process at a time.
-      bs = floor(params.ChunkSize/(8*n2));
-      bs = min(n1,max(1,bs));
-
-      % We need to find the extreme cases. There are two possible
-      % algorithms, depending on how many total elements we will
-      % search for.
-      % 1. Only a very few total elements.
-      % 2. A relatively large number of total elements, forming
-      %    a significant fraction of the total set.
-      %
-      % Case #1 would suggest to retain params.Limit numberr of
-      % elements from each batch, then at the end, sort them all
-      % to find the best few. Case #2 will result in too many
-      % elements to retain, so we must distinguish between these
-      % alternatives.
-      if (8*params.Limit*n1/bs) <= params.ChunkSize
-        % params.Limit is small enough to fall into case #1.
-
-        % Accumulate the result into a cell array. Do it this
-        % way because we don't know in advance how many elements
-        % that we will find satisfying the minimum or maximum
-        % limit specified.
-        accum = cell(0,1);
-
-        % now loop over the chunks
-        batch = (1:bs)';
-        while ~isempty(batch)
-          % One set or two?
-          if dataflag == 1
-            dist = distcomp(data1(batch,:),data1,params);
-            k = find(tril(ones(length(batch),n2),batch(1)-1));
-            if ('s'==params.Subset(1))
-              dist(k) = inf;
-            else
-              dist(k) = -inf;
-            end
-          else
-            dist = distcomp(data1(batch,:),data2,params);
-          end
-
-          % big or small as requested, keeping only the best
-          % params.Limit number of elements
-          if ('s'==params.Subset(1))
-            % minimum value specified
-            [tags,tags] = sort(dist(:),1,'ascend');
-            tags = tags(1:min(bs,params.Limit));
-            [I,J] = ndgrid(batch,1:n2);
-            ijv = [I(tags),J(tags),dist(tags)];
-          else
-            % maximum limit
-            [tags,tags] = sort(dist(:),1,'descend');
-            tags = tags(1:min(bs,params.Limit));
-            [I,J] = ndgrid(batch,1:n2);
-            ijv = [I(tags),J(tags),dist(tags)];
-          end
-          
-          % catch for the bug when batch ends up as a scalar
-          % in that case, matlab indexing of a vector causes a problem.
-          if numel(batch) == 1
-            ijv = reshape(ijv,[],3);
-          end
-          
-          % and stuff them into the cell structure
-          accum{end+1,1} = ijv; %#ok
-
-          % increment the batch
-          batch = batch + bs;
-          if batch(end)>n1
-            batch(batch>n1) = [];
-          end
-        end
-
-        % convert the cells into one flat array
-        accum = cell2mat(accum);
-
-        % keep only the params.Limit best of those singled out
-        accum = sortrows(accum,3);
-        if ('s'==params.Subset(1))
-          % minimum value specified
-          accum = accum(1:params.Limit,:);
-        else
-          % minimum value specified
-          accum = accum(end + 1 - (1:params.Limit),:);
-        end
-        d.rowindex = accum(:,1);
-        d.columnindex = accum(:,2);
-        d.distance = accum(:,3);
-
-        % create the matrix as a sparse one or a struct?
-        if params.Result(1)=='a'
-          % its an array, so make the array sparse.
-          d = sparse(d.rowindex,d.columnindex,d.distance,n1,size(dist,2));
-        end
-
-      else
-        % params.Limit forces us into the domain of case #2.
-        % Here we cannot retain params.Limit elements from each chunk.
-        % so we will grab each chunk and append it to the best elements
-        % found so far, then filter out the best after each chunk is
-        % done. This may be slower than we want, but its the only way.
-        ijv = zeros(0,3);
-
-        % loop over the chunks
-        batch = (1:bs)';
-        while ~isempty(batch)
-          % One set or two?
-          if dataflag == 1
-            dist = distcomp(data1(batch,:),data1,params);
-            k = find(tril(ones(length(batch),n2),batch(1)-1));
-            if ('s'==params.Subset(1))
-              dist(k) = inf;
-            else
-              dist(k) = -inf;
-            end
-          else
-            dist = distcomp(data1(batch,:),data2,params);
-          end
-
-          [I,J] = ndgrid(batch,1:n2);
-          ijv = [ijv;[I(:),J(:),dist(:)]]; %#ok
-
-          % big or small as requested, keeping only the best
-          % params.Limit number of elements
-          if size(ijv,1) > params.Limit
-            if ('s'==params.Subset(1))
-              % minimum value specified
-              [tags,tags] = sort(ijv(:,3),1,'ascend');
-            else
-              [tags,tags] = sort(ijv(:,3),1,'ascend');
-            end
-            ijv = ijv(tags(1:params.Limit),:);
-          end
-
-          % increment the batch
-          batch = batch + bs;
-          if batch(end)>n1
-            batch(batch>n1) = [];
-          end
-        end
-
-        % They are fully trimmed down. stuff a structure
-        d.rowindex = ijv(:,1);
-        d.columnindex = ijv(:,2);
-        d.distance = ijv(:,3);
-
-        % create the matrix as a sparse one or a struct?
-        if params.Result(1)=='a'
-          % its an array, so make the array sparse.
-          d = sparse(d.rowindex,d.columnindex,d.distance,n1,size(dist,2));
-        end
-
-      end
-
-    end
-    
-  case {'nearestneighbor' 'farthestneighbor'}
-    % find the closest/farthest neighbor for every point
-
-    % is this a large problem? Or a 1-d problem?
-    if dim == 1
-      % its a 1-d nearest/farthest neighbor problem. we can
-      % special case these easily enough, and all the distance
-      % metric options are the same in 1-d.
-
-      % first split it into the farthest versus nearest cases.
-      if params.Subset(1) == 'f'
-        % farthest away
-
-        % One set or two?
-        if dataflag == 1
-          [d2min,minind] = min(data1);
-          [d2max,maxind] = max(data1);
-        else
-          [d2min,minind] = min(data2);
-          [d2max,maxind] = max(data2);
-        end
-
-        d.rowindex = (1:n1)';
-        d.columnindex = repmat(maxind,n1,1);
-        d.distance = repmat(d2max,n1,1);
-
-        % which endpoint was further away?
-        k = abs((data1 - d2min)) >= abs((data1 - d2max));
-        if any(k)
-          d.columnindex(k) = minind;
-          d.distance(k) = d2min;
-        end
-
-      else
-        % nearest. this is mainly a sort and some fussing around.
-        d.rowindex = (1:n1)';
-        d.columnindex = ones(n1,1);
-        d.distance = zeros(n1,1);
-
-        % One set or two?
-        if dataflag == 1
-          % if only one data point, then we are done
-          if n1 == 2
-            % if exactly two data points, its trivial
-            d.columnindex = [2 1];
-            d.distance = repmat(abs(diff(data1)),2,1);
-          elseif n1>2
-            % at least three points. do a sort.
-            [sorted_data,tags] = sort(data1);
-
-            % handle the first and last points separately
-            d.columnindex(tags(1)) = tags(2);
-            d.distance(tags(1)) = sorted_data(2) - sorted_data(1);
-            d.columnindex(tags(end)) = tags(end-1);
-            d.distance(tags(end)) = sorted_data(end) - sorted_data(end-1);
-
-            ind = (2:(n1-1))';
-
-            d1 = sorted_data(ind) - sorted_data(ind-1);
-            d2 = sorted_data(ind+1) - sorted_data(ind);
-
-            k = d1 < d2;
-            d.distance(tags(ind(k))) = d1(k);
-            d.columnindex(tags(ind(k))) = tags(ind(k)-1);
-            k = ~k;
-            d.distance(tags(ind(k))) = d2(k);
-            d.columnindex(tags(ind(k))) = tags(ind(k)+1);
-          end % if n1 == 2
-        else
-          % Two sets of data. still really a sort and some fuss.
-          if n2 == 1
-            % there is only one point in data2
-            d.distance = abs(data1 - data2);
-            % d.columnindex is already set correctly
-          else
-            % At least two points in data2
-            % We need to sort all the data points together, but also
-            % know which points from each set went where. ind12 and
-            % bool12 will help keep track.
-            ind12 = [1:n1,1:n2]';
-            bool12 = [zeros(n1,1);ones(n2,1)];
-            [sorted_data,tags] = sort([data1;data2]);
-
-            ind12 = ind12(tags);
-            bool12 = bool12(tags);
-
-            % where did each point end up after the sort?
-            loc1 = find(~bool12);
-            loc2 = find(bool12);
-
-            % for each point in data1, what is the (sorted) data2
-            % element which appears most nearly to the left of it?
-            cs = cumsum(bool12);
-            leftelement = cs(loc1);
-
-            % any points which fell below the minimum element in data2
-            % will have a zero for the index of the element on their
-            % left. fix this.
-            leftelement = max(1,leftelement);
-
-            % likewise, any point greater than the max in data2 will
-            % have an n2 in left element. this too will be a problem
-            % later, so fix it.
-            leftelement = min(n2-1,leftelement);
-
-            % distance to the left hand element
-            dleft = abs(sorted_data(loc1) - sorted_data(loc2(leftelement)));
-            dright = abs(sorted_data(loc1) - sorted_data(loc2(leftelement+1)));
-
-            % find the points which are closer to the left element in data2
-            k = (dleft < dright);
-            d.distance(ind12(loc1(k))) = dleft(k);
-            d.columnindex(ind12(loc1(k))) = ind12(loc2(leftelement(k)));
-            k = ~k;
-            d.distance(ind12(loc1(k))) = dright(k);
-            d.columnindex(ind12(loc1(k))) = ind12(loc2(leftelement(k)+1));
-
-          end % if n2 == 1
-        end % if dataflag == 1
-      end % if params.Subset(1) == 'f'
-
-      % create the matrix as a sparse one or a struct?
-      if params.Result(1)=='a'
-        % its an array, so make the array sparse.
-        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
-      end
-
-    elseif (ntotal>1000) && (((params.Metric == 0) && (params.Subset(1) == 'n')) || ...
-        ((params.Metric == inf) && (params.Subset(1) == 'f')))
-      % nearest/farthest neighbour in n>1 dimensions, but for an
-      % infinity norm metric. Reduce this to a sequence of
-      % 1-d problems, each of which will be faster in general.
-      % do this only if the problem is moderately large, since
-      % we must overcome the extra overhead of the recursive
-      % calls to ipdm.
-
-      % do the first dimension
-      if dataflag == 1
-        d = ipdm(data1(:,1),data1(:,1),'subset',params.Subset,'metric',params.Metric,'result','struct');
-      else
-        d = ipdm(data1(:,1),data2(:,1),'subset',params.Subset,'metric',params.Metric,'result','struct');
-      end
-
-      % its slightly different for nearest versus farthest here
-      % now, loop over dimensions
-      for i = 2:dim
-        if dataflag == 1
-          di = ipdm(data1(:,i),data1(:,i),'subset',params.Subset,'metric',params.Metric,'result','struct');
-        else
-          di = ipdm(data1(:,i),data2(:,i),'subset',params.Subset,'metric',params.Metric,'result','struct');
-        end
-
-        % did any of the distances change?
-        if params.Metric == 0
-          % the 0 norm, with nearest neighbour, so take the
-          % smallest distance in any dimension.
-          k = d.distance > di.distance;
-        else
-          % inf norm. so take the largest distance across dimensions
-          k = d.distance < di.distance;
-        end
-
-        if any(k)
-          d.distance(k) = di.distance(k);
-          d.columnindex(k) = di.columnindex(k);
-        end
-      end
-
-      % create the matrix as a sparse one or a struct?
-      if params.Result(1)=='a'
-        % its an array, so make the array sparse.
-        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
-      end
-
-    elseif ((ntotal*8) <= params.ChunkSize)
-      % None of the other special cases apply, so do it using brute
-      % force for the small potatoes problem.
-
-      % One set or two?
-      if dataflag == 1
-        dist = distcomp(data1,data1,params);
-      else
-        dist = distcomp(data1,data2,params);
-      end
-
-      % if only one data set and if a nearest neighbor
-      % problem, set the diagonal to +inf so we don't find it.
-      if (dataflag==1) && (n1>1) && ('n'==params.Subset(1))
-        diagind = (1:n1) + (0:n1:(n1^2-1));
-        dist(diagind) = +inf;
-      end
-
-      if ('n'==params.Subset(1))
-        % nearest
-        [val,j] = min(dist,[],2);
-      else
-        % farthest
-        [val,j] = max(dist,[],2);
-      end
-
-      % create the matrix as a sparse one or a struct?
-      if params.Result(1)=='a'
-        % its an array, so make the array sparse.
-        d = sparse((1:n1)',j,val,n1,size(dist,2));
-      else
-        % a structure
-        d.rowindex = (1:n1)';
-        d.columnindex = j;
-        d.distance = val;
-      end
-
-    else
-
-      % break it into chunks
-      bs = floor(params.ChunkSize/(8*n2));
-      bs = min(n1,max(1,bs));
-
-      % pre-allocate the result
-      d.rowindex = (1:n1)';
-      d.columnindex = zeros(n1,1);
-      d.distance = zeros(n1,1);
-
-      % now loop over the chunks
-      batch = 1:bs;
-      while ~isempty(batch)
-
-        % One set or two?
-        if dataflag == 1
-          dist = distcomp(data1(batch,:),data1,params);
-        else
-          dist = distcomp(data1(batch,:),data2,params);
-        end
-
-        % if only one data set and if a nearest neighbor
-        % problem, set the diagonal to +inf so we don't find it.
-        if (dataflag==1) && (n1>1) && ('n'==params.Subset(1))
-          diagind = 1:length(batch);
-          diagind = diagind + (diagind-2+batch(1))*length(batch);
-          dist(diagind) = +inf;
-        end
-
-        % big or small as requested
-        if ('n'==params.Subset(1))
-          % nearest
-          [val,j] = min(dist,[],2);
-        else
-          % farthest
-          [val,j] = max(dist,[],2);
-        end
-
-        % and stuff them into the result structure
-        d.columnindex(batch) = j;
-        d.distance(batch) = val;
-
-        % increment the batch
-        batch = batch + bs;
-        if batch(end)>n1
-          batch(batch>n1) = [];
-        end
-
-      end
-
-      % did we need to return a struct or an array?
-      if params.Result(1) == 'a'
-        % an array. make it a sparse one
-        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
-      end
-
-    end % if dim == 1
-
-end  % switch params.Subset
-
-% End of mainline
-
-% ======================================================
-% begin subfunctions
-% ======================================================
-function d = distcomp(set1,set2,params)
-% Subfunction to compute all distances between two sets of points
-dim = size(set1,2);
-% can we take advantage of bsxfun?
-% Note: in theory, there is no need to loop over the dimensions. We
-% could Just let bsxfun do ALL the work, then wrap a sum around the
-% outside. In practice, this tends to create large intermediate
-% arrays, especially in higher numbers of dimensions. Its also when
-% we might gain here by use of a vectorized code. This will only be
-% a serious gain when the number of points is relatively small and
-% the dimension is large.
-if params.usebsxfun
-  % its a recent enough version of matlab that we can
-  % use bsxfun at all.
-  n1 = size(set1,1);
-  n2 = size(set2,1);
-  if (dim>1) && ((n1*n2*dim)<=params.ChunkSize)
-    % its a small enough problem that we might gain by full
-    % use of bsxfun
-    switch params.Metric
-      case 2
-        d = sum(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim])).^2,3);
-      case 1
-        d = sum(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),3);
-      case inf
-        d = max(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),[],3);
-      case 0
-        d = min(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),[],3);
-    end
-  else
-    % too big, so that the ChunkSize will have been exceeded, or just 1-d
-    if params.Metric == 2
-      d = bsxfun(@minus,set1(:,1),set2(:,1)').^2;
-    else
-      d = abs(bsxfun(@minus,set1(:,1),set2(:,1)'));
-    end
-    for i=2:dim
-      switch params.Metric
-        case 2
-          d = d + bsxfun(@minus,set1(:,i),set2(:,i)').^2;
-        case 1
-          d = d + abs(bsxfun(@minus,set1(:,i),set2(:,i)'));
-        case inf
-          d = max(d,abs(bsxfun(@minus,set1(:,i),set2(:,i)')));
-        case 0
-          d = min(d,abs(bsxfun(@minus,set1(:,i),set2(:,i)')));
-      end
-    end
-  end
-else
-  % Cannot use bsxfun. Sigh. Do things the hard (and slower) way.
-  n1 = size(set1,1);
-  n2 = size(set2,1);
-  if params.Metric == 2
-    % Note: While some people might use a different Euclidean
-    % norm computation based on expanding the square of the
-    % difference of two numbers, that computation is inherantly
-    % inaccurate when implemented in floating point arithmetic.
-    % While it might be faster, I won't use it here. Sorry.
-    d = (repmat(set1(:,1),1,n2) - repmat(set2(:,1)',n1,1)).^2;
-  else
-    d = abs(repmat(set1(:,1),1,n2) - repmat(set2(:,1)',n1,1));
-  end
-  for i=2:dim
-    switch params.Metric
-      case 2
-        d = d + (repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)).^2;
-      case 1
-        d = d + abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1));
-      case inf
-        d = max(d,abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)));
-      case 0
-        d = min(d,abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)));
-    end
-  end
-end
-% if 2 norm, then we must sqrt at the end
-if params.Metric==2
-  d = sqrt(d);
-end
-
-% ==============================================================
-%    end main ipdm
-%    begin included function - parse_pv_pairs
-% ==============================================================
-function params=parse_pv_pairs(params,pv_pairs)
-% parse_pv_pairs: parses sets of property value pairs, allows defaults
-% usage: params=parse_pv_pairs(default_params,pv_pairs)
-%
-% arguments: (input)
-%  default_params - structure, with one field for every potential
-%             property/value pair. Each field will contain the default
-%             value for that property. If no default is supplied for a
-%             given property, then that field must be empty.
-%
-%  pv_array - cell array of property/value pairs.
-%             Case is ignored when comparing properties to the list
-%             of field names. Also, any unambiguous shortening of a
-%             field/property name is allowed.
-%
-% arguments: (output)
-%  params   - parameter struct that reflects any updated property/value
-%             pairs in the pv_array.
-%
-% Example usage:
-% First, set default values for the parameters. Assume we
-% have four parameters that we wish to use optionally in
-% the function examplefun.
-%
-%  - 'viscosity', which will have a default value of 1
-%  - 'volume', which will default to 1
-%  - 'pie' - which will have default value 3.141592653589793
-%  - 'description' - a text field, left empty by default
-%
-% The first argument to examplefun is one which will always be
-% supplied.
-%
-%   function examplefun(dummyarg1,varargin)
-%   params.Viscosity = 1;
-%   params.Volume = 1;
-%   params.Pie = 3.141592653589793
-%
-%   params.Description = '';
-%   params=parse_pv_pairs(params,varargin);
-%   params
-%
-% Use examplefun, overriding the defaults for 'pie', 'viscosity'
-% and 'description'. The 'volume' parameter is left at its default.
-%
-%   examplefun(rand(10),'vis',10,'pie',3,'Description','Hello world')
-%
-% params =
-%     Viscosity: 10
-%        Volume: 1
-%           Pie: 3
-%   Description: 'Hello world'
-%
-% Note that capitalization was ignored, and the property 'viscosity'
-% was truncated as supplied. Also note that the order the pairs were
-% supplied was arbitrary.
-
-npv = length(pv_pairs);
-n = npv/2;
-
-if n~=floor(n)
-  error 'Property/value pairs must come in PAIRS.'
-end
-if n<=0
-  % just return the defaults
-  return
-end
-
-if ~isstruct(params)
-  error 'No structure for defaults was supplied'
-end
-
-% there was at least one pv pair. process any supplied
-propnames = fieldnames(params);
-lpropnames = lower(propnames);
-for i=1:n
-  p_i = lower(pv_pairs{2*i-1});
-  v_i = pv_pairs{2*i};
-
-  ind = strmatch(p_i,lpropnames,'exact');
-  if isempty(ind)
-    ind = find(strncmp(p_i,lpropnames,length(p_i)));
-    if isempty(ind)
-      error(['No matching property found for: ',pv_pairs{2*i-1}])
-    elseif length(ind)>1
-      error(['Ambiguous property name: ',pv_pairs{2*i-1}])
-    end
-  end
-  p_i = propnames{ind};
-
-  % override the corresponding default in params.
-  % Use setfield for comptability issues with older releases.
-  params = setfield(params,p_i,v_i); %#ok
-
-end
-
+function d = ipdm(data1,varargin)
+% ipdm: Inter-Point Distance Matrix
+% usage: d = ipdm(data1)
+% usage: d = ipdm(data1,data2)
+% usage: d = ipdm(data1,prop,value)
+% usage: d = ipdm(data1,data2,prop,value)
+%
+% Arguments: (input)
+%  data1 - array of data points, each point is one row. p dimensional
+%          data will be represented by matrix with p columns.
+%          If only data1 is provided, then the distance matrix
+%          is computed between all pairs of rows of data1.
+%
+%          If your data is one dimensional, it MUST form a column
+%          vector. A row vector of length n will be interpreted as
+%          an n-dimensional data set.
+%
+%  data2 - second array, supplied only if distances are to be computed
+%          between two sets of points.
+%
+%
+% Class support: data1 and data2 are assumed to be either
+% single or double precision. I have not tested this code to
+% verify its success on integer data of any class.
+%
+%
+% Additional parameters are expected to be property/value pairs.
+% Property/value pairs are pairs of arguments, the first of which
+% (properties) must always be a character string. These strings
+% may be shortened as long as the shortening is unambiguous.
+% Capitalization is ignored. Valid properties for ipdm are:
+%
+%  'Metric', 'Subset', 'Limit', 'Result'
+%
+%  'Metric' - numeric flag - defines the distance metric used
+%          metric = 2 --> (DEFAULT) Euclidean distance = 2-norm
+%                         The standard distance metric.
+%
+%          metric = 1 --> 1-norm = sum of absolute differences
+%                         Also sometimes known as the "city block
+%                         metric", since this is the sum of the
+%                         differences in each dimension.
+%
+%          metric = inf --> infinity-norm = maximum difference
+%                         over all dimensions. The name refers
+%                         to the limit of the p-norm, as p
+%                         approaches infinity.
+%
+%          metric = 0 --> minimum difference over all dimensions.
+%                         This is not really a useful norm in
+%                         practice.
+%
+%          Note: while other distance metrics exist, IMHO, these
+%          seemed to be the common ones.
+%
+%
+%  'Result' - A string variable that denotes the style of returned
+%          result. Valid result types are 'Array', 'Structure'.
+%          Capitalization is ignored, and the string may be
+%          shortened if you wish.
+%
+%          result = 'Array' --> (DEFAULT) A matrix of all
+%                         interpoint distances will be generated.
+%                         This array may be large. If this option
+%                         is specified along with a minimum or
+%                         maximum value, then those elements above
+%                         or below the limiting values will be
+%                         set as -inf or +inf, as appropriate.
+%
+%                         When any of 'LargestFew', 'SmallestFew',
+%                         or 'NearestNeighbor' are set, then the
+%                         resulting array will be a sparse matrix
+%                         if 'array' is specified as the result.
+%
+%          result = 'Structure' --> A list of all computed distances,
+%                         defined as a structure. This structure
+%                         will have fields named 'rowindex',
+%                         'columnindex', and 'distance'.
+%
+%                         This option will be useful when a subset
+%                         criterion for the distances has been
+%                         specified, since then the distance matrix
+%                         may be very sparsely populated. Distances
+%                         for pairs outside of the criterion will
+%                         not be returned.
+%
+%
+%  'Subset' - Character string, any of:
+%
+%          'All', 'Maximum', 'Minimum', 'LargestFew', 'SmallestFew',
+%          'NearestNeighbor', 'FarthestNeighbor', or empty
+%
+%          Like properties, capitalization is ignored here, and
+%          any unambiguous shortening of the word is acceptable.
+%
+%          DEFAULT = 'All'
+%
+%          Some interpoint distance matrices can be huge. Often
+%          these matrices are too large to be fully retained in
+%          memory, yet only the pair of points with the largest
+%          or smallest distance may be needed. When only some
+%          subset of the complete set of distances is of interest,
+%          these options allow you to specify which distances will
+%          be returned.
+%
+%          If 'result' is defined to be an array, then a sparse
+%          matrix will be returned for the 'LargestFew', 'SmallestFew',
+%          'NearestNeighbor', and 'FarthestNeighbor' subset classes.
+%          'Minimum' and 'Maximum' will yield full matrices by
+%          default. If a structure is specified, then only those
+%          elements which have been identified will be returned.
+%
+%          Where a subset is specified, its limiting value is
+%          specified by the 'Limit' property. Call that value k.
+%
+%
+%          'All' -->     (DEFAULT) Return all interpoint distances
+%
+%          'Minimum' --> Only look for those distances above
+%                        the cutoff k. All other distances will
+%                        be returned as -inf.
+%
+%          'Maximum' --> Only look for those distances below
+%                        the cutoff k. All other distances will
+%                        be returned as +inf.
+%
+%          'SmallestFew' --> Only return the subset of the k
+%                        smallest distances. Where only one data
+%                        set is provided, only the upper triangle
+%                        of the inter-point distance matrix will
+%                        be generated since that matrix is symmetric.
+%
+%          'LargestFew' --> Only return the subset of the k
+%                        largest distances. Where only one data
+%                        set is provided, only the upper triangle
+%                        of the inter-point distance matrix will
+%                        be generated since that matrix is symmetric.
+%
+%          'NearestNeighbor' --> Only return the single nearest
+%                        neighbor in data2 to each point in data1.
+%                        No limiting value is required for this
+%                        option. If multiple points have the same
+%                        nearest distance, then return the first
+%                        such point found. With only one input set,
+%                        a point will not be its own nearest
+%                        neighbor.
+%
+%                        Note that exact replicates in a single set
+%                        will cause problems, since a sparse matrix
+%                        is returned by default. Since they will have
+%                        a zero distance, they will not show up in
+%                        the sparse matrix. A structure return will
+%                        show those points as having a zero distance
+%                        though.
+%
+%          'FarthestNeighbor' --> Only return the single farthest
+%                        neighbor to each point. No limiting value
+%                        is required for this option. If multiple
+%                        points have the same farthest distance,
+%                        then return the first such point found.
+%
+%
+%  'Limit' - scalar numeric value or []. Used only when some
+%           Subset is specified.
+%
+%          DEFAULT = []
+%
+%
+%  'ChunkSize' - allows a user with lower RAM limits
+%          to force the code to only grab smaller chunks of RAM
+%          at a time (where possible). This parameter is specified
+%          in bytes of RAM. The default is 32 megabytes, or 2^22
+%          elements in any piece of the distance matrix. Only some
+%          options will break the problem into chunks, thus as long
+%          as a full matrix is expected to be returned, there seems
+%          no reason to break the problem up into pieces.
+%
+%          DEFAULT = 2^25
+%
+%
+% Arguments: (output)
+%  d     - array of interpoint distances, or a struct wth the
+%          fields {'rowindex', 'columnindex', 'distance'}.
+%
+%          d(i,j) represents the distance between point i
+%          (from data1) and point j (from data2).
+%
+%          If only one (n1 x p) array is supplied, then d will
+%          be an array of size == [n1,n1].
+%
+%          If two arrays (of sizes n1 x p and n2 x p) then d
+%          will be an array of size == [n1,n2].
+%
+%
+% Efficiency considerations:
+%  Where possible, this code will use bsxfun to compute its
+%  distances.
+%
+%
+% Example:
+%  Compute the interpoint distances between all pairs of points
+%  in a list of 5 points, in 2 dimensions and using Euclidean
+%  distance as the distance metric.
+%
+%  A = randn(5,2);
+%  d = ipdm(A,'metric',2)
+%  d =
+%            0       2.3295       3.2263       2.0263       2.8244
+%       2.3295            0       1.1485      0.31798       1.0086
+%       3.2263       1.1485            0       1.4318       1.8479
+%       2.0263      0.31798       1.4318            0       1.0716
+%       2.8244       1.0086       1.8479       1.0716            0
+%
+% (see the demo file for many other examples)
+%
+% See also: pdist
+%
+% Author: John D'Errico
+% e-mail: woodchips@rochester.rr.com
+% Release: 1.0
+% Release date: 2/26/08
+
+% Default property values
+params.Metric = 2;
+params.Result = 'array';
+params.Subset = 'all';
+params.Limit = [];
+params.ChunkSize = 2^25;
+
+% untangle the arguments
+if nargin<1
+  % if called with no arguments, then the user probably
+  % needs help. Give it to them.
+  help ipdm
+  return
+end
+
+% were two sets of data provided?
+pvpairs = {};
+if nargin==1
+  % only 1 set of data provided
+  dataflag = 1;
+  data2 = [];
+else
+  if ischar(varargin{1})
+    dataflag = 1;
+    data2 = [];
+    pvpairs = varargin;
+  else
+    dataflag = 2;
+    data2 = varargin{1};
+    if nargin>2
+      pvpairs = varargin(2:end);
+    end
+  end
+end
+
+% get data sizes for later
+[n1,dim] = size(data1);
+if dataflag == 2
+  n2 = size(data2,1);
+end
+
+% Test the class of the input variables
+if ~(isa(data1,'double') || isa(data1,'single')) || ...
+    ((dataflag == 2) && ~(isa(data2,'double') || isa(data2,'single')))
+  error('data points must be either single or double precision variables.')
+end
+
+% do we need to process any property/value pairs?
+if nargin>2
+  params = parse_pv_pairs(params,pvpairs);
+  
+  % check for problems in the properties
+  
+  % was a legal Subset provided?
+  if ~isempty(params.Subset) && ~ischar(params.Subset)
+    error('If provided, ''Subset'' must be character')
+  elseif isempty(params.Subset)
+    params.Subset = 'all';
+  end
+  valid = {'all','maximum','minimum','largestfew','smallestfew', ...
+    'nearestneighbor','farthestneighbor'};
+  ind = find(strncmpi(params.Subset,valid,length(params.Subset)));
+  if (length(ind)==1)
+    params.Subset = valid{ind};
+  else
+    error(['Invalid Subset: ',params.Subset])
+  end
+  
+  % was a limit provided?
+  if ~ismember(params.Subset,{'all','nearestneighbor','farthestneighbor'}) && ...
+      isempty(params.Limit)
+    error('No limit provided, but a Subset that requires a limit value was specified')
+  end
+  % check the limit values for validity
+  if length(params.Limit)>1
+    error('Limit must be scalar or empty')
+  end
+  
+  switch params.Subset
+    case {'largestfew', 'smallestfew'}
+      % must be at least 1, and an integer
+      if (params.Limit<1) || (round(params.Limit)~=params.Limit)
+        error('Limit must be a positive integer for LargestFew or NearestFew')
+      end
+  end
+  
+  % was a legal Result provided?
+  if isempty(params.Result)
+    params.result = 'Array';
+  elseif ~ischar(params.Result)
+    error('If provided, ''Result'' must be character or empty')
+  end
+  valid = {'array','structure'};
+  ind = find(strncmpi(params.Result,valid,length(params.Result)));
+  if (length(ind)==1)
+    params.Result = valid{ind};
+  else
+    error(['Invalid Result: ',params.Subset])
+  end
+
+  % check for the metric
+  if isempty(params.Metric)
+    params.Metric = 2;
+  elseif (length(params.Metric)~=1) || ~ismember(params.Metric,[0 1 2 inf])
+    error('If supplied, ''Metric'' must be a scalar, and one of [0 1 2 inf]')
+  end
+end % if nargin>2
+  
+% If Metric was given as 2, but the dimension is only 1, then it will
+% be slightly faster (and equivalent) to use the 1-norm Metric.
+if (dim == 1) && (params.Metric == 2)
+  params.Metric = 1;
+end
+
+% Can we use bsxfun to compute the interpoint distances?
+% Older Matlab releases will not have bsxfun, but if it is
+% around, it will ne both faster and less of a memory hog.
+params.usebsxfun = (5==exist('bsxfun','builtin'));
+
+% check for dimension mismatch if 2 sets
+if (dataflag==2) && (size(data2,2)~=dim)
+  error('If 2 point sets provided, then both must have the same number of columns')
+end
+
+% Total number of distances to compute, in case I must do it in batches
+if dataflag==1
+  n2 = n1;
+end
+ntotal = n1*n2;
+
+% FINALLY!!! Compute inter-point distances
+switch params.Subset
+  case 'all'
+    % The complete set of interpoint distances. There is no need
+    % to break this into chunks, since we must return all distances.
+    % If that is too much to compute in memory, then it will fail
+    % anyway when we try to store the result. bsxfun will at least
+    % do the computation efficiently.
+    
+    % One set or two?
+    if dataflag == 1
+      d = distcomp(data1,data1,params);
+    else
+        d = distcomp(data1,data2,params);
+    end
+    
+    % Must we return it as a struct?
+    if params.Result(1) == 's'
+      [rind,cind] = ndgrid(1:size(d,1),1:size(d,2));
+      ds.rowindex = rind(:);
+      ds.columnindex = cind(:);
+      ds.distance = d(:);
+      d = ds;
+    end
+    
+  case {'minimum' 'maximum'}
+    % There is no reason to break this into pieces if the result
+    % sill be filled in the end with +/- inf. Only break it up
+    % if the final result is a struct.
+    if ((ntotal*8)<=params.ChunkSize) || (params.Result(1) == 'a')
+      % its small enough to do it all at once
+      
+      % One set or two?
+      if dataflag == 1
+        d = distcomp(data1,data1,params);
+      else
+          d = distcomp(data1,data2,params);
+      end
+      
+      % Must we return it as a struct?
+      if params.Result(1) == 'a'
+        % its an array, fill the unwanted distances with +/- inf
+        if params.Subset(2) == 'i'
+          % minimum
+          d(d<=params.Limit) = -inf;
+        else
+          % maximum
+          d(d>=params.Limit) = +inf;
+        end
+      else
+        % a struct will be returned
+        if params.Subset(2) == 'i'
+          % minimum
+          [dist.rowindex,dist.columnindex] = find(d>=params.Limit);
+        else
+          % maximum
+          [dist.rowindex,dist.columnindex] = find(d<=params.Limit);
+        end
+        dist.distance = d(dist.rowindex + n1*(dist.columnindex-1));
+        d = dist;
+      end
+      
+    else
+      % we need to break this into chunks. This branch
+      % will always return a struct.
+      
+      % this is the number of rows of data1 that we will
+      % process at a time.
+      bs = floor(params.ChunkSize/(8*n2));
+      bs = min(n1,max(1,bs));
+      
+      % Accumulate the result into a cell array. Do it this
+      % way because we don't know in advance how many elements
+      % that we will find satisfying the minimum or maximum
+      % limit specified.
+      accum = cell(0,1);
+      
+      % now loop over the chunks
+      batch = 1:bs;
+      while ~isempty(batch)
+        
+        % One set or two?
+        if dataflag == 1
+          dist = distcomp(data1(batch,:),data1,params);
+        else
+          dist = distcomp(data1(batch,:),data2,params);
+        end
+        
+        % big or small as requested
+        if ('i'==params.Subset(2))
+          % minimum value specified
+          [I,J,V] = find(dist>=params.Limit);
+        else
+          % maximum limit
+          [I,J] = find(dist<=params.Limit);
+          I = I(:);
+          J = J(:);
+          V = dist(I + (J-1)*length(batch));
+          I = I + (batch(1)-1);
+        end
+
+        % and stuff them into the cell structure
+        if ~isempty(V)
+          accum{end+1,1} = [I,J,V(:)]; %#ok
+        end
+
+        % increment the batch
+        batch = batch + bs;
+        if batch(end)>n1
+          batch(batch>n1) = [];
+        end
+
+      end
+
+      % convert the cells into one flat array
+      accum = cell2mat(accum);
+
+      if isempty(accum)
+        d.rowindex = [];
+        d.columnindex = [];
+        d.distance = [];
+      else
+        % we found something
+
+        % sort on the second column, to put them in a reasonable order
+        accum = sortrows(accum,[2 1]);
+
+        d.rowindex = accum(:,1);
+        d.columnindex = accum(:,2);
+        d.distance = accum(:,3);
+      end
+
+    end
+
+  case {'smallestfew' 'largestfew'}
+    % find the k smallest/largest distances. k is
+    % given by params.Limit
+
+    % if only 1 set, params.Limit must be less than n*(n-1)/2
+    if dataflag == 1
+      params.Limit = min(params.Limit,n1*(n1-1)/2);
+    end
+
+    % is this a large problem?
+    if ((ntotal*8) <= params.ChunkSize)
+      % small potatoes
+
+      % One set or two?
+      if dataflag == 1
+        dist = distcomp(data1,data1,params);
+        % if only one data set, set the diagonal and
+        % below that to +/- inf so we don't find it.
+        temp = find(tril(ones(n1,n1),0));
+        if params.Subset(1) == 's'
+          dist(temp) = inf;
+        else
+          dist(temp) = -inf;
+        end
+      else
+        dist = distcomp(data1,data2,params);
+      end
+
+      % sort the distances to find those we need
+      if ('s'==params.Subset(1))
+        % smallestfew
+        [val,tags] = sort(dist(:),'ascend');
+      else
+        % largestfew
+        [val,tags] = sort(dist(:),'descend');
+      end
+      val = val(1:params.Limit);
+      tags = tags(1:params.Limit);
+
+      % recover the row and column index from the linear
+      % index returned by sort in tags.
+      [d.rowindex,d.columnindex] = ind2sub([n1,size(dist,2)],tags);
+
+      % create the matrix as a sparse one or a struct?
+      if params.Result(1)=='a'
+        % its an array, so make the array sparse.
+        d = sparse(d.rowindex,d.columnindex,val,n1,size(dist,2));
+      else
+        % a structure
+        d.distance = val;
+      end
+
+    else
+      % chunks
+
+      % this is the number of rows of data1 that we will
+      % process at a time.
+      bs = floor(params.ChunkSize/(8*n2));
+      bs = min(n1,max(1,bs));
+
+      % We need to find the extreme cases. There are two possible
+      % algorithms, depending on how many total elements we will
+      % search for.
+      % 1. Only a very few total elements.
+      % 2. A relatively large number of total elements, forming
+      %    a significant fraction of the total set.
+      %
+      % Case #1 would suggest to retain params.Limit numberr of
+      % elements from each batch, then at the end, sort them all
+      % to find the best few. Case #2 will result in too many
+      % elements to retain, so we must distinguish between these
+      % alternatives.
+      if (8*params.Limit*n1/bs) <= params.ChunkSize
+        % params.Limit is small enough to fall into case #1.
+
+        % Accumulate the result into a cell array. Do it this
+        % way because we don't know in advance how many elements
+        % that we will find satisfying the minimum or maximum
+        % limit specified.
+        accum = cell(0,1);
+
+        % now loop over the chunks
+        batch = (1:bs)';
+        while ~isempty(batch)
+          % One set or two?
+          if dataflag == 1
+            dist = distcomp(data1(batch,:),data1,params);
+            k = find(tril(ones(length(batch),n2),batch(1)-1));
+            if ('s'==params.Subset(1))
+              dist(k) = inf;
+            else
+              dist(k) = -inf;
+            end
+          else
+            dist = distcomp(data1(batch,:),data2,params);
+          end
+
+          % big or small as requested, keeping only the best
+          % params.Limit number of elements
+          if ('s'==params.Subset(1))
+            % minimum value specified
+            [tags,tags] = sort(dist(:),1,'ascend');
+            tags = tags(1:min(bs,params.Limit));
+            [I,J] = ndgrid(batch,1:n2);
+            ijv = [I(tags),J(tags),dist(tags)];
+          else
+            % maximum limit
+            [tags,tags] = sort(dist(:),1,'descend');
+            tags = tags(1:min(bs,params.Limit));
+            [I,J] = ndgrid(batch,1:n2);
+            ijv = [I(tags),J(tags),dist(tags)];
+          end
+          
+          % catch for the bug when batch ends up as a scalar
+          % in that case, matlab indexing of a vector causes a problem.
+          if numel(batch) == 1
+            ijv = reshape(ijv,[],3);
+          end
+          
+          % and stuff them into the cell structure
+          accum{end+1,1} = ijv; %#ok
+
+          % increment the batch
+          batch = batch + bs;
+          if batch(end)>n1
+            batch(batch>n1) = [];
+          end
+        end
+
+        % convert the cells into one flat array
+        accum = cell2mat(accum);
+
+        % keep only the params.Limit best of those singled out
+        accum = sortrows(accum,3);
+        if ('s'==params.Subset(1))
+          % minimum value specified
+          accum = accum(1:params.Limit,:);
+        else
+          % minimum value specified
+          accum = accum(end + 1 - (1:params.Limit),:);
+        end
+        d.rowindex = accum(:,1);
+        d.columnindex = accum(:,2);
+        d.distance = accum(:,3);
+
+        % create the matrix as a sparse one or a struct?
+        if params.Result(1)=='a'
+          % its an array, so make the array sparse.
+          d = sparse(d.rowindex,d.columnindex,d.distance,n1,size(dist,2));
+        end
+
+      else
+        % params.Limit forces us into the domain of case #2.
+        % Here we cannot retain params.Limit elements from each chunk.
+        % so we will grab each chunk and append it to the best elements
+        % found so far, then filter out the best after each chunk is
+        % done. This may be slower than we want, but its the only way.
+        ijv = zeros(0,3);
+
+        % loop over the chunks
+        batch = (1:bs)';
+        while ~isempty(batch)
+          % One set or two?
+          if dataflag == 1
+            dist = distcomp(data1(batch,:),data1,params);
+            k = find(tril(ones(length(batch),n2),batch(1)-1));
+            if ('s'==params.Subset(1))
+              dist(k) = inf;
+            else
+              dist(k) = -inf;
+            end
+          else
+            dist = distcomp(data1(batch,:),data2,params);
+          end
+
+          [I,J] = ndgrid(batch,1:n2);
+          ijv = [ijv;[I(:),J(:),dist(:)]]; %#ok
+
+          % big or small as requested, keeping only the best
+          % params.Limit number of elements
+          if size(ijv,1) > params.Limit
+            if ('s'==params.Subset(1))
+              % minimum value specified
+              [tags,tags] = sort(ijv(:,3),1,'ascend');
+            else
+              [tags,tags] = sort(ijv(:,3),1,'ascend');
+            end
+            ijv = ijv(tags(1:params.Limit),:);
+          end
+
+          % increment the batch
+          batch = batch + bs;
+          if batch(end)>n1
+            batch(batch>n1) = [];
+          end
+        end
+
+        % They are fully trimmed down. stuff a structure
+        d.rowindex = ijv(:,1);
+        d.columnindex = ijv(:,2);
+        d.distance = ijv(:,3);
+
+        % create the matrix as a sparse one or a struct?
+        if params.Result(1)=='a'
+          % its an array, so make the array sparse.
+          d = sparse(d.rowindex,d.columnindex,d.distance,n1,size(dist,2));
+        end
+
+      end
+
+    end
+    
+  case {'nearestneighbor' 'farthestneighbor'}
+    % find the closest/farthest neighbor for every point
+
+    % is this a large problem? Or a 1-d problem?
+    if dim == 1
+      % its a 1-d nearest/farthest neighbor problem. we can
+      % special case these easily enough, and all the distance
+      % metric options are the same in 1-d.
+
+      % first split it into the farthest versus nearest cases.
+      if params.Subset(1) == 'f'
+        % farthest away
+
+        % One set or two?
+        if dataflag == 1
+          [d2min,minind] = min(data1);
+          [d2max,maxind] = max(data1);
+        else
+          [d2min,minind] = min(data2);
+          [d2max,maxind] = max(data2);
+        end
+
+        d.rowindex = (1:n1)';
+        d.columnindex = repmat(maxind,n1,1);
+        d.distance = repmat(d2max,n1,1);
+
+        % which endpoint was further away?
+        k = abs((data1 - d2min)) >= abs((data1 - d2max));
+        if any(k)
+          d.columnindex(k) = minind;
+          d.distance(k) = d2min;
+        end
+
+      else
+        % nearest. this is mainly a sort and some fussing around.
+        d.rowindex = (1:n1)';
+        d.columnindex = ones(n1,1);
+        d.distance = zeros(n1,1);
+
+        % One set or two?
+        if dataflag == 1
+          % if only one data point, then we are done
+          if n1 == 2
+            % if exactly two data points, its trivial
+            d.columnindex = [2 1];
+            d.distance = repmat(abs(diff(data1)),2,1);
+          elseif n1>2
+            % at least three points. do a sort.
+            [sorted_data,tags] = sort(data1);
+
+            % handle the first and last points separately
+            d.columnindex(tags(1)) = tags(2);
+            d.distance(tags(1)) = sorted_data(2) - sorted_data(1);
+            d.columnindex(tags(end)) = tags(end-1);
+            d.distance(tags(end)) = sorted_data(end) - sorted_data(end-1);
+
+            ind = (2:(n1-1))';
+
+            d1 = sorted_data(ind) - sorted_data(ind-1);
+            d2 = sorted_data(ind+1) - sorted_data(ind);
+
+            k = d1 < d2;
+            d.distance(tags(ind(k))) = d1(k);
+            d.columnindex(tags(ind(k))) = tags(ind(k)-1);
+            k = ~k;
+            d.distance(tags(ind(k))) = d2(k);
+            d.columnindex(tags(ind(k))) = tags(ind(k)+1);
+          end % if n1 == 2
+        else
+          % Two sets of data. still really a sort and some fuss.
+          if n2 == 1
+            % there is only one point in data2
+            d.distance = abs(data1 - data2);
+            % d.columnindex is already set correctly
+          else
+            % At least two points in data2
+            % We need to sort all the data points together, but also
+            % know which points from each set went where. ind12 and
+            % bool12 will help keep track.
+            ind12 = [1:n1,1:n2]';
+            bool12 = [zeros(n1,1);ones(n2,1)];
+            [sorted_data,tags] = sort([data1;data2]);
+
+            ind12 = ind12(tags);
+            bool12 = bool12(tags);
+
+            % where did each point end up after the sort?
+            loc1 = find(~bool12);
+            loc2 = find(bool12);
+
+            % for each point in data1, what is the (sorted) data2
+            % element which appears most nearly to the left of it?
+            cs = cumsum(bool12);
+            leftelement = cs(loc1);
+
+            % any points which fell below the minimum element in data2
+            % will have a zero for the index of the element on their
+            % left. fix this.
+            leftelement = max(1,leftelement);
+
+            % likewise, any point greater than the max in data2 will
+            % have an n2 in left element. this too will be a problem
+            % later, so fix it.
+            leftelement = min(n2-1,leftelement);
+
+            % distance to the left hand element
+            dleft = abs(sorted_data(loc1) - sorted_data(loc2(leftelement)));
+            dright = abs(sorted_data(loc1) - sorted_data(loc2(leftelement+1)));
+
+            % find the points which are closer to the left element in data2
+            k = (dleft < dright);
+            d.distance(ind12(loc1(k))) = dleft(k);
+            d.columnindex(ind12(loc1(k))) = ind12(loc2(leftelement(k)));
+            k = ~k;
+            d.distance(ind12(loc1(k))) = dright(k);
+            d.columnindex(ind12(loc1(k))) = ind12(loc2(leftelement(k)+1));
+
+          end % if n2 == 1
+        end % if dataflag == 1
+      end % if params.Subset(1) == 'f'
+
+      % create the matrix as a sparse one or a struct?
+      if params.Result(1)=='a'
+        % its an array, so make the array sparse.
+        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
+      end
+
+    elseif (ntotal>1000) && (((params.Metric == 0) && (params.Subset(1) == 'n')) || ...
+        ((params.Metric == inf) && (params.Subset(1) == 'f')))
+      % nearest/farthest neighbour in n>1 dimensions, but for an
+      % infinity norm metric. Reduce this to a sequence of
+      % 1-d problems, each of which will be faster in general.
+      % do this only if the problem is moderately large, since
+      % we must overcome the extra overhead of the recursive
+      % calls to ipdm.
+
+      % do the first dimension
+      if dataflag == 1
+        d = ipdm(data1(:,1),data1(:,1),'subset',params.Subset,'metric',params.Metric,'result','struct');
+      else
+        d = ipdm(data1(:,1),data2(:,1),'subset',params.Subset,'metric',params.Metric,'result','struct');
+      end
+
+      % its slightly different for nearest versus farthest here
+      % now, loop over dimensions
+      for i = 2:dim
+        if dataflag == 1
+          di = ipdm(data1(:,i),data1(:,i),'subset',params.Subset,'metric',params.Metric,'result','struct');
+        else
+          di = ipdm(data1(:,i),data2(:,i),'subset',params.Subset,'metric',params.Metric,'result','struct');
+        end
+
+        % did any of the distances change?
+        if params.Metric == 0
+          % the 0 norm, with nearest neighbour, so take the
+          % smallest distance in any dimension.
+          k = d.distance > di.distance;
+        else
+          % inf norm. so take the largest distance across dimensions
+          k = d.distance < di.distance;
+        end
+
+        if any(k)
+          d.distance(k) = di.distance(k);
+          d.columnindex(k) = di.columnindex(k);
+        end
+      end
+
+      % create the matrix as a sparse one or a struct?
+      if params.Result(1)=='a'
+        % its an array, so make the array sparse.
+        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
+      end
+
+    elseif ((ntotal*8) <= params.ChunkSize)
+      % None of the other special cases apply, so do it using brute
+      % force for the small potatoes problem.
+
+      % One set or two?
+      if dataflag == 1
+        dist = distcomp(data1,data1,params);
+      else
+        dist = distcomp(data1,data2,params);
+      end
+
+      % if only one data set and if a nearest neighbor
+      % problem, set the diagonal to +inf so we don't find it.
+      if (dataflag==1) && (n1>1) && ('n'==params.Subset(1))
+        diagind = (1:n1) + (0:n1:(n1^2-1));
+        dist(diagind) = +inf;
+      end
+
+      if ('n'==params.Subset(1))
+        % nearest
+        [val,j] = min(dist,[],2);
+      else
+        % farthest
+        [val,j] = max(dist,[],2);
+      end
+
+      % create the matrix as a sparse one or a struct?
+      if params.Result(1)=='a'
+        % its an array, so make the array sparse.
+        d = sparse((1:n1)',j,val,n1,size(dist,2));
+      else
+        % a structure
+        d.rowindex = (1:n1)';
+        d.columnindex = j;
+        d.distance = val;
+      end
+
+    else
+
+      % break it into chunks
+      bs = floor(params.ChunkSize/(8*n2));
+      bs = min(n1,max(1,bs));
+
+      % pre-allocate the result
+      d.rowindex = (1:n1)';
+      d.columnindex = zeros(n1,1);
+      d.distance = zeros(n1,1);
+
+      % now loop over the chunks
+      batch = 1:bs;
+      while ~isempty(batch)
+
+        % One set or two?
+        if dataflag == 1
+          dist = distcomp(data1(batch,:),data1,params);
+        else
+          dist = distcomp(data1(batch,:),data2,params);
+        end
+
+        % if only one data set and if a nearest neighbor
+        % problem, set the diagonal to +inf so we don't find it.
+        if (dataflag==1) && (n1>1) && ('n'==params.Subset(1))
+          diagind = 1:length(batch);
+          diagind = diagind + (diagind-2+batch(1))*length(batch);
+          dist(diagind) = +inf;
+        end
+
+        % big or small as requested
+        if ('n'==params.Subset(1))
+          % nearest
+          [val,j] = min(dist,[],2);
+        else
+          % farthest
+          [val,j] = max(dist,[],2);
+        end
+
+        % and stuff them into the result structure
+        d.columnindex(batch) = j;
+        d.distance(batch) = val;
+
+        % increment the batch
+        batch = batch + bs;
+        if batch(end)>n1
+          batch(batch>n1) = [];
+        end
+
+      end
+
+      % did we need to return a struct or an array?
+      if params.Result(1) == 'a'
+        % an array. make it a sparse one
+        d = sparse(d.rowindex,d.columnindex,d.distance,n1,n2);
+      end
+
+    end % if dim == 1
+
+end  % switch params.Subset
+
+% End of mainline
+
+% ======================================================
+% begin subfunctions
+% ======================================================
+function d = distcomp(set1,set2,params)
+% Subfunction to compute all distances between two sets of points
+dim = size(set1,2);
+% can we take advantage of bsxfun?
+% Note: in theory, there is no need to loop over the dimensions. We
+% could Just let bsxfun do ALL the work, then wrap a sum around the
+% outside. In practice, this tends to create large intermediate
+% arrays, especially in higher numbers of dimensions. Its also when
+% we might gain here by use of a vectorized code. This will only be
+% a serious gain when the number of points is relatively small and
+% the dimension is large.
+if params.usebsxfun
+  % its a recent enough version of matlab that we can
+  % use bsxfun at all.
+  n1 = size(set1,1);
+  n2 = size(set2,1);
+  if (dim>1) && ((n1*n2*dim)<=params.ChunkSize)
+    % its a small enough problem that we might gain by full
+    % use of bsxfun
+    switch params.Metric
+      case 2
+        d = sum(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim])).^2,3);
+      case 1
+        d = sum(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),3);
+      case inf
+        d = max(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),[],3);
+      case 0
+        d = min(abs(bsxfun(@minus,reshape(set1,[n1,1,dim]),reshape(set2,[1,n2,dim]))),[],3);
+    end
+  else
+    % too big, so that the ChunkSize will have been exceeded, or just 1-d
+    if params.Metric == 2
+      d = bsxfun(@minus,set1(:,1),set2(:,1)').^2;
+    else
+      d = abs(bsxfun(@minus,set1(:,1),set2(:,1)'));
+    end
+    for i=2:dim
+      switch params.Metric
+        case 2
+          d = d + bsxfun(@minus,set1(:,i),set2(:,i)').^2;
+        case 1
+          d = d + abs(bsxfun(@minus,set1(:,i),set2(:,i)'));
+        case inf
+          d = max(d,abs(bsxfun(@minus,set1(:,i),set2(:,i)')));
+        case 0
+          d = min(d,abs(bsxfun(@minus,set1(:,i),set2(:,i)')));
+      end
+    end
+  end
+else
+  % Cannot use bsxfun. Sigh. Do things the hard (and slower) way.
+  n1 = size(set1,1);
+  n2 = size(set2,1);
+  if params.Metric == 2
+    % Note: While some people might use a different Euclidean
+    % norm computation based on expanding the square of the
+    % difference of two numbers, that computation is inherantly
+    % inaccurate when implemented in floating point arithmetic.
+    % While it might be faster, I won't use it here. Sorry.
+    d = (repmat(set1(:,1),1,n2) - repmat(set2(:,1)',n1,1)).^2;
+  else
+    d = abs(repmat(set1(:,1),1,n2) - repmat(set2(:,1)',n1,1));
+  end
+  for i=2:dim
+    switch params.Metric
+      case 2
+        d = d + (repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)).^2;
+      case 1
+        d = d + abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1));
+      case inf
+        d = max(d,abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)));
+      case 0
+        d = min(d,abs(repmat(set1(:,i),1,n2) - repmat(set2(:,i)',n1,1)));
+    end
+  end
+end
+% if 2 norm, then we must sqrt at the end
+if params.Metric==2
+  d = sqrt(d);
+end
+
+% ==============================================================
+%    end main ipdm
+%    begin included function - parse_pv_pairs
+% ==============================================================
+function params=parse_pv_pairs(params,pv_pairs)
+% parse_pv_pairs: parses sets of property value pairs, allows defaults
+% usage: params=parse_pv_pairs(default_params,pv_pairs)
+%
+% arguments: (input)
+%  default_params - structure, with one field for every potential
+%             property/value pair. Each field will contain the default
+%             value for that property. If no default is supplied for a
+%             given property, then that field must be empty.
+%
+%  pv_array - cell array of property/value pairs.
+%             Case is ignored when comparing properties to the list
+%             of field names. Also, any unambiguous shortening of a
+%             field/property name is allowed.
+%
+% arguments: (output)
+%  params   - parameter struct that reflects any updated property/value
+%             pairs in the pv_array.
+%
+% Example usage:
+% First, set default values for the parameters. Assume we
+% have four parameters that we wish to use optionally in
+% the function examplefun.
+%
+%  - 'viscosity', which will have a default value of 1
+%  - 'volume', which will default to 1
+%  - 'pie' - which will have default value 3.141592653589793
+%  - 'description' - a text field, left empty by default
+%
+% The first argument to examplefun is one which will always be
+% supplied.
+%
+%   function examplefun(dummyarg1,varargin)
+%   params.Viscosity = 1;
+%   params.Volume = 1;
+%   params.Pie = 3.141592653589793
+%
+%   params.Description = '';
+%   params=parse_pv_pairs(params,varargin);
+%   params
+%
+% Use examplefun, overriding the defaults for 'pie', 'viscosity'
+% and 'description'. The 'volume' parameter is left at its default.
+%
+%   examplefun(rand(10),'vis',10,'pie',3,'Description','Hello world')
+%
+% params =
+%     Viscosity: 10
+%        Volume: 1
+%           Pie: 3
+%   Description: 'Hello world'
+%
+% Note that capitalization was ignored, and the property 'viscosity'
+% was truncated as supplied. Also note that the order the pairs were
+% supplied was arbitrary.
+
+npv = length(pv_pairs);
+n = npv/2;
+
+if n~=floor(n)
+  error 'Property/value pairs must come in PAIRS.'
+end
+if n<=0
+  % just return the defaults
+  return
+end
+
+if ~isstruct(params)
+  error 'No structure for defaults was supplied'
+end
+
+% there was at least one pv pair. process any supplied
+propnames = fieldnames(params);
+lpropnames = lower(propnames);
+for i=1:n
+  p_i = lower(pv_pairs{2*i-1});
+  v_i = pv_pairs{2*i};
+
+  ind = strmatch(p_i,lpropnames,'exact');
+  if isempty(ind)
+    ind = find(strncmp(p_i,lpropnames,length(p_i)));
+    if isempty(ind)
+      error(['No matching property found for: ',pv_pairs{2*i-1}])
+    elseif length(ind)>1
+      error(['Ambiguous property name: ',pv_pairs{2*i-1}])
+    end
+  end
+  p_i = propnames{ind};
+
+  % override the corresponding default in params.
+  % Use setfield for comptability issues with older releases.
+  params = setfield(params,p_i,v_i); %#ok
+
+end
+
diff --git a/algorithms/calibration/Utilities/calibGUI_interface.m b/algorithms/calibration/Utilities/calibGUI_interface.m
index 7a1a36e..4d84f2d 100644
--- a/algorithms/calibration/Utilities/calibGUI_interface.m
+++ b/algorithms/calibration/Utilities/calibGUI_interface.m
@@ -1,116 +1,116 @@
-classdef calibGUI_interface < handle  
-
-properties
-    filename='';
-    fid=0;
-    textboxHandle = '';    
-    status='off';
-    quitCalibration = false;
-    jEdit=[];
-end
-methods
-    function this = calibGUI_interface(textboxHandle, filename)
-        % Check the inputs
-        if ~ischar(filename)
-            error('The input for "filename" must be a string for a file name.');
-        end
-        if ~isvalid(textboxHandle)
-            error('The following input for "textboxHandle" must be a valid graphics handle.');
-        end
-
-        % Assign properties
-        this.filename = filename;
-        this.textboxHandle = textboxHandle;
-        
-        % Set status
-        this.status = 'initialised';
-        
-        % Open the file
-        this.fid = fopen(this.filename,'wt+');
-        
-        % Empty text box.
-        this.textboxHandle.String= {};
-        
-        this.quitCalibration = false;
-    end
-    
-    function startDiary(this)
-       % Start the diary.
-        %diary(this.filename);
-        this.status = 'on';
-        
-        % Write initial statement
-        display(' Starting HydroSight model calibration ........');
-        display(' ');
-        
-        % Load the existing diary.
-        frewind(this.fid);
-        updatetextboxFromDiary(this)
-                
-    end
-    
-    function updatetextboxFromDiary(this)
-               
-        % Turn diary off so that the command window is written to the file.
-        diary off;
-        this.status = 'off';        
-        
-        % Read the diary until end-of-file and add each line to the GUI tet
-        % box.
-        while 1
-            
-            % Read line and add line form file to the textbox.
-            txt = fgetl(this.fid);
-            if ischar(txt)
-                this.textboxHandle.String{length(this.textboxHandle.String)+1} = txt;
-            else
-                break;
-            end
-
-        end 
-
-        % Move the vertical scroll bar to the end        
-        %if isempty(this.jEdit)
-        %    this.jEdit = findjobj(this.textboxHandle,'persist');            
-        %end
-        jEdit = findjobj(this.textboxHandle,'persist');
-        
-        try
-            %
-            %jEdit = this.jEdit;
-            jEdit = jEdit.getComponent(0).getComponent(0);
-            jEdit.setCaretPosition(jEdit.getDocument.getLength);
-        catch ME
-            % do nothing
-        end
-        
-        diary(this.filename);
-        this.status = 'on';        
-    end
-    
-    function endDiary(this)
-        % Close file connection to diary file.
-        fclose(this.fid);
-        
-       % Stop the diary.
-        diary off;
-        this.status = 'off';        
-    end    
-    
-    function quitCalibrationListener(this,src,evnt)   
-        this.quitCalibration =  true;
-    end
-    
-    function [doQuit, existFlag, exitStatus] = getCalibrationQuitState(this)   
-        doQuit = this.quitCalibration;
-        existFlag = 0;
-        exitStatus = '';        
-        if doQuit 
-            existFlag = -1;
-            exitStatus = 'User quit';
-            msgbox('The calibration algorithm has now been quit. Model simulations for the last parameter set are now to be undertaken. Click OK and please wait.','Quiting calibration...','warn');
-        end
-    end
-    
-end
-end
+classdef calibGUI_interface < handle  
+
+properties
+    filename='';
+    fid=0;
+    textboxHandle = '';    
+    status='off';
+    quitCalibration = false;
+    jEdit=[];
+end
+methods
+    function this = calibGUI_interface(textboxHandle, filename)
+        % Check the inputs
+        if ~ischar(filename)
+            error('The input for "filename" must be a string for a file name.');
+        end
+        if ~isvalid(textboxHandle)
+            error('The following input for "textboxHandle" must be a valid graphics handle.');
+        end
+
+        % Assign properties
+        this.filename = filename;
+        this.textboxHandle = textboxHandle;
+        
+        % Set status
+        this.status = 'initialised';
+        
+        % Open the file
+        this.fid = fopen(this.filename,'wt+');
+        
+        % Empty text box.
+        this.textboxHandle.String= {};
+        
+        this.quitCalibration = false;
+    end
+    
+    function startDiary(this)
+       % Start the diary.
+        %diary(this.filename);
+        this.status = 'on';
+        
+        % Write initial statement
+        display(' Starting HydroSight model calibration ........');
+        display(' ');
+        
+        % Load the existing diary.
+        frewind(this.fid);
+        updatetextboxFromDiary(this)
+                
+    end
+    
+    function updatetextboxFromDiary(this)
+               
+        % Turn diary off so that the command window is written to the file.
+        diary off;
+        this.status = 'off';        
+        
+        % Read the diary until end-of-file and add each line to the GUI tet
+        % box.
+        while 1
+            
+            % Read line and add line form file to the textbox.
+            txt = fgetl(this.fid);
+            if ischar(txt)
+                this.textboxHandle.String{length(this.textboxHandle.String)+1} = txt;
+            else
+                break;
+            end
+
+        end 
+
+        % Move the vertical scroll bar to the end        
+        %if isempty(this.jEdit)
+        %    this.jEdit = findjobj(this.textboxHandle,'persist');            
+        %end
+        jEdit = findjobj(this.textboxHandle,'persist');
+        
+        try
+            %
+            %jEdit = this.jEdit;
+            jEdit = jEdit.getComponent(0).getComponent(0);
+            jEdit.setCaretPosition(jEdit.getDocument.getLength);
+        catch ME
+            % do nothing
+        end
+        
+        diary(this.filename);
+        this.status = 'on';        
+    end
+    
+    function endDiary(this)
+        % Close file connection to diary file.
+        fclose(this.fid);
+        
+       % Stop the diary.
+        diary off;
+        this.status = 'off';        
+    end    
+    
+    function quitCalibrationListener(this,src,evnt)   
+        this.quitCalibration =  true;
+    end
+    
+    function [doQuit, existFlag, exitStatus] = getCalibrationQuitState(this)   
+        doQuit = this.quitCalibration;
+        existFlag = 0;
+        exitStatus = '';        
+        if doQuit 
+            existFlag = -1;
+            exitStatus = 'User quit';
+            msgbox('The calibration algorithm has now been quit. Model simulations for the last parameter set are now to be undertaken. Click OK and please wait.','Quiting calibration...','warn');
+        end
+    end
+    
+end
+end
diff --git a/algorithms/calibration/Utilities/calibrateOnCluster.m b/algorithms/calibration/Utilities/calibrateOnCluster.m
index 23510f1..bcc2e76 100644
--- a/algorithms/calibration/Utilities/calibrateOnCluster.m
+++ b/algorithms/calibration/Utilities/calibrateOnCluster.m
@@ -1,113 +1,113 @@
-% For use after exporting calibration job toa HPC cluster. vis the GUI
-% Written by Tim Peterson Jan 2016. Edited May 2016 to allow sequential 
-% calibration of multiple models.
-% NOTE: The variable 'modelPath' and 'iModel' should have been set by the matlab task
-% script (see jobSubmission.m').
-
-display(['Working on modle number: ',num2str(iModel)]);
-
-display('Moving to project file path...');
-cd ..
-cd ..
-cd ..
-addpath(genpath(pwd));
-
-display('Loading list of model names...');
-modelName = readtable('ModelNames.csv','ReadVariableNames',false,'Delimiter' ,',');
-modelName = modelName{iModel,:};
-modelName = strtrim(modelName);
-
-% Determine the number of models
-isValidModel = cellfun(@(x) ~isempty(x), modelName);
-modelName = modelName(isValidModel);
-nModels = length(modelName);
-
-display(['Number of models to calibrate = ',num2str(nModels)]);
-
-cd('models');
-
-display(['Starting directory = ',pwd()]);
-
-% Loop through each model name and calibrate
-for i=1:nModels
-
-    % Move to model's folder
-    cd(modelName{i});    
-
-    % Check if the model has already been calibrated.
-    if ~isempty(dir('results.mat'))
-        display(['Model ',num2str(i),'(',modelName{i},') is already calibrated. ...']);
-        cd ..
-        display(['... Moving back to ',pwd()]);
-        continue;
-    end
-
-    % Read in model options
-    display('Reading in model options...');    
-    fid = fopen('options.txt');
-    lineString = strtrim(fgetl(fid));
-    calibStartDate = datenum(lineString);
-    display(['   Calib. start date = ',datestr(calibStartDate)]);    
-    
-    lineString = strtrim(fgetl(fid));
-    calibEndDate = datenum(lineString);
-    display(['   Calib. end date = ',datestr(calibEndDate)]);  
-    
-    calibMethod = strtrim(fgetl(fid));
-    display(['   Calib. method = ',calibMethod]);  
-    
-    while 1
-        try
-            lineString = strtrim(fgetl(fid));
-            ind=strfind(lineString,':');
-
-            calibMethodSettingName = lineString(1:ind-1);
-            calibMethodSettingVal = lineString(ind+1:end);
-            try 
-                calibMethodSetting.(calibMethodSettingName) = str2num(calibMethodSettingVal);
-                display(['   Calib. method setting "',calibMethodSettingName,'" = ',num2str(calibMethodSettingVal)]);  
-            catch ME
-                calibMethodSetting.(calibMethodSettingName) = calibMethodSettingVal;
-                display(['   Calib. method setting "',calibMethodSettingName,'" = ',calibMethodSettingVal]);  
-            end            
-        catch ME
-            break;
-        end
-    end 
-    fclose(fid);
-	
-
-    % Read in model .mat file
-    display('Loading model data file ...');
-    load('HPCmodel.mat');
-
-    % Calibrate model
-    saveResults=false;
-    try
-        display('Starting calibration...');
-        calibrateModel( model, [],calibStartDate, calibEndDate, calibMethod,  calibMethodSetting);
-        saveResults=true;
-    catch ME
-        display(['Calibration failed: ',ME.message]);
-        cd ..
-        continue;
-    end
-
-    if saveResults
-        try
-            display('Saving results...');
-            save('results.mat','model');
-        catch ME
-            display(['Saving results failed: ',ME.message]);
-        end
-    end
-    
-    % Exit model folder
-    cd ..
-end
-
-% Kill matlab. This is an unfortuante requirement when the MEX function
-% doIRFconvolution.c offloads to Xeon Phi Cards (using Intel icc compiler).
-display('Killing matlab...');
-pause(60);
-system(['kill ',num2str(feature('getpid'))]);
+% For use after exporting calibration job toa HPC cluster. vis the GUI
+% Written by Tim Peterson Jan 2016. Edited May 2016 to allow sequential 
+% calibration of multiple models.
+% NOTE: The variable 'modelPath' and 'iModel' should have been set by the matlab task
+% script (see jobSubmission.m').
+
+display(['Working on modle number: ',num2str(iModel)]);
+
+display('Moving to project file path...');
+cd ..
+cd ..
+cd ..
+addpath(genpath(pwd));
+
+display('Loading list of model names...');
+modelName = readtable('ModelNames.csv','ReadVariableNames',false,'Delimiter' ,',');
+modelName = modelName{iModel,:};
+modelName = strtrim(modelName);
+
+% Determine the number of models
+isValidModel = cellfun(@(x) ~isempty(x), modelName);
+modelName = modelName(isValidModel);
+nModels = length(modelName);
+
+display(['Number of models to calibrate = ',num2str(nModels)]);
+
+cd('models');
+
+display(['Starting directory = ',pwd()]);
+
+% Loop through each model name and calibrate
+for i=1:nModels
+
+    % Move to model's folder
+    cd(modelName{i});    
+
+    % Check if the model has already been calibrated.
+    if ~isempty(dir('results.mat'))
+        display(['Model ',num2str(i),'(',modelName{i},') is already calibrated. ...']);
+        cd ..
+        display(['... Moving back to ',pwd()]);
+        continue;
+    end
+
+    % Read in model options
+    display('Reading in model options...');    
+    fid = fopen('options.txt');
+    lineString = strtrim(fgetl(fid));
+    calibStartDate = datenum(lineString);
+    display(['   Calib. start date = ',datestr(calibStartDate)]);    
+    
+    lineString = strtrim(fgetl(fid));
+    calibEndDate = datenum(lineString);
+    display(['   Calib. end date = ',datestr(calibEndDate)]);  
+    
+    calibMethod = strtrim(fgetl(fid));
+    display(['   Calib. method = ',calibMethod]);  
+    
+    while 1
+        try
+            lineString = strtrim(fgetl(fid));
+            ind=strfind(lineString,':');
+
+            calibMethodSettingName = lineString(1:ind-1);
+            calibMethodSettingVal = lineString(ind+1:end);
+            try 
+                calibMethodSetting.(calibMethodSettingName) = str2num(calibMethodSettingVal);
+                display(['   Calib. method setting "',calibMethodSettingName,'" = ',num2str(calibMethodSettingVal)]);  
+            catch ME
+                calibMethodSetting.(calibMethodSettingName) = calibMethodSettingVal;
+                display(['   Calib. method setting "',calibMethodSettingName,'" = ',calibMethodSettingVal]);  
+            end            
+        catch ME
+            break;
+        end
+    end 
+    fclose(fid);
+	
+
+    % Read in model .mat file
+    display('Loading model data file ...');
+    load('HPCmodel.mat');
+
+    % Calibrate model
+    saveResults=false;
+    try
+        display('Starting calibration...');
+        calibrateModel( model, [],calibStartDate, calibEndDate, calibMethod,  calibMethodSetting);
+        saveResults=true;
+    catch ME
+        display(['Calibration failed: ',ME.message]);
+        cd ..
+        continue;
+    end
+
+    if saveResults
+        try
+            display('Saving results...');
+            save('results.mat','model');
+        catch ME
+            display(['Saving results failed: ',ME.message]);
+        end
+    end
+    
+    % Exit model folder
+    cd ..
+end
+
+% Kill matlab. This is an unfortuante requirement when the MEX function
+% doIRFconvolution.c offloads to Xeon Phi Cards (using Intel icc compiler).
+display('Killing matlab...');
+pause(60);
+system(['kill ',num2str(feature('getpid'))]);
diff --git a/algorithms/calibration/Utilities/fminsearchbnd.m b/algorithms/calibration/Utilities/fminsearchbnd.m
index 95d419b..29773be 100644
--- a/algorithms/calibration/Utilities/fminsearchbnd.m
+++ b/algorithms/calibration/Utilities/fminsearchbnd.m
@@ -1,304 +1,304 @@
-function [x,fval,exitflag,output]=fminsearchbnd3(fun,x0,LB,UB,options,varargin)
-% FMINSEARCHBND: FMINSEARCH, but with bound constraints by transformation
-% usage: x=FMINSEARCHBND(fun,x0)
-% usage: x=FMINSEARCHBND(fun,x0,LB)
-% usage: x=FMINSEARCHBND(fun,x0,LB,UB)
-% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options)
-% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options,p1,p2,...)
-% usage: [x,fval,exitflag,output]=FMINSEARCHBND(fun,x0,...)
-% 
-% arguments:
-%  fun, x0, options - see the help for FMINSEARCH
-%
-%  LB - lower bound vector or array, must be the same size as x0
-%
-%       If no lower bounds exist for one of the variables, then
-%       supply -inf for that variable.
-%
-%       If no lower bounds at all, then LB may be left empty.
-%
-%       Variables may be fixed in value by setting the corresponding
-%       lower and upper bounds to exactly the same value.
-%
-%  UB - upper bound vector or array, must be the same size as x0
-%
-%       If no upper bounds exist for one of the variables, then
-%       supply +inf for that variable.
-%
-%       If no upper bounds at all, then UB may be left empty.
-%
-%       Variables may be fixed in value by setting the corresponding
-%       lower and upper bounds to exactly the same value.
-%
-% Notes:
-%
-%  If options is supplied, then TolX will apply to the transformed
-%  variables. All other FMINSEARCH parameters should be unaffected.
-%
-%  Variables which are constrained by both a lower and an upper
-%  bound will use a sin transformation. Those constrained by
-%  only a lower or an upper bound will use a quadratic
-%  transformation, and unconstrained variables will be left alone.
-%
-%  Variables may be fixed by setting their respective bounds equal.
-%  In this case, the problem will be reduced in size for FMINSEARCH.
-%
-%  The bounds are inclusive inequalities, which admit the
-%  boundary values themselves, but will not permit ANY function
-%  evaluations outside the bounds. These constraints are strictly
-%  followed.
-%
-%  If your problem has an EXCLUSIVE (strict) constraint which will
-%  not admit evaluation at the bound itself, then you must provide
-%  a slightly offset bound. An example of this is a function which
-%  contains the log of one of its parameters. If you constrain the
-%  variable to have a lower bound of zero, then FMINSEARCHBND may
-%  try to evaluate the function exactly at zero.
-%
-%
-% Example usage:
-% rosen = @(x) (1-x(1)).^2 + 105*(x(2)-x(1).^2).^2;
-%
-% fminsearch(rosen,[3 3])     % unconstrained
-% ans =
-%    1.0000    1.0000
-%
-% fminsearchbnd(rosen,[3 3],[2 2],[])     % constrained
-% ans =
-%    2.0000    4.0000
-%
-% See test_main.m for other examples of use.
-%
-%
-% See also: fminsearch, fminspleas
-%
-%
-% Author: John D'Errico
-% E-mail: woodchips@rochester.rr.com
-% Release: 4
-% Release date: 7/23/06
-
-% size checks
-xsize = size(x0);
-x0 = x0(:);
-n=length(x0);
-
-if (nargin<3) || isempty(LB)
-  LB = repmat(-inf,n,1);
-else
-  LB = LB(:);
-end
-if (nargin<4) || isempty(UB)
-  UB = repmat(inf,n,1);
-else
-  UB = UB(:);
-end
-
-if (n~=length(LB)) || (n~=length(UB))
-  error 'x0 is incompatible in size with either LB or UB.'
-end
-
-% set default options if necessary
-if (nargin<5) || isempty(options)
-  options = optimset('fminsearch');
-end
-
-% stuff into a struct to pass around
-params.args = varargin;
-params.LB = LB;
-params.UB = UB;
-params.fun = fun;
-params.n = n;
-params.OutputFcn = [];
-
-% 0 --> unconstrained variable
-% 1 --> lower bound only
-% 2 --> upper bound only
-% 3 --> dual finite bounds
-% 4 --> fixed variable
-params.BoundClass = zeros(n,1);
-for i=1:n
-  k = isfinite(LB(i)) + 2*isfinite(UB(i));
-  params.BoundClass(i) = k;
-  if (k==3) && (LB(i)==UB(i))
-    params.BoundClass(i) = 4;
-  end
-end
-
-% transform starting values into their unconstrained
-% surrogates. Check for infeasible starting guesses.
-x0u = x0;
-k=1;
-for i = 1:n
-  switch params.BoundClass(i)
-    case 1
-      % lower bound only
-      if x0(i)<=LB(i)
-        % infeasible starting value. Use bound.
-        x0u(k) = 0;
-      else
-        x0u(k) = sqrt(x0(i) - LB(i));
-      end
-      
-      % increment k
-      k=k+1;
-    case 2
-      % upper bound only
-      if x0(i)>=UB(i)
-        % infeasible starting value. use bound.
-        x0u(k) = 0;
-      else
-        x0u(k) = sqrt(UB(i) - x0(i));
-      end
-      
-      % increment k
-      k=k+1;
-    case 3
-      % lower and upper bounds
-      if x0(i)<=LB(i)
-        % infeasible starting value
-        x0u(k) = -pi/2;
-      elseif x0(i)>=UB(i)
-        % infeasible starting value
-        x0u(k) = pi/2;
-      else
-        x0u(k) = 2*(x0(i) - LB(i))/(UB(i)-LB(i)) - 1;
-        % shift by 2*pi to avoid problems at zero in fminsearch
-        % otherwise, the initial simplex is vanishingly small
-        x0u(k) = 2*pi+asin(max(-1,min(1,x0u(k))));
-      end
-      
-      % increment k
-      k=k+1;
-    case 0
-      % unconstrained variable. x0u(i) is set.
-      x0u(k) = x0(i);
-      
-      % increment k
-      k=k+1;
-    case 4
-      % fixed variable. drop it before fminsearch sees it.
-      % k is not incremented for this variable.
-  end
-  
-end
-% if any of the unknowns were fixed, then we need to shorten
-% x0u now.
-if k<=n
-  x0u(k:n) = [];
-end
-
-% were all the variables fixed?
-if isempty(x0u)
-  % All variables were fixed. quit immediately, setting the
-  % appropriate parameters, then return.
-  
-  % undo the variable transformations into the original space
-  x = xtransform(x0u,params);
-  
-  % final reshape
-  x = reshape(x,xsize);
-  
-  % stuff fval with the final value
-  fval = feval(params.fun,x,params.args{:});
-  
-  % fminsearchbnd was not called
-  exitflag = 0;
-  
-  output.iterations = 0;
-  output.funcount = 1;
-  output.algorithm = 'fminsearch';
-  output.message = 'All variables were held fixed by the applied bounds';
-  
-  % return with no call at all to fminsearch
-  return
-end
-
-% Check for an outputfcn. If there is any, then substitute my
-% own wrapper function.
-if ~isempty(options.OutputFcn)
-  params.OutputFcn = options.OutputFcn;
-  options.OutputFcn = @outfun_wrapper;
-end
-
-% now we can call fminsearch, but with our own
-% intra-objective function.
-[xu,fval,exitflag,output] = fminsearch(@intrafun,x0u,options,params);
-
-% undo the variable transformations into the original space
-x = xtransform(xu,params);
-
-% final reshape
-x = reshape(x,xsize);
-
-% Use a nested function as the OutputFcn wrapper
-  function stop = outfun_wrapper(x,varargin);
-    % we need to transform x first
-    xtrans = xtransform(x,params);
-    
-    % then call the user supplied OutputFcn
-    stop = params.OutputFcn(xtrans,varargin{1:(end-1)});
-    
-  end
-
-end % mainline end
-
-% ======================================
-% ========= begin subfunctions =========
-% ======================================
-function fval = intrafun(x,params)
-% transform variables, then call original function
-
-% transform
-xtrans = xtransform(x,params);
-
-% and call fun
-fval = feval(params.fun,xtrans,params.args{:});
-
-end % sub function intrafun end
-
-% ======================================
-function xtrans = xtransform(x,params)
-% converts unconstrained variables into their original domains
-
-xtrans = zeros(1,params.n);
-% k allows some variables to be fixed, thus dropped from the
-% optimization.
-k=1;
-for i = 1:params.n
-  switch params.BoundClass(i)
-    case 1
-      % lower bound only
-      xtrans(i) = params.LB(i) + x(k).^2;
-      
-      k=k+1;
-    case 2
-      % upper bound only
-      xtrans(i) = params.UB(i) - x(k).^2;
-      
-      k=k+1;
-    case 3
-      % lower and upper bounds
-      xtrans(i) = (sin(x(k))+1)/2;
-      xtrans(i) = xtrans(i)*(params.UB(i) - params.LB(i)) + params.LB(i);
-      % just in case of any floating point problems
-      xtrans(i) = max(params.LB(i),min(params.UB(i),xtrans(i)));
-      
-      k=k+1;
-    case 4
-      % fixed variable, bounds are equal, set it at either bound
-      xtrans(i) = params.LB(i);
-    case 0
-      % unconstrained variable.
-      xtrans(i) = x(k);
-      
-      k=k+1;
-  end
-end
-
-end % sub function xtransform end
-
-
-
-
-
+function [x,fval,exitflag,output]=fminsearchbnd3(fun,x0,LB,UB,options,varargin)
+% FMINSEARCHBND: FMINSEARCH, but with bound constraints by transformation
+% usage: x=FMINSEARCHBND(fun,x0)
+% usage: x=FMINSEARCHBND(fun,x0,LB)
+% usage: x=FMINSEARCHBND(fun,x0,LB,UB)
+% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options)
+% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options,p1,p2,...)
+% usage: [x,fval,exitflag,output]=FMINSEARCHBND(fun,x0,...)
+% 
+% arguments:
+%  fun, x0, options - see the help for FMINSEARCH
+%
+%  LB - lower bound vector or array, must be the same size as x0
+%
+%       If no lower bounds exist for one of the variables, then
+%       supply -inf for that variable.
+%
+%       If no lower bounds at all, then LB may be left empty.
+%
+%       Variables may be fixed in value by setting the corresponding
+%       lower and upper bounds to exactly the same value.
+%
+%  UB - upper bound vector or array, must be the same size as x0
+%
+%       If no upper bounds exist for one of the variables, then
+%       supply +inf for that variable.
+%
+%       If no upper bounds at all, then UB may be left empty.
+%
+%       Variables may be fixed in value by setting the corresponding
+%       lower and upper bounds to exactly the same value.
+%
+% Notes:
+%
+%  If options is supplied, then TolX will apply to the transformed
+%  variables. All other FMINSEARCH parameters should be unaffected.
+%
+%  Variables which are constrained by both a lower and an upper
+%  bound will use a sin transformation. Those constrained by
+%  only a lower or an upper bound will use a quadratic
+%  transformation, and unconstrained variables will be left alone.
+%
+%  Variables may be fixed by setting their respective bounds equal.
+%  In this case, the problem will be reduced in size for FMINSEARCH.
+%
+%  The bounds are inclusive inequalities, which admit the
+%  boundary values themselves, but will not permit ANY function
+%  evaluations outside the bounds. These constraints are strictly
+%  followed.
+%
+%  If your problem has an EXCLUSIVE (strict) constraint which will
+%  not admit evaluation at the bound itself, then you must provide
+%  a slightly offset bound. An example of this is a function which
+%  contains the log of one of its parameters. If you constrain the
+%  variable to have a lower bound of zero, then FMINSEARCHBND may
+%  try to evaluate the function exactly at zero.
+%
+%
+% Example usage:
+% rosen = @(x) (1-x(1)).^2 + 105*(x(2)-x(1).^2).^2;
+%
+% fminsearch(rosen,[3 3])     % unconstrained
+% ans =
+%    1.0000    1.0000
+%
+% fminsearchbnd(rosen,[3 3],[2 2],[])     % constrained
+% ans =
+%    2.0000    4.0000
+%
+% See test_main.m for other examples of use.
+%
+%
+% See also: fminsearch, fminspleas
+%
+%
+% Author: John D'Errico
+% E-mail: woodchips@rochester.rr.com
+% Release: 4
+% Release date: 7/23/06
+
+% size checks
+xsize = size(x0);
+x0 = x0(:);
+n=length(x0);
+
+if (nargin<3) || isempty(LB)
+  LB = repmat(-inf,n,1);
+else
+  LB = LB(:);
+end
+if (nargin<4) || isempty(UB)
+  UB = repmat(inf,n,1);
+else
+  UB = UB(:);
+end
+
+if (n~=length(LB)) || (n~=length(UB))
+  error 'x0 is incompatible in size with either LB or UB.'
+end
+
+% set default options if necessary
+if (nargin<5) || isempty(options)
+  options = optimset('fminsearch');
+end
+
+% stuff into a struct to pass around
+params.args = varargin;
+params.LB = LB;
+params.UB = UB;
+params.fun = fun;
+params.n = n;
+params.OutputFcn = [];
+
+% 0 --> unconstrained variable
+% 1 --> lower bound only
+% 2 --> upper bound only
+% 3 --> dual finite bounds
+% 4 --> fixed variable
+params.BoundClass = zeros(n,1);
+for i=1:n
+  k = isfinite(LB(i)) + 2*isfinite(UB(i));
+  params.BoundClass(i) = k;
+  if (k==3) && (LB(i)==UB(i))
+    params.BoundClass(i) = 4;
+  end
+end
+
+% transform starting values into their unconstrained
+% surrogates. Check for infeasible starting guesses.
+x0u = x0;
+k=1;
+for i = 1:n
+  switch params.BoundClass(i)
+    case 1
+      % lower bound only
+      if x0(i)<=LB(i)
+        % infeasible starting value. Use bound.
+        x0u(k) = 0;
+      else
+        x0u(k) = sqrt(x0(i) - LB(i));
+      end
+      
+      % increment k
+      k=k+1;
+    case 2
+      % upper bound only
+      if x0(i)>=UB(i)
+        % infeasible starting value. use bound.
+        x0u(k) = 0;
+      else
+        x0u(k) = sqrt(UB(i) - x0(i));
+      end
+      
+      % increment k
+      k=k+1;
+    case 3
+      % lower and upper bounds
+      if x0(i)<=LB(i)
+        % infeasible starting value
+        x0u(k) = -pi/2;
+      elseif x0(i)>=UB(i)
+        % infeasible starting value
+        x0u(k) = pi/2;
+      else
+        x0u(k) = 2*(x0(i) - LB(i))/(UB(i)-LB(i)) - 1;
+        % shift by 2*pi to avoid problems at zero in fminsearch
+        % otherwise, the initial simplex is vanishingly small
+        x0u(k) = 2*pi+asin(max(-1,min(1,x0u(k))));
+      end
+      
+      % increment k
+      k=k+1;
+    case 0
+      % unconstrained variable. x0u(i) is set.
+      x0u(k) = x0(i);
+      
+      % increment k
+      k=k+1;
+    case 4
+      % fixed variable. drop it before fminsearch sees it.
+      % k is not incremented for this variable.
+  end
+  
+end
+% if any of the unknowns were fixed, then we need to shorten
+% x0u now.
+if k<=n
+  x0u(k:n) = [];
+end
+
+% were all the variables fixed?
+if isempty(x0u)
+  % All variables were fixed. quit immediately, setting the
+  % appropriate parameters, then return.
+  
+  % undo the variable transformations into the original space
+  x = xtransform(x0u,params);
+  
+  % final reshape
+  x = reshape(x,xsize);
+  
+  % stuff fval with the final value
+  fval = feval(params.fun,x,params.args{:});
+  
+  % fminsearchbnd was not called
+  exitflag = 0;
+  
+  output.iterations = 0;
+  output.funcount = 1;
+  output.algorithm = 'fminsearch';
+  output.message = 'All variables were held fixed by the applied bounds';
+  
+  % return with no call at all to fminsearch
+  return
+end
+
+% Check for an outputfcn. If there is any, then substitute my
+% own wrapper function.
+if ~isempty(options.OutputFcn)
+  params.OutputFcn = options.OutputFcn;
+  options.OutputFcn = @outfun_wrapper;
+end
+
+% now we can call fminsearch, but with our own
+% intra-objective function.
+[xu,fval,exitflag,output] = fminsearch(@intrafun,x0u,options,params);
+
+% undo the variable transformations into the original space
+x = xtransform(xu,params);
+
+% final reshape
+x = reshape(x,xsize);
+
+% Use a nested function as the OutputFcn wrapper
+  function stop = outfun_wrapper(x,varargin);
+    % we need to transform x first
+    xtrans = xtransform(x,params);
+    
+    % then call the user supplied OutputFcn
+    stop = params.OutputFcn(xtrans,varargin{1:(end-1)});
+    
+  end
+
+end % mainline end
+
+% ======================================
+% ========= begin subfunctions =========
+% ======================================
+function fval = intrafun(x,params)
+% transform variables, then call original function
+
+% transform
+xtrans = xtransform(x,params);
+
+% and call fun
+fval = feval(params.fun,xtrans,params.args{:});
+
+end % sub function intrafun end
+
+% ======================================
+function xtrans = xtransform(x,params)
+% converts unconstrained variables into their original domains
+
+xtrans = zeros(1,params.n);
+% k allows some variables to be fixed, thus dropped from the
+% optimization.
+k=1;
+for i = 1:params.n
+  switch params.BoundClass(i)
+    case 1
+      % lower bound only
+      xtrans(i) = params.LB(i) + x(k).^2;
+      
+      k=k+1;
+    case 2
+      % upper bound only
+      xtrans(i) = params.UB(i) - x(k).^2;
+      
+      k=k+1;
+    case 3
+      % lower and upper bounds
+      xtrans(i) = (sin(x(k))+1)/2;
+      xtrans(i) = xtrans(i)*(params.UB(i) - params.LB(i)) + params.LB(i);
+      % just in case of any floating point problems
+      xtrans(i) = max(params.LB(i),min(params.UB(i),xtrans(i)));
+      
+      k=k+1;
+    case 4
+      % fixed variable, bounds are equal, set it at either bound
+      xtrans(i) = params.LB(i);
+    case 0
+      % unconstrained variable.
+      xtrans(i) = x(k);
+      
+      k=k+1;
+  end
+end
+
+end % sub function xtransform end
+
+
+
+
+
diff --git a/algorithms/calibration/Utilities/license.txt b/algorithms/calibration/Utilities/license.txt
index 25fc893..fcefbe2 100644
--- a/algorithms/calibration/Utilities/license.txt
+++ b/algorithms/calibration/Utilities/license.txt
@@ -1,24 +1,24 @@
-Copyright (c) 2012, Wei
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in
-      the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2012, Wei
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/algorithms/models/ExpSmooth/ExpSmooth.m b/algorithms/models/ExpSmooth/ExpSmooth.m
index 4c343c9..9b0c173 100644
--- a/algorithms/models/ExpSmooth/ExpSmooth.m
+++ b/algorithms/models/ExpSmooth/ExpSmooth.m
@@ -1,496 +1,496 @@
-classdef ExpSmooth < model_abstract
-    %ExpSmooth Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-    end
-
-%%  STATIC METHODS        
-% Static methods used by the Graphical User Interface to inform the
-% user of the available model types. Any new models must be listed here
-% in order to be accessable within the GUI.
-    methods(Static)
-        % Provides a simple description of the function for the user
-        % interface.
-        function str = description()
-           
-            str = {['"ExpSmooth" is a double exponential smoothing time-series model for irregular spaced  ', ...
-                   'observations (Cipra et al. 2008). The double exponential smoothing undertakes an ', ...
-                   'smoothing of the trend in the head and exponential smoothing of the component of the ', ...
-                   'head not captured by the trend. Importantly, the trend is linear and is updated at each', ...
-                   'observation. Therefore, when the model is used for interpolation of the head the estimate', ...
-                   'has a discontinuity at each observation because of the updating of the trend.'], ...
-                   '', ...
-                  ['In fitting the exponential model to an observerd hydrograph, the exponential noise model', ...
-                   'from Peterson & Western (2014) was been added. This provides a weighted least squares ', ...
-                   'objective function and hence a means for minimisation all of three model parameters.'], ...
-                   '', ...
-                  ['In using the model, it does not use any forcing data, such as rainfall or extraction rate.', ...
-                   'However, the toolkit user interface does require all models to have forcing data and ', ...
-                   'coordinate data. Therefore, in using this model, forcing and coordinate data files must be', ...
-                   'input but the files can contain junk data, e.g. all rainfall days can be zero.'], ...
-                   '', ...
-                   'For further details of the algorithms see:', ...
-                   '', ...
-                   '   - Peterson, T. J., and A. W. Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355, doi:10.1002/2013WR014800.', ...
-                   '   - Cipra T. and Hanzák T. (2008). Exponential smoothing for irregular time series. _Kybernetika_,  44(3), 385-399.'};                     
-               
-               
-        end        
-    end        
-    
-%%  PUBLIC METHODS                  
-    methods
-        function obj = ExpSmooth(bore_ID, obsHead, forcingData_data,  forcingData_colnames, siteCoordinates, varargin)
-            
-            % Set observed head data
-            obj.inputData.head = obsHead;
-            
-            % Initialise parameters
-            obj.parameters.alpha = -1;            % Exponential smoothing parameter            
-            obj.parameters.beta = -3;             % Noise model parameter            
-            obj.parameters.gamma = -1;            % Exponential trend parameter (optional)
-                        
-            % Set Parameter names
-            obj.variables.param_names = {'Auto-regressive','alpha';'Moving-average','beta';'Auto-regressive', 'gamma'};
-            
-        end
-        
-        function [head, colnames, noise] = solve(obj, time_points, tor_min, tor_max)
-            % Check that the model has first been calibrated.
-            %if ~isfield(obj.variables, 'meanHead_calib') || ~isfield(obj.parameters,'Const')
-            if ~isfield(obj.variables, 'meanHead_calib')
-                error('The model does not appear to have first been calibrated. Please calibrate the model before running a simulation.');
-            end        
-            
-            % Check the time points are al unique
-            if length(unique(time_points)) ~= length(time_points)
-                error('The time points for simulation must be unique.');
-            end
-            
-            % Create logical matrix indicating if the time point is an
-            % observation. If true, then the data point is used to update
-            % the exponential smoothing.  Else, a forecast is made using
-            % the exonential smoothing using the smooth terms from the 
-            % previous observation. 
-            % To calculate this vector, the following steps are undertaken:
-            % 1. Unique time points are derived from the simulation time
-            % points and the observed time points within the calibration
-            % period.
-            % 2. Find the time points within the unique list that are
-            % observations.
-            % 3. Create a logical vector with the time points from 2 as true
-            % 4. Assign vector from 3 to the object for access within the
-            % objective function.
-            time_points_all = [time_points; obj.variables.calibraion_time_points];
-            time_points_all = unique(sort(time_points_all));
-            [~, ind] = intersect(time_points_all,obj.variables.calibraion_time_points);
-            obj.variables.isObsTimePoints = false(size(time_points_all));
-            obj.variables.isObsTimePoints(ind) = true;                                    
-            
-            % Create vector of the time steps for only the time points with
-            % observed heads.
-            obj.variables.delta_t = diff(time_points_all(obj.variables.isObsTimePoints))./365;
-            obj.variables.meanDelta_t = mean(obj.variables.delta_t);
-            
-            % Convert logical to double for MEX input
-            obj.variables.isObsTimePoints = double(obj.variables.isObsTimePoints);
-            
-            % Set percentile for noise 
-            Pnoise = 0.95;            
-            
-            % Calc deterministic component of the head at 'time_points_all'.
-            params = getParameters(obj);
-            obj.variables.doingCalibration = false;
-            [~, headtmp, obj.variables.h_forecast] = objectiveFunction(params, time_points_all, obj);            
-            
-            % Filter 'head' to only those time points input to the
-            % function.
-            [~, ind] = intersect(time_points_all,time_points);            
-            headtmp = [time_points, headtmp(ind,:)];            
-                        
-            if size(params,2)>1
-                head = zeros(size(headtmp,1),size(headtmp,2), size(params,2));
-                noise = zeros(size(headtmp,1),3, size(params,2));
-                head(:,:,1)= headtmp;
-                for ii=1:size(params,2)
-                    
-                    % Calc deterministic component of the head at 'time_points_all'.
-                    params = getParameters(obj);
-                    obj.variables.doingCalibration = false;
-                    [~, headtmp, obj.variables.h_forecast] = objectiveFunction(params(:,ii), time_points_all, obj);            
-
-                    % Filter 'head' to only those time points input to the
-                    % function.
-                    [~, ind] = intersect(time_points_all,time_points);            
-                    head(:,:,ii) = [time_points, head(ind,:)];                                
-                
-                    % Create noise component output.
-                    if isfield(obj.variables,'sigma_n');
-                        noise(:,:,ii) = [head(:,1,ii), ones(size(head,1),2) .* norminv(Pnoise,0,1) .* obj.variables.sigma_n(ii)];
-                    else
-                        noise(:,:,ii) = [head(:,1,ii), zeros(size(head,1),2)];
-                    end                
-                end
-            else
-                head = headtmp;                
-                
-                % Create noise component output.
-                if isfield(obj.variables,'sigma_n');
-                    noise(:,:) = [head(:,1), norminv(Pnoise,0,1) .* obj.variables.sigma_n(ones(size(head,1),2))];
-                else
-                    noise(:,:) = [head(:,1), zeros(size(head,1),2)];
-                end                
-                
-            end            
-                        
-            % Assign column names
-            colnames = {'time','h_star'};
-                                 
-        end
-        
-        function [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
-            
-            % Extract time steps
-            t_filt = find( obj.inputData.head(:,1) >=t_start  ...
-                & obj.inputData.head(:,1) <= t_end );   
-            obj.variables.calibraion_time_points = obj.inputData.head(t_filt,1);
-            time_points =  obj.variables.calibraion_time_points;
-            
-            % STore time difference
-            obj.variables.delta_t = diff(time_points,1)./365;
-            obj.variables.meanDelta_t = mean(obj.variables.delta_t);
-            
-            % Set a flag to indicate that calibration is complete.
-            obj.variables.doingCalibration = true;
-            
-            % Calculate the mean head during the calibration period
-            obj.variables.meanHead_calib = mean(obj.inputData.head(t_filt,2));
-
-            % Create logical matrix indicating if the time point is an
-            % observation. If true, then the data point is used to update
-            % the exponential smoothing.  Else, a forecast is made using
-            % the exonential smoothing using the smooth terms from the 
-            % previous observation. For the calibration, this vector is
-            % true.
-            obj.variables.isObsTimePoints = true(size(obj.variables.calibraion_time_points));
-            
-            % Convert logical to double for MEX input
-            obj.variables.isObsTimePoints = double(obj.variables.isObsTimePoints);
-            
-            % Estimate the initial value and slope by fitting a smoothed
-            % spline.
-            %spline_model = smooth(time_points, obj.inputData.head(t_filt,2), 'rloess');
-            spline_time_points = [0; 1; (time_points(2:end)-time_points(1))]./365;
-            spline_vals = csaps((time_points-time_points(1))./365, obj.inputData.head(t_filt,2) - obj.variables.meanHead_calib,0.1,spline_time_points );
-            obj.variables.initialHead = spline_vals(1);
-            obj.variables.initialTrend = (spline_vals(2) - spline_vals(1))./(spline_time_points(2) - spline_time_points(1) );
-            
-            % Find upper limit for alpha above which numerical errors arise.
-            beta_upperLimit = 1000; 
-            delta_time = diff(obj.inputData.head(:,1),1)./365;
-            while abs(sum( exp( -2.*beta_upperLimit .* delta_time ) )) < eps() ...
-            || exp(mean(log( 1- exp( -2.*beta_upperLimit .* delta_time) ))) < eps()
-                beta_upperLimit = beta_upperLimit - 0.1;
-                if beta_upperLimit <= eps()                                   
-                    break;
-                end
-            end
-            if beta_upperLimit <= eps()
-                obj.variables.beta_upperLimit = 3;
-            else
-                % Transform alpha log10 space.
-                obj.variables.beta_upperLimit = log10(beta_upperLimit);
-            end      
-            
-            % Find lower limit for alpha. This is based on the assumption
-            % that if the best model (ie explains 95% of variance) then 
-            % for such a model the estimate of the
-            % moving average noise should be << than the observed
-            % head standard dev..                        
-            if ~isinf(obj.variables.beta_upperLimit)
-                beta_lowerLimit = -10;         
-                obsHead_std = std(obj.inputData.head(t_filt,2));   
-                while sqrt(mean( 0.05.*obsHead_std^2 ./ (1 - exp( -2 .* 10.^beta_lowerLimit .* obj.variables.delta_t)))) ...
-                > obsHead_std                       
-
-                    if beta_lowerLimit >= (obj.variables.beta_upperLimit - 2)
-                        break;
-                    end
-
-                    beta_lowerLimit = beta_lowerLimit + 0.1;
-                end
-                obj.variables.beta_lowerLimit = beta_lowerLimit;
-            else
-                obj.variables.beta_lowerLimit = -5;
-            end
-            
-            
-            % Assign initial params to outputs
-            params_initial = getParameters(obj);
-            
-            % Clear estimate of constant
-            if isfield(obj.parameters,'Const')
-                obj.parameters.Const = [];
-            end
-        end
-        
-        function calibration_finalise(obj, params, useLikelihood)          
-            
-            % Re-calc objective function and deterministic component of the head and innocations.
-            % Importantly, the drainage elevation (ie the constant term for
-            % the regression) is calculated within 'objectiveFunction' and
-            % assigned to the object. When the calibrated model is solved
-            % for a different time period then this
-            % drainage value will be used by 'objectiveFunction'.
-            [obj.variables.objFn, obj.variables.h_star, obj.variables.h_forecast] = objectiveFunction(params, obj.variables.calibraion_time_points, obj, useLikelihood);                        
- 
-            % Re-calc residuals and assign to object                        
-            t_filt = obj.inputData.head(:,1) >=obj.variables.calibraion_time_points(1)  ...
-                & obj.inputData.head(:,1) <= obj.variables.calibraion_time_points(end);                           
-            obj.variables.resid = obj.inputData.head(t_filt,2)  -  obj.variables.h_forecast;
-            
-            % Calculate mean of noise. This should be zero +- eps()
-            % because the drainage value is approximated assuming n-bar = 0.
-            obj.variables.n_bar = real(mean( obj.variables.resid ));
-            
-            % Calculate innovations
-            innov = obj.variables.resid(2:end) - obj.variables.resid(1:end-1).*exp( -10.^obj.parameters.beta .* obj.variables.delta_t );            
-            
-            % Calculate noise standard deviation.
-            obj.variables.sigma_n = sqrt(mean( innov.^2 ./ (1 - exp( -2 .* 10.^obj.parameters.beta .* obj.variables.delta_t))));                    
-            
-            % Set a flag to indicate that calibration is complete.
-            obj.variables.doingCalibration = false;
-        end
-        
-        function [objFn, h_star, h_forecast] = objectiveFunction(params, time_points, obj, getLikelihood)                
-
-            % Check the required object variables are set.
-            if ~isfield(obj.variables,'isObsTimePoints') ...
-            || ~isfield(obj.variables,'meanHead_calib')    
-                error('The model does not appear to have been initialised for calibration.');
-            end
-               
-            % Check the input time points and the logical vector denoting
-            % observartion time points are of equal length
-            if length(time_points) ~= length(obj.variables.isObsTimePoints)
-                error('The input time points vector must be the same length as the logical vector of observation time points.');
-            end
-                        
-            % Set model parameters         
-            setParameters(obj, params, {'alpha','beta','gamma'});            
-            
-            % Get model parameters and transform them from log10 space.
-            alpha = 10.^obj.parameters.alpha;            
-            beta = 10.^obj.parameters.beta;
-            gamma = 10.^obj.parameters.gamma;
-            
-            % Get time points
-            t = obj.inputData.head(:,1);
-
-            % Create filter for time points and apply to t
-            t_filt =  find(t >=time_points(1) & t <= time_points(end));                   
-            
-            % Setup time-varying weigting terms from input parameters
-            % and their values at t0 using ONLY the time points with
-            % observed head values!
-            q = mean(obj.variables.delta_t);
-            alpha_i = 1 - (1 - alpha)^q;
-            gamma_i = 1 - (1 - gamma)^q;
-            
-            % Initialise the output and subract the mean from the observed
-            % head.
-            h_ar = zeros(length(time_points),1);
-            h_obs = obj.inputData.head(:,2) - obj.variables.meanHead_calib;            
-            
-            % Assign linear regression estimate the initial slope and intercept.
-            %h_trend(1) = obj.variables.initialTrend_calib;
-            %h_trend = obj.variables.initialTrend_calib;
-            h_trend = obj.variables.initialTrend;
-            h_ar(1) = obj.variables.initialHead;
-            h_forecast = h_ar;
-            indPrevObsTimePoint = 1;
-            indPrevObs = 1;
-            
-            % Undertake double exponential smoothing.
-            % Note: It is based on Cipra T. and Hanzák T. (2008). Exponential 
-            % smoothing for irregular time series. Kybernetika,  44(3), 385-399.
-            % Importantly, this can be undertaken for time-points that have observed 
-            % heads and those that are to be interpolated (or extrapolated). 
-            % The for loop cycles though each time point to be simulated.
-            % If the time point is an observation then the alpha, gamma and
-            % h_trend are updates and an index is stored pointng to the last true obs point. 
-            % If the time point is not an observation then a forecast is
-            % undertaken using the most recent values of alpha, gamma and
-            % h_trend
-            for i=2:length(time_points)
-               
-               delta_t = (time_points(i)-time_points(indPrevObsTimePoint))/365;
-                
-               if obj.variables.isObsTimePoints(i)
-                   % Update smoothing model using the observation at the
-                   % current time point.
-                   if indPrevObs==1
-                       gamma_weight = (1-gamma)^delta_t;
-                   else                                              
-                       gamma_weight = delta_t_prev/delta_t * (1-gamma)^delta_t;            
-                   end                    
-                   
-                   alpha_weight = (1-alpha).^delta_t;
-                   
-                   alpha_i = alpha_i./(alpha_weight + alpha_i); 
-                   gamma_i = gamma_i./(gamma_weight + gamma_i);
-                   h_ar(i) = (1-alpha_i) * (h_ar(indPrevObsTimePoint) + delta_t * h_trend) + alpha_i * h_obs(indPrevObs+1);
-                   h_forecast(i) = h_ar(indPrevObsTimePoint) + delta_t * h_trend;
-                   h_trend = (1-gamma_i) * h_trend + gamma_i * (h_ar(i) - h_ar(indPrevObsTimePoint))./delta_t;
-                                                         
-                   indPrevObsTimePoint = i;
-                   indPrevObs = indPrevObs+1;
-                   delta_t_prev = delta_t;
-               else
-                   % Make a forecast to the current non-observation time
-                   % point.
-                   h_forecast(i) = h_ar(indPrevObsTimePoint) + delta_t * h_trend;
-                   h_ar(i) = h_forecast(i);
-               end
-            end            
-            
-            % Add the mean head onto the smoothed estimate and forecast
-            h_ar = h_ar + obj.variables.meanHead_calib;
-            h_forecast = h_forecast + obj.variables.meanHead_calib;
-             
-            % Assign output for non-corrected head
-            h_star = h_ar;
-            
-            %% Calculate the moving average componant.                        
-            if ~obj.variables.doingCalibration
-                objFn=[];
-                return
-            end
-
-            % Create natrix ob observed and forecast heads
-            h_mat = [obj.inputData.head(t_filt,2), h_forecast];
-
-            % Calculate residuals (excluding input outliers).
-            resid = diff(h_mat,1,2);
-
-            % Calculate the innovations
-            innov = resid(2:end,:) - resid(1:end-1,:).*exp( bsxfun(@times, -beta ,obj.variables.delta_t) );
-
-            % Calculate the weighted least squares objective function
-            objFn = sum( bsxfun(@rdivide, exp(mean(log( 1- exp( bsxfun(@times, -2*beta ,obj.variables.delta_t )) ))) ...
-                        ,(1- exp(  bsxfun(@times, -2*beta ,obj.variables.delta_t) ))) .* innov.^2);   
-                    
-            % Calculate log liklihood    
-            if getLikelihood
-                N = size(resid,1);
-                objFn = -0.5 * N * ( log(2*pi) + log(objFn./N)+1); 
-            end                    
-                    
-        end
-        
-        function setParameters(obj, params, param_names)
-            obj.parameters.(param_names{1})= params(1);
-            obj.parameters.(param_names{2})= params(2);
-            obj.parameters.(param_names{3})= params(3);
-        end
-        
-        function [params, param_names] = getParameters(obj)
-            params(1,:) = obj.parameters.alpha;
-            params(2,:) = obj.parameters.beta;
-            params(3,:) = obj.parameters.gamma;
-            param_names = {'alpha','beta','gamma'};
-        end
-        
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)            
-            
-            if isfield(obj.variables,'beta_upperLimit')
-                params_upperLimit = [0; obj.variables.beta_upperLimit; 0];
-            else
-                params_upperLimit = [0; 5 ; 0];
-            end
-            if isfield(obj.variables,'beta_lowerLimit')
-                params_lowerLimit = [-inf; obj.variables.beta_lowerLimit; -inf];
-            else
-                params_lowerLimit = [-inf; -5 ; -inf];
-            end            
-        end
-        
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-
-            params_upperLimit = [0; 2; 0];
-            params_lowerLimit = [-3; -2; -3];
-
-        end        
-        
-        function isValidParameter = getParameterValidity(obj, params, time_points)
-
-            % Get physical limits and test if parames are within the range
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-            isValidParameter = params >= repmat(params_lowerLimit,1,size(params,2)) & params <= repmat(params_upperLimit,1,size(params,2));
-                        
-            % Check the alphanoise parameter is large enough not to cause numerical
-            % problems in the calcuation of the objective function.            
-            filt_noiseErr = exp(mean(log( 1- exp( bsxfun(@times,-2.*10.^params(2,:) , obj.variables.delta_t) )),1)) <= eps() ...
-                         | abs(sum( exp( bsxfun(@times,-2.*10.^params(2,:) , obj.variables.delta_t) ),1)) < eps();                
-            isValidParameter(2,filt_noiseErr)= false;                
-        end
-       
-        %% Get the forcing data from the model
-        function [forcingData, forcingData_colnames] = getForcingData(obj)
-            forcingData = [];
-            forcingData_colnames = {}; 
-        end
-        
-        %% Set the forcing data from the model
-        function setForcingData(obj, forcingData, forcingData_colnames)
-            % do nothing. The model does not use forcing data.
-        end          
-        
-
-        %% Get the obs head data from the model
-        function head = getObservedHead(obj)
-            head = obj.inputData.head;
-        end
-        
-        
-        function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-            propNames = properties(obj);
-            for i=1:length(propNames)
-               if isempty(obj.(propNames{i}))
-                   continue;
-               end                
-               if isobject(obj.(propNames{i}))
-                delete(obj.(propNames{i}));
-               else               
-                obj.(propNames{i}) = []; 
-               end
-            end
-        end            
-    end
-       
-end
-
+classdef ExpSmooth < model_abstract
+    %ExpSmooth Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+    end
+
+%%  STATIC METHODS        
+% Static methods used by the Graphical User Interface to inform the
+% user of the available model types. Any new models must be listed here
+% in order to be accessable within the GUI.
+    methods(Static)
+        % Provides a simple description of the function for the user
+        % interface.
+        function str = description()
+           
+            str = {['"ExpSmooth" is a double exponential smoothing time-series model for irregular spaced  ', ...
+                   'observations (Cipra et al. 2008). The double exponential smoothing undertakes an ', ...
+                   'smoothing of the trend in the head and exponential smoothing of the component of the ', ...
+                   'head not captured by the trend. Importantly, the trend is linear and is updated at each', ...
+                   'observation. Therefore, when the model is used for interpolation of the head the estimate', ...
+                   'has a discontinuity at each observation because of the updating of the trend.'], ...
+                   '', ...
+                  ['In fitting the exponential model to an observerd hydrograph, the exponential noise model', ...
+                   'from Peterson & Western (2014) was been added. This provides a weighted least squares ', ...
+                   'objective function and hence a means for minimisation all of three model parameters.'], ...
+                   '', ...
+                  ['In using the model, it does not use any forcing data, such as rainfall or extraction rate.', ...
+                   'However, the toolkit user interface does require all models to have forcing data and ', ...
+                   'coordinate data. Therefore, in using this model, forcing and coordinate data files must be', ...
+                   'input but the files can contain junk data, e.g. all rainfall days can be zero.'], ...
+                   '', ...
+                   'For further details of the algorithms see:', ...
+                   '', ...
+                   '   - Peterson, T. J., and A. W. Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355, doi:10.1002/2013WR014800.', ...
+                   '   - Cipra T. and Hanzák T. (2008). Exponential smoothing for irregular time series. _Kybernetika_,  44(3), 385-399.'};                     
+               
+               
+        end        
+    end        
+    
+%%  PUBLIC METHODS                  
+    methods
+        function obj = ExpSmooth(bore_ID, obsHead, forcingData_data,  forcingData_colnames, siteCoordinates, varargin)
+            
+            % Set observed head data
+            obj.inputData.head = obsHead;
+            
+            % Initialise parameters
+            obj.parameters.alpha = -1;            % Exponential smoothing parameter            
+            obj.parameters.beta = -3;             % Noise model parameter            
+            obj.parameters.gamma = -1;            % Exponential trend parameter (optional)
+                        
+            % Set Parameter names
+            obj.variables.param_names = {'Auto-regressive','alpha';'Moving-average','beta';'Auto-regressive', 'gamma'};
+            
+        end
+        
+        function [head, colnames, noise] = solve(obj, time_points, tor_min, tor_max)
+            % Check that the model has first been calibrated.
+            %if ~isfield(obj.variables, 'meanHead_calib') || ~isfield(obj.parameters,'Const')
+            if ~isfield(obj.variables, 'meanHead_calib')
+                error('The model does not appear to have first been calibrated. Please calibrate the model before running a simulation.');
+            end        
+            
+            % Check the time points are al unique
+            if length(unique(time_points)) ~= length(time_points)
+                error('The time points for simulation must be unique.');
+            end
+            
+            % Create logical matrix indicating if the time point is an
+            % observation. If true, then the data point is used to update
+            % the exponential smoothing.  Else, a forecast is made using
+            % the exonential smoothing using the smooth terms from the 
+            % previous observation. 
+            % To calculate this vector, the following steps are undertaken:
+            % 1. Unique time points are derived from the simulation time
+            % points and the observed time points within the calibration
+            % period.
+            % 2. Find the time points within the unique list that are
+            % observations.
+            % 3. Create a logical vector with the time points from 2 as true
+            % 4. Assign vector from 3 to the object for access within the
+            % objective function.
+            time_points_all = [time_points; obj.variables.calibraion_time_points];
+            time_points_all = unique(sort(time_points_all));
+            [~, ind] = intersect(time_points_all,obj.variables.calibraion_time_points);
+            obj.variables.isObsTimePoints = false(size(time_points_all));
+            obj.variables.isObsTimePoints(ind) = true;                                    
+            
+            % Create vector of the time steps for only the time points with
+            % observed heads.
+            obj.variables.delta_t = diff(time_points_all(obj.variables.isObsTimePoints))./365;
+            obj.variables.meanDelta_t = mean(obj.variables.delta_t);
+            
+            % Convert logical to double for MEX input
+            obj.variables.isObsTimePoints = double(obj.variables.isObsTimePoints);
+            
+            % Set percentile for noise 
+            Pnoise = 0.95;            
+            
+            % Calc deterministic component of the head at 'time_points_all'.
+            params = getParameters(obj);
+            obj.variables.doingCalibration = false;
+            [~, headtmp, obj.variables.h_forecast] = objectiveFunction(params, time_points_all, obj);            
+            
+            % Filter 'head' to only those time points input to the
+            % function.
+            [~, ind] = intersect(time_points_all,time_points);            
+            headtmp = [time_points, headtmp(ind,:)];            
+                        
+            if size(params,2)>1
+                head = zeros(size(headtmp,1),size(headtmp,2), size(params,2));
+                noise = zeros(size(headtmp,1),3, size(params,2));
+                head(:,:,1)= headtmp;
+                for ii=1:size(params,2)
+                    
+                    % Calc deterministic component of the head at 'time_points_all'.
+                    params = getParameters(obj);
+                    obj.variables.doingCalibration = false;
+                    [~, headtmp, obj.variables.h_forecast] = objectiveFunction(params(:,ii), time_points_all, obj);            
+
+                    % Filter 'head' to only those time points input to the
+                    % function.
+                    [~, ind] = intersect(time_points_all,time_points);            
+                    head(:,:,ii) = [time_points, head(ind,:)];                                
+                
+                    % Create noise component output.
+                    if isfield(obj.variables,'sigma_n');
+                        noise(:,:,ii) = [head(:,1,ii), ones(size(head,1),2) .* norminv(Pnoise,0,1) .* obj.variables.sigma_n(ii)];
+                    else
+                        noise(:,:,ii) = [head(:,1,ii), zeros(size(head,1),2)];
+                    end                
+                end
+            else
+                head = headtmp;                
+                
+                % Create noise component output.
+                if isfield(obj.variables,'sigma_n');
+                    noise(:,:) = [head(:,1), norminv(Pnoise,0,1) .* obj.variables.sigma_n(ones(size(head,1),2))];
+                else
+                    noise(:,:) = [head(:,1), zeros(size(head,1),2)];
+                end                
+                
+            end            
+                        
+            % Assign column names
+            colnames = {'time','h_star'};
+                                 
+        end
+        
+        function [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
+            
+            % Extract time steps
+            t_filt = find( obj.inputData.head(:,1) >=t_start  ...
+                & obj.inputData.head(:,1) <= t_end );   
+            obj.variables.calibraion_time_points = obj.inputData.head(t_filt,1);
+            time_points =  obj.variables.calibraion_time_points;
+            
+            % STore time difference
+            obj.variables.delta_t = diff(time_points,1)./365;
+            obj.variables.meanDelta_t = mean(obj.variables.delta_t);
+            
+            % Set a flag to indicate that calibration is complete.
+            obj.variables.doingCalibration = true;
+            
+            % Calculate the mean head during the calibration period
+            obj.variables.meanHead_calib = mean(obj.inputData.head(t_filt,2));
+
+            % Create logical matrix indicating if the time point is an
+            % observation. If true, then the data point is used to update
+            % the exponential smoothing.  Else, a forecast is made using
+            % the exonential smoothing using the smooth terms from the 
+            % previous observation. For the calibration, this vector is
+            % true.
+            obj.variables.isObsTimePoints = true(size(obj.variables.calibraion_time_points));
+            
+            % Convert logical to double for MEX input
+            obj.variables.isObsTimePoints = double(obj.variables.isObsTimePoints);
+            
+            % Estimate the initial value and slope by fitting a smoothed
+            % spline.
+            %spline_model = smooth(time_points, obj.inputData.head(t_filt,2), 'rloess');
+            spline_time_points = [0; 1; (time_points(2:end)-time_points(1))]./365;
+            spline_vals = csaps((time_points-time_points(1))./365, obj.inputData.head(t_filt,2) - obj.variables.meanHead_calib,0.1,spline_time_points );
+            obj.variables.initialHead = spline_vals(1);
+            obj.variables.initialTrend = (spline_vals(2) - spline_vals(1))./(spline_time_points(2) - spline_time_points(1) );
+            
+            % Find upper limit for alpha above which numerical errors arise.
+            beta_upperLimit = 1000; 
+            delta_time = diff(obj.inputData.head(:,1),1)./365;
+            while abs(sum( exp( -2.*beta_upperLimit .* delta_time ) )) < eps() ...
+            || exp(mean(log( 1- exp( -2.*beta_upperLimit .* delta_time) ))) < eps()
+                beta_upperLimit = beta_upperLimit - 0.1;
+                if beta_upperLimit <= eps()                                   
+                    break;
+                end
+            end
+            if beta_upperLimit <= eps()
+                obj.variables.beta_upperLimit = 3;
+            else
+                % Transform alpha log10 space.
+                obj.variables.beta_upperLimit = log10(beta_upperLimit);
+            end      
+            
+            % Find lower limit for alpha. This is based on the assumption
+            % that if the best model (ie explains 95% of variance) then 
+            % for such a model the estimate of the
+            % moving average noise should be << than the observed
+            % head standard dev..                        
+            if ~isinf(obj.variables.beta_upperLimit)
+                beta_lowerLimit = -10;         
+                obsHead_std = std(obj.inputData.head(t_filt,2));   
+                while sqrt(mean( 0.05.*obsHead_std^2 ./ (1 - exp( -2 .* 10.^beta_lowerLimit .* obj.variables.delta_t)))) ...
+                > obsHead_std                       
+
+                    if beta_lowerLimit >= (obj.variables.beta_upperLimit - 2)
+                        break;
+                    end
+
+                    beta_lowerLimit = beta_lowerLimit + 0.1;
+                end
+                obj.variables.beta_lowerLimit = beta_lowerLimit;
+            else
+                obj.variables.beta_lowerLimit = -5;
+            end
+            
+            
+            % Assign initial params to outputs
+            params_initial = getParameters(obj);
+            
+            % Clear estimate of constant
+            if isfield(obj.parameters,'Const')
+                obj.parameters.Const = [];
+            end
+        end
+        
+        function calibration_finalise(obj, params, useLikelihood)          
+            
+            % Re-calc objective function and deterministic component of the head and innocations.
+            % Importantly, the drainage elevation (ie the constant term for
+            % the regression) is calculated within 'objectiveFunction' and
+            % assigned to the object. When the calibrated model is solved
+            % for a different time period then this
+            % drainage value will be used by 'objectiveFunction'.
+            [obj.variables.objFn, obj.variables.h_star, obj.variables.h_forecast] = objectiveFunction(params, obj.variables.calibraion_time_points, obj, useLikelihood);                        
+ 
+            % Re-calc residuals and assign to object                        
+            t_filt = obj.inputData.head(:,1) >=obj.variables.calibraion_time_points(1)  ...
+                & obj.inputData.head(:,1) <= obj.variables.calibraion_time_points(end);                           
+            obj.variables.resid = obj.inputData.head(t_filt,2)  -  obj.variables.h_forecast;
+            
+            % Calculate mean of noise. This should be zero +- eps()
+            % because the drainage value is approximated assuming n-bar = 0.
+            obj.variables.n_bar = real(mean( obj.variables.resid ));
+            
+            % Calculate innovations
+            innov = obj.variables.resid(2:end) - obj.variables.resid(1:end-1).*exp( -10.^obj.parameters.beta .* obj.variables.delta_t );            
+            
+            % Calculate noise standard deviation.
+            obj.variables.sigma_n = sqrt(mean( innov.^2 ./ (1 - exp( -2 .* 10.^obj.parameters.beta .* obj.variables.delta_t))));                    
+            
+            % Set a flag to indicate that calibration is complete.
+            obj.variables.doingCalibration = false;
+        end
+        
+        function [objFn, h_star, h_forecast] = objectiveFunction(params, time_points, obj, getLikelihood)                
+
+            % Check the required object variables are set.
+            if ~isfield(obj.variables,'isObsTimePoints') ...
+            || ~isfield(obj.variables,'meanHead_calib')    
+                error('The model does not appear to have been initialised for calibration.');
+            end
+               
+            % Check the input time points and the logical vector denoting
+            % observartion time points are of equal length
+            if length(time_points) ~= length(obj.variables.isObsTimePoints)
+                error('The input time points vector must be the same length as the logical vector of observation time points.');
+            end
+                        
+            % Set model parameters         
+            setParameters(obj, params, {'alpha','beta','gamma'});            
+            
+            % Get model parameters and transform them from log10 space.
+            alpha = 10.^obj.parameters.alpha;            
+            beta = 10.^obj.parameters.beta;
+            gamma = 10.^obj.parameters.gamma;
+            
+            % Get time points
+            t = obj.inputData.head(:,1);
+
+            % Create filter for time points and apply to t
+            t_filt =  find(t >=time_points(1) & t <= time_points(end));                   
+            
+            % Setup time-varying weigting terms from input parameters
+            % and their values at t0 using ONLY the time points with
+            % observed head values!
+            q = mean(obj.variables.delta_t);
+            alpha_i = 1 - (1 - alpha)^q;
+            gamma_i = 1 - (1 - gamma)^q;
+            
+            % Initialise the output and subract the mean from the observed
+            % head.
+            h_ar = zeros(length(time_points),1);
+            h_obs = obj.inputData.head(:,2) - obj.variables.meanHead_calib;            
+            
+            % Assign linear regression estimate the initial slope and intercept.
+            %h_trend(1) = obj.variables.initialTrend_calib;
+            %h_trend = obj.variables.initialTrend_calib;
+            h_trend = obj.variables.initialTrend;
+            h_ar(1) = obj.variables.initialHead;
+            h_forecast = h_ar;
+            indPrevObsTimePoint = 1;
+            indPrevObs = 1;
+            
+            % Undertake double exponential smoothing.
+            % Note: It is based on Cipra T. and Hanzák T. (2008). Exponential 
+            % smoothing for irregular time series. Kybernetika,  44(3), 385-399.
+            % Importantly, this can be undertaken for time-points that have observed 
+            % heads and those that are to be interpolated (or extrapolated). 
+            % The for loop cycles though each time point to be simulated.
+            % If the time point is an observation then the alpha, gamma and
+            % h_trend are updates and an index is stored pointng to the last true obs point. 
+            % If the time point is not an observation then a forecast is
+            % undertaken using the most recent values of alpha, gamma and
+            % h_trend
+            for i=2:length(time_points)
+               
+               delta_t = (time_points(i)-time_points(indPrevObsTimePoint))/365;
+                
+               if obj.variables.isObsTimePoints(i)
+                   % Update smoothing model using the observation at the
+                   % current time point.
+                   if indPrevObs==1
+                       gamma_weight = (1-gamma)^delta_t;
+                   else                                              
+                       gamma_weight = delta_t_prev/delta_t * (1-gamma)^delta_t;            
+                   end                    
+                   
+                   alpha_weight = (1-alpha).^delta_t;
+                   
+                   alpha_i = alpha_i./(alpha_weight + alpha_i); 
+                   gamma_i = gamma_i./(gamma_weight + gamma_i);
+                   h_ar(i) = (1-alpha_i) * (h_ar(indPrevObsTimePoint) + delta_t * h_trend) + alpha_i * h_obs(indPrevObs+1);
+                   h_forecast(i) = h_ar(indPrevObsTimePoint) + delta_t * h_trend;
+                   h_trend = (1-gamma_i) * h_trend + gamma_i * (h_ar(i) - h_ar(indPrevObsTimePoint))./delta_t;
+                                                         
+                   indPrevObsTimePoint = i;
+                   indPrevObs = indPrevObs+1;
+                   delta_t_prev = delta_t;
+               else
+                   % Make a forecast to the current non-observation time
+                   % point.
+                   h_forecast(i) = h_ar(indPrevObsTimePoint) + delta_t * h_trend;
+                   h_ar(i) = h_forecast(i);
+               end
+            end            
+            
+            % Add the mean head onto the smoothed estimate and forecast
+            h_ar = h_ar + obj.variables.meanHead_calib;
+            h_forecast = h_forecast + obj.variables.meanHead_calib;
+             
+            % Assign output for non-corrected head
+            h_star = h_ar;
+            
+            %% Calculate the moving average componant.                        
+            if ~obj.variables.doingCalibration
+                objFn=[];
+                return
+            end
+
+            % Create natrix ob observed and forecast heads
+            h_mat = [obj.inputData.head(t_filt,2), h_forecast];
+
+            % Calculate residuals (excluding input outliers).
+            resid = diff(h_mat,1,2);
+
+            % Calculate the innovations
+            innov = resid(2:end,:) - resid(1:end-1,:).*exp( bsxfun(@times, -beta ,obj.variables.delta_t) );
+
+            % Calculate the weighted least squares objective function
+            objFn = sum( bsxfun(@rdivide, exp(mean(log( 1- exp( bsxfun(@times, -2*beta ,obj.variables.delta_t )) ))) ...
+                        ,(1- exp(  bsxfun(@times, -2*beta ,obj.variables.delta_t) ))) .* innov.^2);   
+                    
+            % Calculate log liklihood    
+            if getLikelihood
+                N = size(resid,1);
+                objFn = -0.5 * N * ( log(2*pi) + log(objFn./N)+1); 
+            end                    
+                    
+        end
+        
+        function setParameters(obj, params, param_names)
+            obj.parameters.(param_names{1})= params(1);
+            obj.parameters.(param_names{2})= params(2);
+            obj.parameters.(param_names{3})= params(3);
+        end
+        
+        function [params, param_names] = getParameters(obj)
+            params(1,:) = obj.parameters.alpha;
+            params(2,:) = obj.parameters.beta;
+            params(3,:) = obj.parameters.gamma;
+            param_names = {'alpha','beta','gamma'};
+        end
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)            
+            
+            if isfield(obj.variables,'beta_upperLimit')
+                params_upperLimit = [0; obj.variables.beta_upperLimit; 0];
+            else
+                params_upperLimit = [0; 5 ; 0];
+            end
+            if isfield(obj.variables,'beta_lowerLimit')
+                params_lowerLimit = [-inf; obj.variables.beta_lowerLimit; -inf];
+            else
+                params_lowerLimit = [-inf; -5 ; -inf];
+            end            
+        end
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+
+            params_upperLimit = [0; 2; 0];
+            params_lowerLimit = [-3; -2; -3];
+
+        end        
+        
+        function isValidParameter = getParameterValidity(obj, params, time_points)
+
+            % Get physical limits and test if parames are within the range
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+            isValidParameter = params >= repmat(params_lowerLimit,1,size(params,2)) & params <= repmat(params_upperLimit,1,size(params,2));
+                        
+            % Check the alphanoise parameter is large enough not to cause numerical
+            % problems in the calcuation of the objective function.            
+            filt_noiseErr = exp(mean(log( 1- exp( bsxfun(@times,-2.*10.^params(2,:) , obj.variables.delta_t) )),1)) <= eps() ...
+                         | abs(sum( exp( bsxfun(@times,-2.*10.^params(2,:) , obj.variables.delta_t) ),1)) < eps();                
+            isValidParameter(2,filt_noiseErr)= false;                
+        end
+       
+        %% Get the forcing data from the model
+        function [forcingData, forcingData_colnames] = getForcingData(obj)
+            forcingData = [];
+            forcingData_colnames = {}; 
+        end
+        
+        %% Set the forcing data from the model
+        function setForcingData(obj, forcingData, forcingData_colnames)
+            % do nothing. The model does not use forcing data.
+        end          
+        
+
+        %% Get the obs head data from the model
+        function head = getObservedHead(obj)
+            head = obj.inputData.head;
+        end
+        
+        
+        function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+            propNames = properties(obj);
+            for i=1:length(propNames)
+               if isempty(obj.(propNames{i}))
+                   continue;
+               end                
+               if isobject(obj.(propNames{i}))
+                delete(obj.(propNames{i}));
+               else               
+                obj.(propNames{i}) = []; 
+               end
+            end
+        end            
+    end
+       
+end
+
diff --git a/algorithms/models/TransferNoise/Example_model/Brucknell/climate_Brucknell_Catchment_ETMortonCRAE.csv b/algorithms/models/TransferNoise/Example_model/Brucknell/climate_Brucknell_Catchment_ETMortonCRAE.csv
new file mode 100644
index 0000000..29e0a0e
--- /dev/null
+++ b/algorithms/models/TransferNoise/Example_model/Brucknell/climate_Brucknell_Catchment_ETMortonCRAE.csv
@@ -0,0 +1,23682 @@
+"","catchmentAvg.DN","catchmentAvg.year","catchmentAvg.month","catchmentAvg.day","catchmentAvg.precip_mm","catchmentAvg.Tmin","catchmentAvg.Tmax","catchmentAvg.vprp","catchmentAvg.solarrad","catchmentAvg.solarrad_interp","catchmentAvg.ET_mm","catchmentvar.DN","catchmentvar.year","catchmentvar.month","catchmentvar.day","catchmentvar.precip_mm","catchmentvar.Tmin","catchmentvar.Tmax","catchmentvar.vprp","catchmentvar.solarrad","catchmentvar.solarrad_interp","catchmentvar.ET_mm"
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+"1960",100,1960,5,13,4.50605062027313,4.11963695504091,12.7561056244098,12.5231681232012,0,7.75876973244486,1.91868961812322,100,1960,5,13,3.9255555783378,0.0713590636733071,0.150760848744774,0.018594159134143,NA,0.0134069890389539,0.00043293953954009
+"1961",100,1960,5,14,14.0942794382244,8.45672165311471,12.3629042737686,10.9752365894968,0,7.54920572821894,1.887760817544,100,1960,5,14,0.441754371576217,0.0154666467031518,0.0800819134629282,0.0240228250270067,NA,0.00502054612327135,0.000421051499755207
+"1962",100,1960,5,15,10.4324533029227,7.32143018059474,11.9647306016307,9.62176025084274,0,6.94109059622491,1.85683201696479,100,1960,5,15,5.2811694885277,0.0296923904396881,0.111804145645729,0.022314063550814,NA,0.00702796329865624,0.000410674833448445
+"1963",100,1960,5,16,9.27568742596801,6.76056099462562,12.9053025418776,9.6985697940369,0,6.98041449992814,1.82590321638557,100,1960,5,16,4.64005831500945,0.0405427015996391,0.0565673045778072,0.0442134485301296,NA,0.0060241679604574,0.000401809540619805
+"1964",100,1960,5,17,2.38987900715063,7.66641360829503,13.153916475403,9.12459859438855,0,6.72641511977834,1.79497441580635,100,1960,5,17,0.45830412520303,0.025358473522358,0.0819362146612601,0.0260397451367882,NA,0.00231859592022028,0.000394455621269286
+"1965",100,1960,5,18,0.76754675346567,7.86369643248085,12.4925413152697,10.4781628746142,0,7.39543567227556,1.76404561522714,100,1960,5,18,0.0625146180600452,0.267306467305043,0.0685830378103583,0.0099222377353907,NA,0.0081722710471268,0.000388613075396889
+"1966",100,1960,5,19,0,3.51801981626958,15.3340262968023,11.7599010247209,0,7.0604466397027,1.73311681464792,100,1960,5,19,0,0.109904096134369,0.0221731349789341,0.00133158602807404,NA,0.00154143737092538,0.000384281903002614
+"1967",100,1960,5,20,0.0863586371504303,4.27127610915827,12.5275136855307,12.5795930593845,0,6.62619676011811,1.70218801406871,100,1960,5,20,0.00175438601719706,0.0759228115316204,0.0194871757753363,0.00647018033081973,NA,0.0205018351824426,0.000381462104086459
+"1968",100,1960,5,21,3.16479646569431,5.48704067656178,12.1485368667787,10.5107260820496,0,6.70895254558341,1.67125921348949,100,1960,5,21,0.0528654991395334,0.0463537891343512,0.0573362778112297,0.00975848771710647,NA,0.0096483385069647,0.000380153678648423
+"1969",100,1960,5,22,1.63003299732019,6.93086909356028,12.5925852310802,10.2493509307291,0,6.13139957813827,1.64033041291027,100,1960,5,22,0.0503508672519053,0.0473450460713032,0.0520731138932786,0.00871110615207726,NA,0.00479026163069252,0.000380356626688511
+"1970",100,1960,5,23,1.16182618330021,7.4152585250972,11.8812762062148,10.4982839108038,0,6.97696248428729,1.60940161233106,100,1960,5,23,0.0191812821020175,0.0499719442947752,0.0119093328666439,0.0073953339672576,NA,0.00864859409791655,0.000382070948206721
+"1971",100,1960,5,24,0,2.02066002033725,14.865104474906,11.9994170479517,0,6.67319448045514,1.57847281175184,100,1960,5,24,0,0.0302842037100572,0.0229064404784261,0.00802224824705082,NA,0.00456723011477431,0.000385296643203052
+"1972",100,1960,5,25,0.0863586371504303,4.19182619481984,17.6351705137784,13.2438394118457,0,6.25100109795626,1.54754401117263,100,1960,5,25,0.00175438601719706,0.23038421368349,0.0454719488626478,0.0067485348440136,NA,0.00887715033797632,0.000390033711677504
+"1973",100,1960,5,26,0,6.13473041721172,17.9344006570915,13.9543564065193,0,5.80489284281059,1.51661521059341,100,1960,5,26,0,0.0202263310572059,0.0296983276145793,0.00328422590118011,NA,0.0104869923642608,0.000396282153630078
+"1974",100,1960,5,27,3.39306929900964,5.07729367769197,16.6896040436983,13.154741520142,0,6.19191344029842,1.48568641001419,100,1960,5,27,0.538187180876044,0.072247377826197,0.269467272583681,0.0178924149541613,NA,0.0265957910630505,0.000404041969060772
+"1975",100,1960,5,28,5.81078109856617,8.53331128152946,18.3038282090157,14.5587569573531,0,6.17878006643869,1.45475760943498,100,1960,5,28,0.0809356659337258,0.0103040793793399,0.0795578251981547,0.0394175125680619,NA,0.00557876569954432,0.000413313157969587
+"1976",100,1960,5,29,3.16248628303687,7.30869090150554,13.8409461177746,11.8515622093864,0,6.21801767530851,1.42382880885576,100,1960,5,29,0.0832163657082488,0.10771637539001,0.06433628894302,0.00843742201740024,NA,0.00961406975220803,0.000424095720356526
+"1977",100,1960,5,30,13.6620461460781,6.6369196715528,12.8912432715707,11.1052364594866,0,6.67004848807869,1.39290000827655,100,1960,5,30,0.780233892808935,0.209402309563866,0.0312707504206266,0.0212385760592601,NA,0.0142872713527743,0.000436389656221586
+"1978",100,1960,5,31,4.07953797236528,8.6904179601386,12.1950053864449,11.9917601122715,0,6.15065679090489,1.36197120769733,100,1960,5,31,0.175964888689827,0.0912029176389398,0.110222262064712,0.0124719205087248,NA,0.00551986487728535,0.000450194965564766
+"1979",100,1960,6,1,3.51122111718122,7.60228817743568,12.4411662713398,12.6869306606297,0,6.2488602128617,1.31485084728394,100,1960,6,1,0.218771951602922,0.068609947434386,0.0484561649345246,0.0089508831538057,NA,0.0039790554283129,0.000630217747734533
+"1980",100,1960,6,2,0.664906495409568,6.16644662284222,12.9870957352541,11.9892410932988,0,6.33324654218994,1.26773048687055,100,1960,6,2,0.123157895148149,0.0274590596929825,0.0847777294583737,0.00896197352498699,NA,0.00639378344704877,0.000853245880648844
+"1981",100,1960,6,3,2.03586357209024,5.41954893221294,13.7782398597373,10.7199119194375,0,6.37508699991993,1.22061012645717,100,1960,6,3,0.246491215800685,0.12135964410349,0.0607030012165284,0.00370294323758508,NA,0.00257471217136412,0.0011192793643077
+"1982",100,1960,6,4,0.00363036309040026,3.35112209314823,13.3524752848744,10.5230583069217,0,6.39185592193007,1.17348976604378,100,1960,6,4,0.000526315805159117,0.084632152969521,0.130450827896444,0.0139315733168582,NA,0.0257896177999702,0.00142831819871109
+"1983",100,1960,6,5,0.0650165026189864,3.18003298242231,14.4218811920636,11.7041143925145,0,6.20981023601723,1.12636940563039,100,1960,6,5,0.00228070182235617,0.0623590625327972,0.149950942426612,0.0151040969380843,NA,0.00328825046109424,0.00178036238385903
+"1984",100,1960,6,6,0.133883390333852,6.28609464297069,14.2882178477591,12.3732671748162,0,5.66572961146101,1.079249045217,100,1960,6,6,0.00257309949188902,0.231547392183733,0.168936297123387,0.013564919312302,NA,0.00361530813408492,0.00217541191975151
+"1985",100,1960,6,7,0.0224422445588379,5.65939491836414,14.2083167652092,11.4844003788578,0,6.24501869501017,1.03212868480361,100,1960,6,7,0.00245614042407588,0.203387745546056,0.0721339275271542,0.00571520958847723,NA,0.00825154612448358,0.00261346680638853
+"1986",100,1960,6,8,0.604510459962851,5.07860277614447,13.4057427396869,12.1230362849136,0,5.79285258206969,0.985008324390226,100,1960,6,8,0.0132163729026306,0.0745912150745644,0.0398842414767456,0.00196548404143512,NA,0.00494803857989209,0.00309452704377009
+"1987",100,1960,6,9,1.29460946430336,6.25820687370594,14.4352255234755,12.5878327456769,0,5.89237035651936,0.937887963976838,100,1960,6,9,0.100233923956666,0.0479479774586872,0.138431605418329,0.0211140356678373,NA,0.0143213252368924,0.00361859263189619
+"1988",100,1960,6,10,0.882068195752185,6.48694166649293,11.8818702918075,9.63889983253773,0,6.18151580618553,0.89076760356345,100,1960,6,10,0.00777777751286855,0.055480088520624,0.0816397508521641,0.0103163864649381,NA,0.012391680590461,0.00418566357076684
+"1989",100,1960,6,11,11.8157315364372,2.22496149196352,11.0552915503876,10.2949064032342,0,6.03587733125529,0.843647243150062,100,1960,6,11,1.21315778598458,0.0450561404389997,0.299613471525366,0.00830934787557171,NA,0.00338334902777801,0.00479573986038203
+"1990",100,1960,6,12,1.74444442338283,3.44134215631894,13.5037622777018,12.8008471311647,0,5.32496301691719,0.796526882736674,100,1960,6,12,0.269415222371533,0.0118339219506063,0.108898858638193,0.00502634086396885,NA,0.00607640757060825,0.00544882150074177
+"1991",100,1960,6,13,2.43399339422534,5.92198020160788,13.3576018225373,11.1917601565454,0,5.98021244007442,0.749406522323286,100,1960,6,13,0.0713450400452906,0.0799666545444287,0.238614660979219,0.0100140476093731,NA,0.0133668813613048,0.00614490849184604
+"1992",100,1960,6,14,0.00297029707396385,5.14761272121971,12.4080528001187,9.47676554135363,0,5.94861606110309,0.702286161909898,100,1960,6,14,0.000994152076411666,0.0245040878206837,0.111702913199612,0.000673099163200091,NA,0.0114211913445697,0.00688400083369485
+"1993",100,1960,6,15,5.36644658528277,5.51961498606716,10.9347963721314,10.4533993193288,0,6.22538795844294,0.65516580149651,100,1960,6,15,0.176198852382924,0.0558368302194863,0.165411132049634,0.00631638254986013,NA,0.0165000143846939,0.00766609852628821
+"1994",100,1960,6,16,17.3060507527804,5.54721675218135,12.3974697692166,11.9843345428064,0,6.15086754760939,0.608045441083122,100,1960,6,16,5.45596381561134,0.015887711330896,0.116180675604818,0.00471635961259264,NA,0.00437458530728877,0.00849120156962611
+"1995",100,1960,6,17,4.33861386002225,6.58261827221273,12.2889989353511,12.2528493910602,0,5.84192557635354,0.560925080669734,100,1960,6,17,0.352573093252586,0.081992373225587,0.111848571601194,0.00427019066180904,NA,0.00232760046695561,0.00935930996370855
+"1996",100,1960,6,18,2.2361936055132,6.63744779099988,11.6463476271257,12.071738243103,0,6.00485414236062,0.513804720256346,100,1960,6,18,0.0609941519213036,0.370887764396462,0.0593175481451174,0.0269099350890849,NA,0.00436755577459673,0.0102704237085355
+"1997",100,1960,6,19,0.435973601503865,6.0333222881271,11.8829262390389,11.0327832512598,0,6.11508838722327,0.466684359842958,100,1960,6,19,0.023742689563517,0.0934274862880804,0.101433335367892,0.00823741174673686,NA,0.0154076640707701,0.0112245428041071
+"1998",100,1960,6,20,0.582398257043102,3.57507148276855,12.3566996319459,9.91324539100639,0,5.60658843744594,0.419563999429569,100,1960,6,20,0.00321637433871907,0.0562584695888583,0.0911023246699028,0.00892221739037023,NA,0.0122743618227805,0.0122216672504231
+"1999",100,1960,6,21,0.100000001490116,4.55321232320452,12.9590980385003,10.8775246350548,0,5.71921094924504,0.372443639016181,100,1960,6,21,0,0.0930099140569021,0.0977094118174594,0.0072450363628292,NA,0.0114935585398629,0.0132617970474837
+"2000",100,1960,6,22,0,4.0896480036254,12.4221452706718,9.50853681721703,0,5.69763856035259,0.325323278602793,100,1960,6,22,0,0.179513435548941,0.137395315451437,0.0127730763910201,NA,0.00545963047517543,0.0143449321952889
+"2001",100,1960,6,23,0,1.83177118424666,11.8044333935309,9.69501651159608,0,5.4140803024517,0.278202918189405,100,1960,6,23,0,0.070633921966779,0.119848566604145,0.00329300375383275,NA,0.0047373996980867,0.0154710726938386
+"2002",100,1960,6,24,3.90649063214742,4.35757977367103,11.9315180909647,11.1276017022212,0,5.92964128530964,0.231082557776017,100,1960,6,24,2.28140350750317,0.230432182940003,0.0962888970270052,0.00743217441277809,NA,0.0038663292316584,0.0166402185431328
+"2003",100,1960,6,25,5.92431246303227,5.62479657308497,10.8489218167346,9.07389425313381,0,5.94937141559079,0.183962197362629,100,1960,6,25,0.470292363417797,0.405191224080376,0.0527982519608156,0.0151339441690493,NA,0.00484361868414744,0.0178523697431716
+"2004",100,1960,6,26,0,6.55630358925747,12.2350165647249,10.3299119968225,0,6.45144032858376,0.136841836949241,100,1960,6,26,0,0.0939444559945229,0.0921812455250871,0.00659530822136007,NA,0.0134497423307368,0.0191075262939549
+"2005",100,1960,6,27,0,2.95544558061887,12.8838392830525,11.9812541894525,0,6.24726465747695,0.0952748636531518,100,1960,6,27,0,0.39933744101943,0.0777005917466314,0.0141701863629999,NA,0.00569481608362092,0.0188708812102826
+"2006",100,1960,6,28,0,1.21110010212666,13.2111991834063,10.8703961881212,0,5.94045797574347,0.067158846450978,100,1960,6,28,0,0.118783037223175,0.0678316007626074,0.00137016755493198,NA,0.023551797215854,0.0160087669916746
+"2007",100,1960,6,29,0.00363036309040026,1.5092519242378,12.89489553995,9.374224492819,0,5.60004097597945,0.0470264137944545,100,1960,6,29,0.000526315805159117,0.0525426870979079,0.196454402459627,0.0026497101053893,NA,0.00795992767568609,0.0131303299346206
+"2008",100,1960,6,30,0.00363036309040026,-0.471738175274876,11.1144774786317,9.82448839642952,0,5.40378248794665,0.0339128063879581,100,1960,6,30,0.000526315805159117,0.116376607740649,0.069602327447268,0.00647077318387203,NA,0.00937510888078547,0.0105328265943207
+"2009",100,1960,7,1,0.0746974708600537,0.93603961368074,10.2196149332951,9.69007686891965,0,6.2134391868663,0.0434308146417769,100,1960,7,1,0.00578947385675029,0.0108274874756913,0.0609052117398751,0.00267602096477414,NA,0.00519470983487557,0.0122352190664113
+"2010",100,1960,7,2,0.0118811882958554,-3.22788780016212,9.98996696430202,8.47207924556417,0,6.21260835752847,0.0562073640965118,100,1960,7,2,0.000994152076411666,0.135546792816701,0.0729228345146633,0.014728658667321,NA,0.00685110136823128,0.0140856525210606
+"2011",100,1960,7,3,0.0971397167549144,-1.45548955914211,10.8046095546978,8.28962588126641,0,6.36067221759009,0.0723443738666563,100,1960,7,3,0.0822807043677534,0.0366538038153406,0.127981215612029,0.0174508814137752,NA,0.00788030636586113,0.0161881638155499
+"2012",100,1960,7,4,15.605280385159,1.6940704007091,9.03918589643388,6.96096812511566,0,5.85808543773465,0.0945088311452457,100,1960,7,4,0.424502672563648,0.0140801222968535,0.219527434136102,0.0267257289853224,NA,0.0100227296561663,0.0179570018443673
+"2013",100,1960,7,5,4.71452144165375,4.68135314255264,11.5339053608272,10.5545544430236,0,6.08403246871058,0.122305031072427,100,1960,7,5,0.30222216500178,0.11829823274224,0.0816098928574995,0.00233333312144966,NA,0.010617941039783,0.0189894017455922
+"2014",100,1960,7,6,3.51672169057974,6.15705170542231,11.4443785234122,10.2606710060464,0,6.51323139786851,0.154376889154282,100,1960,7,6,0.0169005787721201,0.0229041002976387,0.208227374124873,0.00924268985780049,NA,0.00643834703623641,0.0187494801507732
+"2015",100,1960,7,7,0.0115511552876372,5.90031910624572,9.43981297496128,7.01684263174814,0,6.5175302520022,0.188712971464048,100,1960,7,7,0.0031578948309547,0.0668356498283443,0.0567923513826135,0.0196701864688892,NA,0.0095936439181551,0.0178914008261223
+"2016",100,1960,7,8,0.000110011002739402,1.93126513895029,12.4705500114869,7.36489547807129,0,6.42301506225303,0.223049053773814,100,1960,7,8,0.000526315805159117,0.0466321635923908,0.0643596317448217,0.0291801217296941,NA,0.0148372999297138,0.0170647619731788
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+"7610",100,1975,11,1,1.79559956250018,10.4894609724072,15.4144885039041,9.69934879487628,0,18.1163154168172,3.77291195734679,100,1975,11,1,0.29163742103075,0.0399474053857689,0.0728245182038107,0.0637370798681199,NA,0.0418351476770849,0.00338090547611269
+"7611",100,1975,11,2,0.348954903213891,8.06345426813342,17.0576455711138,11.0855424690037,0,17.1523567673679,3.81145227208073,100,1975,11,2,0.0133918123583348,0.0291275011542644,0.036028609368711,0.0413839178827774,NA,0.0295391693227099,0.00333304007112201
+"7612",100,1975,11,3,6.06996699449646,10.2557535328881,17.9094609322459,15.699095603263,0,17.3677866578334,3.84999258681467,100,1975,11,3,0.110058470664682,0.00895789655727672,0.0209467482184807,0.0103170416522309,NA,0.0845708601220506,0.00328577166700913
+"7613",100,1975,11,4,10.8775577366811,11.0411111938678,15.4456986059057,14.1190405206712,0,18.8090934361348,3.88853290154861,100,1975,11,4,4.39204709772486,0.0254497100072486,0.0539053220760858,0.0275263888651836,NA,0.028487930049348,0.00323910026377406
+"7614",100,1975,11,5,11.1361936405547,9.94328932872306,14.4661716528327,13.8028690035981,0,18.7151212207415,3.92707321628255,100,1975,11,5,2.99619893637323,0.105598748009763,0.129268943329397,0.0123417868322385,NA,0.0146640883765506,0.00319302586141683
+"7615",100,1975,11,6,8.83223322332233,9.60035202185838,15.4052364288515,12.7350144003353,0,20.4580579144427,3.96561353101649,100,1975,11,6,1.42777777777782,0.0330479633532898,0.02286668451105,0.0249269633527946,NA,0.0778424940028078,0.00314754845993741
+"7616",100,1975,11,7,3.63553357176786,8.8620682339726,15.4970737117352,10.5799208484729,0,20.1155305132962,4.00415384575044,100,1975,11,7,0.17052633101481,0.184249642583004,0.0381608245917045,0.0417415579406001,NA,0.032063485915465,0.0031026680593358
+"7617",100,1975,11,8,0.00847084721093393,4.53224422488407,17.4370076207831,11.253078086148,0,19.2387587513133,4.04269416048438,100,1975,11,8,0.00175438601719706,0.0620578765066224,0.0253386268262344,0.0326086174167018,NA,0.0556834718558233,0.003058384659612
+"7618",100,1975,11,9,0.0592959304765375,10.5900109871255,18.314378406873,13.6947063303361,0,22.5149304257653,4.08123447521832,100,1975,11,9,0.00485380131424519,0.0932912114194444,0.0242175214268189,0.00301809403416569,NA,0.123117737729317,0.00301469826076604
+"7619",100,1975,11,10,0,7.68029705735848,16.7970627993509,15.4527589768598,0,22.6242720108446,4.11977478995226,100,1975,11,10,0,0.15974970604414,0.0449560176563614,0.0263124994926542,NA,0.0437831970280865,0.00297160886279789
+"7620",100,1975,11,11,0.398459851807362,11.9908361613291,16.3214523307037,12.9964994969803,0,22.1976701354767,4.1583151046862,100,1975,11,11,0.00777777718173135,0.01998715968002,0.0526953929302394,0.0160792448975281,NA,0.0502927876397874,0.00292911646570756
+"7621",100,1975,11,12,0,11.6242024198224,19.2860616153092,14.0222199820843,0,21.4775190116753,4.19685541942014,100,1975,11,12,0,0.0405912222299536,0.00740410992386376,0.055432675446562,NA,0.0342006519014201,0.00288722106949505
+"7622",100,1975,11,13,0,7.61075903217916,24.7112538366034,13.6228799882895,0,21.0321182722315,4.23539573415408,100,1975,11,13,0,0.0409585076248684,0.0326590764053802,0.0245393297796983,NA,0.0718129734907854,0.00284592267416034
+"7623",100,1975,11,14,0.387128716377732,13.0570077518425,27.8437842945061,16.7604376936641,0,20.571842699798,4.27393604888802,100,1975,11,14,0.0503508762884559,0.0038385991526693,0.21796993662552,0.0423267426652417,NA,0.112694341403832,0.00280522127970345
+"7624",100,1975,11,15,0.968976901014252,13.8335643124134,16.9811769030144,15.6969834958235,0,23.2030661805291,4.31247636362196,100,1975,11,15,0.103742685952383,0.00559240040701835,0.0608286122597741,0.0708800725363805,NA,0.137851747306798,0.00276511688612439
+"7625",100,1975,11,16,0.381408145505734,10.613355331295,22.7539383706742,16.9496896720693,0,24.7295905096006,4.3510166783559,100,1975,11,16,0.0642690055542871,0.0859707770097342,0.557922428046655,0.0469161358205649,NA,0.0371772754590769,0.00272560949342315
+"7626",100,1975,11,17,0,13.6034323405905,28.6159294094845,19.2024068989769,0,24.148955329114,4.38955699308984,100,1975,11,17,0,0.0770397517065552,0.166203590968869,0.0259911626697069,NA,0.0233373615130368,0.00268669910159972
+"7627",100,1975,11,18,0,17.4520242982703,30.8554891915736,19.2659164298617,0,21.3208002045108,4.42809730782379,100,1975,11,18,0,0.0634040965835499,0.0705578951787153,0.00364782373652448,NA,0.0266730473303951,0.00264838571065411
+"7628",100,1975,11,19,0.00517051712875188,19.3449723649733,32.5516065885,18.410305707237,0,21.1781073595198,4.46663762255773,100,1975,11,19,0.000994152076411666,0.045411117214911,0.00587021081093277,0.0238334227681221,NA,0.0632126673721471,0.0026106693205863
+"7629",100,1975,11,20,5.53938401991253,13.0763255781335,19.8989329930842,16.9644755855514,0,21.419772621482,4.50517793729167,100,1975,11,20,0.33766083527728,0.0300163943709787,0.0420092734757897,0.045917113830756,NA,0.0401605432938883,0.00257354993139634
+"7630",100,1975,11,21,6.5667766643436,12.8772827396036,17.8984927522598,13.6582046378695,0,20.8936445212076,4.54371825202561,100,1975,11,21,0.64830407254182,0.0970479500910671,0.0148385621101433,0.0826825544221007,NA,0.089694491898913,0.00253702754308419
+"7631",100,1975,11,22,1.49042903183579,9.20750273755937,23.0382069167,12.8764665454659,0,21.7678728646009,4.58225856675955,100,1975,11,22,0.139181286479995,0.123480687586566,0.0286801705347387,0.044834072485737,NA,0.0504666338871883,0.00250110215564986
+"7632",100,1975,11,23,13.1856985029214,12.2930693390346,16.6433114071753,12.8344752203644,0,23.7796538643505,4.62079888149349,100,1975,11,23,2.80251431281113,0.0069818905702752,0.0836426968583653,0.0131230483622102,NA,0.0335053760340412,0.00246577376909333
+"7633",100,1975,11,24,1.16061607249106,9.24221121281287,20.2772497065914,12.2178966020725,0,21.6484417658022,4.65933919622743,100,1975,11,24,0.0148537984926115,0.079071947963414,0.0172485510399293,0.0413048815689816,NA,0.0768085766762192,0.00243104238341462
+"7634",100,1975,11,25,6.98404848614935,10.4565235322589,15.8393400515398,10.7293377163911,0,21.806470596292,4.69787951096137,100,1975,11,25,2.13766076417,0.0358941362035864,0.0651356493343995,0.0192572924545362,NA,0.0254827307498443,0.00239690799861373
+"7635",100,1975,11,26,3.46798680980082,8.1262925911789,17.3288119518586,12.2098327894809,0,23.5918701750367,4.73641982569531,100,1975,11,26,0.341461988973349,0.0150497127499979,0.0774274467024186,0.0488800210073696,NA,0.0814231642743358,0.00236337061469065
+"7636",100,1975,11,27,0,7.97195816984271,24.846842708105,14.4748272261079,0,22.9315796857435,4.77496014042925,100,1975,11,27,0,0.0460760518180293,0.0224544250357223,0.0233769725694172,NA,0.0950286898050954,0.00233043023164542
+"7637",100,1975,11,28,0,12.1634433020328,31.452310499185,11.4761693522219,0,23.8526174492598,4.8135004551632,100,1975,11,28,0,0.10559875782538,0.060920505622147,0.0281884098652594,NA,0.020313092640702,0.00229808684947797
+"7638",100,1975,11,29,0,10.251122092781,21.8973925939881,15.6685236268836,0,23.7177808363717,4.85204076989714,100,1975,11,29,0,0.18718078272867,0.180909494500699,0.0378648740036183,NA,0.0339837168510944,0.00226634046818836
+"7639",100,1975,11,30,0,10.1488228882893,26.3653797195821,18.499392821057,0,23.5706844535542,4.89058108463108,100,1975,11,30,0,0.0623806931702752,0.225232142135307,0.035666733001286,NA,0.140510444714971,0.00223519108777656
+"7640",100,1975,12,1,0.0763476359011448,12.5987899117213,23.9746203532707,18.6538250779424,0,24.4642341768072,4.92623351732629,100,1975,12,1,0.00690058500097509,0.0827894632886976,0.0300479991359074,0.141299117073776,NA,0.0801482823874876,0.00221494369791271
+"7641",100,1975,12,2,0,13.2700550889287,34.6934982484455,19.082427730917,0,22.9458659666431,4.96188595002151,100,1975,12,2,0,0.00846493386394307,0.0862812109084593,0.0539105644735354,NA,0.239079508596407,0.00219610692594764
+"7642",100,1975,12,3,2.59614961113211,16.4355993155468,18.0776676308073,15.9134952337435,0,22.5366379903319,4.99753838271673,100,1975,12,3,0.386725142936264,0.00760231586960342,0.0814696418541888,0.0128784942770787,NA,0.0797721152471967,0.00217868077188134
+"7643",100,1975,12,4,0.892299220769188,10.6520351728853,18.4756764578741,12.0621309794477,0,24.2819416690469,5.03319081541195,100,1975,12,4,0.00953216427948074,0.151050819613459,0.036081889303444,0.0348542161850381,NA,0.140476192872302,0.00216266523571383
+"7644",100,1975,12,5,0,11.4094720731343,21.4747083556927,14.514254116776,0,23.5008902931933,5.06884324810716,100,1975,12,5,0,0.281459603785981,0.0182555498762433,0.00459258850951939,NA,0.0570818064680507,0.0021480603174451
+"7645",100,1975,12,6,0,12.1535312906481,30.5984820333382,16.1859482554319,0,21.8704143784494,5.10449568080238,100,1975,12,6,0,0.0270035066794832,0.0403176069162035,0.0316203859032484,NA,0.0593900793951239,0.00213486601707513
+"7646",100,1975,12,7,0,16.4572389663512,26.5785697017959,17.7787537002983,0,23.3618351826918,5.1401481134976,100,1975,12,7,0,0.00629241064455778,0.491058132783136,0.0217157155837423,NA,0.0162162748572307,0.00212308233460396
+"7647",100,1975,12,8,0,11.3121781574749,22.2446975917837,15.3365313600261,0,23.6694910694819,5.17580054619281,100,1975,12,8,0,0.0429286735334564,0.0322287168360136,0.0467909042238381,NA,0.129279162502586,0.00211270927003156
+"7648",100,1975,12,9,0,12.7608690587076,20.9166446115055,12.8735390957957,0,22.7876759391442,5.21145297888803,100,1975,12,9,0,0.071913503640224,0.0293444335093121,0.0648275614315914,NA,0.165676122989782,0.00210374682335793
+"7649",100,1975,12,10,0.153025305843261,8.42970300901054,23.2636965928954,11.2545511415689,0,24.238541578321,5.24710541158325,100,1975,12,10,0.00894736896655714,0.19588421714965,0.061905145362209,0.0334542722791693,NA,0.0396186041760018,0.00209619499458309
+"7650",100,1975,12,11,0,13.7081410132094,39.2343338524679,7.96434218445496,0,25.4006887508904,5.28275784427846,100,1975,12,11,0,0.0885333573235941,0.0513257836515039,0.173732517210518,NA,0.151377861171867,0.00209005378370703
+"7651",100,1975,12,12,0.145874589632447,17.0868204996006,21.4478217839408,16.8218008047677,0,22.9833091473973,5.31841027697368,100,1975,12,12,0.00257309949188902,0.0123999297251834,0.10062519009722,0.0672782803042734,NA,0.198069297236708,0.00208532319072975
+"7652",100,1975,12,13,0.568976910823774,13.091430174111,19.5875908187514,15.049743639754,0,20.9416920464687,5.3540627096689,100,1975,12,13,0.0136842108121394,0.0473666849349194,0.00880529067308621,0.014050370851713,NA,0.0336844790256603,0.00208200321565123
+"7653",100,1975,12,14,1.63421341721112,13.8160175621444,18.6704949293986,16.7265422157984,0,22.225815602627,5.38971514236411,100,1975,12,14,0.12871343724909,0.0237321474778901,0.0522000858733083,0.0964054626205942,NA,0.127330457430969,0.0020800938584715
+"7654",100,1975,12,15,1.88371836955529,14.0751264968721,20.1497138729452,15.1095017106882,0,25.277219288541,5.42536757505933,100,1975,12,15,0.216725132074972,0.0299134631174335,0.0364818526242999,0.0435248708183057,NA,0.046443944515856,0.00207959511919056
+"7655",100,1975,12,16,0,10.522145276547,23.5414191729689,15.0225600608767,0,25.8820264441015,5.46102000775455,100,1975,12,16,0,0.00999180222903061,0.204202782641207,0.0139513519220946,NA,0.128649841255626,0.00208050699780839
+"7656",100,1975,12,17,2.75500548108838,10.7739164595819,17.3399781897517,9.53933666195675,0,23.8749045496677,5.49667244044977,100,1975,12,17,0.394268982340723,0.00788421210805152,0.0253145950231534,0.0239075555179215,NA,0.0626358447336705,0.002082829494325
+"7657",100,1975,12,18,4.15786572572815,9.19096808228949,16.6260285330291,10.9623069312051,0,23.9247434878713,5.53232487314498,100,1975,12,18,0.030643281378959,0.00809415331172714,0.0274801616693016,0.065402573448569,NA,0.0799765227895359,0.00208656260874039
+"7658",100,1975,12,19,0.200550059715185,8.24403749335848,18.2086689322695,9.42954573615549,0,21.2363499027613,5.5679773058402,100,1975,12,19,0.0173099423369818,0.180622244877348,0.047587694090333,0.0617438517054601,NA,0.0392550281828534,0.00209170634105453
+"7659",100,1975,12,20,0,10.8124312473209,18.8327280491492,13.7438470601249,0,23.7726622262736,5.60362973853542,100,1975,12,20,0,0.3084162517504,0.0196718763254041,0.0475709395013145,NA,0.0224309732559426,0.00209826069126749
+"7660",100,1975,12,21,0,8.41282737740327,20.5327172274112,12.5851453470581,0,22.0241265895673,5.63928217123063,100,1975,12,21,0,0.336784141983409,0.143494144573792,0.0271731897818645,NA,0.0632093073326093,0.00210622565937921
+"7661",100,1975,12,22,0,10.6448955640803,22.6445875136372,10.8365753276645,0,22.7499533638933,5.67493460392585,100,1975,12,22,0,0.402925129075141,0.121071913419559,0.0320885274599575,NA,0.0645818976377994,0.00211560124538971
+"7662",100,1975,12,23,0,11.1557316554524,20.0319471306796,12.6221748620632,0,23.8394889921263,5.71058703662107,100,1975,12,23,0,0.415565503163959,0.0182496544736766,0.0276559918197536,NA,0.0387899377047811,0.00212638744929898
+"7663",100,1975,12,24,0,11.3089769151476,19.7823873232431,10.5634730205809,0,26.7952263340267,5.74623946931629,100,1975,12,24,0,0.144391849662907,0.0221245375251725,0.0251748982152057,NA,0.0397091796373021,0.00213858427110703
+"7664",100,1975,12,25,0,9.334675521347,22.9910011102658,13.5739132342952,0,26.7710891212228,5.7818919020115,100,1975,12,25,0,0.0785164432147255,0.0926263285991862,0.0246968026517474,NA,0.0288734946089749,0.00215219171081388
+"7665",100,1975,12,26,0,10.9045436332459,31.1733003727543,14.9121640177056,0,24.9924225073873,5.81754433470672,100,1975,12,26,0,0.0108034891140508,0.262746716955837,0.0365191498403236,NA,0.174040296218994,0.00216720976841949
+"7666",100,1975,12,27,0,14.4055004822801,31.4983278364763,12.2395016048083,0,24.3474257686674,5.85319676740194,100,1975,12,27,0,0.0801982733163805,0.0260538045696135,0.0201112330741397,NA,0.0370867220117061,0.0021836384439239
+"7667",100,1975,12,28,0,15.7513201978996,29.3858635716706,15.7955850752273,0,25.0784154358314,5.88884920009715,100,1975,12,28,0,0.0432831215642698,0.0223585331746507,0.172960924154042,NA,0.0564464351202811,0.00220147773732706
+"7668",100,1975,12,29,2.27381736754847,18.0749945603844,25.1074259226078,17.7494796379433,0,24.6873741235462,5.92450163279237,100,1975,12,29,0.168888885974889,0.0132210445919012,0.0461051681923833,0.0158370041397745,NA,0.0159888769120001,0.00222072764862901
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+"9164",100,1980,2,2,0.329372945463959,13.2648624466328,18.5776128863344,12.6471836417422,0,23.3692480412628,5.75855880481716,100,1980,2,2,0.00578947367375358,0.00881112662429789,0.026392943046838,0.198047869826275,NA,0.0353800765827085,0.00108370838320817
+"9165",100,1980,2,3,0.258745879434844,6.63044003265263,19.3466337180898,9.5018920908929,0,24.2464598636382,5.75753619848277,100,1980,2,3,0.0270175449262586,0.0848596428096185,0.0372065201432814,0.081017555080741,NA,0.0182029744220552,0.00121441006626978
+"9166",100,1980,2,4,0.100660067506553,10.6119471939221,20.9362046815643,13.4479097234141,0,23.1414437401631,5.75651359214838,100,1980,2,4,0.000526315805159117,0.0775695451971491,0.0568818171720804,0.0182152009998039,NA,0.0375595673347797,0.00137986033893635
+"9167",100,1980,2,5,0,11.8794389909381,19.9209132430577,13.807931754193,0,22.0771528800066,5.75549098581399,100,1980,2,5,0,0.0785164365780575,0.0587286226508966,0.0918210269058507,NA,0.0139352692307565,0.00158005920120791
+"9168",100,1980,2,6,1.24488449476995,10.9081407175599,18.1959846672839,8.78633671625219,0,21.9237229235956,5.75446837947961,100,1980,2,6,0.0489473695225196,0.0947356269524901,0.0209474139075865,0.0469263416179386,NA,0.0416414498906271,0.00181500665308442
+"9169",100,1980,2,7,0,6.80883381469022,20.4882398171,10.0459956073656,0,22.0888009242973,5.75344577314522,100,1980,2,7,0,0.152311669663112,0.0603497257302878,0.053413450836772,NA,0.0175583573320285,0.0020847026945659
+"9170",100,1980,2,8,1.65786576979231,9.29392729025863,17.7425195045597,10.5745874369236,0,22.1866014173155,5.75242316681083,100,1980,2,8,0.106900582229883,0.0249666736179662,0.027328657054754,0.13709122413732,NA,0.0428502559241368,0.00238914732565233
+"9171",100,1980,2,9,0.797359735363781,8.26113311161171,17.892893399867,9.58910898957709,0,21.6438105167041,5.75140056047645,100,1980,2,9,0.0904093501825784,0.0943485311955018,0.0202888470122103,0.0673374627922651,NA,0.109403017437708,0.00272834054634374
+"9172",100,1980,2,10,0,6.28056101069854,24.6278107520377,10.8303081755853,0,17.8636470360693,5.75037795414206,100,1980,2,10,0,0.0674778127882435,0.264791863121554,0.132443251956749,NA,0.118921847127515,0.00310228235664012
+"9173",100,1980,2,11,0.0283828387067656,5.87939493202403,27.4311658987249,12.1454124650021,0,19.0001895990484,5.74935534780767,100,1980,2,11,0.00175438601719706,0.0146362658952629,1.0232158258229,0.0673941777637157,NA,0.124614794597885,0.00351097275654147
+"9174",100,1980,2,12,0,11.2122331835387,37.7648741215369,8.50871289340314,0,22.8020727401176,5.74833274147328,100,1980,2,12,0,0.0134450300585517,0.0184667109179396,0.00428715022356338,NA,0.0785552338264747,0.00395441174604778
+"9175",100,1980,2,13,0,13.7126623191456,24.3662375291713,12.5600331320096,0,22.8090300237837,5.7473101351389,100,1980,2,13,0,0.010621084853584,0.308811501192182,0.0323508650685467,NA,0.0486465470496594,0.00443259932515905
+"9176",100,1980,2,14,0,12.1120571749176,37.5287785797623,12.7897250119871,0,23.499533578595,5.74628752880451,100,1980,2,14,0,0.00519414119171096,0.0411983508829774,0.542500064638017,NA,0.0154715266733284,0.00494553549387529
+"9177",100,1980,2,15,0,13.7555555452739,22.1229155176412,12.184642419301,0,20.9534606294226,5.74526492247012,100,1980,2,15,0,0.0669286218454008,0.375202330039636,0.0432818610699637,NA,0.0132668891155866,0.0054932202521965
+"9178",100,1980,2,16,0,7.5027503400746,22.8849836334799,11.5572608270005,0,22.4252820828404,5.74424231613573,100,1980,2,16,0,0.0568479708008201,0.552269741830501,0.0516368764331445,NA,0.148208676285106,0.00607565360012268
+"9179",100,1980,2,17,0,8.81745880534022,23.9636196770159,13.0741035620896,0,23.7961916513606,5.74321970980135,100,1980,2,17,0,0.0728093749577051,0.42984265820707,0.0658034963641794,NA,0.00940797605618017,0.00669283553765381
+"9180",100,1980,2,18,0,9.57339938429191,25.412332062674,15.3949723637143,0,21.511014421912,5.74219710346696,100,1980,2,18,0,0.0302701681751052,0.551189805201888,0.177214057355831,NA,0.058872316714288,0.00734476606478992
+"9181",100,1980,2,19,0,10.7659625608404,33.3009349248066,14.7251925599588,0,21.3607660304071,5.74117449713257,100,1980,2,19,0,0.0387005825333604,0.467427321666122,0.148591893797399,NA,0.0190732034081191,0.00803144518153101
+"9182",100,1980,2,20,0,15.5538282918983,37.2790973902536,17.0255774528411,0,20.7934478617986,5.74015189079819,100,1980,2,20,0,0.0346286678092109,0.0752027411934952,0.112405410740128,NA,0.0185202339682647,0.00875287288787705
+"9183",100,1980,2,21,0.770627067361859,17.9267437140671,21.1504510033904,16.8951814943152,0,19.2397084412227,5.7391292844638,100,1980,2,21,0.00315789540608741,0.0264877338613055,0.242673105331604,0.00925912087009158,NA,0.197870284680301,0.00950904918382807
+"9184",100,1980,2,22,0.701430144450321,12.72942798497,18.6498127780994,11.450264129177,0,18.9772956106669,5.73810667812941,100,1980,2,22,0.0881871308569326,0.00672281784155046,0.0212696106137782,0.0603006447887757,NA,0.0232668150005013,0.010299974069384
+"9185",100,1980,2,23,0.725522552550298,11.1060616070419,22.3321121434043,10.3629924870691,0,19.9743041549194,5.73708407179502,100,1980,2,23,0.0714619874187383,0.120889430745528,0.343175850329854,0.00366725185687745,NA,0.0100829873263694,0.011125647544545
+"9186",100,1980,2,24,2.16545654064489,8.84130907321003,18.2279649069338,9.80844883346977,0,18.7874312302242,5.73606146546064,100,1980,2,24,0.683684225681932,0.0716286289148788,0.0216918587447631,0.0333250790669558,NA,0.0401175167561637,0.0119860696093109
+"9187",100,1980,2,25,1.2108910869975,9.56612760503956,20.1212430624547,10.0155115536731,0,20.8129744264724,5.73503885912625,100,1980,2,25,0.145789472757028,0.197481869399828,0.0554357331707969,0.0646373857643834,NA,0.0120977790662301,0.0128812402636818
+"9188",100,1980,2,26,0,10.0316831391506,27.6245547316649,13.1809570597868,0,20.2956423778945,5.73401625279186,100,1980,2,26,0,0.0536543497989724,0.0501116252834813,0.101909380271472,NA,0.00864262383841193,0.0138111595076576
+"9189",100,1980,2,27,0.0594059414792769,12.1669417078083,24.1798021570422,14.7668867751186,0,18.7624235175121,5.73299364645747,100,1980,2,27,0.00263157902579559,0.0114333290100909,0.540999016562371,0.0154608296579043,NA,0.0245966046264808,0.0147758273412384
+"9190",100,1980,2,28,0,11.5106710385699,24.5718043299005,14.9254454695615,0,17.9139952476356,5.73197104012309,100,1980,2,28,0,0.0489982345581762,0.704365506958848,0.0575485134638326,NA,0.23595334154568,0.0157752437644242
+"9191",100,1980,2,29,0,12.9247854209707,20.9506600655869,12.4082068221928,0,17.6791497343988,5.7309484337887,100,1980,2,29,0,0.16251635962598,0.116421223200127,0.062202352158025,NA,0.0769610871754014,0.016809408777215
+"9192",100,1980,3,1,0.0608360845148891,13.8748404615127,24.7246643754646,15.4575906675903,0,19.7752592142746,5.68239152071906,100,1980,3,1,0.00374269017002039,0.0364543965479828,0.270394272930618,0.103747366237157,NA,0.195925725221559,0.0162692915196166
+"9193",100,1980,3,2,0.929152916891478,12.8671397459914,19.2123320698082,9.39334420559823,0,20.4934099813062,5.63383460764943,100,1980,3,2,0.0511111116409307,0.0137538043552614,0.0314040660438149,0.0329093281149935,NA,0.0166271006672856,0.0157398060155357
+"9194",100,1980,3,3,0.0699669977422595,12.3457534189927,21.4389769592957,11.0558745905642,0,18.9841136765559,5.58527769457979,100,1980,3,3,0.00245614042407588,0.0430245752100926,0.176133898091803,0.0164052704326769,NA,0.0319656887910346,0.0152209522649724
+"9195",100,1980,3,4,0.114521453851717,10.0254235503697,20.8288226195819,15.2726073952267,0,19.0902738560856,5.53672078151015,100,1980,3,4,0.00286549716142186,0.00742282462099956,0.121148580437663,0.0460648979522492,NA,0.0499527191273414,0.0147127302679266
+"9196",100,1980,3,5,0,9.06049512584086,20.9933772910677,10.3072606182203,0,19.4303228256034,5.48816386844052,100,1980,3,5,0,0.191484304289875,0.142528605197105,0.0111251504664505,NA,0.178275180638527,0.0142151400243983
+"9197",100,1980,3,6,0.0190319034739165,8.97162820272582,22.6493622661293,12.289592965339,0,17.8706942354754,5.43960695537088,100,1980,3,6,0.0020467836867299,0.120720495908952,0.185905291324395,0.0258473662192769,NA,0.055412259770432,0.0137281815343874
+"9198",100,1980,3,7,0.326072617234177,10.2853136083605,20.982585124319,17.7894280080092,0,18.4327443469782,5.39105004230125,100,1980,3,7,0.00508771929301715,0.017553782272433,0.103959619346202,0.0478589711198728,NA,0.161908448522134,0.0132518547978942
+"9199",100,1980,3,8,1.65093508582435,13.0465896405009,16.8374588256097,8.21897691048936,0,17.8201645772524,5.34249312923161,100,1980,3,8,0.0269005796365578,0.0223116960983693,0.0114608390721998,0.0461707226424726,NA,0.00723562667630817,0.0127861598149184
+"9200",100,1980,3,9,0,10.2355005738497,16.2709569962505,8.60040704120766,0,18.3921279042805,5.29393621616197,100,1980,3,9,0,0.0991783174325846,0.0386175478909307,0.0279766335181608,NA,0.0180396344826778,0.0123310965854601
+"9201",100,1980,3,10,0.0721672177970475,9.65507153551964,20.323212343474,10.2949284174786,0,17.0609188071771,5.24537930309234,100,1980,3,10,0.0020467836867299,0.210578422956962,0.0719245056155941,0.0382052647686238,NA,0.0408017688827945,0.0118866651095193
+"9202",100,1980,3,11,0.0365236529094814,8.79543451218978,23.6661938624282,13.0960066127043,0,17.4843006479709,5.1968223900227,100,1980,3,11,0.00228070182235617,0.0866227864940885,0.194614572795339,0.146846865934025,NA,0.0106170908107111,0.0114528653870961
+"9203",100,1980,3,12,0.276677673873138,9.20657858623005,25.869757783426,13.7843014692972,0,17.0268388720457,5.14826547695306,100,1980,3,12,0.0687134505189651,0.0246830260918896,0.10828085371795,0.224122318734149,NA,0.0646656786080182,0.0110296974181904
+"9204",100,1980,3,13,0.00286028607122444,13.7618151833647,25.9019799583947,15.3602089729771,0,15.9870540517392,5.09970856388343,100,1980,3,13,0.000526315805159117,0.0946841685200945,0.0146982194200548,0.0953024134765305,NA,0.0363629000224639,0.0106171612028021
+"9205",100,1980,3,14,1.72442244001479,11.3305720758386,20.018613846782,11.8176018764215,0,17.2164572951157,5.05115165081379,100,1980,3,14,0.076198827913632,0.0718766341383157,0.0241982941301082,0.0691649347874549,NA,0.0338604431951135,0.0102152567409314
+"9206",100,1980,3,15,0.693179319630099,12.7399890121191,21.1528493946273,12.3349724959488,0,15.3307898377781,5.00259473774415,100,1980,3,15,0.0235672498376748,0.0160052233446039,0.0426497417831438,0.0525473509025464,NA,0.0432186420005002,0.00982398403257821
+"9207",100,1980,3,16,0.727722776473814,12.9193949494818,20.4738613187414,11.6918370547992,0,15.2170399301019,4.95403782467452,100,1980,3,16,0.0125146168226391,0.0252274954032491,0.0226941512306514,0.0415064382408545,NA,0.0355569057921099,0.00944334307774252
+"9208",100,1980,3,17,0.3864686545169,6.36884486373633,28.9563583525101,14.4226402318386,0,17.0993407368004,4.90548091160488,100,1980,3,17,0.0291812863301116,0.0440988269415689,0.0425116874074248,0.0937953605206323,NA,0.0342508054993436,0.00907333387642435
+"9209",100,1980,3,18,0,15.9043784409073,33.0503190337497,14.5109680142209,0,15.0214471622744,4.85692399853525,100,1980,3,18,0,0.123145070813469,0.0258144920941868,0.376754493211523,NA,0.0382578230868555,0.00871395642862369
+"9210",100,1980,3,19,0.0399339939944028,12.7715181491294,21.2164134160914,11.4846313862648,0,16.5380141086324,4.80836708546561,100,1980,3,19,0.00228070182235617,0.0131116944609554,0.0223338733832638,0.0302005803694619,NA,0.0792785299403369,0.00836521073434051
+"9211",100,1980,3,20,0,5.24715067398692,27.6025632810016,14.7047083815857,0,15.6065733823577,4.75981017239597,100,1980,3,20,0,0.0209040993997328,0.168965237569854,0.260144921716676,NA,0.024464679867295,0.00802709679357487
+"9212",100,1980,3,21,0.0863586371504303,12.7151815752254,19.7512430457523,11.4376678215002,0,14.0129243094166,4.71125325932634,100,1980,3,21,0.00175438601719706,0.00560641912723876,0.0406953009312088,0.0198807051051331,NA,0.0993513710404692,0.00769961460632674
+"9213",100,1980,3,22,0.0490649072217731,6.06425741560782,19.8989215930565,10.678712775605,0,14.1439281156835,4.6626963462567,100,1980,3,22,0.00257309949188902,0.287988821729044,0.0216667048138631,0.0414356884315811,NA,0.0266285263730027,0.0073827641725961
+"9214",100,1980,3,23,0.277777784466088,7.36603956778582,19.7747634598131,12.1252146281294,0,15.2026710722356,4.61413943318706,100,1980,3,23,0.0133918130118945,0.105894187094057,0.0308554845177725,0.0943783447132481,NA,0.011497210454999,0.00707654549238301
+"9215",100,1980,3,24,0.103960397588735,12.1505500050661,20.8520681467256,14.4921451984066,0,13.8597014609501,4.56558252011743,100,1980,3,24,0.00140350881375765,0.0528285752615327,0.00944742800665584,0.0312116878510165,NA,0.0207457668127731,0.0067809585656874
+"9216",100,1980,3,25,0.0234323435834926,11.2900990768365,21.0914522575991,13.3929704124778,0,15.4790264174927,4.51702560704779,100,1980,3,25,0.00368421063611382,0.024758466691645,0.0429924102740911,0.0341707827295637,NA,0.055796690597294,0.00649600339250933
+"9217",100,1980,3,26,0.00066006601643641,11.6423102233014,20.2175247624631,14.787832847654,0,14.1418859408721,4.46846869397816,100,1980,3,26,0.000526315805159117,0.0379800792070508,0.057403539155982,0.0548608608068372,NA,0.100535343847897,0.00622167997284873
+"9218",100,1980,3,27,0.0728272838134839,13.1771177044272,20.3283939970077,12.1193618449179,0,11.2921357656212,4.41991178090852,100,1980,3,27,0.00286549716142186,0.0249590461129607,0.0545467771566992,0.0982496931779168,NA,0.00876688437518285,0.00595798830670569
+"9219",100,1980,3,28,0.836193614404718,8.45024202995174,18.5353463343924,9.15413648250735,0,13.1777204817025,4.37135486783888,100,1980,3,28,0.0114619883319791,0.0963701449121459,0.0497929952880706,0.0886812813966162,NA,0.0484710825279336,0.00570492839408013
+"9220",100,1980,3,29,0.435093517744108,10.7664136173177,17.8772167189024,12.7094940137286,0,12.7985474946694,4.32279795476925,100,1980,3,29,0.0181286542213452,0.0516151801260663,0.0216829709687884,0.0981432587362056,NA,0.0112380031182811,0.00546250023497211
+"9221",100,1980,3,30,1.01430144299506,11.5127282147885,17.05279416377,11.7423211875135,0,12.7613743683858,4.27424104169961,100,1980,3,30,0.0194152084986373,0.0582766235486161,0.115648500998179,0.0300357095618903,NA,0.0133817882840108,0.00523070382938156
+"9222",100,1980,3,31,1.61584158069623,11.1968866973558,19.2588777111964,13.3661057542522,0,12.9653431044323,4.22568412862997,100,1980,3,31,0.334736810079097,0.148688921059573,0.0502263751161655,0.151687147820954,NA,0.0259114059261274,0.00500953917730855
+"9223",100,1980,4,1,1.59790977878277,11.6475138291799,18.5827393169844,9.42229929589334,0,12.7648946744463,4.18891450535553,100,1980,4,1,0.137309927661519,0.040415176503201,0.0233262733061735,0.0540929564962138,NA,0.0240982466482186,0.00459195169844216
+"9224",100,1980,4,2,0.013421342334207,5.48321234196326,25.8294610274245,14.8590980882298,0,12.4086407050415,4.15214488208109,100,1980,4,2,0.00140350881375765,0.0486257253334733,0.0186608590245467,0.451334038474627,NA,0.0455268138164257,0.00419512579491059
+"9225",100,1980,4,3,1.03982397241823,11.2604068697351,16.8733443899123,12.3120791778313,0,13.2088368655946,4.11537525880665,100,1980,4,3,0.0402923967266648,0.0561052603806014,0.0445508832922394,0.026346800660305,NA,0.0472097897293212,0.00381906146671388
+"9226",100,1980,4,4,0.292849292898729,10.5719032130226,21.4541914911553,9.65436756545299,0,13.1999458603884,4.07860563553221,100,1980,4,4,0.00584795349522643,0.0702648815200327,0.104809420388602,0.00792515010059972,NA,0.0194674923423176,0.00346375871385199
+"9227",100,1980,4,5,0.00297029707396385,9.43493951455464,18.4665787112464,10.3360726279919,0,12.9094186273032,4.04183601225777,100,1980,4,5,0.000994152076411666,0.191450860510996,0.0921052325960219,0.0188064351087946,NA,0.0079812746244194,0.00312921753632497
+"9228",100,1980,4,6,0.137953797656514,9.10433444934841,17.1721451762486,12.169020845814,0,12.7148201068485,4.00506638898333,100,1980,4,6,0.00467836293909286,0.599583188769754,0.066636821555253,0.117987144644274,NA,0.0151601890511598,0.00281543793413278
+"9229",100,1980,4,7,0,7.06545660178391,19.1063255796863,10.7925083089058,0,12.5433431697514,3.96829676570889,100,1980,4,7,0,0.279350932639959,0.0161450204193696,0.0356701815221189,NA,0.00842350766789368,0.00252241990727542
+"9230",100,1980,4,8,0.0907590772600064,5.63232126120556,23.1462375044954,12.9894389560645,0,10.8089930102276,3.93152714243445,100,1980,4,8,0.00619883059409627,0.177287701940273,0.0404286671199935,0.13400642039433,NA,0.0536189501198708,0.00225016345575291
+"9231",100,1980,4,9,0,5.71600665143876,26.6581406839872,13.0073046731477,0,11.5600839655916,3.89475751916001,100,1980,4,9,0,0.221160837859454,0.0177801458440089,0.139687756590873,NA,0.0123576005685188,0.00199866857956526
+"9232",100,1980,4,10,0,7.8049614977653,28.8399230507996,11.6315072975536,0,10.9741320310078,3.85798789588557,100,1980,4,10,0,0.0928362100445403,0.0528778099062718,0.0494274833211617,NA,0.0170280551090482,0.00176793527871242
+"9233",100,1980,4,11,0,13.3546315141768,29.643058086517,7.42007703120166,0,11.9290993662251,3.82121827261113,100,1980,4,11,0,0.049627524655973,0.176953910796717,0.0824257642355788,NA,0.03271864201326,0.00155796355319443
+"9234",100,1980,4,12,0,9.11310249744075,23.2622335033186,14.8232894903756,0,11.85008458367,3.78444864933669,100,1980,4,12,0,0.0279163520590639,0.0819362068535353,0.0442760102166777,NA,0.0318227131099981,0.00136875340301129
+"9235",100,1980,4,13,0,9.24047292786987,23.5792956546326,13.5304730785693,0,10.9296754847047,3.74767902606225,100,1980,4,13,0,0.0984262363345375,0.132872017344219,0.0186701884041793,NA,0.0259424307469331,0.00120030482816298
+"9236",100,1980,4,14,0,10.0881739481054,30.8707808231232,11.2811881475585,0,10.9264189812877,3.71090940278781,100,1980,4,14,0,0.0601222935147287,0.0579041225502263,0.0948596861432809,NA,0.0529233801645417,0.00105261782864951
+"9237",100,1980,4,15,0,15.545533653402,30.6122551614826,9.27916391454514,0,10.3417447587532,3.67413977951336,100,1980,4,15,0,0.0428450454243899,0.0842690625109402,0.0762982259048516,NA,0.0314817711161695,0.000925692404470887
+"9238",100,1980,4,16,8.8416942878656,12.9708029953691,18.4729041843393,14.74074800783,0,10.8364803712273,3.63737015623892,100,1980,4,16,1.12426893055793,0.0141333339268236,0.243856161606404,0.00113217011624047,NA,0.0161564659160779,0.000819528555627102
+"9239",100,1980,4,17,1.59075906174411,12.3053353732437,28.992288478268,12.5365346430158,0,10.2744186454349,3.60060053296448,100,1980,4,17,0.0483040892729298,0.0462917612957591,0.0865146609973173,0.0126140476138237,NA,0.00798830768895614,0.000734126282118159
+"9240",100,1980,4,18,0.892739269993093,16.1829811123469,22.4838063436242,18.7177119805868,0,10.945130556365,3.56383090969004,100,1980,4,18,0.0756140314417283,0.0620509084447798,0.0111912436728553,0.0547608604121482,NA,0.0183275191546542,0.000669485583944058
+"9241",100,1980,4,19,6.89328940604517,17.2070846599583,22.6563478245331,18.8681520245912,0,9.97788777318156,3.5270612864156,100,1980,4,19,0.657017498127921,0.0372778311838273,0.139458928136514,0.0496555567426387,NA,0.0296771095285203,0.000625606461104798
+"9242",100,1980,4,20,25.2895490459614,16.0522993652209,24.3481079600956,17.4185810047145,0,10.8813567954977,3.49029166314116,100,1980,4,20,0.639824309767684,0.0122590946243451,0.0867041266238305,0.011049689622327,NA,0.013920561715262,0.000602488913600374
+"9243",100,1980,4,21,2.35786582088575,15.0204730206984,21.3035532234788,17.1023984118001,0,9.14916847747138,3.45352203986672,100,1980,4,21,0.0360818785115341,0.00894973319658175,0.0301999710085383,0.160650785185484,NA,0.0333283274228975,0.000600132941430794
+"9244",100,1980,4,22,6.58646859063043,17.5406600272301,19.9464795581578,20.1305387033225,0,10.022454347844,3.41675241659228,100,1980,4,22,0.0783040775053948,0.0068578646746653,0.0553262935841277,0.0307590523393442,NA,0.0144950328786433,0.000618538544596056
+"9245",100,1980,4,23,24.5007700379794,16.2087790800793,17.6692851143177,12.6770627637639,0,9.71656765683116,3.37998279331784,100,1980,4,23,29.4442686732336,0.004948538333327,0.0335731055949286,0.00725554330618475,NA,0.00756470529417914,0.000657705723096158
+"9246",100,1980,4,24,5.49570957452419,9.24531367483443,15.4416831502295,11.2042684103921,0,9.27733996072705,3.3432131700434,100,1980,4,24,0.706959121324881,0.0108654972657046,0.095789386738225,0.0702035102242794,NA,0.0088198259244684,0.000717634476931102
+"9247",100,1980,4,25,0.895489554135057,9.43863574217911,16.8071175523848,12.2662815906034,0,9.58570959128829,3.30644354676896,100,1980,4,25,0.0400584821464029,0.0298672459987953,0.0351245939871032,0.0260924134796018,NA,0.0192640133289669,0.000798324806100884
+"9248",100,1980,4,26,1.35258522337944,11.5617932486455,14.4025962072106,8.55819584119438,0,10.1104656083393,3.26967392349452,100,1980,4,26,0.00561403776470015,0.0245385522252281,0.0405473995008711,0.00456490987595018,NA,0.0184073354920507,0.000899776710605506
+"9249",100,1980,4,27,0.0662266236491198,9.01468643759212,13.3244114320795,8.49843794756597,0,9.63336081582009,3.23290430022008,100,1980,4,27,0.00619883059409627,0.211520491259921,0.129287107336117,0.00459881834740904,NA,0.013275987443375,0.00102199019044497
+"9250",100,1980,4,28,1.14631462975828,10.2338395029536,13.0280196333613,10.9385367688304,0,8.82896141115506,3.19613467694564,100,1980,4,28,0.0417543825908024,0.0813175766772729,0.0837257530837851,0.105253750603735,NA,0.01694382241701,0.00116496524561928
+"9251",100,1980,4,29,1.01364136148732,9.7365346369308,16.4488887115411,13.6083058093903,0,8.28609058232722,3.1593650536712,100,1980,4,29,0.0316374274513183,0.0391783582565455,0.0513829582395326,0.0380204821804851,NA,0.00920270697563016,0.00132870187612842
+"9252",100,1980,4,30,1.95797578742926,9.66594057849007,15.864070434906,11.3476017520766,0,8.90062632742582,3.12259543039676,100,1980,4,30,0.080058474345517,0.243020492721105,0.108035696868582,0.0331368361780547,NA,0.00536656468211105,0.00151320008197241
+"9253",100,1980,5,1,0.268536861432661,7.66232119718663,17.0526953446459,13.0075909034385,0,8.8130429228584,3.07580928532511,100,1980,5,1,0.00842105280411873,0.0594649250410095,0.00960406127878263,0.0164041186350079,NA,0.0244265944507838,0.00147934083455516
+"9254",100,1980,5,2,0.548734872230012,8.72787675710663,18.6697140075717,13.4890209623952,0,9.71097121814053,3.02902314025347,100,1980,5,2,0.066725145634155,0.275764938539711,0.101987277779699,0.0767655023809529,NA,0.0509767782391309,0.00144610982046277
+"9255",100,1980,5,3,0,8.45024206142614,21.2211220513607,12.5462157057457,0,7.91238174921084,2.98223699518183,100,1980,5,3,0,0.0291362531294498,0.0338719593876847,0.0179497177593013,NA,0.00443888009916981,0.00141350703969523
+"9256",100,1980,5,4,0,8.45405940988539,22.489911789679,12.4967217786346,0,8.94052367179788,2.93545085011019,100,1980,5,4,0,0.0870783959751978,0.0582928400677882,0.0100485357162616,NA,0.0099419404261935,0.00138153249225253
+"9257",100,1980,5,5,0,9.55829481244481,24.0367878241376,14.626006664616,0,8.68450219121984,2.88866470503854,100,1980,5,5,0,0.0687841819764044,0.0714368478698252,0.0713245600762263,NA,0.0110225461288128,0.0013501861781347
+"9258",100,1980,5,6,0.966446660893454,12.0871066931713,22.5713859071301,14.5083387792438,0,7.9951539986729,2.8418785599669,100,1980,5,6,0.198421049613005,0.0327005968775075,0.014387114479305,0.0362871368721318,NA,0.00662100812863615,0.00131946809734171
+"9259",100,1980,5,7,8.94510463362086,13.7095379719246,21.0017161038843,16.4551485877882,0,7.66502311060094,2.79509241489526,100,1980,5,7,0.17479532119133,0.0944005654988274,0.0609472819547559,0.0282700713801976,NA,0.0209203761359687,0.00128937824987358
+"9260",100,1980,5,8,3.71507150452785,11.4367216160589,20.4712981023673,16.11754684742,0,8.12080537062721,2.74830626982362,100,1980,5,8,0.105555560853752,0.322470745175983,0.0262895198847596,0.00785380186142801,NA,0.0105399895965202,0.0012599166357303
+"9261",100,1980,5,9,0.052035204295737,9.18572061578564,21.9375358787176,16.7599448318397,0,6.76254492066568,2.70152012475197,100,1980,5,9,0.00257309949188902,0.0504982983706937,0.12028776180457,0.0540111336605571,NA,0.0145730876040114,0.00123108325491187
+"9262",100,1980,5,10,0,11.6955227395489,18.8003519622668,16.7773928794399,0,8.14627573273007,2.65473397968033,100,1980,5,10,0,0.0650760064042053,0.202115233019016,0.0603064019367657,NA,0.0096624562650062,0.0012028781074183
+"9263",100,1980,5,11,14.0950495339069,13.0396148830619,17.7986138577771,11.8934652414521,0,7.27890263491506,2.60794783460869,100,1980,5,11,1.30397680862612,0.0264467644697824,0.0115204793252778,0.0160690403944612,NA,0.0178061923973474,0.00117530119324958
+"9264",100,1980,5,12,6.21452145266979,9.07016507076352,15.968756802667,11.913091345851,0,7.25531824616007,2.56116168953705,100,1980,5,12,0.0413450521614296,0.0478672602425539,0.143281797937912,0.0250607933892869,NA,0.00495527030601342,0.00114835251240571
+"9265",100,1980,5,13,0.305060515588004,7.96904292751854,18.6697359326387,12.9849945169077,0,7.75876973244486,2.5143755444654,100,1980,5,13,0.00385964918554876,0.239658553772674,0.094969550546958,0.00606552432332376,NA,0.0134069890389539,0.00112203206488669
+"9266",100,1980,5,14,0,9.43053909654271,19.7664249491508,12.9065455488115,0,7.54920572821894,2.46758939939376,100,1980,5,14,0,0.0533929545130037,0.0744210790000037,0.00640234721452262,NA,0.00502054612327135,0.00109633985069252
+"9267",100,1980,5,15,0,9.24369637014055,20.0200769825212,11.7697801075884,0,6.94109059622491,2.42080325432212,100,1980,5,15,0,0.0951145874292195,0.0830250837942386,0.0176777806176666,NA,0.00702796329865624,0.00107127586982321
+"9268",100,1980,5,16,0.957535768778148,10.3769966551442,18.7599998490907,13.850131964395,0,6.98041449992814,2.37401710925048,100,1980,5,16,0.155087721541263,0.0915766384192381,0.0310099750365507,0.0112005741120173,NA,0.0060241679604574,0.00104684012227875
+"9269",100,1980,5,17,6.98811882583484,8.53632571594943,14.5157425988494,9.27073703006287,0,6.72641511977834,2.32723096417883,100,1980,5,17,0.220292447073438,0.0667561967593801,0.0586655082101039,0.00515555102036649,NA,0.00231859592022028,0.00102303260805914
+"9270",100,1980,5,18,0.59350937322946,7.67646863882822,13.5665235582358,9.94083604718199,0,7.39543567227556,2.28044481910719,100,1980,5,18,0.000994152520832319,0.153476570519819,0.117854347755521,0.0885450214743462,NA,0.0081722710471268,0.000999853327164383
+"9271",100,1980,5,19,0.0856985711339939,8.90315742198915,14.9509569625519,13.4262266358395,0,7.0604466397027,2.23365867403555,100,1980,5,19,0.00777777800957362,0.0518941489081097,0.0417917428022993,0.0506912545813135,NA,0.00154143737092538,0.000977302279594483
+"9272",100,1980,5,20,21.5141912697434,6.91260721554982,12.6159186840582,7.67588557714414,0,6.62619676011811,2.18687252896391,100,1980,5,20,1.84584830747043,0.054168993908641,0.138336767756651,0.00593391947050507,NA,0.0205018351824426,0.000955379465349433
+"9273",100,1980,5,21,2.93817381549327,4.15652367384127,12.4179098225794,11.1256656290019,0,6.70895254558341,2.14008638389226,100,1980,5,21,0.334502912543676,0.120648533010221,0.124778379531855,0.00632104210890035,NA,0.0096483385069647,0.000934084884429235
+"9274",100,1980,5,22,0.759295923973467,7.20052805198695,14.4482397744627,9.40614967325208,0,6.13139957813827,2.09330023882062,100,1980,5,22,0.0892397589293143,0.0519929675007573,0.111798280637938,0.0035146198719548,NA,0.00479026163069252,0.000913418536833884
+"9275",100,1980,5,23,0,8.03413640652815,18.0900330559255,11.2133663563576,0,6.97696248428729,2.04651409374898,100,1980,5,23,0,0.0378649107481935,0.0904134600726016,0.00601464061856482,NA,0.00864859409791655,0.00089338042256339
+"9276",100,1980,5,24,1.71870186921656,9.28256327353164,17.2377778715295,13.2679319161393,0,6.67319448045514,1.99972794867733,100,1980,5,24,0.125789472666403,0.0892040533412298,0.0250765571508326,0.00701403453678605,NA,0.00456723011477431,0.000873970541617754
+"9277",100,1980,5,25,0.251485155942482,7.44419137348305,18.9596256281283,14.011419241709,0,6.25100109795626,1.95294180360569,100,1980,5,25,0.005906433164202,0.0439982102701054,0.0999024038663276,0.0252987902703305,NA,0.00887715033797632,0.000855188893996964
+"9278",100,1980,5,26,0,9.26025299768899,19.3980418080413,11.5399451470873,0,5.80489284281059,1.90615565853405,100,1980,5,26,0,0.0476479720913928,0.0433708408068413,0.00439181597756575,NA,0.0104869923642608,0.000837035479701027
+"9279",100,1980,5,27,0,9.68962589007924,18.7349723169644,12.7647524813745,0,6.19191344029842,1.85936951346241,100,1980,5,27,0,0.138136824075887,0.104167243123513,0.00959413134932065,NA,0.0265957910630505,0.000819510298729943
+"9280",100,1980,5,28,0,7.80300338617121,17.9367435862391,13.9277996350699,0,6.17878006643869,1.81258336839076,100,1980,5,28,0,0.0520362608157223,0.0224145990207503,0.0241063999310746,NA,0.00557876569954432,0.000802613351083711
+"9281",100,1980,5,29,0.0458745881423305,8.27431251244708,17.8834211267654,14.8893398551395,0,6.21801767530851,1.76579722331912,100,1980,5,29,0.00257309949188902,0.640560940366671,0.0743251704080803,0.0163362483755961,NA,0.00961406975220803,0.000786344636762331
+"9282",100,1980,5,30,0,8.51536851931195,16.4222991049486,13.6458745511583,0,6.67004848807869,1.71901107824748,100,1980,5,30,0,0.032894143096828,0.21514252805234,0.0419263318023658,NA,0.0142872713527743,0.000770704155765806
+"9283",100,1980,5,31,0,9.03627057804657,14.7566226984408,11.2656435573062,0,6.15065679090489,1.67222493317584,100,1980,5,31,0,0.0515251060352637,0.122283016448032,0.00742747252397695,NA,0.00551986487728535,0.000755691908094131
+"9284",100,1980,6,1,0.425522556112479,9.34697456380846,16.1050383732526,12.566798588886,0,6.2488602128617,1.65496657983136,100,1980,6,1,0.0311111107965311,0.0701818814551609,0.124260704678648,0.0185444636451409,NA,0.0039790554283129,0.000698626066836707
+"9285",100,1980,6,2,1.08019801846432,7.57871288670958,14.8013640226442,8.50887789374793,0,6.33324654218994,1.63770822648688,100,1980,6,2,0.0591228063482996,0.599436814262452,0.173309954169281,0.00104269557093511,NA,0.00639378344704877,0.000645338738270962
+"9286",100,1980,6,3,0,3.7927062789468,10.8698348631822,8.27471952627201,0,6.37508699991993,1.62044987314241,100,1980,6,3,0,0.0684719194657722,0.267132154210459,0.0887544095056929,NA,0.00257471217136412,0.000595829922396898
+"9287",100,1980,6,4,8.33014304357262,5.22226624546534,14.6207589295306,11.7801319904978,0,6.39185592193007,1.60319151979793,100,1980,6,4,0.0632163816865425,0.0260146060966076,0.131995271542258,0.0432940928097393,NA,0.0257896177999702,0.000550099619214514
+"9288",100,1980,6,5,2.0374037332923,5.45642468061122,13.954334321982,10.3894829293682,0,6.20981023601723,1.58593316645346,100,1980,6,5,0.0564912245426981,0.295225767399986,0.0600116848416462,0.0232175879719171,NA,0.00328825046109424,0.00050814782872381
+"9289",100,1980,6,6,0.11782178404865,7.12075908165691,13.4540263617655,10.1886138559306,0,5.66572961146101,1.56867481310898,100,1980,6,6,0.00830409397594414,0.135737423012289,0.102793039703463,0.035244400533102,NA,0.00361530813408492,0.000469974550924785
+"9290",100,1980,6,7,0,6.33195822362197,14.6515950369756,10.1552254568757,0,6.24501869501017,1.5514164597645,100,1980,6,7,0,0.201481315931819,0.0328672487538617,0.0233649172398932,NA,0.00825154612448358,0.000435579785817439
+"9291",100,1980,6,8,0,5.21689768540453,13.6288998460088,10.1994938404516,0,5.79285258206969,1.53415810642003,100,1980,6,8,0,0.119973088145139,0.152549684725395,0.0159894658295742,NA,0.00494803857989209,0.000404963533401776
+"9292",100,1980,6,9,4.72926290856205,6.03016500084838,10.153674365926,10.6271285580592,0,5.89237035651936,1.51689975307555,100,1980,6,9,0.04801169607376,0.0473818620336212,0.240587776094905,0.0450584588414141,NA,0.0143213252368924,0.000378125793677791
+"9293",100,1980,6,10,3.93729371206202,6.58947186370363,13.3531132866972,10.2794500126435,0,6.18151580618553,1.49964139973107,100,1980,6,10,0.704795324621154,0.0854578841594465,0.112121059210343,0.03231170351582,NA,0.012391680590461,0.000355066566645488
+"9294",100,1980,6,11,4.1866886363958,6.32839381812823,13.8996259785853,11.3389110512728,0,6.03587733125529,1.4823830463866,100,1980,6,11,0.298070178980029,0.315856109299047,0.150046822185442,0.0176485598693204,NA,0.00338334902777801,0.000335785852304864
+"9295",100,1980,6,12,2.62871285948423,6.93783281607465,12.4572276450095,10.9355335886067,0,5.32496301691719,1.46512469304212,100,1980,6,12,0.171754379188809,0.186592931901386,0.0735654656528126,0.0335830349660938,NA,0.00607640757060825,0.000320283650655921
+"9296",100,1980,6,13,0.383718378751716,8.27041797721871,12.4251156661114,9.17566550604188,0,5.98021244007442,1.44786633969764,100,1980,6,13,0.00508771898802262,0.0415175542107003,0.224262079725282,0.00392223116564336,NA,0.0133668813613048,0.000308559961698658
+"9297",100,1980,6,14,0,1.02168317445696,14.4866006240593,9.98202424841483,0,5.94861606110309,1.43060798635317,100,1980,6,14,0,0.0783543920490483,0.0315373959993844,0.0106801189802524,NA,0.0114211913445697,0.000300614785433073
+"9298",100,1980,6,15,0.0592959304765375,3.83020903568457,11.4931793149942,11.3226622863702,0,6.22538795844294,1.41334963300869,100,1980,6,15,0.00263157902579559,0.0398432807855538,0.115160851729651,0.00119122482217322,NA,0.0165000143846939,0.000296448121859171
+"9299",100,1980,6,16,1.06061606189468,8.76634757248613,14.6089217728383,11.9228051409076,0,6.15086754760939,1.39609127966421,100,1980,6,16,0.0704093566624053,0.0260953344111532,0.0560210580458543,0.00920291593237704,NA,0.00437458530728877,0.000296059970976948
+"9300",100,1980,6,17,8.17392744981285,7.76787677981017,13.5439053211275,10.0625742953209,0,5.84192557635354,1.37883292631974,100,1980,6,17,1.28315801330483,0.0620649268858984,0.0945941810965525,0.0290485538528187,NA,0.00232760046695561,0.000299450332786402
+"9301",100,1980,6,18,2.09636959187662,8.26294822136823,15.2213751454033,12.3538614436738,0,6.00485414236062,1.36157457297526,100,1980,6,18,0.0184210536075644,0.0981952726275488,0.0565228409215908,0.0232111606174835,NA,0.00436755577459673,0.00030661920728754
+"9302",100,1980,6,19,1.62299228048954,5.1768976055225,13.1880639215769,11.4704950152188,0,6.11508838722327,1.34431621963079,100,1980,6,19,0.127836250157386,0.131150890028003,0.166331601197606,0.00193977233715625,NA,0.0154076640707701,0.000317566594480357
+"9303",100,1980,6,20,2.42574256219224,6.85171612339838,14.7410671970632,11.6566997439948,0,5.60658843744594,1.32705786628631,100,1980,6,20,0.111520469495432,0.193398184561047,0.113714642858335,0.0309608604655093,NA,0.0122743618227805,0.000332292494364854
+"9304",100,1980,6,21,0.249284934748386,7.71178214647064,15.2614631243665,11.5631132618953,0,5.71921094924504,1.30979951294183,100,1980,6,21,0.0092982461525683,0.173975930869834,0.0839690191638,0.0120608301754545,NA,0.0114935585398629,0.000350796906941032
+"9305",100,1980,6,22,0,5.45110011231912,13.1082617483779,9.38444442665092,0,5.69763856035259,1.29254115959736,100,1980,6,22,0,0.176884182644467,0.0608982584011468,0.00269883521681475,NA,0.00545963047517543,0.000373079832208887
+"9306",100,1980,6,23,0.227282732372696,5.41524760243129,13.6723543120952,9.69739285501579,0,5.4140803024517,1.27528280625288,100,1980,6,23,0.0192397663241242,0.0678759991116087,0.187981831107852,0.0385356689498435,NA,0.0047373996980867,0.000399141270168425
+"9307",100,1980,6,24,0,6.88155118860427,15.2520351976451,11.758096721175,0,5.92964128530964,1.2580244529084,100,1980,6,24,0,0.110976067652369,0.0613930253637442,0.0694333921645288,NA,0.0038663292316584,0.000428981220819641
+"9308",100,1980,6,25,0.00110011002739402,8.59708473365037,17.6718590676588,10.1463586127404,0,5.94937141559079,1.24076609956393,100,1980,6,25,0.000994152076411666,0.0371034598925551,0.0802498026889906,0.0136988287921035,NA,0.00484361868414744,0.00046259968416254
+"9309",100,1980,6,26,0,10.0453684421787,16.0808579861396,11.9622111986704,0,6.45144032858376,1.22350774621945,100,1980,6,26,0,0.133891216632835,0.140931506258541,0.0804784402970872,NA,0.0134497423307368,0.000499996660197116
+"9310",100,1980,6,27,2.61386137590943,8.15390532612145,12.654818598849,11.1536633404437,0,6.24726465747695,1.20624939287497,100,1980,6,27,0.0458479547221792,0.013202925089857,0.173683058951812,0.00616898167268039,NA,0.00569481608362092,0.000541172148923375
+"9311",100,1980,6,28,19.5811881809214,6.19403734721235,11.8324423518249,10.3998461147346,0,5.94045797574347,1.1889910395305,100,1980,6,28,1.1264910319817,0.0407719166125671,0.110991783367486,0.0094426964476218,NA,0.023551797215854,0.000586126150341313
+"9312",100,1980,6,29,21.2133113789742,7.79365246471661,12.7089769019283,11.9212541894944,0,5.60004097597945,1.17173268618602,100,1980,6,29,15.1867269254988,0.0688246043858069,0.118755492231642,0.00293742846442698,NA,0.00795992767568609,0.000634858664450928
+"9313",100,1980,6,30,7.35687569449312,9.1450604311835,12.0453574072541,10.5711660038913,0,5.40378248794665,1.15447433284154,100,1980,6,30,9.77619908215713,0.107937417477677,0.180184239872778,0.00660525900864166,NA,0.00937510888078547,0.000687369691252227
+"9314",100,1980,7,1,0.59284928965621,4.99849282737875,12.197832845094,9.6159846218768,0,6.2134391868663,1.15407544105428,100,1980,7,1,0.0248538004724605,0.219368997040699,0.074892901988411,0.00553155386862474,NA,0.00519470983487557,0.000652140843972563
+"9315",100,1980,7,2,9.95544556238995,7.07865783378761,11.4122550867834,9.03202425764732,0,6.21260835752847,1.15367654926701,100,1980,7,2,0.182105317366834,0.0492701473838596,0.140806469998804,0.0339695943933596,NA,0.00685110136823128,0.000618590635171753
+"9316",100,1980,7,3,2.63707373034705,7.42413646605673,12.1603849314489,12.4075247089986,0,6.36067221759009,1.15327765747974,100,1980,7,3,0.0587134653225334,0.155545038768734,0.100755485238051,0.0159368454493307,NA,0.00788030636586113,0.0005867190648498
+"9317",100,1980,7,4,19.7155116535518,5.40812975082985,11.692035182999,9.86005508099714,0,5.85808543773465,1.15287876569247,100,1980,7,4,1.38000020768942,0.133391801878745,0.135559066718987,0.00651461649227734,NA,0.0100227296561663,0.000556526133006701
+"9318",100,1980,7,5,4.19020904303908,6.45105612632072,12.6260286715164,10.2657205793593,0,6.08403246871058,1.1524798739052,100,1980,7,5,0.175087694591962,0.0833163449382394,0.129895366592919,0.00523860713456726,NA,0.010617941039783,0.000528011839642459
+"9319",100,1980,7,6,3.41716171386349,6.99023103818904,12.9528492716673,11.9302310009863,0,6.51323139786851,1.15208098211793,100,1980,7,6,0.0391812783235746,0.225521032201773,0.133715269062053,0.000833916928553535,NA,0.00643834703623641,0.00050117618475707
+"9320",100,1980,7,7,4.44752485602602,8.45833881092806,12.32816271997,9.546270653753,0,6.5175302520022,1.15168209033066,100,1980,7,7,0.0728654927817114,0.0491041105572214,0.112635613250806,0.00351638203456569,NA,0.0095936439181551,0.000476019168350537
+"9321",100,1980,7,8,2.34136416022927,8.32983495116365,12.0436853885126,10.1520131943118,0,6.42301506225303,1.15128319854339,100,1980,7,8,0.0314035150321638,0.144391259403172,0.247525697984813,0.0129760354226824,NA,0.0148372999297138,0.000452540790422862
+"9322",100,1980,7,9,5.07711774209152,6.44189219899697,13.231177107598,10.091661359873,0,6.69997268221247,1.15088430675612,100,1980,7,9,0.154327484376263,0.413438526995719,0.176998856545673,0.00718889584020457,NA,0.00448378253527709,0.000430741050974038
+"9323",100,1980,7,10,4.76897691552526,4.90455440063812,12.5521781436681,9.84112222753342,0,6.15302608546344,1.15048541496885,100,1980,7,10,0.543157897609035,0.318203515461052,0.0459778214773469,0.0132783948017825,NA,0.0109220172285455,0.00041061995000407
+"9324",100,1980,7,11,0.321672176173811,5.1060176268138,9.90841582689611,9.43710666442468,0,5.63910811675357,1.15008652318158,100,1980,7,11,0.00608187131714404,0.217000597069299,0.0574140276390789,0.00815963894145606,NA,0.0224801647023499,0.000392177487512958
+"9325",100,1980,7,12,0.722992308314877,6.55056101785372,14.8868536345898,10.2691639227705,0,6.00168806356483,1.14968763139431,100,1980,7,12,0.133391813904222,0.0358929606198068,0.059228631205961,0.0144368475016962,NA,0.00587089209936512,0.000375413663500701
+"9326",100,1980,7,13,0.128822885748774,7.58360837552414,12.3167437332036,10.7788118219743,0,6.33415174248195,1.14928873960704,100,1980,7,13,0.0169590652903967,0.0886228668927147,0.269125170343034,0.0166871364437866,NA,0.0154330056176504,0.000360328477967298
+"9327",100,1980,7,14,2.19229920233044,5.91768976459147,13.6353794733683,10.7825743084562,0,6.83929972614071,1.14888984781977,100,1980,7,14,0.0613450386928576,0.0380356670034031,0.0673163495226267,0.0074140527915793,NA,0.00364007573593919,0.000346921930912752
+"9328",100,1980,7,15,0.90231023563279,7.77682072573369,15.2062265608046,12.5898019205226,0,6.54582783014973,1.1484909560325,100,1980,7,15,0.104678357902327,0.0645701574917,0.0426655134079094,0.0557484996640293,NA,0.0139256534302546,0.00033519402233706
+"9329",100,1980,7,16,3.12585255808563,6.26554454933561,15.138228760563,11.3809680204318,0,6.68302226302421,1.14809206424524,100,1980,7,16,1.02497078970859,0.0606023260139058,0.0548695807139708,0.0123830236469139,NA,0.0158138375814667,0.000325144752240223
+"9330",100,1980,7,17,1.59548956461505,6.54638063579765,13.6309791046663,11.5536743673948,0,6.90440536236339,1.14769317245797,100,1980,7,17,0.784678381191709,0.0731368043330878,0.150749699870409,0.0165356560334248,NA,0.0101421287539731,0.000316774120622241
+"9331",100,1980,7,18,8.09867985203977,7.27042893870304,13.0880639178,8.78021998872327,0,6.76299585133402,1.1472942806707,100,1980,7,18,0.250000000000047,0.0476777938631133,0.16142577830097,0.0783666289860148,NA,0.00972339492034886,0.000310082127483115
+"9332",100,1980,7,19,0.264246432038948,7.83599560016846,12.5928602512389,9.11454349408711,0,6.90044737368396,1.14689538888343,100,1980,7,19,0.0124561410880926,0.223926334150253,0.0257175366630861,0.00103683614013374,NA,0.0133834453351985,0.000305068772822844
+"9333",100,1980,7,20,1.83014298718099,5.11831680671348,12.0822992713013,10.3434543966329,0,7.67344985290732,1.14649649709616,100,1980,7,20,0.0833918057129409,0.154738558564016,0.0447368322897389,0.0387783798776433,NA,0.0252361522918685,0.000301734056641428
+"9334",100,1980,7,21,8.18019808086232,5.57365239964853,13.3212981355203,10.4856105475011,0,7.19263256455587,1.14609760530889,100,1980,7,21,0.864327362127547,0.240195335570016,0.0517918251194523,0.00158129390702175,NA,0.0157203199546045,0.000300077978938868
+"9335",100,1980,7,22,3.11991199801857,3.62753575968139,14.3848405046956,10.8290098586885,0,7.16359846805276,1.14569871352162,100,1980,7,22,0.950292407978353,0.308270222587484,0.0457140816605668,0.00764677896790601,NA,0.0116613905052299,0.000300100539715162
+"9336",100,1980,7,23,0.258855892035893,5.23727170652551,12.8102311573931,9.82436729343024,0,6.49541765109054,1.14529982173435,100,1980,7,23,0.0202339188275282,0.0878204899236019,0.154784242978092,0.0292485699793514,NA,0.00879378335855493,0.000301801738970311
+"9337",100,1980,7,24,0.0881188131942607,4.14969200584361,14.4784158448575,10.2717820777096,0,7.2468552042594,1.14490092994708,100,1980,7,24,0.000994152076411666,0.175156161817485,0.0157988533132328,0.0135275088796228,NA,0.0144708834296535,0.000305181576704316
+"9338",100,1980,7,25,0,5.37125412098514,14.043872482837,11.8322882510648,0,7.64104235544157,1.14450203815981,100,1980,7,25,0,0.0649654893897621,0.508346647102057,0.0470474015755667,NA,0.00691890336201756,0.000310240052917177
+"9339",100,1980,7,26,0.0863586371504303,3.71579761578567,14.2539054043878,10.8085918478971,0,7.57153447791055,1.14410314637254,100,1980,7,26,0.00175438601719706,0.13116961684144,0.0556227883456792,0.00637192068275291,NA,0.0708813222996603,0.000316977167608891
+"9340",100,1980,7,27,0.252145218459031,6.94482952743211,12.677667626191,11.9029814129484,0,7.94543968215721,1.14370425458527,100,1980,7,27,0.0276023394461961,0.0704263193822246,0.0327386243675791,0.0389146438710934,NA,0.00662021105743442,0.000325392920779461
+"9341",100,1980,7,28,4.194829535563,8.3362486013616,14.1284048407778,10.1315291796056,0,7.4505873842236,1.143305362798,100,1980,7,28,0.262631613274079,0.0173175611619291,0.064709968174356,0.00961522242238455,NA,0.00742667966376097,0.000335487312428887
+"9342",100,1980,7,29,6.20242024097506,5.65561057396061,12.4976128776475,9.70581954540593,0,7.01599521080011,1.14290647101073,100,1980,7,29,1.4406432137852,0.0459385956491125,0.167066658486326,0.00262749640266217,NA,0.0127034942291222,0.000347260342557167
+"9343",100,1980,7,30,6.4991198855527,6.43701860797156,13.2040484746297,9.10014310108684,0,7.4059732340748,1.14250757922346,100,1980,7,30,1.23286557548927,0.0485719254934418,0.138122190306002,0.000546783949260832,NA,0.0203168587026365,0.000360712011164303
+"9344",100,1980,7,31,0.244444450808175,5.30633656918281,14.1130363739232,11.3913312886808,0,7.94959663334499,1.14210868743619,100,1980,7,31,0.00590643309884603,0.266365511314239,0.157422181574565,0.00469412538639953,NA,0.00860844907991845,0.000375842318250294
+"9345",100,1980,8,1,0.831463160589583,7.45463144766091,15.1972715862513,10.2882949579405,0,8.1547555258927,1.17067830124873,100,1980,8,1,0.139122816296349,0.12347017712957,0.0570537818039529,0.00867192803551602,NA,0.0103253125184696,0.000421811382668966
+"9346",100,1980,8,2,5.45599560637941,7.62309130898403,13.2848735993976,9.86196924383753,0,8.05117523381172,1.19924791506126,100,1980,8,2,0.110994157568076,0.0351777784983515,0.0988877595668117,0.0380818594436937,NA,0.0152645027975841,0.000475035093559044
+"9347",100,1980,8,3,2.72574253680289,7.83280523329547,13.719152947857,12.3302969486669,0,8.74682029780232,1.22781752887379,100,1980,8,3,0.0638596500151384,0.0571146356761314,0.0773649078816098,0.00985848231184665,NA,0.0233900694177885,0.000535513450920524
+"9348",100,1980,8,4,1.95159511755009,7.39092413636849,14.4077337262916,9.22327834151366,0,8.13533590326914,1.25638714268632,100,1980,8,4,0.0415204577278692,0.0623339043634199,0.0753204326474641,0.000126312258177214,NA,0.0127443524470768,0.00060324645475341
+"9349",100,1980,8,5,0,5.14278328248245,16.6541145312117,9.69282724046865,0,8.60232768148859,1.28495675649886,100,1980,8,5,0,0.120639741689018,0.105760718242114,0.000402339417835012,NA,0.0138375909784036,0.000678234105057701
+"9350",100,1980,8,6,0.00418041810409726,5.55722772537416,13.2539273105701,9.34390531357366,0,8.18689483979004,1.31352637031139,100,1980,8,6,0.00251461995798245,0.0838251698555484,0.0832000129700555,0.0130321549198797,NA,0.0131524400483247,0.000760476401833394
+"9351",100,1980,8,7,2.86556659909365,7.54473048599377,12.1372167348075,9.31283833720897,0,7.79656071656686,1.34209598412392,100,1980,8,7,0.00596491749524132,0.0486924037520872,0.080805181507846,0.00124268057758551,NA,0.0148809086922211,0.000849973345080493
+"9352",100,1980,8,8,7.98338833474221,5.23422444220817,13.3785369577188,10.8900549755369,0,8.46662999515104,1.37066559793645,100,1980,8,8,0.359181336241212,0.0578888897366211,0.0706918000294387,0.00315788609251493,NA,0.0453573608571879,0.000946724934798997
+"9353",100,1980,8,9,0,7.01035203241279,16.4611769782172,11.0178877302785,0,8.48627767963977,1.39923521174899,100,1980,8,9,0,0.0713052762561565,0.110106385308257,0.00566724146132965,NA,0.0284896934432428,0.0010507311709889
+"9354",100,1980,8,10,0,8.3703080734404,17.9864135339315,11.7412102167362,0,8.50665090395979,1.42780482556152,100,1980,8,10,0,0.0474894179339162,0.159770164026045,0.02785965080716,NA,0.0109519496754911,0.00116199205365022
+"9355",100,1980,8,11,5.71639166990391,9.81266227046518,13.7744223937736,10.1706711125977,0,8.33128179021835,1.45637443937405,100,1980,8,11,0.785380226547971,0.110138615328337,0.109449712806921,0.0113929898937529,NA,0.00344981599972894,0.00128050758278293
+"9356",100,1980,8,12,3.04697467942442,8.97313543095185,14.1455995792603,10.7955776210403,0,9.19804256889691,1.48494405318658,100,1980,8,12,0.197836272172764,0.0248930321008124,0.106564858656389,0.00134326395411272,NA,0.0133189832968741,0.00140627775838705
+"9357",100,1980,8,13,2.43905395223494,3.46455446170895,12.1408250093198,7.96536848542452,0,9.00754871226773,1.51351366699912,100,1980,8,13,0.0102339121333384,0.218735671370774,0.0666227663052045,0.00306199473333946,NA,0.00979145585015858,0.00153930258046258
+"9358",100,1980,8,14,0.195269532198429,5.72012100282675,15.0235203112444,10.1317050858299,0,9.4583680324121,1.54208328081165,100,1980,8,14,0.014736842927877,0.1882894541367,0.0413093321148265,0.0499356365539319,NA,0.0140306244654162,0.00167958204900951
+"9359",100,1980,8,15,18.0334435222697,4.9748295200671,12.7346203880604,9.28756871029358,0,9.08606357143001,1.57065289462418,100,1980,8,15,0.969532142104061,0.0844450338397365,0.134087697447476,0.0127385888418167,NA,0.0209193807794457,0.00182711616402784
+"9360",100,1980,8,16,0.654235428020899,6.98244218070908,12.358140783866,9.51939504663281,0,9.78520517996545,1.59922250843671,100,1980,8,16,0.0220467829495147,0.166191791184196,0.0765162967839052,0.000984214756052799,NA,0.018554057838922,0.00198190492551757
+"9361",100,1980,8,17,0.392079214877946,3.62237624733886,14.3615401302627,9.16493949533427,0,10.1673177554769,1.62779212224925,100,1980,8,17,0.0202923987535706,0.67158135309332,0.0604807035703653,0.00248304244600979,NA,0.00712169557692912,0.00214394833347871
+"9362",100,1980,8,18,1.62398239888243,3.22369639262377,18.4609679861037,7.3060726033579,0,9.74124918425917,1.65636173606178,100,1980,8,18,0.334970785797691,0.0581812802866938,0.0655034343343851,0.0269730867843323,NA,0.0212699716096129,0.00231324638791126
+"9363",100,1980,8,19,0.3413641416479,5.27240919306202,17.9039164586167,11.9113420878831,0,9.14197165460015,1.68493134987431,100,1980,8,19,0.0181871347922331,0.13482866458337,0.0460205249232334,0.00380350988295733,NA,0.0267641396042544,0.00248979908881521
+"9364",100,1980,8,20,5.40869089216813,10.3325632355525,16.1672496669757,10.7679097801939,0,10.5767025050939,1.71350096368684,100,1980,8,20,1.71695892378603,0.0191432924661834,0.0525251238149612,0.053594110725424,NA,0.0590667135849684,0.00267360643619056
+"9365",100,1980,8,21,0.225082513916217,8.44292626312726,15.3722661147417,12.3791969276235,0,9.55263619061929,1.74207057749938,100,1980,8,21,0.0202923985444314,0.0923041100028412,0.0363877385971402,0.0174357207672861,NA,0.0634386620869781,0.00286466843003732
+"9366",100,1980,8,22,1.5411441120902,9.32958188072683,18.7815953182309,9.51064909147088,0,11.2400595679467,1.77064019131191,100,1980,8,22,0.175087708081419,0.0168555408902526,0.0558426937217763,0.00321520953268553,NA,0.0203474342214366,0.00306298507035549
+"9367",100,1980,8,23,0.0407040710135786,8.4178438333526,17.1828382657831,11.1566335994943,0,10.4992852862193,1.79920980512444,100,1980,8,23,0.00263157902579559,0.0442432754606836,0.055489528437695,0.00753391393703418,NA,0.0227721321392173,0.00326855635714506
+"9368",100,1980,8,24,1.41595160082491,7.22028608343127,13.6763585870153,10.6847195599077,0,10.1411665000669,1.82777941893697,100,1980,8,24,0.0505263118576599,0.0425596419819831,0.0381830608814719,0.00370058604686797,NA,0.0250012078932941,0.00348138229040603
+"9369",100,1980,8,25,13.3477446500487,5.38288230602235,11.5791639489572,9.24016503689706,0,10.54021530025,1.85634903274951,100,1980,8,25,1.73029246871242,0.0309315958815223,0.199428581356124,0.0209227828377711,NA,0.0311683278577345,0.0037014628701384
+"9370",100,1980,8,26,5.47777781733061,4.70448839021857,13.7708360569181,7.89261832672639,0,11.1111963644785,1.88491864656204,100,1980,8,26,0.242573105215342,0.19388423524156,0.0411584634636727,0.0138339349088912,NA,0.0173141474196773,0.00392879809634218
+"9371",100,1980,8,27,0.0865786591559091,5.80774473488265,13.7315730775806,9.08817397853067,0,11.527369265378,1.91348826037457,100,1980,8,27,0.00140350881375765,0.126083052422171,0.0825988379451408,0.0010023356521822,NA,0.0109400266012462,0.00416338796901736
+"9372",100,1980,8,28,0.68404839862429,7.77895488728522,15.8864468940676,13.920418146551,0,11.4869607858445,1.9420578741871,100,1980,8,28,0.00204678216175752,0.0713871287340553,0.0541473906267246,0.0439760480713944,NA,0.0146341402662418,0.00440523248816395
+"9373",100,1980,8,29,3.27722768946187,10.0716830873647,17.3188558731667,13.0537072518478,0,11.1688118739146,1.97062748799964,100,1980,8,29,0.605847948057615,0.0858251622764291,0.177623028136458,0.0157146335223199,NA,0.0134887740324177,0.00465433165378194
+"9374",100,1980,8,30,3.05940594321693,8.04233223167058,22.1424973029377,10.4200439012483,0,10.5420096691841,1.99919710181217,100,1980,8,30,0.312280697153331,0.132481876538407,0.192789393910466,0.000838603759281705,NA,0.0139653547214387,0.00491068546587135
+"9375",100,1980,8,31,4.21056106329226,7.09480747636264,13.7361826964862,10.492057135134,0,11.983283664509,2.0277667156247,100,1980,8,31,0.284502953116669,0.0128988362295749,0.0656578846424613,0.00401463472923235,NA,0.0327858943066927,0.00517429392443215
+"9376",100,1980,9,1,4.385148437372,7.99673261631965,13.2686029220178,10.4701649379415,0,10.8990671257069,2.05679998306586,100,1980,9,1,0.753742698675024,0.0100801228016131,0.0875052562602742,0.0134689966659783,NA,0.0129076275973378,0.00511503166872157
+"9377",100,1980,9,2,8.79372938972364,7.37347635472699,13.8333442213774,10.5764355528342,0,11.9963495435151,2.08583325050702,100,1980,9,2,0.746198821207308,0.0202830484825477,0.0805474123838538,0.00265613372319326,NA,0.0402766579949981,0.00505625551365366
+"9378",100,1980,9,3,2.12398237481762,9.30749182181783,14.8540704053621,10.6591528440335,0,11.1944620540827,2.11486651794817,100,1980,9,3,0.743859658673496,0.033911736101353,0.052147410837869,0.000664909311562526,NA,0.00730209090831231,0.00499796545922842
+"9379",100,1980,9,4,0.408580862051255,7.49030805604555,17.4035641614622,12.107998014939,0,11.1282905211815,2.14389978538933,100,1980,9,4,0.0169590643361996,0.0760257432592021,0.0376894764461729,0.0038274786096342,NA,0.0724574576753562,0.00494016150544581
+"9380",100,1980,9,5,0.00814081420271572,8.60258516200436,16.226589747388,10.6244664868899,0,12.6683942253028,2.17293305283049,100,1980,9,5,0.00175438601719706,0.157467192798258,0.0482285829201195,0.0027689944596864,NA,0.0257580342958198,0.00488284365230586
+"9381",100,1980,9,6,0,4.64301429565984,20.0402750743367,10.5009020636446,0,12.7788079425484,2.20196632027164,100,1980,9,6,0,0.068821057278401,0.0583101078728435,0.00732630382343394,NA,0.0388734110142487,0.00482601189980857
+"9382",100,1980,9,7,0,9.91569858232085,16.8208360514625,10.17935083618,0,11.2533337462576,2.2309995877128,100,1980,9,7,0,0.0946362761292088,0.0340478562073641,0.00175087229196477,NA,0.0333374769486358,0.00476966624795394
+"9383",100,1980,9,8,1.3653465430073,5.3554454704847,16.8346649039828,12.080583117058,0,11.8099757146005,2.26003285515396,100,1980,9,8,0.321169611438674,0.0629146057352149,0.133400626132652,0.0157146567228277,NA,0.0177275737482651,0.00471380669674196
+"9384",100,1980,9,9,8.50627070029314,5.82210121165277,11.9663257011355,9.43720566295292,0,11.6671939255137,2.28906612259512,100,1980,9,9,1.98842098938794,0.0306140216749469,0.14568066905737,0.00143976194157249,NA,0.0773490657300521,0.00465843324617263
+"9385",100,1980,9,10,11.0475247805924,6.84793185295969,14.783641224516,12.2937405012359,0,11.5519927533827,2.31809939003627,100,1980,9,10,0.894327515719304,0.0588783689789135,0.0974320728548041,0.00128128920892799,NA,0.0581721018946368,0.00460354589624595
+"9386",100,1980,9,11,0.970517073494635,10.7598459214398,16.3038723885816,12.6554894830265,0,12.4692004362817,2.34713265747743,100,1980,9,11,0.134678366124979,0.0376356839119394,0.0253590361481066,0.00264970871113925,NA,0.142644288894796,0.00454914464696194
+"9387",100,1980,9,12,6.6526952791791,7.54444439051831,14.907227778461,7.35826186850519,0,13.0236326439745,2.37616592491859,100,1980,9,12,2.52894701232691,0.22262568903808,0.0157374390742565,0.00211403130649894,NA,0.0180878007571002,0.00449522949832058
+"9388",100,1980,9,13,1.37579757637448,5.08949390312757,15.1928713140708,11.3149285143358,0,12.3496201036918,2.40519919235975,100,1980,9,13,0.0745029227636025,0.0568485339839746,0.0440093784689835,0.000561990383303374,NA,0.0738123926722521,0.00444180045032187
+"9389",100,1980,9,14,0.819581958917108,6.19968106119808,17.5609901289735,10.2866555697585,0,12.6693035269592,2.4342324598009,100,1980,9,14,0.0356140365586646,0.0122777702543673,0.0478251951031429,0.0757707757738474,NA,0.0146739677063818,0.00438885750296582
+"9390",100,1980,9,15,5.87931790734806,6.39211224355582,12.1963146427939,8.95061600247626,0,13.311804911939,2.46326572724206,100,1980,9,15,0.853157915762314,0.0158473614787749,0.0649918207448148,0.000598832725013939,NA,0.0356872462827949,0.00433640065625242
+"9391",100,1980,9,16,6.86017598833057,7.20319028839682,16.9866006177644,10.9827063154466,0,12.5346883314205,2.49229899468322,100,1980,9,16,0.372573073649288,0.0429801021564916,0.0696431939424698,0.00610001114746836,NA,0.0648038412134949,0.00428442991018168
+"9392",100,1980,9,17,3.20231020647307,9.93606181611585,14.7554895770301,10.7988118401455,0,13.8872262715506,2.52133226212437,100,1980,9,17,0.324444429609515,0.0186374327342268,0.0527433268698587,0.00262515845277353,NA,0.0189509837816842,0.0042329452647536
+"9393",100,1980,9,18,16.4189221578332,6.7998459729949,14.0642795153577,9.50551169409086,0,13.9783453300642,2.55036552956553,100,1980,9,18,1.59590647903815,0.0304888630549328,0.138070701478709,0.00360995072487799,NA,0.0258091570583458,0.00418194671996816
+"9394",100,1980,9,19,4.32321232590083,8.48288221222864,14.5326731160398,9.94914192938306,0,13.4857528639487,2.57939879700669,100,1980,9,19,0.37485378792413,0.0172163753956754,0.112477867889469,0.00283684274557329,NA,0.0295584687146519,0.00413143427582539
+"9395",100,1980,9,20,4.45214522370149,7.39040705079686,15.1836082534035,10.3273157029524,0,14.8402735272686,2.60843206444785,100,1980,9,20,0.394853851279332,0.0365134646109156,0.104850952349286,0.00909297785628366,NA,0.0509434336562759,0.00408140793232526
+"9396",100,1980,9,21,4.16006602076414,8.16169410596455,14.0061826433154,11.6923981929901,0,14.214239101555,2.637465331889,100,1980,9,21,0.0326315864206208,0.034122829214225,0.0403245360503259,0.00158421478278015,NA,0.026764299316053,0.0040318676894678
+"9397",100,1980,9,22,4.9059405961577,9.75321221115566,16.0296149059753,13.5451815136195,0,12.5068166601994,2.66649859933016,100,1980,9,22,0.618713488076879,0.0349719238549624,0.0431374279157954,0.00266550582312166,NA,0.0951561850532158,0.00398281354725298
+"9398",100,1980,9,23,0,7.66177115319717,18.7273594066255,13.0745544097867,0,14.3631195378033,2.69553186677132,100,1980,9,23,0,0.189260839364161,0.0601362799440791,0.0831777706570663,NA,0.0144006947465582,0.00393424550568082
+"9399",100,1980,9,24,0.436083613596734,11.1564907350949,23.543806120114,14.2803630451165,0,14.0949948686381,2.72456513421248,100,1980,9,24,0.0450877196485537,0.105860798165483,0.0264543471356035,0.100771901303664,NA,0.0514537420845912,0.00388616356475133
+"9400",100,1980,9,25,2.24224424690041,10.5034103624367,14.8199119599345,7.95994501355195,0,14.7610192965934,2.75359840165363,100,1980,9,25,0.157192988116842,0.016357896050865,0.0572672596022955,0.0066877097771584,NA,0.0277376964362575,0.00383856772446447
+"9401",100,1980,9,26,3.01067107529005,8.81623758288762,16.3578330859362,12.0595159929315,0,15.2538422754495,2.78263166909479,100,1980,9,26,0.32362572190358,0.144587748220838,0.0390719103185073,0.0271561760306287,NA,0.0196025123436768,0.00379145798482028
+"9402",100,1980,9,27,0.152035205867818,9.38440050412588,17.05834995979,11.9941474759277,0,15.9481312647205,2.81166493653595,100,1980,9,27,0.00701754433021212,0.0258760013090249,0.060345156798129,0.00260058276311164,NA,0.0170012022021127,0.00374483434581874
+"9403",100,1980,9,28,0,6.72084711041256,21.8713201621447,10.1033223753322,0,14.2553177148534,2.84069820397711,100,1980,9,28,0,0.188239744788741,0.0296812819990584,0.0171286894481306,NA,0.0342225572970629,0.00369869680745985
+"9404",100,1980,9,29,0.0717271737860899,11.5369636642657,27.9509900557851,10.3551484853914,0,16.0680693066334,2.86973147141826,100,1980,9,29,0.00426900597517951,0.0513263226850104,0.103480770405802,0.00886491352555726,NA,0.0842898640980503,0.00365304536974363
+"9405",100,1980,9,30,0,12.9342244644501,17.9732340549347,13.1286028388834,0,14.7734150911016,2.89876473885942,100,1980,9,30,0,0.0247467569206758,0.0616988851984095,0.0028812944669641,NA,0.0304814888360167,0.00360788003267006
+"9406",100,1980,10,1,0,8.81041791494137,19.7456874238907,10.192563344412,0,15.1212075313981,2.92937304743122,100,1980,10,1,0,0.0193175373814223,0.0218479808120224,0.0355701652326644,NA,0.015441490077195,0.00355816540946989
+"9407",100,1980,10,2,0,7.88199112465148,19.8472935285243,9.85720570357588,0,15.7031979153855,2.95998135600303,100,1980,10,2,0,0.22857999850605,0.0543356396504494,0.00308128795073274,NA,0.0443168823130399,0.00350892005376572
+"9408",100,1980,10,3,0.415621568660925,5.65474148971675,16.4346536210399,9.43955986203402,0,16.8828401884728,2.99058966457483,100,1980,10,3,0.0149707607585088,0.0522134857233726,0.0387467549243363,0.00476726925059309,NA,0.0710248862236638,0.00346014396555756
+"9409",100,1980,10,4,0.203190322297372,4.05001097779856,21.0167547232247,9.54944977167547,0,16.2660369616368,3.02119797314664,100,1980,10,4,0.00140350902289676,0.0517368405353336,0.0312145231194852,0.0207941609054274,NA,0.0203077403953125,0.00341183714484539
+"9410",100,1980,10,5,0,10.6643454810836,21.4231458901048,14.3397690990184,0,14.4973039880094,3.05180628171844,100,1980,10,5,0,0.0294356928039601,0.0416251009090855,0.00305437849395366,NA,0.0149795450864503,0.00336399959162922
+"9411",100,1980,10,6,20.5727171063817,11.3007149743562,12.293597432253,11.383949357422,0,15.7330451289856,3.08241459029025,100,1980,10,6,0.937660909730915,0.0279473911866172,0.0249023203220441,0.0308175716356441,NA,0.0420501560078127,0.00331663130590906
+"9412",100,1980,10,7,14.8357536370474,6.27377332607643,14.3055775729474,11.2404070970642,0,15.4653439565067,3.11302289886205,100,1980,10,7,2.76701787056295,0.0296099312219231,0.182292426973852,0.0622479357112188,NA,0.0277913927162147,0.00326973228768488
+"9413",100,1980,10,8,2.82288230999862,9.33468656676306,16.0673488159515,13.1703959633939,0,16.8859243343019,3.14363120743386,100,1980,10,8,0.044795333795384,0.00971463498349587,0.100247293523895,0.00502513931906721,NA,0.213807272501053,0.00322330253695672
+"9414",100,1980,10,9,0.606160623930206,9.55368529110983,18.2402090632876,12.8470405876571,0,15.9020071570155,3.17423951600566,100,1980,10,9,0.0071929817032398,0.0381544086702362,0.0271374669662489,0.00748771566879058,NA,0.0770874542956292,0.00317734205372453
+"9415",100,1980,10,10,2.01573155081049,6.97760173432504,21.5121229337518,13.5945105085803,0,16.8936281089517,3.20484782457747,100,1980,10,10,0.208771932334232,0.0342584638528827,0.0878758670474589,0.0104064364629229,NA,0.0252678166582968,0.00313185083798836
+"9416",100,1980,10,11,0.0403740383004329,12.3152913921344,16.4901648900165,12.9991748807716,0,17.1778386468978,3.23545613314927,100,1980,10,11,0.0142690064605565,0.05965909545637,0.0283316156758366,0.00346081741071435,NA,0.0483597978746909,0.00308682888974819
+"9417",100,1980,10,12,4.3727172955428,5.37794281966878,14.9783387210372,10.0901101325342,0,17.1098738644995,3.26606444172108,100,1980,10,12,0.226900568621914,0.0234912518897463,0.0767783812941762,0.00214853051176377,NA,0.0563980117467312,0.00304227620900401
+"9418",100,1980,10,13,8.32145218172482,8.86763468302778,16.7056212724239,13.4886688373008,0,17.3474940204877,3.29667275029288,100,1980,10,13,1.67807014225525,0.065722169452326,0.0526701847903969,0.000581293583537865,NA,0.0284535112552796,0.00299819279575584
+"9419",100,1980,10,14,0.238943900204006,10.3162596726706,17.3798901313471,12.1034872371896,0,19.9639966759439,3.32728105886469,100,1980,10,14,0.010935673084524,0.00440000644268675,0.0659146329737949,0.00730057784194978,NA,0.0448999605102969,0.00295457865000366
+"9420",100,1980,10,15,0.129922994235233,5.70447748276529,18.112101178227,13.2604621639608,0,16.3951004540654,3.35788936743649,100,1980,10,15,0.00228070182235617,0.0244917899092915,0.0414807119809746,0.0129450468789108,NA,0.0718679352916904,0.00291143377174748
+"9421",100,1980,10,16,0,6.95264024388279,16.3785480251669,9.53829480476505,0,18.7320598157167,3.3884976760083,100,1980,10,16,0,0.457950777117433,0.0145929640476898,0.0170315820438052,NA,0.132529742523434,0.0028687581609873
+"9422",100,1980,10,17,0,3.92204621758791,17.321936171965,10.1054234488962,0,19.3571685451549,3.4191059845801,100,1980,10,17,0,0.0718286513574031,0.0270356586213957,0.00805615192221757,NA,0.064814379899403,0.00282655181772313
+"9423",100,1980,10,18,0,6.26829481229793,23.1390980677505,14.8474588131878,0,19.0429078423338,3.4497142931519,100,1980,10,18,0,0.11065612947901,0.0647210544745166,0.103494173574227,NA,0.0450496214481247,0.00278481474195494
+"9424",100,1980,10,19,6.12541252289406,11.616446730065,21.9840375818435,14.5524972384781,0,19.6921585980272,3.48032260172371,100,1980,10,19,0.225789462139746,0.0461543976500029,0.07097078370249,0.0130450525161612,NA,0.0854374496028514,0.00274354693368275
+"9425",100,1980,10,20,0.610231034875703,12.6830473733027,17.5676016665921,11.3710229137156,0,18.5571137742765,3.51093091029551,100,1980,10,20,0.119824560573924,0.046795331263484,0.0580314996599161,0.00258422313717671,NA,0.0774725211459856,0.00270274839290658
+"9426",100,1980,10,21,0,7.34302536069494,23.1657536425869,12.1502311043482,0,18.4484374221346,3.54153921886732,100,1980,10,21,0,0.0474912149251015,0.0154029628333109,0.0249485397830301,NA,0.132315028544623,0.0026624191196264
+"9427",100,1980,10,22,0,9.11810774220885,27.2109680532491,15.1757865481906,0,17.9076164246305,3.57214752743912,100,1980,10,22,0,0.0654263522031997,0.038114056169221,0.305956102694761,NA,0.0212963080820496,0.00262255911384221
+"9428",100,1980,10,23,1.3851485287871,14.0623102125162,16.620660343317,13.044950581751,0,17.6237796359555,3.60275583601093,100,1980,10,23,0.717602336791002,0.046346773555906,0.0724466873840431,0.00598070348110944,NA,0.0542044622713966,0.00258316837555402
+"9429",100,1980,10,24,9.7246424619383,6.99232132390256,17.7657975825277,9.66915293232967,0,18.9065235998279,3.63336414458273,100,1980,10,24,0.0809356804340751,0.537247955258707,0.0142093622489483,0.00955379680618263,NA,0.145625416638008,0.00254424690476185
+"9430",100,1980,10,25,0,4.23132013487737,19.253674485109,11.6320131257816,0,17.9932818335781,3.66397245315454,100,1980,10,25,0,0.0469508640886184,0.0128040939912167,0.00433623947465519,NA,0.0350076853659455,0.00250579470146565
+"9431",100,1980,10,26,0,9.03379548369723,26.1343343921489,11.1455447004967,0,17.6715716572769,3.69458076172634,100,1980,10,26,0,0.0631064469566676,0.0212396993584337,0.0122426891550797,NA,0.0768815857861755,0.00246781176566547
+"9432",100,1980,10,27,0,13.5130801793635,15.1981737254345,12.2360835804535,0,20.1178450330368,3.72518907029815,100,1980,10,27,0,0.00972046641753457,0.146526371493065,0.00217836151351383,NA,0.0371738773190873,0.00243029809736128
+"9433",100,1980,10,28,12.3042904420523,5.69143017211763,14.9317051821416,10.2810781151548,0,20.0354330070819,3.75579737886995,100,1980,10,28,0.118070159934486,0.150543277749422,0.06133859034095,0.0216052908535751,NA,0.109950989328783,0.00239325369655309
+"9434",100,1980,10,29,2.25742572945992,9.29850394707439,15.5268536379891,11.5493730077125,0,20.27930022219,3.78640568744176,100,1980,10,29,0.0181871448483435,0.0218374383425114,0.053287662778365,0.0233561253331062,NA,0.0685217889497587,0.0023566785632409
+"9435",100,1980,10,30,4.0083608220775,8.92115507660919,16.1955773958934,11.8190978745816,0,18.8213629069869,3.81701399601356,100,1980,10,30,0.479883084046221,0.0173064014123167,0.081470139283924,0.00371402541052793,NA,0.0585188608751549,0.00232057269742471
+"9436",100,1980,10,31,4.59064914159911,8.79435639019453,15.820759190978,11.5885258833043,0,19.4828969484941,3.84762230458537,100,1980,10,31,0.216959033374915,0.0930274566004162,0.10617676355033,0.0213467693173171,NA,0.0234263849054395,0.00228493609910452
+"9437",100,1980,11,1,7.10880088963524,9.64014301467912,14.9119362427194,9.66124301801289,0,18.1163154168172,3.8870389558617,100,1980,11,1,0.842573078724399,0.00940231622850038,0.090991192187537,0.000631571273446731,NA,0.0418351476770849,0.00225292910920515
+"9438",100,1980,11,2,1.14059406007477,6.04437844273281,13.2794829464064,12.5327833677151,0,17.1523567673679,3.92645560713804,100,1980,11,2,0.077660818274259,0.0937783474191827,0.0446099076589501,0.044887739812925,NA,0.0295391693227099,0.00222138252431586
+"9439",100,1980,11,3,6.05093510106321,6.15919691973393,15.721364234278,7.73070407569474,0,17.3677866578334,3.96587225841437,100,1980,11,3,1.59222227414451,0.378880670454925,0.00683627739851933,0.0235321718405776,NA,0.0845708601220506,0.00219029634443665
+"9440",100,1980,11,4,0.585148514235111,3.46371836158702,15.4816502391702,10.1811219971828,0,18.8090934361348,4.00528890969071,100,1980,11,4,0.0206432733340572,0.0852584856758394,0.0392929865653559,0.00490059351790616,NA,0.028487930049348,0.00215967056956748
+"9441",100,1980,11,5,3.94906487638014,8.408525875037,19.5748074933378,14.01092413576,0,18.7151212207415,4.04470556096704,100,1980,11,5,0.234853819044022,0.0080842152400963,0.0151877503113286,0.0194479955952752,NA,0.0146640883765506,0.00212950519970841
+"9442",100,1980,11,6,4.92541260950112,11.4076569025273,15.0704180945133,12.5395048831818,0,20.4580579144427,4.08412221224338,100,1980,11,6,2.2469590480983,0.148632179652935,0.0131783874356252,0.00573624812571255,NA,0.0778424940028078,0.0020998002348594
+"9443",100,1980,11,7,1.2426842815674,10.3805940930206,14.6876017871601,10.9100660506648,0,20.1155305132962,4.12353886351972,100,1980,11,7,0.0825146151704408,0.0887941826224731,0.197027577800715,0.00732865064077891,NA,0.032063485915465,0.00207055567502045
+"9444",100,1980,11,8,0.0202420245040499,7.46717268405574,16.8423323216874,12.8261826507854,0,19.2387587513133,4.16295551479605,100,1980,11,8,0.00228070182235617,0.357357842276977,0.114491332048894,0.0379397626777478,NA,0.0556834718558233,0.00204177152019157
+"9445",100,1980,11,9,1.47744774346304,7.5660725772971,18.3477999411269,11.0712541280144,0,22.5149304257653,4.20237216607239,100,1980,11,9,0.096900580319746,0.179781878258354,0.0162000167849903,0.00161520243423103,NA,0.123117737729317,0.00201344777037276
+"9446",100,1980,11,10,0.00110011002739402,5.78310228154735,23.0669306197969,15.1645215688103,0,22.6242720108446,4.24178881734872,100,1980,11,10,0.000994152076411666,0.0263619832245434,0.0463385576038607,0.149956139347076,NA,0.0437831970280865,0.00198558442556402
+"9447",100,1980,11,11,0,10.8412540926792,30.9560836565377,10.5410011960859,0,22.1976701354767,4.28120546862506,100,1980,11,11,0,0.0333830326705501,0.0523841263668401,0.00590410684516327,NA,0.0502927876397874,0.00195818148576536
+"9448",100,1980,11,12,0.0892189232216548,11.4129263402605,28.3696367496705,17.2613203155719,0,21.4775190116753,4.3206221199014,100,1980,11,12,0.00140350881375765,0.080568971495656,0.0137871369838516,0.101154235692897,NA,0.0342006519014201,0.00193123895097675
+"9449",100,1980,11,13,0,15.8779317994322,31.3257534941955,12.0603629574917,0,21.0321182722315,4.36003877117773,100,1980,11,13,0,0.00398070579001009,0.383509805481571,0.175161942737488,NA,0.0718129734907854,0.00190475682119823
+"9450",100,1980,11,14,0.0914191432764428,13.1949176232282,19.8615399980702,11.3016061111383,0,20.571842699798,4.39945542245407,100,1980,11,14,0.00584795339065686,0.00593392669283839,0.00856081295379966,0.00410233378279798,NA,0.112694341403832,0.00187873509642976
+"9451",100,1980,11,15,0,6.80272822490226,21.2355007301725,12.1777448822038,0,23.2030661805291,4.4388720737304,100,1980,11,15,0,0.101992980244308,0.124231596084832,0.0122672345056312,NA,0.137851747306798,0.00185317377667137
+"9452",100,1980,11,16,0.0190319034739165,10.334444421615,34.4442128851862,18.3571725012314,0,24.7295905096006,4.47828872500674,100,1980,11,16,0.0020467836867299,0.0696818577861827,0.240498817775144,0.0193116771143964,NA,0.0371772754590769,0.00182807286192305
+"9453",100,1980,11,17,0,16.341661209845,29.9235753163253,17.8911991685924,0,24.148955329114,4.51770537628308,100,1980,11,17,0,0.0283591517267513,0.0517584530615649,0.0954374076622999,NA,0.0233373615130368,0.00180343235218479
+"9454",100,1980,11,18,0.299559963524866,17.6992187269188,23.350693225336,16.7231136758467,0,21.3208002045108,4.55712202755941,100,1980,11,18,0.0109941519910132,0.00599178386966093,0.0715363493850179,0.0544941969311521,NA,0.0266730473303951,0.00177925224745662
+"9455",100,1980,11,19,16.1052807187877,7.5050494982989,17.4242353659652,9.38518157178419,0,21.1781073595198,4.59653867883575,100,1980,11,19,1.03175452171027,0.0821029216944936,0.0554368344405516,0.0439841952586781,NA,0.0632126673721471,0.00175553254773849
+"9456",100,1980,11,20,0.0836083620819453,6.69822880458517,19.5133443125273,11.7708801724861,0,21.419772621482,4.63595533011208,100,1980,11,20,0.00538011711940431,0.157480698946916,0.0644051346542426,0.0299321827783148,NA,0.0401605432938883,0.00173227325303045
+"9457",100,1980,11,21,0.0222222225533591,9.17850381601499,17.5631023130008,9.44792098495433,0,20.8936445212076,4.67537198138842,100,1980,11,21,0.00175438601719706,0.277678385963288,0.0241315045053565,0.0121116773505555,NA,0.089694491898913,0.00170947436333248
+"9458",100,1980,11,22,0,7.29959293980278,17.1155005833759,10.0254125511161,0,21.7678728646009,4.71478863266475,100,1980,11,22,0,0.146203484976934,0.0206829950169011,0.0129777879927782,NA,0.0504666338871883,0.00168713587864456
+"9459",100,1980,11,23,0,8.24489540996069,18.1147305034306,9.74455444096732,0,23.7796538643505,4.75420528394109,100,1980,11,23,0,0.351076595202718,0.0605316818062696,0.00740292460799091,NA,0.0335053760340412,0.00166525779896673
+"9460",100,1980,11,24,0,10.3832013662105,18.7929375924424,10.7349943647815,0,21.6484417658022,4.79362193521743,100,1980,11,24,0,0.0468953285909632,0.0130654675269068,0.00986726674538489,NA,0.0768085766762192,0.00164384012429895
+"9461",100,1980,11,25,0,7.36929595588457,21.8528272379087,10.1613970191041,0,21.806470596292,4.83303858649376,100,1980,11,25,0,0.0438385845407589,0.212536096789559,0.00232222891921014,NA,0.0254827307498443,0.00162288285464125
+"9462",100,1980,11,26,0,7.90053915426676,30.0613863182278,15.0059406114752,0,23.5918701750367,4.8724552377701,100,1980,11,26,0,0.038021083399055,0.0225783728529832,0.0165356777281072,NA,0.0814231642743358,0.00160238598999362
+"9463",100,1980,11,27,0,15.3864356524611,29.3722443743245,16.128151601953,0,22.9315796857435,4.91187188904643,100,1980,11,27,0,0.129783050579571,0.187037319170188,0.117225687821286,NA,0.0950286898050954,0.00158234953035605
+"9464",100,1980,11,28,1.04587460357626,12.0386798248039,21.1285258818774,13.7917602536964,0,23.8526174492598,4.95128854032277,100,1980,11,28,0.0306432729088081,0.0265117666680432,0.0282338081719873,0.0141666804420523,NA,0.020313092640702,0.00156277347572856
+"9465",100,1980,11,29,1.56875686152409,10.5952144062558,16.34688693803,14.004345529806,0,23.7177808363717,4.99070519159911,100,1980,11,29,0.0938596465434264,0.00851169788077572,0.0365596678708903,0.046069563980621,NA,0.0339837168510944,0.00154365782611113
+"9466",100,1980,11,30,1.12607260148386,11.2727282516765,19.3492741411669,10.8203519846347,0,23.5706844535542,5.03012184287544,100,1980,11,30,0.584444429079694,0.0182801172000804,0.0813719431025887,0.00325904815635809,NA,0.140510444714971,0.00152500258150378
+"9467",100,1980,12,1,0,9.95618266765565,26.1316171673396,15.668943888808,0,24.4642341768072,5.0745964211769,100,1980,12,1,0,0.012048577298687,0.0711204107098175,0.0427871381659863,NA,0.0801482823874876,0.0015545615644721
+"9468",100,1980,12,2,3.21298124543642,15.1480197801579,25.2494387170269,14.6455995551299,0,22.9458659666431,5.11907099947836,100,1980,12,2,2.53333331611422,0.0122876989844957,0.0657619280410946,0.128491230446061,NA,0.239079508596407,0.00159024320147449
+"9469",100,1980,12,3,1.36116611242819,13.7501208900225,17.3166557580593,15.9479867968753,0,22.5366379903319,5.16354557777982,100,1980,12,3,0.236549700979602,0.0331871332203035,0.0182783659686917,0.0382696373612152,NA,0.0797721152471967,0.00163204749251093
+"9470",100,1980,12,4,2.05280526229913,11.0424752534419,17.8753023262989,9.14262930342335,0,24.2819416690469,5.20802015608128,100,1980,12,4,0.0503508816825053,0.110867293169422,0.0388655023634357,0.00755554856201683,NA,0.140476192872302,0.00167997443758145
+"9471",100,1980,12,5,0.290649071934134,4.39495052257911,20.815863616658,9.83631456855631,0,23.5008902931933,5.25249473438274,100,1980,12,5,0.0603508779981681,0.0402619852947394,0.0453538189271781,0.0369830377389731,NA,0.0570818064680507,0.001734024036686
+"9472",100,1980,12,6,0.0784378449531934,7.15644664680473,22.4472058572129,16.5256765825127,0,21.8704143784494,5.2969693126842,100,1980,12,6,0.00362573110220725,0.058980706375549,0.0930801417303053,0.0407928923805602,NA,0.0593900793951239,0.00179419628982463
+"9473",100,1980,12,7,0,12.3959186887584,23.1364796998346,13.4209681071333,0,23.3618351826918,5.34144389098565,100,1980,12,7,0,0.0596004893253508,0.0572433640467012,0.0067917912668007,NA,0.0162162748572307,0.00186049119699731
+"9474",100,1980,12,8,0,9.52331127821416,32.0865127156408,16.8959625567278,0,23.6694910694819,5.38591846928711,100,1980,12,8,0,0.0903116599256357,0.0316385295096041,0.0859989014523371,NA,0.129279162502586,0.00193290875820405
+"9475",100,1980,12,9,0,16.0775358391018,38.2028603086902,9.78552247037982,0,22.7876759391442,5.43039304758857,100,1980,12,9,0,0.0330396915193584,0.0798262353386214,0.0605064293287892,NA,0.165676122989782,0.00201144897344485
+"9476",100,1980,12,10,0,17.7611442058131,41.5735977978596,7.95596253622745,0,24.238541578321,5.47486762589003,100,1980,12,10,0,0.0219923546821562,0.0332480202494863,0.0902953248944666,NA,0.0396186041760018,0.00209611184271972
+"9477",100,1980,12,11,0,15.8841254315098,20.4681627527453,14.7336743127133,0,25.4006887508904,5.51934220419149,100,1980,12,11,0,0.00969415494470353,0.068687694585595,0.0480263340822395,NA,0.151377861171867,0.00218689736602864
+"9478",100,1980,12,12,1.65995598272128,13.5926623171312,17.6690648627622,13.7495048562817,0,22.9833091473973,5.56381678249295,100,1980,12,12,0.104678359784579,0.0345034873271501,0.0871830444962914,0.00340351945764354,NA,0.198069297236708,0.00228380554337161
+"9479",100,1980,12,13,4.87557754264806,12.6524092219975,17.0109570170655,15.2124863174489,0,20.9416920464687,5.60829136079441,100,1980,12,13,1.0576024295294,0.0370561666567594,0.117925840273231,0.0649040022309816,NA,0.0336844790256603,0.00238683637474866
+"9480",100,1980,12,14,7.46875688082839,9.62964784666257,16.2312100119848,10.2520791631852,0,22.225815602627,5.65276593909587,100,1980,12,14,0.0595321910144475,0.0350251223090703,0.0514953007996049,0.019717551899623,NA,0.127330457430969,0.00249598986015976
+"9481",100,1980,12,15,1.39350934823354,7.97701860654472,19.3060174046045,9.5140263449372,0,25.277219288541,5.69724051739733,100,1980,12,15,0.0306432749653432,0.15654853829284,0.0137672243668846,0.00486200111573075,NA,0.046443944515856,0.00261126599960492
+"9482",100,1980,12,16,5.39306933474619,10.2827612716373,17.8870408359272,9.35449944654576,0,25.8820264441015,5.74171509569879,100,1980,12,16,10.0066664731503,0.0562655200378765,0.157387656826789,0.0388619669396289,NA,0.128649841255626,0.00273266479308415
+"9483",100,1980,12,17,3.61298128742852,8.68996692359513,16.8403741775697,10.5041143346016,0,23.8749045496677,5.78618967400024,100,1980,12,17,1.57783632495948,0.0855812310091367,0.0376315264118773,0.0417719118330969,NA,0.0626358447336705,0.00286018624059741
+"9484",100,1980,12,18,4.92684268059641,9.13534655114605,17.4117159680827,9.36312435850976,0,23.9247434878713,5.8306642523017,100,1980,12,18,0.570643301567853,0.100258467550527,0.0436701340838827,0.00886548262041133,NA,0.0799765227895359,0.00299383034214475
+"9485",100,1980,12,19,0.405060513299553,5.3972386778778,25.1832561618818,11.7808470248651,0,21.2363499027613,5.87513883060316,100,1980,12,19,0.0131578947804128,0.0383561302966047,0.0225690323189299,0.0817982296637755,NA,0.0392550281828534,0.00313359709772616
+"9486",100,1980,12,20,0,11.6573597180961,30.2384047366605,11.3896809736363,0,23.7726622262736,5.91961340890462,100,1980,12,20,0,0.0401555690342315,0.278125659363048,0.0666696362301156,NA,0.0224309732559426,0.00327948650734161
+"9487",100,1980,12,21,0,13.677370707194,37.8368097773217,8.18041796679019,0,22.0241265895673,5.96408798720608,100,1980,12,21,0,0.0186263397976163,0.309069202053895,0.429566651068833,NA,0.0632093073326093,0.00343149857099113
+"9488",100,1980,12,22,0,18.5252805317458,32.3370521490855,16.0616503643124,0,22.7499533638933,6.00856256550754,100,1980,12,22,0,0.0185871349671001,0.613073666735476,0.261713556222909,NA,0.0645818976377994,0.00358963328867471
+"9489",100,1980,12,23,0,11.0558855719823,20.9006711164586,11.4196259516432,0,23.8394889921263,6.053037143809,100,1980,12,23,0,0.194692933768647,0.042592434014519,0.225591874980435,NA,0.0387899377047811,0.00375389066039235
+"9490",100,1980,12,24,0.0326732678136023,8.5648625293056,25.3930692662238,14.4588449024918,0,26.7952263340267,6.09751172211046,100,1980,12,24,0.00374269017002039,0.167609367252449,0.509838915583166,0.0848316466951383,NA,0.0397091796373021,0.00392427068614404
+"9491",100,1980,12,25,0,12.4563477236052,22.8502639543892,15.559571020674,0,26.7710891212228,6.14198630041192,100,1980,12,25,0,0.40040425801649,0.071939811822817,0.017449713052309,NA,0.0288734946089749,0.00410077336592978
+"9492",100,1980,12,26,0,12.761947202735,22.1484598153495,16.5469636046323,0,24.9924225073873,6.18646087871338,100,1980,12,26,0,0.133922749230524,0.0875262610008991,0.0413245614706991,NA,0.174040296218994,0.0042833986997496
+"9493",100,1980,12,27,0,15.5379867385847,29.5086576568805,18.2023978721191,0,24.3474257686674,6.23093545701483,100,1980,12,27,0,0.0725818019778746,0.158750855148414,0.0668275509663316,NA,0.0370867220117061,0.00447214668760345
+"9494",100,1980,12,28,0.172497253106831,15.1169416031035,23.714169385803,18.1984047171044,0,25.0784154358314,6.27541003531629,100,1980,12,28,0.00953216412698317,0.0175368211490337,0.409125124109597,0.170625208835903,NA,0.0564464351202811,0.00466701732949141
+"9495",100,1980,12,29,1.20220020088819,13.2538282942064,20.6923101003414,11.463641402745,0,24.6873741235462,6.31988461361775,100,1980,12,29,0.0427485352859175,0.0675063999154387,0.040680744068925,0.00821637716913298,NA,0.0159888769120001,0.00486801062541339
+"9496",100,1980,12,30,0,11.3710890529704,24.6349505601805,14.7643343806923,0,26.9760919811286,6.36435919191921,100,1980,12,30,0,0.556981888952929,0.272991814744098,0.0556035020438563,NA,0.0309081093829142,0.00507512657536946
+"9497",100,1980,12,31,0,11.579097742557,31.5887460194536,17.017997754289,0,25.6653309723101,6.40883377022067,100,1980,12,31,0,0.0549315923791835,0.422678249381996,0.0817497999674391,NA,0.0501234370470788,0.00528836517935957
+"9498",100,1981,1,1,0,15.8578438816553,28.4882620674024,16.8967877315609,0,21.4933723095882,6.42856880788795,100,1981,1,1,0,0.0467432907418367,0.159598325825537,0.0667871550758931,NA,0.113665062200468,0.00510746022425751
+"9499",100,1981,1,2,0.0283828387067656,14.7942793471585,27.4204622065142,16.0611221305083,0,24.9312152524199,6.44830384555523,100,1981,1,2,0.00175438601719706,0.0276731032121333,0.731267326649686,0.0259648775916567,NA,0.116269296888222,0.00494115160609061
+"9500",100,1981,1,3,0,17.4492631750663,20.8846755326778,13.3736852639579,0,26.5967410062153,6.46803888322251,100,1981,1,3,0,0.00623857098448939,0.0673877326744811,0.00374970948520611,NA,0.0285199774543324,0.0047894393248588
+"9501",100,1981,1,4,0.0700770087449989,10.541573152028,22.266622679724,11.2307041250046,0,26.3111357773197,6.48777392088979,100,1981,1,4,0.00228070182235617,0.59852126222141,0.153198774282344,0.014003491487635,NA,0.0850963763836609,0.00465232338056216
+"9502",100,1981,1,5,0,13.4182619046588,23.2254015331877,14.8678877602841,0,23.9883473119327,6.50750895855707,100,1981,1,5,0,0.193349698480595,0.145592944126532,0.0655871891535461,NA,0.046879647748565,0.00452980377320063
+"9503",100,1981,1,6,0,12.8020571466326,23.6373489975798,16.4396373530557,0,24.0440472813795,6.52724399622434,100,1981,1,6,0,0.247676644603044,0.12383967906239,0.0224432495030738,NA,0.026026152994482,0.00442188050277424
+"9504",100,1981,1,7,0,11.566556565439,25.302090362616,15.090979155403,0,22.9117172899045,6.54697903389162,100,1981,1,7,0,0.28171760078794,0.124511211649815,0.0339777966817681,NA,0.112993496208082,0.00432855356928298
+"9505",100,1981,1,8,0,13.9113861467972,37.6474916481211,17.4341587280676,0,24.674535103341,6.5667140715589,100,1981,1,8,0,0.012645033870998,0.050580771392901,0.0233648770295582,NA,0.0422971927609934,0.00424982297272685
+"9506",100,1981,1,9,8.75632579832843,19.322013336702,33.1089987885965,19.7187681082714,0,25.8146201087566,6.58644910922618,100,1981,1,9,0.848187154134181,0.00962453707893075,0.0119480205257627,0.0162988127907398,NA,0.0721808073746579,0.00418568871310586
+"9507",100,1981,1,10,2.04631460027726,15.0607041630677,21.8192739686032,12.1182837669868,0,26.9493202516142,6.60618414689346,100,1981,1,10,0.0270175434274302,0.0478783476021589,0.0217560807571088,0.0180807181308708,NA,0.0177028788771157,0.00413615079042
+"9508",100,1981,1,11,0.0679867996929502,11.8782948660772,21.5985038157212,12.3987789091104,0,26.3301124925092,6.62591918456074,100,1981,1,11,0.00245614042407588,0.38065609074651,0.0240831026939203,0.0272134818473982,NA,0.0168020450131951,0.00410120920466927
+"9509",100,1981,1,12,0.188008805247161,10.9117050936776,24.6601318930111,15.940770227917,0,26.3649291609249,6.64565422222802,100,1981,1,12,0.024502924759137,0.206469559090682,0.105467261474209,0.0314696097302924,NA,0.0225969479608058,0.00408086395585366
+"9510",100,1981,1,13,0,13.9563587155148,33.3082505615369,18.6955002846629,0,25.2431986182527,6.6653892598953,100,1981,1,13,0,0.0749719116412419,0.322639883798734,0.0595229899628433,NA,0.0936036368857892,0.0040751150439732
+"9511",100,1981,1,14,0,19.6471399489803,41.0853130688893,14.476391610795,0,23.7433956383347,6.68512429756258,100,1981,1,14,0,0.0905719728483139,0.0692023051224754,0.475998839937267,NA,0.0672321011006512,0.00408396246902785
+"9512",100,1981,1,15,0.501430145297835,21.6372167652327,26.8384049586599,18.7233443810995,0,25.3748985328658,6.70485933522986,100,1981,1,15,0.22152046189845,0.022394130247539,1.14992562284452,0.0252911837352253,NA,0.0311767743323606,0.00410740623101765
+"9513",100,1981,1,16,1.02871287672302,16.5772825016571,27.1950054819172,18.4839491219935,0,26.3235829356299,6.72459437289713,100,1981,1,16,0.134736835907775,0.00592452051745706,0.664273152696323,0.0235608533315699,NA,0.0447167395274734,0.00414544632994258
+"9514",100,1981,1,17,0,17.8548627314132,36.3861709905274,14.8871067926304,0,24.207612367232,6.74432941056441,100,1981,1,17,0,0.00532103676632474,0.0344620435402493,0.164678309423967,NA,0.0528766704589807,0.00419808276580265
+"9515",100,1981,1,18,0,20.8827063242594,31.8436527000402,18.8622001148555,0,24.1705997348813,6.76406444823169,100,1981,1,18,0,0.0131689140010683,0.540603617391136,0.030447367595871,NA,0.192587833135833,0.00426531553859783
+"9516",100,1981,1,19,0.349394946700276,15.1095708746328,23.8672938897665,15.88641368333,0,26.4963187923028,6.78379948589897,100,1981,1,19,0.00801169549164021,0.036294147710163,0.0437497426130462,0.0337766239055452,NA,0.0486457335737571,0.00434714464832815
+"9517",100,1981,1,20,0,10.7313752955992,27.1286466848208,14.1952146198621,0,20.6468870492452,6.80353452356625,100,1981,1,20,0,0.0127584943760911,0.191338664794769,0.0956023669773431,NA,0.174461157568063,0.0044435700949936
+"9518",100,1981,1,21,0.105610562629826,14.9911992332198,20.7633223685757,9.51600662080368,0,24.009633844067,6.82326956123353,100,1981,1,21,0.00432748550908607,0.00883742644107674,0.038139783991214,0.0132561605197698,NA,0.0406483076735117,0.0045545918785942
+"9519",100,1981,1,22,0.820352030797104,11.0752364579338,21.2008359382386,11.5470957436053,0,22.5955021378006,6.84300459890081,100,1981,1,22,0.0473099392269093,0.0506262983379,0.0286597094577036,0.106294216419325,NA,0.345333595828704,0.00468020999912991
+"9520",100,1981,1,23,0.129042906213318,11.6448736390134,23.9429151453201,15.1288887869538,0,28.2953508361113,6.86273963656809,100,1981,1,23,0.0020467836867299,0.020966686545544,0.077776528605182,0.0304760187473061,NA,0.0314651025705819,0.00482042445660076
+"9521",100,1981,1,24,0,12.7718812640351,26.1329813307793,15.1634763533002,0,23.9323714527054,6.88247467423537,100,1981,1,24,0,0.222004090163093,0.455423193453418,0.140739780816477,NA,0.0599613605108523,0.00497523525100675
+"9522",100,1981,1,25,2.48646863773711,13.0466005977887,37.2757095278162,18.0295160123617,0,26.2258969434073,6.90220971190265,100,1981,1,25,0.592631589237015,0.0286830216971857,0.189225905026481,0.0926764748380334,NA,0.0753529555296814,0.00514464238234786
+"9523",100,1981,1,26,1.10693069921993,18.4561276871248,38.6762371587806,18.8541473854493,0,24.2832999045053,6.92194474956993,100,1981,1,26,0.114678366400346,0.0041912629917347,0.230706296883893,0.500791210973536,NA,0.0286069999763571,0.00532864585062412
+"9524",100,1981,1,27,12.639494044028,16.0423984107929,17.5361937280535,14.483663444603,0,24.0900113398355,6.9416797872372,100,1981,1,27,3.92537989621978,0.0196175347180234,0.0393386238816643,0.012843297929476,NA,0.122690373187351,0.00552724565583549
+"9525",100,1981,1,28,8.74653468326111,14.0755665215722,19.134598649112,15.2546864688987,0,24.5730272549734,6.96141482490448,100,1981,1,28,15.3180712159475,0.0229152372929742,0.0618485080790139,0.00547193742951142,NA,0.0440343149643401,0.00574044179798199
+"9526",100,1981,1,29,0.000110011002739402,13.986842702062,20.6206821068154,16.2828603016399,0,22.2546538068179,6.98114986257176,100,1981,1,29,0.000526315805159117,0.0385175567538447,0.114981270858583,0.00846489733442877,NA,0.149544852119401,0.00596823427706362
+"9527",100,1981,1,30,0.458305833199368,13.2647854487101,22.5647524171668,15.0893728971744,0,24.3973745231152,7.00088490023904,100,1981,1,30,0.00701754354594051,0.0326941366743165,0.0489871047576285,0.0306596289585488,NA,0.0218408287029835,0.00621062309308042
+"9528",100,1981,1,31,0,14.1967105739581,21.7844886863717,14.7386909847868,0,23.3515321540116,7.02061993790632,100,1981,1,31,0,0.0212362363804805,0.065835702068733,0.0111871273866572,NA,0.0235446804634094,0.00646760824603232
+"9529",100,1981,2,1,0,14.7209790736535,25.8799008221516,17.5773815243158,0,23.4129343062141,6.99290565832196,100,1981,2,1,0,0.0064508813691284,0.217224763957453,0.023536860902376,NA,0.0296406499566769,0.00649465667750967
+"9530",100,1981,2,2,0,16.3921783791386,36.3016611147504,16.8793511427406,0,23.3692480412628,6.96519137873759,100,1981,2,2,0,0.00824501917476913,0.029236789325583,0.0755252103931499,NA,0.0353800765827085,0.00652371275009938
+"9531",100,1981,2,3,0,19.9011443277659,39.6140923201054,12.435742443282,0,24.2464598636382,6.93747709915323,100,1981,2,3,0,0.0720116806925454,0.0050982153117187,0.298076555722825,NA,0.0182029744220552,0.0065547764638014
+"9532",100,1981,2,4,2.83410337177178,16.4779206623207,19.4261165887478,18.0569637265011,0,23.1414437401631,6.90976281956886,100,1981,2,4,1.58760232385837,0.0272028798156003,0.0620597409524954,0.01765611314415,NA,0.0375595673347797,0.00658784781861577
+"9533",100,1981,2,5,10.150165061615,14.8817051708108,17.8491088942726,15.0056764426404,0,22.0771528800066,6.8820485399845,100,1981,2,5,4.7550875316866,0.0268655522442384,0.0303338985022864,0.0128596407684683,NA,0.0139352692307565,0.00662292681454249
+"9534",100,1981,2,6,1.01694169961842,13.7830583784315,21.604609247088,14.8658856118079,0,21.9237229235956,6.85433426040013,100,1981,2,6,0.186549703715838,0.100361986972376,0.0133256706375244,0.0377116786043061,NA,0.0416414498906271,0.00666001345158154
+"9535",100,1981,2,7,0.891859185151403,13.5718041735776,27.5106380995613,18.1419585451435,0,22.0888009242973,6.82661998081577,100,1981,2,7,0.147076019278744,0.0986041041213074,0.369724404126775,0.014499972873493,NA,0.0175583573320285,0.00669910772973289
+"9536",100,1981,2,8,0.687018702771797,15.8974587898968,32.3250165840711,21.5219691059377,0,22.1866014173155,6.7989057012314,100,1981,2,8,0.0391228048721255,0.0678941442124144,0.189604220002153,0.086656130834508,NA,0.0428502559241368,0.00674020964899662
+"9537",100,1981,2,9,1.58228823401616,18.1306158869442,22.6078437788389,12.1035204542221,0,21.6438105167041,6.77119142164704,100,1981,2,9,0.605555499924557,0.00647188956760434,0.0564333226525191,0.135102845032533,NA,0.109403017437708,0.00678331920937264
+"9538",100,1981,2,10,0.641914190271638,11.4455224069694,21.1387788774681,10.7494719078307,0,17.8636470360693,6.74347714206268,100,1981,2,10,0.0322806987079266,0.0163538017942881,0.028303585374142,0.0536783608554367,NA,0.118921847127515,0.00682843641086103
+"9539",100,1981,2,11,0,10.8891967326501,20.1257979389858,10.0250164856612,0,19.0001895990484,6.71576286247831,100,1981,2,11,0,0.160573062740626,0.0640099160537239,0.0184807248411925,NA,0.124614794597885,0.00687556125346175
+"9540",100,1981,2,12,0,11.8696147679496,26.8211219816974,15.6814962855004,0,22.8020727401176,6.68804858289395,100,1981,2,12,0,0.0472895042018711,0.553750976727911,0.0569537411986522,NA,0.0785552338264747,0.0069246937371748
+"9541",100,1981,2,13,0,15.4167546869242,36.951342134717,16.9441806155332,0,22.8090300237837,6.66033430330958,100,1981,2,13,0,0.0492356981490021,0.106370301293846,0.0705117715314537,NA,0.0486465470496594,0.00697583386200018
+"9542",100,1981,2,14,0,21.3634435068263,28.4361608251356,21.4990869276594,0,23.499533578595,6.63262002372522,100,1981,2,14,0,0.0396929796678657,0.0343485238060635,0.0719579022685107,NA,0.0154715266733284,0.0070289816279379
+"9543",100,1981,2,15,0.14334433656944,21.7882397319093,40.7137078556946,18.3883605805954,0,20.9534606294226,6.60490574414085,100,1981,2,15,0.00450292411080578,0.0158443580630614,0.043780051749346,0.113455639564042,NA,0.0132668891155866,0.00708413703498798
+"9544",100,1981,2,16,0,17.7478768041294,26.2041032348398,16.1629589907538,0,22.4252820828404,6.57719146455649,100,1981,2,16,0,0.0195696291654622,0.275698814771592,0.0138251256577955,NA,0.148208676285106,0.00714130008315037
+"9545",100,1981,2,17,0,15.1208580917258,23.1028270154896,13.6641363211066,0,23.7961916513606,6.54947718497213,100,1981,2,17,0,0.114091833364532,0.125359668040402,0.117987074708173,NA,0.00940797605618017,0.00720047077242509
+"9546",100,1981,2,18,0,14.2198900069603,20.8291308483799,12.0655555903453,0,21.511014421912,6.52176290538776,100,1981,2,18,0,0.138320409413465,0.07916478169802,0.0333023186890732,NA,0.058872316714288,0.00726164910281215
+"9547",100,1981,2,19,0,13.1675577457457,21.4053026058755,12.2802749619101,0,21.3607660304071,6.4940486258034,100,1981,2,19,0,0.0433619627345369,0.0189719452263867,0.0993590760348982,NA,0.0190732034081191,0.00732483507431157
+"9548",100,1981,2,20,0.0202420245040499,14.4436632456428,19.3857315155801,12.6868207803523,0,20.7934478617986,6.46633434621903,100,1981,2,20,0.00228070182235617,0.0540655048817235,0.051069105869169,0.180225101236813,NA,0.0185202339682647,0.00739002868692328
+"9549",100,1981,2,21,0.348294836525345,14.0881077563933,21.1447413835851,14.8669747960056,0,19.2397084412227,6.43862006663467,100,1981,2,21,0.0350877199605195,0.028524590346231,0.308022472569351,0.0163929927938098,NA,0.197870284680301,0.00745722994064737
+"9550",100,1981,2,22,0.620242024797483,14.1203741193211,22.7819582516342,18.5797029585466,0,18.9772956106669,6.4109057870503,100,1981,2,22,0.115614037051884,0.0174689965655787,0.545327837894265,0.00809122256479193,NA,0.0232668150005013,0.00752643883548377
+"9551",100,1981,2,23,0.161716174658048,14.8132233635427,26.5579209280486,16.7951370339976,0,19.9743041549194,6.38319150746594,100,1981,2,23,0.00619883099494623,0.0655175208153595,0.265454483616988,0.134531464368438,NA,0.0100829873263694,0.00759765537143251
+"9552",100,1981,2,24,0.176017604383043,13.8059515160958,23.2718483680415,15.5391090170647,0,18.7874312302242,6.35547722788157,100,1981,2,24,0.00368421063611382,0.12135963183406,0.0616357274241301,0.0183672428644878,NA,0.0401175167561637,0.00767087954849357
+"9553",100,1981,2,25,0.694169424875866,14.4781737448228,22.3799228017742,13.4855445215542,0,20.8129744264724,6.32776294829721,100,1981,2,25,0.0128654940435067,0.051422769475221,0.0918385324180315,0.0144386006852329,NA,0.0120977790662301,0.00774611136666701
+"9554",100,1981,2,26,0,9.97407043255595,26.2102313424626,17.8822662135293,0,20.2956423778945,6.30004866871285,100,1981,2,26,0,0.0275204415015465,0.158649579982011,0.0694374902693746,NA,0.00864262383841193,0.00782335082595274
+"9555",100,1981,2,27,0,13.9300551010568,33.5772938911933,14.3170186348087,0,18.7624235175121,6.27233438912848,100,1981,2,27,0,0.0149005973660742,0.114778386113746,0.0413707766929345,NA,0.0245966046264808,0.00790259792635086
+"9556",100,1981,2,28,0,17.49074800783,28.4914631607509,18.8965785967635,0,17.9139952476356,6.24462010954412,100,1981,2,28,0,0.0709163643511564,1.25065763964327,0.195802985493871,NA,0.23595334154568,0.00798385266786125
+"9557",100,1981,3,1,0.266886694834243,15.8016612500903,27.7974919381052,17.3417604717091,0,19.7752592142746,6.18432750411447,100,1981,3,1,0.0137426906423262,0.058171934705676,0.0647117156136996,0.0237912384348563,NA,0.195925725221559,0.00773315160868021
+"9558",100,1981,3,2,0.476237632735859,12.607029543458,24.2517381401608,9.64059410577822,0,20.4934099813062,6.12403489868482,100,1981,3,2,0.0770175486559061,0.0134134718622303,0.00437018810092742,0.0286807380677138,NA,0.0166271006672856,0.00748711542407144
+"9559",100,1981,3,3,0.376897697106446,15.0533333244366,22.9001981695362,14.8997359438436,0,18.9841136765559,6.06374229325518,100,1981,3,3,0.010526315536764,0.0111894541824869,0.00943858395429323,0.140322886209393,NA,0.0319656887910346,0.00724574411403495
+"9560",100,1981,3,4,5.43036301773373,13.0313531541982,20.2726622981207,11.6078657962308,0,19.0902738560856,6.00344968782553,100,1981,3,4,0.511111079321987,0.0179654754416408,0.0224761234622157,0.0968952775052638,NA,0.0499527191273414,0.00700903767857075
+"9561",100,1981,3,5,1.22585260356614,11.6562816580005,20.3180525583534,10.9772166859592,0,19.4303228256034,5.94315708239588,100,1981,3,5,0.0191812838448434,0.14549241724522,0.00910991003490237,0.0422871120431277,NA,0.178275180638527,0.00677699611767883
+"9562",100,1981,3,6,0.322772284471753,11.7733004153496,22.1958747314017,11.8371837540428,0,17.8706942354754,5.88286447696624,100,1981,3,6,0.011111111442248,0.230549645875715,0.0629843019589359,0.0594479516481889,NA,0.055412259770432,0.00654961943135914
+"9563",100,1981,3,7,0,10.5604070279464,31.2477008664306,9.39457645248396,0,18.4327443469782,5.82257187153659,100,1981,3,7,0,0.131489529653217,0.0951813608808365,0.306428805642461,NA,0.161908448522134,0.00632690761961174
+"9564",100,1981,3,8,0,15.3320902844336,22.5732785572182,14.3369746685553,0,17.8201645772524,5.76227926610694,100,1981,3,8,0,0.00669532272616789,0.0410245407108226,0.0299210287172992,NA,0.00723562667630817,0.00610886068243665
+"9565",100,1981,3,9,1.97612759972563,11.0313530293497,19.1927943969324,11.8694280166962,0,18.3921279042805,5.70198666067729,100,1981,3,9,0.314502916670684,0.249311658319459,0.0198450270538203,0.0977543953790461,NA,0.0180396344826778,0.00589547861983379
+"9566",100,1981,3,10,3.11012100832428,10.9587238068366,18.6565896843133,11.6898240938176,0,17.0609188071771,5.64169405524765,100,1981,3,10,0.103859658492243,0.0441584951077958,0.023371907275786,0.205747932521146,NA,0.0408017688827945,0.00568676143180324
+"9567",100,1981,3,11,3.16589658357392,11.2138503906619,21.3303301006523,13.696424706672,0,17.4843006479709,5.581401449818,100,1981,3,11,0.136549729213386,0.0675245399408638,0.0294005739761224,0.450480752369514,NA,0.0106170908107111,0.00548270911834495
+"9568",100,1981,3,12,1.39482948777437,11.053795270007,21.226787533566,14.3757645163206,0,17.0268388720457,5.52110884438835,100,1981,3,12,0.0850877232718899,0.0833227780928572,0.0166000350954914,0.145146804678044,NA,0.0646656786080182,0.00528332167945891
+"9569",100,1981,3,13,4.50616062408758,9.48043999708656,16.7440482539312,8.84598459636155,0,15.9870540517392,5.46081623895871,100,1981,3,13,0.46830412139671,0.112664848945666,0.0279982766762515,0.0660538150542746,NA,0.0363629000224639,0.00508859911514518
+"9570",100,1981,3,14,0.770847086269076,10.7880418418658,18.6200220760601,11.9979537968064,0,17.2164572951157,5.40052363352906,100,1981,3,14,0.0437426913621137,0.132148576131962,0.0135146432399587,0.0943427203686718,NA,0.0338604431951135,0.00489854142540371
+"9571",100,1981,3,15,0.445434550641,11.4675688087875,19.9646313880274,12.4062044859195,0,15.3307898377781,5.34023102809941,100,1981,3,15,0.023391813458058,0.0242841967778747,0.0518871280873584,0.094298288724669,NA,0.0432186420005002,0.00471314861023451
+"9572",100,1981,3,16,0,11.4837074342734,20.2333334659455,13.8540043836117,0,15.2170399301019,5.27993842266977,100,1981,3,16,0,0.0686953066977005,0.024891156166834,0.158098856873629,NA,0.0355569057921099,0.0045324206696376
+"9573",100,1981,3,17,0.535423556701316,11.1301538878673,20.8583606892031,12.1399009702492,0,17.0993407368004,5.21964581724012,100,1981,3,17,0.0114035102010471,0.0676338883986584,0.0233362567747963,0.0689578429618721,NA,0.0342508054993436,0.00435635760361295
+"9574",100,1981,3,18,0,9.7357425931001,29.1634653802752,9.99981297491932,0,15.0214471622744,5.15935321181047,100,1981,3,18,0,0.0376912325296733,0.0196485272771662,0.035795323076381,NA,0.0382578230868555,0.00418495941216058
+"9575",100,1981,3,19,0.131463148273585,15.1558306298502,25.9870187614617,12.5748955125463,0,16.5380141086324,5.09906060638083,100,1981,3,19,0.00257309949188902,0.0477333270603472,0.0698701697191721,0.0315163613660354,NA,0.0792785299403369,0.00401822609528048
+"9576",100,1981,3,20,5.14202421831481,11.5019801699027,17.1589879727337,10.6212761966046,0,15.6065733823577,5.03876800095118,100,1981,3,20,0.310233961769031,0.0213783561238686,0.00727016224152455,0.071278359632966,NA,0.024464679867295,0.00385615765297264
+"9577",100,1981,3,21,2.96457649588716,6.85155121118191,17.5217934284273,10.0356655068392,0,14.0129243094166,4.97847539552153,100,1981,3,21,0.0484210587105599,0.107758445019205,0.0128608246343088,0.119169573153754,NA,0.0993513710404692,0.00369875408523711
+"9578",100,1981,3,22,0,8.113575250271,19.8445984491027,12.0898679968285,0,14.1439281156835,4.91818279009188,100,1981,3,22,0,0.227547366657626,0.00954444054533317,0.152973046897419,NA,0.0266285263730027,0.00354601539207384
+"9579",100,1981,3,23,0.181078111877864,7.08306938901593,21.548162781366,13.4311662050757,0,15.2026710722356,4.85789018466224,100,1981,3,23,0.00941520530752273,0.039215213230212,0.0636718137063927,0.11138418348666,NA,0.011497210454999,0.00339794157348282
+"9580",100,1981,3,24,0.10847084870105,11.0678768965802,19.0612101245372,7.86785471636077,0,13.8597014609501,4.79759757923259,100,1981,3,24,0.00175438601719706,0.0489473635133175,0.0224830702758247,0.00574795745048946,NA,0.0207457668127731,0.00325453262946411
+"9581",100,1981,3,25,0.229812986952047,8.89056098159521,18.8604511495995,10.6579537470349,0,15.4790264174927,4.73730497380294,100,1981,3,25,0.00315789519694815,0.519415297480423,0.0138800959227479,0.10389415964947,NA,0.055796690597294,0.00311578856001767
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+"9583",100,1981,3,27,0,8.70634769422911,22.0114409548484,15.1654787315394,0,11.2921357656212,4.61671976294365,100,1981,3,27,0,0.0720005830151698,0.222992337549463,0.0388023432503961,NA,0.00876688437518285,0.00285229504484157
+"9584",100,1981,3,28,1.73036302998252,12.7894059937648,21.7296041650216,14.9810451113089,0,13.1777204817025,4.556427157514,100,1981,3,28,0.718947305093734,0.00791755940405748,0.0629703203260957,0.0281146120462501,NA,0.0484710825279336,0.00272754559911195
+"9585",100,1981,3,29,0,11.3722440648263,22.2229262353039,13.199856953259,0,12.7985474946694,4.49613455208436,100,1981,3,29,0,0.0742362735459024,0.0412263822994252,0.0350023258477832,NA,0.0112380031182811,0.00260746102795459
+"9586",100,1981,3,30,0.114741475857196,12.1954345766074,21.0089658898751,15.0071396602131,0,12.7613743683858,4.43584194665471,100,1981,3,30,0.00274853809360872,0.190080734951238,0.0221245921800811,0.0572385703594937,NA,0.0133817882840108,0.00249204133136952
+"9587",100,1981,3,31,0,11.4838064380474,21.6365897490246,17.0068645792039,0,12.9653431044323,4.37554934122506,100,1981,3,31,0,0.126158495531652,0.0428953209953357,0.0129578450925468,NA,0.0259114059261274,0.00238128650935671
+"9588",100,1981,4,1,2.31078109135329,16.5478545420765,23.3495379847662,19.6877559624096,0,12.7648946744463,4.32694889528731,100,1981,4,1,1.28812871508431,0.0194250709179885,0.0204929859498582,0.0954538184854521,NA,0.0240982466482186,0.00225841337151446
+"9589",100,1981,4,2,0.858635853160464,15.141980288708,28.1068755675464,20.0489989470596,0,12.4086407050415,4.27834844934956,100,1981,4,2,0.0217543835458703,0.0791251394462131,0.0893671146372475,0.0439830504883981,NA,0.0455268138164257,0.00214201318994956
+"9590",100,1981,4,3,0,13.9358636134266,29.4195598606492,17.3846756690668,0,13.2088368655946,4.22974800341181,100,1981,4,3,0,0.0244116744973629,0.0792354738965003,0.077235699007988,NA,0.0472097897293212,0.00203208596466204
+"9591",100,1981,4,4,0.523432350058695,15.05731574773,27.7983500057846,17.411331220822,0,13.1999458603884,4.18114755747406,100,1981,4,4,0.0769005829304982,0.103024515703052,0.099604082806104,0.0215053358560961,NA,0.0194674923423176,0.00192863169565188
+"9592",100,1981,4,5,0.00407040710135786,18.137613090876,30.0226950923471,19.512002105629,0,12.9094186273032,4.13254711153631,100,1981,4,5,0.000994152076411666,0.0125988373875074,0.0619718469813599,0.0128894955286081,NA,0.0079812746244194,0.00183165038291909
+"9593",100,1981,4,6,0,10.9631023375508,19.9247746986918,10.7832562422464,0,12.7148201068485,4.08394666559856,100,1981,4,6,0,0.26790247744964,0.0533918185542807,0.0319649021752071,NA,0.0151601890511598,0.00174114202646367
+"9594",100,1981,4,7,0,7.27029705572181,23.074059277609,13.4295929225758,0,12.5433431697514,4.03534621966081,100,1981,4,7,0,0.0920666902754196,0.0704000225494553,0.0887930232400173,NA,0.00842350766789368,0.00165710662628561
+"9595",100,1981,4,8,0.0726072618080051,7.2306380639113,24.671595299598,15.5503959236103,0,10.8089930102276,3.98674577372306,100,1981,4,8,0.00339181296658098,0.0459871394241285,0.110914059082785,0.110205173450122,NA,0.0536189501198708,0.00157954418238492
+"9596",100,1981,4,9,0.0963696383997159,9.93113311813741,18.7089440835716,12.5751155248963,0,11.5600839655916,3.93814532778531,100,1981,4,9,0.000526315805159117,0.0383222376082829,0.1131036884289,0.0325929709853285,NA,0.0123576005685188,0.0015084546947616
+"9597",100,1981,4,10,0,10.0402309367366,19.9053023456871,11.3866775552563,0,10.9741320310078,3.88954488184756,100,1981,4,10,0,0.493498877136118,0.0134472983514378,0.0377543935943982,NA,0.0170280551090482,0.00144383816341564
+"9598",100,1981,4,11,0,5.67217824797426,27.5799671189882,12.3304950978496,0,11.9290993662251,3.84094443590981,100,1981,4,11,0,0.0384877145700501,0.199000060187756,0.134170794943261,NA,0.03271864201326,0.00138569458834705
+"9599",100,1981,4,12,0,12.2407811524713,22.3081738836039,15.0121122513405,0,11.85008458367,3.79234398997206,100,1981,4,12,0,0.0191912059383213,0.023368961972569,0.102825164484933,NA,0.0318227131099981,0.00133402396955583
+"9600",100,1981,4,13,3.21683168070282,9.77344325142201,18.622057128923,14.2464246561032,0,10.9296754847047,3.74374354403431,100,1981,4,13,0.0215204783768731,0.0685164239181251,0.0498162935851213,0.0784023455191388,NA,0.0259424307469331,0.00128882630704196
+"9601",100,1981,4,14,4.27304739422268,10.6126072414637,17.6235093745199,11.853564390386,0,10.9264189812877,3.69514309809656,100,1981,4,14,0.0769590764854518,0.178439748757435,0.0777813363462048,0.234826359426563,NA,0.0529233801645417,0.00125010160080548
+"9602",100,1981,4,15,0.80055005063187,11.7854014943273,21.312354634435,14.5117932005946,0,10.3417447587532,3.64654265215881,100,1981,4,15,0.0192982437492117,0.0839917826089987,0.0412766139788003,0.0433163536384716,NA,0.0314817711161695,0.00121784985084635
+"9603",100,1981,4,16,1.68173815446063,11.2266777361711,18.0814742013828,13.1940593750957,0,10.8364803712273,3.59794220622106,100,1981,4,16,0.0689473635132567,0.0372228006865458,0.0770263529997443,0.0533035641810359,NA,0.0161564659160779,0.0011920710571646
+"9604",100,1981,4,17,1.7825082246751,10.5323982836783,17.076259619332,13.5837405701019,0,10.2744186454349,3.54934176028331,100,1981,4,17,0.176081876880246,0.0651537883558594,0.0387894708067703,0.0544730964326808,NA,0.00798830768895614,0.0011727652197602
+"9605",100,1981,4,18,0.730693073883833,11.9511111766198,20.970649158994,11.7331903980117,0,10.945130556365,3.50074131434556,100,1981,4,18,0.0187134500832587,0.0484140683961121,0.0281449621727102,0.00684853144649259,NA,0.0183275191546542,0.00115993233863318
+"9606",100,1981,4,19,0.930913104180849,9.69356419885382,16.8405281188596,12.2367437003863,0,9.97788777318156,3.45214086840781,100,1981,4,19,0.0861988315840216,0.196678274600908,0.166680363076501,0.00375440490465974,NA,0.0296771095285203,0.00115357241378352
+"9607",100,1981,4,20,0.0190319034739165,7.48151815842481,17.5293840318099,10.0000220048021,0,10.8813567954977,3.40354042247006,100,1981,4,20,0.00175438601719706,0.186314571560226,0.0631094122817936,0.0284052564119632,NA,0.013920561715262,0.00115368544521123
+"9608",100,1981,4,21,0.631463149060445,8.34589669675586,19.5191529822691,11.6026842434152,0,9.14916847747138,3.35493997653231,100,1981,4,21,0.140935669787098,0.198080170117277,0.0153953299185352,0.0209426872008875,NA,0.0333283274228975,0.00116027143291631
+"9609",100,1981,4,22,0.378327837699514,7.03161716775926,23.8820351515666,15.294884498101,0,10.022454347844,3.30633953059456,100,1981,4,22,0.0170760231164464,0.158925734102819,0.0218356390929298,0.228747502205492,NA,0.0144950328786433,0.00117333037689875
+"9610",100,1981,4,23,0.202310234906584,7.78031908587129,18.3605828238006,9.19438943034089,0,9.71656765683116,3.25773908465681,100,1981,4,23,0.0470760239233748,0.374073183834942,0.0292707779379441,0.0109356916997505,NA,0.00756470529417914,0.00119286227715855
+"9611",100,1981,4,24,0.0998899904873767,8.54418037800637,16.3961603859208,9.49564342897455,0,9.27733996072705,3.20913863871906,100,1981,4,24,0.000526315805159117,0.114716343494977,0.0679486290381439,0.0269894886285392,NA,0.0088198259244684,0.00121886713369573
+"9612",100,1981,4,25,0.726622654594342,8.9390870714345,14.8827281737878,8.1895049654349,0,9.58570959128829,3.16053819278131,100,1981,4,25,0.00485380146238554,0.169573155107609,0.113047376467695,0.0417041159156371,NA,0.0192640133289669,0.00125134494651027
+"9613",100,1981,4,26,0,3.28107811751539,19.3664576758121,6.59273925315429,0,10.1104656083393,3.11193774684356,100,1981,4,26,0,0.0449356708526654,0.12483987084571,0.00803392148828229,NA,0.0184073354920507,0.00129029571560218
+"9614",100,1981,4,27,0.826182612425948,7.96964792738391,18.1760062395018,8.94489544531693,0,9.63336081582009,3.06333730090581,100,1981,4,27,0.00807017339600489,0.0249578548297974,0.0356812405505118,0.00540996073235427,NA,0.013275987443375,0.00133571944097145
+"9615",100,1981,4,28,1.92640262783164,8.84589657505484,14.042827247393,10.0661276574969,0,8.82896141115506,3.01473685496806,100,1981,4,28,0.0478362492371745,0.176816320667005,0.139300591092985,0.0446654764655835,NA,0.01694382241701,0.00138761612261809
+"9616",100,1981,4,29,0.984598456173971,8.61236539060133,14.8422003474304,10.515467516791,0,8.28609058232722,2.96613640903031,100,1981,4,29,0.00385965096323136,0.29756076790218,0.0708064225939125,0.0101801224714827,NA,0.00920270697563016,0.0014459857605421
+"9617",100,1981,4,30,0.263036308828557,5.39250829694557,19.7188997384083,11.1750056253146,0,8.90062632742582,2.91753596309256,100,1981,4,30,0.0687719302271543,0.0444619869600429,0.0170537916754639,0.0170386399521071,NA,0.00536656468211105,0.00151082835474347
+"9618",100,1981,5,1,0,6.34294831608519,23.4100221375821,8.94330017899785,0,8.8130429228584,2.87353344256192,100,1981,5,1,0,0.13911406987733,0.0758672019292986,0.00892631504892009,NA,0.0244265944507838,0.00142332551836992
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+"9620",100,1981,5,3,0.335313537988988,10.4469856801468,18.7247742819707,12.192607259593,0,7.91238174921084,2.78552840150063,100,1981,5,3,0.0173099415047824,0.192657844521244,0.0089649116341382,0.0183509071083264,NA,0.00443888009916981,0.00125799677541805
+"9621",100,1981,5,4,0,5.11372932217958,20.5586360746747,13.5859186237532,0,8.94052367179788,2.74152588096999,100,1981,5,4,0,0.0412245626929415,0.0217204440873316,0.0398263284181965,NA,0.0099419404261935,0.00118017086883974
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+"9624",100,1981,5,7,0.983828383113685,8.56770076478931,15.0030473524457,11.0748296450205,0,7.66502311060094,2.60951831937806,100,1981,5,7,0.0250877196189262,0.168371967145076,0.0762362949952159,0.0111368327983953,NA,0.0209203761359687,0.000966047008695275
+"9625",100,1981,5,8,3.48008798110341,6.43756878572722,14.2563916941812,9.59962595571386,0,8.12080537062721,2.56551579884741,100,1981,5,8,0.0591227981221662,0.226575984330222,0.105578308571782,0.0660923575418543,NA,0.0105399895965202,0.000901123675177281
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+"9627",100,1981,5,10,4.03179315922677,6.89464242597355,14.7695599578001,12.9235423447931,0,8.14627573273007,2.47751075778613,100,1981,5,10,0.0370760172291896,0.152832128475731,0.0519982158416257,0.0802619810004732,NA,0.0096624562650062,0.000780953937936532
+"9628",100,1981,5,11,2.71749175237481,9.73649074168357,15.327051665261,13.0287678963018,0,7.27890263491506,2.43350823725548,100,1981,5,11,0.0289473705124463,0.0734579442744567,0.0503590916952221,0.0580117373997249,NA,0.0178061923973474,0.000725707534213783
+"9629",100,1981,5,12,2.13564353637045,8.213718434765,15.3566447458383,10.5463476097099,0,7.25531824616007,2.38950571672484,100,1981,5,12,0.0135672530514488,0.117836280203772,0.0394117151540129,0.00440408090073414,NA,0.00495527030601342,0.000673686773756109
+"9630",100,1981,5,13,0.0190319034739165,4.13542357372372,18.4009572502279,9.91852578497825,0,7.75876973244486,2.3455031961942,100,1981,5,13,0.0020467836867299,0.0473783635719609,0.132114710325192,0.00435379852616167,NA,0.0134069890389539,0.000624891656563517
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+"9632",100,1981,5,15,0.313641368891999,7.13069308368024,19.2963366010139,9.60457656092376,0,6.94109059622491,2.25749815513291,100,1981,5,15,0.029181285484841,0.111881277300756,0.109413559299302,0.0129760263320967,NA,0.00702796329865624,0.000536978351973566
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+"9636",100,1981,5,19,0.290539058267546,6.98500552035794,12.13925184957,7.72897685662617,0,7.0604466397027,2.08148807301033,100,1981,5,19,0.0324561413791445,0.271603485915,0.050691835387917,0.0180877426326251,NA,0.00154143737092538,0.000399859461974639
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+"9755",100,1981,9,15,0.10110011151751,9.38622660264455,19.0538725753297,11.7230363180666,0,13.311804911939,2.4200986769718,100,1981,9,15,0.00385964923783353,0.151709919687504,0.279772191655813,0.035595299643872,NA,0.0356872462827949,0.0025143045623619
+"9756",100,1981,9,16,0.359295935897675,8.72717273720551,18.6610894009094,11.4057755643385,0,12.5346883314205,2.46012472071956,100,1981,9,16,0.0111695901483123,0.174158490757712,0.016724585246755,0.00636199861723422,NA,0.0648038412134949,0.00252595961848664
+"9757",100,1981,9,17,0,8.79012098196972,27.839559907567,15.045016479702,0,13.8872262715506,2.50015076446731,100,1981,9,17,0,0.0251608255732623,0.093067145699258,0.0351333330790862,NA,0.0189509837816842,0.00253771273315323
+"9758",100,1981,9,18,0,12.5944004342107,16.9400992293825,10.4821781047238,0,13.9783453300642,2.54017680821507,100,1981,9,18,0,0.00763685210608988,0.0107877314878021,0.0714397130308796,NA,0.0258091570583458,0.00254956390636167
+"9759",100,1981,9,19,0,4.78969196281811,15.221144150586,10.9420351190011,0,13.4857528639487,2.58020285196282,100,1981,9,19,0,0.125287162565505,0.0716022757257649,0.00238126862295864,NA,0.0295584687146519,0.00256151313811195
+"9760",100,1981,9,20,0,7.21399342030188,19.5822002066769,11.6395598937183,0,14.8402735272686,2.62022889571058,100,1981,9,20,0,0.0617052591524814,0.0485479244480888,0.0248654732955901,NA,0.0509434336562759,0.00257356042840409
+"9761",100,1981,9,21,0,10.3005720365165,15.7306600566482,9.89749176982212,0,14.214239101555,2.66025493945833,100,1981,9,21,0,0.0268940970416235,0.0395690280022456,0.00932632086685197,NA,0.026764299316053,0.00258570577723807
+"9762",100,1981,9,22,0,4.58051699039304,15.571859272662,8.84141917170996,0,12.5068166601994,2.70028098320609,100,1981,9,22,0,0.0962696080481351,0.0475175366318953,0.000647361302608845,NA,0.0951561850532158,0.0025979491846139
+"9763",100,1981,9,23,3.37810782761988,7.58774475427088,12.2757975910888,8.50469748284032,0,14.3631195378033,2.74030702695384,100,1981,9,23,0.0887134507664509,0.0857111484315908,0.14905436693832,0.00798070340304506,NA,0.0144006947465582,0.00261029065053159
+"9764",100,1981,9,24,4.58921896611372,5.93669967997586,14.3474256790379,10.0357864744986,0,14.0949948686381,2.7803330707016,100,1981,9,24,0.502456202311836,0.015409948016624,0.0582538066753107,0.0428672399978852,NA,0.0514537420845912,0.00262273017499112
+"9765",100,1981,9,25,2.6628162218268,7.05528057369068,15.2583057982693,11.1033993040112,0,14.7610192965934,2.82035911444935,100,1981,9,25,0.0146783672020484,0.0320204421763031,0.175655565431469,0.00407017046395781,NA,0.0277376964362575,0.00263526775799249
+"9766",100,1981,9,26,0.211221126480924,6.51398235369306,17.171771174348,10.1671947619834,0,15.2538422754495,2.86038515819711,100,1981,9,26,0.00912280749856382,0.0341912175942781,0.0789374763046094,0.0148450093186283,NA,0.0196025123436768,0.00264790339953572
+"9767",100,1981,9,27,14.4408140172004,6.31838284808286,14.6627503259741,10.4636742997878,0,15.9481312647205,2.90041120194486,100,1981,9,27,0.28228072272414,0.0202923949002019,0.101015187293682,0.0144263041959819,NA,0.0170012022021127,0.0026606370996208
+"9768",100,1981,9,28,3.37832784914997,8.9173378372612,14.4839163288163,11.2551925048576,0,14.2553177148534,2.94043724569262,100,1981,9,28,0.17228071656144,0.0476707801529498,0.0728988530722825,0.00271638205462646,NA,0.0342225572970629,0.00267346885824771
+"9769",100,1981,9,29,0.0984598474517645,10.7588008842846,17.9983937984253,11.8422002173362,0,16.0680693066334,2.98046328944037,100,1981,9,29,0.000526315805159117,0.035388879140284,0.113959610199786,0.0750175448859074,NA,0.0842898640980503,0.00268639867541648
+"9770",100,1981,9,30,0.387898796413204,9.09128718696149,21.9617933707662,11.9345985450367,0,14.7734150911016,3.02048933318813,100,1981,9,30,0.0114035087370733,0.0917544303582611,0.0590035518315111,0.0429596809834208,NA,0.0304814888360167,0.00269942655112711
+"9771",100,1981,10,1,3.15753575598839,6.9249174933229,20.8396371037784,10.0629153005098,0,15.1212075313981,3.04932825968771,100,1981,10,1,0.201754382897544,0.0831023110953158,0.288154066672842,0.00663215195738452,NA,0.015441490077195,0.00274078028187829
+"9772",100,1981,10,2,1.18008800069786,10.2862597214769,14.8269417545583,13.5075026943345,0,15.7031979153855,3.07816718618729,100,1981,10,2,0.409122792091984,0.0141222127703211,0.0199052753850833,0.00597834517501144,NA,0.0443168823130399,0.0027827826676702
+"9773",100,1981,10,3,20.2770079748072,5.48287133729891,11.7871617284676,9.26232130325536,0,16.8828401884728,3.10700611268688,100,1981,10,3,0.479415083322174,0.0280918127963605,0.111755571280645,0.0351584860060476,NA,0.0710248862236638,0.00282543370850285
+"9774",100,1981,10,4,5.7964795750491,5.23377336527255,15.1964025864638,13.6284048103526,0,16.2660369616368,3.13584503918646,100,1981,10,4,0.0487719483403665,0.0165450239784091,0.0519543571963687,0.0572146121176313,NA,0.0203077403953125,0.00286873340437623
+"9775",100,1981,10,5,0.551265127915885,10.478470701589,21.4229922488709,12.952453139067,0,14.4973039880094,3.16468396568604,100,1981,10,5,0.00953216361720675,0.0548175510117204,0.118226342457012,0.00183157774056765,NA,0.0149795450864503,0.00291268175529035
+"9776",100,1981,10,6,0,9.66290437427685,25.3072716531449,10.5101759609478,0,15.7330451289856,3.19352289218563,100,1981,10,6,0,0.240122233115397,0.140980552678251,0.0407819110402686,NA,0.0420501560078127,0.00295727876124521
+"9777",100,1981,10,7,0.052585259309434,9.58712871790719,16.9423322855967,10.5634324878487,0,15.4653439565067,3.22236181868521,100,1981,10,7,0.00485380131424519,0.01300350945685,0.0572098742572297,0.00140994030921346,NA,0.0277913927162147,0.00300252442224079
+"9778",100,1981,10,8,0.329262935911993,3.71863584969566,21.0672604358367,10.6600879956656,0,16.8859243343019,3.25120074518479,100,1981,10,8,0.00362573094535293,0.0424649069657833,0.0621648660451375,0.0186467638986869,NA,0.213807272501053,0.00304841873827711
+"9779",100,1981,10,9,0.306600669216533,9.67603968944487,19.5958413500728,10.5573376927308,0,15.9020071570155,3.28003967168438,100,1981,10,9,0.00444444457689923,0.0159695686051454,0.0235497158342664,0.0183251474414508,NA,0.0770874542956292,0.00309496170935417
+"9780",100,1981,10,10,0.00363036309040026,6.70536850256757,14.7991419919122,8.32066013474669,0,16.8936281089517,3.30887859818396,100,1981,10,10,0.00140350881375765,0.280776084597656,0.00872513937261105,0.0014362499974191,NA,0.0252678166582968,0.00314215333547196
+"9781",100,1981,10,11,0,2.39380640401305,15.891199101447,9.8327612661817,0,17.1778386468978,3.33771752468354,100,1981,10,11,0,0.0391707531572126,0.0118473742391041,0.0249210599487328,NA,0.0483597978746909,0.00318999361663048
+"9782",100,1981,10,12,0,3.33488449405129,21.1193288545011,10.1694498807028,0,17.1098738644995,3.36655645118313,100,1981,10,12,0,0.0259894712392393,0.0297246158314021,0.00426723922384277,NA,0.0563980117467312,0.00323848255282975
+"9783",100,1981,10,13,0.0136413643396858,8.58963701145353,25.8339822053647,9.87435655730261,0,17.3474940204877,3.39539537768271,100,1981,10,13,0.00175438601719706,0.235725696259285,0.122891159423749,0.00792745804597095,NA,0.0284535112552796,0.00328762014406975
+"9784",100,1981,10,14,4.61903189668561,9.82540144044312,14.5205940722895,10.4264685207992,0,19.9639966759439,3.42423430418229,100,1981,10,14,0.0894151919627262,0.0288485830118355,0.106369015813088,0.00547602583016047,NA,0.0448999605102969,0.00333740639035047
+"9785",100,1981,10,15,2.74312428908773,7.88443352654166,14.494642411915,9.28757987321407,0,16.3951004540654,3.45307323068188,100,1981,10,15,0.0492397671136284,0.101298280531915,0.0400140145375243,0.0120877292031642,NA,0.0718679352916904,0.00338784129167194
+"9786",100,1981,10,16,0.240704077674021,8.76564364648364,15.3677777152906,10.3486138617638,0,18.7320598157167,3.48191215718146,100,1981,10,16,0.00982456200129806,0.0548701704996326,0.0965035213805882,0.0109841902728014,NA,0.132529742523434,0.00343892484803414
+"9787",100,1981,10,17,0.424202424284517,8.26262934373157,16.0433005531236,11.6628933508929,0,19.3571685451549,3.51075108368104,100,1981,10,17,0.0109941520607263,0.289369117783692,0.0227427267109785,0.011222233666388,NA,0.064814379899403,0.00349065705943707
+"9788",100,1981,10,18,0.0855885601312545,8.215346445476,21.1792296041356,11.3368535844406,0,19.0429078423338,3.53959001018063,100,1981,10,18,0.00175438601719706,0.042657912059199,0.0668246037671319,0.0192952940645903,NA,0.0450496214481247,0.00354303792588074
+"9789",100,1981,10,19,0.0565456554080525,10.1160946448382,23.2223323448525,14.2459406134057,0,19.6921585980272,3.56842893668021,100,1981,10,19,0.00263157902579559,0.0583362604554716,0.137670658629392,0.0752093899554468,NA,0.0854374496028514,0.00359606744736515
+"9790",100,1981,10,20,0.0139713975610119,7.82860285640419,18.6151817414102,13.0969417968599,0,18.5571137742765,3.59726786317979,100,1981,10,20,0.00690058521882833,0.0832690451494201,0.00925438663440931,0.0418941956995057,NA,0.0774725211459856,0.00364974562389029
+"9791",100,1981,10,21,13.1023102488586,10.5471397762907,17.8105393169474,13.7239495016167,0,18.4484374221346,3.62610678967937,100,1981,10,21,0.945906478480368,0.185271874734625,0.00895379588718248,0.0378426841625165,NA,0.132315028544623,0.00370407245545616
+"9792",100,1981,10,22,2.00891089006619,9.55106702496116,16.2805720498197,11.6858196373951,0,17.9076164246305,3.65494571617896,100,1981,10,22,1.75485384973866,0.0507719021402116,0.082592414996709,0.0289760258747531,NA,0.0212963080820496,0.00375904794206278
+"9793",100,1981,10,23,0.25797580574343,6.48635855025322,16.5612762795292,9.48764564337903,0,17.6237796359555,3.68378464267854,100,1981,10,23,0.00263157941793143,0.285794106919596,0.0250040963205108,0.00118070460541265,NA,0.0542044622713966,0.00381467208371011
+"9794",100,1981,10,24,0,3.81866884126653,18.6050824688868,11.3415180181119,0,18.9065235998279,3.71262356917812,100,1981,10,24,0,0.0834800983027488,0.0344117456048129,0.00490349571054783,NA,0.145625416638008,0.0038709448803982
+"9795",100,1981,10,25,0,5.73883392262642,17.9095488024755,12.4674148507113,0,17.9932818335781,3.74146249567771,100,1981,10,25,0,0.0367672326428342,0.0858829489594289,0.00421403993310612,NA,0.0350076853659455,0.00392786633212701
+"9796",100,1981,10,26,0.0379537959450936,6.7667986641098,28.1288999886927,12.4605940943635,0,17.6715716572769,3.77030142217729,100,1981,10,26,0.00485380131424519,0.0203543979488817,0.169059036536246,0.373898840178187,NA,0.0768815857861755,0.00398543643889655
+"9797",100,1981,10,27,0.155995601966436,13.2196810190434,27.7778547704548,15.301155247704,0,20.1178450330368,3.79914034867687,100,1981,10,27,0.00596491271117976,0.0255619771099069,0.0160286493005382,0.000909360484134776,NA,0.0371738773190873,0.00404365520070684
+"9798",100,1981,10,28,12.7077007157312,9.91277239582326,19.2106602252251,13.9441583547393,0,20.0354330070819,3.82797927517646,100,1981,10,28,1.52543863553061,0.050413464478209,0.0148941211012918,0.034267265993586,NA,0.109950989328783,0.00410252261755786
+"9799",100,1981,10,29,0,7.49259622760601,20.0697688573789,13.9795818853431,0,20.27930022219,3.85681820167604,100,1981,10,29,0,0.0517485288207516,0.013636289469695,0.0361228259572745,NA,0.0685217889497587,0.00416203868944961
+"9800",100,1981,10,30,0,8.7691968848603,20.1472166583876,12.6434433090411,0,18.8213629069869,3.88565712817562,100,1981,10,30,0,0.0238151648315861,0.016004150685441,0.00755554538310946,NA,0.0585188608751549,0.0042222034163821
+"9801",100,1981,10,31,0,8.76743688572883,18.1111221376425,11.1516831873274,0,19.4828969484941,3.91449605467521,100,1981,10,31,0,0.03577014934701,0.0165426716678332,0.00591169157425547,NA,0.0234263849054395,0.00428301679835532
+"9802",100,1981,11,1,0,9.11774483818163,20.0510563047806,13.1215622223119,0,18.1163154168172,3.94976600461349,100,1981,11,1,0,0.0275473935669024,0.0243619838605044,0.00363157670546453,NA,0.0418351476770849,0.00418130601647358
+"9803",100,1981,11,2,0,10.8358306108397,26.3063038174469,12.1249943954585,0,17.1523567673679,3.98503595455178,100,1981,11,2,0,0.036660828428706,0.126356112193702,0.00966081913735486,NA,0.0295391693227099,0.00408111620049683
+"9804",100,1981,11,3,0,13.4015510808779,27.979295789343,13.9726731423104,0,17.3677866578334,4.02030590449007,100,1981,11,3,0,0.0201999932607833,0.0748040229175291,0.00749299602182086,NA,0.0845708601220506,0.00398244735042507
+"9805",100,1981,11,4,6.2821781643153,12.075621558757,16.8777668977072,11.6908691278254,0,18.8090934361348,4.05557585442835,100,1981,11,4,0.521111102634027,0.0210672767818366,0.046189481510152,0.0202596206866245,NA,0.028487930049348,0.00388529946625831
+"9806",100,1981,11,5,2.79889987640255,9.35837171048877,17.3089218181614,8.99848178720841,0,18.7151212207415,4.09084580436664,100,1981,11,5,0.316081865767991,0.0883806813915417,0.0571562770869578,0.00251111479873442,NA,0.0146640883765506,0.00378967254799655
+"9807",100,1981,11,6,1.54719472941261,10.0191089118143,16.6659735479239,9.26270625824713,0,20.4580579144427,4.12611575430492,100,1981,11,6,0.0137426900584802,0.0970034979793329,0.0204024118081248,0.00398715266408289,NA,0.0778424940028078,0.00369556659563977
+"9808",100,1981,11,7,0.166226626155597,4.81733773119248,17.8929153312289,9.77369647555881,0,20.1155305132962,4.16138570424321,100,1981,11,7,0.00619883099494623,0.12576724132629,0.0294895064196053,0.0299591082969831,NA,0.032063485915465,0.00360298160918798
+"9809",100,1981,11,8,0,6.72634765102525,21.108734586189,14.5692519176387,0,19.2387587513133,4.1966556541815,100,1981,11,8,0,0.0234274868178995,0.317581423339165,0.0174871393795571,NA,0.0556834718558233,0.0035119175886412
+"9810",100,1981,11,9,0.807040711309519,12.1868537120169,20.3489218964697,16.1626732546111,0,22.5149304257653,4.23192560411978,100,1981,11,9,0.00163742642653631,0.0264766265356804,0.0911923733853937,0.075698807577208,NA,0.123117737729317,0.00342237453399939
+"9811",100,1981,11,10,4.35808583218666,13.0862487335541,14.019031823665,12.6834983332585,0,22.6242720108446,4.26719555405807,100,1981,11,10,0.0794152322289711,0.00619825384136255,0.058587705958044,0.00268420278541925,NA,0.0437831970280865,0.0033343524452626
+"9812",100,1981,11,11,4.1749174361444,9.41676551831437,15.5088668965926,12.2190869575811,0,22.1976701354767,4.30246550399636,100,1981,11,11,0.928187112250531,0.107176650157776,0.117770100414041,0.0356538126383044,NA,0.0502927876397874,0.00324785132243081
+"9813",100,1981,11,12,0.448294838236766,9.44662268694216,14.8221232054388,10.2975357892883,0,21.4775190116753,4.33773545393464,100,1981,11,12,0.0298245615603632,0.135825121954376,0.0313134355735483,0.00575380199059414,NA,0.0342006519014201,0.00316287116550397
+"9814",100,1981,11,13,4.94136410472941,9.04957088306792,17.603850099251,10.8942794170317,0,21.0321182722315,4.37300540387293,100,1981,11,13,0.183333322736968,0.015555518468318,0.0189245651560205,0.00626547698315686,NA,0.0718129734907854,0.00307941197448217
+"9815",100,1981,11,14,0,5.53959293491376,16.7420573302753,10.0905059168179,0,20.571842699798,4.40827535381121,100,1981,11,14,0,0.094369599040087,0.012306479546924,0.0148339289303161,NA,0.112694341403832,0.00299747374936533
+"9816",100,1981,11,15,2.72233227901857,7.95319038282002,13.9508800758387,8.09536851645827,0,23.2030661805291,4.4435453037495,100,1981,11,15,0.475847946997976,0.0202508666479458,0.05746899327515,0.00143741400879343,NA,0.137851747306798,0.00291705649015349
+"9817",100,1981,11,16,3.88448849002389,6.02037395082816,14.9873377828315,9.26387234279687,0,24.7295905096006,4.47881525368779,100,1981,11,16,0.723216392171319,0.327057861950543,0.0819094365438503,0.00143860462263249,NA,0.0371772754590769,0.00283816019684666
+"9818",100,1981,11,17,2.96182618125437,7.89952695120548,17.3639051082766,13.6656985015366,0,24.148955329114,4.51408520362607,100,1981,11,17,0.0458479538856233,0.118224573155488,0.14065795965386,0.0411362760845571,NA,0.0233373615130368,0.00276078486944481
+"9819",100,1981,11,18,1.4893289275951,10.4897798752234,18.8670296411965,14.1514081031707,0,21.3208002045108,4.54935515356436,100,1981,11,18,0.0735672477811412,0.0983315008443242,0.0163444763187386,0.00986724265256175,NA,0.0266730473303951,0.00268493050794796
+"9820",100,1981,11,19,0.0541254133477856,8.02128714067314,20.4807919480226,12.9025082415087,0,21.1781073595198,4.58462510350264,100,1981,11,19,0.00257309949188902,0.0117497048506787,0.0552843095347856,0.0215052186264149,NA,0.0632126673721471,0.0026105971123561
+"9821",100,1981,11,20,0,9.1217381148973,19.3354128385403,13.1566445116687,0,21.419772621482,4.61989505344093,100,1981,11,20,0,0.0113450261166972,0.0285817706239735,0.00473684790545361,NA,0.0401605432938883,0.00253778468266924
+"9822",100,1981,11,21,0,7.63465351528592,20.511001379183,12.8926622592183,0,20.8936445212076,4.65516500337922,100,1981,11,21,0,0.0349543865259803,0.0168619802912787,0.0459918212913638,NA,0.089694491898913,0.00246649321888736
+"9823",100,1981,11,22,0,9.45867997558728,28.3373598386221,11.3461385932561,0,21.7678728646009,4.6904349533175,100,1981,11,22,0,0.068692975774887,0.260406683916555,0.0963134581048156,NA,0.0504666338871883,0.0023967227210105
+"9824",100,1981,11,23,0,14.1159186074705,30.8913643546361,11.8425850716099,0,23.7796538643505,4.72570490325579,100,1981,11,23,0,0.0336930165431166,0.0270064168475478,0.0106140370955568,NA,0.0335053760340412,0.00232847318903862
+"9825",100,1981,11,24,0.886028594572027,11.3710780946335,20.1356766011455,9.80325624258211,0,21.6484417658022,4.76097485319408,100,1981,11,24,0.0246783626288702,0.0344578888274959,0.0101473972524766,0.0230719456321875,NA,0.0768085766762192,0.00226174462297172
+"9826",100,1981,11,25,0,6.56796475422002,20.1765677889582,10.5840264887962,0,21.806470596292,4.79624480313236,100,1981,11,25,0,0.0961111100515003,0.0379473427225274,0.0242877393957634,NA,0.0254827307498443,0.00219653702280982
+"9827",100,1981,11,26,0,8.55909782720215,24.9723653845792,13.4519140990523,0,23.5918701750367,4.83151475307065,100,1981,11,26,0,0.0246695805935123,0.432860489900282,0.0150953200335346,NA,0.0814231642743358,0.00213285038855293
+"9828",100,1981,11,27,0,12.6942572787781,32.1916941169596,17.1841035282651,0,22.9315796857435,4.86678470300893,100,1981,11,27,0,0.0377929529804426,0.295598676568569,0.151650887178471,NA,0.0950286898050954,0.00207068472020101
+"9829",100,1981,11,28,0,16.7359076333125,26.7923982995834,17.5748297667215,0,23.8526174492598,4.90205465294722,100,1981,11,28,0,0.00529120135873237,0.237559635345547,0.00180935098772442,NA,0.020313092640702,0.00201004001775409
+"9830",100,1981,11,29,0,10.0407370209563,21.7777998749048,12.4293729511425,0,23.7177808363717,4.93732460288551,100,1981,11,29,0,0.00932863929709618,0.134198208355107,0.0191871346033936,NA,0.0339837168510944,0.00195091628121217
+"9831",100,1981,11,30,0,8.59442240753845,21.3625413770854,12.8535094056586,0,23.5706844535542,4.97259455282379,100,1981,11,30,0,0.116391738824763,0.0192496413563074,0.0524819004639757,NA,0.140510444714971,0.00189331351057525
+"9832",100,1981,12,1,0.205830588502393,7.27797581470183,21.6492630441328,11.4769196872271,0,24.4642341768072,5.00336040003564,100,1981,12,1,0.00941520519423904,0.0677690051675718,0.0268923789936117,0.0114333491432028,NA,0.0801482823874876,0.0018689972740791
+"9833",100,1981,12,2,0.0202420245040499,10.1831902870119,26.7187021882883,14.4747194441238,0,22.9458659666431,5.03412624724749,100,1981,12,2,0.00228070182235617,0.0428841810439283,0.182402962030215,0.102061976619137,NA,0.239079508596407,0.00184989059616076
+"9834",100,1981,12,3,0.469856990019743,13.8131572809419,21.8347085002232,13.8055884179765,0,22.5366379903319,5.06489209445934,100,1981,12,3,0.00695906503158712,0.0351052775690493,0.0191134623569615,0.0193052755256232,NA,0.0797721152471967,0.00183599347682021
+"9835",100,1981,12,4,0,8.14772267441283,28.3350164205721,15.3183497427845,0,24.2819416690469,5.09565794167119,100,1981,12,4,0,0.0185473716100702,0.0414666681081038,0.0906092802728998,NA,0.140476192872302,0.00182730591605744
+"9836",100,1981,12,5,0,13.29159520614,22.1455554500534,13.5981407050121,0,23.5008902931933,5.12642378888304,100,1981,12,5,0,0.0151052488472966,0.0291929982728926,0.0363444254558063,NA,0.0570818064680507,0.00182382791387247
+"9837",100,1981,12,6,0,10.9991419753357,24.8039716399542,13.8228932066981,0,21.8704143784494,5.15718963609489,100,1981,12,6,0,0.0750175065158364,0.0311479398451464,0.0924432318793923,NA,0.0593900793951239,0.00182555947026529
+"9838",100,1981,12,7,0.898129814004216,11.2096700364082,18.0712101750641,9.68863595515588,0,23.3618351826918,5.18795548330674,100,1981,12,7,0.0538596459090363,0.0805321506813393,0.0585537830423467,0.0706315753446869,NA,0.0162162748572307,0.00183250058523591
+"9839",100,1981,12,8,3.36996700661411,8.80628163219154,17.3230254254063,12.3868645834844,0,23.6694910694819,5.21872133051859,100,1981,12,8,0.113976584875103,0.0169561344169853,0.078598376866727,0.0760368451191697,NA,0.129279162502586,0.00184465125878434
+"9840",100,1981,12,9,1.90957093553575,11.0672497376882,18.2005059081729,12.5793618610328,0,22.7876759391442,5.24948717773044,100,1981,12,9,0.00473684436396576,0.0750174973137392,0.0566092676578874,0.0450806861521127,NA,0.165676122989782,0.00186201149091053
+"9841",100,1981,12,10,1.47425741573634,10.183586295813,24.8133993935664,12.8100661664906,0,24.238541578321,5.28025302494229,100,1981,12,10,0.320292378475793,0.0533608320181043,0.0203397665775316,0.0313672249286935,NA,0.0396186041760018,0.00188458128161453
+"9842",100,1981,12,11,0.101650166531207,11.3063255924859,25.7987128573545,16.8357972165014,0,25.4006887508904,5.31101887215414,100,1981,12,11,0.00543859665331088,0.00984969068387025,0.087220545723263,0.0404333489315076,NA,0.151377861171867,0.00191236063089633
+"9843",100,1981,12,12,1.65126512507007,11.550913145571,19.7363477461409,13.9523212781178,0,22.9833091473973,5.34178471936599,100,1981,12,12,0.0484210466502024,0.0110251439926457,0.020587208338796,0.0362496988888369,NA,0.198069297236708,0.00194534953875591
+"9844",100,1981,12,13,5.74972502180714,8.32322324673073,18.4212431682087,10.1276566858995,0,20.9416920464687,5.37255056657785,100,1981,12,13,0.868187152237937,0.236987622509827,0.0210830071926636,0.0554649289338132,NA,0.0336844790256603,0.0019835480051933
+"9845",100,1981,12,14,2.76479649701134,10.066105668432,18.607018769771,9.91825079734307,0,22.225815602627,5.4033164137897,100,1981,12,14,1.49064335683634,0.27646557208062,0.0107210512845389,0.0106064456739877,NA,0.127330457430969,0.00202695603020847
+"9846",100,1981,12,15,0.0222222225533591,11.4245104039594,21.9429922009459,11.7166447749626,0,25.277219288541,5.43408226100155,100,1981,12,15,0.00175438601719706,0.236858367290887,0.0802690559746772,0.0288286576790475,NA,0.046443944515856,0.00207557361380144
+"9847",100,1981,12,16,0,8.0593727859858,26.0019140841544,15.5879208372764,0,25.8820264441015,5.4648481082134,100,1981,12,16,0,0.0390543462753575,0.288803094157055,0.0982672847436195,NA,0.128649841255626,0.0021294007559722
+"9848",100,1981,12,17,0,11.3297248626306,25.0481074997301,14.92590760188,0,23.8749045496677,5.49561395542525,100,1981,12,17,0,0.0185210448840331,0.267858497504107,0.0606731450622663,NA,0.0626358447336705,0.00218843745672077
+"9849",100,1981,12,18,0,12.0640923517897,21.6927283237738,10.413707180254,0,23.9247434878713,5.5263798026371,100,1981,12,18,0,0.261058560667035,0.0223064349305553,0.00550293718312431,NA,0.0799765227895359,0.0022526837160471
+"9850",100,1981,12,19,0.937403732826739,10.4144113381179,18.9838392994191,9.76805279147376,0,21.2363499027613,5.55714564984895,100,1981,12,19,0.120292392278973,0.0462923509141111,0.0217467969975656,0.0269918493327567,NA,0.0392550281828534,0.00232213953395126
+"9851",100,1981,12,20,1.40099011166523,9.35944999195431,20.2022883496006,11.6726402024625,0,23.7726622262736,5.5879114970608,100,1981,12,20,0.174736844175741,0.115802320473736,0.0297923719846194,0.0302871307485315,NA,0.0224309732559426,0.0023968049104332
+"9852",100,1981,12,21,0.203520356387523,8.98259617602996,22.3408801768086,11.7982509233246,0,22.0241265895673,5.61867734427265,100,1981,12,21,0.019824562011755,0.0440450275756541,0.204367396166412,0.0281894822126624,NA,0.0632093073326093,0.00247667984549292
+"9853",100,1981,12,22,0.0403740380053604,8.89606161610653,21.6933774643868,10.6265457130239,0,22.7499533638933,5.6494431914845,100,1981,12,22,0.00245614042407588,0.078709974933722,0.0967982001056953,0.016278357179071,NA,0.0645818976377994,0.00256176433913044
+"9854",100,1981,12,23,0.0299229927451173,11.0254125846888,20.9766117826153,12.7829481939016,0,23.8394889921263,5.68020903869635,100,1981,12,23,0.00228070182235617,0.346427635607908,0.013093007035582,0.0307719423506686,NA,0.0387899377047811,0.00265205839134577
+"9855",100,1981,12,24,0,12.4820462070545,20.2595049162509,9.59257429267707,0,26.7952263340267,5.7109748859082,100,1981,12,24,0,0.313920523044581,0.0586988006281942,0.00657075824188479,NA,0.0397091796373021,0.00274756200213889
+"9856",100,1981,12,25,0,13.0742134434162,20.791034031229,12.0633334725341,0,26.7710891212228,5.74174073312005,100,1981,12,25,0,0.133353840588176,0.0778099133231666,0.0252035370498274,NA,0.0288734946089749,0.0028482751715098
+"9857",100,1981,12,26,0,12.5529591840486,23.6301869580192,16.1318699151638,0,24.9924225073873,5.7725065803319,100,1981,12,26,0,0.172989406300203,0.11461407575593,0.0220140086037496,NA,0.174040296218994,0.00295419789945848
+"9858",100,1981,12,27,0,13.2814302496915,23.0723984847368,16.2289658757326,0,24.3474257686674,5.80327242754375,100,1981,12,27,0,0.0757871414229774,0.0140468336855429,0.0548263836868681,NA,0.0370867220117061,0.00306533018598498
+"9859",100,1981,12,28,0,11.660594085131,22.9868976920351,13.9082508370428,0,25.0784154358314,5.8340382747556,100,1981,12,28,0,0.241513461292606,0.147676656872764,0.0071654774136897,NA,0.0564464351202811,0.00318167203108927
+"9860",100,1981,12,29,0,12.7471618589395,23.4593291266917,12.9788119312954,0,24.6873741235462,5.86480412196745,100,1981,12,29,0,0.199198904048681,0.472153917163529,0.0222813082712369,NA,0.0159888769120001,0.00330322343477136
+"9861",100,1981,12,30,0,11.0015512168473,28.8382289454226,15.1781518231131,0,26.9760919811286,5.8955699691793,100,1981,12,30,0,0.0467286526217699,0.178850937358561,0.238646767579523,NA,0.0309081093829142,0.00342998439703123
+"9862",100,1981,12,31,0,17.1580088524142,34.6792082581976,12.9044884114113,0,25.6653309723101,5.92633581639115,100,1981,12,31,0,0.0043912364540093,0.039820615107133,0.0701374100468265,NA,0.0501234370470788,0.0035619549178689
+"9863",100,1982,1,1,2.33905387868976,12.438734952635,20.8925525614924,11.5189439156661,0,21.4933723095882,5.9651166542141,100,1982,1,1,0.285380107338669,0.0278245664452357,0.041178358290216,0.0207655320062322,NA,0.113665062200468,0.0035207765243857
+"9864",100,1982,1,2,1.82541252795619,12.4133883823525,20.5566668033075,10.7251045124234,0,24.9312152524199,6.00389749203705,100,1982,1,2,0.0487719245682006,0.043324565352112,0.0232163613304666,0.0486210552059956,NA,0.116269296888222,0.00348383895300325
+"9865",100,1982,1,3,0.786578656160923,11.8021892111162,19.8049615517964,14.1538724080958,0,26.5967410062153,6.04267832985999,100,1982,1,3,0.0122222187783987,0.0861432887072597,0.0611257427062704,0.028646805726524,NA,0.0285199774543324,0.00345114220372152
+"9866",100,1982,1,4,0.18327833239985,12.1098129673235,20.7810010542833,9.50394937622272,0,26.3111357773197,6.08145916768294,100,1982,1,4,0.00783625797918667,0.150246740211876,0.0474702075358617,0.0212006005471884,NA,0.0850963763836609,0.00342268627654054
+"9867",100,1982,1,5,0.210231027415287,8.31569863729613,22.9910012319668,11.426094621715,0,23.9883473119327,6.12024000550589,100,1982,1,5,0.00432748578358116,0.053167235995806,0.109548000675023,0.0985145657746615,NA,0.046879647748565,0.0033984711714603
+"9868",100,1982,1,6,0,12.478899958897,26.6433884544079,12.6882287687463,0,24.0440472813795,6.15902084332884,100,1982,1,6,0,0.107815229090283,0.0405023162219502,0.137894153693217,NA,0.026026152994482,0.00337849688848081
+"9869",100,1982,1,7,11.0415842089847,11.3386027868038,20.2321341947885,10.3949284296487,0,22.9117172899045,6.19780168115179,100,1982,1,7,1.15064345878473,0.029498811037016,0.0146134536790078,0.0421649101592787,NA,0.112993496208082,0.00336276342760204
+"9870",100,1982,1,8,2.50880089189091,13.3873926011643,21.7023759995094,16.2842246833021,0,24.674535103341,6.23658251897474,100,1982,1,8,0.25596490919939,0.0432245321084795,0.0379707831224768,0.0288444318138802,NA,0.0422971927609934,0.00335127078882402
+"9871",100,1982,1,9,0.100000001490116,14.0866007296034,25.6437183858538,14.0835204832625,0,25.8146201087566,6.27536335679769,100,1982,1,9,0,0.0312099246845628,0.543705238021383,0.0201116943271008,NA,0.0721808073746579,0.00334401897214674
+"9872",100,1982,1,10,0,13.2152255890262,35.5493614356248,18.6381186872426,0,26.9493202516142,6.31414419462063,100,1982,1,10,0,0.041467270107205,0.0811361301898322,0.12560337120662,NA,0.0177028788771157,0.00334100797757021
+"9873",100,1982,1,11,0.0877887804893115,18.2195490972437,41.3086252705623,10.5887129828744,0,26.3301124925092,6.35292503244358,100,1982,1,11,0.00988304154391876,0.0166140350210676,0.00944802164120875,0.0901023197732189,NA,0.0168020450131951,0.00334223780509441
+"9874",100,1982,1,12,0.0721672177970475,16.8533664216565,20.7534104484667,16.315181683917,0,26.3649291609249,6.39170587026653,100,1982,1,12,0.0020467836867299,0.0341053707728308,0.0487064564088206,0.0296731160172901,NA,0.0225969479608058,0.00334770845471933
+"9875",100,1982,1,13,0.174037406333733,12.5911112324764,22.0930582493445,11.5836304222921,0,25.2431986182527,6.43048670808948,100,1982,1,13,0.00368421063611382,0.111858415476575,0.110593031239952,0.0300584795322082,NA,0.0936036368857892,0.00335741992644502
+"9876",100,1982,1,14,0,10.2792959769305,22.9011329298366,11.680946102499,0,23.7433956383347,6.46926754591243,100,1982,1,14,0,0.18246729206964,0.164421185187049,0.00813392444867175,NA,0.0672321011006512,0.00337137222027145
+"9877",100,1982,1,15,0,11.0885919847898,27.197018794363,16.2561384627003,0,25.3748985328658,6.50804838373538,100,1982,1,15,0,0.0330690194582238,0.0734806732537633,0.017002929330805,NA,0.0311767743323606,0.00338956533619861
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+"10172",100,1982,11,6,0,16.8774806590232,34.1172828254658,14.3937954163001,0,20.4580579144427,4.31614920527499,100,1982,11,6,0,0.0184368321204293,0.0265000008484206,0.48908011071065,NA,0.0778424940028078,0.00128720813130857
+"10173",100,1982,11,7,0.156105613001961,11.2980527447657,18.9903411193781,11.7941034661137,0,20.1155305132962,4.37366084259468,100,1982,11,7,0.0037426902920182,0.013950891909732,0.0537707976584084,0.07440295124396,NA,0.032063485915465,0.00131029611820552
+"10174",100,1982,11,8,0.903300329534659,6.10709569005683,19.1302968939014,10.1342573931771,0,19.2387587513133,4.43117247991437,100,1982,11,8,0.139298241068746,0.0243052749678597,0.0439134305589181,0.0916432447266844,NA,0.0556834718558233,0.00137159924033063
+"10175",100,1982,11,9,0.00110011002739402,7.84422434438573,17.838218103541,11.0460616857699,0,22.5149304257653,4.48868411723407,100,1982,11,9,0.000994152076411666,0.0487443965064451,0.0553830256771777,0.04211113357554,NA,0.123117737729317,0.00147111749768391
+"10176",100,1982,11,10,3.6570956819784,10.1502860177337,16.9803080375176,9.39150720224916,0,22.6242720108446,4.54619575455376,100,1982,11,10,0.301520505927468,0.0288245697245508,0.0670508325412854,0.0472134862532462,NA,0.0437831970280865,0.00160885089026538
+"10177",100,1982,11,11,0.815291533375731,6.48470848070906,19.2174698354387,11.2840374798665,0,22.1976701354767,4.60370739187345,100,1982,11,11,0.00362572993451419,0.0909397611662919,0.0857000150895652,0.0420426632686855,NA,0.0502927876397874,0.001784799418075
+"10178",100,1982,11,12,0,5.90484048457298,23.0532123452366,13.3224861902503,0,21.4775190116753,4.66121902919315,100,1982,11,12,0,0.0180257447705928,0.423535731636149,0.109106464178577,NA,0.0342006519014201,0.00199896308111279
+"10179",100,1982,11,13,0,9.4659185052836,25.7676790738919,14.8066886412953,0,21.0321182722315,4.71873066651284,100,1982,11,13,0,0.0314543886285895,0.913703979033144,0.0377596291637498,NA,0.0718129734907854,0.00225134187937877
+"10180",100,1982,11,14,0,13.1656434968753,23.7573814476022,15.3863036205011,0,20.571842699798,4.77624230383253,100,1982,11,14,0,0.0135812891999753,0.629364640161483,0.0308783138833812,NA,0.112694341403832,0.0025419358128729
+"10181",100,1982,11,15,0.0595159524820163,15.2608581549264,28.4247634014805,14.1332342873837,0,23.2030661805291,4.83375394115223,100,1982,11,15,0.00538011711940431,0.233048013546674,0.0114496851784833,0.0760701551047511,NA,0.137851747306798,0.00287074488159522
+"10182",100,1982,11,16,0.45687569364725,9.51251923736304,16.5760834969834,9.68066021213175,0,24.7295905096006,4.89126557847192,100,1982,11,16,0.1392397724885,0.0126175346843554,0.0388245964946606,0.107205226632809,NA,0.0371772754590769,0.0032377690855457
+"10183",100,1982,11,17,0.574917502102941,7.72645763674192,17.3740922971921,8.39089094976125,0,24.148955329114,4.94877721579161,100,1982,11,17,0.0136842100104395,0.00793567717704059,0.0313953150167616,0.0822654875125704,NA,0.0233373615130368,0.00364300842472433
+"10184",100,1982,11,18,6.07205722355607,6.85363041948039,15.6046753349346,8.24403740890217,0,21.3208002045108,5.0062888531113,100,1982,11,18,0.0681286251335944,0.117931590867756,0.0994689008970167,0.0316222159492355,NA,0.0266730473303951,0.00408646289913115
+"10185",100,1982,11,19,0.00286028607122444,8.9556214843515,17.9611993984814,12.696776663903,0,21.1781073595198,5.063800490431,100,1982,11,19,0.000526315805159117,0.130880709332516,0.0551941860313166,0.043159667161277,NA,0.0632126673721471,0.00456813250876616
+"10186",100,1982,11,20,0,10.6238393689146,19.6059738448744,12.132079412966,0,21.419772621482,5.12131212775069,100,1982,11,20,0,0.0342321679679399,0.108215337581784,0.0350496903024285,NA,0.0401605432938883,0.00508801725362932
+"10187",100,1982,11,21,0,9.04803078822439,21.6924314257598,11.0365786221948,0,20.8936445212076,5.17882376507038,100,1982,11,21,0,0.153573177482738,0.414038419971446,0.0830210871914907,NA,0.089694491898913,0.00564611713372067
+"10188",100,1982,11,22,0,7.79569854883209,26.8315842652609,14.7570406690289,0,21.7678728646009,5.23633540239008,100,1982,11,22,0,0.0182766214014126,0.316731460004935,0.103558448345397,NA,0.0504666338871883,0.00624243214904015
+"10189",100,1982,11,23,0,10.9963365718476,33.6006928119722,11.3160726811626,0,23.7796538643505,5.29384703970977,100,1982,11,23,0,0.0741988186641587,0.0856277385260399,0.278782936707512,NA,0.0335053760340412,0.00687696229958781
+"10190",100,1982,11,24,0,13.1205829519643,33.1539934911613,11.3259625954203,0,21.6484417658022,5.35135867702946,100,1982,11,24,0,0.100205285660232,0.17365352583466,0.304500709112136,NA,0.0768085766762192,0.00754970758536367
+"10191",100,1982,11,25,0.0174917494355649,12.3368207587398,17.7517932287537,9.17118807961576,0,21.806470596292,5.40887031434916,100,1982,11,25,0.00140350881375765,0.0211093525179193,0.0457496735469366,0.0187315905967759,NA,0.0254827307498443,0.00826066800636769
+"10192",100,1982,11,26,0.478327836533978,10.0301429488347,18.620737407336,12.4056876007349,0,23.5918701750367,5.46638195166885,100,1982,11,26,0.00315789477866996,0.0429765812612842,0.0515584773686561,0.157270839809469,NA,0.0814231642743358,0.00900984356259987
+"10193",100,1982,11,27,0.100000001490116,10.0133772515359,17.160979299262,9.56727170996671,0,22.9315796857435,5.52389358898854,100,1982,11,27,0,0.066609943943181,0.0487064275198224,0.0548432662295422,NA,0.0950286898050954,0.00979723425406021
+"10194",100,1982,11,28,0.115401541873632,9.21133111464833,20.9825851264173,10.9778547853527,0,23.8526174492598,5.58140522630824,100,1982,11,28,0.00286549716142186,0.129825793901072,0.109383137061879,0.0248988142337761,NA,0.020313092640702,0.0106228400807487
+"10195",100,1982,11,29,0.121122114016081,10.4981848912926,24.0948403802249,13.269757900301,0,23.7177808363717,5.63891686362793,100,1982,11,29,0.00228070182235617,0.0175806998716522,0.427178254936437,0.0797590054474084,NA,0.0339837168510944,0.0114866610426654
+"10196",100,1982,11,30,0.752145213131333,15.3523542406273,28.7869856827068,13.2265346483035,0,23.5706844535542,5.69642850094762,100,1982,11,30,0.0115204667069064,0.00799120036350487,0.100557862497375,0.11536728517195,NA,0.140510444714971,0.0123886971398103
+"10197",100,1982,12,1,1.31001100871954,10.8950604825917,17.5147522694469,8.70734858853851,0,24.4642341768072,5.69842130703284,100,1982,12,1,0.0980701691156252,0.0390672630846149,0.0208362468475725,0.0409227847339988,NA,0.0801482823874876,0.0120360632987762
+"10198",100,1982,12,2,0.00066006601643641,3.73606160948641,19.4746534181769,8.40013199985617,0,22.9458659666431,5.70041411311806,100,1982,12,2,0.000526315805159117,0.0453263147906309,0.0430362978085112,0.0425116884109644,NA,0.239079508596407,0.0116904062149145
+"10199",100,1982,12,3,0.292299237745692,6.37100109566163,20.826996898756,13.1871617110518,0,22.5366379903319,5.70240691920328,100,1982,12,3,0.00988304085550254,0.0203450409069625,0.0339006803638682,0.157181776249821,NA,0.0797721152471967,0.011351725888225
+"10200",100,1982,12,4,0,8.29623757206043,19.1513092683093,10.2371066715589,0,24.2819416690469,5.70439972528851,100,1982,12,4,0,0.0918672027822922,0.0180427422287269,0.0447895060423174,NA,0.140476192872302,0.0110200223187077
+"10201",100,1982,12,5,0.0797579769860662,8.74442242054787,19.0091859630757,9.20853668943097,0,23.5008902931933,5.70639253137373,100,1982,12,5,0.00228070182235617,0.137098264477902,0.0313029932124967,0.0613596590500444,NA,0.0570818064680507,0.0106952955063628
+"10202",100,1982,12,6,0,9.58654573228624,23.9374477063337,11.5966117290249,0,21.8704143784494,5.70838533745895,100,1982,12,6,0,0.0599099426961785,0.103525639415036,0.161502357648045,NA,0.0593900793951239,0.0103775454511901
+"10203",100,1982,12,7,2.89361938207981,10.6043344241689,14.9360505042165,10.7028711726039,0,23.3618351826918,5.71037814354417,100,1982,12,7,0.290526307228719,0.0630473695521564,0.018522219212928,0.0466888899061931,NA,0.0162162748572307,0.0100667721531898
+"10204",100,1982,12,8,16.4663367413058,8.50910887807378,14.4171396219822,14.1510561144654,0,23.6694910694819,5.71237094962939,100,1982,12,8,18.2383031806056,0.0579649567075037,0.0199619934239085,0.00612629105423808,NA,0.129279162502586,0.00976297561236168
+"10205",100,1982,12,9,3.1124312426522,10.5093288526546,17.715621539075,12.605500639862,0,22.7876759391442,5.71436375571461,100,1982,12,9,1.25339178645821,0.00946724876945548,0.134484144418608,0.0043701647329822,NA,0.165676122989782,0.0094661558287059
+"10206",100,1982,12,10,0,10.5361715837149,20.047084474721,12.7730583226589,0,24.238541578321,5.71635656179983,100,1982,12,10,0,0.156681798702995,0.197970031769717,0.0330701911101886,NA,0.0396186041760018,0.00917631280222237
+"10207",100,1982,12,11,0,10.0745763831144,21.9275469176709,12.3891089117304,0,25.4006887508904,5.71834936788505,100,1982,12,11,0,0.170053799338151,0.276872059908379,0.0593690480707356,NA,0.151377861171867,0.00889344653291112
+"10208",100,1982,12,12,0,10.7513861640452,30.4672606418891,16.0407923529513,0,22.9833091473973,5.72034217397027,100,1982,12,12,0,0.00832513768165233,0.202191059898122,0.0704140746201603,NA,0.198069297236708,0.00861755702077214
+"10209",100,1982,12,13,0,15.7574477798999,28.4183828628758,14.7337074342734,0,20.9416920464687,5.72233498005549,100,1982,12,13,0,0.00948010593220338,0.240636451293263,0.164915252272959,NA,0.0336844790256603,0.00834864426580551
+"10210",100,1982,12,14,0,11.4355445710739,25.8157424003509,12.3820902338647,0,22.225815602627,5.72432778614071,100,1982,12,14,0,0.103098324698805,0.929325854389954,0.0103485389643176,NA,0.127330457430969,0.00808670826801109
+"10211",100,1982,12,15,0,11.4020462455791,20.8755113771646,12.3202859125253,0,25.277219288541,5.72632059222594,100,1982,12,15,0,0.00527308676432687,0.0788800327905708,0.21409410472454,NA,0.046443944515856,0.00783174902738899
+"10212",100,1982,12,16,0,9.17290431674164,22.8980855952264,14.0770736207532,0,25.8820264441015,5.72831339831116,100,1982,12,16,0,0.0360812984021088,0.122305311129577,0.0948584806387479,NA,0.128649841255626,0.00758376654393917
+"10213",100,1982,12,17,0,13.4166225741799,21.2997689871374,11.4910341552382,0,23.8749045496677,5.73030620439638,100,1982,12,17,0,0.0347690168013266,0.0550176134837949,0.00947778566155143,NA,0.0626358447336705,0.00734276081766163
+"10214",100,1982,12,18,0,9.21700777341299,19.7065787551427,13.4271067502868,0,23.9247434878713,5.7322990104816,100,1982,12,18,0,0.319714122272608,0.0241386031260558,0.0536029174671334,NA,0.0799765227895359,0.00710873184855635
+"10215",100,1982,12,19,0,10.2058306233455,18.7852362348433,11.3656874710184,0,21.2363499027613,5.73429181656682,100,1982,12,19,0,0.052317550565564,0.0512800526405087,0.0164485614330963,NA,0.0392550281828534,0.00688167963662341
+"10216",100,1982,12,20,0.159295931966654,11.5235752130892,17.7810012116553,10.8732121428772,0,23.7726622262736,5.73628462265204,100,1982,12,20,0.00263157902579559,0.111453754784288,0.0408730479193019,0.060266653442447,NA,0.0224309732559426,0.0066616041818627
+"10217",100,1982,12,21,0,8.95336626858601,21.6789439152045,10.6827502423781,0,22.0241265895673,5.73827742873726,100,1982,12,21,0,0.468121032513027,0.0951472750036442,0.204095382451829,NA,0.0632093073326093,0.00644850548427428
+"10218",100,1982,12,22,0,9.39998908929437,27.8656766532671,13.5493179652819,0,22.7499533638933,5.74027023482248,100,1982,12,22,0,0.0271362520921599,0.379971753978499,0.127981840588817,NA,0.0645818976377994,0.00624238354385815
+"10219",100,1982,12,23,0,14.2162266910666,28.6999118624478,15.3530472617994,0,23.8394889921263,5.7422630409077,100,1982,12,23,0,0.0100263268655557,1.00181446023711,0.283582931288873,NA,0.0387899377047811,0.00604323836061433
+"10220",100,1982,12,24,0,12.492035131381,19.8240483496973,12.4935752482566,0,26.7952263340267,5.74425584699292,100,1982,12,24,0,0.141335661768847,0.326102328741335,0.0748227720807857,NA,0.0397091796373021,0.00585106993454276
+"10221",100,1982,12,25,0,10.4367987052573,19.0441253313793,11.2818922277856,0,26.7710891212228,5.74624865307814,100,1982,12,25,0,0.159425122895789,0.0436817700707433,0.0163666571723946,NA,0.0288734946089749,0.00566587826564348
+"10222",100,1982,12,26,0,9.59302534810518,22.4679097636174,13.404081319425,0,24.9924225073873,5.74824145916336,100,1982,12,26,0,0.10480059897895,0.0807696497424799,0.0377760392061513,NA,0.174040296218994,0.00548766335391649
+"10223",100,1982,12,27,0.122332235160965,13.7006269335353,25.8544777533402,16.8940151747566,0,24.3474257686674,5.75023426524858,100,1982,12,27,0.00766081909861482,0.0882982067532687,0.0917585022510814,0.0173818740802861,NA,0.0370867220117061,0.00531642519936177
+"10224",100,1982,12,28,0.531243125294367,13.0481409454765,25.588426770419,14.6100769315747,0,25.0784154358314,5.75222707133381,100,1982,12,28,0.049181285910962,0.134604120785521,0.334103167564269,0.0387117562255798,NA,0.0564464351202811,0.00515216380197934
+"10225",100,1982,12,29,0.084708472158518,14.2127172870867,32.1308912124046,18.7016611382513,0,24.6873741235462,5.75421987741903,100,1982,12,29,0.00777777817514208,0.0825174920713069,0.0500133973693335,0.0350579012912973,NA,0.0159888769120001,0.0049948791617692
+"10226",100,1982,12,30,2.18888887648273,14.3203190414294,22.211034143194,15.1282288852436,0,26.9760919811286,5.75621268350425,100,1982,12,30,0.169298244498628,0.0165988368966527,0.285902232807333,0.00488303870492156,NA,0.0309081093829142,0.00484457127873133
+"10227",100,1982,12,31,0,12.3440374735296,28.7741475792477,17.4151046803289,0,25.6653309723101,5.75820548958947,100,1982,12,31,0,0.00559239332417145,0.540580557233348,0.0505708430242117,NA,0.0501234370470788,0.00470124015286575
+"10228",100,1983,1,1,0,12.2722882678931,24.6812649633494,14.506138689316,0,21.4933723095882,5.77657771993851,100,1983,1,1,0,0.137149627989037,0.442827640801444,0.171614675948032,NA,0.113665062200468,0.00483010660886281
+"10229",100,1983,1,2,0.0468646871669851,13.0951705655642,20.6272937199726,10.2076235751245,0,24.9312152524199,5.79494995028755,100,1983,1,2,0.00578947385675029,0.0849923871626434,0.0181894581758767,0.118616336363013,NA,0.116269296888222,0.00496574487836372
+"10230",100,1983,1,3,0,7.8623322039941,22.5728052703723,13.5530473385969,0,26.5967410062153,5.81332218063659,100,1983,1,3,0,0.124939753732104,0.0360918344021341,0.279209339940541,NA,0.0285199774543324,0.00510815496136848
+"10231",100,1983,1,4,0.655555558742339,7.42698567141794,23.1980309921785,9.84279423578344,0,26.3111357773197,5.83169441098564,100,1983,1,4,0.0566666681898968,0.05445905554366,0.287369653829232,0.131291197331795,NA,0.0850963763836609,0.00525733685787705
+"10232",100,1983,1,5,0.247744781726395,11.5238833820859,20.425302339728,9.75778883649702,0,23.9883473119327,5.85006664133468,100,1983,1,5,0.00263157941793143,0.0558449977629953,0.0341338934293013,0.123757945682101,NA,0.046879647748565,0.00541329056788952
+"10233",100,1983,1,6,0,6.80484046086226,22.9898678220407,11.3339935120183,0,24.0440472813795,5.86843887168372,100,1983,1,6,0,0.0632210432532377,0.235139877440113,0.141814511768803,NA,0.026026152994482,0.00557601609140583
+"10234",100,1983,1,7,0,8.76617158547749,29.5132676366926,11.8715841138061,0,22.9117172899045,5.88681110203276,100,1983,1,7,0,0.0357432764756712,0.515624826027896,0.138547999180233,NA,0.112993496208082,0.00574551342842601
+"10235",100,1983,1,8,0,13.6366446235917,30.4599556309162,15.7925631331138,0,24.674535103341,5.9051833323818,100,1983,1,8,0,0.0446140293545939,0.194480672182915,0.0145783481241975,NA,0.0422971927609934,0.00592178257895004
+"10236",100,1983,1,9,0.610561052597526,13.4665787364259,19.9063802851309,10.8196370027246,0,25.8146201087566,5.92355556273085,100,1983,1,9,0.0198830384957165,0.0175099595433292,0.0311028794319116,0.142592365918605,NA,0.0721808073746579,0.0061048235429779
+"10237",100,1983,1,10,3.57348731432286,9.42789879411754,16.9118263068372,9.50580846620734,0,26.9493202516142,5.94192779307989,100,1983,1,10,0.727192986917777,0.0463479697468091,0.021106424392201,0.00303218782445642,NA,0.0177028788771157,0.00629463632050964
+"10238",100,1983,1,11,7.95874585098166,6.21679867638482,15.9455554393521,9.06817381872465,0,26.3301124925092,5.96030002342893,100,1983,1,11,2.17538009615674,0.0324929486726629,0.014391805269654,0.0292485431538052,NA,0.0168020450131951,0.0064912209115452
+"10239",100,1983,1,12,7.94037411997158,6.48331136042529,17.3766337039054,8.86970280394434,0,26.3649291609249,5.97867225377797,100,1983,1,12,0.238947385598378,0.0363321818089873,0.0340731243560339,0.0541338649304129,NA,0.0225969479608058,0.0066945773160846
+"10240",100,1983,1,13,1.1353135636144,8.788778779268,18.2182950543361,12.2346534624089,0,25.2431986182527,5.99704448412702,100,1983,1,13,0.00508772172425952,0.15142621979301,0.0101386062936915,0.0128497142970701,NA,0.0936036368857892,0.00690470553412789
+"10241",100,1983,1,14,1.99240922613112,10.4615401518751,18.3117492998918,11.236149624236,0,23.7433956383347,6.01541671447606,100,1983,1,14,0.085789471230314,0.0630006426226402,0.0400760313338561,0.101870720442907,NA,0.0672321011006512,0.00712160556567501
+"10242",100,1983,1,15,1.02651266702856,10.8047195304476,18.7327941814796,12.3852915779592,0,25.3748985328658,6.0337889448251,100,1983,1,15,0.0460818702435641,0.093947917940552,0.0186689451432129,0.0251093575483782,NA,0.0311767743323606,0.007345277410726
+"10243",100,1983,1,16,0.812431240960447,10.8127722504115,20.0318592919244,13.4302751648151,0,26.3235829356299,6.05216117517414,100,1983,1,16,0.0265497065636153,0.0177842134420622,0.0279543467563998,0.142744460932489,NA,0.0447167395274734,0.00757572106928084
+"10244",100,1983,1,17,2.18437845308264,9.8298789854228,19.3655555481696,10.2266006071051,0,24.207612367232,6.07053340552318,100,1983,1,17,0.309122811437353,0.163694127014817,0.03757600958335,0.118226262258844,NA,0.0528766704589807,0.00781293654133953
+"10245",100,1983,1,18,0.10110011151751,11.1102528996987,18.9167547404307,8.52625963842646,0,24.1705997348813,6.08890563587223,100,1983,1,18,0.000994152076411666,0.0923941096122599,0.0332538807387321,0.172573061770259,NA,0.192587833135833,0.00805692382690205
+"10246",100,1983,1,19,0,8.33280531250604,22.5938613753114,10.785335629949,0,26.4963187923028,6.10727786622127,100,1983,1,19,0,0.120870164214285,0.163983001633366,0.182073069834635,NA,0.0486457335737571,0.00830768292596846
+"10247",100,1983,1,20,0,10.4448514860718,29.5404622358064,13.0607809868321,0,20.6468870492452,6.12565009657031,100,1983,1,20,0,0.0219362983726723,0.175487044369411,0.0870210354476789,NA,0.174461157568063,0.00856521383853868
+"10248",100,1983,1,21,0,11.4272276917175,25.0941144252899,13.907964921496,0,24.009633844067,6.14402232691935,100,1983,1,21,0,0.339681911437318,0.626005327021981,0.0935163562128323,NA,0.0406483076735117,0.00882951656461276
+"10249",100,1983,1,22,0,11.4450054021821,37.8954676637555,13.5091308041899,0,22.5955021378006,6.16239455726839,100,1983,1,22,0,0.0316801362579253,0.15800373393369,0.204206493016356,NA,0.345333595828704,0.00910059110419072
+"10250",100,1983,1,23,0,14.3252365922246,20.2398787877216,12.5496919713791,0,28.2953508361113,6.18076678761744,100,1983,1,23,0,0.0868953228466168,0.0607367565983288,0.0171731082091728,NA,0.0314651025705819,0.00937843745727251
+"10251",100,1983,1,24,0,11.9604839748806,21.2843456499123,10.1174367803945,0,23.9323714527054,6.19913901796648,100,1983,1,24,0,0.119304113521974,0.186175919284329,0.0730374170917452,NA,0.0599613605108523,0.00966305562385818
+"10252",100,1983,1,25,0,9.65369657321338,30.9977337949478,12.8186908382,0,26.2258969434073,6.21751124831552,100,1983,1,25,0,0.0644286331667974,0.275790989341897,0.243069544884795,NA,0.0753529555296814,0.00995444560394769
+"10253",100,1983,1,26,0.479537960359103,15.2554785337123,18.8831133113311,12.4855445173576,0,24.2832999045053,6.23588347866456,100,1983,1,26,0.010994151834159,0.00605907058948026,0.0502817859809224,0.0041801187460497,NA,0.0286069999763571,0.010252607397541
+"10254",100,1983,1,27,12.15236525331,9.10325618676751,21.4296369877848,11.9891089186548,0,24.0900113398355,6.2542557090136,100,1983,1,27,0.775087616792257,0.069459683560009,0.0376315854170853,0.0628427343001277,NA,0.122690373187351,0.0105575410046382
+"10255",100,1983,1,28,0.147194721698105,11.2856215350043,23.2892631178248,16.1673816978866,0,24.5730272549734,6.27262793936265,100,1983,1,28,0.00818713486891742,0.0378590899105818,0.15929990861715,0.0777450103404382,NA,0.0440343149643401,0.0108692464252393
+"10256",100,1983,1,29,0.00847084721093393,11.1918261373791,25.7073596937559,13.6406821197409,0,22.2546538068179,6.29100016971169,100,1983,1,29,0.00175438601719706,0.0170649090952038,0.664258974974723,0.0685479479271789,NA,0.149544852119401,0.0111877236593442
+"10257",100,1983,1,30,0,10.7258966118589,26.8825411529037,14.4375688282177,0,24.3973745231152,6.30937240006073,100,1983,1,30,0,0.0729806315149604,0.551192242101779,0.0502286678917396,NA,0.0218408287029835,0.011512972706953
+"10258",100,1983,1,31,0,12.5594389037331,37.9189664800831,14.1785919485312,0,23.3515321540116,6.32774463040977,100,1983,1,31,0,0.014961999279807,0.234738558426051,0.282691754386912,NA,0.0235446804634094,0.0118449935680656
+"10259",100,1983,2,1,0,18.2345874375111,30.9320350390981,15.3571397657573,0,23.4129343062141,6.3457165272724,100,1983,2,1,0,0.015724581855955,0.207404031956834,0.189147954479092,NA,0.0296406499566769,0.0119999269835275
+"10260",100,1983,2,2,0.0426842690628878,12.2869086805874,22.4204070927417,11.7609791131434,0,23.3692480412628,6.36368842413503,100,1983,2,2,0.00263157902579559,0.0978782659542835,0.14557598963987,0.104037492426454,NA,0.0353800765827085,0.0121569612659451
+"10261",100,1983,2,3,0,8.84001092942241,22.4219253296637,15.3551155379897,0,24.2464598636382,6.38166032099766,100,1983,2,3,0,0.0680029929535415,0.086965399437865,0.0365608322724325,NA,0.0182029744220552,0.0123160964153185
+"10262",100,1983,2,4,0,11.8471507732362,24.1677224481329,13.4539934122654,0,23.1414437401631,6.39963221786029,100,1983,2,4,0,0.212388944202087,0.342744897795897,0.0834449871153195,NA,0.0375595673347797,0.0124773324316478
+"10263",100,1983,2,5,0,10.945599561215,28.0000547822421,15.1971066110861,0,22.0771528800066,6.41760411472292,100,1983,2,5,0,0.0429479514697417,0.473931616260314,0.156027495271648,NA,0.0139352692307565,0.0126406693149327
+"10264",100,1983,2,6,0,14.1296480029854,33.2754783630371,16.6682399680512,0,21.9237229235956,6.43557601158555,100,1983,2,6,0,0.155800580619303,0.434199261538649,0.2666140645793,NA,0.0416414498906271,0.0128061070651734
+"10265",100,1983,2,7,0,19.6384161517958,37.5993617428149,16.492750098603,0,22.0888009242973,6.45354790844818,100,1983,2,7,0,0.00699825410031454,0.0479929119985747,0.0712982440304666,NA,0.0175583573320285,0.01297364568237
+"10266",100,1983,2,8,0,22.430516915484,40.1216282272759,16.6683719370625,0,22.1866014173155,6.47151980531081,100,1983,2,8,0,0.0166702443086932,0.00863687853173663,0.0469730747382203,NA,0.0428502559241368,0.0131432851665223
+"10267",100,1983,2,9,0,13.4754345823567,24.8357098771401,14.4627282810946,0,21.6438105167041,6.48949170217344,100,1983,2,9,0,0.0588479428096918,0.248814715866507,0.0824245872855441,NA,0.109403017437708,0.0133150255176303
+"10268",100,1983,2,10,0,11.9626402062814,25.3684268296748,13.5198570026947,0,17.8636470360693,6.50746359903607,100,1983,2,10,0,0.00484970088987131,0.0854597034032626,0.264136251233267,NA,0.118921847127515,0.0134888667356942
+"10269",100,1983,2,11,0.0541254133477856,14.7417272078847,21.0599778283416,9.43512637177185,0,19.0001895990484,6.5254354958987,100,1983,2,11,0.00257309949188902,0.0261912469630644,0.034669046913228,0.0700561074508564,NA,0.124614794597885,0.0136648088207138
+"10270",100,1983,2,12,0,9.4463587908855,22.4618811570641,10.7045214257487,0,22.8020727401176,6.54340739276133,100,1983,2,12,0,0.0669041025396502,0.17207792587306,0.0225759851556756,NA,0.0785552338264747,0.0138428517726892
+"10271",100,1983,2,13,0,8.04925183599407,24.5266444349971,11.9947854378829,0,22.8090300237837,6.56137928962396,100,1983,2,13,0,0.0239806961416985,0.292113448548263,0.0450473556430121,NA,0.0486465470496594,0.0140229955916203
+"10272",100,1983,2,14,0,10.4173707353531,30.9590211245093,17.2848957804563,0,23.499533578595,6.57935118648659,100,1983,2,14,0,0.0108607903576096,0.248231283279893,0.147844436900041,NA,0.0154715266733284,0.0142052402775072
+"10273",100,1983,2,15,0,13.9172278184964,30.3261165534965,17.9670626658155,0,20.9534606294226,6.59732308334921,100,1983,2,15,0,0.00640527316543165,0.149676083517116,0.348893146831918,NA,0.0132668891155866,0.0143895858303499
+"10274",100,1983,2,16,0,19.9486575383689,42.6033219986885,6.13845987026185,0,22.4252820828404,6.61529498021184,100,1983,2,16,0,0.0323362066484092,0.0216244760445797,0.149933910697821,NA,0.148208676285106,0.0145760322501484
+"10275",100,1983,2,17,0.0469746981697245,13.1570627225114,20.5922444535561,8.82821784423392,0,23.7961916513606,6.63326687707447,100,1983,2,17,0.00245614042407588,0.11208306908642,0.0862578062003796,0.0260005709799382,NA,0.00940797605618017,0.0147645795369027
+"10276",100,1983,2,18,0.105060507616129,10.369097961451,21.6883278291742,10.0294278630591,0,21.511014421912,6.6512387739371,100,1983,2,18,0.00222222228844961,0.414991091867705,0.0635701210293311,0.0991701590912367,NA,0.058872316714288,0.0149552276906127
+"10277",100,1983,2,19,0,12.5920461396573,23.2232562192071,12.0932782826775,0,21.3607660304071,6.66921067079973,100,1983,2,19,0,0.127659036808214,0.253902191559558,0.0342356238628069,NA,0.0190732034081191,0.0151479767112785
+"10278",100,1983,2,20,0,12.4913422644335,25.0348076500384,16.3320900148029,0,20.7934478617986,6.68718256766236,100,1983,2,20,0,0.131005190997744,0.228536848075184,0.0811209802507178,NA,0.0185202339682647,0.0153428265989
+"10279",100,1983,2,21,0,12.3394058875912,30.6068756997389,17.5720130974966,0,19.2397084412227,6.70515446452499,100,1983,2,21,0,0.0142204946150794,0.161571999885791,0.103253859322809,NA,0.197870284680301,0.0155397773534774
+"10280",100,1983,2,22,0.71496149687746,17.2843564288451,26.289945079942,16.4879980265635,0,18.9772956106669,6.72312636138762,100,1983,2,22,0.0427485360527599,0.0164362601478457,0.946582656530505,0.0373204523680986,NA,0.0232668150005013,0.0157388289750105
+"10281",100,1983,2,23,0.631353139705194,14.2280417894504,23.2433444485806,12.8535203367177,0,19.9743041549194,6.74109825825025,100,1983,2,23,0.40672515093496,0.0946655310090615,0.150026279516679,0.00555965522194421,NA,0.0100829873263694,0.0159399814634994
+"10282",100,1983,2,24,0,9.65016500915762,27.0245435481811,14.8562595999018,0,18.7874312302242,6.75907015511288,100,1983,2,24,0,0.0294175456398898,0.337942679872899,0.0618543736441868,NA,0.0401175167561637,0.016143234818944
+"10283",100,1983,2,25,0,13.6618591870936,36.7929264190305,14.6305611172918,0,20.8129744264724,6.77704205197551,100,1983,2,25,0,0.0782584778345627,0.020053783556234,0.0742607881669734,NA,0.0120977790662301,0.0163485890413445
+"10284",100,1983,2,26,0,18.2659846936384,38.870054616393,16.716688678603,0,20.2956423778945,6.79501394883814,100,1983,2,26,0,0.0660386538503503,0.0248205452830305,0.189417679935312,NA,0.00864262383841193,0.0165560441307007
+"10285",100,1983,2,27,0,17.2294607865404,24.7966557598219,15.656303679589,0,18.7624235175121,6.81298584570077,100,1983,2,27,0,0.011350846452672,0.742711169518669,0.0406093314701638,NA,0.0245966046264808,0.0167656000870126
+"10286",100,1983,2,28,0,17.1483169656382,22.5875798719551,18.9023429862212,0,17.9139952476356,6.8309577425634,100,1983,2,28,0,0.00834268825624801,0.0882585264107919,0.0299145818435189,NA,0.23595334154568,0.0169772569102804
+"10287",100,1983,3,1,0.000110011002739402,17.2155112506795,26.4650936399487,18.9734431729458,0,19.7752592142746,6.74791652117139,100,1983,3,1,0.000526315805159117,0.0116783588702078,0.602626029111937,0.0252479788444026,NA,0.195925725221559,0.0161311927996765
+"10288",100,1983,3,2,0.755445542705334,18.0574255566655,26.6209242792413,20.8086688332301,0,20.4934099813062,6.66487529977938,100,1983,3,2,0.0284210526594645,0.0145877300956857,0.0834117735569406,0.100236332707351,NA,0.0166271006672856,0.0153076849368425
+"10289",100,1983,3,3,0.354895496935603,17.0743013828894,31.2703078997017,19.0946094609461,0,18.9841136765559,6.58183407838738,100,1983,3,3,0.0277777761220933,0.0121918482822664,0.0965466539932499,0.0517537590923298,NA,0.0319656887910346,0.0145067333217784
+"10290",100,1983,3,4,2.34037402072231,18.2729042942899,27.3554123457771,23.2650165662776,0,19.0902738560856,6.49879285699537,100,1983,3,4,0.208187133875509,0.0286426793463974,0.12117670476843,0.047424590297484,NA,0.0499527191273414,0.0137283379544843
+"10291",100,1983,3,5,5.88998893542652,16.2894281171205,24.32748068756,17.4577557801938,0,19.4303228256034,6.41575163560336,100,1983,3,5,1.14766076238533,0.0286760225712976,0.0486467727663566,0.0552362503901897,NA,0.178275180638527,0.01297249883496
+"10292",100,1983,3,6,0.769196920957502,13.7465126763607,34.5099885183068,17.4188339371886,0,17.8706942354754,6.33271041421136,100,1983,3,6,0.075555553833644,0.0253052522581948,0.0586983468679695,0.0760209924043457,NA,0.055412259770432,0.0122392159632056
+"10293",100,1983,3,7,1.34136414114792,18.4211113786015,24.6167435871624,21.8488668897102,0,18.4327443469782,6.24966919281935,100,1983,3,7,0.264268990413489,0.0128923577339004,0.0372250575465411,0.214653702781963,NA,0.161908448522134,0.0115284893392212
+"10294",100,1983,3,8,5.60979101707702,18.8204620579551,25.6457205605586,23.5792738072025,0,17.8201645772524,6.16662797142734,100,1983,3,8,0.348187121787288,0.00954970784809312,0.0552134496903488,0.0175467897831384,NA,0.00723562667630817,0.0108403189630067
+"10295",100,1983,3,9,2.7607260639518,13.1500769584748,19.9448845237002,12.7730142427619,0,18.3921279042805,6.08358675003533,100,1983,3,9,0.0202924078946973,0.0814099739945268,0.0190204406205515,0.125326326711665,NA,0.0180396344826778,0.0101747048345621
+"10296",100,1983,3,10,0.331683172193756,11.5001759828121,25.0272605400799,15.3936965352762,0,17.0609188071771,6.00054552864333,100,1983,3,10,0.0799999990728167,0.124902970060913,0.0329209909274747,0.0948251663587611,NA,0.0408017688827945,0.00953164695388734
+"10297",100,1983,3,11,0,12.7749945228249,32.548415999208,12.2962815680257,0,17.4843006479709,5.91750430725132,100,1983,3,11,0,0.0452783541785992,0.0363040377089089,0.0322695887404644,NA,0.0106170908107111,0.00891114532098248
+"10298",100,1983,3,12,0.0998899904873767,15.4746865219015,24.300011086123,14.0537951881736,0,17.0268388720457,5.83446308585931,100,1983,3,12,0.000526315805159117,0.0300830497564133,0.191242557994463,0.0675730730916374,NA,0.0646656786080182,0.00831319993584762
+"10299",100,1983,3,13,0.104400441599692,13.9621782009095,20.9659515040936,12.0073488390747,0,15.9870540517392,5.75142186446731,100,1983,3,13,0.00210526322063647,0.107791809771432,0.0235263560783718,0.0594666698032767,NA,0.0363629000224639,0.00773781079848259
+"10300",100,1983,3,14,0,13.0710449407596,20.7641915362267,9.47144121436527,0,17.2164572951157,5.6683806430753,100,1983,3,14,0,0.226549703419609,0.0925040634157771,0.0549544502437502,NA,0.0338604431951135,0.00718497790888752
+"10301",100,1983,3,15,0.329152923868303,12.468404716391,19.8382620418032,10.6207590291996,0,15.3307898377781,5.58533942168329,100,1983,3,15,0.00473684211572013,0.0917987916199686,0.00938944975221826,0.0652023730384699,NA,0.0432186420005002,0.00665470126706234
+"10302",100,1983,3,16,0.167436747161141,12.9254895064435,21.0094278438388,10.9989878189708,0,15.2170399301019,5.50229820029128,100,1983,3,16,0.016549708370054,0.128724609093993,0.0921250759462129,0.0182929678934542,NA,0.0355569057921099,0.00614698087300706
+"10303",100,1983,3,17,0,11.6713091049782,23.2400657099859,16.63222238228,0,17.0993407368004,5.41925697889928,100,1983,3,17,0,0.0361760422579076,0.0488843026682923,0.0903765293904846,NA,0.0342508054993436,0.0056618167267217
+"10304",100,1983,3,18,0,11.6052144924537,26.1669197858888,17.1591637194878,0,15.0214471622744,5.33621575750727,100,1983,3,18,0,0.0308251741555164,0.390861888783164,0.112835632891185,NA,0.0382578230868555,0.00519920882820623
+"10305",100,1983,3,19,0,14.4077116396561,21.3434654651302,12.6831573406593,0,16.5380141086324,5.25317453611526,100,1983,3,19,0,0.0101450407307912,0.0860607704567319,0.0165760414391977,NA,0.0792785299403369,0.00475915717746071
+"10306",100,1983,3,20,0,11.3504071209428,20.1511220984464,15.1582618856063,0,15.6065733823577,5.17013331472326,100,1983,3,20,0,0.0764590857768791,0.0302339801455886,0.0177842155055834,NA,0.024464679867295,0.00434166177448506
+"10307",100,1983,3,21,0,14.1745434657182,19.444400397071,16.4157315454598,0,14.0129243094166,5.08709209333125,100,1983,3,21,0,0.0763988290241047,0.0271988804247033,0.0338597036667093,NA,0.0993513710404692,0.00394672261927934
+"10308",100,1983,3,22,26.9416939323098,14.0139604165609,16.7621889103888,16.1118042749671,0,14.1439281156835,5.00405087193924,100,1983,3,22,0.985789016143933,0.00864970356908397,0.0336275208191347,0.0234537480139578,NA,0.0266285263730027,0.00357433971184349
+"10309",100,1983,3,23,24.0622664305768,13.7836632597433,15.1408800412588,13.8111332923797,0,15.2026710722356,4.92100965054723,100,1983,3,23,6.41040943837328,0.0193871444837082,0.0294426698451256,0.00416081470922369,NA,0.011497210454999,0.00322451305217758
+"10310",100,1983,3,24,8.3694170520644,11.0958855663589,17.4284819402579,10.9772608162153,0,13.8597014609501,4.83796842915523,100,1983,3,24,0.47052636559251,0.0788374339143696,0.0177227830856324,0.0690619857431934,NA,0.0207457668127731,0.00289724264028156
+"10311",100,1983,3,25,2.38536853562094,5.80474148174324,18.2741032045404,8.8888888238418,0,15.4790264174927,4.75492720776322,100,1983,3,25,0.110526320320826,0.0264537815819202,0.0058257393466405,0.0218923989592408,NA,0.055796690597294,0.00259252847615548
+"10312",100,1983,3,26,0,7.86041804062914,16.1352143093567,11.6545764201283,0,14.1418859408721,4.67188598637121,100,1983,3,26,0,0.0602063997849078,0.0113818715817509,0.0401765942981469,NA,0.100535343847897,0.00231037055979928
+"10313",100,1983,3,27,0.176897692404958,11.5940483605245,17.4577231160616,12.6488998977527,0,11.2921357656212,4.58884476497921,100,1983,3,27,0.00228070182235617,0.0795245886061895,0.00696961611055491,0.0114871111708724,NA,0.00876688437518285,0.00205076889121299
+"10314",100,1983,3,28,1.65808579440426,10.0526841701847,16.979934035617,11.9206490018318,0,13.1777204817025,4.5058035435872,100,1983,3,28,0.467777788241707,0.0349701904141304,0.0180099399904438,0.0354064234126292,NA,0.0484710825279336,0.00181372347039663
+"10315",100,1983,3,29,0.388118818754738,9.69038510716001,21.3047416737371,11.6356875332538,0,12.7985474946694,4.42276232219519,100,1983,3,29,0.0336257310290087,0.0376093638504414,0.0316485651789253,0.0687023740312182,NA,0.0112380031182811,0.00159923429735014
+"10316",100,1983,3,30,1.58734874310929,11.9431352678305,18.1903962071317,11.8398240113547,0,12.7613743683858,4.33972110080318,100,1983,3,30,0.0945029178000351,0.0666321762732585,0.0102175194637769,0.0372924609380055,NA,0.0133817882840108,0.00140730137207358
+"10317",100,1983,3,31,0.300000011920929,10.5046754465638,18.3261496225993,11.5145874033929,0,12.9653431044323,4.25667987941118,100,1983,3,31,0,0.141091825645918,0.0110164144375019,0.156370274188022,NA,0.0259114059261274,0.00123792469456694
+"10318",100,1983,4,1,0.632783288511113,8.99668862140349,17.0567324945767,9.27341029143045,0,12.7648946744463,4.20341756461408,100,1983,4,1,0.0194152041764289,0.0825228228965807,0.0643918206067674,0.00895613651397464,NA,0.0240982466482186,0.00116491836766771
+"10319",100,1983,4,2,0.131463148273585,8.92665576515156,16.6091969388284,9.8562487359881,0,12.4086407050415,4.15015524981699,100,1983,4,2,0.00257309949188902,0.279848141118167,0.106803345048075,0.0107912289871054,NA,0.0455268138164257,0.00109724920422126
+"10320",100,1983,4,3,0.164796482734751,5.35850388932936,17.7805389870118,9.64278326989269,0,13.2088368655946,4.0968929350199,100,1983,4,3,0.00584795377407856,0.059911090087908,0.0265707813867915,0.0279052914135256,NA,0.0472097897293212,0.00103491720422762
+"10321",100,1983,4,4,3.66534653355186,9.67166128672651,13.1558744967705,12.3338064218905,0,13.1999458603884,4.0436306202228,100,1983,4,4,1.00210524561811,0.11241638404898,0.100536858147136,0.0154766081426002,NA,0.0194674923423176,0.000977922367686761
+"10322",100,1983,4,5,11.2194719860131,7.95943895083974,12.6373597481857,8.54918587561881,0,12.9094186273032,3.99036830542571,100,1983,4,5,1.05152046382088,0.0624649235854013,0.0694321279894711,0.00761460683730675,NA,0.0079812746244194,0.000926264694598679
+"10323",100,1983,4,6,9.78800890285714,5.86491750647919,13.9467216225216,8.37954903593158,0,12.7148201068485,3.93710599062862,100,1983,4,6,0.241403584842863,0.11500646109555,0.0619783663566273,0.035891819000309,NA,0.0151601890511598,0.000879944184963401
+"10324",100,1983,4,7,1.30374036409674,7.5622551769051,14.5956877140847,10.1497469829647,0,12.5433431697514,3.88384367583152,100,1983,4,7,0.0232163713968297,0.149321035355051,0.0667894633555967,0.0136561457584587,NA,0.00842350766789368,0.000838960838780902
+"10325",100,1983,4,8,0.360396047908612,6.25467545390785,18.4868317070049,10.3162817383232,0,10.8089930102276,3.83058136103443,100,1983,4,8,0.00590643230586026,0.0231760199831381,0.0249894918300212,0.0087917860020795,NA,0.0536189501198708,0.000803314656051195
+"10326",100,1983,4,9,1.97887787108112,9.28899906229789,15.5478658046659,13.0306492292448,0,11.5600839655916,3.77731904623734,100,1983,4,9,0.334502932621028,0.128839819659461,0.0131701645678886,0.00448069543900383,NA,0.0123576005685188,0.000773005636774271
+"10327",100,1983,4,10,6.87491754231804,12.0822331142111,19.8440814296273,14.0008691550613,0,10.9741320310078,3.72405673144024,100,1983,4,10,0.0521052622098161,0.107570723708806,0.0961718636767701,0.0122929643910602,NA,0.0170280551090482,0.000748033780950142
+"10328",100,1983,4,11,7.37612770886311,10.7571066236339,18.1978547250477,12.4806270148232,0,11.9290993662251,3.67079441664315,100,1983,4,11,0.989298224198236,0.0152210765259122,0.0669842156930401,0.0760818850088587,NA,0.03271864201326,0.000728399088578802
+"10329",100,1983,4,12,4.91837184631129,10.0488228792667,16.9032673033157,12.2143015237269,0,11.85008458367,3.61753210184606,100,1983,4,12,0.0598830349124827,0.117914619622093,0.0173321639794645,0.0817912101813376,NA,0.0318227131099981,0.000714101559660246
+"10330",100,1983,4,13,1.04070408430823,9.91614960522542,15.0623100719305,11.1972936655429,0,10.9296754847047,3.56426978704896,100,1983,4,13,0.0221052654723678,0.162587087740624,0.0532690044091508,0.0318807177984807,NA,0.0259424307469331,0.000705141194194477
+"10331",100,1983,4,14,1.97260723216305,10.0909351069804,15.7028821173018,11.2802969551716,0,10.9264189812877,3.51100747225187,100,1983,4,14,0.0247368428860509,0.0751257718293239,0.0525807021024919,0.0447619591719367,NA,0.0529233801645417,0.000701517992181503
+"10332",100,1983,4,15,2.08206822179725,7.70414739079995,14.0189328419231,8.44343254983228,0,10.3417447587532,3.45774515745478,100,1983,4,15,0.154736857888297,0.284050879979311,0.0358561398891849,0.0278801329094629,NA,0.0314817711161695,0.000703231953621312
+"10333",100,1983,4,16,0.0469746981697245,5.19647963159811,15.7765566287654,9.76383940903398,0,10.8364803712273,3.40448284265768,100,1983,4,16,0.00245614042407588,0.241247372734362,0.0322023379923306,0.0126386026126354,NA,0.0161564659160779,0.000710283078513914
+"10334",100,1983,4,17,0,6.99540160171794,16.2060064466873,10.1061385304752,0,10.2744186454349,3.35122052786059,100,1983,4,17,0,0.044155558755678,0.0316145893925617,0.0134362571694691,NA,0.00798830768895614,0.000722671366859302
+"10335",100,1983,4,18,0.272057211069581,5.12896594801883,18.0358307065219,9.80071508346742,0,10.945130556365,3.2979582130635,100,1983,4,18,0.0536257323875414,0.103033355628137,0.0773153469109748,0.0118245612139559,NA,0.0183275191546542,0.000740396818657479
+"10336",100,1983,4,19,0,6.83457643259214,21.1934654584157,7.73764571734387,0,9.97788777318156,3.2446958982664,100,1983,4,19,0,0.0783584933989411,0.0352251261665409,0.0815619813930593,NA,0.0296771095285203,0.000763459433908447
+"10337",100,1983,4,20,0,9.5805830687973,22.2332014182482,13.6754785323694,0,10.8813567954977,3.19143358346931,100,1983,4,20,0,0.112192373150906,0.0536894784985294,0.0694655036168381,NA,0.013920561715262,0.000791859212612197
+"10338",100,1983,4,21,0,10.5954565104872,24.057942777577,8.7480528016295,0,9.14916847747138,3.13817126867222,100,1983,4,21,0,0.0662929332822961,0.0361830286128892,0.0292222046323199,NA,0.0333283274228975,0.000825596154768735
+"10339",100,1983,4,22,0,13.9668978154987,24.1468870039165,12.5973707069003,0,10.022454347844,3.08490895387512,100,1983,4,22,0,0.0425701930955777,0.138037497903316,0.00611402367718383,NA,0.0144950328786433,0.000864670260378071
+"10340",100,1983,4,23,0.294719480191192,15.5005609021328,22.6696149744216,17.2910890002193,0,9.71656765683116,3.03164663907803,100,1983,4,23,0.0747953250690511,0.0163953119490596,0.0844544388445051,0.0196134907107787,NA,0.00756470529417914,0.000909081529440187
+"10341",100,1983,4,24,8.29702969615084,10.0702970754458,19.2753354965395,11.7959515904174,0,9.27733996072705,2.97838432428094,100,1983,4,24,1.82064303944688,0.0340286810000052,0.0537672714370125,0.00991462417298687,NA,0.0088198259244684,0.000958829961955098
+"10342",100,1983,4,25,3.10143015854167,6.42105615519323,15.415038476027,13.4598240196639,0,9.58570959128829,2.92512200948384,100,1983,4,25,0.0750877188520861,0.0573877328694358,0.0104029217302588,0.033837461353401,NA,0.0192640133289669,0.00101391555792279
+"10343",100,1983,4,26,7.72541247578737,10.3335094451904,15.317678815592,12.5980857559557,0,10.1104656083393,2.87185969468675,100,1983,4,26,2.67783604415541,0.0199246118245355,0.0267584508863216,0.00710059612574483,NA,0.0184073354920507,0.00107433831734328
+"10344",100,1983,4,27,0.0436743680875425,10.0776016363347,13.6368097519324,10.6060396085347,0,9.63336081582009,2.81859737988966,100,1983,4,27,0.00245614042407588,0.11379529069606,0.0347467616054088,0.00383976561789224,NA,0.013275987443375,0.00114009824021655
+"10345",100,1983,4,28,0,7.94299226389466,16.2614192375124,10.7753355615866,0,8.82896141115506,2.76533506509256,100,1983,4,28,0,0.0141444471359621,0.0167654988118551,0.0184988470200923,NA,0.01694382241701,0.00121119532654261
+"10346",100,1983,4,29,0,7.00755773624047,16.6896589738701,9.94400430958394,0,8.28609058232722,2.71207275029547,100,1983,4,29,0,0.0560228039234187,0.0558812698946198,0.0435041400887606,NA,0.00920270697563016,0.00128762957632146
+"10347",100,1983,4,30,0.0578657874409253,8.00595163171179,16.2443124374541,12.9131682997096,0,8.90062632742582,2.65881043549838,100,1983,4,30,0.00263157902579559,0.0878579323685348,0.0577094441646109,0.0103695976905004,NA,0.00536656468211105,0.0013694009895531
+"10348",100,1983,5,1,2.05885589352273,11.267711802022,16.9527611643305,15.1270516524614,0,8.8130429228584,2.62035039998131,100,1983,5,1,0.618947390561915,0.027270161794995,0.0633466909935469,0.0114579128979806,NA,0.0244265944507838,0.00132721968244559
+"10349",100,1983,5,2,9.41804170660978,13.1070516836001,16.0669085919136,16.2159515565509,0,9.71097121814053,2.58189036446425,100,1983,5,2,0.437076073696825,0.0470000131396045,0.0280508646537623,0.00600526779387551,NA,0.0509767782391309,0.001285984685427
+"10350",100,1983,5,3,28.788228927797,11.7983388596504,15.0897689437447,15.1906159341139,0,7.91238174921084,2.54343032894719,100,1983,5,3,2.95315701311826,0.0509397788176777,0.0277584964396046,0.016415215085172,NA,0.00443888009916981,0.00124569599849732
+"10351",100,1983,5,4,4.74785482503138,10.8543013854913,15.6307371819373,15.1232454071213,0,8.94052367179788,2.50497029343013,100,1983,5,4,0.257602319940508,0.0449894243475252,0.0664871642085172,0.0471227547830505,NA,0.0099419404261935,0.00120635362165655
+"10352",100,1983,5,5,2.82508255179041,9.81025307196857,15.8414850895948,13.6710340323621,0,8.68450219121984,2.46651025791307,100,1983,5,5,0.0169590692074033,0.174693050049959,0.0896911098124457,0.0242356952489873,NA,0.0110225461288128,0.00116795755490469
+"10353",100,1983,5,6,2.36050603465803,11.3377008060418,16.7737403340859,14.654807553433,0,7.9951539986729,2.42805022239601,100,1983,5,6,0.469415193858902,0.159295358758308,0.0502362229512371,0.00827660776554716,NA,0.00662100812863615,0.00113050779824176
+"10354",100,1983,5,7,0.446534656979988,9.79195826963754,16.682937426929,14.3536412193961,0,7.66502311060094,2.38959018687895,100,1983,5,7,0.00596491258046785,0.109894122829842,0.0115690264744992,0.0239263027236689,NA,0.0209203761359687,0.00109400435166773
+"10355",100,1983,5,8,0.922662257951478,9.23332228309119,17.1925301672471,13.8245103477251,0,8.12080537062721,2.35113015136189,100,1983,5,8,0.0649707540224872,0.0623982586911823,0.0770264326399298,0.0712987972304594,NA,0.0105399895965202,0.00105844721518262
+"10356",100,1983,5,9,1.92145214707676,10.558613844306,16.7298899693589,14.5837512892334,0,6.76254492066568,2.31267011584482,100,1983,5,9,0.459181263530467,0.133926297536993,0.05075078452071,0.0199982429683651,NA,0.0145730876040114,0.00102383638878642
+"10357",100,1983,5,10,0.376347642133732,11.5470847800226,17.3447084038696,12.1364575533977,0,8.14627573273007,2.27421008032776,100,1983,5,10,0.00690058397270785,0.10131522244243,0.0761029484758757,0.00743743452113178,NA,0.0096624562650062,0.000990171872479141
+"10358",100,1983,5,11,2.02717271522589,7.31707370399248,17.0353794790337,11.3485369362322,0,7.27890263491506,2.2357500448107,100,1983,5,11,0.268011694177553,0.018488881704553,0.0938285480346837,0.00272748374722971,NA,0.0178061923973474,0.00095745366626077
+"10359",100,1983,5,12,0.169306934633926,5.23342132778189,18.2758527992844,13.07425742889,0,7.25531824616007,2.19729000929364,100,1983,5,12,0.0104093575233605,0.0314333268907519,0.0831543361688006,0.0551099487840517,NA,0.00495527030601342,0.000925681770131315
+"10360",100,1983,5,13,0.0254125416328018,8.86655653480387,19.8369636535645,13.4479537770824,0,7.75876973244486,2.15882997377658,100,1983,5,13,0.00257309949188902,0.0436245710540858,0.0574918278836234,0.0255783312146186,NA,0.0134069890389539,0.000894856184090771
+"10361",100,1983,5,14,1.32266227427227,12.1295818500917,16.9062156446434,14.9695489556089,0,7.54920572821894,2.12036993825952,100,1983,5,14,0.376725135727935,0.0735367936988509,0.0436427299860122,0.00495438337499141,NA,0.00502054612327135,0.000864976908139143
+"10362",100,1983,5,15,11.9092408705859,10.6834322978692,15.1287788720545,13.4428823699783,0,6.94109059622491,2.08190990274246,100,1983,5,15,0.662280665503693,0.116815205287438,0.10613688237828,0.0238941756556175,NA,0.00702796329865624,0.000836043942276429
+"10363",100,1983,5,16,12.9480747867076,6.20651262480565,12.6917161154668,8.51833864931751,0,6.98041449992814,2.04344986722539,100,1983,5,16,1.23204666450019,0.274492981248165,0.0624929963006938,0.0574093575394365,NA,0.0060241679604574,0.000808057286502627
+"10364",100,1983,5,17,1.33212320896724,3.66947194766684,12.9832673665583,8.21127610579051,0,6.72641511977834,2.00498983170833,100,1983,5,17,1.4909942150604,0.0678257448034753,0.0542339618638904,0.0069093503785361,NA,0.00231859592022028,0.00078101694081774
+"10365",100,1983,5,18,0.00693069317258231,3.15506049036586,15.5700219891908,11.2033552457266,0,7.39543567227556,1.96652979619127,100,1983,5,18,0.00140350881375765,0.0957497147052654,0.0944338354970648,0.00297662989308195,NA,0.0081722710471268,0.00075492290522177
+"10366",100,1983,5,19,0,6.70077003661555,17.2345874668873,12.9411881376546,0,7.0604466397027,1.92806976067421,100,1983,5,19,0,0.033215193749605,0.0460930203312672,0.00806199294871816,NA,0.00154143737092538,0.00072977517971471
+"10367",100,1983,5,20,0,6.60935097768886,18.244356302276,13.9707699451509,0,6.62619676011811,1.88960972515715,100,1983,5,20,0,0.0287035170449306,0.0768654768427355,0.0605654654632231,NA,0.0205018351824426,0.000705573764296564
+"10368",100,1983,5,21,0,4.97832782355079,17.7415732120392,14.2610010875203,0,6.70895254558341,1.85114968964009,100,1983,5,21,0,0.0496052625723268,0.0211830198012016,0.0252175711626925,NA,0.0096483385069647,0.000682318658967335
+"10369",100,1983,5,22,0.100000001490116,5.03410340352158,17.3719251819439,11.740263961985,0,6.13139957813827,1.81268965412303,100,1983,5,22,0,0.116620472636993,0.0605473199724422,0.00821521326930739,NA,0.00479026163069252,0.000660009863727016
+"10370",100,1983,5,23,0.115401541873632,7.41682068192133,15.449340043944,12.2953465328489,0,6.97696248428729,1.77422961860597,100,1983,5,23,0.00286549716142186,0.0978889109293922,0.117645044244301,0.0117982364677464,NA,0.00864859409791655,0.000638647378575613
+"10371",100,1983,5,24,0.236743679493502,6.31927392401449,13.7292188816469,9.12948291346316,0,6.67319448045514,1.7357695830889,100,1983,5,24,0.0133918132689613,0.0579432895961734,0.0571321853304009,0.00443858473488157,NA,0.00456723011477431,0.000618231203513123
+"10372",100,1983,5,25,0.186578661326331,7.55019800419068,12.9152366220623,11.081232064628,0,6.25100109795626,1.69730954757184,100,1983,5,25,0.0335672521312335,0.207360899790295,0.0175473860378551,0.0108543959077475,NA,0.00887715033797632,0.000598761338539548
+"10373",100,1983,5,26,0.00407040710135786,7.95048400933462,15.1735973211274,10.63349824534,0,5.80489284281059,1.65884951205478,100,1983,5,26,0.000994152076411666,0.129626355625216,0.123847347558498,0.00205789563594743,NA,0.0104869923642608,0.000580237783654885
+"10374",100,1983,5,27,3.61606161402922,9.04380646108663,15.7521231832809,14.1701431410803,0,6.19191344029842,1.62038947653772,100,1983,5,27,0.351754393215076,0.120991754422605,0.099869549147961,0.0274672822540479,NA,0.0265957910630505,0.000562660538859136
+"10375",100,1983,5,28,1.11804180557054,7.93379547782201,12.9467984704163,11.4756654239986,0,6.17878006643869,1.58192944102066,100,1983,5,28,0.374678347257147,0.0974777809991159,0.0421356594621921,0.0153695561973072,NA,0.00557876569954432,0.000546029604152301
+"10376",100,1983,5,29,2.14609459406472,6.11337732560564,10.8598899641971,10.1612649913406,0,6.21801767530851,1.5434694055036,100,1983,5,29,0.240643308873766,0.126802352669941,0.0611894221145504,0.0318210641047678,NA,0.00961406975220803,0.000530344979534381
+"10377",100,1983,5,30,16.5018700053065,5.69774477395287,14.1208801217074,12.0618592525604,0,6.67004848807869,1.50500936998654,100,1983,5,30,1.26397659714479,0.16525609424214,0.0696707747209139,0.0564608363949702,NA,0.0142872713527743,0.000515606665005375
+"10378",100,1983,5,31,4.47788779622782,8.1676017994141,13.5421013910778,11.5903079606781,0,6.15065679090489,1.46654933446948,100,1983,5,31,0.168070177780971,0.305891789916012,0.0716140554886061,0.0333578651875089,NA,0.00551986487728535,0.000501814660565281
+"10379",100,1983,6,1,6.72321236225376,9.17695271247553,14.4508910708957,13.1736082805134,0,6.2488602128617,1.44142381984282,100,1983,6,1,0.382631615421246,0.167080055129829,0.104202926082566,0.0913093621193397,NA,0.0039790554283129,0.000805728760144871
+"10380",100,1983,6,2,1.05753575188361,7.9114632496346,14.0088450113932,10.4819473081952,0,6.33324654218994,1.41629830521617,100,1983,6,2,0.0424561389845027,0.0365245583562491,0.128187144820493,0.0220023521491044,NA,0.00639378344704877,0.00118374729337608
+"10381",100,1983,6,3,0.0635863595833742,4.93294833648061,13.0393069427792,11.6746533722243,0,6.37508699991993,1.39117279058952,100,1983,6,3,0.00450292411080578,0.148491247149255,0.17662581762804,0.0478140714367221,NA,0.00257471217136412,0.00163587026025892
+"10382",100,1983,6,4,0.0222222225533591,6.34679864132234,14.3541144464407,11.3034324792877,0,6.39185592193007,1.36604727596287,100,1983,6,4,0.00175438601719706,0.0318584865559076,0.0626596003817558,0.00876490208817191,NA,0.0257896177999702,0.00216209766079338
+"10383",100,1983,6,5,0,5.2066226934049,15.4602199392875,10.0050714406768,0,6.20981023601723,1.34092176133622,100,1983,6,5,0,0.0471274982207183,0.119254251390991,0.00170409550369372,NA,0.00328825046109424,0.00276242949497947
+"10384",100,1983,6,6,0.00352035208766085,5.43370737363272,15.2122443139356,11.9457425934778,0,5.66572961146101,1.31579624670957,100,1983,6,6,0.000994152076411666,0.0353485340007016,0.157647922344178,0.122976578305401,NA,0.00361530813408492,0.00343686576281717
+"10385",100,1983,6,7,5.08679871013587,7.71709569714906,10.416655703084,9.16058308902485,0,6.24501869501017,1.29067073208292,100,1983,6,7,1.42023392805581,0.0515695725357599,0.0328783672891154,0.0139537968352138,NA,0.00825154612448358,0.0041854064643065
+"10386",100,1983,6,8,18.5058306892319,4.90255223327737,11.528074826869,8.88048401445445,0,5.79285258206969,1.26554521745626,100,1983,6,8,3.80953143225808,0.171303500597117,0.133587121303234,0.0198941634864815,NA,0.00494803857989209,0.00500805159944745
+"10387",100,1983,6,9,4.56820680520716,6.74995598672378,12.7266445464165,11.3055115805732,0,5.89237035651936,1.24041970282961,100,1983,6,9,0.313684199762641,0.278314071654181,0.225361899635902,0.0072093547977873,NA,0.0143213252368924,0.00590480116824003
+"10388",100,1983,6,10,8.11826179470822,5.69730473456472,10.4623101541836,8.82339949655061,0,6.18151580618553,1.21529418820296,100,1983,6,10,0.333391775945516,0.135805273612636,0.170700529808012,0.00214326454750877,NA,0.012391680590461,0.00687565517068422
+"10389",100,1983,6,11,6.30066006988844,3.76368540872966,12.7346644952352,10.8095160978462,0,6.03587733125529,1.19016867357631,100,1983,6,11,0.691345036266863,0.179700604788472,0.11876720479116,0.0352608194987741,NA,0.00338334902777801,0.00792061360678005
+"10390",100,1983,6,12,1.82552252836091,5.73970292765971,12.8304620276977,12.6175356037152,0,5.32496301691719,1.16504315894966,100,1983,6,12,0.0138596461112053,0.0867227898168438,0.118131582260189,0.0138029153568179,NA,0.00607640757060825,0.0090396764765275
+"10391",100,1983,6,13,6.89944997951142,4.7529043327726,12.3796479431841,10.3853575986604,0,5.98021244007442,1.13991764432301,100,1983,6,13,0.0726315672913672,0.188158497433375,0.162509355926572,0.0301473443260444,NA,0.0133668813613048,0.0102328437799266
+"10392",100,1983,6,14,4.05390545375014,6.34882287486027,12.811650211137,12.2737074349448,0,5.94861606110309,1.11479212969636,100,1983,6,14,1.59783625962443,0.147971929956208,0.119616314099494,0.0569339199334499,NA,0.0114211913445697,0.0115001155169773
+"10393",100,1983,6,15,2.88063810236252,6.30232120268416,12.337623750416,10.737315903927,0,6.22538795844294,1.0896666150697,100,1983,6,15,0.00695906708812556,0.0678543975027171,0.0399590896138878,0.0385398040861384,NA,0.0165000143846939,0.0128414916876796
+"10394",100,1983,6,16,0.750275031863266,5.07012101971802,12.0270956446498,8.63573165301836,0,6.15086754760939,1.06454110044305,100,1983,6,16,0.104736842080864,0.0407701817774837,0.123547304100005,0.00514971557087677,NA,0.00437458530728877,0.0142569722920335
+"10395",100,1983,6,17,0,6.31883389249493,10.9843784758229,9.32030803378266,0,5.84192557635354,1.0394155858164,100,1983,6,17,0,0.218892986937869,0.0669210795576182,0.0263275217759752,NA,0.00232760046695561,0.0157465573300391
+"10396",100,1983,6,18,0,1.56642465013089,12.9654565869909,11.4016391819197,0,6.00485414236062,1.01429007118975,100,1983,6,18,0,0.261059657701555,0.0502094215215315,0.0344257170873358,NA,0.00436755577459673,0.0173102468016963
+"10397",100,1983,6,19,0,3.44645762496,14.3380307416842,12.3946863789238,0,6.11508838722327,0.989164556563099,100,1983,6,19,0,0.108636867640875,0.0158813042491112,0.00169297072213143,NA,0.0154076640707701,0.0189480407070051
+"10398",100,1983,6,20,0.670847088131312,6.24997798313271,15.2029041745613,12.8566556873888,0,5.60658843744594,0.964039041936448,100,1983,6,20,0.0235672509879398,0.058391217865324,0.0898385773910148,0.00252631247676299,NA,0.0122743618227805,0.0206599390459655
+"10399",100,1983,6,21,0.159845987242637,7.42590762705955,14.797392846203,12.8633882430258,0,5.71921094924504,0.938913527309797,100,1983,6,21,0.00426900618431862,0.0328649227666699,0.117954391372473,0.00302572427503229,NA,0.0114935585398629,0.0224459418185776
+"10400",100,1983,6,22,0,5.17034105315591,12.6953465049416,11.024565344203,0,5.69763856035259,0.913788012683145,100,1983,6,22,0,0.0610953187953799,0.172422901974599,0.0139497064825179,NA,0.00545963047517543,0.0243060490248413
+"10401",100,1983,6,23,0.478877890096902,6.20739269781165,13.093300347281,11.0823101404608,0,5.4140803024517,0.888662498056494,100,1983,6,23,0.00362573132877467,0.195725695757302,0.126970782017946,0.0204590990614107,NA,0.0047373996980867,0.0262402606647566
+"10402",100,1983,6,24,0.313971408227883,6.3045104538778,12.9574147456288,12.5920682315386,0,5.92964128530964,0.863536983429843,100,1983,6,24,0.00140350860461855,0.0651256912131241,0.0873426219003922,0.0263146024850006,NA,0.0038663292316584,0.0282485767383235
+"10403",100,1983,6,25,7.59207915997479,5.48028599914282,9.06350918256804,7.09332243076908,0,5.94937141559079,0.838411468803191,100,1983,6,25,1.26842116528788,0.036580715247506,0.155549736893085,0.000620465481644491,NA,0.00484361868414744,0.030330997245542
+"10404",100,1983,6,26,8.21364125376619,2.28625963426659,9.77389441805967,7.683927369721,0,6.45144032858376,0.81328595417654,100,1983,6,26,0.0257894412258756,0.207283041628345,0.120638571459282,0.000654384019653981,NA,0.0134497423307368,0.0324875221864122
+"10405",100,1983,6,27,5.25104505708902,2.60993402494718,8.97727155737882,9.54594052087093,0,6.24726465747695,0.788160439549889,100,1983,6,27,1.76608193854841,0.0917146081495055,0.0661251132675732,0.0513145875942885,NA,0.00569481608362092,0.034718151560934
+"10406",100,1983,6,28,7.76105606595282,3.80177118098906,10.6852145494014,7.94136408427105,0,5.94045797574347,0.763034924923237,100,1983,6,28,0.59023399866118,0.10356665853925,0.0823099165353868,0.00260409660342187,NA,0.023551797215854,0.0370228853691074
+"10407",100,1983,6,29,2.10253023497998,1.87072605556912,9.57492842008047,9.25189218111951,0,5.60004097597945,0.737909410296586,100,1983,6,29,0.0706432736826247,0.0259590599188343,0.081909411781788,0.000922227350935843,NA,0.00795992767568609,0.0394017236109325
+"10408",100,1983,6,30,9.09482934844769,4.85180415729485,13.9331464075019,11.7239492928365,0,5.40378248794665,0.712783895669934,100,1983,6,30,2.96093588143072,0.116867266723028,0.160213459226874,0.0172801185497158,NA,0.00937510888078547,0.0418546662864091
+"10409",100,1983,7,1,5.42893295581847,7.52815180223505,13.0347194189023,12.7265786988245,0,6.2134391868663,0.724872662747494,100,1983,7,1,0.0191812781283862,0.213421106450074,0.10271457572186,0.0085467793426246,NA,0.00519470983487557,0.0394636111423013
+"10410",100,1983,7,2,14.6369638180707,8.9049613940047,13.5401760485306,12.7211111570218,0,6.21260835752847,0.736961429825052,100,1983,7,2,0.470233991634133,0.12793973758102,0.031947376753214,0.00335847856402654,NA,0.00685110136823128,0.0371429266140525
+"10411",100,1983,7,3,12.4529152608941,7.26855880854809,13.4249175053881,12.0127172942209,0,6.36067221759009,0.749050196902611,100,1983,7,3,1.81321606463163,0.120414607993916,0.0350146337699464,0.00242515080332175,NA,0.00788030636586113,0.0348926127016629
+"10412",100,1983,7,4,0.55764576989432,4.40778883219552,12.8001320149639,10.7724971802715,0,5.85808543773465,0.76113896398017,100,1983,7,4,0.0115204685717299,0.0218649072178842,0.187714134508624,0.00113333646993064,NA,0.0100227296561663,0.0327126694051324
+"10413",100,1983,7,5,0.200000002980232,3.65123211275233,12.8490540039684,8.76743675668379,0,6.08403246871058,0.773227731057729,100,1983,7,5,0,0.0494286682742459,0.055892419828508,0.00464678641879555,NA,0.010617941039783,0.0306030967244609
+"10414",100,1983,7,6,1.07117712045267,2.83103413917575,11.5064465593059,10.4761496754763,0,6.51323139786851,0.785316498135288,100,1983,7,6,0.126081867172705,0.0268783664497076,0.183550855864226,0.00859531689925647,NA,0.00643834703623641,0.0285638946596486
+"10415",100,1983,7,7,6.21727168887886,3.41261825834302,10.8415841405803,9.26023105898313,0,6.5175302520022,0.797405265212847,100,1983,7,7,1.52760244659516,0.0780766113537841,0.132569537891209,0.015822784171832,NA,0.0095936439181551,0.0265950632106954
+"10416",100,1983,7,8,2.22255224966505,5.81790977054172,11.2036304295522,8.43652359067541,0,6.42301506225303,0.809494032290406,100,1983,7,8,0.0198245753182365,0.209848493265989,0.068405232876842,0.0109941476410111,NA,0.0148372999297138,0.0246966023776013
+"10417",100,1983,7,9,0,5.57651259901762,12.2588668514793,8.04630372705239,0,6.69997268221247,0.821582799367965,100,1983,7,9,0,0.0761473626075673,0.155271895090797,0.0072117234492785,NA,0.00448378253527709,0.0228685121603663
+"10418",100,1983,7,10,0.0179317934465225,1.17052807049914,10.5220570642956,6.6496478921116,0,6.15302608546344,0.833671566445524,100,1983,7,10,0.00140350881375765,0.0301286566076233,0.0906696584936327,0.0115806937201209,NA,0.0109220172285455,0.0211107925589905
+"10419",100,1983,7,11,1.21078107898647,2.39055005106071,10.5180859581472,7.91451052496798,0,5.63910811675357,0.845760333523083,100,1983,7,11,0.0378362540892004,0.0437672602129308,0.053146823104582,0.0280134386609663,NA,0.0224801647023499,0.0194234435734737
+"10420",100,1983,7,12,3.59702968728555,3.14243125128667,11.8698129160832,8.75328942835003,0,6.00168806356483,0.857849100600642,100,1983,7,12,0.0705263180481805,0.192911701869411,0.109700661381592,0.00571755490003937,NA,0.00587089209936512,0.0178064652038161
+"10421",100,1983,7,13,0.813641371178811,5.60271727265042,13.7166336603028,11.233102298019,0,6.33415174248195,0.869937867678201,100,1983,7,13,0.00175438471007788,0.138637421914731,0.127647314377339,0.0141818587120012,NA,0.0154330056176504,0.0162598574500175
+"10422",100,1983,7,14,4.31628161635992,6.60696371789813,13.2777338667934,10.8600221220547,0,6.83929972614071,0.88202663475576,100,1983,7,14,0.648128658679502,0.181125720397792,0.129164827708268,0.000215203598733023,NA,0.00364007573593919,0.0147836203120781
+"10423",100,1983,7,15,2.17964795097397,3.94400444125185,12.0182399476978,11.0846204317049,0,6.54582783014973,0.894115401833319,100,1983,7,15,0.0522222259309584,0.0186386086107058,0.0704175815449135,0.00832456836373272,NA,0.0139256534302546,0.0133777537899978
+"10424",100,1983,7,16,3.11100107149454,6.31132012074537,13.1864137408232,10.8349835214311,0,6.68302226302421,0.906204168910878,100,1983,7,16,0.634970744841285,0.0349783672154498,0.0733730667694725,0.00397659976586946,NA,0.0158138375814667,0.0120422578837765
+"10425",100,1983,7,17,0.232233228758223,3.71761271793588,14.7532123013823,11.7107810575445,0,6.90440536236339,0.918292935988437,100,1983,7,17,0.00368421092368011,0.0278508801348776,0.124369013158419,0.0357841762097344,NA,0.0101421287539731,0.0107771325934144
+"10426",100,1983,7,18,0.928382846158985,6.68800880518159,13.9971837651218,10.8336413502037,0,6.76299585133402,0.930381703065996,100,1983,7,18,0.153859652862912,0.13112280970012,0.0656872043833488,0.0304912751516363,NA,0.00972339492034886,0.00958237791891142
+"10427",100,1983,7,19,3.55225522945685,6.58003293143378,10.5643564755111,8.22723856591287,0,6.90044737368396,0.942470470143555,100,1983,7,19,0.0795321619301557,0.230033321613751,0.203073184431719,0.00205849077555982,NA,0.0133834453351985,0.00845799386026753
+"10428",100,1983,7,20,7.27183722190731,1.5550165001971,10.9092408600945,7.820242041933,0,7.67344985290732,0.954559237221114,100,1983,7,20,2.40690052339213,0.12231169890772,0.0756139818270411,0.00926899784491863,NA,0.0252361522918685,0.00740398041748276
+"10429",100,1983,7,21,0.664906500852016,3.14674367815486,12.2651704991742,10.6466336706684,0,7.19263256455587,0.966648004298673,100,1983,7,21,0.0125730993106353,0.0792596442328599,0.0988707552771345,0.0399286474106362,NA,0.0157203199546045,0.00642033759055709
+"10430",100,1983,7,22,2.86105612428537,5.50586359729074,13.9334102217251,10.1403520466602,0,7.16359846805276,0.978736771376232,100,1983,7,22,0.0922222291098747,0.134654364845784,0.0690830038127239,0.00887017307625774,NA,0.0116613905052299,0.00550706537949052
+"10431",100,1983,7,23,0.0360836088985238,4.19907589871498,11.1796150039656,7.26685371808093,0,6.49541765109054,0.990825538453791,100,1983,7,23,0.00263157902579559,0.115414607868434,0.250498855809516,0.00140234269707163,NA,0.00879378335855493,0.00466416378428307
+"10432",100,1983,7,24,0,2.70355336081208,9.641903202657,6.65335535871969,0,7.2468552042594,1.00291430553135,100,1983,7,24,0,0.172635684162836,0.0198163882830706,0.00428302995003418,NA,0.0144708834296535,0.00389163280493473
+"10433",100,1983,7,25,0,1.8781628040853,12.0746754047239,9.42745871486181,0,7.64104235544157,1.01500307260891,100,1983,7,25,0,0.0431134419349219,0.0837468356796722,0.00693976012341638,NA,0.00691890336201756,0.0031894724414455
+"10434",100,1983,7,26,0,4.61729373344363,12.9650165308164,10.7307479677945,0,7.57153447791055,1.02709183968647,100,1983,7,26,0,0.0279286557705285,0.0431801334805918,0.0236245637481516,NA,0.0708813222996603,0.00255768269381537
+"10435",100,1983,7,27,7.9675467913956,8.98799780414443,13.5679647594658,12.6005169046987,0,7.94543968215721,1.03918060676403,100,1983,7,27,0.12801168095998,0.107124514380202,0.270939659803589,0.00400292127741049,NA,0.00662021105743442,0.00199626356204436
+"10436",100,1983,7,28,0.745874586344028,5.7166666633094,13.8365345127118,10.9128603290017,0,7.4505873842236,1.05126937384159,100,1983,7,28,0.115087722414419,0.0259783473500312,0.155655428738132,0.0267929809883461,NA,0.00742667966376097,0.00150521504613245
+"10437",100,1983,7,29,0.0136413643396858,5.44231013129122,13.4136414144955,11.4294719538673,0,7.01599521080011,1.06335814091915,100,1983,7,29,0.00175438601719706,0.0315467828784195,0.089481891462591,0.000436839013391417,NA,0.0127034942291222,0.00108453714607966
+"10438",100,1983,7,30,0.852035200831914,7.3078218424412,14.4331792930041,11.3158087022234,0,7.4059732340748,1.0754469079967,100,1983,7,30,0.00929824523758502,0.0492894574996485,0.0534877166614641,0.000380702840858668,NA,0.0203168587026365,0.00073422986188597
+"10439",100,1983,7,31,2.83278328567186,7.70462045312846,12.3900991246776,10.2904950971782,0,7.94959663334499,1.08753567507426,100,1983,7,31,0.178128641613747,0.0625373974939776,0.104914553288783,0.0103731011017851,NA,0.00860844907991845,0.000454293193551394
+"10440",100,1983,8,1,4.0619361896326,6.45455441826379,12.9745434822947,8.99415834298884,0,8.1547555258927,1.11555807532042,100,1983,8,1,0.370643301288998,0.0799064164747418,0.0235800951283599,0.00450351610468938,NA,0.0103253125184696,0.000506635843552732
+"10441",100,1983,8,2,3.24851485054092,7.00838283553506,12.2545103780245,8.31665576540335,0,8.05117523381172,1.14358047556658,100,1983,8,2,0.0453801132224488,0.120236819707878,0.0885830194061378,0.00795849616967075,NA,0.0152645027975841,0.000563084386451053
+"10442",100,1983,8,3,0.155995603031976,6.49103399891533,11.6994939999218,8.15184820445851,0,8.74682029780232,1.17160287581274,100,1983,8,3,0.0502923992990751,0.384232167744793,0.0702450635453556,0.00777076883604268,NA,0.0233900694177885,0.000623638822246354
+"10443",100,1983,8,4,0.300880094885629,5.91132014130864,11.0150274186507,8.37713971783226,0,8.13533590326914,1.1996252760589,100,1983,8,4,0.0678362592183359,0.216276042987324,0.108131639369111,0.000745021684079478,NA,0.0127443524470768,0.000688299150938637
+"10444",100,1983,8,5,0,2.14095707773769,13.5144223925566,9.10167206448428,0,8.60232768148859,1.22764767630506,100,1983,8,5,0,0.0320766107305503,0.0739620082699491,0.0214040821611564,NA,0.0138375909784036,0.000757065372527897
+"10445",100,1983,8,6,0,5.24698567521585,14.4983499871098,8.223366303019,0,8.18689483979004,1.25567007655122,100,1983,8,6,0,0.0620731195539179,0.0674719445457695,0.00658130305338192,NA,0.0131524400483247,0.000829937487014137
+"10446",100,1983,8,7,3.9809680937147,8.08726063708399,13.5128492842151,9.96416962500846,0,7.79656071656686,1.28369247679738,100,1983,8,7,0.373216355781117,0.116924504081601,0.123026302237324,0.0243953606031831,NA,0.0148809086922211,0.000906915494397361
+"10447",100,1983,8,8,6.9860285993981,8.3819802350337,12.7502199892688,9.42081391090083,0,8.46662999515104,1.31171487704354,100,1983,8,8,0.741403452945757,0.126276007684671,0.0854450245707558,0.00425556068426216,NA,0.0453573608571879,0.000987999394677563
+"10448",100,1983,8,9,5.71771173246361,7.75532457091496,13.918448890921,11.8913200691064,0,8.48627767963977,1.3397372772897,100,1983,8,9,0.109532177908401,0.129621033193378,0.134904114385306,0.00807191138917713,NA,0.0284896934432428,0.00107318918785475
+"10449",100,1983,8,10,1.96688665847967,8.62097904154963,14.2432893925112,11.9920901307965,0,8.50665090395979,1.36775967753586,100,1983,8,10,0.036900580361575,0.055768969887241,0.113220490889588,0.0261362492589813,NA,0.0109519496754911,0.00116248487392891
+"10450",100,1983,8,11,4.9638063697794,7.88106709974434,13.559207946685,12.1851484688989,0,8.33128179021835,1.39578207778201,100,1983,8,11,0.24432752770989,0.0539338677657849,0.0600052650162384,0.00648011382932507,NA,0.00344981599972894,0.00125588645290005
+"10451",100,1983,8,12,0.176897692404958,8.24822900843437,15.0833773828051,10.0722552709716,0,9.19804256889691,1.42380447802817,100,1983,8,12,0.00245614042407588,0.0598228637394606,0.079813476163253,0.0017286566372599,NA,0.0133189832968741,0.00135339392476818
+"10452",100,1983,8,13,0,5.34572051198307,17.5446093153245,10.465060514192,0,9.00754871226773,1.45182687827433,100,1983,8,13,0,0.0447537880869936,0.0744251213345599,0.00152865333786277,NA,0.00979145585015858,0.00145500728953328
+"10453",100,1983,8,14,0,9.15739268438257,19.2209792855811,12.3508578668727,0,9.4583680324121,1.47984927852049,100,1983,8,14,0,0.109020469676766,0.0367871258520885,0.0241579098395406,NA,0.0140306244654162,0.00156072654719537
+"10454",100,1983,8,15,0.0299229927451173,11.5658085238684,18.5518922921192,13.4815951170045,0,9.08606357143001,1.50787167876665,100,1983,8,15,0.00228070182235617,0.0513479325478935,0.132849757854104,0.0578982099143785,NA,0.0209193807794457,0.00167055169775443
+"10455",100,1983,8,16,6.1962596124286,10.5731352805042,12.601232138404,11.0086798547256,0,9.78520517996545,1.53589407901281,100,1983,8,16,1.82877200238191,0.0103099458371792,0.0623111607446206,0.0568584903204862,NA,0.018554057838922,0.00178448274121048
+"10456",100,1983,8,17,11.1920791149664,3.97295932381591,12.3044444578316,9.92178210016131,0,10.1673177554769,1.56391647925897,100,1983,8,17,0.351754402138439,0.11452751837726,0.0742275335056037,0.0222812725223988,NA,0.00712169557692912,0.00190251967756351
+"10457",100,1983,8,18,5.89834979291272,3.04256324589712,12.1349064141873,8.2160176916091,0,9.74124918425917,1.59193887950513,100,1983,8,18,0.478421081799531,0.0525806992999338,0.0300953148468861,0.00647018568480761,NA,0.0212699716096129,0.00202466250681351
+"10458",100,1983,8,19,0.0174917494355649,2.92594060987005,12.936380554216,6.84289324113114,0,9.14197165460015,1.61996127975129,100,1983,8,19,0.00140350881375765,0.0516988408189123,0.113727516306006,0.0254134401622746,NA,0.0267641396042544,0.0021509112289605
+"10459",100,1983,8,20,0.354125421236057,6.6354235406756,14.9551156319932,11.4320680022371,0,10.5767025050939,1.64798367999745,100,1983,8,20,0.00257309910846733,0.205366647953478,0.0647245376966838,0.000976026410155515,NA,0.0590667135849684,0.00228126584400447
+"10460",100,1983,8,21,1.30154013515699,7.10422438547032,17.7630253530572,11.0313311326097,0,9.55263619061929,1.67600608024361,100,1983,8,21,0.011403503752593,0.0391783666778255,0.0911871419986965,0.00109122559846668,NA,0.0634386620869781,0.00241572635194542
+"10461",100,1983,8,22,3.35797578850464,8.99578647802372,12.4695159484058,12.6253795949015,0,11.2400595679467,1.70402848048977,100,1983,8,22,0.284970783462307,0.0447807140239497,0.112099984868439,0.00352104828601364,NA,0.0203474342214366,0.00255429275278335
+"10462",100,1983,8,23,4.43597358760267,8.69397135798556,17.7810013207665,12.9969197127423,0,10.4992852862193,1.73205088073592,100,1983,8,23,0.0687719269245125,0.0973426638955338,0.0896708581536235,0.0322801716989456,NA,0.0227721321392173,0.00269696504651825
+"10463",100,1983,8,24,0,8.95310228492561,17.719592999835,11.3818261258804,0,10.1411665000669,1.76007328098208,100,1983,8,24,0,0.0366912353293038,0.0243006188111447,0.0149450494220633,NA,0.0250012078932941,0.00284374323315015
+"10464",100,1983,8,25,9.66270621302891,10.354686625851,16.9218151650675,11.1231353296043,0,10.54021530025,1.78809568122824,100,1983,8,25,1.68877199892419,0.102622822120245,0.0256116626721147,0.00276198395629082,NA,0.0311683278577345,0.00299462731267902
+"10465",100,1983,8,26,0.996919692316727,5.55072601662479,14.0597798606612,10.6110670438038,0,11.1111963644785,1.8161180814744,100,1983,8,26,0.508128676780484,0.0805543764861939,0.0129555541569072,0.00463565399667464,NA,0.0173141474196773,0.00314961728510486
+"10466",100,1983,8,27,1.17348735075448,6.58401536259583,12.0805170061302,8.98090214304405,0,11.527369265378,1.84414048172056,100,1983,8,27,0.025614033289124,0.0432941563600819,0.0352485597622147,0.0138222371843762,NA,0.0109400266012462,0.00330871315042769
+"10467",100,1983,8,28,2.06534647744874,5.35456542737938,14.2947526035791,9.78424664299087,0,11.4869607858445,1.87216288196672,100,1983,8,28,0.0381286596275936,0.106673106597334,0.160898249979818,0.000956136157008969,NA,0.0146341402662418,0.00347191490864751
+"10468",100,1983,8,29,1.43157316749245,5.60685363618454,17.696468657524,11.0309020473619,0,11.1688118739146,1.90018528221288,100,1983,8,29,0.0569005914320034,0.023313449856284,0.0492619561940402,0.00535848528996116,NA,0.0134887740324177,0.0036392225597643
+"10469",100,1983,8,30,4.61023104780971,7.77110006680714,14.8697359139639,9.6591969302254,0,10.5420096691841,1.92820768245904,100,1983,8,30,0.299883069462264,0.148348509009995,0.0380918447528997,0.0079321476652471,NA,0.0139653547214387,0.00381063610377808
+"10470",100,1983,8,31,1.34147413971663,9.51321223020816,16.4663365891795,11.6060947338478,0,11.983283664509,1.9562300827052,100,1983,8,31,0.401754389973414,0.0911507905057664,0.0892485147668387,0.0014467801412718,NA,0.0327858943066927,0.00398615554068882
+"10471",100,1983,9,1,0.479427951364496,9.35094604450222,18.4298680623372,11.7135203616454,0,10.8990671257069,1.98317678883199,100,1983,9,1,0.0078362581186128,0.0550356414104292,0.0658094211023382,0.00240994860784364,NA,0.0129076275973378,0.00393194931343654
+"10472",100,1983,9,2,0,10.0616061868447,16.1996921396623,12.427095735296,0,11.9963495435151,2.01012349495878,100,1983,9,2,0,0.137200620501855,0.0206543899874475,0.025113453031922,NA,0.0402766579949981,0.0038782661723858
+"10473",100,1983,9,3,2.55599560661547,7.49921901732257,14.2899339736754,9.94259621417693,0,11.1944620540827,2.03707020108557,100,1983,9,3,0.363976615897402,0.0144479487201801,0.0305496906593474,0.00141110577060424,NA,0.00730209090831231,0.00382510611753662
+"10474",100,1983,9,4,1.68118813683098,5.01563257641262,14.4559956656562,9.81512659112744,0,11.1282905211815,2.06401690721236,100,1983,9,4,0.0160818679988051,0.0713338832654577,0.0549707838806453,0.0011953207518747,NA,0.0724574576753562,0.00377246914888899
+"10475",100,1983,9,5,11.9897688938053,7.83211215756776,14.0313421549446,11.6980967338067,0,12.6683942253028,2.09096361333915,100,1983,9,5,1.29941516675454,0.0413923856635103,0.147172028132291,0.0205579210003273,NA,0.0257580342958198,0.00372035526644292
+"10476",100,1983,9,6,16.4962595015338,8.10556666266145,16.3245432563085,12.6958635401542,0,12.7788079425484,2.11791031946594,100,1983,9,6,1.82152036432666,0.0223596695794568,0.0777777036034172,0.00450527910383329,NA,0.0388734110142487,0.00366876447019839
+"10477",100,1983,9,7,6.54136411637494,8.14049502856398,16.8001432387349,11.4008250451586,0,11.2533337462576,2.14485702559273,100,1983,9,7,0.480409353657789,0.0193801344821955,0.0596596165889093,0.000398245217040408,NA,0.0333374769486358,0.00361769676015542
+"10478",100,1983,9,8,12.282398288924,10.2609241619886,13.8344114406405,13.7695930426401,0,11.8099757146005,2.17180373171952,100,1983,9,8,10.9005258471507,0.0482339437496981,0.0407871599945633,0.00175965884482245,NA,0.0177275737482651,0.003567152136314
+"10479",100,1983,9,9,13.8485149461182,9.52982401559324,14.4200439463616,10.7843454138543,0,11.6671939255137,2.19875043784632,100,1983,9,9,0.909298387382526,0.152964864351699,0.0197093901562437,0.00246550464305462,NA,0.0773490657300521,0.00351713059867415
+"10480",100,1983,9,10,4.15016502830455,6.69436737832719,12.8804180241785,8.20141912529571,0,11.5519927533827,2.22569714397311,100,1983,9,10,0.705964924745402,0.131909990552737,0.145258534530805,0.0106917959024589,NA,0.0581721018946368,0.00346763214723583
+"10481",100,1983,9,11,4.65764568557571,6.67431240270634,11.1291198447199,7.44339929083393,0,12.4692004362817,2.2526438500999,100,1983,9,11,0.0500000052982133,0.0888263097462059,0.0826204463580235,0.00318947434901775,NA,0.142644288894796,0.00341865678199908
+"10482",100,1983,9,12,8.68844886292981,3.87411442999006,12.9158638029864,10.0019581997224,0,13.0236326439745,2.27959055622669,100,1983,9,12,3.35871337650818,0.12243684183535,0.0450877499162786,0.0378561192875588,NA,0.0180878007571002,0.00337020450296388
+"10483",100,1983,9,13,12.4952695938883,6.18639168408838,10.3839382532537,8.79019810788833,0,12.3496201036918,2.30653726235348,100,1983,9,13,0.832631483245411,0.0771707454692721,0.046037382424843,0.00460350322619138,NA,0.0738123926722521,0.00332227531013022
+"10484",100,1983,9,14,14.9441143954941,5.87408140495141,12.8710561601242,8.8563805507748,0,12.6693035269592,2.33348396848027,100,1983,9,14,3.09918140244079,0.0719374337458719,0.0562151918199843,0.00602046826018613,NA,0.0146739677063818,0.00327486920349814
+"10485",100,1983,9,15,1.3156215567859,5.27953795946077,14.8980198756303,7.44097909885402,0,13.311804911939,2.36043067460706,100,1983,9,15,1.09923978848067,0.152256158746213,0.0824941188745645,0.0617801308247324,NA,0.0356872462827949,0.00322798618306759
+"10486",100,1983,9,16,0,3.98077004420088,17.7298348555864,9.60158410402808,0,12.5346883314205,2.38737738073385,100,1983,9,16,0,0.0395152148748811,0.0798064765352706,0.0187988251157107,NA,0.0648038412134949,0.00318162624883861
+"10487",100,1983,9,17,1.24664467304322,9.40198024795918,14.7133333166309,8.17902087745625,0,13.8872262715506,2.41432408686064,100,1983,9,17,0.105438594134928,0.0194029012736685,0.0441561014432829,0.00432573431589844,NA,0.0189509837816842,0.00313578940081116
+"10488",100,1983,9,18,0.48734873595542,7.54928492870268,15.8416611437488,13.652662294008,0,13.9783453300642,2.44127079298743,100,1983,9,18,0.00473684230743102,0.0545467979308639,0.0607607410521848,0.00157309290129329,NA,0.0258091570583458,0.00309047563898528
+"10489",100,1983,9,19,1.25423541701142,10.4923761953222,17.2758305384906,13.1832232711339,0,13.4857528639487,2.46821749911422,100,1983,9,19,0.0369590622221526,0.0626485850775653,0.0173584790774616,0.0497028830501296,NA,0.0295584687146519,0.00304568496336096
+"10490",100,1983,9,20,6.61221109994567,7.60512647534361,14.1036303817111,9.68514861158281,0,14.8402735272686,2.49516420524101,100,1983,9,20,0.0670175114012806,0.259857837982649,0.0532941157313941,0.0545175344345568,NA,0.0509434336562759,0.00300141737393818
+"10491",100,1983,9,21,5.75863581102411,6.42496151709058,14.9189878868716,10.3050715747577,0,14.214239101555,2.5221109113678,100,1983,9,21,0.330994159018104,0.0673221886529026,0.107184220944325,0.00448127752834057,NA,0.026764299316053,0.00295767287071696
+"10492",100,1983,9,22,1.91529150541597,7.15849282114682,20.7447412446781,12.211595168077,0,12.5068166601994,2.54905761749459,100,1983,9,22,0.173216380947516,0.090884212994733,0.165842632262749,0.046325191299357,NA,0.0951561850532158,0.00291445145369729
+"10493",100,1983,9,23,0.85181517772811,9.62277234479277,16.0102420981043,9.94475241620155,0,14.3631195378033,2.57600432362139,100,1983,9,23,0.0247953219581072,0.130806325218778,0.021592384222586,0.0014093514716441,NA,0.0144006947465582,0.00287175312287918
+"10494",100,1983,9,24,3.54587455498766,6.91280534217591,13.4472937054104,9.197183903819,0,14.0949948686381,2.60295102974818,100,1983,9,24,0.212865500003978,0.0901543939523784,0.0575263030516041,0.0509426967933979,NA,0.0514537420845912,0.00282957787826261
+"10495",100,1983,9,25,0.101980199539425,5.30820685356233,14.033828316742,8.15666664027014,0,14.7610192965934,2.62989773587497,100,1983,9,25,0.00543859665331088,0.098671916267909,0.0463396845277484,0.00114794843358401,NA,0.0277376964362575,0.00278792571984761
+"10496",100,1983,9,26,0.709680964434632,2.22727174543836,18.1870187585241,11.264114302246,0,15.2538422754495,2.65684444200176,100,1983,9,26,0.175847946631979,0.0643386030091228,0.0306701545851807,0.0601064545192016,NA,0.0196025123436768,0.00274679664763415
+"10497",100,1983,9,27,0.0907590772600064,7.43125413091007,23.3548736047692,12.3629703731558,0,15.9481312647205,2.68379114812855,100,1983,9,27,0.00619883059409627,0.0169385985937993,0.0922036095291854,0.0788093129298304,NA,0.0170012022021127,0.00270619066162224
+"10498",100,1983,9,28,8.31045108423768,11.4760066893759,17.7010779511942,9.81829482834987,0,14.2553177148534,2.71073785425534,100,1983,9,28,0.51953217668148,0.0448041065172337,0.0420134288010319,0.0172953305607373,NA,0.0342225572970629,0.0026661077618119
+"10499",100,1983,9,29,2.04730473611221,10.0614302575392,17.4726843104766,10.8624643319511,0,16.0680693066334,2.73768456038213,100,1983,9,29,0.529415230207279,0.0192433035689439,0.0521608862852259,0.00702455614776477,NA,0.0842898640980503,0.00262654794820311
+"10500",100,1983,9,30,0.0299229927451173,7.8385699102194,17.3972827210547,13.0140153960426,0,14.7734150911016,2.76463126650892,100,1983,9,30,0.00228070182235617,0.0470485694071312,0.0407544091210147,0.0072479398382307,NA,0.0304814888360167,0.00258751122079586
+"10501",100,1983,10,1,0.013421342334207,9.37753574446876,22.0728273087471,12.7800220136989,0,15.1212075313981,2.79533157921394,100,1983,10,1,0.00140350881375765,0.243089456442275,0.0517538244550866,0.00889297595114191,NA,0.015441490077195,0.00260693148915046
+"10502",100,1983,10,2,0.614741483307777,9.93334422725262,14.0803299139042,8.4064247762934,0,15.7031979153855,2.82603189191895,100,1983,10,2,0.00777777685059467,0.0932690430022881,0.0342023250312897,0.0175210585589385,NA,0.0443168823130399,0.00262662465023107
+"10503",100,1983,10,3,0.844994492001004,4.90810779481307,13.2840815094044,6.85724976930944,0,16.8828401884728,2.85673220462397,100,1983,10,3,0.0147368399302168,0.177891823802103,0.0736211114226375,0.00180408928967505,NA,0.0710248862236638,0.00264659070403767
+"10504",100,1983,10,4,4.21661170926949,6.60245330420264,14.7104399894068,8.68281637743624,0,16.2660369616368,2.88743251732899,100,1983,10,4,0.748421133721789,0.186026333797772,0.0688426752280842,0.00311169289936877,NA,0.0203077403953125,0.00266682965057027
+"10505",100,1983,10,5,0.0605060515066709,6.70866891338487,15.1399449389366,9.40781078926145,0,14.4973039880094,2.918132830034,100,1983,10,5,0.00467836271252549,0.0205894737868465,0.120444402270938,0.00910291375284463,NA,0.0149795450864503,0.00268734148982887
+"10506",100,1983,10,6,0.00297029707396385,4.81887784492065,15.924653334455,10.5765346279501,0,15.7330451289856,2.94883314273902,100,1983,10,6,0.000994152076411666,0.0441356599975247,0.0247210322777604,0.0440988413025128,NA,0.0420501560078127,0.00270812622181347
+"10507",100,1983,10,7,0,4.06842679473827,15.6441583392119,9.34491758273117,0,15.4653439565067,2.97953345544404,100,1983,10,7,0,0.0670941524583788,0.00794970548981864,0.000599997796013418,NA,0.0277913927162147,0.00272918384652406
+"10508",100,1983,10,8,0,3.26111111730108,17.3945982799803,10.3711110346913,0,16.8859243343019,3.01023376814905,100,1983,10,8,0,0.0471023470504994,0.0293251179837437,0.00555788279760566,NA,0.213807272501053,0.00275051436396066
+"10509",100,1983,10,9,0,6.0388778848092,20.5068099165645,12.6413092628957,0,15.9020071570155,3.04093408085407,100,1983,10,9,0,0.0832701892228369,0.0218759905901979,0.00691169691702255,NA,0.0770874542956292,0.00277211777412325
+"10510",100,1983,10,10,0,8.8527722468864,15.34099006443,11.1446094964073,0,16.8936281089517,3.07163439355909,100,1983,10,10,0,0.136857879230562,0.0524538021780096,0.0132766044193647,NA,0.0252678166582968,0.00279399407701186
+"10511",100,1983,10,11,0,5.42707374413284,19.0385918528071,12.5974805798336,0,17.1778386468978,3.1023347062641,100,1983,10,11,0,0.0577953378331862,0.0255619617174909,0.011849701202185,NA,0.0483597978746909,0.00281614327262645
+"10512",100,1983,10,12,0.512541261771069,9.30409241142315,21.264488371292,16.9591643057509,0,17.1098738644995,3.13303501896912,100,1983,10,12,0.0591228052764609,0.0193988232462746,0.111887600253172,0.0585445282831962,NA,0.0563980117467312,0.00283856536096704
+"10513",100,1983,10,13,2.26479646960239,12.6035093490047,21.3226732646409,17.5397251452288,0,17.3474940204877,3.16373533167414,100,1983,10,13,0.124444446828634,0.011905255898907,0.0361941974531884,0.022415287798831,NA,0.0284535112552796,0.00286126034203363
+"10514",100,1983,10,14,5.53377335876784,12.5010450938092,19.8663918544488,14.5596699573026,0,19.9639966759439,3.19443564437915,100,1983,10,14,0.275380143003871,0.0191719242654381,0.136544462161802,0.0212655035298582,NA,0.0448999605102969,0.00288422821582622
+"10515",100,1983,10,15,13.2994497800686,11.0153575955969,13.1188339625779,12.2419362587504,0,16.3951004540654,3.22513595708417,100,1983,10,15,8.84005805623474,0.133059711271219,0.0149152159105866,0.00347075330732319,NA,0.0718679352916904,0.00290746898234481
+"10516",100,1983,10,16,11.2737073898315,10.1570627371995,13.6718261380925,10.7963036620053,0,18.7320598157167,3.25583626978919,100,1983,10,16,1.12701746611579,0.0333701918485446,0.0183701844310857,0.0128450251239782,NA,0.132529742523434,0.00293098264158939
+"10517",100,1983,10,17,1.32035203702641,8.85427946567011,15.2653135314371,12.189207753726,0,19.3571685451549,3.2865365824942,100,1983,10,17,0.109824558384935,0.0972052629950822,0.0362496996138478,0.000600003094208319,NA,0.064814379899403,0.00295476919355998
+"10518",100,1983,10,18,1.25643563388598,7.57275020223771,17.0255776962431,10.8233442768143,0,19.0429078423338,3.31723689519922,100,1983,10,18,0.00707601784265731,0.0569578775896907,0.0164778009204479,0.0150005850452241,NA,0.0450496214481247,0.00297882863825657
+"10519",100,1983,10,19,4.74576452253151,7.38777781162325,14.567755894299,9.3315181805618,0,19.6921585980272,3.34793720790424,100,1983,10,19,0.16695903337491,0.00971168574843214,0.0486356792718429,0.0119374416932175,NA,0.0854374496028514,0.00300316097567916
+"10520",100,1983,10,20,3.31243125230435,8.10152917333169,15.4865897045408,12.0450055979528,0,18.5571137742765,3.37863752060925,100,1983,10,20,0.150233918658479,0.143344403097416,0.086643199218024,0.000387718809774897,NA,0.0774725211459856,0.00302776620582773
+"10521",100,1983,10,21,3.78481848693655,9.84113295715634,17.518404722476,13.232772244872,0,18.4484374221346,3.40933783331427,100,1983,10,21,0.175380087372862,0.0936918020594946,0.0173023772036494,0.0328876928765274,NA,0.132315028544623,0.00305264432870232
+"10522",100,1983,10,22,0.00066006601643641,8.16844888934732,22.7776237735392,13.0345765167337,0,17.9076164246305,3.44003814601929,100,1983,10,22,0.000526315805159117,0.118135713074261,0.0787608433017303,0.0411508870443517,NA,0.0212963080820496,0.00307779534430291
+"10523",100,1983,10,23,0.11034103574762,11.6937952692097,16.3598458297444,12.9126292376628,0,17.6237796359555,3.4707384587243,100,1983,10,23,0.000994152076411666,0.0903578927715436,0.012392997969509,0.00213391191144778,NA,0.0542044622713966,0.00310321925262949
+"10524",100,1983,10,24,0.145874589632447,10.6663366145689,22.9633554116596,14.5497360313424,0,18.9065235998279,3.50143877142932,100,1983,10,24,0.00257309949188902,0.173546824572462,0.105698219130004,0.00264503342485071,NA,0.145625416638008,0.00312891605368208
+"10525",100,1983,10,25,0.187348738468454,9.73774473737962,16.9256654661743,11.3103960454792,0,17.9932818335781,3.53213908413434,100,1983,10,25,0.00649122857733778,0.0207801177577749,0.0185029020702993,0.018898207616618,NA,0.0350076853659455,0.00315488574746065
+"10526",100,1983,10,26,0,4.85906498345605,23.8473707142443,12.9167327419235,0,17.6715716572769,3.56283939683935,100,1983,10,26,0,0.0758093571334008,0.0509397809930082,0.00799122679865981,NA,0.0768815857861755,0.00318112833396523
+"10527",100,1983,10,27,0.0222222225533591,12.8661825643777,17.6372826264636,12.1640154573128,0,20.1178450330368,3.59353970954437,100,1983,10,27,0.00175438601719706,0.0345766073773897,0.0503584326986145,0.0119918240687248,NA,0.0371738773190873,0.00320764381319581
+"10528",100,1983,10,28,1.2306930716544,8.71372922471385,15.2896701101423,9.4542352814879,0,20.0354330070819,3.62424002224939,100,1983,10,28,0.100994146769508,0.111576535299703,0.025678367860192,0.0249690123486161,NA,0.109950989328783,0.0032344321851524
+"10529",100,1983,10,29,0.933223320312626,5.48260735626137,16.7134986096876,11.1491747888664,0,20.27930022219,3.6549403349544,100,1983,10,29,0.0287719351088099,0.0896023380157046,0.00749413605210693,0.0154923853445522,NA,0.0685217889497587,0.00326149344983497
+"10530",100,1983,10,30,0.86633663694195,5.94818481124274,17.5528383727121,11.3205390904996,0,18.8213629069869,3.68564064765942,100,1983,10,30,0.0521052648588935,0.0537005771514497,0.00856725748537577,0.00249530854036802,NA,0.0585188608751549,0.00328882760724355
+"10531",100,1983,10,31,0.230143018230812,6.3970517435483,18.5504178234977,12.152387142706,0,19.4828969484941,3.71634096036444,100,1983,10,31,0.0213450293486927,0.0411210333718386,0.012759053097823,0.00654444520744603,NA,0.0234263849054395,0.00331643465737812
+"10532",100,1983,11,1,0,9.90536849543338,16.5847192120106,12.003597320372,0,18.1163154168172,3.75101160019498,100,1983,11,1,0,0.173725633010041,0.0185561787702522,0.00368949456030579,NA,0.0418351476770849,0.00324868538688504
+"10533",100,1983,11,2,0.719141914506163,9.54331116734034,19.1777335188963,11.2290429177195,0,17.1523567673679,3.78568224002553,100,1983,11,2,0.00584795293752235,0.0615842008959568,0.0368041261819641,0.0371473282976689,NA,0.0295391693227099,0.00318187293703581
+"10534",100,1983,11,3,0.175577561003212,9.50746981827471,16.2193511358582,8.6201760068585,0,17.3677866578334,3.82035287985608,100,1983,11,3,0.00543859701930433,0.00595612879531696,0.048142649709943,0.00653684865503971,NA,0.0845708601220506,0.00311599730783047
+"10535",100,1983,11,4,8.59812975192096,6.59103409501717,14.1597140525172,8.82682070086891,0,18.8090934361348,3.85502351968662,100,1983,11,4,0.0922221787771401,0.448295319534896,0.0350145938940713,0.00437016534644435,NA,0.028487930049348,0.00305105849926899
+"10536",100,1983,11,5,0.0809680980161996,7.48839381294544,16.8964026359835,8.94853680781668,0,18.7151212207415,3.88969415951717,100,1983,11,5,0.0020467836867299,0.117736249413973,0.0145152307837815,0.00189240074388707,NA,0.0146640883765506,0.00298705651135139
+"10537",100,1983,11,6,0,7.22427945194727,23.0471724666516,12.9933112819072,0,20.4580579144427,3.92436479934771,100,1983,11,6,0,0.033944431622844,0.137950958399473,0.00615440445401166,NA,0.0778424940028078,0.00292399134407768
+"10538",100,1983,11,7,0.100000001490116,9.84906478499946,19.8912870094459,13.6718701613356,0,20.1155305132962,3.95903543917826,100,1983,11,7,0,0.0125830577873296,0.0581134884128989,0.0181730979362403,NA,0.032063485915465,0.00286186299744783
+"10539",100,1983,11,8,4.70902095612126,12.7296040013547,16.6182071068893,12.4460286160376,0,19.2387587513133,3.99370607900881,100,1983,11,8,1.72374275282811,0.0622174820750231,0.0347432337670004,0.0452578618702925,NA,0.0556834718558233,0.00280067147146186
+"10540",100,1983,11,9,21.13740370848,8.26372933361528,16.0567216946609,12.0306051126277,0,22.5149304257653,4.02837671883935,100,1983,11,9,0.645614236976958,0.2789923278062,0.0387286583439945,0.0052210550543164,NA,0.123117737729317,0.00274041676611975
+"10541",100,1983,11,10,1.39416941085664,7.82305839956135,14.9133884033354,9.60863580903073,0,22.6242720108446,4.0630473586699,100,1983,11,10,0.0604093491403698,0.166720429112676,0.0300760348471169,0.0306988081347193,NA,0.0437831970280865,0.00268109888142154
+"10542",100,1983,11,11,0,5.1418372155285,19.232783146555,10.5698461091951,0,22.1976701354767,4.09771799850044,100,1983,11,11,0,0.0487146241751492,0.1190430210273,0.121127507545584,NA,0.0502927876397874,0.00262271781736718
+"10543",100,1983,11,12,0,8.81794274207389,16.5816944193656,11.7081518131252,0,21.4775190116753,4.13238863833099,100,1983,11,12,0,0.010000566975604,0.02059175975898,0.0155035117434301,NA,0.0342006519014201,0.00256527357395672
+"10544",100,1983,11,13,0.013421342334207,5.21785475423496,19.6338722850099,10.9730473351557,0,21.0321182722315,4.16705927816154,100,1983,11,13,0.00140350881375765,0.0327654909647221,0.0141912480453171,0.0873286749010093,NA,0.0718129734907854,0.00250876615119012
+"10545",100,1983,11,14,0.902970294911738,11.2292960138604,26.3962375989186,15.6429153574575,0,20.571842699798,4.20172991799208,100,1983,11,14,0.11035086917947,0.0601274598730387,0.0245812547786343,0.0308672257429724,NA,0.112694341403832,0.00245319554906739
+"10546",100,1983,11,15,5.06105613315066,9.90036291179091,14.0942904049545,9.69937297875601,0,23.2030661805291,4.23640055782263,100,1983,11,15,0.372456179362309,0.199736876236843,0.0507210140296103,0.00464327921512534,NA,0.137851747306798,0.00239856176758855
+"10547",100,1983,11,16,13.8001100678648,7.1983718662241,11.9860396442896,7.46071503684334,0,24.7295905096006,4.27107119765317,100,1983,11,16,3.91432741678256,0.0918917986328973,0.0485133964650646,0.00823683701902176,NA,0.0371772754590769,0.00234486480675357
+"10548",100,1983,11,17,7.20484046600308,5.08127608000249,14.2339162742606,9.04718377015772,0,24.148955329114,4.30574183748372,100,1983,11,17,0.172046794277908,0.181503520934359,0.0309730939341545,0.0288778090583712,NA,0.0233373615130368,0.00229210466656248
+"10549",100,1983,11,18,2.40396039386262,7.71277226342095,15.8134654142676,9.37341034766471,0,21.3208002045108,4.34041247731427,100,1983,11,18,0.353625752382114,0.0428924294823425,0.0153912177594752,0.00745906234213138,NA,0.0266730473303951,0.00224028134701526
+"10550",100,1983,11,19,0.0278327836930686,3.75074808484781,16.058921850685,9.864290539581,0,21.1781073595198,4.37508311714481,100,1983,11,19,0.0020467836867299,0.0464093561005217,0.0595163337821435,0.0204146417071948,NA,0.0632126673721471,0.00218939484811192
+"10551",100,1983,11,20,0,6.30654563263829,17.1243014466776,10.9205719734838,0,21.419772621482,4.40975375697536,100,1983,11,20,0,0.0394719279596164,0.0201146101836877,0.00105848269997923,NA,0.0401605432938883,0.00213944516985244
+"10552",100,1983,11,21,0,7.65184821704827,25.5993178087493,13.7378658246417,0,20.8936445212076,4.44442439680591,100,1983,11,21,0,0.0330988228535998,0.0140871599746602,0.0114929790787707,NA,0.089694491898913,0.00209043231223684
+"10553",100,1983,11,22,0.919031911303174,13.4950824607455,22.2511221253046,15.9758194649574,0,21.7678728646009,4.47909503663645,100,1983,11,22,0.272456130279958,0.0122719397963627,0.0231590794393168,0.0865654686310405,NA,0.0504666338871883,0.00204235627526511
+"10554",100,1983,11,23,0.00154015403835162,12.7027612786875,19.6796260032192,14.9911440795798,0,23.7796538643505,4.513765676467,100,1983,11,23,0.000526315805159117,0.137328612047682,0.365309129958352,0.0103877062614076,NA,0.0335053760340412,0.00199521705893728
+"10555",100,1983,11,24,0.155115514288772,10.977139698528,24.056644802702,15.6266665726212,0,21.6484417658022,4.54843631629754,100,1983,11,24,0.0287719313791621,0.0110163863289033,0.174995109161325,0.032178314443281,NA,0.0768085766762192,0.00194901466325329
+"10556",100,1983,11,25,0.682728271804365,13.097986675594,22.7659624999899,17.2619582701831,0,21.806470596292,4.58310695612809,100,1983,11,25,0.0198245591448067,0.0259543882158508,0.0525689249052501,0.0526251016228811,NA,0.0254827307498443,0.00190374908821319
+"10557",100,1983,11,26,0.232123216886659,11.5547195021207,22.1546864903013,16.695544665665,0,23.5918701750367,4.61777759595864,100,1983,11,26,0.0355555571946833,0.0112093425952257,0.0212134483964712,0.0163894886578068,NA,0.0814231642743358,0.00185942033381697
+"10558",100,1983,11,27,0.680308042251893,13.6225193147481,23.6245874335664,17.9743671690014,0,22.9315796857435,4.65244823578918,100,1983,11,27,0.073859649485315,0.0809035527525509,0.0505982359682944,0.164269585093109,NA,0.0950286898050954,0.00181602840006462
+"10559",100,1983,11,28,6.00385029323817,14.6837954641831,26.0473377597083,17.3344334685239,0,23.8526174492598,4.68711887561973,100,1983,11,28,3.74052592004254,0.0552731037742989,0.149578280028744,0.0534374723782796,NA,0.020313092640702,0.00177357328695615
+"10560",100,1983,11,29,1.59790981140467,15.5412651435508,28.6417493557904,17.3758415415211,0,23.7177808363717,4.72178951545027,100,1983,11,29,2.47982459123197,0.0226245297951225,0.11638020456354,0.00173566808896592,NA,0.0339837168510944,0.00173205499449154
+"10561",100,1983,11,30,3.53454342350052,14.5875137505358,16.8188557074015,12.0912429717245,0,23.5706844535542,4.75646015528082,100,1983,11,30,0.394268982898428,0.0466163722457279,0.0112877475719508,0.0244245326639695,NA,0.140510444714971,0.00169147352267082
+"10562",100,1983,12,1,0.620132016565147,9.38381738421416,18.1423982970654,11.0850163890977,0,24.4642341768072,4.80125739646431,100,1983,12,1,0.0111111097865637,0.145517635590939,0.00992281363444057,0.0198590937364087,NA,0.0801482823874876,0.001687008980556
+"10563",100,1983,12,2,0,6.37595158284253,19.460703989329,11.9527172638376,0,22.9458659666431,4.8460546376478,100,1983,12,2,0,0.0398538026754137,0.0983801686251519,0.0215052593946053,NA,0.239079508596407,0.0016864926595813
+"10564",100,1983,12,3,0,10.8417492836091,17.278503634093,10.2573927615998,0,22.5366379903319,4.89085187883129,100,1983,12,3,0,0.18903571079998,0.0244584714748792,0.0241509075901924,NA,0.0797721152471967,0.00168992455974675
+"10565",100,1983,12,4,0,11.1547415820417,19.0976347341002,10.9425852222673,0,24.2819416690469,4.93564912001478,100,1983,12,4,0,0.155571967605735,0.0162315340457583,0.00631638015173535,NA,0.140476192872302,0.00169730468105231
+"10566",100,1983,12,5,0.169526955434526,6.48841591059703,22.4440484650195,12.1148956149849,0,23.5008902931933,4.98044636119827,100,1983,12,5,0.0294152061214224,0.0241918174464995,0.164233366055323,0.0794028740019074,NA,0.0570818064680507,0.00170863302349799
+"10567",100,1983,12,6,7.62794286006092,11.9779538090604,18.5125742808427,12.4387457210763,0,21.8704143784494,5.02524360238177,100,1983,12,6,1.52257318881525,0.0790449984634607,0.0338701435738537,0.0023450224247086,NA,0.0593900793951239,0.00172390958708381
+"10568",100,1983,12,7,1.316061602305,10.8549834241961,17.2281739305217,10.9670188345663,0,23.3618351826918,5.07004084356526,100,1983,12,7,0.212865501049668,0.123170183585601,0.0178561719351325,0.0569953171067102,NA,0.0162162748572307,0.00174313437180976
+"10569",100,1983,12,8,2.0876787629458,10.4014961381163,18.7527282555373,11.0444554925883,0,23.6694910694819,5.11483808474875,100,1983,12,8,0.167836262677848,0.157714685254975,0.00972223069964042,0.0337590348495372,NA,0.129279162502586,0.00176630737767584
+"10570",100,1983,12,9,0.0737073718353991,6.81552253697965,20.241034068159,12.9058967114019,0,22.7876759391442,5.15963532593224,100,1983,12,9,0.00450292411080578,0.0441140315351558,0.0185871582792683,0.130258577306758,NA,0.165676122989782,0.00179342860468205
+"10571",100,1983,12,10,0.0721672177970475,8.14590755154197,21.3470296513523,14.3886250148643,0,24.238541578321,5.20443256711573,100,1983,12,10,0.0020467836867299,0.0278315728494948,0.0239842533717618,0.0151701535030324,NA,0.0396186041760018,0.00182449805282838
+"10572",100,1983,12,11,0,10.6350494632364,26.0883166687717,15.6363476878083,0,25.4006887508904,5.24922980829922,100,1983,12,11,0,0.0272590648428696,0.0512064539549601,0.162295342607277,NA,0.151377861171867,0.00185951572211483
+"10573",100,1983,12,12,0.0583058314518829,15.1219582793736,25.1151926840099,14.982398254512,0,22.9833091473973,5.29402704948271,100,1983,12,12,0.00257309949188902,0.00777600005533432,0.0399918042370573,0.0245584350609081,NA,0.198069297236708,0.00189848161254143
+"10574",100,1983,12,13,0,10.8135203255547,20.3185809421854,11.5601870617588,0,20.9416920464687,5.3388242906662,100,1983,12,13,0,0.182125784690013,0.0234666812475606,0.0520163440437367,NA,0.0336844790256603,0.00194139572410816
+"10575",100,1983,12,14,0,7.36928491518967,26.7452367209759,15.034026316946,0,22.225815602627,5.38362153184969,100,1983,12,14,0,0.0489953250037466,0.043403454429454,0.0598871518119076,NA,0.127330457430969,0.001988258056815
+"10576",100,1983,12,15,0,11.3164686417029,20.007590735575,12.0091860060907,0,25.277219288541,5.42841877303318,100,1983,12,15,0,0.163647464221346,0.0252983068948149,0.0252497126195192,NA,0.046443944515856,0.00203906861066198
+"10577",100,1983,12,16,0.226292634864386,12.4766556820591,19.3802310410637,15.1906710510338,0,25.8820264441015,5.47321601421667,100,1983,12,16,0.00315789519694815,0.185848469440928,0.0307051350469356,0.0140695723685198,NA,0.128649841255626,0.00209382738564909
+"10578",100,1983,12,17,0.280968107029025,12.8959405802526,19.3179756894757,14.9212762016406,0,23.8749045496677,5.51801325540016,100,1983,12,17,0.00175438627862095,0.154949722310172,0.0311982828421201,0.0067543747551067,NA,0.0626358447336705,0.00215253438177632
+"10579",100,1983,12,18,0,9.88910884038843,28.0431355013706,16.0522550503151,0,23.9247434878713,5.56281049658365,100,1983,12,18,0,0.0230023187872022,0.00924973661210715,0.171828491561348,NA,0.0799765227895359,0.00221518959904368
+"10580",100,1983,12,19,0.197579762698597,13.7276458488439,36.9346645132806,12.633476398959,0,21.2363499027613,5.60760773776715,100,1983,12,19,0.0136257316790826,0.0333175751491751,0.0692509533274934,0.150559648752581,NA,0.0392550281828534,0.00228179303745117
+"10581",100,1983,12,20,0,20.840879822197,39.5498577113723,9.4518702992774,0,23.7726622262736,5.65240497895064,100,1983,12,20,0,0.00750058337233209,0.0436485792110757,0.081183034524401,NA,0.0224309732559426,0.0023523446969988
+"10582",100,1983,12,21,0,13.2573707798789,21.8827174599021,12.5167875006647,0,22.0241265895673,5.69720222013413,100,1983,12,21,0,0.0106912314923323,0.037331664709221,0.0186807215596348,NA,0.0632093073326093,0.00242684457768655
+"10583",100,1983,12,22,0,7.60852578606936,22.7807703511287,13.2002751609542,0,22.7499533638933,5.74199946131762,100,1983,12,22,0,0.0251666647593339,0.0644204998666096,0.0169929855704284,NA,0.0645818976377994,0.00250529267951442
+"10584",100,1983,12,23,0,14.20409242758,24.3477998026396,13.3482947417743,0,23.8394889921263,5.78679670250111,100,1983,12,23,0,0.219903469386965,0.172615140350804,0.0195818783052702,NA,0.0387899377047811,0.00258768900248243
+"10585",100,1983,12,24,0,12.9824973715938,27.9311111227776,17.8669857323104,0,26.7952263340267,5.8315939436846,100,1983,12,24,0,0.0365374200364226,0.0780591490362645,0.131313521348661,NA,0.0397091796373021,0.00267403354659056
+"10586",100,1983,12,25,0.00583058314518829,15.9865787713835,21.791914093219,15.3646645089581,0,26.7710891212228,5.87639118486809,100,1983,12,25,0.00140350881375765,0.0393783309939528,0.0397246572129591,0.0151871188649908,NA,0.0288734946089749,0.00276432631183883
+"10587",100,1983,12,26,0,12.5120681745909,20.280231167381,11.0991638223461,0,24.9924225073873,5.92118842605158,100,1983,12,26,0,0.223687688450607,0.016116329894026,0.00847602774866195,NA,0.174040296218994,0.0028585672982272
+"10588",100,1983,12,27,0,10.3284708952615,20.1909350446611,10.9566225947851,0,24.3474257686674,5.96598566723507,100,1983,12,27,0,0.228976594188839,0.0374917966522238,0.0157338941697279,NA,0.0370867220117061,0.00295675650575574
+"10589",100,1983,12,28,0,9.4490429707224,29.2870627173496,14.4878107339504,0,25.0784154358314,6.01078290841856,100,1983,12,28,0,0.0527245643772499,0.0309765980149124,0.0346397826636013,NA,0.0564464351202811,0.00305889393442438
+"10590",100,1983,12,29,0,13.1881078934119,33.786424538221,10.6266447052573,0,24.6873741235462,6.05558014960205,100,1983,12,29,0,0.0229689841511178,0.0765398002057901,0.0145315815976542,NA,0.0159888769120001,0.00316497958423316
+"10591",100,1983,12,30,0.00154015403835162,17.3943454251431,28.3548296098531,17.8283385475083,0,26.9760919811286,6.10037739078554,100,1983,12,30,0.000526315805159117,0.0313648845252116,0.0993397684289544,0.0347222116261515,NA,0.0309081093829142,0.00327501345518207
+"10592",100,1983,12,31,0.122222224043475,17.5121231519743,27.4074698554145,19.9178986544132,0,25.6653309723101,6.14517463196904,100,1983,12,31,0.00175438601719706,0.0597800657353793,0.00794445631262858,0.00881641088154426,NA,0.0501234370470788,0.0033889955472711
+"10593",100,1984,1,1,3.45445547455346,17.7362377347201,27.259361940642,19.4495488660957,0,21.4933723095882,6.14819390008573,100,1984,1,1,1.04023398290602,0.076392969705134,0.128113926112582,0.0985361524931064,NA,0.113665062200468,0.00336405671770494
+"10594",100,1984,1,2,1.77524751588719,15.2668867509882,23.5621894022288,18.1727281199037,0,24.9312152524199,6.15121316820242,100,1984,1,2,0.39356723256976,0.00597074343592172,0.0297333719892181,0.0291064156027933,NA,0.116269296888222,0.00334147665448505
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+"11249",100,1985,10,18,4.27062703490388,10.3839383802005,15.0921232697725,8.16579755986615,0,19.0429078423338,3.48530871559929,100,1985,10,18,0.259239773694551,0.0973813006351717,0.0178982653501487,0.0237842163867605,NA,0.0450496214481247,0.00295509393216159
+"11250",100,1985,10,19,1.11430145398487,6.34237624578612,14.4528823649005,8.56940590928752,0,19.6921585980272,3.51883575945588,100,1985,10,19,0.00315789394211389,0.260228707460791,0.0194333508386628,0.000538594376471151,NA,0.0854374496028514,0.00295025312555089
+"11251",100,1985,10,20,0.601100133459429,5.85792087590603,15.1125851516808,8.74062712620063,0,18.5571137742765,3.55236280331247,100,1985,10,20,0.000994151335710798,0.101088868268341,0.0230812683752904,0.0122596404070576,NA,0.0774725211459856,0.00294551402383201
+"11252",100,1985,10,21,0.344664471992368,2.73023098215412,20.5088340855799,9.71727177228602,0,18.4484374221346,3.58588984716906,100,1985,10,21,0.0198830408659595,0.0272543777750286,0.0320263117295784,0.00948188889717066,NA,0.132315028544623,0.00294087662700498
+"11253",100,1985,10,22,0.112541255802408,10.9081078394018,19.7104839622909,13.4002529370903,0,17.9076164246305,3.61941689102566,100,1985,10,22,0.00497076038205833,0.179627495822638,0.00842224748433113,0.140736790283477,NA,0.0212963080820496,0.00293634093506978
+"11254",100,1985,10,23,12.5550054869111,10.852365132868,13.7002309446681,12.7513641120315,0,17.6237796359555,3.65294393488225,100,1985,10,23,12.6771924905611,0.0724537576174341,0.0870783703207936,0.00606197373021962,NA,0.0542044622713966,0.00293190694802642
+"11255",100,1985,10,24,6.19141912355412,10.0174367608804,15.3232344049301,10.8389988795365,0,18.9065235998279,3.68647097873884,100,1985,10,24,0.178011659209512,0.0751987945044441,0.0685766689591129,0.00560234656538676,NA,0.145625416638008,0.00292757466587487
+"11256",100,1985,10,25,1.31309130674738,9.51357526642786,17.3060393506544,10.7621123037978,0,17.9932818335781,3.71999802259543,100,1985,10,25,1.64888891749912,0.204111603097857,0.306069453667004,0.00128771673068742,NA,0.0350076853659455,0.00292334408861517
+"11257",100,1985,10,26,0,7.25107815504336,19.670495017527,13.9531243789052,0,17.6715716572769,3.75352506645202,100,1985,10,26,0,0.0433579029440395,0.242502969954112,0.0366450501113571,NA,0.0768815857861755,0.0029192152162473
+"11258",100,1985,10,27,0,8.43235434478659,23.0454673620209,12.5253684282041,0,20.1178450330368,3.78705211030861,100,1985,10,27,0,0.0824812830719326,0.0422695889079393,0.0279222097609584,NA,0.0371738773190873,0.00291518804877126
+"11259",100,1985,10,28,0,10.1413970145718,24.0894827910907,13.6628932690594,0,20.0354330070819,3.8205791541652,100,1985,10,28,0,0.0288210760173245,0.0397449848869921,0.00857074951940302,NA,0.109950989328783,0.00291126258618706
+"11260",100,1985,10,29,0,13.0839384055898,23.13889984198,14.2584600343694,0,20.27930022219,3.8541061980218,100,1985,10,29,0,0.0752263128761115,0.00881747857739244,0.0214099178337383,NA,0.0685217889497587,0.00290743882849468
+"11261",100,1985,10,30,7.54774478822127,8.02863601541886,14.9826182584689,10.4962706161935,0,18.8213629069869,3.88763324187839,100,1985,10,30,0.140409375575571,0.0587041514469539,0.0552175384812243,0.00214796526512539,NA,0.0585188608751549,0.00290371677569414
+"11262",100,1985,10,31,3.58162815054127,6.00405940652812,16.7937294547707,10.2348074603527,0,19.4828969484941,3.92116028573498,100,1985,10,31,0.0309356613326542,0.141757903709213,0.0176262587745611,0.00238129109846085,NA,0.0234263849054395,0.00290009642778543
+"11263",100,1985,11,1,0.0473047311779427,4.79924091227902,18.2528163293014,12.4243563776887,0,18.1163154168172,3.95081624007224,100,1985,11,1,0.00368421063611382,0.0243163750988885,0.0230473747391156,0.00665906933352223,NA,0.0418351476770849,0.00285875804708856
+"11264",100,1985,11,2,0,7.25902091551929,17.4754672895039,10.1179318800487,0,17.1523567673679,3.9804721944095,100,1985,11,2,0,0.0251649184634325,0.0467702592596061,0.005733300145576,NA,0.0295391693227099,0.00281796477251994
+"11265",100,1985,11,3,0,5.62064899784504,19.775819675626,12.9140153996097,0,17.3677866578334,4.01012814874677,100,1985,11,3,0,0.0274988386935087,0.080160668468247,0.0843496795632586,NA,0.0845708601220506,0.00277771660407962
+"11266",100,1985,11,4,0,8.62526934742272,21.6667654606113,18.2806048755205,0,18.8090934361348,4.03978410308403,100,1985,11,4,0,0.0142543587490226,0.167680677121927,0.138828677421994,NA,0.028487930049348,0.0027380135417676
+"11267",100,1985,11,5,9.71210111905508,14.8757316171795,19.1794170383835,19.0439493559112,0,18.7151212207415,4.06944005742129,100,1985,11,5,0.633333290947876,0.0108806969515251,0.0241888454017568,0.135527311095581,NA,0.0146640883765506,0.00269885558558384
+"11268",100,1985,11,6,40.7045109898868,14.5781738713498,16.2349508996844,15.5592189113168,0,20.4580579144427,4.09909601175855,100,1985,11,6,10.6891800596013,0.0567363136448785,0.0324146876957127,0.0117538022583091,NA,0.0778424940028078,0.00266024273552837
+"11269",100,1985,11,7,0.234983501884148,12.6376458232028,15.4675358540416,13.7908250097394,0,20.1155305132962,4.12875196609581,100,1985,11,7,0.0516374293370554,0.134847426127938,0.0461432715299728,0.0164818535163967,NA,0.032063485915465,0.0026221749916012
+"11270",100,1985,11,8,0.393729380353449,13.1237623300752,19.8818372195572,16.6087128352804,0,19.2387587513133,4.15840792043308,100,1985,11,8,0.00830409325702849,0.11480528770588,0.0393426768122274,0.00671754228500908,NA,0.0556834718558233,0.00258465235380229
+"11271",100,1985,11,9,0.172717275956545,13.1307480804729,23.4461938104745,16.0391747660369,0,22.5149304257653,4.18806387477034,100,1985,11,9,0.00923976682780084,0.151933316930234,0.0111760656259667,0.067509303614044,NA,0.123117737729317,0.00254767482213167
+"11272",100,1985,11,10,0.164356438691008,13.4889989282169,24.6030145021948,15.4054125711338,0,22.6242720108446,4.2177198291076,100,1985,11,10,0.00508771975050894,0.0927917541125388,0.0655251344337531,0.00263858610459896,NA,0.0437831970280865,0.00251124239658935
+"11273",100,1985,11,11,0.328712881496637,12.5765675491232,18.5566996394998,12.7930362877673,0,22.1976701354767,4.24737578344486,100,1985,11,11,0.00245614005808246,0.093302336089458,0.0598871558832739,0.0204655046653637,NA,0.0502927876397874,0.00247535507717529
+"11274",100,1985,11,12,0,11.8820902317664,23.4828820936751,16.411892095677,0,21.4775190116753,4.27703173778213,100,1985,11,12,0,0.0939356361188657,0.0296807137965728,0.071195258456494,NA,0.0342006519014201,0.00244001286388952
+"11275",100,1985,11,13,0,13.2116061169716,28.7353357224837,16.1024092883036,0,21.0321182722315,4.30668769211939,100,1985,11,13,0,0.087044918009099,0.0278023600485593,0.090027603867873,NA,0.0718129734907854,0.00240521575673205
+"11276",100,1985,11,14,5.8849284850856,15.0107590934493,17.2133225104203,11.4328271888926,0,20.571842699798,4.33634364645665,100,1985,11,14,1.0211111132304,0.00663217342898542,0.0901487366618578,0.0013204648158104,NA,0.112694341403832,0.00237096375570284
+"11277",100,1985,11,15,5.40054996100196,7.79086911638971,16.4440486098018,9.99607261913707,0,23.2030661805291,4.36599960079391,100,1985,11,15,1.84029239303189,0.127535715557152,0.0372332639061297,0.0106871380165379,NA,0.137851747306798,0.00233725686080191
+"11278",100,1985,11,16,0,7.70466446640468,19.5062706950474,9.67774472378268,0,24.7295905096006,4.39565555513117,100,1985,11,16,0,0.0392941408726451,0.0311134351187315,0.0157701660781858,NA,0.0371772754590769,0.00230409507202929
+"11279",100,1985,11,17,5.56721672523927,10.0499779068597,15.397645754127,11.0373817481617,0,24.148955329114,4.42531150946844,100,1985,11,17,0.279181289170939,0.145848576464338,0.0324053044414807,0.00109180512241671,NA,0.0233373615130368,0.00227147838938494
+"11280",100,1985,11,18,1.02046204688657,9.23269528576774,14.9316831332753,9.06264022894294,0,21.3208002045108,4.4549674638057,100,1985,11,18,0.0977777767181403,0.230266717020763,0.0344731237601986,0.0122982429594545,NA,0.0266730473303951,0.00223940681286888
+"11281",100,1985,11,19,0.0863586371504303,9.21431251563648,16.3377228853333,10.7460726107439,0,21.1781073595198,4.48462341814296,100,1985,11,19,0.00175438601719706,0.37505081309943,0.0120497458836177,0.000569595691681118,NA,0.0632126673721471,0.00220788034248109
+"11282",100,1985,11,20,0.0136413643396858,8.174488515088,16.8867213707684,10.6398790273467,0,21.419772621482,4.51427937248022,100,1985,11,20,0.00175438601719706,0.0299397787730719,0.0130719359283894,0.0106175325316261,NA,0.0401605432938883,0.00217689897822158
+"11283",100,1985,11,21,1.10330035125199,8.19512642229875,14.7760835129305,9.95204622228809,0,20.8936445212076,4.54393532681748,100,1985,11,21,0.0109356754286256,0.0573041068518123,0.0445175131302283,0.000920468924991917,NA,0.089694491898913,0.00214646272009037
+"11284",100,1985,11,22,0.584378442060043,6.51584164039268,18.9133442940623,10.5758527717968,0,21.7678728646009,4.57359128115475,100,1985,11,22,0.0592397663746663,0.0175719330614886,0.113781306498051,0.00381518390507244,NA,0.0504666338871883,0.00211657156808744
+"11285",100,1985,11,23,0.181738177713978,7.37002193547449,24.9810890705541,12.151507066028,0,23.7796538643505,4.60324723549201,100,1985,11,23,0.0181871354109363,0.0271924001995076,0.137947237985449,0.0264479381294435,NA,0.0335053760340412,0.00208722552221279
+"11286",100,1985,11,24,0.369416946477622,13.2760396837795,24.3053574326015,14.0806160172482,0,21.6484417658022,4.63290318982927,100,1985,11,24,0.0370760235382103,0.0150655101687606,0.0344508446947306,0.0431841595856937,NA,0.0768085766762192,0.00205842458246642
+"11287",100,1985,11,25,4.17249726357371,12.7724641573311,20.5513532111878,15.5311330096556,0,21.806470596292,4.66255914416653,100,1985,11,25,0.0658479716485153,0.0183333237966551,0.0187479453151572,0.042643302493693,NA,0.0254827307498443,0.00203016874884834
+"11288",100,1985,11,26,0.801430142030726,12.9423102482711,19.3787018006915,16.7043672945633,0,23.5918701750367,4.69221509850379,100,1985,11,26,0.0316374288455788,0.155247406235711,0.0156800332056663,0.0313480145864533,NA,0.0814231642743358,0.00200245802135854
+"11289",100,1985,11,27,0.342134221257156,13.5010890687915,19.5871616883902,15.3229262674078,0,22.9315796857435,4.72187105284106,100,1985,11,27,0.0136257310168088,0.0675204799273474,0.011576049470448,0.0332695713289026,NA,0.0950286898050954,0.00197529239999702
+"11290",100,1985,11,28,0.00297029707396385,12.7173047952264,18.5958196998823,14.0419251858467,0,23.8526174492598,4.75152700717832,100,1985,11,28,0.000994152076411666,0.128362019620403,0.0496204491245679,0.0104356866580695,NA,0.020313092640702,0.00194867188476379
+"11291",100,1985,11,29,0.257535761519365,11.9046974570313,18.24970297399,12.5640595009093,0,23.7177808363717,4.78118296151558,100,1985,11,29,0.0122807025735141,0.0160005866514412,0.0154707624873088,0.00116724549910122,NA,0.0339837168510944,0.00192259647565883
+"11292",100,1985,11,30,0,9.99978003800899,19.7094499632077,14.371672336549,0,23.5706844535542,4.81083891585284,100,1985,11,30,0,0.222191153034486,0.0797041710394663,0.0327432811938377,NA,0.140510444714971,0.00189706617268217
+"11293",100,1985,12,1,0,10.8215071601574,19.7397579050431,12.8811441931394,0,24.4642341768072,4.83679590973695,100,1985,12,1,0,0.0163930150462146,0.0495035221839311,0.0331719284593802,NA,0.0801482823874876,0.0019138166847855
+"11294",100,1985,12,2,1.8916391412286,13.9224312911333,23.4948295156817,16.4254782816233,0,22.9458659666431,4.86275290362106,100,1985,12,2,0.274327485686857,0.130183131297232,0.334906158648345,0.0315023409171634,NA,0.239079508596407,0.0019309386940207
+"11295",100,1985,12,3,26.106600678531,13.5820681705202,22.8098018423821,15.9685150614404,0,22.5366379903319,4.88870989750516,100,1985,12,3,3.79362516035139,0.115366676033959,0.0230648790238987,0.0122537980979833,NA,0.0797721152471967,0.0019484322003878
+"11296",100,1985,12,4,0.354785486842671,13.5327833498796,21.3468095214978,15.6379976492904,0,24.2819416690469,4.91466689138927,100,1985,12,4,0.0220467848361236,0.118931584247878,0.0324818795502723,0.0226146321570838,NA,0.140476192872302,0.00196629720388677
+"11297",100,1985,12,5,0,10.469692132654,22.196897729133,14.4058085547553,0,23.5008902931933,4.94062388527338,100,1985,12,5,0,0.0436245939199474,0.0553801384867575,0.0409140068813408,NA,0.0570818064680507,0.00198453370451762
+"11298",100,1985,12,6,0,11.7516061554123,25.0479208712268,19.4998347971699,0,21.8704143784494,4.96658087915748,100,1985,12,6,0,0.0479765849421077,0.0209368702673696,0.0866322319882342,NA,0.0593900793951239,0.00200314170228035
+"11299",100,1985,12,7,4.35874589336718,14.8965236435104,20.9982948995659,15.1185369029953,0,23.3618351826918,4.99253787304159,100,1985,12,7,1.35040923924475,0.0738332809872813,0.0768355354714791,0.00371753149879065,NA,0.0162162748572307,0.00202212119717497
+"11300",100,1985,12,8,17.5852584450683,11.616952801695,25.0318040102884,14.6413422239364,0,23.6694910694819,5.01849486692569,100,1985,12,8,8.92760186546754,0.066409986375555,0.00825903003123774,0.00928945755545393,NA,0.129279162502586,0.00204147218920146
+"11301",100,1985,12,9,4.70341036002366,14.0012651722554,21.7228491482037,16.3267986163317,0,22.7876759391442,5.0444518608098,100,1985,12,9,0.561403475030837,0.0872940888612205,0.0400817912143937,0.0197134594835868,NA,0.165676122989782,0.00206119467835984
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+"11303",100,1985,12,11,5.5742573937436,13.4625081932059,18.7175138009788,15.739890121653,0,25.4006887508904,5.09636584857801,100,1985,12,11,3.37245610281739,0.0978028949002053,0.0136841923454856,0.00327077867393322,NA,0.151377861171867,0.00210175414807223
+"11304",100,1985,12,12,1.83762375303883,12.6712431682087,18.1317489569468,12.5457756537678,0,22.9833091473973,5.12232284246212,100,1985,12,12,0.113216372902633,0.129958464971727,0.0257052820577042,0.00613977564769716,NA,0.198069297236708,0.00212259112862624
+"11305",100,1985,12,13,0.997689763901651,10.7065236665497,19.5497579710974,13.0981078310506,0,20.9416920464687,5.14827983634623,100,1985,12,13,0.0489473654791632,0.198043298384549,0.00496487966659499,0.00442747740949043,NA,0.0336844790256603,0.00214379960631215
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+"12732",100,1989,11,9,1.50077007915845,7.61125413130875,15.7228161221159,11.0904179469194,0,22.5149304257653,4.03906057415717,100,1989,11,9,0.0384210537330461,0.23733156206346,0.0381338675628158,0.0345760523367339,NA,0.123117737729317,0.00396698882646962
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+"12734",100,1989,11,11,0.26204621263168,8.93247517290944,16.7081958132871,11.4891200270196,0,22.1976701354767,4.11876543351515,100,1989,11,11,0.0113450299961525,0.139798802350222,0.0118672293012838,0.00649415641924989,NA,0.0502927876397874,0.00365182799794302
+"12735",100,1989,11,12,0.022992299572535,7.15312431354334,18.1219141344295,12.7502530384378,0,21.4775190116753,4.15861786319415,100,1989,11,12,0.0020467836867299,0.0163052674723206,0.0377368687861012,0.0177888692644621,NA,0.0342006519014201,0.00350046174152177
+"12736",100,1989,11,13,0,5.8889108994613,18.7664902081715,13.2160835591349,0,21.0321182722315,4.19847029287314,100,1989,11,13,0,0.0483052646057182,0.0824509525770274,0.0273654929825324,NA,0.0718129734907854,0.00335323825699521
+"12737",100,1989,11,14,0,6.47795383161706,19.9950936278625,14.6721671485271,0,20.571842699798,4.23832272255213,100,1989,11,14,0,0.0100315882286996,0.18574519708232,0.00442864660748966,NA,0.112694341403832,0.00321015754436333
+"12738",100,1989,11,15,0.560726073733454,10.2609899917451,19.7506819306427,14.7215841564015,0,23.2030661805291,4.27817515223113,100,1989,11,15,0.0816374224799073,0.020648518956121,0.0504924091586851,0.0863807107491127,NA,0.137851747306798,0.00307121960362619
+"12739",100,1989,11,16,5.0581957640821,12.9383279361872,23.3917710941092,18.92709557058,0,24.7295905096006,4.31802758191012,100,1989,11,16,0.296725201523115,0.0915649258597529,0.265325913774267,0.0975468372995897,NA,0.0371772754590769,0.00293642443478371
+"12740",100,1989,11,17,13.8763476162985,12.8217161438777,14.6522000777577,12.3475027839736,0,24.148955329114,4.35788001158911,100,1989,11,17,1.59707595535196,0.0466912145269582,0.0198497069688731,0.0256666643355261,NA,0.0233373615130368,0.00280577203783593
+"12741",100,1989,11,18,0.960065997079654,9.84727175033788,16.5424422462388,12.5212100472781,0,21.3208002045108,4.39773244126811,100,1989,11,18,0.00368421228308448,0.173109971504957,0.0170783981505481,0.0699286187445871,NA,0.0266730473303951,0.00267926241278285
+"12742",100,1989,11,19,0.907150713524016,10.2523541497712,17.2320129994643,10.2924311336773,0,21.1781073595198,4.4375848709471,100,1989,11,19,0.0116374268029868,0.0757877180468308,0.0136385749954581,0.0192666362763011,NA,0.0632126673721471,0.0025568955596245
+"12743",100,1989,11,20,0.299889994033165,8.1572387834849,20.3900110309798,13.3197248865931,0,21.419772621482,4.47743730062609,100,1989,11,20,0.0169590643361995,0.059821040117849,0.115484303185791,0.0677596306137934,NA,0.0401605432938883,0.00243867147836082
+"12744",100,1989,11,21,0,9.18916398227805,26.3074367594535,18.0062265007934,0,20.8936445212076,4.51728973030509,100,1989,11,21,0,0.0153649306024424,0.0517228053492454,0.179521004160562,NA,0.089694491898913,0.00232459016899183
+"12745",100,1989,11,22,0,13.7413201746505,30.7511003219386,15.6773486688192,0,21.7678728646009,4.55714215998408,100,1989,11,22,0,0.0134578908463647,0.00786083545376668,0.17301405226075,NA,0.0504666338871883,0.00221465163151755
+"12746",100,1989,11,23,0.0717271737860899,17.212794246191,21.3807261795363,15.7999120634643,0,23.7796538643505,4.59699458966307,100,1989,11,23,0.00426900597517951,0.144268910815533,0.712547028034356,0.0155029487276067,NA,0.0335053760340412,0.00210885586593797
+"12747",100,1989,11,24,0.236083613386904,12.0316942496137,18.509394997155,12.5210229896738,0,21.6484417658022,4.63684701934207,100,1989,11,24,0.0291228074192653,0.16500415889459,0.0162719355134735,0.0614246183607847,NA,0.0768085766762192,0.00200720287225309
+"12748",100,1989,11,25,0,10.5918041329966,20.4649724257399,16.2887681094464,0,21.806470596292,4.67669944902106,100,1989,11,25,0,0.0439356650637782,0.107750852761402,0.000414022475067282,NA,0.0254827307498443,0.0019096926504629
+"12749",100,1989,11,26,0,12.6054896752302,22.6932674571626,18.2864248361787,0,23.5918701750367,4.71655187870005,100,1989,11,26,0,0.0782432140127714,0.263457918731669,0.0455479661421187,NA,0.0814231642743358,0.00181632520056741
+"12750",100,1989,11,27,0,12.3605171755464,25.8333441122661,18.4796260481227,0,22.9315796857435,4.75640430837905,100,1989,11,27,0,0.021214648658464,0.271906244624054,0.113802952165448,NA,0.0950286898050954,0.00172710052256664
+"12751",100,1989,11,28,0,14.3003190673224,31.7383501201835,16.9791638549536,0,23.8526174492598,4.79625673805804,100,1989,11,28,0,0.147276709278922,0.0786163959262773,0.0150924784078204,NA,0.020313092640702,0.00164201861646053
+"12752",100,1989,11,29,2.15621561802129,17.7258527271032,26.9301541457475,18.3055775750457,0,23.7177808363717,4.83610916773703,100,1989,11,29,0.205380119524507,0.0198174727101908,0.0281870761672726,0.160257961086192,NA,0.0339837168510944,0.00156107948224914
+"12753",100,1989,11,30,1.39625962365447,16.921023036256,25.6264576822749,19.8134101557128,0,23.5706844535542,4.87596159741603,100,1989,11,30,0.0313450266882699,0.162282939317802,0.0184608252187134,0.0537110627071252,NA,0.140510444714971,0.00148428311993244
+"12754",100,1989,12,1,2.23025302236492,15.3331242499441,28.3046093931293,18.9860289377479,0,24.4642341768072,4.919356989275,100,1989,12,1,0.386198827592954,0.0182929702357967,0.148811671626614,0.033745126164762,NA,0.0801482823874876,0.00143101094863444
+"12755",100,1989,12,2,4.28096808012825,12.9738175315563,20.053190329943,12.6165345730168,0,22.9458659666431,4.96275238113398,100,1989,12,2,0.398947355649639,0.0788210438935366,0.037698233050354,0.126878421955297,NA,0.239079508596407,0.00138359981344614
+"12756",100,1989,12,3,2.34147412365157,10.8727392819848,17.8729153812522,11.1793729784203,0,22.5366379903319,5.00614777299296,100,1989,12,3,0.0866666735543192,0.141859639608438,0.0367205333199021,0.0345397713377487,NA,0.0797721152471967,0.00134204971436754
+"12757",100,1989,12,4,0.289658972720961,9.26708461480303,18.6286135401794,10.1879426360261,0,24.2819416690469,5.04954316485193,100,1989,12,4,0.00941520493717223,0.175346727054507,0.0257333329946103,0.00402456722380446,NA,0.140476192872302,0.00130636065139862
+"12758",100,1989,12,5,0,5.01039607454054,21.0897688828941,12.8426513094844,0,23.5008902931933,5.09293855671091,100,1989,12,5,0,0.0406923782984662,0.0566145997658572,0.011900009621585,NA,0.0570818064680507,0.0012765326245394
+"12759",100,1989,12,6,0,9.37510442996051,23.8171727607484,15.3581738026098,0,21.8704143784494,5.13633394856989,100,1989,12,6,0,0.119316362829448,0.301136238272517,0.12313683138178,NA,0.0593900793951239,0.00125256563378987
+"12760",100,1989,12,7,0.0815181534151302,10.4663697285752,24.8044883237027,15.6350165661937,0,23.3618351826918,5.17972934042887,100,1989,12,7,0.0109356732152359,0.035292965696084,0.28442467404232,0.0188035228217085,NA,0.0162162748572307,0.00123445967915003
+"12761",100,1989,12,8,0,12.9886247972713,27.9598570195231,16.2683169529645,0,23.6694910694819,5.22312473228784,100,1989,12,8,0,0.0694356547685202,0.0585322300652457,0.149580630861588,NA,0.129279162502586,0.0012222147606199
+"12762",100,1989,12,9,0,14.2896699475245,27.8930690878689,16.6016170325452,0,22.7876759391442,5.26652012414682,100,1989,12,9,0,0.035100550695115,0.0651782391199193,0.0525562238997747,NA,0.165676122989782,0.00121583087819945
+"12763",100,1989,12,10,0,16.1678439183335,28.9337844575855,17.534818349236,0,24.238541578321,5.3099155160058,100,1989,12,10,0,0.0539894596057081,0.0233228185824644,0.0416841942640222,NA,0.0396186041760018,0.00121530803188869
+"12764",100,1989,12,11,0,10.7316722051539,23.4853134721813,12.4978548989962,0,25.4006887508904,5.35331090786478,100,1989,12,11,0,0.0170134265141814,0.0544876709746322,0.0904450580887604,NA,0.151377861171867,0.00122064622168763
+"12765",100,1989,12,12,0.112541255802408,10.4264465841917,20.7083829768551,12.0989987674457,0,22.9833091473973,5.39670629972375,100,1989,12,12,0.00228070182235617,0.0702093441679278,0.0175841869424073,0.0489555713230344,NA,0.198069297236708,0.00123184544759626
+"12766",100,1989,12,13,0.0973597374243705,8.65829486553163,19.1094280595433,9.92941684429139,0,20.9416920464687,5.44010169158273,100,1989,12,13,0.00140350881375765,0.00910291698752902,0.0101034958870354,0.0349053128070382,NA,0.0336844790256603,0.0012489057096146
+"12767",100,1989,12,14,6.75269523364614,9.3790977496912,17.5394388184164,9.57246422531581,0,22.225815602627,5.48349708344171,100,1989,12,14,0.145906415180873,0.126343264846417,0.0312952838923157,0.0478812885552841,NA,0.127330457430969,0.00127182700774262
+"12768",100,1989,12,15,0.462926294839028,10.1501979418714,18.4827613662703,13.1342244845937,0,25.277219288541,5.52689247530069,100,1989,12,15,0.00479532106578003,0.131036315732863,0.0220000343325351,0.0466555221982274,NA,0.046443944515856,0.00130060934198034
+"12769",100,1989,12,16,0.209680973295004,10.8559296799965,21.4151372416447,11.5063806561091,0,25.8820264441015,5.57028786715966,100,1989,12,16,0.00555555613504518,0.289936183005958,0.136987313026462,0.0867813013891735,NA,0.128649841255626,0.00133525271232774
+"12770",100,1989,12,17,0.179647967473443,8.62536846765197,30.8445765597068,11.9836413359354,0,23.8749045496677,5.61368325901864,100,1989,12,17,0.00356725156830068,0.0278287021392316,0.0334152325194098,0.0804894653053734,NA,0.0626358447336705,0.00137575711878484
+"12771",100,1989,12,18,0,14.1036744122983,27.6464468155495,11.0418372086041,0,23.9247434878713,5.65707865087762,100,1989,12,18,0,0.13403155536326,0.0641607568755313,0.02169415018188,NA,0.0799765227895359,0.00142212256135164
+"12772",100,1989,12,19,0.000110011002739402,11.5478876541943,23.8960837184793,12.0483939367028,0,21.2363499027613,5.70047404273659,100,1989,12,19,0.000526315805159117,0.0126064536826237,0.0641140179554443,0.00877195347133323,NA,0.0392550281828534,0.00147434904002813
+"12773",100,1989,12,20,1.73729370752446,8.52037404944794,18.6826621791055,8.06443332235674,0,23.7726622262736,5.74386943459557,100,1989,12,20,0.0365497011190286,0.111680066600691,0.023513993278043,0.00581460996377033,NA,0.0224309732559426,0.00153243655481431
+"12774",100,1989,12,21,0.419251931792307,9.22564360174802,19.168536942653,9.19908692891842,0,22.0241265895673,5.78726482645455,100,1989,12,21,0.146081868797018,0.227156220814306,0.023433864397559,0.0109619752873569,NA,0.0632093073326093,0.00159638510571018
+"12775",100,1989,12,22,0,8.74773380229182,22.0428710576594,11.4425082726054,0,22.7499533638933,5.83066021831353,100,1989,12,22,0,0.0869368352321821,0.0630729709428443,0.113259057525794,NA,0.0645818976377994,0.00166619469271576
+"12776",100,1989,12,23,0,8.60293720736362,25.2619800525661,13.6725853572715,0,23.8394889921263,5.8740556101725,100,1989,12,23,0,0.114354401417836,0.280992840634434,0.110042707070757,NA,0.0387899377047811,0.00174186531583103
+"12777",100,1989,12,24,0,11.240902015216,31.5258306748796,10.7737293180459,0,26.7952263340267,5.91745100203148,100,1989,12,24,0,0.0318707434761037,0.0160888687983885,0.247827550397339,NA,0.0397091796373021,0.00182339697505598
+"12778",100,1989,12,25,0,15.0983387671157,34.2642796078924,14.2924092453305,0,26.7710891212228,5.96084639389046,100,1989,12,25,0,0.0598701969905939,0.00922860047749001,0.30578778502711,NA,0.0288734946089749,0.00191078967039064
+"12779",100,1989,12,26,3.84213423860086,12.9666006058881,19.4444226012109,12.8901649397461,0,24.9924225073873,6.00424178574944,100,1989,12,26,0.263391795967071,0.146002887290908,0.0847981656619308,0.0133690104368236,NA,0.174040296218994,0.00200404340183497
+"12780",100,1989,12,27,8.60077012726183,10.5433992232689,18.3903300806765,11.6812980261573,0,24.3474257686674,6.04763717760841,100,1989,12,27,1.53134489081761,0.25889295882966,0.035816390112569,0.00741521697023682,NA,0.0370867220117061,0.00210315816938901
+"12781",100,1989,12,28,3.92882285233509,10.0769525807027,20.1959955972938,11.087271757514,0,25.0784154358314,6.09103256946739,100,1989,12,28,0.0492397503825043,0.0511093028264855,0.018864827416216,0.0208760357193925,NA,0.0564464351202811,0.00220813397305275
+"12782",100,1989,12,29,0,8.6588887517864,26.2832562516887,13.4410891664041,0,24.6873741235462,6.13442796132637,100,1989,12,29,0,0.0313941273584346,0.0848204943075131,0.0653731164386513,NA,0.0159888769120001,0.00231897081282615
+"12783",100,1989,12,30,0,13.3435863404646,22.4014303104581,11.9209351397977,0,26.9760919811286,6.17782335318535,100,1989,12,30,0,0.139047349296364,0.061547398988099,0.025198830587922,NA,0.0309081093829142,0.00243566868870928
+"12784",100,1989,12,31,0,11.5544885104508,34.1570413755243,13.229966927414,0,25.6653309723101,6.22121874504432,100,1989,12,31,0,0.238255548816352,0.125298378839326,0.0491467707249833,NA,0.0501234370470788,0.0025582276007021
+"12785",100,1990,1,1,0,13.5464686130402,22.1653796238999,12.6297801115332,34.0555113403186,34.0555113403186,6.25327947898875,100,1990,1,1,0,0.197391240156816,0.256880513455851,0.22222225507107,0.123700487390225,0.123700487390225,0.00256731883876421
+"12786",100,1990,1,2,0.000110011002739402,9.42851483730069,31.8843014048796,11.8811441585176,26.0696477942472,26.0696477942472,6.28534021293318,100,1990,1,2,0.000526315805159117,0.0538485443628985,0.0653595263838778,0.113414677534144,1.0479478712029,1.0479478712029,0.00258056944456725
+"12787",100,1990,1,3,0,15.8118702287328,27.693883373399,14.9959185918172,0,28.1710230572389,6.3174009468776,100,1990,1,3,0,0.0320064198589513,0.842987497448153,0.0300245709492591,NA,0.269809174294628,0.00259797941811118
+"12788",100,1990,1,4,0,11.460891195912,19.880659764356,11.1021451729752,30.2723983202306,30.2723983202306,6.34946168082203,100,1990,1,4,0,0.160869039214379,0.0459930884782211,0.0583719711326836,2.94393417884011,2.94393417884011,0.00261954875939603
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+"12790",100,1990,1,6,0,8.8555335117252,21.4494942181443,13.311529145907,26.3017054789662,26.3017054789662,6.41358314871088,100,1990,1,6,0,0.0295321895086867,0.189470722288031,0.0465140439954618,0.610399927181927,0.610399927181927,0.00267516554518845
+"12791",100,1990,1,7,0,11.549516002206,22.473080493436,14.2915950802424,23.5173377004525,23.5173377004525,6.44564388265531,100,1990,1,7,0,0.0392251066019175,0.062002797712751,0.0288151654450617,0.89282248170673,0.89282248170673,0.00270921298969604
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+"16010",100,1998,10,31,1.58844886951321,7.0533773828261,14.4494388294954,9.61881210045977,23.027964756696,23.027964756696,3.61941643810587,100,1998,10,31,0.0556140377647005,0.267326333604797,0.0387134312747298,0.0131695835381929,1.71379436588419,1.71379436588419,0.00241816301695333
+"16011",100,1998,11,1,0.0222222225533591,7.70916382314348,16.8357756943068,11.0384487743818,28.9254015218569,28.9254015218569,3.66334526099784,100,1998,11,1,0.00175438601719706,0.20997189192634,0.071287720690682,0.0953356728225638,0.0593140449572386,0.0593140449572386,0.00229668181282439
+"16012",100,1998,11,2,0,4.28880090734484,22.7009242554047,10.4450274027876,30.3977556805668,30.3977556805668,3.70727408388981,100,1998,11,2,0,0.0688374265804777,0.0491814000986842,0.0770701794652917,0.00196198620736869,0.00196198620736869,0.00218125502758156
+"16013",100,1998,11,3,0,6.21045098918499,26.5644883742296,10.4474257782872,30.3402642102131,30.3402642102131,3.75120290678178,100,1998,11,3,0,0.0537608003304214,0.0292321327212411,0.0136485401712277,0.0506029245837012,0.0506029245837012,0.00207188266122486
+"16014",100,1998,11,4,0,12.7201869280556,24.3054346497959,12.3233553851792,10.2019581107548,10.2019581107548,3.79513172967375,100,1998,11,4,0,0.0177555630155051,0.151149521757813,0.209731583279673,2.02473679129848,2.02473679129848,0.00196856471375428
+"16015",100,1998,11,5,0,7.3655665175225,17.3927612934175,11.3958415869701,24.9780421283248,24.9780421283248,3.83906055256572,100,1998,11,5,0,0.0157228148689294,0.0166619799476595,0.0190818902178257,0.684126229729362,0.684126229729362,0.00187130118516981
+"16016",100,1998,11,6,0,5.07925190049561,23.4343013175906,14.328492774166,29.9465899703526,29.9465899703526,3.88298937545769,100,1998,11,6,0,0.0387274835408246,0.01753973823263,0.0796151577286716,0.0456694849920692,0.0456694849920692,0.00178009207547147
+"16017",100,1998,11,7,0,12.1788998825191,22.2741255996251,13.7940045987288,7.41814071615406,7.41814071615406,3.92691819834966,100,1998,11,7,0,0.075632164516852,0.0134596309240986,0.0561560654490443,0.474499908235366,0.474499908235366,0.00169493738465926
+"16018",100,1998,11,8,4.07238717887006,6.29635866194537,16.135665537894,10.9841473593046,19.6422663332999,19.6422663332999,3.97084702124163,100,1998,11,8,0.064268997175671,0.0577450254819742,0.0206842164158859,0.00870000089019131,1.79539864120075,1.79539864120075,0.00161583711273316
+"16019",100,1998,11,9,0,3.69264025814069,18.1914410900624,10.7729262865022,30.8036633691903,30.8036633691903,4.0147758441336,100,1998,11,9,0,0.0141093612821513,0.0201894352877384,0.019098251238,0.16040239251149,0.16040239251149,0.00154279125969318
+"16020",100,1998,11,10,0,2.94421340661212,20.8586909920469,12.7772607331229,27.9081077114059,27.9081077114059,4.05870466702558,100,1998,11,10,0,0.0211397693037336,0.0695696717739551,0.0177140160522277,0.179892839804625,0.179892839804625,0.00147579982553932
+"16021",100,1998,11,11,0.389548959864183,10.8329703621607,15.6432452972966,13.757194640744,5.35988996786909,5.35988996786909,4.10263348991755,100,1998,11,11,0.0142690054017898,0.0867718847286799,0.0510141173112784,0.00243156956907253,0.160198244957082,0.160198244957082,0.00141486281027159
+"16022",100,1998,11,12,8.74554438218556,10.8793728390936,14.7325962295364,13.2673705691683,4.37451040862811,4.37451040862811,4.14656231280952,100,1998,11,12,0.656959061650567,0.0765830583004466,0.0564093206863692,0.00681169239520088,0.468842647370696,0.468842647370696,0.00135998021388997
+"16023",100,1998,11,13,8.11694175575433,11.227920778776,14.8190759138437,12.4231351552361,13.5545875362568,13.5545875362568,4.19049113570149,100,1998,11,13,0.710350824266914,0.0517730982641701,0.0435731709576005,0.0053146383564908,1.59002462900624,1.59002462900624,0.00131115203639447
+"16024",100,1998,11,14,0.218151820768224,5.32851488807938,23.1880088697041,13.5564135361557,23.5302310729578,23.5302310729578,4.23441995859346,100,1998,11,14,0.00672514679574829,0.0726099806568298,0.0158116800812635,0.021215186187193,0.716649831528894,0.716649831528894,0.0012683782777851
+"16025",100,1998,11,15,2.21914190244098,12.9201210408059,17.859537655502,12.5946534149455,26.0991532039328,26.0991532039328,4.27834878148543,100,1998,11,15,0.0289473741375276,0.127245007529531,0.0282468268458785,0.0357432463038653,2.07977817496607,2.07977817496607,0.00123165893806184
+"16026",100,1998,11,16,1.3339933867764,6.62399332908907,16.4788670114952,10.1348404354519,25.317238827612,25.317238827612,4.3222776043774,100,1998,11,16,0.0115204655217858,0.0593368312122081,0.0195485200227969,0.00152104853141441,1.25666895286186,1.25666895286186,0.00120099401722471
+"16027",100,1998,11,17,0.193399342897833,4.84498356199107,15.2038613519784,10.431903085824,0,27.9765457346363,4.36620642726937,100,1998,11,17,0.00274853826789132,0.0304005927314712,0.013291207738606,0.000709357240530632,NA,0.708038493233512,0.00117638351527371
+"16028",100,1998,11,18,0.47348735283966,6.51218918054411,14.9096809215147,8.55796468795592,30.6358526416607,30.6358526416607,4.41013525016134,100,1998,11,18,0.00543859730687067,0.169380695322952,0.0217912494994784,0.00353333083265766,0.800702607241071,0.800702607241071,0.00115782743220881
+"16029",100,1998,11,19,0,5.55343233660372,18.0958195152325,10.729724954326,29.7892850575799,29.7892850575799,4.45406407305331,100,1998,11,19,0,0.0850362666938878,0.053931636226097,0.035043226697222,0.461039722884582,0.461039722884582,0.00114532576803004
+"16030",100,1998,11,20,0,6.07018702782945,20.9299671332566,13.409384019304,32.1332450295963,32.1332450295963,4.49799289594529,100,1998,11,20,0,0.0596029262676758,0.0352099751123275,0.0560139856372657,0.026678373259619,0.026678373259619,0.00113887852273739
+"16031",100,1998,11,21,0,10.3072056838519,18.1827284033411,12.2006820305215,23.9248404969739,23.9248404969739,4.54192171883726,100,1998,11,21,0,0.0940889308930261,0.0237210700455634,0.0122093639441421,0.619814373703722,0.619814373703722,0.00113848569633086
+"16032",100,1998,11,22,0.211991204426299,7.29971401027851,16.8752365678844,11.3463695765328,21.1655665650488,21.1655665650488,4.58585054172923,100,1998,11,22,0.00842105314396977,0.11474969567083,0.0137385989810833,0.0346041106353134,1.76036558195889,1.76036558195889,0.00114414728881046
+"16033",100,1998,11,23,0.868536857390168,7.49390539372846,19.1366775269293,12.8403851149237,20.4565345860682,20.4565345860682,4.6297793646212,100,1998,11,23,0.0611695928322645,0.138038609852727,0.0231918164260675,0.0152397810730499,1.52950036539929,1.52950036539929,0.00115586330017616
+"16034",100,1998,11,24,0,8.97534655055853,20.3304840356472,13.1558965897009,24.7952254854544,24.7952254854544,4.67370818751317,100,1998,11,24,0,0.0294432922007234,0.0541246361050252,0.0127479467760886,0.908648180996538,0.908648180996538,0.001173633730428
+"16035",100,1998,11,25,0,9.93345439394709,22.1607811763079,14.7236633185375,31.2610668018706,31.2610668018706,4.71763701040514,100,1998,11,25,0,0.0325485011943895,0.0560889535695291,0.0594918163724128,0.637059059759349,0.637059059759349,0.00119745857956596
+"16036",100,1998,11,26,0,11.7607701923719,30.6816941524627,12.5707150334441,32.3018261413239,32.3018261413239,4.76156583329711,100,1998,11,26,0,0.122981234350904,0.166704144358851,0.00702283335912084,0.0657102390413196,0.0657102390413196,0.00122733784759003
+"16037",100,1998,11,27,3.43256331663845,15.6539933379858,21.0082505292232,11.2900000394899,15.781980282665,15.781980282665,4.80549465618908,100,1998,11,27,1.70426916062484,0.0593620281978745,0.0402584541100767,0.00617777976997238,1.38073635381077,1.38073635381077,0.00126327153450023
+"16038",100,1998,11,28,16.6792078857506,7.27543456221309,16.3754564396488,10.478954876479,23.9142908006087,23.9142908006087,4.84942347908105,100,1998,11,28,0.793742429833842,0.0381561261048975,0.0289029765974394,0.0153099282695259,3.2891329609343,3.2891329609343,0.00130525964029654
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+"16040",100,1998,11,30,0.943894389111086,11.0009681063779,17.4763036671251,11.7695378711646,21.7220021259404,21.7220021259404,4.93728112486499,100,1998,11,30,0.209122801799524,0.231538681646068,0.0256000126311889,0.0160666476780406,3.89301270405302,3.89301270405302,0.00140739910854754
+"16041",100,1998,12,1,0.248734881849376,6.1876787672473,23.0076896008616,13.4718042212089,34.0181183096337,34.0181183096337,4.96745791006116,100,1998,12,1,0.0087134511620678,0.0639385844894514,0.0225064737556718,0.0551175218013945,0.00387602101945746,0.00387602101945746,0.00133513088301818
+"16042",100,1998,12,2,0,11.4867546713129,31.9050605950182,9.17031892350536,25.4692301000043,25.4692301000043,4.99763469525732,100,1998,12,2,0,0.0125321318643797,0.0106941262678426,0.0367368737048969,0.221328545357673,0.221328545357673,0.0012676934042792
+"16043",100,1998,12,3,0,8.57565466143248,20.1954343961542,11.6993508921205,26.9591967054982,26.9591967054982,5.02781148045348,100,1998,12,3,0,0.0177450357159082,0.0207496629504745,0.0303508739026251,0.424206455985098,0.424206455985098,0.00120508667233059
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+"16046",100,1998,12,6,1.25610561877063,11.6056654691958,19.2806711710981,14.1421123972558,14.5055995506815,14.5055995506815,5.11834183604197,100,1998,12,6,0.00912280118953598,0.0732807116146698,0.030836261348019,0.0267263078678978,1.74882208459655,1.74882208459655,0.00104625095722706
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+"16048",100,1998,12,8,0.128382840196882,6.06257421978236,21.1657536761595,14.2184817751642,0,26.0843289829586,5.1786954064343,100,1998,12,8,0.00175438601719706,0.0239356530172997,0.120870212706963,0.0164877341957228,NA,0.732259485648275,0.000964514214443257
+"16049",100,1998,12,9,0,8.54476340454404,22.9184157452305,15.0997470849418,33.8623872889151,33.8623872889151,5.20887219163046,100,1998,12,9,0,0.104836802457038,0.112704049370627,0.0800029335357242,0.160216567101779,0.160216567101779,0.000930891963236936
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+"16051",100,1998,12,11,0,17.1721562489425,41.2885476711429,11.5263916441579,26.7844553633754,26.7844553633754,5.26922576202279,100,1998,12,11,0,0.011326284371056,0.0209204379178288,0.036771902630981,7.20863228003996,7.20863228003996,0.000878139701195425
+"16052",100,1998,12,12,0.128932895210579,22.127898605481,30.9039716059619,17.5673268111494,14.1810892312834,14.1810892312834,5.29940254721895,100,1998,12,12,0.00473684224643205,0.0216981988742404,0.232012007284395,0.0699251919621403,4.08779130620692,4.08779130620692,0.000859009690360243
+"16053",100,1998,12,13,0.0854785491285151,13.3113531998151,17.8123100226206,11.3797139512955,12.3735644505231,12.3735644505231,5.32957933241511,100,1998,12,13,0.00584795339065686,0.0597830515276623,0.014724538310464,0.00162279452504993,0.868143005817345,0.868143005817345,0.000844710426315434
+"16054",100,1998,12,14,0.11771177293116,12.5261606126204,18.8845215410289,13.7448735683009,11.2996590386654,11.2996590386654,5.35975611761128,100,1998,12,14,0.00228070182235617,0.125509934747748,0.0660115972153677,0.0026766142639788,3.07451178927739,3.07451178927739,0.000835241909061003
+"16055",100,1998,12,15,0,13.1210561853037,17.1554126949331,12.5039603901644,5.61763476014006,5.61763476014006,5.38993290280744,100,1998,12,15,0,0.0532005932189467,0.0303765949766141,0.00453567724852882,0.886609923280206,0.886609923280206,0.000830604138596956
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+"16057",100,1998,12,17,0,7.8294059446018,19.600979235306,12.8258086090172,18.2936632662061,18.2936632662061,5.45028647319977,100,1998,12,17,0,0.0150257160265078,0.0284619646353726,0.0126146425861476,1.017936837229,1.017936837229,0.000835820838040009
+"16058",100,1998,12,18,0,7.46855887485416,21.2862046223925,13.1241364830529,34.7971071257974,34.7971071257974,5.48046325839593,100,1998,12,18,0,0.0156134351518604,0.117528880479975,0.0111315728015425,0.00600995429121271,0.00600995429121271,0.000845675307947093
+"16059",100,1998,12,19,0,8.14311325117306,29.3430252285025,13.4983829324133,30.6829371247748,30.6829371247748,5.51064004359209,100,1998,12,19,0,0.0175807158498745,0.0739562218429365,0.173515107263117,0.419991127728366,0.419991127728366,0.000860360524644573
+"16060",100,1998,12,20,11.2251924802237,13.5488888265276,18.3814189856333,9.53969205409387,25.6062263367069,25.6062263367069,5.54081682878826,100,1998,12,20,1.25368428436645,0.0248286293922665,0.0151988290264896,0.0127286757553616,3.73944867660827,3.73944867660827,0.000879876488132418
+"16061",100,1998,12,21,1.47403741918906,7.96834977730142,19.9153354464322,11.0019472350906,20.2108582495594,20.2108582495594,5.57099361398442,100,1998,12,21,0.199532157942568,0.0478321688535464,0.0167228140493939,0.0389508644706843,1.73326749739656,1.73326749739656,0.00090422319841065
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+"20431",100,2010,12,8,53.8886688282781,17.8925741718154,23.2622223278083,18.5601177005747,9.07039604722076,9.07039604722076,4.67032119130198,100,2010,12,8,19.0498856545622,0.0549870783669741,0.00505848365940687,0.00258048563146023,2.18645348326593,2.18645348326593,0.00783944652853786
+"20432",100,2010,12,9,2.25489549878145,10.9839713303301,21.6361056014125,15.8449031611611,17.6310670451887,17.6310670451887,4.71772318686813,100,2010,12,9,0.015438599670146,0.0143210761511776,0.0246579351819252,0.0476242615886697,5.43708706156216,5.43708706156216,0.00783893862461463
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+"20444",100,2010,12,21,3.56501648397204,10.6373047204432,16.4226181840215,9.63477113721657,24.9907586923396,24.9907586923396,5.28654713366197,100,2010,12,21,0.00707602160716242,0.116011725208266,0.00972044232501992,0.00537457110580394,2.66288364751745,2.66288364751745,0.00817759022637783
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+"20737",100,2011,10,10,0.784708467778331,3.87103412542144,14.3438833927033,11.7645313286021,10.6884487821455,10.6884487821455,3.21592683717438,100,2011,10,10,0.00777777751286847,0.100781272441468,0.0698871558273588,0.0204273398093344,0.727858869059695,0.727858869059695,0.00384755735912739
+"20738",100,2011,10,11,3.22035203462649,8.21722757383542,14.754521390917,9.99937183497631,16.546138608154,16.546138608154,3.23927490889792,100,2011,10,11,0.096900598654279,0.156935634049801,0.0480590037541812,0.000953475774202172,7.158146787425,7.158146787425,0.00387839739928792
+"20739",100,2011,10,12,0.257535759789912,7.56231030851307,15.082695248628,11.0134752323919,13.2647635698056,13.2647635698056,3.26262298062145,100,2011,10,12,0.0133918134911716,0.203271942434287,0.00833567435065082,0.0125089393616355,5.84890586698,5.84890586698,0.00391154299343805
+"20740",100,2011,10,13,0,5.80518152899999,18.6219582426535,12.359206829134,18.7456875078224,18.7456875078224,3.28597105234499,100,2011,10,13,0,0.0287690144143108,0.0193315236994313,0.00891844777523583,0.28437020505517,0.28437020505517,0.0039469941415778
+"20741",100,2011,10,14,0,8.64148525200268,26.1709901098371,12.0687119281462,21.8233993307854,21.8233993307854,3.30931912406852,100,2011,10,14,0,0.086368968742843,0.0792162246261637,0.00536851824582348,0.0909356367101601,0.0909356367101601,0.00398475084370716
+"20742",100,2011,10,15,0,12.2667766336036,17.6594717804224,10.9536513065216,15.085159538865,15.085159538865,3.33266719579206,100,2011,10,15,0,0.0824929691404646,0.0362590409846415,0.015676218703474,0.471073284394694,0.471073284394694,0.00402481309982614
+"20743",100,2011,10,16,6.03069310403369,7.59823978275093,15.2753574866535,9.48239838775366,22.5117380417088,22.5117380417088,3.35601526751559,100,2011,10,16,0.238421010301839,0.136684193538674,0.0363766136482681,0.0355134440707301,0.493491865182908,0.493491865182908,0.0040671809099347
+"20744",100,2011,10,17,0.239273932966331,4.36987897297992,18.1935201756107,9.96381747053795,23.700836198427,23.700836198427,3.37936333923912,100,2011,10,17,0.00707602379876272,0.0787719103049304,0.0083426753174679,0.0177807035614087,1.07369221778583,1.07369221778583,0.00411185427403291
+"20745",100,2011,10,18,0,5.55038503189423,24.9580638306369,9.99202407862094,26.1137733186695,26.1137733186695,3.40271141096266,100,2011,10,18,0,0.0536830256289425,0.0348596055552837,0.0182497256363343,0.00113625232648031,0.00113625232648031,0.00415883319212072
+"20746",100,2011,10,19,0,10.6808249281578,29.7171835526906,15.2278867675395,25.255797454364,25.255797454364,3.42605948268619,100,2011,10,19,0,0.0588064320972245,0.0295426046542528,0.033654014727125,0.005554421939408,0.005554421939408,0.00420811766419815
+"20747",100,2011,10,20,0,17.5937072346837,24.0199119916188,16.1176667008856,7.19936188770206,7.19936188770206,3.44940755440973,100,2011,10,20,0,0.00969126602562113,0.0412362973935341,0.0962224018134751,0.648867248742653,0.648867248742653,0.00425970769026519
+"20748",100,2011,10,21,2.02156211440713,12.1288338609786,17.5954016107406,13.3622871929794,16.3799890879095,16.3799890879095,3.47275562613326,100,2011,10,21,0.00988303616730289,0.0497134136959277,0.0242473928622089,0.0118768139022452,0.336758537721149,0.336758537721149,0.00431360327032184
+"20749",100,2011,10,22,0.00407040710135786,9.81696376548742,17.1370516127617,14.6276007433011,9.7941142387516,9.7941142387516,3.4961036978568,100,2011,10,22,0.000994152076411666,0.0385555816227472,0.0237251643578802,0.0220423871754262,1.28556887233584,1.28556887233584,0.00436980440436811
+"20750",100,2011,10,23,0.097579759479028,11.4896590271668,24.4815180498381,17.0940142485699,23.4260943342488,23.4260943342488,3.51945176958033,100,2011,10,23,0.00807017583160373,0.125698814157908,0.310476658113088,0.00796623172228512,0.101113345439774,0.101113345439774,0.00442831109240398
+"20751",100,2011,10,24,9.61397136083924,10.8672717003146,12.9951594534225,11.3379968133303,6.85894382222913,6.85894382222913,3.54279984130386,100,2011,10,24,0.523976375066827,0.0196619989875584,0.0350543570558324,0.00254809495810251,0.871113368974685,0.871113368974685,0.00448912333442947
+"20752",100,2011,10,25,6.14345436180123,6.98532451654818,14.9468207731761,10.2646523990778,19.3675577700859,19.3675577700859,3.5661479130274,100,2011,10,25,0.32023392024795,0.262114581801897,0.0494590830664562,0.0081401173450726,3.64821450116587,3.64821450116587,0.00455224113044458
+"20753",100,2011,10,26,0,6.15842679152788,18.0643566853405,11.4551693913173,26.1599559846884,26.1599559846884,3.58949598475093,100,2011,10,26,0,0.184359626725421,0.0127473205168172,0.00503214034640026,0.114226175190335,0.114226175190335,0.00461766448044929
+"20754",100,2011,10,27,0,9.02532456233294,25.5250386090168,13.7385357593415,23.9786579123687,23.9786579123687,3.61284405647447,100,2011,10,27,0,0.0271134460383152,0.0310766051580148,0.0645084834568958,0.542760984356216,0.542760984356216,0.00468539338444364
+"20755",100,2011,10,28,0,13.1730582590806,27.3151376554281,18.4295148503269,11.4821564031251,11.4821564031251,3.636192128198,100,2011,10,28,0,0.00958421106846991,0.0324871138368853,0.0893872337787186,0.593371901586941,0.593371901586941,0.00475542784242757
+"20756",100,2011,10,29,2.00374035368396,12.2203739825124,16.0485808757534,13.2797679985055,12.218074762913,12.218074762913,3.65954019992154,100,2011,10,29,0.207192965529819,0.0826251842187016,0.0395870539215293,0.0028768728696388,0.961550968040875,0.961550968040875,0.00482776785440112
+"20757",100,2011,10,30,2.14917486676551,9.5507701384877,13.7018481161204,9.55529047291402,8.26523645010718,8.26523645010718,3.68288827164507,100,2011,10,30,0.0202924085918273,0.188635595671986,0.0208608090407,0.00155161283063859,0.531833369043207,0.531833369043207,0.00490241342036429
+"20758",100,2011,10,31,0.0137513753424252,8.47061620477271,15.3443563427731,10.7761705543342,11.9488008297709,11.9488008297709,3.7062363433686,100,2011,10,31,0.0031578948309547,0.183433968102342,0.0245362543185136,0.00348610782630363,0.473489359084958,0.473489359084958,0.00497936454031708
+"20759",100,2011,11,1,0.45896589936334,10.2764465239706,15.8755336190739,13.0809658856282,12.3109902454288,12.3109902454288,3.74416408708724,100,2011,11,1,0.00479532200690603,0.11515088805935,0.022411122936755,0.00151580187875826,2.21790958923211,2.21790958923211,0.00492028295117388
+"20760",100,2011,11,2,2.3823982676526,11.2706160812882,15.1300661120609,13.7425279858613,8.82579760241954,8.82579760241954,3.78209183080587,100,2011,11,2,0.288713440379208,0.0901321057124903,0.0109982494823484,0.0186782213009601,0.268198785714995,0.268198785714995,0.00486385366991515
+"20761",100,2011,11,3,1.29658967787677,9.78586362769502,16.3755444105965,12.3110209098875,14.2288449458425,14.2288449458425,3.8200195745245,100,2011,11,3,0.0287719262552553,0.126411835930439,0.0284538312235138,0.00595633938118285,3.57136523359211,3.57136523359211,0.00481007669654084
+"20762",100,2011,11,4,0.226512656919043,9.59987906234624,21.2399340756524,13.368151765452,22.814961371511,22.814961371511,3.85794731824313,100,2011,11,4,0.0039766086977825,0.131659039496331,0.0904028999241517,0.0272619814800955,0.143259808041998,0.143259808041998,0.00475895203105102
+"20763",100,2011,11,5,0,9.25965900337211,30.8242682676242,12.462769902841,28.8307261178465,28.8307261178465,3.89587506196177,100,2011,11,5,0,0.0408871120922221,0.0398537501957344,0.0176748727403055,0.0105894753486561,0.0105894753486561,0.00471047967344563
+"20764",100,2011,11,6,0,16.1819689470549,23.4162153825246,15.4891286531035,17.3984050058296,17.3984050058296,3.9338028056804,100,2011,11,6,0,0.0287584823743717,0.0116098876153248,0.00762284490074254,1.66539512512954,1.66539512512954,0.00466465962372471
+"20765",100,2011,11,7,0,8.11627059774955,26.5272718813553,14.2049812541412,27.232343079889,27.232343079889,3.97173054939903,100,2011,11,7,0,0.0180871277223982,0.104453811413736,0.060768618611717,0.139536953526036,0.139536953526036,0.00462149188188822
+"20766",100,2011,11,8,4.61034105136187,14.6505059430046,24.3506600030578,18.2532763297539,18.1251595584211,18.1251595584211,4.00965829311766,100,2011,11,8,0.73701754324619,0.0664140463891032,0.0233473306938035,0.001441882882015,0.551226312140148,0.551226312140148,0.0045809764479362
+"20767",100,2011,11,9,2.54785478390483,16.0080748212875,29.9917821978578,19.3986007367293,16.4608801494468,16.4608801494468,4.0475860368363,100,2011,11,9,0.0942689968410334,0.022565485295151,0.0132538132563842,0.0499296711873684,0.521513511709185,0.521513511709185,0.00454311332186861
+"20768",100,2011,11,10,11.023432428425,10.178085796117,18.6757316085765,11.6827700471196,22.025665797285,22.025665797285,4.08551378055493,100,2011,11,10,0.824503070764478,0.0244988226372405,0.0373872234770912,0.018948543400868,5.21682178315297,5.21682178315297,0.0045079025036855
+"20769",100,2011,11,11,0.100000001490116,6.79757976427068,20.8045105865949,12.7495687199373,23.2357204970222,23.2357204970222,4.12344152427356,100,2011,11,11,0,0.0409227895023027,0.0192619933370746,0.0814371433339918,0.214632295588693,0.214632295588693,0.00447534399338682
+"20770",100,2011,11,12,0,7.93382836699617,20.3626623520888,13.8035292211014,25.7382176507293,25.7382176507293,4.16136926799219,100,2011,11,12,0,0.03508889931578,0.0699941417406293,0.0329847687117438,0.688869145814176,0.688869145814176,0.00444543779097261
+"20771",100,2011,11,13,0.00407040710135786,12.2517050815494,27.0254233185083,13.0577865533441,15.476886737727,15.476886737727,4.19929701171083,100,2011,11,13,0.000994152076411666,0.0480029502334421,0.00885966395822589,0.0514803806609956,0.644578338977891,0.644578338977891,0.00441818389644285
+"20772",100,2011,11,14,0.749614960486346,11.2842022766768,21.4004510915188,14.0796898473607,21.1684158438503,21.1684158438503,4.23722475542946,100,2011,11,14,0.00707602279228101,0.0367432790857365,0.0303041453001864,0.00570512238657996,1.76150013224316,1.76150013224316,0.00439358230979753
+"20773",100,2011,11,15,0.19691969527237,7.86702964696685,24.8169087199095,13.6010647596437,25.2393180461082,25.2393180461082,4.27515249914809,100,2011,11,15,0.0409356741093056,0.0260690116447541,0.0354928826630582,0.0399179121527569,0.395509928278693,0.395509928278693,0.00437163303103668
+"20774",100,2011,11,16,0.348734880019926,6.70265127670909,18.4697029147342,8.98834994392689,31.0785151104985,31.0785151104985,4.31308024286672,100,2011,11,16,0.0414035078970313,0.0204999905692379,0.0299099145322862,0.00945438318538766,0.0873252245275144,0.0873252245275144,0.00435233606016027
+"20775",100,2011,11,17,0,6.92803083079876,27.3048072139291,12.3585017081535,26.1357975488711,26.1357975488711,4.35100798658536,100,2011,11,17,0,0.0321029305062303,0.0215929320130958,0.0258157068733799,0.319815022215679,0.319815022215679,0.0043356913971683
+"20776",100,2011,11,18,0,13.9589218993654,31.0209459928003,16.7005589383401,12.4549504942102,12.4549504942102,4.38893573030399,100,2011,11,18,0,0.0563777889252453,0.0486508851483865,0.0753475018876604,0.49580224155857,0.49580224155857,0.00432169904206081
+"20777",100,2011,11,19,9.92387224371546,15.3328492255888,17.9437957717509,13.7743652593447,8.14564355948839,8.14564355948839,4.42686347402262,100,2011,11,19,0.348771993514456,0.0101210394898968,0.0118315811427695,0.00167668984927358,4.0514039045602,4.0514039045602,0.00431035899483778
+"20778",100,2011,11,20,0.407150720859846,7.00345434948425,19.2573706057205,11.18513530001,21.1867988594819,21.1867988594819,4.46479121774125,100,2011,11,20,0.0459649101518401,0.020687718516364,0.0371117080244908,0.0210447144317085,3.14866888747669,3.14866888747669,0.00430167125549917
+"20779",100,2011,11,21,0.122332235046215,7.82532452959957,20.5522882820356,10.8075334593014,20.3514079690898,20.3514079690898,4.50271896145989,100,2011,11,21,0.0020467836867299,0.124322228813197,0.0504702045465862,0.057012628300476,2.50982015756553,2.50982015756553,0.00429563582404504
+"20780",100,2011,11,22,0.638503848260517,8.50096814834377,18.3882181106752,11.0184906843078,21.7649944313813,21.7649944313813,4.54064670517852,100,2011,11,22,0.014795319176557,0.0582397273326305,0.0179252113925648,0.0270019386282044,0.999492187928633,0.999492187928633,0.00429225270047533
+"20781",100,2011,11,23,0.570187022026354,7.62236528375624,17.6863258034483,9.5933860824971,25.3474916430852,25.3474916430852,4.57857444889715,100,2011,11,23,0.0569005814978952,0.0711210755064085,0.0150134466696871,0.016813066295311,1.92703704466823,1.92703704466823,0.00429152188479008
+"20782",100,2011,11,24,0,9.27876789630181,21.0587349945169,12.8939911597895,31.7205717634447,31.7205717634447,4.61650219261578,100,2011,11,24,0,0.171233891403525,0.037520418691744,0.0290436540556089,0.0495764994619371,0.0495764994619371,0.00429344337698932
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+"21541",100,2013,12,22,5.7257425583104,11.8934432104214,27.8009792617445,19.2010308151329,23.0737846823546,23.0737846823546,5.50369332830452,100,2013,12,22,0.49508771082116,0.0464274355035954,0.0919115770667671,0.0144984851865222,1.31847330201381,1.31847330201381,0.00145176926504676
+"21542",100,2013,12,23,3.51309129848207,11.7764907035366,17.3464577363269,12.2409429266901,26.4496701957107,26.4496701957107,5.55501762559432,100,2013,12,23,0.0783040801544686,0.0420508623224469,0.0193385719034034,0.0742649301229308,5.62389425512864,5.62389425512864,0.00145654590974895
+"21543",100,2013,12,24,0.0278327836930686,11.9268096172639,21.2753243923712,12.1324389376918,29.7937182750639,29.7937182750639,5.60634192288411,100,2013,12,24,0.0020467836867299,0.054865478482221,0.0853784513912391,0.0362460983620803,0.377033861434923,0.377033861434923,0.00146568956352624
+"21544",100,2013,12,25,0,9.13180412947148,30.2563475429422,10.3952222179921,33.3146861069011,33.3146861069011,5.65766622017391,100,2013,12,25,0,0.0857496641215779,0.0491145298296437,0.0535125059016893,0.132315446295805,0.132315446295805,0.00147920022637863
+"21545",100,2013,12,26,0,12.5451156230125,20.6679099600176,14.861811878133,23.9792960054672,23.9792960054672,5.70899051746371,100,2013,12,26,0,0.0828930601756513,0.237311690856118,0.0115160555535793,1.68369228259106,1.68369228259106,0.00149707789830614
+"21546",100,2013,12,27,0,10.994037393165,22.9677776110054,13.4574116464495,32.2508694068565,32.2508694068565,5.7603148147535,100,2013,12,27,0,0.108971899773301,0.0713811678571206,0.0702093926984056,0.857057776008742,0.857057776008742,0.00151932257930875
+"21547",100,2013,12,28,0,11.3142903876646,33.4953247632655,15.2230772463271,33.1644554515876,33.1644554515876,5.8116391120433,100,2013,12,28,0,0.107876641377298,0.222251215805752,0.073087659656373,0.185098751502388,0.185098751502388,0.00154593426938647
+"21548",100,2013,12,29,0,9.33116615303803,18.7300219855817,9.47996694315122,34.429768599037,34.429768599037,5.8629634093331,100,2013,12,29,0,0.276393014767367,0.0939988506118941,0.0642666701847564,0.180209869930159,0.180209869930159,0.00157691296853932
+"21549",100,2013,12,30,0,2.91929593259352,19.7591422912967,11.278885708128,35.0252039062701,35.0252039062701,5.91428770662289,100,2013,12,30,0,0.0338175360066208,0.0940495359412694,0.111563006850244,0.119403426366591,0.119403426366591,0.00161225867676724
+"21550",100,2013,12,31,0,7.07206822648169,24.7080307153716,12.7800990955533,34.4984929847507,34.4984929847507,5.96561200391269,100,2013,12,31,0,0.0635052650608,0.0635562240559383,0.147598304822216,0.0178473631309976,0.0178473631309976,0.00165197139407029
+"21551",100,2014,1,1,0.000110011002739402,13.8565127547949,24.3328164943112,19.6123982713823,3.39656765180321,3.39656765180321,6.01303467238571,100,2014,1,1,0.000526315805159117,0.161536844304917,0.0289496262849694,0.284931561873023,0.0643871270135176,0.0643871270135176,0.00154160157825579
+"21552",100,2014,1,2,13.0814082174018,11.2449833499585,20.277029662767,12.8500253763398,25.2039491089001,25.2039491089001,6.06045734085874,100,2014,1,2,2.81175445445107,0.0601099326106538,0.0931537991978474,0.0604050998353324,0.792616206708112,0.792616206708112,0.00148413419400392
+"21553",100,2014,1,3,0,8.19524753211748,20.9609680385611,11.0053388901932,24.6215951854509,24.6215951854509,6.10788000933176,100,2014,1,3,0,0.0398953381221067,0.0316245318142153,0.0857752585814215,3.81132739579087,3.81132739579087,0.00147956924131466
+"21554",100,2014,1,4,2.06655665382956,10.2278877957032,18.7616173520733,11.5497392920902,22.3953793948502,22.3953793948502,6.15530267780478,100,2014,1,4,0.125906432065352,0.0449339101401723,0.0407894650958489,0.0758085561468154,4.7187021057756,4.7187021057756,0.00152790672018803
+"21555",100,2014,1,5,0.706050607171914,7.85831683802001,21.6409461647764,13.7826437158029,19.5444224665005,19.5444224665005,6.2027253462778,100,2014,1,5,0.0333918116036914,0.0593830141435894,0.141025328648687,0.0977142201357188,0.988669055345627,0.988669055345627,0.00162914663062399
+"21556",100,2014,1,6,6.5447744499601,10.4673047522114,17.1610999154572,9.69561391563961,18.9790320128891,18.9790320128891,6.25014801475083,100,2014,1,6,0.346198854948362,0.155891164799799,0.0312322444629548,0.0610408036057735,1.930904659334,1.930904659334,0.00178328897262259
+"21557",100,2014,1,7,0.159185921177022,10.3313531676272,18.799108698292,12.276109018735,25.8794939483878,25.8794939483878,6.29757068322385,100,2014,1,7,0.0033918130755076,0.266650937092726,0.0564620302437276,0.0736234683260607,3.26735576858112,3.26735576858112,0.00199033374618378
+"21558",100,2014,1,8,0,10.2687458504151,22.7108249496443,14.223069277009,33.6574038766792,33.6574038766792,6.34499335169687,100,2014,1,8,0,0.121342633177198,0.100391891702846,0.173615221987369,0.00728196620967373,0.00728196620967373,0.00225028095130761
+"21559",100,2014,1,9,0,11.5777006757797,28.8104619843469,16.5278658756722,32.6543896153684,32.6543896153684,6.3924160201699,100,2014,1,9,0,0.0499543812557394,0.228810159426943,0.226510032892755,0.00155550384640759,0.00155550384640759,0.00256313058799404
+"21560",100,2014,1,10,0,16.3235861481351,34.744389608486,15.6268536742895,30.7228603845645,30.7228603845645,6.43983868864292,100,2014,1,10,0,0.0105151892235689,0.0557354193091275,0.486922851480052,0.683503545116924,0.683503545116924,0.0029288826562431
+"21561",100,2014,1,11,0,12.5537293353359,21.3278987725051,14.8468756156393,23.6783279213313,23.6783279213313,6.48726135711594,100,2014,1,11,0,0.0396695727299018,0.273227544224895,0.0436029460773952,4.54072558707553,4.54072558707553,0.00334753715605475
+"21562",100,2014,1,12,0,12.4681957484078,22.8414083151403,14.772937193431,30.0286249846909,30.0286249846909,6.53468402558896,100,2014,1,12,0,0.100733318159378,0.537180909773881,0.0916929515371032,0.140103005428559,0.140103005428559,0.00381909408742906
+"21563",100,2014,1,13,0.0299229927451173,8.34589644286237,32.4124750970352,16.1009902177733,33.4183385322327,33.4183385322327,6.58210669406199,100,2014,1,13,0.0020467836867299,0.0403227970324671,0.940583567217731,0.421611546658265,0.000435669704667859,0.000435669704667859,0.00434355345036596
+"21564",100,2014,1,14,0,15.1802309621679,41.2578879672177,13.1742134241119,32.1165011146806,32.1165011146806,6.62952936253501,100,2014,1,14,0,0.0100760357952195,0.178825685496228,0.546083143997275,0.0419110248151422,0.0419110248151422,0.00492091524486548
+"21565",100,2014,1,15,0,20.5103299987592,36.026325439856,16.5801025575275,21.4036635555188,21.4036635555188,6.67695203100803,100,2014,1,15,0,0.0737427044475827,0.45137014535975,0.375057514292406,1.94032069287239,1.94032069287239,0.00555117947092761
+"21566",100,2014,1,16,0,20.0336632471536,41.4520132318713,14.9147558988649,28.6568317539228,28.6568317539228,6.72437469948106,100,2014,1,16,0,0.0156719335904816,0.106882935954678,0.220189301346713,1.99613739732028,1.99613739732028,0.00623434612855235
+"21567",100,2014,1,17,0.721012108462347,22.82555518869,39.4785811085381,16.5306194240373,25.4587788408739,25.4587788408739,6.77179736795408,100,2014,1,17,0.0937426947954807,0.122891756403817,0.188914942648058,0.13302406995303,2.67624025538957,2.67624025538957,0.00697041521773972
+"21568",100,2014,1,18,0.106600661769231,16.0747963356631,21.1165344292837,14.5358120967584,18.5954344322448,18.5954344322448,6.8192200364271,100,2014,1,18,0.00941520512888307,0.0214433007114108,0.144675919105921,0.00582503413573499,1.22591483932447,1.22591483932447,0.00775938673848969
+"21569",100,2014,1,19,0,12.0185370356074,21.3385146637299,12.3543378601242,27.7153685520453,27.7153685520453,6.86664270490013,100,2014,1,19,0,0.22263684234624,0.164503492960372,0.0289326937925856,0.677925326739154,0.677925326739154,0.00860126069080229
+"21570",100,2014,1,20,0.164356438691008,13.0051265701865,21.0189110469503,13.9478692641222,19.9033662042733,19.9033662042733,6.91406537337315,100,2014,1,20,0.00508771975050894,0.103548518137407,0.115070025282572,0.00479269613425081,8.23620385253221,8.23620385253221,0.00949603707467753
+"21571",100,2014,1,21,0,10.5519141196155,21.8713421459639,12.7956688338512,25.0311769017554,25.0311769017554,6.96148804184617,100,2014,1,21,0,0.0693806956576588,0.0849374810338214,0.0603659510316365,1.79798184722574,1.79798184722574,0.0104437158901153
+"21572",100,2014,1,22,0,12.9517710930181,22.7379867881045,11.0839967926999,32.126303254181,32.126303254181,7.00891071031919,100,2014,1,22,0,0.0508064681026123,0.159791145454422,0.09827221556981,0.00240640950480092,0.00240640950480092,0.0114442971371158
+"21573",100,2014,1,23,0,8.66855888345716,28.9507038863448,13.3685952009279,31.6373816784984,31.6373816784984,7.05633337879222,100,2014,1,23,0,0.0728467778055921,0.360049792976029,0.11056659743877,0.011884219258133,0.011884219258133,0.0124977808156788
+"21574",100,2014,1,24,0,15.5512321197292,22.8363587093039,14.095372026903,22.9940372085152,22.9940372085152,7.10375604726524,100,2014,1,24,0,0.00662748520398059,0.0640894515236354,0.045186312394786,1.39301444701761,1.39301444701761,0.0136041669258045
+"21575",100,2014,1,25,0,11.1456324962368,20.1674036292484,10.1826656759113,28.8570077956969,28.8570077956969,7.15117871573826,100,2014,1,25,0,0.218256185170285,0.0653672381576946,0.0763408753175301,0.553491984866253,0.553491984866253,0.0147634554674928
+"21576",100,2014,1,26,0,6.30250831641773,28.8095711245395,12.6962300343613,31.5110123826332,31.5110123826332,7.19860138421129,100,2014,1,26,0,0.0442450353087169,0.301944874782849,0.194059567691772,0.000749726974997126,0.000749726974997126,0.0159756464407437
+"21577",100,2014,1,27,0,12.2860945029096,36.1953354511324,12.2576821057579,31.4316721770367,31.4316721770367,7.24602405268431,100,2014,1,27,0,0.0345719600321187,0.194970091088329,0.230232575477969,0.00054502675034227,0.00054502675034227,0.0172407398455573
+"21578",100,2014,1,28,0,15.9233773937582,42.358503943885,7.1245248425256,31.1648402009467,31.1648402009467,7.29344672115733,100,2014,1,28,0,0.115895271827867,0.0217942716557211,0.134026352206233,0.00672099667404539,0.00672099667404539,0.0185587356819334
+"21579",100,2014,1,29,0,14.0978986343535,22.5058968295359,11.6748991526655,31.7113088832305,31.7113088832305,7.34086938963035,100,2014,1,29,0,0.036565480788904,0.14692226994859,0.0122341652697702,0.00123157988904314,0.00123157988904314,0.0199296339498722
+"21580",100,2014,1,30,0,7.8776016954017,27.259174934446,14.5255775462152,30.215588618951,30.215588618951,7.38829205810338,100,2014,1,30,0,0.0652923802047165,0.750402568857638,0.226780034983299,0.0252373823336525,0.0252373823336525,0.0213534346493736
+"21581",100,2014,1,31,0,13.7968758204327,26.390187000153,16.7346536659434,30.3920462244284,30.3920462244284,7.4357147265764,100,2014,1,31,0,0.0560607744719536,0.294828926537398,0.0516807640792623,0.147446887711478,0.147446887711478,0.0228301377804375
+"21582",100,2014,2,1,0,13.9016391441505,32.8751378033159,13.9036194000832,29.8122551732331,29.8122551732331,7.40346804779021,100,2014,2,1,0,0.013169555652995,0.691283751987734,0.510592337497991,0.0986719843637905,0.0986719843637905,0.0226993021770935
+"21583",100,2014,2,2,0,12.9119251962542,41.7308140577394,10.8022994019411,26.3606272341788,26.3606272341788,7.37122136900402,100,2014,2,2,0,0.0172257391300024,0.0919508163642604,0.312347992991908,0.782804356112345,0.782804356112345,0.0225732394842319
+"21584",100,2014,2,3,0,19.4732892715236,22.3592407973555,15.6116723321845,14.1699119836453,14.1699119836453,7.33897469021783,100,2014,2,3,0,0.0356357919619172,0.262853862762808,0.0112783664927195,2.17267018947499,2.17267018947499,0.0224519497018528
+"21585",100,2014,2,4,0,11.3629152824645,21.3832780441435,9.95608356526189,30.9280307064749,30.9280307064749,7.30672801143164,100,2014,2,4,0,0.199913338972474,0.0784537887263006,0.00852631179635896,0.0323608498203176,0.0323608498203176,0.0223354328299562
+"21586",100,2014,2,5,0,10.9575247308209,28.9233223733598,13.2724531559792,29.3666338170453,29.3666338170453,7.27448133264545,100,2014,2,5,0,0.0257889112261352,0.187569413021457,0.0889877006776946,0.00915377548432328,0.00915377548432328,0.022223688868542
+"21587",100,2014,2,6,0,14.81555564595,35.5517493731642,14.5534434439194,28.2745982779659,28.2745982779659,7.24223465385926,100,2014,2,6,0,0.0923274452477778,0.0712339398137134,0.0918584797241211,0.157660891549908,0.157660891549908,0.0221167178176102
+"21588",100,2014,2,7,0,15.7847085633818,24.0497359180346,16.3421673811439,19.1134322788587,19.1134322788587,7.20998797507307,100,2014,2,7,0,0.0266719572815599,0.800536456084913,0.0454689893936093,3.72925943853835,3.72925943853835,0.022014519677161
+"21589",100,2014,2,8,0,13.3493729466521,37.0128606664072,13.3173706675782,25.1131132522432,25.1131132522432,7.17774129628688,100,2014,2,8,0,0.0160263229504779,0.497351291322739,0.127835626700411,1.13447960885052,1.13447960885052,0.0219170944471941
+"21590",100,2014,2,9,0,18.6224862739722,26.2135534884513,8.5992850848157,23.9376898116142,23.9376898116142,7.14549461750069,100,2014,2,9,0,0.03822116929476,0.762680400500608,0.0890263419347289,1.13434754188524,1.13434754188524,0.0218244421277098
+"21591",100,2014,2,10,0,8.0791309947359,23.2834763962312,11.9164026971697,28.4722884191801,28.4722884191801,7.11324793871449,100,2014,2,10,0,0.0446888794157695,0.153000544243802,0.139092396882347,0.0709608533273384,0.0709608533273384,0.0217365627187079
+"21592",100,2014,2,11,0,6.24328932064463,27.3365790846586,13.2265126183219,29.2675023996922,29.2675023996922,7.0810012599283,100,2014,2,11,0,0.119653757269357,0.219711635304345,0.0894765301309288,0.019202861835292,0.019202861835292,0.0216534562201884
+"21593",100,2014,2,12,0,13.5983608348666,29.9726843503442,16.7087897310163,22.030362998954,22.030362998954,7.04875458114211,100,2014,2,12,0,0.0104479630142139,0.223211778679026,0.064554339354523,0.348470906285736,0.348470906285736,0.0215751226321514
+"21594",100,2014,2,13,0,16.7117494849613,28.53964781053,14.3867106159659,9.42762378445028,9.42762378445028,7.01650790235592,100,2014,2,13,0,0.0263813088447571,0.10422104318648,0.0454497339327411,0.116660753975222,0.116660753975222,0.0215015619545969
+"21595",100,2014,2,14,0,15.4846315415386,28.9136084754868,16.4072496993314,15.2218040281659,15.2218040281659,6.98426122356973,100,2014,2,14,0,0.0438830916980444,0.254968878165123,0.163554323805722,1.00997216796213,1.00997216796213,0.0214327741875248
+"21596",100,2014,2,15,0.231683174588762,16.0583281406868,27.8275793702951,17.8332673063373,22.6014852597244,22.6014852597244,6.95201454478354,100,2014,2,15,0.0104093573490779,0.00736725871460645,0.199534048488938,0.0421222494339434,1.35096883950522,1.35096883950522,0.0213687593309351
+"21597",100,2014,2,16,0.336853693781382,14.4957535694403,22.2018700338433,12.4515071707328,23.1196261679772,23.1196261679772,6.91976786599735,100,2014,2,16,0.00707602303627641,0.0679731558170423,0.103311539539832,0.0455620438113605,3.58863770727888,3.58863770727888,0.021309517384828
+"21598",100,2014,2,17,0.134213424137127,9.79281626702404,23.582112212648,10.2607590420411,27.4072385206737,27.4072385206737,6.88752118721116,100,2014,2,17,0.00812865544829453,0.125047940018943,0.309620477455844,0.0797543588828721,0.0690473980956096,0.0690473980956096,0.0212550483492033
+"21599",100,2014,2,18,0,9.44785470616306,25.7979644758604,14.4426293032135,26.8171837138395,26.8171837138395,6.85527450842497,100,2014,2,18,0,0.0220818636934121,0.368421175884514,0.111305303611607,0.0149555535214024,0.0149555535214024,0.021205352224061
+"21600",100,2014,2,19,0.0365236529094814,10.2359076396073,20.2982729459622,12.4689328001671,15.4297360387704,15.4297360387704,6.82302782963878,100,2014,2,19,0.00228070182235617,0.0131871301529231,0.147469538500552,0.0146274997265656,2.10662037342161,2.10662037342161,0.0211604290094012
+"21601",100,2014,2,20,6.50231021832843,9.44680968789246,18.9970957312253,11.0344004374002,18.333696161822,18.333696161822,6.79078115085259,100,2014,2,20,0.885906467716618,0.0748690134037958,0.0205807085943176,0.00456901160578659,1.95160258190572,1.95160258190572,0.0211202787052239
+"21602",100,2014,2,21,2.77205721694644,11.0379756805789,19.0231573379735,9.26760180214188,16.669768955579,16.669768955579,6.7585344720664,100,2014,2,21,0.592514684353662,0.124909318054009,0.0457442835493205,0.0479362528528237,1.15824858739105,1.15824858739105,0.021084901311529
+"21603",100,2014,2,22,0.0458745881423305,11.5524531963504,19.8104952171166,13.0677447932782,14.0407038920521,14.0407038920521,6.72628779328021,100,2014,2,22,0.00257309949188902,0.0587040277203052,0.131036907972565,0.0379912841864465,0.232604061077719,0.232604061077719,0.0210542968283165
+"21604",100,2014,2,23,0,9.71106705702308,23.2175133435508,12.5067545709306,26.238998757206,26.238998757206,6.69404111449402,100,2014,2,23,0,0.0815987747684141,0.305909792328067,0.0901432954666096,0.0343877413524007,0.0343877413524007,0.0210284652555866
+"21605",100,2014,2,24,0,11.1651376319273,25.0209902164304,13.3517600396285,25.4049173760073,25.4049173760073,6.66179443570783,100,2014,2,24,0,0.060070760628863,0.314925104514004,0.1163356563591,0.0281673453618918,0.0281673453618918,0.0210074065933391
+"21606",100,2014,2,25,0,10.017106783272,24.5764575975038,14.6107261886429,19.9820241330087,19.9820241330087,6.62954775692164,100,2014,2,25,0,0.0328701873088408,0.533078372206147,0.150178349043267,0.614215763824134,0.614215763824134,0.020991120841574
+"21607",100,2014,2,26,0.533883396925444,10.908492797541,20.0572716531449,11.289604031738,17.2214413255748,17.2214413255748,6.59730107813545,100,2014,2,26,0.0236257313148322,0.0957894710073023,0.106737587301122,0.0399806909439342,0.885287896601692,0.885287896601692,0.0209796080002914
+"21608",100,2014,2,27,0.221342139749309,9.49243129860319,20.537964643556,10.2237733456955,22.3614078916208,22.3614078916208,6.56505439934926,100,2014,2,27,0.00918128703247038,0.208993003840817,0.0780397921291339,0.0739719226392119,0.595553754019346,0.595553754019346,0.0209728680694913
+"21609",100,2014,2,28,0,10.3183279100424,21.6000989970594,11.6153134093164,24.1540485293952,24.1540485293952,6.53280772056307,100,2014,2,28,0,0.190972039288612,0.147920499911225,0.0909731261137271,0.807806400486798,0.807806400486798,0.0209709010491735
+"21610",100,2014,3,1,0,9.93399355671193,22.7868756838758,15.1272498331185,18.6214628481891,18.6214628481891,6.45864413678067,100,2014,3,1,0,0.186339793726233,0.0818555324133246,0.0308257068322507,0.646395342567159,0.646395342567159,0.0201077665354108
+"21611",100,2014,3,2,0,13.2516282273598,22.6966447966589,13.8622441339021,17.4522220676619,17.4522220676619,6.38448055299827,100,2014,3,2,0,0.16441165101801,0.0900051864360616,0.124067313556879,1.85113575511982,1.85113575511982,0.0192649251373146
+"21612",100,2014,3,3,0,7.01137517125431,24.6191418315187,12.3280418677167,25.373245247651,25.373245247651,6.31031696921588,100,2014,3,3,0,0.0686040916844573,0.329289450863271,0.117976708306263,0.00841639848256723,0.00841639848256723,0.0184423768548849
+"21613",100,2014,3,4,0,10.5136304809184,35.2664024470532,14.1275357790906,20.9253579486977,20.9253579486977,6.23615338543348,100,2014,3,4,0,0.0262041194068923,0.0243923410709283,0.121776024940054,0.93157621745082,0.93157621745082,0.0176401216881218
+"21614",100,2014,3,5,0,15.8533113467024,19.6750494429249,11.0902749723596,12.7727394250884,12.7727394250884,6.16198980165108,100,2014,3,5,0,0.0123093487790628,0.0233287180006364,0.0105333333758183,0.710220630739484,0.710220630739484,0.0168581596370252
+"21615",100,2014,3,6,0,8.76959295975755,20.8015954570539,9.65016503643544,19.3162263612149,19.3162263612149,6.08782621786869,100,2014,3,6,0,0.0864619781539297,0.0313098748863082,0.0353801348056863,1.1363354690822,1.1363354690822,0.0160964907015951
+"21616",100,2014,3,7,0.669636968437201,10.9822661323254,19.4472606394551,16.5909792246467,7.76680968487092,7.76680968487092,6.01366263408629,100,2014,3,7,0.0265497050125002,0.241450935856873,0.108792353870283,0.0240175499723256,1.27391412109357,1.27391412109357,0.0153551148818316
+"21617",100,2014,3,8,0.0114411442848978,13.4738723641575,24.797766808236,17.7855009051702,15.6207370086603,15.6207370086603,5.93949905030389,100,2014,3,8,0.00175438601719706,0.0524719486170371,0.0619654460619588,0.0212116583485719,0.502805204239955,0.502805204239955,0.0146340321777347
+"21618",100,2014,3,9,0.0103410342575038,13.6028053212349,33.7050829007156,11.0339824416325,23.9205828814617,23.9205828814617,5.8653354665215,100,2014,3,9,0.000994152076411666,0.0516274816346536,0.0182929531713997,0.0727842673163145,0.0230648835972423,0.0230648835972423,0.0139332425893043
+"21619",100,2014,3,10,0,15.267975814534,29.6627504015126,12.8780309238581,13.0922111389529,13.0922111389529,5.7911718827391,100,2014,3,10,0,0.0857140089359512,0.0985940903693831,0.315014712629345,0.591220541216819,0.591220541216819,0.0132527461165404
+"21620",100,2014,3,11,0.188228825769081,15.1545213735012,33.0997027784291,15.818855875265,8.78111115068492,8.78111115068492,5.7170082989567,100,2014,3,11,0.003216374547858,0.0142450193305258,0.156664803560925,0.14948428049825,0.766766678132032,0.766766678132032,0.012592542759443
+"21621",100,2014,3,12,3.97601758660001,11.5934763164541,20.4927722838583,11.0755335091233,18.8305721261976,18.8305721261976,5.64284471517431,100,2014,3,12,0.0169005768201543,0.0585146121756443,0.0150941714149114,0.0453672151800446,1.5358501181534,1.5358501181534,0.0119526325180122
+"21622",100,2014,3,13,0,7.37502746088932,22.2432452223875,13.1835093923134,17.756985602468,17.756985602468,5.56868113139191,100,2014,3,13,0,0.0458064447905118,0.0312022807832933,0.0590912297478402,1.62305128627828,1.62305128627828,0.011333015392248
+"21623",100,2014,3,14,0,7.89111104237102,27.0895601446741,12.9842244275201,20.7460728384087,20.7460728384087,5.49451754760952,100,2014,3,14,0,0.0572561835640805,0.0945257047110291,0.0632204872087604,0.382375992285574,0.382375992285574,0.0107336913821503
+"21624",100,2014,3,15,0.969636973112461,12.8926513145203,24.4077119690881,18.5378437105185,4.61386136017223,4.61386136017223,5.42035396382712,100,2014,3,15,0.0388888902134374,0.0114584780889111,0.275480139160358,0.00883625110868999,0.563902298610552,0.563902298610552,0.0101546604877192
+"21625",100,2014,3,16,7.41397153688605,9.51642463545595,18.9242024899054,11.5102751210447,15.7286910097985,15.7286910097985,5.34619038004472,100,2014,3,16,1.15923977243973,0.031692968379763,0.0152356669066819,0.146559679303597,1.48311163272315,1.48311163272315,0.00959592270895456
+"21626",100,2014,3,17,2.60572059424666,11.3387788367612,21.2479756610228,14.7316391433951,12.8508690455303,12.8508690455303,5.27202679626233,100,2014,3,17,0.355847964091613,0.0196450319525233,0.035858447444335,0.0125806859290265,1.51766563922598,1.51766563922598,0.00905747804585652
+"21627",100,2014,3,18,1.49933994163906,9.90300334431026,23.6367106757673,12.0801649387389,19.527073506606,19.527073506606,5.19786321247993,100,2014,3,18,0.141345029400107,0.0384590459344822,0.0384730030844217,0.11618011576788,0.403366793230199,0.403366793230199,0.008539326498425
+"21628",100,2014,3,19,0.127062708164009,9.00058302653767,22.8087457293856,11.3586358887659,20.4036190675037,20.4036190675037,5.12369962869753,100,2014,3,19,0.0020467836867299,0.0640274858687226,0.0476584755215193,0.113247321486958,0.141780655442835,0.141780655442835,0.00804146806666003
+"21629",100,2014,3,20,0,7.18852585980339,27.5550715925932,10.2163915434817,21.2315841628642,21.2315841628642,5.04953604491514,100,2014,3,20,0,0.0297485451280185,0.154202923851933,0.130573047124916,0.00347017121808219,0.00347017121808219,0.0075639027505616
+"21630",100,2014,3,21,0.0842684280983817,10.8230473137531,16.086952619689,15.5154345116862,2.99136413310883,2.99136413310883,4.97537246113274,100,2014,3,21,0.0020467836867299,0.0323666722192444,0.0363063715306969,0.00386900655519118,0.0504877142131028,0.0504877142131028,0.00710663055012971
+"21631",100,2014,3,22,18.9008800655571,8.59980202298222,17.724587435245,9.88701880699468,12.2671065535089,12.2671065535089,4.90120887735034,100,2014,3,22,5.35923991800078,0.195198836081294,0.0185766097312052,0.00386724461568307,0.941047518760405,0.941047518760405,0.00666965146536435
+"21632",100,2014,3,23,0,9.45931792495274,18.8555995618025,10.2290318712543,12.5847854624749,12.5847854624749,4.82704529356795,100,2014,3,23,0,0.0471362597327296,0.00618070315555853,0.0657204819663306,0.460954529482383,0.460954529482383,0.00625296549626557
+"21633",100,2014,3,24,1.95676569320975,9.49752475667183,20.1727393709525,11.4096369213528,13.3617382993793,13.3617382993793,4.75288170978555,100,2014,3,24,0.283157911175178,0.177405270477306,0.0294913266949811,0.0204145869964293,3.42664726758377,3.42664726758377,0.00585657264283331
+"21634",100,2014,3,25,0.211991204229584,11.4313201369232,21.7589111370091,15.0958527417073,11.3951594611861,11.3951594611861,4.67871812600315,100,2014,3,25,0.0138596498844219,0.0860537721110573,0.0222135039885014,0.00589529803993806,2.91053228391687,2.91053228391687,0.00548047290506761
+"21635",100,2014,3,26,0,12.4010561564312,25.6313534748174,18.4617053368697,16.7007042640376,16.7007042640376,4.60455454222076,100,2014,3,26,0,0.0823801021576576,0.0626398372853933,0.0170554991831767,0.285576526833939,0.285576526833939,0.00512466628296843
+"21636",100,2014,3,27,2.42178216350354,16.1954124878735,19.9579868190753,20.338844933001,3.93627065164421,3.93627065164421,4.53039095843836,100,2014,3,27,0.89263155528677,0.0462339268737175,0.023269584044835,0.0171941739625052,0.0748274764373388,0.0748274764373388,0.0047891527765358
+"21637",100,2014,3,28,1.52079206711651,14.8185587317506,22.0595598986702,16.6257647586734,8.09367437173825,8.09367437173825,4.45622737465596,100,2014,3,28,0.153391807232685,0.0758818373720151,0.0993450974137726,0.00769295693595662,0.921764859947997,0.921764859947997,0.00447393238576974
+"21638",100,2014,3,29,0.0436743680875425,14.0371837678916,24.6651484654157,15.5500439768708,17.1177556527854,17.1177556527854,4.38206379087357,100,2014,3,29,0.00245614042407588,0.0589140224635991,0.0502759904073024,0.0652544361472585,0.165411609569604,0.165411609569604,0.00417900511067021
+"21639",100,2014,3,30,0,11.0357315841943,24.2161716528327,17.0678657263157,16.8399007228604,16.8399007228604,4.30790020709117,100,2014,3,30,0,0.0206894781414435,0.140092803339684,0.0296988576703891,0.058392908669926,0.058392908669926,0.0039043709512372
+"21640",100,2014,3,31,0,13.025511497187,33.269757857286,13.1823980784652,13.6313861431462,13.6313861431462,4.23373662330877,100,2014,3,31,0,0.0145783590551697,0.0427256179641597,0.0882041718087743,0.302114665875844,0.302114665875844,0.00365002990747076
+"21641",100,2014,4,1,0.067656766988001,16.1210451430351,35.64525851427,11.4375797382938,16.8773598728663,16.8773598728663,4.18313133730727,100,2014,4,1,0.0109941527230001,0.0292695463884346,0.0272340708072175,0.0385497088628223,0.411606534059235,0.411606534059235,0.00332435625329527
+"21642",100,2014,4,2,2.16633661207717,15.8032123232045,17.5342903116224,13.4226732295994,6.47805272837808,6.47805272837808,4.13252605130577,100,2014,4,2,0.0853801303439691,0.00717311127654183,0.0562917981602283,0.0329982541447569,0.508025785984927,0.508025785984927,0.00301990925398611
+"21643",100,2014,4,3,1.4779977835182,11.0666116288524,18.9031351800799,13.0964467528105,12.340659989251,12.340659989251,4.08192076530427,100,2014,4,3,0.0580116957530655,0.127781795852022,0.038839903928861,0.00420935940442098,2.04991743545986,2.04991743545986,0.00273668890954324
+"21644",100,2014,4,4,0.436633666733442,9.51806391242838,18.8430912785798,11.0768096344699,13.2644223411485,13.2644223411485,4.03131547930277,100,2014,4,4,0.0153801164501592,0.164870774785621,0.0111807042709688,0.00438947518339997,0.340387247926687,0.340387247926687,0.0024746952199667
+"21645",100,2014,4,5,0.608470857700761,12.1528052391917,21.9792188365336,16.4685148337756,11.1117821893807,11.1117821893807,3.98071019330127,100,2014,4,5,0.0233333318763311,0.0992374379476818,0.0250262444929001,0.0216251190902033,0.249102399313048,0.249102399313048,0.00223392818525645
+"21646",100,2014,4,6,0,10.4318149637992,22.6978106860674,15.3773926387657,15.2918811468664,15.2918811468664,3.93010490729977,100,2014,4,6,0,0.0511427352839321,0.0412204686039097,0.0361508694766716,0.30948724621342,0.30948724621342,0.00201438780541254
+"21647",100,2014,4,7,0.11881188296675,12.3581188483076,25.6679866316557,14.3519911907687,11.3575466290297,11.3575466290297,3.87949962129827,100,2014,4,7,0.00450292425023185,0.0530444683075591,0.0570811161334051,0.0104894769613443,0.216548028872367,0.216548028872367,0.00181607408043491
+"21648",100,2014,4,8,1.51683167432926,16.3285368729477,17.4155114785542,18.8591969556148,3.41127617052286,3.41127617052286,3.82889433529677,100,2014,4,8,0.0365497053018103,0.090554459305497,0.0582000023740029,0.0229251451374157,0.00347076358293761,0.00347076358293761,0.0016389870103236
+"21649",100,2014,4,9,18.7667765821954,15.9919361380985,21.276314581188,18.9668537429457,6.07407033981139,6.07407033981139,3.77828904929527,100,2014,4,9,6.9658479213159,0.0836608144080648,0.181338486663886,0.0152696123539138,0.735044936825958,0.735044936825958,0.00148312659507861
+"21650",100,2014,4,10,0.172937296609608,15.71662270931,20.1497688167559,18.8271838294135,5.15710680634275,5.15710680634275,3.72768376329376,100,2014,4,10,0.00362573129391811,0.0269918151454731,0.0953098938035611,0.00739124618023559,0.820202835932407,0.820202835932407,0.00134849283469993
+"21651",100,2014,4,11,3.05940592590601,14.9742903861538,18.9419252397728,15.2667106475243,10.0022663068194,10.0022663068194,3.67707847729226,100,2014,4,11,0.0537426818044611,0.0759315223761708,0.0274731549584989,0.00392573725389012,1.3624444090526,1.3624444090526,0.00123508572918755
+"21652",100,2014,4,12,3.08030801900018,13.7608470276769,19.9802860830746,16.6580085061958,11.1462706302521,11.1462706302521,3.62647319129076,100,2014,4,12,0.323684224468928,0.0736597082864004,0.0623537261027615,0.0074029403912875,1.07292510799392,1.07292510799392,0.00114290527854149
+"21653",100,2014,4,13,0.133553357325634,10.8749174720252,20.299053995785,13.0655775825576,14.3244884247565,14.3244884247565,3.57586790528926,100,2014,4,13,0.00397660830564666,0.0557397711682128,0.0432894602997257,0.0227029287483972,0.0950813545741235,0.0950813545741235,0.00107195148276173
+"21654",100,2014,4,14,0,8.77490655607385,19.7019803117473,12.9261496022458,12.6055226026982,12.6055226026982,3.52526261928776,100,2014,4,14,0,0.065842628470143,0.0172356521609571,0.0256222273933178,1.70700626214016,1.70700626214016,0.00102222434184829
+"21655",100,2014,4,15,2.58459845286916,9.8041804495162,19.1237624035154,14.1298789694758,11.4720791679273,11.4720791679273,3.47465733328626,100,2014,4,15,0.498421044977091,0.0867806814317165,0.0112473677431675,0.00747190193720194,0.676298781893602,0.676298781893602,0.000993723855801151
+"21656",100,2014,4,16,0,7.19267334271841,19.975577496066,14.0064356072639,12.0941474073135,12.0941474073135,3.42405204728476,100,2014,4,16,0,0.047698251537992,0.0409485234314581,0.0128608103568908,0.0734654932546895,0.0734654932546895,0.000986450024620331
+"21657",100,2014,4,17,0.0222222225533591,7.10223322313349,22.7297027575301,13.415665655902,12.0229483741869,12.0229483741869,3.37344676128326,100,2014,4,17,0.00175438601719706,0.0416444503148554,0.011043241075018,0.0913760289377077,0.421457902747826,0.421457902747826,0.00100040284830582
+"21658",100,2014,4,18,2.06446645283463,9.11261819875149,17.4058193966369,10.2081077153927,11.4178989334862,11.4178989334862,3.32284147528176,100,2014,4,18,0.125847956060668,0.0273374230837256,0.0378895575454966,0.030450878424468,1.05122532190919,1.05122532190919,0.00103558232685762
+"21659",100,2014,4,19,0.784488450084189,8.60632572027192,16.1376127792795,10.8435203743191,9.13324532335741,9.13324532335741,3.27223618928026,100,2014,4,19,0.0670760220707511,0.077338611522549,0.0250286254417304,0.00509416422041981,0.779425767754751,0.779425767754751,0.00109198846027572
+"21660",100,2014,4,20,0.0436743680875425,10.0304179249292,19.7662044940609,11.7296918936164,9.50184826279106,9.50184826279106,3.22163090327875,100,2014,4,20,0.00245614042407588,0.0530035292553272,0.0589543820478334,0.0891619693511529,0.535532225748321,0.535532225748321,0.00116962124856014
+"21661",100,2014,4,21,0.16182618502966,10.3496810128324,22.7383059385193,10.4601979848444,5.06852583024893,5.06852583024893,3.17102561727725,100,2014,4,21,0.00257309949188902,0.0748818067094101,0.023006470989495,0.0290707941022388,1.15150633662711,1.15150633662711,0.00126848069171087
+"21662",100,2014,4,22,16.0273925856789,13.4353244905293,17.5598571135266,13.0449285591134,9.82344329947292,9.82344329947292,3.12042033127575,100,2014,4,22,1.61760244447601,0.139817593397242,0.0226811947965142,0.0380812858872256,0.93240363798091,0.93240363798091,0.00138856678972792
+"21663",100,2014,4,23,0.350275036427054,4.78411440802093,18.0158744409139,11.2945543379411,11.3183498382568,11.3183498382568,3.06981504527425,100,2014,4,23,0.00561403441847418,0.0432263379950482,0.0571035043418486,0.0166356851500203,0.0318543896502984,0.0318543896502984,0.00152987954261126
+"21664",100,2014,4,24,2.72860288633109,10.6403631466319,15.9407590838811,9.24113310655483,11.4616391608948,11.4616391608948,3.01920975927275,100,2014,4,24,0.477836277136333,0.0673321741294424,0.0276151914363653,0.0127116704060166,0.392453861238901,0.392453861238901,0.00169241895036093
+"21665",100,2014,4,25,0,2.79687567328987,18.6852147298546,8.88217816389564,13.0960506495863,13.0960506495863,2.96860447327125,100,2014,4,25,0,0.0535397597625274,0.0390765669218281,0.00830642264972143,0.00513216109254899,0.00513216109254899,0.0018761850129769
+"21666",100,2014,4,26,4.44609461260839,9.65202427749718,15.1955334670735,9.31477441462484,8.79121021547727,8.79121021547727,2.91799918726975,100,2014,4,26,0.140526327306082,0.0501912623668426,0.0469222106086488,0.020850888982936,0.26220994656328,0.26220994656328,0.00208117773045918
+"21667",100,2014,4,27,0,3.43700768819081,16.4003520352875,9.48030795463503,11.4607040690641,11.4607040690641,2.86739390126825,100,2014,4,27,0,0.0809426923735156,0.0282987924678182,0.0272620017805331,0.0638801147729362,0.0638801147729362,0.00230739710280777
+"21668",100,2014,4,28,0.00363036309040026,4.53442244272683,21.3100661455077,11.70354230438,5.35059403061736,5.35059403061736,2.81678861526675,100,2014,4,28,0.000526315805159117,0.0508041015982187,0.0392339016877076,0.162859714162332,0.256036238267708,0.256036238267708,0.00255484313002267
+"21669",100,2014,4,29,17.3030805304499,11.4413971990117,15.9844334322234,14.1749393696045,2.80644667686278,2.80644667686278,2.76618332926524,100,2014,4,29,0.287310055515455,0.0347035274350264,0.0520830258644911,0.00985787657583914,0.0269339233772157,0.0269339233772157,0.00282351581210388
+"21670",100,2014,4,30,19.0521451490547,8.26118819758181,15.7557094897112,9.08511549988464,9.88665567005691,9.88665567005691,2.71557804326374,100,2014,4,30,0.93023436630007,0.0893543352462346,0.0403397789582493,0.00984500831475078,0.434859600312592,0.434859600312592,0.00311341514905141
+"21671",100,2014,5,1,0,4.9013311361978,15.0835973479436,9.57095713347885,8.71930696678371,8.71930696678371,2.68237058814506,100,2014,5,1,0,0.160581885265348,0.152580725526019,0.00958712235175578,0.044902986067055,0.044902986067055,0.00294499663676148
+"21672",100,2014,5,2,0.683058300165191,8.99322322178202,14.2930802700937,11.5355227200767,5.88216722024681,5.88216722024681,2.64916313302638,100,2014,5,2,0.00426900478134401,0.155000013775378,0.0349099008125827,0.00331403364668792,0.222033959479102,0.222033959479102,0.00278274805414805
+"21673",100,2014,5,3,13.8210121103377,5.38845988967107,14.1203410218913,12.6416172965525,6.13088012135068,6.13088012135068,2.6159556779077,100,2014,5,3,8.95923999663694,0.180624520726805,0.0235286528136358,0.0176806837026875,0.67768310240809,0.67768310240809,0.00262666940121113
+"21674",100,2014,5,4,5.86226622735706,7.13896591776144,14.5993400632483,10.5340813237055,7.61126520924836,7.61126520924836,2.58274822278901,100,2014,5,4,1.43368412787456,0.196691199329465,0.095491800243593,0.00571461087952895,0.71504688370022,0.71504688370022,0.00247676067795071
+"21675",100,2014,5,5,2.20044002163135,8.46551151401532,15.4185699225783,12.1412541375826,7.29919691421542,7.29919691421542,2.54954076767033,100,2014,5,5,0.0305263243502384,0.101725104036463,0.05979414156424,0.00249239541671164,0.286869001864851,0.286869001864851,0.0023330218843668
+"21676",100,2014,5,6,1.58217821446451,8.50324535317416,14.6612871000082,11.3071177548701,8.07850384213875,8.07850384213875,2.51633331255165,100,2014,5,6,0.0750877180852403,0.233491220714138,0.0791035704072496,0.00125438599845747,0.604450859585184,0.604450859585184,0.00219545302045939
+"21677",100,2014,5,7,1.31177117614069,7.6981298401542,13.6476898550069,9.14973588718964,6.75305834960098,6.75305834960098,2.48312585743297,100,2014,5,7,0.151169583271122,0.0622333396699988,0.0670596001318912,0.00370994158299908,0.22619183828233,0.22619183828233,0.00206405408622849
+"21678",100,2014,5,8,0.0278327836930686,2.24328933441468,16.8276569903618,10.2881298621233,11.6907371283889,11.6907371283889,2.44991840231429,100,2014,5,8,0.0020467836867299,0.124850875413909,0.0567485965263602,0.00592456251230586,0.000691813860511401,0.000691813860511401,0.0019388250816741
+"21679",100,2014,5,9,0,6.13863590185923,16.5046534627447,11.6710010891569,4.43085813181366,4.43085813181366,2.4167109471956,100,2014,5,9,0,0.0869432889938569,0.0279368412444264,0.00640351518560285,0.471957927127759,0.471957927127759,0.00181976600679621
+"21680",100,2014,5,10,4.08195820840934,11.7681297138579,18.031573193826,13.7491639644006,7.46290433052743,7.46290433052743,2.38350349207692,100,2014,5,10,0.332631588512011,0.153691294925623,0.115077897898708,0.0212584921899243,0.303681364466609,0.303681364466609,0.00170687686159483
+"21681",100,2014,5,11,0.136523654399597,9.8164905814579,18.1684929246556,13.7212431501634,8.69537958768335,8.69537958768335,2.35029603695824,100,2014,5,11,0.00228070182235617,0.131148505872389,0.0491718612450882,0.0192362129792158,0.382465481052112,0.382465481052112,0.00160015764606996
+"21682",100,2014,5,12,0.127832785183185,5.46284919682116,19.747238669065,12.8373817815246,10.6954125560681,10.6954125560681,2.31708858183956,100,2014,5,12,0.0020467836867299,0.0668871463519244,0.159249745535418,0.0144134368272568,0.00681402175642789,0.00681402175642789,0.00149960836022158
+"21683",100,2014,5,13,0,6.41113319334024,19.5350274756403,11.8039714427146,10.4929812906599,10.4929812906599,2.28388112672088,100,2014,5,13,0,0.0794830106345027,0.0969876376237802,0.00573860609719629,0.0212315641615931,0.0212315641615931,0.00140522900404972
+"21684",100,2014,5,14,0,7.26691967478417,20.9588231127648,10.7690098545339,9.23964808351792,9.23964808351792,2.25067367160219,100,2014,5,14,0,0.0660134655088207,0.0180099512560728,0.0096432767054546,0.0381701587677941,0.0381701587677941,0.00131701957755436
+"21685",100,2014,5,15,0,11.5818591909965,23.2059073957017,13.5002749572099,9.29176017238755,9.29176017238755,2.21746621648351,100,2014,5,15,0,0.061970723737825,0.035222813870849,0.027120428377633,0.20208007091283,0.20208007091283,0.0012349800807355
+"21686",100,2014,5,16,0.0365236529094814,10.0759296081509,24.1252474915994,12.2441474979598,8.73705164696386,8.73705164696386,2.18425876136483,100,2014,5,16,0.00228070182235617,0.0287163993323074,0.0266368972916949,0.0534152475770471,0.196173074033023,0.196173074033023,0.00115911051359316
+"21687",100,2014,5,17,0,14.3098348107668,23.0081077823282,12.8493288499687,5.98046208896784,5.98046208896784,2.15105130624615,100,2014,5,17,0,0.0153508871759633,0.0424361352667765,0.0990316369553878,0.431406446713468,0.431406446713468,0.00108941087612732
+"21688",100,2014,5,18,0.657535755070392,13.7107700530452,21.7362598864028,14.8292299329382,6.06935091669148,6.06935091669148,2.11784385112746,100,2014,5,18,0.0113450264233598,0.0557450307188548,0.012243255851958,0.0511649120666047,0.127988917096477,0.127988917096477,0.00102588116833798
+"21689",100,2014,5,19,0.10847084870105,12.4892188933554,21.0143672829807,14.2441804117889,5.16809675061401,5.16809675061401,2.08463639600878,100,2014,5,19,0.00175438601719706,0.0217356935758002,0.0236321797647232,0.0391444540236564,0.203011135037756,0.203011135037756,0.000968521390225148
+"21690",100,2014,5,20,0.342684275975781,13.5204621022291,17.4603081316051,14.0138062895721,5.21183715165645,5.21183715165645,2.0514289408901,100,2014,5,20,0.0157894748126903,0.0595467909874836,0.0238485369457186,0.00270293410240061,0.169470756279811,0.169470756279811,0.000917331541788822
+"21691",100,2014,5,21,1.78734872954907,5.81458747085303,21.3161164148413,15.299857065098,8.62788792516794,8.62788792516794,2.01822148577142,100,2014,5,21,0.191462002149106,0.0498619713342945,0.0307374600997719,0.0139999838935682,0.0101339350784555,0.0101339350784555,0.000872311623029007
+"21692",100,2014,5,22,0.00154015403835162,9.96853691726366,18.6977119382852,17.0120792409899,2.92609460702693,2.92609460702693,1.98501403065274,100,2014,5,22,0.000526315805159117,0.0194695607860816,0.0395145147895217,0.0328578976239452,0.128159656746072,0.128159656746072,0.000833461633945691
+"21693",100,2014,5,23,12.4391639560494,13.8315069872161,17.0990429407168,13.2543125131605,6.59574258393056,6.59574258393056,1.95180657553405,100,2014,5,23,1.4339766805772,0.0592607566008878,0.0440486281949505,0.00733217061591412,0.0394649151417403,0.0394649151417403,0.000800781574538885
+"21694",100,2014,5,24,0.573487350747924,8.30047302225111,17.0324861990212,13.245302576961,5.85242021936263,5.85242021936263,1.91859912041537,100,2014,5,24,0.0361988290917805,0.0959690434016005,0.0960801864806513,0.0396818600170024,0.0844140028825504,0.0844140028825504,0.000774271444808584
+"21695",100,2014,5,25,1.77799779353756,10.6602531240062,19.1508911015308,15.3539054501306,5.80584163949041,5.80584163949041,1.88539166529669,100,2014,5,25,0.0715204571841074,0.0726841918544645,0.0290315602712236,0.01262046543153,0.235580083143241,0.235580083143241,0.000753931244754787
+"21696",100,2014,5,26,0.0976897704325887,10.3554015059938,17.8651375807289,16.0971179496337,2.81256326774035,2.81256326774035,1.85218421017801,100,2014,5,26,0.00497076038205833,0.0703473719413842,0.0134397710658023,0.0188760848197904,0.0105619874273866,0.0105619874273866,0.000739760974377498
+"21697",100,2014,5,27,9.35247515478019,13.3826841274635,18.856325541917,14.7270517118431,6.60960399409463,6.60960399409463,1.81897675505932,100,2014,5,27,0.803333157433462,0.0959783244283968,0.0642719215930486,0.00368421816691227,0.337611143218161,0.337611143218161,0.000731760633676712
+"21698",100,2014,5,28,2.13630359833783,10.2400220728288,15.3171837736409,13.3452146454613,5.6670957503408,5.6670957503408,1.78576929994064,100,2014,5,28,1.18654970955431,0.100809958017433,0.0681708267435989,0.00225613571307128,0.125556146819149,0.125556146819149,0.000729930222652436
+"21699",100,2014,5,29,1.91199116108835,7.32559952038218,17.0332012826985,11.5824862239909,6.6394719107054,6.6394719107054,1.75256184482196,100,2014,5,29,0.10941518929967,0.0718146518361714,0.140677666812762,0.0158818683803315,0.0418432849181494,0.0418432849181494,0.000734269741304663
+"21700",100,2014,5,30,0.859955999553531,5.60965899665757,16.4422884606423,10.8810010528145,8.3107920993935,8.3107920993935,1.71935438970328,100,2014,5,30,0.0948538013299308,0.0595449973145579,0.101159567131607,0.00882220539524124,0.00240233984166278,0.00240233984166278,0.000744779189633397
+"21701",100,2014,5,31,0,5.92998900901366,15.5484157358721,11.0499669758007,5.04038497244958,5.04038497244958,1.6861469345846,100,2014,5,31,0,0.0396029227541377,0.0693257517163019,0.024062001358916,0.110948540329533,0.110948540329533,0.000761458567638635
+"21702",100,2014,6,1,0.57645764797762,6.94959295018934,15.8720571163333,16.1926731008901,4.02389443746888,4.02389443746888,1.6495677705957,100,2014,6,1,0.349415208686862,0.0299690106475766,0.0988151245654463,0.00851172972599872,0.100942686210887,0.100942686210887,0.00099254558268804
+"21703",100,2014,6,2,2.01639163900654,8.88630367715498,16.6057096529584,14.000924134543,6.99141909816478,6.99141909816478,1.61298860660681,100,2014,6,2,0.670994171015702,0.160444414986585,0.0436536933678204,0.00256199861502082,0.0416871443910423,0.0416871443910423,0.00141738912641131
+"21704",100,2014,6,3,0.137403742421513,9.06205718352063,15.1342353002466,14.5308910260762,3.59273927597323,3.59273927597323,1.57640944261791,100,2014,6,3,0.00426900597517951,0.0247468032659196,0.11940288744258,0.000764912271840293,0.0331152127489049,0.0331152127489049,0.00203598919880844
+"21705",100,2014,6,4,3.56380635781912,8.1470188375878,14.4063037194566,14.6810780631171,4.02036302580167,4.02036302580167,1.53983027862902,100,2014,6,4,0.116198830743984,0.0622362879290869,0.136015236929333,0.00553861813466151,0.0579543920416684,0.0579543920416684,0.00284834579987943
+"21706",100,2014,6,5,2.88668867044061,6.39096806676212,14.0219031618242,11.3025633305213,6.34055001822242,6.34055001822242,1.50325111464013,100,2014,6,5,0.0316374287410123,0.143158452287211,0.0800888632880923,0.000958481524358772,0.146725130806212,0.146725130806212,0.00385445892962428
+"21707",100,2014,6,6,0.922002204508409,6.47244228598046,15.2096259386757,13.4266005590542,6.65952699858495,6.65952699858495,1.46667195065123,100,2014,6,6,0.0278362523184886,0.11385091404611,0.136800061204774,0.00167017598968738,0.596900596981183,0.596900596981183,0.00505432858804299
+"21708",100,2014,6,7,3.27953794923159,9.03451046036153,14.8986027542383,14.1711992372905,3.77568760537209,3.77568760537209,1.43009278666234,100,2014,6,7,0.357602357585533,0.0746695678778467,0.107591713112366,0.0047473625451448,0.306187743691123,0.306187743691123,0.00644795477513557
+"21709",100,2014,6,8,2.84785478290814,7.96299221229763,13.7431022960885,9.69481856594778,4.37099010165375,4.37099010165375,1.39351362267345,100,2014,6,8,0.592514606693338,0.142700601757385,0.1268666175419,0.0125537943188152,0.0801924176361369,0.0801924176361369,0.00803533749090202
+"21710",100,2014,6,9,0,2.88843783424764,14.3216503109738,11.7858746342927,8.03975817656229,8.03975817656229,1.35693445868455,100,2014,6,9,0,0.0707830488952321,0.0600426944666327,0.00918421930464053,0.00106725056971486,0.00106725056971486,0.00981647673534231
+"21711",100,2014,6,10,1.52750274378343,5.2955775837956,16.6510448770555,12.9761714505153,7.38433440841071,7.38433440841071,1.32035529469566,100,2014,6,10,0.704736846211363,0.0307315783885669,0.0731000380624981,0.00103743345482133,0.0250116909205737,0.0250116909205737,0.0117913725084565
+"21712",100,2014,6,11,0,6.48325629281526,14.1767437319026,12.2159625411165,3.99511551227507,3.99511551227507,1.28377613070676,100,2014,6,11,0,0.0623035349963228,0.0271812830027854,0.00154795012452678,0.0300689822369957,0.0300689822369957,0.0139600248102445
+"21713",100,2014,6,12,0.129372939934628,8.96891083418339,13.8019141594831,12.203267289467,3.53970297151404,3.53970297151404,1.24719696671787,100,2014,6,12,0.0159649130004889,0.0551146389052785,0.0575807212875195,0.000650874975610605,0.0550099669467738,0.0550099669467738,0.0163224336407064
+"21714",100,2014,6,13,1.97898790400938,8.70234326298612,16.0957207622045,13.3180637842227,5.07988995687403,5.07988995687403,1.21061780272898,100,2014,6,13,0.164269011606253,0.0603092912462767,0.0224672809714182,0.0137426995952995,0.131804080171216,0.131804080171216,0.0188785989998421
+"21715",100,2014,6,14,10.1993399274887,7.81798688596887,13.0422441369236,11.9643454829721,3.23631463271163,3.23631463271163,1.17403863874008,100,2014,6,14,15.2578359030005,0.330347925002163,0.0954953421922372,0.0314941187742143,0.0218590677807954,0.0218590677807954,0.0216285208876518
+"21716",100,2014,6,15,1.85126510600183,6.12267327229969,14.4622882899671,12.5822991317648,6.39243125338497,6.39243125338497,1.13745947475119,100,2014,6,15,0.285789449619275,0.0471585045831412,0.128191843195751,0.00810058250655793,0.217725169918009,0.217725169918009,0.0245721993041352
+"21717",100,2014,6,16,9.98107816047794,7.30818477374623,14.8099560407129,12.1185919737527,5.88290429403811,5.88290429403811,1.10088031076229,100,2014,6,16,5.05163762036824,0.123292353234115,0.0841373946653364,0.00468712481011203,0.356928683220729,0.356928683220729,0.0277096342492926
+"21718",100,2014,6,17,2.51210121117016,7.10971391371506,13.4897469603451,11.6069636769814,3.79205725951032,3.79205725951032,1.0643011467734,100,2014,6,17,0.058011706698016,0.0884707859608415,0.0436368493621813,0.00191520930740531,0.0450315665479383,0.0450315665479383,0.0310408257231238
+"21719",100,2014,6,18,0.0797579769860662,8.59658959517778,13.9392739401923,10.1016282699551,4.0703080998789,4.0703080998789,1.02772198278451,100,2014,6,18,0.00228070182235617,0.0619227582876979,0.140420506659243,0.00638186371794312,0.040904110191313,0.040904110191313,0.0345657737256288
+"21720",100,2014,6,19,0,5.71894390695822,11.9455994216785,10.8581737176289,4.35676571900564,4.35676571900564,0.991142818795614,100,2014,6,19,0,0.038309950181628,0.121746762977339,0.0145929919884999,0.0688204562968133,0.0688204562968133,0.0382844782568077
+"21721",100,2014,6,20,0,6.99448847534633,14.6269636395479,12.8192189450573,4.42188123009517,4.42188123009517,0.95456365480672,100,2014,6,20,0,0.0508321743692098,0.0810619750129518,0.0303894956957547,0.173061955819504,0.173061955819504,0.0421969393166605
+"21722",100,2014,6,21,0.0176017604383043,9.00283822499224,15.9691859047012,13.9939382949678,4.81375130935602,4.81375130935602,0.917984490817826,100,2014,6,21,0.00175438601719706,0.0841842631402258,0.122727420392188,0.0315029138041476,0.195614025216371,0.195614025216371,0.0463031569051872
+"21723",100,2014,6,22,0.38382838629555,7.44572058920026,15.0443013320268,12.7532452424892,5.11713972920501,5.11713972920501,0.881405326828932,100,2014,6,22,0.0556140342790482,0.0910508495063296,0.0733373869333003,0.00643159289227282,0.0885841670488254,0.0885841670488254,0.0506031310223877
+"21724",100,2014,6,23,1.4086908654149,10.0598131815592,12.0344223288944,10.9456987255084,2.97848181624879,2.97848181624879,0.844826162840038,100,2014,6,23,0.0350877191588208,0.0410649261163136,0.0252029182078044,0.0216093840504897,0.0361257428453737,0.0361257428453737,0.0550968616682621
+"21725",100,2014,6,24,25.3009900259893,4.29467547923425,13.0938723126654,11.3827611274845,5.05557752835869,5.05557752835869,0.808246998851144,100,2014,6,24,3.74228016100462,0.094569600833669,0.0995702276509265,0.0676941545765956,0.084071944840208,0.084071944840208,0.0597843488428103
+"21726",100,2014,6,25,13.9628161964375,8.40118817265409,14.3019250873948,12.9270735993506,4.67895493806392,4.67895493806392,0.771667834862251,100,2014,6,25,16.2316381791323,0.20120406977118,0.0918690135488424,0.00523683448708339,0.117058495962152,0.117058495962152,0.0646655925460324
+"21727",100,2014,6,26,8.70836087238408,8.66019806688768,13.8101430385157,9.80225519194986,4.78796484446762,4.78796484446762,0.735088670873357,100,2014,6,26,3.01766088151102,0.139187099802661,0.109991125831919,0.00515088331888082,0.3998818396139,0.3998818396139,0.0697405927779284
+"21728",100,2014,6,27,0.789328931253998,6.91366335267674,14.7862705979804,9.70522551751635,7.37411435025491,7.37411435025491,0.698509506884463,100,2014,6,27,0.0206432731597748,0.0465801339668325,0.0960765670665724,0.0156263192518123,0.00908185948825727,0.00908185948825727,0.0750093495384982
+"21729",100,2014,6,28,0.297249734696775,7.87430147028337,11.499405933292,10.3198238767282,4.48984600792099,4.48984600792099,0.661930342895569,100,2014,6,28,0.00584795309437648,0.0587111093309644,0.0789164137857752,0.0290999916501251,0.613747365916417,0.613747365916417,0.0804718628277418
+"21730",100,2014,6,29,23.8236520198574,7.46005503598875,11.1563146001566,9.54192516066716,4.8441363826181,4.8441363826181,0.625351178906676,100,2014,6,29,1.4428649072482,0.0920368101677915,0.112120466468845,0.00505907957969548,1.49100939015842,1.49100939015842,0.0861281326456594
+"21731",100,2014,6,30,10.3523652409301,5.03576448910569,13.2526292832378,11.509515918652,4.56268422123623,4.56268422123623,0.588772014917782,100,2014,6,30,2.40783653571599,0.13663566791692,0.135502896916871,0.00210351850730457,0.40071337145255,0.40071337145255,0.0919781589922508
+"21732",100,2014,7,1,4.26809687740339,7.17507149651237,13.3323322407352,11.9064906081482,3.91280529234144,3.91280529234144,0.607439556180042,100,2014,7,1,0.0387134651273509,0.137433898874488,0.122868988887103,0.0159473682314833,0.0264204793902273,0.0264204793902273,0.0873107287009129
+"21733",100,2014,7,2,0.225082514448987,7.30966992425446,14.1458966440887,12.1815950259386,5.76199127581253,5.76199127581253,0.626107097442303,100,2014,7,2,0.0125730996025585,0.133147958143161,0.111877765189342,0.0254812870539327,0.0401146303444718,0.0401146303444718,0.0827677572757163
+"21734",100,2014,7,3,0,4.25864689001287,14.4785807109115,9.91561065648649,8.42888881297264,8.42888881297264,0.644774638704563,100,2014,7,3,0,0.0227163659369221,0.0625111317529296,0.00469296332694976,0.000811105855388382,0.000811105855388382,0.0783492447166611
+"21735",100,2014,7,4,0,7.5478877454701,14.1758635880792,11.1201429734267,6.61117710978022,6.61117710978022,0.663442179966823,100,2014,7,4,0,0.0761508416192725,0.0445813112181635,0.0459800668482966,0.0715607901657113,0.0715607901657113,0.0740551910237473
+"21736",100,2014,7,5,7.58888890850793,8.65971398851921,13.5445216024669,12.1705939457624,5.95888887432673,5.95888887432673,0.682109721229084,100,2014,7,5,1.60701749422399,0.108909977731174,0.0667912386800054,0.00513331824413133,0.23563160643663,0.23563160643663,0.0698855961969748
+"21737",100,2014,7,6,13.3796479620687,7.13641361880748,13.0098242471189,11.9560285301754,4.0888999460554,4.0888999460554,0.700777262491344,100,2014,7,6,1.7226316478797,0.107622843285067,0.0987333738963432,0.00282047034824251,0.422571961127954,0.422571961127954,0.0658404602363438
+"21738",100,2014,7,7,9.38976892727305,3.0437403782235,13.2979758947727,10.2259517361229,7.80314643049922,7.80314643049922,0.719444803753605,100,2014,7,7,1.31146198679836,0.0330707619616912,0.0670146241551127,0.0113830470816602,0.082288927438564,0.082288927438564,0.061919783141854
+"21739",100,2014,7,8,0,6.4577777556198,15.1788009500871,8.90840488639471,6.06629258492599,6.06629258492599,0.738112345015865,100,2014,7,8,0,0.0292608138212943,0.0672210659630025,0.0219116996497965,0.214670742346126,0.214670742346126,0.0581235649135057
+"21740",100,2014,7,9,0.669856993540941,7.38693071644429,11.856919687311,10.3758965682144,4.2955665585756,4.2955665585756,0.756779886278126,100,2014,7,9,0.085614034885552,0.033883034198627,0.117067287868988,0.000147368307328801,0.322309334808497,0.322309334808497,0.0544518055512987
+"21741",100,2014,7,10,21.2059407082066,5.96698564533616,12.3805390254106,10.4523763194992,4.52922988383815,4.52922988383815,0.775447427540386,100,2014,7,10,1.61842072693255,0.108649731008085,0.0581607743425747,0.0325163461295701,0.510411601900932,0.510411601900932,0.0509045050552331
+"21742",100,2014,7,11,6.17579758731183,7.52672165481433,14.0647524748698,11.7835643063284,6.14433441854546,6.14433441854546,0.794114968802646,100,2014,7,11,0.700526323151191,0.0918397879483593,0.103491839305212,0.0175315810064768,0.215915180373627,0.215915180373627,0.0474816634253089
+"21743",100,2014,7,12,8.12816265838506,5.46521450793914,11.1187788393631,8.3951045373092,7.74118817154199,7.74118817154199,0.812782510064907,100,2014,7,12,1.30228057331511,0.279043304563846,0.10872281547687,0.000839763922488212,0.724404039156577,0.724404039156577,0.044183280661526
+"21744",100,2014,7,13,1.13509351913423,4.33720571032189,12.4720352947122,10.7712981105506,6.23904286752833,6.23904286752833,0.831450051327167,100,2014,7,13,0.0250877150178661,0.182867237753077,0.0936210902856088,0.0048695799889642,0.444568981880072,0.444568981880072,0.0410093567638845
+"21745",100,2014,7,14,3.17117712337716,2.44878989503984,11.3964466288014,10.3079648474262,6.48701864290814,6.48701864290814,0.850117592589428,100,2014,7,14,1.27263161602077,0.0585315785134802,0.0747485426184204,0.000425149654445874,0.0491397917597113,0.0491397917597113,0.0379598917323844
+"21746",100,2014,7,15,0.19834984009481,5.41429040822783,9.67730472531125,9.51836075693598,3.11986797503775,3.11986797503775,0.868785133851688,100,2014,7,15,0.0109356732675207,0.0576982520956933,0.0443099383751067,0.00203391675898524,0.0223286578211904,0.0223286578211904,0.0350348855670256
+"21747",100,2014,7,16,0.37106711439567,7.65682067021285,14.1663476337563,10.6964137997433,8.48224424817512,8.48224424817512,0.887452675113948,100,2014,7,16,0.00362573056193124,0.0424590690066527,0.0710356775385099,0.0190982615555154,0.153817579075419,0.153817579075419,0.0322343382678082
+"21748",100,2014,7,17,4.8863586130971,5.09698568266479,9.92192518488146,8.24144105942729,4.58345441377596,4.58345441377596,0.906120216376209,100,2014,7,17,0.240994148310186,0.0633321323183143,0.111905259356675,0.00475849256911965,0.207721015540919,0.207721015540919,0.0295582498347322
+"21749",100,2014,7,18,11.1347634962814,4.79057211713298,12.187777847609,9.42335528509058,7.79608366014672,7.79608366014672,0.924787757638469,100,2014,7,18,0.784678151984055,0.146624568566686,0.0919258156894459,0.00518419754302669,0.287221098198273,0.287221098198273,0.0270066202677975
+"21750",100,2014,7,19,0.757865784519708,4.99683164579771,12.6583718875847,10.8318591616204,6.80333333860005,6.80333333860005,0.94345529890073,100,2014,7,19,0.0224561404624184,0.0305146076637713,0.14365792916957,0.0459578869424564,0.119943267070498,0.119943267070498,0.0245794495670042
+"21751",100,2014,7,20,2.75764572187619,5.85862485698872,13.2894168576785,10.9669636872211,7.60322330860939,7.60322330860939,0.96212284016299,100,2014,7,20,0.261754395473775,0.0255386021843029,0.114549735175318,0.00950934720410063,0.0743579095137993,0.0743579095137993,0.0222767377323523
+"21752",100,2014,7,21,0.514961499069193,2.43324531947557,12.3156545594974,10.1850605525068,8.07330032963433,8.07330032963433,0.98079038142525,100,2014,7,21,0.097602339118545,0.0348537984089563,0.0294468115623113,0.00397834871085236,0.0470590634954533,0.0470590634954533,0.0200984847638418
+"21753",100,2014,7,22,0.0960396053914977,0.522277228853884,13.0492190769141,9.19963704897101,9.21503844224449,9.21503844224449,0.999457922687511,100,2014,7,22,0.00140350881375765,0.0385543867475807,0.0545812901369541,0.00491170084332922,0.0650760147139924,0.0650760147139924,0.0180446906614726
+"21754",100,2014,7,23,0.0541254133477856,-0.0409020911984154,11.5727942207597,9.94343236308418,7.30898786456672,7.30898786456672,1.01812546394977,100,2014,7,23,0.00257309949188902,0.0675912290187695,0.111170109210189,0.00067309637466806,0.199225135323607,0.199225135323607,0.0161153554252448
+"21755",100,2014,7,24,1.74972495243232,2.41441143831142,11.729284979985,11.9741804051583,3.83276122133068,3.83276122133068,1.03679300521203,100,2014,7,24,0.165380102612124,0.0560175524315699,0.0532596655483976,0.00866959209339278,0.0954812729857917,0.0954812729857917,0.0143104790551583
+"21756",100,2014,7,25,4.38514850110766,7.83849283413525,14.0941475499975,13.2002307967384,5.48685367089031,5.48685367089031,1.05546054647429,100,2014,7,25,0.23555555131702,0.0729339033305569,0.101669045830937,0.00335555233436536,0.451291268745803,0.451291268745803,0.0126300615512132
+"21757",100,2014,7,26,11.2491748561167,9.29891092575292,14.2082728961907,11.7899779937711,4.56737072554359,4.56737072554359,1.07412808773655,100,2014,7,26,3.43023392682889,0.0532689996686676,0.112257949407553,0.00856200206165377,0.735136785753162,0.735136785753162,0.0110741029134095
+"21758",100,2014,7,27,1.03806380535772,4.33595158322023,15.2831023062023,11.8700658884248,8.53465338193937,8.53465338193937,1.09279562899881,100,2014,7,27,0.0342690062244042,0.0629052752679127,0.0913579050299382,0.00898947349242805,0.161735720959113,0.161735720959113,0.00964260314174717
+"21759",100,2014,7,28,1.77458744401848,7.61360839753523,16.1837845089937,11.5251154097,9.48033014198866,9.48033014198866,1.11146317026107,100,2014,7,28,0.988011652893492,0.10624974456477,0.074360898820088,0.00995440044075808,0.0767831396783895,0.0767831396783895,0.0083355622362262
+"21760",100,2014,7,29,0.558195829866874,10.0765346678177,15.6990429371497,13.5197358451398,4.8047304476055,4.8047304476055,1.13013071152333,100,2014,7,29,0.0300584794084233,0.174131624634606,0.107002947957987,0.00600292225893403,0.66193327030607,0.66193327030607,0.00715298019684659
+"21761",100,2014,7,30,7.72662270790411,10.3243345453663,15.8889880699686,13.5044444611888,6.99618265206533,6.99618265206533,1.14879825278559,100,2014,7,30,1.18444453239447,0.0961760649564873,0.0906801336479252,0.0016941575993833,0.517614655329631,0.517614655329631,0.00609485702360836
+"21762",100,2014,7,31,2.44158413058723,11.9890978942216,14.6337294846085,8.8044114821028,3.37158414790339,3.37158414790339,1.16746579404785,100,2014,7,31,0.430292378852245,0.0706251862487701,0.0305052476381722,0.00126491340957111,0.099443274842096,0.099443274842096,0.00516119271651149
+"21763",100,2014,8,1,15.8081407599454,3.50346537050765,9.91959285106596,7.48292624806151,4.04799778548011,4.04799778548011,1.1810695593578,100,2014,8,1,1.51228046974961,0.107102947334942,0.101591860468964,0.00312866043628934,0.733906420863873,0.733906420863873,0.00497425019678166
+"21764",100,2014,8,2,4.803740434783,1.63036302791046,12.2029262790323,6.59672177454295,10.1561276967769,10.1561276967769,1.19467332466774,100,2014,8,2,0.228187148501316,0.0883385863170307,0.0824689424125077,0.00386548853200845,0.0830584688466353,0.0830584688466353,0.00479385790823169
+"21765",100,2014,8,3,0.492959302029397,0.106831683895097,12.5191529896131,7.7790649553599,10.9860176835516,10.9860176835516,1.20827708997768,100,2014,8,3,0.224678366410802,0.0472590637341491,0.0750561162068094,0.0262316062124491,0.00197661216921458,0.00197661216921458,0.00462001585086158
+"21766",100,2014,8,4,1.0543454348041,1.58715072008643,13.3057205705884,9.56369640279,8.5627503819985,8.5627503819985,1.22188085528762,100,2014,8,4,0.717309908983653,0.0846695866515078,0.0468701684696009,0.0133374117511968,0.225048580332602,0.225048580332602,0.00445272402467132
+"21767",100,2014,8,5,0.410781083425673,6.25889986550192,13.253223179984,11.9791199817385,7.06811872488595,7.06811872488595,1.23548462059756,100,2014,8,5,0.00140350860461855,0.163021082255738,0.0363432121991446,0.00322748873312302,0.555165567672498,0.555165567672498,0.00429198242966092
+"21768",100,2014,8,6,0.0379537959450936,9.37523648605095,13.9172607596033,12.4394497719273,6.91955996503924,6.91955996503924,1.2490883859075,100,2014,8,6,0.00485380131424519,0.0815053215228811,0.0592064608368482,0.000398243265030756,0.464657864213725,0.464657864213725,0.00413779106583037
+"21769",100,2014,8,7,4.21023101932538,9.02069306976856,12.53959291498,12.4800000877926,4.50465345697434,4.50465345697434,1.26269215121745,100,2014,8,7,0.239122789143131,0.101450883098035,0.0394982510885302,0.00677604875406163,0.0700918118259576,0.0700918118259576,0.00399014993317969
+"21770",100,2014,8,8,2.22893288597153,8.89966989166797,14.4483387320742,9.39316829412815,8.97708481556774,8.97708481556774,1.27629591652739,100,2014,8,8,0.299883044644409,0.0650151763180655,0.106964915727379,0.0128561446631279,0.308811664794406,0.308811664794406,0.00384905903170885
+"21771",100,2014,8,9,0.0651265142528531,5.15844885822963,15.8512653386501,10.0848514254731,7.91580855807062,7.91580855807062,1.28989968183733,100,2014,8,9,0.0209941529861668,0.0237912174369984,0.0750146420240425,0.0112485493777675,0.442242684052043,0.442242684052043,0.00371451836141787
+"21772",100,2014,8,10,4.45720576794103,7.53007704511334,11.3762706708331,8.17155105288666,8.71225520879915,8.71225520879915,1.30350344714727,100,2014,8,10,0.193157863589075,0.173576670715857,0.079220490209188,0.00235905681643222,0.576487684615821,0.576487684615821,0.00358652792230675
+"21773",100,2014,8,11,3.39328936171873,4.70475250974347,11.2102089704591,8.59161705965519,8.83764579613479,8.83764579613479,1.31710721245721,100,2014,8,11,0.0573099356645759,0.179528607963,0.0607607848318611,0.00343565957595466,0.50048014008044,0.50048014008044,0.00346508771437549
+"21774",100,2014,8,12,3.37744772578492,4.44936187353858,11.959361941901,8.68210111960064,8.733795386861,8.733795386861,1.33071097776715,100,2014,8,12,1.08286555178682,0.174080645464752,0.0244450340070902,0.00197309280757344,0.318298256472601,0.318298256472601,0.00335019773762408
+"21775",100,2014,8,13,0.162486252095243,6.93304728255151,13.52048403464,10.6033883446252,9.85715059295084,9.85715059295084,1.34431474307709,100,2014,8,13,0.0139766089043074,0.174835648707363,0.0579052706669096,0.0011812762099615,0.4357543311817,0.4357543311817,0.00324185799205252
+"21776",100,2014,8,14,0.192299232788474,9.08365229804917,12.1883388588531,10.7296480749569,6.64517048404555,6.64517048404555,1.35791850838704,100,2014,8,14,0.00140350881375765,0.126309395748883,0.0393152215723901,0.00562281862902217,0.122999949773184,0.122999949773184,0.00314006847766083
+"21777",100,2014,8,15,0,1.36212322224091,14.5123322070366,10.9676348609631,12.2470627659881,12.2470627659881,1.37152227369698,100,2014,8,15,0,0.0451005858373935,0.0466690155476047,0.0197397608618404,0.0334116704372814,0.0334116704372814,0.00304482919444899
+"21778",100,2014,8,16,0.119031904964033,2.75394938933705,12.3065567562158,11.3102421314672,8.92002209158752,8.92002209158752,1.38512603900692,100,2014,8,16,0.0020467836867299,0.0225339308772217,0.0501812395241412,0.00216900034460546,0.64664320262002,0.64664320262002,0.002956140142417
+"21779",100,2014,8,17,0,5.06121010407887,13.3081298656065,9.7882178781843,9.91021995313621,9.91021995313621,1.39872980431686,100,2014,8,17,0,0.0655818851816896,0.0506871570900202,0.00185555996373636,0.230682910524428,0.230682910524428,0.00287400132156488
+"21780",100,2014,8,18,0.000110011002739402,8.21301432044068,13.0842794516955,9.70520348753473,10.4393399304683,10.4393399304683,1.4123335696268,100,2014,8,18,0.000526315805159117,0.151776015486962,0.068569543512878,0.0017894679288033,0.175157891033737,0.175157891033737,0.00279841273189261
+"21781",100,2014,8,19,0.0595159524820163,9.10039603041344,12.4781077828738,8.67959298027886,6.41055006083876,6.41055006083876,1.42593733493674,100,2014,8,19,0.00538011711940431,0.124847995780435,0.0537648716932828,0.00225438347764825,0.258892408808236,0.258892408808236,0.00272937437340019
+"21782",100,2014,8,20,0.23069307583459,7.06141912189647,12.8169306145512,9.49838279077847,9.60177115323913,9.60177115323913,1.43954110024669,100,2014,8,20,0.00508771990736328,0.066306416537205,0.0758649567253555,0.00362515492809491,1.67438765373683,1.67438765373683,0.00266688624608763
+"21783",100,2014,8,21,0,4.25947191324433,14.1047854019601,10.3578438470335,10.5536853813364,10.5536853813364,1.45314486555663,100,2014,8,21,0,0.0469034990483729,0.0437204829925135,0.00193624979660774,0.283575972373102,0.283575972373102,0.00261094834995492
+"21784",100,2014,8,22,0.0545654573587432,3.34452144514741,18.3445545108405,11.3360836723588,12.889736039148,12.889736039148,1.46674863086657,100,2014,8,22,0.00263157902579559,0.0940473828979473,0.0867468345868527,0.00528303660349481,0.256837420551776,0.256837420551776,0.00256156068500207
+"21785",100,2014,8,23,0,5.60089108705258,18.1967985637904,12.8062266789385,10.3376786165898,10.3376786165898,1.48035239617651,100,2014,8,23,0,0.0679894626282941,0.0147895019711685,0.000680119649523317,1.34422058479583,1.34422058479583,0.00251872325122908
+"21786",100,2014,8,24,4.22365233580796,7.84553347116519,12.3110012310435,11.8620681710238,4.47818484059786,4.47818484059786,1.49395616148645,100,2014,8,24,0.0354386100434604,0.0359034863857004,0.0534590846949315,0.0124152078796433,0.455066611438338,0.455066611438338,0.00248243604863595
+"21787",100,2014,8,25,8.1891089267332,9.53654565381007,14.6707480601614,12.3456875797939,11.235093594122,11.235093594122,1.50755992679639,100,2014,8,25,0.702105309893552,0.0812543709961951,0.118414612338493,0.00970293067805268,0.950106419328009,0.950106419328009,0.00245269907722267
+"21788",100,2014,8,26,0.0541254133477856,3.66529154541469,16.017986659563,13.4773927579487,10.8406049824915,10.8406049824915,1.52116369210634,100,2014,8,26,0.00257309949188902,0.0918841958062753,0.0562040891236606,0.00217660877614015,0.155904130324522,0.155904130324522,0.00242951233698925
+"21789",100,2014,8,27,0.0469746981697245,3.48364137790122,16.9630473297421,12.9864687410781,14.4045654208747,14.4045654208747,1.53476745741628,100,2014,8,27,0.00245614042407588,0.0292877266939656,0.0944339397321624,0.0104654986978975,0.00512454747437345,0.00512454747437345,0.00241287582793567
+"21790",100,2014,8,28,0.0685368547066472,3.76143015545718,17.3129923981015,13.0616061606161,14.4212101085482,14.4212101085482,1.54837122272622,100,2014,8,28,0.00257309949188902,0.0774444434907764,0.0409843138046061,0.00112456352510289,0.00648714156579434,0.00648714156579434,0.00240278955006197
+"21791",100,2014,8,29,0.0194719474848741,3.20298133142973,16.421067044811,12.8407810219575,14.6928272467635,14.6928272467635,1.56197498803616,100,2014,8,29,0.00175438601719706,0.0469608096752986,0.129458579725018,0.0162982265740895,0.00203391793018039,0.00203391793018039,0.00239925350336811
+"21792",100,2014,8,30,0.065456546629944,6.85810784748023,18.9415623042712,13.9826182500757,14.650077045554,14.650077045554,1.5755787533461,100,2014,8,30,0.00263157902579559,0.0523894699632226,0.0198730622634248,0.00714502542736791,0.0462286446242897,0.0462286446242897,0.00240226768785411
+"21793",100,2014,8,31,0,7.92707368223318,20.3984925634134,12.1111439798269,13.2331022822818,13.2331022822818,1.58918251865604,100,2014,8,31,0,0.0635555625492118,0.0625135270907691,0.00371461363458727,0.146865502496982,0.146865502496982,0.00241183210351997
+"21794",100,2014,9,1,3.25907590496789,10.5663366118411,13.6222110859501,11.2800000529609,7.14929592858578,7.14929592858578,1.63169981665768,100,2014,9,1,0.0636842146812169,0.0617474182932572,0.130011662700808,0.00191345277763019,0.874377865854925,0.874377865854925,0.00251221467600339
+"21795",100,2014,9,2,3.21947195244045,7.09117710839535,13.4379648370187,8.62883384524137,13.8333774258201,13.8333774258201,1.67421711465932,100,2014,9,2,0.303391857983778,0.19481637088264,0.0600151924356811,0.00243567832595752,0.654247308592858,0.654247308592858,0.00261768200361731
+"21796",100,2014,9,3,0.609460945897894,6.43768972768248,13.4048184886886,8.20123200998841,10.6433442454658,10.6433442454658,1.71673441266096,100,2014,9,3,0.0371929812466193,0.111080691857384,0.0746806905959038,0.00267602129937778,0.557331536938917,0.557331536938917,0.00272823408636175
+"21797",100,2014,9,4,0,3.48192519821612,14.4032123637016,10.0667657484972,11.8823323653738,11.8823323653738,1.7592517106626,100,2014,9,4,0,0.0443374194223328,0.0452321569142409,0.00393684184660072,0.794447898052775,0.794447898052775,0.00284387092423669
+"21798",100,2014,9,5,0.679977998588279,1.74414740852003,15.3534433345984,10.7202750752599,15.1158964253626,15.1158964253626,1.80176900866424,100,2014,9,5,0.348888870643246,0.0390034999088952,0.0172719393502397,0.015991220016975,0.242680592852652,0.242680592852652,0.00296459251724215
+"21799",100,2014,9,6,0.0299229927451173,2.41379537230933,18.0847747160657,11.4703852073325,16.242750134274,16.242750134274,1.84428630666587,100,2014,9,6,0.0020467836867299,0.0469818756655696,0.0676871315810243,0.0285362676029774,0.0902620769815502,0.0902620769815502,0.00309039886537811
+"21800",100,2014,9,7,0.0365236529094814,4.31438948166515,19.6280089463338,11.6174367740996,16.774147510004,16.774147510004,1.88680360466751,100,2014,9,7,0.00228070182235617,0.0327941439567431,0.0513316107903555,0.00989179463980775,0.000933920837269397,0.000933920837269397,0.00322128996864459
+"21801",100,2014,9,8,0.320022009034427,8.97211205447861,20.7596702512735,11.6239493708931,8.98095719386773,8.98095719386773,1.92932090266915,100,2014,9,8,0.0173099428554725,0.0796427264052675,0.0865539007400907,0.0119444243114044,0.195391833980269,0.195391833980269,0.00335726582704157
+"21802",100,2014,9,9,1.86149613049426,12.7525963725561,18.7959516418256,12.359020946574,15.3400000361326,15.3400000361326,1.97183820067079,100,2014,9,9,0.0854385882232648,0.0308204210694767,0.0811671785189614,0.00399180514026558,0.142470688401671,0.142470688401671,0.00349832644056907
+"21803",100,2014,9,10,0.823322328928411,7.90245320852047,16.987579473699,11.5156434901608,11.7672277498822,11.7672277498822,2.01435549867243,100,2014,9,10,0.012456140295107,0.0966496682329158,0.0654595976625467,0.00766550136149445,1.05084815510542,1.05084815510542,0.00364447180922708
+"21804",100,2014,9,11,3.52530256094057,8.59759066784211,13.7893288825342,9.5707589391828,13.7667325442643,13.7667325442643,2.05687279667407,100,2014,9,11,0.183976636518517,0.0644339128506523,0.0509584811897296,0.00775847876038346,0.698627449321289,0.698627449321289,0.00379570193301559
+"21805",100,2014,9,12,0.318261837015058,4.02852581803686,14.2996589180135,9.85089110383893,15.3735313237172,15.3735313237172,2.09939009467571,100,2014,9,12,0.00339181246116149,0.119202897291023,0.0825543864468315,0.0133321628526526,0.610036781273191,0.610036781273191,0.00395201681193461
+"21806",100,2014,9,13,0,7.76508242121362,14.6840263887076,10.2006050514834,8.50205715943222,8.50205715943222,2.14190739267734,100,2014,9,13,0,0.223287139526759,0.0781228631895983,0.00530234342884222,0.0986701449032621,0.0986701449032621,0.00411341644598414
+"21807",100,2014,9,14,0.00363036309040026,3.53562155808553,16.457634738045,11.7941144265489,17.2355885290601,17.2355885290601,2.18442469067898,100,2014,9,14,0.000526315805159117,0.0395801184152344,0.033535675753994,0.00414738259572042,0.0263707691083468,0.0263707691083468,0.00427990083516419
+"21808",100,2014,9,15,5.44433437846806,9.59888893437989,14.9864576736299,13.1426403215616,13.2228382045549,13.2228382045549,2.22694198868062,100,2014,9,15,0.0492397374158939,0.0550333168454677,0.0990368544551327,0.0234210834169328,0.878170126253616,0.878170126253616,0.00445146997947474
+"21809",100,2014,9,16,0.520902098512033,9.90209030449325,14.5623982715921,11.3695708999802,11.4168865971833,11.4168865971833,2.26945928668226,100,2014,9,16,0.0298830410158427,0.106583062092801,0.068684207849401,0.00791461365469336,3.8071597458087,3.8071597458087,0.00462812387891579
+"21810",100,2014,9,17,3.74048405459481,6.8257535954382,12.6333002535292,8.65480748628757,14.5111773032429,14.5111773032429,2.3119765846839,100,2014,9,17,0.575906422514677,0.106787750968056,0.0519982450653074,0.00733683140187865,1.34334760106859,1.34334760106859,0.00480986253348737
+"21811",100,2014,9,18,2.96072605760446,6.56211214359313,13.6318591624597,9.15269523721324,15.765203581916,15.765203581916,2.35449388268554,100,2014,9,18,0.0622807032880768,0.130316350911258,0.0326953045896091,0.0043473716736361,0.96423837820819,0.96423837820819,0.00499668594318946
+"21812",100,2014,9,19,1.35632562716015,3.70869088854858,14.7190318543001,11.2803850373288,14.0566007437879,14.0566007437879,2.39701118068718,100,2014,9,19,0.100000000000001,0.0916561526543966,0.0690596215811995,0.00420642430718645,1.88688080424268,1.88688080424268,0.00518859410802206
+"21813",100,2014,9,20,0.622002203423198,2.93883388428011,15.2405281990144,10.3380307364385,14.4448736503442,14.4448736503442,2.43952847868881,100,2014,9,20,0.0580701727511592,0.097059655121478,0.0490426852199312,0.000720463952470257,2.34987373546815,2.34987373546815,0.00538558702798515
+"21814",100,2014,9,21,0,1.65524751898741,15.4735534498007,10.5425413654189,16.8898570078565,16.8898570078565,2.48204577669045,100,2014,9,21,0,0.151353777889622,0.0201029264015882,0.00518421817811053,0.136425688287787,0.136425688287787,0.00558766470307876
+"21815",100,2014,9,22,0.040814082016318,2.68937296285094,21.4360285739038,9.88521447617098,20.0633886167319,20.0633886167319,2.52456307469209,100,2014,9,22,0.00228070182235617,0.0424204601103833,0.0962485054866373,0.00901169723385454,0.00164501921482941,0.00164501921482941,0.00579482713330288
+"21816",100,2014,9,23,0,8.5714191503913,24.5896589302256,11.537073764864,18.6707257878269,18.6707257878269,2.56708037269373,100,2014,9,23,0,0.0198730966178533,0.0501111056013149,0.0613321303608298,0.0027584966071027,0.0027584966071027,0.00600707431865752
+"21817",100,2014,9,24,0.318481851061775,12.6412870073476,19.1670407399093,15.7728383417832,5.62422444455826,5.62422444455826,2.60959767069537,100,2014,9,24,0.0806432729610924,0.0160842247433875,0.0260778567846916,0.0391672304633643,0.198315204127369,0.198315204127369,0.00622440625914266
+"21818",100,2014,9,25,4.68877888879891,10.5911110761535,14.1721122466823,11.5318042379532,10.7669307165282,10.7669307165282,2.65211496869701,100,2014,9,25,0.0848538228085412,0.0363286010162951,0.0785286520328464,0.00590057632942592,1.87570390963199,1.87570390963199,0.00644682295475833
+"21819",100,2014,9,26,0.753025304464617,7.12473054871176,17.6603079209364,13.6713532194971,17.2746975010115,17.2746975010115,2.69463226669864,100,2014,9,26,0.0705263189021612,0.0683356678533469,0.0564872166330149,0.0418204343874754,1.36588180641957,1.36588180641957,0.00667432440550448
+"21820",100,2014,9,27,0,7.60435644208532,22.7804620908563,14.4856435421145,18.9277116022225,18.9277116022225,2.73714956470028,100,2014,9,27,0,0.0532368472250066,0.0582029522502445,0.0454175889624087,0.0176982047190192,0.0176982047190192,0.00690691061138114
+"21821",100,2014,9,28,0.24444444915249,13.9968317103202,27.5462048617658,10.0407590194635,13.5728495149854,13.5728495149854,2.77966686270192,100,2014,9,28,0.0514035094063185,0.0413227795093991,0.0126813177145049,0.217967170902824,2.03985115975951,2.03985115975951,0.00714458157238833
+"21822",100,2014,9,29,0.126952697210448,8.79298133241593,19.8218594604593,12.6186248865327,18.4862598003727,18.4862598003727,2.82218416070356,100,2014,9,29,0.00450292425023185,0.0284930009184786,0.0683356291209913,0.0193251624325632,0.0550695915983623,0.0550695915983623,0.00738733728852601
+"21823",100,2014,9,30,0,8.65468650498931,18.5552366338547,9.94871279377617,8.8099230298377,8.8099230298377,2.8647014587052,100,2014,9,30,0,0.0648163817579284,0.0196151493633296,0.00373625946944315,1.46022281679863,1.46022281679863,0.00763517775979422
+"21824",100,2014,10,1,9.53014297821078,5.41302528265942,14.3522993650111,9.42032990387433,16.5221233430869,16.5221233430869,2.89912801507348,100,2014,10,1,0.477777841356153,0.259770227241533,0.0356953145948056,0.00404503638736974,1.91006871431599,1.91006871431599,0.00738763957334714
+"21825",100,2014,10,2,0.404510456736725,8.56096816823559,15.5237953964502,12.7501871667155,10.9384819582613,10.9384819582613,2.93355457144176,100,2014,10,2,0.00140350860461855,0.105642593772043,0.0247783305096419,0.0191473759032784,0.625748476407746,0.625748476407746,0.00714700501027034
+"21826",100,2014,10,3,0.599229928200329,8.7043344950912,18.383597142101,13.2545544264471,16.5571837126225,16.5571837126225,2.96798112781004,100,2014,10,3,0.0503508757438229,0.0467695978645067,0.0262098936205771,0.0457731386531341,0.116354489122711,0.116354489122711,0.00691327407056379
+"21827",100,2014,10,4,0,6.49882290885262,27.1036082751418,12.4322662080737,18.9586358694616,18.9586358694616,3.00240768417832,100,2014,10,4,0,0.0421023519583199,0.0290229063963944,0.100780643686284,0.698210431336823,0.698210431336823,0.00668644675422752
+"21828",100,2014,10,5,0,10.795995588481,15.9651376464055,9.95822880894962,19.2794388161503,19.2794388161503,3.0368342405466,100,2014,10,5,0,0.0702608102408767,0.0185760112450672,0.00225905770425845,0.295776518588987,0.295776518588987,0.00646652306126151
+"21829",100,2014,10,6,0.148514853812943,7.23200216335301,23.271331090476,15.738492699215,3.41475245451639,3.41475245451639,3.07126079691488,100,2014,10,6,0.00368421076682577,0.0677585081513363,0.0654595511500516,0.0367151878268561,0.026498835985846,0.026498835985846,0.00625350299166579
+"21830",100,2014,10,7,4.65654562225174,9.58445554571708,15.861133214533,10.5943234814013,17.2341364114591,17.2341364114591,3.10568735328317,100,2014,10,7,0.150526316458734,0.0838842297761142,0.0357444255406915,0.00469299171410903,0.872427396174364,0.872427396174364,0.00604738654544031
+"21831",100,2014,10,8,1.63212320342971,7.45191420606523,17.4047082600945,13.1612431208293,18.6219144051093,18.6219144051093,3.14011390965145,100,2014,10,8,0.0861988239678743,0.078555555131723,0.0255759807164794,0.0248701871750577,0.914383480496156,0.914383480496156,0.00584817372258511
+"21832",100,2014,10,9,0.0275027506848504,4.91199113669569,19.6273816825271,12.630715075368,19.2646863033013,19.2646863033013,3.17454046601973,100,2014,10,9,0.0020467836867299,0.0593473676581317,0.022865450117758,0.0490555123224006,0.147498270519086,0.147498270519086,0.00565586452310018
+"21833",100,2014,10,10,0,10.5985479805991,26.902244345452,12.5673486858574,18.0536192399834,18.0536192399834,3.20896702238801,100,2014,10,10,0,0.0940701469845722,0.0125579147877954,0.165264852686715,1.00251761247984,1.00251761247984,0.00547045894698552
+"21834",100,2014,10,11,0,11.5716722517779,17.8404180151139,12.4735973964561,18.75719502683,18.75719502683,3.24339357875629,100,2014,10,11,0,0.0573006042213394,0.0405608546477479,0.0102368526682416,0.095791197477033,0.095791197477033,0.00529195699424112
+"21835",100,2014,10,12,0,6.08763479373374,20.34608371764,14.0646866587522,19.7421452550605,19.7421452550605,3.27782013512457,100,2014,10,12,0,0.164733350266369,0.0666765761842518,0.00334503975815809,0.980845032452379,0.980845032452379,0.00512035866486699
+"21836",100,2014,10,13,1.40010999898837,6.45528048598202,13.1923102367305,9.05877897999074,14.1747194716115,14.1747194716115,3.31224669149285,100,2014,10,13,0.1080701554449,0.0848952950115335,0.0291707953894399,0.00567779926730413,1.96027841346265,1.96027841346265,0.00495566395886313
+"21837",100,2014,10,14,5.20088003282369,6.29610563452357,14.6081298612001,9.17970287524434,16.1382950540423,16.1382950540423,3.34667324786113,100,2014,10,14,0.0565496931578126,0.284009305827953,0.0166590788145048,0.0157093391709322,1.0006500301943,1.0006500301943,0.00479787287622954
+"21838",100,2014,10,15,0.111441145775014,5.66429037308142,14.694752447676,9.1901429494222,11.7680857399247,11.7680857399247,3.38109980422941,100,2014,10,15,0.00385964923783353,0.0494590664746653,0.0186690069924321,0.00778187482297675,1.30778148642431,1.30778148642431,0.00464698541696622
+"21839",100,2014,10,16,5.32607265457724,8.84696368835416,15.723806290999,10.0704511321417,16.4277555446814,16.4277555446814,3.4155263605977,100,2014,10,16,0.914269001776727,0.0857356547150034,0.0145450502965179,0.0349093756648424,1.72465348867908,1.72465348867908,0.00450300158107316
+"21840",100,2014,10,17,1.29493948354973,4.77181514628781,17.0822553582186,9.75188116927614,23.0311110908836,23.0311110908836,3.44995291696598,100,2014,10,17,0.118771924031409,0.166543273527597,0.0180941582755473,0.0080040851883852,0.1483673345513,0.1483673345513,0.00436592136855038
+"21841",100,2014,10,18,0,6.95207921427862,26.3179096876591,9.93295922714754,15.6052256543251,15.6052256543251,3.48437947333426,100,2014,10,18,0,0.101213448563538,0.0512315783275588,0.0532204167149428,0.330616487924781,0.330616487924781,0.00423574477939785
+"21842",100,2014,10,19,0,10.0038283970227,20.6600662730839,14.6123652725723,11.3475578421413,11.3475578421413,3.51880602970254,100,2014,10,19,0,0.021681866296763,0.57360040350607,0.0211005793499835,2.47764851028942,2.47764851028942,0.0041124718136156
+"21843",100,2014,10,20,0.11485148800744,6.15130908628239,16.6072935601666,9.42581954700063,26.4547745732978,26.4547745732978,3.55323258607082,100,2014,10,20,0.0183040945963902,0.138495334016247,0.0553579681176129,0.013425120174196,0.00889120843481972,0.00889120843481972,0.00399610247120361
+"21844",100,2014,10,21,0.103630364580516,6.67938395301894,25.3381848770662,12.8721781700227,23.9770518408881,23.9770518408881,3.5876591424391,100,2014,10,21,0.00163742694938392,0.059862006274455,0.0550541985008664,0.0311982587491481,0.0886117366458884,0.0886117366458884,0.0038866367521619
+"21845",100,2014,10,22,0.497689772425967,11.9202530391932,28.9629043457401,15.3051924563871,19.1772387979841,19.1772387979841,3.62208569880738,100,2014,10,22,0.0149707591463949,0.0225029173622785,0.171367174066338,0.0110368512539201,1.82262473397082,1.82262473397082,0.00378407465649045
+"21846",100,2014,10,23,0,12.4849063655069,19.1960724535817,14.887986802163,17.1589549351053,17.1589549351053,3.65651225517566,100,2014,10,23,0,0.0534585195040276,0.0445940485862795,0.00833214428110838,0.318078587145321,0.318078587145321,0.00368841618418926
+"21847",100,2014,10,24,0,13.7179099377757,25.2097469934142,17.0655228013646,22.9857866184415,22.9857866184415,3.69093881154394,100,2014,10,24,0,0.0449538131647467,0.105278467448602,0.0883175202712632,0.799748288249673,0.799748288249673,0.00359966133525836
+"21848",100,2014,10,25,0.183168320602054,11.7766005512905,17.2818040753355,13.1206820669478,10.5605391364942,10.5605391364942,3.72536536791222,100,2014,10,25,0.00508771975050894,0.0552777926127818,0.080404226283383,0.00780408869299772,0.72119167652028,0.72119167652028,0.00351781010969771
+"21849",100,2014,10,26,0,10.9875026646227,24.3122770731205,13.0868758001212,15.1091527896877,15.1091527896877,3.75979192428051,100,2014,10,26,0,0.107860813703113,0.0768758730261189,0.062093005708033,0.724425135890372,0.724425135890372,0.00344286250750734
+"21850",100,2014,10,27,2.48019801678568,12.1853576509079,17.1028711279102,10.5345103365622,20.7928054272407,20.7928054272407,3.79421848064879,100,2014,10,27,0.0806432770567373,0.0175385643496959,0.0248035252089078,0.00340410015271427,4.1556230882793,4.1556230882793,0.00337481852868723
+"21851",100,2014,10,28,1.31276127843574,9.62409244512174,17.7805608575231,10.9955554192085,18.2909131422557,18.2909131422557,3.82864503701707,100,2014,10,28,0.0537426801313444,0.0863140304343326,0.0262730992460002,0.0254175223167108,1.27923131165568,1.27923131165568,0.00331367817323738
+"21852",100,2014,10,29,0.735863583643969,8.18647958893981,19.3618591065192,12.5050934811499,11.74544550326,11.74544550326,3.86307159338535,100,2014,10,29,0.00450292438094398,0.144075941582232,0.035332175825093,0.0197362649237992,0.326192511416944,0.326192511416944,0.00325944144115781
+"21853",100,2014,10,30,0,7.66215628339644,19.0842574175173,12.6476237776518,14.8756875456756,14.8756875456756,3.89749814975363,100,2014,10,30,0,0.230114099229321,0.0460578716660942,0.0308040823016859,1.35249173792579,1.35249173792579,0.00321210833244851
+"21854",100,2014,10,31,0,6.54862486752215,32.0920127998747,8.93095696851103,18.6377337889047,18.6377337889047,3.93192470612191,100,2014,10,31,0,0.0219649121301463,0.0149433648368454,0.0422222052680777,1.34864886899387,1.34864886899387,0.00317167884710947
+"21855",100,2014,11,1,5.84741471230787,10.106688673609,14.3212540724097,8.30002203711582,20.0344992554752,20.0344992554752,3.98632710629508,100,2014,11,1,0.3539766167758,0.0375210146118411,0.017342682690155,0.0184035352406559,1.57232794707032,1.57232794707032,0.00309288714145963
+"21856",100,2014,11,2,3.46512648689471,6.93913088537285,15.5035643320535,9.3828603473827,17.1257976966329,17.1257976966329,4.04072950646824,100,2014,11,2,0.0746783521719188,0.210242678302098,0.0175953440705954,0.0190988536277585,2.17422559041052,2.17422559041052,0.00301812145100265
+"21857",100,2014,11,3,1.2497249515471,4.50039602453821,22.6454674077637,9.66342139794882,25.5408248964316,25.5408248964316,4.0951319066414,100,2014,11,3,1.02374267169084,0.0408561566481314,0.0213151456466423,0.0358830258665817,0.215595280849428,0.215595280849428,0.00294738177573852
+"21858",100,2014,11,4,0.0959295943887583,10.0355556202669,27.8256544841267,11.2048845910134,18.6452805505465,18.6452805505465,4.14953430681457,100,2014,11,4,0.000994152076411666,0.0363496768082205,0.0815397664078582,0.432832153293616,1.45401404962855,1.45401404962855,0.00288066811566724
+"21859",100,2014,11,5,0.0596259634847557,9.60820683184499,16.928514503672,9.22178219227639,24.7418262221501,24.7418262221501,4.20393670698773,100,2014,11,5,0.00245614042407588,0.260491138792973,0.0188321515538067,0.0079941620075122,0.408380975971229,0.408380975971229,0.00281798047078881
+"21860",100,2014,11,6,0,5.10386128630182,21.8908690566933,10.8219362475035,27.1262927270434,27.1262927270434,4.2583391071609,100,2014,11,6,0,0.0322210678033869,0.0224812856934036,0.0692731351841838,0.096194088589195,0.096194088589195,0.00275931884110324
+"21861",100,2014,11,7,0,8.54411453146352,33.5463152592725,7.58655668695112,28.2510229977313,28.2510229977313,4.31274150733406,100,2014,11,7,0,0.0525432696962185,0.00902745536339949,0.000276608468231898,0.0260321636982851,0.0260321636982851,0.00270468322661051
+"21862",100,2014,11,8,0,17.7329593528353,27.1384491432618,14.3873048494882,13.983124252462,13.983124252462,4.36714390750723,100,2014,11,8,0,0.0456760711808172,0.426703407171798,0.00905614733784997,2.28246201149356,2.28246201149356,0.00265407362731063
+"21863",100,2014,11,9,0.799339931036117,8.69247522553464,18.3511221332781,10.263949379538,29.3638610650997,29.3638610650997,4.42154630768039,100,2014,11,9,0.0367251434242519,0.125528716090756,0.0785982782646089,0.0227163335901434,0.00995845320441029,0.00995845320441029,0.00260749004320361
+"21864",100,2014,11,10,0.228602865823556,6.92324536179814,18.8815511235572,10.6872497229162,22.0051373921343,22.0051373921343,4.47594870785356,100,2014,11,10,0.00228070216220724,0.120799999533773,0.0253596394859999,0.010456126932578,1.53465797812507,1.53465797812507,0.00256493247428944
+"21865",100,2014,11,11,0,8.96992293967403,18.4989107087894,9.66024214048984,26.0201208347535,26.0201208347535,4.53035110802672,100,2014,11,11,0,0.101905209776741,0.0405607973157761,0.0360467867601036,0.0508876374630761,0.0508876374630761,0.00252640092056812
+"21866",100,2014,11,12,0,4.58026406054187,23.8715840602996,9.96108918247706,27.5484154809295,27.5484154809295,4.58475350819989,100,2014,11,12,0,0.05784503719823,0.193914681070188,0.240206391614552,0.085970811587675,0.085970811587675,0.00249189538203966
+"21867",100,2014,11,13,0,8.3225633644297,32.1802200489443,10.8439384026103,17.1233443154229,17.1233443154229,4.63915590837305,100,2014,11,13,0,0.169898830909128,0.135311038463064,0.0586730882545059,1.11350322254053,1.11350322254053,0.00246141585870406
+"21868",100,2014,11,14,0.00297029707396385,13.4592628479004,16.7067435068397,10.3208911715299,10.0424531269388,10.0424531269388,4.69355830854622,100,2014,11,14,0.000994152076411666,0.00945087157584854,0.0229257560600268,0.00760350269078042,0.164413342129108,0.164413342129108,0.00243496235056128
+"21869",100,2014,11,15,0.0121012103013342,5.83178208868365,19.2077886688434,9.42086909916272,15.3410671326456,15.3410671326456,4.74796070871938,100,2014,11,15,0.0020467836867299,0.0803842521790373,0.0240321193274074,0.0183175693490531,1.94474753632584,1.94474753632584,0.00241253485761139
+"21870",100,2014,11,16,7.91177120145791,10.5267656145841,18.413575234324,11.4354235320726,20.8802308648071,20.8802308648071,4.80236310889255,100,2014,11,16,0.228888960944309,0.0474386235734278,0.0131912581507164,0.0406339073271142,2.53611497329387,2.53611497329387,0.00239413337985432
+"21871",100,2014,11,17,3.42288228649773,10.5973597716446,18.2945763301535,9.94331125169173,22.5185037178568,22.5185037178568,4.85676550906571,100,2014,11,17,0.444853740575029,0.0520263287951991,0.0390830433542659,0.0238368132318872,0.918492115159427,0.918492115159427,0.00237975791729013
+"21872",100,2014,11,18,0,7.23913088988419,18.3862487910473,11.8488228423367,17.3133223945945,17.3133223945945,4.91116790923888,100,2014,11,18,0,0.15725847168306,0.0193544118471346,0.0154064396976048,4.02356173776563,4.02356173776563,0.00236940846991876
+"21873",100,2014,11,19,0,5.17192514606304,27.7298456680919,11.2011440841541,27.7751372623758,27.7751372623758,4.96557030941204,100,2014,11,19,0,0.0320830291547433,0.0457427441337589,0.0779607750586769,0.170643334417068,0.170643334417068,0.00236308503774027
+"21874",100,2014,11,20,0,13.031210062134,27.2367875127509,17.8795268370373,2.94454346211961,2.94454346211961,5.0199727095852,100,2014,11,20,0,0.0208894587422622,0.0160807388442477,0.0350695458665536,0.0350497059738476,0.0350497059738476,0.00236078762075461
+"21875",100,2014,11,21,3.75324532720778,11.7542684196246,21.9443346029854,11.2828053399937,27.6456103959624,27.6456103959624,5.07437510975837,100,2014,11,21,0.792456116258059,0.0428368884773399,0.0532923365590144,0.0302988331578059,0.200622724656101,0.200622724656101,0.00236251621896183
+"21876",100,2014,11,22,0.260506058831026,9.96814082453091,32.6279432130988,10.9143454296754,18.965016177254,18.965016177254,5.12877750993153,100,2014,11,22,0.00467836295652113,0.0816497117896566,0.00697777506632237,0.120257911115568,2.03558126866774,2.03558126866774,0.00236827083236188
+"21877",100,2014,11,23,0.011221122279419,8.63787673303921,23.0833002726237,12.2122331709489,26.2079536429595,26.2079536429595,5.1831799101047,100,2014,11,23,0.0102339184336495,0.0131029333951869,0.0889697030902293,0.0704116653956398,0.254328509463778,0.254328509463778,0.00237805146095479
+"21878",100,2014,11,24,2.348184821367,10.8928712943468,18.947403701094,14.1764796344098,5.52809687311237,5.52809687311237,5.23758231027786,100,2014,11,24,0.15830409208934,0.0570895149566458,0.0255877542331291,0.0123719302083474,0.959931605317866,0.959931605317866,0.00239185810474055
+"21879",100,2014,11,25,3.23465349558819,7.94574260659213,18.610979193508,10.2121342490084,23.6884267762943,23.6884267762943,5.29198471045103,100,2014,11,25,0.909824578218304,0.0506830264153142,0.016981839931089,0.0264316117426838,1.62477010435168,1.62477010435168,0.00240969076371915
+"21880",100,2014,11,26,0.0436743680875425,8.49139722030942,18.5792520038366,13.5658196280367,17.4259514939798,17.4259514939798,5.34638711062419,100,2014,11,26,0.00245614042407588,0.165153759299467,0.0147006042171964,0.0191590495907899,3.49786884962851,3.49786884962851,0.00243154943789062
+"21881",100,2014,11,27,0.296809692538217,11.2300879160563,19.0165454142689,10.5805721157061,28.9174585982387,28.9174585982387,5.40078951079736,100,2014,11,27,0.00111111114422481,0.150105281640309,0.0497147217067418,0.0179163706863662,0.223561978136328,0.223561978136328,0.00245743412725494
+"21882",100,2014,11,28,0,5.61974694154444,20.8495163219859,11.0729372456785,32.1432784303974,32.1432784303974,5.45519191097052,100,2014,11,28,0,0.114728685771672,0.119061895283761,0.0613374256603152,0.0541718294566396,0.0541718294566396,0.0024873448318121
+"21883",100,2014,11,29,0,7.84933989905682,24.6159733771229,12.1760175209759,32.4521783130004,32.4521783130004,5.50959431114369,100,2014,11,29,0,0.0743356666710388,0.242215326494697,0.0504806928624265,0.000947412012613988,0.000947412012613988,0.00252128155156212
+"21884",100,2014,11,30,0,11.6395708719889,31.27304712092,13.6075138075255,21.9935643014604,21.9935643014604,5.56399671131685,100,2014,11,30,0,0.0291766133269868,0.23839532726382,0.231421043842219,3.71843199002472,3.71843199002472,0.00255924428650499
+"21885",100,2014,12,1,0.0749174928655325,13.9070515627384,23.7367655289317,14.9600769930547,17.4811001212159,17.4811001212159,5.56713256279321,100,2014,12,1,0.00362573110220725,0.0829578395152662,0.17934782208915,0.0158245317225595,1.46133672076475,1.46133672076475,0.0024619805735395
+"21886",100,2014,12,2,0,13.725940584743,20.8686465629519,15.0873816850031,14.803531314149,14.803531314149,5.57026841426956,100,2014,12,2,0,0.127471940028396,0.169402433179268,0.0403262918551114,0.323614684681693,0.323614684681693,0.00236846949482203
+"21887",100,2014,12,3,0.347634769385994,12.3886907701314,18.6479100016477,18.060561174869,3.81130913722895,3.81130913722895,5.57340426574592,100,2014,12,3,0.0192982452741841,0.0384005306534578,0.0374164428312033,0.0238619750265283,0.19763978027378,0.19763978027378,0.0022787110503526
+"21888",100,2014,12,4,10.5174917414112,13.4266667413239,24.4900550779336,17.9515844288439,24.3192301562386,24.3192301562386,5.57654011722227,100,2014,12,4,1.06929809949559,0.0173093421423816,0.13360403424112,0.0219449712657644,4.40908978837706,4.40908978837706,0.00219270524013116
+"21889",100,2014,12,5,0.153905394488108,15.5930802489009,19.66546746895,16.3445101067571,14.6242464052962,14.6242464052962,5.57967596869862,100,2014,12,5,0.0124561412623751,0.0386759825277673,0.0118210773224007,0.0213135181139792,2.89938165614939,2.89938165614939,0.00211045206415778
+"21890",100,2014,12,6,0.124092411090045,12.8441254560179,18.9391310558592,15.6253134619416,10.6965675595308,10.6965675595308,5.58281182017498,100,2014,12,6,0.00175438601719706,0.0455427277281336,0.00526549606810376,0.00110233074885709,0.408013365657529,0.408013365657529,0.00203195152243242
+"21891",100,2014,12,7,0.168646868297828,13.1924641969049,19.1205609450639,14.6709571131254,14.1278327610364,14.1278327610364,5.58594767165133,100,2014,12,7,0.0298830415691897,0.0322263175942877,0.093392939009077,0.0202731085705344,1.15242588715365,1.15242588715365,0.00195720361495509
+"21892",100,2014,12,8,0.486578662296792,13.0920462603092,20.3185368276665,14.0348403592839,18.6186689129232,18.6186689129232,5.58908352312769,100,2014,12,8,0.10023391890247,0.102305284861561,0.0292988043359172,0.0050479523999499,16.8165738369348,16.8165738369348,0.00188620834172579
+"21893",100,2014,12,9,0,9.48492846263386,21.5554785607803,11.5067548059394,30.5921563734972,30.5921563734972,5.59221937460404,100,2014,12,9,0,0.248704069101957,0.126837495172899,0.0169175335377449,0.500650264039932,0.500650264039932,0.00181896570274449
+"21894",100,2014,12,10,0.468096810566186,11.096589682257,22.8320021089023,15.1191749100638,25.0227720766309,25.0227720766309,5.5953552260804,100,2014,12,10,0.105847951848256,0.0628467674880606,0.0576485395691496,0.0455894941431016,1.79942056589883,1.79942056589883,0.00175547569801124
+"21895",100,2014,12,11,0,12.9855775056761,20.2193509386186,11.6067107151313,32.9443340889036,32.9443340889036,5.59849107755675,100,2014,12,11,0,0.0920830542515182,0.0381262996208045,0.0338146137506045,0.501965974543001,0.501965974543001,0.00169573832752601
+"21896",100,2014,12,12,0.645434545096916,9.47055010695924,23.9098020853645,14.3357425916313,32.6490204352619,32.6490204352619,5.60162692903311,100,2014,12,12,0.164736841080482,0.162450875055934,0.186084202714302,0.0349023686192233,0.0658690871224754,0.0658690871224754,0.00163975359128879
+"21897",100,2014,12,13,0,12.5519581113843,33.8791533240391,10.1372386221052,32.0847409503295,32.0847409503295,5.60476278050946,100,2014,12,13,0,0.117817513054369,0.0402719460662464,0.0465227612256069,0.155735438716864,0.155735438716864,0.00158752148929961
+"21898",100,2014,12,14,0.199889992657799,10.9824752786634,23.2970735489076,14.780913178808,29.9729921678768,29.9729921678768,5.60789863198582,100,2014,12,14,0.0295321642916802,0.0430830730349831,0.252150827972533,0.0384222379473491,0.547583121700563,0.547583121700563,0.00153904202155846
+"21899",100,2014,12,15,0,10.8184488682595,23.9069196808063,13.2596810249606,26.9231904494618,26.9231904494618,5.61103448346217,100,2014,12,15,0,0.153184261478244,0.172404025710506,0.0183274683813632,0.455027615914423,0.455027615914423,0.00149431518806533
+"21900",100,2014,12,16,0.0914191432764428,10.1711112065415,23.1762926019851,14.8939274514076,19.2198460309288,19.2198460309288,5.61417033493852,100,2014,12,16,0.0020467836867299,0.018336856822018,0.175692408498456,0.139684284801448,1.27104864546152,1.27104864546152,0.00145334098882023
+"21901",100,2014,12,17,3.37799779731448,7.63694167950235,20.593465284677,9.94975795158327,29.7791196720304,29.7791196720304,5.61730618641488,100,2014,12,17,0.301637429772768,0.0840263386442128,0.0421589679654115,0.0584778225369816,4.49563361955187,4.49563361955187,0.00141611942382315
+"21902",100,2014,12,18,0.228272832167817,6.39360838597364,20.9600438788386,13.4097578386531,17.731594977468,17.731594977468,5.62044203789123,100,2014,12,18,0.0505263166330015,0.0482579073175508,0.0108064567256518,0.00386083817517047,1.37843413287637,1.37843413287637,0.00138265049307411
+"21903",100,2014,12,19,4.76292619820606,8.41545644661512,19.7203299980877,10.1177337235219,30.598360823326,30.598360823326,5.62357788936759,100,2014,12,19,0.0516374382777661,0.124859611243569,0.0257432507657822,0.0437876970994407,3.51833846319159,3.51833846319159,0.00135293419657309
+"21904",100,2014,12,20,0,8.07299225663457,21.8676789307883,12.8747085690892,30.8968537522621,30.8968537522621,5.62671374084394,100,2014,12,20,0,0.0840695937006596,0.0940952720823892,0.0272041125917256,0.732604253709022,0.732604253709022,0.00132697053432008
+"21905",100,2014,12,21,0,10.2368867315999,29.7467437194388,15.1538394543991,31.4480638409605,31.4480638409605,5.6298495923203,100,2014,12,21,0,0.122011678294214,0.127025130708259,0.131568994963877,0.422102026175942,0.422102026175942,0.00130475950631511
+"21906",100,2014,12,22,0.290319033196591,12.8429262761367,24.7313201031407,17.7489878798213,7.22800875339571,7.22800875339571,5.63298544379665,100,2014,12,22,0.088187131641204,0.0764819681246336,0.089042758000618,0.0255164385079515,0.525757834565029,0.525757834565029,0.00128630111255817
+"21907",100,2014,12,23,0.0818481860381148,16.3536082226844,19.8803407188558,16.2122773148439,9.45556654473736,9.45556654473736,5.63612129527301,100,2014,12,23,0.0020467836867299,0.0433929419091035,0.151465364837882,0.00243566310082474,1.41652122568617,1.41652122568617,0.00127159535304924
+"21908",100,2014,12,24,0.0222222225533591,7.06833881749572,21.9471949188098,10.1658525498393,31.584785434148,31.584785434148,5.63925714674936,100,2014,12,24,0.00175438601719706,0.0411561280512796,0.0478783547968257,0.00879296903116887,0.349272585622857,0.349272585622857,0.00126064222778835
+"21909",100,2014,12,25,0,8.6027613084833,21.2022437734572,11.4423324198875,26.7274807089626,26.7274807089626,5.64239299822571,100,2014,12,25,0,0.0647999770695095,0.0429426048058889,0.0440543798770848,0.926650953768256,0.926650953768256,0.00125344173677549
+"21910",100,2014,12,26,1.08987900204916,7.88591864295263,19.7000441136796,8.97668870709779,31.813036531505,31.813036531505,5.64552884970207,100,2014,12,26,0.0845029255800086,0.120433922180978,0.0254831064373496,0.0319888411204853,2.10696527985332,2.10696527985332,0.00124999388001065
+"21911",100,2014,12,27,0,4.29046201810847,22.2382178353791,11.6759846176382,33.3738840143017,33.3738840143017,5.64866470117842,100,2014,12,27,0,0.033066657108753,0.0807479392696388,0.114783018400034,0.106725836540072,0.106725836540072,0.00125029865749384
+"21912",100,2014,12,28,0,8.07198024556713,29.9003299339639,12.0765675396809,33.2352475375101,33.2352475375101,5.65180055265478,100,2014,12,28,0,0.0149134581538539,0.0885806542963174,0.256583037721112,0.00623327111250078,0.00623327111250078,0.00125435606922503
+"21913",100,2014,12,29,0.638283831451592,10.5245215968855,20.2944332853009,11.081001079253,21.9706928895252,21.9706928895252,5.65493640413113,100,2014,12,29,0.103859643225085,0.0175204635308589,0.0487982324301018,0.0232497321057218,2.93181450339007,2.93181450339007,0.00126216611520427
+"21914",100,2014,12,30,4.36413634492226,11.7188339191433,20.066611683408,10.1465567451368,22.4746205924761,22.4746205924761,5.65807225560749,100,2014,12,30,0.194853758142714,0.0659322082542344,0.020637381459243,0.0311912222009942,1.79763626377073,1.79763626377073,0.00127372879543154
+"21915",100,2014,12,31,0.024092409599929,9.18510459988031,22.737568900399,11.0271727359465,28.9289328488055,28.9289328488055,5.66120810708384,100,2014,12,31,0.00175438601719706,0.146047868231235,0.158913909528724,0.0378117197673185,1.5387709703237,1.5387709703237,0.00128904410990683
+"21916",100,2015,1,1,0.240374042215061,8.73911993679303,24.4301978385094,15.1152035752014,20.593145983185,20.593145983185,5.67043865149487,100,2015,1,1,0.0655555548601681,0.0279228064063483,0.207146803150039,0.144176666141626,3.19292248102851,3.19292248102851,0.00123600424251285
+"21917",100,2015,1,2,0.0572057214244889,14.0673376498836,41.5434874103408,9.07006600613903,27.2190540028353,27.2190540028353,5.67966919590591,100,2015,1,2,0.00263157902579559,0.0314953169170685,0.0337286094903615,0.0388690105706882,3.07307306385633,3.07307306385633,0.00118967805444901
+"21918",100,2015,1,3,0.388668875891318,17.9382398624231,37.1572828513168,19.5519140556176,18.5940152809303,18.5940152809303,5.68889974031694,100,2015,1,3,0.0460818708709807,0.0477315711441885,0.675322574398147,0.168567111924656,1.22457019669858,1.22457019669858,0.0011500655457153
+"21919",100,2015,1,4,1.56897689839794,14.3168207027993,22.4281518556366,11.2842905340415,34.3419802574435,34.3419802574435,5.69813028472797,100,2015,1,4,0.215789475148185,0.0419760691192048,0.12789525395915,0.015791835615431,0.0203444390201652,0.0203444390201652,0.00111716671631173
+"21920",100,2015,1,5,0.698239827772441,6.53745873821582,23.9039162655737,11.178074780077,33.4393842674062,33.4393842674062,5.70736082913901,100,2015,1,5,0.102456136101868,0.0795368596082742,0.12758762181178,0.031043326013186,0.22857801361193,0.22857801361193,0.00109098156623828
+"21921",100,2015,1,6,0,8.09908699071316,26.4048624804573,15.6885370909184,31.7340374454544,31.7340374454544,5.71659137355004,100,2015,1,6,0,0.037706433051991,0.240189428706735,0.110814069228278,0.186851177416817,0.186851177416817,0.00107151009549497
+"21922",100,2015,1,7,0,15.4363257153199,39.5395928478346,16.2196921567843,26.3893619784952,26.3893619784952,5.72582191796107,100,2015,1,7,0,0.0209099509948291,0.0640372460893027,0.246937459762906,1.10673891957189,1.10673891957189,0.00105875230408178
+"21923",100,2015,1,8,7.17678765128023,18.9752584757453,24.3241475171382,22.8343014418095,5.06114411826181,5.06114411826181,5.7350524623721,100,2015,1,8,0.592748604545832,0.021103469831115,0.021384230724414,0.064957848974099,0.441098860228156,0.441098860228156,0.00105270819199874
+"21924",100,2015,1,9,5.7644664640605,12.900164918931,17.7652253632498,12.0815952519248,9.28674362253959,9.28674362253959,5.74428300678314,100,2015,1,9,0.470409299114312,0.0555356149417567,0.0054263067505784,0.0161707548778071,0.333425128093567,0.333425128093567,0.0010533777592458
+"21925",100,2015,1,10,0.193179321269391,13.3269745267526,18.7988668007426,14.623586246545,7.75096804605197,7.75096804605197,5.75351355119417,100,2015,1,10,0.0445029255773938,0.105881812777226,0.0105275122037519,0.00271402871212505,0.311482992403155,0.311482992403155,0.00106076100582302
+"21926",100,2015,1,11,0.0970297044161523,14.1452035977371,24.0243451692352,17.2375028272404,26.1125742027862,26.1125742027862,5.7627440956052,100,2015,1,11,0.00695906453488166,0.0478895004362343,0.0797650310184418,0.0346134496634775,2.1604989402461,2.1604989402461,0.00107485793173035
+"21927",100,2015,1,12,0.0528052813149128,13.0417159975427,28.978580642717,18.4821781162644,29.3911771910681,29.3911771910681,5.77197464001624,100,2015,1,12,0.00538011711940431,0.0401590665895936,0.177826537077976,0.0719099179677124,1.46015463396077,1.46015463396077,0.00109566853696784
+"21928",100,2015,1,13,34.5150709750235,17.7779427896632,25.6199341156039,22.3925085120206,5.41075908652495,5.41075908652495,5.78120518442727,100,2015,1,13,3.00111190372155,0.054970150203833,0.0475589464557854,0.0511981956172492,1.27752749996745,1.27752749996745,0.00112319282153544
+"21929",100,2015,1,14,13.4541255250098,15.7653465543774,18.1928271051287,15.3106269322344,9.86734870243387,9.86734870243387,5.7904357288383,100,2015,1,14,6.6445036466499,0.0238731404087798,0.0116368357210174,0.0146163718776447,0.454202870981395,0.454202870981395,0.00115743078543317
+"21930",100,2015,1,15,2.39625962326104,12.8745764374602,20.1545873732194,13.8911551610865,22.1974806518051,22.1974806518051,5.79966627324933,100,2015,1,15,0.208947368434998,0.0555818031713488,0.0352894084108381,0.0349210764567905,3.09374759917716,3.09374759917716,0.00119838242866104
+"21931",100,2015,1,16,0.0959295943887583,9.86883391746462,24.7682509469514,12.8206381635173,23.8370734320747,23.8370734320747,5.80889681766037,100,2015,1,16,0.00730994173832107,0.0795620591594151,0.0355889285620243,0.0964264322225988,1.01893667719898,1.01893667719898,0.00124604775121904
+"21932",100,2015,1,17,1.27986799473941,12.949120096641,22.0625081371815,11.9030363357762,25.7463588462804,25.7463588462804,5.8181273620714,100,2015,1,17,0.173157898720943,0.0657620043905952,0.034291750331579,0.108687195593929,1.7322117362489,1.7322117362489,0.00130042675310717
+"21933",100,2015,1,18,0.248404848029708,13.369560010887,19.4278551086997,10.925511621406,25.3265126063616,25.3265126063616,5.82735790648243,100,2015,1,18,0.00578947422274374,0.0984982108892204,0.0361134527420769,0.0445579048938212,2.38408764876013,2.38408764876013,0.00136151943432542
+"21934",100,2015,1,19,0.530693074411685,9.96613864562955,22.0437734098193,11.8884599242929,30.2290869273237,30.2290869273237,5.83658845089347,100,2015,1,19,0.133216373986668,0.172370743108017,0.0755140945347571,0.090298268859224,0.073548450041958,0.073548450041958,0.00142932579487381
+"21935",100,2015,1,20,0,11.7877228572161,29.1996368475349,13.5949063725991,10.4545874191721,10.4545874191721,5.8458189953045,100,2015,1,20,0,0.0975538362581627,0.040527438140679,0.0139672414969836,0.900500664986629,0.900500664986629,0.00150384583475236
+"21936",100,2015,1,21,0.72651265535352,14.6547524207758,24.0929590944934,17.6783607197542,29.1012760011276,29.1012760011276,5.85504953971553,100,2015,1,21,0.0814035104311003,0.0146286631802176,0.854203349728395,0.115180003736449,6.43745009636256,6.43745009636256,0.001585079553961
+"21937",100,2015,1,22,0,14.730308011289,31.2509900230517,17.2339493528058,21.5679319295684,21.5679319295684,5.86428008412657,100,2015,1,22,0,0.0595812887984732,0.318056157109099,0.00897190239470148,0.824440022188951,0.824440022188951,0.00167302695249979
+"21938",100,2015,1,23,0,13.4184818141925,22.5228604399594,14.3387899703056,26.3632449684101,26.3632449684101,5.8735106285376,100,2015,1,23,0,0.00827309214056234,0.0928819785049253,0.0349362513247345,0.627800587177996,0.627800587177996,0.00176768803036871
+"21939",100,2015,1,24,0,10.7338504612905,25.7906377706328,12.9432563131267,30.5228710783066,30.5228710783066,5.88274117294863,100,2015,1,24,0,0.011511695003001,0.0792005741901523,0.0969649372547835,0.316709884385096,0.316709884385096,0.00186906278756775
+"21940",100,2015,1,25,2.91067108279145,11.0414300494724,19.0556105539219,10.8556215687029,25.3021563812189,25.3021563812189,5.89197171735966,100,2015,1,25,0.337660824680889,0.039456139603604,0.0339778009204921,0.0389608081193526,2.47530966782185,2.47530966782185,0.00197715122409693
+"21941",100,2015,1,26,0.906710669021271,7.93386141668976,19.1775689140798,10.762772355536,24.7942573979612,24.7942573979612,5.9012022617707,100,2015,1,26,0.0394152079269907,0.0570274840784733,0.0280064577161246,0.062624543849279,11.8356318160076,11.8356318160076,0.00209195333995624
+"21942",100,2015,1,27,0.303190325500548,8.0393179902936,19.7282067436327,11.4912211792697,17.4409021067016,17.4409021067016,5.91043280618173,100,2015,1,27,0.0702339188449565,0.131538610706025,0.0393262510423861,0.0970579046149306,4.41713879639279,4.41713879639279,0.00221346913514568
+"21943",100,2015,1,28,0,11.5124423044874,23.1343455655609,13.5650715088293,30.1377778908338,30.1377778908338,5.91966335059276,100,2015,1,28,0,0.201936885648632,0.0570818631358687,0.114298204901681,0.181662061372519,0.181662061372519,0.00234169860966528
+"21944",100,2015,1,29,0,10.7953244283779,22.3808360000124,11.5788228797703,29.1252474496336,29.1252474496336,5.9288938950038,100,2015,1,29,0,0.108394178979516,0.0317561462315316,0.132594217035068,0.704765509095874,0.704765509095874,0.00247664176351498
+"21945",100,2015,1,30,0,9.45042905817987,21.4248515801592,12.373619395383,28.2511110442175,28.2511110442175,5.93812443941483,100,2015,1,30,0,0.0392257219861958,0.0961508385242279,0.0836871242077873,0.349242761132604,0.349242761132604,0.0026182985966948
+"21946",100,2015,1,31,0,12.7882948949917,18.4349284596963,13.084477456096,11.5581958128674,11.5581958128674,5.94735498382586,100,2015,1,31,0,0.165482949108619,0.0585093814027603,0.0793952611087374,0.894438535378064,0.894438535378064,0.00276666910920478
+"21947",100,2015,2,1,0,13.0155115211495,18.6523761098797,12.7819691407274,11.9167767232007,11.9167767232007,5.93114838432611,100,2015,2,1,0,0.0639537788213373,0.0757695348552819,0.00378713966067514,1.24549129344435,1.24549129344435,0.00296179688754832
+"21948",100,2015,2,2,0.481518156088815,13.0160616076294,19.236413666124,12.4677227332909,13.2485699249704,13.2485699249704,5.91494178482635,100,2015,2,2,0.16005848001057,0.047943303566682,0.156759305470241,0.00475380562682148,0.747015411814259,0.747015411814259,0.00317295711288689
+"21949",100,2015,2,3,0,13.3327613070984,19.9620131103381,12.4342904106619,21.5932671806075,21.5932671806075,5.8987351853266,100,2015,2,3,0,0.0501450302459404,0.345939847805764,0.0368210589738748,2.26194540727883,2.26194540727883,0.00340014978522045
+"21950",100,2015,2,4,0.100000001490116,12.1655115467487,19.4666993526211,10.079130827397,23.4111328649573,23.4111328649573,5.88252858582684,100,2015,2,4,0,0.0733005529571698,0.0517555108815632,0.03258127125308,1.25823731219672,1.25823731219672,0.00364337490454906
+"21951",100,2015,2,5,0,11.5413202363403,23.1053463502554,14.4775467223198,27.1819801309583,27.1819801309583,5.86632198632709,100,2015,2,5,0,0.115353732817434,0.177128709469857,0.0960473931653675,0.163202354600948,0.163202354600948,0.00390263247087266
+"21952",100,2015,2,6,0,12.5090869141884,33.8525631509074,13.8820132107625,27.1807921947819,27.1807921947819,5.85011538682733,100,2015,2,6,0,0.0124397530071132,0.350135926918616,0.04869236501958,0.630478612572234,0.630478612572234,0.00417792248419128
+"21953",100,2015,2,7,0.013971397347904,18.4516500529676,33.1903081249745,15.0454013371232,8.81912009655708,8.81912009655708,5.83390878732758,100,2015,2,7,0.00140350881375765,0.00490407367311884,0.0949100148088839,0.115924534069383,1.91638328845604,1.91638328845604,0.00446924494450492
+"21954",100,2015,2,8,0.244004407280808,15.449273896296,21.614334175689,15.1030364629328,17.3852475840922,17.3852475840922,5.81770218782782,100,2015,2,8,0.00561403559488165,0.04154855429897,0.276813705761504,0.0644590314787761,1.39038856548774,1.39038856548774,0.00477659985181356
+"21955",100,2015,2,9,0.116501651901026,10.7190867592924,23.2586359458395,13.4859955643926,26.4732894288956,26.4732894288956,5.80149558832807,100,2015,2,9,0.00321637436486127,0.181347362170373,0.205394275273062,0.0525701958283117,1.12309522162123,1.12309522162123,0.00509998720611722
+"21956",100,2015,2,10,0,9.87276136337465,26.5783827081896,16.4654124063758,23.7764024975801,23.7764024975801,5.78528898882831,100,2015,2,10,0,0.0741695572591495,0.405604109620502,0.121136107434962,0.435039863827668,0.435039863827668,0.00543940700741589
+"21957",100,2015,2,11,0,13.4053134383148,26.1149946974449,16.9884929615017,13.5661275620245,13.5661275620245,5.76908238932856,100,2015,2,11,0,0.0964116980101618,0.715217906175841,0.205902430011546,1.97085106912142,1.97085106912142,0.00579485925570959
+"21958",100,2015,2,12,0,13.1841364274062,22.1199119135623,11.9522661958197,28.3558194692379,28.3558194692379,5.7528757898288,100,2015,2,12,0,0.066942674195209,0.179169659709765,0.0763766238542515,0.0287672170050545,0.0287672170050545,0.0061663439509983
+"21959",100,2015,2,13,0,14.5094720422894,30.7396040581765,17.9054343828929,17.0682838039167,17.0682838039167,5.73666919032905,100,2015,2,13,0,0.0171315605209111,0.153949842227966,0.0182204781741927,4.99262552946463,4.99262552946463,0.00655386109328203
+"21960",100,2015,2,14,14.5665567039263,17.3891967357975,26.920473356845,18.4930912957858,27.1135092190783,27.1135092190783,5.72046259082929,100,2015,2,14,6.62830406356286,0.0295133597571895,0.0750730276946523,0.0282034779558129,0.034643236439335,0.034643236439335,0.00695741068256074
+"21961",100,2015,2,15,0.908580863444653,15.3733663527486,35.1287899290112,18.6209790971544,18.9035864862541,18.9035864862541,5.70425599132954,100,2015,2,15,0.612748542548273,0.0358251418644151,0.122687793255709,0.0223690147983279,1.51084547722169,1.51084547722169,0.00737699271883448
+"21962",100,2015,2,16,0,13.1724862999911,21.9065898311938,16.1020573499573,15.4724640242993,15.4724640242993,5.68804939182978,100,2015,2,16,0,0.0591894600606553,0.0822468627175006,0.0242876997431985,5.50578830405356,5.50578830405356,0.00781260720210324
+"21963",100,2015,2,17,0,14.3676127485185,20.4513420046228,14.1243564450439,13.6808031226936,13.6808031226936,5.67184279233003,100,2015,2,17,0,0.0405368395755931,0.155335504507625,0.0227812881604164,1.37873177776233,1.37873177776233,0.00826425413236702
+"21964",100,2015,2,18,0,13.7109239691555,21.1044552302597,14.9637294849022,13.2489990018251,13.2489990018251,5.65563619283027,100,2015,2,18,0,0.0528953281894017,0.0293912228459985,0.0245707610527312,1.27918920853607,1.27918920853607,0.00873193350962583
+"21965",100,2015,2,19,0,14.2587790714763,26.6765567036745,17.8171946879136,23.8889876450643,23.8889876450643,5.63942959333052,100,2015,2,19,0,0.0469783713759873,0.307902800875993,0.0644760644346307,0.0626760220803957,0.0626760220803957,0.00921564533387963
+"21966",100,2015,2,20,0,12.9479428145489,25.5733331904815,16.957777778701,22.5370185629632,22.5370185629632,5.62322299383076,100,2015,2,20,0,0.0796579178928017,0.333758095202938,0.0278028772769622,0.21514959733782,0.21514959733782,0.00971538960512844
+"21967",100,2015,2,21,0.0661166126463804,13.1869527493635,34.9323759383232,14.7754784511654,24.9468316706625,24.9468316706625,5.60701639433101,100,2015,2,21,0.00257309949188902,0.0131356732821193,0.372157609148842,0.0877982874865437,0.164439792637782,0.164439792637782,0.0102311663233723
+"21968",100,2015,2,22,1.82156212822963,18.1365457268307,34.4737189684239,17.7983499092631,12.2681188352562,12.2681188352562,5.59080979483125,100,2015,2,22,0.101754377738777,0.0135321623598876,0.572110987346764,0.151056123669134,2.67639811269533,2.67639811269533,0.0107629754886111
+"21969",100,2015,2,23,4.04576464271126,17.2213200337291,21.7236522070252,14.4876457241633,15.4911331086531,15.4911331086531,5.5746031953315,100,2015,2,23,1.12023389877633,0.0075912582355935,0.0540152114648535,0.00186083078004986,1.78743718606397,1.78743718606397,0.011310817100845
+"21970",100,2015,2,24,0.00297029707396385,9.70573156501594,20.1638174696986,10.7580968124507,20.5386469560881,20.5386469560881,5.55839659583174,100,2015,2,24,0.000994152076411666,0.166671902626511,0.0238584739018888,0.0268175252792352,0.567879989022104,0.567879989022104,0.0118746911600738
+"21971",100,2015,2,25,0.065456546629944,12.9482287710125,23.2590209092244,16.4816059914097,12.4361386115533,12.4361386115533,5.54218999633199,100,2015,2,25,0.00263157902579559,0.113755556869593,0.0868696241370387,0.0421320900188635,1.04177310757783,1.04177310757783,0.0124545976662977
+"21972",100,2015,2,26,0.0565456554080525,14.341265150685,22.98961479934,15.5075688566705,22.2596148899024,22.2596148899024,5.52598339683223,100,2015,2,26,0.00263157902579559,0.0355151580476628,0.14075443287922,0.0406210966768934,0.150122897299042,0.150122897299042,0.0130505366195166
+"21973",100,2015,2,27,0.577667773786289,12.3270518121415,22.8445215697336,14.3525523090258,16.4578107082673,16.4578107082673,5.50977679733248,100,2015,2,27,0.015438596323917,0.0138608170939611,0.139371982945018,0.0463029389477057,0.537424531459474,0.537424531459474,0.0136625080197306
+"21974",100,2015,2,28,0,15.2076678690475,31.4902860328834,16.5481409675086,7.42479649628743,7.42479649628743,5.49357019783273,100,2015,2,28,0,0.0317596776951858,0.0587022688484004,0.167475957991234,1.15933859106033,1.15933859106033,0.0142905118669395
+"21975",100,2015,3,1,3.90770078885673,13.1905830905776,20.1073376222281,13.4457095432596,17.1903520055337,17.1903520055337,5.44159670914107,100,2015,3,1,0.162573086588018,0.0601339147780455,0.0460887755504237,0.0491374372181754,4.37717347266909,4.37717347266909,0.0136576498478939
+"21976",100,2015,3,2,1.52233221890903,4.25386142993,24.6420134427917,10.1369416695355,19.977370670264,19.977370670264,5.38962322044941,100,2015,3,2,0.922748539552356,0.0356877009486777,0.0327918272277314,0.0486818291313428,0.3517033510668,0.3517033510668,0.0130418521533453
+"21977",100,2015,3,3,0.013421342334207,9.30621562512925,21.8817490807461,12.6745215165209,23.1610338150209,23.1610338150209,5.33764973175775,100,2015,3,3,0.00140350881375765,0.0176029372768378,0.0640361732619684,0.0650362638217669,0.883781442131635,0.883781442131635,0.0124431187832939
+"21978",100,2015,3,4,0,9.45984583352134,22.4627396506969,13.8619803417109,11.7957975840805,11.7957975840805,5.28567624306609,100,2015,3,4,0,0.063978454953541,0.0197953351341062,0.0651672446736654,1.9538686593843,1.9538686593843,0.0118614497377395
+"21979",100,2015,3,5,3.46699671750546,10.8505611125917,18.5662925078137,9.72324520001973,16.3751596549425,16.3751596549425,5.23370275437443,100,2015,3,5,0.243976622324943,0.121762057138283,0.0152543818827506,0.0417257703391825,0.928317973963613,0.928317973963613,0.0112968450166823
+"21980",100,2015,3,6,1.6797579650176,12.8458965026637,18.6978659970794,17.3554563658728,9.78518152813969,9.78518152813969,5.18172926568277,100,2015,3,6,0.60543857381358,0.109326345294345,0.0197608702277521,0.0352321680127088,0.383903515185607,0.383903515185607,0.0107493046201222
+"21981",100,2015,3,7,3.96644658827283,13.9015400532973,21.1390758132515,16.3287791838609,13.5876568279119,13.5876568279119,5.12975577699111,100,2015,3,7,0.121345012229794,0.0422479524423521,0.0307100258858405,0.104580802913336,1.73736097363043,1.73736097363043,0.0102188285480592
+"21982",100,2015,3,8,0.520132021837109,12.5760505516799,22.2181079201441,14.9700219759716,11.7113091518121,11.7113091518121,5.07778228829945,100,2015,3,8,0.0191228071081709,0.0348163645806577,0.0478222051836899,0.0785760531844541,1.27227005203878,1.27227005203878,0.00970541680049333
+"21983",100,2015,3,9,0.166116614136497,11.9021563713569,22.1414520528057,13.5893289788459,16.3299891741494,16.3299891741494,5.02580879960779,100,2015,3,9,0.00257309949188902,0.0464579186423579,0.0407694803118494,0.0170397761897896,0.883460952709909,0.883460952709909,0.00920906937742453
+"21984",100,2015,3,10,0.193619365034455,7.64818479046963,21.1012430872985,10.4687568215516,22.178768036258,22.178768036258,4.97383531091613,100,2015,3,10,0.0231578959411348,0.0780543576636784,0.0726245227126224,0.0530006127854643,0.126442600834941,0.126442600834941,0.00872978627885284
+"21985",100,2015,3,11,0,5.76997800812338,23.3723213753947,12.4928822784927,20.5930691898459,20.5930691898459,4.92186182222447,100,2015,3,11,0,0.0334537839019674,0.0218584576392055,0.0978760904078696,0.175822317081253,0.175822317081253,0.0082675675047783
+"21986",100,2015,3,12,0.0172717274300861,10.6269857251343,18.9483938521416,10.8038283947146,17.5782617772504,17.5782617772504,4.86988833353281,100,2015,3,12,0.00245614042407588,0.0642871266884745,0.0173133936790138,0.0528256798170651,0.802833229912945,0.802833229912945,0.00782241305520085
+"21987",100,2015,3,13,0,12.8056875235177,20.7063916167541,10.7739933347545,19.7726182885165,19.7726182885165,4.81791484484115,100,2015,3,13,0,0.132324547795609,0.0218100100961205,0.0238918108243737,0.521869148513594,0.521869148513594,0.00739432293012049
+"21988",100,2015,3,14,0,7.29256328996127,26.6462707519531,11.5750496143555,16.7267767640755,16.7267767640755,4.76594135614949,100,2015,3,14,0,0.0611701611044933,0.107313434056703,0.322139721818046,0.559433687817248,0.559433687817248,0.00698329712953729
+"21989",100,2015,3,15,2.12079204781221,10.9019252695266,18.7108249076785,10.0546094749627,16.3468757562249,16.3468757562249,4.71396786745783,100,2015,3,15,0.13707605870844,0.107387697929322,0.0166093442865701,0.0471923983591376,0.289661889677667,0.289661889677667,0.00658933565345115
+"21990",100,2015,3,16,1.79416941691547,8.21894389961419,21.6668096045063,11.3795929574075,11.9982949887434,11.9982949887434,4.66199437876617,100,2015,3,16,0.567309938341557,0.0827000171026377,0.0367806819897728,0.0368210632124233,0.165709908042655,0.165709908042655,0.00621243850186213
+"21991",100,2015,3,17,0,13.9231682631573,31.5493399847721,13.9876018341618,11.3444005034544,11.3444005034544,4.61002089007451,100,2015,3,17,0,0.0581813301176502,0.00580992316114475,0.521665322628432,0.775209925141853,0.775209925141853,0.00585260567477024
+"21992",100,2015,3,18,6.53784380546629,15.7605061064197,23.9503519144258,16.7596918993657,13.0433663062923,13.0433663062923,4.55804740138285,100,2015,3,18,0.0809356876841892,0.0112117036898185,0.0107321884470845,0.0331760961657706,2.3915366921074,2.3915366921074,0.00550983717217542
+"21993",100,2015,3,19,0.183058309746851,14.3545985216617,28.3688118602052,16.841859389453,9.11446637131593,9.11446637131593,4.50607391269119,100,2015,3,19,0.0157894744946246,0.0302227533039933,0.230048537891405,0.0598029299020156,1.39430230657572,1.39430230657572,0.00518413299407773
+"21994",100,2015,3,20,1.68712869152115,10.2077887821512,19.2359735721802,10.2629481956641,17.9119362101959,17.9119362101959,4.45410042399953,100,2015,3,20,0.133976619508533,0.0643093614723793,0.0204812719962187,0.0731766156805164,0.97568998448133,0.97568998448133,0.00487549314047717
+"21995",100,2015,3,21,0.00363036309040026,6.81020898944867,20.5068973671354,10.5638944071905,20.0944331665375,20.0944331665375,4.40212693530787,100,2015,3,21,0.000526315805159117,0.0780029242443271,0.0288894858692684,0.0463941459857135,0.0971396612515103,0.0971396612515103,0.0045839176113737
+"21996",100,2015,3,22,0,6.22479649345474,29.2173596834323,13.4974696754229,19.0009681567369,19.0009681567369,4.35015344661621,100,2015,3,22,0,0.0955999887890461,0.0311911674903138,0.071592962909802,0.114498756382282,0.114498756382282,0.00430940640676737
+"21997",100,2015,3,23,0.585148522890572,11.0174808271385,23.0716941422231,15.5999339736334,7.31950489470143,7.31950489470143,4.29817995792455,100,2015,3,23,0.0105263157197608,0.0344508551235872,0.161565466233117,0.0243590690078028,1.02173680560913,1.02173680560913,0.0040519595266581
+"21998",100,2015,3,24,0.968426843484243,11.1799999799403,15.275995651094,9.32138610332057,6.1115181317555,6.1115181317555,4.2462064692329,100,2015,3,24,0.114678361729572,0.0587099310423795,0.0181432499244767,0.0446256925014873,0.199537441335107,0.199537441335107,0.00381157697104596
+"21999",100,2015,3,25,0.269856990104986,7.89352033266796,20.5911000737525,8.22657869777533,14.1955996430484,14.1955996430484,4.19423298054124,100,2015,3,25,0.0259649126013817,0.208978302979098,0.01880536150155,0.0379690217124662,0.440376654371823,0.440376654371823,0.00358825873993093
+"22000",100,2015,3,26,7.98206836083541,7.0797470132641,14.8881077509378,12.2430361757184,11.4377226152829,11.4377226152829,4.14225949184958,100,2015,3,26,1.16251464899526,0.0402146246045831,0.0264368273462386,0.189568959404653,0.434398791178263,0.434398791178263,0.00338200483331301
+"22001",100,2015,3,27,16.4149617078674,8.59402630431424,16.6388668001551,12.1714080730812,10.0901870601641,10.0901870601641,4.09028600315792,100,2015,3,27,1.15228049941948,0.0252801137757378,0.00646900828885716,0.110605265377618,0.253882980770597,0.253882980770597,0.00319281525119221
+"22002",100,2015,3,28,1.30693068415156,10.7549175623358,16.5213641511856,12.4330253160433,8.11143005746688,8.11143005746688,4.03831251446626,100,2015,3,28,0.273684203276161,0.0391784098999842,0.0133251179390834,0.0186163662002342,0.207102321647128,0.207102321647128,0.0030206899935685
+"22003",100,2015,3,29,0.127722774180445,6.53007698741027,21.4759404063881,12.7371616888099,16.7317049416772,16.7317049416772,3.9863390257746,100,2015,3,29,0.00286549716142186,0.0307584742094528,0.0265093893444588,0.127299935743629,0.162450715720084,0.162450715720084,0.00286562906044191
+"22004",100,2015,3,30,0.0283828387067656,8.50855889126281,21.2160288597753,14.9068645911642,15.2782397904936,15.2782397904936,3.93436553708294,100,2015,3,30,0.00175438601719706,0.0510813328124822,0.0361152299627182,0.122776538699476,0.58324780636848,0.58324780636848,0.00272763245181243
+"22005",100,2015,3,31,0.100000001490116,8.8025852388019,25.2927723665311,14.4231242745361,17.6749943102678,17.6749943102678,3.88239204839128,100,2015,3,31,0,0.0397373803591204,0.11419229867527,0.159904078580689,0.0105444460553941,0.0105444460553941,0.00260670016768006
+"22006",100,2015,4,1,0,11.3424642120126,28.7916941627024,8.96138618960239,8.95598462548586,8.95598462548586,3.8343296887475,100,2015,4,1,0,0.0357339202145505,0.0142584301958341,0.00630528264192483,1.81109114344865,1.81109114344865,0.00245378867076679
+"22007",100,2015,4,2,3.23421343272538,10.9054455269288,16.3431132267279,8.68559964497884,15.1511771177957,15.1511771177957,3.78626732910373,100,2015,4,2,0.149415216111304,0.246464938292099,0.0187497605711435,0.00996667679685461,0.206480662735225,0.206480662735225,0.00230726974940159
+"22008",100,2015,4,3,0.0826182630572907,6.88942797296774,17.8628603239657,10.5894279301626,15.9546312955346,15.9546312955346,3.73820496945996,100,2015,4,3,0.00321637436486127,0.270106489208301,0.0424146523372767,0.00585787299539137,0.306604067669803,0.306604067669803,0.00216714340358444
+"22009",100,2015,4,4,0,10.9541364791501,19.9792519035381,12.9784819127703,13.0916171761105,13.0916171761105,3.69014260981618,100,2015,4,4,0,0.114324464284979,0.0193690491305354,0.125689472118243,1.87932778436547,1.87932778436547,0.00203340963331535
+"22010",100,2015,4,5,0,9.68639157235426,16.49370732533,13.178856023992,7.52057206224162,7.52057206224162,3.64208025017241,100,2015,4,5,0,0.0308766333129229,0.0553706780465184,0.0185450146146113,1.62070919218791,1.62070919218791,0.00190606843859434
+"22011",100,2015,4,6,1.81177117578005,7.62498355147862,15.3805061073849,12.5262155564311,8.90160621720703,8.90160621720703,3.59401789052863,100,2015,4,6,0.106725150102762,0.12462457441702,0.0103339015693947,0.0357607666507238,0.660167251756484,0.660167251756484,0.00178511981942138
+"22012",100,2015,4,7,11.0839383489359,11.1077007853945,13.5326402787984,10.4702860147122,3.81306933438686,3.81306933438686,3.54595553088486,100,2015,4,7,4.5284213391801,0.06494674248506,0.0207374396877002,0.00418714559017171,0.0179374375131467,0.0179374375131467,0.00167056377579649
+"22013",100,2015,4,8,1.19174918378278,10.7810340394544,17.0192188854658,12.4824862296563,11.6881408733372,11.6881408733372,3.49789317124108,100,2015,4,8,0.0195321597411627,0.10801343927447,0.016358494425316,0.0647093743174449,1.20787328547881,1.20787328547881,0.00156240030771965
+"22014",100,2015,4,9,0.0742574271195793,12.6282947800472,17.4968539721633,12.5180198000078,7.43750270365095,7.43750270365095,3.44983081159731,100,2015,4,9,0.00783625773519104,0.0975256944712777,0.031425659175543,0.0276561418099181,1.22520075933674,1.22520075933674,0.00146062941519088
+"22015",100,2015,4,10,0.0365236529094814,10.2065127040162,17.1071944976404,13.3354234905264,5.61030811891042,5.61030811891042,3.40176845195353,100,2015,4,10,0.00228070182235617,0.172260798952931,0.0272316085996523,0.0395152139407619,0.518509252851623,0.518509252851623,0.00136525109821018
+"22016",100,2015,4,11,0.0283828387067656,7.73508252037896,19.9803628386444,13.0043124753912,13.9992078281734,13.9992078281734,3.35370609230976,100,2015,4,11,0.00175438601719706,0.0473783418912228,0.04538775020878,0.043525729149383,0.113456182334939,0.113456182334939,0.00127626535677753
+"22017",100,2015,4,12,0,10.7055226306055,18.6312760456954,11.6812762070541,12.6502529077142,12.6502529077142,3.30564373266598,100,2015,4,12,0,0.186917529633814,0.0469315546006594,0.0521116634637477,0.206289488653077,0.206289488653077,0.00119367219089294
+"22018",100,2015,4,13,0,2.42157318725838,20.483872647595,11.2366885908104,15.2227062292487,15.2227062292487,3.25758137302221,100,2015,4,13,0,0.051209354948306,0.0336264050492475,0.0262139930636672,0.0824064609238036,0.0824064609238036,0.00111747160055642
+"22019",100,2015,4,14,0.293509360226599,10.3158747460058,25.8068757849296,13.5184158916914,9.31660059078036,9.31660059078036,3.20951901337844,100,2015,4,14,0.00608187131714404,0.147087060948512,0.160415210300277,0.00905613244716658,0.472259167906672,0.472259167906672,0.00104766358576796
+"22020",100,2015,4,15,0.200000002980232,13.0703409654473,22.9012322399614,16.5278766556542,12.5256106814142,12.5256106814142,3.16145665373466,100,2015,4,15,0,0.0147321658709593,0.0926918618009822,0.0965765652486369,0.349767291224139,0.349767291224139,0.000984248146527565
+"22021",100,2015,4,16,0.342354244819962,12.6583719211574,16.8300438074127,10.1171726933931,10.6130913334711,10.6130913334711,3.11339429409089,100,2015,4,16,0.00263157863365977,0.118370232616734,0.015957894862091,0.00993393930810016,0.0525000349681351,0.0525000349681351,0.000927225282835228
+"22022",100,2015,4,17,0.0278327836930686,5.30118808861744,16.6275024623892,10.7278437724601,7.26594057649669,7.26594057649669,3.06533193444711,100,2015,4,17,0.0020467836867299,0.0247040845670412,0.0354912975158266,0.00910292825314736,0.0575579310445301,0.0575579310445301,0.000876594994690956
+"22023",100,2015,4,18,0.000110011002739402,10.257876687842,20.2602310852118,13.4006160918636,7.8451815207537,7.8451815207537,3.01726957480334,100,2015,4,18,0.000526315805159117,0.0216485124198448,0.0136608523321787,0.0658877672965751,0.68957190370396,0.68957190370396,0.000832357282094743
+"22024",100,2015,4,19,6.56809679876984,8.27057209109316,13.9715290887914,8.49148528305742,11.3972167213364,11.3972167213364,2.96920721515956,100,2015,4,19,0.100409345069145,0.229844967881197,0.0405918387854271,0.00392105795217184,0.639665429060719,0.639665429060719,0.000794512145046597
+"22025",100,2015,4,20,1.12178218299144,5.18590757791752,15.6846645982614,9.73561065451409,10.9606490245353,10.9606490245353,2.92114485551579,100,2015,4,20,0.0478362590667109,0.192348012678821,0.0344263248310555,0.0097730991230162,0.961303667545939,0.961303667545939,0.00076305958354651
+"22026",100,2015,4,21,0,8.96524744065288,16.5926403637373,11.0304840369062,8.24734874763111,8.24734874763111,2.87308249587201,100,2015,4,21,0,0.191709871111468,0.0124356454774504,0.00301521510084047,0.234015168103082,0.234015168103082,0.000737999597594485
+"22027",100,2015,4,22,0,7.30251930045872,17.7462046531954,13.5112540199943,10.4046973195931,10.4046973195931,2.82502013622824,100,2015,4,22,0,0.0762152043961849,0.0259034037899574,0.00829120718085957,0.475069064833087,0.475069064833087,0.000719332187190521
+"22028",100,2015,4,23,0,12.13464250659,17.4990648683017,14.9076346774044,6.36097906656129,6.36097906656129,2.77695777658446,100,2015,4,23,0,0.146973085278596,0.0215620370522602,0.0213321726348022,0.586980654280384,0.586980654280384,0.000707057352334618
+"22029",100,2015,4,24,0.0305830587615537,11.798547752095,18.4308031730526,14.0159076138823,5.49402648780999,5.49402648780999,2.72889541694069,100,2015,4,24,0.00228070182235617,0.0371783975969749,0.0510708417080779,0.0142485308285375,0.277737440669208,0.277737440669208,0.000701175093026778
+"22030",100,2015,4,25,3.04719471315084,8.50480751314572,14.7996809532409,13.9060286405456,5.47543445698368,5.47543445698368,2.68083305729692,100,2015,4,25,0.398304087181541,0.0566450034872552,0.0371332942963685,0.00719530545958198,0.0788608180419844,0.0788608180419844,0.000701685409267011
+"22031",100,2015,4,26,8.13234330220322,9.56078110958221,13.863399375521,10.383509354754,7.00706265389723,7.00706265389723,2.63277069765314,100,2015,4,26,3.20222243520954,0.156305344100969,0.0190397853696154,0.00409239392648322,0.521122238095623,0.521122238095623,0.000708588301055296
+"22032",100,2015,4,27,2.32310227382563,9.56785488863065,14.8545655742599,11.6238392881303,10.2598129624974,10.2598129624974,2.58470833800937,100,2015,4,27,0.451169569171664,0.141753831983965,0.015357885856082,0.0481450291528309,0.47622635854093,0.47622635854093,0.000721883768391641
+"22033",100,2015,4,28,0.940924094651792,8.74889997077329,14.8277446969245,10.5414191026761,6.62944998966716,6.62944998966716,2.53664597836559,100,2015,4,28,0.0280701759614445,0.169947424236166,0.0345321852701463,0.0177146303768915,0.256276048675911,0.256276048675911,0.000741571811276052
+"22034",100,2015,4,29,1.20770079940066,9.06652365580644,15.2065345692818,11.3587458865477,7.14487341134855,7.14487341134855,2.48858361872182,100,2015,4,29,0.00842104876947833,0.165871932093404,0.0153830233345791,0.0511543860497571,0.23752628383862,0.23752628383862,0.000767652429708526
+"22035",100,2015,4,30,0.123542356461582,3.29768974447932,18.4292187937284,10.9891528873423,11.3538063414419,11.3538063414419,2.44052125907804,100,2015,4,30,0.0091812871283258,0.0349093568840953,0.012750912770358,0.049665467598098,0.249850976832485,0.249850976832485,0.00080012562368906
+"22036",100,2015,5,1,0,7.66607255956652,22.5101539477526,12.0612100605393,12.0139273516547,12.0139273516547,2.41307593616824,100,2015,5,1,0,0.0635830424671533,0.103416239425298,0.00574443401764953,0.00339183099097612,0.00339183099097612,0.000782475689420445
+"22037",100,2015,5,2,0.740704068584148,12.0258746540586,21.0997687949337,12.4364686542087,6.69825082774734,6.69825082774734,2.38563061325843,100,2015,5,2,0.0417543858498861,0.0265695873707402,0.0176941332724204,0.0506953636170038,0.7563700874407,0.7563700874407,0.000766333235555883
+"22038",100,2015,5,3,0.865016504515647,10.0737403705962,15.7249174548192,13.5115181982714,5.14545657060327,5.14545657060327,2.35818529034863,100,2015,5,3,0.0447953243841208,0.131361984116905,0.0808648898008342,0.0155052428419463,0.470069508332832,0.470069508332832,0.000751698262095368
+"22039",100,2015,5,4,0.343454352699884,5.4450715183556,22.0328382422822,12.4271398246354,10.2492189827007,10.2492189827007,2.33073996743882,100,2015,5,4,0.0394152046156209,0.0898175290102439,0.0385064140145116,0.0181333029853949,0.162436899725121,0.162436899725121,0.000738570769038911
+"22040",100,2015,5,5,3.19570956660314,10.6399450574902,14.334785512834,9.54306927084005,8.2724972012544,8.2724972012544,2.30329464452902,100,2015,5,5,0.301754382479266,0.0302672721729738,0.0738093419862314,0.0134099424061553,0.683389472907937,0.683389472907937,0.000726950756386503
+"22041",100,2015,5,6,7.49130910562866,7.93107804528164,15.1374807641058,10.4808361013599,6.82272824727007,6.82272824727007,2.27584932161921,100,2015,5,6,2.87228046322431,0.0846356258325707,0.0638923900694129,0.0110316057016302,0.668321089993332,0.668321089993332,0.000716838224138144
+"22042",100,2015,5,7,7.01320131173884,7.55497245347933,13.6562928230193,10.949493922285,5.93932894485356,5.93932894485356,2.24840399870941,100,2015,5,7,0.0714619844559485,0.0915911584452819,0.0603052519793397,0.0155672610322728,0.370770160668377,0.370770160668377,0.000708233172293844
+"22043",100,2015,5,8,4.83795387280656,8.3180967977207,14.5152584470407,12.8981518100197,6.38523651507035,6.38523651507035,2.2209586757996,100,2015,5,8,0.023216389661664,0.106802908268372,0.041475982262311,0.0116538119021387,0.639835801992383,0.639835801992383,0.000701135600853592
+"22044",100,2015,5,9,2.74147412446466,10.7190649422875,16.2169968769758,14.0616392003428,4.86612759250225,4.86612759250225,2.1935133528898,100,2015,5,9,0.457660815952818,0.0951707801529255,0.0661917846034705,0.0113760317819991,0.306161970583615,0.306161970583615,0.000695545509817393
+"22045",100,2015,5,10,17.36171605747,11.4700218972856,14.1075908631513,11.1566667336442,6.46029698861839,6.46029698861839,2.16606802997999,100,2015,5,10,1.31204617673221,0.104614069843932,0.0537731012534697,0.0190701978472347,0.194033888962976,0.194033888962976,0.000691462899185245
+"22046",100,2015,5,11,8.20495050365251,10.5363916013107,15.452255238532,11.5497690049729,7.14647968269155,7.14647968269155,2.13862270707019,100,2015,5,11,7.8547958964075,0.0964806811952529,0.108139832120819,0.0425842170582426,0.428220476751732,0.428220476751732,0.000688887768957148
+"22047",100,2015,5,12,3.1812981696281,10.94264037276,13.4629042785947,9.027073814006,5.96905387588853,5.96905387588853,2.11117738416038,100,2015,5,12,0.205847919056996,0.110465512528963,0.0793812768546538,0.00200409339780443,0.723924626893622,0.723924626893622,0.0006878201191331
+"22048",100,2015,5,13,7.85962597476636,6.12416938743969,12.4056656011785,10.1377227455869,6.85965898773982,6.85965898773982,2.08373206125058,100,2015,5,13,0.674970798492478,0.241276578062203,0.0952759463484148,0.0250596374646289,0.826548587482316,0.826548587482316,0.000688259949713112
+"22049",100,2015,5,14,3.94004400544959,8.26171624673606,14.1222440364993,13.2503190035343,6.19826178818253,6.19826178818253,2.05628673834078,100,2015,5,14,0.072573088428437,0.320857939166958,0.0409695985538305,0.00380936178470625,0.0731041243994012,0.0731041243994012,0.000690207260697169
+"22050",100,2015,5,15,3.05775578029872,10.1715622325935,13.7606821070672,11.5618261582781,6.4439603755183,6.4439603755183,2.02884141543097,100,2015,5,15,0.0224561348853807,0.13789420059613,0.0253052573332869,0.00931635970633121,0.224106458852464,0.224106458852464,0.000693662052085279
+"22051",100,2015,5,16,0.698569861001963,9.32793195701406,14.7992519739569,11.934037372224,6.95025295996168,6.95025295996168,2.00139609252117,100,2015,5,16,0.10485380482604,0.124360885267719,0.0159941752473639,0.00155438439448161,0.0952140625089435,0.0952140625089435,0.000698624323877443
+"22052",100,2015,5,17,0.00154015403835162,2.61771178061944,18.5760175581157,12.4742245060383,10.1738833888005,10.1738833888005,1.97395076961136,100,2015,5,17,0.000526315805159117,0.0622204802067023,0.049747915466772,0.0305052759695385,0.000891227653068072,0.000891227653068072,0.000705094076073658
+"22053",100,2015,5,18,0.362376241548227,4.83435646902741,19.2612764339636,12.6271727963774,9.87195824789922,9.87195824789922,1.94650544670156,100,2015,5,18,0.0725146175825109,0.0530813107328979,0.038081826706767,0.00617777553141859,0.00155904668627683,0.00155904668627683,0.000713071308673926
+"22054",100,2015,5,19,1.09537955597289,10.2572166350546,17.0781298701388,13.7877888191651,5.8321451710658,5.8321451710658,1.91906012379175,100,2015,5,19,0.0106432728181815,0.0353801114378741,0.13121763439552,0.0123730884195815,0.106880657203871,0.106880657203871,0.000722556021678244
+"22055",100,2015,5,20,4.48173811621923,11.6231462375821,13.5328601904304,12.2760836797448,3.32635862992542,3.32635862992542,1.89161480088195,100,2015,5,20,1.33064327078257,0.122257943384342,0.0123619938655862,0.0259146195886701,0.0800374222699693,0.0800374222699693,0.000733548215086615
+"22056",100,2015,5,21,7.53300321272629,8.81199110406722,13.6693619580159,12.4328932242818,7.22975795995547,7.22975795995547,1.86416947797214,100,2015,5,21,0.482046738395948,0.129075990465015,0.038709916095924,0.0590210938169931,0.267647910883306,0.267647910883306,0.000746047888899038
+"22057",100,2015,5,22,1.96578654935389,8.53138622959586,12.4324642282115,9.79865772259904,3.9814411856816,3.9814411856816,1.83672415506234,100,2015,5,22,1.03999992238151,0.141314697908201,0.056546785220825,0.00661171458293014,0.0453473650536457,0.0453473650536457,0.000760055043115514
+"22058",100,2015,5,23,0,8.62398242635695,11.9990209583664,10.0482839620022,6.33786577963331,6.33786577963331,1.80927883215253,100,2015,5,23,0,0.231211155955085,0.0711363156815476,0.00560993860040204,0.0490204646852407,0.0490204646852407,0.00077556967773604
+"22059",100,2015,5,24,0.0283828387067656,1.56187016539054,14.4609792602338,10.8807041369649,8.18356426163475,8.18356426163475,1.78183350924273,100,2015,5,24,0.00175438601719706,0.0445204711838783,0.105895314213336,0.012004066795292,0.0086730917959323,0.0086730917959323,0.000792591792760618
+"22060",100,2015,5,25,0,4.24334431271611,17.2399782615133,11.3062156817832,8.86446644580535,8.86446644580535,1.75438818633292,100,2015,5,25,0,0.0242204544881854,0.0685134608802879,0.0165894553157473,0.0350116561198854,0.0350116561198854,0.000811121388189247
+"22061",100,2015,5,26,0.0798679882592887,3.32134212623991,15.9516941192258,11.9128051491329,7.51842685632317,7.51842685632317,1.72694286342312,100,2015,5,26,0.0087134507394325,0.0697689991778151,0.10466195660211,0.00424853473250338,0.0301099393309388,0.0301099393309388,0.000831158464021928
+"22062",100,2015,5,27,0.702750283761649,7.53727174880612,18.5384158521596,13.2351484602959,8.28949396051589,8.28949396051589,1.69949754051331,100,2015,5,27,0.250935672132941,0.0226941524014995,0.0292134365955617,0.0201508300023471,0.0577245608637204,0.0577245608637204,0.000852703020258663
+"22063",100,2015,5,28,0.371837191373864,11.6293069358968,17.9250826442202,12.3473047774748,6.32699672407312,6.32699672407312,1.67205221760351,100,2015,5,28,0.0102339182332246,0.0639485240669289,0.0294099409451976,0.0629982381386454,0.133398825878583,0.133398825878583,0.000875755056899451
+"22064",100,2015,5,29,6.6403740378234,6.0528272963462,13.9790867930329,10.8126293283091,6.60733772556905,6.60733772556905,1.6446068946937,100,2015,5,29,1.73251461140597,0.138584215457563,0.0378099253136407,0.0282889038086568,0.209098803239147,0.209098803239147,0.000900314573944288
+"22065",100,2015,5,30,0.32585259108129,7.40155113700724,14.8355885737538,11.9129043805717,6.59030806611736,6.59030806611736,1.6171615717839,100,2015,5,30,0.0336257316259265,0.0967924039182807,0.11140354011499,0.00231345502625809,0.303416409357544,0.303416409357544,0.000926381571393177
+"22066",100,2015,5,31,1.89713971593855,9.6709791672374,14.2008030044757,10.6580307963658,5.39072599872635,5.39072599872635,1.58971624887409,100,2015,5,31,0.544268995795336,0.103156099725518,0.0806251494402911,0.0111842114186971,0.565884211405275,0.565884211405275,0.000953956049246118
+"22067",100,2015,6,1,5.7908691441921,4.58888886890264,11.3045215166048,8.73860308203367,7.49623764938254,7.49623764938254,1.57296395388904,100,2015,6,1,0.520409285450553,0.389877770063628,0.0998561984592792,0.00399533175199426,0.188795307176168,0.188795307176168,0.00108194117250476
+"22068",100,2015,6,2,2.23729372286823,3.90199118164113,11.4105941680136,10.2533992792514,5.67473048502856,5.67473048502856,1.55621165890398,100,2015,6,2,0.4848538007792,0.0837052596599788,0.0586052099641229,0.0244689850545889,0.189955560408717,0.189955560408717,0.00122630075402401
+"22069",100,2015,6,3,0.289658972950462,6.65594058881367,11.3743893399884,8.3132674266534,6.10982395503649,6.10982395503649,1.53945936391893,100,2015,6,3,0.0584210544249468,0.0729315692846051,0.104831540508244,0.00332223336966718,0.273459618682212,0.273459618682212,0.00138703479380388
+"22070",100,2015,6,4,1.36083607613319,4.31510454724462,9.70332248822035,9.53236508553046,3.23029699918329,3.23029699918329,1.52270706893387,100,2015,6,4,0.0776023356816933,0.0501028888680333,0.159702411832385,0.026909335710908,0.0237204731071244,0.0237204731071244,0.00156414329184436
+"22071",100,2015,6,5,5.14301425526769,6.06269526875058,14.1609789813706,12.2636082998597,6.30509343635131,6.30509343635131,1.50595477394881,100,2015,6,5,0.0333333439297301,0.111688922754951,0.138726357815895,0.00868304216492745,0.207525727431556,0.207525727431556,0.00175762624814546
+"22072",100,2015,6,6,0.546644664718898,9.1340704247503,16.571990960502,13.9070957643364,5.08078110309848,5.08078110309848,1.48920247896376,100,2015,6,6,0.188771929019376,0.192781291952853,0.13769165110029,0.0228222009447234,0.0570707878782289,0.0570707878782289,0.00196748366270717
+"22073",100,2015,6,7,0,10.1360615561373,16.8290651256364,12.1551814021582,8.14642460459005,8.14642460459005,1.4724501839787,100,2015,6,7,0,0.106622865241954,0.165744539981769,0.0218508910910187,0.00744970337612954,0.00744970337612954,0.0021937155355295
+"22074",100,2015,6,8,0.024642464613626,10.9094058709307,16.141925421485,13.5782287648015,5.14887794383419,5.14887794383419,1.45569788899364,100,2015,6,8,0.00175438601719706,0.139889500135048,0.0991988395225187,0.0192672633724087,0.235862054616262,0.235862054616262,0.00243632186661245
+"22075",100,2015,6,9,5.75863587397291,7.88080306205288,12.4718151732509,9.2419692963788,6.80365238514933,6.80365238514933,1.43894559400859,100,2015,6,9,0.440935700120998,0.265895270238313,0.0966561557078905,0.00148714519086194,0.42602045724547,0.42602045724547,0.00269530265595601
+"22076",100,2015,6,10,1.29944994570267,6.16629262752134,12.2742135233612,9.29689776858087,5.52243134841667,5.52243134841667,1.42219329902353,100,2015,6,10,0.0517543855814917,0.32073740728503,0.0974228050455853,0.000143276950832613,0.24038951683269,0.24038951683269,0.00297065790356019
+"22077",100,2015,6,11,0.0365236529094814,7.68172713815838,11.5298349949131,10.2541033615767,5.34521449438416,5.34521449438416,1.40544100403848,100,2015,6,11,0.00228070182235617,0.0980321364877095,0.0557719504373562,0.000735673453860802,0.105437445056104,0.105437445056104,0.00326238760942499
+"22078",100,2015,6,12,0.0545654573587432,2.10621563986977,11.6774917545885,11.4499120628349,6.30006598167293,6.30006598167293,1.38868870905342,100,2015,6,12,0.00263157902579559,0.0611163788600304,0.028370166026865,0.00732279059656858,0.0738842363569703,0.0738842363569703,0.00357049177355039
+"22079",100,2015,6,13,0.065456546629944,5.59477442194789,17.8145324320945,12.8950605654743,5.87697468706221,5.87697468706221,1.37193641406836,100,2015,6,13,0.00263157902579559,0.0560146116736577,0.094239697261491,0.0117561380888836,0.188798871711838,0.188798871711838,0.00389497039593642
+"22080",100,2015,6,14,0,6.06040706414201,15.6439494541114,12.325445440605,7.11275032985591,7.11275032985591,1.35518411908331,100,2015,6,14,0,0.165633328946451,0.0557690778030157,0.00552632170121414,0.122691213372448,0.122691213372448,0.00423582347658306
+"22081",100,2015,6,15,0,8.1139825499884,13.9802200109652,13.0706160978647,4.90006588263349,4.90006588263349,1.33843182409825,100,2015,6,15,0,0.0856590878046643,0.118383094241112,0.0239245768118096,0.0691877345113176,0.0691877345113176,0.00459305101549032
+"22082",100,2015,6,16,6.94686468048851,10.146050610558,13.3009792155821,13.7455774324037,3.51477449247152,3.51477449247152,1.3216795291132,100,2015,6,16,0.0326315588142561,0.0702895008221834,0.0795807233844509,0.00594795199396036,0.0114146172266996,0.0114146172266996,0.00496665301265819
+"22083",100,2015,6,17,0.755445555295094,9.3347965215299,13.7100879178189,13.2134872747071,4.27177118179691,4.27177118179691,1.30492723412814,100,2015,6,17,0.0402339192580064,0.0668655506268758,0.0830871748763425,0.00062456043541561,0.14052455951067,0.14052455951067,0.00535662946808668
+"22084",100,2015,6,18,4.23344327829065,9.69766779648851,12.9198349710345,10.9325633369001,5.78134211822443,5.78134211822443,1.28817493914308,100,2015,6,18,1.08374266153192,0.0838362521122179,0.0923163289991107,0.0159175343296578,0.380964947165125,0.380964947165125,0.00576298038177578
+"22085",100,2015,6,19,1.11100112001757,5.33650164452061,11.1614083047747,10.1585589577787,6.07206823959602,6.07206823959602,1.27142264415803,100,2015,6,19,0.0281871348375475,0.163484239127641,0.0786842140399008,0.00608945596266292,0.170492988068895,0.170492988068895,0.0061857057537255
+"22086",100,2015,6,20,1.74554453936085,2.9711550795468,11.6289769572393,9.75374051839998,6.05316833720611,6.05316833720611,1.25467034917297,100,2015,6,20,0.242748519391347,0.0939578938662699,0.0432672945592229,0.0238473600510965,0.369036908663944,0.369036908663944,0.00662480558393583
+"22087",100,2015,6,21,0.0222222225533591,3.19705168799599,12.2820352065419,8.97610550745092,8.17606148966337,8.17606148966337,1.23791805418792,100,2015,6,21,0.00175438601719706,0.121991212936875,0.0637474043283522,0.00119532833663502,0.00225789999494843,0.00225789999494843,0.00708027987240679
+"22088",100,2015,6,22,0.281848190296995,3.52360836507464,11.8440154215159,9.86379544412342,4.00338833035678,4.00338833035678,1.22116575920286,100,2015,6,22,0.0511695919242519,0.0899497153806463,0.0717544227734685,0.0153432665307072,0.141733948869329,0.141733948869329,0.00755212861913835
+"22089",100,2015,6,23,0.952475257865404,7.29474144747811,14.3959626375121,11.1911111030117,5.54746973501443,5.54746973501443,1.2044134642178,100,2015,6,23,0.0527485425988141,0.078511695649909,0.177109862429163,0.0196485378177258,0.144887723806742,0.144887723806742,0.00804035182413053
+"22090",100,2015,6,24,0.122662268439666,7.7140044174572,13.1317820312953,12.1580968579837,5.62733765718567,5.62733765718567,1.18766116923275,100,2015,6,24,0.0091812871283258,0.190861867171888,0.0568912256899797,0.00112632775118705,0.260217525737624,0.260217525737624,0.00854494948738333
+"22091",100,2015,6,25,1.98404838648042,8.0418152436696,13.6752693944245,12.1357536095597,6.72715071246962,6.72715071246962,1.17090887424769,100,2015,6,25,0.128011687094708,0.113453766585282,0.112136186617646,0.0162929853718968,0.230043213702661,0.230043213702661,0.00906592160889674
+"22092",100,2015,6,26,1.84323432094062,6.79248627839964,13.7088448846563,10.7678988375942,6.68724970245781,6.68724970245781,1.15415657926263,100,2015,6,26,0.0325146104996684,0.109339747375376,0.132453791748905,0.000592399570471927,0.180047389356178,0.180047389356178,0.00960326818867077
+"22093",100,2015,6,27,0.0190319034739165,7.99697476461513,13.39832784424,12.619328839813,6.01183716928211,6.01183716928211,1.13740428427758,100,2015,6,27,0.00175438601719706,0.0777783739770829,0.0868146342964095,0.0336205023961571,0.125355558586142,0.125355558586142,0.0101569892267054
+"22094",100,2015,6,28,0,7.27727175545771,13.7878217906973,10.7376348581513,4.71321228969477,4.71321228969477,1.12065198929252,100,2015,6,28,0,0.073043256305696,0.0869595918733924,0.00535965476685614,0.0661064230534271,0.0661064230534271,0.0107270847230007
+"22095",100,2015,6,29,0.00693069317258231,9.01418034288094,12.5253465776265,11.0648405245035,4.60715072094673,4.60715072094673,1.10389969430747,100,2015,6,29,0.00140350881375765,0.219881304873716,0.105033261744248,0.00658012972834571,0.0315807046790085,0.0315807046790085,0.0113135546775566
+"22096",100,2015,6,30,0.512541257623661,8.82355337908821,12.9459624547507,11.5580857059743,4.71498350682694,4.71498350682694,1.08714739932241,100,2015,6,30,0.146198830557497,0.133713957760974,0.106648623793401,0.0291397628985759,0.0840163794913697,0.0840163794913697,0.011916399090373
+"22097",100,2015,7,1,1.63212321562604,6.50801981820001,13.7280969021737,11.155918716204,7.62557753058288,7.62557753058288,1.08719083312983,100,2015,7,1,0.218128657271291,0.0247146137628148,0.0903140306127649,0.0293690155007404,0.0130894644731845,0.0130894644731845,0.0114660202308595
+"22098",100,2015,7,2,1.77634763979938,7.46839379005306,11.6213090863034,8.00530256086713,7.43544553949757,7.43544553949757,1.08723426693726,100,2015,7,2,0.11146199934664,0.145284214250689,0.0882554990133387,0.000459065877957557,0.0588649070282638,0.0588649070282638,0.011025100635756
+"22099",100,2015,7,3,1.53619362602402,2.66150714173438,10.4884159394486,7.50778879662945,6.46388337740673,6.46388337740673,1.08727770074468,100,2015,7,3,0.0664912240128784,0.160384193661901,0.0707695864427494,0.0073152088790088,0.0256339133781484,0.0256339133781484,0.0105936403050624
+"22100",100,2015,7,4,0.00858085821367333,5.90368540662088,11.4702749892299,11.8109240568642,4.28020908126999,4.28020908126999,1.0873211345521,100,2015,7,4,0.0020467836867299,0.0927801583641691,0.064276084168288,0.0169473749127926,0.0529204681775923,0.0529204681775923,0.0101716392387787
+"22101",100,2015,7,5,0,7.41356434942734,14.3575797705236,10.7988117813933,5.91729372274233,5.91729372274233,1.08736456835953,100,2015,7,5,0,0.109473114697444,0.0274988756972354,0.0225274723611327,0.063336847424665,0.063336847424665,0.00975909743690506
+"22102",100,2015,7,6,0,7.42925197089335,13.3631022141712,12.1161276772208,4.36627062345364,4.36627062345364,1.08740800216695,100,2015,7,6,0,0.0578374105297523,0.200922790311008,0.0278245394523371,0.113724548750332,0.113724548750332,0.00935601489944134
+"22103",100,2015,7,7,1.99691968389077,5.35196916918025,14.1660836600628,11.4651594749509,7.41732674830555,7.41732674830555,1.08745143597438,100,2015,7,7,0.0792397616899523,0.0150731093144727,0.114809970688466,0.00234795439877555,0.0596888931910391,0.0596888931910391,0.00896239162638758
+"22104",100,2015,7,8,0.160616064081491,2.4380637906959,14.8312320751194,10.8535972737899,7.32517054319644,7.32517054319644,1.0874948697818,100,2015,7,8,0.00450292432865902,0.0435877073382824,0.100404057856379,0.0281701867645352,0.12722575148311,0.12722575148311,0.00857822761774378
+"22105",100,2015,7,9,0.00517051712875188,5.90325632504504,14.5232563323051,10.841210133434,4.92266220359257,4.92266220359257,1.08753830358922,100,2015,7,9,0.000994152076411666,0.0407134576875775,0.128427445241087,0.0128555527581893,0.0800228278322077,0.0800228278322077,0.00820352287350993
+"22106",100,2015,7,10,0.0958195833860189,7.81711769628577,15.1356765602288,10.5293618664883,5.84954894572595,5.84954894572595,1.08758173739665,100,2015,7,10,0.00432748550908607,0.0774538303141342,0.0682841932754764,0.00837602384028117,0.0562619871518963,0.0562619871518963,0.00783827739368605
+"22107",100,2015,7,11,4.731463153239,7.10110008231353,11.248976933025,8.87000001775156,5.53694166366023,5.53694166366023,1.08762517120407,100,2015,7,11,0.130233895876267,0.103180124668781,0.147533357111692,0.000592394662673637,0.228281263030225,0.228281263030225,0.00748249117827213
+"22108",100,2015,7,12,3.5117711509415,0.69705170566755,12.2537844629571,11.8899671556664,4.13044008773283,4.13044008773283,1.08766860501149,100,2015,7,12,0.178947335516507,0.029842691456505,0.153567253469782,0.0046765912642492,0.100942690881674,0.100942690881674,0.00713616422726817
+"22109",100,2015,7,13,15.8409242399193,7.610011011067,11.9676237641388,11.8855665362183,4.46051705762236,4.46051705762236,1.08771203881892,100,2015,7,13,1.35649147446413,0.217056146607233,0.0956023472903783,0.000771924559919772,0.214811680343153,0.214811680343153,0.00679929654067416
+"22110",100,2015,7,14,4.38646862241957,5.98801977904585,9.13897688816352,10.3396480159529,3.67433440462329,3.67433440462329,1.08775547262634,100,2015,7,14,0.0992982765108653,0.11101406120614,0.105035685413294,0.00358421526241685,0.1555672390726,0.1555672390726,0.00647188811849011
+"22111",100,2015,7,15,11.6741472428913,2.07817380945019,9.59099014941091,10.4331573647897,5.74412545643755,5.74412545643755,1.08779890643376,100,2015,7,15,1.89561399102912,0.0873578945826398,0.123976646111116,0.0157064368667046,0.12512452215453,0.12512452215453,0.00615393896071602
+"22112",100,2015,7,16,3.55390539447335,2.53376235243249,11.3162706761208,9.16948287741448,7.27206825396933,7.27206825396933,1.08784234024119,100,2015,7,16,0.0525731006421593,0.122838604043663,0.0394251589235098,0.000325145690250835,0.255677692243808,0.255677692243808,0.0058454490673519
+"22113",100,2015,7,17,0.970407033517416,5.74393840040704,12.0278437607097,11.1555005609661,6.83697465281807,6.83697465281807,1.08788577404861,100,2015,7,17,0.0187719336169511,0.126695322419342,0.0817052982754948,0.0176362762630513,0.173817499407285,0.173817499407285,0.00554641843839773
+"22114",100,2015,7,18,2.46369638589874,3.70449943851979,10.4691748676783,8.6458306884346,6.83664473186363,6.83664473186363,1.08792920785603,100,2015,7,18,0.165614048110118,0.110967248794498,0.0579147075163294,0.0255731140494241,0.112911713804306,0.112911713804306,0.00525684707385352
+"22115",100,2015,7,19,0.216721675855623,3.12646865031638,10.6071176161729,7.55327834390571,7.89129810049982,7.89129810049982,1.08797264166346,100,2015,7,19,0.0398830423813466,0.114117558286348,0.0633140354871076,0.00523392317589172,0.628011734750581,0.628011734750581,0.00497673497371927
+"22116",100,2015,7,20,0.355555560214422,-0.894158413766897,12.2539933186815,8.41273930888496,9.63007710711791,9.63007710711791,1.08801607547088,100,2015,7,20,0.0638596478191734,0.0284432755259747,0.0310654816031269,0.000258480433490548,0.00114678458951948,0.00114678458951948,0.00470608213799498
+"22117",100,2015,7,21,0,2.40559957339556,14.3331354894523,10.0861056666694,5.88404834965537,5.88404834965537,1.0880595092783,100,2015,7,21,0,0.0214701756321228,0.0471093701301986,0.00142631376618536,0.239903508732905,0.239903508732905,0.00444488856668065
+"22118",100,2015,7,22,0.0589658974683193,8.60905392964681,14.4506489803033,10.9873927245439,8.02778870568942,8.02778870568942,1.08810294308573,100,2015,7,22,0.00263157902579559,0.0401877315165333,0.0515619854085455,0.0231029100831359,0.292380682796985,0.292380682796985,0.00419315425977628
+"22119",100,2015,7,23,0.100000001490116,2.53701869830309,15.0192079512593,9.62335533251201,9.66617159429032,9.66617159429032,1.08814637689315,100,2015,7,23,0,0.0356543843263831,0.0385649347606897,0.00771344305346582,0.00133742136600971,0.00133742136600971,0.00395087921728186
+"22120",100,2015,7,24,0.136413643396858,5.60576458890053,15.0692408292076,10.8463915823841,5.93751374801787,5.93751374801787,1.08818981070058,100,2015,7,24,0.0031578948309547,0.0313052745830444,0.0155257672182383,0.0133485403808844,0.178360827193912,0.178360827193912,0.00371806343919741
+"22121",100,2015,7,25,5.56138618708444,6.28315735597684,13.2543014404666,9.67957100065628,8.05654563106457,8.05654563106457,1.088233244508,100,2015,7,25,0.416900629523227,0.0510175247136763,0.068691811428957,0.0099064216614809,0.120002360843702,0.120002360843702,0.00349470692552291
+"22122",100,2015,7,26,10.7727171684911,4.14260730963729,9.96874589888569,8.25872388111614,6.95829482755252,6.95829482755252,1.08827667831542,100,2015,7,26,3.15397654650512,0.0825245789243643,0.0927918843364793,0.00418887825021051,1.82739297284469,1.82739297284469,0.00328080967625837
+"22123",100,2015,7,27,7.83993399287477,3.81029704642637,12.1295818983525,10.2465897267408,7.64629263767708,7.64629263767708,1.08832011212285,100,2015,7,27,1.2214619376088,0.233317522752786,0.0964765699286891,0.0011655011272598,0.432546814070846,0.432546814070846,0.0030763716914038
+"22124",100,2015,7,28,3.13641363375782,4.23889991867267,11.9175576838461,11.3089988706398,8.40658961297131,8.40658961297131,1.08836354593027,100,2015,7,28,0.561403488834009,0.0555923958059103,0.0577450287446056,0.0168654872158936,0.308457909501567,0.308457909501567,0.00288139297095918
+"22125",100,2015,7,29,1.0436743566043,3.7340594256016,11.3558306447481,9.36544548009488,5.86123212231006,5.86123212231006,1.08840697973769,100,2015,7,29,0.0424561395422066,0.0291204765966822,0.0721759991362052,0.0101450292978312,0.123793003636648,0.123793003636648,0.00269587351492452
+"22126",100,2015,7,30,0.0907590772600064,4.63827278118322,11.2562046932309,9.22926299826409,4.75616058290857,4.75616058290857,1.08845041354512,100,2015,7,30,0.00619883059409627,0.0365040973908943,0.0552783995757508,0.0136175560332825,0.134843259704336,0.134843259704336,0.00251981332329982
+"22127",100,2015,7,31,4.98536859434692,2.36253025154076,11.6032452483644,8.01116610202852,8.71135306804225,8.71135306804225,1.08849384735254,100,2015,7,31,0.2806432607439,0.184622803833057,0.0716140248148868,0.00832280904540296,0.203225162885406,0.203225162885406,0.00235321239608508
+"22128",100,2015,8,1,3.3166116681954,6.04985704883622,11.3604510278985,7.45760171038352,9.61644670803292,9.61644670803292,1.10476266063951,100,2015,8,1,0.216491253640922,0.172304131530201,0.0823929635589806,0.0053923967450852,0.073116408117263,0.073116408117263,0.00240668819316386
+"22129",100,2015,8,2,0.00297029707396385,4.68024206371328,12.0910892140354,10.6577447842975,6.49843786101137,6.49843786101137,1.12103147392649,100,2015,8,2,0.000994152076411666,0.105861991037976,0.0410432534558636,0.00869883308641023,0.0812929673858732,0.0812929673858732,0.0024654969812234
+"22130",100,2015,8,3,12.6026403660035,5.49430149747725,9.80468635433184,6.90325633186449,10.1822991984906,10.1822991984906,1.13730028721346,100,2015,8,3,1.28894713630462,0.128281872753811,0.0675444305844548,0.00318011094926514,0.14325904653903,0.14325904653903,0.0025296387602637
+"22131",100,2015,8,4,1.14554456853368,2.29597360990753,11.480990022716,9.86264014585052,8.2763256108669,8.2763256108669,1.15356910050043,100,2015,8,4,0.253625733733875,0.0639362566061237,0.0691134715030966,0.0153538020842929,0.153559591638062,0.153559591638062,0.00259911353028477
+"22132",100,2015,8,5,9.69823969472753,5.40864687574447,10.5200330064897,8.48685381357426,9.72701867001809,9.72701867001809,1.16983791378741,100,2015,8,5,0.191345051882638,0.0969251672332251,0.0684702131015725,0.0030426946562526,0.520721161319298,0.520721161319298,0.00267392129128659
+"22133",100,2015,8,6,6.07216716311028,6.36602857797453,12.3161166183757,12.1860947131586,8.04767876384807,8.04767876384807,1.18610672707438,100,2015,8,6,0.334795336974316,0.142787695799953,0.0502368840112276,0.0139929808076657,0.297333332591629,0.297333332591629,0.00275406204326917
+"22134",100,2015,8,7,4.29944993744064,7.53609463195465,12.6250603647515,11.6744113529738,9.44542347558654,9.44542347558654,1.20237554036135,100,2015,8,7,0.024327500605459,0.107871917988982,0.0366543654369989,0.00371461251916914,0.129670720177465,0.129670720177465,0.00283953578623251
+"22135",100,2015,8,8,2.0311330812599,5.92678772681879,12.1086577834076,10.3361605863498,6.97411440019429,6.97411440019429,1.21864435364833,100,2015,8,8,0.195380104438605,0.0697450296145607,0.0350035098249437,0.00319825164400254,0.347876019774568,0.347876019774568,0.00293034252017662
+"22136",100,2015,8,9,0.281738183941647,3.87534653261812,15.0386029674669,8.75160616695291,9.37221120450363,9.37221120450363,1.2349131669353,100,2015,8,9,0.00397660849735753,0.0139175436655758,0.0998479409023456,0.000376032127796702,0.0441251174726635,0.0441251174726635,0.00302648224510149
+"22137",100,2015,8,10,0.617271731247996,7.45506048202515,14.5647085019858,10.5550275693501,8.98381741841634,8.98381741841634,1.25118198022228,100,2015,8,10,0.0798245608283765,0.080715190821658,0.050814011896183,0.000381290339939039,0.652953807108095,0.652953807108095,0.00312795496100712
+"22138",100,2015,8,11,2.09823980425844,5.46070405122864,14.2697909284871,9.31091316147606,11.379416814076,11.379416814076,1.26745079350925,100,2015,8,11,0.0187719291553185,0.0627315558350036,0.0423829870615062,0.00162632069063157,0.0248099594228675,0.0248099594228675,0.00323476066789351
+"22139",100,2015,8,12,0.139823984678495,6.6802420280423,12.6753135078942,10.8506820718579,9.24895488520791,9.24895488520791,1.28371960679622,100,2015,8,12,0.0502339189939681,0.0614444612927012,0.0353339159581359,0.00719298284659823,0.215225787337951,0.215225787337951,0.00334689936576065
+"22140",100,2015,8,13,4.6789878188449,6.94661163470664,11.978613742245,9.33634780971917,7.65815188205413,7.65815188205413,1.2999884200832,100,2015,8,13,0.0245029246458623,0.160458538108981,0.068433373938732,0.00536725321548977,0.142744475237562,0.142744475237562,0.00346437105460857
+"22141",100,2015,8,14,0.37403740284696,6.61829484282809,13.6221892311759,13.3956104977296,7.5054894464113,7.5054894464113,1.31625723337017,100,2015,8,14,0.130058477343175,0.103356142649754,0.0377778000302253,0.00410351508298579,0.317487146573922,0.317487146573922,0.00358717573443723
+"22142",100,2015,8,15,2.09636963633421,6.93259625964695,14.9839604076641,11.2247855456093,11.8254125152353,11.8254125152353,1.33252604665714,100,2015,8,15,0.160994160844571,0.0734356566758382,0.0648813040371771,0.0135467742117742,0.0721579173908815,0.0721579173908815,0.00371531340524667
+"22143",100,2015,8,16,0.200000003365466,4.64737079429417,14.8456765585082,11.4841144098045,8.81004402057828,8.81004402057828,1.34879485994412,100,2015,8,16,0.00222222245401807,0.0923730851011774,0.0430830565269096,0.0102257395426943,0.268750951742504,0.268750951742504,0.00384878406703686
+"22144",100,2015,8,17,5.785698564926,7.11629269172912,10.7645984613987,8.15909788868215,11.3403410937789,11.3403410937789,1.36506367323109,100,2015,8,17,0.0191228271506916,0.104513467756365,0.0546450301121455,0.00264736975739184,0.222466668319734,0.222466668319734,0.00398758771980782
+"22145",100,2015,8,18,2.16105610060089,3.35760173519584,10.802299072509,8.83137499827101,9.93002199208645,9.93002199208645,1.38133248651806,100,2015,8,18,0.139532163212874,0.14538712602638,0.0271930023104661,0.00283566908589622,0.404135691173225,0.404135691173225,0.00413172436355953
+"22146",100,2015,8,19,0.295159525357851,0.331001102760418,12.6334983922205,8.64154004263799,10.7219141633859,10.7219141633859,1.39760129980504,100,2015,8,19,0.00473684237714401,0.0189263140014041,0.12333682296938,0.000547954318471168,0.0908099429594075,0.0908099429594075,0.00428119399829201
+"22147",100,2015,8,20,0.156655668704161,4.59403738487672,17.9257316988031,10.5866446982909,11.9624862093868,11.9624862093868,1.41387011309201,100,2015,8,20,0.0280116967107469,0.0439029058802403,0.0752369063753066,0.0105467799895817,0.167589475437716,0.167589475437716,0.00443599662400524
+"22148",100,2015,8,21,0,9.95616060305219,16.7016941024394,10.6878547248798,5.02392734867511,5.02392734867511,1.43013892637898,100,2015,8,21,0,0.106003443948932,0.0298098232875386,0.0102257281655591,0.0896608297337038,0.0896608297337038,0.00459613224069924
+"22149",100,2015,8,22,1.36600659363078,8.6434103291158,15.8371947228712,11.9245213486574,9.52445545972902,9.52445545972902,1.44640773966596,100,2015,8,22,0.0933918058453955,0.0819655216307146,0.049394185549307,0.00311754708772566,0.435437532030144,0.435437532030144,0.004761600848374
+"22150",100,2015,8,23,0.96787680297008,5.83162821139177,12.4401980453592,10.8763255884152,10.150747911896,10.150747911896,1.46267655295293,100,2015,8,23,0.078421057086242,0.0669707734849887,0.0418035165419188,0.0040251355378334,0.789015145265087,0.789015145265087,0.00493240244702952
+"22151",100,2015,8,24,1.45698568057699,5.94660066797657,11.3930252765534,9.77096814972864,9.03135302200569,9.03135302200569,1.4789453662399,100,2015,8,24,0.0405263167096866,0.214980145161081,0.0350140472189744,0.00750233198251456,0.493848043307991,0.493848043307991,0.00510853703666579
+"22152",100,2015,8,25,0.212321235270652,5.05018705214867,12.7472826450965,9.13808586993495,12.8868206787949,12.8868206787949,1.49521417952688,100,2015,8,25,0.0570175448966306,0.25468186425552,0.046139754064559,0.00256081770520303,0.306766747538343,0.306766747538343,0.00529000461728284
+"22153",100,2015,8,26,1.00143015135502,7.08961496835757,13.8751265826923,11.2386579597482,8.33726080494745,8.33726080494745,1.51148299281385,100,2015,8,26,0.0198830453275941,0.108522781085427,0.0681871416416109,0.00206083301977396,0.1084164123134,0.1084164123134,0.00547680518888064
+"22154",100,2015,8,27,0.554785484724706,5.10833886590334,14.4171947756223,12.0092188485778,10.5206710817528,10.5206710817528,1.52775180610082,100,2015,8,27,0.0180701743127312,0.145125710258729,0.0305707728091034,0.00940645259175675,0.362933984727539,0.362933984727539,0.0056689387514592
+"22155",100,2015,8,28,1.99614958112652,7.5187128799321,12.8259076505604,10.637953788665,10.5665237250501,10.5665237250501,1.5440206193878,100,2015,8,28,0.0424561311069319,0.202561996188943,0.0402841743470114,0.0355029014900639,0.821558439834863,0.821558439834863,0.00586640530501852
+"22156",100,2015,8,29,1.33498348654693,3.55875685952022,11.1292079058942,10.2096697604827,11.0348733748802,11.0348733748802,1.56028943267477,100,2015,8,29,0.0480701772371937,0.406715196485135,0.0315842104326676,0.00143684017347214,0.505681271279864,0.505681271279864,0.00606920484955861
+"22157",100,2015,8,30,1.38305830594861,7.12764570395676,11.2908910209983,9.5093949646315,7.42537955325035,7.42537955325035,1.57655824596175,100,2015,8,30,0.224970743244853,0.133380089653881,0.0312368579552906,0.0108841972396317,0.0930140594549525,0.0930140594549525,0.00627733738507945
+"22158",100,2015,8,31,0.537953801510489,5.95528057778236,11.4589658460208,8.77414748062789,10.816677559327,10.816677559327,1.59282705924872,100,2015,8,31,0.0822222170564867,0.0763818810624899,0.0282923694075983,0.0228537927706538,2.55039447526551,2.55039447526551,0.00649080291158106
+"22159",100,2015,9,1,0,-0.19922992289763,15.2748295948713,10.1163586898737,14.9811881708496,14.9811881708496,1.62836587306888,100,2015,9,1,0,0.0263228068329263,0.0499467902803386,0.0180807271099024,0.0455152539170102,0.0455152539170102,0.00638243335752329
+"22160",100,2015,9,2,0,5.88423540568588,13.76071500411,11.6726842120667,5.6426622361371,5.6426622361371,1.66390468688904,100,2015,9,2,0,0.014124569849407,0.0307649061928422,0.0121608399397579,0.0933397733476596,0.0933397733476596,0.00627547973312123
+"22161",100,2015,9,3,7.6369637225983,9.16739285975793,13.3506270713932,10.9327061918571,8.58447746606288,8.58447746606288,1.69944350070921,100,2015,9,3,0.776725116417451,0.0493666525099739,0.0534806870288366,0.00530525387555252,0.698136189311359,0.698136189311359,0.00616994203837489
+"22162",100,2015,9,4,0.38921892334788,3.44995597827815,12.7392629564661,9.43471940778138,10.3498789479893,10.3498789479893,1.73498231452937,100,2015,9,4,0.154269003265086,0.0499912120250278,0.0884473678968852,0.000733322397962035,0.246148574916168,0.246148574916168,0.00606582027328426
+"22163",100,2015,9,5,0.467766784270867,7.71391636992183,12.9471288487987,11.2755225499471,9.37217816372778,9.37217816372778,1.77052112834953,100,2015,9,5,0.0228654976790412,0.0526537805200557,0.0228895014400898,0.00237017819595778,0.310398274552272,0.310398274552272,0.00596311443784935
+"22164",100,2015,9,6,0.227062711973723,6.03528058332185,14.3686028513053,10.8738063134507,7.7890648799892,7.7890648799892,1.80605994216969,100,2015,9,6,0.00362573145077244,0.0484982406147904,0.0438608303115206,0.0297807027025247,0.223539708523488,0.223539708523488,0.00586182453207016
+"22165",100,2015,9,7,19.9537953186874,7.14149617991432,11.3146204166811,9.31182634292787,10.9493070839524,10.9493070839524,1.84159875598986,100,2015,9,7,2.51701754542147,0.0703362591224988,0.116133339648864,0.0467830508361169,1.87268775806322,1.87268775806322,0.00576195055594668
+"22166",100,2015,9,8,7.02794280435124,7.0715401033626,12.6039494418993,11.4632452974225,10.6929704154154,10.6929704154154,1.87713756981002,100,2015,9,8,0.144269063068441,0.0668801123134134,0.0431912171035458,0.0646942141729064,0.443481837153341,0.443481837153341,0.00566349250947892
+"22167",100,2015,9,9,1.3944994247917,9.31227722839423,14.384015469399,10.3155665875006,15.8254897481669,15.8254897481669,1.91267638363018,100,2015,9,9,0.00807017928675647,0.0944870784001786,0.0449655001256153,0.0108204624511236,0.191737410675973,0.191737410675973,0.00556645039266688
+"22168",100,2015,9,10,0.353355341864498,7.27407038828196,14.8569966611033,11.6658747298489,14.6150165627105,14.6150165627105,1.94821519745034,100,2015,9,10,0.0133918128724684,0.0609830454464064,0.0363895096829845,0.00432455892738896,2.89550968316746,2.89550968316746,0.00547082420551055
+"22169",100,2015,9,11,0.0379537959450936,5.178536861512,18.9732121844234,11.8401870601641,17.4820682685105,17.4820682685105,1.9837540112705,100,2015,9,11,0.00485380131424519,0.0279450315977567,0.068380658972098,0.00830291416332329,0.0678111762155599,0.0678111762155599,0.00537661394800994
+"22170",100,2015,9,12,0.0706270637586959,10.3194499933811,22.717084687404,13.5946094368157,17.1362487092139,17.1362487092139,2.01929282509067,100,2015,9,12,0.00245614042407588,0.0379467482517287,0.159956109900553,0.0171368392008116,0.0132894667690247,0.0132894667690247,0.00528381962016505
+"22171",100,2015,9,13,0,10.8275467061629,24.1077118108768,11.9552254440761,16.4384377556141,16.4384377556141,2.05483163891083,100,2015,9,13,0,0.0301432688083718,0.0215918531633322,0.0029362646203784,0.136954332434353,0.136954332434353,0.00519244122197587
+"22172",100,2015,9,14,0,13.074829477157,25.5040592008954,12.8392518644679,13.3204839305647,13.3204839305647,2.09037045273099,100,2015,9,14,0,0.0379496819547025,0.0118930074281414,0.0511480024193663,0.802349820963609,0.802349820963609,0.0051024787534424
+"22173",100,2015,9,15,6.59614961768928,8.92420243430059,14.2847964535452,10.0449285003612,14.9368207604185,14.9368207604185,2.12590926655115,100,2015,9,15,0.779532120520628,0.0666690357923136,0.0606098884070048,0.00629531623894896,0.687328427949532,0.687328427949532,0.00501393221456466
+"22174",100,2015,9,16,1.44488448396374,5.09815183698279,13.6192190308775,11.3497028739014,13.9758637179636,13.9758637179636,2.16144808037132,100,2015,9,16,0.122514609007809,0.108827528858767,0.040465488603459,0.00411638758257476,1.87167862662382,1.87167862662382,0.00492680160534264
+"22175",100,2015,9,17,2.95368538332982,6.14860283974374,13.6361166342388,10.8358635078825,11.5459186230818,11.5459186230818,2.19698689419148,100,2015,9,17,0.0860818766989953,0.0911151978386977,0.0387152224379675,0.0070941480804822,0.362844402991464,0.362844402991464,0.00484108692577631
+"22176",100,2015,9,18,1.4265126436457,8.24253030447545,14.4157975262935,11.5838504682149,10.5228493326437,10.5228493326437,2.23252570801164,100,2015,9,18,0.249298226944886,0.088632165319925,0.0324450180790533,0.000331573435113042,1.7224732503256,1.7224732503256,0.00475678817586572
+"22177",100,2015,9,19,0.173707373710749,2.60378441034239,15.6939492388265,11.4149945493054,15.3550935268927,15.3550935268927,2.2680645218318,100,2015,9,19,0.00450292432865902,0.042935680369889,0.0212666653104335,0.0125695762612929,1.36499881702683,1.36499881702683,0.00467390535561084
+"22178",100,2015,9,20,0.0572057214244889,5.09167221906555,20.2531791011361,11.4291529241044,18.9173045751154,18.9173045751154,2.30360333565197,100,2015,9,20,0.00263157902579559,0.0470251471324419,0.0686778062611809,0.0403690099683711,0.12599232061761,0.12599232061761,0.00459243846501168
+"22179",100,2015,9,21,0.181848189003593,10.2423541726846,13.5021780578479,9.1119692385918,12.0610451346839,12.0610451346839,2.33914214947213,100,2015,9,21,0.00888888956771959,0.0604140506945989,0.0591111382379458,0.0265274725395809,1.44027689390133,1.44027689390133,0.00451238750406822
+"22180",100,2015,9,22,2.7634763625851,5.77206823403555,12.0245763906682,7.85785481743556,17.2581739436151,17.2581739436151,2.37468096329229,100,2015,9,22,0.20204674625955,0.160105302163751,0.0545467682331999,0.00334502882707689,1.40305061731187,1.40305061731187,0.0044337524727805
+"22181",100,2015,9,23,0.529482953979893,5.05541258729068,12.1446974201433,8.0730362914183,13.9028273037951,13.9028273037951,2.41021977711245,100,2015,9,23,0.0159649126432097,0.223498288605247,0.0552099805219279,0.000339764625223644,1.09448945338161,1.09448945338161,0.00435653337114848
+"22182",100,2015,9,24,0.013421342334207,5.10701873252625,14.614950560894,10.2345653949398,17.7339057817449,17.7339057817449,2.44575859093262,100,2015,9,24,0.00140350881375765,0.143071935454052,0.0199338890589702,0.00638305522415604,1.71885946864995,1.71885946864995,0.00428073019917219
+"22183",100,2015,9,25,0.013971397347904,1.30304732076143,15.5829153018947,10.8707589945777,19.597568734382,19.597568734382,2.48129740475278,100,2015,9,25,0.00140350881375765,0.0943163743599126,0.0265005984970789,0.00965790618100828,0.134022103288755,0.134022103288755,0.0042063429568516
+"22184",100,2015,9,26,0.109020903714747,5.29037401253896,17.7116832817086,11.9801430287797,20.3935864325797,20.3935864325797,2.51683621857294,100,2015,9,26,0.00608187152628313,0.034684205495841,0.0592731254803378,0.0156151984857064,0.292380106006021,0.292380106006021,0.00413337164418674
+"22185",100,2015,9,27,0,3.75427944563141,19.8861714248741,12.5092629840796,20.1542245216496,20.1542245216496,2.5523750323931,100,2015,9,27,0,0.0444584927090496,0.0796333356225255,0.0613479796739893,0.392564884690249,0.392564884690249,0.00406181626117758
+"22186",100,2015,9,28,0,4.1548625009157,16.6292738646957,10.7663917048405,13.2405171766795,13.2405171766795,2.58791384621326,100,2015,9,28,0,0.0419988250771585,0.0160479092786166,0.00926901599828129,1.07043849733148,1.07043849733148,0.00399167680782416
+"22187",100,2015,9,29,0.0497249732382096,5.20375134580337,15.347502729418,11.0835753714684,10.8126843969683,10.8126843969683,2.62345266003343,100,2015,9,29,0.00374269017002039,0.166021104948718,0.0820245385800811,0.00413976691402373,0.689106502827487,0.689106502827487,0.00392295328412645
+"22188",100,2015,9,30,0.0797579769860662,5.68561054036693,15.3777446788792,9.69616038885841,14.4080307942675,14.4080307942675,2.65899147385359,100,2015,9,30,0.00228070182235617,0.263557916309979,0.0517251471469934,0.00489532459778275,1.04319804350876,1.04319804350876,0.00385564569008444
+"22189",100,2015,10,1,0.0970297044161523,3.08069307392318,17.3498019542631,12.7369745535688,21.2749836127488,21.2749836127488,2.73752797564181,100,2015,10,1,0.00222222228844961,0.0378023455062166,0.0275672262094368,0.0482912128694679,0.0204023217946364,0.0204023217946364,0.00349377173370536
+"22190",100,2015,10,2,0,9.9719253682723,24.7572056316044,14.1969087462221,21.221001460345,21.221001460345,2.81606447743004,100,2015,10,2,0,0.0841666338179408,0.0164918345315378,0.0496263120685452,0.0108707899443081,0.0108707899443081,0.00316161898726346
+"22191",100,2015,10,3,0,8.78966984260987,29.8508141352923,11.177117767376,21.1118260290232,21.1118260290232,2.89460097921826,100,2015,10,3,0,0.0452473661925447,0.01300998739531,0.0805052214818184,0.442380599476197,0.442380599476197,0.00285918745075875
+"22192",100,2015,10,4,0,8.03352031193682,23.9158305911997,13.9871836799731,22.217106956591,22.217106956591,2.97313748100648,100,2015,10,4,0,0.0285315930417476,0.0527895273134115,0.0494479739563652,0.00370935408402509,0.00370935408402509,0.00258647712419121
+"22193",100,2015,10,5,0,7.63911986429699,32.8046316728078,9.75760180407231,21.6856106987881,21.6856106987881,3.0516739827947,100,2015,10,5,0,0.0397479963615241,0.0134759442615241,0.158124575772277,0.198302909200103,0.198302909200103,0.00234348800756086
+"22194",100,2015,10,6,0,15.1864686888306,25.4099121177682,14.4953687351004,19.7886360365697,19.7886360365697,3.13021048458293,100,2015,10,6,0,0.0195426605916765,0.353747887760034,0.0304538351606239,0.0582332948475458,0.0582332948475458,0.00213022010086769
+"22195",100,2015,10,7,0,6.46166111323962,15.8315291934543,9.206853708156,22.7444335782226,22.7444335782226,3.20874698637115,100,2015,10,7,0,0.329573040387802,0.012291817445487,0.00582632734741092,0.117742785827623,0.117742785827623,0.00194667340411171
+"22196",100,2015,10,8,0,4.90210118676701,19.0935091977597,12.1415841540094,23.4831685673679,23.4831685673679,3.28728348815937,100,2015,10,8,0,0.111032155165221,0.0845146390460052,0.00789299514735565,0.00193218091573077,0.00193218091573077,0.00179284791729292
+"22197",100,2015,10,9,0,8.36300335622857,25.1023652755519,16.0614192477941,21.0287900007728,21.0287900007728,3.3658199899476,100,2015,10,9,0,0.0556432929235055,0.16987087656683,0.0574450215369075,0.38322055558343,0.38322055558343,0.0016687436404113
+"22198",100,2015,10,10,0,12.0856543961662,27.4545765615532,16.4473926577762,10.1756654420439,10.1756654420439,3.44435649173582,100,2015,10,10,0,0.0231151724966734,0.0179555001156762,0.11741118969408,0.736594082416394,0.736594082416394,0.00157436057346686
+"22199",100,2015,10,11,0,11.2853683339487,17.8418701956637,14.0366226506836,12.3640594020797,12.3640594020797,3.52289299352404,100,2015,10,11,0,0.0949929778964031,0.186445086018695,0.00282515087695424,1.19615842553979,1.19615842553979,0.00150969871645961
+"22200",100,2015,10,12,0.494169423780819,9.90397141816461,16.3096922401286,11.2878878428729,14.8780417458059,14.8780417458059,3.60142949531226,100,2015,10,12,0.0128654968494562,0.101206469626278,0.0171783248726825,0.0285918494598864,0.997728870951936,0.997728870951936,0.00147475806938954
+"22201",100,2015,10,13,0.258195825806349,8.75904295179579,16.9250936980295,10.8116941651364,16.2259630144495,16.2259630144495,3.67996599710049,100,2015,10,13,0.0124561409312382,0.0846695805935083,0.0411894742599607,0.00330058920795179,1.50665808635307,1.50665808635307,0.00146953863225665
+"22202",100,2015,10,14,0.0299229927451173,5.01386139075486,25.1010780901012,13.1932232345816,23.1947082985352,23.1947082985352,3.75850249888871,100,2015,10,14,0.0020467836867299,0.0560140278833158,0.147580791335511,0.0497263684568977,0.0109560858522615,0.0109560858522615,0.00149404040506095
+"22203",100,2015,10,15,0.013421342334207,8.53540159294707,33.5019582280494,7.30020901078832,23.2183499624758,23.2183499624758,3.83703900067693,100,2015,10,15,0.00140350881375765,0.0217023367430828,0.0139474311083751,0.141731616775349,0.279635863080117,0.279635863080117,0.00154826338780244
+"22204",100,2015,10,16,0.134873489661775,12.3442245982792,19.0855662930261,13.068184916598,20.7204618989044,20.7204618989044,3.91557550246516,100,2015,10,16,0.00485380156695495,0.0302807380833161,0.0420474487576227,0.0238204273380859,0.305547997931198,0.305547997931198,0.00163220758048109
+"22205",100,2015,10,17,0,6.21218923635871,18.3543895410888,10.5002861264253,25.4609901713591,25.4609901713591,3.99411200425338,100,2015,10,17,0,0.0160204683995544,0.0275537677166513,0.0424485648239366,0.048070749329909,0.048070749329909,0.00174587298309695
+"22206",100,2015,10,18,0,7.97422444308945,18.6357538938785,10.6930582342368,24.8929816037253,24.8929816037253,4.0726485060416,100,2015,10,18,0,0.113492381239825,0.0656042136880807,0.0320321187917918,0.0336433150311746,0.0336433150311746,0.00188925959564999
+"22207",100,2015,10,19,0,6.20935093582791,26.5120902024742,10.2632892554087,25.9737843134747,25.9737843134747,4.15118500782982,100,2015,10,19,0,0.0703035124037201,0.148958367384917,0.126269555623978,0.00928188587682885,0.00928188587682885,0.00206236741814019
+"22208",100,2015,10,20,0,11.4849944434675,31.0721891610929,11.569031886404,14.7736083577306,14.7736083577306,4.22972150961805,100,2015,10,20,0,0.0433584778390484,0.0688356703020239,0.179054360134359,1.86895344369692,1.86895344369692,0.00226519645056759
+"22209",100,2015,10,21,0.866336642154897,12.307392725719,15.7623102819697,13.0818591794558,11.5686468485296,11.5686468485296,4.30825801140627,100,2015,10,21,0.104269005583044,0.0413450502653193,0.0259140302491395,0.00996957397023967,1.20255967510967,1.20255967510967,0.00249774669293217
+"22210",100,2015,10,22,0.619031916881683,8.99490646954023,14.8526842895776,9.54608374722589,15.4177557060821,15.4177557060821,4.38679451319449,100,2015,10,22,0.00555555489328198,0.134598337927584,0.0195005845767778,0.00134852913759045,3.0046693899837,3.0046693899837,0.00276001814523393
+"22211",100,2015,10,23,0.00154015403835162,9.51319034186134,16.4437733913543,8.91704077767854,16.5166007095438,16.5166007095438,4.46533101498272,100,2015,10,23,0.000526315805159117,0.0896930003027432,0.0185116360315727,0.00988303820297367,1.22492054974761,1.22492054974761,0.00305201080747288
+"22212",100,2015,10,24,0,0.908151808089287,20.5869087307367,9.46372943454319,23.9497137373955,23.9497137373955,4.54386751677094,100,2015,10,24,0,0.0531561441500993,0.0590497196490477,0.0629672240520024,0.393738406684797,0.393738406684797,0.00337372467964901
+"22213",100,2015,10,25,0,8.37621546316199,32.5245433173688,9.74121023876832,18.1969858805339,18.1969858805339,4.62240401855916,100,2015,10,25,0,0.145225617963163,0.0152029136357637,0.166998817115992,3.34154074607108,3.34154074607108,0.00372515976176232
+"22214",100,2015,10,26,0.841034108492145,11.5420242273899,15.8985478198699,9.05059408643196,18.3227174295188,18.3227174295188,4.70094052034738,100,2015,10,26,0.175380114595793,0.0396116745062697,0.013792392407313,0.0148608353197312,0.492345286832444,0.492345286832444,0.00410631605381282
+"22215",100,2015,10,27,0.0270627066738928,8.82686462759054,20.1987567687585,9.75968093788139,28.3270739690699,28.3270739690699,4.77947702213561,100,2015,10,27,0.00245614042407588,0.0846023169623927,0.0587585234215011,0.0134982290703713,0.00215439267546907,0.00215439267546907,0.00451719355580048
+"22216",100,2015,10,28,0,8.18645754951586,25.0712649719943,11.4939054241537,25.2812432455938,25.2812432455938,4.85801352392383,100,2015,10,28,0,0.148167227435044,0.111014023170983,0.0684701267689527,1.75792661248403,1.75792661248403,0.00495779226772535
+"22217",100,2015,10,29,0,9.85293719897045,24.3943231664475,13.1674036848532,27.1167655940628,27.1167655940628,4.93655002571205,100,2015,10,29,0,0.0803649467201233,0.163358943480032,0.099909883133366,0.0416221820410181,0.0416221820410181,0.00542811218958738
+"22218",100,2015,10,30,0,11.2327062262691,26.880946032416,12.2355665588799,22.9182397215018,22.9182397215018,5.01508652750028,100,2015,10,30,0,0.0569730879555879,0.108393002252629,0.0602286858498137,0.450367507528916,0.450367507528916,0.00592815332138664
+"22219",100,2015,10,31,7.60902088624809,13.9025743377484,29.4056985475311,15.9456545039766,24.5367766570206,24.5367766570206,5.0936230292885,100,2015,10,31,4.33877190746064,0.0751040615238724,0.129889434605045,0.0624443518322949,0.708300481013549,0.708300481013549,0.00645791566312304
+"22220",100,2015,11,1,0.0416941700382332,11.7270076183071,20.0788560202151,13.5355334224218,16.0397471269496,16.0397471269496,5.10000371536655,100,2015,11,1,0.00479532178033862,0.0429368483270954,0.0564508847155966,0.0302450295811686,1.00883714779876,1.00883714779876,0.00620515656982711
+"22221",100,2015,11,2,2.94466445915508,11.4522111523401,15.7419581775225,10.616930719256,16.6474915746808,16.6474915746808,5.10638440144461,100,2015,11,2,1.35941526033726,0.0259117062976667,0.0140806475701804,0.0160473632724131,0.583848167955178,0.583848167955178,0.00596214513929926
+"22222",100,2015,11,3,0.0555555563833978,4.79595161559689,18.2926512068779,9.91556663429252,23.5156544600383,23.5156544600383,5.11276508752267,100,2015,11,3,0.00485380131424519,0.153150854535707,0.0619052362678319,0.0651912192004985,0.346302456910861,0.346302456910861,0.00572888137153948
+"22223",100,2015,11,4,0,10.4595379929076,22.4706158569806,15.1230692249714,10.9768646301085,10.9768646301085,5.11914577360072,100,2015,11,4,0,0.0456982552133008,0.0507689567255522,0.0299877007780744,0.512160815679625,0.512160815679625,0.00550536526654777
+"22224",100,2015,11,5,3.3436744039995,14.7986138535805,24.1176678874705,18.9552697117704,15.042145063906,15.042145063906,5.12552645967878,100,2015,11,5,0.0264912328943223,0.0308941553886457,0.0207988360804757,0.0356497248512877,0.847947348131863,0.847947348131863,0.00529159682432412
+"22225",100,2015,11,6,1.4181518095358,13.3223872955876,16.2222992673565,15.0488999579737,9.89700768725707,9.89700768725707,5.13190714575684,100,2015,11,6,0.480409358520258,0.0141888750712608,0.0175152663876744,0.000288890457229348,1.11888932307515,1.11888932307515,0.00508757604486854
+"22226",100,2015,11,7,0.330583066010501,9.22817380567326,16.8618701834096,9.62297026421239,28.7473046792747,28.7473046792747,5.13828783183489,100,2015,11,7,0.019824562748099,0.13701636624039,0.0416246197083739,0.0116163818940113,0.424520573041907,0.424520573041907,0.00489330292818104
+"22227",100,2015,11,8,0,4.04991200366298,27.3074479748314,9.68203515660251,27.1134980897305,27.1134980897305,5.14466851791295,100,2015,11,8,0,0.084936272405892,0.0405942316694074,0.142976040218913,0.292105277323912,0.292105277323912,0.00470877747426161
+"22228",100,2015,11,9,0,11.5678107373916,34.6274477808651,10.5833993309533,25.3536192135449,25.3536192135449,5.151049203991,100,2015,11,9,0,0.016925169631396,0.0144164628870561,0.227350752970007,0.827949812959751,0.827949812959751,0.00453399968311023
+"22229",100,2015,11,10,0.905170518572968,12.0094389679408,15.6900219912052,11.7746975817958,13.4471947509463,13.4471947509463,5.15742989006906,100,2015,11,10,0.0892982457046625,0.0358052623749362,0.0454730972581045,0.0073005959115565,0.678473054950631,0.678473054950631,0.00436896955472692
+"22230",100,2015,11,11,0.844994506623485,6.6703739811485,19.6230694715208,13.0874147341721,27.3107478977955,27.3107478977955,5.16381057614712,100,2015,11,11,0.0606432750489983,0.197849694861045,0.0453099700975549,0.0112760419835244,0.49077322180705,0.49077322180705,0.00421368708911169
+"22231",100,2015,11,12,0.261936200301115,10.8454234508267,21.2408469801295,13.538580835718,28.2611332485254,28.2611332485254,5.17019126222517,100,2015,11,12,0.0109356729930256,0.0182204861491263,0.208154374092815,0.0635877205698663,0.583171874808337,0.583171874808337,0.00406815228626453
+"22232",100,2015,11,13,0,12.2573268017491,18.5296369265146,11.1714961888111,24.5490208406522,24.5490208406522,5.17657194830323,100,2015,11,13,0,0.0876561298192582,0.0248813184819786,0.0170368648507639,3.68719941830136,3.68719941830136,0.00393236514618542
+"22233",100,2015,11,14,0,5.76238721799273,18.6112542252074,11.8009681575763,21.1886690851092,21.1886690851092,5.18295263438128,100,2015,11,14,0,0.116476004152144,0.0408094512184118,0.0386701433974355,3.6939870710721,3.6939870710721,0.0038063256688744
+"22234",100,2015,11,15,0.0572057214244889,10.1535092312904,19.2717052833213,11.0941363304231,28.7375247900767,28.7375247900767,5.18933332045934,100,2015,11,15,0.00263157902579559,0.123491217897723,0.0715655192148777,0.0070064472980097,0.560191039619926,0.560191039619926,0.00369003385433142
+"22235",100,2015,11,16,0,6.29253024446427,25.5614849694885,12.4417051088692,30.3466996968252,30.3466996968252,5.1957140065374,100,2015,11,16,0,0.0596871500461619,0.168225681675408,0.135392975293074,0.0526953309134923,0.0526953309134923,0.00358348970255653
+"22236",100,2015,11,17,0,10.556028638867,30.093553308082,11.5373048457113,30.1492301345003,30.1492301345003,5.20209469261545,100,2015,11,17,0,0.0164508768517323,0.0758054149073732,0.135980153292446,0.0167561296119968,0.0167561296119968,0.00348669321354971
+"22237",100,2015,11,18,0,9.15611671850626,28.4400880244961,9.50202402738061,29.0261606755692,29.0261606755692,5.20847537869351,100,2015,11,18,0,0.0344374236236206,0.207211205970186,0.14753562311772,0.244665107754175,0.244665107754175,0.00339964438731095
+"22238",100,2015,11,19,0.976237640981496,14.768932926904,24.3074478636218,16.7467873618417,12.6653794298078,12.6653794298078,5.21485606477157,100,2015,11,19,0.0614035120832994,0.0311672466702545,0.251169445305834,0.0376818825707605,1.2544361091436,1.2544361091436,0.00332234322384026
+"22239",100,2015,11,20,3.06743672447498,12.2240153324224,19.3176678131909,13.8146094845729,11.9109240532971,11.9109240532971,5.22123675084962,100,2015,11,20,0.0395321646071358,0.0659262715814309,0.052787812221066,0.00612045287201462,2.31933966168418,2.31933966168418,0.00325478972313764
+"22240",100,2015,11,21,0.356655674995762,10.1440814441056,16.7446535234273,10.0020901299152,13.8051374842494,13.8051374842494,5.22761743692768,100,2015,11,21,0.0091812868843302,0.0683485836029911,0.0142941320054941,0.00952748410641133,0.904136479947796,0.904136479947796,0.0031969838852031
+"22241",100,2015,11,22,0,11.8570077054703,18.3478437807694,11.4593509415982,18.9621231931009,18.9621231931009,5.23399812300573,100,2015,11,22,0,0.0707005926612473,0.0205923866319369,0.0215263033415949,6.18724729879982,6.18724729879982,0.0031489257100366
+"22242",100,2015,11,23,0.959185925253941,11.1369637016154,20.0500548817012,11.9982618619375,21.9672936816158,21.9672936816158,5.24037880908379,100,2015,11,23,0.0244444455040831,0.0535140468174118,0.0310695154606181,0.0524374293902397,4.65932088870688,4.65932088870688,0.00311061519763819
+"22243",100,2015,11,24,0,8.27282732731701,23.3390100141301,11.0964907592673,24.7469635744168,24.7469635744168,5.24675949516185,100,2015,11,24,0,0.121638580427191,0.12422861185388,0.087125703990192,0.512369795276499,0.512369795276499,0.00308205234800784
+"22244",100,2015,11,25,0,9.84360832638211,31.4338615425874,7.09302540161166,9.06343232523097,9.06343232523097,5.2531401812399,100,2015,11,25,0,0.0758140759206773,0.0172263298083093,0.0854760373467013,0.55028082491787,0.55028082491787,0.00306323716114556
+"22245",100,2015,11,26,7.92838285045393,7.1783608697822,14.9930033447719,7.41731567456253,26.4159846751735,26.4159846751735,5.25952086731796,100,2015,11,26,0.217192959980696,0.0383152100669202,0.022653832414501,0.000764911323696056,0.932698580946321,0.932698580946321,0.00305416963705135
+"22246",100,2015,11,27,1.55896589556674,5.3585588210749,17.2617603801396,10.3744223482407,22.0945654072777,22.0945654072777,5.26590155339601,100,2015,11,27,0.0595321622926603,0.0985579006384825,0.013109902070969,0.0111608048155442,4.27032126509053,4.27032126509053,0.00305484977572521
+"22247",100,2015,11,28,0.86116611737885,9.96155130325502,19.0297689784085,11.5350165456304,17.972035257992,17.972035257992,5.27228223947407,100,2015,11,28,0.0469590640102913,0.214891251757061,0.083322355228242,0.0396029083096533,0.498920317698124,0.498920317698124,0.00306527757716713
+"22248",100,2015,11,29,2.24609458774361,10.8115401682418,19.4823099394443,10.8997359375487,25.4494498728132,25.4494498728132,5.27866292555213,100,2015,11,29,0.0838596471429596,0.117061896030889,0.0223818584645893,0.00538069363872169,5.89974410425858,5.89974410425858,0.00308545304137714
+"22249",100,2015,11,30,0.891419127435968,7.55874586315176,32.3723098662558,10.7399010317292,18.9026732470992,18.9026732470992,5.28504361163018,100,2015,11,30,0.00807017517368716,0.0766877012721478,0.0162262644013526,0.0216812809169489,5.1530211847898,5.1530211847898,0.0031153761683552
+"22250",100,2015,12,1,0.736083612321365,7.61213426988642,19.4030803034146,9.48565445743641,18.781869991122,18.781869991122,5.36439849620367,100,2015,12,1,0.0381286534928444,0.0294263318022951,0.0770151219198186,0.00713218265337934,0.866446518466567,0.866446518466567,0.00314673653474176
+"22251",100,2015,12,2,2.60374036401805,4.99424640468769,17.6901210558296,8.7031023027611,26.6104395901016,26.6104395901016,5.44375338077715,100,2015,12,2,0.138421053245057,0.152164900008998,0.0219467464784328,0.037999999576221,7.24234790856906,7.24234790856906,0.00318943607578171
+"22252",100,2015,12,3,0.108140815692832,10.6146535338348,20.6953130959153,12.8983388193632,25.8346644633412,25.8346644633412,5.52310826535063,100,2015,12,3,0.00175438601719706,0.150127418770236,0.195438533582352,0.0165672629173112,0.147987701135197,0.147987701135197,0.00324347479147502
+"22253",100,2015,12,4,0,8.67027508281376,28.5070737958348,11.0766994782669,31.2544885033166,31.2544885033166,5.60246314992412,100,2015,12,4,0,0.0639467395404018,0.305395429412916,0.088591825422815,0.233109841058165,0.233109841058165,0.00330885268182171
+"22254",100,2015,12,5,0,11.3357426734647,27.5886247213131,13.4293178174362,26.9206380455932,26.9206380455932,5.6818180344976,100,2015,12,5,0,0.0417117001986048,0.441080845031164,0.126920420226217,0.709843211128562,0.709843211128562,0.00338556974682176
+"22255",100,2015,12,6,0.00286028607122444,14.0477887416961,27.0739052041267,15.4112101797223,11.7720133075357,11.7720133075357,5.76117291907108,100,2015,12,6,0.000526315805159117,0.106720426017416,0.16317314338927,0.023421077393727,0.0858841665101502,0.0858841665101502,0.00347362598647518
+"22256",100,2015,12,7,0.137843786514435,15.5502200614501,29.559086995812,16.4279426138262,9.66425740417212,9.66425740417212,5.84052780364457,100,2015,12,7,0.00590643318598732,0.034356141774198,0.145391065171738,0.00962630303394307,0.697688937801859,0.697688937801859,0.00357302140078197
+"22257",100,2015,12,8,2.90891093389429,18.0456655961846,32.0784599337772,13.2762046652396,30.2772717680475,30.2772717680475,5.91988268821805,100,2015,12,8,0.4332163868313,0.0634537764569375,0.0650144223452398,0.157237386438102,0.0533982948752602,0.0533982948752602,0.00368375598974214
+"22258",100,2015,12,9,0.347084715880445,14.4116282783063,22.3874477201825,12.5264467432423,26.9254675530496,26.9254675530496,5.99923757279154,100,2015,12,9,0.0387719319298951,0.0500778300815883,0.0627274340003653,0.0266385910793238,1.22145044502148,1.22145044502148,0.00380582975335567
+"22259",100,2015,12,10,0.254675472899042,7.60354239082966,23.5770516516221,11.6451156121014,31.8822113053896,31.8822113053896,6.07859245736502,100,2015,12,10,0.0483040949937545,0.0389321570792628,0.206061005919509,0.103749694259868,0.375370769063926,0.375370769063926,0.00393924269162258
+"22260",100,2015,12,11,0.634543458423992,9.52348736384259,17.311870201455,9.62754690634011,20.4640702687212,20.4640702687212,6.1579473419385,100,2015,12,11,0.0302339150473391,0.0360917958645299,0.0124941533409498,0.0171719185433995,4.52068820440312,4.52068820440312,0.00408399480454285
+"22261",100,2015,12,12,0.218701875699957,7.752816262156,18.3752805607023,8.88730472149235,22.1124532613555,22.1124532613555,6.23730222651198,100,2015,12,12,0.0183040944613212,0.295404178320485,0.0209163153847106,0.0214204562873994,3.5978242191071,3.5978242191071,0.0042400860921165
+"22262",100,2015,12,13,0.00693069317258231,6.21790980558322,24.3352583810703,10.3808030589054,30.1850385592453,30.1850385592453,6.31665711108547,100,2015,12,13,0.00140350881375765,0.0274093741032305,0.213828491818106,0.155976147822439,0.153458589440391,0.153458589440391,0.00440751655434352
+"22263",100,2015,12,14,0.0771177129203206,5.41832782883849,24.2235755269939,11.6946754518515,31.9392628706459,31.9392628706459,6.39601199565895,100,2015,12,14,0.00543859665331088,0.0314257390942625,0.156144380273122,0.108776560550345,0.0628720417720062,0.0628720417720062,0.00458628619122389
+"22264",100,2015,12,15,0,12.0120682784564,26.6118041564136,13.4273267909639,32.0737515964655,32.0737515964655,6.47536688023244,100,2015,12,15,0,0.0792830640537123,0.259564620427732,0.0773912610707662,0.0724356269620649,0.0724356269620649,0.00477639500275764
+"22265",100,2015,12,16,0,12.1875468150224,27.3388448868385,14.697029825091,33.0126624028675,33.0126624028675,6.55472176480592,100,2015,12,16,0,0.0166666666667113,0.157387131455973,0.200549680341809,0.0173653809410555,0.0173653809410555,0.00497784298894478
+"22266",100,2015,12,17,0,14.8560725223638,29.2503959458522,13.5464576190323,32.3794168654842,32.3794168654842,6.6340766493794,100,2015,12,17,0,0.0410338778423923,0.581002700404454,0.279203495567807,0.183373067885489,0.183373067885489,0.00519063014978528
+"22267",100,2015,12,18,0.0464246431560275,13.0061167527084,30.5638724118307,16.3466447991769,28.2245984177123,28.2245984177123,6.71343153395289,100,2015,12,18,0.00245614042407588,0.0186953357540979,0.815069773298485,0.225099883779158,0.391593986770188,0.391593986770188,0.00541475648527913
+"22268",100,2015,12,19,0.611991199952672,13.9667876477551,42.6610560653233,13.6022222915498,20.6768098816489,20.6768098816489,6.79278641852637,100,2015,12,19,0.0349707592718784,0.0190035478715172,0.0277373400608374,0.417113959028077,1.61325427514701,1.61325427514701,0.00565022199542635
+"22269",100,2015,12,20,1.20638064307348,21.4264906724819,29.7341910447225,17.4724313905924,13.9824532067159,13.9824532067159,6.87214130309985,100,2015,12,20,0.0598245578620875,0.0116999421864777,0.27812448246867,0.00844679082712252,0.90666984971933,0.90666984971933,0.00589702668022699
+"22270",100,2015,12,21,2.22233220820117,13.2610121780496,19.6683828565809,9.93097902472132,23.7157645020941,23.7157645020941,6.95149618767334,100,2015,12,21,0.0281871457127845,0.0710871781333087,0.0573496126054704,0.00872045845351335,1.20882645971323,1.20882645971323,0.00615517053968096
+"22271",100,2015,12,22,1.0264026361658,8.52524762263786,22.5101651841133,10.4110452003605,33.9897690029165,33.9897690029165,7.03085107224682,100,2015,12,22,0.673333320617678,0.148021103141807,0.139313501962757,0.0378210539991531,0.0124251762359425,0.0124251762359425,0.00642465357378831
+"22272",100,2015,12,23,0,9.25949394532425,26.2194170873157,14.0206160277817,33.2658306705152,33.2658306705152,7.1102059568203,100,2015,12,23,0,0.0535035266564655,0.141943256842533,0.137476013315307,0.0487023812826935,0.0487023812826935,0.00670547578254902
+"22273",100,2015,12,24,0,12.5609901719885,34.7722553928824,11.8090208607538,33.2856437003258,33.2856437003258,7.18956084139379,100,2015,12,24,0,0.0123485321380206,0.0366543053175535,0.0781497156490039,0.00081640227137158,0.00081640227137158,0.00699763716596312
+"22274",100,2015,12,25,0,17.587282587855,38.2487568661194,10.7123542519161,27.1685917102071,27.1685917102071,7.26891572596727,100,2015,12,25,0,0.0471718583674438,0.00521345181080543,0.00668131566195711,0.872775950140956,0.872775950140956,0.00730113772403058
+"22275",100,2015,12,26,17.9870186680877,11.1058855319049,19.1289989127315,10.8498348397653,26.3774146089459,26.3774146089459,7.34827061054075,100,2015,12,26,1.93339170734811,0.0546227740795883,0.0116807283861578,0.0129578845220036,5.99805871257703,5.99805871257703,0.0076159774567514
+"22276",100,2015,12,27,0.12552255439614,8.7199009117907,20.0852035002084,10.3583278981241,29.6159186347483,29.6159186347483,7.42762549511424,100,2015,12,27,0.00619883074223647,0.203189383052865,0.0189983561380018,0.036384220908613,5.10534306949415,5.10534306949415,0.00794215636412561
+"22277",100,2015,12,28,0,7.8635973515946,22.6247414629845,10.5607483006678,33.6075353129338,33.6075353129338,7.50698037968772,100,2015,12,28,0,0.045028660660497,0.0701340172843268,0.0253485363988648,0.000514031626482914,0.000514031626482914,0.00827967444615319
+"22278",100,2015,12,29,1.19559956747707,7.49332233099523,26.1556326212532,11.083894298415,33.41929513157,33.41929513157,7.5863352642612,100,2015,12,29,0.992748528140332,0.0573386233179356,0.254128741125216,0.202592507575502,0.000370176546551336,0.000370176546551336,0.00862853170283413
+"22279",100,2015,12,30,0.0535753583340886,11.1173376066588,35.1451043148901,11.265280560954,31.119911926152,31.119911926152,7.66569014883469,100,2015,12,30,0.00257309949188902,0.0815251581248448,0.0305134827200982,0.183146747479872,0.350176072385337,0.350176072385337,0.00898872813416841
+"22280",100,2015,12,31,0,17.6731464524474,39.5778986990649,9.16699671456785,32.0839938026319,32.0839938026319,7.74504503340817,100,2015,12,31,0,0.0368246276368197,0.0178285781068595,0.365280751691297,0.0050041307874556,0.0050041307874556,0.0093602637401561
+"22281",100,2016,1,1,0,13.6106710119216,24.6274917904693,15.6930252899825,16.9276348340629,16.9276348340629,7.71012420011209,100,2016,1,1,0,0.0274175330581018,0.877856146280667,0.169213487402162,2.27812109309437,2.27812109309437,0.00924106947182845
+"22282",100,2016,1,2,0,14.0636083951222,20.7888557950262,15.7726072714274,11.0847524951393,11.0847524951393,7.67520336681601,100,2016,1,2,0,0.0993146660297742,0.248732125337217,0.00138773352202343,1.13816214654494,1.13816214654494,0.00912855591607545
+"22283",100,2016,1,3,0,14.4479757944743,25.3482290043427,15.5978878501749,27.7384929468136,27.7384929468136,7.64028253351993,100,2016,1,3,0,0.0207555551741551,0.0594731989947523,0.0845035526900994,0.48757067279054,0.48757067279054,0.00902272307289708
+"22284",100,2016,1,4,0,16.1670296399376,25.2441583786598,15.1027832356485,29.791771367307,29.791771367307,7.60536170022385,100,2016,1,4,0,0.0413842789100873,0.0528893874455351,0.0693397690979855,0.311300774611338,0.311300774611338,0.00892357094229339
+"22285",100,2016,1,5,0,14.6434984532389,24.8559074464804,14.8906380083695,26.560989996781,26.560989996781,7.57044086692777,100,2016,1,5,0,0.0280584934413787,0.25702745538482,0.0760005630605381,0.401224851472459,0.401224851472459,0.00883109952426432
+"22286",100,2016,1,6,0,14.8040373742384,24.0689550259195,16.2663148506509,23.6485478408528,23.6485478408528,7.53552003363168,100,2016,1,6,0,0.021238595188476,0.0741175755197033,0.0396813299059102,13.2733548976745,13.2733548976745,0.0087453088188099
+"22287",100,2016,1,7,0,11.9117161621748,21.9382396316109,12.1872166181424,28.1992408513236,28.1992408513236,7.5005992003356,100,2016,1,7,0,0.156717577754758,0.105242668932913,0.0587649572451334,0.672298199681892,0.672298199681892,0.00866619882593011
+"22288",100,2016,1,8,0,12.4203301105562,21.5963035894044,11.9703629801113,30.1821562889779,30.1821562889779,7.46567836703952,100,2016,1,8,0,0.105835680429621,0.167770768166945,0.0749035242650131,1.49028748116772,1.49028748116772,0.008593769545625
+"22289",100,2016,1,9,0,9.72605061190094,22.9493509445778,13.1812211677711,30.9104510979815,30.9104510979815,7.43075753374344,100,2016,1,9,0,0.150298253801227,0.487691184734609,0.0424555545807848,1.10685055044544,1.10685055044544,0.00852802097789452
+"22290",100,2016,1,10,0,9.44519252693168,29.9974916497997,11.8004951393119,25.4014630648169,25.4014630648169,7.39583670044736,100,2016,1,10,0,0.0852449386753687,0.977381340343862,0.398854356500374,0.459271808156179,0.459271808156179,0.00846895312273867
+"22291",100,2016,1,11,0,13.1662376412202,32.6553027968202,17.1294059102947,13.5729481840816,13.5729481840816,7.36091586715128,100,2016,1,11,0,0.0232034924000539,0.311832232221046,0.0873707721646135,1.19114677700473,1.19114677700473,0.00841656598015752
+"22292",100,2016,1,12,0.00352035208766085,12.3555554158092,28.8041035281812,14.8608470985992,31.5810451213807,31.5810451213807,7.3259950338552,100,2016,1,12,0.000994152076411666,0.035698866191726,0.130111712619972,0.199191721493518,0.0120315511472622,0.0120315511472622,0.00837085955015097
+"22293",100,2016,1,13,0.00363036309040026,15.481595056154,41.2229156620038,13.7194608304367,21.0263804172394,21.0263804172394,7.29107420055912,100,2016,1,13,0.000526315805159117,0.0554210283492025,0.0441843128327603,0.175347388393659,3.14150931695443,3.14150931695443,0.00833183383271909
+"22294",100,2016,1,14,0.357535761788209,14.4084928290154,19.7631131729277,9.47377348208454,15.588943786747,15.588943786747,7.25615336726303,100,2016,1,14,0.0113450299961525,0.0528812791858479,0.0562830945379111,0.0311894673443064,1.36554989505807,1.36554989505807,0.00829948882786183
+"22295",100,2016,1,15,0.100000001490116,10.9511110339359,20.4108249571982,9.51112205098302,20.8200439436339,20.8200439436339,7.22123253396695,100,2016,1,15,0,0.196800007163227,0.050903392636008,0.0451093894490808,5.42415894396184,5.42415894396184,0.00827382453557926
+"22296",100,2016,1,16,0,9.41238717224994,24.1857755973656,12.1838614093457,31.3311880676135,31.3311880676135,7.18631170067087,100,2016,1,16,0,0.0946701917949995,0.23922611685443,0.101932170653814,0.0461731140764923,0.0461731140764923,0.00825484095587129
+"22297",100,2016,1,17,0,11.4340483966571,31.1509133014742,12.6105501575701,31.6004842122396,31.6004842122396,7.15139086737479,100,2016,1,17,0,0.0219912700877156,0.407464584975714,0.18366206447316,0.0431983526446346,0.0431983526446346,0.00824253808873801
+"22298",100,2016,1,18,0,14.5199560589261,33.0114851973631,13.2044664137434,31.17426850641,31.17426850641,7.11647003407871,100,2016,1,18,0,0.117822807963479,0.634712180405517,0.403454505556851,0.0290666239424466,0.0290666239424466,0.00823691593417935
+"22299",100,2016,1,19,0,8.23218913020605,31.8898239933094,12.0492850210276,26.4503078628557,26.4503078628557,7.08154920078263,100,2016,1,19,0,0.0259467926115369,0.651567264981407,0.483931651395446,0.945795750780065,0.945795750780065,0.00823797449219535
+"22300",100,2016,1,20,0.151375140146454,14.8883827710965,27.2492850820879,17.8409682484743,23.7447963101898,23.7447963101898,7.04662836748655,100,2016,1,20,0.0139766089043074,0.0315257425565973,0.235367108435666,0.0189000386558371,3.45584538012522,3.45584538012522,0.00824571376278599
+"22301",100,2016,1,21,0,14.8980307489863,25.9145764706552,15.042090204027,24.9656216922504,24.9656216922504,7.01170753419047,100,2016,1,21,0,0.0557584596534241,0.326142486603756,0.0225830401638914,0.706383110158342,0.706383110158342,0.00826013374595126
+"22302",100,2016,1,22,3.43894392088039,14.4858085655405,23.5439055981022,19.8739822752798,13.5915181765331,13.5915181765331,6.97678670089438,100,2016,1,22,0.881520438528906,0.0449140290333619,0.0388702083791249,0.00940058814167751,3.25536501219461,3.25536501219461,0.00828123444169121
+"22303",100,2016,1,23,1.80033003313444,14.4982177127968,21.7387130347022,14.271650099256,16.6155224182162,16.6155224182162,6.9418658675983,100,2016,1,23,0.0887719308842016,0.0497321826020697,0.0363981188860401,0.0337918179585513,1.92303263315146,1.92303263315146,0.00830901585000577
+"22304",100,2016,1,24,1.75698569492407,13.8876127855744,22.2056215787747,16.6259844835573,19.3824201055092,19.3824201055092,6.90694503430222,100,2016,1,24,0.0677777769830504,0.0550450364319855,0.0409930747587035,0.0963666465761076,9.16438900219461,9.16438900219461,0.00834347797089501
+"22305",100,2016,1,25,0.43905391124445,14.3788118509307,22.8013091428314,12.6912210011246,27.5858416352728,27.5858416352728,6.87202420100614,100,2016,1,25,0.0539766070072415,0.0618655276494148,0.122044932230696,0.0537584624084824,0.351228590304233,0.351228590304233,0.00838462080435886
+"22306",100,2016,1,26,0,13.2972277430418,25.2804509615085,16.2740378133272,25.2549837341141,25.2549837341141,6.83710336771006,100,2016,1,26,0,0.0611397465913132,0.122658613051419,0.0746672796196212,0.475156157091115,0.475156157091115,0.00843244435039741
+"22307",100,2016,1,27,0.220022005749286,16.8998900309648,31.0284818391202,15.6022660598503,25.6198129811303,25.6198129811303,6.80218253441398,100,2016,1,27,0.0243274860136342,0.0113777666730443,0.169938749391751,0.0306397808120126,2.20800486823225,2.20800486823225,0.00848694860901055
+"22308",100,2016,1,28,0,15.7657096545951,27.3399776211142,14.1586798656367,19.6593286390483,19.6593286390483,6.7672617011179,100,2016,1,28,0,0.0164894659077065,0.0343690355225297,0.0129397639604087,1.62240555618951,1.62240555618951,0.00854813358019841
+"22309",100,2016,1,29,0.00165016504109103,10.0530363257044,22.1103409367426,11.1146753938547,24.799515859522,24.799515859522,6.73234086782182,100,2016,1,29,0.000994152076411666,0.0120427068811471,0.0321210186654662,0.0185146101679152,7.12509364724449,7.12509364724449,0.00861599926396087
+"22310",100,2016,1,30,0.271507153769072,7.84878989178749,23.1939164173223,14.1361606102703,23.612475383662,23.612475383662,6.69742003452573,100,2016,1,30,0.0303508774003788,0.0195801279101765,0.0270596632085019,0.0459871134351605,3.12094777921745,3.12094777921745,0.00869054566029796
+"22311",100,2016,1,31,14.6687569791334,14.0492846779566,24.033410360842,15.7341912860262,18.8893068321992,18.8893068321992,6.66249920122965,100,2016,1,31,13.2602927990966,0.0258427146354787,0.00618186421134633,0.128009318973151,3.46544701544811,3.46544701544811,0.00877177276920972
+"22312",100,2016,2,1,5.08250827954547,10.3080307474755,26.0454565306308,14.7627282336731,29.5074806989747,29.5074806989747,6.62206093443832,100,2016,2,1,14.3176612578219,0.0287731114594703,0.12020539134549,0.090002858970695,0.0113210247646211,0.0113210247646211,0.0088984613027077
+"22313",100,2016,2,2,0,14.4196040674929,29.4054234591779,15.0072387909338,14.0584819067692,14.0584819067692,6.58162266764698,100,2016,2,2,0,0.0996807280736467,0.0448287535375704,0.133522856738062,1.30054852653646,1.30054852653646,0.00903254547693715
+"22314",100,2016,2,3,7.2319032487565,14.6188450310752,21.3540813647481,13.321881226056,23.7418042970831,23.7418042970831,6.54118440085565,100,2016,2,3,0.578128755803726,0.0177584724025264,0.0798877975913876,0.0462251240357455,3.09434566270294,3.09434566270294,0.00917402529189813
+"22315",100,2016,2,4,0.153685371122017,13.5680637391094,21.2322662970414,12.5936414007306,23.6979208926294,23.6979208926294,6.50074613406432,100,2016,2,4,0.00619883069430876,0.143673038953425,0.152605105796331,0.0391473476834453,3.68965816718692,3.68965816718692,0.00932290074759059
+"22316",100,2016,2,5,0,13.2376897295709,25.5123981857719,15.0906159712536,28.7726732365238,28.7726732365238,6.46030786727298,100,2016,2,5,0,0.0783338937837885,0.229000678695112,0.108105217247734,0.0200093777888182,0.0200093777888182,0.00947917184401455
+"22317",100,2016,2,6,0,13.3757205014706,28.6999996991047,16.082475301325,29.017458876892,29.017458876892,6.41986960048165,100,2016,2,6,0,0.0400386182239116,0.471621097056339,0.242916398850568,0.00760525715727906,0.00760525715727906,0.00964283858117001
+"22318",100,2016,2,7,0.076127613895666,13.4939493687108,25.6072824601949,17.4943564869259,28.133179350917,28.133179350917,6.37943133369031,100,2016,2,7,0.00245614042407588,0.0406368116390647,0.674055948795306,0.193031417788921,0.270224438726712,0.270224438726712,0.00981390095905698
+"22319",100,2016,2,8,0,14.6724752703122,23.8766886225366,14.1314741875341,28.5314962640978,28.5314962640978,6.33899306689898,100,2016,2,8,0,0.0793689813692828,0.247676068258379,0.122249684433121,0.137948410975935,0.137948410975935,0.00999235897767542
+"22320",100,2016,2,9,0,13.5483388481098,25.2680308422764,13.1655445707382,27.5786360141599,27.5786360141599,6.29855480010764,100,2016,2,9,0,0.0858765847926449,0.341460853349524,0.0836917958689576,0.157042574761267,0.157042574761267,0.0101782126370254
+"22321",100,2016,2,10,0,9.82563255171571,24.2681517858054,13.5543124562967,27.3188668661254,27.3188668661254,6.25811653331631,100,2016,2,10,0,0.0423696304054435,0.463000728174576,0.109058512057537,0.0283578781264937,0.0283578781264937,0.0103714619371068
+"22322",100,2016,2,11,0,10.6311221652561,25.241781891507,15.4857866667023,26.9591746776149,26.9591746776149,6.21767826652498,100,2016,2,11,0,0.0351608425832387,0.181132282047789,0.140410063740331,0.250359020755427,0.250359020755427,0.0105721068779198
+"22323",100,2016,2,12,0,11.3172826515173,26.7647304262134,13.1673707584343,26.9057203257176,26.9057203257176,6.17723999973364,100,2016,2,12,0,0.0207596720735174,0.380004143502192,0.0845923925545475,0.116400631732059,0.116400631732059,0.0107801474594643
+"22324",100,2016,2,13,0,11.8260175476242,27.1156986161034,15.6585920583559,26.8545431382585,26.8545431382585,6.13680173294231,100,2016,2,13,0,0.0172941440181316,0.387783203691911,0.383753830065514,0.0187783182514147,0.0187783182514147,0.0109955836817402
+"22325",100,2016,2,14,0.100000001490116,12.0128273560007,23.7803741073189,11.9112981097533,21.7952144841025,21.7952144841025,6.09636346615097,100,2016,2,14,0,0.0296099590793363,0.0495509237816645,0.0983468008053077,0.680459268603709,0.680459268603709,0.0112184155447477
+"22326",100,2016,2,15,0.36952695834099,10.0747414789315,23.6431792713497,13.2404510363756,18.1920790866394,18.1920790866394,6.05592519935964,100,2016,2,15,0.0338596483977915,0.116277705934413,0.044262033380252,0.0973590484508142,2.97911998587301,2.97911998587301,0.0114486430484866
+"22327",100,2016,2,16,1.81155113261132,12.6855663817839,18.8742462510716,9.45092409488523,16.3811112803595,16.3811112803595,6.0154869325683,100,2016,2,16,0.026959061218285,0.0745812783693855,0.0396759339408227,0.0641035090597813,1.66874323554915,1.66874323554915,0.0116862661929571
+"22328",100,2016,2,17,0,12.385676575966,20.6732231198889,17.612189338021,13.2308800196884,13.2308800196884,5.97504866577697,100,2016,2,17,0,0.125382949383014,0.144920516173932,0.192548615858725,2.06546643159661,2.06546643159661,0.011931284978159
+"22329",100,2016,2,18,1.20088009773964,13.8042683585642,23.3749393782075,15.2744774246635,18.5342023669034,18.5342023669034,5.93461039898564,100,2016,2,18,0.0687134491072772,0.0424251340164013,0.110369577557582,0.0937362094433592,4.40266876094482,4.40266876094482,0.0121836994040924
+"22330",100,2016,2,19,1.22453245261584,14.3746644734549,17.3652804724061,13.25880073006,11.0381298894011,11.0381298894011,5.8941721321943,100,2016,2,19,0.0336842069988372,0.0485357043082746,0.0743163877031245,0.0106117026591951,0.253133306164278,0.253133306164278,0.0124435094707574
+"22331",100,2016,2,20,1.09471946951449,11.5354896121555,20.5046311201173,11.618822925555,17.099417018156,17.099417018156,5.85373386540297,100,2016,2,20,0.113567247944966,0.195687071705515,0.0587766563642123,0.0485719120807086,12.371398539018,12.371398539018,0.0127107151781538
+"22332",100,2016,2,21,0.00286028607122444,8.90815165701217,26.9783716736847,12.7783500043997,25.8067434854371,25.8067434854371,5.81329559861163,100,2016,2,21,0.000526315805159117,0.0505877401787692,0.132437420578791,0.142121089995612,0.0506257723537968,0.0506257723537968,0.0129853165262818
+"22333",100,2016,2,22,0.196699673348855,13.6536964850851,27.0103739810855,15.6501870338935,3.49984596245097,3.49984596245097,5.7728573318203,100,2016,2,22,0.0311695918510532,0.0118257242214342,0.154169459605477,0.33700291315723,0.026446790165932,0.026446790165932,0.0132673135151412
+"22334",100,2016,2,23,1.05819583345692,16.8227503433479,39.0726401535722,13.6248734928463,10.3979759321223,10.3979759321223,5.73241906502896,100,2016,2,23,0.00672514940563039,0.00844794452106662,0.19570936934404,0.306473019614517,0.324443287069799,0.324443287069799,0.0135567061447322
+"22335",100,2016,2,24,3.04103408486667,17.5607701250167,19.5936524818177,20.1302747736932,2.95749174827265,2.95749174827265,5.69198079823763,100,2016,2,24,0.459239775228225,0.00974847786921217,0.339424950105931,0.0947765617106996,0.0364619980220954,0.0364619980220954,0.0138534944150546
+"22336",100,2016,2,25,19.0701869622578,14.9347084190192,23.9566118557199,16.8848625283824,20.603399159229,20.603399159229,5.6515425314463,100,2016,2,25,1.69257302602152,0.0124251110389903,0.195364610089993,0.0919695868867767,2.71876102990968,2.71876102990968,0.0141576783261085
+"22337",100,2016,2,26,0.32948295667161,12.6054895650698,21.0633443553324,13.7910781384039,13.9918373221218,13.9918373221218,5.61110426465496,100,2016,2,26,0.00783625702934657,0.0250930053957293,0.0262497203048946,0.071847997263937,3.14842059111806,3.14842059111806,0.0144692578778939
+"22338",100,2016,2,27,0.348184825924232,14.504268473131,21.8998898421184,15.7799449349919,12.838019916589,12.838019916589,5.57066599786363,100,2016,2,27,0.00578947367375358,0.0503321487761916,0.186193030039711,0.102581249446845,1.04043334787163,1.04043334787163,0.0147882330704109
+"22339",100,2016,2,28,0,13.3467765845875,22.2618480006723,14.5648293941066,17.6962157733107,17.6962157733107,5.53022773107229,100,2016,2,28,0,0.087509374777,0.125414625589895,0.132336750255878,5.48855965680589,5.48855965680589,0.0151146039036593
+"22340",100,2016,2,29,0.354455452777109,13.8272716597755,24.1647747761608,14.2918043084139,21.2735642348186,21.2735642348186,5.48978946428096,100,2016,2,29,0.0135672519534652,0.111515199808559,0.151965498374538,0.0675092760075716,0.393458388220661,0.393458388220661,0.0154483703776392
+"22341",100,2016,3,1,0,12.8416281302508,30.8163037777472,13.4362376163764,20.2607037719458,20.2607037719458,5.44445653195852,100,2016,3,1,0,0.0417210554558562,0.0660584545918527,0.1697257429224,1.65851293942674,1.65851293942674,0.0149496489920017
+"22342",100,2016,3,2,0,13.3602530193014,27.4659296839413,17.8170736814358,21.5141361496761,21.5141361496761,5.39912359963608,100,2016,3,2,0,0.0265871652169334,0.771809293432698,0.26672749641854,0.379111777603771,0.379111777603771,0.014462589863949
+"22343",100,2016,3,3,0,13.1094499006785,24.9633993960843,14.7963036064005,23.0868318324829,23.0868318324829,5.35379066731364,100,2016,3,3,0,0.0499690240046396,0.14936088225629,0.127221043989495,0.00692749665049313,0.00692749665049313,0.0139871929934812
+"22344",100,2016,3,4,0,11.6554895333856,28.4470958415956,18.1739937873563,23.0158084831615,23.0158084831615,5.3084577349912,100,2016,3,4,0,0.0214584615808538,0.614895737912759,0.134322779922343,0.0385783543819056,0.0385783543819056,0.0135234583805983
+"22345",100,2016,3,5,0,17.0754894365703,23.3758305380709,17.3830473934463,11.6343894608081,11.6343894608081,5.26312480266876,100,2016,3,5,0,0.0571146069267034,0.0472100614227984,0.0337035412216933,1.69079188759349,1.69079188759349,0.0130713860253003
+"22346",100,2016,3,6,0,12.3390648771565,25.3876125956788,17.3120463154103,18.9216721097235,18.9216721097235,5.21779187034632,100,2016,3,6,0,0.0359397644846765,0.287725784382354,0.105787082975843,0.128611504529481,0.128611504529481,0.0126309759275872
+"22347",100,2016,3,7,0.487238730436111,15.9912981205385,25.3887679558514,17.6183388818084,15.7490648966287,15.7490648966287,5.17245893802388,100,2016,3,7,0.0322807009736001,0.0266695834713323,0.160781243008876,0.143920438719989,1.99410504083491,1.99410504083491,0.0122022280874589
+"22348",100,2016,3,8,0.09009901124357,15.6030253188969,33.5731688899176,18.7488227189571,19.6074696363527,19.6074696363527,5.12712600570143,100,2016,3,8,0.00274853809360872,0.019346796444105,0.783154025403847,0.263000044081336,0.506327505881714,0.506327505881714,0.0117851425049156
+"22349",100,2016,3,9,0.0183718374656765,17.8693288607959,21.519218893859,21.5563148439783,4.60727176152178,4.60727176152178,5.08179307337899,100,2016,3,9,0.00578947407460353,0.00993160515087888,0.108403396093643,0.0333209983070409,0.876453780076113,0.876453780076113,0.0113797191799571
+"22350",100,2016,3,10,2.0596259501901,15.877414730647,18.9807370105068,19.5298566681848,4.4224422250775,4.4224422250775,5.03646014105655,100,2016,3,10,0.0480116930063727,0.013002341485791,0.0375818784394152,0.0610607146863807,0.470447942169938,0.470447942169938,0.0109859581125835
+"22351",100,2016,3,11,2.88415842612322,15.9907589283976,25.6615070200334,20.7134982005204,14.6046753275906,14.6046753275906,4.99112720873411,100,2016,3,11,0.0891228050376979,0.0181952760017738,0.236542776293226,0.090705278153673,2.66803298793384,2.66803298793384,0.0106038593027948
+"22352",100,2016,3,12,0.184048407583019,16.6761937681729,21.5152695931748,19.6912101728819,11.1113860630753,11.1113860630753,4.94579427641167,100,2016,3,12,0.0020467836867299,0.00771342771677899,0.0501672960383171,0.0538537694476431,3.66375528242329,3.66375528242329,0.010233422750591
+"22353",100,2016,3,13,0.0970297044161523,15.508162855184,22.5826621606405,17.8061828151657,13.7562046837885,13.7562046837885,4.90046134408923,100,2016,3,13,0.000994152076411666,0.0536262917792673,0.166359669646636,0.0334701201324622,0.266658467557237,0.266658467557237,0.00987464845597207
+"22354",100,2016,3,14,0.272937301148807,15.1042463052915,20.3765345427594,13.3137294320252,15.2747194837816,15.2747194837816,4.85512841176679,100,2016,3,14,0.00561403543802732,0.0247028913041153,0.0447034394295004,0.0716040872451906,0.220165518315729,0.220165518315729,0.00952753641893803
+"22355",100,2016,3,15,0.451155121275301,13.9534213797356,23.4238616515308,16.954345581424,13.0296589743317,13.0296589743317,4.80979547944435,100,2016,3,15,0.0122807011618252,0.0576204689450499,0.0569321091951418,0.128253937513108,3.83713435154973,3.83713435154973,0.00919208663948887
+"22356",100,2016,3,16,0,15.1132452915473,29.9336416015793,16.5810889138116,20.1263695476603,20.1263695476603,4.76446254712191,100,2016,3,16,0,0.0253870937771651,0.103727532647003,0.201102829796215,0.00928889448399712,0.00928889448399712,0.00886829911762465
+"22357",100,2016,3,17,0,14.5200989841759,35.2457427432959,13.5268095633378,17.4602968973426,17.4602968973426,4.71912961479947,100,2016,3,17,0,0.0450929423193522,0.00455794387840127,0.0396941538292594,1.31298778228109,1.31298778228109,0.00855617385334525
+"22358",100,2016,3,18,14.8455444803857,13.5457536090981,17.5781075421995,12.5984708284519,11.106358591086,11.106358591086,4.67379668247703,100,2016,3,18,0.262514554185533,0.0382654962573905,0.00828713294607451,0.0559204470629663,2.30990971437206,2.30990971437206,0.00825571084665077
+"22359",100,2016,3,19,2.52002202546505,11.4844333913067,17.6028714384576,10.8558306804191,9.45003299587237,9.45003299587237,4.62846375015459,100,2016,3,19,0.194678362893784,0.130828544275462,0.00686493831010905,0.0771175503893536,1.4890262514193,1.4890262514193,0.00796691009754116
+"22360",100,2016,3,20,0,12.2222772971763,19.8628162723957,12.6908580310012,13.4162486467687,13.4162486467687,4.58313081783215,100,2016,3,20,0,0.0209286722173075,0.0222578928006596,0.189153830250625,1.21953163887389,1.21953163887389,0.00768977160601644
+"22361",100,2016,3,21,1.23311331645228,9.11646862785415,21.382398114346,13.2178328727076,15.4648734954062,15.4648734954062,4.53779788550971,100,2016,3,21,0.503157892112147,0.0639929789114235,0.0318496942311479,0.114181873546931,0.443037475929614,0.443037475929614,0.00742429537207658
+"22362",100,2016,3,22,0,9.428690850538,24.8887459804254,13.3573158262062,21.5181078559363,21.5181078559363,4.49246495318727,100,2016,3,22,0,0.102354371937594,0.0509486222567218,0.158937340391813,0.00186491834445842,0.00186491834445842,0.00717048139572167
+"22363",100,2016,3,23,0,12.0825632575846,25.763663590938,14.7451594313904,10.5398019343713,10.5398019343713,4.44713202086483,100,2016,3,23,0,0.0359608108855839,0.0899450123349107,0.343422820315564,0.159811120478406,0.159811120478406,0.00692832967695158
+"22364",100,2016,3,24,0,14.8960617357092,20.6593287880271,14.3596809143805,12.4859186336152,12.4859186336152,4.40179908854239,100,2016,3,24,0,0.0116064323894577,0.0651396977477896,0.0356467637090555,0.77860402871969,0.77860402871969,0.00669784021576643
+"22365",100,2016,3,25,0,11.4277117900198,21.0354783715981,12.271364046271,13.9988228763291,13.9988228763291,4.35646615621995,100,2016,3,25,0,0.0196175447007489,0.0207620280932149,0.121467296932827,0.947738643552594,0.947738643552594,0.00647901301216614
+"22366",100,2016,3,26,1.18173815695235,13.279131064714,19.0958304767168,15.3505830282163,9.63749186093002,9.63749186093002,4.3111332238975,100,2016,3,26,0.0346783611230695,0.0516146454973543,0.00669301321949743,0.0684760424720475,1.90267589313896,1.90267589313896,0.00627184806615074
+"22367",100,2016,3,27,0.95038504116606,12.013245357014,18.1483721696373,10.3003412127101,8.68039601024884,8.68039601024884,4.26580029157506,100,2016,3,27,0.0302923986856006,0.127080199017543,0.00779884330821441,0.018298852867042,1.84740638845014,1.84740638845014,0.00607634537772022
+"22368",100,2016,3,28,0,11.5251155293027,17.8564906524222,10.8326842766522,6.64742570663049,6.64742570663049,4.22046735925262,100,2016,3,28,0,0.0654654560335155,0.0085876978157624,0.150835685170124,0.0778251346197939,0.0778251346197939,0.00589250494687462
+"22369",100,2016,3,29,0.487128716490843,11.8812211480471,17.9815072457258,10.277623815505,7.92432341402513,7.92432341402513,4.17513442693018,100,2016,3,29,0.00888888849152467,0.0200467789076122,0.00799294858364176,0.0166432920757934,0.457242684553985,0.457242684553985,0.00572032677361388
+"22370",100,2016,3,30,0,12.7901980921511,19.861650481607,9.60591867969374,11.2955664927417,11.2955664927417,4.12980149460774,100,2016,3,30,0,0.0242555532668174,0.0129111066184421,0.0906175529436036,0.228533954024796,0.228533954024796,0.00555981085793804
+"22371",100,2016,3,31,0,10.1512871680349,22.8980857651882,14.5683828370668,12.6417380890044,12.6417380890044,4.0844685622853,100,2016,3,31,0,0.0567110972510898,0.0148063720214701,0.0830152294338245,1.0533842688489,1.0533842688489,0.00541095719984707
+"22372",100,2016,4,1,0,11.5275026904737,26.8411331323638,9.88071513516937,8.68924083751683,8.68924083751683,4.0487673296312,100,2016,4,1,0,0.0316690092455746,0.0224322167427547,0.050160844869855,0.0982760671136898,0.0982760671136898,0.00498108494200865
+"22373",100,2016,4,2,1.23509349097775,13.0063255507298,19.3074808215151,9.53223315743592,13.8660065637301,13.8660065637301,4.0130660969771,100,2016,4,2,0.0349707557165147,0.0403765966493878,0.0243052070397388,0.0620251221752108,0.344899944559819,0.344899944559819,0.0045730670813955
+"22374",100,2016,4,3,0.0418041810409726,7.24746974309286,19.1771836884082,10.0155114833802,14.9604290793307,14.9604290793307,3.977364864323,100,2016,4,3,0.00245614042407588,0.0729508944026487,0.0202287328086997,0.0091830348255028,0.507905300950253,0.507905300950253,0.00418690361800756
+"22375",100,2016,4,4,0.216611665746298,5.08393838145945,22.804983353064,11.4043454945546,16.0830034260178,16.0830034260178,3.9416636316689,100,2016,4,4,0.00286549748820173,0.0440719261850055,0.0304432606795746,0.0672315949914636,0.00695965935589242,0.00695965935589242,0.00382259455184489
+"22376",100,2016,4,5,0,10.1513310947565,28.1217929600882,12.813234325027,11.3148514733981,11.3148514733981,3.9059623990148,100,2016,4,5,0,0.0190923817685578,0.0448783882367711,0.422368910563138,0.115526263309561,0.115526263309561,0.00348013988290746
+"22377",100,2016,4,6,14.7103409961243,12.8815400582073,16.6468206286037,11.538591909461,9.03260728733243,9.03260728733243,3.8702611663607,100,2016,4,6,0.367777722676649,0.0568953268621097,0.0537397735331351,0.00175964914074269,0.602295231651888,0.602295231651888,0.00315953961119526
+"22378",100,2016,4,7,2.15808578424066,7.47471938746991,17.03820717479,12.0039495042186,8.43213421598126,8.43213421598126,3.8345599337066,100,2016,4,7,0.116549701132973,0.0909578869981466,0.0243818814196218,0.00794677150802884,0.270880676678954,0.270880676678954,0.00286079373670831
+"22379",100,2016,4,8,0.0298129817423779,11.3790869413823,19.2629154066835,12.9894500560362,8.88940590679055,8.88940590679055,3.7988587010525,100,2016,4,8,0.00257309949188902,0.149264873109845,0.0803209928192472,0.113132224414287,0.790616519184074,0.790616519184074,0.0025839022594466
+"22380",100,2016,4,9,0.160506053078752,10.6481957619209,20.1444444708829,14.6849944625619,9.05556653015422,9.05556653015422,3.7631574683984,100,2016,4,9,0.00467836293909286,0.0549497482992039,0.0250672824259863,0.129888909233994,0.569469657519905,0.569469657519905,0.00232886517941013
+"22381",100,2016,4,10,0.10110011151751,12.2607810510398,21.0310342597752,13.8813861452445,10.2613201288238,10.2613201288238,3.7274562357443,100,2016,4,10,0.000994152076411666,0.0973151726840429,0.030271895871824,0.111660832165345,1.13671505029696,1.13671505029696,0.0020956824965989
+"22382",100,2016,4,11,3.2256325377096,13.5895930008002,18.2545874252571,15.7303960084653,10.6095159717388,10.6095159717388,3.6917550030902,100,2016,4,11,0.0592982492112328,0.0789117061526481,0.0210093033241485,0.130671863040786,0.420691854684493,0.420691854684493,0.00188435421101291
+"22383",100,2016,4,12,1.00682068520253,12.5305282741752,17.781771201374,13.6836853436511,9.38613872087435,9.38613872087435,3.6560537704361,100,2016,4,12,0.0354385981573704,0.0937274477083993,0.0206877136367189,0.070792962874123,1.43689501332074,1.43689501332074,0.00169488032265217
+"22384",100,2016,4,13,0.672717272071424,10.9350603867416,18.3396260919351,13.0426512488438,10.5203300073202,10.5203300073202,3.620352537782,100,2016,4,13,0.0733918106172519,0.106348009901517,0.0253584119521489,0.0736982586376255,0.491891046332291,0.491891046332291,0.00152726083151667
+"22385",100,2016,4,14,0.0278327836930686,9.45018700745502,20.8364026727456,13.9891749519457,11.850088077541,11.850088077541,3.5846513051279,100,2016,4,14,0.0020467836867299,0.0742211020799419,0.0492286072807878,0.0635801107998373,0.0753841658185662,0.0753841658185662,0.0013814957376064
+"22386",100,2016,4,15,0.0797579769860662,7.09366334468225,23.4539933409234,13.3956765708881,13.780869128287,13.780869128287,3.5489500724738,100,2016,4,15,0.00228070182235617,0.0292140465418714,0.0679152137580738,0.213221126239681,0.073825097828078,0.073825097828078,0.00125758504092138
+"22387",100,2016,4,16,0.603300337268837,12.6436194303406,17.2271615900222,12.2436304564523,8.41321238695067,8.41321238695067,3.5132488398197,100,2016,4,16,0.0331578939996269,0.0455228154456531,0.101236875411639,0.00180292915441822,0.302733885753963,0.302733885753963,0.00115552874146161
+"22388",100,2016,4,17,0,12.2121782093027,18.1431681063309,12.0584708361736,13.0574037291692,13.0574037291692,3.4775476071656,100,2016,4,17,0,0.121687223111052,0.0183151769675182,0.0166830620415216,0.0615619359959539,0.0615619359959539,0.00107532683922707
+"22389",100,2016,4,18,0.117491750925681,6.19577560068095,22.2921122203697,12.3660176574069,11.8787458679988,11.8787458679988,3.4418463745115,100,2016,4,18,0.0020467836867299,0.0563286699763711,0.0170316017866907,0.0770427105062322,0.11739416861737,0.11739416861737,0.00101697933421777
+"22390",100,2016,4,19,0,7.82523649885054,26.3706488908321,10.7168427730682,13.5711989838167,13.5711989838167,3.40614514185739,100,2016,4,19,0,0.0211222264396083,0.0221544558076666,0.0814982237387361,0.134021104692219,0.134021104692219,0.000980486226433723
+"22391",100,2016,4,20,0,10.5075028538048,30.4400879867268,10.3230913653232,13.4918811166509,13.4918811166509,3.37044390920329,100,2016,4,20,0,0.0470029375288951,0.0587701650968627,0.0119432474728307,0.0505151415396349,0.0505151415396349,0.00096584751587491
+"22392",100,2016,4,21,3.10286028261888,15.8195599166736,17.7115513934816,17.0482950409909,3.41222222462477,3.41222222462477,3.33474267654919,100,2016,4,21,0.0306432716191205,0.00806551699506546,0.0207759944449272,0.0395579683050496,0.0764368588007034,0.0764368588007034,0.000973063202541345
+"22393",100,2016,4,22,1.86963695727035,11.6671617873038,16.0627723658177,9.31972500095011,7.50454351164983,7.50454351164983,3.29904144389509,100,2016,4,22,0.121520472032986,0.168511555889307,0.0470063733465336,0.0108766083277627,0.0787783891912289,0.0787783891912289,0.00100213328643302
+"22394",100,2016,4,23,0.0194719474848741,11.219031839612,18.3040812254214,14.2687679192152,9.45869084946787,9.45869084946787,3.26334021124099,100,2016,4,23,0.00175438601719706,0.112342649841381,0.0153596779675875,0.080435678069404,0.971503957741798,0.971503957741798,0.00105305776754993
+"22395",100,2016,4,24,0,7.82255227390033,19.8021012233822,12.2686688976057,11.855984672068,11.855984672068,3.22763897858689,100,2016,4,24,0,0.107747933502674,0.0345930526111675,0.0549877669775589,0.0365941940129802,0.0365941940129802,0.00112583664589209
+"22396",100,2016,4,25,0.0930693083175338,7.10592960445794,26.4552146336689,9.50669972888707,13.0366997629634,13.0366997629634,3.19193774593279,100,2016,4,25,0.00140350881375765,0.0228397606933509,0.0265760950906308,0.0512672834832179,0.00040409338216492,0.00040409338216492,0.00122046992145949
+"22397",100,2016,4,26,0,11.3070077319088,27.5691200168219,9.15966994429317,12.6685039448922,12.6685039448922,3.15623651327869,100,2016,4,26,0,0.0411093633931361,0.0430024089123834,0.0524654920946881,0.0333766074911863,0.0333766074911863,0.00133695759425212
+"22398",100,2016,4,27,0,14.1235863199853,26.8517599452053,9.58971393383769,6.18744769557999,6.18744769557999,3.12053528062459,100,2016,4,27,0,0.0404841463591739,0.0511801326108086,0.0154730854904976,0.557462015508924,0.557462015508924,0.00147529966427001
+"22399",100,2016,4,28,0.250715077669248,15.2201430149729,20.4465784780001,15.4469857021789,6.56486252248615,6.56486252248615,3.08483404797049,100,2016,4,28,0.0126315793761036,0.00689299621812182,0.0172134301484907,0.0130485348462523,0.226988923772197,0.226988923772197,0.00163549613151313
+"22400",100,2016,4,29,2.48151819671866,13.7388558466442,18.2374918342817,17.1656105410804,2.34821782804558,2.34821782804558,3.04913281531639,100,2016,4,29,0.0759064428708744,0.020753810858096,0.0223315985653701,0.0300445079382482,0.0683923948921013,0.0683923948921013,0.00181754699598149
+"22401",100,2016,4,30,8.95566555060963,13.168723748462,23.9762816749128,11.9680966837834,12.2730253993875,12.2730253993875,3.01343158266229,100,2016,4,30,0.420526252099708,0.00732221090536726,0.00956082047164193,0.00430526230806134,0.00341170033019626,0.00341170033019626,0.0020214522576751
+"22402",100,2016,5,1,12.3353135310384,9.87173821981197,14.6704621425163,10.5773156819695,5.35894399848577,5.35894399848577,2.9701372074683,100,2016,5,1,11.7578363279711,0.0411204234252123,0.158287229540276,0.00285380492857114,0.792333957540512,0.792333957540512,0.00199439533731686
+"22403",100,2016,5,2,11.6360835861189,7.25788777889592,18.0782617247931,10.811078084184,10.6981957837431,10.6981957837431,2.92684283227431,100,2016,5,2,12.1257889002527,0.173238588919854,0.0573653871862959,0.0835152149000361,0.0988029884718408,0.0988029884718408,0.00196997310347715
+"22404",100,2016,5,3,2.0291529158161,12.1144334858138,16.1467656008612,11.7156324365614,3.81958195583524,3.81958195583524,2.88354845708032,100,2016,5,3,0.450994164337194,0.149838659987027,0.017706413923002,0.00140233695276027,0.310200003327271,0.310200003327271,0.00194818555615597
+"22405",100,2016,5,4,9.56721679028636,6.94090207470263,16.7835974614612,10.5540923705064,10.2329923831197,10.2329923831197,2.84025408188633,100,2016,5,4,0.799239679414723,0.106020456715652,0.0529035578055678,0.0266099373623212,0.0649432660666792,0.0649432660666792,0.00192903269535333
+"22406",100,2016,5,5,0,10.2414410510341,21.0968758086822,12.7673266760193,10.4138613712407,10.4138613712407,2.79695970669234,100,2016,5,5,0,0.103854323707342,0.0307539136432411,0.0792140002803487,0.083706440904487,0.083706440904487,0.00191251452106921
+"22407",100,2016,5,6,0,11.7557425881901,19.6059403288351,14.0635423209145,7.9186469572212,7.9186469572212,2.75366533149835,100,2016,5,6,0,0.0703006077014169,0.0279999995765292,0.0162690120162695,0.425292473564419,0.425292473564419,0.00189863103330363
+"22408",100,2016,5,7,0.190319034821129,9.79485139227805,21.2504071948027,13.8388338802409,11.0067876840975,11.0067876840975,2.71037095630436,100,2016,5,7,0.00274853826789132,0.023702292717894,0.201961872547278,0.204248599816534,0.0198812989620388,0.0198812989620388,0.00188738223205657
+"22409",100,2016,5,8,0.342574264456665,13.2002310464353,17.4349945979019,16.8030693402516,1.99113312753776,1.99113312753776,2.66707658111037,100,2016,5,8,0.0120467835882603,0.0397374013287813,0.0231999960160465,0.0256584755437583,0.028869587926423,0.028869587926423,0.00187876811732805
+"22410",100,2016,5,9,21.6312432703536,14.045588606655,18.6394278691022,13.1345764722499,5.08733772156131,5.08733772156131,2.62378220591638,100,2016,5,9,3.73719220211659,0.0474099108512597,0.0566385861718134,0.006394142862577,0.127784219604636,0.127784219604636,0.00187278868911807
+"22411",100,2016,5,10,16.6913090832818,12.1507260340406,15.6486579131241,10.8593948907716,8.84375135864493,8.84375135864493,2.58048783072239,100,2016,5,10,0.356198742180929,0.0981368701422554,0.0909356575682244,0.0362432429799661,0.882892409154042,0.882892409154042,0.00186944394742661
+"22412",100,2016,5,11,8.72321238061383,9.84605055726138,15.4588668212639,13.565709588289,6.31572053246241,6.31572053246241,2.5371934555284,100,2016,5,11,0.96467826569994,0.13136961853749,0.0615275289793258,0.0185222404056811,0.256787094592505,0.256787094592505,0.00186873389225369
+"22413",100,2016,5,12,5.57315731835444,11.1026292649826,17.6159954889379,16.3759626550118,5.08613868698691,5.08613868698691,2.49389908033441,100,2016,5,12,0.275906421817553,0.0957141437888211,0.0913211650829546,0.101535623240566,0.175914636710113,0.175914636710113,0.00187065852359931
+"22414",100,2016,5,13,1.48954894610889,13.420187054163,19.3594387671342,13.9732783331205,9.91574258458103,9.91574258458103,2.45060470514042,100,2016,5,13,0.0798245637737536,0.0696590814579916,0.062525052651974,0.0477696172056897,0.0649841995976347,0.0649841995976347,0.00187521784146344
+"22415",100,2016,5,14,0,9.51927394468268,21.2481961098179,11.8695709933542,10.6417932206123,10.6417932206123,2.40731032994643,100,2016,5,14,0,0.0241251579062658,0.0394426086651458,0.117654361410393,0.000355552758185041,0.000355552758185041,0.00188241184584611
+"22416",100,2016,5,15,0,11.5514302112088,18.2730363398889,12.0830143759615,8.09938403036204,8.09938403036204,2.36401595475244,100,2016,5,15,0,0.106114066174235,0.0477656008049069,0.0373608760878942,0.562698820632853,0.562698820632853,0.00189224053674732
+"22417",100,2016,5,16,0.237843790848722,4.91005492446446,19.8135861353775,13.3911771449057,9.68416943356018,9.68416943356018,2.32072157955845,100,2016,5,16,0.005906433164202,0.0413263117539436,0.0629690086902591,0.0624871155655801,0.0759362341920268,0.0759362341920268,0.00190470391416705
+"22418",100,2016,5,17,5.79317929589971,10.8388007796637,14.9618701179429,11.6469968159993,4.46817382648833,4.46817382648833,2.27742720436445,100,2016,5,17,0.198187126248913,0.0719940910228626,0.0631450187795521,0.00687776836827136,0.352703511479048,0.352703511479048,0.00191980197810532
+"22419",100,2016,5,18,1.21100109087752,9.01753565659224,17.4640373276143,12.5105059553425,9.13979098610621,9.13979098610621,2.23413282917046,100,2016,5,18,0.190994133182438,0.0296315971620742,0.0704250624855445,0.0853782816257346,0.256211793368679,0.256211793368679,0.00193753472856212
+"22420",100,2016,5,19,1.3650164731396,12.0226072210683,17.1540485797542,15.977062885255,4.65441141217717,4.65441141217717,2.19083845397647,100,2016,5,19,0.00842105427680723,0.082524580987937,0.0879941685328405,0.00829123634887589,1.35999286583963,1.35999286583963,0.00195790216553746
+"22421",100,2016,5,20,5.80869086583455,9.26792092003313,16.2317491296363,12.106864748746,9.0265785695696,9.0265785695696,2.14754407878248,100,2016,5,20,0.231637404480892,0.0908818374277632,0.0616339054757574,0.0328953299740746,0.0743467322523291,0.0743467322523291,0.00198090428903132
+"22422",100,2016,5,21,0.0179317934465225,6.64727171917822,18.7231684245161,14.236963652935,9.47045105702282,9.47045105702282,2.10424970358849,100,2016,5,21,0.00140350881375765,0.0353040861553764,0.0904538129085182,0.0392485183917591,0.0194339391742336,0.0194339391742336,0.00200654109904372
+"22423",100,2016,5,22,0,9.98377341405787,19.7475907458986,13.1943783314183,6.74222221626307,6.74222221626307,2.0609553283945,100,2016,5,22,0,0.0551321666138371,0.0156696011060285,0.0459479915909671,0.0472923883248609,0.0472923883248609,0.00203481259557465
+"22424",100,2016,5,23,11.9701869280556,9.15537956812725,14.0708470937311,11.7733774520908,5.53444449421596,5.53444449421596,2.01766095320051,100,2016,5,23,0.642105165559716,0.099047921793132,0.0547450302280877,0.073158456581594,0.446920487039154,0.446920487039154,0.00206571877862411
+"22425",100,2016,5,24,4.72156214189477,7.88577560282121,15.941485067143,12.1963806603477,8.84399344399162,8.84399344399162,1.97436657800652,100,2016,5,24,0.508947363345961,0.0866479827546829,0.0985719297599889,0.0891706849640196,0.0530064684796253,0.0530064684796253,0.00209925964819211
+"22426",100,2016,5,25,0,8.87958198857911,13.4482068910588,11.9848295448899,3.26262925245581,3.26262925245581,1.93107220281253,100,2016,5,25,0,0.111022213914744,0.0161163594519869,0.0324760310502942,0.0765479401555031,0.0765479401555031,0.00213543520427863
+"22427",100,2016,5,26,16.2663366393288,10.1864907901541,13.8927282557891,10.571177068192,7.17895492390044,7.17895492390044,1.88777782761854,100,2016,5,26,0.625789503131103,0.0925999558343637,0.0821924184364652,0.00845907030616324,0.646665545375463,0.646665545375463,0.00217424544688369
+"22428",100,2016,5,27,4.73575360127146,5.09556661633113,14.4517710909198,10.2459625772911,7.73475252073852,7.73475252073852,1.84448345242455,100,2016,5,27,0.141462022993316,0.0456385950780211,0.137402847979438,0.0204841817176096,0.237783044857483,0.237783044857483,0.00221569037600728
+"22429",100,2016,5,28,0.621672172521469,6.34391642665968,13.2466995797404,9.97665569884549,6.59585255548374,6.59585255548374,1.80118907723056,100,2016,5,28,0.0261988281227694,0.123569588129166,0.0647245932998018,0.0122719385136773,0.151602391031067,0.151602391031067,0.00225976999164939
+"22430",100,2016,5,29,0.233773384353902,8.02215632668423,12.1711110250391,8.75464254532448,5.47157311308371,5.47157311308371,1.75789470203657,100,2016,5,29,0.00619883111694405,0.0988801148286741,0.0680496980768426,0.00422048831300429,0.0203029153957905,0.0203029153957905,0.00230648429381005
+"22431",100,2016,5,30,0.0925192533120333,3.07047309051908,13.3973816870594,8.53209019441678,9.39095713780134,9.39095713780134,1.71460032684258,100,2016,5,30,0.00766081909861482,0.0413807132726567,0.0685022977283142,0.0200783360442978,0.00438303226901713,0.00438303226901713,0.00235583328248923
+"22432",100,2016,5,31,0,2.36542351857008,14.7617052285978,9.91113301498531,9.38565442218508,9.38565442218508,1.67130595164859,100,2016,5,31,0,0.0516286626977579,0.0355930018487436,0.0129426993923054,0.000225727775130359,0.000225727775130359,0.00240781695768695
+"22433",100,2016,6,1,0.065456546629944,3.30964797715543,15.847480789663,11.6317382661423,9.33561062471832,9.33561062471832,1.63100010311975,100,2016,6,1,0.00263157902579559,0.0539538074314981,0.0561374347420209,0.0136608417355549,0.000246787393665706,0.000246787393665706,0.00267056212559533
+"22434",100,2016,6,2,0.0545654573587432,3.94917495730687,14.4299450875378,12.4317932317753,9.10324535747566,9.10324535747566,1.59069425459092,100,2016,6,2,0.00263157902579559,0.0741315896664571,0.0513543885683503,0.0140596527568922,0.0252707470855424,0.0252707470855424,0.00296526151794741
+"22435",100,2016,6,3,0.0661166126463804,5.23299233853096,14.4425522077202,12.6987789068023,9.14711751297887,9.14711751297887,1.55038840606208,100,2016,6,3,0.00257309949188902,0.0977812893594458,0.0610174967448835,0.0102719321781393,0.0335671977551707,0.0335671977551707,0.00329191513474319
+"22436",100,2016,6,4,0,8.8447524344567,14.6667106374524,14.562486136576,4.90540149371878,4.90540149371878,1.51008255753325,100,2016,6,4,0,0.0332924179278776,0.055559028572022,0.00525792026193642,0.344543307569877,0.344543307569877,0.00365052297598268
+"22437",100,2016,6,5,0.317051711640801,10.6564906438192,15.7311330308484,12.0373817670463,5.70995593831615,5.70995593831615,1.46977670900441,100,2016,6,5,0.0200000000993411,0.0271356793164531,0.234365515881976,0.00450409328623316,0.0554619946173227,0.0554619946173227,0.00404108504166586
+"22438",100,2016,6,6,0.548514856953826,7.09018698462559,13.7651595429356,12.3461055367431,5.95792075640822,5.95792075640822,1.42947086047557,100,2016,6,6,0.106491225274683,0.140716329206519,0.0559812817223113,0.0348034993869599,0.530167227217486,0.530167227217486,0.00446360133179276
+"22439",100,2016,6,7,14.5422443519987,7.69117712371289,12.8930583026412,14.1292299211878,4.68286026166742,4.68286026166742,1.38916501194674,100,2016,6,7,4.25093551401519,0.0832952939084083,0.0500444750892786,0.0115368392297787,0.142580689958404,0.142580689958404,0.00491807184636335
+"22440",100,2016,6,8,6.08943892364586,9.39989007083234,12.2509901258681,12.6980528794762,3.05976893642161,3.05976893642161,1.3488591634179,100,2016,6,8,0.0421052441959689,0.0960321824370155,0.049036264836801,0.0275473621123792,0.216636842719857,0.216636842719857,0.00540449658537764
+"22441",100,2016,6,9,10.9930691797741,9.60405927286683,14.2209350839831,14.1869857408295,6.18023104295217,6.18023104295217,1.30855331488906,100,2016,6,9,0.786900624280888,0.108153797859154,0.0999654680576231,0.013198281815814,0.384089468728463,0.384089468728463,0.00592287554883564
+"22442",100,2016,6,10,3.5807480649455,7.29469744626707,14.5928052665115,12.4222330475273,6.24567659302513,6.24567659302513,1.26824746636023,100,2016,6,10,0.148947331668347,0.12119119930716,0.0693204399645201,0.0248257309919572,0.190500603220776,0.190500603220776,0.00647320873673734
+"22443",100,2016,6,11,9.63476350047801,5.85093506465782,11.9806051201815,8.45970308662641,8.0460725822071,8.0460725822071,1.22794161783139,100,2016,6,11,5.28818693618334,0.104568992587994,0.106627566573006,0.0141666878595029,0.201795327498942,0.201795327498942,0.00705549614908274
+"22444",100,2016,6,12,0.30451045915632,5.61319034196625,12.5874475840032,9.68739274888411,6.74161718334958,6.74161718334958,1.18763576930256,100,2016,6,12,0.0131578953032605,0.148042678203941,0.110000553033004,0.0355163705792976,0.0465111119376443,0.0465111119376443,0.00766973778587184
+"22445",100,2016,6,13,0,2.46682069718641,12.295291466288,9.45005498283898,8.94938391506082,8.94938391506082,1.14732992077372,100,2016,6,13,0,0.0630052473731955,0.0680134186393907,0.00568070581009619,0.000428658754274251,0.000428658754274251,0.00831593364710464
+"22446",100,2016,6,14,3.70759076424295,2.46843782404993,14.2682618412903,10.9594831109965,8.90268428517122,8.90268428517122,1.10702407224488,100,2016,6,14,5.73368443586674,0.0132982502401962,0.0832257398996533,0.0251449856295842,0.00357075827532381,0.00357075827532381,0.00899408373278115
+"22447",100,2016,6,15,0,2.97796477710191,15.3611771895154,11.8549396191755,8.92192499498592,8.92192499498592,1.06671822371605,100,2016,6,15,0,0.0551163600207813,0.120967385362433,0.021873114999755,0.000298825461496238,0.000298825461496238,0.00970418804290136
+"22448",100,2016,6,16,0.0863586371504303,4.29343226352016,12.2590540360303,11.6845655367844,3.76836083483512,3.76836083483512,1.02641237518721,100,2016,6,16,0.00175438601719706,0.0282894591002627,0.0665035093899371,0.0441315748315773,0.0936163824014568,0.0936163824014568,0.0104462465774653
+"22449",100,2016,6,17,1.73234321995012,8.38667760246789,13.1028602639965,13.9594718588985,3.52762377721117,3.52762377721117,0.986106526658377,100,2016,6,17,0.17052630731237,0.170016345599709,0.0958152241490108,0.000742685630370278,0.0430263099335844,0.0430263099335844,0.0112202593364729
+"22450",100,2016,6,18,1.63729370991127,9.04952696788691,12.2741474292197,9.30545666508942,6.31183713707331,6.31183713707331,0.94580067812954,100,2016,6,18,0.290643258645522,0.153237455303444,0.0859830631814486,0.00153977153850224,0.227376642581894,0.227376642581894,0.0120262263199242
+"22451",100,2016,6,19,0.397909798580493,3.6657976120612,13.9674697273766,12.4034763248053,7.84055008641695,7.84055008641695,0.905494829600704,100,2016,6,19,0.0273099427352175,0.0633707473213992,0.148473219975322,0.0159982648750118,0.101426317614327,0.101426317614327,0.0128641475278192
+"22452",100,2016,6,20,1.10792078774492,6.01547855381394,14.4054785855401,11.7549285112303,6.50015401892667,6.50015401892667,0.865188981071868,100,2016,6,20,0.423391793554988,0.0532982674593388,0.0794672282459591,0.0350041411595732,0.159606407426557,0.159606407426557,0.0137340229601579
+"22453",100,2016,6,21,0.359295937979575,6.43915294735345,13.2640262295311,12.4747744886526,5.41943896223347,5.41943896223347,0.824883132543032,100,2016,6,21,0.00263157863365977,0.0418801252242646,0.155391816858743,0.045111138449846,0.717666802512298,0.717666802512298,0.0146358526169404
+"22454",100,2016,6,22,5.64851482630563,7.84012095290836,13.4554123799793,11.6246205404384,6.90368537252361,6.90368537252361,0.784577284014196,100,2016,6,22,0.970350862079214,0.0873783435643269,0.0493812371460985,0.025947363992951,0.544458484329559,0.544458484329559,0.0155696364981665
+"22455",100,2016,6,23,4.87953794435306,8.96152925019217,11.9487788365094,10.2670297307937,2.75941694189351,2.75941694189351,0.74427143548536,100,2016,6,23,0.0711111450195499,0.109536905395852,0.0168508893621516,0.000838599464972163,0.0785000036557554,0.0785000036557554,0.0165353746038363
+"22456",100,2016,6,24,13.8435643440557,3.36904290957813,9.21881196679849,7.45350925311266,4.8559075296253,4.8559075296253,0.703965586956524,100,2016,6,24,1.96731042940027,0.0857877124000061,0.174369626144541,0.00192514877321043,1.15312047292699,1.15312047292699,0.0175330669339498
+"22457",100,2016,6,25,4.56028600966576,2.61209021674262,12.6216940748678,9.6079537608836,8.14399345112581,8.14399345112581,0.663659738427688,100,2016,6,25,0.429883003179112,0.207273103333641,0.109114056213656,0.006105264563409,0.145702364527894,0.145702364527894,0.0185627134885071
+"22458",100,2016,6,26,0.100000001490116,1.0210230983261,11.7940264062913,8.4597250190374,8.97264020015435,8.97264020015435,0.623353889898852,100,2016,6,26,0,0.0391321642825485,0.129304101810182,0.0106450150986996,0.000827484443245382,0.000827484443245382,0.019624314267508
+"22459",100,2016,6,27,2.3562156087888,4.00371836383219,13.0552913751802,9.90774460174594,8.16699675076341,8.16699675076341,0.583048041370016,100,2016,6,27,1.20777773910099,0.091387734902266,0.157094094094694,0.0040151930603496,0.0763175297185469,0.0763175297185469,0.0207178692709526
+"22460",100,2016,6,28,0.164906493745687,5.67734872862057,14.8660507621807,13.2057206811685,7.63519251123645,7.63519251123645,0.54274219284118,100,2016,6,28,0.00654970825502749,0.133488891241309,0.106392990897622,0.0305245333822834,0.573905253785165,0.573905253785165,0.021843378498841
+"22461",100,2016,6,29,1.06281628427726,6.67488453438049,14.1132562803094,9.68284924300459,8.58971388872438,8.58971388872438,0.502436344312344,100,2016,6,29,0.0705263185361679,0.0763578773398331,0.0649918771287101,0.0143649030630582,0.0181386261701208,0.0181386261701208,0.023000841951173
+"22462",100,2016,6,30,2.08888888123012,7.84866887849025,11.0921452021835,9.67705158300788,3.65817384043149,3.65817384043149,0.462130495783507,100,2016,6,30,0.215087721891575,0.116478347818505,0.112889500770842,0.00250234224427669,0.162702365174773,0.162702365174773,0.0241902596279488
+"22463",100,2016,7,1,11.6558856082828,7.10267330238921,12.8084488555019,11.9461056569753,5.61870183881753,5.61870183881753,0.477914568966629,100,2016,7,1,0.685965023263921,0.116876001297818,0.103292402925647,0.020291206154703,0.383476604902277,0.383476604902277,0.0241759993774434
+"22464",100,2016,7,2,3.1242024290024,8.15430134698765,14.7070406451084,11.7666227730981,5.91962596790494,5.91962596790494,0.49369864214975,100,2016,7,2,0.33654975960827,0.101428614405633,0.0937368628073065,0.00308773644215305,0.100526300703591,0.100526300703591,0.0241654987420602
+"22465",100,2016,7,3,0.561496155818041,8.59067106089576,13.5171727147957,13.200968215699,5.89888892682603,5.89888892682603,0.509482715332871,100,2016,7,3,0.0335672513643901,0.0743005963911919,0.0381116967475028,0.0302386116519296,0.226759084591126,0.226759084591126,0.0241587577217992
+"22466",100,2016,7,4,3.37205718190494,8.24647964502719,11.7053025385203,11.1490978822194,3.58610561316294,3.58610561316294,0.525266788515992,100,2016,7,4,0.0906432786183094,0.0532134247273009,0.0649474017049194,0.0193146302809504,0.10518422793785,0.10518422793785,0.0241557763166602
+"22467",100,2016,7,5,6.95918589194353,6.61671066808753,12.112464109532,11.8193180485003,5.14319027847189,5.14319027847189,0.541050861699113,100,2016,7,5,0.0935672168843427,0.111037446505034,0.118627507824417,0.00180057837731947,0.0702619797455831,0.0702619797455831,0.0241565545266434
+"22468",100,2016,7,6,2.87348735450518,7.70734879364668,13.7390098959962,12.2047745072015,6.99854785669493,6.99854785669493,0.556834934882234,100,2016,7,6,0.0739766136247565,0.124931588809842,0.07030988382046,0.00803333337579295,0.326864952726558,0.326864952726558,0.0241610923517487
+"22469",100,2016,7,7,0.373597367958661,8.59844885197672,12.584213500238,12.9131573634048,5.49206829018588,5.49206829018588,0.572619008065355,100,2016,7,7,0.0247953218535374,0.11676719655941,0.12031170967288,0.00998011426434044,0.0608807007717297,0.0608807007717297,0.0241693897919761
+"22470",100,2016,7,8,5.47051704782333,8.66660059980302,12.8717821517793,12.0738503641815,4.83067103187637,4.83067103187637,0.588403081248476,100,2016,7,8,0.281169556009856,0.0521876794046178,0.0482257286897714,0.022169562300836,0.0572257201680174,0.0572257201680174,0.0241814468473257
+"22471",100,2016,7,9,0.12684268615853,4.07625964398694,14.7561166729733,11.897942787481,8.81976903783213,8.81976903783213,0.604187154431597,100,2016,7,9,0.00362573110220725,0.0806485240032913,0.0971210400252831,0.0215701381728714,0.0517666447534081,0.0517666447534081,0.0241972635177974
+"22472",100,2016,7,10,0,6.24598462300988,11.189504980123,11.5527614356399,3.21976897084411,3.21976897084411,0.619971227614718,100,2016,7,10,0,0.0873257225148349,0.0481590898123996,0.00527661320877236,0.0508052621518015,0.0508052621518015,0.0242168398033912
+"22473",100,2016,7,11,5.68932893908325,8.13283833523657,14.4799779413557,11.8320461741113,4.05341033258847,4.05341033258847,0.635755300797839,100,2016,7,11,0.06877193668434,0.0853000455220976,0.085146782425643,0.0575496378224665,1.53402282548705,1.53402282548705,0.0242401757041072
+"22474",100,2016,7,12,6.10110009909988,7.58801980957602,9.01880086766611,8.14800874942994,5.27639164101041,5.27639164101041,0.65153937398096,100,2016,7,12,0.033391803551854,0.0223982559583533,0.042692926596573,0.009738582071282,0.606703427541758,0.606703427541758,0.0242672712199452
+"22475",100,2016,7,13,20.722992226629,2.81688668232153,11.1331903942347,9.55171609213381,7.8361055706725,7.8361055706725,0.667323447164082,100,2016,7,13,1.0933925711207,0.144620473398247,0.0977707408392713,0.0105929895234277,0.441781880494769,0.441781880494769,0.0242981263509054
+"22476",100,2016,7,14,3.88492850187195,5.48295934601585,12.8131793340047,11.571452083105,8.1817821548848,8.1817821548848,0.683107520347203,100,2016,7,14,0.225614029594346,0.124698258905256,0.0932672609296628,0.0318941540389625,0.183633406935854,0.183633406935854,0.0243327410969878
+"22477",100,2016,7,15,0.965456547910231,7.41132013291547,13.2937622993562,11.4429151767945,8.15672159667062,8.15672159667062,0.698891593530324,100,2016,7,15,0.0492982443382871,0.0913930229097912,0.100476586894043,0.00214854350629431,0.132162006934829,0.132162006934829,0.0243711154581922
+"22478",100,2016,7,16,0,6.05017598994625,12.2794831289579,9.22873488091531,6.32488453217728,6.32488453217728,0.714675666713445,100,2016,7,16,0,0.241536852084842,0.0474818907599145,0.00939532407355408,0.0220619990666923,0.0220619990666923,0.0244132494345188
+"22479",100,2016,7,17,0.147744778334701,2.03184818880524,13.4263367049634,10.3514741964728,8.85536842954697,8.85536842954697,0.730459739896566,100,2016,7,17,0.0122807026780837,0.018826320175263,0.0576005984346187,0.0139988665062963,0.0233812629231999,0.0233812629231999,0.0244591430259675
+"22480",100,2016,7,18,0,5.75896591505464,16.5190979482317,12.0912540790403,6.2392849806774,6.2392849806774,0.746243813079687,100,2016,7,18,0,0.00703392785726996,0.0160812830655599,0.0225023399019134,0.448117466694593,0.448117466694593,0.0245087962325384
+"22481",100,2016,7,19,0,10.2085259241371,13.5084819552397,13.3377007735182,5.35574258986873,5.35574258986873,0.762027886262808,100,2016,7,19,0,0.0633619676423568,0.061646864921274,0.00865089379256896,0.0444561273898482,0.0444561273898482,0.0245622090542313
+"22482",100,2016,7,20,0.309240931765933,8.34697455593986,15.082959240157,12.5889327434292,8.60124308310195,8.60124308310195,0.777811959445929,100,2016,7,20,0.0111111111111111,0.0865444526249049,0.0508924097340821,0.0227830247801558,0.11170528204627,0.11170528204627,0.0246193814910464
+"22483",100,2016,7,21,4.31364147786391,5.55137510163294,17.1475136843976,14.6651375322583,7.14498350460275,7.14498350460275,0.79359603262905,100,2016,7,21,0.0178362548002904,0.0709163905350236,0.0583274710585974,0.018691792466986,0.155036306988027,0.155036306988027,0.0246803135429836
+"22484",100,2016,7,22,14.0488447518763,9.66064905517041,14.8808800012234,11.5565017641443,6.55033006814971,6.55033006814971,0.809380105812171,100,2016,7,22,0.431520561642163,0.0693777916802694,0.0358315872326678,0.00274735961163289,0.386714031623274,0.386714031623274,0.024745005210043
+"22485",100,2016,7,23,15.9503851965053,5.52653468796129,9.8491420326191,8.71081424625006,7.4992189753567,7.4992189753567,0.825164178995292,100,2016,7,23,3.34953269166578,0.146491800929533,0.0966941733265937,0.0152888968150737,0.370924629938727,0.370924629938727,0.0248134564922245
+"22486",100,2016,7,24,5.04378438468027,3.07647966473016,10.5666996816335,9.60926301513437,6.86594057607703,6.86594057607703,0.840948252178413,100,2016,7,24,0.594736835691684,0.060302933786073,0.0676028366112155,0.0150093497360577,0.10519124056053,0.10519124056053,0.0248856673895281
+"22487",100,2016,7,25,6.26666663162517,5.82503849130259,12.9691198221003,10.9514191924411,7.09366342599111,7.09366342599111,0.856732325361535,100,2016,7,25,0.660233908647704,0.0780333190706262,0.0661672580474303,0.0443251211624519,0.0905508353183311,0.0905508353183311,0.0249616379019538
+"22488",100,2016,7,26,15.7580859810606,4.18698576233699,9.92920798751781,9.04074803384879,6.51706272645621,6.51706272645621,0.872516398544656,100,2016,7,26,2.59473709084158,0.112403517098466,0.108262009019535,0.0194146329623856,0.074677767795999,0.074677767795999,0.0250413680295016
+"22489",100,2016,7,27,2.40693070838685,5.62559955217133,11.4127831946899,8.86330034551841,7.18949387700382,7.18949387700382,0.888300471727777,100,2016,7,27,0.0242690027805848,0.0581110908720411,0.0934578874443775,0.00170526107663322,0.450928636301073,0.450928636301073,0.0251248577721716
+"22490",100,2016,7,28,2.25775577513167,7.94396044213911,14.8873486440174,12.5263476555366,7.24011000600716,7.24011000600716,0.904084544910898,100,2016,7,28,0.203859651869504,0.0970830134275136,0.111400644280148,0.0298701661495694,0.239498196628655,0.239498196628655,0.0252121071299637
+"22491",100,2016,7,29,1.78976896218341,9.5917382444879,14.8242463425572,12.7047636149609,8.04112216112244,8.04112216112244,0.919868618094019,100,2016,7,29,0.125789467647064,0.0794257461436847,0.0761590888866112,0.0175426881087918,0.715122823988521,0.715122823988521,0.025303116102878
+"22492",100,2016,7,30,2.25632560935089,6.66968095577983,13.7143233795502,11.6030801473015,7.5297249619848,7.5297249619848,0.93565269127714,100,2016,7,30,0.340935689524605,0.0807257200174316,0.0693227775239909,0.009436837853396,0.122569015448299,0.122569015448299,0.0253978846909143
+"22493",100,2016,7,31,1.2410340964204,7.14754677238506,14.8827171976155,10.3426292784537,10.7308030574366,10.7308030574366,0.951436764460261,100,2016,7,31,0.0343274808348282,0.0702356693602724,0.107473187639634,0.0106783757238345,0.0190245857953382,0.0190245857953382,0.0254964128940728
+"22494",100,2016,8,1,1.08943895639891,7.4274477470826,13.4964575972101,11.0447194442497,5.09597360445197,5.09597360445197,0.978506283464056,100,2016,8,1,0.690233918839729,0.0378151943452397,0.0162023378361801,0.00233743306659685,0.150264989268454,0.150264989268454,0.0244151366970412
+"22495",100,2016,8,2,2.49053909492702,5.9963146189783,13.0378217655178,10.9542793173208,9.75660064223051,9.75660064223051,1.00557580246785,100,2016,8,2,0.0446783502757264,0.193746750936479,0.0664930190661292,0.00112105542909605,0.243371973921206,0.243371973921206,0.0233580185653943
+"22496",100,2016,8,3,1.19504951591408,6.73627063910691,12.898811762089,10.678767843215,8.61794282107463,8.61794282107463,1.03264532147164,100,2016,8,3,0.0281286503278731,0.133591834485479,0.0556139910849309,0.000992976100642621,0.638325162415838,0.638325162415838,0.0223250584991322
+"22497",100,2016,8,4,1.21650166060403,5.50232125272845,13.4662266994598,9.4578107974448,9.22896586576573,9.22896586576573,1.05971484047544,100,2016,8,4,0.0692982444986276,0.0961321800990611,0.0922374921788078,0.00436725311510334,0.58185783584108,0.58185783584108,0.0213162564982549
+"22498",100,2016,8,5,0.137403742421513,1.80323432407232,11.4034543378387,8.48645761697599,6.91025306053288,6.91025306053288,1.08678435947923,100,2016,8,5,0.00426900597517951,0.0941432567660606,0.0663696188720949,0.00272749584941298,0.0787362171117528,0.0787362171117528,0.0203316125627624
+"22499",100,2016,8,6,0,3.59356435585861,14.1894388933255,10.2548953844245,9.09815182072101,9.09815182072101,1.11385387848303,100,2016,8,6,0,0.0258877303067765,0.0689560955226668,0.0152251479166275,0.0549701765273222,0.0549701765273222,0.0193711266926546
+"22500",100,2016,8,7,0,5.78309129330978,14.6982506729982,8.86357537197201,8.67981289295998,8.67981289295998,1.14092339748682,100,2016,8,7,0,0.0159842109914551,0.0191672760835597,0.0231538345003083,0.182033906803941,0.182033906803941,0.0184347988879316
+"22501",100,2016,8,8,0,5.63225524784839,15.7253904500023,9.47990097280907,8.66115506280242,8.66115506280242,1.16799291649062,100,2016,8,8,0,0.0925362729401846,0.147014078372381,0.0180485202344036,0.707516304469019,0.707516304469019,0.0175226291485934
+"22502",100,2016,8,9,0.212101216117541,8.30568746581461,14.5403300433269,11.1686799004264,5.03542352221062,5.03542352221062,1.19506243549441,100,2016,8,9,0.00941520556894662,0.0591561552896179,0.0293894779718873,0.0206918087854463,0.67623629864812,0.67623629864812,0.01663461747464
+"22503",100,2016,8,10,4.96809683328677,6.17449942044299,12.3252476019697,10.2930913847534,8.29882290497078,8.29882290497078,1.22213195449821,100,2016,8,10,0.113157869640172,0.0707116757822519,0.0447830277136947,0.0168888503181648,0.428732206427194,0.428732206427194,0.0157707638660713
+"22504",100,2016,8,11,7.19647965572848,4.57078100483541,13.4203410500085,9.73497237991316,9.57607260893936,9.57607260893936,1.249201473502,100,2016,8,11,0.374795395087108,0.121736219476418,0.0635257228472926,0.00700232482159392,0.294692451173741,0.294692451173741,0.0149310683228875
+"22505",100,2016,8,12,1.54719470436424,6.59370734956529,14.775808652242,10.649218951646,8.93489547872176,8.93489547872176,1.2762709925058,100,2016,8,12,0.362105258542893,0.0950117178855853,0.0627619603654037,0.00111345112457175,0.295433863538399,0.295433863538399,0.0141155308450884
+"22506",100,2016,8,13,4.58107818786067,5.78233227032115,14.5887897489357,10.2936084766199,8.41497245322753,8.41497245322753,1.30334051150959,100,2016,8,13,0.280233927107707,0.11313389335657,0.0682918515657766,0.00140408578625826,0.538870675887924,0.538870675887924,0.0133241514326741
+"22507",100,2016,8,14,3.074917498726,6.27419146190513,15.1963806509054,12.1678217897321,9.17024211023245,9.17024211023245,1.33041003051338,100,2016,8,14,0.263976592264685,0.0427508813155482,0.037422792218358,0.0147649217639985,0.301243353963102,0.301243353963102,0.0125569300856445
+"22508",100,2016,8,15,1.1942794338049,5.59655669918417,16.892915496994,10.8511111749412,13.212618206725,13.212618206725,1.35747954951718,100,2016,8,15,0.270292392000121,0.0459655145031791,0.0455065005478166,0.00230935759078442,0.000228655287615202,0.000228655287615202,0.0118138668039998
+"22509",100,2016,8,16,0.0797579769860662,7.87904298423541,17.4622992210262,10.9977668073967,11.7351265918828,11.7351265918828,1.38454906852097,100,2016,8,16,0.00228070182235617,0.0206702038023812,0.0519092835100692,0.0010894868595408,0.158131567257931,0.158131567257931,0.0110949615877398
+"22510",100,2016,8,17,2.64862490749464,9.70106711141085,15.3899561595602,9.1868426193892,12.8222332357442,12.8222332357442,1.41161858752477,100,2016,8,17,0.192573083186015,0.071550830957155,0.0159619888952992,0.00233625857040272,0.0704134594031492,0.0704134594031492,0.0104002144368646
+"22511",100,2016,8,18,0.0338833888437357,6.40744783382605,21.4671834246947,11.9148403331391,12.8853905743892,12.8853905743892,1.43868810652856,100,2016,8,18,0.00257309949188902,0.0928427094264575,0.11013166606105,0.00397778290648629,0.0351590661434758,0.0351590661434758,0.00972962535137416
+"22512",100,2016,8,19,19.0623762153819,8.90625960703599,12.3649505147315,9.83237620837355,6.82792081822394,6.82792081822394,1.46575762553236,100,2016,8,19,1.74941525598745,0.0729730953618951,0.123739799075747,0.000309362911272,0.0927321594517268,0.0927321594517268,0.00908319433126852
+"22513",100,2016,8,20,3.86017593561095,4.77397145217795,12.364400459726,9.35064918425742,10.5855775398783,10.5855775398783,1.49282714453615,100,2016,8,20,0.417602283299333,0.0766099448075988,0.0910989050258298,0.00603334778688751,0.964967288890705,0.964967288890705,0.00846092137654765
+"22514",100,2016,8,21,8.3300330767406,6.94273925116091,11.8607151306371,11.6064135789609,7.59717267732022,7.59717267732022,1.51989666353995,100,2016,8,21,0.511403528570465,0.0984386056877957,0.0207099358163408,0.00656724328613915,0.10677196224952,0.10677196224952,0.00786280648721157
+"22515",100,2016,8,22,9.76226628600436,4.36487346540059,13.0303850583117,11.0296589491522,8.66642468728379,8.66642468728379,1.54696618254374,100,2016,8,22,1.69029233029021,0.0790596369180288,0.0484339121925352,0.00935378385851468,0.243073130892123,0.243073130892123,0.00728884966326025
+"22516",100,2016,8,23,0.69306931230757,2.97078106584329,12.8170847048198,7.63156206668145,11.2611550613336,11.2611550613336,1.57403570154753,100,2016,8,23,0.0109941496556269,0.047091228724367,0.0366830428565238,0.000795319813744898,0.28575916008762,0.28575916008762,0.00673905090469372
+"22517",100,2016,8,24,0.118261827953053,2.66126510517301,11.5553246729969,9.16487341273342,8.9925413797922,8.9925413797922,1.60110522055133,100,2016,8,24,0.00339181310600705,0.12159881916158,0.100202930979205,0.0129561600044422,0.314554478808243,0.314554478808243,0.00621341021151197
+"22518",100,2016,8,25,5.20286028613352,4.58684264551295,12.8673708205438,9.293025383986,12.9844994508263,12.9844994508263,1.62817473955512,100,2016,8,25,1.05140352951854,0.194137452387684,0.047567228440066,0.00338187629976032,0.264859635280671,0.264859635280671,0.00571192758371499
+"22519",100,2016,8,26,1.63828383787762,3.99129820106053,13.1290978776871,9.64874580014001,9.16971395721268,9.16971395721268,1.65524425855892,100,2016,8,26,0.0356140283673839,0.107971973986943,0.085266612116548,0.00755380485945182,0.250491260372474,0.250491260372474,0.00523460302130279
+"22520",100,2016,8,27,0.0840484060929029,5.31150715946495,13.8592189373356,8.71550044866547,11.3040593601558,11.3040593601558,1.68231377756271,100,2016,8,27,0.0020467836867299,0.143184207464533,0.0635543683660834,0.00407076058420191,0.247925719139645,0.247925719139645,0.00478143652427537
+"22521",100,2016,8,28,0,7.65735964622959,16.7112761981154,10.8645985627463,13.697854899206,13.697854899206,1.70938329656651,100,2016,8,28,0,0.0884707517177902,0.0695589738725654,0.0103649286505204,0.158244385274336,0.158244385274336,0.00435242809263273
+"22522",100,2016,8,29,0,6.49517049359279,18.6994388819528,10.2063696602128,13.7035533539926,13.7035533539926,1.7364528155703,100,2016,8,29,0,0.0236777765910069,0.0304286499427179,0.00776958045297606,0.247573029144554,0.247573029144554,0.00394757772637487
+"22523",100,2016,8,30,1.39053905534797,10.1017381156107,15.0151924273886,13.1359846820139,5.03320126853498,5.03320126853498,1.7635223345741,100,2016,8,30,0.0492982461856828,0.0839175368058804,0.0284321982713472,0.00431169055479442,0.302136316562698,0.302136316562698,0.00356688542550178
+"22524",100,2016,8,31,0.696919697149359,9.43921890300755,16.0936303563637,12.5533663652124,10.5434542782892,10.5434542782892,1.79059185357789,100,2016,8,31,0.00988304076836144,0.0269467791597023,0.0462022676771585,0.0115924034632547,0.864794182890151,0.864794182890151,0.00321035119001347
+"22525",100,2016,9,1,1.64081408370315,8.94554444009846,14.1710450505004,11.414796445236,9.96733776151282,9.96733776151282,1.81706081923746,100,2016,9,1,0.178011691242632,0.0576256583365207,0.0432877234008033,0.000458480386649235,0.972915192565077,0.972915192565077,0.00323516585331619
+"22526",100,2016,9,2,0.473927397178476,4.78554453131127,14.2381187938359,10.3834543070777,13.789625935822,13.789625935822,1.84352978489703,100,2016,9,2,0.0248538000193257,0.0815871447211989,0.0526315736492586,0.001249706696686,0.176380089397407,0.176380089397407,0.00326159215242337
+"22527",100,2016,9,3,0.76545655129045,3.20167217684789,14.8037513086636,10.9118591115551,10.7395599541491,10.7395599541491,1.8699987505566,100,2016,9,3,0.113625729047416,0.0767812894402937,0.0800579229299723,0.00701755640807411,0.79251112221618,0.79251112221618,0.00328963008733501
+"22528",100,2016,9,4,1.83751373912635,7.01559953668592,14.7502529954228,11.6421671322863,9.92127611770882,9.92127611770882,1.89646771621617,100,2016,9,4,0.0659649021862574,0.0594812970758361,0.070133916172317,0.000402926080345136,0.319387762377608,0.319387762377608,0.0033192796580511
+"22529",100,2016,9,5,2.59713972309897,9.6650386218584,15.8785258955163,12.487458758884,10.3634872730285,10.3634872730285,1.92293668187574,100,2016,9,5,0.6424561138181,0.0779263834903961,0.0519579557520695,0.00948420180612252,0.132081348305478,0.132081348305478,0.00335054086457165
+"22530",100,2016,9,6,0.0594059414792769,5.23744781890718,17.999064713028,13.1532013308753,15.5366556851646,15.5366556851646,1.94940564753531,100,2016,9,6,0.00263157902579559,0.0259730822780876,0.0212561800061003,0.00123976249031659,0.422442726585971,0.422442726585971,0.00338341370689667
+"22531",100,2016,9,7,0.0299229927451173,6.90173816575994,19.0283497137861,12.8599559313918,16.3778548329839,16.3778548329839,1.97587461319488,100,2016,9,7,0.0020467836867299,0.0205666804207919,0.103777832031504,0.0100584702185672,0.0926338999990804,0.0926338999990804,0.00341789818502614
+"22532",100,2016,9,8,5.45082509032439,11.2773267171039,19.8711659040126,15.0902859946944,7.81897677892637,7.81897677892637,2.00234357885445,100,2016,9,8,5.1433917140682,0.0713982356691415,0.0146274667665148,0.0448801276447446,1.52700351928356,1.52700351928356,0.00345399429896007
+"22533",100,2016,9,9,28.6683168862388,12.2813970347573,14.5955225773508,14.1765454736086,5.75495049662323,5.75495049662323,2.02881254451402,100,2016,9,9,10.94152105008,0.031016392854007,0.012360832363912,0.00259881853701684,0.135198275240418,0.135198275240418,0.00349170204869846
+"22534",100,2016,9,10,14.7248625896945,9.84658959098119,13.973542357173,10.6192849540081,12.7443343793074,12.7443343793074,2.05528151017359,100,2016,9,10,18.1728663427232,0.0964894303261054,0.0273064537249895,0.0291473759032946,0.556558329297885,0.556558329297885,0.00353102143424131
+"22535",100,2016,9,11,0.725632571134761,7.04399342689053,15.5790210205598,12.4875247239804,11.2237952885979,11.2237952885979,2.08175047583316,100,2016,9,11,0.423684218538958,0.13807190935349,0.022370725596098,0.010467245021706,0.676900635936895,0.676900635936895,0.00357195245558862
+"22536",100,2016,9,12,6.9639163311034,7.88850387671862,13.1965348314006,9.38161720091229,11.755038564617,11.755038564617,2.10821944149273,100,2016,9,12,0.572046699189318,0.0421818824645842,0.0488397367845895,0.014933923134665,0.634494146291356,0.634494146291356,0.00361449511274038
+"22537",100,2016,9,13,6.24059408935908,7.65730481100555,12.955709611622,11.8420683731734,6.2580968009101,6.2580968009101,2.1346884071523,100,2016,9,13,2.21912297020184,0.055225165707354,0.0643210741991903,0.00144970899222436,0.339180723310785,0.339180723310785,0.00365864940569661
+"22538",100,2016,9,14,29.178987754847,8.53710665141526,14.8380967938598,10.1699560834761,8.75250822220436,8.75250822220436,2.16115737281187,100,2016,9,14,1.71479558286641,0.0474696182519432,0.0109397489247454,0.0221801421361604,1.51886953747516,1.51886953747516,0.0037044153344573
+"22539",100,2016,9,15,7.03465350783697,7.53682073901589,13.7205169612687,10.5318263172448,8.56018707697148,8.56018707697148,2.18762633847144,100,2016,9,15,0.575964910719161,0.0396877020194626,0.0629783126943406,0.000544446055175807,1.00522212484154,1.00522212484154,0.00375179289902245
+"22540",100,2016,9,16,0.777337736029043,7.13601763518599,15.1558305196898,10.5100439992806,14.0409350725684,14.0409350725684,2.214095304131,100,2016,9,16,0.19140349860777,0.103933346494059,0.117669610006812,0.00863568451427055,0.388815273862747,0.388815273862747,0.00380078209939205
+"22541",100,2016,9,17,0.762816281667506,6.84244227120847,19.6731900780639,11.1459625819073,17.3053134376853,17.3053134376853,2.24056426979057,100,2016,9,17,0.0498830393462158,0.0129590624480459,0.0453953014756527,0.00324327218134635,0.151642742277705,0.151642742277705,0.0038513829355661
+"22542",100,2016,9,18,1.15687569015228,8.11882281119805,12.1005391004455,9.53222226939186,12.6779318233528,12.6779318233528,2.26703323545014,100,2016,9,18,0.13210526313001,0.0882210327684225,0.0313695860233129,0.000325726496912839,1.41804861917113,1.41804861917113,0.00390359540754463
+"22543",100,2016,9,19,1.68657866982606,6.21202425752143,15.0124092227948,9.23836081938119,16.3549396107824,16.3549396107824,2.29350220110971,100,2016,9,19,0.0159649107609588,0.0662766099907702,0.0396315736269364,0.00113567971799291,0.262002499996643,0.262002499996643,0.00395741951532761
+"22544",100,2016,9,20,0.000110011002739402,7.31884488755196,18.361034389114,10.7342684958765,16.4879537504761,16.4879537504761,2.31997116676928,100,2016,9,20,0.000526315805159117,0.0433245603327304,0.072857900970142,0.00346958924014525,0.499564868550221,0.499564868550221,0.00401285525891505
+"22545",100,2016,9,21,0.854455430384385,10.3792078723215,15.184741473434,12.1118593121519,9.17316829463174,9.17316829463174,2.34644013242885,100,2016,9,21,0.0214619874187383,0.0817093345866059,0.0724701427862246,0.00368945669216421,0.389815148256616,0.389815148256616,0.00406990263830695
+"22546",100,2016,9,22,2.20902091402425,7.41112206241872,12.6313420286273,8.85819588609786,11.7889329303872,11.7889329303872,2.37290909808842,100,2016,9,22,0.0578362714338062,0.128002918527865,0.0173766035984327,0.0160286675370186,0.439322232522016,0.439322232522016,0.00412856165350331
+"22547",100,2016,9,23,0.00110011002739402,6.16237624381373,15.9281957540313,10.6274366871883,17.4116831969376,17.4116831969376,2.39937806374799,100,2016,9,23,0.000994152076411666,0.0412619821046737,0.055166605419725,0.015076616713417,0.273724711154117,0.273724711154117,0.00418883230450412
+"22548",100,2016,9,24,0,6.62678763522829,17.6048514090224,12.0611552205941,11.6849945234125,11.6849945234125,2.42584702940756,100,2016,9,24,0,0.025959063279018,0.0166339620269855,0.0227982364008392,0.624855553182035,0.624855553182035,0.0042507145913094
+"22549",100,2016,9,25,5.97172713778069,5.15685366220338,13.9669087045919,8.86048393018699,18.543454441956,18.543454441956,2.45231599506713,100,2016,9,25,0.405438599781705,0.0391432762648149,0.0461444480896934,0.000906431242822835,0.967689623903212,0.967689623903212,0.00431420851391913
+"22550",100,2016,9,26,0.832233224961624,4.6620681839283,16.7435643413279,9.80643567839603,14.6001541055862,14.6001541055862,2.4787849607267,100,2016,9,26,0.245789468678815,0.0237333405177039,0.0723244619092578,0.000233916957550022,0.466177661090524,0.466177661090524,0.00437931407233332
+"22551",100,2016,9,27,8.17799783618537,4.6374257956401,15.4321672651503,9.40130904064451,16.2124201645552,16.2124201645552,2.50525392638627,100,2016,9,27,0.350350813782074,0.0647842132523889,0.0789730500651594,0.00849237789360393,0.340180814094156,0.340180814094156,0.00444603126655199
+"22552",100,2016,9,28,0.194059410340453,7.217557731897,17.5687680260183,11.0338394460898,17.8191308796865,17.8191308796865,2.53172289204584,100,2016,9,28,0.0548538038674852,0.125770789298137,0.0473204163404022,0.00380235284289712,0.0230831100110961,0.0230831100110961,0.00451436009657509
+"22553",100,2016,9,29,9.31903186856848,7.75590761266526,14.6008690234983,9.37867985254336,17.8510011687662,17.8510011687662,2.55819185770541,100,2016,9,29,1.45894723535288,0.0101052651042962,0.0605654763941899,0.0163982570292218,0.737265721469689,0.737265721469689,0.00458430056240267
+"22554",100,2016,9,30,5.81881195011705,6.51920790562142,15.5749285921405,10.9719800476981,17.9605391001937,17.9605391001937,2.58466082336498,100,2016,9,30,0.48701757280452,0.0497731089664689,0.0337690141355215,0.000542690640786904,4.06838123280727,4.06838123280727,0.00465585266403469
+"22555",100,2016,10,1,4.75874584867354,10.591089189905,15.0546535611546,11.5702861999915,10.1054894213367,10.1054894213367,2.62126755642709,100,2016,10,1,0.355847989467174,0.108258493394544,0.0379672920919361,0.00127660243392365,0.672325671540713,0.672325671540713,0.00463427517778386
+"22556",100,2016,10,2,0.901100106988147,8.19062704929818,20.2096370365491,11.1661496985995,9.58074794167077,9.58074794167077,2.6578742894892,100,2016,10,2,0.360058448251926,0.0873175613571316,0.0264052149413949,0.00474795026729071,1.47305832336944,1.47305832336944,0.00461442907694971
+"22557",100,2016,10,3,11.7279427688901,7.10697468462819,14.6645764370825,10.5514300255099,14.0162045748451,14.0162045748451,2.69448102255131,100,2016,10,3,4.35029286278629,0.034556150092877,0.0397204812524522,0.00537778235547837,1.49175767778328,1.49175767778328,0.00459631436153224
+"22558",100,2016,10,4,5.23817378590734,8.73776678834418,14.6842243301593,9.29877885707272,18.0159295725219,18.0159295725219,2.73108775561342,100,2016,10,4,0.88385974571721,0.0477578846803811,0.0348871265479021,0.00975088502992437,1.78515054083296,1.78515054083296,0.00457993103153145
+"22559",100,2016,10,5,2.90627061756793,6.98220021758798,16.2021124014104,12.3895599577162,10.172431303723,10.172431303723,2.76769448867553,100,2016,10,5,0.204678399381587,0.0707619828910314,0.0637485015826027,0.0202041191949703,0.274031530489636,0.274031530489636,0.00456527908694734
+"22560",100,2016,10,6,0.760726062443652,7.18509351879325,25.1714301408321,14.3099120258629,22.2133770975212,22.2133770975212,2.80430122173763,100,2016,10,6,0.0181286522519521,0.0489286736895654,0.0967356673081561,0.00984271258310624,0.0731321023330783,0.0731321023330783,0.00455235852777991
+"22561",100,2016,10,7,0,12.9420350780844,17.6856656279108,14.093564353498,12.0323761896987,12.0323761896987,2.84090795479974,100,2016,10,7,0,0.0559930158091426,0.0262509022186403,0.00124970714286495,0.461103394038862,0.461103394038862,0.00454116935402917
+"22562",100,2016,10,8,0,4.93907593841469,16.7032784880585,9.35130919088231,23.0090760796508,23.0090760796508,2.87751468786185,100,2016,10,8,0,0.0605596223452078,0.020638625501093,0.00618772302823877,0.0150707677119775,0.0150707677119775,0.00453171156569511
+"22563",100,2016,10,9,0.366226630619388,9.87573152275631,22.0903408902444,13.0510670721728,9.13889988919165,9.13889988919165,2.91412142092396,100,2016,10,9,0.00485380084368222,0.228592331999049,0.0320509019198142,0.0548052899924756,0.648220392761531,0.648220392761531,0.00452398516277773
+"22564",100,2016,10,10,3.29922989387848,8.54202420116127,14.3166887799505,8.49203513347932,18.6379539040711,18.6379539040711,2.95072815398607,100,2016,10,10,0.23052628531094,0.0777537661078948,0.049987109932772,0.000347953696078623,1.75391371242258,1.75391371242258,0.00451799014527704
+"22565",100,2016,10,11,7.9471946712112,5.84078112424928,14.0189328954296,9.88624849518796,17.7539382091056,17.7539382091056,2.98733488704818,100,2016,10,11,0.102748494287726,0.128892406800496,0.0508760080015209,0.0127619742522325,0.750359185055145,0.750359185055145,0.00451372651319303
+"22566",100,2016,10,12,7.30121008397722,8.27801986574733,14.9986798451154,10.8723982232894,17.212651119505,17.212651119505,3.02394162011029,100,2016,10,12,0.683625696416514,0.108859036220388,0.0484146568990779,0.00884735798760433,1.34526474566343,1.34526474566343,0.0045111942665257
+"22567",100,2016,10,13,1.21397140277888,4.17946096825259,15.2705610493491,8.72349827412856,20.8594827368708,20.8594827368708,3.0605483531724,100,2016,10,13,0.045438595989295,0.0605368514825227,0.0094204686907177,0.0027064326058445,0.210780198625149,0.210780198625149,0.00451039340527505
+"22568",100,2016,10,14,0,3.643861408412,20.4107589658731,11.7289658872732,24.2918041783197,24.2918041783197,3.09715508623451,100,2016,10,14,0,0.0538286714263895,0.0781145652551475,0.0129397597218602,0.000568991740580889,0.000568991740580889,0.00451132392944109
+"22569",100,2016,10,15,0,8.98674359861905,21.112002174453,11.4620792464455,13.1414851963979,13.1414851963979,3.13376181929662,100,2016,10,15,0,0.0490022957428558,0.01193214187649,0.0233765904254541,0.441788829845919,0.441788829845919,0.0045139858390238
+"22570",100,2016,10,16,0.73553356167638,14.1168757953791,16.3134984015369,13.2881518511882,3.36872389030666,3.36872389030666,3.17036855235873,100,2016,10,16,0.079122810133717,0.0530707705782982,0.0122871131587739,0.00569531091395225,0.291483029323161,0.291483029323161,0.00451837913402321
+"22571",100,2016,10,17,11.7988997712256,5.60767874701975,15.3182068206821,10.198152003902,13.4198348136625,13.4198348136625,3.20697528542084,100,2016,10,17,0.128713500932016,0.0517274646848438,0.0441690341191633,0.0139163721810265,2.23985870840514,2.23985870840514,0.00452450381443929
+"22572",100,2016,10,18,5.73267324319636,6.62971401581801,13.7931022486671,8.85537952742036,16.173971487744,16.173971487744,3.24358201848294,100,2016,10,18,0.342456070805182,0.0430537947872423,0.0481584810648052,0.00313216284375919,3.41650376571096,3.41650376571096,0.00453235988027206
+"22573",100,2016,10,19,2.9925192548628,3.50729373920344,15.6557646290828,8.45618252811914,19.2328273492022,19.2328273492022,3.28018875154505,100,2016,10,19,0.049824562992969,0.0904871425807175,0.035942709955214,0.00369823605061563,1.26632494628664,1.26632494628664,0.0045419473315215
+"22574",100,2016,10,20,0.16908691121046,5.02322327107093,22.3391860483503,10.5343564204519,25.3314961327447,25.3314961327447,3.31679548460716,100,2016,10,20,0.00368421063611382,0.0242040883371246,0.0767783433705182,0.0136222502815197,0.00520290705398435,0.00520290705398435,0.00455326616818764
+"22575",100,2016,10,21,16.4913093033928,10.6806050375088,12.8326183860451,10.1037734160722,9.25443335859427,9.25443335859427,3.35340221766927,100,2016,10,21,0.293625501666366,0.0312930094647176,0.126525766593606,0.000625149272963698,1.13294854284485,1.13294854284485,0.00456631639027045
+"22576",100,2016,10,22,9.53971403686389,5.83083606159726,13.233685198659,7.66445541329379,16.6456877096783,16.6456877096783,3.39000895073138,100,2016,10,22,0.0533918008191553,0.165313981200119,0.0472339028810946,0.00435847936152337,2.21446688776762,2.21446688776762,0.00458109799776995
+"22577",100,2016,10,23,1.71694168215669,5.4142794488418,13.3449063925329,8.63677663142138,13.010219891866,13.010219891866,3.42661568379349,100,2016,10,23,0.0425730946189494,0.228476631532654,0.0225707697752289,0.00121344700199731,0.946014749761268,0.946014749761268,0.00459761099068613
+"22578",100,2016,10,24,1.31023103013487,6.77773376450156,18.3819581232186,11.0484928596925,25.1676567526671,25.1676567526671,3.4632224168556,100,2016,10,24,1.29561409861372,0.032491230931216,0.0364508088678334,0.00493684177964544,0.182078377961596,0.182078377961596,0.00461585536901898
+"22579",100,2016,10,25,0,7.03209024005466,25.3684269912434,11.6251924470706,21.9669194551978,21.9669194551978,3.49982914991771,100,2016,10,25,0,0.0606701817730242,0.025084185157677,0.0736619550515841,0.245965020475582,0.245965020475582,0.00463583113276853
+"22580",100,2016,10,26,0.68800879911621,11.5025084330828,15.6975028454536,10.5108581612212,14.8174806548686,14.8174806548686,3.53643588297982,100,2016,10,26,0.0122807000289886,0.0278175464608259,0.0448140454254135,0.0163596418169193,1.82036205735469,1.82036205735469,0.00465753828193475
+"22581",100,2016,10,27,0.747634764092721,7.13468647422833,15.6696479658876,8.60617161741351,24.4634543398951,24.4634543398951,3.57304261604193,100,2016,10,27,0.0136842085464659,0.191436238480752,0.0167368607103287,0.00034970492766948,0.346776785662295,0.346776785662295,0.00468097681651766
+"22582",100,2016,10,28,0,2.98441141561838,16.9899888977622,9.61242014818852,25.8780529895107,25.8780529895107,3.60964934910404,100,2016,10,28,0,0.0307257339600272,0.0178608230728474,0.000331578175574944,0.0292029941228337,0.0292029941228337,0.00470614673651726
+"22583",100,2016,10,29,0.0299229927451173,5.02638064700254,22.7018261186623,13.4465346845201,26.3574916054838,26.3574916054838,3.64625608216615,100,2016,10,29,0.0020467836867299,0.0313765918977302,0.100816259721376,0.00655378925486666,0.0757584888214248,0.0757584888214248,0.00473304804193353
+"22584",100,2016,10,30,3.47084710542912,11.3009350958175,19.9758414068107,10.5144995153278,18.6058413659779,18.6058413659779,3.68286281522826,100,2016,10,30,1.71040942129337,0.0361210578272295,0.0215251010162085,0.0319999930064683,3.74851389240265,3.74851389240265,0.00476168073276648
+"22585",100,2016,10,31,9.72310241745381,4.43715070760158,15.345401520514,9.70054991777712,19.882134201503,19.882134201503,3.71946954829036,100,2016,10,31,0.602748519942135,0.0713537936517574,0.0497695638110567,0.00260058934403979,2.51395983897003,2.51395983897003,0.00479204480901612
+"22586",100,2016,11,1,2.28437844715496,7.02641368620466,16.3608913400648,10.9509459570034,18.2648182597229,18.2648182597229,3.74451363903646,100,2016,11,1,0.0358479631295719,0.0950812827974944,0.0220211468214176,0.00188773014234192,4.27745893902047,4.27745893902047,0.00448933041826568
+"22587",100,2016,11,2,2.15885588044774,7.63551157824408,15.2422662478994,11.1689438520878,11.4624531843481,11.4624531843481,3.76955772978256,100,2016,11,2,0.502514630005378,0.0739877215357825,0.0309362780833919,0.0032052677801807,1.00490326872491,1.00490326872491,0.00420076745314647
+"22588",100,2016,11,3,0.0572057216457932,8.81273923376606,18.0834544027599,12.2695709455131,16.0295600072779,16.0295600072779,3.79460182052866,100,2016,11,3,0.0102923983248354,0.118656101588242,0.0151760474001035,0.018135671664748,0.277373118535704,0.277373118535704,0.00392635591365853
+"22589",100,2016,11,4,0,5.69179320833733,24.3113750776704,12.2703190940966,26.0708691226636,26.0708691226636,3.81964591127476,100,2016,11,4,0,0.0263812885719646,0.0143257662726172,0.0853134901282073,0.25810973520584,0.25810973520584,0.00366609579980182
+"22590",100,2016,11,5,3.10319032763491,8.11606154111352,15.0945873753597,9.59367439114746,15.4898239016139,15.4898239016139,3.84469000202086,100,2016,11,5,0.166900573195078,0.134731595075116,0.0379871140263351,0.0210140424115861,2.06805118979531,2.06805118979531,0.00341998711157636
+"22591",100,2016,11,6,0.779537951002027,9.87771173042826,19.7023214333915,12.777887685333,24.0721891296185,24.0721891296185,3.86973409276696,100,2016,11,6,0.0209941512520553,0.132709335969695,0.0389843051266405,0.0331813018420046,0.328837541997608,0.328837541997608,0.00318802984898215
+"22592",100,2016,11,7,0.435753581408161,10.1380199730331,17.3842024766441,12.9279427586084,9.60031899262314,9.60031899262314,3.89477818351306,100,2016,11,7,0.118304095938366,0.0610227667246001,0.0336818420144599,0.000546780379972809,0.481581179577618,0.481581179577618,0.00297022401201918
+"22593",100,2016,11,8,2.55632558120753,4.79112216426988,16.0678328124866,10.9930582214372,22.9778544548715,22.9778544548715,3.91982227425916,100,2016,11,8,0.00929824251878014,0.0384204579660217,0.0170175237267066,0.0027111080594056,0.806391791137573,0.806391791137573,0.00276656960068746
+"22594",100,2016,11,9,0,5.11343233970919,16.4077225235036,11.3110341375286,20.0849396335279,20.0849396335279,3.94486636500526,100,2016,11,9,0,0.0538274872540234,0.0186707087560476,0.00552866204919565,3.01932793667477,3.01932793667477,0.00257706661498699
+"22595",100,2016,11,10,0,7.85634761648734,16.7179977723343,9.99734872874647,21.2375689087921,21.2375689087921,3.96991045575136,100,2016,11,10,0,0.069878412636016,0.0220035015488081,0.00391462819961338,1.88043165391653,1.88043165391653,0.00240171505491777
+"22596",100,2016,11,11,0,4.8395929609326,19.6291638952408,13.7465456573352,28.0073158339699,28.0073158339699,3.99495454649746,100,2016,11,11,0,0.069048555386439,0.0584374109192842,0.0174999904633541,0.0837584992172069,0.0837584992172069,0.00224051492047979
+"22597",100,2016,11,12,0.716831680833966,11.4941583335465,21.0689548454662,13.214455588816,12.1438064302417,12.1438064302417,4.01999863724356,100,2016,11,12,0.0177777756585018,0.0366385652576419,0.0253485056695652,0.0313929761184774,2.1327646013294,2.1327646013294,0.00209346621167305
+"22598",100,2016,11,13,14.1233223819628,10.0382728136019,14.8021892688193,14.1652254706824,6.70774476362927,6.70774476362927,4.04504272798966,100,2016,11,13,3.28871355826398,0.0746783828736088,0.0144918140301039,0.00175788636466965,1.29721491630291,1.29721491630291,0.00196056892849757
+"22599",100,2016,11,14,11.8924092417634,10.5088779409595,15.525500516818,11.1895049675332,17.5848295129959,17.5848295129959,4.07008681873576,100,2016,11,14,5.39912272949675,0.109070830576143,0.0115467891805273,0.0446987775836714,0.962782899073823,0.962782899073823,0.00184182307095331
+"22600",100,2016,11,15,0.493399354441725,8.92297048117593,17.0018813591717,12.1517272305043,20.4143012962719,20.4143012962719,4.09513090948185,100,2016,11,15,0.0380701737585125,0.0980263292302247,0.0196480064796933,0.00296198731145965,2.20457084442968,2.20457084442968,0.00173722863904033
+"22601",100,2016,11,16,0.100000001490116,9.93653463189489,20.6795707908269,14.7713421012702,26.8704070352485,26.8704070352485,4.12017500022795,100,2016,11,16,0,0.115769584602276,0.0281322439900391,0.0521362326282319,0.136846781944067,0.136846781944067,0.00164678563275857
+"22602",100,2016,11,17,0.0278327836930686,10.177007768545,30.1936633526558,12.8605170171253,27.0133221314685,27.0133221314685,4.14521909097405,100,2016,11,17,0.0020467836867299,0.0800812587605025,0.0225765964132734,0.0141929624123273,0.675862014043565,0.675862014043565,0.00157049405210807
+"22603",100,2016,11,18,0.271067116184138,11.8762375125528,16.9677445754753,12.4865675381701,21.1970847269358,21.1970847269358,4.17026318172015,100,2016,11,18,0.00690058530596964,0.0335584807480274,0.0317555850560893,0.00512456108234487,0.376132232635108,0.376132232635108,0.00150835389708881
+"22604",100,2016,11,19,0.361826188854127,6.97180420127508,19.726061492601,13.830142921347,29.7131242594703,29.7131242594703,4.19530727246625,100,2016,11,19,0.0111695901483123,0.155291206015226,0.0936024160778049,0.0143818718827453,0.0244263505413961,0.0244263505413961,0.0014603651677008
+"22605",100,2016,11,20,0,9.92002204752336,29.803894517183,18.2546208699544,24.2246203490741,24.2246203490741,4.22035136321235,100,2016,11,20,0,0.0774227650581771,0.0554759597426811,0.0742386829936055,1.4299464007241,1.4299464007241,0.00142652786394404
+"22606",100,2016,11,21,0.734983498884244,15.9570518625845,32.8393396392252,16.5191639101807,20.2692628468093,20.2692628468093,4.24539545395845,100,2016,11,21,0.315906425327586,0.0150117336966876,0.0283941028543342,0.0239671983865945,2.2504141298888,2.2504141298888,0.00140684198581853
+"22607",100,2016,11,22,1.84268425216507,11.7092079596944,18.2678879925651,10.4904510017538,22.6994060289742,22.6994060289742,4.27043954470455,100,2016,11,22,1.12005851070784,0.0321245516905899,0.0195151863904171,0.00211638085671992,0.740135614924163,0.740135614924163,0.00140130753332425
+"22608",100,2016,11,23,2.38525852957706,7.98082517869402,15.7034104151039,9.55704061006687,23.632596157565,23.632596157565,4.29548363545065,100,2016,11,23,0.0357894701149069,0.035473099628294,0.0124321598008922,0.00829298847271828,1.7712547120884,1.7712547120884,0.00140992450646123
+"22609",100,2016,11,24,1.67447745511503,8.29162805072545,16.993311545243,9.95496145149793,21.0802970336478,21.0802970336478,4.32052772619675,100,2016,11,24,0.016959059029296,0.114945023321458,0.0132596808855356,0.00823214656324317,0.780534114008014,0.780534114008014,0.00143269290522945
+"22610",100,2016,11,25,0.229262931872778,9.33986809964489,16.8792739444309,10.0706051395278,16.8764578501383,16.8764578501383,4.34557181694285,100,2016,11,25,0.00368421092368011,0.131089471913003,0.00980994732096847,0.0030029145960906,2.00746458441785,2.00746458441785,0.00146961272962891
+"22611",100,2016,11,26,0,10.6718151629692,15.9986687871096,12.069361933256,12.4686139498082,12.4686139498082,4.37061590768895,100,2016,11,26,0,0.166138586483851,0.0164988551292077,0.0129696116375739,2.304335308374,2.304335308374,0.00152068397965963
+"22612",100,2016,11,27,0.294609466409854,10.9027944235387,15.5931242532594,12.2927062828811,11.7737953691724,11.7737953691724,4.39565999843505,100,2016,11,27,0.0143274852929757,0.141189480840731,0.0261508694208677,0.00453566442130275,1.0700001006657,1.0700001006657,0.00158590665532159
+"22613",100,2016,11,28,0.0801980209970238,5.13969195452985,18.4159184131685,10.6516500982908,29.2896038449899,29.2896038449899,4.42070408918115,100,2016,11,28,0.00432748550908607,0.0673040827589694,0.116928827805796,0.0111426809635145,0.0711656175761866,0.0711656175761866,0.00166528075661479
+"22614",100,2016,11,29,0,7.23546752625435,20.0287019878593,12.3791309121681,30.8627171364292,30.8627171364292,4.44574817992725,100,2016,11,29,0,0.0789116902022611,0.104356065257442,0.0276719424354487,0.0378315736025367,0.0378315736025367,0.00175880628353924
+"22615",100,2016,11,30,0,8.95983500318034,19.1289880687516,12.8599889700693,19.2332344821053,19.2332344821053,4.47079227067334,100,2016,11,30,0,0.0581257239448365,0.0665431924656635,0.00070057832826348,5.09470345614732,5.09470345614732,0.00186648323609494
+"22616",100,2016,12,1,0,10.4911990317837,21.6065675338896,12.5325082583789,23.4107592533393,23.4107592533393,4.50403389751634,100,2016,12,1,0,0.0530251650515263,0.1963257081375,0.0431590931586725,0.433795331408656,0.433795331408656,0.00191303677444503
+"22617",100,2016,12,2,0.238723879788641,7.83517044999025,18.8185919980929,10.5627393061572,26.0378874893105,26.0378874893105,4.53727552435934,100,2016,12,2,0.00953216448426249,0.0229918163054514,0.0679671591911671,0.00541753681702378,0.651070160737751,0.651070160737751,0.00197532344606563
+"22618",100,2016,12,3,0.0840484060929029,4.27954896500926,21.9338614502625,10.0723872263439,31.5152584953014,31.5152584953014,4.57051715120234,100,2016,12,3,0.0020467836867299,0.0217918084440325,0.134076090517264,0.048902361826379,0.00474677415421179,0.00474677415421179,0.00205334325095672
+"22619",100,2016,12,4,0,10.3591858829209,30.0046533347488,15.3596697336245,13.6299561226722,13.6299561226722,4.60375877804533,100,2016,12,4,0,0.02757659585524,0.020949678575264,0.0944952971966606,0.848789538511844,0.848789538511844,0.00214709618911831
+"22620",100,2016,12,5,1.04917491990479,12.5124532785615,19.1390759370508,12.2222112838191,20.0567877111655,20.0567877111655,4.63700040488833,100,2016,12,5,0.203742688392339,0.0431619733220175,0.0600999855044589,0.0093175499297577,1.04083724449592,1.04083724449592,0.00225658226055039
+"22621",100,2016,12,6,0.013421342334207,7.41729370648056,19.41464238854,9.80386149424269,29.5189439754675,29.5189439754675,4.67024203173133,100,2016,12,6,0.00140350881375765,0.013533332422065,0.0471842828497246,0.0181397898468489,0.364128531540051,0.364128531540051,0.00238180146525299
+"22622",100,2016,12,7,0,5.27481855417636,26.2060945760561,10.4192958324,31.7392957706263,31.7392957706263,4.70348365857433,100,2016,12,7,0,0.0332362354379256,0.0787256499086716,0.1776619254487,0.00431639785346152,0.00431639785346152,0.00252275380322609
+"22623",100,2016,12,8,4.46908694866336,14.2918262964297,19.2515951750433,12.2216061391715,16.6019803394448,16.6019803394448,4.73672528541733,100,2016,12,8,0.0371929803926532,0.0413134460583843,0.0131578832484454,0.0517777330611267,4.23055861481138,4.23055861481138,0.00267943927446968
+"22624",100,2016,12,9,4.56589660969767,8.92282721810084,16.047249627192,10.1145436094932,15.0167656081213,15.0167656081213,4.76996691226032,100,2016,12,9,0.641637406321311,0.169869046029503,0.014120440959583,0.0193719527172676,1.62335768577408,1.62335768577408,0.00285185787898377
+"22625",100,2016,12,10,0.521672176973786,8.76370741799064,16.0335205852395,12.3033443331325,12.0980638004635,12.0980638004635,4.80320853910332,100,2016,12,10,0.013625731426373,0.0313356748191203,0.0168485267061745,0.018739779834946,1.71840906439895,1.71840906439895,0.00304000961676836
+"22626",100,2016,12,11,0.302750282637095,8.38213409134264,19.6423430930663,11.8022441811556,17.3757648027376,17.3757648027376,4.83645016594632,100,2016,12,11,0.00766081871519314,0.0992374744215148,0.0473204950881979,0.043858501173407,1.37492090779379,1.37492090779379,0.00324389448782344
+"22627",100,2016,12,12,0.0566556664107919,7.63005498323766,32.5972498651385,12.1902751020341,29.4272499357251,29.4272499357251,4.86969179278932,100,2016,12,12,0.00450292411080578,0.070794105330159,0.0363432542511639,0.0696098819487986,0.100757912365055,0.100757912365055,0.00346351249214904
+"22628",100,2016,12,13,0,13.3874586816668,30.780462002728,13.9331462732112,8.69113316446772,8.69113316446772,4.90293341963231,100,2016,12,13,0,0.0391538186503306,0.0741913555705156,0.0851818991343739,0.2903818699754,0.2903818699754,0.00369886362974514
+"22629",100,2016,12,14,0.0986798694572433,12.5447305505163,18.0214743640425,9.0372717335935,24.2544555685045,24.2544555685045,4.93617504647531,100,2016,12,14,0.00432748550908607,0.0962620407194424,0.025124656561599,0.0290257267177537,0.387670726892511,0.387670726892511,0.00394994790061174
+"22630",100,2016,12,15,0,7.74532452196178,19.0299449119106,9.64180437304137,28.47548952302,28.47548952302,4.96941667331831,100,2016,12,15,0,0.1980543146368,0.0434672332767522,0.037524551987296,1.29823985649726,1.29823985649726,0.00421676530474883
+"22631",100,2016,12,16,0,6.08877893223359,23.0147856973579,12.0502750608656,31.9354673958454,31.9354673958454,5.00265830016131,100,2016,12,16,0,0.0847783472685975,0.289509320546014,0.140013969140375,0.0616538230009269,0.0616538230009269,0.00449931584215643
+"22632",100,2016,12,17,0.185808583659635,9.1034984745995,17.7297800775408,8.10124323837566,21.6462265325196,21.6462265325196,5.0358999270043,100,2016,12,17,0.00543859674480924,0.0690022789002163,0.0165801163436368,0.0101152114299731,3.28068204063313,3.28068204063313,0.00479759951283451
+"22633",100,2016,12,18,0.00418041810409726,6.62103407139039,18.5968425989938,8.44365243628474,28.7851926239148,28.7851926239148,5.0691415538473,100,2016,12,18,0.00251461995798245,0.249671930701288,0.04735902474861,0.0147426923897788,2.68075356474336,2.68075356474336,0.00511161631678312
+"22634",100,2016,12,19,0.201210125010334,7.26826185230637,30.1379648928333,7.58350935181638,19.6394938835085,19.6394938835085,5.1023831806903,100,2016,12,19,0.0233918141203317,0.0849730909336539,0.027878322784601,0.0786707296137436,1.1942872894826,1.1942872894826,0.0054413662540022
+"22635",100,2016,12,20,4.30484040551978,11.9396148562038,21.0906601686551,11.2090868976119,29.7750164804154,29.7750164804154,5.1356248075333,100,2016,12,20,0.558011691918861,0.0408812819408901,0.0281924057099355,0.147195291461206,0.375180644998118,0.375180644998118,0.00578684932449181
+"22636",100,2016,12,21,0.756655671710622,10.88181522105,19.7375907268461,12.1582067393103,19.3903632001384,19.3903632001384,5.16886643437629,100,2016,12,21,0.0716374326170542,0.128283060997453,0.0176871181959046,0.0381152067397103,2.95414772335327,2.95414772335327,0.00614806552825192
+"22637",100,2016,12,22,0.00363036309040026,12.456325618085,21.8775359469541,13.6582177385639,24.7732561801789,24.7732561801789,5.20210806121929,100,2016,12,22,0.000526315805159117,0.0797806945600697,0.196120642045507,0.0319982781929441,0.7851435761037,0.7851435761037,0.00652501486528249
+"22638",100,2016,12,23,0,10.3831024065007,28.3880966656541,14.6010230728502,26.8686795439264,26.8686795439264,5.23534968806229,100,2016,12,23,0,0.0172649044751731,0.293676168912923,0.223721166360924,1.03176690125527,1.03176690125527,0.0069176973355836
+"22639",100,2016,12,24,0,13.4508580374639,26.8433219124906,17.0077556761185,31.5029921809701,31.5029921809701,5.26859131490529,100,2016,12,24,0,0.0209444262187251,0.687842778024405,0.107139684563695,0.176964963857553,0.176964963857553,0.00732611293915518
+"22640",100,2016,12,25,0.0403740380053604,13.8425852688495,33.0364686065775,15.9326073129316,31.1505501624381,31.1505501624381,5.30183294174828,100,2016,12,25,0.00245614042407588,0.0371912869950659,0.763136796874507,0.460832084662627,0.096278366994898,0.096278366994898,0.00775026167599728
+"22641",100,2016,12,26,0.0963696383997159,14.1693509472216,22.2118482264486,18.6291420960715,9.00570953429991,9.00570953429991,5.33507456859128,100,2016,12,26,0.00163742694938392,0.0223649016911011,0.0300140261380279,0.00604732992194426,0.650105262957162,0.650105262957162,0.00819014354610989
+"22642",100,2016,12,27,1.64730474513487,14.4631903137442,24.5085807334472,23.396050496201,10.4050825505105,10.4050825505105,5.36831619543428,100,2016,12,27,0.0722807000394463,0.0549146082784271,0.137864856818436,0.0653719039071919,0.675561987371739,0.675561987371739,0.00864575854949298
+"22643",100,2016,12,28,7.18679867011092,18.5576127202335,31.6264903736849,25.8197137415081,11.5123652152889,11.5123652152889,5.40155782227728,100,2016,12,28,3.53450298476641,0.0918006155764046,0.141006446919052,0.0232134237236596,1.43283219328101,1.43283219328101,0.00911710668614655
+"22644",100,2016,12,29,14.5732674645905,18.1560507880317,27.5950385745209,18.8461717599296,17.3959953887234,17.3959953887234,5.43479944912027,100,2016,12,29,6.25579027242842,0.00704678931462525,0.0521316441767053,0.172083004671842,0.692292788491337,0.692292788491337,0.00960418795607066
+"22645",100,2016,12,30,0.39262926818678,14.617557792118,25.0025081214863,14.8738284567402,23.9454675995477,23.9454675995477,5.46804107596327,100,2016,12,30,0.0361988295405581,0.141591229190108,0.124989462940908,0.108813426482944,2.21984736734615,2.21984736734615,0.0101070023592653
+"22646",100,2016,12,31,0.252145220581913,11.76350937687,19.7979869737615,16.0992850900614,7.29983494121774,7.29983494121774,5.50128270280627,100,2016,12,31,0.0169005850288603,0.123222881152117,0.0426000512020144,0.0215485018749025,0.40658001012861,0.40658001012861,0.0106255498957304
+"22647",100,2017,1,1,0.741034100879275,14.0531572786757,19.1269415193396,15.0932673107017,11.8865235258381,11.8865235258381,5.52170427753314,100,2017,1,1,0.0264912268641401,0.024260836017999,0.021262035365796,0.0254590495930339,1.24720998033228,1.24720998033228,0.0106658009291583
+"22648",100,2017,1,2,0.990759079605833,11.7759186038614,18.521452155706,13.0418923496544,13.1176566930756,13.1176566930756,5.54212585226001,100,2017,1,2,0.0703508742441218,0.0491041395020817,0.0947426400216394,0.0486175329443003,0.578627547031052,0.578627547031052,0.0107106610128417
+"22649",100,2017,1,3,0.414631468841214,12.8431023785515,21.2400771184067,13.5219801943688,27.6820570459985,27.6820570459985,5.56254742698688,100,2017,1,3,0.139415199997133,0.0815175654205744,0.259724546229729,0.0794246018750682,0.128542670418592,0.128542670418592,0.0107601301467806
+"22650",100,2017,1,4,0,10.4021563388333,25.9580087189627,16.3509463456073,31.7082070622376,31.7082070622376,5.58296900171375,100,2017,1,4,0,0.067998767469144,0.456187288117269,0.120311767618659,0.121925829632277,0.121925829632277,0.010814208330975
+"22651",100,2017,1,5,0.0661166126463804,14.6934544602112,28.6488997938872,17.8198021614906,31.6186360148313,31.6186360148313,5.60339057644062,100,2017,1,5,0.00257309949188902,0.0242257227112144,0.669447440498968,0.0703782996955298,0.053339795042631,0.053339795042631,0.0108728955654249
+"22652",100,2017,1,6,0,15.9154566050363,33.242276904082,17.9208690571969,30.7074148075284,30.7074148075284,5.62381215116749,100,2017,1,6,0,0.0310543492926132,0.147971921089808,0.0591063087466816,0.23320261281801,0.23320261281801,0.0109361918501303
+"22653",100,2017,1,7,0.547634760096128,18.4891640355747,38.6743448716973,12.1342684774116,25.7817269672524,25.7817269672524,5.64423372589436,100,2017,1,7,0.258713448669827,0.0236952752992911,0.0338559503487838,0.0638140396921864,0.0982649920236842,0.0982649920236842,0.0110040971850912
+"22654",100,2017,1,8,0,18.5255004056085,25.4811769344888,16.1099559964389,29.9834103830839,29.9834103830839,5.66465530062123,100,2017,1,8,0,0.0202672633614804,0.355595523227695,0.0938608995226803,0.271876532803707,0.271876532803707,0.0110766115703076
+"22655",100,2017,1,9,0,12.5238723649968,24.8827722966999,13.122508068945,26.8242684564706,26.8242684564706,5.6850768753481,100,2017,1,9,0,0.130364931717876,0.202926208259956,0.0937274599221154,0.13559165279143,0.13559165279143,0.0111537350057795
+"22656",100,2017,1,10,0,11.5442903590019,24.7789881845774,14.2777997448106,22.4591749210169,22.4591749210169,5.70549845007497,100,2017,1,10,0,0.189843222672847,0.393865700682967,0.0146871412623714,0.267994075173149,0.267994075173149,0.011235467491507
+"22657",100,2017,1,11,0,12.1564135808494,23.2495488501487,13.0705721543567,24.0769856686902,24.0769856686902,5.72592002480184,100,2017,1,11,0,0.0475134391963631,0.117373023447343,0.025378378220134,0.751613984827584,0.751613984827584,0.0113218090274899
+"22658",100,2017,1,12,0,8.22698570163337,28.7070189770823,11.9919140388732,31.0299011100375,31.0299011100375,5.74634159952871,100,2017,1,12,0,0.0499368718956727,0.395092148693076,0.364446674757484,0.00919180241665648,0.00919180241665648,0.0114127596137283
+"22659",100,2017,1,13,4.44763472607427,15.7534102921439,22.9596590602359,17.1415510880541,12.5959845732803,12.5959845732803,5.76676317425558,100,2017,1,13,0.104795296317669,0.0401333299002082,0.0663876116080868,0.184268940373771,0.417003047898569,0.417003047898569,0.0115083192502222
+"22660",100,2017,1,14,6.76182627022201,13.0448295542903,20.7547412574357,12.7820022192725,16.0855446092629,16.0855446092629,5.78718474898245,100,2017,1,14,0.164736862238401,0.0629245912674984,0.0285537157831332,0.0150245317426362,2.06886556460301,2.06886556460301,0.0116084879369716
+"22661",100,2017,1,15,0.261276134874823,13.211034186209,20.9951595898115,12.2524422256335,14.9725523309739,14.9725523309739,5.80760632370932,100,2017,1,15,0.00538011729368691,0.128259001030364,0.0963895014849388,0.0371228283999981,0.418392575762711,0.418392575762711,0.0117132656739766
+"22662",100,2017,1,16,2.6569856761968,6.99975800435535,30.543960556601,12.9727942935704,30.1827944093543,30.1827944093543,5.82802789843619,100,2017,1,16,4.78666674176853,0.0233561278705884,0.394266927132765,0.194625779391822,0.0371999142120096,0.0371999142120096,0.011822652461237
+"22663",100,2017,1,17,0.142794281555743,14.2200000152336,31.5513752180882,13.7713971867157,7.22536853332855,7.22536853332855,5.84844947316306,100,2017,1,17,0.00263157902579559,0.0258432849572518,0.271191187567898,0.39465919411428,0.216328706645435,0.216328706645435,0.0119366482987529
+"22664",100,2017,1,18,0.253575365445336,12.8671397417948,20.6286468421928,10.2130802970777,24.5875577139776,24.5875577139776,5.86887104788993,100,2017,1,18,0.00707602390768934,0.058694197062525,0.0920246646771923,0.0428245928803883,1.27785119129242,1.27785119129242,0.0120552531865243
+"22665",100,2017,1,19,0,8.02815175922004,23.5986357333243,11.0475467423795,26.7631462588169,26.7631462588169,5.8892926226168,100,2017,1,19,0,0.0471274735980959,0.221373123120131,0.16117009052709,0.403170837897965,0.403170837897965,0.0121784671245512
+"22666",100,2017,1,20,28.3479650096663,13.2991749099379,18.3871948548538,12.5803849426958,11.8670516501952,11.8670516501952,5.90971419734367,100,2017,1,20,0.330350798668496,0.0406619670199386,0.0139731149822438,0.016839748853357,1.18322038464693,1.18322038464693,0.0123062901128336
+"22667",100,2017,1,21,0.202750280228975,8.20635868508955,22.0186802032101,13.7960176635759,27.026380578808,27.026380578808,5.93013577207054,100,2017,1,21,0.007660819312111,0.031053773784174,0.142842666706531,0.0660450727273679,1.26933896653503,1.26933896653503,0.0124387221513716
+"22668",100,2017,1,22,0.040814082016318,9.73658959752787,28.7225193027878,15.4647084331617,29.949637034283,29.949637034283,5.95055734679741,100,2017,1,22,0.00228070182235617,0.0278485468837418,0.243614069464893,0.159476480160659,0.0269362722165755,0.0269362722165755,0.012575763240165
+"22669",100,2017,1,23,0,16.4368206880274,33.9895709785822,14.9145985342095,16.5538833007561,16.5538833007561,5.97097892152428,100,2017,1,23,0,0.0308608475224794,0.459709397407707,0.404717485086611,0.397225412315583,0.397225412315583,0.0127174133792139
+"22670",100,2017,1,24,6.41826183069395,14.4885477941028,20.6352037297617,11.9565566139515,25.492442199237,25.492442199237,5.99140049625115,100,2017,1,24,0.0691812895613274,0.0647538031171599,0.0765544740735781,0.0602784223858595,1.41960224089176,1.41960224089176,0.0128636725685183
+"22671",100,2017,1,25,0,6.74455444463933,21.4595820906401,12.0493949831385,28.9843124372862,28.9843124372862,6.01182207097802,100,2017,1,25,0,0.0182339077219117,0.168660683013098,0.0995035281511216,0.0204093986647113,0.0204093986647113,0.0130145408080782
+"22672",100,2017,1,26,0,10.9683607623915,20.9250386660904,14.5315620739206,9.46353141860206,9.46353141860206,6.03224364570489,100,2017,1,26,0,0.128244995360435,0.169400564272157,0.0601584761904383,0.262456235160685,0.262456235160685,0.0131700180978937
+"22673",100,2017,1,27,0,14.6602750438275,24.4992521430793,13.8798019159483,26.0040042423966,26.0040042423966,6.05266522043176,100,2017,1,27,0,0.086853778393013,0.329984350010717,0.0822619735606715,0.140565578398533,0.140565578398533,0.0133301044379646
+"22674",100,2017,1,28,0,8.19957095063297,23.6485476352201,14.2287678618898,27.418206874818,27.418206874818,6.07308679515863,100,2017,1,28,0,0.0191719393234419,0.302687193263005,0.0979263111561288,0.215661907847847,0.215661907847847,0.013494799828291
+"22675",100,2017,1,29,0.571177115124969,9.03410345545434,28.7997690794623,15.5159076212263,28.9861056135826,28.9861056135826,6.0935083698855,100,2017,1,29,0.0557894734820429,0.028739733489729,0.276771876195954,0.115769065087366,0.0676287437352755,0.0676287437352755,0.0136641042688729
+"22676",100,2017,1,30,0.23894390165478,14.5455446285252,26.3383716125824,21.2512102997867,4.01502754008941,4.01502754008941,6.11392994461237,100,2017,1,30,0.00538011760739557,0.0574578629723166,0.238332307577153,0.050560908968234,0.283650906243251,0.283650906243251,0.0138380177597103
+"22677",100,2017,1,31,1.17018702091819,14.098525818425,21.6627944404929,12.2297029967355,24.2686137838332,24.2686137838332,6.13435151933925,100,2017,1,31,0.0717543904823181,0.0851035840040987,0.103500399932278,0.105605269950826,0.729047760157284,0.729047760157284,0.0140165403008033
+"22678",100,2017,2,1,0.013421342334207,7.24082513427315,21.5378219134474,10.507975813317,16.4606380420681,16.4606380420681,6.10532091235772,100,2017,2,1,0.00140350881375765,0.0137251469573196,0.00983562383401461,0.0822807511670719,1.30884340672657,1.30884340672657,0.0138264054833977
+"22679",100,2017,2,2,0,10.5591638793777,23.0995707139455,12.658118649809,24.036094648741,24.036094648741,6.0762903053762,100,2017,2,2,0,0.0535485277857296,0.106005373311464,0.101242703778079,1.19366929593931,1.19366929593931,0.0136395271403949
+"22680",100,2017,2,3,0,9.36390529607389,24.6404292439208,13.7796919783874,26.7023872085924,26.7023872085924,6.04725969839468,100,2017,2,3,0,0.0450111518860611,0.285661756486895,0.12486078953218,0.0904940631267753,0.0904940631267753,0.0134559052717946
+"22681",100,2017,2,4,0.00418041810409726,9.43873490647252,28.1927942815262,15.5291089570955,27.1101432838062,27.1101432838062,6.01822909141316,100,2017,2,4,0.00251461995798245,0.0204064270377232,0.920614221138142,0.171945161888719,0.335825032721999,0.335825032721999,0.0132755398775969
+"22682",100,2017,2,5,1.16479646775982,14.814543508985,26.8156216309802,20.043872340153,4.50719469897162,4.50719469897162,5.98919848443164,100,2017,2,5,1.06023385619734,0.0270251221752553,0.360435562847856,0.082633995645399,0.67771640535267,0.67771640535267,0.0130984309578019
+"22683",100,2017,2,6,20.8772278062844,13.7440703730903,18.2371180128343,10.9122771293548,11.5943454935475,11.5943454935475,5.96016787745012,100,2017,2,6,6.56842130248329,0.066131590982707,0.00498185211119286,0.0424175526335209,2.62585428291683,2.62585428291683,0.0129245785124094
+"22684",100,2017,2,7,0.0210121015232257,11.0266775911791,24.0146314602087,17.5062597440545,25.4732233893098,25.4732233893098,5.9311372704686,100,2017,2,7,0.0159064332225867,0.107714608961022,0.0380918190985859,0.0348561110114713,1.95943211979151,1.95943211979151,0.0127539825414197
+"22685",100,2017,2,8,0,14.4783607405273,37.2248621102345,20.795093150291,27.341375078174,27.341375078174,5.90210666348708,100,2017,2,8,0,0.0401450170841158,0.0170649233734887,0.0974478542397137,0.0266807263784326,0.0266807263784326,0.0125866430448325
+"22686",100,2017,2,9,0,19.2024533761741,27.1341691095837,19.6785149611,20.8615401827201,20.8615401827201,5.87307605650556,100,2017,2,9,0,0.0115760355276709,1.30452468443338,0.00485848153130596,0.180464934120736,0.180464934120736,0.012422560022648
+"22687",100,2017,2,10,0,15.5254014731765,26.6380856517125,18.7718701818989,23.0993838651214,23.0993838651214,5.84404544952404,100,2017,2,10,0,0.0531766374867418,0.739513326071909,0.0492924316364139,0.26170302635809,0.26170302635809,0.012261733474866
+"22688",100,2017,2,11,0,13.3098569655969,23.2697909830427,13.9981077370471,22.8703849549078,22.8703849549078,5.81501484254252,100,2017,2,11,0,0.0162257249754922,0.301316431851098,0.109591848946828,0.541414697689734,0.541414697689734,0.0121041634014867
+"22689",100,2017,2,12,1.71265127569667,12.4131572438021,18.5752476061663,9.77005507633893,15.2672827471994,15.2672827471994,5.78598423556099,100,2017,2,12,0.205789489481187,0.0409006047165726,0.0227023548016548,0.0348029552214992,1.8501662864687,1.8501662864687,0.0119498498025101
+"22690",100,2017,2,13,2.43476346853149,11.9516391198103,19.5810452283937,14.0703850191156,14.1745764435452,14.1745764435452,5.75695362857947,100,2017,2,13,0.254502912320593,0.0448275010182192,0.0193163705259995,0.0726649473246908,1.42378779631507,1.42378779631507,0.0117987926779361
+"22691",100,2017,2,14,1.63465346558259,12.6459075447225,21.2724642737864,12.0926403396069,20.2989440599028,20.2989440599028,5.72792302159795,100,2017,2,14,0.100994152869399,0.066513434076678,0.131286946583799,0.0729204426348024,0.14389125539359,0.14389125539359,0.0116509920277646
+"22692",100,2017,2,15,0,8.86135309783801,32.3895044987745,11.7777227752148,23.9394940123437,23.9394940123437,5.69889241461643,100,2017,2,15,0,0.0724368672109444,0.0447869891265396,0.3229028193915,0.296659098669003,0.296659098669003,0.0115064478519958
+"22693",100,2017,2,16,0.104070408591474,12.9169857908528,20.8906820001382,12.4597800310427,16.2536304167526,16.2536304167526,5.66986180763491,100,2017,2,16,0.000994152076411666,0.0595473717383739,0.114831461481782,0.0055766090393832,0.77589246617389,0.77589246617389,0.0113651601506296
+"22694",100,2017,2,17,0.0136413643396858,10.6173047641716,22.9251704452061,14.0628273358571,13.1580419204678,13.1580419204678,5.64083120065339,100,2017,2,17,0.00175438601719706,0.0733210852528737,0.116302206278364,0.144435682988358,1.39707862290505,1.39707862290505,0.0112271289236661
+"22695",100,2017,2,18,2.4587458414082,7.90432339637849,17.9376127014328,10.1553906660007,19.275940783871,19.275940783871,5.61180059367187,100,2017,2,18,0.302748523427734,0.0407175697070513,0.0600117227657084,0.0845701626783985,3.25656607809029,3.25656607809029,0.0110923541711051
+"22696",100,2017,2,19,11.6733773895616,8.59696376389272,16.0813534634866,11.2835642433796,15.1454676543132,15.1454676543132,5.58276998669035,100,2017,2,19,1.86690079348833,0.0981579295934225,0.0267655021022795,0.037332208372481,3.84835915044387,3.84835915044387,0.0109608358929468
+"22697",100,2017,2,20,9.98866867153558,9.99269531321342,18.7455555251723,11.8889329446555,19.8453465473272,19.8453465473272,5.55373937970883,100,2017,2,20,3.64175451183881,0.0883696132727705,0.0469818967052008,0.0492035115739144,0.990383848652416,0.990383848652416,0.0108325740891912
+"22698",100,2017,2,21,0.0222222225533591,5.40469745231016,22.7900109747455,11.0287460173973,24.7673927722591,24.7673927722591,5.52470877272731,100,2017,2,21,0.00175438601719706,0.0513093768984482,0.139084183886186,0.114731488079582,0.132014567771477,0.132014567771477,0.0107075687598381
+"22699",100,2017,2,22,0,11.0602531296716,34.374664180743,7.51392735899872,23.8128054254782,23.8128054254782,5.49567816574579,100,2017,2,22,0,0.120969523486857,0.0279766391519912,0.0632666487799951,0.137600630447228,0.137600630447228,0.0105858199048877
+"22700",100,2017,2,23,0,14.889042953978,21.4763367907836,15.6073597560752,9.450704034799,9.450704034799,5.46664755876427,100,2017,2,23,0,0.0463362888649499,0.162947951525729,0.0970385845721041,1.54781397830009,1.54781397830009,0.0104673275243398
+"22701",100,2017,2,24,0.067106711671035,13.2223983592588,19.3225631462072,11.7974697538991,10.2492629912558,10.2492629912558,5.43761695178274,100,2017,2,24,0.00228070182235617,0.0875397510038412,0.0344098954365694,0.0648210428785072,0.798656017709522,0.798656017709522,0.0103520916181946
+"22702",100,2017,2,25,0.00517051712875188,11.4861166485072,19.4461165471177,10.2299229104658,19.3779316682889,19.3779316682889,5.40858634480122,100,2017,2,25,0.000994152076411666,0.166327457863046,0.131146911077067,0.0471145901396064,0.703647730817593,0.703647730817593,0.0102401121864521
+"22703",100,2017,2,26,0,10.3122002084394,25.3017270641096,15.5810452179022,24.005324624946,24.005324624946,5.3795557378197,100,2017,2,26,0,0.084115202657313,0.274406496817945,0.14188312752497,0.0193718776503778,0.0193718776503778,0.0101313892291121
+"22704",100,2017,2,27,0,14.2294719043476,31.1240921733928,14.3186908528881,23.673058340914,23.673058340914,5.35052513083818,100,2017,2,27,0,0.0245163481707611,0.252313643084445,0.144987743721281,0.12992517757335,0.12992517757335,0.0100259227461748
+"22705",100,2017,2,28,0,15.2138064891198,32.4990103357565,14.4977116516583,22.8043233705695,22.8043233705695,5.32149452385666,100,2017,2,28,0,0.0121584726546282,0.157687317966504,0.173867255769762,0.573154140637806,0.573154140637806,0.00992371273764008
+"22706",100,2017,3,1,0,15.1588229545535,32.7277453373236,13.465038467424,22.9683607896694,22.9683607896694,5.3028626032136,100,2017,3,1,0,0.0406286741558449,0.14027085383118,0.241402945309979,0.399403437742982,0.399403437742982,0.0100441692150706
+"22707",100,2017,3,2,0,12.9776457281921,27.9035204645037,15.5043233141254,23.1805061134699,23.1805061134699,5.28423068257055,100,2017,3,2,0,0.0844819032301732,0.342016433555371,0.205222155041284,0.011747928494802,0.011747928494802,0.0101661761041347
+"22708",100,2017,3,3,0,12.963344319032,23.8983501522454,17.151551224611,15.4333552275554,15.4333552275554,5.26559876192749,100,2017,3,3,0,0.122142751892377,0.249113266045061,0.0696690740176415,2.68906093985364,2.68906093985364,0.0102897334048323
+"22709",100,2017,3,4,0,14.7116831537127,27.6758856065202,17.1342241981767,22.6226731332878,22.6226731332878,5.24696684128444,100,2017,3,4,0,0.0182450205686152,0.16466884903958,0.10491404331084,0.0110432530099499,0.0110432530099499,0.0104148411171635
+"22710",100,2017,3,5,0,13.6439053993939,23.4647303078697,16.5998899566852,16.7494832018946,16.7494832018946,5.22833492064138,100,2017,3,5,0,0.0319608320114107,0.104988922289038,0.0486321248865873,0.824224516258691,0.824224516258691,0.0105414992411283
+"22711",100,2017,3,6,0,15.2912762340802,24.1524203964586,16.0052255498301,19.6555007519108,19.6555007519108,5.20970299999833,100,2017,3,6,0,0.0446262937758223,0.0918467473663988,0.0688953148492146,0.247802354694704,0.247802354694704,0.0106697077767268
+"22712",100,2017,3,7,0.065456546629944,12.8609791840657,25.0374917469927,14.5148405110744,22.1266774785007,22.1266774785007,5.19107107935527,100,2017,3,7,0.00263157902579559,0.0375005710468446,0.064687652244685,0.0419426541736266,0.0202362022046464,0.0202362022046464,0.0107994667239587
+"22713",100,2017,3,8,0,13.2953795526418,29.5991970141991,14.128415908226,21.7500548179131,21.7500548179131,5.17243915871221,100,2017,3,8,0,0.075742705707736,0.173611709876065,0.217728674695728,0.0513479309307162,0.0513479309307162,0.0109307760828243
+"22714",100,2017,3,9,0,14.2381078438921,31.706600640342,12.3779208631274,20.07178215094,20.07178215094,5.15380723806916,100,2017,3,9,0,0.0490357201136445,0.12044870343656,0.117948521178062,0.131544937785452,0.131544937785452,0.0110636358533234
+"22715",100,2017,3,10,0,15.3625634102145,23.6714520721939,15.2433991426944,16.2760834206055,16.2760834206055,5.1351753174261,100,2017,3,10,0,0.0822485934810143,0.399023005110016,0.0693245549118782,0.018417548718618,0.018417548718618,0.0111980460354562
+"22716",100,2017,3,11,0,12.325005394922,23.2339381437228,15.5968315672166,6.66079201871412,6.66079201871412,5.11654339678305,100,2017,3,11,0,0.0984338729859164,0.286776542503227,0.130825088157534,1.70534850474913,1.70534850474913,0.0113340066292225
+"22717",100,2017,3,12,6.41705172788454,14.333300353408,22.688085675633,16.8231022753994,12.7030472750186,12.7030472750186,5.09791147613999,100,2017,3,12,4.43806993746623,0.0241385970467858,0.12708421012061,0.0912006987590976,2.69465524838769,2.69465524838769,0.0114715176346224
+"22718",100,2017,3,13,0.119581961362931,16.4476897803077,22.7689548886911,18.1332448743226,14.058173762952,14.058173762952,5.07927955549694,100,2017,3,13,0.0183040945963902,0.0345695104859271,0.161402266068784,0.0159000051712477,4.77625856290689,4.77625856290689,0.0116105790516558
+"22719",100,2017,3,14,4.80528061644341,13.4159075597463,29.7376127421397,17.7867105775672,19.5526405477157,19.5526405477157,5.06064763485388,100,2017,3,14,6.39777809725869,0.0234163549814439,0.169016363820027,0.171036820859206,0.106125740809988,0.106125740809988,0.0117511908803229
+"22720",100,2017,3,15,0,16.8886028684274,33.9527941830743,14.7657865528489,18.5738613686808,18.5738613686808,5.04201571421082,100,2017,3,15,0,0.0153322259070299,0.00526192712775236,0.033946747850199,0.0795151602899309,0.0795151602899309,0.0118933531206236
+"22721",100,2017,3,16,1.73069306175307,16.8623763127427,22.1814411780228,12.4328273735424,15.6244445628721,15.6244445628721,5.02338379356777,100,2017,3,16,0.00766081288544241,0.0250953158509389,0.0344783137095884,0.0391117132444192,0.902217867114091,0.902217867114091,0.0120370657725578
+"22722",100,2017,3,17,0.438283834799026,11.8461055766107,21.2612761276128,11.4667547234345,19.8649396340315,19.8649396340315,5.00475187292471,100,2017,3,17,0.0225730980923999,0.103321024574632,0.0304099772405586,0.116600588462871,0.0140544297918448,0.0140544297918448,0.0121823288361256
+"22723",100,2017,3,18,0,8.7052256759375,31.3767878235501,13.9103741262874,19.617117761123,19.617117761123,4.98611995228166,100,2017,3,18,0,0.0673830179583262,0.0558321430543672,0.0841449663999247,0.0188256833754321,0.0188256833754321,0.012329142311327
+"22724",100,2017,3,19,0,13.5602089815801,33.8539824994615,14.0743673154623,19.3472717839106,19.3472717839106,4.9674880316386,100,2017,3,19,0,0.124817604049416,0.0336251796353595,0.147864871931967,0.0180397462858022,0.0180397462858022,0.012477506198162
+"22725",100,2017,3,20,0,13.2533332784839,28.0441031146495,17.819625617385,11.2704509298662,11.2704509298662,4.94885611099555,100,2017,3,20,0,0.0687449470297848,0.0510589965823895,0.00822048765513229,0.863865332374897,0.863865332374897,0.0126274204966306
+"22726",100,2017,3,21,6.99944988361942,16.9809238971001,19.148283585988,20.3608358569927,0.0334323426284412,0.0334323426284412,4.93022419035249,100,2017,3,21,2.20397667003658,0.0253812235829673,0.0599981943366949,0.00690000644289822,0.0225707595900485,0.0225707595900485,0.0127788852067327
+"22727",100,2017,3,22,32.8555555270188,15.595104558502,20.6976567237946,18.0799119233823,8.08375141303269,8.08375141303269,4.91159226970944,100,2017,3,22,26.0980710931976,0.0360508645643841,0.0143842512703135,0.0259175800615568,1.46695860936689,1.46695860936689,0.0129319003284685
+"22728",100,2017,3,23,0,13.981166119885,22.5018591235573,16.0666446580876,17.9339933762587,17.9339933762587,4.89296034906638,100,2017,3,23,0,0.0499801510728152,0.0275666930307306,0.0703589339208313,0.107606496291317,0.107606496291317,0.0130864658618378
+"22729",100,2017,3,24,0.0222222225533591,13.3025081653406,24.2906158959249,17.1099561223365,12.6260725982381,12.6260725982381,4.87432842842332,100,2017,3,24,0.00175438601719706,0.029532180975818,0.0230315498533616,0.0759988987752366,0.525913511793876,0.525913511793876,0.0132425818068406
+"22730",100,2017,3,25,0,15.244037314479,28.0545874911435,19.2632012068242,17.6678766978718,17.6678766978718,4.85569650778027,100,2017,3,25,0,0.0132929917631771,0.12863860879218,0.0818321805768206,0.0612982748157079,0.0612982748157079,0.0134002481634771
+"22731",100,2017,3,26,0,15.1503850718667,29.3499341467426,18.2362593891072,13.2205171522134,13.2205171522134,4.83706458713721,100,2017,3,26,0,0.0560356552860808,0.047247393776751,0.0133800886502094,1.23530235965803,1.23530235965803,0.0135594649317472
+"22732",100,2017,3,27,0.0157315733917344,17.0648185490775,30.0501650567889,14.6543783611721,8.5555334888538,8.5555334888538,4.81843266649416,100,2017,3,27,0.0020467836867299,0.0362426937395516,0.12230071051992,0.0585578795852337,1.05310506100687,1.05310506100687,0.0137202321116509
+"22733",100,2017,3,28,2.15951594464456,11.3767436188046,22.6316389329363,12.6782728914905,15.3794609285948,15.3794609285948,4.7998007458511,100,2017,3,28,0.073859649206467,0.0521427210512777,0.0207953502369002,0.043976041211645,0.194925207968104,0.194925207968104,0.0138825497031881
+"22734",100,2017,3,29,0.14576457943296,11.1178769173533,24.2534102449323,13.744114471872,8.24492859971536,8.24492859971536,4.78116882520805,100,2017,3,29,0.0289473692576091,0.0361924034454184,0.0116093710562249,0.074428693900838,0.971304322471453,0.971304322471453,0.0140464177063589
+"22735",100,2017,3,30,7.62178227977522,8.09159503072271,17.0706599689815,10.2196150644384,13.8317821248792,13.8317821248792,4.76253690456499,100,2017,3,30,0.242280795114111,0.076603541629695,0.0183357001726151,0.0679783889325133,1.26913396146908,1.26913396146908,0.0142118361211633
+"22736",100,2017,3,31,2.2559956165299,6.79053908999604,16.7870075343335,9.6857206126382,7.38480746549348,7.38480746549348,4.74390498392193,100,2017,3,31,0.298187144513719,0.0633064363412917,0.0084783667583619,0.0562877278289626,0.123660753618234,0.123660753618234,0.0143788049476013
+"22737",100,2017,4,1,0.767766767864836,10.0603410659975,16.9122663336356,10.2421671423582,8.67452142524509,8.67452142524509,4.67241284072976,100,2017,4,1,0.0436842096932454,0.109121112671889,0.0164164323017205,0.0461321586832947,0.641758442464985,0.641758442464985,0.0133619846875778
+"22738",100,2017,4,2,1.47172717400903,10.5011661758255,17.3806051667636,10.1624424213623,10.4629923135403,10.4629923135403,4.60092069753758,100,2017,4,2,0.184269003662451,0.121569499476533,0.0232842048201943,0.0210251344001096,1.77372022009071,1.77372022009071,0.0123839070389637
+"22739",100,2017,4,3,0.0299229927451173,9.26441136707436,20.2431792142761,12.7476016968915,14.6411660795558,14.6411660795558,4.52942855434541,100,2017,4,3,0.0020467836867299,0.184616424301859,0.0177374374347706,0.0484561373281692,0.163877747599352,0.163877747599352,0.0114445720017587
+"22740",100,2017,4,4,0.200220026002072,9.74100116482138,23.5457755257719,12.9548515701714,14.9085367305575,14.9085367305575,4.45793641115323,100,2017,4,4,0.0038596495515422,0.0503175498628139,0.0272035210774442,0.105692440845054,0.0360590869301914,0.0360590869301914,0.010543979575963
+"22741",100,2017,4,5,0.0541254133477856,9.20821775933697,26.0091858476695,10.0118811222324,14.6964685242824,14.6964685242824,4.38644426796106,100,2017,4,5,0.00257309949188902,0.027837455620192,0.0219813694382655,0.109347351540524,0.417303446851229,0.417303446851229,0.00968212976157647
+"22742",100,2017,4,6,0,11.5083499033459,26.4831682882949,10.733124374163,13.7941804923634,13.7941804923634,4.31495212476888,100,2017,4,6,0,0.0576275260167549,0.0337544049048093,0.115061966045081,0.344114563012891,0.344114563012891,0.0088590225585992
+"22743",100,2017,4,7,0,12.904180487915,28.1252913648146,8.94752475268508,15.3782508428341,15.3782508428341,4.24345998157671,100,2017,4,7,0,0.0402245840642477,0.029427459619038,0.0204052438971117,0.133003494398859,0.133003494398859,0.00807465796703108
+"22744",100,2017,4,8,0.624202423740273,14.112794334739,26.8645212469321,13.5505611600131,8.19367425538788,8.19367425538788,4.17196783838453,100,2017,4,8,0.0748538040016831,0.0332619430431039,0.0295040795668651,0.00239824106771606,0.595809969422459,0.595809969422459,0.00732903598687224
+"22745",100,2017,4,9,10.695269536395,9.70088016396702,12.9324643163398,11.9989990406435,1.44676567422281,1.44676567422281,4.10047569519236,100,2017,4,9,3.71315738387981,0.0631034976380048,0.0782373564782186,0.00730292856323428,0.349313453473684,0.349313453473684,0.00662215661812258
+"22746",100,2017,4,10,31.5715069471806,10.0817711455594,14.9860395981271,11.0260176485521,5.79551153791488,5.79551153791488,4.02898355200018,100,2017,4,10,17.1805250986566,0.10815612984155,0.0329204441963774,0.0229163479878331,0.209381303094584,0.209381303094584,0.00595401986078212
+"22747",100,2017,4,11,0.986578667255649,11.1706380057256,16.4278656945895,13.8699339865589,6.78124308035319,6.78124308035319,3.957491408808,100,2017,4,11,0.033567249900417,0.180638635929916,0.0193274886150477,0.021365492993692,0.228195265349461,0.228195265349461,0.00532462571485094
+"22748",100,2017,4,12,0,12.6361935650161,19.2468315812752,13.3958856007709,12.7642465146592,12.7642465146592,3.88599926561583,100,2017,4,12,0,0.0620818450660626,0.0177374121150338,0.0148672657972953,0.369599053698629,0.369599053698629,0.00473397418032893
+"22749",100,2017,4,13,0.065456546629944,8.90588572578724,21.3650163958962,15.1971067201973,14.0049503219403,14.0049503219403,3.81450712242365,100,2017,4,13,0.00263157902579559,0.0615286447492609,0.0436608040349099,0.0714718689880197,0.0800146595358711,0.0800146595358711,0.00418206525721614
+"22750",100,2017,4,14,0.148734875818421,9.35606150107809,17.3089663040782,12.0183829705183,9.45089118630186,9.45089118630186,3.74301497923148,100,2017,4,14,0.00672514653868146,0.0271204688111719,0.0179538100083309,0.00234269390002524,0.515903048574552,0.515903048574552,0.00366889894551258
+"22751",100,2017,4,15,0,4.35888885218974,15.5364135011981,9.36490636349249,6.43979102891139,6.43979102891139,3.6715228360393,100,2017,4,15,0,0.051138602963962,0.0571146739625847,0.021242705506958,0.0774766338025098,0.0774766338025098,0.00319447524521824
+"22752",100,2017,4,16,0,7.84773377721721,18.4391968646328,13.1223431546303,11.3209460293106,11.3209460293106,3.60003069284713,100,2017,4,16,0,0.0466649278161456,0.017242763943502,0.029281854589435,0.448661927612544,0.448661927612544,0.00275879415633311
+"22753",100,2017,4,17,0.0797579769860662,5.90293722498928,19.0675796932644,12.1439384737424,14.1607921168093,14.1607921168093,3.52853854965495,100,2017,4,17,0.00228070182235617,0.0367766040177544,0.0468204349453601,0.00510641843568159,0.103532227276435,0.103532227276435,0.0023618556788572
+"22754",100,2017,4,18,0.0541254133477856,7.68796484751014,23.5312102289483,13.2429593173322,14.2041582919583,14.2041582919583,3.45704640646278,100,2017,4,18,0.00257309949188902,0.0619052462505668,0.100947264331387,0.0458801536226115,0.000404094051441329,0.000404094051441329,0.0020036598127905
+"22755",100,2017,4,19,0.215181522858222,11.9493510725737,26.5144776070472,14.3213641278946,12.3361717650075,12.3361717650075,3.3855542632706,100,2017,4,19,0.00538011742439885,0.0746397930927088,0.0433063662659625,0.0508479574549955,0.132037384142581,0.132037384142581,0.00168420655813302
+"22756",100,2017,4,20,0,13.9381409027133,22.1088227124104,16.0736084057815,4.3125303135191,4.3125303135191,3.31406212007843,100,2017,4,20,0,0.0382941460704855,0.0196731160172739,0.106298232876209,0.543177701515635,0.543177701515635,0.00140349591488476
+"22757",100,2017,4,21,15.1827281998067,15.6069967523791,19.2417824811274,17.5902414961879,3.15404842448051,3.15404842448051,3.24256997688625,100,2017,4,21,0.492514513138537,0.0579163822866528,0.0498432305366858,0.0355485461677213,0.470914674996501,0.470914674996501,0.00116152788304572
+"22758",100,2017,4,22,0.08096809813095,15.319790969194,18.8065347744949,17.5486907958984,3.43049501874397,3.43049501874397,3.17107783369408,100,2017,4,22,0.0102923981461957,0.0850941651685405,0.0270028628525239,0.0303953073873121,0.589478299418116,0.589478299418116,0.000958302462615901
+"22759",100,2017,4,23,1.13883388652266,15.2281516991039,17.1628054061739,16.4700661125225,3.02720571377359,3.02720571377359,3.0995856905019,100,2017,4,23,0.0499999953640845,0.0608649176302548,0.0636479538992483,0.00333977764222515,0.119828667720445,0.119828667720445,0.00079381965359529
+"22760",100,2017,4,24,1.19119912418726,13.0171507655984,18.3897797214185,17.1558194427994,5.54042898415208,5.54042898415208,3.02809354730972,100,2017,4,24,0.0291228036504055,0.0552432516355515,0.0236708347972036,0.00157018039130118,0.200969583272678,0.200969583272678,0.000668079455983896
+"22761",100,2017,4,25,18.8653465717933,13.8593069544457,18.5523101798247,14.0086139127104,10.9577777251945,10.9577777251945,2.95660140411755,100,2017,4,25,15.680409653424,0.0471204748901924,0.0379667071028324,0.0278496904831085,0.892394280827484,0.892394280827484,0.000581081869781726
+"22762",100,2017,4,26,19.5457645989094,9.25287121121246,12.771430051235,9.20756870475408,10.1761716227851,10.1761716227851,2.88510926092537,100,2017,4,26,8.2104072705768,0.109967207967845,0.0541152416966331,0.0563544012282428,0.528160727997346,0.528160727997346,0.000532826894988765
+"22763",100,2017,4,27,3.02013200239511,7.05976899255096,14.829317968933,11.422321404561,7.71139713534952,7.71139713534952,2.8136171177332,100,2017,4,27,0.0526315795050785,0.229242679846924,0.0646877337249803,0.024504118327181,0.437392894905515,0.437392894905515,0.000523314531605029
+"22764",100,2017,4,28,1.99658965264479,10.4060175468688,16.8706381433737,16.24603946925,6.52443345282862,6.52443345282862,2.74212497454102,100,2017,4,28,0.0869005892848439,0.12826957015776,0.0384496937448502,0.0210759885466737,0.287578344409801,0.287578344409801,0.000552544779630504
+"22765",100,2017,4,29,2.35819580137926,13.4428271922079,16.8052474295739,14.2500439351148,8.0179316585738,8.0179316585738,2.67063283134885,100,2017,4,29,0.421345005746477,0.0681367853154285,0.0151286555064127,0.00773334083562225,0.170899945301543,0.170899945301543,0.000620517639065201
+"22766",100,2017,4,30,0.527392743739358,9.90479649123054,17.0199671347674,13.2878108260655,5.88952685136868,5.88952685136868,2.59914068815667,100,2017,4,30,0.126549707487313,0.199516376892498,0.0381146380914607,0.132291131678868,0.0737485004112921,0.0737485004112921,0.000727233109909115
+"22767",100,2017,5,1,2.40033000635498,12.936259610687,17.4050053910191,14.0756324279164,4.41036304913469,4.41036304913469,2.56191215968438,100,2017,5,1,0.706549690090427,0.121087728098873,0.0113134312236687,0.00598713189516828,0.156147947973411,0.156147947973411,0.000741681862317653
+"22768",100,2017,5,2,4.93168318153608,7.70883392946686,14.3640814100293,8.82851485975242,9.4623321532154,9.4623321532154,2.52468363121208,100,2017,5,2,0.164678367759769,0.175054410316546,0.0945052686190048,0.0103918415092724,0.83866553771267,0.83866553771267,0.000756972479705735
+"22769",100,2017,5,3,0.412431253425621,2.59266225575614,14.7279648355918,9.12672169793426,6.53320133515579,6.53320133515579,2.48745510273979,100,2017,5,3,0.0202923964617546,0.0591497125146051,0.0342731216319753,0.015716383359666,0.285500577622144,0.285500577622144,0.00077310496207335
+"22770",100,2017,5,4,0,6.28828374098892,16.489252096749,9.89899892880447,10.7301868652747,10.7301868652747,2.45022657426749,100,2017,5,4,0,0.0773333188163043,0.0370555924311372,0.00862512999872025,0.0463888782925463,0.0463888782925463,0.000790079309420507
+"22771",100,2017,5,5,0,4.00927397913665,17.3733664282871,10.6821891786766,10.5227172198993,10.5227172198993,2.4129980457952,100,2017,5,5,0,0.0528619923123258,0.0236702254359661,0.0116783641682166,0.0741766215252714,0.0741766215252714,0.000807895521747202
+"22772",100,2017,5,6,0,5.17911988018107,15.4036743617294,10.3987238824171,5.39939496226043,5.39939496226043,2.3757695173229,100,2017,5,6,0,0.0296877224314461,0.0425871495899947,0.00198303729282796,0.579983074346642,0.579983074346642,0.000826553599053437
+"22773",100,2017,5,7,4.00418036264686,7.05636960995866,14.2444774808139,9.57526959628031,9.19876780735515,9.19876780735515,2.33854098885061,100,2017,5,7,0.0995321465096332,0.308058476626544,0.0708579158828308,0.0406707538516228,0.44170506878019,0.44170506878019,0.000846053541339211
+"22774",100,2017,5,8,1.71463146802485,6.53365239582964,14.8714741537936,12.837678789699,8.19910897253895,8.19910897253895,2.30131246037831,100,2017,5,8,0.242222230434421,0.0691321781972893,0.0295432997899462,0.00653684960307891,0.45078197160694,0.45078197160694,0.00086639534860452
+"22775",100,2017,5,9,0.725192524353401,9.22523649192617,14.0892739227765,10.8928164386644,5.20981303929496,5.20981303929496,2.26408393190602,100,2017,5,9,0.0138596455883565,0.15455079567675,0.0500953354261881,0.00432748610519039,0.0564730707001563,0.0564730707001563,0.000887579020849372
+"22776",100,2017,5,10,0,2.35169415216897,15.4110010879399,11.8318591909965,10.9658524856316,10.9658524856316,2.22685540343372,100,2017,5,10,0,0.0734818630726062,0.0311760105803108,0.0171479338663609,0.00365088185766613,0.00365088185766613,0.000909604558073762
+"22777",100,2017,5,11,0.065456546629944,4.38354237402233,17.6986248637452,12.877150725479,9.41766777605113,9.41766777605113,2.18962687496143,100,2017,5,11,0.00263157902579559,0.0781145939765806,0.0916163944983065,0.0572579236973894,0.0671806611491339,0.0671806611491339,0.000932471960277689
+"22778",100,2017,5,12,0.10847084870105,6.20550055131398,14.9374037523343,11.4828932733819,8.15467543544287,8.15467543544287,2.15239834648913,100,2017,5,12,0.00321637436486127,0.188921095150976,0.0359889468723712,0.0113134509661819,0.151999949561318,0.151999949561318,0.000956181227461159
+"22779",100,2017,5,13,0,3.1346424669847,14.3664906337054,10.4139273006662,8.76515951251039,8.76515951251039,2.11516981801684,100,2017,5,13,0,0.0942397769571091,0.0192263023466359,0.0153871328164963,0.3333139725223,0.3333139725223,0.00098073235962416
+"22780",100,2017,5,14,0,4.40886697097711,14.8051816166037,10.8429373687643,8.66480761392675,8.66480761392675,2.07794128954454,100,2017,5,14,0,0.0753175208342886,0.0466818516291589,0.00277075344337928,0.207471966909735,0.207471966909735,0.00100612535676671
+"22781",100,2017,5,15,0,4.61502748327811,16.0292191248391,13.5064796462442,7.88829480644369,7.88829480644369,2.04071276107225,100,2017,5,15,0,0.161738637679942,0.0238286878508932,0.00751753830499242,0.587505197366689,0.587505197366689,0.0010323602188888
+"22782",100,2017,5,16,0.0556655673861372,4.87324527860081,16.3533993732549,12.2154565345336,8.14481852838833,8.14481852838833,2.00348423259995,100,2017,5,16,0.00374269017002039,0.0894117094374821,0.025859031039582,0.0365398008737733,0.11927667587799,0.11927667587799,0.00105943694599042
+"22783",100,2017,5,17,1.76578658222496,8.64424643710633,13.7289330300981,13.0780967213009,2.76292634456202,2.76292634456202,1.96625570412766,100,2017,5,17,0.00842104723579203,0.0331585086711736,0.0322655064745432,0.0224333620707783,0.172343257515335,0.172343257515335,0.00108735553807158
+"22784",100,2017,5,18,7.49724983670662,11.0456546210613,17.8165344850983,13.8939165035621,8.36680964353454,8.36680964353454,1.92902717565536,100,2017,5,18,0.0726315806762717,0.0611766462092663,0.030359023990149,0.0111877001623693,0.225344996234887,0.225344996234887,0.00111611599513228
+"22785",100,2017,5,19,1.72046205463714,12.3682178732323,16.024961495688,14.3468756219341,5.19752473537416,5.19752473537416,1.89179864718307,100,2017,5,19,1.25818712330527,0.0603544061472061,0.0505275215172551,0.0181760331450448,0.0511023533637258,0.0511023533637258,0.00114571831717252
+"22786",100,2017,5,20,0.399449950077943,10.1765896898947,17.9701540981582,13.5234653810726,8.76572055627804,8.76572055627804,1.85457011871077,100,2017,5,20,0.134678364652291,0.0825426783489975,0.0194830671301702,0.00718948233537299,0.131169516580208,0.131169516580208,0.0011761625041923
+"22787",100,2017,5,21,0.0592959304765375,9.99621577100261,20.5288779087717,14.4553245737477,9.38517047076335,9.38517047076335,1.81734159023848,100,2017,5,21,0.00263157902579559,0.0421450486278791,0.0923871766298739,0.00404326541303168,0.0553029143640785,0.0553029143640785,0.00120744855619162
+"22788",100,2017,5,22,0.14763476687296,10.3087457209924,20.4708802083669,12.5950165613256,9.39638061334591,9.39638061334591,1.78011306176618,100,2017,5,22,0.0206432756040877,0.0528473660296989,0.0861585410405852,0.0100760146582328,0.0341672552254824,0.0341672552254824,0.00123957647317047
+"22789",100,2017,5,23,2.91485148451903,11.7863367183505,17.8825523493969,12.0770847270198,9.36021996698495,9.36021996698495,1.74288453329389,100,2017,5,23,0.0347953164786662,0.0620708243521285,0.0498210948567753,0.00110935502757111,0.0293590785445702,0.0293590785445702,0.00127254625512887
+"22790",100,2017,5,24,0.712541259620318,10.0579756767181,15.8668645695098,12.1885369209567,8.22897685138044,8.22897685138044,1.70565600482159,100,2017,5,24,0.113216375865435,0.110631555546153,0.0819228002828716,0.0442707616528062,0.206073092460712,0.206073092460712,0.0013063579020668
+"22791",100,2017,5,25,0.156215624111255,10.7931243353026,16.5878658693353,14.494895544776,6.0756875474592,6.0756875474592,1.6684274763493,100,2017,5,25,0.00450292432865902,0.131437455725047,0.0678222412111582,0.0428695641167049,0.619025730816818,0.619025730816818,0.00134101141398428
+"22792",100,2017,5,26,5.43124314581994,5.0742684207996,15.5847414140523,9.71264024719809,9.03526948020272,9.03526948020272,1.63119894787701,100,2017,5,26,0.527777745988651,0.0793561452598148,0.0876894375740566,0.0106941740628316,0.162798845706106,0.162798845706106,0.00137650679088129
+"22793",100,2017,5,27,0,7.58096806837781,17.878404841827,12.5428382172705,7.00522551258536,7.00522551258536,1.59397041940471,100,2017,5,27,0,0.0744321530303362,0.0761584696543618,0.00958304599750234,0.0878619985725641,0.0878619985725641,0.00141284403275784
+"22794",100,2017,5,28,10.9576459250959,7.50476348098486,11.5435203346613,9.62597358607092,5.15666667725256,5.15666667725256,1.55674189093242,100,2017,5,28,3.48666701528767,0.0575479640280482,0.0688093872717941,0.00496080749473538,0.509914034401201,0.509914034401201,0.00145002313961393
+"22795",100,2017,5,29,10.5490649541219,4.95225519027122,13.73466437878,10.4698569934623,8.29403754665513,8.29403754665513,1.51951336246012,100,2017,5,29,1.64140337403066,0.124015198023857,0.0853228269254128,0.0222695785345082,0.0892696223198336,0.0892696223198336,0.00148804411144956
+"22796",100,2017,5,30,4.77931788573564,7.68469740507757,12.1322443199368,9.15910888688661,8.0535312989364,8.0535312989364,1.48228483398783,100,2017,5,30,0.0544444571601254,0.105326316812335,0.0713695770443087,0.00716492354417052,0.599217501042494,0.599217501042494,0.00152690694826473
+"22797",100,2017,5,31,3.7007700730734,4.56286031761841,13.298998935519,10.1434323475568,6.43190319755814,6.43190319755814,1.44505630551553,100,2017,5,31,0.0770760086963115,0.339569562101097,0.105328678882955,0.0103654855316372,0.468492990991254,0.468492990991254,0.00156661165005944
+"22798",100,2017,6,1,0.152365239179305,5.27264023718923,12.9315621467313,9.3396150286835,5.41176017230362,5.41176017230362,1.42516529742959,100,2017,6,1,0.00801169641533791,0.17128946726505,0.102227468611138,0.0139719374740851,0.0650274977087443,0.0650274977087443,0.00176399739390292
+"22799",100,2017,6,2,0,3.76573160405469,13.8759077216926,9.76045108287379,6.62067104041642,6.62067104041642,1.40527428934365,100,2017,6,2,0,0.094480153657691,0.259087172299736,0.0135374303316687,1.12251754639165,1.12251754639165,0.00198300564168904
+"22800",100,2017,6,3,0.0661166126463804,1.34679866401014,14.3369527382426,9.27336646123032,9.25058308214244,9.25058308214244,1.38538328125771,100,2017,6,3,0.00257309949188902,0.0989730980555227,0.116770780670495,0.00108304533936547,0.000332172367142826,0.000332172367142826,0.0022236363934178
+"22801",100,2017,6,4,0.0685368547066472,0.637007704629625,14.0434322451601,10.7218701905019,8.81666666288974,8.81666666288974,1.36549227317177,100,2017,6,4,0.00257309949188902,0.0622871327826854,0.16555387007737,0.0502134353571457,0.0960843286437129,0.0960843286437129,0.0024858896490892
+"22802",100,2017,6,5,0.177007703407697,4.53738173662108,15.1882068856452,12.7745764997795,5.77114410945947,5.77114410945947,1.34560126508583,100,2017,6,5,0.0020467836867299,0.0379485210385501,0.0750175907292112,0.00442631211536159,0.266570775391223,0.266570775391223,0.00276976540870324
+"22803",100,2017,6,6,5.17689770571601,8.80084702887289,13.2368537632152,9.42027517094208,7.82895504182453,7.82895504182453,1.32571025699988,100,2017,6,6,0.281637406711714,0.289869431667517,0.102369618693658,0.0117093744624369,0.121257938431913,0.121257938431913,0.00307526367225993
+"22804",100,2017,6,7,0,4.87574259649934,13.06767883028,9.29860282390162,7.59592967773035,7.59592967773035,1.30581924891394,100,2017,6,7,0,0.0877099584088879,0.100936171840706,0.00076198827295155,0.122636335407537,0.122636335407537,0.00340238443975926
+"22805",100,2017,6,8,0,1.80825081831551,13.1215181571029,10.0557314145683,5.92935094791408,5.92935094791408,1.285928240828,100,2017,6,8,0,0.069444452524189,0.19480055302411,0.00939124509806778,0.148244441435087,0.148244441435087,0.00375112771120122
+"22806",100,2017,6,9,2.26754675341649,5.81541259585172,13.8867326323086,12.1934653157317,6.65990100520672,6.65990100520672,1.26603723274206,100,2017,6,9,0.199122813779713,0.215217500752474,0.115437402977365,0.000546781997299048,0.25360639207295,0.25360639207295,0.00412149348658583
+"22807",100,2017,6,10,1.85489551737757,6.67231019600259,12.8642574369055,11.9947744512191,5.37435639101287,5.37435639101287,1.24614622465612,100,2017,6,10,0.281345037633218,0.157469608296865,0.152533312606896,0.00903566021619479,0.0870134661669193,0.0870134661669193,0.00451348176591308
+"22808",100,2017,6,11,0.356105615678925,2.80064904493074,13.6853464984789,11.6810779875786,7.29828381984278,7.29828381984278,1.22625521657018,100,2017,6,11,0.0198245609399171,0.129806451563812,0.0636356778217975,0.00860293168861653,0.0701473339527236,0.0701473339527236,0.00492709254918297
+"22809",100,2017,6,12,0.0365236529094814,6.44315726319031,14.7149395601715,12.7365236586601,5.54740381870333,5.54740381870333,1.20636420848424,100,2017,6,12,0.00228070182235617,0.145531581084504,0.143439784617829,0.00675614628717245,0.221300649685354,0.221300649685354,0.00536232583639551
+"22810",100,2017,6,13,0.887898791757616,5.70423542784386,12.9259186147725,11.2116061442494,5.92466440452601,5.92466440452601,1.1864732003983,100,2017,6,13,0.399415183311319,0.0814870853770111,0.096532200807804,0.00116900688089761,0.0662006046183801,0.0662006046183801,0.00581918162755068
+"22811",100,2017,6,14,0,4.38324524939257,15.1466117739284,12.1557425791674,6.51836083404826,6.51836083404826,1.16658219231236,100,2017,6,14,0,0.052469591035922,0.0944620346159216,0.0113274811193092,0.180606418569482,0.180606418569482,0.0062976599226485
+"22812",100,2017,6,15,0,7.22786580086804,16.4758306593523,11.3351816171073,8.02815177023608,8.02815177023608,1.14669118422642,100,2017,6,15,0,0.0254251366877685,0.102359693672532,0.0033596571873002,0.186120510008817,0.186120510008817,0.00679776072168896
+"22813",100,2017,6,16,0.0691969207230836,4.15222226222618,16.854939772351,10.8941254683978,8.92058287411764,8.92058287411764,1.12680017614047,100,2017,6,16,0.00245614042407588,0.0392005590204829,0.117746907132976,0.00731402166718653,0.000332158312971177,0.000332158312971177,0.00731948402467205
+"22814",100,2017,6,17,0.0685368547066472,2.83623763777898,14.9939823748648,12.8384158225736,8.23342130102865,8.23342130102865,1.10690916805453,100,2017,6,17,0.00257309949188902,0.071698824533948,0.0911205162829577,0.0700707708348671,0.280481237920181,0.280481237920181,0.00786282983159779
+"22815",100,2017,6,18,0.736193622496262,7.17127614498663,15.3506710705059,13.081936180526,6.81059410600904,6.81059410600904,1.08701815996859,100,2017,6,18,0.0369005860780418,0.0509473676179687,0.0657842213280067,0.00520527043488727,0.114066645664662,0.114066645664662,0.00842779814246617
+"22816",100,2017,6,19,0.472387244271235,8.23442233067797,15.4624532903119,12.5553244998877,6.14184819432375,6.14184819432375,1.06712715188265,100,2017,6,19,0.0547368406691748,0.0687139794556515,0.135662022344197,0.00221345608143666,0.310492325530715,0.310492325530715,0.0090143889572772
+"22817",100,2017,6,20,0.0278327836930686,5.96083600345356,15.5749724485693,11.537755833994,8.90587448208245,8.90587448208245,1.04723614379671,100,2017,6,20,0.0020467836867299,0.057102929084079,0.0992811885098756,0.0106894764683294,0.000789475134323984,0.000789475134323984,0.00962260227603086
+"22818",100,2017,6,21,0.897029701304777,7.95029715159283,12.9451595029422,9.72937284182138,5.48591860528826,5.48591860528826,1.02734513571077,100,2017,6,21,0.0417543846473363,0.0766672609139857,0.10373162138208,0.00443861306626382,0.0529877352664525,0.0529877352664525,0.0102524380987272
+"22819",100,2017,6,22,0.0448844891176759,6.41052811478887,14.3337293945917,11.8179099121765,6.2353025447942,6.2353025447942,1.00745412762483,100,2017,6,22,0.00374269017002039,0.0741134370926678,0.155598865406478,0.00142631560663564,0.169795351837134,0.169795351837134,0.0109038964253661
+"22820",100,2017,6,23,0,6.42048397106175,13.3274037399963,11.5371837038936,6.75264022688661,6.75264022688661,0.987563119538888,100,2017,6,23,0,0.0540029078924286,0.117222143809063,0.0123584863830894,0.247845046707086,0.247845046707086,0.0115769772559477
+"22821",100,2017,6,24,3.96215623781101,5.20832777810175,11.3023871326342,8.27756864927521,6.46865788597216,6.46865788597216,0.967672111452947,100,2017,6,24,0.0576023525661992,0.146098807399611,0.0809806958963693,0.0105813139955245,0.3309765333767,0.3309765333767,0.0122716805904719
+"22822",100,2017,6,25,0,4.89366333135808,12.5933994622645,11.0579757302245,5.74618266622881,5.74618266622881,0.947781103367005,100,2017,6,25,0,0.0599988438801689,0.108749755495819,0.01986726300875,0.106044986952417,0.106044986952417,0.0129880064289388
+"22823",100,2017,6,26,5.39856986821157,5.19209020392205,12.5798018773397,10.2451374654067,6.51595162715849,6.51595162715849,0.927890095281064,100,2017,6,26,0.490350868269729,0.109438631637772,0.0892134682170981,0.000764916064247046,0.399714016113518,0.399714016113518,0.0137259547713483
+"22824",100,2017,6,27,0.370187024832821,4.57239824081018,13.0094829261369,9.19325615680388,5.21195823524651,5.21195823524651,0.907999087195124,100,2017,6,27,0.0102339177190909,0.0974923780999226,0.237881244942805,0.0283730997298388,0.178420500881362,0.178420500881362,0.0144855256177005
+"22825",100,2017,6,28,0.0528052813149128,6.8202639937532,14.1386689772569,11.3753024763269,7.68630361819293,7.68630361819293,0.888108079109183,100,2017,6,28,0.00538011711940431,0.0730175432908127,0.0570485600098452,0.00531638785922265,0.113256168606435,0.113256168606435,0.0152667189679953
+"22826",100,2017,6,29,2.38316830812377,5.32854788531565,11.8202310411057,10.794224505115,4.82193618455474,4.82193618455474,0.868217071023242,100,2017,6,29,0.310643271354211,0.0488321477053696,0.0708467540696972,0.00699882838159139,0.263673042801545,0.263673042801545,0.0160695348222327
+"22827",100,2017,6,30,3.67216724609778,6.68085805863568,12.0193620247416,8.00041802829117,6.59401535541967,6.59401535541967,0.848326062937301,100,2017,6,30,0.137602318490469,0.306809360468479,0.11607014573535,0.00111111217082035,0.16900644791699,0.16900644791699,0.0168939731804128
+"22828",100,2017,7,1,0,0.408811881351392,12.4069967322355,8.3872167778225,9.10261835385733,9.10261835385733,0.862075081682488,100,2017,7,1,0,0.0406146209685776,0.0698227931620138,0.00213391932888235,0.000276606906705287,0.000276606906705287,0.0161990234583882
+"22829",100,2017,7,2,0,1.08232123630144,12.4894829006216,7.20367435207724,9.13904283857188,9.13904283857188,0.875824100427676,100,2017,7,2,0,0.0613134462535042,0.0351696207147446,0.00819531481783731,0.000194148720696809,0.000194148720696809,0.0155200550527376
+"22830",100,2017,7,3,0,1.90563255896007,10.1881847822233,9.65804180820914,2.84536850491766,2.84536850491766,0.889573119172863,100,2017,7,3,0,0.102749708318155,0.0371496853767735,0.000584210555362059,0.173459653706025,0.173459653706025,0.014857067963461
+"22831",100,2017,7,4,5.45401540841207,5.85411438454102,14.1031352442877,11.9761386478957,5.67630369041619,5.67630369041619,0.903322137918051,100,2017,7,4,1.17093573046011,0.0695040858129578,0.0752561085194201,0.00700234300276,0.0995877054170102,0.0995877054170102,0.0142100621905583
+"22832",100,2017,7,5,6.05775575197176,6.4639934475797,13.6827832705641,10.2464685754807,7.12012102527849,7.12012102527849,0.917071156663238,100,2017,7,5,1.06035097791441,0.10428126260079,0.0996672284779395,0.00241755023540591,0.404802847277791,0.404802847277791,0.0135790377340295
+"22833",100,2017,7,6,0.679647972776551,2.19598462426885,14.0669857115373,9.60492855382569,9.30371837699898,9.30371837699898,0.930820175408426,100,2017,7,6,0.0384210541861798,0.0422830494083187,0.0902005807040828,0.0129339058681495,0.000214617995837413,0.000214617995837413,0.0129639945938747
+"22834",100,2017,7,7,0.533003303690581,4.59136417649104,13.6113090515137,10.3612321312278,9.26423546714489,9.26423546714489,0.944569194153613,100,2017,7,7,0.231169597188458,0.0292655114157407,0.127054366124095,0.00699999936430643,0.0143842197039543,0.0143842197039543,0.0123649327700938
+"22835",100,2017,7,8,1.81342132020705,3.94156216106268,13.2580197778079,9.97192519935969,7.47113314766039,7.47113314766039,0.958318212898801,100,2017,7,8,0.0448537943237715,0.0633257509605321,0.0668368946789997,0.0231450191141443,0.151204115921178,0.151204115921178,0.0117818522626869
+"22836",100,2017,7,9,5.16336633243708,6.20029702884267,12.1683498221,9.91407036230509,5.6465016728056,5.6465016728056,0.972067231643988,100,2017,7,9,1.07286548706525,0.112725752871793,0.0663894853670486,0.0100561444266441,0.314226277640288,0.314226277640288,0.0112147530716539
+"22837",100,2017,7,10,5.73707365438883,5.03112210010407,13.8718042163828,10.7179099241368,8.53634761583687,8.53634761583687,0.985816250389176,100,2017,7,10,0.515847922431118,0.126556128208555,0.0644028825347837,0.00682867236894692,0.217616321595032,0.217616321595032,0.0106636351969948
+"22838",100,2017,7,11,7.75852576865353,3.20487349418917,14.6882067786323,10.6104511349115,9.41218925912519,9.41218925912519,0.999565269134364,100,2017,7,11,2.18339152302663,0.0619982596910541,0.13685376076956,0.00158420095173255,0.00857601542787036,0.00857601542787036,0.0101284986387097
+"22839",100,2017,7,12,0.242464253611297,2.65957097461646,14.808789769415,9.63130912528966,9.43883375707108,9.43883375707108,1.01331428787955,100,2017,7,12,0.00801169645890857,0.0383450229572294,0.0868544122931089,0.00670526726713561,0.0020274794216771,0.0020274794216771,0.00960934339679856
+"22840",100,2017,7,13,0,4.24881179078315,13.815786666996,8.84941697514097,5.67972498338739,5.67972498338739,1.02706330662474,100,2017,7,13,0,0.0585537942797225,0.17269241059515,0.0200005466194427,0.279148576555772,0.279148576555772,0.00910616947126135
+"22841",100,2017,7,14,7.63289334514354,7.03107810571249,12.2255775684571,9.41976888108962,9.00457646482193,9.00457646482193,1.04081232536993,100,2017,7,14,0.334502898043391,0.108317549773558,0.0555426636478815,0.000414617725898645,0.208870149373824,0.208870149373824,0.00861897686209808
+"22842",100,2017,7,15,4.53905390748883,3.27864685625133,12.1440594655321,8.43544558303715,8.52870196372893,8.52870196372893,1.05456134411511,100,2017,7,15,0.363976594774363,0.0818473699307643,0.100966672770227,0.00659237914735792,0.232533976288344,0.232533976288344,0.00814776556930874
+"22843",100,2017,7,16,0.243014308559422,3.95959297632358,10.3229043486357,8.30264024661057,5.44380638439401,5.44380638439401,1.0683103628603,100,2017,7,16,0.00450292456394053,0.145750856018075,0.0704894755671115,0.0123426926173135,0.0913028950005451,0.0913028950005451,0.00769253559289336
+"22844",100,2017,7,17,6.42365242388382,6.67881186488438,14.092178162175,11.169240893835,7.93698565952062,7.93698565952062,1.08205938160549,100,2017,7,17,0.520994163870132,0.143531591452238,0.0848812607034994,0.0191257543285502,0.271938650655493,0.271938650655493,0.00725328693285191
+"22845",100,2017,7,18,5.22233224518359,7.77400446829885,13.9933004494679,10.4006050695287,9.2026842986003,9.2026842986003,1.09580840035068,100,2017,7,18,0.33210529868368,0.0553005924481533,0.0763117585724082,0.00611754487921676,0.105756048421214,0.105756048421214,0.00683001958918441
+"22846",100,2017,7,19,11.5354234739499,7.46559956050155,11.7656327133263,9.12961490345736,6.52174923941903,6.52174923941903,1.10955741909586,100,2017,7,19,1.41315787019794,0.138259018235477,0.0628356692532392,0.000948546073944841,0.330281315878287,0.330281315878287,0.00642273356189086
+"22847",100,2017,7,20,1.92805279547101,6.58699670378262,11.2514412473924,8.71584155876907,8.26051697662824,8.26051697662824,1.12330643784105,100,2017,7,20,0.101111098395456,0.180725142612807,0.10837081844269,0.00122749081892622,0.413549717953388,0.413549717953388,0.00603142885097125
+"22848",100,2017,7,21,0.391639170375201,-1.01168318228884,13.2876567945491,8.15149612132997,10.1809790163281,10.1809790163281,1.13705545658624,100,2017,7,21,0.00274853768404465,0.0491666671302588,0.0599719062872883,0.000378358979334245,0.00652866432465195,0.00652866432465195,0.00565610545642557
+"22849",100,2017,7,22,0,2.37115514160383,14.2293399710073,9.20718377704012,8.12531346299074,8.12531346299074,1.15080447533143,100,2017,7,22,0,0.0305052614970581,0.0917023648513797,0.0136649217706664,0.708837454465702,0.708837454465702,0.00529676337825385
+"22850",100,2017,7,23,0.928602857951677,8.69672169281442,13.8463917198223,11.5803409246984,6.67422443910269,6.67422443910269,1.16455349407661,100,2017,7,23,0.027076029261651,0.157911121453258,0.0701871550599692,0.00284853851815314,0.309955554050889,0.309955554050889,0.00495340261645607
+"22851",100,2017,7,24,7.97172731928306,6.03364140759207,14.7097689489065,11.3511882274195,9.32090208685175,9.32090208685175,1.1783025128218,100,2017,7,24,0.0446783707713916,0.030209356074884,0.0539485191591517,0.00474503374165611,0.285743335313484,0.285743335313484,0.00462602317103223
+"22852",100,2017,7,25,0.101760177533947,7.50600662640613,16.3584159876254,12.0814300879131,7.59068206224767,7.59068206224767,1.19205153156699,100,2017,7,25,0.00362573110220725,0.0490485179644733,0.0304153053331359,0.00116724198554767,0.281834007014495,0.281834007014495,0.00431462504198234
+"22853",100,2017,7,26,3.64829481867674,9.08822876077281,13.6668536969931,11.050110025899,9.53042915814256,9.53042915814256,1.20580055031218,100,2017,7,26,0.251754367839532,0.0831450519629667,0.0638426752280761,0.00389884581099392,0.141367133409451,0.141367133409451,0.00401920822930638
+"22854",100,2017,7,27,0.400880093375842,4.0357976447631,13.752321371282,10.4110452318349,7.29799776633318,7.29799776633318,1.21954956905736,100,2017,7,27,0.0714619893924884,0.068364919861026,0.0738041192440367,0.0122064323046735,0.0431707660095273,0.0431707660095273,0.00373977273300437
+"22855",100,2017,7,28,9.49471946229504,6.61017596524934,13.58849288974,9.39994499156184,9.16057194813643,9.16057194813643,1.23329858780255,100,2017,7,28,0.106198830297908,0.0906251308407923,0.0589818966269823,0.0162690232818985,0.0520210457094617,0.0520210457094617,0.00347631855307631
+"22856",100,2017,7,29,0.150165018821248,8.12210122822928,15.0218591438268,9.78184816677316,3.27704069270815,3.27704069270815,1.24704760654774,100,2017,7,29,0.00485380155824082,0.0982561342797658,0.0260889186012389,0.0213672168977966,0.201703477512053,0.201703477512053,0.00322884568952219
+"22857",100,2017,7,30,2.12618262828118,8.44611659328012,14.1049614204432,10.7686247946274,9.37353129958687,9.37353129958687,1.26079662529293,100,2017,7,30,0.162865484457967,0.0470164126413772,0.064405229073318,0.00912280790992006,0.235795408014744,0.235795408014744,0.00299735414234201
+"22858",100,2017,7,31,1.90319030408156,2.84701868934338,14.7861825661822,9.90559946087459,10.6287899195689,10.6287899195689,1.27454564403811,100,2017,7,31,0.103742687869492,0.0571871481220683,0.104680046489858,0.00598888223442782,0.0666696711424207,0.0666696711424207,0.00278184391153578
+"22859",100,2017,8,1,0.338063812696829,1.01547854148647,15.3695599783634,10.4685478388804,11.2815404006488,11.2815404006488,1.28891458451662,100,2017,8,1,0.0242690070330749,0.0364555537064879,0.0554918002993745,0.0126006067443863,0.00753685154391562,0.00753685154391562,0.00276408609344528
+"22860",100,2017,8,2,0.161826185324732,2.44158417766768,12.4930032010388,8.89719479941692,10.6015291455293,10.6015291455293,1.30328352499512,100,2017,8,2,0.00578947419660135,0.0793731005133286,0.0741474148310437,0.00414327099455828,0.116605276743668,0.116605276743668,0.00274695413428235
+"22861",100,2017,8,3,0,3.02595158772107,9.41617146104869,10.0249285724166,5.30569857050746,5.30569857050746,1.31765246547363,100,2017,8,3,0,0.0432052687700778,0.0486175400271525,0.00210643966192681,0.171625173438244,0.171625173438244,0.00273044803404697
+"22862",100,2017,8,4,1.16193623209682,2.98272822057978,12.3051924584854,9.90094609570057,7.70083606728364,7.70083606728364,1.33202140595213,100,2017,8,4,0.00339181274001414,0.0277590673190139,0.0608035073509684,0.0077397356759182,0.111880176421544,0.111880176421544,0.00271456779273915
+"22863",100,2017,8,5,5.82750271282049,5.48924093854965,13.1082839672059,9.89082505548223,10.2838503026595,10.2838503026595,1.34639034643063,100,2017,8,5,1.05760230153627,0.0430204721417606,0.0533497153790088,0.0113000044081636,0.146298280448319,0.146298280448319,0.0026993134103589
+"22864",100,2017,8,6,2.23135312437618,6.17799776431882,12.3587019241551,8.09444449312485,9.73337730348963,9.73337730348963,1.36075928690914,100,2017,8,6,0.343742710010353,0.0459110977173027,0.0622924568110196,0.00308070273824969,0.0809163718241183,0.0809163718241183,0.0026846848869062
+"22865",100,2017,8,7,10.7990100407364,4.80940595895413,12.3801760311567,9.03932906193833,8.06982397272511,8.06982397272511,1.37512822738764,100,2017,8,7,1.78029262598501,0.0459941686385014,0.0530245947297585,0.00105906662973967,0.804654396439745,0.804654396439745,0.00267068222238106
+"22866",100,2017,8,8,4.88107804313089,4.99686477207901,14.1538502574623,12.6763694981406,5.91103409671679,5.91103409671679,1.38949716786615,100,2017,8,8,1.52023400055737,0.105126321125613,0.0986116386012907,0.000787721616141117,0.166317534514781,0.166317534514781,0.00265730541678349
+"22867",100,2017,8,9,0.119801981983208,4.98584157798943,15.89236536645,10.9772385879449,10.2461276353389,10.2461276353389,1.40386610834465,100,2017,8,9,0.00473684224643205,0.0419333402422003,0.0527211041323974,0.00035379812459468,0.0464133854961615,0.0464133854961615,0.00264455447011347
+"22868",100,2017,8,10,0.0379537959450936,8.79735975611721,16.9130034787689,9.25838280773268,9.10492849349976,9.10492849349976,1.41823504882315,100,2017,8,10,0.00485380131424519,0.0321812616985184,0.0357731341360141,0.0335649402262182,1.41879119225672,1.41879119225672,0.00263242938237101
+"22869",100,2017,8,11,1.67040702323578,6.87046210128482,13.6543124223044,9.44794279251686,10.6978656760406,10.6978656760406,1.43260398930166,100,2017,8,11,0.246900566516565,0.0767023471720921,0.0585759695288331,0.00176082620236075,0.370027473866935,0.370027473866935,0.00262093015355611
+"22870",100,2017,8,12,0.777777779351498,8.45396035155579,15.3776348321745,11.8113971401756,9.76936192087608,9.76936192087608,1.44697292978016,100,2017,8,12,0.0238596473590675,0.045462006775332,0.0541672800990566,0.0166151805164488,0.133570776121852,0.133570776121852,0.00261005678366877
+"22871",100,2017,8,13,1.50550055005501,4.7087348506789,17.9159735668086,9.9957975979292,12.6135754212819,12.6135754212819,1.46134187025867,100,2017,8,13,0.0276608242347238,0.0288356729228823,0.0774228525621516,0.00453683897329518,0.00662749485226738,0.00662749485226738,0.002599809272709
+"22872",100,2017,8,14,0.56798680545506,8.7038284853609,17.3980527632307,13.4336522766466,11.6059954877209,11.6059954877209,1.47571081073717,100,2017,8,14,0.167076020014217,0.0593889329699049,0.028973079735221,0.000645027870116563,0.149922223960097,0.149922223960097,0.00259018762067678
+"22873",100,2017,8,15,0.988008799201453,7.98555554703648,17.2627504598452,15.6063475739969,5.05190323786636,5.05190323786636,1.49007975121568,100,2017,8,15,0.0336257326672657,0.0459023485864555,0.064827489306566,0.000560816165930537,0.41441636319637,0.41441636319637,0.00258119182757212
+"22874",100,2017,8,16,9.84576452630844,8.35459828822657,14.5830693218705,9.72817382245961,11.8810012794302,11.8810012794302,1.50444869169418,100,2017,8,16,0.981169710215132,0.0909579418205393,0.0562029867942648,0.000380114282158464,0.249405449143428,0.249405449143428,0.00257282189339502
+"22875",100,2017,8,17,9.1920791836855,7.62684270231375,12.7299559984532,9.79069310682442,11.075192515475,11.075192515475,1.51881763217268,100,2017,8,17,1.26035089760499,0.0565275000622648,0.0479707546235031,0.00854501896469761,0.375816352578841,0.375816352578841,0.00256507781814548
+"22876",100,2017,8,18,14.454565457385,5.14015408947129,10.5108251991314,8.69629266736793,8.00917489405381,8.00917489405381,1.53318657265119,100,2017,8,18,0.476900426407379,0.101057901279847,0.102371934614213,0.000709943847279689,1.14656665626106,1.14656665626106,0.0025579596018235
+"22877",100,2017,8,19,3.29768978408461,3.65099010504249,11.8579647937099,7.98132002183182,11.2364135077029,11.2364135077029,1.54755551312969,100,2017,8,19,0.0864912224652512,0.196802343537522,0.0380274342364801,0.000228655510638195,0.426857853408795,0.426857853408795,0.00255146724442909
+"22878",100,2017,8,20,0.527392748886734,-0.710484050812632,12.2788229964354,6.6486028659724,12.9970736047222,12.9970736047222,1.5619244536082,100,2017,8,20,0.0165497077034232,0.0566619853083855,0.0297924013194455,0.00115906599280234,0.107281863535037,0.107281863535037,0.00254560074596223
+"22879",100,2017,8,21,5.46369639481648,4.86697475928547,12.3712101738052,9.73533541193627,10.0485369007711,10.0485369007711,1.5762933940867,100,2017,8,21,0.74397668743693,0.0926005764331182,0.0703368599228674,0.000277779897148496,0.119850890778742,0.119850890778742,0.00254036010642293
+"22880",100,2017,8,22,0.851265124230757,2.39255225146958,14.9996919946702,10.5906381701479,9.15591857876584,9.15591857876584,1.5906623345652,100,2017,8,22,0.00561403368648758,0.0475356757091566,0.0822608959935083,0.00410407860075984,0.133046845798673,0.133046845798673,0.00253574532581118
+"22881",100,2017,8,23,0.322332238305126,7.67947195806388,13.3090538721536,10.1621891982747,6.70588562433476,6.70588562433476,1.60503127504371,100,2017,8,23,0.063157893238012,0.0957841984063142,0.0195117162738878,0.000817543136924823,0.296183012857505,0.296183012857505,0.00253175640412701
+"22882",100,2017,8,24,11.9886688740209,4.00460946310734,13.1135092851746,8.76113316144618,12.6327392045159,12.6327392045159,1.61940021552221,100,2017,8,24,0.66695902305743,0.172561985899284,0.0366467635306047,0.00360234692226712,0.427673048668649,0.427673048668649,0.00252839334137038
+"22883",100,2017,8,25,0.117491750925681,4.45286034488573,14.0428052730162,10.7789659007023,9.09277216698339,9.09277216698339,1.63376915600072,100,2017,8,25,0.0020467836867299,0.163425171580528,0.0810894420200655,0.00199123121569888,0.330333410051217,0.330333410051217,0.00252565613754133
+"22884",100,2017,8,26,0,7.69891086060091,14.6754786037114,10.1607591589161,11.1651594825048,11.1651594825048,1.64813809647922,100,2017,8,26,0,0.107546810836146,0.0884877644010039,0.00492222607937298,0.40007843776957,0.40007843776957,0.00252354479263982
+"22885",100,2017,8,27,7.96479643558381,4.28609465765874,10.4988118277656,7.86818485291484,12.2823212500846,12.2823212500846,1.66250703695772,100,2017,8,27,0.457602224851961,0.169567255814885,0.0430432831592139,0.00172864894088038,0.275825878159009,0.275825878159009,0.00252205930666588
+"22886",100,2017,8,28,3.50990096964065,2.75520349755408,11.7202530389834,9.34100106997852,11.6589438959841,11.6589438959841,1.67687597743623,100,2017,8,28,0.153391804444167,0.0941146128994728,0.0609911839893352,0.000169003880425104,0.244467100754753,0.244467100754753,0.0025211996796195
+"22887",100,2017,8,29,1.56281627804795,3.44363038164817,13.1514741196753,10.0976897041396,10.6660065834541,10.6660065834541,1.69124491791473,100,2017,8,29,0.131637422815402,0.157814043509618,0.0817538148623966,0.0160362551104411,0.441047337506488,0.441047337506488,0.00252096591150068
+"22888",100,2017,8,30,3.16446645398869,4.3211001140712,13.6668536298477,8.64617165585424,12.8426512465356,12.8426512465356,1.70561385839324,100,2017,8,30,0.00578947937279844,0.0831783515472938,0.0573760492269956,0.00882748362901058,0.192987172417954,0.192987172417954,0.00252135800230942
+"22889",100,2017,8,31,0.118811882958554,0.0431463126035774,14.1510231146062,9.10396041912083,15.6202200395439,15.6202200395439,1.71998279887174,100,2017,8,31,0.00228070182235617,0.0765608181832435,0.0820784122601711,0.0453175815962514,0.00185847080081136,0.00185847080081136,0.00252237595204572
+"22890",100,2017,9,1,0,2.92323431848037,17.6469965642042,9.51054994081638,12.9795380540938,12.9795380540938,1.74609106874149,100,2017,9,1,0,0.047667255771932,0.149745060734688,0.0132473681667932,0.347826225097889,0.347826225097889,0.00255225473707667
+"22891",100,2017,9,2,1.25335533712039,7.10605062509921,18.1221451859007,13.7331684708464,8.36280518396459,8.36280518396459,1.77219933861123,100,2017,9,2,0.740292374732204,0.0262397779944587,0.0235246193470944,0.00575614780411225,0.486369067958395,0.486369067958395,0.00258327370508874
+"22892",100,2017,9,3,2.47645766113457,8.9859405209129,13.9703630577482,9.42957093456004,11.8300330148409,11.8300330148409,1.79830760848098,100,2017,9,3,0.16719298361338,0.0677578512181567,0.0381561514805337,0.00108304137967551,0.576743479747756,0.576743479747756,0.00261543285608195
+"22893",100,2017,9,4,17.9858084228566,4.29370742085481,10.1784377344633,7.74020897778216,7.46557760028818,7.46557760028818,1.82441587835073,100,2017,9,4,3.41807025474445,0.0568654798820147,0.0237800924045353,0.000446788239090552,5.80976084856071,5.80976084856071,0.00264873219005628
+"22894",100,2017,9,5,13.4457645332328,3.77363033446804,11.2099339712833,9.22218909331805,8.144378441264,8.144378441264,1.85052414822048,100,2017,9,5,0.226198754784714,0.100653790734392,0.117425718280802,0.000647957390313619,2.59037614305512,2.59037614305512,0.00268317170701174
+"22895",100,2017,9,6,13.8431242910287,5.08482947365286,13.7278547360428,10.8285697553024,12.0694829733065,12.0694829733065,1.87663241809022,100,2017,9,6,3.36467817339997,0.137427472328739,0.0813759801943335,0.00245555352111621,1.11109172442259,1.11109172442259,0.00271875140694833
+"22896",100,2017,9,7,5.69680964435288,6.95094607398324,14.2973048605672,11.5088120419594,10.7528603661834,10.7528603661834,1.90274068795997,100,2017,9,7,0.281111072964156,0.0703953312846174,0.0519040848604158,0.000613455061975614,0.779798156236413,0.779798156236413,0.00275547128986604
+"22897",100,2017,9,8,5.91782178501091,5.63937285657239,12.2670957200205,9.6633333157916,13.034433463488,13.034433463488,1.92884895782972,100,2017,9,8,0.523157812085087,0.132405250650829,0.0603701956186512,0.0107339190433841,0.855273144375165,0.855273144375165,0.00279333135576489
+"22898",100,2017,9,9,2.73696371693291,6.46779976636007,13.4739054269654,10.6368647996086,12.5009020500057,12.5009020500057,1.95495722769946,100,2017,9,9,0.049415221130645,0.133665507791775,0.0372251409788088,0.000567246308880295,0.675037430287029,0.675037430287029,0.00283233160464486
+"22899",100,2017,9,10,0.32145215283231,4.79567648422862,14.9257866478596,9.63748085957812,12.1362376312743,12.1362376312743,1.98106549756921,100,2017,9,10,0.00888888915379844,0.0785035285804875,0.020580692699584,0.000256140866259711,1.06865109529114,1.06865109529114,0.00287247203650596
+"22900",100,2017,9,11,1.01958195634014,6.99387234613316,19.9851485358344,13.0650715266648,9.23399340017925,9.23399340017925,2.00717376743896,100,2017,9,11,0.441520457693011,0.0726497118899869,0.0464813314920214,0.00442749709649785,0.97496730687668,0.97496730687668,0.00291375265134818
+"22901",100,2017,9,12,5.59812972306943,9.85887798984977,14.1422771877713,13.9621452891787,5.08656759702727,5.08656759702727,2.03328203730871,100,2017,9,12,0.12146197692694,0.0196895047550865,0.0518110605028793,0.00292866210054332,0.225670768630731,0.225670768630731,0.00295617344917154
+"22902",100,2017,9,13,5.47887794622625,8.34799762134111,12.131650154609,9.61801974817996,9.82741476023289,9.82741476023289,2.05939030717845,100,2017,9,13,0.675087745621905,0.0143175413969078,0.0371929893160155,0.000391811727852214,1.28690934714385,1.28690934714385,0.00299973442997602
+"22903",100,2017,9,14,13.9918591170946,5.10277232767069,14.6805501010421,11.1118152395989,12.9523872652463,12.9523872652463,2.0854985770482,100,2017,9,14,1.08929814611965,0.147002312830895,0.0723538832079883,0.00166666348784887,1.53226961668146,1.53226961668146,0.00304443559376163
+"22904",100,2017,9,15,4.41595151715546,8.89419151742597,15.23355337443,11.8448734766055,9.91156210652804,9.91156210652804,2.11160684691795,100,2017,9,15,0.195087673176119,0.107309994614016,0.0577433177234881,0.000969583886206514,1.10783589345039,1.10783589345039,0.00309027694052837
+"22905",100,2017,9,16,17.2137513333815,6.11539049169543,12.849460969008,7.72958201438812,16.3831132567755,16.3831132567755,2.13771511678769,100,2017,9,16,6.42918152368584,0.153815170659535,0.0534064031345219,0.00423860218205851,0.214650947488469,0.214650947488469,0.00313725847027624
+"22906",100,2017,9,17,0.0190319034739165,1.58777778337498,16.9636081447958,9.14996703413322,18.4993292280812,18.4993292280812,2.16382338665744,100,2017,9,17,0.00175438601719706,0.0306023373562005,0.0608257334350245,0.00470407461208936,0.0495386054862409,0.0495386054862409,0.00318538018300523
+"22907",100,2017,9,18,3.86974697734657,9.03616057606814,16.0479098719732,11.2859405144082,7.0085478390273,7.0085478390273,2.18993165652719,100,2017,9,18,7.91140358326033,0.0435818667719482,0.0197286119876358,0.0499444281260835,0.250649676302879,0.250649676302879,0.00323464207871534
+"22908",100,2017,9,19,6.79449952632287,7.01563264041057,13.2838174475826,9.07460952041173,14.171804152175,14.171804152175,2.21603992639694,100,2017,9,19,1.11666685210337,0.121869044306151,0.0519929337039647,0.00719882643860255,0.767569662315041,0.767569662315041,0.0032850441574066
+"22909",100,2017,9,20,0.0906490669621624,3.81991201370332,17.6012211433469,11.586919692221,16.7733554777139,16.7733554777139,2.24214819626668,100,2017,9,20,0.0231578954052158,0.042783051703277,0.0312562373382372,0.00439825164174404,0.117114604807476,0.117114604807476,0.00333658641907898
+"22910",100,2017,9,21,0,9.95960391184153,19.3598239613314,13.1995820086388,10.6485696872338,10.6485696872338,2.26825646613643,100,2017,9,21,0,0.0467818730494454,0.0152830083838796,0.0505164195457401,1.33574260548827,1.33574260548827,0.00338926886373248
+"22911",100,2017,9,22,0.632123215904724,5.88546749624876,22.5483718049539,13.6477447715398,17.8616281568152,17.8616281568152,2.29436473600618,100,2017,9,22,0.056959066364849,0.0105818714900502,0.0324509176647957,0.000406430718559496,0.351543314212346,0.351543314212346,0.00344309149136711
+"22912",100,2017,9,23,1.24697469196173,11.3467106635552,21.3068977435692,13.8690099789627,14.3070628603693,14.3070628603693,2.32047300587592,100,2017,9,23,0.138713447873356,0.0123432871991822,0.0448988500943973,0.00388948179774991,0.726904226126947,0.726904226126947,0.00349805430198288
+"22913",100,2017,9,24,2.76402639241108,8.77072602241608,16.3990427212353,12.6113201735174,15.0412322297217,15.0412322297217,2.34658127574567,100,2017,9,24,0.229181284012156,0.0450274392335319,0.0647807048779597,0.0115649148172303,4.17659871123255,4.17659871123255,0.00355415729557976
+"22914",100,2017,9,25,9.68679865594744,8.87732679746857,13.2404181487752,9.29563274425511,14.4829593276558,14.4829593276558,2.37268954561542,100,2017,9,25,2.34257272764962,0.0397543634560553,0.0502690172698212,0.00233567692725557,1.48803847831824,1.48803847831824,0.00361140047215778
+"22915",100,2017,9,26,2.84125413477618,4.44798685038182,14.9794938923633,8.94983488236061,15.8655555219409,15.8655555219409,2.39879781548517,100,2017,9,26,0.203216368691968,0.106573096944594,0.0553579019179454,0.00482573762868849,0.477794305217127,0.477794305217127,0.00366978383171693
+"22916",100,2017,9,27,0.0203520355067893,8.56303640036169,20.0690868134284,11.020352060383,10.6452255962443,10.6452255962443,2.42490608535491,100,2017,9,27,0.00356725156830068,0.126813504695428,0.0739918113311284,0.000976030592985473,1.69923189451216,1.69923189451216,0.0037293073742572
+"22917",100,2017,9,28,2.77326735831199,8.8759295806633,14.5004729783968,10.4870627332966,14.3827392087124,14.3827392087124,2.45101435522466,100,2017,9,28,0.0247953280231455,0.0771263184263618,0.0466245956711816,0.00119181717332273,2.5592255115086,2.5592255115086,0.0037899710997786
+"22918",100,2017,9,29,0.452035215059326,5.65367434913963,17.1413203429336,11.4925851580596,15.8135421981644,15.8135421981644,2.47712262509441,100,2017,9,29,0.0367251475242495,0.0704842207568663,0.0619063933080996,0.00378304071933886,0.750147996742016,0.750147996742016,0.00385177500828112
+"22919",100,2017,9,30,5.77436742740627,7.87211230826719,14.5533111905894,9.41459834851054,16.4117160782431,16.4117160782431,2.50323089496415,100,2017,9,30,0.334444426430614,0.153915244420415,0.0485818840607272,0.00494327158795434,0.724188886727365,0.724188886727365,0.00391471909976478
+"22920",100,2017,10,1,0.668976901912584,7.79282726942509,15.5628603357162,13.0591640650767,13.7691087932608,13.7691087932608,2.5460224225295,100,2017,10,1,0.0102923946910438,0.0814152366683137,0.0213163623609742,0.000392984327866899,0.728909339815625,0.728909339815625,0.00378230920786652
+"22921",100,2017,10,2,2.68635864370596,7.15718371062913,15.2976897158901,11.6814741159823,11.1104510635695,11.1104510635695,2.58881395009484,100,2017,10,2,1.55146203048048,0.112233913114906,0.0417426549454895,0.000367247973058484,0.111446731119058,0.111446731119058,0.00365559158050845
+"22922",100,2017,10,3,0.0691969207230836,3.47020904616554,18.5956213293296,13.271199108875,21.0504952505215,21.0504952505215,2.63160547766018,100,2017,10,3,0.00245614042407588,0.0338473639967896,0.0502006701601884,0.00173216467746312,0.191544964889896,0.191544964889896,0.00353456621769063
+"22923",100,2017,10,4,0,7.54391639224767,22.4238944331674,14.0638174459879,12.865929584692,12.865929584692,2.67439700522552,100,2017,10,4,0,0.0950830105497429,0.0711115742424492,0.0153473839319777,0.784633948496834,0.784633948496834,0.00341923311941301
+"22924",100,2017,10,5,0.232343238744602,11.1745764613807,15.2013421452085,12.6833002889904,11.9314961491114,11.9314961491114,2.71718853279086,100,2017,10,5,0.0331578964962249,0.0425496901908919,0.0141619861937992,0.0203783755988983,1.09642567094287,1.09642567094287,0.00330959228567563
+"22925",100,2017,10,6,1.60693068131886,8.99893294860034,15.1189549302373,12.0805940051021,14.1429703306444,14.1429703306444,2.7599800603562,100,2017,10,6,0.174970757013178,0.134132180344505,0.0155426764751174,0.00292631160230309,0.391554485450526,0.391554485450526,0.00320564371647847
+"22926",100,2017,10,7,0.00363036309040026,8.34641372357527,20.8233554890447,12.2309242359745,17.409724924824,17.409724924824,2.80277158792154,100,2017,10,7,0.000526315805159117,0.0949917688504346,0.0521451304434165,0.0120251559163463,0.900209432898908,0.900209432898908,0.00310738741182153
+"22927",100,2017,10,8,0,11.2375248844999,22.393839144733,15.2152255460112,17.0966998730818,17.0966998730818,2.84556311548688,100,2017,10,8,0,0.0733918424239057,0.0595356600715179,0.00454268027869153,0.322179923516383,0.322179923516383,0.00301482337170481
+"22928",100,2017,10,9,1.64477448849001,9.47946106771169,15.8559956419455,10.3630803069397,21.5180967307851,21.5180967307851,2.88835464305222,100,2017,10,9,0.0325730930573774,0.055013452525536,0.0196275074786651,0.0155619693578259,0.501447828987007,0.501447828987007,0.00292795159612831
+"22929",100,2017,10,10,0.0546754683614826,4.33972495564795,18.7833662736009,10.7980418084609,19.6862373834658,19.6862373834658,2.93114617061756,100,2017,10,10,0.00257309949188902,0.0920484988435872,0.0132620082389919,0.0078783777182029,0.467449571005213,0.467449571005213,0.00284677208509203
+"22930",100,2017,10,11,0.0379537959450936,8.51403740189388,20.0021122490743,13.9073266290595,5.3186467897774,5.3186467897774,2.9739376981829,100,2017,10,11,0.00485380131424519,0.135861991790919,0.012232153601627,0.125031590543208,0.453780714258136,0.453780714258136,0.00277128483859598
+"22931",100,2017,10,12,21.0920789275888,7.68240932112086,16.1415622132053,11.2848074486022,12.6746645068178,12.6746645068178,3.01672922574824,100,2017,10,12,6.65339117061355,0.0736228431066157,0.0570146897412912,0.0122251448269469,0.842293956016066,0.842293956016066,0.00270148985664015
+"22932",100,2017,10,13,1.29185917052236,9.19913091124481,16.3856764081025,11.3623212524767,20.8827392905459,20.8827392905459,3.05952075331358,100,2017,10,13,0.02385964544196,0.0349222642263471,0.0279695525769376,0.0185467968545517,0.925035593949924,0.925035593949924,0.00263738713922453
+"22933",100,2017,10,14,0,7.35005503047024,16.9447085461338,11.6994168561677,21.9800770825679,21.9800770825679,3.10231228087893,100,2017,10,14,0,0.140627467424285,0.0166053041829671,0.0465228032207801,0.198125265387046,0.198125265387046,0.00257897668634915
+"22934",100,2017,10,15,0,5.61105615156318,17.7468204729103,13.6439493680813,23.7215731802291,23.7215731802291,3.14510380844427,100,2017,10,15,0,0.149283009593824,0.0578204812795163,0.0112140044966083,0.0333275079785636,0.0333275079785636,0.00252625849801398
+"22935",100,2017,10,16,0.3614961581819,7.59595164948433,25.4911769829174,15.7105610829638,24.0741034068159,24.0741034068159,3.18789533600961,100,2017,10,16,0.0100584801456385,0.0193099399087204,0.0445585603772689,0.0371322237562272,0.0138005767378975,0.0138005767378975,0.00247923257421904
+"22936",100,2017,10,17,0,10.6602639092339,29.1303740082794,12.0774587110849,24.7080086098515,24.7080086098515,3.23068686357495,100,2017,10,17,0,0.0701098574321464,0.0458135060452528,0.0989134031196156,0.000337416134982837,0.000337416134982837,0.00243789891496431
+"22937",100,2017,10,18,0,15.6983389030851,30.4382065897859,14.5570845850492,24.2356984943184,24.2356984943184,3.27347839114029,100,2017,10,18,0,0.0942841523288649,0.0544397994418799,0.142635730424477,0.0757808269481331,0.0757808269481331,0.00240225752024981
+"22938",100,2017,10,19,12.5528053078059,15.7384819737887,21.0952148773227,14.4265347125113,10.3483608411615,10.3483608411615,3.31626991870563,100,2017,10,19,0.331520359753245,0.0664369032498058,0.0104731321148259,0.0952374236592849,1.16819989323094,1.16819989323094,0.00237230839007553
+"22939",100,2017,10,20,0.138943897459832,8.85147406532951,15.2123541858199,9.23188111438478,19.9073819222361,19.9073819222361,3.35906144627097,100,2017,10,20,0.0109356732718777,0.19797084965631,0.0128923912517533,0.00134679145821526,0.577632171912286,0.577632171912286,0.00234805152444147
+"22940",100,2017,10,21,0,10.239505107909,14.9624311763986,10.9128161987456,13.1469415511497,13.1469415511497,3.40185297383631,100,2017,10,21,0,0.12565850733268,0.0307041181018544,0.00224326934820492,1.36325461770228,1.36325461770228,0.00232948692334764
+"22941",100,2017,10,22,1.18283830473263,10.0893729957941,16.1832784940176,14.0636963943968,14.8420901765393,14.8420901765393,3.44464450140165,100,2017,10,22,0.00871344775484459,0.098255566830173,0.0177544213683395,0.00682105813730923,1.16294288605593,1.16294288605593,0.00231661458679402
+"22942",100,2017,10,23,1.26006599700097,9.70056093276793,15.9631352251512,12.8770077802954,11.3977337187797,11.3977337187797,3.48743602896699,100,2017,10,23,0.00228069740429467,0.0595620356242714,0.0262467868248747,0.000621054458685665,0.520511769077285,0.520511769077285,0.00230943451478063
+"22943",100,2017,10,24,0,9.53195813308061,18.259834713406,14.1154894330452,13.3970186718226,13.3970186718226,3.53022755653233,100,2017,10,24,0,0.0744251592580956,0.0387210419373614,0.00638945407986577,0.303839822057576,0.303839822057576,0.00230794670730746
+"22944",100,2017,10,25,2.74697470560063,12.1218261949562,17.84389431432,14.0985146764875,18.3801320456829,18.3801320456829,3.57301908409767,100,2017,10,25,0.383976621321074,0.0808064060301314,0.0458098638213287,0.0128204716867158,1.2761206923638,1.2761206923638,0.00231215116437452
+"22945",100,2017,10,26,0,8.72064904883356,16.2357867233562,11.3221783695704,20.5191088803399,20.5191088803399,3.61581061166302,100,2017,10,26,0,0.149970186454435,0.0412620091535527,0.000369003889366133,4.42016480293517,4.42016480293517,0.00232204788598179
+"22946",100,2017,10,27,0.377117720874164,5.25271733864175,25.186479872734,12.9586909937255,23.8680970010453,23.8680970010453,3.65860213922836,100,2017,10,27,0.0456140377803854,0.0211485414259836,0.0283942330212585,0.0644152421004016,0.0915596605093802,0.0915596605093802,0.00233763687212928
+"22947",100,2017,10,28,0,9.62379533203259,19.8907146663687,12.0962485847431,20.0842682321211,20.0842682321211,3.7013936667937,100,2017,10,28,0,0.0276894887155434,0.0279784250719334,0.0531701553100365,1.15201037941676,1.15201037941676,0.002358918122817
+"22948",100,2017,10,29,2.51606160539998,8.53833879233718,26.83013214766,11.0231244178495,19.2968315191657,19.2968315191657,3.74418519435904,100,2017,10,29,0.528304075274555,0.0406110649109746,0.00945849110599494,0.0611485252805201,0.350573104010846,0.350573104010846,0.00238589163804494
+"22949",100,2017,10,30,7.58943892154757,6.2909461326725,14.9972388075523,9.26119899749756,20.2837184637424,20.2837184637424,3.78697672192438,100,2017,10,30,1.04561410814703,0.0763093383889578,0.0144982423883219,0.00312456300084891,2.81279807455544,2.81279807455544,0.0024185574178131
+"22950",100,2017,10,31,2.37854786254916,5.01656767873481,15.9592409920771,10.7579647301316,16.164818592722,16.164818592722,3.82976824948972,100,2017,10,31,0.0216374289919808,0.127337413965208,0.050342654883062,0.0208923749891021,1.77933684408199,1.77933684408199,0.00245691546212149
+"22951",100,2017,11,1,0.640044001725116,9.84977999982005,16.9844223710701,13.2126733246941,19.3300879597008,19.3300879597008,3.88588317379134,100,2017,11,1,0.017076021391049,0.0335321582326542,0.0122274768096939,0.0177555397034432,1.74017059526093,1.74017059526093,0.00241016591135696
+"22952",100,2017,11,2,2.12178217604084,9.72713962194025,14.6083718112068,10.3471838161103,12.6714413394236,12.6714413394236,3.94199809809296,100,2017,11,2,0.0283041047631688,0.105531529218092,0.0220356939238233,0.00972632110782616,0.587743131282936,0.587743131282936,0.00236504372048195
+"22953",100,2017,11,3,0.520462054919095,6.10627067836598,14.0713750967229,8.89853679543675,20.9705498215914,20.9705498215914,3.99811302239459,100,2017,11,3,0.00842105345767855,0.0587040665643572,0.0119146467376813,0.00230349682151534,1.24607621218626,1.24607621218626,0.00232154888949645
+"22954",100,2017,11,4,1.25632565013646,3.82742574742131,15.5147524803254,8.81705167768287,20.1477559722296,20.1477559722296,4.05422794669621,100,2017,11,4,0.00912280153810099,0.131637427848687,0.0106228237599047,0.0321286437163678,1.60678762880322,1.60678762880322,0.00227968141840046
+"22955",100,2017,11,5,0.00704070417532171,3.63773376267604,16.7204181788647,9.73306920583492,17.287590752865,17.287590752865,4.11034287099783,100,2017,11,5,0.00286549716142186,0.0834731024530245,0.0316498187018374,0.0175759922385198,0.930714224209964,0.930714224209964,0.00223944130719399
+"22956",100,2017,11,6,0,2.84930695663847,15.6513090469394,9.29379542196545,23.1455554437585,23.1455554437585,4.16645779529946,100,2017,11,6,0,0.107273096362335,0.0132368354575006,0.0244766278407223,0.707633547126162,0.707633547126162,0.00220082855587702
+"22957",100,2017,11,7,0.266336641517213,9.71819572637577,14.8678988592066,9.16972487253456,16.123685436018,16.123685436018,4.22257271960108,100,2017,11,7,0.012631579672384,0.111339731597991,0.0175777643840583,0.014802926667067,1.1743479817042,1.1743479817042,0.00216384316444957
+"22958",100,2017,11,8,0.113641365829802,9.51573153035213,17.5448733750481,12.0031793350958,20.8731351942644,20.8731351942644,4.2786876439027,100,2017,11,8,0.00175438601719706,0.112676040540187,0.0333941576198051,0.0146918239103388,0.328544984610271,0.328544984610271,0.00212848513291162
+"22959",100,2017,11,9,0,5.06620467325511,21.1686248527502,12.4355775499501,28.3427173312348,28.3427173312348,4.33480256820433,100,2017,11,9,0,0.0505877232523906,0.0819649312875645,0.0802099953568375,0.0376912910331327,0.0376912910331327,0.00209475446126319
+"22960",100,2017,11,10,0.526952695195759,8.82114410715135,24.0729923772864,15.2044553788188,25.3006494037389,25.3006494037389,4.39091749250595,100,2017,11,10,0.212573099031783,0.0173093530176193,0.112120445889745,0.142406528796453,0.968657757006525,0.968657757006525,0.00206265114950427
+"22961",100,2017,11,11,0,12.0685368948119,25.3804951574412,14.3981848382058,29.0591750569863,29.0591750569863,4.44703241680757,100,2017,11,11,0,0.0309894570735898,0.110738555904178,0.128644381332485,0.00134795111980087,0.00134795111980087,0.00203217519763486
+"22962",100,2017,11,12,0,12.0110781504901,23.7241251014903,16.1308801239735,28.9777557091876,28.9777557091876,4.50314734110919,100,2017,11,12,0,0.0218116690273916,0.0965806518647563,0.0700952714576764,0.0636099239553281,0.0636099239553281,0.00200332660565496
+"22963",100,2017,11,13,0,12.6096149251537,30.1475357101827,17.1213969114197,29.3749286097662,29.3749286097662,4.55926226541082,100,2017,11,13,0,0.0989116975082524,0.0971767841187081,0.0977251004005666,0.000764901452624288,0.000764901452624288,0.00197610537356459
+"22964",100,2017,11,14,0,15.7862375708434,34.3384598877826,13.170935170223,26.7297027722181,26.7297027722181,4.61537718971244,100,2017,11,14,0,0.0861783283413835,0.0898717709291762,0.0302035263419235,1.41407871568495,1.41407871568495,0.00195051150136371
+"22965",100,2017,11,15,0.00693069317258231,16.938855946523,24.6151595750395,17.7125080599643,10.0906600427575,10.0906600427575,4.67149211401406,100,2017,11,15,0.00140350881375765,0.0506925208202433,0.0104151671339476,0.0817731004951453,2.00858420516796,2.00858420516796,0.00192654498905235
+"22966",100,2017,11,16,35.4106712802933,15.1744774271815,20.6229261706765,18.7684049197156,11.2108250650505,11.2108250650505,4.72760703831568,100,2017,11,16,1.80146237200595,0.00925613200992334,0.0252596492971127,0.000584209384443477,0.553170268691284,0.553170268691284,0.0019042058366305
+"22967",100,2017,11,17,4.94884488973418,14.7143123813457,23.2903301356518,16.8476458424651,19.1155117441981,19.1155117441981,4.78372196261731,100,2017,11,17,1.59040942256215,0.0308450332329876,0.025622212558571,0.118637572885549,2.20162059133017,2.20162059133017,0.00188349404409816
+"22968",100,2017,11,18,1.97326730711363,10.939703024391,25.4965785648694,17.8008361728278,29.0669964852244,29.0669964852244,4.83983688691893,100,2017,11,18,0.088187137375105,0.0294356660341803,0.0428028824078237,0.0782398108769687,0.259731673739512,0.259731673739512,0.00186440961145533
+"22969",100,2017,11,19,0,13.6455555392308,27.2425742049684,18.1286136618804,29.5067657349002,29.5067657349002,4.89595181122055,100,2017,11,19,0,0.030768988068388,0.0310579220824461,0.059957863362946,0.248498102694851,0.248498102694851,0.00184695253870203
+"22970",100,2017,11,20,0.855775586662382,15.1409682346256,29.1764357391626,15.0318041277928,30.1189219429679,30.1189219429679,4.95206673552218,100,2017,11,20,0.332456140490304,0.107116384370236,0.0471497220182516,0.0454338976141494,0.0291035477089148,0.0291035477089148,0.0018311228258382
+"22971",100,2017,11,21,0,14.6848845801862,31.1588231329084,14.3473157872199,29.8845106655746,29.8845106655746,5.0081816598238,100,2017,11,21,0,0.0821893885295512,0.0654175446808034,0.0402385927457688,0.0674257623954846,0.0674257623954846,0.00181692047286392
+"22972",100,2017,11,22,0,15.389438712033,32.1310671291204,12.2144112906965,29.8374917541269,29.8374917541269,5.06429658412542,100,2017,11,22,0,0.0233731251053818,0.104599962616607,0.0185590975265848,0.329753696428679,0.329753696428679,0.00180434547977913
+"22973",100,2017,11,23,0,13.9627504044502,29.9684047971753,16.3980749233065,28.6776346263319,28.6776346263319,5.12041150842704,100,2017,11,23,0,0.110971926632363,0.100557803826925,0.266044660738069,0.533284308999308,0.533284308999308,0.00179339784658386
+"22974",100,2017,11,24,0,16.3081299180638,24.0792740338182,17.5861496941091,16.3287570867339,16.3287570867339,5.17652643272867,100,2017,11,24,0,0.0605368072288826,0.0886819515476976,0.0355245066684642,0.898142752160098,0.898142752160098,0.00178407757327811
+"22975",100,2017,11,25,0.616281632608575,15.5914521358981,27.3889990870578,18.3848954548012,18.2177889921484,18.2177889921484,5.23264135703029,100,2017,11,25,0.0738596510538582,0.0438759765692238,0.0876315488314599,0.0901000448441542,7.8825405303957,7.8825405303957,0.00177638465986185
+"22976",100,2017,11,26,3.79977996042459,17.3044883509805,19.7945431834138,19.3021013345918,8.49584156556754,8.49584156556754,5.28875628133191,100,2017,11,26,1.09263158714563,0.0143889011808595,0.182995328361283,0.0099800966410882,0.274659048888083,0.274659048888083,0.00177031910633512
+"22977",100,2017,11,27,1.75357534481485,14.3796700203773,20.4999120416421,14.1705940800531,24.2957867012821,24.2957867012821,5.34487120563354,100,2017,11,27,1.76333324366146,0.053184216025361,0.0175163817114092,0.00601345552598088,2.29760410480198,2.29760410480198,0.0017658809126979
+"22978",100,2017,11,28,0.151925194996222,9.1170407988713,26.5686355166965,19.4957642864735,31.0941585901678,31.0941585901678,5.40098612993516,100,2017,11,28,0.033216375864563,0.0472163444988273,0.0675478447632464,0.0582022358101004,0.000725152958491688,0.000725152958491688,0.00176307007895019
+"22979",100,2017,11,29,0,16.1933884090847,34.6168203993861,18.625863401541,27.9447086825229,27.9447086825229,5.45710105423678,100,2017,11,29,0,0.037413436124813,0.0755766042442999,0.16505390435414,0.160568945451027,0.160568945451027,0.00176188660509199
+"22980",100,2017,11,30,0.00396039609861846,18.5175468205619,27.5702749258614,20.844257703053,7.26295927727577,7.26295927727577,5.5132159785384,100,2017,11,30,0.00140350881375765,0.0130585452523796,0.647699780273685,0.127827497806018,0.999868966277753,0.999868966277753,0.0017623304911233
+"22981",100,2017,12,1,9.21936199347703,16.9707696996506,17.6019911603435,16.6217713287824,5.32288228613053,5.32288228613053,5.50745806189566,100,2017,12,1,2.88333324326414,0.0130579202754479,0.111262023988535,0.00158770921525129,0.359200645436761,0.359200645436761,0.0016228583754808
+"22982",100,2017,12,2,0.90693069468237,13.2480417488694,16.0123983263576,12.9827062619401,7.09126514543926,7.09126514543926,5.50170014525291,100,2017,12,2,0.0116374268029868,0.0932759946121202,0.0293607835425032,0.0037426970856311,0.179791232372321,0.179791232372321,0.00150647490694615
+"22983",100,2017,12,3,1.00121011758762,11.0652034935778,17.2830802741224,11.333091393818,18.5186798150259,18.5186798150259,5.49594222861017,100,2017,12,3,0.693859649392946,0.0737052425385138,0.00732047300309141,0.0578000370450519,1.19583811964544,1.19583811964544,0.00141318008551934
+"22984",100,2017,12,4,1.60847082966887,10.4993068581761,17.9716942412625,11.1680417349367,26.22686441923,26.22686441923,5.49018431196742,100,2017,12,4,0.821403484574537,0.121750931743232,0.0110842458805841,0.0282812870115952,0.777378536028318,0.777378536028318,0.00134297391120037
+"22985",100,2017,12,5,0,9.27811878716329,20.3789769226175,14.4110780271104,18.9891635970314,18.9891635970314,5.48442639532468,100,2017,12,5,0,0.184945046689275,0.0140321558012562,0.023925150089483,6.85276524703955,6.85276524703955,0.00129585638398923
+"22986",100,2017,12,6,0.0600660074957133,10.4298238796238,21.5884706814035,14.6634762074688,23.8368867385243,23.8368867385243,5.47866847868193,100,2017,12,6,0.00368421063611382,0.0447707892257026,0.0393765532156211,0.0492596085398484,2.6611823534561,2.6611823534561,0.00127182750388593
+"22987",100,2017,12,7,1.56303629400444,10.1756985740955,20.1057644163159,13.6826954013837,10.7869527646811,10.7869527646811,5.47291056203919,100,2017,12,7,0.287836251440106,0.137528703520128,0.0130807246562212,0.0652561752766182,1.38526950427057,1.38526950427057,0.00127088727089048
+"22988",100,2017,12,8,8.93740381423396,7.86643558602915,18.9856985971348,11.1245655903328,27.4509129498002,27.4509129498002,5.46715264539644,100,2017,12,8,0.173684238969272,0.0994766234347964,0.0123783978291485,0.0445052603092112,1.38773976255753,1.38773976255753,0.00129303568500285
+"22989",100,2017,12,9,0.937073710498506,12.5597579345451,20.8549065144017,14.7417271543782,19.4516172020873,19.4516172020873,5.4613947287537,100,2017,12,9,0.236549691428916,0.0767538209414139,0.0342256899741443,0.0910590684144148,1.05744839935339,1.05744839935339,0.00133827274622307
+"22990",100,2017,12,10,0.200000002980232,11.0392629992713,20.8049723831865,14.2685039234896,15.8658856023656,15.8658856023656,5.45563681211095,100,2017,12,10,0,0.265678475720211,0.069891209037174,0.00533157912366477,2.05688415986234,2.05688415986234,0.00140659845455113
+"22991",100,2017,12,11,0.205060510598,12.9869527407605,20.5360397489944,14.9829373092148,19.6460836114663,19.6460836114663,5.44987889546821,100,2017,12,11,0.0117543865766442,0.149628639477807,0.107156148591946,0.085062003745901,1.08494747866829,1.08494747866829,0.00149801280998703
+"22992",100,2017,12,12,0,7.47991194530945,26.784642338097,14.1271726778238,31.1221340334717,31.1221340334717,5.44412097882546,100,2017,12,12,0,0.0941619821504188,0.135002999400765,0.65949984677642,0.0837805718766658,0.0837805718766658,0.00161251581253078
+"22993",100,2017,12,13,0,12.4306710833895,39.0393403560022,10.1271837400262,24.8614850248834,24.8614850248834,5.43836306218272,100,2017,12,13,0,0.0711620019545144,0.0229758796571789,0.350654293113908,0.355026348671978,0.355026348671978,0.00175010746218234
+"22994",100,2017,12,14,0.0691969207230836,15.6384377726103,21.7814852459596,13.3200439257984,24.4386907164151,24.4386907164151,5.43260514553998,100,2017,12,14,0.00245614042407588,0.035722266939052,0.0818165363873464,0.0695368882742998,0.288198411091937,0.288198411091937,0.00191078775894175
+"22995",100,2017,12,15,0,7.48708472813186,23.7784598427113,12.7225741448313,28.6078107538003,28.6078107538003,5.42684722889723,100,2017,12,15,0,0.0226467817563253,0.127725034925975,0.194487098310206,0.780014222226788,0.780014222226788,0.00209455670280902
+"22996",100,2017,12,16,0,8.02343248140694,23.1908141389014,14.6931243094936,26.4600660703888,26.4600660703888,5.42108931225448,100,2017,12,16,0,0.0313146050225162,0.0558034354631162,0.132873069535714,0.375776456014562,0.375776456014562,0.00230141429378411
+"22997",100,2017,12,17,0,12.6295159248629,25.09524753516,15.2814630900804,27.8462486141192,27.8462486141192,5.41533139561174,100,2017,12,17,0,0.0782981681043869,0.105739748525753,0.0647356996213342,0.371431487542284,0.371431487542284,0.00253136053186705
+"22998",100,2017,12,18,0,14.9762266052045,34.1575797598223,15.1499011183467,14.3925631704635,14.3925631704635,5.409573478969,100,2017,12,18,0,0.104824497379152,0.0298369053542908,0.133315209648673,0.382402975024473,0.382402975024473,0.00278439541705782
+"22999",100,2017,12,19,0,18.5700440611383,31.496644424002,16.3835861281593,20.6098680066066,20.6098680066066,5.40381556232625,100,2017,12,19,0,0.0330286047732049,0.256647564630945,0.0364497766418744,5.87979279788975,5.87979279788975,0.00306051894935642
+"23000",100,2017,12,20,0,13.1699449278996,20.420143060296,12.0749615269526,19.6650495518683,19.6650495518683,5.39805764568351,100,2017,12,20,0,0.0547028755210959,0.01937778108407,0.0134707604459048,4.2862440266932,4.2862440266932,0.00335973112876287
+"23001",100,2017,12,21,0.478437847823593,9.18052804378262,22.0423101209047,14.4023761864674,18.7921564628844,18.7921564628844,5.39229972904076,100,2017,12,21,0.00777777817514218,0.114076055270275,0.104848027681376,0.136048475974976,0.953646687995436,0.953646687995436,0.00368203195527717
+"23002",100,2017,12,22,0,9.19919679660608,24.1354346826132,11.9617381006709,25.554059396876,25.554059396876,5.38654181239801,100,2017,12,22,0,0.0279316129807957,0.166622160848175,0.0922988967137789,1.52076557249578,1.52076557249578,0.0040274214288993
+"23003",100,2017,12,23,0,8.63732659724942,24.6260178170451,14.0694389280313,15.448338838038,15.448338838038,5.38078389575527,100,2017,12,23,0,0.0208929793308117,0.116884035104103,0.1806426271785,2.14466091545298,2.14466091545298,0.00439589954962926
+"23004",100,2017,12,24,0,12.1530253019008,19.5320353974866,10.2006380786203,25.4756656831378,25.4756656831378,5.37502597911253,100,2017,12,24,0,0.0781952928443298,0.0369818747316437,0.0750450613614166,0.649470070095187,0.649470070095187,0.00478746631746705
+"23005",100,2017,12,25,0,11.5084599284056,22.1015618789052,11.6881958612121,27.2626733559586,27.2626733559586,5.36926806246978,100,2017,12,25,0,0.110470180676638,0.0537918138874992,0.0528204869678232,0.332660622624937,0.332660622624937,0.00520212173241271
+"23006",100,2017,12,26,0,9.76028601748191,26.7629152912774,14.8452695640925,30.7016173363781,30.7016173363781,5.36351014582704,100,2017,12,26,0,0.0644620305446929,0.23548726131629,0.035395317202619,0.0221766016376018,0.0221766016376018,0.0056398657944662
+"23007",100,2017,12,27,0,13.6530803107586,37.4774590000199,10.6757204613932,26.4369305861403,26.4369305861403,5.35775222918429,100,2017,12,27,0,0.0303116813415062,0.0131919266297264,0.0144028996005359,0.0881758940538089,0.0881758940538089,0.00610069850362754
+"23008",100,2017,12,28,0.530803087684843,18.0710558246071,22.5371950273335,17.7970518861273,8.26657862395737,8.26657862395737,5.35199431254155,100,2017,12,28,0.00286549804154904,0.0218029654611546,0.33260518800188,0.0109064219293048,5.2784430376477,5.2784430376477,0.0065846198598967
+"23009",100,2017,12,29,2.97777778061047,12.181672021763,19.9958525029215,18.8704181016475,6.6253025350791,6.6253025350791,5.3462363958988,100,2017,12,29,0.570409361019473,0.0456812928629878,0.0376806550368456,0.00168422772625682,0.768911728628076,0.768911728628076,0.00709162986327368
+"23010",100,2017,12,30,6.13531350984563,11.2058745206911,19.9328165064813,12.9276127149038,18.1505388222118,18.1505388222118,5.34047847925606,100,2017,12,30,0.089824569378697,0.11078425110942,0.0149584977092279,0.113814618602622,4.51830519071018,4.51830519071018,0.00762172851375852
+"23011",100,2017,12,31,1.19372937584868,8.09034103607581,22.2495266103377,14.1121233089267,18.5225964389881,18.5225964389881,5.33472056261331,100,2017,12,31,0.0413450241716293,0.0754052808171048,0.0608672864770187,0.158167295335553,3.31583091592428,3.31583091592428,0.00817491581135121
+"23012",100,2018,1,1,0.00363036309040026,13.9464026473143,23.0255446229437,14.3264686313793,20.1307370759735,20.1307370759735,5.38522310548343,100,2018,1,1,0.000526315805159117,0.117445048473956,0.132149759161206,0.0658385871954725,1.65240062240717,1.65240062240717,0.00845213873601104
+"23013",100,2018,1,2,1.95995596723326,10.3626733765219,20.7872826562594,14.2034433593582,19.9660835580857,19.9660835580857,5.43572564835355,100,2018,1,2,0.969122781872055,0.132314549826567,0.0860264071639765,0.195546780881898,2.29490325048815,2.29490325048815,0.00874882137115544
+"23014",100,2018,1,3,0.150055007736544,11.058085653517,21.9865567319595,13.0157424488215,27.1444446093703,27.1444446093703,5.48622819122367,100,2018,1,3,0.00450292411080578,0.145347933339832,0.0912911957146856,0.305178316473337,0.494270902194532,0.494270902194532,0.00906496371678449
+"23015",100,2018,1,4,0,9.2632343325809,23.3558307601543,13.9108471015368,28.7716612716188,28.7716612716188,5.53673073409379,100,2018,1,4,0,0.0929801200867459,0.159737247235192,0.0393842419563995,0.256000460498963,0.256000460498963,0.00940056577289813
+"23016",100,2018,1,5,0,7.58587458267464,33.1655113715412,12.2359405796651,30.2253248237803,30.2253248237803,5.58723327696391,100,2018,1,5,0,0.0248830365298242,0.0715132926996492,0.14169240201765,0.0217718937079521,0.0217718937079521,0.00975562753949637
+"23017",100,2018,1,6,0,16.6950052532032,42.8972499918754,11.4408911588562,24.9223761899505,24.9223761899505,5.63773581983403,100,2018,1,6,0,0.0441444540238985,0.0330115725429782,0.450564843609558,2.5858914291653,2.5858914291653,0.0101301490165792
+"23018",100,2018,1,7,0,14.6048514310545,23.6459405771052,11.8930583603442,28.5126402406934,28.5126402406934,5.68823836270415,100,2018,1,7,0,0.0543250994677591,0.305606264531793,0.041838554391332,0.0632620940139542,0.0632620940139542,0.0105241302041467
+"23019",100,2018,1,8,0,11.4597250053985,23.6072716980484,12.6608800657249,28.373113065663,28.373113065663,5.73874090557427,100,2018,1,8,0,0.143333307902097,0.20304435492649,0.0823368511446035,0.194717684075686,0.194717684075686,0.0109375711021988
+"23020",100,2018,1,9,1.10264026948197,9.65269525399958,20.9735756067291,10.2528933775832,23.3791970440788,23.3791970440788,5.78924344844439,100,2018,1,9,0.0860818778492565,0.0607333446503468,0.070420480168581,0.069283069351907,0.650969714235288,0.650969714235288,0.0113704717107354
+"23021",100,2018,1,10,0,8.11465339115088,24.5963918016558,13.2872607867972,25.8071287090104,25.8071287090104,5.83974599131451,100,2018,1,10,0,0.134702259774279,0.123654563065355,0.0450561371207564,0.375889479935275,0.375889479935275,0.0118228320297567
+"23022",100,2018,1,11,0,11.908052923918,33.5700326338328,10.4856325837776,23.9854567499444,23.9854567499444,5.89024853418463,100,2018,1,11,0,0.0306760373277992,0.154172223865338,0.13848770723629,0.33884332418644,0.33884332418644,0.0122946520592626
+"23023",100,2018,1,12,0,19.7788999135738,30.1182948344349,16.3987346854803,4.02267328635825,4.02267328635825,5.94075107705475,100,2018,1,12,0,0.0093315538151994,0.228731725338351,0.0237005692603087,0.262636239022273,0.262636239022273,0.0127859317992532
+"23024",100,2018,1,13,4.2029703306024,10.9599120346758,19.7234653477097,13.9131904314585,15.230340981772,15.230340981772,5.99125361992487,100,2018,1,13,1.04251468756047,0.029566658486306,0.0140052166188235,0.00894501050540826,1.55623199070725,1.55623199070725,0.0132966712497283
+"23025",100,2018,1,14,5.0518152097402,9.60081410591621,20.4384818942633,10.6288448413475,28.3843124943598,28.3843124943598,6.04175616279499,100,2018,1,14,0.858771845053526,0.2506930736297,0.054492452057376,0.201022747651106,0.316281200422644,0.316281200422644,0.013826870410688
+"23026",100,2018,1,15,1.44994499312245,12.6499670425264,21.6051374064027,15.9197250414472,18.0390100447652,18.0390100447652,6.09225870566511,100,2018,1,15,0.240409362364932,0.109176668260894,0.11405087579677,0.118158524398543,1.02282478004615,1.02282478004615,0.0143765292821324
+"23027",100,2018,1,16,0,11.9727611499782,26.26317957342,14.0985148653339,29.2093510370706,29.2093510370706,6.14276124853523,100,2018,1,16,0,0.284777727339069,0.139347559731797,0.185646762370586,0.0219333116322331,0.0219333116322331,0.0149456478640613
+"23028",100,2018,1,17,0,8.76960385619479,32.2811763750838,12.0854345359425,29.0628821684582,29.0628821684582,6.19326379140535,100,2018,1,17,0,0.07300239792547,0.215620873819142,0.701955551825689,0.0218701819662118,0.0218701819662118,0.0155342261564749
+"23029",100,2018,1,18,0,13.391837233364,41.6766007342617,7.08211221841827,28.7685478029996,28.7685478029996,6.24376633427547,100,2018,1,18,0,0.0206590822834357,0.0185871742307842,0.156167252414568,0.0231205257977101,0.0231205257977101,0.0161422641593731
+"23030",100,2018,1,19,0,16.2479760880255,33.6315621018279,14.786314610732,28.409020737584,28.409020737584,6.29426887714559,100,2018,1,19,0,0.147247801695036,1.37416231379038,0.609658555473751,0.0399818940507095,0.0399818940507095,0.0167697618727558
+"23031",100,2018,1,20,0,16.5685698925728,25.08295938389,16.9410448315645,11.94063803229,11.94063803229,6.34477142001571,100,2018,1,20,0,0.0526596804125451,0.559800302378694,0.111500679810574,4.97657319189382,4.97657319189382,0.0174167192966232
+"23032",100,2018,1,21,0.55082509718319,16.9523106166894,26.1455006866959,19.0734983912133,14.7250825118179,14.7250825118179,6.39527396288583,100,2018,1,21,0.0125731006351829,0.0227818269164207,0.87797623569441,0.0492468504257274,3.88759569938397,3.88759569938397,0.0180831364309752
+"23033",100,2018,1,22,0,15.9397577457827,24.0147855420842,13.9408800546879,26.9416389968922,26.9416389968922,6.44577650575595,100,2018,1,22,0,0.110570701869146,0.462984113008644,0.021204067205755,0.346548261894784,0.346548261894784,0.0187690132758118
+"23034",100,2018,1,23,0,11.4746203322877,24.4883606641075,13.3788999372845,27.1523763281022,27.1523763281022,6.49627904862607,100,2018,1,23,0,0.262773182354833,0.152226304822964,0.0867912291432784,0.158625173946071,0.158625173946071,0.019474349831133
+"23035",100,2018,1,24,0,8.01831691834268,24.4174147334167,13.663157300456,26.3002311803064,26.3002311803064,6.54678159149619,100,2018,1,24,0,0.0439917965236791,0.272606382519807,0.0533479213715419,0.083256034534576,0.083256034534576,0.0201991460969389
+"23036",100,2018,1,25,0,9.75484063890245,28.5979317034563,15.4200660560784,27.7350823724493,27.7350823724493,6.59728413436631,100,2018,1,25,0,0.0392730679250501,0.445589484495164,0.0350321611996305,0.0194022812606685,0.0194022812606685,0.0209434020732293
+"23037",100,2018,1,26,0,13.6911881156225,27.2631133449878,19.1637843901044,22.6717269989786,22.6717269989786,6.64778667723643,100,2018,1,26,0,0.0638982773296458,0.255502319122224,0.0284115852761412,0.829483169970907,0.829483169970907,0.0217071177600043
+"23038",100,2018,1,27,0,14.9573267190763,33.8895928285303,19.6991639509715,26.5806159385622,26.5806159385622,6.69828922010655,100,2018,1,27,0,0.0231818511383608,0.143537837791829,0.0748256813007897,0.0384204335667866,0.0384204335667866,0.022490293157264
+"23039",100,2018,1,28,0,20.5580306645929,41.4096364230081,16.9036632722492,26.9636413335013,26.9636413335013,6.74879176297667,100,2018,1,28,0,0.0316374126236659,0.0248812302542484,0.0823134781154475,0.0905895337961701,0.0905895337961701,0.0232929282650082
+"23040",100,2018,1,29,0.278767882056362,20.5168319509105,26.0154456085104,19.9802748718933,4.80843778044739,4.80843778044739,6.79929430584679,100,2018,1,29,0.0689473674346133,0.00998303413652197,0.401869322419169,0.00694325806934534,0.796181311401024,0.796181311401024,0.0241150230832371
+"23041",100,2018,1,30,6.42915289944941,13.1611551016208,19.7407589357416,10.4279977853018,17.1533882909089,17.1533882909089,6.84979684871691,100,2018,1,30,0.154853845730183,0.0840473668640272,0.0210555678477143,0.06689881426724,0.522178219131847,0.522178219131847,0.0249565776119506
+"23042",100,2018,1,31,0.12310231206539,11.1848185573867,19.3261714805209,8.74490664894431,18.3755116961052,18.3755116961052,6.90029939158704,100,2018,1,31,0.00245614042407588,0.148684247390653,0.0424830566675612,0.0214426682611877,0.72863860801141,0.72863860801141,0.0258175918511487
+"23043",100,2018,2,1,0.00517051712875188,10.9226181651368,21.0604950954156,10.5197139578422,26.1287790031979,26.1287790031979,6.86631017708817,100,2018,2,1,0.000994152076411666,0.153547319548364,0.102292422400873,0.0171812979492403,1.37472625740483,1.37472625740483,0.0256269716969907
+"23044",100,2018,2,2,0,12.6234652432147,23.4936746926722,13.4849614278712,23.0373379504851,23.0373379504851,6.8323209625893,100,2018,2,2,0,0.0845251252983988,0.2213785290682,0.0805140041865614,0.0703731246371478,0.0703731246371478,0.0254391626626233
+"23045",100,2018,2,3,0,11.2529483203447,25.8817820806052,15.1476897018315,27.14817369417,27.14817369417,6.79833174809043,100,2018,2,3,0,0.0748544156058681,0.133636390191119,0.0598719256909481,0.10519285398367,0.10519285398367,0.0252541647480467
+"23046",100,2018,2,4,0.0624862495559801,13.2843013979552,27.8468976183431,13.8224313282731,27.9798679540653,27.9798679540653,6.76434253359156,100,2018,2,4,0.00263157902579559,0.0504953346074733,0.19283688217547,0.134137412846482,0.0110139841317558,0.0110139841317558,0.0250719779532606
+"23047",100,2018,2,5,0,9.51008803091689,28.1441584084556,12.2615291450677,24.9891858473338,24.9891858473338,6.73035331909269,100,2018,2,5,0,0.026532146766223,0.435169578775596,0.118691179201981,0.211477773369892,0.211477773369892,0.0248926022782652
+"23048",100,2018,2,6,0.00517051712875188,10.4826951840005,30.1155337867695,13.1549724635511,27.6838174259702,27.6838174259702,6.69636410459382,100,2018,2,6,0.000994152076411666,0.0922760313759793,0.180728577119938,0.0739485099012566,0.0122871400400758,0.0122871400400758,0.0247160377230605
+"23049",100,2018,2,7,0,14.4229922520183,34.0805830331263,14.2382948521865,27.3412652388133,27.3412652388133,6.66237489009495,100,2018,2,7,0,0.0124695865743351,0.260409678758172,0.0789245818869268,0.0270251052770086,0.0270251052770086,0.0245422842876464
+"23050",100,2018,2,8,0,16.9754676084445,26.1157316537318,15.9660174524037,11.4902310355662,11.4902310355662,6.62838567559608,100,2018,2,8,0,0.0072591098498421,0.243405126913413,0.137225670253556,1.99718043125007,1.99718043125007,0.0243713419720229
+"23051",100,2018,2,9,0.0865786591559091,16.8500550797801,29.5252257620934,17.7785478052658,23.2853466512346,23.2853466512346,6.59439646109722,100,2018,2,9,0.00140350881375765,0.0337708416055004,0.0766474055734128,0.0739041865079407,0.772737478481849,0.772737478481849,0.0242032107761901
+"23052",100,2018,2,10,0.111331134772274,16.4195489663102,35.9858199813054,16.1607149709569,17.5919032815528,17.5919032815528,6.56040724659835,100,2018,2,10,0.00251461995798245,0.0668689741751808,0.0604053497507117,0.101721019930351,2.52624709391037,2.52624709391037,0.024037890700148
+"23053",100,2018,2,11,0,12.3588228330623,20.8711884375846,11.0187899051326,14.8461495956572,14.8461495956572,6.52641803209948,100,2018,2,11,0,0.076405229162553,0.0570923974067371,0.0649263387401881,1.43099542692985,1.43099542692985,0.0238753817438965
+"23054",100,2018,2,12,0.0436743680875425,13.4527391931011,23.7162815194712,13.5332012889933,22.5557315100407,22.5557315100407,6.49242881760061,100,2018,2,12,0.00245614042407588,0.0790987730395191,0.0760708304423511,0.156713383501813,1.21550422361072,1.21550422361072,0.0237156839074356
+"23055",100,2018,2,13,0.485918605107941,8.48853692389426,27.2462487761075,12.0724643858353,24.1165237070048,24.1165237070048,6.45843960310174,100,2018,2,13,0.0264912291995267,0.0324210177305328,0.0971999587167779,0.0416362892129331,0.0535912510300445,0.0535912510300445,0.0235587971907654
+"23056",100,2018,2,14,4.1089109110229,12.6496258044269,20.6483497577663,9.05980198506606,21.560836154111,21.560836154111,6.42445038860287,100,2018,2,14,0.0828655029877048,0.0895245275375191,0.0345111268787852,0.07168949932303,1.07328716953158,1.07328716953158,0.0234047215938858
+"23057",100,2018,2,15,0.900440041479891,12.4530691728078,20.4520901385182,14.4632783161663,6.64124312259183,6.64124312259183,6.390461174104,100,2018,2,15,0.21555555038982,0.0830619212279967,0.0765596661977187,0.115468996197509,0.220260843424236,0.220260843424236,0.0232534571167969
+"23058",100,2018,2,16,2.64268421575968,12.4141363672691,21.643652432298,11.0955335545723,21.8282177928257,21.8282177928257,6.35647195960513,100,2018,2,16,0.00690058934061586,0.0995222300212334,0.103025164884478,0.0435596097913571,0.429904163463495,0.429904163463495,0.0231050037594987
+"23059",100,2018,2,17,0,6.72444443025998,23.8974148736666,11.8231243609858,24.7577887724991,24.7577887724991,6.32248274510627,100,2018,2,17,0,0.0359386023973316,0.19804279521052,0.142670711666871,0.0711286524278583,0.0711286524278583,0.0229593615219911
+"23060",100,2018,2,18,0,9.70689766845032,22.167161452626,15.8429043447748,9.81056106051203,9.81056106051203,6.2884935306074,100,2018,2,18,0,0.0352111557431325,0.238973066796568,0.0611923625657196,1.09963716731164,1.09963716731164,0.0228165304042741
+"23061",100,2018,2,19,0.325082513062146,14.2782838336705,23.2067768529172,15.0564025799171,19.8858530004688,19.8858530004688,6.25450431610853,100,2018,2,19,0.0244444445106718,0.0552876863469236,0.0750228005886209,0.00577193448703061,2.72393960564629,2.72393960564629,0.0226765104063478
+"23062",100,2018,2,20,0,14.6985259029863,27.478206817073,14.0665236316761,24.6616720151324,24.6616720151324,6.22051510160966,100,2018,2,20,0,0.0363953645405539,0.0869814524246349,0.0831806736863102,0.0414163022450716,0.0414163022450716,0.0225393015282122
+"23063",100,2018,2,21,0,15.6374807431228,28.4111880815462,14.5709130577784,9.46981281442086,9.46981281442086,6.18652588711079,100,2018,2,21,0,0.0929262330217979,0.301016366474794,0.24337007556721,2.86158040282158,2.86158040282158,0.0224049037698672
+"23064",100,2018,2,22,0.458745880423337,18.5371179717078,26.268206842924,18.4336746329128,8.27460936849529,8.27460936849529,6.15253667261192,100,2018,2,22,0.0187719293470272,0.0390029702440183,0.126471944668625,0.0406450044467941,0.515359044401922,0.515359044401922,0.0222733171313128
+"23065",100,2018,2,23,8.45764572680718,18.3060612715248,31.0492738339767,18.8177448541287,10.718239829354,10.718239829354,6.11854745811305,100,2018,2,23,0.24812866104981,0.0221450020599469,0.759247031568052,0.163536741776752,1.68662113537042,1.68662113537042,0.0221445416125492
+"23066",100,2018,2,24,2.40451044821241,18.1621012294253,19.9459296689175,16.1553245288442,6.57816280495084,6.57816280495084,6.08455824361418,100,2018,2,24,0.0743274976775011,0.0127929735597994,0.192883045308418,0.0027473790758141,0.33738067448713,0.33738067448713,0.0220185772135761
+"23067",100,2018,2,25,0.329812986335673,13.4080417840788,19.2435863293437,10.0782398039227,11.8543234060306,11.8543234060306,6.05056902911531,100,2018,2,25,0.0183040936116935,0.0609210521855145,0.0498211576543741,0.0378578797770398,2.1627147861571,2.1627147861571,0.0218954239343937
+"23068",100,2018,2,26,0.199119916187786,10.5940484014412,25.5307041415812,13.7466116224316,23.0004730287558,23.0004730287558,6.01657981461645,100,2018,2,26,0.0276023406400318,0.0663942029830927,0.0843654785160211,0.0643760807707191,0.0377561400298511,0.0377561400298511,0.0217750817750019
+"23069",100,2018,2,27,0,13.2469746219312,34.6010558251107,11.6932783399609,23.9482069298773,23.9482069298773,5.98259060011758,100,2018,2,27,0,0.0957923282490406,0.0103765578344138,0.0191812597242024,0.0142555935333654,0.0142555935333654,0.0216575507354008
+"23070",100,2018,2,28,0,17.3111661044416,23.6258746651795,14.8815621500886,11.0469196894512,11.0469196894512,5.94860138561871,100,2018,2,28,0,0.00968304260016748,0.0999468179985204,0.106558521928954,0.889706377872739,0.889706377872739,0.0215428308155904
+"23071",100,2018,3,1,1.42178217478187,14.0748184768542,21.3376787414383,14.2129922170188,16.1708688924808,16.1708688924808,5.91627710531349,100,2018,3,1,0.0921052567442967,0.040754363835263,0.106265647250423,0.05327313235105,0.304625649781505,0.304625649781505,0.0207677240710773
+"23072",100,2018,3,2,0.0970297044161523,10.114213353861,26.2369197471963,13.0820352231184,19.935841297028,19.935841297028,5.88395282500827,100,2018,3,2,0.000994152076411666,0.0395163843099775,0.069842652072622,0.130698246847752,0.288649304364692,0.288649304364692,0.020008959308707
+"23073",100,2018,3,3,0,12.2180308204542,23.8064356490199,15.3797690850018,16.9980966234365,16.9980966234365,5.85162854470306,100,2018,3,3,0,0.0140228115305519,0.227560603881692,0.042258509579098,0.491587547258035,0.491587547258035,0.0192665365284798
+"23074",100,2018,3,4,0,11.0880528283198,20.4655002640157,10.1522771596122,10.5576567487224,10.5576567487224,5.81930426439784,100,2018,3,4,0,0.0606251546826926,0.0250876889594009,0.0255649340691549,2.7270813516517,2.7270813516517,0.0185404557303954
+"23075",100,2018,3,5,0,7.71400442952239,21.2229922329239,10.9396260422055,15.9851375900873,15.9851375900873,5.78697998409262,100,2018,3,5,0,0.0814385211621468,0.0140467988848079,0.0299783740194992,2.30593657851877,2.30593657851877,0.017830716914454
+"23076",100,2018,3,6,0,12.5387677936533,22.8320792085922,13.4494278564705,20.474125464495,20.474125464495,5.7546557037874,100,2018,3,6,0,0.0844579530192288,0.0498374724408853,0.0518040831828447,0.285524565785279,0.285524565785279,0.0171373200806554
+"23077",100,2018,3,7,0,9.54614956334348,27.1634985039336,14.5280638139765,22.4766224734198,22.4766224734198,5.72233142348219,100,2018,3,7,0,0.0763696374436578,0.282967286682365,0.197159075434806,0.00447601357195203,0.00447601357195203,0.0164602652289998
+"23078",100,2018,3,8,0,9.9129702029842,28.4180967857604,14.1001649516644,22.2120789778639,22.2120789778639,5.69000714317697,100,2018,3,8,0,0.034539788013116,0.256411659919283,0.264303455863717,0.0047953175107233,0.0047953175107233,0.0157995523594871
+"23079",100,2018,3,9,0,11.6162156544634,30.5096697371916,13.0506930891568,21.9579316948113,21.9579316948113,5.65768286287175,100,2018,3,9,0,0.028378346843662,0.205475913564548,0.233209375700559,0.00554677675511264,0.00554677675511264,0.0151551814721173
+"23080",100,2018,3,10,0,13.0968978575485,35.7980636453996,10.5635533537408,21.6934871379823,21.6934871379823,5.62535858256654,100,2018,3,10,0,0.019935683512648,0.0427375044719635,0.0695111342960689,0.00695438140097628,0.00695438140097628,0.0145271525668904
+"23081",100,2018,3,11,0,14.2659735653398,21.6706159505645,13.3480416907467,14.1522001973604,14.1522001973604,5.59303430226132,100,2018,3,11,0,0.028005259573031,0.132236736699503,0.0173432714719739,0.275647298226261,0.275647298226261,0.0139154656438064
+"23082",100,2018,3,12,0,14.4521451882928,20.0392844868441,12.3881958100137,9.70123209601844,9.70123209601844,5.5607100219561,100,2018,3,12,0,0.142509317277674,0.043470772882804,0.0600064136907344,0.343975967985095,0.343975967985095,0.0133201207028653
+"23083",100,2018,3,13,0.0930693083175338,14.3755225841493,21.5461275816226,12.021111092814,13.5218591396302,13.5218591396302,5.52838574165088,100,2018,3,13,0.00140350881375765,0.106402354799317,0.0514256878415506,0.0360473288062768,0.854003028914329,0.854003028914329,0.0127411177440672
+"23084",100,2018,3,14,0,13.8353795050526,21.4796590123108,11.5621671194028,18.6788997335402,18.6788997335402,5.49606146134567,100,2018,3,14,0,0.0720286985899973,0.105014179395187,0.0703608174397083,0.189270044654919,0.189270044654919,0.0121784567674119
+"23085",100,2018,3,15,0,8.76223315290361,20.9112761574085,13.5767106716127,11.4513420518344,11.4513420518344,5.46373718104045,100,2018,3,15,0,0.0454117486051047,0.0652029158657128,0.167758564200681,0.603202561397522,0.603202561397522,0.0116321377728996
+"23086",100,2018,3,16,0,7.74466444976521,24.4599669197343,12.53953810546,12.2447633711812,12.2447633711812,5.43141290073523,100,2018,3,16,0,0.0756917637786314,0.112387752082607,0.166080664390494,1.06613658694895,1.06613658694895,0.0111021607605301
+"23087",100,2018,3,17,0.147964798684495,12.4540922827978,34.2727400608713,11.1761165876987,14.8054674859881,14.8054674859881,5.39908862043002,100,2018,3,17,0.00368421063611382,0.0235000139872793,0.00831752417570631,0.0217730858273932,0.439491240362046,0.439491240362046,0.0105885257303036
+"23088",100,2018,3,18,0.533553355417498,12.7770407921148,20.2561493129751,9.36385028197034,16.8337183848466,16.8337183848466,5.3667643401248,100,2018,3,18,0.0811695890451037,0.0386783441801037,0.0163385521383554,0.0919543685177756,2.35887206490528,2.35887206490528,0.01009123268222
+"23089",100,2018,3,19,2.89284927118467,10.774521507708,23.0709680026383,14.9331902912085,9.09246421306178,9.09246421306178,5.33444005981958,100,2018,3,19,0.183684215434118,0.0724427191684756,0.0155473268880502,0.0576953126205356,1.98978786579385,1.98978786579385,0.00961028161627932
+"23090",100,2018,3,20,1.33575355783679,13.0348183985459,17.9094718622558,9.66442235625616,10.6452147019054,10.6452147019054,5.30211577951436,100,2018,3,20,0.00918128193470598,0.131032149588246,0.0125339184546035,0.0245982343061003,1.27579131013309,1.27579131013309,0.00914567253248151
+"23091",100,2018,3,21,0.261496157773716,11.0583389531923,22.3087788236679,13.035962509768,15.2803191236406,15.2803191236406,5.26979149920915,100,2018,3,21,0.0100584796576472,0.0762257071847346,0.0442368956229548,0.0954649378013395,0.370835656281077,0.370835656281077,0.00869740543082663
+"23092",100,2018,3,22,0,13.9380639981646,28.1765237143069,13.5148513540052,18.2643233992741,18.2643233992741,5.23746721890393,100,2018,3,22,0,0.075684170293605,0.0476619753076592,0.157865518380399,0.162731513477634,0.162731513477634,0.00826548031131466
+"23093",100,2018,3,23,0,14.4213641484578,31.2054894660303,11.8149615309813,18.2239605008703,18.2239605008703,5.20514293859871,100,2018,3,23,0,0.0768005509382887,0.0372877161621093,0.0355619589845499,0.0770205656801451,0.0770205656801451,0.0078498971739456
+"23094",100,2018,3,24,7.26028605787405,17.1740594702323,26.998162574894,14.5423322683907,15.3682510839699,15.3682510839699,5.17281865829349,100,2018,3,24,1.03093578193623,0.051872989070364,0.00919879819665187,0.00735613633102533,0.209211799581526,0.209211799581526,0.00745065601871948
+"23095",100,2018,3,25,0.883388340932177,15.1965455578761,21.6481409775804,9.82856983503755,12.0943894632841,12.0943894632841,5.14049437798828,100,2018,3,25,0.0580701746856962,0.0518204629976685,0.0146321639704024,0.0465760180713808,0.251035606821583,0.251035606821583,0.00706775684563623
+"23096",100,2018,3,26,10.8169417108508,8.01195824762644,17.5091749638221,11.368041781309,12.583883368405,12.583883368405,5.10817009768306,100,2018,3,26,4.76052618612332,0.0908870865772216,0.0218496793962626,0.00587603148081978,0.491759580766181,0.491759580766181,0.0067011996546959
+"23097",100,2018,3,27,0.287898793267404,8.4718263256799,21.8163257888441,10.0318480828414,11.7273706492811,11.7273706492811,5.07584581737784,100,2018,3,27,0.0692397628776861,0.165233914141137,0.00523740029176857,0.0370561350907034,0.473200615415594,0.473200615415594,0.0063509844458985
+"23098",100,2018,3,28,0.182948298342485,10.3264907169657,22.7737955108072,16.474037555447,13.377436692434,13.377436692434,5.04352153707263,100,2018,3,28,0.0325146212267597,0.0681620003706697,0.143404147892435,0.0882801556261373,1.08980357417509,1.08980357417509,0.00601711121924401
+"23099",100,2018,3,29,0,13.6981297273709,22.5630801718096,11.6293068823904,14.6260615643626,14.6260615643626,5.01119725676741,100,2018,3,29,0,0.021367237588583,0.0145812556709015,0.0566981902296296,0.186687099133387,0.186687099133387,0.00569957997473245
+"23100",100,2018,3,30,0.0456545661368517,12.8412761730198,21.486391655027,13.6347193523864,13.9445764858468,13.9445764858468,4.97887297646219,100,2018,3,30,0.00368421063611382,0.0890444271936168,0.0344648119389178,0.0679801474700326,0.321505234855594,0.321505234855594,0.00539839071236376
+"23101",100,2018,3,31,0,9.59702965345057,21.1593509564961,10.1212100846277,13.6385918576332,13.6385918576332,4.94654869615697,100,2018,3,31,0,0.0726480046613427,0.0211211133189874,0.0530485373001337,0.221258556381671,0.221258556381671,0.00511354343213799
+"23102",100,2018,4,1,0,8.2312651152658,24.6636633605453,11.3948185220935,15.3051155539367,15.3051155539367,4.88547105210631,100,2018,4,1,0,0.0442175520166949,0.0321620435483353,0.140676098229109,0.398098213157958,0.398098213157958,0.00472561388564508
+"23103",100,2018,4,2,0,13.3268427759638,20.2842243056093,13.5489108418212,11.6477118146957,11.6477118146957,4.82439340805564,100,2018,4,2,0,0.137389445480066,0.0239812065662553,0.0850339533109456,0.606183076017552,0.606183076017552,0.00435459350398088
+"23104",100,2018,4,3,0.809130910206156,11.4548515093209,19.5287460163482,12.3329702866222,10.3139494307364,10.3139494307364,4.76331576400497,100,2018,4,3,0.0331578981736952,0.0988116761994828,0.0331877039326168,0.0170245603305949,0.315911859666334,0.315911859666334,0.00400048228714537
+"23105",100,2018,4,4,0.136413643700127,10.1473486525784,20.5117602563403,11.8829592829621,13.3561165781304,13.3561165781304,4.70223811995431,100,2018,4,4,0.00596491271117976,0.364170135779194,0.157760880846618,0.0788497604638353,1.05837205010362,1.05837205010362,0.00366328023513858
+"23106",100,2018,4,5,0,7.4713531904357,19.4950166687582,12.1010890788633,11.5441913888006,11.5441913888006,4.64116047590364,100,2018,4,5,0,0.0389777876748162,0.0270579496776578,0.0753134138342176,0.816182804248275,0.816182804248275,0.00334298734796051
+"23107",100,2018,4,6,0.490319040053748,11.4245545279206,19.9435641180695,11.842519124349,13.5387017383303,13.5387017383303,4.58008283185297,100,2018,4,6,0.0258479535161404,0.0674332772997805,0.0263561369558576,0.0381988255350989,0.203911821430118,0.203911821430118,0.00303960362561116
+"23108",100,2018,4,7,0.434763484219513,5.79378433143607,23.5701539403666,12.0190538977108,15.9368757503917,15.9368757503917,4.5190051878023,100,2018,4,7,0.0535672490673457,0.0264064116338684,0.0222029507556578,0.0821222621494567,0.0206924023634669,0.0206924023634669,0.00275312906809049
+"23109",100,2018,4,8,0,11.4920792438016,30.506314736126,11.2948075883066,7.90187011168997,7.90187011168997,4.45792754375164,100,2018,4,8,0,0.044794132529439,0.0153022661313664,0.44095798950427,0.43778017002023,0.43778017002023,0.00248356367539855
+"23110",100,2018,4,9,0.0820682080435936,12.0797908539557,24.6322109843507,13.6072606421409,14.231980219926,14.231980219926,4.39684989970097,100,2018,4,9,0.00321637436486127,0.109469592017525,0.0560116841056458,0.0873140318174097,0.0389830781502459,0.0389830781502459,0.00223090744753531
+"23111",100,2018,4,10,0.188448847905703,14.7058854475535,33.453421010436,12.7627722841941,9.42072619856781,9.42072619856781,4.3357722556503,100,2018,4,10,0.0258479545269794,0.124720501329834,0.0834095090014448,0.0390321285850878,1.03307655700556,1.03307655700556,0.00199516038450079
+"23112",100,2018,4,11,0.184268429588498,14.95146308559,28.4327391948637,15.5401980491361,11.5825631642106,11.5825631642106,4.27469461159964,100,2018,4,11,0.0020467836867299,0.0592912259643478,0.0350251318460318,0.0195433002918754,0.195080058052176,0.195080058052176,0.00177632248629497
+"23113",100,2018,4,12,0.581958205032401,15.4480528070851,21.847964864884,13.4682178371417,11.6777997735572,11.6777997735572,4.21361696754897,100,2018,4,12,0.0153801163107333,0.0635578896239039,0.0317608481203912,0.0275111200969112,0.253669009279016,0.253669009279016,0.00157439375291786
+"23114",100,2018,4,13,0,8.66424644268779,23.4452586640882,10.8037294339557,10.8076348845059,10.8076348845059,4.1525393234983,100,2018,4,13,0,0.0466397634562946,0.0257486242554845,0.043832153282488,0.442705289229531,0.442705289229531,0.00138937418436946
+"23115",100,2018,4,14,3.401100086038,10.0072717488271,14.6191747957068,10.6688119911387,7.48152910941767,7.48152910941767,4.09146167944764,100,2018,4,14,1.04485381962961,0.0762245345289217,0.0783450096978785,0.0117877105066705,1.64306872763946,1.64306872763946,0.00122126378064977
+"23116",100,2018,4,15,15.9028600098932,9.97029705488249,17.0460728896071,15.3190539961207,7.40512647460921,7.40512647460921,4.03038403539697,100,2018,4,15,5.55099379656622,0.0551690304829575,0.070270803190785,0.0233485668161034,1.080957845254,1.080957845254,0.00107006254175879
+"23117",100,2018,4,16,10.5966997136115,13.7677777324966,16.1679865183479,12.2151924727118,7.43937289518098,7.43937289518098,3.9693063913463,100,2018,4,16,10.8770764050847,0.0995706824164034,0.0317917641625747,0.0062315718021275,0.441815245999813,0.441815245999813,0.000935770467696515
+"23118",100,2018,4,17,1.07249725481333,11.5970737134138,15.7318481753762,9.41397149701848,7.67851479953141,7.67851479953141,3.90822874729563,100,2018,4,17,0.0206432722186492,0.139513489478996,0.0354842483633154,0.00614736428070229,0.213047911012686,0.213047911012686,0.000818387558462958
+"23119",100,2018,4,18,0,3.97918593162226,19.3678986598687,10.3922001342438,14.2418810495056,14.2418810495056,3.84715110324497,100,2018,4,18,0,0.0856374523430645,0.0381064193505536,0.0327508598997773,0.00109240516983803,0.00109240516983803,0.000717913814058111
+"23120",100,2018,4,19,0,7.65389441761902,23.8574697160878,11.877975856248,13.9818152206303,13.9818152206303,3.7860734591943,100,2018,4,19,0,0.0488701820273352,0.17384437671266,0.0487702230978554,0.008922202555416,0.008922202555416,0.000634349234481969
+"23121",100,2018,4,20,0,9.41777786196131,24.6019471969017,13.561309183916,13.9142904407514,13.9142904407514,3.72499581514363,100,2018,4,20,0,0.0496877008762307,0.123950597051717,0.114447869609909,0.00649123626848641,0.00649123626848641,0.000567693819734535
+"23122",100,2018,4,21,0,10.5600222072454,22.7770188334751,14.3577557753677,7.05564358945202,7.05564358945202,3.66391817109297,100,2018,4,21,0,0.251394094955786,0.0544285966931929,0.0287649124057089,0.518532073322123,0.518532073322123,0.000517947569815813
+"23123",100,2018,4,22,0.0114411442848978,12.9290648744707,20.0550382758918,14.9206048505928,11.2217160792503,11.2217160792503,3.6028405270423,100,2018,4,22,0.00175438601719706,0.301544972809118,0.0397590253018258,0.0276497269480483,0.236894199436089,0.236894199436089,0.000485110484725799
+"23124",100,2018,4,23,0.113641365829802,9.81401536960413,25.7165785224,13.4374587412583,11.7500879499647,11.7500879499647,3.54176288299163,100,2018,4,23,0.00175438601719706,0.0575649307006403,0.084798275418092,0.0909608521556809,0.0194918191051776,0.0194918191051776,0.000469182564464499
+"23125",100,2018,4,24,0.282398242122269,12.5665785020465,23.6612320219067,17.7647636736711,5.71634766659459,5.71634766659459,3.48068523894097,100,2018,4,24,0.0726315801969746,0.0143918130217348,0.0906609031724295,0.0157396876245018,0.294670146258412,0.294670146258412,0.000470163809031901
+"23126",100,2018,4,25,2.30473051684918,8.84621571497818,19.2177116910223,13.2001211011108,7.14400438428319,7.14400438428319,3.4196075948903,100,2018,4,25,0.103157914507463,0.119381946782421,0.0235561572429462,0.132458466589041,0.39360935720257,0.39360935720257,0.000488054218428019
+"23127",100,2018,4,26,2.43113313361232,10.7627833757726,17.3623322422879,11.6382288906572,10.944334427778,10.944334427778,3.35852995083963,100,2018,4,26,0.578070164772505,0.0850578927024187,0.0320432429042638,0.0397895115635538,0.550949611762029,0.550949611762029,0.000522853792652842
+"23128",100,2018,4,27,0.0863586371504303,6.88463146694423,17.8598022314057,11.9381739812584,8.67859174027564,8.67859174027564,3.29745230678896,100,2018,4,27,0.00175438601719706,0.0736479421593552,0.0139870972842167,0.0253760186313347,0.403820521534345,0.403820521534345,0.000574562531706378
+"23129",100,2018,4,28,0,5.36047305735556,19.161232024005,11.8339383741154,12.1042463671912,12.1042463671912,3.2363746627383,100,2018,4,28,0,0.125460826562594,0.0225263730993774,0.0542701277527424,0.034002931371813,0.034002931371813,0.000643180435588618
+"23130",100,2018,4,29,0,6.16425741466359,18.9837624305415,12.6341254283624,11.0341474115521,11.0341474115521,3.17529701868763,100,2018,4,29,0,0.0704146263981635,0.0137063910460179,0.087022774353989,0.179511149130988,0.179511149130988,0.000728707504299575
+"23131",100,2018,4,30,0.100000001490116,6.40647966607307,19.9987789443617,11.0138834920785,12.3451485796468,12.3451485796468,3.11421937463696,100,2018,4,30,0,0.0199000049591331,0.0302702172534285,0.143921063194587,0.00321519213233395,0.00321519213233395,0.000831143737839238
+"23132",100,2018,5,1,0.0818481860381148,3.91522557717083,24.0439273636989,8.02443350056479,11.9362046710729,11.9362046710729,3.06250992948766,100,2018,5,1,0.0020467836867299,0.0463005846988536,0.0231315801968973,0.0275204798715616,0.0114034997371945,0.0114034997371945,0.000790571560514743
+"23133",100,2018,5,2,0,10.9259736309744,23.4436743267299,8.28744765870248,10.8689218831666,10.8689218831666,3.01080048433836,100,2018,5,2,0,0.0555842545025009,0.0511041068543153,0.00826551047885005,0.179669009992871,0.179669009992871,0.00075168525007181
+"23134",100,2018,5,3,0.507920791730039,12.3089548746745,21.8764907157067,13.1028053107435,2.74240929432566,2.74240929432566,2.95909103918905,100,2018,5,3,0.192573095516504,0.0553140560441002,0.0141111276418342,0.398444963859076,0.0678590627084732,0.0678590627084732,0.000714484806510445
+"23135",100,2018,5,4,10.8271727556705,9.38949393072013,15.8536302673541,10.8746094404668,8.97938390769581,8.97938390769581,2.90738159403975,100,2018,5,4,0.623625830377108,0.0727591421072359,0.0802818655093842,0.0295356307917288,0.502048369888456,0.502048369888456,0.000678970229830643
+"23136",100,2018,5,5,3.93179316331845,10.5570077046309,19.3592848531222,13.6688888474266,10.1082839598619,10.1082839598619,2.85567214889045,100,2018,5,5,0.202280738674423,0.142977789052411,0.0716760525979997,0.0145818373697566,0.46753568290359,0.46753568290359,0.000645141520032405
+"23137",100,2018,5,6,0.414631468414998,10.5332232444856,19.7311220536269,13.227260742775,10.3147744314112,10.3147744314112,2.80396270374114,100,2018,5,6,0.0147368428930205,0.133933954098434,0.0535117509187128,0.0465099371347776,0.212626287886488,0.212626287886488,0.000612998677115735
+"23138",100,2018,5,7,1.34807479735648,10.711815208754,18.7658744786832,9.24248613749925,4.57899884684513,4.57899884684513,2.75225325859184,100,2018,5,7,0.825380101106322,0.0882164128622223,0.0618467879672788,0.00888187542974978,0.306849707044093,0.306849707044093,0.000582541701080624
+"23139",100,2018,5,8,3.14543453235962,8.43886692479368,16.5460176730182,9.06559948034675,11.3636963763515,11.3636963763515,2.70054381344254,100,2018,5,8,0.167836249014094,0.0252204682244756,0.0624450415586489,0.0158731174090606,0.000592986121454599,0.000592986121454599,0.00055377059192708
+"23140",100,2018,5,9,0,5.83619361191299,17.7470076322818,12.8128823510098,8.90216726674499,8.90216726674499,2.64883436829323,100,2018,5,9,0,0.180036239924095,0.0680450319927097,0.0251029132955276,0.354077774768499,0.354077774768499,0.000526685349655101
+"23141",100,2018,5,10,24.4634764097442,6.62668865558469,11.8568537369026,8.99870181529567,2.964202461463,2.964202461463,2.59712492314393,100,2018,5,10,1.66099383549411,0.160513995179535,0.10040406148147,0.00354269434394038,0.753957960832616,0.753957960832616,0.000501285974264688
+"23142",100,2018,5,11,25.7988998572556,6.67292631472429,12.9702750081145,11.2999120812998,3.42317932745804,3.42317932745804,2.54541547799463,100,2018,5,11,0.974678859822564,0.227461944485282,0.0726508392602796,0.00325965506355013,0.171392971838014,0.171392971838014,0.000477572465755838
+"23143",100,2018,5,12,0.904070384169307,10.1526071844321,14.5651155782349,13.779384025515,7.06645768441514,7.06645768441514,2.49370603284533,100,2018,5,12,0.0017543868014688,0.135624568075964,0.0397374711533648,0.00126958858431091,0.0652719050736626,0.0652719050736626,0.000455544824128551
+"23144",100,2018,5,13,1.28206821007304,10.8826513731047,14.5915292072611,12.2904731542757,5.86016500743702,5.86016500743702,2.44199658769602,100,2018,5,13,0.0906432736686804,0.240914561420061,0.0421134604828067,0.0073099725289238,0.257016363061395,0.257016363061395,0.00043520304938283
+"23145",100,2018,5,14,1.05841583957767,7.62101216048691,15.4249285843768,11.4972166674103,8.77078099455377,8.77078099455377,2.39028714254672,100,2018,5,14,0.0991812866473067,0.129914692575368,0.0181608280495019,0.0387608226665392,0.0598368571120278,0.0598368571120278,0.000416547141518673
+"23146",100,2018,5,15,1.63410340467564,10.0356765281249,14.7519250750148,8.8467766234059,8.34643560038148,8.34643560038148,2.33857769739742,100,2018,5,15,0.0631578849073065,0.0938098881036117,0.0654140388712503,0.00979123044829476,0.25618952238972,0.25618952238972,0.000399577100536079
+"23147",100,2018,5,16,0.47601760864848,7.7472496835312,13.0702419920985,8.94726069401069,4.81701872293705,4.81701872293705,2.28686825224811,100,2018,5,16,0.0567251431053147,0.288257888829684,0.0519871430938935,0.00572457064584751,0.185628659794919,0.185628659794919,0.000384292926435052
+"23148",100,2018,5,17,0.739933999917164,9.13309136947783,14.1667546155823,12.8865457666983,5.21873481937236,5.21873481937236,2.23515880709881,100,2018,5,17,0.0543274884196056,0.103733886049588,0.102446771095298,0.00817600721178178,0.256764856434437,0.256764856434437,0.000370694619215589
+"23149",100,2018,5,18,9.184818351754,10.2852364152965,15.1360175580737,12.6756764465433,7.2619362239397,7.2619362239397,2.18344936194951,100,2018,5,18,1.72608187681058,0.10864509191685,0.0575918665925903,0.00248889473815532,0.424225715600429,0.424225715600429,0.00035878217887769
+"23150",100,2018,5,19,0.872497250406918,6.36552260339063,14.32556637679,11.0636523711537,6.72432337971804,6.72432337971804,2.1317399168002,100,2018,5,19,0.033859651709161,0.235898804100236,0.124424562947305,0.0120912314432644,0.136661973394446,0.136661973394446,0.000348555605421358
+"23151",100,2018,5,20,0.292959304524399,10.2215291328556,15.4868646439153,14.5523983490611,3.74465347096996,3.74465347096996,2.0800304716509,100,2018,5,20,0.00918128692790083,0.125502325591239,0.0648035247067293,0.00692456222229568,0.170904073856873,0.170904073856873,0.000340014898846586
+"23152",100,2018,5,21,13.763256330039,11.4552474226495,14.7438393877153,13.0609902192001,5.45572063762887,5.45572063762887,2.0283210265016,100,2018,5,21,0.525438446468829,0.169647926727744,0.083121110284891,0.00128303899047581,0.285137422232609,0.285137422232609,0.000333160059153384
+"23153",100,2018,5,22,4.39218920528299,11.4602310140796,14.8192078209553,15.0444884431375,4.40785474378546,4.40785474378546,1.97661158135229,100,2018,5,22,1.79146201267579,0.151659042898336,0.07364790353841,0.000860814506214884,0.065425717382848,0.065425717382848,0.00032799108634174
+"23154",100,2018,5,23,2.0423542244075,11.7201321038476,14.4386247618101,12.0362265852287,3.84181522352598,3.84181522352598,1.92490213620299,100,2018,5,23,0.174970761823378,0.124489573566891,0.1040023483902,0.0116912069979715,0.0147953227388993,0.0147953227388993,0.000324507980411665
+"23155",100,2018,5,24,0.239933997269708,11.8934324241445,15.8767878162061,10.6649615536429,7.11509350974961,7.11509350974961,1.87319269105369,100,2018,5,24,0.0247368425113416,0.195710106696935,0.112117591492236,0.017827490555673,0.546392977763643,0.546392977763643,0.000322710741363153
+"23156",100,2018,5,25,0.955005510182533,9.95542358066907,14.8355336393854,10.0046535267426,6.67332232719732,6.67332232719732,1.82148324590438,100,2018,5,25,0.0598830472342458,0.0720760856874599,0.0781321235813665,0.00628773797917235,0.0709918219326658,0.0709918219326658,0.000322599369196206
+"23157",100,2018,5,26,0.0299229927451173,3.18677670675011,18.0657427633556,11.6660726933327,9.67971381154915,9.67971381154915,1.76977380075508,100,2018,5,26,0.0020467836867299,0.0469093506294425,0.0420145580673525,0.00949298518005687,0.00200350032167064,0.00200350032167064,0.000324173863910823
+"23158",100,2018,5,27,0.0995599577496416,7.00194723036948,19.3723761618334,12.4944554392916,8.24690875335626,8.24690875335626,1.71806435560578,100,2018,5,27,0.00918128700197092,0.021222782683809,0.0138321650501362,0.0175485478741968,0.0253619989630218,0.0253619989630218,0.000327434225507005
+"23159",100,2018,5,28,1.26127613292407,13.335885501144,20.2896810223167,13.4744775665082,6.98708468301855,6.98708468301855,1.66635491045647,100,2018,5,28,0.123216375788751,0.0567655255257099,0.0822362127786917,0.0313052824188359,0.140638583840283,0.140638583840283,0.000332380453984751
+"23160",100,2018,5,29,5.96292631696947,11.2672057755054,14.1757867349388,9.11161716385643,7.73047291878426,7.73047291878426,1.61464546530717,100,2018,5,29,0.276081826896017,0.0889578714605859,0.111869064467191,0.00599239518245593,0.493387047956161,0.493387047956161,0.000339012549344061
+"23161",100,2018,5,30,2.31232123150684,4.65850381410555,14.4570187588598,11.100440099819,6.98465344299971,6.98465344299971,1.56293602015787,100,2018,5,30,0.609883030171983,0.121105266264327,0.0866427210847561,0.0619649112099952,0.0966766027863462,0.0966766027863462,0.000347330511584937
+"23162",100,2018,5,31,6.66721666344453,7.4822992402466,14.0917381626545,9.35620457845421,9.38080310611704,9.38080310611704,1.51122657500856,100,2018,5,31,0.344970726046664,0.223945071908641,0.0568929227126866,0.0200023649986391,0.00112105029821626,0.00112105029821626,0.000357334340707377
+"23163",100,2018,6,1,0,1.80391637261289,15.0988889217901,9.71733776885684,9.35360843775952,9.35360843775952,1.49559297672397,100,2018,6,1,0,0.0618888720406454,0.0835497092532278,0.0227760298924811,0.000315203993560259,0.000315203993560259,0.000367669176619526
+"23164",100,2018,6,2,0.065456546629944,4.15712873534401,15.353993433668,11.2536632959599,9.28649067537751,9.28649067537751,1.47995937843937,100,2018,6,2,0.00263157902579559,0.0659134426803141,0.103433355924938,0.0224368587895968,0.00116959783774919,0.00116959783774919,0.000380668206507696
+"23165",100,2018,6,3,0,3.38443344260993,14.825665588295,10.1254015703275,9.15096802496412,9.15096802496412,1.46432578015477,100,2018,6,3,0,0.041592407325281,0.169836230239047,0.00231168622700054,0.0181578814077958,0.0181578814077958,0.000396331430371891
+"23166",100,2018,6,4,0,2.89422442443562,13.8956985505107,11.601298150712,6.32784373689406,6.32784373689406,1.44869218187017,100,2018,6,4,0,0.107009351609886,0.0869117633954226,0.00107192529830763,0.380780695692162,0.380780695692162,0.000414658848212109
+"23167",100,2018,6,5,0.880968091380347,4.5231023107556,15.1972276238587,10.5616613583203,8.99997800568936,8.99997800568936,1.43305858358558,100,2018,6,5,0.0543274838882581,0.134669562049801,0.0735648692862599,0.00278012030089512,0.0304555605147449,0.0304555605147449,0.000435650460028347
+"23168",100,2018,6,6,0.0365236529094814,4.08677674074246,17.3265455285839,10.2968428037872,9.1388340447471,9.1388340447471,1.41742498530098,100,2018,6,6,0.00228070182235617,0.0870696044308554,0.0667205165887372,0.00238187006463534,0.00261521095595867,0.00261521095595867,0.000459306265820609
+"23169",100,2018,6,7,0.419361942852422,6.25570962452652,16.0255775052984,9.42544559336076,4.0407811129185,4.0407811129185,1.40179138701638,100,2018,6,7,0.0135672518576098,0.0293210461466113,0.0927830204300707,0.00107018103804153,0.233838583801796,0.233838583801796,0.000485626265588896
+"23170",100,2018,6,8,0.488228830124679,10.6407592718882,15.6501758520884,12.0921011778912,7.16127613590102,7.16127613590102,1.38615778873178,100,2018,6,8,0.0703508763877971,0.0659894982879732,0.0979806408285988,0.0211163635790479,0.0696666535271728,0.0696666535271728,0.000514610459333203
+"23171",100,2018,6,9,0.890869092757684,6.73064904532941,14.6365235919344,11.8603851456847,6.92878988893381,6.92878988893381,1.37052419044719,100,2018,6,9,0.0581286535834712,0.0291356764497982,0.092617572106373,0.00282047151939511,0.363614039432263,0.363614039432263,0.000546258847053533
+"23172",100,2018,6,10,0.130693071639643,4.74429036288372,15.6940814019299,9.87631462378339,9.0244007163053,9.0244007163053,1.35489059216259,100,2018,6,10,0.00953216432305109,0.0601497405002195,0.0912619198649327,0.00631753083398614,0.000754383968379385,0.000754383968379385,0.000580571428749886
+"23173",100,2018,6,11,0.247854793220922,4.16585258026459,15.9730142251362,10.6898899875721,6.71925189130508,6.71925189130508,1.33925699387799,100,2018,6,11,0.0148538014989848,0.0622619862484117,0.0276145812387544,0.035049658847884,0.237965522623358,0.237965522623358,0.000617548204422263
+"23174",100,2018,6,12,7.82233228620523,9.18860280107219,14.9486247787643,13.5751705736217,4.73753574238096,4.73753574238096,1.32362339559339,100,2018,6,12,1.27760228374553,0.130614628387004,0.0834608432324727,0.00263858443151165,0.339984207405421,0.339984207405421,0.000657189174070661
+"23175",100,2018,6,13,5.4433443412529,5.43774482152118,13.0366117122805,11.3540923744932,5.85746972500557,5.85746972500557,1.3079897973088,100,2018,6,13,0.520292390745299,0.0540590820167658,0.0246479669939729,0.000655550596529367,0.499414080291911,0.499414080291911,0.000699494337695083
+"23176",100,2018,6,14,1.63267324178001,5.44641363607644,14.0923542352137,10.7172606744126,7.64409240821276,7.64409240821276,1.2923561990242,100,2018,6,14,0.201345008270087,0.0805912058055293,0.0701585437842283,0.0167877104508803,0.125615250491588,0.125615250491588,0.000744463695295525
+"23177",100,2018,6,15,17.0708471440902,6.86609460969176,11.5726843646126,9.93127593952175,5.08878990211109,5.08878990211109,1.2767226007396,100,2018,6,15,1.58818708564823,0.115198249753337,0.094114008585655,0.000958483197488628,1.6893245739435,1.6893245739435,0.000792097246871993
+"23178",100,2018,6,16,19.6316831544681,5.07132013192927,10.8873818432144,9.12276127839377,4.05678768939573,4.05678768939573,1.261089002455,100,2018,6,16,2.37485385024789,0.130949693549337,0.0955731432731445,0.00206549296917356,0.968498175586463,0.968498175586463,0.000842394992424483
+"23179",100,2018,6,17,26.7194719241135,5.4633883380785,12.0346865386459,10.4497579652222,5.9919692412986,5.9919692412986,1.24545540417041,100,2018,6,17,2.14052647774718,0.0677496855987024,0.0811321993956796,0.00946198726684109,0.514859652708863,0.514859652708863,0.000895356931952994
+"23180",100,2018,6,18,2.71188120296424,7.4686248729987,11.8275577148589,9.44321228358874,6.26211226655312,6.26211226655312,1.22982180588581,100,2018,6,18,0.434795315223832,0.176014579075838,0.0820262341527768,0.00603216726745569,0.182903491466383,0.182903491466383,0.000950983065457529
+"23181",100,2018,6,19,0.123542356461582,0.928074809846574,12.5254564264295,8.93952685273258,7.95207918595166,7.95207918595166,1.21418820760121,100,2018,6,19,0.0091812871283258,0.060931580607376,0.0820766024138327,0.00394794783347628,0.00781112111414788,0.00781112111414788,0.00100927339293808
+"23182",100,2018,6,20,0,3.13220023558085,11.6097909216047,8.83519249046334,7.52232127299797,7.52232127299797,1.19855460931661,100,2018,6,20,0,0.0315274826339879,0.0955689429412059,0.00559123617261571,0.0983064401615572,0.0983064401615572,0.00107022791439467
+"23183",100,2018,6,21,0,3.9313421320207,13.5812102067064,10.3230363124012,7.38137511084444,7.38137511084444,1.18292101103202,100,2018,6,21,0,0.102116365882254,0.0783152450629196,0.00327660542322322,0.14481521018962,0.14481521018962,0.00113384662982727
+"23184",100,2018,6,22,0.201980203250782,3.37437845168203,13.9510559883579,10.1730691250926,8.15213419029815,8.15213419029815,1.16728741274742,100,2018,6,22,0.00695906509258596,0.107831580601523,0.149547996157442,0.0258830096373878,0.0459666770087998,0.0459666770087998,0.0012001295392359
+"23185",100,2018,6,23,0.968536859232732,4.37427943977717,11.8778877877297,11.4853575772578,5.13844889618776,5.13844889618776,1.15165381446282,100,2018,6,23,0.0503508726198081,0.0780841956088568,0.072404124814365,0.00432633045940683,0.122326292780848,0.122326292780848,0.00126907664262054
+"23186",100,2018,6,24,0.698679873062046,6.84457642017025,11.3628712214521,8.57990108359896,4.51746978329616,4.51746978329616,1.13602021617822,100,2018,6,24,0.0542690070470176,0.129321070434634,0.0871584829164133,0.00103624841801442,0.0273988364024635,0.0273988364024635,0.00134068793998121
+"23187",100,2018,6,25,0.732783281862146,7.05765669626502,12.4121232573086,10.5146754509283,6.24784380846685,6.24784380846685,1.12038661789363,100,2018,6,25,0.0624561433450524,0.0803719214746316,0.0634877318868098,0.0207110917410095,0.188391805029774,0.188391805029774,0.00141496343131791
+"23188",100,2018,6,26,0.475907598867012,5.70702972873734,13.3927613049582,9.93181509284428,8.35781081202794,8.35781081202794,1.10475301960903,100,2018,6,26,0.0305263155315356,0.24792630030874,0.120602367423499,0.00121520704532066,0.0781760176408221,0.0781760176408221,0.00149190311663062
+"23189",100,2018,6,27,0.0125412543122918,4.63132010985522,12.3813641614253,10.1834103665074,7.6244664580384,7.6244664580384,1.08911942132443,100,2018,6,27,0.00228070182235617,0.102914620179752,0.129492497677369,0.0021584826933167,0.0302093443073336,0.0302093443073336,0.00157150699591936
+"23190",100,2018,6,28,0,1.99867985482001,13.2736853934226,10.1233773457073,9.00757991887293,9.00757991887293,1.07348582303983,100,2018,6,28,0,0.0539152084763327,0.113470697034942,0.000645034395267686,0.00075438469341686,0.00075438469341686,0.00165377506918413
+"23191",100,2018,6,29,0,4.16408140247542,13.409912023345,8.64585258202715,7.60103408364442,7.60103408364442,1.05785222475524,100,2018,6,29,0,0.0534005683681617,0.103425724850564,0.00257601744674742,0.257715186700238,0.257715186700238,0.00173870733642491
+"23192",100,2018,6,30,6.2057205112067,6.99657864198171,12.2829922779952,9.44550054380209,8.14859190606179,8.14859190606179,1.04221862647064,100,2018,6,30,0.16397659351953,0.0782274704403553,0.10180646513562,0.00100351879285222,0.206726325798079,0.206726325798079,0.00182630379764172
+"23193",100,2018,7,1,0.574257436496328,3.40754675393057,13.4955225263623,9.92018708773572,8.96306933139679,8.96306933139679,1.04865458770449,100,2018,7,1,0.019239765426569,0.0972274910447222,0.105738585350592,0.00483742157792764,0.00732517438975573,0.00732517438975573,0.00176205203466701
+"23194",100,2018,7,2,0.719691970380488,1.95816279126473,13.2864357661886,8.86163915711792,9.11166116926405,9.11166116926405,1.05509054893835,100,2018,7,2,0.223216377601289,0.0510508826060639,0.0518357554855096,0.00212632651308981,0.00052573141364152,0.00052573141364152,0.00170058701674913
+"23195",100,2018,7,3,0,4.59123209469652,13.8502640299278,8.1049615185384,5.96884491944602,5.96884491944602,1.0615265101722,100,2018,7,3,0,0.0227672633611813,0.0405064450560424,0.0152871284340361,0.13412459558745,0.13412459558745,0.00164190874388809
+"23196",100,2018,7,4,0.572607261135896,8.81157317649413,14.5030803565014,7.90863579360828,5.03308034153006,5.03308034153006,1.06796247140605,100,2018,7,4,0.0435672501287266,0.071449761583741,0.0243818347953865,0.00367193193939161,0.382158482442276,0.382158482442276,0.00158601721608387
+"23197",100,2018,7,5,3.92200219539395,9.59520350876946,13.3502089339908,10.6833443405605,2.16128712033544,2.16128712033544,1.07439843263991,100,2018,7,5,0.196959070127621,0.0724151337049904,0.0222163451680793,0.0174701609115779,0.0626783562027255,0.0626783562027255,0.00153291243333649
+"23198",100,2018,7,6,10.4451045255588,7.90002200574634,12.3901978840004,9.41866872596531,8.06348735139971,8.06348735139971,1.08083439387376,100,2018,7,6,0.632573230252698,0.0446649065899362,0.0932227716189996,0.0133274805393314,0.173749750309153,0.173749750309153,0.00148259439564594
+"23199",100,2018,7,7,11.1950496279105,7.07984601913637,11.8107810340437,8.54586367507448,5.93519249712542,5.93519249712542,1.08727035510761,100,2018,7,7,4.03386013499486,0.0941701775233075,0.144449778783329,0.000377780236188072,1.44492090227173,1.44492090227173,0.00143506310301221
+"23200",100,2018,7,8,6.08965897586348,7.98576463672063,13.1908581506039,11.8877997424605,6.53676563206286,6.53676563206286,1.09370631634147,100,2018,7,8,0.605906394155416,0.111400623282538,0.0863818627698258,0.00863334494704504,0.183360842502903,0.183360842502903,0.00139031855543532
+"23201",100,2018,7,9,5.87590764720317,8.36810785079553,12.1821122363587,10.3528823422389,6.6704070759554,6.6704070759554,1.10014227757532,100,2018,7,9,2.60040939721451,0.215991800544747,0.0703427204132514,0.00343742034430485,0.188370147427361,0.188370147427361,0.00134836075291526
+"23202",100,2018,7,10,0.677887796044219,7.41347634123497,11.7551704832692,9.30783291198764,6.65941691687136,6.65941691687136,1.10657823880917,100,2018,7,10,0.0120467808302387,0.0381649249551142,0.0506262971108959,0.00416725422159639,0.0262257398382407,0.0262257398382407,0.00130918969545202
+"23203",100,2018,7,11,4.7579757495813,6.24513748534048,11.2382728683673,9.33629265607911,5.49440044512187,5.49440044512187,1.11301420004303,100,2018,7,11,1.71450285225586,0.101557882619061,0.436170310374115,0.00253858888864186,0.803739640548235,0.803739640548235,0.00127280538304562
+"23204",100,2018,7,12,0.118151818212568,0.0460396045419348,10.8947964281139,9.58546752006438,6.68823980839208,6.68823980839208,1.11945016127688,100,2018,7,12,0.0209941529861668,0.0275830402479012,0.0471871127636575,0.0121286707650004,0.443498876411065,0.443498876411065,0.00123920781569605
+"23205",100,2018,7,13,0.0244224426163436,2.0136193569475,13.5675578112125,8.68019798119338,8.74272835503842,8.74272835503842,1.12588612251074,100,2018,7,13,0.00707602381183391,0.0410286610675886,0.0819988510802006,0.00213742156898789,0.124546838559678,0.124546838559678,0.00120839699340331
+"23206",100,2018,7,14,0,1.09941693297838,12.5009682049977,8.1177668534752,9.72814075440595,9.72814075440595,1.13232208374459,100,2018,7,14,0,0.0515941544159132,0.0776731351128312,0.000949703615945068,0.00037193134604204,0.00037193134604204,0.0011803729161674
+"23207",100,2018,7,15,0,3.81502751365091,14.0366667012046,10.3246425527944,9.7283387598556,9.7283387598556,1.13875804497844,100,2018,7,15,0,0.0200672579843473,0.0707468192608838,0.0264333364275338,0.0132888932123066,0.0132888932123066,0.00115513558398832
+"23208",100,2018,7,16,0,6.13477442261934,14.2661936060168,9.71291520755545,8.19482944927069,8.19482944927069,1.1451940062123,100,2018,7,16,0,0.0331000057432613,0.0727444450379509,0.00196082275352526,0.560778313878898,0.560778313878898,0.00113268499686608
+"23209",100,2018,7,17,2.05005499284522,8.72036305564989,15.205302521734,10.1507260770556,5.24158418506417,5.24158418506417,1.15162996744615,100,2018,7,17,0.604444449875092,0.118578390465126,0.0387035109938951,0.00086900731366768,0.0728988560559703,0.0728988560559703,0.00111302115480066
+"23210",100,2018,7,18,8.45049516386194,5.06842688076829,14.6391638828189,10.4771396240385,8.74270619181516,8.74270619181516,1.15806592868,100,2018,7,18,0.783976635681997,0.0821210630026678,0.0565953233909562,0.0333286840361846,0.0971053007472125,0.0971053007472125,0.00109614405779207
+"23211",100,2018,7,19,2.89735975832042,8.27541256265672,10.5843894421333,9.86093501499121,4.13683170821145,4.13683170821145,1.16450188991386,100,2018,7,19,0.0880701740722256,0.147564924537979,0.0966432437228497,0.000605263091013283,0.178870160276866,0.178870160276866,0.00108205370584031
+"23212",100,2018,7,20,10.2910891502473,4.22915284127423,11.1816171220165,9.93108915154821,6.16851487018094,6.16851487018094,1.17093785114771,100,2018,7,20,1.54912287059591,0.132750849610053,0.184280710265037,0.00262632943545666,0.24513686083411,0.24513686083411,0.00107075009894539
+"23213",100,2018,7,21,8.26633654123355,4.40194720456047,12.6886248268572,9.09392748519008,9.292926294182,9.292926294182,1.17737381238156,100,2018,7,21,1.8265496176447,0.0834619844336441,0.0719929992342126,0.00353974524835378,0.0397151585697036,0.0397151585697036,0.00106223323710729
+"23214",100,2018,7,22,0,5.45894388003711,14.2056875952793,8.19688674037081,10.3182618536703,10.3182618536703,1.18380977361542,100,2018,7,22,0,0.0632204409471209,0.0871696239271214,0.00530409404696028,0.000539762961013542,0.000539762961013542,0.00105650312032603
+"23215",100,2018,7,23,1.8440044231803,7.17465337484714,15.1353464441331,7.30003302640254,6.11911992848378,6.11911992848378,1.19024573484927,100,2018,7,23,1.26040938385746,0.127847331145233,0.0794923890923376,0.029843288286687,0.178469007373845,0.178469007373845,0.00105355974860159
+"23216",100,2018,7,24,3.41914189845422,7.4461386437201,13.2342575008195,10.442409130344,7.00816282108672,7.00816282108672,1.19668169608312,100,2018,7,24,0.148771921012839,0.0877578862419139,0.0970474430129707,0.00887837190690568,0.545492298008005,0.545492298008005,0.00105340312193399
+"23217",100,2018,7,25,6.88492854750983,6.29970301207405,13.7068756090926,10.423938324218,8.58387235184052,8.58387235184052,1.20311765731698,100,2018,7,25,0.554502957773507,0.0910695869579857,0.112948505841139,0.00037661098242246,0.410591233579237,0.410591233579237,0.00105603324032321
+"23218",100,2018,7,26,0.843234318645612,4.67611665012288,14.2492958830528,11.1461495566289,9.95558866179816,9.95558866179816,1.20955361855083,100,2018,7,26,0.0259649139607864,0.0778941492315037,0.105224565102245,0.010060809730021,0.0723777880775317,0.0723777880775317,0.00106145010376927
+"23219",100,2018,7,27,0,4.73953798921457,18.0600439686455,11.3096148603164,10.7406381401423,10.7406381401423,1.21598957978468,100,2018,7,27,0,0.0310426919736595,0.102057977005265,0.0151204672719577,0.00226901111275696,0.00226901111275696,0.00106965371227215
+"23220",100,2018,7,28,0.34598460393508,4.1895819735868,18.08691974258,12.8654235172586,10.7052584439352,10.7052584439352,1.22242554101854,100,2018,7,28,0.0698830418680844,0.0403473547779471,0.0976065290843609,0.00260348986360478,0.0275064169811645,0.0275064169811645,0.00108064406583187
+"23221",100,2018,7,29,4.93586359270597,7.12058304428923,10.8936963348892,9.70583043869573,5.45424638250873,5.45424638250873,1.22886150225239,100,2018,7,29,1.41368422750843,0.0397607935431655,0.071859094550666,0.0025099493930994,0.363865495419663,0.363865495419663,0.00109442116444842
+"23222",100,2018,7,30,11.6977997990725,6.41397139053009,13.449890009772,9.88712871979565,9.64431254488669,9.64431254488669,1.23529746348624,100,2018,7,30,2.92473658745756,0.0892245718058276,0.125525121439064,0.00633567113829103,0.157070712643978,0.157070712643978,0.0011109850081218
+"23223",100,2018,7,31,0.489328937037419,7.12124311753494,14.5332564206013,10.9467987389979,7.97586354013323,7.97586354013323,1.2417334247201,100,2018,7,31,0.0121052635622304,0.0931111136542632,0.0654830591436738,0.0231654945798217,0.274900483176071,0.274900483176071,0.00113033559685201
+"23224",100,2018,8,1,7.93080314525021,5.494422535954,13.9067107096757,9.51897688614916,10.3237185158221,10.3237185158221,1.26260753955972,100,2018,8,1,0.346900584014572,0.0731467917336556,0.0608356120550929,0.00185379980883769,0.055880112073644,0.055880112073644,0.00111693247123646
+"23225",100,2018,8,2,0.0892189232216548,4.37210124029447,15.843982401723,9.9953355033799,11.0170737571842,11.0170737571842,1.28348165439933,100,2018,8,2,0.00140350881375765,0.0657654762915132,0.0863228421954741,0.00612748674323309,0.0166538141887228,0.0166538141887228,0.00110550576373494
+"23226",100,2018,8,3,0.186578661326331,8.12742580386541,11.9266885803609,10.1151265332145,3.46657866167419,3.46657866167419,1.30435576923895,100,2018,8,3,0.0335672521312335,0.104206424697548,0.0757356975467095,0.00194326872136047,0.187737438243179,0.187737438243179,0.00109605547434743
+"23227",100,2018,8,4,10.4991198456851,5.50122114481575,13.9636964504213,10.8047745393054,6.41136413570022,6.41136413570022,1.32522988407857,100,2018,8,4,0.500526249534269,0.0799812871152349,0.0671274566717829,0.00197660257670482,0.398638679815624,0.398638679815624,0.00108858160307394
+"23228",100,2018,8,5,0.690649076296289,9.14361936319517,17.326204444709,8.5182838565839,11.7076236905307,11.7076236905307,1.34610399891819,100,2018,8,5,0.0360818726207779,0.100947351277326,0.078269603452892,0.0148152205350798,0.00723858971185918,0.00723858971185918,0.00108308414991446
+"23229",100,2018,8,6,3.0317932295196,6.38405947165914,11.4457645531666,9.36982385763372,8.00025305627334,8.00025305627334,1.3669781137578,100,2018,8,6,0.797602384745733,0.0819496925900723,0.0395479484179773,0.000158483775239855,0.274191272026247,0.274191272026247,0.00107956311486901
+"23230",100,2018,8,7,11.1493949606867,5.56512650667113,12.9677997448526,9.8680968971798,9.60653463265028,9.60653463265028,1.38785222859742,100,2018,8,7,2.00719306689264,0.0828924047481403,0.0643204288104052,0.0221321671491995,0.171370176812858,0.171370176812858,0.00107801849793757
+"23231",100,2018,8,8,2.69416941353197,7.55078110311947,12.8359296376949,8.99584148793068,7.71533546143501,7.71533546143501,1.40872634343704,100,2018,8,8,0.0913450166077689,0.0862917839942798,0.0386584616232237,0.000269598578379586,0.417821002355693,0.417821002355693,0.00107845029912014
+"23232",100,2018,8,9,2.95984598459846,7.0762156663817,14.4794389317663,9.27654559384085,9.35168315186621,9.35168315186621,1.42960045827666,100,2018,8,9,0.101111103693649,0.168776581798819,0.100804066909085,0.0074894755447935,0.0845870942434274,0.0845870942434274,0.00108085851841674
+"23233",100,2018,8,10,1.152585266701,9.81222211357259,17.2278217550683,9.59050607209158,9.1329483157075,9.1329483157075,1.45047457311627,100,2018,8,10,0.850292413799388,0.0898953315690854,0.00987657164217535,0.0410731236642655,0.423400042788239,0.423400042788239,0.00108524315582735
+"23234",100,2018,8,11,13.9688668466113,5.41980198114225,10.8053023744338,8.39471946796044,8.06215617260655,8.06215617260655,1.47134868795589,100,2018,8,11,3.45543804402946,0.0539257516426173,0.0212888578204076,0.000567248316603146,0.314881253174494,0.314881253174494,0.00109160421135198
+"23235",100,2018,8,12,9.2843784708919,3.77720571298673,12.7300880513962,11.1427172334543,9.61317950165836,9.61317950165836,1.49222280279551,100,2018,8,12,1.59497049710911,0.146216374570171,0.0653398018798699,0.0130923978415799,0.326400601113818,0.326400601113818,0.00109994168499064
+"23236",100,2018,8,13,5.56127607599475,6.44492848189619,13.4040264721357,11.3195160182789,7.23383942584131,7.23383942584131,1.51309691763513,100,2018,8,13,3.25426880669179,0.126331625848087,0.118798291713936,0.00191755417279382,0.176695342803431,0.176695342803431,0.0011102555767433
+"23237",100,2018,8,14,1.49867987383579,8.36480751446765,16.501319899942,10.1472167842852,12.6771066338316,12.6771066338316,1.53397103247475,100,2018,8,14,0.0302924032518051,0.107105286581534,0.0541315389714886,0.0070871302153493,0.00901343725557217,0.00901343725557217,0.00112254588660998
+"23238",100,2018,8,15,0.927502749645539,9.92345441056557,13.6247745709057,9.48632552101798,8.9843453553119,8.9843453553119,1.55484514731436,100,2018,8,15,0.0955555537674167,0.0255812834333462,0.0614620251126379,0.000176024087911083,0.409232199589757,0.409232199589757,0.00113681261459068
+"23239",100,2018,8,16,4.46732678376671,5.86431246254966,13.098492890957,8.57612763143609,10.2626512703722,10.2626512703722,1.57571926215398,100,2018,8,16,0.564444419542961,0.113895315295232,0.0639040938729373,0.00111344560329535,1.66070408023375,1.66070408023375,0.0011530557606854
+"23240",100,2018,8,17,1.56248625342471,4.45565453797939,14.7408250666032,10.2602422051173,11.4056215118391,11.4056215118391,1.5965933769936,100,2018,8,17,0.0376608215438005,0.1243485182846,0.0423695878525958,0.0256397282762982,0.121926353385517,0.121926353385517,0.00117127532489414
+"23241",100,2018,8,18,6.66457648272037,5.59457646318526,11.0125302450098,8.17632558191046,9.63757974951968,9.63757974951968,1.61746749183322,100,2018,8,18,1.41385961348554,0.116302352647635,0.0849877455617197,0.000491227189060248,0.877204076564048,0.877204076564048,0.00119147130721689
+"23242",100,2018,8,19,13.0768976033193,5.48618260835788,10.9951703781867,8.04408146288529,11.2192299206002,11.2192299206002,1.63834160667283,100,2018,8,19,0.369824612266196,0.113402319306964,0.0458672324354256,0.014755567148042,0.73052556694664,0.73052556694664,0.00121364370765366
+"23243",100,2018,8,20,3.16017603087347,4.64026405840161,11.9637073069909,9.63811886769579,9.92678769114781,9.92678769114781,1.65921572151245,100,2018,8,20,1.43362580698137,0.16884853723818,0.0626227601035185,0.000291812559938649,0.553757897942568,0.553757897942568,0.00123779252620445
+"23244",100,2018,8,21,5.05874585895517,6.33644659789351,12.0171946178306,9.89642470373441,9.8201759860854,9.8201759860854,1.68008983635207,100,2018,8,21,0.0527485316538904,0.131992983423218,0.0859537911800443,0.000522803176140477,0.325258960072551,0.325258960072551,0.00126391776286925
+"23245",100,2018,8,22,1.32640263638218,3.20524749163092,14.3716831396122,9.01962590191362,11.6267216916394,11.6267216916394,1.70096395119169,100,2018,8,22,0.0720467889796929,0.0242578917882629,0.120565587767748,0.00239121913133916,0.607169616230807,0.607169616230807,0.00129201941764807
+"23246",100,2018,8,23,0.000110011002739402,3.49614962046952,16.7049396334439,9.31248623204834,14.2622663437074,14.2622663437074,1.72183806603131,100,2018,8,23,0.000526315805159117,0.0280485434487588,0.14550988656233,0.00835848893735357,0.0012064314792813,0.0012064314792813,0.00132209749054092
+"23247",100,2018,8,24,0,2.06839386189338,16.1428601534584,10.8929482639426,14.4470407225774,14.4470407225774,1.74271218087092,100,2018,8,24,0,0.0325888842158903,0.0348339617234617,0.0281894504235993,0.000433335155986237,0.000433335155986237,0.00135415198154777
+"23248",100,2018,8,25,0.000440044010957607,3.33079208189374,14.3413971889399,10.9740814003352,12.6258746211154,12.6258746211154,1.76358629571054,100,2018,8,25,0.000526315805159117,0.0766818597018419,0.0525819369423192,0.00031403524172403,0.0286210227476451,0.0286210227476451,0.00138818289066865
+"23249",100,2018,8,26,0,3.84419145153956,13.709394919728,11.6609900698017,8.22907589449741,8.22907589449741,1.78446041055016,100,2018,8,26,0,0.0419655193301093,0.0819397365883199,0.00382863921902498,0.283513963078136,0.283513963078136,0.00142419021790354
+"23250",100,2018,8,27,1.38965896608019,3.03267328442782,12.0967877852772,7.99709564204788,13.1646645035025,13.1646645035025,1.80533452538978,100,2018,8,27,0.0254385961426639,0.181478344123688,0.045028698556547,0.00809882731523457,0.353336766306631,0.353336766306631,0.00146217396325245
+"23251",100,2018,8,28,0.215731578568617,1.43091307555882,12.1253024375085,7.30957101595284,12.0467766445987,12.0467766445987,1.82620864022939,100,2018,8,28,0.00918128710218342,0.112642688053952,0.0361356303611974,0.000977779091741542,0.718433955303056,0.718433955303056,0.00150213412671538
+"23252",100,2018,8,29,0,0.548811877095135,13.5891529047581,7.65851481755575,15.1336412776028,15.1336412776028,1.84708275506901,100,2018,8,29,0,0.0557257287934163,0.0745806657736073,0.00984677834094859,0.0126210609615018,0.0126210609615018,0.00154407070829232
+"23253",100,2018,8,30,0.0371837189259178,5.99990102088097,12.376996775796,8.66911990204529,3.7186028739669,3.7186028739669,1.86795686990863,100,2018,8,30,0.00485380131424519,0.138739750834253,0.0462274996864447,0.00384328252565285,0.0456655038236913,0.0456655038236913,0.00158798370798328
+"23254",100,2018,8,31,3.9157316446042,7.72642467832408,15.4742904721838,12.4484266725966,9.15956001890243,9.15956001890243,1.88883098474825,100,2018,8,31,0.0032163739901567,0.0597496970372275,0.0987988367717221,0.00225847717648156,0.837980129846575,0.837980129846575,0.00163387312578826
+"23255",100,2018,9,1,5.04829475090186,8.27726066283005,12.4020682524795,8.66221126917303,9.71809686738403,9.71809686738403,1.91718645421199,100,2018,9,1,0.194970721306178,0.0960619917998548,0.0689924551579732,0.0042923971143529,0.477161732404913,0.477161732404913,0.00166484227963067
+"23256",100,2018,9,2,2.82574254398954,3.75546755255646,12.7603740723613,8.59420242771195,12.1258636417955,12.1258636417955,1.94554192367574,100,2018,9,2,0.0533918037470365,0.188191795470152,0.0578251171358375,0.00139298186284535,0.534337449775433,0.534337449775433,0.00169690715194889
+"23257",100,2018,9,3,1.63542353838059,4.6608360996603,12.6419581873844,8.29454356740148,9.44350948375706,9.44350948375706,1.97389739313949,100,2018,9,3,0.563625736062292,0.150481871290398,0.0182877163736877,0.00121636966242365,0.396582971019683,0.396582971019683,0.0017300677427429
+"23258",100,2018,9,4,0,0.953784385071074,17.016226427521,9.77025312270531,15.1828384535803,15.1828384535803,2.00225286260324,100,2018,9,4,0,0.0508783663702296,0.064998846529535,0.00241638276624974,0.198402366934965,0.198402366934965,0.00176432405201271
+"23259",100,2018,9,5,0.0278327836930686,6.2751155119918,18.478877668727,11.1338943842352,13.7583937524307,13.7583937524307,2.03060833206698,100,2018,9,5,0.0020467836867299,0.0285877115629069,0.0862667267695207,0.001546782376544,0.38521165841541,0.38521165841541,0.00179967607975833
+"23260",100,2018,9,6,0,11.7546755630191,18.2271507208628,10.619956070047,9.02927383521471,9.02927383521471,2.05896380153073,100,2018,9,6,0,0.0602227963587871,0.0204174913644812,0.0187251481285455,0.867469425453573,0.867469425453573,0.00183612382597975
+"23261",100,2018,9,7,4.99031906526605,4.08800882238759,14.018019851416,9.71903179240043,14.4111221636614,14.4111221636614,2.08731927099448,100,2018,9,7,0.901169593989518,0.0630111313290208,0.0344333264669575,0.00462748221470762,0.431713907255301,0.431713907255301,0.00187366729067697
+"23262",100,2018,9,8,0.323872392843462,2.35269526915975,15.7690980237703,8.63719463872962,15.8680638459125,15.8680638459125,2.11567474045823,100,2018,9,8,0.0171929822975432,0.0374654966259626,0.0775667057462228,0.0168093396104008,0.0885765761572417,0.0885765761572417,0.00191230647384999
+"23263",100,2018,9,9,0.000110011002739402,8.69633673887179,16.0523105019128,12.943399348537,9.95460946279259,9.95460946279259,2.14403020992197,100,2018,9,9,0.000526315805159117,0.0302152022602659,0.0699247016707737,0.00140000262797391,0.55824796944118,0.55824796944118,0.00195204137549881
+"23264",100,2018,9,10,1.3921891941358,10.8776677460036,19.0523105889919,13.4699449014611,14.4498791091382,14.4498791091382,2.17238567938572,100,2018,9,10,0.0291228129570958,0.115095319029667,0.117748435282824,0.0103116907667164,0.145356170930991,0.145356170930991,0.00199287199562345
+"23265",100,2018,9,11,0.0608360845148891,8.62112205807525,23.7500547046053,10.5554345847488,17.2883388070252,17.2883388070252,2.20074114884947,100,2018,9,11,0.00374269017002039,0.0406759787688661,0.0541146176124529,0.00281462597547156,0.0305841604179355,0.0305841604179355,0.00203479833422388
+"23266",100,2018,9,12,0.565236528435818,6.59806389226378,14.5129813161751,9.86682058928168,12.2437513464748,12.2437513464748,2.22909661831322,100,2018,9,12,0.0359649107644434,0.0464988235908813,0.0445783207966534,0.000495319326381571,0.497256173302336,0.497256173302336,0.0020778203913001
+"23267",100,2018,9,13,0.465456553254322,6.16185924794414,16.4863036588998,12.0211111033055,11.6476677820103,11.6476677820103,2.25745208777696,100,2018,9,13,0.0115204679268843,0.081881900437132,0.0284005140340269,0.00668187315652006,0.246509834182535,0.246509834182535,0.00212193816685213
+"23268",100,2018,9,14,0,6.35642465060562,21.9458965893232,9.70355342008887,17.9770078333822,17.9770078333822,2.28580755724071,100,2018,9,14,0,0.030089460658488,0.0465701095629369,0.0425386012697217,0.00708183536222371,0.00708183536222371,0.00216715166087996
+"23269",100,2018,9,15,1.83828381867823,7.45590760920307,10.7122772277647,7.68090203204433,7.96863585022023,7.96863585022023,2.31416302670446,100,2018,9,15,0.162573093838164,0.0558696201882378,0.0780701824657589,0.00314970354672863,1.22483581529367,1.22483581529367,0.00221346087338361
+"23270",100,2018,9,16,7.68338829560904,3.49924092534089,13.1105501239974,9.19299223976429,15.1135644251757,15.1135644251757,2.34251849616821,100,2018,9,16,0.41654969834448,0.159228674818349,0.109266733763056,0.000947367952637799,1.02178108014788,1.02178108014788,0.00226086580436304
+"23271",100,2018,9,17,2.70858085325973,4.63257425662839,16.9098566997432,9.06508249339491,14.4718261368335,14.4718261368335,2.37087396563195,100,2018,9,17,1.49818723242186,0.110400023608756,0.0634117471219501,0.0124117063088248,0.112925138946524,0.112925138946524,0.00230936645381828
+"23272",100,2018,9,18,0,7.22330035575808,16.5583936714365,9.61200224159837,10.4717381008387,10.4717381008387,2.3992294350957,100,2018,9,18,0,0.0215578903711548,0.0281538669030703,0.0147374512991295,1.84115353771239,1.84115353771239,0.00235896282174932
+"23273",100,2018,9,19,8.18316830948766,4.41828387483905,13.5581079185075,8.62862484263639,15.942310140209,15.942310140209,2.42758490455945,100,2018,9,19,1.50368432424227,0.0585362662086854,0.0542783844173393,0.000309942769783117,2.11127022025366,2.11127022025366,0.00240965490815616
+"23274",100,2018,9,20,2.1165016389916,2.84072609152338,15.719757980246,10.9755225312723,15.0249396314715,15.0249396314715,2.4559403740232,100,2018,9,20,0.117836257588798,0.0752707565804022,0.0335917975934009,0.0116672345346171,0.408864898802229,0.408864898802229,0.00246144271303881
+"23275",100,2018,9,21,0,7.30625958647272,16.5719692788371,12.3055776191099,12.0102310117715,12.0102310117715,2.48429584348694,100,2018,9,21,0,0.0613578995364685,0.0598175584852433,0.0326760840411098,0.270336278502769,0.270336278502769,0.00251432623639725
+"23276",100,2018,9,22,0,7.42885590648756,15.067678851263,11.8960726227042,9.52782175905503,9.52782175905503,2.51265131295069,100,2018,9,22,0,0.314606457201666,0.132158482247109,0.0016321672942175,0.178616380421676,0.178616380421676,0.0025683054782315
+"23277",100,2018,9,23,1.31760175491717,7.41177119726133,12.1548073457019,8.35586370440862,13.4899780202096,13.4899780202096,2.54100678241444,100,2018,9,23,0.200409347962219,0.147114628927913,0.0524596022132755,0.002703505706875,1.14499877458546,1.14499877458546,0.00262338043854155
+"23278",100,2018,9,24,0,6.6209901027029,12.0625303644027,8.43509344535299,10.3188668734694,10.3188668734694,2.56936225187819,100,2018,9,24,0,0.101984247147428,0.0425321730229567,0.00753977381394004,0.38373865190704,0.38373865190704,0.0026795511173274
+"23279",100,2018,9,25,0,7.69799782498048,12.7370848524557,8.86105621556113,12.7165676091764,12.7165676091764,2.59771772134193,100,2018,9,25,0,0.166014539813472,0.0278812897822292,0.00244502092335639,0.610838671520373,0.610838671520373,0.00273681751458906
+"23280",100,2018,9,26,0,0.383685366880677,17.9552808201352,9.16903180729831,20.2876018528367,20.2876018528367,2.62607319080568,100,2018,9,26,0,0.0768397671148182,0.0424690716885252,0.0186356635445993,0.0145619780694326,0.0145619780694326,0.00279517963032651
+"23281",100,2018,9,27,0,6.25795381166229,17.4315731271003,12.2844114083268,17.7926842403097,17.7926842403097,2.65442866026943,100,2018,9,27,0,0.0156695876093811,0.0495029130458443,0.00320000237367465,0.974454394228198,0.974454394228198,0.00285463746453978
+"23282",100,2018,9,28,4.44675472152509,7.14005501855193,13.3434654252626,9.23237632944508,13.7947964547622,13.7947964547622,2.68278412973317,100,2018,9,28,0.393742727787193,0.113059665483642,0.124369108302771,0.00727020077854363,0.866181219268317,0.866181219268317,0.00291519101722884
+"23283",100,2018,9,29,3.59713970342747,3.63634764190817,13.3090759671823,8.54995616749175,15.626116484043,15.626116484043,2.71113959919692,100,2018,9,29,0.467076041433556,0.240173132979529,0.0485152096018697,0.00091637673189357,1.37054441108719,1.37054441108719,0.00297684028839369
+"23284",100,2018,9,30,1.75555553019244,7.27077013168922,15.8653795223425,10.1884708383558,13.3184048764908,13.3184048764908,2.73949506866067,100,2018,9,30,0.114736833586332,0.0663064507523665,0.0294783249750019,0.00503741316554874,0.646792447251941,0.646792447251941,0.00303958527803436
+"23285",100,2018,10,1,0.0995599577496416,4.69924093735362,21.1007260825112,10.2912431418961,20.9704069320125,20.9704069320125,2.7766845311317,100,2018,10,1,0.00918128700197092,0.0720485226157355,0.0920321254845518,0.0570649202492636,0.0184432821176963,0.0184432821176963,0.00296060770734778
+"23286",100,2018,10,2,0,11.6269086779016,25.8386688756995,10.4778988400702,17.4752806526075,17.4752806526075,2.81387399360274,100,2018,10,2,0,0.0752356742681386,0.0801595490173847,0.00522573881772847,0.90378096016364,0.90378096016364,0.00288433003706608
+"23287",100,2018,10,3,0.114191420843499,9.83282721239348,16.8280966919247,12.0696700109769,11.8337844160393,11.8337844160393,2.85106345607377,100,2018,10,3,0.00286549716142186,0.0384093650015439,0.0120485183495752,0.00172630870448084,0.551380183259048,0.551380183259048,0.00281075226718928
+"23288",100,2018,10,4,0.013421342334207,8.88726061746494,13.9588888292134,9.93268424790553,9.63038491101155,9.63038491101155,2.8882529185448,100,2018,10,4,0.00140350881375765,0.111560778732835,0.0162432778922548,0.00619415991943178,0.256409970927175,0.256409970927175,0.00273987439771736
+"23289",100,2018,10,5,0.841364135934968,9.12499464935202,16.5197908361622,11.4043894212763,15.3881408871859,15.3881408871859,2.92544238101584,100,2018,10,5,0.220409356592692,0.101826348027104,0.026500599835887,0.0411912313473146,0.380636698648221,0.380636698648221,0.00267169642865032
+"23290",100,2018,10,6,0.013421342334207,3.90293733200224,20.7237952581727,13.0800770349366,22.1536634233263,22.1536634233263,2.96263184348687,100,2018,10,6,0.00140350881375765,0.0349222210460341,0.0617397407624052,0.0131257149100295,0.0560244674283697,0.0560244674283697,0.00260621835998818
+"23291",100,2018,10,7,0.518921897012015,9.76455437625595,21.5731900008467,13.9101430433418,10.0142793949157,10.0142793949157,2.9998213059579,100,2018,10,7,0.0287134490305919,0.0432719577433388,0.053335607035872,0.00839823341598196,0.506069043684912,0.506069043684912,0.00254344019173092
+"23292",100,2018,10,8,1.33707368059127,11.8497030480598,23.8497027217752,13.4084269174255,12.9187899852874,12.9187899852874,3.03701076842894,100,2018,10,8,0.0183040899561176,0.0182578774525471,0.0170140368236761,0.0470561419504637,0.966103458063255,0.966103458063255,0.00248336192387856
+"23293",100,2018,10,9,6.49989004365944,12.7161055733793,14.7616392624522,14.1084269029473,6.69147412244505,6.69147412244505,3.07420023089997,100,2018,10,9,0.031461984902112,0.0538496711753917,0.0124497167889254,0.00359531299530355,0.31690532485897,0.31690532485897,0.00242598355643108
+"23294",100,2018,10,10,3.13498348135366,8.23313533617194,14.49481841571,8.85894389619397,16.9530694287996,16.9530694287996,3.111389693371,100,2018,10,10,0.0113450296301633,0.224775966375521,0.0460204793027079,0.00451402566927781,0.361713856671714,0.361713856671714,0.00237130508938848
+"23295",100,2018,10,11,0,6.30706266680173,16.3773047905682,11.2644775514424,21.7014631328016,21.7014631328016,3.14857915584204,100,2018,10,11,0,0.0843368608541847,0.0327595699916494,0.00364677003341185,0.27659835435783,0.27659835435783,0.00231932652275077
+"23296",100,2018,10,12,0.701100111581442,5.29520352419191,21.6642356448703,12.7272277187855,23.6389876534574,23.6389876534574,3.18576861831307,100,2018,10,12,0.338421049489264,0.048695327165413,0.095020528670952,0.0332257175914758,0.000255550300134502,0.000255550300134502,0.00227004785651796
+"23297",100,2018,10,13,0,9.00865784782519,23.2575245826814,13.5943344733109,22.3589769180852,22.3589769180852,3.22295808078411,100,2018,10,13,0,0.0863204739063846,0.0217813557097351,0.128681806990483,0.759364573348574,0.759364573348574,0.00222346909069002
+"23298",100,2018,10,14,0,11.2106931096781,25.3794169473176,14.5964686978112,19.4936084894195,19.4936084894195,3.26014754325514,100,2018,10,14,0,0.0662006045069085,0.0155952724839451,0.0889338852219236,0.228409453119017,0.228409453119017,0.00217959022526698
+"23299",100,2018,10,15,0.191969201231029,16.6155885207509,21.7996367359056,15.9582289526827,8.68475251780091,8.68475251780091,3.29733700572617,100,2018,10,15,0.0256140359033618,0.0521655328497675,0.0398590488414476,0.0552690904964655,0.164311064741418,0.164311064741418,0.00213841126024883
+"23300",100,2018,10,16,1.58723871818077,17.101176892439,24.5233771824601,17.0489326640718,15.8256435478219,15.8256435478219,3.33452646819721,100,2018,10,16,0.0467251509114325,0.0375262702587723,0.0201666891312415,0.00495963980266259,2.82730587477224,2.82730587477224,0.00209993219563555
+"23301",100,2018,10,17,2.34664466947612,11.8675797140375,18.6510230149373,15.4205500116967,12.4128823936051,12.4128823936051,3.37171593066824,100,2018,10,17,0.40701756059078,0.0720111703237483,0.0133760141921727,0.0345467439730347,1.8153361994203,1.8153361994203,0.00206415303142718
+"23302",100,2018,10,18,12.7739272741857,12.3927613070565,17.097656636086,15.7191639859291,7.93855881402464,7.93855881402464,3.40890539313927,100,2018,10,18,10.5158480713383,0.0435210275952121,0.0090590431171339,0.00113684395246469,0.39147183106414,0.39147183106414,0.00203107376762368
+"23303",100,2018,10,19,1.02266226153169,9.25441142460956,26.1294829680188,14.6172167250294,19.7591859672723,19.7591859672723,3.44609485561031,100,2018,10,19,0.278011687024993,0.0507689745162046,0.0204396992111262,0.0761315315871878,0.311149590132352,0.311149590132352,0.00200069440422508
+"23304",100,2018,10,20,2.14444442288448,9.20767869519191,15.9453025624828,10.6760616124135,21.0052033960491,21.0052033960491,3.48328431808134,100,2018,10,20,0.0724561438783617,0.0543690429956141,0.029458495177016,0.0010315823338284,0.60501746491131,0.60501746491131,0.00197301494123136
+"23305",100,2018,10,21,0.607700775657157,7.0788778157124,17.6238943861656,12.6607811259489,12.492706254764,12.492706254764,3.52047378055237,100,2018,10,21,0.0733918114712365,0.167342690888367,0.0128701241211961,0.0188385831465442,0.464989535776857,0.464989535776857,0.00194803537864253
+"23306",100,2018,10,22,0,7.65958198855812,27.6337403988812,10.1582287252277,22.8092408300889,22.8092408300889,3.55766324302341,100,2018,10,22,0,0.0454690450802507,0.0124322107194796,0.0986818357122454,0.279653689173889,0.279653689173889,0.00192575571645858
+"23307",100,2018,10,23,1.03685370110574,9.93146322688909,16.0418372642089,9.62129808075488,18.7684707725533,18.7684707725533,3.59485270549444,100,2018,10,23,0.0606432739684463,0.00944267893568426,0.0167228106472987,0.00126549388159225,1.3325777029252,1.3325777029252,0.00190617595467953
+"23308",100,2018,10,24,0.21056106154642,4.20325633506439,15.6883717275689,8.44612761246752,19.9973266379143,19.9973266379143,3.63204216796547,100,2018,10,24,0.00766081939925229,0.309698826145722,0.0149923939108958,0.00594853988344267,0.574276726434236,0.574276726434236,0.00188929609330535
+"23309",100,2018,10,25,0.201650169021291,3.41955993382713,18.3584268609814,10.3392188771985,24.4506271307749,24.4506271307749,3.66923163043651,100,2018,10,25,0.0224561415603984,0.0951093628325695,0.0394252336560885,0.0218801076008643,0.131787058369916,0.131787058369916,0.00187511613233608
+"23310",100,2018,10,26,0.352475253670457,5.8992518848843,16.9255884273349,11.629835077376,18.0093622952536,18.0093622952536,3.70642109290754,100,2018,10,26,0.0153801168771515,0.203169523166654,0.0230035530137748,0.018848524787479,1.47002709434935,1.47002709434935,0.00186363607177169
+"23311",100,2018,10,27,0.100660067506553,6.30053909908165,17.5865898593949,12.0366336814117,15.324774609934,15.324774609934,3.74361055537857,100,2018,10,27,0.000526315805159117,0.0627023408588785,0.0255561171774846,0.0225368104411511,2.18850271154496,2.18850271154496,0.00185485591161217
+"23312",100,2018,10,28,0.0959295943887583,4.7783277871454,16.1043787243867,9.03343221561612,24.9691200550109,24.9691200550109,3.78080001784961,100,2018,10,28,0.000994152076411666,0.169284253050167,0.0241397340096121,0.0022479496538192,0.136635746720719,0.136635746720719,0.00184877565185756
+"23313",100,2018,10,29,0,4.77017601147474,17.8845873414093,10.4180088001247,25.3933112770811,25.3933112770811,3.81798948032064,100,2018,10,29,0,0.114892971645622,0.054302234743661,0.0427099351247775,0.190821282401672,0.190821282401672,0.00184539529250783
+"23314",100,2018,10,30,0,3.27174913424208,20.9327720898082,13.2897028886315,26.0008802235585,26.0008802235585,3.85517894279167,100,2018,10,30,0,0.0333315908855924,0.134854190572318,0.105873051990327,0.117809396764195,0.117809396764195,0.00184471483356298
+"23315",100,2018,10,31,0.0771177129203206,11.057546819135,19.8015733200594,14.6012099762299,12.3666556770652,12.3666556770652,3.89236840526271,100,2018,10,31,0.00543859665331088,0.10180289608255,0.0604801314642487,0.00383391550081126,0.227781938847419,0.227781938847419,0.00184673427502303
+"23316",100,2018,11,1,1.17194721866624,11.82089112222,32.0578881380188,12.8952694568697,11.1413641741829,11.1413641741829,3.90845312980047,100,2018,11,1,0.0161988274674679,0.0688362507737366,0.0222473963429004,0.0912397809503272,0.270511778446724,0.270511778446724,0.0017394140072824
+"23317",100,2018,11,2,0.24763477071546,15.5285369581384,25.6490981707347,12.7130582912265,22.6674698325011,22.6674698325011,3.92453785433824,100,2018,11,2,0.00245614079006933,0.0159433018043752,0.0758941869549659,0.0352210643680002,0.484711138153344,0.484711138153344,0.00163932114401602
+"23318",100,2018,11,3,0.00517051712875188,9.52403732127745,16.7044115035054,10.8665346968161,14.4107921703158,14.4107921703158,3.940622578876,100,2018,11,3,0.000994152076411666,0.129164917108278,0.0161806528506675,0.0159432579130491,0.990343473438972,0.990343473438972,0.00154645568522385
+"23319",100,2018,11,4,0,7.38407040009535,20.8846753039638,12.8567986546045,17.7305392988182,17.7305392988182,3.95670730341376,100,2018,11,4,0,0.223539749877238,0.0283064300672894,0.0645228465209052,0.544147973218209,0.544147973218209,0.00146081763090592
+"23320",100,2018,11,5,6.62376235000896,11.6262485831484,24.0432453722057,19.4642135527792,15.3558417782925,15.3558417782925,3.97279202795153,100,2018,11,5,0.670994176976193,0.0741648989828426,0.0274175238004031,0.0112210187367826,1.8746694700432,1.8746694700432,0.0013824069810622
+"23321",100,2018,11,6,1.88195819075745,10.6090539223028,18.8646424065853,15.6157976419094,15.0166117423701,15.0166117423701,3.98887675248929,100,2018,11,6,0.347076016071947,0.010520443643129,0.0248005936251683,0.000502923296291598,1.43666499596776,1.43666499596776,0.00131122373569272
+"23322",100,2018,11,7,2.80682068833686,5.7954345531065,14.8659185759961,9.08413628828932,22.2276568795719,22.2276568795719,4.00496147702706,100,2018,11,7,1.26707597332392,0.025347945748727,0.0204953218917793,0.00987600098558591,0.99510641051644,0.99510641051644,0.00124726789479747
+"23323",100,2018,11,8,2.12299230271834,4.77401545391356,16.1069307977741,9.82550046520002,19.3371176032474,19.3371176032474,4.02104620156482,100,2018,11,8,0.0459649247175108,0.109767260494577,0.0260099234488543,0.014198232113196,0.935317593865934,0.935317593865934,0.00119053945837645
+"23324",100,2018,11,9,0.655885594676692,7.7650935332505,18.2492409990434,11.5539163551708,15.2422991470404,15.2422991470404,4.03713092610258,100,2018,11,9,0.0156140338363706,0.0462134405604821,0.0098982398409794,0.072126328409292,3.69113108034872,3.69113108034872,0.00114103842642965
+"23325",100,2018,11,10,1.75632564554645,9.11473038172958,17.8454454734643,11.1188778756606,17.4153906535788,17.4153906535788,4.05321565064035,100,2018,11,10,0.0125730935593111,0.142964949122753,0.0169701248617601,0.000400585843869803,0.806159004506235,0.806159004506235,0.00109876479895708
+"23326",100,2018,11,11,0,6.42784371444232,19.3766335675163,12.1876128755911,24.3844333329741,24.3844333329741,4.06930037517811,100,2018,11,11,0,0.102392993061464,0.0547030535963344,0.0138322078817148,0.526742750536369,0.526742750536369,0.00106371857595873
+"23327",100,2018,11,12,0,10.2693620152993,30.4291531276388,11.6611879294199,15.6739714436799,15.6739714436799,4.08538509971588,100,2018,11,12,0,0.0801695942795507,0.0302385683742252,0.0495052108408238,0.257506573361486,0.257506573361486,0.00103589975743463
+"23328",100,2018,11,13,0,13.8224421565158,27.2599008159407,15.2693068490695,17.4258196340798,17.4258196340798,4.10146982425364,100,2018,11,13,0,0.027680144500813,0.527477738613856,0.121314542587546,0.727111460961509,0.727111460961509,0.00101530834338474
+"23329",100,2018,11,14,0.437513759722411,11.8305608961317,18.5292960629605,13.7552696255305,14.6875469356743,14.6875469356743,4.1175545487914,100,2018,11,14,0.0170175432426889,0.0428608123088319,0.0201696297498694,0.0187263181073698,1.38491687499611,1.38491687499611,0.00100194433380908
+"23330",100,2018,11,15,0.124312433398793,7.78084707574876,17.7664358854556,10.1546425247612,23.390000109363,23.390000109363,4.13363927332917,100,2018,11,15,0.00479532204176251,0.1366432366957,0.0142316021080197,0.031395349482542,0.172335545420777,0.172335545420777,0.000995807728707644
+"23331",100,2018,11,16,0,2.61646865959084,18.7981408311196,8.4092740624389,29.2243894339919,29.2243894339919,4.14972399786693,100,2018,11,16,0,0.0433508793624568,0.0525666803150245,0.0356672628771754,0.0262877396526164,0.0262877396526164,0.000996898528080443
+"23332",100,2018,11,17,0,5.66038500961035,19.1473706416433,10.8723872376747,29.2788890716922,29.2788890716922,4.1658087224047,100,2018,11,17,0,0.189159638205206,0.0494116893881824,0.062065488730621,0.0689210360345343,0.0689210360345343,0.00100521673192747
+"23333",100,2018,11,18,0,5.66499444648902,27.0781407057255,13.252398135245,29.862431118066,29.862431118066,4.18189344694246,100,2018,11,18,0,0.0565608350051281,0.0267427447360689,0.133046765879656,0.00304736180028184,0.00304736180028184,0.00102076234024873
+"23334",100,2018,11,19,0,12.1630474673902,32.057854982886,9.09324539717537,25.6067661205665,25.6067661205665,4.19797817148022,100,2018,11,19,0,0.0838830491451815,0.0264228717033797,0.00782573921257039,0.189882081434874,0.189882081434874,0.0010435353530442
+"23335",100,2018,11,20,0,17.9666008461427,19.4638944527235,16.1152584791446,8.14086922245844,8.14086922245844,4.21406289601799,100,2018,11,20,0,0.0162982138028479,0.373520840374113,0.0221760545164216,0.834305388126088,0.834305388126088,0.00107353577031391
+"23336",100,2018,11,21,2.73531352962204,6.18877887358629,21.2893837004474,8.79075915375427,17.6608691367641,17.6608691367641,4.23014762055575,100,2018,11,21,0.0104093646166667,0.0284116812009078,0.0328928765239009,0.00468070074615572,1.30829183812525,1.30829183812525,0.00111076359205785
+"23337",100,2018,11,22,5.78580852388942,6.30287127132856,14.7500221170608,11.0069636794994,18.6488996616947,18.6488996616947,4.24623234509352,100,2018,11,22,2.15596499348246,0.0175818719696724,0.021270156619503,0.0392760589600276,5.24033732002907,5.24033732002907,0.00115521881827602
+"23338",100,2018,11,23,4.46182616027144,8.99422445811323,13.5009570798465,12.3601759489876,4.46071507959607,4.46071507959607,4.26231706963128,100,2018,11,23,0.924970786501801,0.0276426845439825,0.0111666702694875,0.000837425414919758,0.425155558172869,0.425155558172869,0.00120690144896842
+"23339",100,2018,11,24,6.32068213377849,10.4739933339151,16.6660726062535,11.95271722397,12.3042683900386,12.3042683900386,4.27840179416904,100,2018,11,24,0.100233917961362,0.0533719703518983,0.0300731058848177,0.00535380392472173,0.757469552275532,0.757469552275532,0.00126581148413503
+"23340",100,2018,11,25,0.0717271742942703,11.0673818661697,16.6168645055118,13.712893236326,11.3393399277405,11.3393399277405,4.29448651870681,100,2018,11,25,0.0154385972650428,0.0556766543852159,0.025422743832178,0.00792105601133789,1.81829412716434,1.81829412716434,0.00133194892377589
+"23341",100,2018,11,26,0.266996708115598,10.6435424586465,17.5326730814179,12.3422992725183,14.4989218475795,14.4989218475795,4.31057124324457,100,2018,11,26,0.00426900619303276,0.056747388902279,0.0921210249116537,0.0164152144717205,1.2907925938278,1.2907925938278,0.00140531376789097
+"23342",100,2018,11,27,0,7.97860271623819,18.118536750869,13.0051375243268,19.8591092994111,19.8591092994111,4.32665596778233,100,2018,11,27,0,0.0753204452793802,0.0591140072474499,0.00937190720862567,0.738788072044196,0.738788072044196,0.00148590601648027
+"23343",100,2018,11,28,0.693839383203991,11.9138504055598,18.5819253323495,13.2670297517766,17.2230251215734,17.2230251215734,4.3427406923201,100,2018,11,28,0.226081863791622,0.040284202009112,0.0465244721355213,0.0062690330974789,2.45528269852334,2.45528269852334,0.00157372566954381
+"23344",100,2018,11,29,0.0970297044161523,12.1237843820889,17.294004593483,12.0269087097957,11.389790998696,11.389790998696,4.35882541685786,100,2018,11,29,0.00654970779753568,0.0591619950613123,0.0242620080605041,0.000967253576871169,0.3375877391526,0.3375877391526,0.00166877272708157
+"23345",100,2018,11,30,0,8.95702973817966,20.0781625048949,10.6352036070115,23.147800076257,23.147800076257,4.37491014139563,100,2018,11,30,0,0.0226941450398645,0.0173912313453826,0.0512397406841025,1.01367772572872,1.01367772572872,0.00177104718909356
+"23346",100,2018,12,1,0,7.72376239575175,32.9303081751657,9.55972513087643,22.6159184165258,22.6159184165258,4.42990621305064,100,2018,12,1,0,0.0429509064769495,0.0309608091579135,0.0435064386089584,1.91484591291139,1.91484591291139,0.00187981548617578
+"23347",100,2018,12,2,9.12409238846782,10.2909902241101,18.0446093866665,10.2603960257552,26.4121453082732,26.4121453082732,4.48490228470565,100,2018,12,2,1.67766071018426,0.0407543901589224,0.017831559548439,0.0138918046896137,3.17571394015789,3.17571394015789,0.00199910405007872
+"23348",100,2018,12,3,2.61199118634655,11.8665126668345,18.676523901055,14.4241144654512,14.5595930286235,14.5595930286235,4.53989835636067,100,2018,12,3,0.612046771941832,0.100069556295467,0.0236263269706961,0.0656924218273379,1.80213660223229,1.80213660223229,0.00212891288080239
+"23349",100,2018,12,4,1.73707371156732,11.6512541429962,18.1313201350347,14.25651268702,17.1280526181128,17.1280526181128,4.59489442801568,100,2018,12,4,0.0484210427462724,0.0873778357612249,0.0578706330955127,0.0143871101848679,7.88068411251569,7.88068411251569,0.00226924197834676
+"23350",100,2018,12,5,0,9.09480746639575,25.0378769244036,14.2030691486774,30.7702312805209,30.7702312805209,4.64989049967069,100,2018,12,5,0,0.0824287013406027,0.118214069781715,0.231132156285118,0.0988819396216431,0.0988819396216431,0.00242009134271187
+"23351",100,2018,12,6,0,12.1837294791529,36.4187124061375,11.5603960423317,30.2242354024755,30.2242354024755,4.70488657132571,100,2018,12,6,0,0.0277561707592356,0.0365940041719066,0.0296672409147515,0.200991936903693,0.200991936903693,0.00258146097389772
+"23352",100,2018,12,7,0,15.3605059381365,38.8825748892638,12.2902969721258,23.6778109627064,23.6778109627064,4.75988264298072,100,2018,12,7,0,0.0137596394143651,0.133035688559261,0.364146849305548,0.449268935131436,0.449268935131436,0.00275335087190427
+"23353",100,2018,12,8,0,16.9411220739384,22.0629814806813,17.8873819365884,14.5168207481225,14.5168207481225,4.81487871463573,100,2018,12,8,0,0.0111134521843514,0.670376104852592,0.0227819302033195,2.37689272672207,2.37689272672207,0.00293576103673156
+"23354",100,2018,12,9,0,10.461782174273,23.4652143148962,13.1885369440379,31.6908140969355,31.6908140969355,4.86987478629075,100,2018,12,9,0,0.0264648757734818,0.215870808794571,0.0353152016512159,0.0154450222843378,0.0154450222843378,0.00312869146837957
+"23355",100,2018,12,10,0.134983500656318,12.4187458959481,21.1473265774835,12.4946864068311,30.0566227307545,30.0566227307545,4.92487085794576,100,2018,12,10,0.00707602372469261,0.0762719200648031,0.111358444120467,0.0618871559166253,0.125469142539286,0.125469142539286,0.0033321421668483
+"23356",100,2018,12,11,0,7.60891086066386,24.2600987710313,12.0347634977502,31.8611112139275,31.8611112139275,4.97986692960077,100,2018,12,11,0,0.131811116133809,0.173716335810124,0.207000698850404,0.016009299085693,0.016009299085693,0.00354611313213777
+"23357",100,2018,12,12,0.102090210845434,13.2899011018121,33.8773926335199,12.319835056435,18.6252804033303,18.6252804033303,5.03486300125579,100,2018,12,12,0.00842105317882628,0.0322894387273818,0.0978689966407606,0.0257427113963424,0.606800083861201,0.606800083861201,0.00377060436424794
+"23358",100,2018,12,13,7.17282727842677,15.1650935163592,18.2855995354479,17.2663588675991,4.48762378965405,4.48762378965405,5.0898590729108,100,2018,12,13,0.84339179200738,0.0104041085517177,0.0442988993686812,0.0240374512122692,2.32065448995558,2.32065448995558,0.00400561586317885
+"23359",100,2018,12,14,5.80143013986686,14.9942133980091,20.098790046012,19.1639383649669,6.22217821733917,6.22217821733917,5.14485514456581,100,2018,12,14,0.438011680290731,0.0374467806320529,0.0574006543079095,0.00313333901333315,0.489625837448766,0.489625837448766,0.00425114762893048
+"23360",100,2018,12,15,7.1331133296912,15.8106600107795,24.0197358881549,19.0546642676963,19.1224203770704,19.1224203770704,5.19985121622083,100,2018,12,15,0.500233896489749,0.0673362504279454,0.0677205969428723,0.100471895769191,1.39159095390756,1.39159095390756,0.00450719966150285
+"23361",100,2018,12,16,2.30539056996439,11.2389879677818,23.6680749425269,16.0758524895763,29.400330171071,29.400330171071,5.25484728787584,100,2018,12,16,1.74807019856938,0.00944327290411746,0.0183117138781173,0.0175953357608774,1.09180301152748,1.09180301152748,0.00477377196089594
+"23362",100,2018,12,17,3.26743673268456,13.7333552819012,19.6212321156585,16.189218999529,16.6613972100488,16.6613972100488,5.30984335953085,100,2018,12,17,2.48362564531684,0.0619649143331361,0.0493537889895762,0.00455382859554075,0.340269399868533,0.340269399868533,0.00505086452710974
+"23363",100,2018,12,18,0.115731574881851,13.2458085866913,21.8803414952244,14.6241804874114,26.8317602764,26.8317602764,5.36483943118586,100,2018,12,18,0.00555555572112401,0.0557841934428139,0.213160574015416,0.0141707343766125,0.120848589347609,0.120848589347609,0.00533847736014429
+"23364",100,2018,12,19,0.0495049512327307,13.8646864140912,24.4141805101149,17.6602860357371,16.8682619730632,16.8682619730632,5.41983550284088,100,2018,12,19,0.00467836271252549,0.0594818470693402,0.0904132965536875,0.0945818928057687,0.843579857100333,0.843579857100333,0.00563661045999954
+"23365",100,2018,12,20,0.0640264035943318,14.7717050462142,18.9618921248433,14.4036303279948,11.7733883369874,11.7733883369874,5.47483157449589,100,2018,12,20,0.00339181296658098,0.0402426873927317,0.0499918291105515,0.0102777851953064,1.94821657727343,1.94821657727343,0.00594526382667554
+"23366",100,2018,12,21,0.660726079387371,12.845467497151,16.3846206497176,13.6250385015842,9.19706266128322,9.19706266128322,5.5298276461509,100,2018,12,21,0.0209941485681036,0.057146724067434,0.00963688169813076,0.00672046120857167,0.401854454511295,0.401854454511295,0.00626443746017222
+"23367",100,2018,12,22,0.699559957739806,9.90410343543662,19.6557536906797,11.0872606974099,27.1603080657187,27.1603080657187,5.58482371780592,100,2018,12,22,0.00953216330349817,0.152614578171321,0.0459888536242517,0.067709988385567,0.692156194769874,0.692156194769874,0.00659413136048967
+"23368",100,2018,12,23,0,6.30096809515203,24.4724973756226,11.8326072955158,32.2800772722536,32.2800772722536,5.63981978946093,100,2018,12,23,0,0.0356374224501164,0.223726427584945,0.205243162179773,0.0011648835130281,0.0011648835130281,0.0069343455276278
+"23369",100,2018,12,24,0,9.0823320560854,34.2805833176549,9.59305810666058,32.2263809430717,32.2263809430717,5.69481586111594,100,2018,12,24,0,0.0303830383368621,0.0139924006156041,0.114226287009773,0.00330294712534452,0.00330294712534452,0.0072850799615867
+"23370",100,2018,12,25,0,9.38888889623292,23.7210782012268,16.4138941581231,28.175698770286,28.175698770286,5.74981193277096,100,2018,12,25,0,0.0513912273407463,0.442047845298725,0.0629496862492759,2.0389829459744,2.0389829459744,0.00764633466236629
+"23371",100,2018,12,26,0,11.9365345983222,26.06425723463,13.6958196312681,32.1646752562066,32.1646752562066,5.80480800442597,100,2018,12,26,0,0.155825669298782,0.539355803680778,0.127295358390203,0.00643217933290347,0.00643217933290347,0.00801810962996663
+"23372",100,2018,12,27,0.214081412166914,10.6700219289698,34.595896411388,13.1006270388697,24.1646204529816,24.1646204529816,5.85980407608098,100,2018,12,27,0.0370175458168426,0.0435649234950954,0.773991715281791,0.440625048663121,0.719688830142895,0.719688830142895,0.0084004048643877
+"23373",100,2018,12,28,0.120792081712758,18.5985921077078,30.2755004223948,16.7700881433435,28.4973157729515,28.4973157729515,5.914800147736,100,2018,12,28,0.0204093572898218,0.0566819264733344,0.95671803908808,0.212391063476346,0.727369799947682,0.727369799947682,0.00879322036562944
+"23374",100,2018,12,29,1.00968097283108,16.9780526785436,21.9061274554732,15.8125082253098,12.183399304305,12.183399304305,5.96979621939101,100,2018,12,29,0.012514622748247,0.0268052111667479,0.0681578246894255,0.0339140161057864,0.138415188070031,0.138415188070031,0.00919655613369198
+"23375",100,2018,12,30,0.262486252806696,14.5514960629974,22.5120021685778,15.4502640735723,27.1155992996837,27.1155992996837,6.02479229104602,100,2018,12,30,0.0411695919268661,0.0358145867354186,0.188458519950361,0.00842106043950884,0.333736861022963,0.333736861022963,0.00961041216857521
+"23376",100,2018,12,31,0,14.3635204512425,24.8447635323301,13.1627502095188,29.1678986942807,29.1678986942807,6.07978836270104,100,2018,12,31,0,0.0906433077584313,0.452491417399976,0.0168040854694173,0.353133762658871,0.353133762658871,0.0100347884702791
+"23377",100,2019,1,1,0,7.33548953850539,24.3943783095961,15.0272386580279,31.4594612163548,31.4594612163548,6.11181700546297,100,2019,1,1,0,0.0188760297463121,0.363616276688802,0.133980789780258,0.0640396576555434,0.0640396576555434,0.0107560052313518
+"23378",100,2019,1,2,0.280308036209464,15.200396053075,23.310142982911,14.3993399958931,25.4523212676263,25.4523212676263,6.1438456482249,100,2019,1,2,0.0353801176387665,0.244464877301599,0.285825133554841,0.129704078850638,0.152309253858045,0.152309253858045,0.0115154181993423
+"23379",100,2019,1,3,0,9.32196924316608,31.8585697379705,14.730164970633,31.4277227483567,31.4277227483567,6.17587429098683,100,2019,1,3,0,0.0590842508104846,0.232031789510181,0.17578074267885,0.00571462281472686,0.00571462281472686,0.0123130273742506
+"23380",100,2019,1,4,0,16.6985916603516,33.1035200251211,17.8536195618616,19.6900987635614,19.6900987635614,6.20790293374877,100,2019,1,4,0,0.010167231790951,2.66407147486353,0.0507468960012593,3.7068177455169,3.7068177455169,0.0131488327560766
+"23381",100,2019,1,5,0,12.6780418316261,22.6565015413056,11.3333553801013,24.6510891762241,24.6510891762241,6.2399315765107,100,2019,1,5,0,0.0609028719495628,0.0827315913331446,0.316903625410252,0.60537839405913,0.60537839405913,0.0140228343448204
+"23382",100,2019,1,6,0.226292634815207,9.05994499739509,21.6153464574363,13.948514892192,16.1302200774811,16.1302200774811,6.27196021927263,100,2019,1,6,0.0205263168369121,0.0933251104656714,0.157379904340245,0.098917616724922,2.27445933447964,2.27445933447964,0.014935032140482
+"23383",100,2019,1,7,0,13.871716280057,23.8496037368858,12.2708360401317,23.4175245937603,23.4175245937603,6.30398886203457,100,2019,1,7,0,0.0722596757655482,0.215053813113734,0.100960843065101,0.593065238408599,0.593065238408599,0.0158854261430614
+"23384",100,2019,1,8,0,9.74361929563012,23.3595049326176,13.6830363341815,25.1539273319727,25.1539273319727,6.3360175047965,100,2019,1,8,0,0.0162760018845866,0.218933768940816,0.0425316072185907,0.488558514600895,0.488558514600895,0.0168740163525586
+"23385",100,2019,1,9,0.491969201693309,10.9501870032584,20.4867985120045,9.03848184453379,25.5017604701983,25.5017604701983,6.36804614755843,100,2019,1,9,0.0200584780124197,0.13865845783083,0.0603596738405648,0.0505041231680591,0.850360435133691,0.850360435133691,0.0179008027689735
+"23386",100,2019,1,10,0,7.79369640350342,21.7742464277479,10.3224314006642,22.9041474702204,22.9041474702204,6.40007479032037,100,2019,1,10,0,0.0852701951400978,0.178414070571413,0.0879924116124348,0.908170878498828,0.908170878498828,0.0189657853923062
+"23387",100,2019,1,11,0,9.78742586897545,32.2271614746161,11.1810890728622,30.2380749709798,30.2380749709798,6.4321034330823,100,2019,1,11,0,0.0411871338003081,0.129216402534258,0.396016477346203,0.021902307026567,0.021902307026567,0.0200689642225567
+"23388",100,2019,1,12,0,15.7036634000352,23.3276786888131,12.7109241789848,28.7606489897037,28.7606489897037,6.46413207584423,100,2019,1,12,0,0.0223485478652585,0.432322108120723,0.0328052625757169,0.0675655928765385,0.0675655928765385,0.021210339259725
+"23389",100,2019,1,13,0,9.87690867685249,25.7168097459313,15.0295930092353,29.9431902420665,29.9431902420665,6.49616071860616,100,2019,1,13,0,0.161777712928012,0.308620943999999,0.0588537132308688,0.0180141033465562,0.0180141033465562,0.0223899105038111
+"23390",100,2019,1,14,0,12.0818371673097,30.3913532819423,17.7295709238587,29.6124642564125,29.6124642564125,6.5281893613681,100,2019,1,14,0,0.0394146311220049,0.766047181001575,0.454081187051093,0.0270316167332258,0.0270316167332258,0.0236076779548148
+"23391",100,2019,1,15,0,13.4573925991919,34.9245763973828,12.5265236037268,22.8328932834537,22.8328932834537,6.56021800413003,100,2019,1,15,0,0.0545250948477284,0.478158874861176,0.268750843269043,1.16943795174079,1.16943795174079,0.0248636416127365
+"23392",100,2019,1,16,0,14.4108911635983,29.1432341060492,17.0439713963843,17.3783166600008,17.3783166600008,6.59224664689196,100,2019,1,16,0,0.120089507338432,0.150483209166446,0.0736221909419913,0.970759166278377,0.970759166278377,0.0261578014775759
+"23393",100,2019,1,17,0.0190319034739165,18.4087900680022,33.609516223534,16.908812035035,8.52687577865567,8.52687577865567,6.6242752896539,100,2019,1,17,0.00140350881375765,0.0288929799668646,0.13551525815631,0.184714614627596,1.72780970489553,1.72780970489553,0.027490157549333
+"23394",100,2019,1,18,1.63333333981181,17.8472497098648,23.108041922776,18.6518262835881,12.3894829555969,12.3894829555969,6.65630393241583,100,2019,1,18,0.0665496988324408,0.00970351605807422,0.125687766730136,0.00297072244428452,0.319202967854617,0.319202967854617,0.028860709828008
+"23395",100,2019,1,19,0,10.0663695749801,23.0106159688616,11.1188228290335,28.8037952532207,28.8037952532207,6.68833257517776,100,2019,1,19,0,0.0411701914715477,0.13909889177499,0.0763783822802167,0.0580701713118963,0.0580701713118963,0.0302694583136007
+"23396",100,2019,1,20,0,12.6534214355502,24.6605940148382,16.8430802037876,21.8660616313401,21.8660616313401,6.72036121793969,100,2019,1,20,0,0.131321000615694,0.381525252042396,0.122609360950599,0.860814979316965,0.860814979316965,0.0317164030061113
+"23397",100,2019,1,21,0,14.9906930304465,26.9519254224922,16.598492990626,26.8899339690591,26.8899339690591,6.75238986070163,100,2019,1,21,0,0.0550035205886225,0.66370919413461,0.152405421336412,0.0457374954584078,0.0457374954584078,0.0332015439055396
+"23398",100,2019,1,22,0.137953797738479,11.5777776758007,29.0773157029524,15.7673928236673,28.4878987395199,28.4878987395199,6.78441850346356,100,2019,1,22,0.00485380155824082,0.130095274524882,1.12455908117485,0.241158007192492,0.0833986633080383,0.0833986633080383,0.0347248810118856
+"23399",100,2019,1,23,0,12.5378877022872,26.3994611020398,16.5364354860665,28.0858635918142,28.0858635918142,6.81644714622549,100,2019,1,23,0,0.043515169893307,0.266417392918407,0.153115217893907,0.0261871956499392,0.0261871956499392,0.0362864143251494
+"23400",100,2019,1,24,0,13.1313860707551,42.9078437628919,10.0236854154547,28.5346644121428,28.5346644121428,6.84847578898743,100,2019,1,24,0,0.0289929548744112,0.142736923888109,0.228439802464375,0.0286690779885142,0.0286690779885142,0.037886143845331
+"23401",100,2019,1,25,0,21.0222878471853,31.4841364807028,17.5278767248978,18.0199996792968,18.0199996792968,6.88050443174936,100,2019,1,25,0,0.0224052435402632,0.83368836996612,0.0977117021395837,1.22361173374584,1.22361173374584,0.0395240695724305
+"23402",100,2019,1,26,0.328932902895578,16.0459186849814,25.6621891751935,13.5924863154345,23.796490422701,23.796490422701,6.91253307451129,100,2019,1,26,0.00473684211572013,0.044565534685541,0.442591331629381,0.140214669360415,0.370202328924268,0.370202328924268,0.0412001915064476
+"23403",100,2019,1,27,0,12.2554345912535,25.4178438490898,12.7283608318031,26.4790652120861,26.4790652120861,6.94456171727322,100,2019,1,27,0,0.0186894549075152,0.133714117320395,0.309928668324499,0.171437324497504,0.171437324497504,0.0429145096473826
+"23404",100,2019,1,28,0,9.4718262155195,28.6201100868754,13.2459185561462,27.8286138729687,27.8286138729687,6.97659036003516,100,2019,1,28,0,0.0244707413612825,0.35989425060012,0.46197830277834,0.0305145893434685,0.0305145893434685,0.0446670239952353
+"23405",100,2019,1,29,0.093399341629021,10.3055995410294,28.563388381723,14.8108911440842,27.5978436884445,27.5978436884445,7.00861900279709,100,2019,1,29,0.0091812871283258,0.0271690415031585,0.507947900551555,0.390109843712612,0.0340052226421361,0.0340052226421361,0.0464577345500058
+"23406",100,2019,1,30,0,10.6254675627017,34.0160835232541,13.7679537216035,24.3685588144233,24.3685588144233,7.04064764555902,100,2019,1,30,0,0.01335851163594,0.583509220226841,0.186859145413333,0.376592197503185,0.376592197503185,0.0482866413116942
+"23407",100,2019,1,31,0.013421342334207,13.6863697794798,20.4713644105347,8.80471938776367,21.5526071491808,21.5526071491808,7.07267628832096,100,2019,1,31,0.00140350881375765,0.077621109091407,0.0691730212968262,0.0369433124453855,1.76935975699202,1.76935975699202,0.0501537442803002
+"23408",100,2019,2,1,0,10.6775907709522,22.2325523154046,10.2270847494715,27.421089119906,27.421089119906,7.0234360431597,100,2019,2,1,0,0.112311730843335,0.0714987994146192,0.118875982369384,0.0367245008103842,0.0367245008103842,0.0488099971139362
+"23409",100,2019,2,2,0,7.17448846787641,30.7524093988836,11.894939576832,27.4204290450865,27.4204290450865,6.97419579799844,100,2019,2,2,0,0.0919888797972102,0.625288991123305,0.207180671689718,0.0241952994143312,0.0241952994143312,0.0474864575218833
+"23410",100,2019,2,3,0,10.9222992088142,32.7139270895779,15.7839932153196,25.9647303834082,25.9647303834082,6.92495555283718,100,2019,2,3,0,0.105900617599558,1.30955024770657,0.512613479913353,0.162072985956188,0.162072985956188,0.0461831255041417
+"23411",100,2019,2,4,0.054235424350525,15.9789439779435,21.6656985435024,15.1384269045,14.1537955392181,14.1537955392181,6.87571530767593,100,2019,2,4,0.00263157902579559,0.0399608554015904,0.171209879008763,0.0336719229604164,0.886333091312087,0.886333091312087,0.0449000010607113
+"23412",100,2019,2,5,0.0338833888437357,13.3702091158289,23.8645980948269,14.7911331588023,20.8065235821983,20.8065235821983,6.82647506251467,100,2019,2,5,0.00257309949188902,0.179088857777975,0.132892047637078,0.0086497087557461,5.18211264251386,5.18211264251386,0.0436370841915922
+"23413",100,2019,2,6,0,16.753289227963,30.7364576253692,22.3913859439762,9.12742574983435,9.12742574983435,6.77723481735341,100,2019,2,6,0,0.0481467977292675,0.201836314820787,0.060124455118823,1.74429132911175,1.74429132911175,0.0423943748967843
+"23414",100,2019,2,7,19.0449947172528,16.8921014661967,25.6047415528754,19.7692301491044,12.9983717862791,12.9983717862791,6.72799457219215,100,2019,2,7,5.5076018779601,0.012489465060152,0.174305189416208,0.0181538586491184,0.365760781652963,0.365760781652963,0.0411718731762876
+"23415",100,2019,2,8,0.00154015403835162,15.9181298737479,21.8539273689027,12.7231463001113,17.0154457144743,17.0154457144743,6.6787543270309,100,2019,2,8,0.000526315805159117,0.0357689861165725,0.0927274992516697,0.104981847091672,0.875587764271873,0.875587764271873,0.0399695790301021
+"23416",100,2019,2,9,6.76006600303356,12.0444225042698,18.7953243297581,9.61367439882721,19.0467433153075,19.0467433153075,6.62951408186964,100,2019,2,9,0.862573178609238,0.0484918134277277,0.0253982388595108,0.0545924065529575,1.05208309167372,1.05208309167372,0.0387874924582278
+"23417",100,2019,2,10,0.137953797517174,10.371100009209,21.2725411552538,13.3803410965486,8.04084717506098,8.04084717506098,6.58027383670838,100,2019,2,10,0.00596491271117976,0.0912707404935039,0.110481862841396,0.0602140727149811,0.970133417320284,0.970133417320284,0.0376256134606648
+"23418",100,2019,2,11,0.905940597092096,11.5310671159012,25.0840481442325,16.3371726267933,11.1591310469624,11.1591310469624,6.53103359154712,100,2019,2,11,0.443567263208636,0.0331590725793336,0.0511672989830924,0.191610002781116,1.56875400620188,1.56875400620188,0.0364839420374131
+"23419",100,2019,2,12,7.01628163256923,10.9225192631301,17.6767987900704,8.11162808704691,18.1438502877197,18.1438502877197,6.48179334638587,100,2019,2,12,1.48467827021733,0.0128689774541889,0.022684183265988,0.061604079242121,3.55465574323884,3.55465574323884,0.0353624781884725
+"23420",100,2019,2,13,5.85390539819782,8.30257440688718,18.6719361946265,9.37722778530142,19.7856875819342,19.7856875819342,6.43255310122461,100,2019,2,13,0.252514595567153,0.0999433224226788,0.0211251605611132,0.0497368569402819,2.56138901550143,2.56138901550143,0.0342612219138432
+"23421",100,2019,2,14,0.461276130691351,9.24578650425238,21.8160615822401,13.2564245566021,17.8903517392602,17.8903517392602,6.38331285606335,100,2019,2,14,0.0115204683451625,0.169359631577496,0.0959585607743916,0.121025102722502,1.94726650763636,1.94726650763636,0.033180173213525
+"23422",100,2019,2,15,0,12.9719582097103,23.0576347732964,14.6379097727659,17.5100110099129,17.5100110099129,6.33407261090209,100,2019,2,15,0,0.0585204486847432,0.180536980117355,0.0702561600512807,0.332584255986234,0.332584255986234,0.0321193320875182
+"23423",100,2019,2,16,0,11.7943015355613,23.706589630597,15.0706491166084,18.1338066021339,18.1338066021339,6.28483236574084,100,2019,2,16,0,0.113436784380749,0.345343032351008,0.0832544110728165,2.3179372261853,2.3179372261853,0.0310786985358226
+"23424",100,2019,2,17,0,10.1881298814276,30.8308251300136,12.7988669402791,17.2530803281744,17.2530803281744,6.23559212057958,100,2019,2,17,0,0.0143561271066223,0.046636874760214,0.0482841763212502,0.526344184918189,0.526344184918189,0.0300582725584381
+"23425",100,2019,2,18,0.016171617402692,13.2212651266385,22.1019033362763,12.3915951480173,18.6556324533897,18.6556324533897,6.18635187541832,100,2019,2,18,0.00175438601719706,0.0152140563184847,0.131894065419992,0.0374052777274274,0.922236592142565,0.922236592142565,0.029058054155365
+"23426",100,2019,2,19,0.404730478725811,11.2052916378865,20.2731462568864,9.93510457889737,18.1949616548645,18.1949616548645,6.13711163025706,100,2019,2,19,0.00654970682155321,0.105671910055138,0.0335052655631316,0.0373245781347152,1.19650316131366,1.19650316131366,0.028078043326603
+"23427",100,2019,2,20,0,9.34856978921082,21.5839275511185,13.3221891967639,17.8634432277533,17.8634432277533,6.08787138509581,100,2019,2,20,0,0.124480072079566,0.0839052308916582,0.0608064783643455,1.43752792075566,1.43752792075566,0.0271182400721523
+"23428",100,2019,2,21,0.013971397347904,10.081925256811,20.4194278947853,11.6226732539396,14.2978658392878,14.2978658392878,6.03863113993455,100,2019,2,21,0.00140350881375765,0.0402163513809039,0.095446807089805,0.0471479696151996,1.16120323928039,1.16120323928039,0.0261786443920127
+"23429",100,2019,2,22,0.0202420245040499,12.102024285051,22.7151704144032,13.1989328470429,23.8749173461276,23.8749173461276,5.98939089477329,100,2019,2,22,0.00228070182235617,0.134271891588405,0.290224554059991,0.0834321822652229,0.292555604722956,0.292555604722956,0.0252592562861845
+"23430",100,2019,2,23,0,12.2607591490541,24.5304621517068,13.8739934581341,25.0372166649343,25.0372166649343,5.94015064961203,100,2019,2,23,0,0.159253853120927,0.362800049760967,0.153483038363607,0.0111175452698821,0.0111175452698821,0.0243600757546674
+"23431",100,2019,2,24,0,12.841859067365,34.8339051308543,12.3906381290213,24.7689990787485,24.7689990787485,5.89091040445078,100,2019,2,24,0,0.0170777722253326,0.0516542531169538,0.101460790886332,0.0231561520941205,0.0231561520941205,0.0234811027974616
+"23432",100,2019,2,25,0,16.4457646927031,36.6666339290942,8.65024206793085,24.0528714250285,24.0528714250285,5.84167015928952,100,2019,2,25,0,0.0111473387380889,0.0104316448828046,0.0556923488238947,0.0402063718163696,0.0402063718163696,0.0226223374145671
+"23433",100,2019,2,26,0,13.1097249837861,22.397249574315,14.3936303676945,22.2928713954846,22.2928713954846,5.79242991412826,100,2019,2,26,0,0.0281579062814443,0.374232041480748,0.0409830296634431,1.3000506557712,1.3000506557712,0.0217837796059837
+"23434",100,2019,2,27,0,9.74041797251853,28.4723322462327,12.928415871296,24.0628272162543,24.0628272162543,5.743189668967,100,2019,2,27,0,0.0400052392503062,0.465986963970619,0.0971929902640773,0.0298800872672138,0.0298800872672138,0.0209654293717116
+"23435",100,2019,2,28,0,13.9539274240878,36.6211551250798,12.071572940604,16.9837625281121,16.9837625281121,5.69394942380575,100,2019,2,28,0,0.0225467766768119,0.652503160230406,0.461006318702543,1.05224694118625,1.05224694118625,0.0201672867117507
+"23436",100,2019,3,1,0,15.4339714984034,40.7613532273027,8.67158416815192,23.3421229267015,23.3421229267015,5.65980054549455,100,2019,3,1,0,0.197192257371506,0.0156472965542054,0.00862455365373077,0.138122216797174,0.138122216797174,0.0202029115951639
+"23437",100,2019,3,2,0,21.9252253260681,39.7716389352864,9.08558855067254,22.8332455596252,22.8332455596252,5.62565166718336,100,2019,3,2,0,0.0973586023300365,0.0164228092407779,0.00681460936705699,0.339478493736559,0.339478493736559,0.0202406803087425
+"23438",100,2019,3,3,0,15.7215290950863,32.6080087844295,15.4903297969873,11.306314770538,11.306314770538,5.59150278887217,100,2019,3,3,0,0.0852373830027068,0.783670187834408,0.590221063141032,0.403538682906459,0.403538682906459,0.0202805928524865
+"23439",100,2019,3,4,0,9.63771169282684,25.3638176744921,13.7644553777277,22.9832233076442,22.9832233076442,5.55735391056098,100,2019,3,4,0,0.0386766249049442,0.40764839133127,0.144110010254251,0.0262147022541386,0.0262147022541386,0.0203226492263959
+"23440",100,2019,3,5,0.00297029707396385,10.8221563395887,25.5070187177333,14.7314740874455,20.8189220554364,20.8189220554364,5.52320503224978,100,2019,3,5,0.000994152076411666,0.0464427032292783,0.097013453005264,0.212517697864218,0.481248716633476,0.481248716633476,0.0203668494304708
+"23441",100,2019,3,6,4.689658983837,9.75970296366642,17.0362484095776,9.14290417670155,17.6843674631402,17.6843674631402,5.48905615393859,100,2019,3,6,0.390233917933472,0.0460555890402098,0.0088988625423112,0.0742988931221765,3.48991702883788,3.48991702883788,0.020413193464711
+"23442",100,2019,3,7,1.29350936622641,6.93768980322105,22.6600329123183,8.49674362081899,21.1246422346931,21.1246422346931,5.4549072756274,100,2019,3,7,0.0224561365584896,0.082232146741653,0.0438941631074312,0.0881478771924675,0.180436346881993,0.180436346881993,0.0204616813291166
+"23443",100,2019,3,8,0.451485158185033,8.6020352989927,22.6503299024895,10.58353123177,9.22859177731051,9.22859177731051,5.42075839731621,100,2019,3,8,0.145614034015882,0.0316479206264445,0.0908730922010949,0.195622730177082,0.102078401329167,0.102078401329167,0.0205123130236876
+"23444",100,2019,3,9,0.306160623484319,8.85798688454203,24.8198129005558,12.7119691259134,22.210450685457,22.210450685457,5.38660951900501,100,2019,3,9,0.0106432745871488,0.0306484878362176,0.0981005422405435,0.0624877083628288,0.00184796073436802,0.00184796073436802,0.0205650885484241
+"23445",100,2019,3,10,0.00286028607122444,9.64683166667573,20.496534760898,14.8856435530257,7.54611662853144,7.54611662853144,5.35246064069382,100,2019,3,10,0.000526315805159117,0.0192163815638664,0.0282145968256011,0.0372924181621188,0.648055563502878,0.648055563502878,0.0206200079033259
+"23446",100,2019,3,11,1.80539053422783,14.2845105484898,20.7117159395459,12.655005504327,9.12449934668798,9.12449934668798,5.31831176238263,100,2019,3,11,0.0892982408596068,0.0163596676386652,0.0161321881303231,0.00526900148676562,1.52576859508208,1.52576859508208,0.0206770710883932
+"23447",100,2019,3,12,5.31133106124677,12.3265126011159,17.8332675203632,9.29346530272229,14.7202089506932,14.7202089506932,5.28416288407144,100,2019,3,12,0.305906423434893,0.0493257246676312,0.00992108337255314,0.0765245452502531,0.906922925822009,0.906922925822009,0.0207362781036259
+"23448",100,2019,3,13,0.290539060537965,5.71466450224353,18.6037733667623,9.267854677962,15.3835534168155,15.3835534168155,5.25001400576025,100,2019,3,13,0.01953216486507,0.0361286506987672,0.0116917778777329,0.0239479414198903,0.43920997941771,0.43920997941771,0.0207976289490239
+"23449",100,2019,3,14,0,5.49298130692166,21.750891021376,9.70612763361831,21.0816504559239,21.0816504559239,5.21586512744905,100,2019,3,14,0,0.0295023314861043,0.0672620728879412,0.0528063968614328,0.00265381233737316,0.00265381233737316,0.0208611236245874
+"23450",100,2019,3,15,0,8.09447742111743,22.094037426318,14.4193838862303,14.791078074406,14.791078074406,5.18171624913786,100,2019,3,15,0,0.0194134504910146,0.0557334071692385,0.128313454680497,0.85248434705464,0.85248434705464,0.0209267621303162
+"23451",100,2019,3,16,0.00154015403835162,11.1721781970906,26.0141474426907,14.6963145085032,20.5645106706944,20.5645106706944,5.14756737082667,100,2019,3,16,0.000526315805159117,0.0391275353415721,0.165191378704176,0.12929301623524,0.0154099311742022,0.0154099311742022,0.0209945444662105
+"23452",100,2019,3,17,0.0278327836930686,12.2575688105081,27.4367219024759,14.946996640582,19.9488561502253,19.9488561502253,5.11341849251548,100,2019,3,17,0.0020467836867299,0.0738256856060329,0.287861821278652,0.183213299076515,0.098849892416949,0.098849892416949,0.0210644706322702
+"23453",100,2019,3,18,0,13.4672936060772,25.7325959410211,17.3891421403035,20.0519470806562,20.0519470806562,5.07926961420428,100,2019,3,18,0,0.0264579031939464,0.0661578991095762,0.0852367680646659,0.0178321561938768,0.0178321561938768,0.0211365406284953
+"23454",100,2019,3,19,0.436413649973696,15.1269085981665,22.5358748713998,18.852222264272,8.18075904856683,8.18075904856683,5.04512073589309,100,2019,3,19,0.0524561376529829,0.0131877217232209,0.0356398313959406,0.0359228019672293,1.18140409674509,1.18140409674509,0.0212107544548858
+"23455",100,2019,3,20,0.319801986602732,15.8728382353998,20.7139163987734,19.8248842132367,6.14185916472583,6.14185916472583,5.0109718575819,100,2019,3,20,0.0232163746559132,0.0303888854981225,0.152233719563917,0.00913691089956355,1.90596896574123,1.90596896574123,0.0212871121114417
+"23456",100,2019,3,21,0,17.2306159452768,23.2600552020687,20.0261933103253,10.1360946624848,10.1360946624848,4.97682297927071,100,2019,3,21,0,0.0204146347360615,0.192031687003915,0.0270701465051388,5.74849825386031,5.74849825386031,0.021365613598163
+"23457",100,2019,3,22,0.067656766684732,17.3289329919091,26.7294279125789,17.6521121844469,14.5116722832943,14.5116722832943,4.94267410095951,100,2019,3,22,0.00356725156830068,0.0190204853483308,0.324333848598777,0.10274255884207,0.150478347327769,0.150478347327769,0.0214462589150496
+"23458",100,2019,3,23,0,17.2065675796324,29.7288337514477,16.2363147788053,18.3046972843418,18.3046972843418,4.90852522264832,100,2019,3,23,0,0.0121555249534991,0.476989506196364,0.074414607475428,0.0262923286620782,0.0262923286620782,0.0215290480621018
+"23459",100,2019,3,24,0,13.6455885852524,29.003212235286,15.0457425248636,12.7395380049518,12.7395380049518,4.87437634433713,100,2019,3,24,0,0.045780106715236,0.0403209995116594,0.0988386803544033,0.666565518880114,0.666565518880114,0.0216139810393193
+"23460",100,2019,3,25,8.06336635069223,9.32469749870342,16.3356876268376,7.55332234616589,13.7466887598909,13.7466887598909,4.84022746602594,100,2019,3,25,2.09397667237895,0.0155783549170077,0.0109783295036599,0.0263052829764917,1.35829825867555,1.35829825867555,0.0217010578467022
+"23461",100,2019,3,26,10.1918593334286,8.45370753973362,19.2702864971098,10.1944664324602,15.4377117545167,15.4377117545167,4.80607858771475,100,2019,3,26,3.66005890929915,0.0216608377423808,0.0174719114741796,0.114138561744097,0.360843295832526,0.360843295832526,0.0217902784842505
+"23462",100,2019,3,27,0,10.0206051019683,21.2518809227267,11.0907479381666,18.1508690652543,18.1508690652543,4.77192970940355,100,2019,3,27,0,0.101384198600425,0.0585555784440317,0.155248457256197,0.0403210736864093,0.0403210736864093,0.0218816429519643
+"23463",100,2019,3,28,0,6.43555549216611,26.4162155315034,11.0760065997788,17.8000548754064,17.8000548754064,4.73778083109236,100,2019,3,28,0,0.0336146115386851,0.0703842323530419,0.30671101671861,0.0342209961163675,0.0342209961163675,0.0219751512498434
+"23464",100,2019,3,29,0,11.1120352015899,24.1138944405534,13.4984598359128,5.01511548454612,5.01511548454612,4.70363195278117,100,2019,3,29,0,0.00998070672694294,0.231143270972608,0.110567294159899,0.304178315396922,0.304178315396922,0.0220708033778879
+"23465",100,2019,3,30,14.5122113091455,6.90151810829658,13.9846866259349,9.15363038884531,7.7302309687775,7.7302309687775,4.66948307446998,100,2019,3,30,11.7758487226253,0.0327052735022133,0.00781404232453518,0.0191496944450607,0.863975972028414,0.863975972028414,0.0221685993360978
+"23466",100,2019,3,31,7.88393831462881,8.21403739003852,17.2232783603983,13.0839164238689,10.2377447856404,10.2377447856404,4.63533419615878,100,2019,3,31,1.46257308993428,0.0797204558769007,0.0106578804598934,0.01144796484347,0.429283075486643,0.429283075486643,0.0222685391244732
+"23467",100,2019,4,1,0.598239828585529,12.1703191165483,18.0088119590768,10.1115403369446,8.94172719340644,8.94172719340644,4.5781648926236,100,2019,4,1,0.0306432728739512,0.111436822550015,0.0252561456070173,0.017502934483806,0.42983327484138,0.42983327484138,0.0209079192987321
+"23468",100,2019,4,2,0,7.66481843010427,25.223322588225,10.4691749274796,17.0849287265992,17.0849287265992,4.52099558908842,100,2019,4,2,0,0.0565806672849429,0.0173941211011948,0.024524548674523,0.00489414552868649,0.00489414552868649,0.019592089913627
+"23469",100,2019,4,3,0.235093515878594,9.97411441698064,16.9333881193524,16.2590649188286,4.29943896861228,4.29943896861228,4.46382628555324,100,2019,4,3,0.00760233965184955,0.0247888707479603,0.0337063970691177,0.00681517511579495,0.165069006469248,0.165069006469248,0.0183210509691581
+"23470",100,2019,4,4,0.572827288698573,11.6313970700087,23.2786469475271,12.9582067957543,16.6359295315213,16.6359295315213,4.40665698201806,100,2019,4,4,0.0457894709444884,0.182637449922692,0.0283818608515173,0.0521766080511357,0.00934792961463607,0.00934792961463607,0.0170948024653251
+"23471",100,2019,4,5,0,13.248129726112,30.4049947463771,11.9862266095689,11.6116502854166,11.6116502854166,4.34948767848288,100,2019,4,5,0,0.0925151985146633,0.0652806599492254,0.0127497154080682,0.241638482048181,0.241638482048181,0.0159133444021283
+"23472",100,2019,4,6,2.2479648057646,12.6518151815182,17.9340266866652,11.7635093674277,11.4886031030166,11.4886031030166,4.2923183749477,100,2019,4,6,0.0247953231432327,0.0680952944014535,0.0261760592682187,0.00921638206576898,0.823335709207172,0.823335709207172,0.0147766767795675
+"23473",100,2019,4,7,0,5.55038504448399,21.1158088247637,11.755709509645,12.0248406119604,12.0248406119604,4.23514907141252,100,2019,4,7,0,0.0790432956963467,0.0226537836713452,0.0604028976931978,0.471320358796204,0.471320358796204,0.0136847995976427
+"23474",100,2019,4,8,2.48140812195567,10.5138173433814,19.713685389268,14.024697425473,10.9216391518302,10.9216391518302,4.17797976787734,100,2019,4,8,0.144736848030875,0.0313485283010049,0.0343813168980424,0.0082842105085116,0.866724868294869,0.866724868294869,0.012637712856354
+"23475",100,2019,4,9,5.73619362334869,7.9940704087613,15.6722881004493,10.027029827483,12.6040703791334,12.6040703791334,4.12081046434216,100,2019,4,9,0.326725142071838,0.126060823047986,0.0485701497507826,0.00794502261876446,0.512506580946244,0.512506580946244,0.0116354165557014
+"23476",100,2019,4,10,0.963256331429099,6.93531350596379,15.1952475231997,10.7672387344478,9.13316825776473,9.13316825776473,4.06364116080698,100,2019,4,10,0.0900584820766901,0.118145059491888,0.0338151943899195,0.0266707620944624,1.70349162255345,1.70349162255345,0.0106779106956848
+"23477",100,2019,4,11,0.903300331698524,11.2903079944606,15.6506269987923,11.0629262598959,9.04607254338868,9.04607254338868,4.0064718572718,100,2019,4,11,0.0636842127292482,0.121270775294261,0.014089446950195,0.0131619737681693,0.568359783312111,0.568359783312111,0.00976519527630432
+"23478",100,2019,4,12,0.0842684280983817,5.46300332695738,22.3686576377441,9.80341038006236,15.3235866317917,15.3235866317917,3.94930255373662,100,2019,4,12,0.0020467836867299,0.109124559559748,0.0194022499177153,0.16457031666055,0.000324561481653152,0.000324561481653152,0.00889727029755987
+"23479",100,2019,4,13,0,7.79145216548403,19.0625743719086,15.2099230475683,5.24778880275647,5.24778880275647,3.89213325020144,100,2019,4,13,0,0.0747783456233866,0.0250204720081701,0.0758871661337465,0.223336278552913,0.223336278552913,0.00807413575945148
+"23480",100,2019,4,14,0,11.9122993041186,22.1065126509294,14.3715842480969,12.2861497425797,12.2861497425797,3.83496394666626,100,2019,4,14,0,0.164161942246705,0.0287695615357915,0.0507263662149417,0.292291676402023,0.292291676402023,0.00729579166197914
+"23481",100,2019,4,15,0,10.8847413991544,26.4620352725122,11.953476256401,13.5546754557963,13.5546754557963,3.77779464313108,100,2019,4,15,0,0.0399093883247455,0.0151690206117213,0.153104069298274,0.176189376048122,0.176189376048122,0.00656223800514287
+"23482",100,2019,4,16,0,14.9966886549762,32.5115510879701,8.80880079594645,13.4711990996425,13.4711990996425,3.72062533959589,100,2019,4,16,0,0.0105538105145564,0.0390580468080946,0.156538556664547,0.284137359474198,0.284137359474198,0.00587347478894266
+"23483",100,2019,4,17,0.0379537959450936,20.7216942622454,26.7296699066498,13.5071286924339,3.04477448064764,3.04477448064764,3.66345603606071,100,2019,4,17,0.00485380131424519,0.0278584238869626,0.0749556933936363,0.364503938913711,0.211987718239729,0.211987718239729,0.00522950201337849
+"23484",100,2019,4,18,1.09273928882528,8.56689767396883,17.2388227985244,9.69381737997561,12.1538393243049,12.1538393243049,3.60628673252553,100,2019,4,18,0.06608187685236,0.175953853391953,0.012894170201493,0.0361701820463343,0.0632111860487805,0.0632111860487805,0.00463031967845039
+"23485",100,2019,4,19,0,3.7662376116867,22.5982285825857,9.7440704087613,14.0552145839393,14.0552145839393,3.54911742899035,100,2019,4,19,0,0.114894178600211,0.0557942250974765,0.0876918298443034,0.00304737089063352,0.00304737089063352,0.00407592778415834
+"23486",100,2019,4,20,0,11.2359625865655,31.315973093014,10.6860505944431,13.7251705173874,13.7251705173874,3.49194812545517,100,2019,4,20,0,0.0532263397239925,0.203494146470085,0.0177035016913844,0.0349140033789521,0.0349140033789521,0.00356632633050235
+"23487",100,2019,4,21,0,18.0286028854655,25.4885920282244,13.5334542497943,9.65501661059355,9.65501661059355,3.43477882191999,100,2019,4,21,0,0.0444702453259574,0.0120806570179284,0.0956912118053187,0.30934727279269,0.30934727279269,0.00310151531748243
+"23488",100,2019,4,22,0.384598466437576,4.8141474309403,17.9585701089488,10.0131353665762,12.4818590634202,12.4818590634202,3.37760951838481,100,2019,4,22,0.0167251466842075,0.0448695907804947,0.0160023926497129,0.0189163734079855,0.202898833329565,0.202898833329565,0.00268149474509856
+"23489",100,2019,4,23,0,3.17033008091783,17.9872715176791,10.9346863733004,13.2415840287413,13.2415840287413,3.32044021484963,100,2019,4,23,0,0.0578052588250939,0.0257538669076404,0.0106496882099077,0.0196783815908823,0.0196783815908823,0.00230626461335074
+"23490",100,2019,4,24,0,6.66877890577411,18.8957425191982,12.9876347091725,10.4535755444937,10.4535755444937,3.26327091131445,100,2019,4,24,0,0.0232988092266501,0.0458941551658544,0.0407578751102304,0.333056146624027,0.333056146624027,0.00197582492223899
+"23491",100,2019,4,25,0.190319034821129,9.03210127865127,17.9535091576403,11.5102970072932,5.60799785284582,5.60799785284582,3.20610160777927,100,2019,4,25,0.00274853826789132,0.096600004365768,0.0294023764049359,0.0662368330816883,0.341424543712033,0.341424543712033,0.0016901756717633
+"23492",100,2019,4,26,4.88206817739212,10.2496478874954,14.1889549051837,9.46605059914332,9.49688669819512,9.49688669819512,3.14893230424409,100,2019,4,26,0.166959075342163,0.107817574122965,0.0230274852663953,0.0409321712784742,0.635969002410327,0.635969002410327,0.00144931686192366
+"23493",100,2019,4,27,0.779317936586468,7.25930689821149,14.2855555541707,11.7321891543364,3.74558855152235,3.74558855152235,3.09176300070891,100,2019,4,27,0.0559649138353026,0.209836826772878,0.0756608327899876,0.0231608221935821,0.300333917212912,0.300333917212912,0.00125324849272008
+"23494",100,2019,4,28,2.39405940861592,11.2574916688522,15.5883497966267,12.89330027468,7.06075905232277,7.06075905232277,3.03459369717373,100,2019,4,28,0.0417543839850659,0.11478189420879,0.0191689869931497,0.0360619676512534,0.20092859773582,0.20092859773582,0.00110197056415256
+"23495",100,2019,4,29,1.83377335318113,10.0815620527278,16.843124257456,11.0415401584638,6.71789878429753,6.71789878429753,2.97742439363855,100,2019,4,29,0.187602324346355,0.0962386045021315,0.014565483544031,0.0204052883461282,0.11612108074881,0.11612108074881,0.000995483076221104
+"23496",100,2019,4,30,0.0299229927451173,6.53270628586067,20.4609239534183,9.85850389667339,9.8029702955609,9.8029702955609,2.92025509010337,100,2019,4,30,0.00228070182235617,0.0184508656195264,0.0115590413325338,0.0513262905612427,0.0894648703302473,0.0894648703302473,0.000933786028925702
+"23497",100,2019,5,1,0.12310231206539,12.6340594947404,22.8967988588104,11.8307041885829,9.45128709648309,9.45128709648309,2.88014786061597,100,2019,5,1,0.00245614042407588,0.046256158478566,0.0458649488619169,0.0107596491518626,0.651350886551334,0.651350886551334,0.000871176032712941
+"23498",100,2019,5,2,15.5964796949666,11.3211990897805,20.2854237131553,10.0194501404715,10.1308470443793,10.1308470443793,2.84004063112857,100,2019,5,2,26.7435681655395,0.0189052484033576,0.0970147691026243,0.000405267264934125,0.0566783796166313,0.0566783796166313,0.000814928651337693
+"23499",100,2019,5,3,0,7.08266222831046,18.8586027084535,9.35698576046951,9.81970290906883,9.81970290906883,2.79993340164117,100,2019,5,3,0,0.0590806886171186,0.0857911168330045,0.00794267518792011,0.204373028109453,0.204373028109453,0.000765043884799953
+"23500",100,2019,5,4,1.71276126049533,10.6861275829235,14.9573047090285,11.2256546933265,6.54674368355796,6.54674368355796,2.75982617215377,100,2019,5,4,0.123684193167772,0.120560847118522,0.0289801108734718,0.00556549702927526,0.319646801142129,0.319646801142129,0.00072152173309972
+"23501",100,2019,5,5,1.14158416361567,10.0110671008774,14.7491968394113,12.3833663655062,6.86751374894112,6.86751374894112,2.71971894266638,100,2019,5,5,0.15093567930467,0.086803528755665,0.0329029501173906,0.0515731235750244,0.999606485588815,0.999606485588815,0.00068436219623699
+"23502",100,2019,5,6,1.8602860169311,10.3589878911101,16.1344883869452,10.7221012503663,9.51037403773947,9.51037403773947,2.67961171317898,100,2019,5,6,0.185380107268955,0.185733885358015,0.0218035071169312,0.0203152038262668,0.387962037616394,0.387962037616394,0.000653565274211772
+"23503",100,2019,5,7,0.100000001490116,6.48513748307433,20.7360286754612,9.68687565701761,10.9777007853106,10.9777007853106,2.63950448369158,100,2019,5,7,0,0.158564900489738,0.0857783351834742,0.0237286728441187,0.031448530926695,0.031448530926695,0.000629130967024065
+"23504",100,2019,5,8,15.3108911524774,9.85474150881122,14.7171836691459,10.5383057735934,8.50381755251827,8.50381755251827,2.59939725420418,100,2019,5,8,0.40888893551304,0.0978812639605379,0.0636783576207473,0.00276081447941711,0.287534006153419,0.287534006153419,0.000611059274673866
+"23505",100,2019,5,9,1.27722771385453,7.77123221050132,14.9371947937935,11.0222441481285,4.32768984491413,4.32768984491413,2.55929002471678,100,2019,5,9,0.0104093507437695,0.0661953467129503,0.0103579085545885,0.0377450048190812,0.189070762452489,0.189070762452489,0.000599350197161173
+"23506",100,2019,5,10,32.8073709695646,9.15778880544228,13.4718152152167,9.0369088211731,9.74807485206948,9.74807485206948,2.51918279522939,100,2019,5,10,1.08432760673744,0.147422181574565,0.0961222365061858,0.000739767487361567,0.866622744108577,0.866622744108577,0.000594003734485991
+"23507",100,2019,5,11,1.131573167899,8.11937291026771,15.1343784688985,13.8773157447085,6.73330031710752,6.73330031710752,2.47907556574199,100,2019,5,11,0.0332163744572324,0.208294236808957,0.0739152551841365,0.00743213805046189,0.736198951765832,0.736198951765832,0.000595019886648313
+"23508",100,2019,5,12,1.65665567931038,10.5872387838836,16.7090981234812,12.3929483762013,6.28071504798528,6.28071504798528,2.43896833625459,100,2019,5,12,0.0994152046086506,0.119922297075136,0.056775984450947,0.00285846264718234,0.163381295967127,0.163381295967127,0.000602398653648146
+"23509",100,2019,5,13,0.500660072155584,9.90376230330095,16.179516024322,10.6878987806465,5.16820690262042,5.16820690262042,2.39886110676719,100,2019,5,13,0.278713455810185,0.0616332618714168,0.0448174904452449,0.00978421754676908,0.262537408620175,0.262537408620175,0.000616140035485485
+"23510",100,2019,5,14,0.27381738676916,8.2979978938045,17.2659846139033,13.4388558489524,8.36355333674465,8.36355333674465,2.35875387727979,100,2019,5,14,0.0294152047228047,0.0508953199577308,0.0480609529153064,0.00258420785608607,0.186880147002669,0.186880147002669,0.000636244032160336
+"23511",100,2019,5,15,0.0334433448327781,8.99345437816792,14.6212540344305,12.1624311724118,6.48610560361571,6.48610560361571,2.3186466477924,100,2019,5,15,0.00263157902579559,0.0963929093612672,0.123865547871805,0.0347479396709175,0.435337475753274,0.435337475753274,0.000662710643672693
+"23512",100,2019,5,16,0.00517051712875188,6.09888884100584,15.9065676700689,10.573762416315,9.53125402033001,9.53125402033001,2.278539418305,100,2019,5,16,0.000994152076411666,0.0648707634362828,0.138205243743268,0.0068064115402359,0.242935701939158,0.242935701939158,0.00069553987002256
+"23513",100,2019,5,17,0,4.31115512590859,18.8408803929328,11.1963585768596,10.4100659625365,10.4100659625365,2.2384321888176,100,2019,5,17,0,0.0574362452178148,0.178553754599227,0.0434473827875757,0.000258479206550283,0.000258479206550283,0.000734731711209934
+"23514",100,2019,5,18,0,5.25876791065413,19.2367764064843,11.8149286077099,10.3362265650851,10.3362265650851,2.1983249593302,100,2019,5,18,0,0.053060841764511,0.0359801391828672,0.0538262669703477,0.000884212855384227,0.000884212855384227,0.000780286167234818
+"23515",100,2019,5,19,0,8.46081412245076,22.4817489166595,11.2739274042799,9.27240927542003,9.27240927542003,2.1582177298428,100,2019,5,19,0,0.0342842415057606,0.056292366139627,0.00364445020895481,0.0578227627894348,0.0578227627894348,0.000832203238097211
+"23516",100,2019,5,20,6.31111109453459,12.8119692576863,19.6479207344181,13.1122331661229,8.06515955216814,8.06515955216814,2.11811050035541,100,2019,5,20,0.351111082500898,0.073744431474557,0.0319022624102196,0.00161111280658322,0.791822750001234,0.791822750001234,0.000890482923797107
+"23517",100,2019,5,21,0,10.2900549455313,18.9763367488177,13.7516942249797,7.13523648359595,7.13523648359595,2.07800327086801,100,2019,5,21,0,0.0809672743234682,0.0818668093367402,0.0170286540294282,0.0724228301143277,0.0724228301143277,0.000955125224334517
+"23518",100,2019,5,22,0.0278327836930686,7.58345438964558,18.4607263172683,13.930230970561,8.94522539040174,8.94522539040174,2.03789604138061,100,2019,5,22,0.0020467836867299,0.0613362477620631,0.0359619466774225,0.0418561665920601,0.0940175030134615,0.0940175030134615,0.00102613013970943
+"23519",100,2019,5,23,0.0818481860381148,4.47080304153157,21.0757753772966,13.5943785616011,9.75547844138738,9.75547844138738,1.99778881189321,100,2019,5,23,0.0020467836867299,0.0495479784502877,0.0527040230470299,0.0377338901877085,0.0193479432223843,0.0193479432223843,0.00110349766992185
+"23520",100,2019,5,24,2.41980198533884,9.25614950287067,21.6085037592352,14.1458416635578,6.39447745028371,6.39447745028371,1.95768158240581,100,2019,5,24,0.0870175530060052,0.0205485304403849,0.0384473040400751,0.0350362735816125,0.955098884485502,0.955098884485502,0.00118722781497178
+"23521",100,2019,5,25,0.979427940971387,10.1567766139216,15.9482288140275,12.8858965264164,6.81104513573306,6.81104513573306,1.91757435291842,100,2019,5,25,0.012222225401137,0.115513936035042,0.0321251634499042,0.00937835107108494,0.604204089145784,0.604204089145784,0.00127732057485922
+"23522",100,2019,5,26,5.79405941480588,7.00370737270947,14.5295049554051,11.8182507180276,7.97049508183965,7.97049508183965,1.87746712343102,100,2019,5,26,1.09766093248514,0.0618485774012599,0.12751163308672,0.00763566344417538,0.162535613915565,0.162535613915565,0.00137377594958417
+"23523",100,2019,5,27,13.613311365755,5.61005500073742,12.7051815058139,10.01410351492,7.28908694301895,7.28908694301895,1.83735989394362,100,2019,5,27,1.27508772108298,0.15536544880674,0.0859052783297509,0.00339824378931409,0.373870081584811,0.373870081584811,0.00147659393914663
+"23524",100,2019,5,28,5.63982397704759,7.51979091537274,14.5615181980616,10.3360946674158,6.34719465956567,6.34719465956567,1.79725266445622,100,2019,5,28,0.630058427219516,0.18615843117255,0.0610830720534121,0.00210525668863573,0.145136205769163,0.145136205769163,0.0015857745435466
+"23525",100,2019,5,29,16.7031902821019,5.68973594451501,10.0832341921211,8.29261841579895,4.4881958623137,4.4881958623137,1.75714543496883,100,2019,5,29,2.28871283157543,0.216594143847437,0.152706424742213,0.000836258503523179,0.849378426333213,0.849378426333213,0.00170131776278407
+"23526",100,2019,5,30,8.66853680469022,4.42579754036252,12.6292519543168,10.9107920381233,5.98408132811191,5.98408132811191,1.71703820548143,100,2019,5,30,0.215964897724665,0.0978274711196402,0.0707953650157381,0.0038204644811789,0.967158538296364,0.967158538296364,0.00182322359685905
+"23527",100,2019,5,31,2.99427943418522,9.33730470071925,14.1926732928839,11.5673598613676,4.77676570218782,4.77676570218782,1.67693097599403,100,2019,5,31,0.0646783634375462,0.291025627312539,0.0882584486666476,0.00509999927950761,0.24170646649747,0.24170646649747,0.00195149204577154
+"23528",100,2019,6,1,0.00693069317258231,10.0090098092527,14.2728714308198,11.0512652937466,5.76427941642316,5.76427941642316,1.65718852168987,100,2019,6,1,0.00140350881375765,0.149116315694502,0.0629784012577647,0.000658471751140969,0.0486274972837803,0.0486274972837803,0.00186448549031625
+"23529",100,2019,6,2,0.100000001490116,8.50699672027521,11.4464137095167,10.4978216714723,3.24805280713752,3.24805280713752,1.63744606738571,100,2019,6,2,0,0.093278356599039,0.0944608581677444,0.00458069795313988,0.228213481473652,0.228213481473652,0.00178005693342982
+"23530",100,2019,6,3,17.0259627603462,7.66722781062782,12.2109902133249,8.93096798140355,6.19944995464665,6.19944995464665,1.61770361308155,100,2019,6,3,0.283742822569265,0.158813465382798,0.100448007351694,0.00227777078425948,0.984502179372267,0.984502179372267,0.00169820637511223
+"23531",100,2019,6,4,3.89570956786211,7.14268420736651,12.1386688566051,9.7419470691576,6.81339933376501,6.81339933376501,1.59796115877739,100,2019,6,4,0.150409391498017,0.197346736931402,0.108527414627585,0.00418712768783756,0.360424552479143,0.360424552479143,0.0016189338153635
+"23532",100,2019,6,5,0.731023112850876,8.17867997613284,11.8616390983657,9.83742567572263,4.99916391194326,4.99916391194326,1.57821870447323,100,2019,6,5,0.104269010410672,0.167617552502993,0.0948362497586974,0.0108619908428489,0.0859386035127401,0.0859386035127401,0.00154223925418362
+"23533",100,2019,6,6,2.89471947163245,9.0960507104368,13.1767766394369,12.9903958921779,4.64975792136785,4.64975792136785,1.55847625016906,100,2019,6,6,0.338421092702636,0.132892936867238,0.0708578671952498,0.00387603596477967,0.266269558635526,0.266269558635526,0.00146812269157259
+"23534",100,2019,6,7,1.02134213130204,9.25864679401595,12.8411001314556,10.9982509466157,5.41547856420049,5.41547856420049,1.5387337958649,100,2019,6,7,0.0720467784181681,0.0445801671168408,0.0366619811744566,0.00118772258205705,0.0362695871899858,0.0362695871899858,0.00139658412753041
+"23535",100,2019,6,8,0.133553357325634,3.99321232167277,15.872948285639,10.9116061171814,9.0752587365632,9.0752587365632,1.51899134156074,100,2019,6,8,0.00397660830564666,0.0131818742830259,0.0423257614984535,0.00360234536072855,0.000370176433593158,0.000370176433593158,0.00132762356205708
+"23536",100,2019,6,9,0,6.43978000867485,16.4924422768739,9.70567675654513,7.68289330349241,7.68289330349241,1.49924888725658,100,2019,6,9,0,0.0246263249793378,0.029027474212885,0.0134701869452509,0.13953979027177,0.13953979027177,0.00126124099515261
+"23537",100,2019,6,10,1.34444444751976,9.37821773071625,15.7155225814635,12.2315950739895,6.8702310041757,6.8702310041757,1.47950643295242,100,2019,6,10,0.21035087508765,0.0291918346517447,0.0772952610150561,0.00399824599111664,0.390559666069223,0.390559666069223,0.00119743642681698
+"23538",100,2019,6,11,2.11551157541663,6.89609463380115,17.6193508823844,11.8245765572727,8.99454333994648,8.99454333994648,1.45976397864826,100,2019,6,11,0.982573082670138,0.0491695793999691,0.101571967539063,0.00535905299057581,0.000359658113107766,0.000359658113107766,0.00113620985705021
+"23539",100,2019,6,12,9.87876786912891,9.50130911471427,14.7242573663609,14.6874368445183,2.61210121573395,2.61210121573395,1.4400215243441,100,2019,6,12,2.00777772055735,0.0318561604015603,0.0869263063710467,0.00439180783504052,0.212355563248533,0.212355563248533,0.00107756128585229
+"23540",100,2019,6,13,11.2356436040142,7.30261826331597,15.0843234686437,12.4023542572038,7.66179317688391,7.66179317688391,1.42027907003994,100,2019,6,13,0.0909356719569943,0.0663648962924529,0.0718543619324164,0.00149823807171673,0.353545035835208,0.353545035835208,0.00102149071322322
+"23541",100,2019,6,14,2.05511549006988,6.62392732307594,14.3651376122033,12.4562485593118,6.19722770464302,6.19722770464302,1.40053661573578,100,2019,6,14,0.538187090597657,0.0602924008397563,0.08648012888732,0.00149122689905538,0.2573210132086,0.2573210132086,0.000967998139163003
+"23542",100,2019,6,15,1.86336631769657,6.73136416992339,14.4626842746378,11.3392519536454,7.8577668029483,7.8577668029483,1.38079416143162,100,2019,6,15,0.0370175401788024,0.0689362317882968,0.100936285110474,0.00109239457341632,0.134724552615266,0.134724552615266,0.000917083563671636
+"23543",100,2019,6,16,0.403300337378669,6.92965902458585,15.7941035626352,11.4473597208659,8.66470842917498,8.66470842917498,1.36105170712746,100,2019,6,16,0.0373099414080557,0.0190175424988693,0.114721162111274,0.00357778366947192,0.0342140695472222,0.0342140695472222,0.000868746986749121
+"23544",100,2019,6,17,0.630253029472888,5.04212318867347,15.8002199523389,10.2837184070885,8.90797565111888,8.90797565111888,1.3413092528233,100,2019,6,17,0.0192982440803486,0.0364374124750218,0.0806952675803953,0.00279182616791943,0.000304097002788705,0.000304097002788705,0.000822988408395459
+"23545",100,2019,6,18,5.88327837777216,7.61638058189249,11.4588999291851,9.09236530161271,5.44796475954969,5.44796475954969,1.32156679851914,100,2019,6,18,0.225906440500651,0.0308005590918596,0.140625038010964,0.00122455828713691,0.644683044747039,0.644683044747039,0.000779807828610647
+"23546",100,2019,6,19,3.14840482760577,3.6997689938519,11.8601319572189,8.98127613969893,7.27234322901475,7.27234322901475,1.30182434421497,100,2019,6,19,1.27005851070784,0.127948555936878,0.0947595784461571,0.00301404846823859,0.331436300218629,0.331436300218629,0.000739205247394688
+"23547",100,2019,6,20,1.14488449302575,2.53433441210895,11.5285149879581,8.96497251281906,7.63480750798392,7.63480750798392,1.28208188991081,100,2019,6,20,0.0702923955903424,0.195228652227137,0.0878924190254121,0.00314444957316934,0.260343343549628,0.260343343549628,0.000701180664747577
+"23548",100,2019,6,21,6.45896588483922,3.31622662376387,12.3013311455352,9.4366004123415,6.94104504611495,6.94104504611495,1.26233943560665,100,2019,6,21,1.82941522581537,0.108425160649513,0.0969175767597909,0.00100351477736397,0.619276107619682,0.619276107619682,0.000665734080669321
+"23549",100,2019,6,22,3.85170512125962,2.82356435211316,11.8444224138333,9.04251924835809,6.31524745229841,6.31524745229841,1.24259698130249,100,2019,6,22,0.27497072222638,0.131611113601265,0.106556096140631,0.00557193531467579,0.189927519817259,0.189927519817259,0.000632865495159915
+"23550",100,2019,6,23,3.28998903682654,1.11130913229796,12.4025853307071,10.2764467579303,8.82960394847773,8.82960394847773,1.22285452699833,100,2019,6,23,0.794444504049095,0.0307537989371014,0.073736261421433,0.00182865993219448,0.0101801226387978,0.0101801226387978,0.000602574908219361
+"23551",100,2019,6,24,0.600440055885986,-0.828371833128766,13.7288448734514,8.55611666856688,8.87904288666476,8.87904288666476,1.20311207269417,100,2019,6,24,0.0114035088764996,0.0574672534589882,0.0894649319565605,0.00152748263405944,0.00822046729869423,0.00822046729869423,0.00057486231984766
+"23552",100,2019,6,25,0,3.58297030471995,15.262926348234,10.2067215891168,8.94870175990072,8.94870175990072,1.18336961839001,100,2019,6,25,0,0.0747830435524266,0.0723175737102846,0.00578421837220086,0.000394156202876022,0.000394156202876022,0.000549727730044807
+"23553",100,2019,6,26,0.0797579769860662,3.8693179858662,16.3543346686201,11.2468757178261,8.95817366685018,8.95817366685018,1.16362716408585,100,2019,6,26,0.00228070182235617,0.033627468074004,0.0630496666224495,0.00723742570045826,0.000527486381830194,0.000527486381830194,0.000527171138810808
+"23554",100,2019,6,27,0,5.64144117735138,17.1056876712375,10.9104951369618,8.98585265697819,8.98585265697819,1.14388470978169,100,2019,6,27,0,0.0395607991949381,0.167031627218022,0.000689474906758674,0.000395323281593033,0.000395323281593033,0.000507192546145662
+"23555",100,2019,6,28,0,9.5713202788098,18.5981958135389,12.4217271080898,8.48044002226608,8.48044002226608,1.12414225547753,100,2019,6,28,0,0.0443584167035502,0.131762147598073,0.0143537850297073,0.0188444360521891,0.0188444360521891,0.000489791952049365
+"23556",100,2019,6,29,0.732783282837524,12.211276123626,13.7599670580118,11.9582178306789,2.26688670023571,2.26688670023571,1.10439980117337,100,2019,6,29,0.467836261065731,0.0969432539979703,0.0569029122604252,0.0209175203313032,0.270958486627281,0.270958486627281,0.000474969356521918
+"23557",100,2019,6,30,28.6888889568736,5.886754628193,13.3973047709701,10.5600660353473,7.98931795535701,7.98931795535701,1.08465734686921,100,2019,6,30,5.0565498791525,0.0834140530034376,0.124417512512289,0.00900937333813755,0.130695331911494,0.130695331911494,0.000462724759563327
+"23558",100,2019,7,1,4.26776678242175,7.63854784435696,12.7359405744194,9.02438945371588,8.06470849359259,8.06470849359259,1.08926179021977,100,2019,7,1,3.9002339395724,0.0612783566491863,0.0809099993477971,0.0104397813050271,0.0376695919371967,0.0376695919371967,0.000483005623475619
+"23559",100,2019,7,2,0.479207924096891,8.16526950966276,14.1408800727332,11.7886467808806,6.24393839306302,6.24393839306302,1.09386623357034,100,2019,7,2,0.00432748596222087,0.118059598698601,0.0608941256072051,0.00390527048846026,0.373198869398477,0.373198869398477,0.000506395901686756
+"23560",100,2019,7,3,1.83399337851437,7.71713977547237,13.8432011651521,11.8593180040584,7.11287132321936,7.11287132321936,1.0984706769209,100,2019,7,3,0.0946783486026103,0.0784672723156921,0.0702034648828837,0.00181637147387745,0.136700552941504,0.136700552941504,0.000532895594196745
+"23561",100,2019,7,4,0.0840484060929029,4.80871285070287,13.1013862800808,10.1530582587449,7.11603964332438,7.11603964332438,1.10307512027147,100,2019,7,4,0.0020467836867299,0.0374251466204713,0.0398450251016728,0.000459649640414847,0.00783566897433031,0.00783566897433031,0.00056250470100558
+"23562",100,2019,7,5,0.0863586371504303,4.40515953374512,16.0461278554499,12.2966229026467,9.21050590519333,9.21050590519333,1.10767956362204,100,2019,7,5,0.00175438601719706,0.0450427006292328,0.0679030109630204,0.000922219933474682,0.000287713841753004,0.000287713841753004,0.000595223222113264
+"23563",100,2019,7,6,0,6.39086910075743,17.8125410363226,12.6736192608824,9.04660073417773,9.04660073417773,1.1122840069726,100,2019,7,6,0,0.0540894740545421,0.0705286225082367,0.0167988264764871,0.010295328686869,0.010295328686869,0.000631051157519797
+"23564",100,2019,7,7,0,8.78020894120891,14.6751705102532,13.3844003184269,4.3906380450896,4.3906380450896,1.11688845032317,100,2019,7,7,0,0.0540316041065897,0.0124245445096453,0.00569825532484865,0.0820497059515522,0.0820497059515522,0.00066998850722518
+"23565",100,2019,7,8,5.90583058054035,5.9433332963614,13.7660066281477,11.749141934419,8.09255227423606,8.09255227423606,1.12149289367374,100,2019,7,8,0.243157927585636,0.0455696045870203,0.0463690007550771,0.00763684725406862,0.0791707327034694,0.0791707327034694,0.000712035271229407
+"23566",100,2019,7,9,0.582288243327335,5.31168316325994,14.7021671815543,11.9743675760704,7.33853682957598,7.33853682957598,1.1260973370243,100,2019,7,9,0.00941520470624799,0.0358719211901976,0.120891773045461,0.00431520567780199,0.0980040781333689,0.0980040781333689,0.000757191449532486
+"23567",100,2019,7,10,0.322222227876139,8.11473044205551,13.4963036852964,9.68078101192764,5.49995594842993,5.49995594842993,1.13070178037487,100,2019,7,10,0.0237426911059179,0.0981473626968251,0.0246140446914771,0.00938070463663062,0.0925561351965977,0.0925561351965977,0.000805457042134412
+"23568",100,2019,7,11,24.4057206236752,6.9940703930241,12.5384708253464,10.0427284146299,5.8035753421133,5.8035753421133,1.13530622372543,100,2019,7,11,10.0322812726208,0.0732731109630565,0.0663941469895887,0.00144853131706967,0.400914081557897,0.400914081557897,0.000856832049035187
+"23569",100,2019,7,12,8.33080312206407,8.61147417565777,12.0639603827784,8.99190304381619,6.19242027752733,6.19242027752733,1.139910667076,100,2019,7,12,0.657836355856349,0.0657035058964656,0.0562479589228808,0.00197662968116807,1.02519169998283,1.02519169998283,0.000911316470234809
+"23570",100,2019,7,13,13.6731573394423,5.38229924381369,11.5321452441913,9.04278344866728,7.40234325585192,7.40234325585192,1.14451511042657,100,2019,7,13,0.360233987284044,0.133925738369255,0.100399924002822,0.000936261631105422,0.895181224103585,0.895181224103585,0.000968910305733281
+"23571",100,2019,7,14,3.49163912756346,5.93029702168749,12.5817162101418,10.358910740012,5.67492846949528,5.67492846949528,1.14911955377713,100,2019,7,14,0.0345029236977602,0.147517564405494,0.056993024442403,0.00305555979416807,0.152868999009408,0.152868999009408,0.0010296135555306
+"23572",100,2019,7,15,4.76622656574606,8.80579757270771,12.8824972062483,13.4089547808808,5.64989004827569,5.64989004827569,1.1537239971277,100,2019,7,15,3.62830401211456,0.145443248409746,0.060356128880089,0.00344443363621118,0.167171945536225,0.167171945536225,0.00109342621962677
+"23573",100,2019,7,16,4.25115510995107,9.52002200618698,12.9310120119907,12.6256324979994,6.06150718097246,6.06150718097246,1.15832844047826,100,2019,7,16,0.632280705992955,0.115965533353662,0.0430397578859369,0.00226550000512595,0.127584212680191,0.127584212680191,0.00116034829802178
+"23574",100,2019,7,17,1.95797577142978,8.46889988843626,13.2764907213721,11.8810341418511,4.94654570633035,4.94654570633035,1.16293288382883,100,2019,7,17,0.00842105183685168,0.0906432711730017,0.0620064363112218,0.0126894849231283,0.21067663818384,0.21067663818384,0.00123037979071565
+"23575",100,2019,7,18,6.07645755501339,6.06355330023435,13.3268316434686,10.1280417353144,7.09476348669222,7.09476348669222,1.1675373271794,100,2019,7,18,0.020409354605876,0.0758479718437208,0.0701321712985409,0.032680730772908,0.0505286586471625,0.0505286586471625,0.00130352069770836
+"23576",100,2019,7,19,4.74158417509727,5.80183719723138,13.9336412574592,9.22183712139906,8.04614960478477,8.04614960478477,1.17214177052996,100,2019,7,19,0.134268966139431,0.0703988641705684,0.0775619888551683,0.00619415802321511,0.0660397944378282,0.0660397944378282,0.00137977101899992
+"23577",100,2019,7,20,0,6.44896583483689,16.1716941050833,9.2856545589938,10.0734543050214,10.0734543050214,1.17674621388053,100,2019,7,20,0,0.0473672677492032,0.110481368255853,0.00104269010538786,0.000664908474981642,0.000664908474981642,0.00145913075459034
+"23578",100,2019,7,21,0.229482954640528,8.74136411291276,14.0154894240225,8.38336649455122,9.37092405472389,9.37092405472389,1.18135065723109,100,2019,7,21,0.00783625782668941,0.0460450518692394,0.095958472154898,0.00472279075942243,0.0798228724675746,0.0798228724675746,0.00154159990447959
+"23579",100,2019,7,22,1.04411441268045,7.55270624790255,16.6030911213756,9.49500563068621,10.0976458896767,10.0976458896767,1.18595510058166,100,2019,7,22,0.275555541581579,0.0732701935729259,0.0933094206561186,0.00111404710074907,0.00636901770930739,0.00636901770930739,0.0016271784686677
+"23580",100,2019,7,23,6.94532456676034,6.96260722792975,11.8877227581767,10.4675026947122,5.67136421350494,5.67136421350494,1.19055954393223,100,2019,7,23,0.0200584764090271,0.039067237374407,0.045527472941135,0.00786724111330522,0.0592145995469312,0.0592145995469312,0.00171586644715466
+"23581",100,2019,7,24,11.5267326178724,4.40579764940033,13.6831683881737,12.4889329253513,7.60353137374055,7.60353137374055,1.19516398728279,100,2019,7,24,0.856900511959213,0.0804918356020076,0.0664210271111055,0.0138584747382301,0.10964439214073,0.10964439214073,0.00180766383994045
+"23582",100,2019,7,25,0.131463148273585,7.82564347228332,15.0143125647365,11.1615070934736,7.44072610028375,7.44072610028375,1.19976843063336,100,2019,7,25,0.00257309949188902,0.097726282927333,0.0736497515874032,0.0215918273413256,0.108614636217099,0.108614636217099,0.00190257064702511
+"23583",100,2019,7,26,0.993619360267395,9.61491751592151,13.6918920940823,10.8100989806508,7.97798676905197,7.97798676905197,1.20437287398393,100,2019,7,26,0.185614034597986,0.136246724473452,0.0942450520120123,0.0265245250613791,0.704213926161422,0.704213926161422,0.00200058686840861
+"23584",100,2019,7,27,0.217051708527786,3.03190319346647,12.4286359151204,9.42853673508983,7.30107814067005,7.30107814067005,1.20897731733449,100,2019,7,27,0.0553801181128151,0.012711104615534,0.0559023232109579,0.00142105499629406,0.0322508874837522,0.0322508874837522,0.00210171250409096
+"23585",100,2019,7,28,0.0760176028929266,6.57694170682213,13.1347745479924,8.91463137564748,9.15382833869019,9.15382833869019,1.21358176068506,100,2019,7,28,0.00251461995798245,0.0193385951873374,0.0930468178019361,0.00538128905719792,0.045689451483189,0.045689451483189,0.00220594755407216
+"23586",100,2019,7,29,0.0416941700382332,4.81130910995114,13.6670737848817,11.822992252438,8.44151819919465,8.44151819919465,1.21818620403562,100,2019,7,29,0.00479532178033862,0.0160204711044236,0.073566563966635,0.00335904983393453,0.407959645401894,0.407959645401894,0.0023132920183522
+"23587",100,2019,7,30,1.64213422724385,6.80825074723583,11.9194279703239,9.8666886874158,9.01317927965892,9.01317927965892,1.22279064738619,100,2019,7,30,0.0233918128654987,0.117628615156273,0.0668929435150858,0.000276606962492947,0.233113998446594,0.233113998446594,0.0024237458969311
+"23588",100,2019,7,31,0.136523654399597,5.45693068247293,10.8017162358669,9.76041806622831,6.38089108755617,6.38089108755617,1.22739509073676,100,2019,7,31,0.00228070182235617,0.0825555533303303,0.0785877006598655,0.00854447106262847,0.152455593787322,0.152455593787322,0.00253730918980884
+"23589",100,2019,8,1,0.67502750264536,6.62187017675805,13.1830143493132,11.3572166367333,8.31081410734305,8.31081410734305,1.24203431169082,100,2019,8,1,0.00695906363732659,0.0282245649014529,0.0769175275145138,0.00391403350614271,0.145649638406929,0.145649638406929,0.00258691509950531
+"23590",100,2019,8,2,0.839823984568662,7.48687567621699,12.3426731894381,10.9446532382692,6.89033007018506,6.89033007018506,1.25667353264488,100,2019,8,2,0.11555555038982,0.0412953292891509,0.0279093714486317,0.00255789866984173,0.106883072451562,0.106883072451562,0.00263753109901755
+"23591",100,2019,8,3,1.3900990078945,8.53723864980263,12.4046093916604,11.0661606909287,5.45073708764004,5.45073708764004,1.27131275359894,100,2019,8,3,0.0806432816926509,0.0744210832384511,0.0377427425496881,0.00335438514404112,0.110536267692181,0.110536267692181,0.00268915718834555
+"23592",100,2019,8,4,3.3213421086667,7.67809682999245,12.9452914048081,10.9779097092296,6.63512648476495,6.63512648476495,1.285951974553,100,2019,8,4,0.348070188739846,0.0660467539670375,0.051177747980796,0.00250936708067052,0.0887152143511991,0.0887152143511991,0.00274179336748932
+"23593",100,2019,8,5,0.48316831781034,5.29920792448508,15.4577446571409,9.88368524629028,11.5944884586649,11.5944884586649,1.30059119550706,100,2019,8,5,0.0880116930569126,0.0224175505420935,0.122903017711198,0.0048666573842936,0.0221701549865307,0.0221701549865307,0.00279543963644884
+"23594",100,2019,8,6,0,8.57501650748342,15.2407260418463,9.63283823556764,6.34822885798674,6.34822885798674,1.31523041646112,100,2019,8,6,0,0.0425496884619957,0.12298423771112,0.00354501422420635,0.196838599816278,0.196838599816278,0.00285009599522414
+"23595",100,2019,8,7,3.46336628527793,6.12943887867943,12.5862045151697,8.10612761830077,9.48162804237425,9.48162804237425,1.32986963741518,100,2019,8,7,0.379298213016224,0.0309374201769172,0.0637777419620836,0.00696548181061226,0.299156066252127,0.299156066252127,0.00290576244381518
+"23596",100,2019,8,8,0.0408140824015516,5.12050603430132,11.080594125754,8.19019806634213,9.23170505288673,9.23170505288673,1.34450885836924,100,2019,8,8,0.0104093574536474,0.0383034929063757,0.0668368311564751,0.00486080564987575,0.0528357078463968,0.0528357078463968,0.002962438982222
+"23597",100,2019,8,9,13.7355336135764,6.01781083045095,10.0998130143672,8.18864677605456,6.8665675907114,6.8665675907114,1.3591480793233,100,2019,8,9,0.653157784422999,0.101870727525698,0.0941140152223815,0.000271926043405877,0.14820526323487,0.14820526323487,0.00302012561044458
+"23598",100,2019,8,10,8.55533556418844,1.63146314329833,11.854268484252,9.42794277959137,8.66577560122651,8.66577560122651,1.37378730027737,100,2019,8,10,2.0135671151591,0.0546257304950079,0.0560152699454565,0.000254384615327155,0.644611148940285,0.644611148940285,0.00307882232848292
+"23599",100,2019,8,11,10.3782177978616,4.37743675931714,11.8313972548683,9.96668855480366,8.88969201695408,8.88969201695408,1.38842652123143,100,2019,8,11,3.43257313098134,0.169717521922925,0.0381807079851335,0.00306959279164695,0.885168862223546,0.885168862223546,0.00313852913633703
+"23600",100,2019,8,12,2.66996700089626,4.12712875320049,11.6215071662424,9.25103404944224,9.76311330931677,9.76311330931677,1.40306574218549,100,2019,8,12,0.146549704702283,0.0820210439944245,0.0683496734954617,0.00656959958444515,0.59029247490293,0.59029247490293,0.0031992460340069
+"23601",100,2019,8,13,3.11166117660808,6.82403728382291,12.0685699576198,11.5184599285734,6.77303623602335,6.77303623602335,1.41770496313955,100,2019,8,13,0.0970760072183937,0.0640888858583436,0.0178485192773543,0.000822810997407133,0.300871961261251,0.300871961261251,0.00326097302149253
+"23602",100,2019,8,14,0.533443349668689,4.70639167340806,13.1364686774998,9.14680970908523,8.60062711073621,8.60062711073621,1.43234418409361,100,2019,8,14,0.0524561374177015,0.0201321581645534,0.132027578916129,0.0015052688197623,0.258117568782378,0.258117568782378,0.00332371009879393
+"23603",100,2019,8,15,0.0771177129203206,5.9679208053614,15.0525411725438,9.73254121543288,5.3561716431176,5.3561716431176,1.44698340504767,100,2019,8,15,0.00543859665331088,0.0460152182183361,0.0115842122173671,0.00819530042910458,0.150989448289727,0.150989448289727,0.00338745726591108
+"23604",100,2019,8,16,0.870407043707253,8.87891085890129,12.3748184713987,10.1856106389867,7.93774482571777,7.93774482571777,1.46162262600173,100,2019,8,16,0.101345027911733,0.0696911558230536,0.0277596416563401,0.00720234663673948,0.244492355239655,0.244492355239655,0.00345221452284401
+"23605",100,2019,8,17,2.609900972893,4.48146316215675,14.3177556823714,8.83144127811142,13.322684289074,13.322684289074,1.47626184695579,100,2019,8,17,0.0166666666666708,0.0949912307081305,0.0515445151435713,0.000539760060980652,0.00181462855208971,0.00181462855208971,0.00351798186959269
+"23606",100,2019,8,18,0,6.76524754535772,14.425082463767,9.69585246955863,7.88899890293251,7.88899890293251,1.49090106790985,100,2019,8,18,0,0.06043860494047,0.069939720492994,0.00262222569788144,0.716654514622865,0.716654514622865,0.00358475930615714
+"23607",100,2019,8,19,16.8762376195658,3.81936194112461,11.6526844383466,9.61403743819435,10.8416393225474,10.8416393225474,1.50554028886391,100,2019,8,19,0.763742709020659,0.102692987107403,0.0744449647960071,0.00158947137760688,0.568614557078902,0.568614557078902,0.00365254683253735
+"23608",100,2019,8,20,2.58195821654023,6.54297028385242,13.6665676240743,12.3588339592626,7.06817378829939,7.06817378829939,1.52017950981797,100,2019,8,20,0.065555536482074,0.189543273689334,0.0463262689780797,0.000514035194856123,0.464421039681701,0.464421039681701,0.00372134444873333
+"23609",100,2019,8,21,3.16677668989033,9.61368528162554,13.9647194103833,11.3941694685597,10.4386137761954,10.4386137761954,1.53481873077204,100,2019,8,21,0.0559649149855722,0.077843304956534,0.0399760393600722,0.0144689949259729,0.267114621315286,0.267114621315286,0.00379115215474507
+"23610",100,2019,8,22,3.58646861607223,9.19831680700724,12.3617161770727,8.49383939086276,12.50416940431,12.50416940431,1.5494579517261,100,2019,8,22,0.381403495582234,0.146537479376178,0.0454064437130617,0.0208362324029617,0.571143158985278,0.571143158985278,0.00386196995057257
+"23611",100,2019,8,23,0.489438951376117,2.44597360416345,15.0579647834283,10.4630252976622,11.4392079016556,11.4392079016556,1.56409717268016,100,2019,8,23,0.00807017639366516,0.157243278857007,0.0869813186869227,0.00209824735635553,0.106054393295471,0.106054393295471,0.00393379783621583
+"23612",100,2019,8,24,0.00407040710135786,6.75682064618739,17.3032122592066,11.5752034056174,6.9469306618467,6.9469306618467,1.57873639363422,100,2019,8,24,0.000994152076411666,0.0277871458064981,0.0454256863469273,0.0276537928732483,0.490478433297832,0.490478433297832,0.00400663581167486
+"23613",100,2019,8,25,3.3630363185807,3.38600661888374,12.6489108382541,9.37496153618505,12.2155885612479,12.2155885612479,1.59337561458828,100,2019,8,25,0.0250877237319995,0.117316352492332,0.0760098859709668,0.011698825356609,0.393506541895827,0.393506541895827,0.00408048387694965
+"23614",100,2019,8,26,1.50330033062315,3.38866882591751,13.413674359799,10.5713639327533,12.2117710795471,12.2117710795471,1.60801483554234,100,2019,8,26,0.340058474443113,0.0406035040794135,0.0372923670763758,0.00578070804532052,0.391926333252368,0.391926333252368,0.00415534203204021
+"23615",100,2019,8,27,0,4.57878989512377,14.741859206272,11.0367548253277,11.0657426248683,11.0657426248683,1.6226540564964,100,2019,8,27,0,0.0830730937489478,0.0962426712639336,0.00748890427492274,0.318558996916718,0.318558996916718,0.00423121027694652
+"23616",100,2019,8,28,0.115291531141376,6.8115071447769,10.8832124410027,9.26739277120995,8.36778865316914,8.36778865316914,1.63729327745046,100,2019,8,28,0.00654970795439001,0.12100527682836,0.0352473717137783,0.00920995883840418,0.386824512470598,0.386824512470598,0.0043080886116686
+"23617",100,2019,8,29,5.71716172729257,4.79167223422572,12.1000549790621,8.3106932320086,9.46136414808015,9.46136414808015,1.65193249840452,100,2019,8,29,0.0426315837994121,0.259620471613607,0.0373023589107276,0.000180114161684665,0.483009278093358,0.483009278093358,0.00438597703620645
+"23618",100,2019,8,30,0,0.472706270690012,16.109559859642,9.52486261990991,15.2788120087224,15.2788120087224,1.66657171935858,100,2019,8,30,0,0.048122807779856,0.111783005104456,0.0013134556508985,0.000484206702779571,0.000484206702779571,0.00446487555056006
+"23619",100,2019,8,31,0,5.01561055797161,17.855984514696,9.89636960150254,14.0284378956122,14.0284378956122,1.68121094031264,100,2019,8,31,0,0.0735479360480201,0.0792202900493971,0.0108467747047691,0.227798371922955,0.227798371922955,0.00454478415472942
+"23620",100,2019,9,1,4.28514857522988,9.9919691704812,16.5033995079653,11.5811881693808,9.45467553595112,9.45467553595112,1.70674055291493,100,2019,9,1,1.1078363290865,0.0298689849608878,0.0104924071510518,0.00844736521988428,1.05598017060918,1.05598017060918,0.00450256328151334
+"23621",100,2019,9,2,2.32090207111455,3.87779981258548,16.4562927640573,10.6767325889159,12.4664246608453,12.4664246608453,1.73227016551721,100,2019,9,2,0.00467836801072065,0.0760801222884959,0.0104228033949618,0.0243999841902824,0.170033309427932,0.170033309427932,0.00446125448161839
+"23622",100,2019,9,3,0.503740381027343,6.31435639000568,16.0403081948477,13.3759516001535,13.1863036884858,13.1863036884858,1.75779977811949,100,2019,9,3,0.0314035083571374,0.0599286626358611,0.0303251355960038,0.000620469469278802,0.254765536869397,0.254765536869397,0.00442085775504459
+"23623",100,2019,9,4,0.0278327836930686,5.00697466196662,16.2096258411051,13.5618591707269,10.6970407981159,10.6970407981159,1.78332939072178,100,2019,9,4,0.0020467836867299,0.040000600772461,0.0318274424371551,0.0152508698268996,1.40422523459503,1.40422523459503,0.00438137310179192
+"23624",100,2019,9,5,2.55852586601434,5.13543447593127,19.2269637335514,14.2607371820213,13.6420682082475,13.6420682082475,1.80885900332406,100,2019,9,5,0.180292411408234,0.0132538116109474,0.282948151853098,0.00263216226370819,0.44351462762404,0.44351462762404,0.0043428005218604
+"23625",100,2019,9,6,6.97766776132111,5.88763474736145,13.3737514315396,10.9410121737271,10.6151593452764,10.6151593452764,1.83438861592634,100,2019,9,6,0.0824561308699209,0.0197362624698389,0.0803871335414846,0.0236035109894242,0.433958607699344,0.433958607699344,0.00430514001525001
+"23626",100,2019,9,7,6.05951594939195,6.78606161216173,11.6415732502282,10.1554786002282,12.8052254856223,12.8052254856223,1.85991822852863,100,2019,9,7,0.189122780638143,0.110239786343284,0.0512672665847957,0.0180473752964788,1.49852275690899,1.49852275690899,0.00426839158196076
+"23627",100,2019,9,8,1.69768977768481,6.51757972652238,12.335401590639,10.4864245499715,8.74060506002344,8.74060506002344,1.88544784113091,100,2019,9,8,0.0165497039214926,0.0838596345109737,0.0482339353840967,0.0027444482168568,1.6965497512985,1.6965497512985,0.00423255522199265
+"23628",100,2019,9,9,10.4336633755691,6.39150714139865,12.694686536086,8.95753559788199,13.8810891024481,13.8810891024481,1.91097745373319,100,2019,9,9,0.425380254265936,0.127503508179674,0.0605596638105671,0.00230291138595611,0.278528545009391,0.278528545009391,0.00419763093534567
+"23629",100,2019,9,10,1.19680967805671,5.44203517303215,13.0789659214754,10.6684926969908,9.1316281550526,9.1316281550526,1.93650706633548,100,2019,9,10,0.0160818664651185,0.0810877037550462,0.0349204780267167,0.00161403758631306,0.627554495868419,0.627554495868419,0.00416361872201985
+"23630",100,2019,9,11,0.199889991977493,3.0753465732201,18.7101651056372,12.1364246269788,17.3120020561092,17.3120020561092,1.96203667893776,100,2019,9,11,0.000526315805159117,0.0388561546766309,0.0283988577237175,0.00128946712569739,0.0163619882999106,0.0163619882999106,0.00413051858201515
+"23631",100,2019,9,12,0,10.4938174479603,15.031936346501,14.0587568954535,8.89638060390359,8.89638060390359,1.98756629154005,100,2019,9,12,0,0.0571176272410279,0.0190912372768013,0.00193333176515839,0.201573013205362,0.201573013205362,0.0040983305153316
+"23632",100,2019,9,13,0.153685371679376,4.75545659185898,16.8953137098759,11.5199118604754,11.9130802123067,11.9130802123067,2.01309590414233,100,2019,9,13,0.00760233982613214,0.106728646961032,0.14304262656085,0.0625801565985424,0.310526374515809,0.310526374515809,0.00406705452196918
+"23633",100,2019,9,14,0,5.45136414214198,18.8534986833797,12.5537403356386,17.5334430984145,17.5334430984145,2.03862551674461,100,2019,9,14,0,0.0558578950804036,0.0930006526614519,0.011017537903411,0.045509902066575,0.045509902066575,0.00403669060192791
+"23634",100,2019,9,15,0,11.0363586255819,18.5537406010727,16.1619690915014,7.43454343021506,7.43454343021506,2.0641551293469,100,2019,9,15,0,0.0612052282613043,0.0754800994518784,0.0218140011772517,0.21160987495514,0.21160987495514,0.00400723875520777
+"23635",100,2019,9,16,16.5925190708425,7.29238720852943,11.7554345471893,7.76411448899407,15.8970847554726,15.8970847554726,2.08968474194918,100,2019,9,16,0.240292465812518,0.184032155633691,0.0556813058017575,0.00121111780804951,0.473925750917888,0.473925750917888,0.00397869898180878
+"23636",100,2019,9,17,0.465236529462015,0.737854783455006,12.5101869276779,7.5204180501344,17.3773485924413,17.3773485924413,2.11521435455146,100,2019,9,17,0.00877193097482658,0.0346339190342973,0.0348029063666191,0.00299415372549378,0.254432126260789,0.254432126260789,0.00395107128173092
+"23637",100,2019,9,18,0,1.00193619144501,16.6293949718916,9.75432354808509,18.878008726013,18.878008726013,2.14074396715375,100,2019,9,18,0,0.0387444426669024,0.0596479298511381,0.00563569118441475,0.0020801084467262,0.0020801084467262,0.0039243556549742
+"23638",100,2019,9,19,0,5.7477998329599,21.2494717798348,12.0707371998148,14.8507920108875,14.8507920108875,2.16627357975603,100,2019,9,19,0,0.0486286723655747,0.0249432760497762,0.0525988617033225,0.847328483162209,0.847328483162209,0.00389855210153863
+"23639",100,2019,9,20,0,14.0256656273232,23.5750823907464,14.3943013620324,13.4678108164973,13.4678108164973,2.19180319235831,100,2019,9,20,0,0.0478263317309508,0.0137800982606225,0.00338305029401443,1.20837020199879,1.20837020199879,0.00387366062142418
+"23640",100,2019,9,21,0.0876787703390038,11.1393068636736,13.3163145608766,11.2926183643908,8.68238725284539,8.68238725284539,2.2173328049606,100,2019,9,21,0.0254385972580715,0.0541830241733821,0.0145052664439811,0.0069426911494069,0.810757814404043,0.810757814404043,0.00384968121463089
+"23641",100,2019,9,22,10.4033005198236,2.85742574189231,14.4336193219008,9.16614972902472,15.4204180909462,15.4204180909462,2.24286241756288,100,2019,9,22,1.37783610829144,0.0434590722859302,0.065114638347583,0.0118362519114436,0.376673177063348,0.376673177063348,0.00382661388115872
+"23642",100,2019,9,23,4.40946094483563,5.3041363984707,14.026138598376,10.7471506089398,15.7080857777359,15.7080857777359,2.26839203016516,100,2019,9,23,0.125614042839825,0.0238988130458108,0.0469362376387042,0.00341638883409684,1.13307739520616,1.13307739520616,0.0038044586210077
+"23643",100,2019,9,24,1.09636964715353,6.01108916233344,14.2785039372963,11.3028271814646,16.0878437512254,16.0878437512254,2.29392164276745,100,2019,9,24,0.0403508749970227,0.0907315602631738,0.0688274874214133,0.0165701531751379,1.72697598841948,1.72697598841948,0.00378321543417781
+"23644",100,2019,9,25,0.1589658990404,5.70786577640194,15.706215683252,12.0143895637084,15.9393070449661,15.9393070449661,2.31945125536973,100,2019,9,25,0.00479532201562012,0.056068983536454,0.0447988965465146,0.0474374379788879,0.591336136611759,0.591336136611759,0.00376288432066907
+"23645",100,2019,9,26,0.0930693083175338,6.60133111647385,19.0765676739717,11.7079426846226,17.7551699996126,17.7551699996126,2.34498086797201,100,2019,9,26,0.00140350881375765,0.0940689943503436,0.118889467241941,0.00229123440798322,0.336347847386536,0.336347847386536,0.00374346528048147
+"23646",100,2019,9,27,0,8.95008800682848,14.395027457553,9.7150713697125,10.6103848896929,10.6103848896929,2.3705104805743,100,2019,9,27,0,0.053520420688031,0.0154596423345001,0.000414623470267181,0.522437573501041,0.522437573501041,0.003724958313615
+"23647",100,2019,9,28,1.79812978596577,5.08470850651807,13.7604179739034,9.5888447871696,10.0715291051581,10.0715291051581,2.39604009317658,100,2019,9,28,0.00538011171664621,0.233078355391001,0.0505923647697113,0.007350880293905,0.624849667427654,0.624849667427654,0.00370736342006968
+"23648",100,2019,9,29,0.140154018875199,7.65190322380779,14.7928271519207,11.7258855549022,15.2658855608194,15.2658855608194,2.42156970577886,100,2019,9,29,0.0164912289930018,0.105203492544996,0.0347596653922431,0.0120339285823634,1.41877576346605,1.41877576346605,0.00369068059984549
+"23649",100,2019,9,30,0.491639167217925,8.24229915228614,13.4632232947187,9.11719473911197,12.2922881423312,12.2922881423312,2.44709931838115,100,2019,9,30,0.0647953184619983,0.139425178666202,0.0314204728511969,0.00155847463560753,0.450450960351223,0.450450960351223,0.00367490985294244
+"23650",100,2019,10,1,0,1.52925191500006,19.5969304356507,11.7636744653431,21.5100992389507,21.5100992389507,2.48794637938862,100,2019,10,1,0,0.0438005817987775,0.0307432790971247,0.0366509134460501,0.000521068229727727,0.000521068229727727,0.00349799753730683
+"23651",100,2019,10,2,0,7.23759074415704,25.4479205296247,11.6947964709191,18.1215183186715,18.1215183186715,2.52879344039609,100,2019,10,2,0,0.0373877215759299,0.0326982415274428,0.0727110911052185,0.391988736216513,0.391988736216513,0.00332750818298328
+"23652",100,2019,10,3,0.0469746981697245,7.67070412976776,17.247337702215,15.1717600638848,8.38139706161549,8.38139706161549,2.56964050140356,100,2019,10,3,0.00245614042407588,0.126981875735297,0.0295777181840818,0.000900592558659357,0.118270728173808,0.118270728173808,0.0031634417899718
+"23653",100,2019,10,4,0.101980199539425,9.70609455297489,12.7600770127786,12.1775136828029,6.95183722230599,6.95183722230599,2.61048756241103,100,2019,10,4,0.00543859665331088,0.101991178456892,0.0123596490001924,0.00427194613191646,0.51127606950618,0.51127606950618,0.00300579835827239
+"23654",100,2019,10,5,2.31155116880688,6.12767877096128,24.3223540911449,13.8852143838461,11.1501869709447,11.1501869709447,2.65133462341851,100,2019,10,5,0.00988304969163495,0.0759596333219535,0.136020298495216,0.0349204354738903,0.543373014624178,0.543373014624178,0.00285457788788501
+"23655",100,2019,10,6,0.00297029707396385,10.8381848676239,17.2371727887816,15.5282398838677,7.79081407972951,7.79081407972951,2.69218168442598,100,2019,10,6,0.000994152076411666,0.0484035283808699,0.00950287534510741,0.0132374331625582,0.507825105424002,0.507825105424002,0.00270978037880972
+"23656",100,2019,10,7,2.21639163292149,6.21913089374504,16.6692634137681,11.294114384583,11.6480637729758,11.6480637729758,2.73302874543345,100,2019,10,7,0.522456149873681,0.0324812822074475,0.043016464943039,0.0358286137096651,0.9697140477633,0.9697140477633,0.00257140583104649
+"23657",100,2019,10,8,12.3166116739657,6.63017601541954,13.1014411116328,9.80097915682987,18.2228933519,18.2228933519,2.77387580644092,100,2019,10,8,0.275964891199548,0.0471374447694461,0.0163877148946654,0.0274321545585092,3.03659247490526,3.03659247490526,0.00243945424459531
+"23658",100,2019,10,9,0.628162829068103,6.21220024820208,13.8908031409067,11.2306490741809,14.9822883679397,14.9822883679397,2.81472286744839,100,2019,10,9,0.00397660694624277,0.172121074742342,0.0212163863935631,0.00138070941064579,4.11332608439812,4.11332608439812,0.00231392561945619
+"23659",100,2019,10,10,0.470627064457034,6.31925186675505,14.6776566552644,11.2045103803326,18.0275028782709,18.0275028782709,2.85556992845586,100,2019,10,10,0.00245614005808246,0.0948953116589992,0.016347943311595,0.00490409380599673,1.1707939750228,1.1707939750228,0.00219481995562914
+"23660",100,2019,10,11,0.238173824348728,6.39586351938111,16.0798239115179,12.6807261384097,15.0175137094932,15.0175137094932,2.89641698946334,100,2019,10,11,0.00672514679574829,0.189311673795714,0.0555830212242707,0.020914060476709,1.46535402076017,1.46535402076017,0.00208213725311414
+"23661",100,2019,10,12,0.448404843690503,8.71025303671725,18.7341471379346,13.8613090641034,23.2188228716289,23.2188228716289,2.93726405047081,100,2019,10,12,0.00578947353432754,0.0505924226036444,0.0783386395640434,0.0627152586998632,0.0965586160139202,0.0965586160139202,0.00197587751191122
+"23662",100,2019,10,13,0,8.64247519531921,22.9352474065766,13.8039384008896,19.4583607895015,19.4583607895015,2.97811111147828,100,2019,10,13,0,0.14311455276229,0.0338368927050662,0.0822913392006453,0.111279948684298,0.111279948684298,0.00187604073202035
+"23663",100,2019,10,14,0.266336636951785,11.761529047497,18.9995490767644,14.8728382668742,16.1155443034156,16.1155443034156,3.01895817248575,100,2019,10,14,0.0600584806859145,0.118488892830797,0.0286204830998201,0.0151116934347348,0.975403740397859,0.975403740397859,0.00178262691344155
+"23664",100,2019,10,15,0,6.06201323686522,17.1327503632398,14.2165565941856,13.9549724675379,13.9549724675379,3.05980523349322,100,2019,10,15,0,0.0247117036507374,0.0200543970435564,0.0268620443712865,0.664769516183149,0.664769516183149,0.00169563605617481
+"23665",100,2019,10,16,4.98173817943032,9.7489988538954,16.9673487827985,14.1479098107031,13.4818041437399,13.4818041437399,3.10065229450069,100,2019,10,16,0.901345057348075,0.0164432767299954,0.0208427330957026,0.00706723209857859,1.88661385107666,1.88661385107666,0.00161506816022012
+"23666",100,2019,10,17,12.009460980087,6.53660069938802,14.0335754146933,12.281771165703,14.4351815275102,14.4351815275102,3.14149935550817,100,2019,10,17,0.136958990487526,0.0717023325881264,0.0563111700694793,0.0300988073829385,3.44971346455855,3.44971346455855,0.00154092322557751
+"23667",100,2019,10,18,6.98349829737765,7.60655661594487,16.9377226520031,11.6792629816876,11.4940373837227,11.4940373837227,3.18234641651564,100,2019,10,18,0.157017544975072,0.0958760548485967,0.0117988654603419,0.0500053029401408,1.18530502817261,1.18530502817261,0.00147320125224695
+"23668",100,2019,10,19,8.21353142594609,5.87559959833378,14.0868208122463,11.7493398622318,19.6718150412682,19.6718150412682,3.22319347752311,100,2019,10,19,0.744853961286539,0.06339531613736,0.0213175256473323,0.0377250927596876,1.91905878722901,1.91905878722901,0.00141190224022846
+"23669",100,2019,10,20,2.54543459192492,7.91946097228131,14.5029043862791,11.7950824552899,12.6378767194003,12.6378767194003,3.26404053853058,100,2019,10,20,0.0450877158404829,0.0908029594767631,0.0331257279045499,0.00992866246861811,0.843384180843129,0.843384180843129,0.00135702618952202
+"23670",100,2019,10,21,0,10.0235423538158,15.3339383804103,12.7282949286063,13.7822223029646,13.7822223029646,3.30488759953805,100,2019,10,21,0,0.0983508110716392,0.0250783425136853,0.00224737464732403,0.539494343439837,0.539494343439837,0.00130857310012765
+"23671",100,2019,10,22,0,3.54525850865707,20.7313201849741,11.65863594521,24.2070406797302,24.2070406797302,3.34573466054552,100,2019,10,22,0,0.0355894875342455,0.0610204333481508,0.0793608083602798,0.103471946988778,0.103471946988778,0.00126654297204534
+"23672",100,2019,10,23,0.118151816942117,5.33447748442294,27.4718704874104,12.1844445051271,25.8557647436497,25.8557647436497,3.386581721553,100,2019,10,23,0.00321637436486127,0.0503444231245454,0.0398474497748384,0.0408761041496998,0.000569000440775294,0.000569000440775294,0.00123093580527509
+"23673",100,2019,10,24,0,12.1272606372309,32.1175134467869,12.3595050690067,25.9618369208442,25.9618369208442,3.42742878256047,100,2019,10,24,0,0.0541397529714444,0.0137719530258006,0.265336227138207,0.000936838478274264,0.000936838478274264,0.0012017515998169
+"23674",100,2019,10,25,0.0314631467834689,12.7857646186753,19.6352806133274,9.53056102643574,18.327370653058,18.327370653058,3.46827584356794,100,2019,10,25,0.00257309949188902,0.0276783780439244,0.0264555734425621,0.0251093502982268,2.58873700898184,2.58873700898184,0.00117899035567078
+"23675",100,2019,10,26,4.55291528145735,6.84493948104489,14.8321891497202,9.97782174562595,19.4064247626545,19.4064247626545,3.50912290457541,100,2019,10,26,0.124327491403343,0.0633140350408834,0.0424982341566158,0.0187251523113488,2.17540920960309,2.17540920960309,0.00116265207283672
+"23676",100,2019,10,27,6.92959293778842,7.75093514743549,16.1847966207792,14.3966997878911,15.925522609119,15.925522609119,3.54996996558288,100,2019,10,27,0.365380121643799,0.119031633637006,0.0264023315432166,0.00996490593410782,1.70739303237985,1.70739303237985,0.00115273675131471
+"23677",100,2019,10,28,1.91826181613704,5.14627066015279,19.2900548794351,11.5298239862171,26.0307482288222,26.0307482288222,3.59081702659035,100,2019,10,28,0.246959060437501,0.0811543709080338,0.0868702810145981,0.0285029262186391,0.0836139314889051,0.0836139314889051,0.00114924439110478
+"23678",100,2019,10,29,0.0467546761642457,5.531265161743,27.9414743333235,10.806556850639,26.6807590354525,26.6807590354525,3.63166408759783,100,2019,10,29,0.00257309949188902,0.0143140462953623,0.0321730560082646,0.115791306117643,0.0325655209325683,0.0325655209325683,0.0011521749922069
+"23679",100,2019,10,30,0,8.62980194217694,23.738063891991,16.55526992118,26.3879097811591,26.3879097811591,3.6725111486053,100,2019,10,30,0,0.0839800972654882,0.260592438052326,0.078498793703759,0.0896226988455358,0.0896226988455358,0.00116152855462108
+"23680",100,2019,10,31,0,12.6495049700092,31.4268866267272,11.48080318858,24.9538721372061,24.9538721372061,3.71335820961277,100,2019,10,31,0,0.114439787729813,0.0162695680052837,0.0242830634402357,0.121588821750425,0.121588821750425,0.00117730507834733
+"23681",100,2019,11,1,0,19.2664466035379,27.1325524249355,17.0088891280104,14.8233002493746,14.8233002493746,3.79540149730631,100,2019,11,1,0,0.0222818575677689,0.307605167813089,0.0833831103590727,1.07189871204004,1.07189871204004,0.00134707817693843
diff --git a/algorithms/models/TransferNoise/Example_model/Brucknell/obsFlow_Brucknell.csv b/algorithms/models/TransferNoise/Example_model/Brucknell/obsFlow_Brucknell.csv
new file mode 100644
index 0000000..98bc327
--- /dev/null
+++ b/algorithms/models/TransferNoise/Example_model/Brucknell/obsFlow_Brucknell.csv
@@ -0,0 +1,16325 @@
+"stream_ID","year","month","day","Value","q_mm","q_ML_day","q_mm_5th_root"
+"FLOW_236212",1975,1,24,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1975,1,25,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1975,1,26,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1975,1,27,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1975,1,28,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1975,1,29,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1975,1,30,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1975,1,31,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1975,2,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1975,2,2,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1975,2,3,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1975,2,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1975,2,5,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1975,2,6,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1975,2,7,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1975,2,8,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1975,2,9,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1975,2,10,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1975,2,11,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1975,2,12,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1975,2,13,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1975,2,14,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1975,2,15,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1975,2,16,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1975,2,17,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1975,2,18,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1975,2,19,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1975,2,20,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1975,2,21,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1975,2,22,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1975,2,23,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1975,2,24,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1975,2,25,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",1975,2,26,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",1975,2,27,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1975,2,28,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1975,3,1,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1975,3,2,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1975,3,3,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1975,3,4,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1975,3,5,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1975,3,6,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1975,3,7,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1975,3,8,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1975,3,9,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1975,3,10,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1975,3,11,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1975,3,12,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1975,3,13,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1975,3,14,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1975,3,15,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1975,3,16,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1975,3,17,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1975,3,18,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1975,3,19,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1975,3,20,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1975,3,21,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1975,3,22,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1975,3,23,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1975,3,24,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1975,3,25,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1975,3,26,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1975,3,27,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1975,3,28,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1975,3,29,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1975,3,30,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1975,3,31,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1975,4,1,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1975,4,2,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1975,4,3,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1975,4,4,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1975,4,5,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1975,4,6,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1975,4,7,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1975,4,8,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1975,4,9,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1975,4,10,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1975,4,11,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1975,4,12,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1975,4,13,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1975,4,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1975,4,15,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1975,4,16,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1975,4,17,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1975,4,18,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1975,4,19,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1975,4,20,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1975,4,21,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1975,4,22,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1975,4,23,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1975,4,24,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1975,4,25,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1975,4,26,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1975,4,27,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1975,4,28,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1975,4,29,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1975,4,30,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1975,5,1,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1975,5,2,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1975,5,3,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1975,5,4,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1975,5,5,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1975,5,6,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1975,5,7,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1975,5,8,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1975,5,9,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1975,5,10,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1975,5,11,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1975,5,12,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1975,5,13,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1975,5,14,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1975,5,15,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1975,5,16,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1975,5,17,0.49,0.189847533632287,42.336,0.717267233870557
+"FLOW_236212",1975,5,18,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",1975,5,19,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1975,5,20,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1975,5,21,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1975,5,22,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1975,5,23,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1975,5,24,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1975,5,25,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1975,5,26,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1975,5,27,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1975,5,28,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1975,5,29,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1975,5,30,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1975,5,31,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1975,6,1,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1975,6,2,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1975,6,3,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1975,6,4,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1975,6,5,0.63,0.244089686098655,54.432,0.754240564311294
+"FLOW_236212",1975,6,6,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",1975,6,7,0.477,0.184810762331839,41.2128,0.713420281757027
+"FLOW_236212",1975,6,8,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",1975,6,9,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1975,6,10,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1975,6,11,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1975,6,12,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1975,6,13,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1975,6,14,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1975,6,15,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1975,6,16,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1975,6,17,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1975,6,18,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1975,6,19,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1975,6,20,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1975,6,21,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1975,6,22,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1975,6,23,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1975,6,24,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1975,6,25,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1975,6,26,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1975,6,27,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1975,6,28,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1975,6,29,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1975,6,30,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1975,7,1,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1975,7,2,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1975,7,3,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1975,7,4,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1975,7,5,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",1975,7,6,1.741,0.674539910313901,150.4224,0.924275689139499
+"FLOW_236212",1975,7,7,1.615,0.62572197309417,139.536,0.910492308146133
+"FLOW_236212",1975,7,8,2.183,0.845790134529148,188.6112,0.967058002314743
+"FLOW_236212",1975,7,9,1.873,0.725682511210762,161.8272,0.937884441333725
+"FLOW_236212",1975,7,10,1.137,0.440523766816144,98.2368,0.848777520259195
+"FLOW_236212",1975,7,11,0.805,0.311892376681614,69.552,0.79213819049272
+"FLOW_236212",1975,7,12,0.717,0.27779730941704,61.9488,0.774008280749771
+"FLOW_236212",1975,7,13,1.405,0.544358744394619,121.392,0.885476501963313
+"FLOW_236212",1975,7,14,2.758,1.06857040358744,238.2912,1.01335269831247
+"FLOW_236212",1975,7,15,4.817,1.86631748878924,416.1888,1.13291442134493
+"FLOW_236212",1975,7,16,6.112,2.36805739910314,528.0768,1.18816962322751
+"FLOW_236212",1975,7,17,3.286,1.27314080717489,283.9104,1.04948270912035
+"FLOW_236212",1975,7,18,1.884,0.729944394618834,162.7776,0.9389834886515
+"FLOW_236212",1975,7,19,1.177,0.456021524663677,101.6928,0.854667261630504
+"FLOW_236212",1975,7,20,2.353,0.911655605381166,203.2992,0.981671451698213
+"FLOW_236212",1975,7,21,3.572,1.38394977578475,308.6208,1.06714655307413
+"FLOW_236212",1975,7,22,1.909,0.739630493273543,164.9376,0.941462360244878
+"FLOW_236212",1975,7,23,1.2,0.464932735426009,103.68,0.857981700533435
+"FLOW_236212",1975,7,24,0.905,0.350636771300448,78.192,0.810907825908105
+"FLOW_236212",1975,7,25,0.875,0.339013452914798,75.6,0.805458903399461
+"FLOW_236212",1975,7,26,0.912,0.353348878923767,78.7968,0.812158405718966
+"FLOW_236212",1975,7,27,0.718,0.278184753363229,62.0352,0.774224062294529
+"FLOW_236212",1975,7,28,0.572,0.221617937219731,49.4208,0.739811335992901
+"FLOW_236212",1975,7,29,0.963,0.373108520179372,83.2032,0.821045137327274
+"FLOW_236212",1975,7,30,1.892,0.733043946188341,163.4688,0.939779575839252
+"FLOW_236212",1975,7,31,7.182,2.78262242152466,620.5248,1.22713079817958
+"FLOW_236212",1975,8,1,8.445,3.27196412556054,729.648,1.26754009728531
+"FLOW_236212",1975,8,2,3.536,1.37000179372197,305.5104,1.06498680543854
+"FLOW_236212",1975,8,3,5.043,1.9538798206278,435.7152,1.1433509830824
+"FLOW_236212",1975,8,4,3.84,1.48778475336323,331.776,1.08269971076235
+"FLOW_236212",1975,8,5,2.822,1.0933668161435,243.8208,1.01801265730816
+"FLOW_236212",1975,8,6,3.529,1.36728968609865,304.9056,1.06456481347774
+"FLOW_236212",1975,8,7,4.565,1.76868161434978,394.416,1.12080467109919
+"FLOW_236212",1975,8,8,2.94,1.13908520179372,254.016,1.02638723455866
+"FLOW_236212",1975,8,9,1.99,0.771013452914798,171.936,0.949319476714161
+"FLOW_236212",1975,8,10,1.349,0.522661883408072,116.5536,0.878302594830664
+"FLOW_236212",1975,8,11,1.265,0.490116591928251,109.296,0.867081421631968
+"FLOW_236212",1975,8,12,3.134,1.21424932735426,270.7776,1.03958875769285
+"FLOW_236212",1975,8,13,4.708,1.82408609865471,406.7712,1.12774021280006
+"FLOW_236212",1975,8,14,4.066,1.57534708520179,351.3024,1.09515414375224
+"FLOW_236212",1975,8,15,2.71,1.0499730941704,234.144,1.00980062244659
+"FLOW_236212",1975,8,16,2.05,0.794260089686099,177.12,0.954976199787959
+"FLOW_236212",1975,8,17,1.662,0.643931838565022,143.5968,0.915731133436516
+"FLOW_236212",1975,8,18,1.425,0.552107623318386,123.12,0.887983198847355
+"FLOW_236212",1975,8,19,1.613,0.624947085201794,139.3632,0.910266687425038
+"FLOW_236212",1975,8,20,2.907,1.12629955156951,251.1648,1.02407268463314
+"FLOW_236212",1975,8,21,15.054,5.83258116591928,1300.6656,1.42289272277149
+"FLOW_236212",1975,8,22,26.879,10.4141058295964,2322.3456,1.59780728649682
+"FLOW_236212",1975,8,23,27.976,10.839131838565,2417.1264,1.61064155472937
+"FLOW_236212",1975,8,24,16.999,6.58615964125561,1468.7136,1.45789568535231
+"FLOW_236212",1975,8,25,7.482,2.89885560538117,646.4448,1.23721539856712
+"FLOW_236212",1975,8,26,3.877,1.5021201793722,334.9728,1.08477816797287
+"FLOW_236212",1975,8,27,8.984,3.48079641255605,776.2176,1.28332225144573
+"FLOW_236212",1975,8,28,9.232,3.57688251121076,797.6448,1.29033041704875
+"FLOW_236212",1975,8,29,6.941,2.68924843049327,599.7024,1.21878242992053
+"FLOW_236212",1975,8,30,4.039,1.56488609865471,348.9696,1.09369580547954
+"FLOW_236212",1975,8,31,2.918,1.13056143497758,252.1152,1.02484652624822
+"FLOW_236212",1975,9,1,2.123,0.822543497757848,183.4272,0.961682636353493
+"FLOW_236212",1975,9,2,1.562,0.605187443946188,134.9568,0.90443629405767
+"FLOW_236212",1975,9,3,7.794,3.01973811659193,673.4016,1.24736586803965
+"FLOW_236212",1975,9,4,12.088,4.68342242152466,1044.4032,1.3617979188952
+"FLOW_236212",1975,9,5,5.253,2.03524304932735,453.8592,1.15271848097172
+"FLOW_236212",1975,9,6,3.468,1.34365560538117,299.6352,1.06085882532917
+"FLOW_236212",1975,9,7,2.628,1.01820269058296,227.0592,1.00361431694218
+"FLOW_236212",1975,9,8,1.867,0.723357847533632,161.3088,0.937282782905296
+"FLOW_236212",1975,9,9,1.324,0.512975784753363,114.3936,0.875022807170014
+"FLOW_236212",1975,9,10,0.94,0.36419730941704,81.216,0.817085204673696
+"FLOW_236212",1975,9,11,0.761,0.294844843049327,65.7504,0.783283011390142
+"FLOW_236212",1975,9,12,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",1975,9,13,0.555,0.215031390134529,47.952,0.735360626221547
+"FLOW_236212",1975,9,14,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1975,9,15,0.49,0.189847533632287,42.336,0.717267233870557
+"FLOW_236212",1975,9,16,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",1975,9,17,0.738,0.285933632286996,63.7632,0.778490017904793
+"FLOW_236212",1975,9,18,15.392,5.96353721973094,1329.8688,1.42922560075646
+"FLOW_236212",1975,9,19,16.805,6.51099551569507,1451.952,1.45455276171142
+"FLOW_236212",1975,9,20,14.161,5.48659372197309,1223.5104,1.40559615488443
+"FLOW_236212",1975,9,21,7.295,2.82640358744395,630.288,1.23096820375772
+"FLOW_236212",1975,9,22,3.539,1.37116412556054,305.7696,1.06516745457346
+"FLOW_236212",1975,9,23,2.588,1.00270493273543,223.6032,1.00054040216237
+"FLOW_236212",1975,9,24,1.93,0.747766816143498,166.752,0.943524621545207
+"FLOW_236212",1975,9,25,1.276,0.494378475336323,110.2464,0.868584171417039
+"FLOW_236212",1975,9,26,0.916,0.35489865470852,79.1424,0.812869578793101
+"FLOW_236212",1975,9,27,0.707,0.273922869955157,61.0848,0.771837115312275
+"FLOW_236212",1975,9,28,0.628,0.243314798206278,54.2592,0.753761072147175
+"FLOW_236212",1975,9,29,0.641,0.248351569506726,55.3824,0.756856215452741
+"FLOW_236212",1975,9,30,0.603,0.23362869955157,52.0992,0.747661879782813
+"FLOW_236212",1975,10,1,0.559,0.216581165919283,48.2976,0.736417562854411
+"FLOW_236212",1975,10,2,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",1975,10,3,0.563,0.218130941704036,48.6432,0.737468466258041
+"FLOW_236212",1975,10,4,0.52,0.201470852017937,44.928,0.725842585090593
+"FLOW_236212",1975,10,5,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1975,10,6,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",1975,10,7,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1975,10,8,0.531,0.205732735426009,45.8784,0.728887798254835
+"FLOW_236212",1975,10,9,0.621,0.24060269058296,53.6544,0.752073170521953
+"FLOW_236212",1975,10,10,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1975,10,11,1.224,0.474231390134529,105.7536,0.861386496842621
+"FLOW_236212",1975,10,12,4.168,1.614866367713,360.1152,1.10059444871601
+"FLOW_236212",1975,10,13,7.88,3.05305829596413,680.832,1.25010651745029
+"FLOW_236212",1975,10,14,10.517,4.07474798206278,908.6688,1.32440298425614
+"FLOW_236212",1975,10,15,5.678,2.1999067264574,490.5792,1.17079498505817
+"FLOW_236212",1975,10,16,3.497,1.35489147982063,302.1408,1.06262713644248
+"FLOW_236212",1975,10,17,2.381,0.92250403587444,205.7184,0.983996729485429
+"FLOW_236212",1975,10,18,1.6,0.619910313901345,138.24,0.908794672388616
+"FLOW_236212",1975,10,19,1.049,0.40642869955157,90.6336,0.835212284718555
+"FLOW_236212",1975,10,20,0.911,0.352961434977578,78.7104,0.811980222637809
+"FLOW_236212",1975,10,21,5.981,2.31730224215247,516.7584,1.18303212064425
+"FLOW_236212",1975,10,22,4.718,1.82796053811659,407.6352,1.12821888029541
+"FLOW_236212",1975,10,23,2.383,0.923278923766816,205.8912,0.984161982115371
+"FLOW_236212",1975,10,24,1.624,0.629208968609866,140.3136,0.91150484379797
+"FLOW_236212",1975,10,25,3.518,1.36302780269058,303.9552,1.063900328312
+"FLOW_236212",1975,10,26,5.386,2.0867730941704,465.3504,1.15849735266551
+"FLOW_236212",1975,10,27,5.685,2.20261883408072,491.184,1.17108352068311
+"FLOW_236212",1975,10,28,2.835,1.09840358744395,244.944,1.01894886181579
+"FLOW_236212",1975,10,29,1.607,0.622622421524664,138.8448,0.909588479776893
+"FLOW_236212",1975,10,30,1.327,0.514138116591928,114.6528,0.875418984274981
+"FLOW_236212",1975,10,31,4.685,1.81517488789238,404.784,1.12663618306326
+"FLOW_236212",1975,11,1,4.761,1.84462062780269,411.3504,1.1302679481959
+"FLOW_236212",1975,11,2,2.678,1.03757488789238,231.3792,1.00740450912681
+"FLOW_236212",1975,11,3,2.089,0.809370403587444,180.4896,0.958582428679358
+"FLOW_236212",1975,11,4,8.052,3.11969865470852,695.6928,1.25551680223037
+"FLOW_236212",1975,11,5,14.988,5.80700986547085,1294.9632,1.42164287496236
+"FLOW_236212",1975,11,6,16.392,6.35098116591928,1416.2688,1.44733200217706
+"FLOW_236212",1975,11,7,12.503,4.84421165919283,1080.2592,1.37102262633765
+"FLOW_236212",1975,11,8,6.613,2.5621668161435,571.3632,1.20703949428129
+"FLOW_236212",1975,11,9,3.074,1.19100269058296,265.5936,1.03557736306495
+"FLOW_236212",1975,11,10,2.124,0.822930941704036,183.5136,0.961773215863092
+"FLOW_236212",1975,11,11,1.452,0.562568609865471,125.4528,0.891322970876005
+"FLOW_236212",1975,11,12,1.08,0.418439461883408,93.312,0.840091377012999
+"FLOW_236212",1975,11,13,0.879,0.340563228699552,75.9456,0.806193980054242
+"FLOW_236212",1975,11,14,0.69,0.267336322869955,59.616,0.768089085813831
+"FLOW_236212",1975,11,15,0.598,0.231691479820628,51.6672,0.746417843138657
+"FLOW_236212",1975,11,16,0.537,0.208057399103139,46.3968,0.730527607407242
+"FLOW_236212",1975,11,17,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1975,11,18,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",1975,11,19,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1975,11,20,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1975,11,21,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1975,11,22,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1975,11,23,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1975,11,24,0.561,0.217356053811659,48.4704,0.73694376386518
+"FLOW_236212",1975,11,25,0.502,0.194496860986547,43.3728,0.720746462189665
+"FLOW_236212",1975,11,26,0.48,0.185973094170404,41.472,0.714315416671224
+"FLOW_236212",1975,11,27,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",1975,11,28,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1975,11,29,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1975,11,30,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1975,12,1,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1975,12,2,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1975,12,3,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1975,12,4,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1975,12,5,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1975,12,6,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1975,12,7,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1975,12,8,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1975,12,9,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1975,12,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1975,12,11,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1975,12,12,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1975,12,13,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1975,12,14,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1975,12,15,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1975,12,16,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1975,12,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1975,12,18,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1975,12,19,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1975,12,20,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1975,12,21,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1975,12,22,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1975,12,23,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1975,12,24,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1975,12,25,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1975,12,26,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1975,12,27,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1975,12,28,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1975,12,29,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1975,12,30,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1975,12,31,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1976,1,1,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1976,1,2,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1976,1,3,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1976,1,4,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1976,1,5,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1976,1,6,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1976,1,7,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1976,1,8,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1976,1,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1976,1,10,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1976,1,11,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1976,1,12,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1976,1,13,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1976,1,14,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1976,1,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1976,1,16,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1976,1,17,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1976,1,18,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1976,1,19,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1976,1,20,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1976,1,21,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1976,1,22,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1976,1,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1976,1,24,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1976,1,25,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1976,1,26,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1976,1,27,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1976,1,28,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1976,1,29,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1976,1,30,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1976,1,31,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1976,2,1,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1976,2,2,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1976,2,3,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1976,2,4,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1976,2,5,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1976,2,6,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1976,2,7,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1976,2,8,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1976,2,9,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1976,2,10,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1976,2,11,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1976,2,12,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1976,2,13,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1976,2,14,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1976,2,15,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1976,2,16,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1976,2,17,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1976,2,18,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1976,2,19,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1976,2,20,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1976,2,21,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1976,2,22,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1976,2,23,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1976,2,24,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1976,2,25,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1976,2,26,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1976,2,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1976,2,28,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1976,2,29,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1976,3,1,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1976,3,2,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1976,3,3,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1976,3,4,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1976,3,5,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1976,3,6,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1976,3,7,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1976,3,8,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1976,3,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1976,3,10,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1976,3,11,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1976,3,12,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1976,3,13,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1976,3,14,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1976,3,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1976,3,16,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1976,3,17,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1976,3,18,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1976,3,19,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1976,3,20,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1976,3,21,0.238,0.0922116591928251,20.5632,0.620807803455985
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+"FLOW_236212",1976,3,23,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1976,3,24,0.24,0.0929865470852018,20.736,0.62184768835424
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+"FLOW_236212",1976,4,11,0.24,0.0929865470852018,20.736,0.62184768835424
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+"FLOW_236212",1976,9,25,2.869,1.11157668161435,247.8816,1.02138126058535
+"FLOW_236212",1976,9,26,1.867,0.723357847533632,161.3088,0.937282782905296
+"FLOW_236212",1976,9,27,1.209,0.468419730941704,104.4576,0.859264829450014
+"FLOW_236212",1976,9,28,0.866,0.335526457399103,74.8224,0.803795099851657
+"FLOW_236212",1976,9,29,0.691,0.267723766816143,59.7024,0.768311591380086
+"FLOW_236212",1976,9,30,0.577,0.223555156950673,49.8528,0.741100213733267
+"FLOW_236212",1976,10,1,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",1976,10,2,0.503,0.194884304932735,43.4592,0.721033383642924
+"FLOW_236212",1976,10,3,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1976,10,4,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",1976,10,5,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1976,10,6,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1976,10,7,0.855,0.331264573991031,73.872,0.801742671453037
+"FLOW_236212",1976,10,8,2.392,0.926765919282511,206.6688,0.984904248763499
+"FLOW_236212",1976,10,9,2.251,0.872136322869955,194.4864,0.973009039261639
+"FLOW_236212",1976,10,10,1.82,0.70514798206278,157.248,0.932515487603246
+"FLOW_236212",1976,10,11,1.289,0.499415246636771,111.3696,0.870346845025966
+"FLOW_236212",1976,10,12,0.894,0.346374887892377,77.2416,0.808926903706272
+"FLOW_236212",1976,10,13,0.715,0.277022421524664,61.776,0.773575994369569
+"FLOW_236212",1976,10,14,0.607,0.235178475336323,52.4448,0.748651181133953
+"FLOW_236212",1976,10,15,0.686,0.265786547085202,59.2704,0.767196475515567
+"FLOW_236212",1976,10,16,4.815,1.86554260089686,416.016,1.13282032936894
+"FLOW_236212",1976,10,17,9.691,3.75471928251121,837.3024,1.30291323179024
+"FLOW_236212",1976,10,18,5.93,2.29754260089686,512.352,1.18100766029398
+"FLOW_236212",1976,10,19,3.388,1.3126600896861,292.7232,1.05591863933115
+"FLOW_236212",1976,10,20,2.071,0.802396412556054,178.9344,0.956924768289145
+"FLOW_236212",1976,10,21,1.305,0.505614349775785,112.752,0.87249687115994
+"FLOW_236212",1976,10,22,1.135,0.439748878923767,98.064,0.84847870746895
+"FLOW_236212",1976,10,23,1.109,0.42967533632287,95.8176,0.844555287236869
+"FLOW_236212",1976,10,24,0.939,0.363809865470852,81.1296,0.816911282732716
+"FLOW_236212",1976,10,25,0.811,0.314217040358744,70.0704,0.793315512792699
+"FLOW_236212",1976,10,26,0.642,0.248739013452915,55.4688,0.757092216813081
+"FLOW_236212",1976,10,27,0.541,0.209607174887892,46.7424,0.731612688474363
+"FLOW_236212",1976,10,28,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",1976,10,29,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",1976,10,30,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1976,10,31,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1976,11,1,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1976,11,2,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",1976,11,3,2.735,1.05965919282511,236.304,1.01165688612438
+"FLOW_236212",1976,11,4,3.326,1.28863856502242,287.3664,1.05202539843605
+"FLOW_236212",1976,11,5,1.878,0.727619730941704,162.2592,0.938384646653589
+"FLOW_236212",1976,11,6,1.031,0.399454708520179,89.0784,0.832326091438438
+"FLOW_236212",1976,11,7,0.669,0.2592,57.8016,0.763355781923635
+"FLOW_236212",1976,11,8,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1976,11,9,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",1976,11,10,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1976,11,11,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1976,11,12,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1976,11,13,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1976,11,14,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1976,11,15,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1976,11,16,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1976,11,17,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1976,11,18,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1976,11,19,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1976,11,20,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1976,11,21,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1976,11,22,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1976,11,23,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1976,11,24,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1976,11,25,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1976,11,26,0.548,0.212319282511211,47.3472,0.733496232652639
+"FLOW_236212",1976,11,27,2.51,0.972484304932735,216.864,0.994435272276315
+"FLOW_236212",1976,11,28,1.269,0.491666367713004,109.6416,0.867629081264516
+"FLOW_236212",1976,11,29,0.585,0.226654708520179,50.544,0.743143954430343
+"FLOW_236212",1976,11,30,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1976,12,1,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",1976,12,2,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1976,12,3,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1976,12,4,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1976,12,5,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1976,12,6,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1976,12,7,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1976,12,8,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1976,12,9,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1976,12,10,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1976,12,11,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1976,12,12,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1976,12,13,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1976,12,14,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",1976,12,15,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1976,12,16,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1976,12,17,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1976,12,18,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1976,12,19,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1976,12,20,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1976,12,21,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1976,12,22,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1976,12,23,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1976,12,24,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1976,12,25,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1976,12,26,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1976,12,27,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1976,12,28,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1976,12,29,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1976,12,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1976,12,31,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1977,1,1,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1977,1,2,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1977,1,3,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1977,1,4,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1977,1,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1977,1,6,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1977,1,7,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1977,1,8,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1977,1,9,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1977,1,10,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1977,1,11,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1977,1,12,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1977,1,13,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1977,1,14,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1977,1,15,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1977,1,16,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1977,1,17,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1977,1,18,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1977,1,19,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1977,1,20,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1977,1,21,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1977,1,22,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1977,1,23,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1977,1,24,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1977,1,25,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1977,1,26,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1977,1,27,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1977,1,28,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1977,1,29,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1977,1,30,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1977,1,31,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1977,2,1,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1977,2,2,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1977,2,3,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1977,2,4,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1977,2,5,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1977,2,6,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1977,2,7,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1977,2,8,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1977,2,9,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1977,2,10,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1977,2,11,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1977,2,12,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1977,2,13,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1977,2,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1977,2,15,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1977,2,16,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1977,2,17,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1977,2,18,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1977,2,19,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1977,2,20,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1977,2,21,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1977,2,22,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1977,2,23,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1977,2,24,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1977,2,25,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1977,2,26,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1977,2,27,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1977,2,28,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1977,3,1,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1977,3,2,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1977,3,3,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1977,3,4,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1977,3,5,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1977,3,6,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1977,3,7,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1977,3,8,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1977,3,9,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1977,3,10,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1977,3,11,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1977,3,12,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1977,3,13,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1977,3,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1977,3,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1977,3,16,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1977,3,17,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1977,3,18,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1977,3,19,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,3,20,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,3,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1977,3,22,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1977,3,23,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1977,3,24,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1977,3,25,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1977,3,26,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1977,3,27,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1977,3,28,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1977,3,29,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1977,3,30,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1977,3,31,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1977,4,1,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1977,4,2,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1977,4,3,0.243,0.0941488789237668,20.9952,0.623394592271931
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+"FLOW_236212",1977,10,9,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",1977,10,10,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1977,10,11,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1977,10,12,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1977,10,13,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1977,10,14,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1977,10,15,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1977,10,16,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1977,10,17,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1977,10,18,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1977,10,19,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1977,10,20,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1977,10,21,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1977,10,22,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1977,10,23,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1977,10,24,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1977,10,25,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1977,10,26,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",1977,10,27,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1977,10,28,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1977,10,29,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,10,30,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,10,31,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,11,1,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,11,2,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1977,11,3,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1977,11,4,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1977,11,5,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1977,11,6,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,11,7,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1977,11,8,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1977,11,9,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1977,11,10,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1977,11,11,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1977,11,12,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1977,11,13,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1977,11,14,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1977,11,15,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1977,11,16,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1977,11,17,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1977,11,18,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1977,11,19,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1977,11,20,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1977,11,21,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1977,11,22,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1977,11,23,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1977,11,24,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1977,11,25,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1977,11,26,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1977,11,27,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1977,11,28,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1977,11,29,0.724,0.280509417040359,62.5536,0.77551372640652
+"FLOW_236212",1977,11,30,0.531,0.205732735426009,45.8784,0.728887798254835
+"FLOW_236212",1977,12,1,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1977,12,2,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",1977,12,3,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1977,12,4,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1977,12,5,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1977,12,6,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1977,12,7,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1977,12,8,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1977,12,9,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1977,12,10,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1977,12,11,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1977,12,12,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1977,12,13,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1977,12,14,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1977,12,15,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1977,12,16,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,12,17,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,12,18,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1977,12,19,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1977,12,20,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1977,12,21,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1977,12,22,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,12,23,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1977,12,24,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1977,12,25,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1977,12,26,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1977,12,27,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,12,28,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1977,12,29,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1977,12,30,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1977,12,31,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1978,1,1,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1978,1,2,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1978,1,3,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1978,1,4,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1978,1,5,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1978,1,6,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,1,7,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1978,1,8,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1978,1,9,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1978,1,10,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1978,1,11,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1978,1,12,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1978,1,13,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1978,1,14,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1978,1,15,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1978,1,16,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1978,1,17,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1978,1,18,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1978,1,19,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1978,1,20,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1978,1,21,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1978,1,22,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1978,1,23,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1978,1,24,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1978,1,25,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1978,1,26,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1978,1,27,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1978,1,28,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1978,1,29,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1978,1,30,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1978,1,31,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1978,2,1,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1978,2,2,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1978,2,3,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1978,2,4,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1978,2,5,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1978,2,6,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1978,2,7,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1978,2,8,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,2,9,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1978,2,10,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1978,2,11,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,2,12,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1978,2,13,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1978,2,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1978,2,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1978,2,16,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1978,2,17,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1978,2,18,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1978,2,19,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1978,2,20,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1978,2,21,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1978,2,22,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1978,2,23,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1978,2,24,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1978,2,25,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1978,2,26,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1978,2,27,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1978,2,28,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1978,3,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,3,2,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1978,3,3,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1978,3,4,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,3,5,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1978,3,6,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1978,3,7,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1978,3,8,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1978,3,9,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1978,3,10,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1978,3,11,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1978,3,12,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1978,3,13,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,3,14,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1978,3,15,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1978,3,16,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1978,3,17,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1978,3,18,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1978,3,19,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1978,3,20,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1978,3,21,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,3,22,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1978,3,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1978,3,24,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1978,3,25,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1978,3,26,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1978,3,27,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1978,3,28,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1978,3,29,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1978,3,30,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1978,3,31,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1978,4,1,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1978,4,2,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1978,4,3,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1978,4,4,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1978,4,5,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1978,4,6,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1978,4,7,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1978,4,8,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1978,4,9,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1978,4,10,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1978,4,11,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1978,4,12,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1978,4,13,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1978,4,14,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1978,4,15,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1978,4,16,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1978,4,17,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1978,4,18,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1978,4,19,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,4,20,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,4,21,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1978,4,22,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1978,4,23,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1978,4,24,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,4,25,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,4,26,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1978,4,27,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1978,4,28,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1978,4,29,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1978,4,30,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1978,5,1,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1978,5,2,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1978,5,3,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1978,5,4,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1978,5,5,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1978,5,6,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1978,5,7,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,5,8,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1978,5,9,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1978,5,10,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1978,5,11,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1978,5,12,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1978,5,13,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1978,5,14,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1978,5,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1978,5,16,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,5,17,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1978,5,18,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1978,5,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,5,20,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1978,5,21,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1978,5,22,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1978,5,23,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,5,24,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1978,5,25,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1978,5,26,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1978,5,27,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1978,5,28,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1978,5,29,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1978,5,30,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1978,5,31,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1978,6,1,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1978,6,2,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1978,6,3,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1978,6,4,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,6,5,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1978,6,6,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1978,6,7,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1978,6,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1978,6,9,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1978,6,10,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1978,6,11,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1978,6,12,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1978,6,13,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1978,6,14,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1978,6,15,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1978,6,16,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1978,6,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1978,6,18,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1978,6,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1978,6,20,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1978,6,21,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1978,6,22,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1978,6,23,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1978,6,24,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1978,6,25,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1978,6,26,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1978,6,27,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1978,6,28,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1978,6,29,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1978,6,30,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1978,7,1,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1978,7,2,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1978,7,3,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1978,7,4,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1978,7,5,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1978,7,6,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",1978,7,7,0.89,0.344825112107623,76.896,0.808201732747543
+"FLOW_236212",1978,7,8,0.728,0.282059192825112,62.8992,0.776368760156004
+"FLOW_236212",1978,7,9,0.607,0.235178475336323,52.4448,0.748651181133953
+"FLOW_236212",1978,7,10,0.673,0.260749775784753,58.1472,0.764266438593352
+"FLOW_236212",1978,7,11,2.515,0.974421524663677,217.296,0.994831146315885
+"FLOW_236212",1978,7,12,5.05,1.95659192825112,436.32,1.14366821555747
+"FLOW_236212",1978,7,13,3.548,1.37465112107623,306.5472,1.06570866792184
+"FLOW_236212",1978,7,14,1.548,0.599763228699552,133.7472,0.902809181470543
+"FLOW_236212",1978,7,15,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",1978,7,16,0.585,0.226654708520179,50.544,0.743143954430343
+"FLOW_236212",1978,7,17,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1978,7,18,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",1978,7,19,0.517,0.200308520179372,44.6688,0.725003134981235
+"FLOW_236212",1978,7,20,2.581,0.999992825112108,222.9984,0.999998565018303
+"FLOW_236212",1978,7,21,8.659,3.35487713004484,748.1376,1.27389995952757
+"FLOW_236212",1978,7,22,5.864,2.27197130044843,506.6496,1.17836699067441
+"FLOW_236212",1978,7,23,10.586,4.10148161434978,914.6304,1.32613627179192
+"FLOW_236212",1978,7,24,9.895,3.83375784753363,854.928,1.30835300311449
+"FLOW_236212",1978,7,25,3.938,1.52575426008969,340.2432,1.08817042513219
+"FLOW_236212",1978,7,26,2.267,0.878335426008969,195.8688,0.974388343518923
+"FLOW_236212",1978,7,27,1.343,0.520337219730942,116.0352,0.877519908963673
+"FLOW_236212",1978,7,28,1.017,0.394030493273543,87.8688,0.830053273163631
+"FLOW_236212",1978,7,29,1.161,0.449822421524664,100.3104,0.852330873308713
+"FLOW_236212",1978,7,30,0.951,0.368459192825112,82.1664,0.818988642769627
+"FLOW_236212",1978,7,31,0.684,0.265011659192825,59.0976,0.766748608032454
+"FLOW_236212",1978,8,1,0.541,0.209607174887892,46.7424,0.731612688474363
+"FLOW_236212",1978,8,2,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",1978,8,3,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",1978,8,4,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1978,8,5,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1978,8,6,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",1978,8,7,4.044,1.56682331838565,349.4016,1.0939664553037
+"FLOW_236212",1978,8,8,13.423,5.2006600896861,1159.7472,1.39063031942699
+"FLOW_236212",1978,8,9,27.115,10.5055426008969,2342.736,1.60060326199055
+"FLOW_236212",1978,8,10,19.048,7.38003228699552,1645.7472,1.49146014427312
+"FLOW_236212",1978,8,11,7.394,2.86476053811659,638.8416,1.2342912922974
+"FLOW_236212",1978,8,12,9.237,3.5788197309417,798.0768,1.29047015393929
+"FLOW_236212",1978,8,13,13.959,5.40833004484305,1206.0576,1.40156304306496
+"FLOW_236212",1978,8,14,7.152,2.77099910313901,617.9328,1.22610390904356
+"FLOW_236212",1978,8,15,5.336,2.06740089686099,461.0304,1.15633837874251
+"FLOW_236212",1978,8,16,7.484,2.89963049327354,646.6176,1.23728153506178
+"FLOW_236212",1978,8,17,6.675,2.58618834080717,576.72,1.20929436699474
+"FLOW_236212",1978,8,18,3.945,1.528466367713,340.848,1.08855700627806
+"FLOW_236212",1978,8,19,2.568,0.994956053811659,221.8752,0.998989169269447
+"FLOW_236212",1978,8,20,1.84,0.712896860986547,158.976,0.934556022412971
+"FLOW_236212",1978,8,21,1.641,0.635795515695067,141.7824,0.913405226053883
+"FLOW_236212",1978,8,22,1.899,0.735756053811659,164.0736,0.940473946138055
+"FLOW_236212",1978,8,23,2.713,1.05113542600897,234.4032,1.01002409560711
+"FLOW_236212",1978,8,24,1.943,0.752803587443946,167.8752,0.944792280116112
+"FLOW_236212",1978,8,25,1.298,0.502902242152466,112.1472,0.871558844401469
+"FLOW_236212",1978,8,26,0.99,0.383569506726457,85.536,0.825598347112573
+"FLOW_236212",1978,8,27,0.873,0.338238565022422,75.4272,0.805090356503674
+"FLOW_236212",1978,8,28,0.797,0.308792825112108,68.8608,0.790557458129397
+"FLOW_236212",1978,8,29,0.696,0.269660986547085,60.1344,0.769420270728911
+"FLOW_236212",1978,8,30,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1978,8,31,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",1978,9,1,0.683,0.264624215246637,59.0112,0.766524281309425
+"FLOW_236212",1978,9,2,2.169,0.840365919282511,187.4016,0.965814422188171
+"FLOW_236212",1978,9,3,2.022,0.783411659192825,174.7008,0.952353113891698
+"FLOW_236212",1978,9,4,2.429,0.94110134529148,209.8656,0.987932514107891
+"FLOW_236212",1978,9,5,2.485,0.962798206278027,214.704,0.992446385804638
+"FLOW_236212",1978,9,6,1.889,0.731881614349776,163.2096,0.939481359285899
+"FLOW_236212",1978,9,7,1.36,0.526923766816144,117.504,0.879730314582357
+"FLOW_236212",1978,9,8,0.977,0.378532735426009,84.4128,0.823418629741507
+"FLOW_236212",1978,9,9,0.807,0.312667264573991,69.7248,0.792531408949302
+"FLOW_236212",1978,9,10,0.697,0.270048430493274,60.2208,0.769641241549598
+"FLOW_236212",1978,9,11,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",1978,9,12,0.553,0.214256502242153,47.7792,0.734829870976035
+"FLOW_236212",1978,9,13,0.879,0.340563228699552,75.9456,0.806193980054242
+"FLOW_236212",1978,9,14,3.346,1.29638744394619,289.0944,1.05328758011002
+"FLOW_236212",1978,9,15,3.746,1.45136502242152,323.6544,1.07734631493736
+"FLOW_236212",1978,9,16,4.049,1.56876053811659,349.8336,1.09423683755504
+"FLOW_236212",1978,9,17,2.687,1.04106188340807,232.1568,1.00808072099656
+"FLOW_236212",1978,9,18,5.052,1.9573668161435,436.4928,1.14375878879181
+"FLOW_236212",1978,9,19,12.197,4.72565381165919,1053.8208,1.36424503108039
+"FLOW_236212",1978,9,20,10.371,4.01818116591928,896.0544,1.32070524546614
+"FLOW_236212",1978,9,21,4.201,1.62765201793722,362.9664,1.1023317393339
+"FLOW_236212",1978,9,22,2.43,0.941488789237668,209.952,0.988013845508854
+"FLOW_236212",1978,9,23,1.645,0.637345291479821,142.128,0.913850084526679
+"FLOW_236212",1978,9,24,1.175,0.455246636771301,101.52,0.854376607843952
+"FLOW_236212",1978,9,25,0.889,0.344437668161435,76.8096,0.808020032699328
+"FLOW_236212",1978,9,26,0.722,0.279734529147982,62.3808,0.775084791560821
+"FLOW_236212",1978,9,27,0.673,0.260749775784753,58.1472,0.764266438593352
+"FLOW_236212",1978,9,28,0.671,0.259974887892377,57.9744,0.763811653124739
+"FLOW_236212",1978,9,29,0.652,0.252613452914798,56.3328,0.75943620043054
+"FLOW_236212",1978,9,30,0.642,0.248739013452915,55.4688,0.757092216813081
+"FLOW_236212",1978,10,1,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",1978,10,2,0.453,0.175512107623318,39.1392,0.706092203731679
+"FLOW_236212",1978,10,3,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",1978,10,4,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1978,10,5,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1978,10,6,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1978,10,7,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1978,10,8,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1978,10,9,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1978,10,10,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1978,10,11,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1978,10,12,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1978,10,13,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1978,10,14,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1978,10,15,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1978,10,16,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1978,10,17,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1978,10,18,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1978,10,19,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1978,10,20,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1978,10,21,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1978,10,22,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1978,10,23,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1978,10,24,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1978,10,25,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1978,10,26,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1978,10,27,4.47,1.73187443946188,386.208,1.11610044299752
+"FLOW_236212",1978,10,28,12.861,4.98291659192825,1111.1904,1.3787855508945
+"FLOW_236212",1978,10,29,4.841,1.87561614349776,418.2624,1.1340410943598
+"FLOW_236212",1978,10,30,2.077,0.804721076233184,179.4528,0.957478597984817
+"FLOW_236212",1978,10,31,1.115,0.432,96.336,0.8454671719826
+"FLOW_236212",1978,11,1,0.753,0.291745291479821,65.0592,0.781629192435301
+"FLOW_236212",1978,11,2,0.563,0.218130941704036,48.6432,0.737468466258041
+"FLOW_236212",1978,11,3,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",1978,11,4,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1978,11,5,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1978,11,6,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1978,11,7,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1978,11,8,0.707,0.273922869955157,61.0848,0.771837115312275
+"FLOW_236212",1978,11,9,1.45,0.561793721973094,125.28,0.891077291960258
+"FLOW_236212",1978,11,10,1.077,0.417277130044843,93.0528,0.839624140140425
+"FLOW_236212",1978,11,11,0.726,0.281284304932735,62.7264,0.775941714374881
+"FLOW_236212",1978,11,12,0.577,0.223555156950673,49.8528,0.741100213733267
+"FLOW_236212",1978,11,13,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",1978,11,14,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",1978,11,15,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1978,11,16,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1978,11,17,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1978,11,18,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1978,11,19,3.822,1.48081076233184,330.2208,1.0816827712301
+"FLOW_236212",1978,11,20,9.633,3.73224753363229,832.2912,1.301349914693
+"FLOW_236212",1978,11,21,5.02,1.94496860986547,433.728,1.14230616141483
+"FLOW_236212",1978,11,22,2.188,0.84772735426009,189.0432,0.967500591974852
+"FLOW_236212",1978,11,23,1.222,0.473456502242153,105.5808,0.861104813820679
+"FLOW_236212",1978,11,24,0.784,0.303756053811659,67.7376,0.787961482136515
+"FLOW_236212",1978,11,25,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",1978,11,26,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",1978,11,27,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1978,11,28,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1978,11,29,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1978,11,30,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1978,12,1,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1978,12,2,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1978,12,3,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1978,12,4,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1978,12,5,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",1978,12,6,0.514,0.199146188340807,44.4096,0.724159778915107
+"FLOW_236212",1978,12,7,0.451,0.174737219730942,38.9664,0.705467618761141
+"FLOW_236212",1978,12,8,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1978,12,9,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",1978,12,10,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1978,12,11,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1978,12,12,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1978,12,13,1.474,0.571092376681614,127.3536,0.89400772726163
+"FLOW_236212",1978,12,14,7.649,2.96355874439462,660.8736,1.24268972677591
+"FLOW_236212",1978,12,15,4.711,1.82524843049327,407.0304,1.12788389838352
+"FLOW_236212",1978,12,16,2.058,0.797359641255605,177.8112,0.955720386313721
+"FLOW_236212",1978,12,17,1.179,0.456796412556054,101.8656,0.854957520573873
+"FLOW_236212",1978,12,18,2.578,0.998830493273543,222.7392,0.999765989158187
+"FLOW_236212",1978,12,19,2.724,1.05539730941704,235.3536,1.01084180956974
+"FLOW_236212",1978,12,20,1.692,0.655555156950673,146.1888,0.919013408062652
+"FLOW_236212",1978,12,21,0.996,0.385894170403587,86.0544,0.826596655165714
+"FLOW_236212",1978,12,22,0.683,0.264624215246637,59.0112,0.766524281309425
+"FLOW_236212",1978,12,23,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",1978,12,24,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1978,12,25,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1978,12,26,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1978,12,27,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1978,12,28,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1978,12,29,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1978,12,30,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1978,12,31,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1979,1,1,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1979,1,2,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1979,1,3,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1979,1,4,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1979,1,5,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1979,1,6,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1979,1,7,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1979,1,8,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1979,1,9,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1979,1,10,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1979,1,11,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1979,1,12,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1979,1,13,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1979,1,14,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,1,15,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1979,1,16,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1979,1,17,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1979,1,18,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1979,1,19,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1979,1,20,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1979,1,21,0.823,0.318866367713005,71.1072,0.795649406075272
+"FLOW_236212",1979,1,22,0.79,0.306080717488789,68.256,0.789163870461388
+"FLOW_236212",1979,1,23,0.644,0.249513901345292,55.6416,0.757563338656869
+"FLOW_236212",1979,1,24,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1979,1,25,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1979,1,26,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1979,1,27,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1979,1,28,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1979,1,29,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,1,30,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1979,1,31,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1979,2,1,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,2,2,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,2,3,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1979,2,4,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1979,2,5,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1979,2,6,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1979,2,7,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,2,8,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1979,2,9,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,2,10,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1979,2,11,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1979,2,12,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,2,13,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,2,14,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1979,2,15,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1979,2,16,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1979,2,17,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1979,2,18,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1979,2,19,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1979,2,20,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1979,2,21,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1979,2,22,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1979,2,23,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1979,2,24,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1979,2,25,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1979,2,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1979,2,27,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1979,2,28,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1979,3,1,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1979,3,2,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,3,3,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1979,3,4,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1979,3,5,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1979,3,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1979,3,7,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,3,8,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,3,9,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1979,3,10,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1979,3,11,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1979,3,12,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1979,3,13,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1979,3,14,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1979,3,15,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1979,3,16,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1979,3,17,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1979,3,18,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1979,3,19,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1979,3,20,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1979,3,21,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1979,3,22,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1979,3,23,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1979,3,24,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,3,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1979,3,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,3,27,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1979,3,28,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1979,3,29,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1979,3,30,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1979,3,31,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1979,4,1,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1979,4,2,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1979,4,3,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1979,4,4,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1979,4,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1979,4,6,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1979,4,7,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1979,4,8,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1979,4,9,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1979,4,10,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1979,4,11,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1979,4,12,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1979,4,13,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1979,4,14,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1979,4,15,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1979,4,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1979,4,17,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1979,4,18,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1979,4,19,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1979,4,20,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1979,4,21,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,4,22,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,4,23,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1979,4,24,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1979,4,25,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1979,4,26,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1979,4,27,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1979,4,28,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1979,4,29,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1979,4,30,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1979,5,1,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1979,5,2,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1979,5,3,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1979,5,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1979,5,5,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1979,5,6,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,5,7,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1979,5,8,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1979,5,9,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1979,5,10,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1979,5,11,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1979,5,12,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,5,13,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,5,14,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1979,5,15,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,5,16,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,5,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1979,5,18,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1979,5,19,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1979,5,20,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1979,5,21,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,5,22,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1979,5,23,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,5,24,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1979,5,25,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1979,5,26,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,5,27,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,5,28,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,5,29,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1979,5,30,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1979,5,31,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1979,6,1,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1979,6,2,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1979,6,3,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1979,6,4,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1979,6,5,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1979,6,6,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1979,6,7,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,6,8,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,6,9,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,6,10,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1979,6,11,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1979,6,12,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1979,6,13,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,6,14,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,6,15,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,6,16,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,6,17,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1979,6,18,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1979,6,19,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,20,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,21,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,22,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,23,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,24,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,25,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,26,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1979,6,27,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1979,6,28,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1979,6,29,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1979,6,30,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1979,7,1,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1979,7,2,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1979,7,3,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1979,7,4,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1979,7,5,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",1979,7,6,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1979,7,7,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1979,7,8,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1979,7,9,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1979,7,10,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1979,7,11,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1979,7,12,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,7,13,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1979,7,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1979,7,15,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1979,7,16,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1979,7,17,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1979,7,18,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1979,7,19,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1979,7,20,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1979,7,21,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1979,7,22,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1979,7,23,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1979,7,24,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1979,7,25,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1979,7,26,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1979,7,27,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1979,7,28,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1979,7,29,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1979,7,30,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1979,7,31,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1979,8,1,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1979,8,2,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1979,8,3,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,8,4,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1979,8,5,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1979,8,6,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1979,8,7,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",1979,8,8,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1979,8,9,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1979,8,10,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1979,8,11,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",1979,8,12,2.259,0.875235874439462,195.1776,0.973699668323346
+"FLOW_236212",1979,8,13,7.065,2.73729147982063,610.416,1.22310631682157
+"FLOW_236212",1979,8,14,2.777,1.07593183856502,239.9328,1.01474507421052
+"FLOW_236212",1979,8,15,1.287,0.498640358744395,111.1968,0.870076592887204
+"FLOW_236212",1979,8,16,0.749,0.290195515695067,64.7136,0.780797006153262
+"FLOW_236212",1979,8,17,0.64,0.247964125560538,55.296,0.756619919366877
+"FLOW_236212",1979,8,18,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",1979,8,19,0.728,0.282059192825112,62.8992,0.776368760156004
+"FLOW_236212",1979,8,20,1.418,0.549395515695067,122.5152,0.8871090750329
+"FLOW_236212",1979,8,21,1.746,0.676477130044843,150.8544,0.924805968139747
+"FLOW_236212",1979,8,22,1.531,0.59317668161435,132.2784,0.900817498932225
+"FLOW_236212",1979,8,23,1.417,0.549008071748879,122.4288,0.886983918555706
+"FLOW_236212",1979,8,24,1.28,0.495928251121076,110.592,0.869128056734721
+"FLOW_236212",1979,8,25,0.773,0.299494170403588,66.7872,0.78573785090246
+"FLOW_236212",1979,8,26,0.581,0.225104932735426,50.1984,0.742124898206603
+"FLOW_236212",1979,8,27,1.646,0.637732735426009,142.2144,0.913961163913033
+"FLOW_236212",1979,8,28,3.791,1.4688,327.5424,1.07992235839492
+"FLOW_236212",1979,8,29,2.104,0.815182062780269,181.7856,0.959955106040469
+"FLOW_236212",1979,8,30,1.237,0.479268161434978,106.8768,0.863208515156312
+"FLOW_236212",1979,8,31,1.306,0.506001793721973,112.8384,0.872630546188609
+"FLOW_236212",1979,9,1,1.888,0.731494170403587,163.1232,0.939381869570728
+"FLOW_236212",1979,9,2,1.35,0.52304932735426,116.64,0.878432771595658
+"FLOW_236212",1979,9,3,0.898,0.34792466367713,77.5872,0.809649483602681
+"FLOW_236212",1979,9,4,0.768,0.297556950672646,66.3552,0.784718732230276
+"FLOW_236212",1979,9,5,0.926,0.358773094170404,80.0064,0.814636703257581
+"FLOW_236212",1979,9,6,2.037,0.78922331838565,175.9968,0.953761926434465
+"FLOW_236212",1979,9,7,2.732,1.05849686098655,236.0448,1.01143485298729
+"FLOW_236212",1979,9,8,2.953,1.14412197309417,255.1392,1.02729332275893
+"FLOW_236212",1979,9,9,2.327,0.901582062780269,201.0528,0.979492359275055
+"FLOW_236212",1979,9,10,1.781,0.690037668161435,153.8784,0.928484289839427
+"FLOW_236212",1979,9,11,3.199,1.2394331838565,276.3936,1.04386569136156
+"FLOW_236212",1979,9,12,7.78,3.01431390134529,672.192,1.24691742865114
+"FLOW_236212",1979,9,13,6.181,2.39479103139013,534.0384,1.19084030312455
+"FLOW_236212",1979,9,14,9.438,3.65669596412556,815.4432,1.29603810503472
+"FLOW_236212",1979,9,15,3.415,1.32312107623318,295.056,1.05759628715285
+"FLOW_236212",1979,9,16,1.737,0.672990134529148,150.0768,0.92385058803325
+"FLOW_236212",1979,9,17,1.114,0.431612556053812,96.2496,0.845315464244141
+"FLOW_236212",1979,9,18,1.023,0.396355156950673,88.3872,0.83103038390201
+"FLOW_236212",1979,9,19,0.911,0.352961434977578,78.7104,0.811980222637809
+"FLOW_236212",1979,9,20,0.716,0.277409865470852,61.8624,0.773792258310098
+"FLOW_236212",1979,9,21,0.595,0.230529147982063,51.408,0.745667421537325
+"FLOW_236212",1979,9,22,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",1979,9,23,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",1979,9,24,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1979,9,25,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",1979,9,26,0.879,0.340563228699552,75.9456,0.806193980054242
+"FLOW_236212",1979,9,27,1.131,0.438199103139013,97.7184,0.847879815954419
+"FLOW_236212",1979,9,28,12.119,4.6954331838565,1047.0816,1.36249567694694
+"FLOW_236212",1979,9,29,21.686,8.40210941704036,1873.6704,1.53065624091948
+"FLOW_236212",1979,9,30,10.705,4.14758744394619,924.912,1.32910444429608
+"FLOW_236212",1979,10,1,4.062,1.57379730941704,350.9568,1.09493858342774
+"FLOW_236212",1979,10,2,2.171,0.841140807174888,187.5744,0.965992468918345
+"FLOW_236212",1979,10,3,1.347,0.521886995515695,116.3808,0.878042009531901
+"FLOW_236212",1979,10,4,0.84,0.325452914798206,72.576,0.798909587604632
+"FLOW_236212",1979,10,5,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",1979,10,6,0.824,0.319253811659193,71.1936,0.7958426656089
+"FLOW_236212",1979,10,7,1.132,0.438586547085202,97.8048,0.848029697493916
+"FLOW_236212",1979,10,8,1.6,0.619910313901345,138.24,0.908794672388616
+"FLOW_236212",1979,10,9,1.358,0.526148878923767,117.3312,0.879471418035274
+"FLOW_236212",1979,10,10,9.111,3.53000179372197,787.1904,1.28693017854342
+"FLOW_236212",1979,10,11,19.06,7.38468161434978,1646.784,1.49164801715583
+"FLOW_236212",1979,10,12,12.728,4.9313865470852,1099.6992,1.37592198241455
+"FLOW_236212",1979,10,13,12.746,4.93836053811659,1101.2544,1.37631092957896
+"FLOW_236212",1979,10,14,8.149,3.15728071748879,704.0736,1.2585272963359
+"FLOW_236212",1979,10,15,4.156,1.61021704035874,359.0784,1.09995997801244
+"FLOW_236212",1979,10,16,3.146,1.21889865470852,271.8144,1.04038365253634
+"FLOW_236212",1979,10,17,2.016,0.781086995515695,174.1824,0.951787247114192
+"FLOW_236212",1979,10,18,1.341,0.519562331838565,115.8624,0.877258392031811
+"FLOW_236212",1979,10,19,0.888,0.344050224215247,76.7232,0.807838169067869
+"FLOW_236212",1979,10,20,0.646,0.250288789237668,55.8144,0.758033291461797
+"FLOW_236212",1979,10,21,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",1979,10,22,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",1979,10,23,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1979,10,24,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1979,10,25,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1979,10,26,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1979,10,27,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1979,10,28,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1979,10,29,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1979,10,30,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1979,10,31,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1979,11,1,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1979,11,2,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1979,11,3,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1979,11,4,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1979,11,5,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1979,11,6,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1979,11,7,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1979,11,8,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1979,11,9,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1979,11,10,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1979,11,11,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1979,11,12,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1979,11,13,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1979,11,14,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1979,11,15,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1979,11,16,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",1979,11,17,0.6,0.232466367713004,51.84,0.746916452697148
+"FLOW_236212",1979,11,18,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1979,11,19,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1979,11,20,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1979,11,21,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1979,11,22,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1979,11,23,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1979,11,24,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1979,11,25,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1979,11,26,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1979,11,27,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1979,11,28,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1979,11,29,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1979,11,30,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1979,12,1,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1979,12,2,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1979,12,3,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1979,12,4,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1979,12,5,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1979,12,6,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1979,12,7,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1979,12,8,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,12,9,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1979,12,10,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1979,12,11,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1979,12,12,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1979,12,13,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1979,12,14,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1979,12,15,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1979,12,16,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1979,12,17,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1979,12,18,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1979,12,19,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1979,12,20,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1979,12,21,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1979,12,22,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1979,12,23,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1979,12,24,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1979,12,25,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1979,12,26,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1979,12,27,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1979,12,28,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,12,29,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1979,12,30,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1979,12,31,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1980,1,1,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1980,1,2,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1980,1,3,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1980,1,4,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1980,1,5,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1980,1,6,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1980,1,7,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1980,1,8,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1980,1,9,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1980,1,10,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1980,1,11,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1980,1,12,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1980,1,13,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1980,1,14,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1980,1,15,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1980,1,16,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1980,1,17,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1980,1,18,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1980,1,19,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1980,1,20,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1980,1,21,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1980,1,22,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1980,1,23,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1980,1,24,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1980,1,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1980,1,26,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1980,1,27,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1980,1,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1980,1,29,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1980,1,30,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1980,1,31,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1980,2,1,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1980,2,2,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1980,2,3,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1980,2,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1980,2,5,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,2,6,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1980,2,7,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1980,2,8,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1980,2,9,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1980,2,10,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1980,2,11,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1980,2,12,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1980,2,13,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1980,2,14,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1980,2,15,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1980,2,16,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1980,2,17,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1980,2,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1980,2,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1980,2,20,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1980,2,21,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1980,2,22,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1980,2,23,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1980,2,24,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1980,2,25,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1980,2,26,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1980,2,27,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1980,2,28,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1980,2,29,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1980,3,1,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1980,3,2,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1980,3,3,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1980,3,4,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1980,3,5,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1980,3,6,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1980,3,7,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1980,3,8,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1980,3,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,3,10,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1980,3,11,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1980,3,12,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1980,3,13,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1980,3,14,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1980,3,15,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1980,3,16,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1980,3,17,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1980,3,18,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1980,3,19,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1980,3,20,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1980,3,21,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1980,3,22,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1980,3,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1980,3,24,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1980,3,25,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,3,26,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1980,3,27,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1980,3,28,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1980,3,29,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1980,3,30,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,3,31,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1980,4,1,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1980,4,2,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1980,4,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,4,4,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1980,4,5,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1980,4,6,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1980,4,7,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1980,4,8,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,4,9,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1980,4,10,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1980,4,11,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1980,4,12,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1980,4,13,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1980,4,14,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1980,4,15,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1980,4,16,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1980,4,17,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1980,4,18,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1980,4,19,1.803,0.698561434977578,155.7792,0.930766880182505
+"FLOW_236212",1980,4,20,14.415,5.58500448430493,1245.456,1.41060268197084
+"FLOW_236212",1980,4,21,1.783,0.690812556053812,154.0512,0.928692727236085
+"FLOW_236212",1980,4,22,0.732,0.283608968609866,63.2448,0.77722004373713
+"FLOW_236212",1980,4,23,1.568,0.607512107623318,135.4752,0.905130058331241
+"FLOW_236212",1980,4,24,1.928,0.746991928251121,166.5792,0.943328991293771
+"FLOW_236212",1980,4,25,1.145,0.44362331838565,98.928,0.849968582684275
+"FLOW_236212",1980,4,26,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",1980,4,27,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1980,4,28,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1980,4,29,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1980,4,30,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1980,5,1,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1980,5,2,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1980,5,3,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1980,5,4,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1980,5,5,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1980,5,6,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1980,5,7,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1980,5,8,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1980,5,9,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1980,5,10,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1980,5,11,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1980,5,12,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1980,5,13,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1980,5,14,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",1980,5,15,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1980,5,16,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1980,5,17,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",1980,5,18,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1980,5,19,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1980,5,20,1.622,0.628434080717489,140.1408,0.911280224538602
+"FLOW_236212",1980,5,21,2.758,1.06857040358744,238.2912,1.01335269831247
+"FLOW_236212",1980,5,22,1.719,0.666016143497758,148.5216,0.921927885433143
+"FLOW_236212",1980,5,23,0.917,0.355286098654709,79.2288,0.813046983781364
+"FLOW_236212",1980,5,24,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",1980,5,25,0.461,0.178611659192825,39.8304,0.708568694902007
+"FLOW_236212",1980,5,26,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1980,5,27,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1980,5,28,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1980,5,29,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1980,5,30,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1980,5,31,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1980,6,1,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1980,6,2,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1980,6,3,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1980,6,4,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1980,6,5,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1980,6,6,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1980,6,7,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1980,6,8,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1980,6,9,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1980,6,10,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1980,6,11,0.474,0.183648430493274,40.9536,0.712520631628183
+"FLOW_236212",1980,6,12,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",1980,6,13,0.521,0.201858295964126,45.0144,0.726121540816752
+"FLOW_236212",1980,6,14,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",1980,6,15,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1980,6,16,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1980,6,17,0.428,0.16582600896861,36.9792,0.698120722844581
+"FLOW_236212",1980,6,18,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",1980,6,19,0.566,0.219293273542601,48.9024,0.738252730845171
+"FLOW_236212",1980,6,20,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",1980,6,21,0.543,0.210382062780269,46.9152,0.732152823938167
+"FLOW_236212",1980,6,22,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1980,6,23,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1980,6,24,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1980,6,25,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1980,6,26,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1980,6,27,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1980,6,28,2.868,1.11118923766816,247.7952,1.02131004945305
+"FLOW_236212",1980,6,29,24.922,9.65587802690583,2153.2608,1.57383193784936
+"FLOW_236212",1980,6,30,20.794,8.05650941704036,1796.6016,1.51785184831651
+"FLOW_236212",1980,7,1,9.79,3.79307623318386,845.856,1.30556444501505
+"FLOW_236212",1980,7,2,5.744,2.22547802690583,496.2816,1.1735042374108
+"FLOW_236212",1980,7,3,11.323,4.38702780269058,978.3072,1.34410772569582
+"FLOW_236212",1980,7,4,18.302,7.09099910313901,1581.2928,1.47959033616095
+"FLOW_236212",1980,7,5,16.503,6.39398744394619,1425.8592,1.44928686380134
+"FLOW_236212",1980,7,6,9.074,3.515666367713,783.9936,1.28588322531769
+"FLOW_236212",1980,7,7,9.03,3.49861883408072,780.192,1.28463374497656
+"FLOW_236212",1980,7,8,9.611,3.72372376681614,830.3904,1.30075496217551
+"FLOW_236212",1980,7,9,5.804,2.24872466367713,501.4656,1.17594566817109
+"FLOW_236212",1980,7,10,5.757,2.23051479820628,497.4048,1.1740349394676
+"FLOW_236212",1980,7,11,4.821,1.86786726457399,416.5344,1.13310251157592
+"FLOW_236212",1980,7,12,3.288,1.27391569506726,284.0832,1.04961043003311
+"FLOW_236212",1980,7,13,2.515,0.974421524663677,217.296,0.994831146315885
+"FLOW_236212",1980,7,14,2.197,0.851214349775785,189.8208,0.968295218464221
+"FLOW_236212",1980,7,15,2.512,0.973259192825112,217.0368,0.994593697530856
+"FLOW_236212",1980,7,16,2.947,1.14179730941704,254.6208,1.02687552556677
+"FLOW_236212",1980,7,17,3.426,1.32738295964126,296.0064,1.05827673218923
+"FLOW_236212",1980,7,18,8.332,3.22818295964126,719.8848,1.26412968019069
+"FLOW_236212",1980,7,19,4.784,1.85353183856502,413.3376,1.13135789038422
+"FLOW_236212",1980,7,20,2.899,1.1232,250.4736,1.02350841805465
+"FLOW_236212",1980,7,21,4.265,1.65244843049327,368.496,1.10567014483621
+"FLOW_236212",1980,7,22,3.323,1.28747623318386,287.1072,1.05183554783445
+"FLOW_236212",1980,7,23,2.18,0.844627802690583,188.352,0.966792059107684
+"FLOW_236212",1980,7,24,1.542,0.597438565022422,133.2288,0.902108241819682
+"FLOW_236212",1980,7,25,1.225,0.474618834080718,105.84,0.8615272002962
+"FLOW_236212",1980,7,26,1.015,0.393255605381166,87.696,0.829726544751732
+"FLOW_236212",1980,7,27,0.976,0.378145291479821,84.3264,0.823250000067369
+"FLOW_236212",1980,7,28,2.441,0.94575067264574,210.9024,0.988906728358397
+"FLOW_236212",1980,7,29,4.854,1.88065291479821,419.3856,1.13464951096297
+"FLOW_236212",1980,7,30,7.679,2.97518206278027,663.4656,1.24366298712104
+"FLOW_236212",1980,7,31,4.964,1.92327174887892,428.8896,1.1397461410718
+"FLOW_236212",1980,8,1,3.106,1.20340089686099,268.3584,1.03772449020219
+"FLOW_236212",1980,8,2,3.108,1.20417578475336,268.5312,1.03785809706772
+"FLOW_236212",1980,8,3,4.137,1.60285560538117,357.4368,1.09895239558689
+"FLOW_236212",1980,8,4,2.687,1.04106188340807,232.1568,1.00808072099656
+"FLOW_236212",1980,8,5,1.772,0.68655067264574,153.1008,0.927543997754614
+"FLOW_236212",1980,8,6,1.387,0.537384753363229,119.8368,0.883195948413924
+"FLOW_236212",1980,8,7,1.259,0.487791928251121,108.7776,0.866257328818237
+"FLOW_236212",1980,8,8,2.025,0.78457399103139,174.96,0.952635543690447
+"FLOW_236212",1980,8,9,2.673,1.03563766816143,230.9472,1.00702804993752
+"FLOW_236212",1980,8,10,1.972,0.764039461883408,170.3808,0.947595867339278
+"FLOW_236212",1980,8,11,1.883,0.729556950672646,162.6912,0.938883787705458
+"FLOW_236212",1980,8,12,4.135,1.60208071748879,357.264,1.09884611906098
+"FLOW_236212",1980,8,13,3.752,1.45368968609865,324.1728,1.0776912129853
+"FLOW_236212",1980,8,14,2.768,1.07244484304933,239.1552,1.01408648090523
+"FLOW_236212",1980,8,15,10.837,4.19873004484305,936.3168,1.33236615016945
+"FLOW_236212",1980,8,16,10.931,4.23514977578475,944.4384,1.33466955748767
+"FLOW_236212",1980,8,17,3.94,1.52652914798206,340.416,1.0882809329465
+"FLOW_236212",1980,8,18,2.36,0.914367713004484,203.904,0.98225483781029
+"FLOW_236212",1980,8,19,1.717,0.665241255605381,148.3488,0.921713258966986
+"FLOW_236212",1980,8,20,1.404,0.54397130044843,121.3056,0.885350419585714
+"FLOW_236212",1980,8,21,2.094,0.811307623318386,180.9216,0.959040861423048
+"FLOW_236212",1980,8,22,1.976,0.765589237668161,170.7264,0.947979976045672
+"FLOW_236212",1980,8,23,1.535,0.594726457399103,132.624,0.901287715813186
+"FLOW_236212",1980,8,24,1.101,0.426575784753363,95.1264,0.843333281339109
+"FLOW_236212",1980,8,25,2.15,0.833004484304933,185.76,0.964116394283596
+"FLOW_236212",1980,8,26,7.716,2.98951748878924,666.6624,1.2448591610694
+"FLOW_236212",1980,8,27,3.936,1.52497937219731,340.0704,1.08805987240968
+"FLOW_236212",1980,8,28,2.3,0.891121076233184,198.72,0.977208741147379
+"FLOW_236212",1980,8,29,1.91,0.740017937219731,165.024,0.94156097367118
+"FLOW_236212",1980,8,30,1.755,0.679964125560538,151.632,0.925757416622102
+"FLOW_236212",1980,8,31,1.514,0.586590134529148,130.8096,0.898808044784943
+"FLOW_236212",1980,9,1,1.606,0.622234977578475,138.7584,0.909475248293267
+"FLOW_236212",1980,9,2,3.591,1.39131121076233,310.2624,1.06827940759325
+"FLOW_236212",1980,9,3,8.301,3.21617219730942,717.2064,1.26318761413873
+"FLOW_236212",1980,9,4,4.236,1.64121255605381,365.9904,1.10416243084145
+"FLOW_236212",1980,9,5,2.433,0.942651121076233,210.2112,0.988257679162814
+"FLOW_236212",1980,9,6,1.691,0.655167713004484,146.1024,0.918904751946105
+"FLOW_236212",1980,9,7,1.196,0.463382959641256,103.3344,0.85740894855381
+"FLOW_236212",1980,9,8,0.887,0.343662780269058,76.6368,0.807656141521427
+"FLOW_236212",1980,9,9,1.585,0.61409865470852,136.944,0.907084256234235
+"FLOW_236212",1980,9,10,4.618,1.78921614349776,398.9952,1.12339519403795
+"FLOW_236212",1980,9,11,4.683,1.8144,404.6112,1.12653997571092
+"FLOW_236212",1980,9,12,2.93,1.13521076233184,253.152,1.02568806004406
+"FLOW_236212",1980,9,13,2.538,0.983332735426009,219.2832,0.996644098396138
+"FLOW_236212",1980,9,14,1.991,0.771400896860987,172.0224,0.949414866534889
+"FLOW_236212",1980,9,15,1.762,0.682676233183857,152.2368,0.926494737151678
+"FLOW_236212",1980,9,16,2.873,1.1131264573991,248.2272,1.02166590669953
+"FLOW_236212",1980,9,17,2.6,1.00735426008969,224.64,1.00146654420118
+"FLOW_236212",1980,9,18,8.056,3.12124843049327,696.0384,1.2556415183153
+"FLOW_236212",1980,9,19,10.299,3.99028520179372,889.8336,1.31886634955176
+"FLOW_236212",1980,9,20,6.145,2.38084304932735,530.928,1.18944989778395
+"FLOW_236212",1980,9,21,6.006,2.32698834080717,518.9184,1.18401946313131
+"FLOW_236212",1980,9,22,5.358,2.07592466367713,462.9312,1.15729031253982
+"FLOW_236212",1980,9,23,3.695,1.43160538116592,319.248,1.07439669678712
+"FLOW_236212",1980,9,24,2.545,0.986044843049327,219.888,0.997193257159038
+"FLOW_236212",1980,9,25,1.912,0.740792825112108,165.1968,0.941758076676452
+"FLOW_236212",1980,9,26,1.546,0.598988340807175,133.5744,0.902575776790561
+"FLOW_236212",1980,9,27,1.265,0.490116591928251,109.296,0.867081421631968
+"FLOW_236212",1980,9,28,0.93,0.360322869955157,80.352,0.81533928015676
+"FLOW_236212",1980,9,29,0.717,0.27779730941704,61.9488,0.774008280749771
+"FLOW_236212",1980,9,30,0.573,0.222005381165919,49.5072,0.740069830582908
+"FLOW_236212",1980,10,1,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",1980,10,2,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",1980,10,3,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1980,10,4,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1980,10,5,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1980,10,6,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",1980,10,7,3.128,1.21192466367713,270.2592,1.03919039683078
+"FLOW_236212",1980,10,8,3.91,1.51490582959641,337.824,1.08661858159928
+"FLOW_236212",1980,10,9,2.79,1.08096860986547,241.056,1.01569336757512
+"FLOW_236212",1980,10,10,2.047,0.793097757847534,176.8608,0.954696530802128
+"FLOW_236212",1980,10,11,1.575,0.610224215246637,136.08,0.905936770894238
+"FLOW_236212",1980,10,12,1.399,0.542034080717489,120.8736,0.884718927916134
+"FLOW_236212",1980,10,13,1.633,0.632695964125561,141.0912,0.912512900279922
+"FLOW_236212",1980,10,14,1.471,0.569930044843049,127.0944,0.893643519755898
+"FLOW_236212",1980,10,15,1.075,0.416502242152466,92.88,0.839312070126612
+"FLOW_236212",1980,10,16,0.789,0.305693273542601,68.1696,0.788963980904211
+"FLOW_236212",1980,10,17,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",1980,10,18,0.515,0.199533632286996,44.496,0.724441334124209
+"FLOW_236212",1980,10,19,0.481,0.186360538116592,41.5584,0.714612800378232
+"FLOW_236212",1980,10,20,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",1980,10,21,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",1980,10,22,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",1980,10,23,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1980,10,24,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1980,10,25,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1980,10,26,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1980,10,27,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1980,10,28,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1980,10,29,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1980,10,30,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1980,10,31,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1980,11,1,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1980,11,2,0.444,0.172025112107623,38.3616,0.703263973134143
+"FLOW_236212",1980,11,3,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",1980,11,4,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1980,11,5,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",1980,11,6,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",1980,11,7,0.737,0.285546188340807,63.6768,0.7782789305587
+"FLOW_236212",1980,11,8,0.623,0.241377578475336,53.8272,0.752556974827375
+"FLOW_236212",1980,11,9,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",1980,11,10,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",1980,11,11,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1980,11,12,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1980,11,13,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1980,11,14,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1980,11,15,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1980,11,16,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1980,11,17,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1980,11,18,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1980,11,19,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1980,11,20,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1980,11,21,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1980,11,22,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1980,11,23,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1980,11,24,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1980,11,25,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1980,11,26,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1980,11,27,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1980,11,28,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1980,11,29,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1980,11,30,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1980,12,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1980,12,2,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1980,12,3,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1980,12,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1980,12,5,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1980,12,6,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1980,12,7,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1980,12,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1980,12,9,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1980,12,10,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1980,12,11,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1980,12,12,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1980,12,13,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1980,12,14,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1980,12,15,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1980,12,16,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1980,12,17,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1980,12,18,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1980,12,19,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1980,12,20,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1980,12,21,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1980,12,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1980,12,23,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1980,12,24,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1980,12,25,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1980,12,26,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1980,12,27,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1980,12,28,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1980,12,29,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1980,12,30,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1980,12,31,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1981,1,1,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,1,2,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1981,1,3,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,1,4,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,1,5,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1981,1,6,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1981,1,7,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1981,1,8,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1981,1,9,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1981,1,10,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1981,1,11,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1981,1,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,1,13,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1981,1,14,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1981,1,15,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1981,1,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1981,1,17,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,1,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1981,1,19,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1981,1,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1981,1,21,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1981,1,22,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1981,1,23,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,1,24,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1981,1,25,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,1,26,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1981,1,27,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1981,1,28,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1981,1,29,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1981,1,30,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1981,1,31,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1981,2,1,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,2,2,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,2,3,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1981,2,4,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1981,2,5,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1981,2,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,2,7,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1981,2,8,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1981,2,9,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,2,10,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,2,11,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1981,2,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,2,13,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,2,14,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,2,15,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1981,2,16,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1981,2,17,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,2,18,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1981,2,19,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1981,2,20,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1981,2,21,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1981,2,22,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,2,23,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1981,2,24,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1981,2,25,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1981,2,26,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1981,2,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1981,2,28,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1981,3,1,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1981,3,2,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,3,3,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1981,3,4,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1981,3,5,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,3,6,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,3,7,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1981,3,8,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1981,3,9,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1981,3,10,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1981,3,11,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,3,12,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1981,3,13,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,3,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1981,3,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1981,3,16,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1981,3,17,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1981,3,18,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1981,3,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1981,3,20,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1981,3,21,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1981,3,22,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,3,23,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1981,3,24,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1981,3,25,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1981,3,26,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1981,3,27,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1981,3,28,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,3,29,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1981,3,30,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1981,3,31,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1981,4,1,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1981,4,2,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,4,3,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1981,4,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,4,5,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,4,6,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1981,4,7,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1981,4,8,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1981,4,9,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,4,10,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,4,11,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1981,4,12,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1981,4,13,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1981,4,14,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,4,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1981,4,16,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1981,4,17,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1981,4,18,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1981,4,19,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1981,4,20,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,4,21,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1981,4,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1981,4,23,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1981,4,24,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1981,4,25,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1981,4,26,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1981,4,27,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1981,4,28,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1981,4,29,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1981,4,30,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1981,5,1,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1981,5,2,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1981,5,3,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1981,5,4,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1981,5,5,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1981,5,6,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1981,5,7,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,5,8,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,5,9,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,5,10,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1981,5,11,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1981,5,12,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,5,13,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1981,5,14,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1981,5,15,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,5,16,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1981,5,17,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,5,18,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1981,5,19,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,5,20,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,5,21,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1981,5,22,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,5,23,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,5,24,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1981,5,25,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1981,5,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1981,5,27,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1981,5,28,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1981,5,29,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1981,5,30,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1981,5,31,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1981,6,1,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1981,6,2,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1981,6,3,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1981,6,4,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1981,6,5,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1981,6,6,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1981,6,7,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1981,6,8,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1981,6,9,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,6,10,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1981,6,11,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1981,6,12,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,6,13,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1981,6,14,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1981,6,15,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1981,6,16,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1981,6,17,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,6,18,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1981,6,19,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1981,6,20,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1981,6,21,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1981,6,22,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1981,6,23,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1981,6,24,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1981,6,25,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1981,6,26,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1981,6,27,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1981,6,28,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1981,6,29,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1981,6,30,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1981,7,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1981,7,2,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1981,7,3,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1981,7,4,0.548,0.212319282511211,47.3472,0.733496232652639
+"FLOW_236212",1981,7,5,0.594,0.230141704035874,51.3216,0.745416608355256
+"FLOW_236212",1981,7,6,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1981,7,7,0.622,0.240990134529148,53.7408,0.752315228238822
+"FLOW_236212",1981,7,8,0.772,0.299106726457399,66.7008,0.785534449930101
+"FLOW_236212",1981,7,9,1.259,0.487791928251121,108.7776,0.866257328818237
+"FLOW_236212",1981,7,10,1.806,0.699723766816144,156.0384,0.931076413633128
+"FLOW_236212",1981,7,11,1.209,0.468419730941704,104.4576,0.859264829450014
+"FLOW_236212",1981,7,12,0.736,0.285158744394619,63.5904,0.778067613956656
+"FLOW_236212",1981,7,13,0.68,0.263461883408072,58.752,0.765849720908347
+"FLOW_236212",1981,7,14,0.659,0.255325560538117,56.9376,0.761059933464859
+"FLOW_236212",1981,7,15,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",1981,7,16,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1981,7,17,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1981,7,18,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1981,7,19,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1981,7,20,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1981,7,21,0.487,0.188685201793722,42.0768,0.716386788620938
+"FLOW_236212",1981,7,22,0.811,0.314217040358744,70.0704,0.793315512792699
+"FLOW_236212",1981,7,23,0.737,0.285546188340807,63.6768,0.7782789305587
+"FLOW_236212",1981,7,24,0.627,0.24292735426009,54.1728,0.753520867805153
+"FLOW_236212",1981,7,25,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",1981,7,26,0.628,0.243314798206278,54.2592,0.753761072147175
+"FLOW_236212",1981,7,27,0.643,0.249126457399103,55.5552,0.757327924273715
+"FLOW_236212",1981,7,28,0.838,0.32467802690583,72.4032,0.798528791631944
+"FLOW_236212",1981,7,29,0.905,0.350636771300448,78.192,0.810907825908105
+"FLOW_236212",1981,7,30,1.359,0.526536322869955,117.4176,0.879600904409856
+"FLOW_236212",1981,7,31,0.881,0.341338116591928,76.1184,0.806560515318288
+"FLOW_236212",1981,8,1,0.603,0.23362869955157,52.0992,0.747661879782813
+"FLOW_236212",1981,8,2,0.482,0.18674798206278,41.6448,0.714909689886756
+"FLOW_236212",1981,8,3,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",1981,8,4,0.423,0.163888789237668,36.5472,0.696481923722871
+"FLOW_236212",1981,8,5,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",1981,8,6,0.474,0.183648430493274,40.9536,0.712520631628183
+"FLOW_236212",1981,8,7,0.737,0.285546188340807,63.6768,0.7782789305587
+"FLOW_236212",1981,8,8,1.23,0.476556053811659,106.272,0.862229342386107
+"FLOW_236212",1981,8,9,4.276,1.65671031390135,369.4464,1.10623989131349
+"FLOW_236212",1981,8,10,13.161,5.09914977578475,1137.1104,1.38515876250932
+"FLOW_236212",1981,8,11,9.673,3.74774529147982,835.7472,1.30242886729168
+"FLOW_236212",1981,8,12,3.54,1.37155156950673,305.856,1.06522764372763
+"FLOW_236212",1981,8,13,2.456,0.951562331838565,212.1984,0.990119122758863
+"FLOW_236212",1981,8,14,3.81,1.47616143497758,329.184,1.08100268092724
+"FLOW_236212",1981,8,15,12.43,4.81592825112108,1073.952,1.36941790402194
+"FLOW_236212",1981,8,16,11.594,4.49202511210762,1001.7216,1.35048086517515
+"FLOW_236212",1981,8,17,6.708,2.59897399103139,579.5712,1.21048771623055
+"FLOW_236212",1981,8,18,14.706,5.69775067264574,1270.5984,1.41625249580959
+"FLOW_236212",1981,8,19,16.518,6.39979910313901,1427.1552,1.44955022686867
+"FLOW_236212",1981,8,20,11.096,4.29907802690583,958.6944,1.33867473048585
+"FLOW_236212",1981,8,21,12.108,4.69117130044843,1046.1312,1.36224824899331
+"FLOW_236212",1981,8,22,15.054,5.83258116591928,1300.6656,1.42289272277149
+"FLOW_236212",1981,8,23,9.902,3.83646995515695,855.5328,1.3085380638631
+"FLOW_236212",1981,8,24,3.905,1.51296860986547,337.392,1.08634053177228
+"FLOW_236212",1981,8,25,2.186,0.846952466367713,188.8704,0.967323653303181
+"FLOW_236212",1981,8,26,1.49,0.577291479820628,128.736,0.895940212693118
+"FLOW_236212",1981,8,27,1.298,0.502902242152466,112.1472,0.871558844401469
+"FLOW_236212",1981,8,28,3.267,1.26577937219731,282.2688,1.04826624834164
+"FLOW_236212",1981,8,29,3.398,1.31653452914798,293.5872,1.05654123330126
+"FLOW_236212",1981,8,30,3.258,1.26229237668161,281.4912,1.04768805369942
+"FLOW_236212",1981,8,31,2.757,1.06818295964126,238.2048,1.01327920304782
+"FLOW_236212",1981,9,1,2.243,0.869036771300448,193.7952,0.972316443816776
+"FLOW_236212",1981,9,2,1.701,0.659042152466368,146.9664,0.919989008358094
+"FLOW_236212",1981,9,3,1.459,0.565280717488789,126.0576,0.89218072071746
+"FLOW_236212",1981,9,4,1.765,0.683838565022422,152.496,0.926810014471898
+"FLOW_236212",1981,9,5,1.753,0.679189237668162,151.4592,0.925546321533081
+"FLOW_236212",1981,9,6,1.327,0.514138116591928,114.6528,0.875418984274981
+"FLOW_236212",1981,9,7,0.94,0.36419730941704,81.216,0.817085204673696
+"FLOW_236212",1981,9,8,0.685,0.265399103139013,59.184,0.766972672538289
+"FLOW_236212",1981,9,9,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",1981,9,10,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",1981,9,11,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1981,9,12,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1981,9,13,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1981,9,14,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1981,9,15,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1981,9,16,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1981,9,17,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1981,9,18,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1981,9,19,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1981,9,20,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1981,9,21,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1981,9,22,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1981,9,23,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1981,9,24,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1981,9,25,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1981,9,26,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1981,9,27,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1981,9,28,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",1981,9,29,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1981,9,30,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1981,10,1,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1981,10,2,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1981,10,3,0.786,0.304530941704036,67.9104,0.78836309370197
+"FLOW_236212",1981,10,4,2.836,1.09879103139013,245.0304,1.01902073519323
+"FLOW_236212",1981,10,5,2.593,1.00464215246637,224.0352,1.00092671131311
+"FLOW_236212",1981,10,6,1.54,0.596663677130045,133.056,0.901874110399135
+"FLOW_236212",1981,10,7,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",1981,10,8,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",1981,10,9,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1981,10,10,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1981,10,11,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1981,10,12,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1981,10,13,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1981,10,14,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1981,10,15,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1981,10,16,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1981,10,17,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1981,10,18,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1981,10,19,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1981,10,20,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1981,10,21,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1981,10,22,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1981,10,23,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1981,10,24,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1981,10,25,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1981,10,26,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1981,10,27,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1981,10,28,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1981,10,29,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1981,10,30,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1981,10,31,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1981,11,1,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1981,11,2,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1981,11,3,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1981,11,4,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1981,11,5,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1981,11,6,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1981,11,7,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1981,11,8,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1981,11,9,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1981,11,10,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1981,11,11,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1981,11,12,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1981,11,13,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1981,11,14,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1981,11,15,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1981,11,16,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1981,11,17,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1981,11,18,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1981,11,19,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1981,11,20,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1981,11,21,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1981,11,22,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1981,11,23,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1981,11,24,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1981,11,25,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,11,26,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1981,11,27,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1981,11,28,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,11,29,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1981,11,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,12,1,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1981,12,2,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1981,12,3,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1981,12,4,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1981,12,5,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1981,12,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,12,7,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1981,12,8,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1981,12,9,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1981,12,10,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,12,11,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1981,12,12,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1981,12,13,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1981,12,14,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1981,12,15,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1981,12,16,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1981,12,17,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1981,12,18,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1981,12,19,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1981,12,20,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1981,12,21,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1981,12,22,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1981,12,23,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1981,12,24,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,12,25,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1981,12,26,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1981,12,27,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1981,12,28,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1981,12,29,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1981,12,30,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1981,12,31,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,1,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1982,1,3,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1982,1,4,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1982,1,5,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,1,6,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1982,1,7,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,1,8,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1982,1,9,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1982,1,10,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1982,1,11,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1982,1,12,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1982,1,13,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1982,1,14,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1982,1,15,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,16,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,17,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,1,18,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1982,1,19,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,1,20,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,21,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,1,22,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1982,1,23,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,1,24,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1982,1,25,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1982,1,26,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1982,1,27,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1982,1,28,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1982,1,29,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1982,1,30,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1982,1,31,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1982,2,1,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1982,2,2,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,2,3,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1982,2,4,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,2,5,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1982,2,6,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1982,2,7,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1982,2,8,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1982,2,9,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1982,2,10,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1982,2,11,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1982,2,12,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1982,2,13,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1982,2,14,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1982,2,15,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1982,2,16,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1982,2,17,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1982,2,18,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,2,19,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,2,20,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,2,21,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1982,2,22,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,2,23,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,2,24,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1982,2,25,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,2,26,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,2,27,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,2,28,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1982,3,1,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1982,3,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1982,3,3,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1982,3,4,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,3,5,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1982,3,6,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1982,3,7,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1982,3,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,3,9,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1982,3,10,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,3,11,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1982,3,12,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1982,3,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,3,14,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1982,3,15,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1982,3,16,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,3,17,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1982,3,18,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1982,3,19,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1982,3,20,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1982,3,21,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1982,3,22,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,3,23,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,3,24,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1982,3,25,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1982,3,26,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1982,3,27,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1982,3,28,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,3,29,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1982,3,30,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1982,3,31,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1982,4,1,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1982,4,2,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,4,3,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1982,4,4,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,4,5,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,4,6,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,4,7,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1982,4,8,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,4,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1982,4,10,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1982,4,11,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1982,4,12,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,4,13,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1982,4,14,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1982,4,15,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,4,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1982,4,17,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1982,4,18,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1982,4,19,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1982,4,20,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1982,4,21,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1982,4,22,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1982,4,23,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1982,4,24,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1982,4,25,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1982,4,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1982,4,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1982,4,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1982,4,29,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1982,4,30,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1982,5,1,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1982,5,2,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1982,5,3,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1982,5,4,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1982,5,5,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1982,5,6,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1982,5,7,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1982,5,8,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1982,5,9,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1982,5,10,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1982,5,11,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,5,12,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1982,5,13,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1982,5,14,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1982,5,15,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,5,16,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1982,5,17,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,5,18,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1982,5,19,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1982,5,20,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1982,5,21,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,5,22,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1982,5,23,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1982,5,24,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1982,5,25,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1982,5,26,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1982,5,27,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1982,5,28,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1982,5,29,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1982,5,30,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1982,5,31,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1982,6,1,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1982,6,2,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1982,6,3,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1982,6,4,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,6,5,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1982,6,6,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1982,6,7,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,6,8,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1982,6,9,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1982,6,10,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1982,6,11,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1982,6,12,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1982,6,13,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1982,6,14,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1982,6,15,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1982,6,16,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1982,6,17,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1982,6,18,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,6,19,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,6,20,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1982,6,21,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1982,6,22,0.664,0.257262780269058,57.3696,0.762211315332639
+"FLOW_236212",1982,6,23,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1982,6,24,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1982,6,25,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1982,6,26,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1982,6,27,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1982,6,28,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1982,6,29,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1982,6,30,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1982,7,1,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1982,7,2,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1982,7,3,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1982,7,4,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1982,7,5,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1982,7,6,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1982,7,7,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1982,7,8,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1982,7,9,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1982,7,10,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1982,7,11,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1982,7,12,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1982,7,13,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1982,7,14,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1982,7,15,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1982,7,16,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1982,7,17,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,7,18,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1982,7,19,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1982,7,20,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1982,7,21,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1982,7,22,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1982,7,23,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1982,7,24,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1982,7,25,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,7,26,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,7,27,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1982,7,28,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1982,7,29,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1982,7,30,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1982,7,31,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1982,8,1,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",1982,8,2,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",1982,8,3,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1982,8,4,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1982,8,5,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1982,8,6,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1982,8,7,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1982,8,8,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1982,8,9,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1982,8,10,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1982,8,11,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1982,8,12,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1982,8,13,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1982,8,14,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1982,8,15,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1982,8,16,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1982,8,17,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1982,8,18,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1982,8,19,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1982,8,20,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1982,8,21,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1982,8,22,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1982,8,23,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,8,24,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,8,25,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,8,26,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,8,27,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1982,8,28,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1982,8,29,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1982,8,30,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1982,8,31,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,9,1,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,9,2,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1982,9,3,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1982,9,4,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1982,9,5,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1982,9,6,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1982,9,7,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1982,9,8,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1982,9,9,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1982,9,10,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1982,9,11,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1982,9,12,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1982,9,13,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1982,9,14,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1982,9,15,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1982,9,16,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1982,9,17,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1982,9,18,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1982,9,19,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1982,9,20,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1982,9,21,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1982,9,22,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1982,9,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1982,9,24,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1982,9,25,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1982,9,26,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1982,9,27,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1982,9,28,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1982,9,29,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1982,9,30,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1982,10,1,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1982,10,2,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1982,10,3,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1982,10,4,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1982,10,5,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1982,10,6,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1982,10,7,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1982,10,8,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1982,10,9,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1982,10,10,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1982,10,11,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1982,10,12,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1982,10,13,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1982,10,14,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1982,10,15,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1982,10,16,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1982,10,17,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1982,10,18,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1982,10,19,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1982,10,20,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1982,10,21,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1982,10,22,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1982,10,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1982,10,24,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1982,10,25,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1982,10,26,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1982,10,27,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1982,10,28,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1982,10,29,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1982,10,30,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1982,10,31,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1982,11,1,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1982,11,2,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1982,11,3,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1982,11,4,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1982,11,5,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1982,11,6,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1982,11,7,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,11,8,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1982,11,9,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1982,11,10,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1982,11,11,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,11,12,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1982,11,13,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1982,11,14,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,11,15,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1982,11,16,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1982,11,17,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1982,11,18,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1982,11,19,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1982,11,20,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1982,11,21,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1982,11,22,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1982,11,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1982,11,24,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1982,11,25,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1982,11,26,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,11,27,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1982,11,28,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,11,29,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1982,11,30,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1982,12,1,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,12,2,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1982,12,3,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1982,12,4,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1982,12,5,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1982,12,6,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1982,12,7,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1982,12,8,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1982,12,9,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1982,12,10,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1982,12,11,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1982,12,12,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1982,12,13,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1982,12,14,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1982,12,15,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1982,12,16,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1982,12,17,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1982,12,18,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1982,12,19,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1982,12,20,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1982,12,21,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1982,12,22,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1982,12,23,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1982,12,24,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1982,12,25,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1982,12,26,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1982,12,27,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1982,12,28,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1982,12,29,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1982,12,30,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1982,12,31,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1983,1,1,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1983,1,2,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1983,1,3,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1983,1,4,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1983,1,5,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1983,1,6,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",1983,1,7,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",1983,1,8,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",1983,1,9,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1983,1,10,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1983,1,11,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1983,1,12,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1983,1,13,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1983,1,14,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1983,1,15,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1983,1,16,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1983,1,17,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1983,1,18,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1983,1,19,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1983,1,20,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1983,1,21,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1983,1,22,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1983,1,23,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",1983,1,24,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1983,1,25,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",1983,1,26,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1983,1,27,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1983,1,28,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1983,1,29,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1983,1,30,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1983,1,31,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",1983,2,1,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",1983,2,2,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",1983,2,3,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",1983,2,4,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",1983,2,5,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1983,2,6,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",1983,2,7,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",1983,2,8,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",1983,2,9,0.094,0.036419730941704,8.1216,0.51554591095497
+"FLOW_236212",1983,2,10,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",1983,2,11,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1983,2,12,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1983,2,13,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",1983,2,14,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1983,2,15,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",1983,2,16,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1983,2,17,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1983,2,18,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1983,2,19,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1983,2,20,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1983,2,21,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",1983,2,22,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",1983,2,23,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1983,2,24,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1983,2,25,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",1983,2,26,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",1983,2,27,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",1983,2,28,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1983,3,1,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",1983,3,2,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1983,3,3,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",1983,3,4,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1983,3,5,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1983,3,6,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1983,3,7,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1983,3,8,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1983,3,9,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1983,3,10,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1983,3,11,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1983,3,12,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1983,3,13,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1983,3,14,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1983,3,15,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1983,3,16,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1983,3,17,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1983,3,18,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1983,3,19,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",1983,3,20,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1983,3,21,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1983,3,22,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1983,3,23,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1983,3,24,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",1983,3,25,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1983,3,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1983,3,27,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1983,3,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1983,3,29,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1983,3,30,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1983,3,31,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1983,4,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1983,4,2,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1983,4,3,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1983,4,4,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1983,4,5,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1983,4,6,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1983,4,7,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1983,4,8,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1983,4,9,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1983,4,10,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1983,4,11,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1983,4,12,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1983,4,13,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1983,4,14,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1983,4,15,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1983,4,16,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1983,4,17,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1983,4,18,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1983,4,19,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1983,4,20,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1983,4,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1983,4,22,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1983,4,23,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1983,4,24,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1983,4,25,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1983,4,26,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1983,4,27,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1983,4,28,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1983,4,29,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1983,4,30,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1983,5,1,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1983,5,2,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1983,5,3,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1983,5,4,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1983,5,5,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1983,5,6,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1983,5,7,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1983,5,8,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1983,5,9,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1983,5,10,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1983,5,11,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1983,5,12,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1983,5,13,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1983,5,14,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1983,5,15,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1983,5,16,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1983,5,17,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1983,5,18,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1983,5,19,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1983,5,20,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1983,5,21,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1983,5,22,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1983,5,23,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1983,5,24,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1983,5,25,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1983,5,26,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1983,5,27,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1983,5,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1983,5,29,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1983,5,30,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1983,5,31,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1983,6,1,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1983,6,2,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1983,6,3,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1983,6,4,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1983,6,5,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1983,6,6,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1983,6,7,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1983,6,8,0.684,0.265011659192825,59.0976,0.766748608032454
+"FLOW_236212",1983,6,9,1.755,0.679964125560538,151.632,0.925757416622102
+"FLOW_236212",1983,6,10,1.558,0.603637668161435,134.6112,0.903972599225759
+"FLOW_236212",1983,6,11,3.478,1.34753004484305,300.4992,1.06146991952799
+"FLOW_236212",1983,6,12,2.153,0.834166816143498,186.0192,0.964385299976492
+"FLOW_236212",1983,6,13,2.909,1.12707443946188,251.3376,1.02421355714478
+"FLOW_236212",1983,6,14,3.047,1.18054170403587,263.2608,1.03375177134758
+"FLOW_236212",1983,6,15,1.787,0.692362331838565,154.3968,0.929109041525182
+"FLOW_236212",1983,6,16,1.429,0.553657399103139,123.4656,0.888481156933532
+"FLOW_236212",1983,6,17,0.979,0.379307623318386,84.5856,0.82375547527445
+"FLOW_236212",1983,6,18,0.688,0.266561434977578,59.4432,0.767643299624964
+"FLOW_236212",1983,6,19,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",1983,6,20,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",1983,6,21,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1983,6,22,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1983,6,23,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1983,6,24,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1983,6,25,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1983,6,26,0.854,0.330877130044843,73.7856,0.801555041507031
+"FLOW_236212",1983,6,27,3.551,1.3758134529148,306.8064,1.06588882830651
+"FLOW_236212",1983,6,28,14.135,5.4765201793722,1221.264,1.40507963247692
+"FLOW_236212",1983,6,29,10.614,4.11233004484305,917.0496,1.32683705759086
+"FLOW_236212",1983,6,30,5.271,2.04221704035874,455.4144,1.15350738451484
+"FLOW_236212",1983,7,1,6.259,2.42501165919283,540.7776,1.19383076427151
+"FLOW_236212",1983,7,2,18.484,7.16151390134529,1597.0176,1.48252138904437
+"FLOW_236212",1983,7,3,34.747,13.4625147982063,3002.1408,1.6819970847416
+"FLOW_236212",1983,7,4,15.551,6.02514080717489,1343.6064,1.43216626682697
+"FLOW_236212",1983,7,5,4.606,1.7845668161435,397.9584,1.12281075158466
+"FLOW_236212",1983,7,6,2.57,0.995730941704036,222.048,0.999144726606369
+"FLOW_236212",1983,7,7,2.357,0.913205381165919,203.6448,0.982004984927909
+"FLOW_236212",1983,7,8,3.504,1.35760358744395,302.7456,1.06305221172308
+"FLOW_236212",1983,7,9,2.428,0.940713901345292,209.7792,0.987851155915722
+"FLOW_236212",1983,7,10,1.634,0.633083408071749,141.1776,0.912624631996297
+"FLOW_236212",1983,7,11,1.147,0.444398206278027,99.1008,0.850265307693813
+"FLOW_236212",1983,7,12,0.938,0.363422421524664,81.0432,0.816737212552251
+"FLOW_236212",1983,7,13,0.966,0.374270852017937,83.4624,0.821556055667657
+"FLOW_236212",1983,7,14,1.13,0.437811659192825,97.632,0.847729828360393
+"FLOW_236212",1983,7,15,1.569,0.607899551569507,135.5616,0.90524547915345
+"FLOW_236212",1983,7,16,1.803,0.698561434977578,155.7792,0.930766880182505
+"FLOW_236212",1983,7,17,1.873,0.725682511210762,161.8272,0.937884441333725
+"FLOW_236212",1983,7,18,1.41,0.546295964125561,121.824,0.886105839131917
+"FLOW_236212",1983,7,19,1.067,0.41340269058296,92.1888,0.83805912619862
+"FLOW_236212",1983,7,20,1.976,0.765589237668161,170.7264,0.947979976045672
+"FLOW_236212",1983,7,21,3.274,1.26849147982063,282.8736,1.04871507496273
+"FLOW_236212",1983,7,22,2.245,0.869811659192825,193.968,0.972489777715628
+"FLOW_236212",1983,7,23,1.842,0.713671748878924,159.1488,0.934759098490907
+"FLOW_236212",1983,7,24,1.475,0.571479820627803,127.44,0.894128997983301
+"FLOW_236212",1983,7,25,1.038,0.402166816143498,89.6832,0.833453254138332
+"FLOW_236212",1983,7,26,0.803,0.311117488789238,69.3792,0.791744189705586
+"FLOW_236212",1983,7,27,0.884,0.342500448430493,76.3776,0.807109072059093
+"FLOW_236212",1983,7,28,2.084,0.807433183856502,180.0576,0.958123117290307
+"FLOW_236212",1983,7,29,1.771,0.686163228699552,153.0144,0.927439285175996
+"FLOW_236212",1983,7,30,1.192,0.461833183856502,102.9888,0.85683466207148
+"FLOW_236212",1983,7,31,0.921,0.356835874439462,79.5744,0.813755059737436
+"FLOW_236212",1983,8,1,0.842,0.326227802690583,72.7488,0.799289658941048
+"FLOW_236212",1983,8,2,1.255,0.486242152466368,108.432,0.865706186441681
+"FLOW_236212",1983,8,3,1.511,0.585427802690583,130.5504,0.898451563428142
+"FLOW_236212",1983,8,4,1.053,0.407978475336323,90.9792,0.835848274250574
+"FLOW_236212",1983,8,5,0.773,0.299494170403588,66.7872,0.78573785090246
+"FLOW_236212",1983,8,6,0.637,0.246801793721973,55.0368,0.755909254443587
+"FLOW_236212",1983,8,7,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",1983,8,8,0.674,0.261137219730942,58.2336,0.764493425992641
+"FLOW_236212",1983,8,9,1.883,0.729556950672646,162.6912,0.938883787705458
+"FLOW_236212",1983,8,10,3.748,1.4521399103139,323.8272,1.07746133002645
+"FLOW_236212",1983,8,11,2.633,1.0201399103139,227.4912,1.00399591945438
+"FLOW_236212",1983,8,12,1.894,0.733818834080718,163.6416,0.939978176781722
+"FLOW_236212",1983,8,13,1.376,0.533122869955157,118.8864,0.881790595503693
+"FLOW_236212",1983,8,14,0.968,0.375045739910314,83.6352,0.821895963130161
+"FLOW_236212",1983,8,15,0.735,0.284771300448431,63.504,0.777856067537677
+"FLOW_236212",1983,8,16,0.958,0.371171300448431,82.7712,0.820190770088631
+"FLOW_236212",1983,8,17,6.619,2.56449147982063,571.8816,1.20725844513664
+"FLOW_236212",1983,8,18,5.84,2.26267264573991,504.576,1.17740085082014
+"FLOW_236212",1983,8,19,3.82,1.48003587443946,330.048,1.08156954158549
+"FLOW_236212",1983,8,20,2.178,0.843852914798206,188.1792,0.966614600935095
+"FLOW_236212",1983,8,21,1.757,0.680739013452915,151.8048,0.925968319347403
+"FLOW_236212",1983,8,22,1.561,0.6048,134.8704,0.90432045948752
+"FLOW_236212",1983,8,23,2.208,0.855476233183857,190.7712,0.969262899818629
+"FLOW_236212",1983,8,24,1.968,0.762489686098655,170.0352,0.947211134826159
+"FLOW_236212",1983,8,25,1.639,0.635020627802691,141.6096,0.913182471430126
+"FLOW_236212",1983,8,26,2.16,0.836878923766816,186.624,0.965011582822025
+"FLOW_236212",1983,8,27,1.941,0.75202869955157,167.7024,0.944597698227476
+"FLOW_236212",1983,8,28,2.017,0.781474439461883,174.2688,0.951881651722984
+"FLOW_236212",1983,8,29,1.96,0.759390134529148,169.344,0.946439789230391
+"FLOW_236212",1983,8,30,1.457,0.564505829596413,125.8848,0.891935985878617
+"FLOW_236212",1983,8,31,1.363,0.528086098654709,117.7632,0.880118088894593
+"FLOW_236212",1983,9,1,1.188,0.460283408071749,102.6432,0.856258831805151
+"FLOW_236212",1983,9,2,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",1983,9,3,0.746,0.289033183856502,64.4544,0.780170530080621
+"FLOW_236212",1983,9,4,0.745,0.288645739910314,64.368,0.779961256839643
+"FLOW_236212",1983,9,5,1.997,0.773725560538117,172.5408,0.949986401934372
+"FLOW_236212",1983,9,6,10.453,4.04995156950673,903.1392,1.32278714569339
+"FLOW_236212",1983,9,7,15.385,5.96082511210762,1329.264,1.42909557997084
+"FLOW_236212",1983,9,8,19.564,7.5799533632287,1690.3296,1.49945455114139
+"FLOW_236212",1983,9,9,29.232,11.3257614349776,2525.6448,1.62485077500218
+"FLOW_236212",1983,9,10,14.477,5.60902600896861,1250.8128,1.4118140213968
+"FLOW_236212",1983,9,11,10.304,3.99222242152466,890.2656,1.31899438240046
+"FLOW_236212",1983,9,12,11.873,4.60012197309417,1025.8272,1.35691883069623
+"FLOW_236212",1983,9,13,15.702,6.08364484304933,1356.6528,1.43493678994873
+"FLOW_236212",1983,9,14,26.923,10.4311533632287,2326.1472,1.59833005543902
+"FLOW_236212",1983,9,15,14.883,5.76632825112108,1285.8912,1.41964537607739
+"FLOW_236212",1983,9,16,4.762,1.84500807174888,411.4368,1.13031542448237
+"FLOW_236212",1983,9,17,2.492,0.965510313901345,215.3088,0.993004881592039
+"FLOW_236212",1983,9,18,1.937,0.750478923766816,167.3568,0.944208052761971
+"FLOW_236212",1983,9,19,1.517,0.587752466367713,131.0688,0.899163961492945
+"FLOW_236212",1983,9,20,1.376,0.533122869955157,118.8864,0.881790595503693
+"FLOW_236212",1983,9,21,2.323,0.900032286995516,200.7072,0.979155387539898
+"FLOW_236212",1983,9,22,2.723,1.05500986547085,235.2672,1.01076758120871
+"FLOW_236212",1983,9,23,1.988,0.770238565022422,171.7632,0.949128581971853
+"FLOW_236212",1983,9,24,1.468,0.568767713004484,126.8352,0.89327871754431
+"FLOW_236212",1983,9,25,1.183,0.458346188340807,102.2112,0.855536858744601
+"FLOW_236212",1983,9,26,0.889,0.344437668161435,76.8096,0.808020032699328
+"FLOW_236212",1983,9,27,0.73,0.282834080717489,63.072,0.776794868403954
+"FLOW_236212",1983,9,28,0.712,0.275860089686099,61.5168,0.772925747447752
+"FLOW_236212",1983,9,29,0.794,0.307630493273543,68.6016,0.789961410109347
+"FLOW_236212",1983,9,30,0.663,0.25687533632287,57.2832,0.761981595183807
+"FLOW_236212",1983,10,1,0.55,0.213094170403587,47.52,0.734030851465829
+"FLOW_236212",1983,10,2,0.478,0.185198206278027,41.2992,0.713719159231734
+"FLOW_236212",1983,10,3,0.428,0.16582600896861,36.9792,0.698120722844581
+"FLOW_236212",1983,10,4,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1983,10,5,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1983,10,6,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1983,10,7,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1983,10,8,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1983,10,9,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1983,10,10,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1983,10,11,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1983,10,12,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1983,10,13,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1983,10,14,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1983,10,15,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1983,10,16,1.16,0.449434977578475,100.224,0.852183995672329
+"FLOW_236212",1983,10,17,2.104,0.815182062780269,181.7856,0.959955106040469
+"FLOW_236212",1983,10,18,1.459,0.565280717488789,126.0576,0.89218072071746
+"FLOW_236212",1983,10,19,1.071,0.414952466367713,92.5344,0.838686534070795
+"FLOW_236212",1983,10,20,0.977,0.378532735426009,84.4128,0.823418629741507
+"FLOW_236212",1983,10,21,0.994,0.385119282511211,85.8816,0.826264421676049
+"FLOW_236212",1983,10,22,0.882,0.341725560538117,76.2048,0.806743533380091
+"FLOW_236212",1983,10,23,0.711,0.27547264573991,61.4304,0.772708511396718
+"FLOW_236212",1983,10,24,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",1983,10,25,0.45,0.174349775784753,38.88,0.705154494972067
+"FLOW_236212",1983,10,26,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1983,10,27,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1983,10,28,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1983,10,29,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1983,10,30,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1983,10,31,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1983,11,1,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1983,11,2,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1983,11,3,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1983,11,4,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1983,11,5,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1983,11,6,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1983,11,7,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1983,11,8,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1983,11,9,0.516,0.199921076233184,44.5824,0.724722452305139
+"FLOW_236212",1983,11,10,0.514,0.199146188340807,44.4096,0.724159778915107
+"FLOW_236212",1983,11,11,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1983,11,12,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1983,11,13,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1983,11,14,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1983,11,15,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1983,11,16,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",1983,11,17,0.72,0.278959641255605,62.208,0.774654905111265
+"FLOW_236212",1983,11,18,0.673,0.260749775784753,58.1472,0.764266438593352
+"FLOW_236212",1983,11,19,0.739,0.286321076233184,63.8496,0.778700876553795
+"FLOW_236212",1983,11,20,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",1983,11,21,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",1983,11,22,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1983,11,23,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1983,11,24,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1983,11,25,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1983,11,26,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1983,11,27,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1983,11,28,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1983,11,29,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1983,11,30,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1983,12,1,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1983,12,2,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1983,12,3,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1983,12,4,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1983,12,5,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1983,12,6,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1983,12,7,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1983,12,8,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1983,12,9,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1983,12,10,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1983,12,11,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1983,12,12,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1983,12,13,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1983,12,14,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1983,12,15,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1983,12,16,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1983,12,17,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1983,12,18,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1983,12,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1983,12,20,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1983,12,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1983,12,22,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1983,12,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1983,12,24,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1983,12,25,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1983,12,26,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1983,12,27,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1983,12,28,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1983,12,29,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1983,12,30,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1983,12,31,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1984,1,1,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1984,1,2,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1984,1,3,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1984,1,4,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1984,1,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1984,1,6,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1984,1,7,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1984,1,8,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1984,1,9,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1984,1,10,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1984,1,11,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1984,1,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1984,1,13,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1984,1,14,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1984,1,15,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1984,1,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1984,1,17,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1984,1,18,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1984,1,19,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1984,1,20,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1984,1,21,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1984,1,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1984,1,23,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1984,1,24,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1984,1,25,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1984,1,26,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1984,1,27,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1984,1,28,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1984,1,29,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1984,1,30,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1984,1,31,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1984,2,1,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1984,2,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1984,2,3,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1984,2,4,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1984,2,5,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1984,2,6,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1984,2,7,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1984,2,8,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1984,2,9,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1984,2,10,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1984,2,11,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1984,2,12,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1984,2,13,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1984,2,14,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1984,2,15,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1984,2,16,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1984,2,17,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1984,2,18,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1984,2,19,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1984,2,20,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1984,2,21,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1984,2,22,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1984,2,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1984,2,24,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1984,2,25,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1984,2,26,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1984,2,27,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1984,2,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1984,2,29,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1984,3,1,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1984,3,2,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1984,3,3,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1984,3,4,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1984,3,5,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1984,3,6,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1984,3,7,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1984,3,8,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1984,3,9,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1984,3,10,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1984,3,11,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1984,3,12,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1984,3,13,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1984,3,14,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,3,15,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1984,3,16,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1984,3,17,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1984,3,18,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1984,3,19,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1984,3,20,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,3,21,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1984,3,22,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1984,3,23,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1984,3,24,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1984,3,25,0.683,0.264624215246637,59.0112,0.766524281309425
+"FLOW_236212",1984,3,26,0.693,0.26849865470852,59.8752,0.768755830816311
+"FLOW_236212",1984,3,27,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",1984,3,28,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1984,3,29,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1984,3,30,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1984,3,31,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1984,4,1,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1984,4,2,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1984,4,3,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1984,4,4,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1984,4,5,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1984,4,6,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1984,4,7,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1984,4,8,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1984,4,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,4,10,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1984,4,11,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1984,4,12,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1984,4,13,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1984,4,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1984,4,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1984,4,16,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1984,4,17,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1984,4,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,4,19,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1984,4,20,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1984,4,21,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1984,4,22,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1984,4,23,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1984,4,24,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1984,4,25,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1984,4,26,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1984,4,27,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1984,4,28,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1984,4,29,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1984,4,30,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1984,5,1,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1984,5,2,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,5,3,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1984,5,4,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,5,5,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1984,5,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1984,5,7,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1984,5,8,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,5,9,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1984,5,10,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1984,5,11,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1984,5,12,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,5,13,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1984,5,14,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1984,5,15,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1984,5,16,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,5,17,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1984,5,18,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,5,19,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,5,20,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1984,5,21,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,5,22,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1984,5,23,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1984,5,24,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1984,5,25,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1984,5,26,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1984,5,27,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1984,5,28,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,5,29,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1984,5,30,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1984,5,31,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,6,1,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1984,6,2,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1984,6,3,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1984,6,4,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1984,6,5,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1984,6,6,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1984,6,7,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1984,6,8,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1984,6,9,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1984,6,10,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1984,6,11,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1984,6,12,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1984,6,13,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1984,6,14,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1984,6,15,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1984,6,16,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1984,6,17,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1984,6,18,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1984,6,19,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1984,6,20,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1984,6,21,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1984,6,22,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1984,6,23,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1984,6,24,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,6,25,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,6,26,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,6,27,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,6,28,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,6,29,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,6,30,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,7,1,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,7,2,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1984,7,3,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1984,7,4,0.704,0.272760538116592,60.8256,0.771180976266069
+"FLOW_236212",1984,7,5,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",1984,7,6,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1984,7,7,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1984,7,8,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1984,7,9,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1984,7,10,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",1984,7,11,0.416,0.16117668161435,35.9424,0.694161370705451
+"FLOW_236212",1984,7,12,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",1984,7,13,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",1984,7,14,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",1984,7,15,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1984,7,16,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1984,7,17,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1984,7,18,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1984,7,19,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",1984,7,20,1.049,0.40642869955157,90.6336,0.835212284718555
+"FLOW_236212",1984,7,21,1.064,0.412240358744395,91.9296,0.837587334310341
+"FLOW_236212",1984,7,22,0.807,0.312667264573991,69.7248,0.792531408949302
+"FLOW_236212",1984,7,23,0.642,0.248739013452915,55.4688,0.757092216813081
+"FLOW_236212",1984,7,24,0.797,0.308792825112108,68.8608,0.790557458129397
+"FLOW_236212",1984,7,25,1.268,0.491278923766816,109.5552,0.867492295969797
+"FLOW_236212",1984,7,26,0.889,0.344437668161435,76.8096,0.808020032699328
+"FLOW_236212",1984,7,27,0.629,0.243702242152466,54.3456,0.754000970691041
+"FLOW_236212",1984,7,28,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",1984,7,29,0.461,0.178611659192825,39.8304,0.708568694902007
+"FLOW_236212",1984,7,30,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",1984,7,31,0.734,0.284383856502242,63.4176,0.777644290738638
+"FLOW_236212",1984,8,1,1.07,0.414565022421525,92.448,0.838529858077405
+"FLOW_236212",1984,8,2,0.93,0.360322869955157,80.352,0.81533928015676
+"FLOW_236212",1984,8,3,0.758,0.293682511210762,65.4912,0.782664466518846
+"FLOW_236212",1984,8,4,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1984,8,5,0.501,0.194109417040359,43.2864,0.720459083126107
+"FLOW_236212",1984,8,6,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1984,8,7,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",1984,8,8,0.591,0.228979372197309,51.0624,0.744662136557278
+"FLOW_236212",1984,8,9,0.561,0.217356053811659,48.4704,0.73694376386518
+"FLOW_236212",1984,8,10,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",1984,8,11,0.475,0.184035874439462,41.04,0.712821019859571
+"FLOW_236212",1984,8,12,0.458,0.17744932735426,39.5712,0.707644069704618
+"FLOW_236212",1984,8,13,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",1984,8,14,0.561,0.217356053811659,48.4704,0.73694376386518
+"FLOW_236212",1984,8,15,0.655,0.253775784753363,56.592,0.76013378537976
+"FLOW_236212",1984,8,16,0.765,0.296394618834081,66.096,0.784104710560857
+"FLOW_236212",1984,8,17,0.908,0.351799103139014,78.4512,0.81144473290655
+"FLOW_236212",1984,8,18,1.604,0.621460089686099,138.5856,0.90924861600788
+"FLOW_236212",1984,8,19,3.452,1.33745650224215,298.2528,1.0598781360393
+"FLOW_236212",1984,8,20,3.299,1.27817757847534,285.0336,1.05031178618894
+"FLOW_236212",1984,8,21,3.983,1.54318923766816,344.1312,1.09064606595385
+"FLOW_236212",1984,8,22,8.048,3.11814887892377,695.3472,1.25539203657132
+"FLOW_236212",1984,8,23,3.704,1.43509237668161,320.0256,1.07491957435433
+"FLOW_236212",1984,8,24,2.559,0.991469058295964,221.0976,0.99828795952167
+"FLOW_236212",1984,8,25,2.153,0.834166816143498,186.0192,0.964385299976492
+"FLOW_236212",1984,8,26,2.417,0.93645201793722,208.8288,0.986954441884457
+"FLOW_236212",1984,8,27,3.7,1.43354260089686,319.68,1.07468730994751
+"FLOW_236212",1984,8,28,13.412,5.19639820627803,1158.7968,1.39040232344625
+"FLOW_236212",1984,8,29,14.633,5.66946726457399,1264.2912,1.41484365141692
+"FLOW_236212",1984,8,30,6.761,2.61950852017937,584.1504,1.21239451546661
+"FLOW_236212",1984,8,31,3.114,1.20650044843049,269.0496,1.03825850540336
+"FLOW_236212",1984,9,1,1.95,0.755515695067265,168.48,0.945472057359995
+"FLOW_236212",1984,9,2,1.404,0.54397130044843,121.3056,0.885350419585714
+"FLOW_236212",1984,9,3,1.491,0.577678923766816,128.8224,0.896060440852666
+"FLOW_236212",1984,9,4,13.453,5.21228340807175,1162.3392,1.39125136782625
+"FLOW_236212",1984,9,5,10.486,4.06273721973094,905.9904,1.32362129773547
+"FLOW_236212",1984,9,6,3.555,1.37736322869955,307.152,1.06612885283759
+"FLOW_236212",1984,9,7,2.024,0.784186547085202,174.8736,0.952541437638656
+"FLOW_236212",1984,9,8,1.398,0.5416466367713,120.7872,0.884592412977752
+"FLOW_236212",1984,9,9,1.026,0.397517488789238,88.6464,0.831517221016325
+"FLOW_236212",1984,9,10,0.856,0.33165201793722,73.9584,0.801930125920911
+"FLOW_236212",1984,9,11,1.225,0.474618834080718,105.84,0.8615272002962
+"FLOW_236212",1984,9,12,3.024,1.17163049327354,261.2736,1.03218640168131
+"FLOW_236212",1984,9,13,2.452,0.950012556053812,211.8528,0.989796398073995
+"FLOW_236212",1984,9,14,1.605,0.621847533632287,138.672,0.909361960391358
+"FLOW_236212",1984,9,15,1.087,0.421151569506726,93.9168,0.841177571931339
+"FLOW_236212",1984,9,16,0.865,0.335139013452915,74.736,0.803609380076777
+"FLOW_236212",1984,9,17,5.361,2.0770869955157,463.1904,1.15741987930444
+"FLOW_236212",1984,9,18,15.082,5.84342959641256,1303.0848,1.42342163718677
+"FLOW_236212",1984,9,19,36.915,14.3024932735426,3189.456,1.70248130040567
+"FLOW_236212",1984,9,20,32.735,12.6829775784753,2828.304,1.66205061334974
+"FLOW_236212",1984,9,21,18.587,7.20142062780269,1605.9168,1.48416995511116
+"FLOW_236212",1984,9,22,21.211,8.2180735426009,1832.6304,1.52389135275108
+"FLOW_236212",1984,9,23,12.26,4.75006278026906,1059.264,1.36565144923016
+"FLOW_236212",1984,9,24,8.98,3.4792466367713,775.872,1.28320795484154
+"FLOW_236212",1984,9,25,9.08,3.51799103139013,784.512,1.28605323324202
+"FLOW_236212",1984,9,26,5.712,2.2130798206278,493.5168,1.17219378831443
+"FLOW_236212",1984,9,27,3.237,1.25415605381166,279.6768,1.04633394718285
+"FLOW_236212",1984,9,28,2.593,1.00464215246637,224.0352,1.00092671131311
+"FLOW_236212",1984,9,29,2.187,0.847339910313901,188.9568,0.967412138819966
+"FLOW_236212",1984,9,30,1.86,0.720645739910314,160.704,0.936578889880537
+"FLOW_236212",1984,10,1,1.474,0.571092376681614,127.3536,0.89400772726163
+"FLOW_236212",1984,10,2,1.106,0.428513004484305,95.5584,0.844097863992855
+"FLOW_236212",1984,10,3,0.95,0.368071748878924,82.08,0.818816332919998
+"FLOW_236212",1984,10,4,2.456,0.951562331838565,212.1984,0.990119122758863
+"FLOW_236212",1984,10,5,2.623,1.01626547085202,226.6272,1.0032321331615
+"FLOW_236212",1984,10,6,1.569,0.607899551569507,135.5616,0.90524547915345
+"FLOW_236212",1984,10,7,1.043,0.404104035874439,90.1152,0.834254652979247
+"FLOW_236212",1984,10,8,0.762,0.295232286995516,65.8368,0.783488759517135
+"FLOW_236212",1984,10,9,0.606,0.234791031390135,52.3584,0.748404345873839
+"FLOW_236212",1984,10,10,0.539,0.208832286995516,46.5696,0.731070953197764
+"FLOW_236212",1984,10,11,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",1984,10,12,0.421,0.163113901345291,36.3744,0.695822062827897
+"FLOW_236212",1984,10,13,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1984,10,14,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1984,10,15,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1984,10,16,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",1984,10,17,0.421,0.163113901345291,36.3744,0.695822062827897
+"FLOW_236212",1984,10,18,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",1984,10,19,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1984,10,20,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1984,10,21,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1984,10,22,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1984,10,23,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1984,10,24,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1984,10,25,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1984,10,26,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1984,10,27,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",1984,10,28,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1984,10,29,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1984,10,30,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1984,10,31,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1984,11,1,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1984,11,2,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1984,11,3,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1984,11,4,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1984,11,5,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1984,11,6,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1984,11,7,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1984,11,8,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1984,11,9,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1984,11,10,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1984,11,11,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1984,11,12,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1984,11,13,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1984,11,14,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1984,11,15,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1984,11,16,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1984,11,17,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1984,11,18,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1984,11,19,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1984,11,20,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1984,11,21,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1984,11,22,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1984,11,23,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1984,11,24,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1984,11,25,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1984,11,26,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1984,11,27,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1984,11,28,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1984,11,29,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1984,11,30,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1984,12,1,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1984,12,2,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1984,12,3,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1984,12,4,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1984,12,5,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1984,12,6,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1984,12,7,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1984,12,8,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1984,12,9,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1984,12,10,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1984,12,11,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1984,12,12,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1984,12,13,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1984,12,14,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1984,12,15,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1984,12,16,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1984,12,17,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1984,12,18,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1984,12,19,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1984,12,20,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1984,12,21,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1984,12,22,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1984,12,23,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1984,12,24,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1984,12,25,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1984,12,26,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1984,12,27,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1984,12,28,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1984,12,29,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1984,12,30,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1984,12,31,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,1,1,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1985,1,2,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1985,1,3,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1985,1,4,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1985,1,5,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1985,1,6,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1985,1,7,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1985,1,8,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1985,1,9,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1985,1,10,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1985,1,11,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1985,1,12,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1985,1,13,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1985,1,14,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1985,1,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1985,1,16,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1985,1,17,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1985,1,18,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1985,1,19,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1985,1,20,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1985,1,21,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1985,1,22,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1985,1,23,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1985,1,24,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1985,1,25,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1985,1,26,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1985,1,27,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1985,1,28,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1985,1,29,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1985,1,30,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1985,1,31,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1985,2,1,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1985,2,2,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1985,2,3,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,4,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1985,2,5,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1985,2,6,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1985,2,7,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1985,2,8,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1985,2,9,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1985,2,10,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1985,2,11,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1985,2,12,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,13,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1985,2,14,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,15,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,16,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1985,2,17,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1985,2,18,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1985,2,19,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1985,2,20,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1985,2,21,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,22,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1985,2,23,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1985,2,24,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1985,2,25,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1985,2,26,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1985,2,27,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1985,2,28,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1985,3,1,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1985,3,2,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1985,3,3,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1985,3,4,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1985,3,5,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1985,3,6,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1985,3,7,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1985,3,8,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1985,3,9,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1985,3,10,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1985,3,11,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1985,3,12,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1985,3,13,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1985,3,14,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1985,3,15,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1985,3,16,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1985,3,17,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1985,3,18,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1985,3,19,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1985,3,20,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1985,3,21,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1985,3,22,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,3,23,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,3,24,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,3,25,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1985,3,26,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1985,3,27,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1985,3,28,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,3,29,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,3,30,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1985,3,31,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1985,4,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1985,4,2,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",1985,4,3,0.584,0.226267264573991,50.4576,0.742889714222194
+"FLOW_236212",1985,4,4,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1985,4,5,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1985,4,6,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1985,4,7,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1985,4,8,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1985,4,9,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1985,4,10,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1985,4,11,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1985,4,12,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1985,4,13,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1985,4,14,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1985,4,15,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1985,4,16,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1985,4,17,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1985,4,18,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1985,4,19,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1985,4,20,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1985,4,21,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1985,4,22,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1985,4,23,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1985,4,24,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1985,4,25,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1985,4,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1985,4,27,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1985,4,28,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1985,4,29,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1985,4,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1985,5,1,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1985,5,2,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1985,5,3,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1985,5,4,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1985,5,5,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1985,5,6,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1985,5,7,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1985,5,8,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1985,5,9,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1985,5,10,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1985,5,11,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1985,5,12,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1985,5,13,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1985,5,14,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1985,5,15,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1985,5,16,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1985,5,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1985,5,18,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1985,5,19,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1985,5,20,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1985,5,21,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1985,5,22,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1985,5,23,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1985,5,24,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1985,5,25,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1985,5,26,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1985,5,27,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1985,5,28,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1985,5,29,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1985,5,30,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1985,5,31,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1985,6,1,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1985,6,2,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1985,6,3,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1985,6,4,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1985,6,5,2.031,0.78689865470852,175.4784,0.953199400577678
+"FLOW_236212",1985,6,6,1.158,0.448660089686099,100.0512,0.851889936252506
+"FLOW_236212",1985,6,7,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",1985,6,8,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1985,6,9,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1985,6,10,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1985,6,11,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",1985,6,12,1.639,0.635020627802691,141.6096,0.913182471430126
+"FLOW_236212",1985,6,13,1.3,0.503677130044843,112.32,0.871827264176557
+"FLOW_236212",1985,6,14,0.78,0.302206278026906,67.392,0.787155793863912
+"FLOW_236212",1985,6,15,0.632,0.244864573991031,54.6048,0.75471884025973
+"FLOW_236212",1985,6,16,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",1985,6,17,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",1985,6,18,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1985,6,19,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1985,6,20,1.843,0.714059192825112,159.2352,0.934860570388488
+"FLOW_236212",1985,6,21,3.048,1.18092914798206,263.3472,1.0338196161845
+"FLOW_236212",1985,6,22,1.951,0.755903139013453,168.5664,0.945569008967587
+"FLOW_236212",1985,6,23,1.439,0.557531838565023,124.3296,0.889721191289285
+"FLOW_236212",1985,6,24,1.063,0.411852914798206,91.8432,0.837429833860257
+"FLOW_236212",1985,6,25,1.026,0.397517488789238,88.6464,0.831517221016325
+"FLOW_236212",1985,6,26,0.845,0.327390134529148,73.008,0.799858414017954
+"FLOW_236212",1985,6,27,0.694,0.268886098654709,59.9616,0.768977566022169
+"FLOW_236212",1985,6,28,0.742,0.287483408071749,64.1088,0.779332085875151
+"FLOW_236212",1985,6,29,0.682,0.264236771300448,58.9248,0.766299691677741
+"FLOW_236212",1985,6,30,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",1985,7,1,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",1985,7,2,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",1985,7,3,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1985,7,4,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1985,7,5,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1985,7,6,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1985,7,7,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1985,7,8,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1985,7,9,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1985,7,10,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",1985,7,11,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1985,7,12,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1985,7,13,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1985,7,14,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1985,7,15,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1985,7,16,3.223,1.24873183856502,278.4672,1.04542730007837
+"FLOW_236212",1985,7,17,10.091,3.90969686098655,871.8624,1.31349558431333
+"FLOW_236212",1985,7,18,13.459,5.21460807174888,1162.8576,1.39137544452914
+"FLOW_236212",1985,7,19,7.628,2.95542242152466,659.0592,1.24200662597133
+"FLOW_236212",1985,7,20,3.138,1.21579910313901,271.1232,1.03985399277207
+"FLOW_236212",1985,7,21,2.168,0.839978475336323,187.3152,0.965725349566331
+"FLOW_236212",1985,7,22,4.151,1.6082798206278,358.6464,1.09969518260909
+"FLOW_236212",1985,7,23,9.876,3.82639641255605,853.2864,1.30785016689052
+"FLOW_236212",1985,7,24,4.453,1.72528789237668,384.7392,1.11525021313729
+"FLOW_236212",1985,7,25,2.339,0.906231390134529,202.0896,0.980500501757397
+"FLOW_236212",1985,7,26,1.862,0.721420627802691,160.8768,0.936780218121385
+"FLOW_236212",1985,7,27,1.912,0.740792825112108,165.1968,0.941758076676452
+"FLOW_236212",1985,7,28,1.589,0.615648430493274,137.2896,0.907541629090584
+"FLOW_236212",1985,7,29,1.282,0.496703139013453,110.7648,0.869399489659845
+"FLOW_236212",1985,7,30,1.608,0.623009865470852,138.9312,0.909701654905416
+"FLOW_236212",1985,7,31,3.984,1.54357668161435,344.2176,1.09070082550957
+"FLOW_236212",1985,8,1,4.08,1.58077130044843,352.512,1.09590727139293
+"FLOW_236212",1985,8,2,2.502,0.969384753363229,216.1728,0.993800559601821
+"FLOW_236212",1985,8,3,1.759,0.681513901345291,151.9776,0.926179030102946
+"FLOW_236212",1985,8,4,1.36,0.526923766816144,117.504,0.879730314582357
+"FLOW_236212",1985,8,5,1.916,0.742342600896861,165.5424,0.942151788384562
+"FLOW_236212",1985,8,6,4.607,1.78495426008969,398.0448,1.12285950161747
+"FLOW_236212",1985,8,7,4.03,1.56139910313901,348.192,1.09320795960716
+"FLOW_236212",1985,8,8,4.597,1.7810798206278,397.1808,1.12237161983356
+"FLOW_236212",1985,8,9,7.119,2.7582134529148,615.0816,1.2249703432286
+"FLOW_236212",1985,8,10,5.156,1.99766098654709,445.4784,1.14842954712799
+"FLOW_236212",1985,8,11,3.256,1.26151748878924,281.3184,1.04755939250756
+"FLOW_236212",1985,8,12,2.161,0.837266367713005,186.7104,0.965100919204118
+"FLOW_236212",1985,8,13,1.604,0.621460089686099,138.5856,0.90924861600788
+"FLOW_236212",1985,8,14,1.201,0.465320179372197,103.7664,0.858124649841692
+"FLOW_236212",1985,8,15,0.961,0.372333632286996,83.0304,0.820703817260273
+"FLOW_236212",1985,8,16,1.102,0.426963228699552,95.2128,0.843486419769643
+"FLOW_236212",1985,8,17,2.522,0.977133632286996,217.9008,0.995384313551503
+"FLOW_236212",1985,8,18,3.104,1.20262600896861,268.1856,1.03759081449374
+"FLOW_236212",1985,8,19,2.818,1.09181704035874,243.4752,1.01772390030315
+"FLOW_236212",1985,8,20,2.247,0.870586547085202,194.1408,0.972662988124286
+"FLOW_236212",1985,8,21,1.702,0.659429596412556,147.0528,0.920097153301526
+"FLOW_236212",1985,8,22,1.633,0.632695964125561,141.0912,0.912512900279922
+"FLOW_236212",1985,8,23,4.099,1.58813273542601,354.1536,1.09692607333688
+"FLOW_236212",1985,8,24,7.579,2.93643766816143,654.8256,1.240406853963
+"FLOW_236212",1985,8,25,4.979,1.92908340807175,430.1856,1.14043411711086
+"FLOW_236212",1985,8,26,5.03,1.94884304932735,434.592,1.14276090127287
+"FLOW_236212",1985,8,27,6.138,2.37813094170404,530.3232,1.18917878482281
+"FLOW_236212",1985,8,28,3.482,1.3490798206278,300.8448,1.06171396367829
+"FLOW_236212",1985,8,29,2.894,1.12126278026906,250.0416,1.02315511855103
+"FLOW_236212",1985,8,30,2.376,0.920566816143498,205.2864,0.98358311154626
+"FLOW_236212",1985,8,31,1.851,0.717158744394619,159.9264,0.935670763844523
+"FLOW_236212",1985,9,1,1.886,0.730719282511211,162.9504,0.939182763603373
+"FLOW_236212",1985,9,2,9.618,3.72643587443946,830.9952,1.30094438332264
+"FLOW_236212",1985,9,3,6.453,2.50017578475336,557.5392,1.20114132465537
+"FLOW_236212",1985,9,4,3.265,1.26500448430493,282.096,1.0481378708657
+"FLOW_236212",1985,9,5,2.24,0.867874439461884,193.536,0.972056210990529
+"FLOW_236212",1985,9,6,1.734,0.671827802690583,149.8176,0.923531247967975
+"FLOW_236212",1985,9,7,1.317,0.510263677130045,113.7888,0.874095593513835
+"FLOW_236212",1985,9,8,0.972,0.376595515695067,83.9808,0.822574096035855
+"FLOW_236212",1985,9,9,0.781,0.302593721973094,67.4784,0.787357525257363
+"FLOW_236212",1985,9,10,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",1985,9,11,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1985,9,12,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",1985,9,13,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",1985,9,14,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",1985,9,15,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1985,9,16,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",1985,9,17,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1985,9,18,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1985,9,19,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1985,9,20,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1985,9,21,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1985,9,22,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1985,9,23,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1985,9,24,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1985,9,25,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1985,9,26,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1985,9,27,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1985,9,28,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1985,9,29,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1985,9,30,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1985,10,1,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1985,10,2,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1985,10,3,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1985,10,4,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1985,10,5,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1985,10,6,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1985,10,7,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1985,10,8,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1985,10,9,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1985,10,10,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1985,10,11,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1985,10,12,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1985,10,13,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1985,10,14,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1985,10,15,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1985,10,16,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1985,10,17,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1985,10,18,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1985,10,19,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1985,10,20,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1985,10,21,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1985,10,22,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1985,10,23,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1985,10,24,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",1985,10,25,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1985,10,26,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1985,10,27,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1985,10,28,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1985,10,29,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1985,10,30,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1985,10,31,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1985,11,1,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1985,11,2,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1985,11,3,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1985,11,4,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1985,11,5,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1985,11,6,5.281,2.04609147982063,456.2784,1.15394473341194
+"FLOW_236212",1985,11,7,3.845,1.48972197309417,332.208,1.08298151707611
+"FLOW_236212",1985,11,8,1.531,0.59317668161435,132.2784,0.900817498932225
+"FLOW_236212",1985,11,9,0.842,0.326227802690583,72.7488,0.799289658941048
+"FLOW_236212",1985,11,10,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",1985,11,11,0.421,0.163113901345291,36.3744,0.695822062827897
+"FLOW_236212",1985,11,12,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1985,11,13,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1985,11,14,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1985,11,15,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1985,11,16,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1985,11,17,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1985,11,18,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1985,11,19,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1985,11,20,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1985,11,21,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1985,11,22,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1985,11,23,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1985,11,24,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1985,11,25,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1985,11,26,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1985,11,27,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1985,11,28,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1985,11,29,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1985,11,30,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1985,12,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1985,12,2,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1985,12,3,0.951,0.368459192825112,82.1664,0.818988642769627
+"FLOW_236212",1985,12,4,2.904,1.12513721973094,250.9056,1.02386123041634
+"FLOW_236212",1985,12,5,1.499,0.580778475336323,129.5136,0.897019950952688
+"FLOW_236212",1985,12,6,0.808,0.313054708520179,69.8112,0.792727725894111
+"FLOW_236212",1985,12,7,0.613,0.237503139013453,52.9632,0.750125399016538
+"FLOW_236212",1985,12,8,0.766,0.296782062780269,66.1824,0.784309598153651
+"FLOW_236212",1985,12,9,0.59,0.228591928251121,50.976,0.74440996510202
+"FLOW_236212",1985,12,10,1.032,0.399842152466368,89.1648,0.832487488792331
+"FLOW_236212",1985,12,11,5.418,2.09917130044843,468.1152,1.15987069560904
+"FLOW_236212",1985,12,12,3.932,1.52342959641256,339.7248,1.08783863207573
+"FLOW_236212",1985,12,13,1.958,0.758615246636771,169.1712,0.946246559367562
+"FLOW_236212",1985,12,14,1.146,0.444010762331839,99.0144,0.850116996973521
+"FLOW_236212",1985,12,15,0.771,0.298719282511211,66.6144,0.7853308380702
+"FLOW_236212",1985,12,16,0.623,0.241377578475336,53.8272,0.752556974827375
+"FLOW_236212",1985,12,17,0.854,0.330877130044843,73.7856,0.801555041507031
+"FLOW_236212",1985,12,18,4.945,1.91591031390135,427.248,1.13887231320688
+"FLOW_236212",1985,12,19,5.308,2.05655246636771,458.6112,1.15512227499161
+"FLOW_236212",1985,12,20,2.362,0.915142600896861,204.0768,0.982421265274574
+"FLOW_236212",1985,12,21,1.275,0.493991031390135,110.16,0.868447986999647
+"FLOW_236212",1985,12,22,0.969,0.375433183856502,83.7216,0.822065706218397
+"FLOW_236212",1985,12,23,1.276,0.494378475336323,110.2464,0.868584171417039
+"FLOW_236212",1985,12,24,2.542,0.984882511210762,219.6288,0.996958051552907
+"FLOW_236212",1985,12,25,1.453,0.562956053811659,125.5392,0.891445708827626
+"FLOW_236212",1985,12,26,0.953,0.369234080717489,82.3392,0.819332828074103
+"FLOW_236212",1985,12,27,0.736,0.285158744394619,63.5904,0.778067613956656
+"FLOW_236212",1985,12,28,0.596,0.230916591928251,51.4944,0.745917897717971
+"FLOW_236212",1985,12,29,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",1985,12,30,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1985,12,31,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1986,1,1,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1986,1,2,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1986,1,3,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1986,1,4,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1986,1,5,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1986,1,6,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1986,1,7,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1986,1,8,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1986,1,9,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1986,1,10,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1986,1,11,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1986,1,12,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1986,1,13,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1986,1,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1986,1,15,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1986,1,16,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1986,1,17,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1986,1,18,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1986,1,19,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1986,1,20,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1986,1,21,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1986,1,22,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1986,1,23,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1986,1,24,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1986,1,25,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1986,1,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1986,1,27,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1986,1,28,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1986,1,29,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1986,1,30,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1986,1,31,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1986,2,1,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1986,2,2,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1986,2,3,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1986,2,4,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1986,2,5,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1986,2,6,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1986,2,7,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1986,2,8,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1986,2,9,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1986,2,10,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1986,2,11,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1986,2,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1986,2,13,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1986,2,14,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1986,2,15,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1986,2,16,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1986,2,17,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1986,2,18,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1986,2,19,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1986,2,20,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1986,2,21,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1986,2,22,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1986,2,23,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1986,2,24,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1986,2,25,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1986,2,26,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1986,2,27,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1986,2,28,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1986,3,1,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1986,3,2,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1986,3,3,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1986,3,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1986,3,5,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1986,3,6,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1986,3,7,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1986,3,8,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1986,3,9,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1986,3,10,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1986,3,11,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1986,3,12,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1986,3,13,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1986,3,14,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1986,3,15,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1986,3,16,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1986,3,17,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1986,3,18,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1986,3,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1986,3,20,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1986,3,21,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1986,3,22,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1986,3,23,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1986,3,24,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1986,3,25,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1986,3,26,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1986,3,27,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1986,3,28,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1986,3,29,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1986,3,30,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1986,3,31,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1986,4,1,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1986,4,2,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1986,4,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1986,4,4,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1986,4,5,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1986,4,6,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1986,4,7,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1986,4,8,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1986,4,9,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1986,4,10,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1986,4,11,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1986,4,12,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1986,4,13,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1986,4,14,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1986,4,15,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1986,4,16,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1986,4,17,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1986,4,18,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1986,4,19,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1986,4,20,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1986,4,21,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1986,4,22,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1986,4,23,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1986,4,24,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1986,4,25,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1986,4,26,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1986,4,27,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1986,4,28,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1986,4,29,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1986,4,30,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1986,5,1,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1986,5,2,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1986,5,3,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1986,5,4,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1986,5,5,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1986,5,6,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1986,5,7,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1986,5,8,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1986,5,9,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",1986,5,10,0.509,0.197208968609865,43.9776,0.722745393545393
+"FLOW_236212",1986,5,11,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1986,5,12,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1986,5,13,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1986,5,14,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1986,5,15,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1986,5,16,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1986,5,17,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1986,5,18,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1986,5,19,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1986,5,20,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",1986,5,21,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",1986,5,22,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1986,5,23,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1986,5,24,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1986,5,25,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1986,5,26,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1986,5,27,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1986,5,28,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1986,5,29,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1986,5,30,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1986,5,31,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1986,6,1,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1986,6,2,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1986,6,3,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1986,6,4,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1986,6,5,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1986,6,6,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1986,6,7,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1986,6,8,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1986,6,9,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1986,6,10,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",1986,6,11,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",1986,6,12,0.458,0.17744932735426,39.5712,0.707644069704618
+"FLOW_236212",1986,6,13,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1986,6,14,0.437,0.169313004484305,37.7568,0.701032356130901
+"FLOW_236212",1986,6,15,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1986,6,16,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1986,6,17,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1986,6,18,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1986,6,19,4.761,1.84462062780269,411.3504,1.1302679481959
+"FLOW_236212",1986,6,20,10.952,4.24328609865471,946.2528,1.33518198173339
+"FLOW_236212",1986,6,21,7.625,2.9542600896861,658.8,1.24190891736683
+"FLOW_236212",1986,6,22,7.741,2.99920358744395,668.8224,1.24566479154021
+"FLOW_236212",1986,6,23,7.757,3.00540269058296,670.2048,1.24617930333677
+"FLOW_236212",1986,6,24,7.172,2.77874798206278,619.6608,1.22678888372186
+"FLOW_236212",1986,6,25,5.171,2.00347264573991,446.7744,1.14909698048097
+"FLOW_236212",1986,6,26,8.1,3.13829596412556,699.84,1.25701013598298
+"FLOW_236212",1986,6,27,11.146,4.31845022421525,963.0144,1.33987900980958
+"FLOW_236212",1986,6,28,8.317,3.22237130044843,718.5888,1.26367419255852
+"FLOW_236212",1986,6,29,3.974,1.53970224215247,343.3536,1.09015273436561
+"FLOW_236212",1986,6,30,2.294,0.888796412556054,198.2016,0.976698360258931
+"FLOW_236212",1986,7,1,1.561,0.6048,134.8704,0.90432045948752
+"FLOW_236212",1986,7,2,1.287,0.498640358744395,111.1968,0.870076592887204
+"FLOW_236212",1986,7,3,2.943,1.14024753363229,254.2752,1.02659661589688
+"FLOW_236212",1986,7,4,6.486,2.51296143497758,560.3904,1.20236732280766
+"FLOW_236212",1986,7,5,6.718,2.60284843049327,580.4352,1.210848409893
+"FLOW_236212",1986,7,6,3.408,1.32040896860987,294.4512,1.05716236320331
+"FLOW_236212",1986,7,7,2.09,0.809757847533632,180.576,0.958674185398631
+"FLOW_236212",1986,7,8,1.691,0.655167713004484,146.1024,0.918904751946105
+"FLOW_236212",1986,7,9,3.857,1.49437130044843,333.2448,1.08365665803424
+"FLOW_236212",1986,7,10,7.81,3.02593721973094,674.784,1.24787758181342
+"FLOW_236212",1986,7,11,6.653,2.57766457399103,574.8192,1.2084961761435
+"FLOW_236212",1986,7,12,3.567,1.38201255605381,308.1888,1.06684763242411
+"FLOW_236212",1986,7,13,2.357,0.913205381165919,203.6448,0.982004984927909
+"FLOW_236212",1986,7,14,1.667,0.645869058295964,144.0288,0.916281453024665
+"FLOW_236212",1986,7,15,1.263,0.489341704035874,109.1232,0.866807072131803
+"FLOW_236212",1986,7,16,1.051,0.407203587443946,90.8064,0.835530521535924
+"FLOW_236212",1986,7,17,1.055,0.4087533632287,91.152,0.836165544516303
+"FLOW_236212",1986,7,18,1.176,0.455634080717489,101.6064,0.85452198416815
+"FLOW_236212",1986,7,19,1.198,0.464157847533632,103.5072,0.857695515779771
+"FLOW_236212",1986,7,20,1.106,0.428513004484305,95.5584,0.844097863992855
+"FLOW_236212",1986,7,21,1.033,0.400229596412556,89.2512,0.83264876108045
+"FLOW_236212",1986,7,22,0.967,0.374658295964126,83.5488,0.821726079700363
+"FLOW_236212",1986,7,23,4.755,1.84229596412556,410.832,1.12998292282912
+"FLOW_236212",1986,7,24,12.353,4.78609506726457,1067.2992,1.36771706035245
+"FLOW_236212",1986,7,25,12.542,4.85932197309417,1083.6288,1.37187687400001
+"FLOW_236212",1986,7,26,8.465,3.27971300448431,731.376,1.26813990358595
+"FLOW_236212",1986,7,27,4.454,1.72567533632287,384.8256,1.11530029847513
+"FLOW_236212",1986,7,28,3.42,1.32505829596413,295.488,1.05790579738648
+"FLOW_236212",1986,7,29,3.593,1.39208609865471,310.4352,1.06839837629241
+"FLOW_236212",1986,7,30,3.291,1.27507802690583,284.3424,1.04980189491817
+"FLOW_236212",1986,7,31,2.804,1.08639282511211,242.2656,1.01671066158434
+"FLOW_236212",1986,8,1,3.538,1.37077668161435,305.6832,1.06510725181184
+"FLOW_236212",1986,8,2,2.815,1.09065470852018,243.216,1.01750711728853
+"FLOW_236212",1986,8,3,2.083,0.807045739910314,179.9712,0.958031149241263
+"FLOW_236212",1986,8,4,1.615,0.62572197309417,139.536,0.910492308146133
+"FLOW_236212",1986,8,5,1.263,0.489341704035874,109.1232,0.866807072131803
+"FLOW_236212",1986,8,6,1.146,0.444010762331839,99.0144,0.850116996973521
+"FLOW_236212",1986,8,7,1.023,0.396355156950673,88.3872,0.83103038390201
+"FLOW_236212",1986,8,8,0.881,0.341338116591928,76.1184,0.806560515318288
+"FLOW_236212",1986,8,9,0.765,0.296394618834081,66.096,0.784104710560857
+"FLOW_236212",1986,8,10,0.679,0.263074439461883,58.6656,0.765624338373449
+"FLOW_236212",1986,8,11,0.61,0.236340807174888,52.704,0.749389740131935
+"FLOW_236212",1986,8,12,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1986,8,13,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1986,8,14,0.497,0.192559641255605,42.9408,0.71930495772163
+"FLOW_236212",1986,8,15,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",1986,8,16,0.541,0.209607174887892,46.7424,0.731612688474363
+"FLOW_236212",1986,8,17,1.474,0.571092376681614,127.3536,0.89400772726163
+"FLOW_236212",1986,8,18,11.632,4.50674798206278,1005.0048,1.35136496256083
+"FLOW_236212",1986,8,19,11.408,4.41996053811659,985.6512,1.34611969484764
+"FLOW_236212",1986,8,20,8.992,3.48389596412556,776.9088,1.28355072257494
+"FLOW_236212",1986,8,21,5.459,2.11505650224215,471.6576,1.16162083968763
+"FLOW_236212",1986,8,22,3.24,1.25531838565022,279.936,1.0465278204493
+"FLOW_236212",1986,8,23,2.29,0.887246636771301,197.856,0.976357512728588
+"FLOW_236212",1986,8,24,2.01,0.778762331838565,173.664,0.95122003142684
+"FLOW_236212",1986,8,25,3.897,1.50986905829596,336.7008,1.08589505905828
+"FLOW_236212",1986,8,26,3.104,1.20262600896861,268.1856,1.03759081449374
+"FLOW_236212",1986,8,27,2.262,0.876398206278027,195.4368,0.973958149822029
+"FLOW_236212",1986,8,28,1.746,0.676477130044843,150.8544,0.924805968139747
+"FLOW_236212",1986,8,29,1.418,0.549395515695067,122.5152,0.8871090750329
+"FLOW_236212",1986,8,30,1.647,0.638120179372197,142.3008,0.914072189324948
+"FLOW_236212",1986,8,31,4.493,1.74078565022422,388.1952,1.11724664608328
+"FLOW_236212",1986,9,1,4.374,1.6946798206278,377.9136,1.11126473246666
+"FLOW_236212",1986,9,2,2.921,1.13172376681614,252.3744,1.02505716887897
+"FLOW_236212",1986,9,3,2.359,0.913980269058296,203.8176,0.982171581762492
+"FLOW_236212",1986,9,4,1.856,0.719095964125561,160.3584,0.936175713282572
+"FLOW_236212",1986,9,5,1.536,0.595113901345292,132.7104,0.901405116848277
+"FLOW_236212",1986,9,6,1.944,0.753191031390135,167.9616,0.94488951097956
+"FLOW_236212",1986,9,7,2.809,1.08833004484305,242.6976,1.01707299619581
+"FLOW_236212",1986,9,8,2.922,1.13211121076233,252.4608,1.02512734462885
+"FLOW_236212",1986,9,9,3.171,1.22858475336323,273.9744,1.04203192387836
+"FLOW_236212",1986,9,10,2.263,0.876785650224215,195.5232,0.974044249376931
+"FLOW_236212",1986,9,11,1.731,0.670665470852018,149.5584,0.923211465603203
+"FLOW_236212",1986,9,12,1.415,0.548233183856502,122.256,0.886733393471652
+"FLOW_236212",1986,9,13,1.559,0.604025112107623,134.6976,0.904088612140158
+"FLOW_236212",1986,9,14,2.824,1.09414170403587,243.9936,1.0181569130398
+"FLOW_236212",1986,9,15,2.583,1.00076771300448,223.1712,1.00015349547194
+"FLOW_236212",1986,9,16,2.902,1.12436233183857,250.7328,1.02372016383198
+"FLOW_236212",1986,9,17,10.937,4.23747443946188,944.9568,1.33481604472014
+"FLOW_236212",1986,9,18,12.573,4.87133273542601,1086.3072,1.37255437676929
+"FLOW_236212",1986,9,19,10.438,4.0441399103139,901.8432,1.32240728907506
+"FLOW_236212",1986,9,20,4.79,1.85585650224215,413.856,1.131641533548
+"FLOW_236212",1986,9,21,3.025,1.17201793721973,261.36,1.03225465894958
+"FLOW_236212",1986,9,22,2.592,1.00425470852018,223.9488,1.0008494971865
+"FLOW_236212",1986,9,23,1.908,0.739243049327354,164.8512,0.941363705484224
+"FLOW_236212",1986,9,24,1.519,0.58852735426009,131.2416,0.899400926596746
+"FLOW_236212",1986,9,25,1.177,0.456021524663677,101.6928,0.854667261630504
+"FLOW_236212",1986,9,26,0.917,0.355286098654709,79.2288,0.813046983781364
+"FLOW_236212",1986,9,27,0.801,0.310342600896861,69.2064,0.79134940307706
+"FLOW_236212",1986,9,28,0.706,0.273535426008969,60.9984,0.771618650200755
+"FLOW_236212",1986,9,29,0.612,0.237115695067265,52.8768,0.749880500042019
+"FLOW_236212",1986,9,30,0.548,0.212319282511211,47.3472,0.733496232652639
+"FLOW_236212",1986,10,1,0.515,0.199533632286996,44.496,0.724441334124209
+"FLOW_236212",1986,10,2,0.941,0.364584753363229,81.3024,0.817258978658932
+"FLOW_236212",1986,10,3,1.479,0.573029596412556,127.7856,0.894613424109673
+"FLOW_236212",1986,10,4,1.195,0.462995515695067,103.248,0.85726552115392
+"FLOW_236212",1986,10,5,1.484,0.574966816143498,128.2176,0.895217485041194
+"FLOW_236212",1986,10,6,1.983,0.76830134529148,171.3312,0.948650672070861
+"FLOW_236212",1986,10,7,6.568,2.54473183856502,567.4752,1.20539227730803
+"FLOW_236212",1986,10,8,5.014,1.94264394618834,433.2096,1.14203296954013
+"FLOW_236212",1986,10,9,2.554,0.989531838565022,220.6656,0.99789754565192
+"FLOW_236212",1986,10,10,1.65,0.639282511210762,142.56,0.914404942303305
+"FLOW_236212",1986,10,11,1.124,0.435486995515695,97.1136,0.846827666157952
+"FLOW_236212",1986,10,12,0.831,0.321965919282511,71.7984,0.797190254040957
+"FLOW_236212",1986,10,13,0.677,0.262299551569507,58.4928,0.765172775488641
+"FLOW_236212",1986,10,14,0.711,0.27547264573991,61.4304,0.772708511396718
+"FLOW_236212",1986,10,15,1.073,0.41572735426009,92.7072,0.838999535290296
+"FLOW_236212",1986,10,16,3.029,1.17356771300448,261.7056,1.03252750763871
+"FLOW_236212",1986,10,17,2.456,0.951562331838565,212.1984,0.990119122758863
+"FLOW_236212",1986,10,18,1.772,0.68655067264574,153.1008,0.927543997754614
+"FLOW_236212",1986,10,19,3.06,1.18557847533632,264.384,1.03463236845934
+"FLOW_236212",1986,10,20,3.589,1.39053632286996,310.0896,1.06816038587479
+"FLOW_236212",1986,10,21,3.468,1.34365560538117,299.6352,1.06085882532917
+"FLOW_236212",1986,10,22,11.082,4.29365381165919,957.4848,1.33833675442337
+"FLOW_236212",1986,10,23,30.178,11.6922834080718,2607.3792,1.63523384287623
+"FLOW_236212",1986,10,24,20.225,7.83605381165919,1747.44,1.50945260741648
+"FLOW_236212",1986,10,25,12.77,4.94765919282511,1103.328,1.3768288428353
+"FLOW_236212",1986,10,26,7.285,2.82252914798206,629.424,1.23063053584138
+"FLOW_236212",1986,10,27,3.634,1.40797130044843,313.9776,1.07082563761543
+"FLOW_236212",1986,10,28,2.396,0.928315695067265,207.0144,0.985233428064557
+"FLOW_236212",1986,10,29,1.759,0.681513901345291,151.9776,0.926179030102946
+"FLOW_236212",1986,10,30,1.317,0.510263677130045,113.7888,0.874095593513835
+"FLOW_236212",1986,10,31,0.956,0.370396412556054,82.5984,0.81984802413936
+"FLOW_236212",1986,11,1,0.881,0.341338116591928,76.1184,0.806560515318288
+"FLOW_236212",1986,11,2,0.816,0.316154260089686,70.5024,0.794291303582111
+"FLOW_236212",1986,11,3,0.714,0.276634977578475,61.6896,0.773359488319869
+"FLOW_236212",1986,11,4,0.614,0.237890582959641,53.0496,0.750369978592176
+"FLOW_236212",1986,11,5,0.53,0.205345291479821,45.792,0.728613057204925
+"FLOW_236212",1986,11,6,0.509,0.197208968609865,43.9776,0.722745393545393
+"FLOW_236212",1986,11,7,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",1986,11,8,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",1986,11,9,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1986,11,10,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",1986,11,11,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",1986,11,12,0.411,0.159239461883408,35.5104,0.692484632829216
+"FLOW_236212",1986,11,13,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1986,11,14,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1986,11,15,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1986,11,16,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1986,11,17,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1986,11,18,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1986,11,19,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1986,11,20,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1986,11,21,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1986,11,22,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1986,11,23,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1986,11,24,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1986,11,25,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1986,11,26,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1986,11,27,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1986,11,28,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1986,11,29,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1986,11,30,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1986,12,1,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1986,12,2,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1986,12,3,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1986,12,4,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1986,12,5,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1986,12,6,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1986,12,7,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",1986,12,8,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",1986,12,9,0.437,0.169313004484305,37.7568,0.701032356130901
+"FLOW_236212",1986,12,10,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1986,12,11,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1986,12,12,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1986,12,13,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",1986,12,14,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",1986,12,15,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1986,12,16,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1986,12,17,0.817,0.316541704035874,70.5888,0.794485887462295
+"FLOW_236212",1986,12,18,0.649,0.251451121076233,56.0736,0.758736042939418
+"FLOW_236212",1986,12,19,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",1986,12,20,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1986,12,21,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1986,12,22,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1986,12,23,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1986,12,24,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1986,12,25,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1986,12,26,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1986,12,27,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1986,12,28,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1986,12,29,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1986,12,30,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1986,12,31,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1987,1,1,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1987,1,2,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1987,1,3,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1987,1,4,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1987,1,5,1.343,0.520337219730942,116.0352,0.877519908963673
+"FLOW_236212",1987,1,6,1.218,0.471906726457399,105.2352,0.860540339514474
+"FLOW_236212",1987,1,7,1.12,0.433937219730942,96.768,0.846224082033301
+"FLOW_236212",1987,1,8,0.761,0.294844843049327,65.7504,0.783283011390142
+"FLOW_236212",1987,1,9,0.55,0.213094170403587,47.52,0.734030851465829
+"FLOW_236212",1987,1,10,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",1987,1,11,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1987,1,12,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1987,1,13,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1987,1,14,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1987,1,15,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1987,1,16,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1987,1,17,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1987,1,18,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1987,1,19,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1987,1,20,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1987,1,21,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1987,1,22,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1987,1,23,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1987,1,24,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1987,1,25,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1987,1,26,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1987,1,27,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1987,1,28,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1987,1,29,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1987,1,30,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1987,1,31,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1987,2,1,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1987,2,2,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1987,2,3,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1987,2,4,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1987,2,5,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1987,2,6,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1987,2,7,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1987,2,8,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1987,2,9,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1987,2,10,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1987,2,11,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1987,2,12,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1987,2,13,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1987,2,14,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1987,2,15,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1987,2,16,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1987,2,17,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1987,2,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1987,2,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1987,2,20,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1987,2,21,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1987,2,22,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1987,2,23,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1987,2,24,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1987,2,25,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1987,2,26,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1987,2,27,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1987,2,28,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1987,3,1,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1987,3,2,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1987,3,3,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,3,4,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,3,5,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1987,3,6,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1987,3,7,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1987,3,8,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1987,3,9,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1987,3,10,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1987,3,11,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1987,3,12,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1987,3,13,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1987,3,14,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1987,3,15,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1987,3,16,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,3,17,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1987,3,18,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1987,3,19,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1987,3,20,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1987,3,21,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1987,3,22,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1987,3,23,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1987,3,24,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1987,3,25,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1987,3,26,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1987,3,27,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1987,3,28,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1987,3,29,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1987,3,30,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",1987,3,31,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1987,4,1,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1987,4,2,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1987,4,3,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1987,4,4,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1987,4,5,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1987,4,6,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1987,4,7,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1987,4,8,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1987,4,9,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1987,4,10,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1987,4,11,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1987,4,12,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1987,4,13,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1987,4,14,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,4,15,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,4,16,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1987,4,17,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,4,18,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,4,19,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,4,20,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1987,4,21,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1987,4,22,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1987,4,23,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,4,24,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1987,4,25,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1987,4,26,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1987,4,27,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1987,4,28,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1987,4,29,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1987,4,30,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1987,5,1,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1987,5,2,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1987,5,3,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1987,5,4,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1987,5,5,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1987,5,6,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1987,5,7,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1987,5,8,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1987,5,9,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1987,5,10,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1987,5,11,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1987,5,12,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1987,5,13,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1987,5,14,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",1987,5,15,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1987,5,16,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1987,5,17,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1987,5,18,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1987,5,19,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1987,5,20,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1987,5,21,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1987,5,22,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1987,5,23,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1987,5,24,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1987,5,25,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1987,5,26,0.503,0.194884304932735,43.4592,0.721033383642924
+"FLOW_236212",1987,5,27,0.74,0.286708520179372,63.936,0.778911507062446
+"FLOW_236212",1987,5,28,0.731,0.283221524663677,63.1584,0.777007572397622
+"FLOW_236212",1987,5,29,0.564,0.218518385650224,48.7296,0.737730258466806
+"FLOW_236212",1987,5,30,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",1987,5,31,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",1987,6,1,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1987,6,2,0.411,0.159239461883408,35.5104,0.692484632829216
+"FLOW_236212",1987,6,3,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1987,6,4,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1987,6,5,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1987,6,6,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",1987,6,7,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",1987,6,8,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1987,6,9,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1987,6,10,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1987,6,11,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1987,6,12,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1987,6,13,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1987,6,14,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1987,6,15,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",1987,6,16,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1987,6,17,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1987,6,18,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1987,6,19,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1987,6,20,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1987,6,21,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1987,6,22,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",1987,6,23,0.938,0.363422421524664,81.0432,0.816737212552251
+"FLOW_236212",1987,6,24,1.05,0.406816143497758,90.72,0.835371463743786
+"FLOW_236212",1987,6,25,1.209,0.468419730941704,104.4576,0.859264829450014
+"FLOW_236212",1987,6,26,2.86,1.10808968609865,247.104,1.02073964415465
+"FLOW_236212",1987,6,27,3.703,1.43470493273543,319.9392,1.07486152707165
+"FLOW_236212",1987,6,28,3.156,1.2227730941704,272.6784,1.04104421411798
+"FLOW_236212",1987,6,29,2.701,1.04648609865471,233.3664,1.00912901339804
+"FLOW_236212",1987,6,30,3.44,1.33280717488789,297.216,1.05914022991452
+"FLOW_236212",1987,7,1,2.715,1.05191031390135,234.576,1.01017296791008
+"FLOW_236212",1987,7,2,1.76,0.68190134529148,152.064,0.926284313614829
+"FLOW_236212",1987,7,3,1.205,0.466869955156951,104.112,0.85869549661307
+"FLOW_236212",1987,7,4,0.876,0.339400896860987,75.6864,0.805642924187095
+"FLOW_236212",1987,7,5,0.71,0.275085201793722,61.344,0.772491030779259
+"FLOW_236212",1987,7,6,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1987,7,7,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",1987,7,8,0.49,0.189847533632287,42.336,0.717267233870557
+"FLOW_236212",1987,7,9,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",1987,7,10,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1987,7,11,0.497,0.192559641255605,42.9408,0.71930495772163
+"FLOW_236212",1987,7,12,0.814,0.315379372197309,70.3296,0.793901562813638
+"FLOW_236212",1987,7,13,1.057,0.409528251121076,91.3248,0.836482333978136
+"FLOW_236212",1987,7,14,0.987,0.382407174887892,85.2768,0.825097376872724
+"FLOW_236212",1987,7,15,2.469,0.956599103139013,213.3216,0.991165082234677
+"FLOW_236212",1987,7,16,5.725,2.21811659192825,494.64,1.17272686483964
+"FLOW_236212",1987,7,17,4.523,1.75240896860987,390.7872,1.11873466021037
+"FLOW_236212",1987,7,18,2.883,1.11700089686099,249.0912,1.02237613742168
+"FLOW_236212",1987,7,19,4.102,1.58929506726457,354.4128,1.09708659128044
+"FLOW_236212",1987,7,20,5.635,2.1832466367713,486.864,1.16901628180323
+"FLOW_236212",1987,7,21,3.063,1.18674080717489,264.6432,1.03483515804146
+"FLOW_236212",1987,7,22,1.919,0.743504932735426,165.8016,0.942446640869302
+"FLOW_236212",1987,7,23,1.339,0.518787443946188,115.6896,0.876996562887401
+"FLOW_236212",1987,7,24,1.02,0.395192825112108,88.128,0.830542403304738
+"FLOW_236212",1987,7,25,0.84,0.325452914798206,72.576,0.798909587604632
+"FLOW_236212",1987,7,26,0.722,0.279734529147982,62.3808,0.775084791560821
+"FLOW_236212",1987,7,27,0.634,0.245639461883408,54.7776,0.755195906911555
+"FLOW_236212",1987,7,28,0.561,0.217356053811659,48.4704,0.73694376386518
+"FLOW_236212",1987,7,29,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",1987,7,30,0.484,0.187522869955157,41.8176,0.715501993673772
+"FLOW_236212",1987,7,31,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1987,8,1,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1987,8,2,0.446,0.1728,38.5344,0.703896405787105
+"FLOW_236212",1987,8,3,0.434,0.16815067264574,37.4976,0.700067186306734
+"FLOW_236212",1987,8,4,0.438,0.169700448430493,37.8432,0.701352901466612
+"FLOW_236212",1987,8,5,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",1987,8,6,0.591,0.228979372197309,51.0624,0.744662136557278
+"FLOW_236212",1987,8,7,0.665,0.257650224215247,57.456,0.762440758876723
+"FLOW_236212",1987,8,8,0.663,0.25687533632287,57.2832,0.761981595183807
+"FLOW_236212",1987,8,9,0.69,0.267336322869955,59.616,0.768089085813831
+"FLOW_236212",1987,8,10,0.899,0.348312107623318,77.6736,0.809829726159191
+"FLOW_236212",1987,8,11,0.862,0.33397668161435,74.4768,0.803051188257518
+"FLOW_236212",1987,8,12,0.708,0.274310313901345,61.1712,0.772055333361098
+"FLOW_236212",1987,8,13,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",1987,8,14,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",1987,8,15,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",1987,8,16,0.483,0.187135426008969,41.7312,0.715206087040821
+"FLOW_236212",1987,8,17,0.48,0.185973094170404,41.472,0.714315416671224
+"FLOW_236212",1987,8,18,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1987,8,19,0.45,0.174349775784753,38.88,0.705154494972067
+"FLOW_236212",1987,8,20,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",1987,8,21,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",1987,8,22,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1987,8,23,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1987,8,24,1.187,0.459895964125561,102.5568,0.856114631926341
+"FLOW_236212",1987,8,25,3.497,1.35489147982063,302.1408,1.06262713644248
+"FLOW_236212",1987,8,26,2.152,0.833779372197309,185.9328,0.964295698070274
+"FLOW_236212",1987,8,27,1.269,0.491666367713004,109.6416,0.867629081264516
+"FLOW_236212",1987,8,28,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",1987,8,29,0.665,0.257650224215247,57.456,0.762440758876723
+"FLOW_236212",1987,8,30,0.556,0.215418834080718,48.0384,0.735625430260761
+"FLOW_236212",1987,8,31,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1987,9,1,0.827,0.320416143497758,71.4528,0.796421320606559
+"FLOW_236212",1987,9,2,1.723,0.667565919282511,148.8672,0.922356539755565
+"FLOW_236212",1987,9,3,1.63,0.631533632286995,140.832,0.912177376387142
+"FLOW_236212",1987,9,4,2.251,0.872136322869955,194.4864,0.973009039261639
+"FLOW_236212",1987,9,5,1.775,0.687713004484305,153.36,0.927857852100469
+"FLOW_236212",1987,9,6,1.255,0.486242152466368,108.432,0.865706186441681
+"FLOW_236212",1987,9,7,0.995,0.385506726457399,85.968,0.826430605201494
+"FLOW_236212",1987,9,8,0.909,0.352186547085202,78.5376,0.811623386541857
+"FLOW_236212",1987,9,9,1.394,0.540096860986547,120.4416,0.884085628105899
+"FLOW_236212",1987,9,10,2.414,0.935289686098655,208.5696,0.98670931698248
+"FLOW_236212",1987,9,11,2.721,1.05423497757848,235.0944,1.01061905903916
+"FLOW_236212",1987,9,12,1.925,0.745829596412556,166.32,0.943035241291404
+"FLOW_236212",1987,9,13,1.414,0.547845739910314,122.1696,0.886608024684867
+"FLOW_236212",1987,9,14,1.11,0.430062780269058,95.904,0.84470754167028
+"FLOW_236212",1987,9,15,0.993,0.384731838565022,85.7952,0.826098104347418
+"FLOW_236212",1987,9,16,0.916,0.35489865470852,79.1424,0.812869578793101
+"FLOW_236212",1987,9,17,0.819,0.317316591928251,70.7616,0.794874484316053
+"FLOW_236212",1987,9,18,0.707,0.273922869955157,61.0848,0.771837115312275
+"FLOW_236212",1987,9,19,0.612,0.237115695067265,52.8768,0.749880500042019
+"FLOW_236212",1987,9,20,0.583,0.225879820627803,50.3712,0.742635125500896
+"FLOW_236212",1987,9,21,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",1987,9,22,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",1987,9,23,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1987,9,24,0.421,0.163113901345291,36.3744,0.695822062827897
+"FLOW_236212",1987,9,25,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1987,9,26,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1987,9,27,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1987,9,28,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1987,9,29,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1987,9,30,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1987,10,1,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1987,10,2,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1987,10,3,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1987,10,4,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1987,10,5,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1987,10,6,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1987,10,7,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",1987,10,8,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1987,10,9,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1987,10,10,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1987,10,11,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1987,10,12,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1987,10,13,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1987,10,14,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1987,10,15,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1987,10,16,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1987,10,17,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1987,10,18,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",1987,10,19,1.146,0.444010762331839,99.0144,0.850116996973521
+"FLOW_236212",1987,10,20,1.509,0.584652914798206,130.3776,0.898213594499655
+"FLOW_236212",1987,10,21,1.047,0.405653811659193,90.4608,0.834893562135835
+"FLOW_236212",1987,10,22,0.723,0.28012197309417,62.4672,0.775299377637887
+"FLOW_236212",1987,10,23,0.535,0.207282511210762,46.224,0.729982640289904
+"FLOW_236212",1987,10,24,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1987,10,25,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",1987,10,26,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1987,10,27,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1987,10,28,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1987,10,29,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1987,10,30,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1987,10,31,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1987,11,1,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,11,2,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1987,11,3,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1987,11,4,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1987,11,5,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,11,6,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1987,11,7,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,11,8,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1987,11,9,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1987,11,10,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1987,11,11,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1987,11,12,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1987,11,13,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1987,11,14,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1987,11,15,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1987,11,16,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1987,11,17,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1987,11,18,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1987,11,19,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1987,11,20,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1987,11,21,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1987,11,22,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1987,11,23,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1987,11,24,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1987,11,25,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1987,11,26,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1987,11,27,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1987,11,28,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1987,11,29,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1987,11,30,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1987,12,1,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1987,12,2,0.9,0.348699551569507,77.76,0.810009808393178
+"FLOW_236212",1987,12,3,0.594,0.230141704035874,51.3216,0.745416608355256
+"FLOW_236212",1987,12,4,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1987,12,5,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",1987,12,6,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1987,12,7,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1987,12,8,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1987,12,9,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1987,12,10,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1987,12,11,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1987,12,12,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1987,12,13,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1987,12,14,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1987,12,15,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1987,12,16,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1987,12,17,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1987,12,18,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1987,12,19,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1987,12,20,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1987,12,21,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1987,12,22,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1987,12,23,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1987,12,24,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1987,12,25,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1987,12,26,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1987,12,27,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1987,12,28,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1987,12,29,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1987,12,30,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1987,12,31,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1988,1,1,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1988,1,2,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1988,1,3,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1988,1,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1988,1,5,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1988,1,6,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1988,1,7,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1988,1,8,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1988,1,9,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1988,1,10,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1988,1,11,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1988,1,12,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1988,1,13,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1988,1,14,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1988,1,15,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1988,1,16,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1988,1,17,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1988,1,18,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1988,1,19,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1988,1,20,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1988,1,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1988,1,22,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1988,1,23,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1988,1,24,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1988,1,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1988,1,26,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,1,27,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1988,1,28,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1988,1,29,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1988,1,30,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1988,1,31,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1988,2,1,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1988,2,2,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1988,2,3,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1988,2,4,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1988,2,5,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1988,2,6,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1988,2,7,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1988,2,8,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1988,2,9,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1988,2,10,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,2,11,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1988,2,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1988,2,13,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1988,2,14,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1988,2,15,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,2,16,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1988,2,17,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1988,2,18,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1988,2,19,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1988,2,20,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1988,2,21,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1988,2,22,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1988,2,23,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1988,2,24,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1988,2,25,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1988,2,26,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1988,2,27,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1988,2,28,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1988,2,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1988,3,1,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1988,3,2,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1988,3,3,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1988,3,4,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1988,3,5,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1988,3,6,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1988,3,7,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1988,3,8,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1988,3,9,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1988,3,10,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1988,3,11,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1988,3,12,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1988,3,13,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1988,3,14,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1988,3,15,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1988,3,16,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1988,3,17,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1988,3,18,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1988,3,19,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1988,3,20,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1988,3,21,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1988,3,22,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,3,23,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1988,3,24,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1988,3,25,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1988,3,26,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1988,3,27,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1988,3,28,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1988,3,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1988,3,30,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1988,3,31,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1988,4,1,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1988,4,2,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1988,4,3,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1988,4,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1988,4,5,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1988,4,6,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1988,4,7,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1988,4,8,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1988,4,9,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1988,4,10,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1988,4,11,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1988,4,12,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1988,4,13,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1988,4,14,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1988,4,15,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1988,4,16,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1988,4,17,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1988,4,18,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1988,4,19,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1988,4,20,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1988,4,21,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1988,4,22,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1988,4,23,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1988,4,24,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,4,25,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1988,4,26,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1988,4,27,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1988,4,28,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1988,4,29,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1988,4,30,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1988,5,1,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1988,5,2,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1988,5,3,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1988,5,4,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,5,5,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1988,5,6,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1988,5,7,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1988,5,8,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1988,5,9,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1988,5,10,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1988,5,11,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1988,5,12,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1988,5,13,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1988,5,14,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1988,5,15,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1988,5,16,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1988,5,17,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1988,5,18,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1988,5,19,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1988,5,20,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1988,5,21,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1988,5,22,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1988,5,23,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1988,5,24,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1988,5,25,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1988,5,26,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1988,5,27,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1988,5,28,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1988,5,29,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1988,5,30,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1988,5,31,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1988,6,1,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1988,6,2,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1988,6,3,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1988,6,4,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1988,6,5,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1988,6,6,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1988,6,7,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1988,6,8,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1988,6,9,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1988,6,10,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1988,6,11,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1988,6,12,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1988,6,13,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1988,6,14,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1988,6,15,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1988,6,16,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1988,6,17,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1988,6,18,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1988,6,19,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1988,6,20,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1988,6,21,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1988,6,22,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1988,6,23,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1988,6,24,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1988,6,25,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1988,6,26,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1988,6,27,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1988,6,28,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1988,6,29,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1988,6,30,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1988,7,1,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1988,7,2,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1988,7,3,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1988,7,4,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1988,7,5,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1988,7,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1988,7,7,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1988,7,8,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1988,7,9,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1988,7,10,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1988,7,11,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1988,7,12,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1988,7,13,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1988,7,14,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1988,7,15,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1988,7,16,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1988,7,17,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1988,7,18,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1988,7,19,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1988,7,20,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1988,7,21,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1988,7,22,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1988,7,23,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1988,7,24,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1988,7,25,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1988,7,26,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",1988,7,27,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",1988,7,28,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1988,7,29,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1988,7,30,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1988,7,31,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1988,8,1,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1988,8,2,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1988,8,3,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1988,8,4,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1988,8,5,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1988,8,6,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1988,8,7,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",1988,8,8,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",1988,8,9,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",1988,8,10,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",1988,8,11,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",1988,8,12,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",1988,8,13,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",1988,8,14,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1988,8,15,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1988,8,16,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1988,8,17,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1988,8,18,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",1988,8,19,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1988,8,20,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1988,8,21,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",1988,8,22,1.335,0.517237668161435,115.344,0.876471964597777
+"FLOW_236212",1988,8,23,0.93,0.360322869955157,80.352,0.81533928015676
+"FLOW_236212",1988,8,24,0.7,0.271210762331839,60.48,0.770302635744216
+"FLOW_236212",1988,8,25,0.54,0.209219730941704,46.656,0.731342021478882
+"FLOW_236212",1988,8,26,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1988,8,27,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1988,8,28,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1988,8,29,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1988,8,30,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1988,8,31,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1988,9,1,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1988,9,2,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1988,9,3,0.842,0.326227802690583,72.7488,0.799289658941048
+"FLOW_236212",1988,9,4,1.564,0.605962331838565,135.1296,0.904667785332956
+"FLOW_236212",1988,9,5,1.095,0.424251121076233,94.608,0.842412106795319
+"FLOW_236212",1988,9,6,0.742,0.287483408071749,64.1088,0.779332085875151
+"FLOW_236212",1988,9,7,0.564,0.218518385650224,48.7296,0.737730258466806
+"FLOW_236212",1988,9,8,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",1988,9,9,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",1988,9,10,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1988,9,11,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1988,9,12,0.752,0.291357847533632,64.9728,0.781421478031143
+"FLOW_236212",1988,9,13,5.108,1.97906367713004,441.3312,1.14628326914594
+"FLOW_236212",1988,9,14,2.817,1.09142959641256,243.3888,1.01765165982114
+"FLOW_236212",1988,9,15,1.375,0.532735426008969,118.8,0.881662390991802
+"FLOW_236212",1988,9,16,0.876,0.339400896860987,75.6864,0.805642924187095
+"FLOW_236212",1988,9,17,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",1988,9,18,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1988,9,19,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",1988,9,20,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1988,9,21,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",1988,9,22,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",1988,9,23,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1988,9,24,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1988,9,25,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1988,9,26,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1988,9,27,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1988,9,28,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1988,9,29,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1988,9,30,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1988,10,1,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1988,10,2,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1988,10,3,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1988,10,4,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1988,10,5,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1988,10,6,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1988,10,7,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1988,10,8,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1988,10,9,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1988,10,10,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1988,10,11,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1988,10,12,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1988,10,13,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1988,10,14,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1988,10,15,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1988,10,16,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1988,10,17,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1988,10,18,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",1988,10,19,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",1988,10,20,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",1988,10,21,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1988,10,22,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1988,10,23,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1988,10,24,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1988,10,25,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1988,10,26,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1988,10,27,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1988,10,28,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1988,10,29,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1988,10,30,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1988,10,31,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1988,11,1,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1988,11,2,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1988,11,3,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1988,11,4,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1988,11,5,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1988,11,6,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1988,11,7,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1988,11,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1988,11,9,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1988,11,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1988,11,11,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1988,11,12,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1988,11,13,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1988,11,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1988,11,15,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1988,11,16,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1988,11,17,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1988,11,18,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1988,11,19,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1988,11,20,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1988,11,21,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1988,11,22,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1988,11,23,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1988,11,24,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1988,11,25,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1988,11,26,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1988,11,27,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1988,11,28,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1988,11,29,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1988,11,30,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1988,12,1,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1988,12,2,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1988,12,3,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1988,12,4,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1988,12,5,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1988,12,6,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1988,12,7,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1988,12,8,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1988,12,9,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1988,12,10,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1988,12,11,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1988,12,12,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1988,12,13,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1988,12,14,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1988,12,15,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1988,12,16,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1988,12,17,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1988,12,18,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1988,12,19,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1988,12,20,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1988,12,21,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1988,12,22,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1988,12,23,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1988,12,24,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1988,12,25,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1988,12,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1988,12,27,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1988,12,28,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1988,12,29,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1988,12,30,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1988,12,31,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1989,1,1,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1989,1,2,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1989,1,3,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1989,1,4,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1989,1,5,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1989,1,6,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1989,1,7,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1989,1,8,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1989,1,9,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1989,1,10,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1989,1,11,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1989,1,12,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1989,1,13,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1989,1,14,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1989,1,15,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1989,1,16,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1989,1,17,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1989,1,18,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1989,1,19,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1989,1,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1989,1,21,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1989,1,22,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1989,1,23,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1989,1,24,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1989,1,25,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1989,1,26,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1989,1,27,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1989,1,28,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1989,1,29,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1989,1,30,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",1989,1,31,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1989,2,1,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1989,2,2,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1989,2,3,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1989,2,4,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",1989,2,5,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1989,2,6,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1989,2,7,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1989,2,8,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1989,2,9,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1989,2,10,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1989,2,11,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1989,2,12,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1989,2,13,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1989,2,14,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1989,2,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1989,2,16,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1989,2,17,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1989,2,18,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1989,2,19,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1989,2,20,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1989,2,21,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1989,2,22,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1989,2,23,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1989,2,24,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1989,2,25,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1989,2,26,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1989,2,27,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1989,2,28,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1989,3,1,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1989,3,2,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1989,3,3,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1989,3,4,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1989,3,5,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1989,3,6,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1989,3,7,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1989,3,8,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1989,3,9,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1989,3,10,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1989,3,11,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1989,3,12,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1989,3,13,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1989,3,14,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1989,3,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1989,3,16,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1989,3,17,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1989,3,18,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1989,3,19,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1989,3,20,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1989,3,21,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1989,3,22,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1989,3,23,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1989,3,24,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1989,3,25,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1989,3,26,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1989,3,27,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1989,3,28,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1989,3,29,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1989,3,30,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1989,3,31,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1989,4,1,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1989,4,2,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1989,4,3,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1989,4,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1989,4,5,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1989,4,6,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1989,4,7,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1989,4,8,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1989,4,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1989,4,10,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1989,4,11,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1989,4,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1989,4,13,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1989,4,14,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1989,4,15,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1989,4,16,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1989,4,17,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1989,4,18,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1989,4,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1989,4,20,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1989,4,21,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1989,4,22,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1989,4,23,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1989,4,24,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1989,4,25,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1989,4,26,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1989,4,27,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1989,4,28,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1989,4,29,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1989,4,30,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1989,5,1,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1989,5,2,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1989,5,3,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1989,5,4,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1989,5,5,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1989,5,6,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1989,5,7,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1989,5,8,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1989,5,9,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1989,5,10,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1989,5,11,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1989,5,12,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1989,5,13,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1989,5,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1989,5,15,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1989,5,16,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1989,5,17,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1989,5,18,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1989,5,19,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1989,5,20,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1989,5,21,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1989,5,22,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1989,5,23,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1989,5,24,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1989,5,25,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1989,5,26,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1989,5,27,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1989,5,28,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1989,5,29,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1989,5,30,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1989,5,31,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1989,6,1,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1989,6,2,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1989,6,3,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1989,6,4,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1989,6,5,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1989,6,6,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1989,6,7,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1989,6,8,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1989,6,9,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1989,6,10,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1989,6,11,0.509,0.197208968609865,43.9776,0.722745393545393
+"FLOW_236212",1989,6,12,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1989,6,13,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1989,6,14,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1989,6,15,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1989,6,16,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",1989,6,17,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",1989,6,18,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1989,6,19,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",1989,6,20,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1989,6,21,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1989,6,22,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1989,6,23,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1989,6,24,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1989,6,25,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1989,6,26,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1989,6,27,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1989,6,28,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1989,6,29,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1989,6,30,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1989,7,1,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1989,7,2,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1989,7,3,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1989,7,4,0.651,0.25222600896861,56.2464,0.759203101491453
+"FLOW_236212",1989,7,5,0.575,0.222780269058296,49.68,0.740585738947927
+"FLOW_236212",1989,7,6,0.638,0.247189237668161,55.1232,0.756146439697201
+"FLOW_236212",1989,7,7,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",1989,7,8,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",1989,7,9,0.501,0.194109417040359,43.2864,0.720459083126107
+"FLOW_236212",1989,7,10,0.424,0.164276233183857,36.6336,0.696810918637591
+"FLOW_236212",1989,7,11,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1989,7,12,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",1989,7,13,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1989,7,14,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1989,7,15,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1989,7,16,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1989,7,17,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1989,7,18,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1989,7,19,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1989,7,20,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1989,7,21,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1989,7,22,0.695,0.269273542600897,60.048,0.769199045772625
+"FLOW_236212",1989,7,23,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1989,7,24,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",1989,7,25,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1989,7,26,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1989,7,27,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1989,7,28,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",1989,7,29,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",1989,7,30,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1989,7,31,3.027,1.17279282511211,261.5328,1.03239111934947
+"FLOW_236212",1989,8,1,9.763,3.78261524663677,843.5232,1.3048435218001
+"FLOW_236212",1989,8,2,3.636,1.40874618834081,314.1504,1.07094347911027
+"FLOW_236212",1989,8,3,1.962,0.760165022421525,169.5168,0.946632861418885
+"FLOW_236212",1989,8,4,1.211,0.469194618834081,104.6304,0.859548930960111
+"FLOW_236212",1989,8,5,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",1989,8,6,0.768,0.297556950672646,66.3552,0.784718732230276
+"FLOW_236212",1989,8,7,1.303,0.504839461883408,112.5792,0.872229275074883
+"FLOW_236212",1989,8,8,3.132,1.21347443946188,270.6048,1.03945603858247
+"FLOW_236212",1989,8,9,4.75,1.84035874439462,410.4,1.12974518181862
+"FLOW_236212",1989,8,10,5.633,2.18247174887892,486.6912,1.16893328749748
+"FLOW_236212",1989,8,11,5.508,2.13404125560538,475.8912,1.16369873495564
+"FLOW_236212",1989,8,12,2.521,0.976746188340807,217.8144,0.995305364921579
+"FLOW_236212",1989,8,13,1.47,0.569542600896861,127.008,0.893521985204992
+"FLOW_236212",1989,8,14,1.051,0.407203587443946,90.8064,0.835530521535924
+"FLOW_236212",1989,8,15,0.824,0.319253811659193,71.1936,0.7958426656089
+"FLOW_236212",1989,8,16,0.715,0.277022421524664,61.776,0.773575994369569
+"FLOW_236212",1989,8,17,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",1989,8,18,0.595,0.230529147982063,51.408,0.745667421537325
+"FLOW_236212",1989,8,19,0.537,0.208057399103139,46.3968,0.730527607407242
+"FLOW_236212",1989,8,20,0.511,0.197983856502242,44.1504,0.723312475760448
+"FLOW_236212",1989,8,21,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",1989,8,22,0.918,0.355673542600897,79.3152,0.813224234067184
+"FLOW_236212",1989,8,23,9.307,3.60594080717489,804.1248,1.29242014472147
+"FLOW_236212",1989,8,24,11.886,4.60515874439462,1026.9504,1.35721584449443
+"FLOW_236212",1989,8,25,5.718,2.21540448430493,494.0352,1.17243994410857
+"FLOW_236212",1989,8,26,3.759,1.45640179372197,324.7776,1.07809303681549
+"FLOW_236212",1989,8,27,3.322,1.28708878923767,287.0208,1.05177223383003
+"FLOW_236212",1989,8,28,3.182,1.2328466367713,274.9248,1.04275387148578
+"FLOW_236212",1989,8,29,4.726,1.8310600896861,408.3264,1.12860123025369
+"FLOW_236212",1989,8,30,8.133,3.15108161434978,702.6912,1.25803270141628
+"FLOW_236212",1989,8,31,4.215,1.63307623318386,364.176,1.10306547479822
+"FLOW_236212",1989,9,1,6.005,2.32660089686099,518.832,1.1839800326176
+"FLOW_236212",1989,9,2,6.781,2.62725739910314,585.8784,1.21311095533185
+"FLOW_236212",1989,9,3,2.867,1.11080179372197,247.7088,1.02123881845435
+"FLOW_236212",1989,9,4,1.726,0.668728251121076,149.1264,0.922677508354255
+"FLOW_236212",1989,9,5,1.209,0.468419730941704,104.4576,0.859264829450014
+"FLOW_236212",1989,9,6,0.921,0.356835874439462,79.5744,0.813755059737436
+"FLOW_236212",1989,9,7,0.749,0.290195515695067,64.7136,0.780797006153262
+"FLOW_236212",1989,9,8,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",1989,9,9,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",1989,9,10,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",1989,9,11,0.563,0.218130941704036,48.6432,0.737468466258041
+"FLOW_236212",1989,9,12,0.718,0.278184753363229,62.0352,0.774224062294529
+"FLOW_236212",1989,9,13,0.917,0.355286098654709,79.2288,0.813046983781364
+"FLOW_236212",1989,9,14,1.152,0.446335426008969,99.5328,0.85100531343378
+"FLOW_236212",1989,9,15,1.217,0.471519282511211,105.1488,0.860398989253969
+"FLOW_236212",1989,9,16,1.674,0.648581165919282,144.6336,0.917049686248389
+"FLOW_236212",1989,9,17,1.544,0.598213452914798,133.4016,0.902342130428207
+"FLOW_236212",1989,9,18,1.369,0.530410762331839,118.2816,0.880891593609321
+"FLOW_236212",1989,9,19,2.937,1.13792286995516,253.7568,1.02617768222707
+"FLOW_236212",1989,9,20,2.762,1.0701201793722,238.6368,1.01364646635671
+"FLOW_236212",1989,9,21,1.64,0.635408071748879,141.696,0.913293875907204
+"FLOW_236212",1989,9,22,1.298,0.502902242152466,112.1472,0.871558844401469
+"FLOW_236212",1989,9,23,1.626,0.629983856502242,140.4864,0.911729241866476
+"FLOW_236212",1989,9,24,1.459,0.565280717488789,126.0576,0.89218072071746
+"FLOW_236212",1989,9,25,1.734,0.671827802690583,149.8176,0.923531247967975
+"FLOW_236212",1989,9,26,2.718,1.05307264573991,234.8352,1.01039611194951
+"FLOW_236212",1989,9,27,1.848,0.715996412556054,159.6672,0.935367270182259
+"FLOW_236212",1989,9,28,1.264,0.489729147982063,109.2096,0.866944290291622
+"FLOW_236212",1989,9,29,0.924,0.357998206278027,79.8336,0.814284503945924
+"FLOW_236212",1989,9,30,0.715,0.277022421524664,61.776,0.773575994369569
+"FLOW_236212",1989,10,1,0.603,0.23362869955157,52.0992,0.747661879782813
+"FLOW_236212",1989,10,2,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",1989,10,3,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",1989,10,4,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",1989,10,5,0.802,0.310730044843049,69.2928,0.791546894841881
+"FLOW_236212",1989,10,6,1.014,0.392868161434978,87.6096,0.829562987367691
+"FLOW_236212",1989,10,7,1.657,0.641994618834081,143.1648,0.915179487774907
+"FLOW_236212",1989,10,8,1.9,0.736143497757847,164.16,0.940572974669907
+"FLOW_236212",1989,10,9,5.802,2.24794977578475,501.2928,1.17586461318905
+"FLOW_236212",1989,10,10,13.553,5.25102780269058,1170.9792,1.39331355929841
+"FLOW_236212",1989,10,11,13.698,5.30720717488789,1183.5072,1.39628222138708
+"FLOW_236212",1989,10,12,6.686,2.59045022421525,577.6704,1.20969267345707
+"FLOW_236212",1989,10,13,3.877,1.5021201793722,334.9728,1.08477816797287
+"FLOW_236212",1989,10,14,5.999,2.32427623318386,518.3136,1.18374333915931
+"FLOW_236212",1989,10,15,6.678,2.58735067264574,576.9792,1.20940304807558
+"FLOW_236212",1989,10,16,3.54,1.37155156950673,305.856,1.06522764372763
+"FLOW_236212",1989,10,17,2.404,0.931415246636771,207.7056,0.985890469947932
+"FLOW_236212",1989,10,18,1.58,0.612161434977579,136.512,0.90651123982209
+"FLOW_236212",1989,10,19,1.104,0.427738116591928,95.3856,0.843792363419142
+"FLOW_236212",1989,10,20,1.227,0.475393721973094,106.0128,0.861808331764218
+"FLOW_236212",1989,10,21,1.961,0.759777578475336,169.4304,0.946536345015836
+"FLOW_236212",1989,10,22,1.567,0.60712466367713,135.3888,0.905014578605826
+"FLOW_236212",1989,10,23,1.104,0.427738116591928,95.3856,0.843792363419142
+"FLOW_236212",1989,10,24,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",1989,10,25,0.71,0.275085201793722,61.344,0.772491030779259
+"FLOW_236212",1989,10,26,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",1989,10,27,2.251,0.872136322869955,194.4864,0.973009039261639
+"FLOW_236212",1989,10,28,9.713,3.76324304932735,839.2032,1.3035042564071
+"FLOW_236212",1989,10,29,6.533,2.53117130044843,564.4512,1.2041048550923
+"FLOW_236212",1989,10,30,2.961,1.14722152466368,255.8304,1.02784933056596
+"FLOW_236212",1989,10,31,3.627,1.40525919282511,313.3728,1.07041278336933
+"FLOW_236212",1989,11,1,4.009,1.55326278026906,346.3776,1.09206625395484
+"FLOW_236212",1989,11,2,2.398,0.929090582959641,207.1872,0.985397852878132
+"FLOW_236212",1989,11,3,1.531,0.59317668161435,132.2784,0.900817498932225
+"FLOW_236212",1989,11,4,1.015,0.393255605381166,87.696,0.829726544751732
+"FLOW_236212",1989,11,5,0.756,0.292907623318386,65.3184,0.782251014377559
+"FLOW_236212",1989,11,6,0.607,0.235178475336323,52.4448,0.748651181133953
+"FLOW_236212",1989,11,7,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1989,11,8,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",1989,11,9,0.437,0.169313004484305,37.7568,0.701032356130901
+"FLOW_236212",1989,11,10,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1989,11,11,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1989,11,12,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1989,11,13,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1989,11,14,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1989,11,15,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1989,11,16,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1989,11,17,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1989,11,18,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",1989,11,19,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1989,11,20,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1989,11,21,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1989,11,22,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1989,11,23,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1989,11,24,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1989,11,25,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1989,11,26,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1989,11,27,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1989,11,28,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1989,11,29,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1989,11,30,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1989,12,1,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1989,12,2,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1989,12,3,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1989,12,4,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1989,12,5,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1989,12,6,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1989,12,7,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1989,12,8,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1989,12,9,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1989,12,10,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1989,12,11,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1989,12,12,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1989,12,13,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1989,12,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1989,12,15,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1989,12,16,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1989,12,17,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1989,12,18,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1989,12,19,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1989,12,20,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1989,12,21,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1989,12,22,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1989,12,23,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1989,12,24,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1989,12,25,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1989,12,26,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1989,12,27,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1989,12,28,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1989,12,29,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1989,12,30,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1989,12,31,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1990,1,1,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1990,1,2,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1990,1,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1990,1,4,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1990,1,5,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1990,1,6,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1990,1,7,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1990,1,8,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1990,1,9,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1990,1,10,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1990,1,11,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,1,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1990,1,13,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1990,1,14,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1990,1,15,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1990,1,16,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1990,1,17,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1990,1,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,1,19,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1990,1,20,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1990,1,21,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,1,22,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1990,1,23,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1990,1,24,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1990,1,25,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,1,26,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1990,1,27,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1990,1,28,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1990,1,29,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1990,1,30,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1990,1,31,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1990,2,1,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1990,2,2,0.747,0.289420627802691,64.5408,0.780379579020089
+"FLOW_236212",1990,2,3,1.121,0.43432466367713,96.8544,0.846375139537042
+"FLOW_236212",1990,2,4,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",1990,2,5,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1990,2,6,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1990,2,7,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1990,2,8,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1990,2,9,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1990,2,10,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1990,2,11,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1990,2,12,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1990,2,13,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1990,2,14,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1990,2,15,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1990,2,16,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1990,2,17,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1990,2,18,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1990,2,19,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1990,2,20,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1990,2,21,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1990,2,22,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1990,2,23,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,2,24,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1990,2,25,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,2,26,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1990,2,27,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1990,2,28,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1990,3,1,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1990,3,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1990,3,3,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1990,3,4,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1990,3,5,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1990,3,6,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1990,3,7,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1990,3,8,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1990,3,9,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1990,3,10,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1990,3,11,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1990,3,12,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1990,3,13,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1990,3,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1990,3,15,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1990,3,16,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1990,3,17,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1990,3,18,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1990,3,19,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1990,3,20,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1990,3,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1990,3,22,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1990,3,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1990,3,24,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1990,3,25,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1990,3,26,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1990,3,27,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1990,3,28,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1990,3,29,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1990,3,30,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1990,3,31,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1990,4,1,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1990,4,2,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1990,4,3,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1990,4,4,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1990,4,5,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1990,4,6,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1990,4,7,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1990,4,8,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1990,4,9,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1990,4,10,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1990,4,11,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1990,4,12,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1990,4,13,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1990,4,14,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1990,4,15,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1990,4,16,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1990,4,17,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1990,4,18,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1990,4,19,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1990,4,20,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1990,4,21,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1990,4,22,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1990,4,23,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1990,4,24,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1990,4,25,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1990,4,26,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1990,4,27,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1990,4,28,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1990,4,29,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1990,4,30,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1990,5,1,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1990,5,2,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1990,5,3,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1990,5,4,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1990,5,5,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1990,5,6,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1990,5,7,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1990,5,8,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1990,5,9,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1990,5,10,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1990,5,11,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1990,5,12,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1990,5,13,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1990,5,14,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1990,5,15,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1990,5,16,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1990,5,17,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1990,5,18,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1990,5,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1990,5,20,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1990,5,21,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1990,5,22,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1990,5,23,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1990,5,24,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1990,5,25,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1990,5,26,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1990,5,27,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1990,5,28,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1990,5,29,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1990,5,30,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1990,5,31,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1990,6,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1990,6,2,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1990,6,3,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1990,6,4,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1990,6,5,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1990,6,6,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1990,6,7,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1990,6,8,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1990,6,9,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1990,6,10,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1990,6,11,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1990,6,12,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1990,6,13,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1990,6,14,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1990,6,15,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1990,6,16,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1990,6,17,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1990,6,18,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1990,6,19,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1990,6,20,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1990,6,21,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1990,6,22,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1990,6,23,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1990,6,24,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1990,6,25,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1990,6,26,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1990,6,27,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",1990,6,28,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",1990,6,29,0.662,0.256487892376682,57.1968,0.761751597678812
+"FLOW_236212",1990,6,30,0.564,0.218518385650224,48.7296,0.737730258466806
+"FLOW_236212",1990,7,1,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",1990,7,2,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",1990,7,3,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",1990,7,4,1.059,0.410303139013453,91.4976,0.83679864427239
+"FLOW_236212",1990,7,5,1.404,0.54397130044843,121.3056,0.885350419585714
+"FLOW_236212",1990,7,6,3.267,1.26577937219731,282.2688,1.04826624834164
+"FLOW_236212",1990,7,7,3.183,1.23323408071749,275.0112,1.04281940403227
+"FLOW_236212",1990,7,8,2.795,1.08290582959641,241.488,1.01605715469121
+"FLOW_236212",1990,7,9,1.844,0.7144466367713,159.3216,0.934961998249223
+"FLOW_236212",1990,7,10,1.126,0.436261883408072,97.2864,0.847128814052798
+"FLOW_236212",1990,7,11,0.867,0.335913901345291,74.9088,0.803980648140094
+"FLOW_236212",1990,7,12,0.709,0.274697757847534,61.2576,0.772273304974995
+"FLOW_236212",1990,7,13,0.613,0.237503139013453,52.9632,0.750125399016538
+"FLOW_236212",1990,7,14,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",1990,7,15,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",1990,7,16,2.665,1.03253811659193,230.256,1.00642454179548
+"FLOW_236212",1990,7,17,6.607,2.55984215246637,570.8448,1.20682038444427
+"FLOW_236212",1990,7,18,2.833,1.09762869955157,244.7712,1.01880505419426
+"FLOW_236212",1990,7,19,4.257,1.64934887892377,367.8048,1.10525504494168
+"FLOW_236212",1990,7,20,2.669,1.03408789237668,230.6016,1.00672647676073
+"FLOW_236212",1990,7,21,1.581,0.612548878923767,136.5984,0.90662595904113
+"FLOW_236212",1990,7,22,1.1,0.426188340807175,95.04,0.843180031595871
+"FLOW_236212",1990,7,23,0.873,0.338238565022422,75.4272,0.805090356503674
+"FLOW_236212",1990,7,24,0.731,0.283221524663677,63.1584,0.777007572397622
+"FLOW_236212",1990,7,25,0.617,0.239052914798206,53.3088,0.75110181026194
+"FLOW_236212",1990,7,26,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",1990,7,27,1.473,0.570704932735426,127.2672,0.893886390703514
+"FLOW_236212",1990,7,28,6.561,2.5420197309417,566.8704,1.20513523268765
+"FLOW_236212",1990,7,29,8.698,3.36998744394619,751.5072,1.27504542280481
+"FLOW_236212",1990,7,30,4.442,1.72102600896861,383.7888,1.11469867969546
+"FLOW_236212",1990,7,31,2.544,0.985657399103139,219.8016,0.997114879946943
+"FLOW_236212",1990,8,1,1.751,0.678414349775785,151.2864,0.925335033685116
+"FLOW_236212",1990,8,2,1.345,0.521112107623318,116.208,0.877781114519393
+"FLOW_236212",1990,8,3,1.028,0.398292376681614,88.8192,0.8318411467948
+"FLOW_236212",1990,8,4,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",1990,8,5,1.889,0.731881614349776,163.2096,0.939481359285899
+"FLOW_236212",1990,8,6,17.913,6.94028340807175,1547.6832,1.47324657820507
+"FLOW_236212",1990,8,7,19.436,7.53036053811659,1679.2704,1.49748732097761
+"FLOW_236212",1990,8,8,12.987,5.03173452914798,1122.0768,1.38147663476961
+"FLOW_236212",1990,8,9,6.424,2.4889399103139,555.0336,1.20005978477511
+"FLOW_236212",1990,8,10,11.373,4.4064,982.6272,1.34529269423511
+"FLOW_236212",1990,8,11,12.885,4.99221524663677,1113.264,1.37929975943839
+"FLOW_236212",1990,8,12,7.155,2.77216143497758,618.192,1.22620675285553
+"FLOW_236212",1990,8,13,5.076,1.96666547085202,438.5664,1.14484343634515
+"FLOW_236212",1990,8,14,4.642,1.79851479820628,401.0688,1.12456044352549
+"FLOW_236212",1990,8,15,3.237,1.25415605381166,279.6768,1.04633394718285
+"FLOW_236212",1990,8,16,2.703,1.04726098654709,233.5392,1.0092784143985
+"FLOW_236212",1990,8,17,4.566,1.76906905829596,394.5024,1.12085377105431
+"FLOW_236212",1990,8,18,3.498,1.35527892376682,302.2272,1.0626879031345
+"FLOW_236212",1990,8,19,2.175,0.842690582959641,187.92,0.966348169086189
+"FLOW_236212",1990,8,20,1.747,0.676864573991032,150.9408,0.924911878125134
+"FLOW_236212",1990,8,21,3.286,1.27314080717489,283.9104,1.04948270912035
+"FLOW_236212",1990,8,22,9.978,3.86591569506726,862.0992,1.31054058809313
+"FLOW_236212",1990,8,23,16.32,6.32308520179372,1410.048,1.44605831407651
+"FLOW_236212",1990,8,24,9.958,3.8581668161435,860.3712,1.31001479430774
+"FLOW_236212",1990,8,25,10.622,4.11542959641256,917.7408,1.32703701043988
+"FLOW_236212",1990,8,26,14.141,5.47884484304933,1221.7824,1.40519889737593
+"FLOW_236212",1990,8,27,5.833,2.25996053811659,503.9712,1.17711846176703
+"FLOW_236212",1990,8,28,2.829,1.09607892376682,244.4256,1.0185171951233
+"FLOW_236212",1990,8,29,2.034,0.788060986547085,175.7376,0.953480829442968
+"FLOW_236212",1990,8,30,1.942,0.752416143497758,167.7888,0.944695009211124
+"FLOW_236212",1990,8,31,1.728,0.669503139013453,149.2992,0.922891239558184
+"FLOW_236212",1990,9,1,1.312,0.508326457399103,113.3568,0.873430881266787
+"FLOW_236212",1990,9,2,1.781,0.690037668161435,153.8784,0.928484289839427
+"FLOW_236212",1990,9,3,7.648,2.96317130044843,660.7872,1.24265723220884
+"FLOW_236212",1990,9,4,10.749,4.16463497757848,928.7136,1.33019523709438
+"FLOW_236212",1990,9,5,5.266,2.0402798206278,454.9824,1.15328846110098
+"FLOW_236212",1990,9,6,3.182,1.2328466367713,274.9248,1.04275387148578
+"FLOW_236212",1990,9,7,2.238,0.867099551569507,193.3632,0.971882567497668
+"FLOW_236212",1990,9,8,1.636,0.633858295964126,141.3504,0.912847931419164
+"FLOW_236212",1990,9,9,1.212,0.469582062780269,104.7168,0.859690840977911
+"FLOW_236212",1990,9,10,0.923,0.357610762331839,79.7472,0.814108175539618
+"FLOW_236212",1990,9,11,0.731,0.283221524663677,63.1584,0.777007572397622
+"FLOW_236212",1990,9,12,1.151,0.44594798206278,99.4464,0.850857518129026
+"FLOW_236212",1990,9,13,1.901,0.736530941704036,164.2464,0.940671961514308
+"FLOW_236212",1990,9,14,1.456,0.564118385650224,125.7984,0.89181351766229
+"FLOW_236212",1990,9,15,1.05,0.406816143497758,90.72,0.835371463743786
+"FLOW_236212",1990,9,16,0.838,0.32467802690583,72.4032,0.798528791631944
+"FLOW_236212",1990,9,17,0.706,0.273535426008969,60.9984,0.771618650200755
+"FLOW_236212",1990,9,18,0.593,0.229754260089686,51.2352,0.745165457149857
+"FLOW_236212",1990,9,19,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",1990,9,20,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1990,9,21,0.78,0.302206278026906,67.392,0.787155793863912
+"FLOW_236212",1990,9,22,1.611,0.624172197309417,139.1904,0.9100408427905
+"FLOW_236212",1990,9,23,1.115,0.432,96.336,0.8454671719826
+"FLOW_236212",1990,9,24,0.762,0.295232286995516,65.8368,0.783488759517135
+"FLOW_236212",1990,9,25,0.674,0.261137219730942,58.2336,0.764493425992641
+"FLOW_236212",1990,9,26,1.133,0.43897399103139,97.8912,0.848179473147465
+"FLOW_236212",1990,9,27,1.278,0.4951533632287,110.4192,0.868856284306101
+"FLOW_236212",1990,9,28,1.083,0.419601793721973,93.5712,0.840557576732363
+"FLOW_236212",1990,9,29,0.87,0.337076233183857,75.168,0.804536267639635
+"FLOW_236212",1990,9,30,0.689,0.266948878923767,59.5296,0.767866322120373
+"FLOW_236212",1990,10,1,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",1990,10,2,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",1990,10,3,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",1990,10,4,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",1990,10,5,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1990,10,6,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",1990,10,7,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",1990,10,8,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",1990,10,9,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",1990,10,10,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1990,10,11,2.029,0.786123766816143,175.3056,0.953011596523971
+"FLOW_236212",1990,10,12,9.782,3.78997668161435,845.1648,1.30535100413307
+"FLOW_236212",1990,10,13,4.357,1.6880932735426,376.4448,1.11039957752246
+"FLOW_236212",1990,10,14,1.858,0.719870852017937,160.5312,0.936377388379549
+"FLOW_236212",1990,10,15,1.07,0.414565022421525,92.448,0.838529858077405
+"FLOW_236212",1990,10,16,0.887,0.343662780269058,76.6368,0.807656141521427
+"FLOW_236212",1990,10,17,0.824,0.319253811659193,71.1936,0.7958426656089
+"FLOW_236212",1990,10,18,0.69,0.267336322869955,59.616,0.768089085813831
+"FLOW_236212",1990,10,19,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",1990,10,20,0.754,0.292132735426009,65.1456,0.781836686277241
+"FLOW_236212",1990,10,21,1.315,0.509488789237668,113.616,0.873829951284691
+"FLOW_236212",1990,10,22,3.038,1.17705470852018,262.4832,1.03314036486449
+"FLOW_236212",1990,10,23,2.669,1.03408789237668,230.6016,1.00672647676073
+"FLOW_236212",1990,10,24,1.606,0.622234977578475,138.7584,0.909475248293267
+"FLOW_236212",1990,10,25,1.047,0.405653811659193,90.4608,0.834893562135835
+"FLOW_236212",1990,10,26,0.757,0.293295067264574,65.4048,0.78245784968262
+"FLOW_236212",1990,10,27,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",1990,10,28,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1990,10,29,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",1990,10,30,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1990,10,31,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1990,11,1,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1990,11,2,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1990,11,3,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1990,11,4,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1990,11,5,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1990,11,6,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1990,11,7,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1990,11,8,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1990,11,9,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1990,11,10,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1990,11,11,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1990,11,12,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1990,11,13,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1990,11,14,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1990,11,15,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1990,11,16,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1990,11,17,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1990,11,18,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1990,11,19,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1990,11,20,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1990,11,21,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1990,11,22,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1990,11,23,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1990,11,24,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1990,11,25,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1990,11,26,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1990,11,27,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1990,11,28,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1990,11,29,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1990,11,30,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1990,12,1,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1990,12,2,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1990,12,3,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1990,12,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1990,12,5,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1990,12,6,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1990,12,7,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1990,12,8,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1990,12,9,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1990,12,10,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1990,12,11,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1990,12,12,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1990,12,13,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1990,12,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1990,12,15,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1990,12,16,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1990,12,17,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1990,12,18,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1990,12,19,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1990,12,20,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1990,12,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1990,12,22,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1990,12,23,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1990,12,24,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1990,12,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1990,12,26,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1990,12,27,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1990,12,28,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1990,12,29,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1990,12,30,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1990,12,31,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1991,1,1,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1991,1,2,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1991,1,3,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1991,1,4,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1991,1,5,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1991,1,6,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1991,1,7,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,1,8,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1991,1,9,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1991,1,10,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1991,1,11,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1991,1,12,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1991,1,13,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1991,1,14,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1991,1,15,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1991,1,16,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1991,1,17,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1991,1,18,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1991,1,19,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1991,1,20,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1991,1,21,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1991,1,22,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1991,1,23,4.262,1.65128609865471,368.2368,1.1055145554293
+"FLOW_236212",1991,1,24,11.052,4.28203049327354,954.8928,1.33761136822231
+"FLOW_236212",1991,1,25,2.516,0.974808968609866,217.3824,0.99491024555727
+"FLOW_236212",1991,1,26,0.965,0.373883408071749,83.376,0.821385890769942
+"FLOW_236212",1991,1,27,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",1991,1,28,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1991,1,29,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",1991,1,30,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",1991,1,31,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1991,2,1,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1991,2,2,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1991,2,3,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1991,2,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1991,2,5,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1991,2,6,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1991,2,7,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1991,2,8,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1991,2,9,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1991,2,10,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1991,2,11,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1991,2,12,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1991,2,13,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1991,2,14,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1991,2,15,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1991,2,16,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1991,2,17,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1991,2,18,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1991,2,19,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1991,2,20,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1991,2,21,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1991,2,22,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1991,2,23,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1991,2,24,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1991,2,25,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1991,2,26,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1991,2,27,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1991,2,28,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1991,3,1,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1991,3,2,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1991,3,3,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1991,3,4,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1991,3,5,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1991,3,6,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1991,3,7,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1991,3,8,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1991,3,9,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1991,3,10,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1991,3,11,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1991,3,12,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1991,3,13,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1991,3,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1991,3,15,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1991,3,16,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1991,3,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1991,3,18,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1991,3,19,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1991,3,20,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1991,3,21,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1991,3,22,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1991,3,23,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1991,3,24,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1991,3,25,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1991,3,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1991,3,27,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1991,3,28,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1991,3,29,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1991,3,30,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1991,3,31,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1991,4,1,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1991,4,2,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1991,4,3,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1991,4,4,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1991,4,5,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1991,4,6,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1991,4,7,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1991,4,8,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1991,4,9,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1991,4,10,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1991,4,11,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1991,4,12,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1991,4,13,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1991,4,14,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1991,4,15,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1991,4,16,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1991,4,17,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1991,4,18,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1991,4,19,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,4,20,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,4,21,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,4,22,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1991,4,23,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1991,4,24,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,4,25,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1991,4,26,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1991,4,27,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1991,4,28,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1991,4,29,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1991,4,30,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1991,5,1,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1991,5,2,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1991,5,3,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1991,5,4,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1991,5,5,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1991,5,6,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,5,7,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,5,8,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,5,9,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1991,5,10,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1991,5,11,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1991,5,12,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,5,13,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1991,5,14,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1991,5,15,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1991,5,16,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1991,5,17,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1991,5,18,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1991,5,19,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1991,5,20,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1991,5,21,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1991,5,22,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1991,5,23,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1991,5,24,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1991,5,25,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1991,5,26,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1991,5,27,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1991,5,28,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1991,5,29,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1991,5,30,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1991,5,31,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1991,6,1,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,6,2,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1991,6,3,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1991,6,4,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1991,6,5,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1991,6,6,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1991,6,7,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1991,6,8,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1991,6,9,0.501,0.194109417040359,43.2864,0.720459083126107
+"FLOW_236212",1991,6,10,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1991,6,11,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1991,6,12,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1991,6,13,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1991,6,14,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1991,6,15,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1991,6,16,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1991,6,17,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1991,6,18,0.451,0.174737219730942,38.9664,0.705467618761141
+"FLOW_236212",1991,6,19,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1991,6,20,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1991,6,21,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1991,6,22,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1991,6,23,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1991,6,24,0.588,0.227817040358744,50.8032,0.743904594666505
+"FLOW_236212",1991,6,25,0.494,0.19139730941704,42.6816,0.718434477206278
+"FLOW_236212",1991,6,26,1.128,0.437036771300448,97.4592,0.847429534332357
+"FLOW_236212",1991,6,27,1.577,0.610999103139014,136.2528,0.906166733297494
+"FLOW_236212",1991,6,28,0.916,0.35489865470852,79.1424,0.812869578793101
+"FLOW_236212",1991,6,29,1.126,0.436261883408072,97.2864,0.847128814052798
+"FLOW_236212",1991,6,30,1.705,0.660591928251121,147.312,0.920421283426572
+"FLOW_236212",1991,7,1,1.779,0.689262780269058,153.7056,0.928275665104318
+"FLOW_236212",1991,7,2,2.158,0.836104035874439,186.4512,0.964832810749209
+"FLOW_236212",1991,7,3,2.203,0.853539013452915,190.3392,0.968823523890136
+"FLOW_236212",1991,7,4,1.301,0.504064573991031,112.4064,0.871961350197077
+"FLOW_236212",1991,7,5,0.899,0.348312107623318,77.6736,0.809829726159191
+"FLOW_236212",1991,7,6,1.348,0.522274439461883,116.4672,0.878172340843794
+"FLOW_236212",1991,7,7,2.282,0.884147085201794,197.1648,0.975674386285358
+"FLOW_236212",1991,7,8,1.455,0.563730941704036,125.712,0.891690982137237
+"FLOW_236212",1991,7,9,0.97,0.375820627802691,83.808,0.822235309225658
+"FLOW_236212",1991,7,10,0.908,0.351799103139014,78.4512,0.81144473290655
+"FLOW_236212",1991,7,11,0.82,0.317704035874439,70.848,0.795068498125917
+"FLOW_236212",1991,7,12,0.923,0.357610762331839,79.7472,0.814108175539618
+"FLOW_236212",1991,7,13,1.199,0.464545291479821,103.5936,0.857838655893849
+"FLOW_236212",1991,7,14,1.034,0.400617040358744,89.3376,0.832809908520638
+"FLOW_236212",1991,7,15,0.786,0.304530941704036,67.9104,0.78836309370197
+"FLOW_236212",1991,7,16,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",1991,7,17,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",1991,7,18,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",1991,7,19,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1991,7,20,0.509,0.197208968609865,43.9776,0.722745393545393
+"FLOW_236212",1991,7,21,0.537,0.208057399103139,46.3968,0.730527607407242
+"FLOW_236212",1991,7,22,0.555,0.215031390134529,47.952,0.735360626221547
+"FLOW_236212",1991,7,23,0.631,0.244477130044843,54.5184,0.754479853878591
+"FLOW_236212",1991,7,24,1.071,0.414952466367713,92.5344,0.838686534070795
+"FLOW_236212",1991,7,25,3.115,1.20688789237668,269.136,1.03832518010885
+"FLOW_236212",1991,7,26,2.618,1.01432825112108,226.1952,1.00284936611637
+"FLOW_236212",1991,7,27,1.757,0.680739013452915,151.8048,0.925968319347403
+"FLOW_236212",1991,7,28,4.685,1.81517488789238,404.784,1.12663618306326
+"FLOW_236212",1991,7,29,17.341,6.71866547085202,1498.2624,1.46371526987448
+"FLOW_236212",1991,7,30,13.365,5.17818834080717,1154.736,1.38942647042224
+"FLOW_236212",1991,7,31,6.38,2.47189237668161,551.232,1.1984113447798
+"FLOW_236212",1991,8,1,3.894,1.5087067264574,336.4416,1.08572781816646
+"FLOW_236212",1991,8,2,4.894,1.89615067264574,422.8416,1.1365134216483
+"FLOW_236212",1991,8,3,3.35,1.29793721973094,289.44,1.05353929183195
+"FLOW_236212",1991,8,4,2.444,0.946913004484305,211.1616,0.989149683115842
+"FLOW_236212",1991,8,5,1.696,0.657104932735426,146.5344,0.919447519455229
+"FLOW_236212",1991,8,6,2.855,1.10615246636771,246.672,1.02038249233725
+"FLOW_236212",1991,8,7,9.829,3.8081865470852,849.2256,1.30660297560505
+"FLOW_236212",1991,8,8,16.782,6.50208430493274,1449.9648,1.45415439166011
+"FLOW_236212",1991,8,9,14.322,5.54897219730942,1237.4208,1.40877783409696
+"FLOW_236212",1991,8,10,9.897,3.83453273542601,855.1008,1.3084058882984
+"FLOW_236212",1991,8,11,7.433,2.87987085201794,642.2112,1.23559061930644
+"FLOW_236212",1991,8,12,3.793,1.46957488789238,327.7152,1.08003628026658
+"FLOW_236212",1991,8,13,2.625,1.01704035874439,226.8,1.00338507655349
+"FLOW_236212",1991,8,14,4.533,1.75628340807175,391.6512,1.11922891030837
+"FLOW_236212",1991,8,15,10.395,4.0274798206278,898.128,1.32131594117626
+"FLOW_236212",1991,8,16,17.753,6.87829237668161,1533.8592,1.47060529892683
+"FLOW_236212",1991,8,17,9.21,3.56835874439462,795.744,1.28971485450865
+"FLOW_236212",1991,8,18,3.923,1.51994260089686,338.9472,1.08734018223352
+"FLOW_236212",1991,8,19,2.472,0.957761434977578,213.5808,0.991405831615093
+"FLOW_236212",1991,8,20,1.823,0.706310313901345,157.5072,0.932822707795427
+"FLOW_236212",1991,8,21,5.549,2.1499264573991,479.4336,1.16542604862653
+"FLOW_236212",1991,8,22,13.021,5.04490762331839,1125.0144,1.38219922036221
+"FLOW_236212",1991,8,23,12.695,4.91860089686099,1096.848,1.37520776827206
+"FLOW_236212",1991,8,24,17.978,6.96546726457399,1553.2992,1.47431420882134
+"FLOW_236212",1991,8,25,14.415,5.58500448430493,1245.456,1.41060268197084
+"FLOW_236212",1991,8,26,7.777,3.01315156950673,671.9328,1.24682125051748
+"FLOW_236212",1991,8,27,3.912,1.51568071748879,337.9968,1.08672972188816
+"FLOW_236212",1991,8,28,2.753,1.0666331838565,237.8592,1.01298500855732
+"FLOW_236212",1991,8,29,2.111,0.817894170403588,182.3904,0.960593011078904
+"FLOW_236212",1991,8,30,2.058,0.797359641255605,177.8112,0.955720386313721
+"FLOW_236212",1991,8,31,2.915,1.12939910313901,251.856,1.02463571029656
+"FLOW_236212",1991,9,1,3.008,1.16543139013453,259.8912,1.03109182190994
+"FLOW_236212",1991,9,2,2.394,0.927540807174888,206.8416,0.985068893414753
+"FLOW_236212",1991,9,3,2.656,1.02905112107623,229.4784,1.00574386026203
+"FLOW_236212",1991,9,4,2.888,1.11893811659193,249.5232,1.02273051398426
+"FLOW_236212",1991,9,5,2.059,0.797747085201794,177.8976,0.955813246827164
+"FLOW_236212",1991,9,6,1.437,0.556756950672646,124.1568,0.889473737158022
+"FLOW_236212",1991,9,7,1.073,0.41572735426009,92.7072,0.838999535290296
+"FLOW_236212",1991,9,8,1.072,0.415339910313901,92.6208,0.838843093076304
+"FLOW_236212",1991,9,9,1.161,0.449822421524664,100.3104,0.852330873308713
+"FLOW_236212",1991,9,10,1.006,0.389768609865471,86.9184,0.828249861688572
+"FLOW_236212",1991,9,11,1.446,0.560243946188341,124.9344,0.890585119722866
+"FLOW_236212",1991,9,12,7.277,2.81942959641256,628.7328,1.23036013442312
+"FLOW_236212",1991,9,13,5.063,1.96162869955157,437.4432,1.1442564294626
+"FLOW_236212",1991,9,14,2.559,0.991469058295964,221.0976,0.99828795952167
+"FLOW_236212",1991,9,15,1.677,0.649743497757848,144.8928,0.917378142523995
+"FLOW_236212",1991,9,16,1.287,0.498640358744395,111.1968,0.870076592887204
+"FLOW_236212",1991,9,17,15.813,6.12665112107623,1366.2432,1.43695983796511
+"FLOW_236212",1991,9,18,33.148,12.8429919282511,2863.9872,1.66622344844541
+"FLOW_236212",1991,9,19,22.724,8.80427623318386,1963.3536,1.5450364585047
+"FLOW_236212",1991,9,20,12.046,4.66714977578475,1040.7744,1.36085028207359
+"FLOW_236212",1991,9,21,21.826,8.45635156950673,1885.7664,1.53262747249049
+"FLOW_236212",1991,9,22,28.226,10.9359928251121,2438.7264,1.61350993299225
+"FLOW_236212",1991,9,23,12.706,4.92286278026906,1097.7984,1.37544600450934
+"FLOW_236212",1991,9,24,4.88,1.8907264573991,421.632,1.1358624438909
+"FLOW_236212",1991,9,25,2.992,1.15923228699552,258.5088,1.02999257439717
+"FLOW_236212",1991,9,26,2.454,0.950787443946188,212.0256,0.989957813020296
+"FLOW_236212",1991,9,27,2.226,0.862450224215247,192.3264,0.970838091609773
+"FLOW_236212",1991,9,28,1.778,0.68887533632287,153.6192,0.928171282366369
+"FLOW_236212",1991,9,29,1.679,0.650518385650224,145.0656,0.917596852321777
+"FLOW_236212",1991,9,30,1.6,0.619910313901345,138.24,0.908794672388616
+"FLOW_236212",1991,10,1,1.318,0.510651121076233,113.8752,0.874228293625237
+"FLOW_236212",1991,10,2,1.019,0.394805381165919,88.0416,0.830379487952203
+"FLOW_236212",1991,10,3,0.812,0.314604484304933,70.1568,0.793511055215469
+"FLOW_236212",1991,10,4,0.687,0.26617399103139,59.3568,0.767420017650114
+"FLOW_236212",1991,10,5,0.606,0.234791031390135,52.3584,0.748404345873839
+"FLOW_236212",1991,10,6,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",1991,10,7,0.517,0.200308520179372,44.6688,0.725003134981235
+"FLOW_236212",1991,10,8,0.492,0.190622421524664,42.5088,0.717851804519562
+"FLOW_236212",1991,10,9,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1991,10,10,0.461,0.178611659192825,39.8304,0.708568694902007
+"FLOW_236212",1991,10,11,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",1991,10,12,0.429,0.166213452914798,37.0656,0.698446643025294
+"FLOW_236212",1991,10,13,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1991,10,14,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1991,10,15,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1991,10,16,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1991,10,17,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1991,10,18,0.411,0.159239461883408,35.5104,0.692484632829216
+"FLOW_236212",1991,10,19,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1991,10,20,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1991,10,21,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1991,10,22,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1991,10,23,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1991,10,24,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1991,10,25,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1991,10,26,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1991,10,27,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1991,10,28,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",1991,10,29,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1991,10,30,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1991,10,31,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1991,11,1,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1991,11,2,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1991,11,3,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1991,11,4,0.679,0.263074439461883,58.6656,0.765624338373449
+"FLOW_236212",1991,11,5,1.362,0.52769865470852,117.6768,0.879988906724209
+"FLOW_236212",1991,11,6,0.989,0.383182062780269,85.4496,0.825431492137924
+"FLOW_236212",1991,11,7,0.622,0.240990134529148,53.7408,0.752315228238822
+"FLOW_236212",1991,11,8,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",1991,11,9,0.969,0.375433183856502,83.7216,0.822065706218397
+"FLOW_236212",1991,11,10,1.141,0.442073542600897,98.5824,0.849373886574319
+"FLOW_236212",1991,11,11,0.822,0.318478923766816,71.0208,0.795455958591646
+"FLOW_236212",1991,11,12,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",1991,11,13,0.455,0.176286995515695,39.312,0.706714586543881
+"FLOW_236212",1991,11,14,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1991,11,15,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1991,11,16,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1991,11,17,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1991,11,18,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1991,11,19,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1991,11,20,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1991,11,21,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1991,11,22,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1991,11,23,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1991,11,24,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1991,11,25,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1991,11,26,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1991,11,27,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1991,11,28,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1991,11,29,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1991,11,30,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1991,12,1,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1991,12,2,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1991,12,3,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1991,12,4,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1991,12,5,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1991,12,6,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1991,12,7,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,12,8,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1991,12,9,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,12,10,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,12,11,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1991,12,12,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1991,12,13,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1991,12,14,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1991,12,15,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1991,12,16,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1991,12,17,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1991,12,18,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1991,12,19,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",1991,12,20,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1991,12,21,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1991,12,22,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1991,12,23,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1991,12,24,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1991,12,25,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1991,12,26,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1991,12,27,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1991,12,28,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1991,12,29,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1991,12,30,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1991,12,31,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1992,1,1,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1992,1,2,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1992,1,3,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1992,1,4,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1992,1,5,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1992,1,6,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1992,1,7,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1992,1,8,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1992,1,9,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1992,1,10,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1992,1,11,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1992,1,12,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1992,1,13,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1992,1,14,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1992,1,15,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1992,1,16,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1992,1,17,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1992,1,18,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1992,1,19,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1992,1,20,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1992,1,21,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1992,1,22,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1992,1,23,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1992,1,24,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1992,1,25,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1992,1,26,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1992,1,27,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1992,1,28,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1992,1,29,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1992,1,30,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1992,1,31,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1992,2,1,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1992,2,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1992,2,3,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1992,2,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1992,2,5,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1992,2,6,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1992,2,7,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1992,2,8,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1992,2,9,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1992,2,10,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1992,2,11,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1992,2,12,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1992,2,13,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1992,2,14,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1992,2,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1992,2,16,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1992,2,17,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1992,2,18,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1992,2,19,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1992,2,20,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1992,2,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1992,2,22,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1992,2,23,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1992,2,24,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1992,2,25,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1992,2,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1992,2,27,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1992,2,28,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1992,2,29,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1992,3,1,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1992,3,2,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1992,3,3,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1992,3,4,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1992,3,5,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1992,3,6,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1992,3,7,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1992,3,8,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1992,3,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1992,3,10,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1992,3,11,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1992,3,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1992,3,13,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1992,3,14,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1992,3,15,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1992,3,16,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1992,3,17,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1992,3,18,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1992,3,19,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1992,3,20,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1992,3,21,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1992,3,22,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1992,3,23,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1992,3,24,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1992,3,25,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1992,3,26,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1992,3,27,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1992,3,28,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1992,3,29,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1992,3,30,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1992,3,31,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1992,4,1,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1992,4,2,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1992,4,3,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1992,4,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1992,4,5,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1992,4,6,0.676,0.261912107623318,58.4064,0.764946593724164
+"FLOW_236212",1992,4,7,0.794,0.307630493273543,68.6016,0.789961410109347
+"FLOW_236212",1992,4,8,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1992,4,9,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1992,4,10,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1992,4,11,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1992,4,12,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1992,4,13,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1992,4,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1992,4,15,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1992,4,16,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1992,4,17,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1992,4,18,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1992,4,19,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1992,4,20,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1992,4,21,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1992,4,22,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1992,4,23,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1992,4,24,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1992,4,25,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1992,4,26,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1992,4,27,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1992,4,28,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1992,4,29,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1992,4,30,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1992,5,1,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1992,5,2,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1992,5,3,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1992,5,4,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1992,5,5,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1992,5,6,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1992,5,7,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1992,5,8,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1992,5,9,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1992,5,10,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1992,5,11,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1992,5,12,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1992,5,13,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1992,5,14,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1992,5,15,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1992,5,16,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",1992,5,17,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1992,5,18,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1992,5,19,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1992,5,20,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1992,5,21,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1992,5,22,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1992,5,23,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1992,5,24,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1992,5,25,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1992,5,26,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1992,5,27,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1992,5,28,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1992,5,29,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1992,5,30,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1992,5,31,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1992,6,1,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1992,6,2,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1992,6,3,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1992,6,4,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1992,6,5,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1992,6,6,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1992,6,7,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1992,6,8,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1992,6,9,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1992,6,10,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",1992,6,11,0.972,0.376595515695067,83.9808,0.822574096035855
+"FLOW_236212",1992,6,12,1.883,0.729556950672646,162.6912,0.938883787705458
+"FLOW_236212",1992,6,13,1.261,0.488566816143498,108.9504,0.866532374858387
+"FLOW_236212",1992,6,14,0.781,0.302593721973094,67.4784,0.787357525257363
+"FLOW_236212",1992,6,15,0.557,0.215806278026906,48.1248,0.735889853560662
+"FLOW_236212",1992,6,16,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",1992,6,17,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1992,6,18,0.613,0.237503139013453,52.9632,0.750125399016538
+"FLOW_236212",1992,6,19,2.008,0.777987443946188,173.4912,0.95103065852084
+"FLOW_236212",1992,6,20,1.983,0.76830134529148,171.3312,0.948650672070861
+"FLOW_236212",1992,6,21,1.139,0.44129865470852,98.4096,0.849075912851927
+"FLOW_236212",1992,6,22,0.751,0.290970403587444,64.8864,0.781213542536543
+"FLOW_236212",1992,6,23,0.664,0.257262780269058,57.3696,0.762211315332639
+"FLOW_236212",1992,6,24,1.009,0.390930941704036,87.1776,0.828743259493989
+"FLOW_236212",1992,6,25,1.083,0.419601793721973,93.5712,0.840557576732363
+"FLOW_236212",1992,6,26,1.095,0.424251121076233,94.608,0.842412106795319
+"FLOW_236212",1992,6,27,0.845,0.327390134529148,73.008,0.799858414017954
+"FLOW_236212",1992,6,28,0.679,0.263074439461883,58.6656,0.765624338373449
+"FLOW_236212",1992,6,29,0.582,0.225492376681614,50.2848,0.742380187189683
+"FLOW_236212",1992,6,30,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",1992,7,1,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",1992,7,2,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",1992,7,3,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",1992,7,4,2.731,1.05810941704036,235.9584,1.01136079859691
+"FLOW_236212",1992,7,5,3.734,1.44671569506726,322.6176,1.07665519089036
+"FLOW_236212",1992,7,6,2.134,0.826805381165919,184.3776,0.962677139654608
+"FLOW_236212",1992,7,7,1.672,0.647806278026906,144.4608,0.916830453690158
+"FLOW_236212",1992,7,8,2.699,1.04571121076233,233.1936,1.0089795238702
+"FLOW_236212",1992,7,9,2.801,1.08523049327354,242.0064,1.01649301264297
+"FLOW_236212",1992,7,10,2.102,0.814407174887892,181.6128,0.959772535638131
+"FLOW_236212",1992,7,11,1.477,0.572254708520179,127.6128,0.89437134223844
+"FLOW_236212",1992,7,12,1.36,0.526923766816144,117.504,0.879730314582357
+"FLOW_236212",1992,7,13,2.603,1.00851659192825,224.8992,1.00169754527393
+"FLOW_236212",1992,7,14,2.211,0.856638565022421,191.0304,0.969526143447849
+"FLOW_236212",1992,7,15,1.416,0.548620627802691,122.3424,0.886858691398581
+"FLOW_236212",1992,7,16,0.967,0.374658295964126,83.5488,0.821726079700363
+"FLOW_236212",1992,7,17,0.759,0.294069955156951,65.5776,0.782870865404754
+"FLOW_236212",1992,7,18,0.65,0.251838565022422,56.16,0.758969715925821
+"FLOW_236212",1992,7,19,0.619,0.239827802690583,53.4816,0.751588118089578
+"FLOW_236212",1992,7,20,2.403,0.931027802690583,207.6192,0.985808435459365
+"FLOW_236212",1992,7,21,4.173,1.61680358744395,360.5472,1.10085838026883
+"FLOW_236212",1992,7,22,2.183,0.845790134529148,188.6112,0.967058002314743
+"FLOW_236212",1992,7,23,1.484,0.574966816143498,128.2176,0.895217485041194
+"FLOW_236212",1992,7,24,1.686,0.653230493273543,145.6704,0.918360699017527
+"FLOW_236212",1992,7,25,1.924,0.745442152466368,166.2336,0.942937243238649
+"FLOW_236212",1992,7,26,3.195,1.23788340807175,276.048,1.04360451269838
+"FLOW_236212",1992,7,27,2.031,0.78689865470852,175.4784,0.953199400577678
+"FLOW_236212",1992,7,28,1.326,0.51375067264574,114.5664,0.875287004925656
+"FLOW_236212",1992,7,29,1.097,0.42502600896861,94.7808,0.842719612665461
+"FLOW_236212",1992,7,30,1.224,0.474231390134529,105.7536,0.861386496842621
+"FLOW_236212",1992,7,31,1.832,0.70979730941704,158.2848,0.933741947986768
+"FLOW_236212",1992,8,1,2.612,1.01200358744395,225.6768,1.00238927292702
+"FLOW_236212",1992,8,2,1.771,0.686163228699552,153.0144,0.927439285175996
+"FLOW_236212",1992,8,3,3.27,1.26694170403587,282.528,1.04845869672038
+"FLOW_236212",1992,8,4,3.284,1.27236591928251,283.7376,1.04935492600331
+"FLOW_236212",1992,8,5,2.138,0.828355156950673,184.7232,0.963037760523603
+"FLOW_236212",1992,8,6,1.867,0.723357847533632,161.3088,0.937282782905296
+"FLOW_236212",1992,8,7,2.079,0.805495964125561,179.6256,0.957662923457711
+"FLOW_236212",1992,8,8,2.412,0.934514798206278,208.3968,0.986545764956754
+"FLOW_236212",1992,8,9,3.45,1.33668161434978,298.08,1.05975529434197
+"FLOW_236212",1992,8,10,3.163,1.22548520179372,273.2832,1.04150561163356
+"FLOW_236212",1992,8,11,3.699,1.43315515695067,319.5936,1.07462921246042
+"FLOW_236212",1992,8,12,4.565,1.76868161434978,394.416,1.12080467109919
+"FLOW_236212",1992,8,13,4.234,1.64043766816143,365.8176,1.1040581465148
+"FLOW_236212",1992,8,14,15.665,6.06930941704036,1353.456,1.43425989815335
+"FLOW_236212",1992,8,15,10.085,3.90737219730942,871.344,1.3133393490829
+"FLOW_236212",1992,8,16,4.306,1.668333632287,372.0384,1.10778780766632
+"FLOW_236212",1992,8,17,2.763,1.07050762331839,238.7232,1.0137198551836
+"FLOW_236212",1992,8,18,2.197,0.851214349775785,189.8208,0.968295218464221
+"FLOW_236212",1992,8,19,5.517,2.13752825112108,476.6688,1.16407878034531
+"FLOW_236212",1992,8,20,10.8,4.18439461883408,933.12,1.33145510447318
+"FLOW_236212",1992,8,21,8.017,3.10613811659193,692.6688,1.25442341694148
+"FLOW_236212",1992,8,22,4.204,1.62881434977578,363.2256,1.10248913285885
+"FLOW_236212",1992,8,23,6.724,2.6051730941704,580.9536,1.21106461996747
+"FLOW_236212",1992,8,24,12.76,4.94378475336323,1102.464,1.37661314035834
+"FLOW_236212",1992,8,25,8.153,3.15883049327354,704.4192,1.25865082366597
+"FLOW_236212",1992,8,26,4.661,1.80587623318386,402.7104,1.12547951944422
+"FLOW_236212",1992,8,27,2.898,1.12281255605381,250.3872,1.02343779717369
+"FLOW_236212",1992,8,28,2.537,0.982945291479821,219.1968,0.996565548261304
+"FLOW_236212",1992,8,29,3.059,1.18519103139013,264.2976,1.03456473658738
+"FLOW_236212",1992,8,30,15.643,6.06078565022422,1351.5552,1.43385681537791
+"FLOW_236212",1992,8,31,17.827,6.90696322869955,1540.2528,1.47182924753749
+"FLOW_236212",1992,9,1,17.046,6.60436950672646,1472.7744,1.45870097321575
+"FLOW_236212",1992,9,2,8.137,3.15263139013453,703.0368,1.25815642307199
+"FLOW_236212",1992,9,3,3.712,1.43819192825112,320.7168,1.07538350183587
+"FLOW_236212",1992,9,4,2.498,0.967834977578475,215.8272,0.993482594232088
+"FLOW_236212",1992,9,5,1.947,0.7543533632287,168.2208,0.945180963691081
+"FLOW_236212",1992,9,6,1.941,0.75202869955157,167.7024,0.944597698227476
+"FLOW_236212",1992,9,7,2.449,0.948850224215247,211.5936,0.989554078051945
+"FLOW_236212",1992,9,8,2.985,1.1565201793722,257.904,1.02951017434468
+"FLOW_236212",1992,9,9,2.222,0.860900448430493,191.9808,0.970488931982798
+"FLOW_236212",1992,9,10,1.63,0.631533632286995,140.832,0.912177376387142
+"FLOW_236212",1992,9,11,1.481,0.573804484304933,127.9584,0.894855244235318
+"FLOW_236212",1992,9,12,5.063,1.96162869955157,437.4432,1.1442564294626
+"FLOW_236212",1992,9,13,7.279,2.82020448430493,628.9056,1.23042775706413
+"FLOW_236212",1992,9,14,3.831,1.48429775784753,330.9984,1.08219171880697
+"FLOW_236212",1992,9,15,2.566,0.994181165919283,221.7024,0.998833514981853
+"FLOW_236212",1992,9,16,2.259,0.875235874439462,195.1776,0.973699668323346
+"FLOW_236212",1992,9,17,1.734,0.671827802690583,149.8176,0.923531247967975
+"FLOW_236212",1992,9,18,1.822,0.705922869955157,157.4208,0.932720346034178
+"FLOW_236212",1992,9,19,4.363,1.69041793721973,376.9632,1.11070523419423
+"FLOW_236212",1992,9,20,4.171,1.61602869955157,360.3744,1.10075283802249
+"FLOW_236212",1992,9,21,3.018,1.16930582959641,260.7552,1.03177647843773
+"FLOW_236212",1992,9,22,11.581,4.48698834080717,1000.5984,1.35017787861891
+"FLOW_236212",1992,9,23,19.958,7.73260627802691,1724.3712,1.50544599119054
+"FLOW_236212",1992,9,24,8.746,3.38858475336323,755.6544,1.2764495968209
+"FLOW_236212",1992,9,25,4.082,1.58154618834081,352.6848,1.09601469222144
+"FLOW_236212",1992,9,26,2.947,1.14179730941704,254.6208,1.02687552556677
+"FLOW_236212",1992,9,27,2.548,0.987207174887892,220.1472,0.997428241064754
+"FLOW_236212",1992,9,28,3.076,1.19177757847534,265.7664,1.03571208108214
+"FLOW_236212",1992,9,29,13.633,5.28202331838565,1177.8912,1.39495456620012
+"FLOW_236212",1992,9,30,8.719,3.37812376681614,753.3216,1.27566050978308
+"FLOW_236212",1992,10,1,4.203,1.6284269058296,363.1392,1.10243667833712
+"FLOW_236212",1992,10,2,2.857,1.10692735426009,246.8448,1.02052541306453
+"FLOW_236212",1992,10,3,2.07,0.802008968609865,178.848,0.956832338579288
+"FLOW_236212",1992,10,4,1.556,0.602862780269058,134.4384,0.903740394571627
+"FLOW_236212",1992,10,5,1.298,0.502902242152466,112.1472,0.871558844401469
+"FLOW_236212",1992,10,6,1.093,0.423476233183857,94.4352,0.842104151272749
+"FLOW_236212",1992,10,7,1.069,0.414177578475336,92.3616,0.83837306489926
+"FLOW_236212",1992,10,8,1.865,0.722582959641256,161.136,0.937081886352079
+"FLOW_236212",1992,10,9,13.11,5.07939013452915,1132.704,1.38408357291297
+"FLOW_236212",1992,10,10,14.323,5.5493596412556,1237.5072,1.408797506468
+"FLOW_236212",1992,10,11,10.854,4.20531659192825,937.7856,1.33278390462832
+"FLOW_236212",1992,10,12,7.106,2.75317668161435,613.9584,1.22452263261775
+"FLOW_236212",1992,10,13,3.468,1.34365560538117,299.6352,1.06085882532917
+"FLOW_236212",1992,10,14,2.177,0.843465470852018,188.0928,0.966525822957759
+"FLOW_236212",1992,10,15,1.518,0.588139910313901,131.1552,0.899282475265547
+"FLOW_236212",1992,10,16,1.157,0.44827264573991,99.9648,0.851742754153834
+"FLOW_236212",1992,10,17,1.868,0.723745291479821,161.3952,0.937383166626449
+"FLOW_236212",1992,10,18,8.747,3.38897219730942,755.7408,1.27647878482049
+"FLOW_236212",1992,10,19,5.986,2.31923946188341,517.1904,1.18322985291681
+"FLOW_236212",1992,10,20,3.856,1.49398385650224,333.1584,1.08360046052017
+"FLOW_236212",1992,10,21,2.435,0.94342600896861,210.384,0.988420101339254
+"FLOW_236212",1992,10,22,1.618,0.626884304932736,139.7952,0.910830320480894
+"FLOW_236212",1992,10,23,1.11,0.430062780269058,95.904,0.84470754167028
+"FLOW_236212",1992,10,24,0.853,0.330489686098655,73.6992,0.801367235712859
+"FLOW_236212",1992,10,25,0.696,0.269660986547085,60.1344,0.769420270728911
+"FLOW_236212",1992,10,26,0.595,0.230529147982063,51.408,0.745667421537325
+"FLOW_236212",1992,10,27,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",1992,10,28,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",1992,10,29,0.453,0.175512107623318,39.1392,0.706092203731679
+"FLOW_236212",1992,10,30,0.429,0.166213452914798,37.0656,0.698446643025294
+"FLOW_236212",1992,10,31,5.636,2.18363408071749,486.9504,1.1690577701194
+"FLOW_236212",1992,11,1,4.612,1.78689147982063,398.4768,1.1231031248779
+"FLOW_236212",1992,11,2,2.366,0.916692376681614,204.4224,0.982753782279254
+"FLOW_236212",1992,11,3,1.452,0.562568609865471,125.4528,0.891322970876005
+"FLOW_236212",1992,11,4,1.151,0.44594798206278,99.4464,0.850857518129026
+"FLOW_236212",1992,11,5,1.127,0.43664932735426,97.3728,0.847279227559094
+"FLOW_236212",1992,11,6,0.892,0.3456,77.0688,0.808564643415972
+"FLOW_236212",1992,11,7,0.724,0.280509417040359,62.5536,0.77551372640652
+"FLOW_236212",1992,11,8,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",1992,11,9,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1992,11,10,0.546,0.211544394618834,47.1744,0.732960050622791
+"FLOW_236212",1992,11,11,0.681,0.26384932735426,58.8384,0.766074838443101
+"FLOW_236212",1992,11,12,0.697,0.270048430493274,60.2208,0.769641241549598
+"FLOW_236212",1992,11,13,0.588,0.227817040358744,50.8032,0.743904594666505
+"FLOW_236212",1992,11,14,0.581,0.225104932735426,50.1984,0.742124898206603
+"FLOW_236212",1992,11,15,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",1992,11,16,0.477,0.184810762331839,41.2128,0.713420281757027
+"FLOW_236212",1992,11,17,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1992,11,18,0.423,0.163888789237668,36.5472,0.696481923722871
+"FLOW_236212",1992,11,19,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1992,11,20,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",1992,11,21,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",1992,11,22,0.823,0.318866367713005,71.1072,0.795649406075272
+"FLOW_236212",1992,11,23,0.652,0.252613452914798,56.3328,0.75943620043054
+"FLOW_236212",1992,11,24,0.662,0.256487892376682,57.1968,0.761751597678812
+"FLOW_236212",1992,11,25,0.722,0.279734529147982,62.3808,0.775084791560821
+"FLOW_236212",1992,11,26,0.913,0.353736322869955,78.8832,0.812336432567664
+"FLOW_236212",1992,11,27,1.244,0.481980269058296,107.4816,0.864183265117153
+"FLOW_236212",1992,11,28,1.114,0.431612556053812,96.2496,0.845315464244141
+"FLOW_236212",1992,11,29,0.856,0.33165201793722,73.9584,0.801930125920911
+"FLOW_236212",1992,11,30,0.711,0.27547264573991,61.4304,0.772708511396718
+"FLOW_236212",1992,12,1,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",1992,12,2,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",1992,12,3,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1992,12,4,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1992,12,5,0.573,0.222005381165919,49.5072,0.740069830582908
+"FLOW_236212",1992,12,6,0.645,0.24990134529148,55.728,0.757798460781196
+"FLOW_236212",1992,12,7,0.613,0.237503139013453,52.9632,0.750125399016538
+"FLOW_236212",1992,12,8,0.618,0.239440358744395,53.3952,0.751345121557008
+"FLOW_236212",1992,12,9,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",1992,12,10,0.458,0.17744932735426,39.5712,0.707644069704618
+"FLOW_236212",1992,12,11,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1992,12,12,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1992,12,13,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1992,12,14,0.515,0.199533632286996,44.496,0.724441334124209
+"FLOW_236212",1992,12,15,0.48,0.185973094170404,41.472,0.714315416671224
+"FLOW_236212",1992,12,16,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1992,12,17,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1992,12,18,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1992,12,19,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",1992,12,20,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",1992,12,21,0.532,0.206120179372197,45.9648,0.729162125693567
+"FLOW_236212",1992,12,22,0.635,0.246026905829596,54.864,0.755433988896739
+"FLOW_236212",1992,12,23,0.688,0.266561434977578,59.4432,0.767643299624964
+"FLOW_236212",1992,12,24,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",1992,12,25,0.508,0.196821524663677,43.8912,0.722461183707755
+"FLOW_236212",1992,12,26,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1992,12,27,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1992,12,28,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",1992,12,29,0.478,0.185198206278027,41.2992,0.713719159231734
+"FLOW_236212",1992,12,30,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",1992,12,31,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1993,1,1,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1993,1,2,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",1993,1,3,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1993,1,4,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",1993,1,5,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1993,1,6,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1993,1,7,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1993,1,8,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1993,1,9,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1993,1,10,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1993,1,11,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1993,1,12,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1993,1,13,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1993,1,14,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1993,1,15,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1993,1,16,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1993,1,17,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1993,1,18,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1993,1,19,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1993,1,20,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1993,1,21,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1993,1,22,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1993,1,23,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1993,1,24,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1993,1,25,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1993,1,26,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1993,1,27,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1993,1,28,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1993,1,29,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1993,1,30,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1993,1,31,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1993,2,1,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1993,2,2,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1993,2,3,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,2,4,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1993,2,5,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1993,2,6,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1993,2,7,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1993,2,8,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,2,9,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1993,2,10,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1993,2,11,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1993,2,12,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1993,2,13,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1993,2,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1993,2,15,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1993,2,16,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1993,2,17,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1993,2,18,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1993,2,19,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1993,2,20,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1993,2,21,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1993,2,22,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1993,2,23,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1993,2,24,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1993,2,25,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1993,2,26,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1993,2,27,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1993,2,28,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",1993,3,1,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",1993,3,2,0.461,0.178611659192825,39.8304,0.708568694902007
+"FLOW_236212",1993,3,3,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1993,3,4,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1993,3,5,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1993,3,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1993,3,7,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1993,3,8,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1993,3,9,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1993,3,10,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1993,3,11,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1993,3,12,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1993,3,13,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1993,3,14,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1993,3,15,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1993,3,16,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1993,3,17,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1993,3,18,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1993,3,19,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1993,3,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1993,3,21,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1993,3,22,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1993,3,23,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1993,3,24,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1993,3,25,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1993,3,26,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1993,3,27,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1993,3,28,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1993,3,29,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1993,3,30,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,3,31,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1993,4,1,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1993,4,2,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1993,4,3,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1993,4,4,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1993,4,5,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,4,6,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1993,4,7,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1993,4,8,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1993,4,9,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1993,4,10,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1993,4,11,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1993,4,12,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1993,4,13,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1993,4,14,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1993,4,15,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1993,4,16,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1993,4,17,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1993,4,18,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1993,4,19,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1993,4,20,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1993,4,21,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1993,4,22,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1993,4,23,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1993,4,24,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1993,4,25,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1993,4,26,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1993,4,27,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1993,4,28,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1993,4,29,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1993,4,30,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1993,5,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1993,5,2,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1993,5,3,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1993,5,4,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1993,5,5,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1993,5,6,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1993,5,7,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1993,5,8,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1993,5,9,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1993,5,10,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1993,5,11,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1993,5,12,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1993,5,13,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1993,5,14,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1993,5,15,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1993,5,16,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1993,5,17,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1993,5,18,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1993,5,19,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1993,5,20,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1993,5,21,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1993,5,22,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,5,23,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1993,5,24,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1993,5,25,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1993,5,26,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1993,5,27,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1993,5,28,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1993,5,29,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1993,5,30,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1993,5,31,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1993,6,1,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1993,6,2,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1993,6,3,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1993,6,4,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1993,6,5,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1993,6,6,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1993,6,7,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1993,6,8,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1993,6,9,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1993,6,10,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1993,6,11,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1993,6,12,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1993,6,13,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1993,6,14,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",1993,6,15,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1993,6,16,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1993,6,17,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1993,6,18,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1993,6,19,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1993,6,20,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1993,6,21,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1993,6,22,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1993,6,23,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",1993,6,24,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1993,6,25,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1993,6,26,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1993,6,27,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1993,6,28,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1993,6,29,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1993,6,30,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1993,7,1,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1993,7,2,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1993,7,3,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",1993,7,4,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1993,7,5,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1993,7,6,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1993,7,7,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1993,7,8,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",1993,7,9,0.835,0.323515695067265,72.144,0.797956232157998
+"FLOW_236212",1993,7,10,0.48,0.185973094170404,41.472,0.714315416671224
+"FLOW_236212",1993,7,11,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",1993,7,12,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1993,7,13,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1993,7,14,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1993,7,15,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1993,7,16,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1993,7,17,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1993,7,18,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1993,7,19,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1993,7,20,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1993,7,21,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1993,7,22,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",1993,7,23,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1993,7,24,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1993,7,25,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1993,7,26,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1993,7,27,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1993,7,28,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1993,7,29,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1993,7,30,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1993,7,31,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1993,8,1,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1993,8,2,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1993,8,3,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",1993,8,4,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1993,8,5,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1993,8,6,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1993,8,7,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",1993,8,8,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1993,8,9,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1993,8,10,0.424,0.164276233183857,36.6336,0.696810918637591
+"FLOW_236212",1993,8,11,1.007,0.390156053811659,87.0048,0.828414458255961
+"FLOW_236212",1993,8,12,0.857,0.332039461883408,74.0448,0.802117405279478
+"FLOW_236212",1993,8,13,0.557,0.215806278026906,48.1248,0.735889853560662
+"FLOW_236212",1993,8,14,0.768,0.297556950672646,66.3552,0.784718732230276
+"FLOW_236212",1993,8,15,1.773,0.686938116591928,153.1872,0.927648663069597
+"FLOW_236212",1993,8,16,2.789,1.08058116591928,240.9696,1.01562054757383
+"FLOW_236212",1993,8,17,4.965,1.92365919282511,428.976,1.13979205784501
+"FLOW_236212",1993,8,18,6.507,2.52109775784753,562.2048,1.20314490764669
+"FLOW_236212",1993,8,19,7.082,2.74387802690583,611.8848,1.22369436560678
+"FLOW_236212",1993,8,20,2.978,1.15380807174888,257.2992,1.02902686843645
+"FLOW_236212",1993,8,21,1.531,0.59317668161435,132.2784,0.900817498932225
+"FLOW_236212",1993,8,22,1.031,0.399454708520179,89.0784,0.832326091438438
+"FLOW_236212",1993,8,23,0.823,0.318866367713005,71.1072,0.795649406075272
+"FLOW_236212",1993,8,24,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",1993,8,25,0.536,0.207669955156951,46.3104,0.73025532719464
+"FLOW_236212",1993,8,26,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",1993,8,27,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1993,8,28,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1993,8,29,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",1993,8,30,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1993,8,31,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1993,9,1,0.636,0.246414349775785,54.9504,0.755671771124804
+"FLOW_236212",1993,9,2,4.133,1.60130582959641,357.0912,1.0987398014044
+"FLOW_236212",1993,9,3,4.137,1.60285560538117,357.4368,1.09895239558689
+"FLOW_236212",1993,9,4,2.313,0.896157847533632,199.8432,0.978310922818232
+"FLOW_236212",1993,9,5,1.287,0.498640358744395,111.1968,0.870076592887204
+"FLOW_236212",1993,9,6,0.848,0.328552466367713,73.2672,0.800425555982974
+"FLOW_236212",1993,9,7,0.627,0.24292735426009,54.1728,0.753520867805153
+"FLOW_236212",1993,9,8,1.599,0.619522869955157,138.1536,0.90868104464408
+"FLOW_236212",1993,9,9,4.01,1.55365022421525,346.464,1.09212072925086
+"FLOW_236212",1993,9,10,3.158,1.22354798206278,272.8512,1.04117612545193
+"FLOW_236212",1993,9,11,2.011,0.779149775784753,173.7504,0.951314661356232
+"FLOW_236212",1993,9,12,1.615,0.62572197309417,139.536,0.910492308146133
+"FLOW_236212",1993,9,13,1.51,0.585040358744395,130.464,0.898332610482965
+"FLOW_236212",1993,9,14,1.053,0.407978475336323,90.9792,0.835848274250574
+"FLOW_236212",1993,9,15,0.772,0.299106726457399,66.7008,0.785534449930101
+"FLOW_236212",1993,9,16,0.668,0.258812556053812,57.7152,0.76312743734734
+"FLOW_236212",1993,9,17,0.578,0.223942600896861,49.9392,0.741356916328187
+"FLOW_236212",1993,9,18,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",1993,9,19,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",1993,9,20,9.025,3.49668161434978,779.76,1.28449145058395
+"FLOW_236212",1993,9,21,13.529,5.24172914798206,1168.9056,1.39281974631459
+"FLOW_236212",1993,9,22,5.137,1.99029955156951,443.8368,1.14758189794849
+"FLOW_236212",1993,9,23,2.938,1.13831031390135,253.8432,1.02624755202292
+"FLOW_236212",1993,9,24,1.888,0.731494170403587,163.1232,0.939381869570728
+"FLOW_236212",1993,9,25,1.237,0.479268161434978,106.8768,0.863208515156312
+"FLOW_236212",1993,9,26,0.881,0.341338116591928,76.1184,0.806560515318288
+"FLOW_236212",1993,9,27,0.676,0.261912107623318,58.4064,0.764946593724164
+"FLOW_236212",1993,9,28,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",1993,9,29,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",1993,9,30,0.492,0.190622421524664,42.5088,0.717851804519562
+"FLOW_236212",1993,10,1,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",1993,10,2,0.607,0.235178475336323,52.4448,0.748651181133953
+"FLOW_236212",1993,10,3,0.66,0.255713004484305,57.024,0.761290767578741
+"FLOW_236212",1993,10,4,2.513,0.9736466367713,217.1232,0.994672872320709
+"FLOW_236212",1993,10,5,2.909,1.12707443946188,251.3376,1.02421355714478
+"FLOW_236212",1993,10,6,1.608,0.623009865470852,138.9312,0.909701654905416
+"FLOW_236212",1993,10,7,1.089,0.421926457399103,94.0896,0.841486885334306
+"FLOW_236212",1993,10,8,0.791,0.306468161434978,68.3424,0.789363557701142
+"FLOW_236212",1993,10,9,0.614,0.237890582959641,53.0496,0.750369978592176
+"FLOW_236212",1993,10,10,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",1993,10,11,0.599,0.232078923766816,51.7536,0.74666731439864
+"FLOW_236212",1993,10,12,0.537,0.208057399103139,46.3968,0.730527607407242
+"FLOW_236212",1993,10,13,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1993,10,14,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",1993,10,15,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",1993,10,16,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",1993,10,17,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1993,10,18,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1993,10,19,0.658,0.254938116591928,56.8512,0.76082881895716
+"FLOW_236212",1993,10,20,1.273,0.493216143497758,109.9872,0.868175361616444
+"FLOW_236212",1993,10,21,1.541,0.597051121076233,133.1424,0.901991206496357
+"FLOW_236212",1993,10,22,1.134,0.439361434977578,97.9776,0.848329143083228
+"FLOW_236212",1993,10,23,0.803,0.311117488789238,69.3792,0.791744189705586
+"FLOW_236212",1993,10,24,0.609,0.2359533632287,52.6176,0.749143877303363
+"FLOW_236212",1993,10,25,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",1993,10,26,0.488,0.18907264573991,42.1632,0.716680751292182
+"FLOW_236212",1993,10,27,0.471,0.182486098654709,40.6944,0.711616414714403
+"FLOW_236212",1993,10,28,0.434,0.16815067264574,37.4976,0.700067186306734
+"FLOW_236212",1993,10,29,0.54,0.209219730941704,46.656,0.731342021478882
+"FLOW_236212",1993,10,30,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",1993,10,31,0.455,0.176286995515695,39.312,0.706714586543881
+"FLOW_236212",1993,11,1,0.423,0.163888789237668,36.5472,0.696481923722871
+"FLOW_236212",1993,11,2,1.404,0.54397130044843,121.3056,0.885350419585714
+"FLOW_236212",1993,11,3,4.823,1.86864215246637,416.7072,1.13319650987756
+"FLOW_236212",1993,11,4,3.679,1.42540627802691,317.8656,1.07346461561618
+"FLOW_236212",1993,11,5,4.582,1.77526816143498,395.8848,1.12163820277062
+"FLOW_236212",1993,11,6,3.582,1.38782421524664,309.4848,1.06774339156434
+"FLOW_236212",1993,11,7,2.093,0.810920179372197,180.8352,0.958949244984481
+"FLOW_236212",1993,11,8,1.351,0.523436771300449,116.7264,0.878562871241708
+"FLOW_236212",1993,11,9,0.924,0.357998206278027,79.8336,0.814284503945924
+"FLOW_236212",1993,11,10,0.674,0.261137219730942,58.2336,0.764493425992641
+"FLOW_236212",1993,11,11,0.531,0.205732735426009,45.8784,0.728887798254835
+"FLOW_236212",1993,11,12,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",1993,11,13,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1993,11,14,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1993,11,15,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1993,11,16,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1993,11,17,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1993,11,18,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1993,11,19,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1993,11,20,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",1993,11,21,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1993,11,22,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1993,11,23,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1993,11,24,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1993,11,25,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",1993,11,26,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1993,11,27,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1993,11,28,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1993,11,29,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1993,11,30,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1993,12,1,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1993,12,2,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1993,12,3,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1993,12,4,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1993,12,5,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1993,12,6,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1993,12,7,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1993,12,8,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1993,12,9,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1993,12,10,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1993,12,11,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1993,12,12,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1993,12,13,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1993,12,14,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1993,12,15,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1993,12,16,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1993,12,17,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1993,12,18,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1993,12,19,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1993,12,20,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1993,12,21,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1993,12,22,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1993,12,23,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",1993,12,24,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",1993,12,25,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1993,12,26,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1993,12,27,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1993,12,28,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1993,12,29,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",1993,12,30,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1993,12,31,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1994,1,1,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1994,1,2,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1994,1,3,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1994,1,4,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1994,1,5,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1994,1,6,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1994,1,7,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1994,1,8,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1994,1,9,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1994,1,10,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1994,1,11,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1994,1,12,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1994,1,13,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1994,1,14,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1994,1,15,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1994,1,16,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1994,1,17,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1994,1,18,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1994,1,19,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1994,1,20,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1994,1,21,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1994,1,22,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,1,23,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1994,1,24,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1994,1,25,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1994,1,26,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1994,1,27,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1994,1,28,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1994,1,29,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1994,1,30,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1994,1,31,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1994,2,1,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1994,2,2,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1994,2,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1994,2,4,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1994,2,5,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1994,2,6,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1994,2,7,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1994,2,8,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1994,2,9,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1994,2,10,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1994,2,11,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1994,2,12,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1994,2,13,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1994,2,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1994,2,15,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1994,2,16,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1994,2,17,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1994,2,18,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1994,2,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1994,2,20,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1994,2,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1994,2,22,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1994,2,23,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1994,2,24,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1994,2,25,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1994,2,26,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1994,2,27,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1994,2,28,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1994,3,1,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1994,3,2,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1994,3,3,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1994,3,4,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1994,3,5,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1994,3,6,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1994,3,7,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1994,3,8,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1994,3,9,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1994,3,10,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1994,3,11,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1994,3,12,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1994,3,13,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1994,3,14,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1994,3,15,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1994,3,16,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1994,3,17,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1994,3,18,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1994,3,19,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1994,3,20,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1994,3,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1994,3,22,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1994,3,23,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1994,3,24,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1994,3,25,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1994,3,26,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1994,3,27,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1994,3,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1994,3,29,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1994,3,30,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1994,3,31,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1994,4,1,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1994,4,2,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1994,4,3,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1994,4,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1994,4,5,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1994,4,6,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1994,4,7,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1994,4,8,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1994,4,9,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1994,4,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1994,4,11,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1994,4,12,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1994,4,13,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1994,4,14,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,4,15,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1994,4,16,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1994,4,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1994,4,18,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1994,4,19,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1994,4,20,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1994,4,21,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1994,4,22,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1994,4,23,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1994,4,24,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1994,4,25,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,4,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1994,4,27,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1994,4,28,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1994,4,29,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,4,30,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,5,1,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,5,2,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,5,3,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1994,5,4,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1994,5,5,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1994,5,6,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1994,5,7,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1994,5,8,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1994,5,9,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1994,5,10,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1994,5,11,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1994,5,12,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1994,5,13,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1994,5,14,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1994,5,15,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1994,5,16,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1994,5,17,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1994,5,18,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1994,5,19,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1994,5,20,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1994,5,21,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1994,5,22,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1994,5,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1994,5,24,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",1994,5,25,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1994,5,26,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1994,5,27,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1994,5,28,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1994,5,29,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1994,5,30,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1994,5,31,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1994,6,1,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1994,6,2,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1994,6,3,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1994,6,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1994,6,5,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1994,6,6,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1994,6,7,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1994,6,8,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1994,6,9,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,6,10,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,6,11,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,6,12,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1994,6,13,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1994,6,14,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1994,6,15,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1994,6,16,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1994,6,17,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1994,6,18,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1994,6,19,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1994,6,20,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1994,6,21,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1994,6,22,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1994,6,23,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1994,6,24,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",1994,6,25,0.514,0.199146188340807,44.4096,0.724159778915107
+"FLOW_236212",1994,6,26,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",1994,6,27,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1994,6,28,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1994,6,29,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1994,6,30,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1994,7,1,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1994,7,2,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1994,7,3,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1994,7,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1994,7,5,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1994,7,6,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1994,7,7,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1994,7,8,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1994,7,9,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1994,7,10,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1994,7,11,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1994,7,12,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1994,7,13,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1994,7,14,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1994,7,15,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1994,7,16,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1994,7,17,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1994,7,18,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1994,7,19,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1994,7,20,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",1994,7,21,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1994,7,22,0.661,0.256100448430493,57.1104,0.761521322063055
+"FLOW_236212",1994,7,23,0.52,0.201470852017937,44.928,0.725842585090593
+"FLOW_236212",1994,7,24,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1994,7,25,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",1994,7,26,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1994,7,27,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",1994,7,28,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",1994,7,29,0.416,0.16117668161435,35.9424,0.694161370705451
+"FLOW_236212",1994,7,30,0.885,0.342887892376682,76.464,0.807291593351405
+"FLOW_236212",1994,7,31,2.319,0.898482511210762,200.3616,0.978817951296198
+"FLOW_236212",1994,8,1,2.746,1.06392107623318,237.2544,1.01246934433393
+"FLOW_236212",1994,8,2,2.359,0.913980269058296,203.8176,0.982171581762492
+"FLOW_236212",1994,8,3,1.865,0.722582959641256,161.136,0.937081886352079
+"FLOW_236212",1994,8,4,1.367,0.529635874439462,118.1088,0.880634060574907
+"FLOW_236212",1994,8,5,1.136,0.440136322869955,98.1504,0.848628166471965
+"FLOW_236212",1994,8,6,0.884,0.342500448430493,76.3776,0.807109072059093
+"FLOW_236212",1994,8,7,0.803,0.311117488789238,69.3792,0.791744189705586
+"FLOW_236212",1994,8,8,1.696,0.657104932735426,146.5344,0.919447519455229
+"FLOW_236212",1994,8,9,6.256,2.42384932735426,540.5184,1.19371629936823
+"FLOW_236212",1994,8,10,3.576,1.38549955156951,308.9664,1.06738544867745
+"FLOW_236212",1994,8,11,1.868,0.723745291479821,161.3952,0.937383166626449
+"FLOW_236212",1994,8,12,1.134,0.439361434977578,97.9776,0.848329143083228
+"FLOW_236212",1994,8,13,0.833,0.322740807174888,71.9712,0.797573610916482
+"FLOW_236212",1994,8,14,1.046,0.405266367713005,90.3744,0.834734018160464
+"FLOW_236212",1994,8,15,2.006,0.777212556053812,173.3184,0.950841134659926
+"FLOW_236212",1994,8,16,1.638,0.634633183856502,141.5232,0.913071012562966
+"FLOW_236212",1994,8,17,1.245,0.482367713004484,107.568,0.86432215668092
+"FLOW_236212",1994,8,18,2.825,1.09452914798206,244.08,1.01822901025855
+"FLOW_236212",1994,8,19,2.315,0.896932735426009,200.016,0.978480049100761
+"FLOW_236212",1994,8,20,1.289,0.499415246636771,111.3696,0.870346845025966
+"FLOW_236212",1994,8,21,0.906,0.351024215246637,78.2784,0.811086952902811
+"FLOW_236212",1994,8,22,0.729,0.2824466367713,62.9856,0.776581931182144
+"FLOW_236212",1994,8,23,0.858,0.332426905829596,74.1312,0.802304509896357
+"FLOW_236212",1994,8,24,3.317,1.28515156950673,286.5888,1.05145543490693
+"FLOW_236212",1994,8,25,2.135,0.827192825112108,184.464,0.962767345535095
+"FLOW_236212",1994,8,26,1.302,0.50445201793722,112.4928,0.872095353791879
+"FLOW_236212",1994,8,27,0.926,0.358773094170404,80.0064,0.814636703257581
+"FLOW_236212",1994,8,28,0.714,0.276634977578475,61.6896,0.773359488319869
+"FLOW_236212",1994,8,29,0.59,0.228591928251121,50.976,0.74440996510202
+"FLOW_236212",1994,8,30,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",1994,8,31,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",1994,9,1,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",1994,9,2,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",1994,9,3,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",1994,9,4,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",1994,9,5,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",1994,9,6,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",1994,9,7,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1994,9,8,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1994,9,9,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1994,9,10,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",1994,9,11,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",1994,9,12,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1994,9,13,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",1994,9,14,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1994,9,15,0.784,0.303756053811659,67.7376,0.787961482136515
+"FLOW_236212",1994,9,16,0.908,0.351799103139014,78.4512,0.81144473290655
+"FLOW_236212",1994,9,17,1.119,0.433549775784753,96.6816,0.846072916592779
+"FLOW_236212",1994,9,18,1.335,0.517237668161435,115.344,0.876471964597777
+"FLOW_236212",1994,9,19,2.27,0.879497757847534,196.128,0.974646095519593
+"FLOW_236212",1994,9,20,3.099,1.20068878923767,267.7536,1.03725632351045
+"FLOW_236212",1994,9,21,4.372,1.69390493273543,377.7408,1.11116308929909
+"FLOW_236212",1994,9,22,3.707,1.43625470852018,320.2848,1.07509364101158
+"FLOW_236212",1994,9,23,1.948,0.754740807174888,168.3072,0.945278034758517
+"FLOW_236212",1994,9,24,1.297,0.502514798206278,112.0608,0.871424510417836
+"FLOW_236212",1994,9,25,1.072,0.415339910313901,92.6208,0.838843093076304
+"FLOW_236212",1994,9,26,0.954,0.369621524663677,82.4256,0.819504704075818
+"FLOW_236212",1994,9,27,1.113,0.431225112107623,96.1632,0.845163647520222
+"FLOW_236212",1994,9,28,1.854,0.718321076233184,160.1856,0.935973864252746
+"FLOW_236212",1994,9,29,1.758,0.681126457399103,151.8912,0.92607369869682
+"FLOW_236212",1994,9,30,2.687,1.04106188340807,232.1568,1.00808072099656
+"FLOW_236212",1994,10,1,2.106,0.815956950672646,181.9584,0.960137537658745
+"FLOW_236212",1994,10,2,3.968,1.53737757847534,342.8352,1.08982334985555
+"FLOW_236212",1994,10,3,9.059,3.50985470852018,782.6976,1.28545781171816
+"FLOW_236212",1994,10,4,3.817,1.4788735426009,329.7888,1.08139960816217
+"FLOW_236212",1994,10,5,2.308,0.894220627802691,199.4112,0.977887594696528
+"FLOW_236212",1994,10,6,2.047,0.793097757847534,176.8608,0.954696530802128
+"FLOW_236212",1994,10,7,8.577,3.3231067264574,741.0528,1.27147802281035
+"FLOW_236212",1994,10,8,21.189,8.20954977578475,1830.7296,1.52357510620451
+"FLOW_236212",1994,10,9,13.685,5.30217040358744,1182.384,1.3960170941642
+"FLOW_236212",1994,10,10,5.336,2.06740089686099,461.0304,1.15633837874251
+"FLOW_236212",1994,10,11,2.539,0.983720179372197,219.3696,0.996722623775178
+"FLOW_236212",1994,10,12,1.527,0.591626905829596,131.9328,0.900346298204089
+"FLOW_236212",1994,10,13,0.991,0.383956950672646,85.6224,0.82576506730935
+"FLOW_236212",1994,10,14,0.735,0.284771300448431,63.504,0.777856067537677
+"FLOW_236212",1994,10,15,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",1994,10,16,0.471,0.182486098654709,40.6944,0.711616414714403
+"FLOW_236212",1994,10,17,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1994,10,18,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",1994,10,19,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",1994,10,20,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1994,10,21,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1994,10,22,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1994,10,23,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1994,10,24,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1994,10,25,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1994,10,26,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1994,10,27,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1994,10,28,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",1994,10,29,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1994,10,30,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1994,10,31,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1994,11,1,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1994,11,2,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1994,11,3,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1994,11,4,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1994,11,5,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1994,11,6,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1994,11,7,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1994,11,8,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1994,11,9,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",1994,11,10,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",1994,11,11,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1994,11,12,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1994,11,13,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1994,11,14,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",1994,11,15,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1994,11,16,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1994,11,17,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1994,11,18,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1994,11,19,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1994,11,20,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1994,11,21,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1994,11,22,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1994,11,23,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1994,11,24,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1994,11,25,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1994,11,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1994,11,27,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1994,11,28,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1994,11,29,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1994,11,30,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1994,12,1,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1994,12,2,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1994,12,3,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1994,12,4,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1994,12,5,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1994,12,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1994,12,7,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1994,12,8,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1994,12,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1994,12,10,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1994,12,11,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1994,12,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1994,12,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1994,12,14,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1994,12,15,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1994,12,16,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1994,12,17,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1994,12,18,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1994,12,19,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1994,12,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1994,12,21,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1994,12,22,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1994,12,23,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1994,12,24,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1994,12,25,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1994,12,26,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1994,12,27,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1994,12,28,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1994,12,29,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1994,12,30,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1994,12,31,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1995,1,1,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1995,1,2,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1995,1,3,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1995,1,4,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1995,1,5,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1995,1,6,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1995,1,7,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1995,1,8,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1995,1,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1995,1,10,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1995,1,11,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1995,1,12,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1995,1,13,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1995,1,14,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1995,1,15,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1995,1,16,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1995,1,17,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1995,1,18,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1995,1,19,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1995,1,20,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1995,1,21,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1995,1,22,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1995,1,23,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1995,1,24,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1995,1,25,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",1995,1,26,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1995,1,27,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1995,1,28,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1995,1,29,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1995,1,30,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1995,1,31,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1995,2,1,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1995,2,2,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1995,2,3,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1995,2,4,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1995,2,5,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1995,2,6,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1995,2,7,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1995,2,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1995,2,9,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1995,2,10,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1995,2,11,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1995,2,12,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1995,2,13,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1995,2,14,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1995,2,15,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",1995,2,16,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1995,2,17,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1995,2,18,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1995,2,19,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1995,2,20,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1995,2,21,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1995,2,22,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1995,2,23,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1995,2,24,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1995,2,25,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1995,2,26,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1995,2,27,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1995,2,28,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1995,3,1,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1995,3,2,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1995,3,3,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1995,3,4,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1995,3,5,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1995,3,6,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",1995,3,7,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1995,3,8,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1995,3,9,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1995,3,10,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1995,3,11,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1995,3,12,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1995,3,13,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1995,3,14,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1995,3,15,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1995,3,16,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1995,3,17,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1995,3,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1995,3,19,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1995,3,20,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1995,3,21,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1995,3,22,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1995,3,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1995,3,24,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1995,3,25,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1995,3,26,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1995,3,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1995,3,28,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1995,3,29,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1995,3,30,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1995,3,31,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,4,1,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,4,2,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,4,3,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,4,4,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,4,5,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1995,4,6,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1995,4,7,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1995,4,8,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",1995,4,9,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1995,4,10,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1995,4,11,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1995,4,12,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1995,4,13,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1995,4,14,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,4,15,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1995,4,16,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1995,4,17,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1995,4,18,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1995,4,19,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1995,4,20,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1995,4,21,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1995,4,22,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1995,4,23,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1995,4,24,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,4,25,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1995,4,26,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1995,4,27,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1995,4,28,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1995,4,29,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1995,4,30,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1995,5,1,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1995,5,2,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1995,5,3,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1995,5,4,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1995,5,5,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1995,5,6,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1995,5,7,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1995,5,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,5,9,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1995,5,10,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,5,11,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1995,5,12,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,5,13,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,5,14,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1995,5,15,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1995,5,16,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1995,5,17,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1995,5,18,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1995,5,19,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,5,20,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,5,21,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1995,5,22,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1995,5,23,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1995,5,24,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1995,5,25,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,5,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1995,5,27,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1995,5,28,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1995,5,29,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1995,5,30,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1995,5,31,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1995,6,1,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,6,2,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1995,6,3,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1995,6,4,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,6,5,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1995,6,6,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1995,6,7,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1995,6,8,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1995,6,9,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1995,6,10,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1995,6,11,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1995,6,12,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1995,6,13,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1995,6,14,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1995,6,15,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",1995,6,16,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",1995,6,17,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1995,6,18,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1995,6,19,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",1995,6,20,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1995,6,21,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1995,6,22,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",1995,6,23,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1995,6,24,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1995,6,25,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1995,6,26,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1995,6,27,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1995,6,28,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1995,6,29,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",1995,6,30,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1995,7,1,0.787,0.304918385650224,67.9968,0.788563592988764
+"FLOW_236212",1995,7,2,2.055,0.79619730941704,177.552,0.955441588020548
+"FLOW_236212",1995,7,3,1.778,0.68887533632287,153.6192,0.928171282366369
+"FLOW_236212",1995,7,4,1.956,0.757840358744395,168.9984,0.946053171540375
+"FLOW_236212",1995,7,5,1.62,0.627659192825112,139.968,0.9110553835972
+"FLOW_236212",1995,7,6,1.014,0.392868161434978,87.6096,0.829562987367691
+"FLOW_236212",1995,7,7,0.966,0.374270852017937,83.4624,0.821556055667657
+"FLOW_236212",1995,7,8,0.975,0.377757847533632,84.24,0.823081232115489
+"FLOW_236212",1995,7,9,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",1995,7,10,0.785,0.304143497757848,67.824,0.78816239024069
+"FLOW_236212",1995,7,11,0.693,0.26849865470852,59.8752,0.768755830816311
+"FLOW_236212",1995,7,12,0.661,0.256100448430493,57.1104,0.761521322063055
+"FLOW_236212",1995,7,13,0.796,0.308405381165919,68.7744,0.790358975187442
+"FLOW_236212",1995,7,14,5.388,2.08754798206278,465.5232,1.15858337756827
+"FLOW_236212",1995,7,15,10.649,4.12589058295964,920.0736,1.32771096307249
+"FLOW_236212",1995,7,16,4.961,1.92210941704036,428.6304,1.13960834633805
+"FLOW_236212",1995,7,17,4.275,1.65632286995516,369.36,1.10618814466291
+"FLOW_236212",1995,7,18,8.626,3.34209147982063,745.2864,1.27292749318139
+"FLOW_236212",1995,7,19,10.735,4.15921076233184,927.504,1.32984855474336
+"FLOW_236212",1995,7,20,9.294,3.60090403587444,803.0016,1.29205889282383
+"FLOW_236212",1995,7,21,3.799,1.47189955156951,328.2336,1.0803777577908
+"FLOW_236212",1995,7,22,2.157,0.835716591928251,186.3648,0.964743375003257
+"FLOW_236212",1995,7,23,1.878,0.727619730941704,162.2592,0.938384646653589
+"FLOW_236212",1995,7,24,1.741,0.674539910313901,150.4224,0.924275689139499
+"FLOW_236212",1995,7,25,2.026,0.784961434977579,175.0464,0.95272961257188
+"FLOW_236212",1995,7,26,1.789,0.693137219730942,154.5696,0.929316919170229
+"FLOW_236212",1995,7,27,1.351,0.523436771300449,116.7264,0.878562871241708
+"FLOW_236212",1995,7,28,1.556,0.602862780269058,134.4384,0.903740394571627
+"FLOW_236212",1995,7,29,5.721,2.2165668161435,494.2944,1.17256294452505
+"FLOW_236212",1995,7,30,5.677,2.19951928251121,490.4928,1.17075374245608
+"FLOW_236212",1995,7,31,3.261,1.26345470852018,281.7504,1.04788092706553
+"FLOW_236212",1995,8,1,2.502,0.969384753363229,216.1728,0.993800559601821
+"FLOW_236212",1995,8,2,11.648,4.51294708520179,1006.3872,1.35173652295865
+"FLOW_236212",1995,8,3,12.939,5.01313721973094,1117.9296,1.38045393313667
+"FLOW_236212",1995,8,4,9.021,3.49513183856502,779.4144,1.28437756965474
+"FLOW_236212",1995,8,5,6.91,2.67723766816143,597.024,1.21769181086726
+"FLOW_236212",1995,8,6,5.556,2.15263856502242,480.0384,1.16571993469512
+"FLOW_236212",1995,8,7,11.609,4.49783677130045,1003.0176,1.35083012751458
+"FLOW_236212",1995,8,8,13.22,5.12200896860987,1142.208,1.38639845878909
+"FLOW_236212",1995,8,9,7.205,2.791533632287,622.512,1.22791575844902
+"FLOW_236212",1995,8,10,3.601,1.39518565022422,311.1264,1.06887372195691
+"FLOW_236212",1995,8,11,2.323,0.900032286995516,200.7072,0.979155387539898
+"FLOW_236212",1995,8,12,1.657,0.641994618834081,143.1648,0.915179487774907
+"FLOW_236212",1995,8,13,1.244,0.481980269058296,107.4816,0.864183265117153
+"FLOW_236212",1995,8,14,0.989,0.383182062780269,85.4496,0.825431492137924
+"FLOW_236212",1995,8,15,0.819,0.317316591928251,70.7616,0.794874484316053
+"FLOW_236212",1995,8,16,0.701,0.271598206278027,60.5664,0.770522596555566
+"FLOW_236212",1995,8,17,0.611,0.236728251121076,52.7904,0.749635280728596
+"FLOW_236212",1995,8,18,0.543,0.210382062780269,46.9152,0.732152823938167
+"FLOW_236212",1995,8,19,0.488,0.18907264573991,42.1632,0.716680751292182
+"FLOW_236212",1995,8,20,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",1995,8,21,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",1995,8,22,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",1995,8,23,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",1995,8,24,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1995,8,25,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1995,8,26,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1995,8,27,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",1995,8,28,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1995,8,29,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1995,8,30,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",1995,8,31,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",1995,9,1,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",1995,9,2,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",1995,9,3,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",1995,9,4,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",1995,9,5,0.935,0.362260089686099,80.784,0.816214109723788
+"FLOW_236212",1995,9,6,1.903,0.737305829596413,164.4192,0.940869810298561
+"FLOW_236212",1995,9,7,1.8,0.697399103139014,155.52,0.930456934432707
+"FLOW_236212",1995,9,8,1.449,0.561406278026906,125.1936,0.890954350828099
+"FLOW_236212",1995,9,9,1.102,0.426963228699552,95.2128,0.843486419769643
+"FLOW_236212",1995,9,10,1.122,0.434712107623318,96.9408,0.846526089277255
+"FLOW_236212",1995,9,11,0.932,0.361097757847534,80.5248,0.815689662443497
+"FLOW_236212",1995,9,12,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",1995,9,13,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",1995,9,14,0.675,0.26152466367713,58.32,0.764720144130376
+"FLOW_236212",1995,9,15,1.357,0.525761434977579,117.2448,0.879341855357576
+"FLOW_236212",1995,9,16,1.21,0.468807174887892,104.544,0.859406927163996
+"FLOW_236212",1995,9,17,0.82,0.317704035874439,70.848,0.795068498125917
+"FLOW_236212",1995,9,18,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",1995,9,19,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",1995,9,20,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",1995,9,21,0.552,0.213869058295964,47.6928,0.734563917279502
+"FLOW_236212",1995,9,22,0.633,0.24525201793722,54.6912,0.75495752431767
+"FLOW_236212",1995,9,23,0.701,0.271598206278027,60.5664,0.770522596555566
+"FLOW_236212",1995,9,24,0.641,0.248351569506726,55.3824,0.756856215452741
+"FLOW_236212",1995,9,25,0.581,0.225104932735426,50.1984,0.742124898206603
+"FLOW_236212",1995,9,26,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",1995,9,27,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",1995,9,28,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",1995,9,29,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1995,9,30,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",1995,10,1,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",1995,10,2,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",1995,10,3,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1995,10,4,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",1995,10,5,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1995,10,6,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",1995,10,7,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",1995,10,8,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",1995,10,9,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1995,10,10,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1995,10,11,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1995,10,12,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1995,10,13,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1995,10,14,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1995,10,15,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1995,10,16,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1995,10,17,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",1995,10,18,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1995,10,19,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1995,10,20,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1995,10,21,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1995,10,22,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1995,10,23,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1995,10,24,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1995,10,25,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1995,10,26,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1995,10,27,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1995,10,28,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1995,10,29,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1995,10,30,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1995,10,31,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1995,11,1,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1995,11,2,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1995,11,3,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1995,11,4,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1995,11,5,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1995,11,6,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",1995,11,7,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",1995,11,8,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1995,11,9,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1995,11,10,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1995,11,11,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1995,11,12,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1995,11,13,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1995,11,14,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1995,11,15,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1995,11,16,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1995,11,17,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1995,11,18,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1995,11,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1995,11,20,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1995,11,21,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1995,11,22,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1995,11,23,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,11,24,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1995,11,25,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1995,11,26,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1995,11,27,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1995,11,28,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1995,11,29,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1995,11,30,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1995,12,1,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1995,12,2,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1995,12,3,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1995,12,4,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1995,12,5,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1995,12,6,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1995,12,7,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1995,12,8,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1995,12,9,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1995,12,10,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1995,12,11,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1995,12,12,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1995,12,13,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1995,12,14,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1995,12,15,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1995,12,16,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1995,12,17,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1995,12,18,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1995,12,19,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1995,12,20,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1995,12,21,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1995,12,22,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1995,12,23,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1995,12,24,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1995,12,25,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1995,12,26,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1995,12,27,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1995,12,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1995,12,29,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1995,12,30,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1995,12,31,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1996,1,1,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1996,1,2,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1996,1,3,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1996,1,4,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1996,1,5,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1996,1,6,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1996,1,7,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1996,1,8,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1996,1,9,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1996,1,10,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1996,1,11,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1996,1,12,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1996,1,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1996,1,14,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1996,1,15,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1996,1,16,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1996,1,17,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1996,1,18,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1996,1,19,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1996,1,20,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1996,1,21,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1996,1,22,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1996,1,23,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1996,1,24,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1996,1,25,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1996,1,26,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1996,1,27,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1996,1,28,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1996,1,29,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1996,1,30,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1996,1,31,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1996,2,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1996,2,2,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,2,3,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,2,4,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1996,2,5,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1996,2,6,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1996,2,7,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1996,2,8,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1996,2,9,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1996,2,10,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1996,2,11,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1996,2,12,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1996,2,13,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1996,2,14,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1996,2,15,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1996,2,16,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1996,2,17,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1996,2,18,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1996,2,19,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1996,2,20,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1996,2,21,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1996,2,22,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1996,2,23,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1996,2,24,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1996,2,25,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1996,2,26,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1996,2,27,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1996,2,28,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",1996,2,29,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1996,3,1,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1996,3,2,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1996,3,3,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1996,3,4,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1996,3,5,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1996,3,6,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1996,3,7,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1996,3,8,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1996,3,9,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1996,3,10,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1996,3,11,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1996,3,12,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",1996,3,13,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1996,3,14,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1996,3,15,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1996,3,16,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1996,3,17,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1996,3,18,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1996,3,19,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1996,3,20,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,3,21,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1996,3,22,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1996,3,23,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1996,3,24,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1996,3,25,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1996,3,26,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1996,3,27,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1996,3,28,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1996,3,29,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1996,3,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1996,3,31,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1996,4,1,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1996,4,2,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1996,4,3,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1996,4,4,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1996,4,5,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1996,4,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,4,7,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1996,4,8,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,4,9,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1996,4,10,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1996,4,11,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1996,4,12,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1996,4,13,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1996,4,14,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1996,4,15,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1996,4,16,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1996,4,17,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1996,4,18,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1996,4,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1996,4,20,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1996,4,21,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1996,4,22,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1996,4,23,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1996,4,24,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1996,4,25,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1996,4,26,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1996,4,27,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1996,4,28,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1996,4,29,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1996,4,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1996,5,1,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1996,5,2,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1996,5,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1996,5,4,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1996,5,5,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1996,5,6,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1996,5,7,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1996,5,8,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1996,5,9,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1996,5,10,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1996,5,11,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1996,5,12,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1996,5,13,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1996,5,14,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1996,5,15,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,5,16,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,5,17,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1996,5,18,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1996,5,19,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1996,5,20,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1996,5,21,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1996,5,22,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1996,5,23,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1996,5,24,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1996,5,25,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1996,5,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1996,5,27,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1996,5,28,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1996,5,29,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1996,5,30,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1996,5,31,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1996,6,1,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1996,6,2,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1996,6,3,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1996,6,4,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1996,6,5,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1996,6,6,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",1996,6,7,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1996,6,8,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1996,6,9,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1996,6,10,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1996,6,11,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1996,6,12,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1996,6,13,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1996,6,14,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1996,6,15,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1996,6,16,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1996,6,17,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1996,6,18,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1996,6,19,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1996,6,20,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1996,6,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1996,6,22,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1996,6,23,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1996,6,24,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",1996,6,25,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1996,6,26,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1996,6,27,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1996,6,28,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1996,6,29,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1996,6,30,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1996,7,1,0.468,0.181323766816144,40.4352,0.710707578523312
+"FLOW_236212",1996,7,2,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",1996,7,3,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",1996,7,4,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",1996,7,5,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1996,7,6,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",1996,7,7,0.598,0.231691479820628,51.6672,0.746417843138657
+"FLOW_236212",1996,7,8,0.496,0.192172197309417,42.8544,0.71901526573971
+"FLOW_236212",1996,7,9,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",1996,7,10,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",1996,7,11,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1996,7,12,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1996,7,13,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1996,7,14,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",1996,7,15,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1996,7,16,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",1996,7,17,0.485,0.187910313901345,41.904,0.715797411608359
+"FLOW_236212",1996,7,18,0.568,0.220068161434978,49.0752,0.738773728351126
+"FLOW_236212",1996,7,19,0.819,0.317316591928251,70.7616,0.794874484316053
+"FLOW_236212",1996,7,20,1.325,0.513363228699552,114.48,0.875154945926746
+"FLOW_236212",1996,7,21,1.964,0.760939910313901,169.6896,0.946825776222235
+"FLOW_236212",1996,7,22,3.257,1.26190493273543,281.4048,1.04762373100408
+"FLOW_236212",1996,7,23,2.284,0.88492197309417,197.3376,0.975845347288721
+"FLOW_236212",1996,7,24,1.776,0.688100448430493,153.4464,0.927962375912078
+"FLOW_236212",1996,7,25,2.137,0.827967713004484,184.6368,0.962947655941138
+"FLOW_236212",1996,7,26,1.756,0.680351569506726,151.7184,0.925862892005565
+"FLOW_236212",1996,7,27,1.029,0.398679820627803,88.9056,0.832002920658999
+"FLOW_236212",1996,7,28,0.695,0.269273542600897,60.048,0.769199045772625
+"FLOW_236212",1996,7,29,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",1996,7,30,2.818,1.09181704035874,243.4752,1.01772390030315
+"FLOW_236212",1996,7,31,19.501,7.55554439461884,1684.8864,1.49848759600273
+"FLOW_236212",1996,8,1,15.096,5.84885381165919,1304.2944,1.42368579986729
+"FLOW_236212",1996,8,2,11.11,4.30450224215247,959.904,1.33901236557648
+"FLOW_236212",1996,8,3,3.646,1.41262062780269,315.0144,1.07153190994849
+"FLOW_236212",1996,8,4,3.252,1.25996771300448,280.9728,1.04730188033472
+"FLOW_236212",1996,8,5,4.306,1.668333632287,372.0384,1.10778780766632
+"FLOW_236212",1996,8,6,3.198,1.23904573991031,276.3072,1.04380042119964
+"FLOW_236212",1996,8,7,3.05,1.18170403587444,263.52,1.03395525245474
+"FLOW_236212",1996,8,8,8.687,3.36572556053812,750.5568,1.27472276012312
+"FLOW_236212",1996,8,9,8.414,3.2599533632287,726.9696,1.26660814781506
+"FLOW_236212",1996,8,10,12.504,4.84459910313901,1080.3456,1.37104455673461
+"FLOW_236212",1996,8,11,8.265,3.20222421524664,714.096,1.26209006332657
+"FLOW_236212",1996,8,12,3.23,1.25144394618834,279.072,1.04588101660492
+"FLOW_236212",1996,8,13,1.92,0.743892376681614,165.888,0.942544843084717
+"FLOW_236212",1996,8,14,2.103,0.814794618834081,181.6992,0.959863838202154
+"FLOW_236212",1996,8,15,3.475,1.34636771300448,300.24,1.06128673902015
+"FLOW_236212",1996,8,16,2.533,0.981395515695067,218.8512,0.996251099812387
+"FLOW_236212",1996,8,17,1.791,0.693912107623318,154.7424,0.92952461098205
+"FLOW_236212",1996,8,18,3.404,1.31885919282511,294.1056,1.05691408643492
+"FLOW_236212",1996,8,19,10.17,3.94030493273543,878.688,1.3155457820171
+"FLOW_236212",1996,8,20,6.886,2.66793901345291,594.9504,1.21684476919568
+"FLOW_236212",1996,8,21,3.647,1.41300807174888,315.1008,1.07159068199305
+"FLOW_236212",1996,8,22,2.387,0.92482869955157,206.2368,0.984492154791246
+"FLOW_236212",1996,8,23,2.275,0.881434977578475,196.56,0.975075077241891
+"FLOW_236212",1996,8,24,2.535,0.982170403587444,219.024,0.996408373653963
+"FLOW_236212",1996,8,25,1.908,0.739243049327354,164.8512,0.941363705484224
+"FLOW_236212",1996,8,26,1.607,0.622622421524664,138.8448,0.909588479776893
+"FLOW_236212",1996,8,27,2.696,1.04454887892377,232.9344,1.00875512333116
+"FLOW_236212",1996,8,28,8.269,3.20377399103139,714.4416,1.26221220206191
+"FLOW_236212",1996,8,29,4.618,1.78921614349776,398.9952,1.12339519403795
+"FLOW_236212",1996,8,30,2.604,1.00890403587444,224.9856,1.00177449830129
+"FLOW_236212",1996,8,31,1.667,0.645869058295964,144.0288,0.916281453024665
+"FLOW_236212",1996,9,1,1.201,0.465320179372197,103.7664,0.858124649841692
+"FLOW_236212",1996,9,2,0.923,0.357610762331839,79.7472,0.814108175539618
+"FLOW_236212",1996,9,3,0.795,0.308017937219731,68.688,0.790160292664795
+"FLOW_236212",1996,9,4,0.739,0.286321076233184,63.8496,0.778700876553795
+"FLOW_236212",1996,9,5,0.764,0.296007174887892,66.0096,0.783899608594372
+"FLOW_236212",1996,9,6,0.89,0.344825112107623,76.896,0.808201732747543
+"FLOW_236212",1996,9,7,0.947,0.366909417040359,81.8208,0.818298531283392
+"FLOW_236212",1996,9,8,0.879,0.340563228699552,75.9456,0.806193980054242
+"FLOW_236212",1996,9,9,1.43,0.554044843049327,123.552,0.88860547219752
+"FLOW_236212",1996,9,10,5.871,2.27468340807175,507.2544,1.17864818552932
+"FLOW_236212",1996,9,11,7.661,2.96820807174888,661.9104,1.24307939673472
+"FLOW_236212",1996,9,12,8.366,3.24135605381166,722.8224,1.26515969519675
+"FLOW_236212",1996,9,13,21.09,8.17119282511211,1822.176,1.52214873760739
+"FLOW_236212",1996,9,14,44.529,17.2524914798206,3847.3056,1.76754453191668
+"FLOW_236212",1996,9,15,25.344,9.81937937219731,2189.7216,1.57912608616726
+"FLOW_236212",1996,9,16,8.683,3.36417578475336,750.2112,1.27460534719736
+"FLOW_236212",1996,9,17,3.539,1.37116412556054,305.7696,1.06516745457346
+"FLOW_236212",1996,9,18,2.227,0.862837668161435,192.4128,0.97092530308186
+"FLOW_236212",1996,9,19,4.905,1.90041255605381,423.792,1.13702385985998
+"FLOW_236212",1996,9,20,8.335,3.22934529147982,720.144,1.26422069898491
+"FLOW_236212",1996,9,21,6.933,2.68614887892377,599.0112,1.21850135348487
+"FLOW_236212",1996,9,22,4.371,1.69351748878924,377.6544,1.11111225376546
+"FLOW_236212",1996,9,23,2.626,1.01742780269058,226.8864,1.00346151329366
+"FLOW_236212",1996,9,24,2.22,0.860125560538117,191.808,0.97031416357024
+"FLOW_236212",1996,9,25,2.018,0.781861883408072,174.3552,0.951976018895626
+"FLOW_236212",1996,9,26,1.767,0.684613452914798,152.6688,0.927019961263058
+"FLOW_236212",1996,9,27,1.546,0.598988340807175,133.5744,0.902575776790561
+"FLOW_236212",1996,9,28,1.362,0.52769865470852,117.6768,0.879988906724209
+"FLOW_236212",1996,9,29,1.198,0.464157847533632,103.5072,0.857695515779771
+"FLOW_236212",1996,9,30,1.934,0.749316591928251,167.0976,0.943915396009969
+"FLOW_236212",1996,10,1,8.045,3.1169865470852,695.088,1.25529842976733
+"FLOW_236212",1996,10,2,11.073,4.2901668161435,956.7072,1.33811930368273
+"FLOW_236212",1996,10,3,5.66,2.19293273542601,489.024,1.17005172743234
+"FLOW_236212",1996,10,4,3.462,1.34133094170404,299.1168,1.0604914918528
+"FLOW_236212",1996,10,5,2.471,0.95737399103139,213.4944,0.991325607805043
+"FLOW_236212",1996,10,6,2.173,0.841915695067265,187.7472,0.966170384478608
+"FLOW_236212",1996,10,7,1.622,0.628434080717489,140.1408,0.911280224538602
+"FLOW_236212",1996,10,8,1.188,0.460283408071749,102.6432,0.856258831805151
+"FLOW_236212",1996,10,9,0.908,0.351799103139014,78.4512,0.81144473290655
+"FLOW_236212",1996,10,10,0.733,0.283996412556054,63.3312,0.777432282994267
+"FLOW_236212",1996,10,11,0.621,0.24060269058296,53.6544,0.752073170521953
+"FLOW_236212",1996,10,12,0.559,0.216581165919283,48.2976,0.736417562854411
+"FLOW_236212",1996,10,13,0.511,0.197983856502242,44.1504,0.723312475760448
+"FLOW_236212",1996,10,14,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",1996,10,15,0.428,0.16582600896861,36.9792,0.698120722844581
+"FLOW_236212",1996,10,16,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",1996,10,17,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",1996,10,18,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1996,10,19,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1996,10,20,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1996,10,21,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",1996,10,22,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1996,10,23,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",1996,10,24,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",1996,10,25,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1996,10,26,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",1996,10,27,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1996,10,28,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",1996,10,29,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1996,10,30,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",1996,10,31,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1996,11,1,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1996,11,2,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",1996,11,3,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1996,11,4,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1996,11,5,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1996,11,6,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1996,11,7,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1996,11,8,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1996,11,9,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1996,11,10,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1996,11,11,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1996,11,12,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1996,11,13,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",1996,11,14,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1996,11,15,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",1996,11,16,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1996,11,17,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1996,11,18,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1996,11,19,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",1996,11,20,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1996,11,21,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1996,11,22,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1996,11,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1996,11,24,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1996,11,25,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1996,11,26,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1996,11,27,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1996,11,28,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1996,11,29,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1996,11,30,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1996,12,1,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1996,12,2,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",1996,12,3,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1996,12,4,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1996,12,5,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1996,12,6,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1996,12,7,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1996,12,8,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1996,12,9,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1996,12,10,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1996,12,11,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",1996,12,12,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1996,12,13,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1996,12,14,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1996,12,15,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1996,12,16,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1996,12,17,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1996,12,18,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1996,12,19,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1996,12,20,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1996,12,21,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1996,12,22,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1996,12,23,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1996,12,24,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1996,12,25,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1996,12,26,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1996,12,27,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1996,12,28,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1996,12,29,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1996,12,30,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1996,12,31,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1997,1,1,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1997,1,2,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,1,3,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1997,1,4,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1997,1,5,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1997,1,6,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1997,1,7,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1997,1,8,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1997,1,9,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1997,1,10,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1997,1,11,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1997,1,12,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1997,1,13,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1997,1,14,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1997,1,15,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1997,1,16,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1997,1,17,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,1,18,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1997,1,19,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1997,1,20,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1997,1,21,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1997,1,22,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1997,1,23,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1997,1,24,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1997,1,25,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1997,1,26,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,1,27,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1997,1,28,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1997,1,29,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1997,1,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1997,1,31,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1997,2,1,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1997,2,2,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1997,2,3,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1997,2,4,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1997,2,5,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1997,2,6,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1997,2,7,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1997,2,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1997,2,9,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1997,2,10,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,2,11,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1997,2,12,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1997,2,13,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1997,2,14,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1997,2,15,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1997,2,16,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1997,2,17,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,2,18,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1997,2,19,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1997,2,20,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1997,2,21,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1997,2,22,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",1997,2,23,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1997,2,24,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,2,25,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,2,26,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1997,2,27,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1997,2,28,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1997,3,1,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1997,3,2,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1997,3,3,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1997,3,4,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1997,3,5,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1997,3,6,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1997,3,7,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1997,3,8,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,3,9,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,3,10,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1997,3,11,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1997,3,12,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1997,3,13,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1997,3,14,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1997,3,15,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1997,3,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1997,3,17,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1997,3,18,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1997,3,19,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1997,3,20,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1997,3,21,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1997,3,22,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1997,3,23,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1997,3,24,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1997,3,25,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1997,3,26,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1997,3,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1997,3,28,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1997,3,29,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1997,3,30,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1997,3,31,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1997,4,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1997,4,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1997,4,3,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,4,4,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1997,4,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1997,4,6,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1997,4,7,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1997,4,8,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1997,4,9,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1997,4,10,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1997,4,11,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1997,4,12,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1997,4,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1997,4,14,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1997,4,15,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,4,16,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1997,4,17,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,4,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",1997,4,19,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,4,20,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1997,4,21,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,4,22,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1997,4,23,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1997,4,24,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1997,4,25,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1997,4,26,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1997,4,27,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1997,4,28,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,4,29,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1997,4,30,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1997,5,1,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1997,5,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1997,5,3,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1997,5,4,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",1997,5,5,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",1997,5,6,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1997,5,7,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1997,5,8,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1997,5,9,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",1997,5,10,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1997,5,11,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1997,5,12,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1997,5,13,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,5,14,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,5,15,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1997,5,16,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1997,5,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1997,5,18,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1997,5,19,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1997,5,20,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,5,21,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,5,22,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,5,23,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,5,24,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1997,5,25,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,5,26,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1997,5,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1997,5,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1997,5,29,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1997,5,30,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1997,5,31,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1997,6,1,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1997,6,2,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1997,6,3,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,6,4,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,6,5,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1997,6,6,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1997,6,7,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1997,6,8,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1997,6,9,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1997,6,10,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,6,11,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1997,6,12,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1997,6,13,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1997,6,14,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1997,6,15,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",1997,6,16,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",1997,6,17,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1997,6,18,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1997,6,19,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1997,6,20,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,6,21,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,6,22,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,6,23,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1997,6,24,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1997,6,25,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1997,6,26,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1997,6,27,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1997,6,28,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1997,6,29,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1997,6,30,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,7,1,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1997,7,2,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1997,7,3,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1997,7,4,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,7,5,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1997,7,6,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,7,7,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,7,8,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1997,7,9,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1997,7,10,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1997,7,11,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1997,7,12,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1997,7,13,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,7,14,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,7,15,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1997,7,16,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1997,7,17,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",1997,7,18,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1997,7,19,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1997,7,20,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1997,7,21,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1997,7,22,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1997,7,23,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1997,7,24,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1997,7,25,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,7,26,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1997,7,27,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,7,28,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1997,7,29,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1997,7,30,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1997,7,31,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,8,1,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1997,8,2,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,8,3,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,8,4,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1997,8,5,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1997,8,6,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1997,8,7,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1997,8,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1997,8,9,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1997,8,10,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1997,8,11,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1997,8,12,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1997,8,13,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1997,8,14,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1997,8,15,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1997,8,16,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1997,8,17,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1997,8,18,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1997,8,19,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1997,8,20,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1997,8,21,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1997,8,22,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1997,8,23,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1997,8,24,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1997,8,25,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",1997,8,26,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",1997,8,27,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",1997,8,28,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",1997,8,29,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1997,8,30,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1997,8,31,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",1997,9,1,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",1997,9,2,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",1997,9,3,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",1997,9,4,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",1997,9,5,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",1997,9,6,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",1997,9,7,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",1997,9,8,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",1997,9,9,0.575,0.222780269058296,49.68,0.740585738947927
+"FLOW_236212",1997,9,10,0.516,0.199921076233184,44.5824,0.724722452305139
+"FLOW_236212",1997,9,11,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",1997,9,12,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",1997,9,13,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1997,9,14,0.405,0.156914798206278,34.992,0.690450868963381
+"FLOW_236212",1997,9,15,0.642,0.248739013452915,55.4688,0.757092216813081
+"FLOW_236212",1997,9,16,0.699,0.27082331838565,60.3936,0.77008242340526
+"FLOW_236212",1997,9,17,0.543,0.210382062780269,46.9152,0.732152823938167
+"FLOW_236212",1997,9,18,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",1997,9,19,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",1997,9,20,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1997,9,21,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",1997,9,22,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1997,9,23,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1997,9,24,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",1997,9,25,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",1997,9,26,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1997,9,27,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1997,9,28,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1997,9,29,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1997,9,30,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1997,10,1,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1997,10,2,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1997,10,3,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1997,10,4,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1997,10,5,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,10,6,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,10,7,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,10,8,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,10,9,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1997,10,10,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,10,11,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1997,10,12,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",1997,10,13,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,10,14,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,10,15,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1997,10,16,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",1997,10,17,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1997,10,18,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1997,10,19,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1997,10,20,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1997,10,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1997,10,22,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1997,10,23,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,10,24,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1997,10,25,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1997,10,26,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1997,10,27,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1997,10,28,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1997,10,29,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1997,10,30,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1997,10,31,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1997,11,1,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",1997,11,2,0.494,0.19139730941704,42.6816,0.718434477206278
+"FLOW_236212",1997,11,3,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",1997,11,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1997,11,5,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1997,11,6,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,11,7,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1997,11,8,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1997,11,9,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1997,11,10,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1997,11,11,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1997,11,12,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1997,11,13,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1997,11,14,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1997,11,15,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1997,11,16,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1997,11,17,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1997,11,18,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1997,11,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1997,11,20,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1997,11,21,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1997,11,22,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1997,11,23,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1997,11,24,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,11,25,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1997,11,26,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1997,11,27,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1997,11,28,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1997,11,29,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1997,11,30,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1997,12,1,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1997,12,2,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1997,12,3,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1997,12,4,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1997,12,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1997,12,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1997,12,7,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1997,12,8,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1997,12,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1997,12,10,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1997,12,11,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",1997,12,12,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1997,12,13,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1997,12,14,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1997,12,15,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1997,12,16,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1997,12,17,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1997,12,18,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1997,12,19,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1997,12,20,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1997,12,21,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1997,12,22,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1997,12,23,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1997,12,24,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1997,12,25,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1997,12,26,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1997,12,27,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1997,12,28,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1997,12,29,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1997,12,30,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",1997,12,31,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",1998,1,1,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1998,1,2,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1998,1,3,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",1998,1,4,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,1,5,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1998,1,6,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1998,1,7,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1998,1,8,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1998,1,9,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",1998,1,10,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1998,1,11,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1998,1,12,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",1998,1,13,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1998,1,14,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",1998,1,15,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",1998,1,16,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1998,1,17,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1998,1,18,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",1998,1,19,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,1,20,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1998,1,21,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1998,1,22,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1998,1,23,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1998,1,24,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,1,25,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,1,26,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1998,1,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1998,1,28,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1998,1,29,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1998,1,30,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1998,1,31,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1998,2,1,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1998,2,2,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1998,2,3,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1998,2,4,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1998,2,5,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1998,2,6,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,2,7,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,2,8,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1998,2,9,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1998,2,10,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1998,2,11,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1998,2,12,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1998,2,13,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1998,2,14,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1998,2,15,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1998,2,16,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1998,2,17,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1998,2,18,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1998,2,19,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1998,2,20,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1998,2,21,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1998,2,22,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1998,2,23,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1998,2,24,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1998,2,25,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,2,26,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",1998,2,27,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,2,28,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,3,1,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1998,3,2,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1998,3,3,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1998,3,4,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1998,3,5,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1998,3,6,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,3,7,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1998,3,8,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1998,3,9,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1998,3,10,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1998,3,11,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1998,3,12,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",1998,3,13,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,3,14,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1998,3,15,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1998,3,16,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1998,3,17,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1998,3,18,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",1998,3,19,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1998,3,20,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",1998,3,21,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1998,3,22,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1998,3,23,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1998,3,24,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",1998,3,25,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1998,3,26,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1998,3,27,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1998,3,28,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1998,3,29,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1998,3,30,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1998,3,31,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1998,4,1,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1998,4,2,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1998,4,3,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1998,4,4,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1998,4,5,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1998,4,6,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1998,4,7,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1998,4,8,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1998,4,9,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",1998,4,10,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1998,4,11,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1998,4,12,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1998,4,13,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1998,4,14,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1998,4,15,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1998,4,16,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1998,4,17,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1998,4,18,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1998,4,19,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1998,4,20,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1998,4,21,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",1998,4,22,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",1998,4,23,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1998,4,24,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1998,4,25,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1998,4,26,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,4,27,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1998,4,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1998,4,29,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1998,4,30,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1998,5,1,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1998,5,2,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1998,5,3,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1998,5,4,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1998,5,5,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1998,5,6,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1998,5,7,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1998,5,8,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1998,5,9,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1998,5,10,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1998,5,11,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1998,5,12,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1998,5,13,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1998,5,14,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1998,5,15,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1998,5,16,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1998,5,17,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1998,5,18,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1998,5,19,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1998,5,20,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1998,5,21,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1998,5,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1998,5,23,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1998,5,24,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1998,5,25,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1998,5,26,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1998,5,27,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1998,5,28,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1998,5,29,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1998,5,30,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1998,5,31,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1998,6,1,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1998,6,2,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1998,6,3,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1998,6,4,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1998,6,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1998,6,6,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1998,6,7,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1998,6,8,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1998,6,9,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1998,6,10,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",1998,6,11,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1998,6,12,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,13,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1998,6,14,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1998,6,15,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1998,6,16,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1998,6,17,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1998,6,18,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1998,6,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,21,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,22,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,23,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1998,6,24,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1998,6,25,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1998,6,26,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,6,27,0.677,0.262299551569507,58.4928,0.765172775488641
+"FLOW_236212",1998,6,28,0.774,0.299881614349776,66.8736,0.785941041478094
+"FLOW_236212",1998,6,29,0.45,0.174349775784753,38.88,0.705154494972067
+"FLOW_236212",1998,6,30,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",1998,7,1,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",1998,7,2,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",1998,7,3,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",1998,7,4,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",1998,7,5,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1998,7,6,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1998,7,7,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1998,7,8,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1998,7,9,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",1998,7,10,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1998,7,11,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1998,7,12,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1998,7,13,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1998,7,14,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1998,7,15,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1998,7,16,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,7,17,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1998,7,18,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,7,19,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",1998,7,20,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1998,7,21,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",1998,7,22,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1998,7,23,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1998,7,24,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1998,7,25,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1998,7,26,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1998,7,27,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1998,7,28,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",1998,7,29,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1998,7,30,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",1998,7,31,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",1998,8,1,0.652,0.252613452914798,56.3328,0.75943620043054
+"FLOW_236212",1998,8,2,1.489,0.57690403587444,128.6496,0.895819919964198
+"FLOW_236212",1998,8,3,1.213,0.469969506726457,104.8032,0.859832657356624
+"FLOW_236212",1998,8,4,0.635,0.246026905829596,54.864,0.755433988896739
+"FLOW_236212",1998,8,5,0.535,0.207282511210762,46.224,0.729982640289904
+"FLOW_236212",1998,8,6,0.53,0.205345291479821,45.792,0.728613057204925
+"FLOW_236212",1998,8,7,0.468,0.181323766816144,40.4352,0.710707578523312
+"FLOW_236212",1998,8,8,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1998,8,9,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1998,8,10,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1998,8,11,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1998,8,12,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",1998,8,13,2.469,0.956599103139013,213.3216,0.991165082234677
+"FLOW_236212",1998,8,14,1.857,0.719483408071749,160.4448,0.936276572551589
+"FLOW_236212",1998,8,15,0.885,0.342887892376682,76.464,0.807291593351405
+"FLOW_236212",1998,8,16,0.57,0.220843049327354,49.248,0.739293260319341
+"FLOW_236212",1998,8,17,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",1998,8,18,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",1998,8,19,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1998,8,20,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1998,8,21,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",1998,8,22,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1998,8,23,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",1998,8,24,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",1998,8,25,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",1998,8,26,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",1998,8,27,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",1998,8,28,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",1998,8,29,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",1998,8,30,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1998,8,31,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",1998,9,1,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1998,9,2,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1998,9,3,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",1998,9,4,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",1998,9,5,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1998,9,6,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",1998,9,7,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",1998,9,8,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",1998,9,9,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1998,9,10,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",1998,9,11,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",1998,9,12,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",1998,9,13,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1998,9,14,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",1998,9,15,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",1998,9,16,1.787,0.692362331838565,154.3968,0.929109041525182
+"FLOW_236212",1998,9,17,1.855,0.718708520179372,160.272,0.936074810530359
+"FLOW_236212",1998,9,18,1.015,0.393255605381166,87.696,0.829726544751732
+"FLOW_236212",1998,9,19,0.611,0.236728251121076,52.7904,0.749635280728596
+"FLOW_236212",1998,9,20,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",1998,9,21,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",1998,9,22,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",1998,9,23,10.765,4.17083408071749,930.096,1.33059100345474
+"FLOW_236212",1998,9,24,16.318,6.32231031390135,1409.8752,1.44602286973327
+"FLOW_236212",1998,9,25,8.427,3.26499013452915,728.0928,1.26699929920858
+"FLOW_236212",1998,9,26,3.617,1.40138475336323,312.5088,1.06982188442595
+"FLOW_236212",1998,9,27,1.693,0.655942600896861,146.2752,0.919122012817288
+"FLOW_236212",1998,9,28,1.026,0.397517488789238,88.6464,0.831517221016325
+"FLOW_236212",1998,9,29,0.662,0.256487892376682,57.1968,0.761751597678812
+"FLOW_236212",1998,9,30,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",1998,10,1,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",1998,10,2,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",1998,10,3,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",1998,10,4,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",1998,10,5,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1998,10,6,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1998,10,7,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",1998,10,8,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",1998,10,9,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",1998,10,10,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",1998,10,11,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",1998,10,12,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",1998,10,13,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",1998,10,14,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",1998,10,15,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",1998,10,16,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",1998,10,17,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",1998,10,18,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",1998,10,19,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",1998,10,20,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",1998,10,21,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",1998,10,22,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",1998,10,23,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",1998,10,24,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1998,10,25,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",1998,10,26,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",1998,10,27,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",1998,10,28,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",1998,10,29,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",1998,10,30,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",1998,10,31,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",1998,11,1,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",1998,11,2,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",1998,11,3,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1998,11,4,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1998,11,5,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1998,11,6,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",1998,11,7,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1998,11,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1998,11,9,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1998,11,10,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",1998,11,11,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1998,11,12,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1998,11,13,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",1998,11,14,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1998,11,15,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",1998,11,16,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",1998,11,17,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1998,11,18,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1998,11,19,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1998,11,20,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1998,11,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1998,11,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1998,11,23,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1998,11,24,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1998,11,25,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1998,11,26,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1998,11,27,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1998,11,28,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1998,11,29,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1998,11,30,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1998,12,1,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1998,12,2,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1998,12,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1998,12,4,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1998,12,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",1998,12,6,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1998,12,7,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1998,12,8,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1998,12,9,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1998,12,10,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1998,12,11,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1998,12,12,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1998,12,13,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,12,14,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1998,12,15,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",1998,12,16,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1998,12,17,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1998,12,18,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1998,12,19,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1998,12,20,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1998,12,21,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",1998,12,22,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1998,12,23,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1998,12,24,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1998,12,25,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1998,12,26,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",1998,12,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1998,12,28,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1998,12,29,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1998,12,30,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1998,12,31,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1999,1,1,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1999,1,2,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1999,1,3,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1999,1,4,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1999,1,5,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1999,1,6,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1999,1,7,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1999,1,8,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1999,1,9,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",1999,1,10,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1999,1,11,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",1999,1,12,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1999,1,13,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",1999,1,14,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",1999,1,15,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",1999,1,16,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1999,1,17,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1999,1,18,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1999,1,19,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1999,1,20,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1999,1,21,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1999,1,22,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",1999,1,23,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1999,1,24,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1999,1,25,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",1999,1,26,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",1999,1,27,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",1999,1,28,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",1999,1,29,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1999,1,30,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1999,1,31,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1999,2,1,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",1999,2,2,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",1999,2,3,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1999,2,4,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",1999,2,5,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",1999,2,6,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",1999,2,7,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,2,8,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,2,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,2,10,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,2,11,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",1999,2,12,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1999,2,13,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1999,2,14,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1999,2,15,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1999,2,16,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1999,2,17,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1999,2,18,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,2,19,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,2,20,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1999,2,21,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1999,2,22,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,2,23,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,2,24,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1999,2,25,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",1999,2,26,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1999,2,27,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1999,2,28,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",1999,3,1,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1999,3,2,0.654,0.253388340807175,56.5056,0.759901541588952
+"FLOW_236212",1999,3,3,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",1999,3,4,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",1999,3,5,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,3,6,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,3,7,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,3,8,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,3,9,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1999,3,10,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",1999,3,11,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",1999,3,12,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1999,3,13,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",1999,3,14,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,3,15,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,3,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,3,17,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,3,18,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,3,19,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1999,3,20,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,3,21,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,3,22,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1999,3,23,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,3,24,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,3,25,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1999,3,26,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1999,3,27,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,3,28,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",1999,3,29,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,3,30,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,3,31,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,4,1,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,4,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,4,3,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1999,4,4,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,4,5,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1999,4,6,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,4,7,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1999,4,8,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1999,4,9,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",1999,4,10,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,4,11,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1999,4,12,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,4,13,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",1999,4,14,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1999,4,15,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1999,4,16,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1999,4,17,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1999,4,18,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,4,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1999,4,20,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1999,4,21,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,4,22,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,4,23,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,4,24,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,4,25,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",1999,4,26,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,4,27,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",1999,4,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",1999,4,29,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1999,4,30,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,5,1,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",1999,5,2,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",1999,5,3,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,5,4,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1999,5,5,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1999,5,6,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,5,7,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1999,5,8,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",1999,5,9,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,5,10,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",1999,5,11,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,5,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,5,13,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",1999,5,14,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,5,15,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1999,5,16,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",1999,5,17,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1999,5,18,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,5,19,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,5,20,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1999,5,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1999,5,22,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1999,5,23,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,5,24,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,5,25,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,5,26,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1999,5,27,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1999,5,28,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1999,5,29,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1999,5,30,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1999,5,31,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1999,6,1,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1999,6,2,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1999,6,3,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1999,6,4,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,6,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1999,6,6,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",1999,6,7,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1999,6,8,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1999,6,9,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1999,6,10,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1999,6,11,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,6,12,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1999,6,13,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,6,14,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1999,6,15,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",1999,6,16,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",1999,6,17,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1999,6,18,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",1999,6,19,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",1999,6,20,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",1999,6,21,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",1999,6,22,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",1999,6,23,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",1999,6,24,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1999,6,25,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1999,6,26,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",1999,6,27,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1999,6,28,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1999,6,29,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1999,6,30,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,7,1,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1999,7,2,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,7,3,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1999,7,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,7,5,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,7,6,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1999,7,7,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",1999,7,8,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,7,9,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,7,10,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,7,11,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,7,12,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1999,7,13,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1999,7,14,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1999,7,15,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,7,16,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1999,7,17,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,7,18,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,7,19,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,7,20,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1999,7,21,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",1999,7,22,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",1999,7,23,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",1999,7,24,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",1999,7,25,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",1999,7,26,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",1999,7,27,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1999,7,28,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1999,7,29,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1999,7,30,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1999,7,31,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,8,1,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,8,2,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1999,8,3,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1999,8,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,8,5,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,8,6,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1999,8,7,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",1999,8,8,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,8,9,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,8,10,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,8,11,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,8,12,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,8,13,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1999,8,14,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",1999,8,15,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",1999,8,16,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",1999,8,17,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",1999,8,18,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1999,8,19,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1999,8,20,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",1999,8,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",1999,8,22,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",1999,8,23,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",1999,8,24,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1999,8,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1999,8,26,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",1999,8,27,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1999,8,28,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",1999,8,29,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",1999,8,30,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",1999,8,31,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1999,9,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",1999,9,2,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1999,9,3,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",1999,9,4,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",1999,9,5,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1999,9,6,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1999,9,7,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",1999,9,8,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",1999,9,9,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",1999,9,10,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",1999,9,11,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",1999,9,12,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",1999,9,13,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1999,9,14,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1999,9,15,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1999,9,16,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1999,9,17,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",1999,9,18,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,9,19,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,9,20,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1999,9,21,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,9,22,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1999,9,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",1999,9,24,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1999,9,25,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",1999,9,26,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,9,27,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",1999,9,28,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,9,29,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,9,30,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1999,10,1,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",1999,10,2,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,10,3,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1999,10,4,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1999,10,5,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,10,6,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,10,7,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1999,10,8,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1999,10,9,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1999,10,10,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",1999,10,11,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",1999,10,12,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",1999,10,13,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",1999,10,14,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",1999,10,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1999,10,16,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,10,17,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,10,18,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1999,10,19,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",1999,10,20,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,10,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,10,22,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,10,23,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",1999,10,24,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",1999,10,25,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,10,26,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,10,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1999,10,28,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",1999,10,29,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1999,10,30,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1999,10,31,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",1999,11,1,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,11,2,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",1999,11,3,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,11,4,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",1999,11,5,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1999,11,6,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",1999,11,7,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",1999,11,8,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",1999,11,9,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",1999,11,10,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",1999,11,11,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",1999,11,12,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1999,11,13,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1999,11,14,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1999,11,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",1999,11,16,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,11,17,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,11,18,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",1999,11,19,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",1999,11,20,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",1999,11,21,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",1999,11,22,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",1999,11,23,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",1999,11,24,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",1999,11,25,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",1999,11,26,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",1999,11,27,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",1999,11,28,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1999,11,29,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",1999,11,30,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1999,12,1,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",1999,12,2,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",1999,12,3,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",1999,12,4,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",1999,12,5,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1999,12,6,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,12,7,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1999,12,8,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",1999,12,9,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",1999,12,10,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",1999,12,11,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",1999,12,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",1999,12,13,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",1999,12,14,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",1999,12,15,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1999,12,16,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1999,12,17,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",1999,12,18,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",1999,12,19,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",1999,12,20,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",1999,12,21,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",1999,12,22,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",1999,12,23,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",1999,12,24,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",1999,12,25,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",1999,12,26,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",1999,12,27,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",1999,12,28,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",1999,12,29,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",1999,12,30,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",1999,12,31,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2000,1,1,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2000,1,2,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2000,1,3,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2000,1,4,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2000,1,5,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2000,1,6,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2000,1,7,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2000,1,8,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2000,1,9,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2000,1,10,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2000,1,11,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2000,1,12,0.088,0.034095067264574,7.6032,0.50878969941032
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+"FLOW_236212",2000,1,14,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2000,1,15,0.095,0.0368071748878924,8.208,0.516638179036212
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+"FLOW_236212",2000,2,20,0.09,0.0348699551569507,7.776,0.51108163770667
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+"FLOW_236212",2000,7,20,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2000,7,21,0.816,0.316154260089686,70.5024,0.794291303582111
+"FLOW_236212",2000,7,22,4.45,1.72412556053812,384.48,1.11509990311613
+"FLOW_236212",2000,7,23,2.593,1.00464215246637,224.0352,1.00092671131311
+"FLOW_236212",2000,7,24,1.003,0.388606278026906,86.6592,0.827755285383325
+"FLOW_236212",2000,7,25,0.746,0.289033183856502,64.4544,0.780170530080621
+"FLOW_236212",2000,7,26,3.774,1.4622134529148,326.0736,1.07895207618223
+"FLOW_236212",2000,7,27,4.353,1.68654349775785,376.0992,1.11019561928166
+"FLOW_236212",2000,7,28,2.509,0.972096860986547,216.7776,0.994356021775446
+"FLOW_236212",2000,7,29,1.575,0.610224215246637,136.08,0.905936770894238
+"FLOW_236212",2000,7,30,1.061,0.41107802690583,91.6704,0.837114477026735
+"FLOW_236212",2000,7,31,0.713,0.276247533632287,61.6032,0.773142739550297
+"FLOW_236212",2000,8,1,0.527,0.204182959641256,45.5328,0.727786338293044
+"FLOW_236212",2000,8,2,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",2000,8,3,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",2000,8,4,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2000,8,5,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2000,8,6,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2000,8,7,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2000,8,8,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2000,8,9,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2000,8,10,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",2000,8,11,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2000,8,12,0.847,0.328165022421525,73.1808,0.80023668726067
+"FLOW_236212",2000,8,13,0.632,0.244864573991031,54.6048,0.75471884025973
+"FLOW_236212",2000,8,14,0.491,0.190234977578475,42.4224,0.717559757309554
+"FLOW_236212",2000,8,15,1.167,0.452147085201794,100.8288,0.8532100199678
+"FLOW_236212",2000,8,16,4.201,1.62765201793722,362.9664,1.1023317393339
+"FLOW_236212",2000,8,17,3.236,1.25376860986547,279.5904,1.04626929081782
+"FLOW_236212",2000,8,18,1.495,0.57922869955157,129.168,0.89654070935356
+"FLOW_236212",2000,8,19,0.835,0.323515695067265,72.144,0.797956232157998
+"FLOW_236212",2000,8,20,0.583,0.225879820627803,50.3712,0.742635125500896
+"FLOW_236212",2000,8,21,0.468,0.181323766816144,40.4352,0.710707578523312
+"FLOW_236212",2000,8,22,0.428,0.16582600896861,36.9792,0.698120722844581
+"FLOW_236212",2000,8,23,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2000,8,24,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2000,8,25,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2000,8,26,0.57,0.220843049327354,49.248,0.739293260319341
+"FLOW_236212",2000,8,27,0.563,0.218130941704036,48.6432,0.737468466258041
+"FLOW_236212",2000,8,28,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2000,8,29,1.391,0.538934529147982,120.1824,0.883704775453841
+"FLOW_236212",2000,8,30,5.625,2.17937219730942,486,1.16860107435276
+"FLOW_236212",2000,8,31,3.355,1.29987443946188,289.872,1.05385359358593
+"FLOW_236212",2000,9,1,1.665,0.645094170403588,143.856,0.916061483859222
+"FLOW_236212",2000,9,2,1.463,0.566830493273543,126.4032,0.892669386337548
+"FLOW_236212",2000,9,3,3.787,1.46725022421525,327.1968,1.0796943703325
+"FLOW_236212",2000,9,4,3.789,1.46802511210762,327.3696,1.07980838843213
+"FLOW_236212",2000,9,5,4.51,1.74737219730942,389.664,1.11809082647628
+"FLOW_236212",2000,9,6,8.343,3.23244484304933,720.8352,1.26446328770518
+"FLOW_236212",2000,9,7,5.231,2.02671928251121,451.9584,1.15175132326221
+"FLOW_236212",2000,9,8,10.556,4.08985829596413,912.0384,1.32538378240701
+"FLOW_236212",2000,9,9,12.298,4.76478565022421,1062.5472,1.36649697182603
+"FLOW_236212",2000,9,10,9.816,3.80314977578475,848.1024,1.30625716561171
+"FLOW_236212",2000,9,11,8.018,3.10652556053812,692.7552,1.2544547094657
+"FLOW_236212",2000,9,12,8.589,3.32775605381166,742.0896,1.27183360645621
+"FLOW_236212",2000,9,13,5.446,2.1100197309417,470.5344,1.16106705781878
+"FLOW_236212",2000,9,14,2.849,1.10382780269058,246.1536,1.01995324904192
+"FLOW_236212",2000,9,15,2.023,0.783799103139013,174.7872,0.952447294383442
+"FLOW_236212",2000,9,16,1.47,0.569542600896861,127.008,0.893521985204992
+"FLOW_236212",2000,9,17,1.015,0.393255605381166,87.696,0.829726544751732
+"FLOW_236212",2000,9,18,0.745,0.288645739910314,64.368,0.779961256839643
+"FLOW_236212",2000,9,19,0.611,0.236728251121076,52.7904,0.749635280728596
+"FLOW_236212",2000,9,20,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",2000,9,21,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",2000,9,22,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",2000,9,23,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",2000,9,24,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2000,9,25,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",2000,9,26,0.619,0.239827802690583,53.4816,0.751588118089578
+"FLOW_236212",2000,9,27,1.082,0.419214349775785,93.4848,0.840402291745459
+"FLOW_236212",2000,9,28,1.041,0.403329147982063,89.9424,0.833934463035357
+"FLOW_236212",2000,9,29,0.728,0.282059192825112,62.8992,0.776368760156004
+"FLOW_236212",2000,9,30,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2000,10,1,3.196,1.23827085201794,276.1344,1.04366983187725
+"FLOW_236212",2000,10,2,5.713,2.21346726457399,493.6032,1.17223482864043
+"FLOW_236212",2000,10,3,3.027,1.17279282511211,261.5328,1.03239111934947
+"FLOW_236212",2000,10,4,1.709,0.662141704035874,147.6576,0.920852747946832
+"FLOW_236212",2000,10,5,1.012,0.392093273542601,87.4368,0.829235485099232
+"FLOW_236212",2000,10,6,0.665,0.257650224215247,57.456,0.762440758876723
+"FLOW_236212",2000,10,7,0.868,0.33630134529148,74.9952,0.804166025297948
+"FLOW_236212",2000,10,8,2.402,0.930640358744395,207.5328,0.985726373655559
+"FLOW_236212",2000,10,9,1.932,0.748541704035875,166.9248,0.94372008968329
+"FLOW_236212",2000,10,10,1.156,0.447885201793722,99.8784,0.851595470251863
+"FLOW_236212",2000,10,11,0.853,0.330489686098655,73.6992,0.801367235712859
+"FLOW_236212",2000,10,12,0.659,0.255325560538117,56.9376,0.761059933464859
+"FLOW_236212",2000,10,13,0.531,0.205732735426009,45.8784,0.728887798254835
+"FLOW_236212",2000,10,14,0.487,0.188685201793722,42.0768,0.716386788620938
+"FLOW_236212",2000,10,15,2.284,0.88492197309417,197.3376,0.975845347288721
+"FLOW_236212",2000,10,16,4.918,1.90544932735426,424.9152,1.13762592570108
+"FLOW_236212",2000,10,17,2.029,0.786123766816143,175.3056,0.953011596523971
+"FLOW_236212",2000,10,18,1.083,0.419601793721973,93.5712,0.840557576732363
+"FLOW_236212",2000,10,19,1.608,0.623009865470852,138.9312,0.909701654905416
+"FLOW_236212",2000,10,20,4.639,1.79735246636771,400.8096,1.12441505128952
+"FLOW_236212",2000,10,21,2.673,1.03563766816143,230.9472,1.00702804993752
+"FLOW_236212",2000,10,22,1.435,0.555982062780269,123.984,0.889226007350149
+"FLOW_236212",2000,10,23,0.899,0.348312107623318,77.6736,0.809829726159191
+"FLOW_236212",2000,10,24,8.329,3.22702062780269,719.6256,1.26403863517509
+"FLOW_236212",2000,10,25,16.198,6.27581704035875,1399.5072,1.44388982117663
+"FLOW_236212",2000,10,26,8.274,3.20571121076233,714.8736,1.26236480903071
+"FLOW_236212",2000,10,27,4.204,1.62881434977578,363.2256,1.10248913285885
+"FLOW_236212",2000,10,28,2.451,0.949625112107623,211.7664,0.989715651099673
+"FLOW_236212",2000,10,29,1.667,0.645869058295964,144.0288,0.916281453024665
+"FLOW_236212",2000,10,30,1.151,0.44594798206278,99.4464,0.850857518129026
+"FLOW_236212",2000,10,31,0.816,0.316154260089686,70.5024,0.794291303582111
+"FLOW_236212",2000,11,1,0.708,0.274310313901345,61.1712,0.772055333361098
+"FLOW_236212",2000,11,2,2.514,0.974034080717489,217.2096,0.994752021909705
+"FLOW_236212",2000,11,3,3.335,1.29212556053812,288.144,1.05259412946192
+"FLOW_236212",2000,11,4,2.173,0.841915695067265,187.7472,0.966170384478608
+"FLOW_236212",2000,11,5,1.252,0.485079820627803,108.1728,0.86529190669226
+"FLOW_236212",2000,11,6,0.822,0.318478923766816,71.0208,0.795455958591646
+"FLOW_236212",2000,11,7,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",2000,11,8,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",2000,11,9,0.424,0.164276233183857,36.6336,0.696810918637591
+"FLOW_236212",2000,11,10,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2000,11,11,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",2000,11,12,0.374,0.144904035874439,32.3136,0.679541674977205
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+"FLOW_236212",2000,11,15,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2000,11,16,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2000,11,17,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2000,11,18,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2000,11,19,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2000,11,20,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2000,11,21,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2000,11,22,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2000,11,23,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2000,11,24,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2000,11,25,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2000,11,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2000,11,27,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2000,11,28,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2000,11,29,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2000,11,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2000,12,1,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2000,12,2,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2000,12,3,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2000,12,4,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2000,12,5,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2000,12,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2000,12,7,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2000,12,8,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2000,12,9,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2000,12,10,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2000,12,11,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2000,12,12,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2000,12,13,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2000,12,14,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2000,12,15,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2000,12,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2000,12,17,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2000,12,18,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2000,12,19,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2000,12,20,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2000,12,21,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2000,12,22,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2000,12,23,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2000,12,24,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2000,12,25,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2000,12,26,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2000,12,27,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2000,12,28,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2000,12,29,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2000,12,30,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2000,12,31,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2001,1,1,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2001,1,2,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2001,1,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2001,1,4,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2001,1,5,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2001,1,6,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2001,1,7,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2001,1,8,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2001,1,9,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2001,1,10,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2001,1,11,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2001,1,12,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2001,1,13,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2001,1,14,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2001,1,15,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2001,1,16,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2001,1,17,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2001,1,18,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2001,1,19,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2001,1,20,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2001,1,21,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2001,1,22,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2001,1,23,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2001,1,24,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2001,1,25,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2001,1,26,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2001,1,27,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2001,1,28,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2001,1,29,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2001,1,30,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2001,1,31,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2001,2,1,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2001,2,2,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2001,2,3,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2001,2,4,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2001,2,5,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2001,2,6,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2001,2,7,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2001,2,8,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2001,2,9,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2001,2,10,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2001,2,11,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2001,2,12,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2001,2,13,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2001,2,14,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2001,2,15,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2001,2,16,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2001,2,17,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2001,2,18,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2001,2,19,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2001,2,20,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2001,2,21,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2001,2,22,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2001,2,23,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2001,2,24,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2001,2,25,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2001,2,26,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2001,2,27,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2001,2,28,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2001,3,1,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2001,3,2,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2001,3,3,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2001,3,4,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2001,3,5,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2001,3,6,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2001,3,7,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2001,3,8,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2001,3,9,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2001,3,10,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2001,3,11,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2001,3,12,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2001,3,13,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2001,3,14,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2001,3,15,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2001,3,16,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2001,3,17,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2001,3,18,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2001,3,19,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2001,3,20,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2001,3,21,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,3,22,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2001,3,23,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2001,3,24,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2001,3,25,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2001,3,26,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2001,3,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2001,3,28,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2001,3,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2001,3,30,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2001,3,31,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,4,1,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2001,4,2,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2001,4,3,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2001,4,4,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2001,4,5,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2001,4,6,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2001,4,7,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2001,4,8,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2001,4,9,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2001,4,10,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2001,4,11,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2001,4,12,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2001,4,13,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2001,4,14,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2001,4,15,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2001,4,16,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2001,4,17,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2001,4,18,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2001,4,19,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,4,20,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,4,21,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2001,4,22,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2001,4,23,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2001,4,24,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2001,4,25,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2001,4,26,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2001,4,27,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2001,4,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2001,4,29,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2001,4,30,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2001,5,1,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2001,5,2,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2001,5,3,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2001,5,4,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2001,5,5,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2001,5,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,7,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,8,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,9,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,10,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,11,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,12,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,13,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,14,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,15,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2001,5,16,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2001,5,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2001,5,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2001,5,19,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2001,5,20,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2001,5,21,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2001,5,22,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2001,5,23,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2001,5,24,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2001,5,25,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2001,5,26,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2001,5,27,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2001,5,28,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2001,5,29,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2001,5,30,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2001,5,31,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2001,6,1,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2001,6,2,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2001,6,3,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2001,6,4,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2001,6,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2001,6,6,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2001,6,7,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2001,6,8,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2001,6,9,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2001,6,10,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,6,11,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,6,12,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2001,6,13,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2001,6,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2001,6,15,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2001,6,16,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2001,6,17,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2001,6,18,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2001,6,19,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2001,6,20,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2001,6,21,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2001,6,22,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2001,6,23,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2001,6,24,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2001,6,25,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2001,6,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,6,27,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,6,28,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2001,6,29,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2001,6,30,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2001,7,1,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2001,7,2,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2001,7,3,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2001,7,4,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2001,7,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2001,7,6,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2001,7,7,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2001,7,8,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2001,7,9,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2001,7,10,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2001,7,11,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2001,7,12,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2001,7,13,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2001,7,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2001,7,15,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2001,7,16,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2001,7,17,0.446,0.1728,38.5344,0.703896405787105
+"FLOW_236212",2001,7,18,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",2001,7,19,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",2001,7,20,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2001,7,21,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",2001,7,22,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",2001,7,23,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2001,7,24,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2001,7,25,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2001,7,26,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2001,7,27,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2001,7,28,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2001,7,29,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2001,7,30,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2001,7,31,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2001,8,1,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2001,8,2,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2001,8,3,0.593,0.229754260089686,51.2352,0.745165457149857
+"FLOW_236212",2001,8,4,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2001,8,5,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",2001,8,6,0.953,0.369234080717489,82.3392,0.819332828074103
+"FLOW_236212",2001,8,7,1.829,0.708634977578475,158.0256,0.9334359367954
+"FLOW_236212",2001,8,8,1.484,0.574966816143498,128.2176,0.895217485041194
+"FLOW_236212",2001,8,9,2.436,0.943813452914798,210.4704,0.988501272408867
+"FLOW_236212",2001,8,10,1.393,0.539709417040359,120.3552,0.883958750140267
+"FLOW_236212",2001,8,11,0.774,0.299881614349776,66.8736,0.785941041478094
+"FLOW_236212",2001,8,12,0.54,0.209219730941704,46.656,0.731342021478882
+"FLOW_236212",2001,8,13,0.424,0.164276233183857,36.6336,0.696810918637591
+"FLOW_236212",2001,8,14,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2001,8,15,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2001,8,16,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2001,8,17,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",2001,8,18,1.587,0.614873542600897,137.1168,0.907313057942306
+"FLOW_236212",2001,8,19,3.197,1.23865829596413,276.2208,1.04373513470794
+"FLOW_236212",2001,8,20,6.656,2.5788269058296,575.0784,1.20860514457208
+"FLOW_236212",2001,8,21,8.079,3.13015964125561,698.0256,1.25635767597302
+"FLOW_236212",2001,8,22,11.636,4.50829775784753,1005.3504,1.35145789097338
+"FLOW_236212",2001,8,23,9.609,3.72294887892377,830.2176,1.30070082157629
+"FLOW_236212",2001,8,24,3.961,1.53466547085202,342.2304,1.08943856395062
+"FLOW_236212",2001,8,25,1.903,0.737305829596413,164.4192,0.940869810298561
+"FLOW_236212",2001,8,26,1.491,0.577678923766816,128.8224,0.896060440852666
+"FLOW_236212",2001,8,27,6.195,2.40021524663677,535.248,1.19137926704891
+"FLOW_236212",2001,8,28,13.093,5.07280358744395,1131.2352,1.38372443279593
+"FLOW_236212",2001,8,29,5.139,1.99107443946188,444.0096,1.14767124217457
+"FLOW_236212",2001,8,30,2.181,0.845015246636771,188.4384,0.966880739356755
+"FLOW_236212",2001,8,31,1.354,0.524599103139014,116.9856,0.878952708492318
+"FLOW_236212",2001,9,1,0.931,0.360710313901345,80.4384,0.815514546570238
+"FLOW_236212",2001,9,2,0.701,0.271598206278027,60.5664,0.770522596555566
+"FLOW_236212",2001,9,3,0.575,0.222780269058296,49.68,0.740585738947927
+"FLOW_236212",2001,9,4,0.645,0.24990134529148,55.728,0.757798460781196
+"FLOW_236212",2001,9,5,0.812,0.314604484304933,70.1568,0.793511055215469
+"FLOW_236212",2001,9,6,0.801,0.310342600896861,69.2064,0.79134940307706
+"FLOW_236212",2001,9,7,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",2001,9,8,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",2001,9,9,0.872,0.337851121076233,75.3408,0.804905829700763
+"FLOW_236212",2001,9,10,1.624,0.629208968609866,140.3136,0.91150484379797
+"FLOW_236212",2001,9,11,1.183,0.458346188340807,102.2112,0.855536858744601
+"FLOW_236212",2001,9,12,2.231,0.864387443946188,192.7584,0.971273835991584
+"FLOW_236212",2001,9,13,1.605,0.621847533632287,138.672,0.909361960391358
+"FLOW_236212",2001,9,14,0.933,0.361485201793722,80.6112,0.815864628067156
+"FLOW_236212",2001,9,15,0.681,0.26384932735426,58.8384,0.766074838443101
+"FLOW_236212",2001,9,16,0.547,0.211931838565022,47.2608,0.73322833768245
+"FLOW_236212",2001,9,17,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2001,9,18,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2001,9,19,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",2001,9,20,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2001,9,21,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2001,9,22,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2001,9,23,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2001,9,24,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2001,9,25,0.874,0.33862600896861,75.5136,0.805274714287262
+"FLOW_236212",2001,9,26,2.209,0.855863677130045,190.8576,0.969350679470478
+"FLOW_236212",2001,9,27,1.643,0.636570403587444,141.9552,0.91362776359426
+"FLOW_236212",2001,9,28,0.959,0.371558744394619,82.8576,0.82036192844454
+"FLOW_236212",2001,9,29,0.643,0.249126457399103,55.5552,0.757327924273715
+"FLOW_236212",2001,9,30,0.471,0.182486098654709,40.6944,0.711616414714403
+"FLOW_236212",2001,10,1,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2001,10,2,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2001,10,3,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2001,10,4,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2001,10,5,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2001,10,6,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2001,10,7,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",2001,10,8,0.571,0.221230493273543,49.3344,0.739552479618804
+"FLOW_236212",2001,10,9,0.476,0.18442331838565,41.1264,0.713120902599212
+"FLOW_236212",2001,10,10,0.471,0.182486098654709,40.6944,0.711616414714403
+"FLOW_236212",2001,10,11,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",2001,10,12,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",2001,10,13,0.563,0.218130941704036,48.6432,0.737468466258041
+"FLOW_236212",2001,10,14,1.577,0.610999103139014,136.2528,0.906166733297494
+"FLOW_236212",2001,10,15,1.935,0.74970403587444,167.184,0.944012988588153
+"FLOW_236212",2001,10,16,5.573,2.15922511210762,481.5072,1.16643242673839
+"FLOW_236212",2001,10,17,5.102,1.97673901345292,440.8128,1.14601385123718
+"FLOW_236212",2001,10,18,2.363,0.915530044843049,204.1632,0.982504436734052
+"FLOW_236212",2001,10,19,1.806,0.699723766816144,156.0384,0.931076413633128
+"FLOW_236212",2001,10,20,1.346,0.521499551569507,116.2944,0.877911600791626
+"FLOW_236212",2001,10,21,0.929,0.359935426008969,80.2656,0.81516386291157
+"FLOW_236212",2001,10,22,0.676,0.261912107623318,58.4064,0.764946593724164
+"FLOW_236212",2001,10,23,0.543,0.210382062780269,46.9152,0.732152823938167
+"FLOW_236212",2001,10,24,0.521,0.201858295964126,45.0144,0.726121540816752
+"FLOW_236212",2001,10,25,0.496,0.192172197309417,42.8544,0.71901526573971
+"FLOW_236212",2001,10,26,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",2001,10,27,0.752,0.291357847533632,64.9728,0.781421478031143
+"FLOW_236212",2001,10,28,1.373,0.531960538116592,118.6272,0.881405758029958
+"FLOW_236212",2001,10,29,3.065,1.18751569506726,264.816,1.03497026283855
+"FLOW_236212",2001,10,30,4.919,1.90583677130045,425.0016,1.13767218570146
+"FLOW_236212",2001,10,31,2.593,1.00464215246637,224.0352,1.00092671131311
+"FLOW_236212",2001,11,1,1.823,0.706310313901345,157.5072,0.932822707795427
+"FLOW_236212",2001,11,2,1.318,0.510651121076233,113.8752,0.874228293625237
+"FLOW_236212",2001,11,3,0.907,0.351411659192825,78.3648,0.811265921797753
+"FLOW_236212",2001,11,4,0.676,0.261912107623318,58.4064,0.764946593724164
+"FLOW_236212",2001,11,5,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",2001,11,6,1.277,0.494765919282511,110.3328,0.868720270479099
+"FLOW_236212",2001,11,7,1.747,0.676864573991032,150.9408,0.924911878125134
+"FLOW_236212",2001,11,8,1.167,0.452147085201794,100.8288,0.8532100199678
+"FLOW_236212",2001,11,9,0.712,0.275860089686099,61.5168,0.772925747447752
+"FLOW_236212",2001,11,10,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",2001,11,11,0.675,0.26152466367713,58.32,0.764720144130376
+"FLOW_236212",2001,11,12,3.641,1.41068340807175,314.5824,1.07123785614186
+"FLOW_236212",2001,11,13,5.617,2.17627264573991,485.3088,1.16826848300801
+"FLOW_236212",2001,11,14,2.83,1.096466367713,244.512,1.01858919040182
+"FLOW_236212",2001,11,15,1.536,0.595113901345292,132.7104,0.901405116848277
+"FLOW_236212",2001,11,16,0.954,0.369621524663677,82.4256,0.819504704075818
+"FLOW_236212",2001,11,17,0.687,0.26617399103139,59.3568,0.767420017650114
+"FLOW_236212",2001,11,18,4.75,1.84035874439462,410.4,1.12974518181862
+"FLOW_236212",2001,11,19,7.241,2.80548161434978,625.6224,1.22914037698231
+"FLOW_236212",2001,11,20,3.527,1.36651479820628,304.7328,1.06444412135815
+"FLOW_236212",2001,11,21,1.719,0.666016143497758,148.5216,0.921927885433143
+"FLOW_236212",2001,11,22,1.025,0.397130044843049,88.56,0.8313550686598
+"FLOW_236212",2001,11,23,0.725,0.280896860986547,62.64,0.775727838456387
+"FLOW_236212",2001,11,24,0.763,0.295619730941704,65.9232,0.783694291748861
+"FLOW_236212",2001,11,25,0.818,0.316929147982063,70.6752,0.794680280900655
+"FLOW_236212",2001,11,26,2.023,0.783799103139013,174.7872,0.952447294383442
+"FLOW_236212",2001,11,27,2.724,1.05539730941704,235.3536,1.01084180956974
+"FLOW_236212",2001,11,28,1.641,0.635795515695067,141.7824,0.913405226053883
+"FLOW_236212",2001,11,29,1.037,0.401779372197309,89.5968,0.833292603923206
+"FLOW_236212",2001,11,30,0.72,0.278959641255605,62.208,0.774654905111265
+"FLOW_236212",2001,12,1,0.575,0.222780269058296,49.68,0.740585738947927
+"FLOW_236212",2001,12,2,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",2001,12,3,1.264,0.489729147982063,109.2096,0.866944290291622
+"FLOW_236212",2001,12,4,1.505,0.583103139013453,130.032,0.897736898678711
+"FLOW_236212",2001,12,5,1.084,0.419989237668161,93.6576,0.840712747054315
+"FLOW_236212",2001,12,6,0.721,0.279347085201794,62.2944,0.774869967583366
+"FLOW_236212",2001,12,7,0.657,0.25455067264574,56.7648,0.760597423288142
+"FLOW_236212",2001,12,8,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",2001,12,9,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",2001,12,10,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2001,12,11,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",2001,12,12,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",2001,12,13,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2001,12,14,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",2001,12,15,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2001,12,16,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2001,12,17,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",2001,12,18,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2001,12,19,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2001,12,20,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2001,12,21,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2001,12,22,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2001,12,23,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2001,12,24,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2001,12,25,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2001,12,26,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2001,12,27,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2001,12,28,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2001,12,29,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2001,12,30,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2001,12,31,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2002,1,1,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2002,1,2,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2002,1,3,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2002,1,4,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2002,1,5,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2002,1,6,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2002,1,7,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2002,1,8,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2002,1,9,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2002,1,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2002,1,11,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2002,1,12,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2002,1,13,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,1,14,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2002,1,15,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2002,1,16,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2002,1,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2002,1,18,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,1,19,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2002,1,20,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,1,21,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2002,1,22,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2002,1,23,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2002,1,24,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2002,1,25,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2002,1,26,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2002,1,27,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2002,1,28,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2002,1,29,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2002,1,30,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2002,1,31,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2002,2,1,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2002,2,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2002,2,3,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2002,2,4,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2002,2,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2002,2,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2002,2,7,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,2,8,0.93,0.360322869955157,80.352,0.81533928015676
+"FLOW_236212",2002,2,9,1.046,0.405266367713005,90.3744,0.834734018160464
+"FLOW_236212",2002,2,10,0.453,0.175512107623318,39.1392,0.706092203731679
+"FLOW_236212",2002,2,11,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2002,2,12,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2002,2,13,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2002,2,14,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2002,2,15,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2002,2,16,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2002,2,17,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2002,2,18,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2002,2,19,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2002,2,20,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2002,2,21,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2002,2,22,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,2,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2002,2,24,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2002,2,25,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2002,2,26,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2002,2,27,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2002,2,28,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,3,1,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2002,3,2,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2002,3,3,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2002,3,4,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2002,3,5,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2002,3,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2002,3,7,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2002,3,8,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2002,3,9,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2002,3,10,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2002,3,11,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2002,3,12,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2002,3,13,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2002,3,14,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2002,3,15,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2002,3,16,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2002,3,17,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2002,3,18,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2002,3,19,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2002,3,20,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2002,3,21,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2002,3,22,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2002,3,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2002,3,24,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2002,3,25,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2002,3,26,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2002,3,27,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2002,3,28,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2002,3,29,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2002,3,30,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2002,3,31,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2002,4,1,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2002,4,2,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2002,4,3,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2002,4,4,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2002,4,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2002,4,6,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,4,7,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,4,8,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2002,4,9,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2002,4,10,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2002,4,11,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2002,4,12,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2002,4,13,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2002,4,14,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2002,4,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2002,4,16,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2002,4,17,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2002,4,18,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2002,4,19,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2002,4,20,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2002,4,21,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2002,4,22,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2002,4,23,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2002,4,24,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2002,4,25,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2002,4,26,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2002,4,27,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2002,4,28,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2002,4,29,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2002,4,30,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,5,1,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2002,5,2,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2002,5,3,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2002,5,4,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2002,5,5,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2002,5,6,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2002,5,7,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2002,5,8,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2002,5,9,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2002,5,10,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2002,5,11,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2002,5,12,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2002,5,13,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2002,5,14,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2002,5,15,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2002,5,16,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2002,5,17,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2002,5,18,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2002,5,19,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2002,5,20,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2002,5,21,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",2002,5,22,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2002,5,23,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",2002,5,24,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2002,5,25,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2002,5,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2002,5,27,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2002,5,28,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2002,5,29,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2002,5,30,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2002,5,31,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2002,6,1,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2002,6,2,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2002,6,3,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2002,6,4,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,6,5,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2002,6,6,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2002,6,7,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2002,6,8,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2002,6,9,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2002,6,10,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2002,6,11,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2002,6,12,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2002,6,13,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2002,6,14,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2002,6,15,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",2002,6,16,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2002,6,17,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2002,6,18,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2002,6,19,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2002,6,20,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2002,6,21,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2002,6,22,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2002,6,23,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2002,6,24,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2002,6,25,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2002,6,26,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2002,6,27,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2002,6,28,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2002,6,29,0.496,0.192172197309417,42.8544,0.71901526573971
+"FLOW_236212",2002,6,30,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2002,7,1,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2002,7,2,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2002,7,3,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2002,7,4,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2002,7,5,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2002,7,6,2.254,0.87329865470852,194.7456,0.97326825492029
+"FLOW_236212",2002,7,7,5.817,2.25376143497758,502.5888,1.17647198159839
+"FLOW_236212",2002,7,8,2.18,0.844627802690583,188.352,0.966792059107684
+"FLOW_236212",2002,7,9,1.053,0.407978475336323,90.9792,0.835848274250574
+"FLOW_236212",2002,7,10,0.85,0.32932735426009,73.44,0.800802759522755
+"FLOW_236212",2002,7,11,0.927,0.359160538116592,80.0928,0.814812574755678
+"FLOW_236212",2002,7,12,0.696,0.269660986547085,60.1344,0.769420270728911
+"FLOW_236212",2002,7,13,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",2002,7,14,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",2002,7,15,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2002,7,16,0.434,0.16815067264574,37.4976,0.700067186306734
+"FLOW_236212",2002,7,17,0.444,0.172025112107623,38.3616,0.703263973134143
+"FLOW_236212",2002,7,18,0.799,0.309567713004484,69.0336,0.790953827071735
+"FLOW_236212",2002,7,19,1.465,0.567605381165919,126.576,0.892913318437462
+"FLOW_236212",2002,7,20,1.086,0.420764125560538,93.8304,0.841022744464206
+"FLOW_236212",2002,7,21,1.835,0.710959641255605,158.544,0.934047558552338
+"FLOW_236212",2002,7,22,2.168,0.839978475336323,187.3152,0.965725349566331
+"FLOW_236212",2002,7,23,1.22,0.472681614349776,105.408,0.860822761741209
+"FLOW_236212",2002,7,24,2.954,1.14450941704036,255.2256,1.02736288958579
+"FLOW_236212",2002,7,25,5.885,2.28010762331839,508.464,1.17920977155144
+"FLOW_236212",2002,7,26,1.971,0.76365201793722,170.2944,0.947499742781879
+"FLOW_236212",2002,7,27,1.172,0.454084304932735,101.2608,0.853939884183484
+"FLOW_236212",2002,7,28,0.826,0.320028699551569,71.3664,0.796228622466257
+"FLOW_236212",2002,7,29,0.804,0.311504932735426,69.4656,0.791941288109326
+"FLOW_236212",2002,7,30,0.805,0.311892376681614,69.552,0.79213819049272
+"FLOW_236212",2002,7,31,0.714,0.276634977578475,61.6896,0.773359488319869
+"FLOW_236212",2002,8,1,0.617,0.239052914798206,53.3088,0.75110181026194
+"FLOW_236212",2002,8,2,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",2002,8,3,0.491,0.190234977578475,42.4224,0.717559757309554
+"FLOW_236212",2002,8,4,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",2002,8,5,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2002,8,6,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",2002,8,7,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2002,8,8,0.769,0.297944394618834,66.4416,0.784922979715581
+"FLOW_236212",2002,8,9,1.261,0.488566816143498,108.9504,0.866532374858387
+"FLOW_236212",2002,8,10,0.943,0.365359641255605,81.4752,0.817606083892927
+"FLOW_236212",2002,8,11,0.691,0.267723766816143,59.7024,0.768311591380086
+"FLOW_236212",2002,8,12,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",2002,8,13,2.403,0.931027802690583,207.6192,0.985808435459365
+"FLOW_236212",2002,8,14,3.288,1.27391569506726,284.0832,1.04961043003311
+"FLOW_236212",2002,8,15,1.751,0.678414349775785,151.2864,0.925335033685116
+"FLOW_236212",2002,8,16,1.082,0.419214349775785,93.4848,0.840402291745459
+"FLOW_236212",2002,8,17,0.764,0.296007174887892,66.0096,0.783899608594372
+"FLOW_236212",2002,8,18,0.598,0.231691479820628,51.6672,0.746417843138657
+"FLOW_236212",2002,8,19,0.512,0.198371300448431,44.2368,0.723595351277449
+"FLOW_236212",2002,8,20,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2002,8,21,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2002,8,22,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",2002,8,23,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",2002,8,24,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",2002,8,25,0.455,0.176286995515695,39.312,0.706714586543881
+"FLOW_236212",2002,8,26,0.421,0.163113901345291,36.3744,0.695822062827897
+"FLOW_236212",2002,8,27,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2002,8,28,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",2002,8,29,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2002,8,30,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",2002,8,31,0.515,0.199533632286996,44.496,0.724441334124209
+"FLOW_236212",2002,9,1,0.59,0.228591928251121,50.976,0.74440996510202
+"FLOW_236212",2002,9,2,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",2002,9,3,0.488,0.18907264573991,42.1632,0.716680751292182
+"FLOW_236212",2002,9,4,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2002,9,5,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",2002,9,6,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",2002,9,7,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",2002,9,8,0.64,0.247964125560538,55.296,0.756619919366877
+"FLOW_236212",2002,9,9,0.831,0.321965919282511,71.7984,0.797190254040957
+"FLOW_236212",2002,9,10,0.895,0.346762331838565,77.328,0.809107790777962
+"FLOW_236212",2002,9,11,1.409,0.545908520179372,121.7376,0.885980114688839
+"FLOW_236212",2002,9,12,1.17,0.453309417040359,101.088,0.853648237980127
+"FLOW_236212",2002,9,13,0.787,0.304918385650224,67.9968,0.788563592988764
+"FLOW_236212",2002,9,14,0.581,0.225104932735426,50.1984,0.742124898206603
+"FLOW_236212",2002,9,15,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",2002,9,16,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",2002,9,17,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",2002,9,18,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2002,9,19,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",2002,9,20,0.437,0.169313004484305,37.7568,0.701032356130901
+"FLOW_236212",2002,9,21,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",2002,9,22,0.453,0.175512107623318,39.1392,0.706092203731679
+"FLOW_236212",2002,9,23,0.444,0.172025112107623,38.3616,0.703263973134143
+"FLOW_236212",2002,9,24,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",2002,9,25,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",2002,9,26,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2002,9,27,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2002,9,28,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",2002,9,29,1.929,0.747379372197309,166.6656,0.943426826702564
+"FLOW_236212",2002,9,30,2.48,0.960860986547085,214.272,0.992046689184495
+"FLOW_236212",2002,10,1,1.665,0.645094170403588,143.856,0.916061483859222
+"FLOW_236212",2002,10,2,1.054,0.408365919282511,91.0656,0.836006969586535
+"FLOW_236212",2002,10,3,0.682,0.264236771300448,58.9248,0.766299691677741
+"FLOW_236212",2002,10,4,0.527,0.204182959641256,45.5328,0.727786338293044
+"FLOW_236212",2002,10,5,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",2002,10,6,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",2002,10,7,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",2002,10,8,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2002,10,9,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2002,10,10,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2002,10,11,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2002,10,12,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2002,10,13,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2002,10,14,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",2002,10,15,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2002,10,16,0.487,0.188685201793722,42.0768,0.716386788620938
+"FLOW_236212",2002,10,17,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2002,10,18,0.497,0.192559641255605,42.9408,0.71930495772163
+"FLOW_236212",2002,10,19,0.423,0.163888789237668,36.5472,0.696481923722871
+"FLOW_236212",2002,10,20,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",2002,10,21,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2002,10,22,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2002,10,23,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",2002,10,24,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",2002,10,25,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2002,10,26,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",2002,10,27,0.52,0.201470852017937,44.928,0.725842585090593
+"FLOW_236212",2002,10,28,0.54,0.209219730941704,46.656,0.731342021478882
+"FLOW_236212",2002,10,29,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",2002,10,30,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2002,10,31,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2002,11,1,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2002,11,2,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2002,11,3,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2002,11,4,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2002,11,5,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2002,11,6,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2002,11,7,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2002,11,8,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2002,11,9,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2002,11,10,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2002,11,11,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2002,11,12,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2002,11,13,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2002,11,14,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2002,11,15,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2002,11,16,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2002,11,17,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2002,11,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2002,11,19,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2002,11,20,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2002,11,21,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2002,11,22,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2002,11,23,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2002,11,24,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2002,11,25,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2002,11,26,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2002,11,27,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2002,11,28,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2002,11,29,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2002,11,30,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2002,12,1,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2002,12,2,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2002,12,3,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2002,12,4,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2002,12,5,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2002,12,6,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2002,12,7,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2002,12,8,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2002,12,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2002,12,10,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2002,12,11,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2002,12,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2002,12,13,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2002,12,14,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2002,12,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2002,12,16,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2002,12,17,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2002,12,18,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2002,12,19,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2002,12,20,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2002,12,21,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2002,12,22,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2002,12,23,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2002,12,24,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2002,12,25,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2002,12,26,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2002,12,27,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2002,12,28,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2002,12,29,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2002,12,30,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2002,12,31,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2003,1,1,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2003,1,2,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2003,1,3,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2003,1,4,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2003,1,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2003,1,6,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2003,1,7,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2003,1,8,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2003,1,9,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2003,1,10,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2003,1,11,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2003,1,12,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2003,1,13,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2003,1,14,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2003,1,15,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2003,1,16,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2003,1,17,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2003,1,18,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2003,1,19,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2003,1,20,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2003,1,21,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2003,1,22,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2003,1,23,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2003,1,24,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2003,1,25,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2003,1,26,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2003,1,27,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2003,1,28,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2003,1,29,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2003,1,30,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2003,1,31,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2003,2,1,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2003,2,2,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2003,2,3,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2003,2,4,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2003,2,5,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2003,2,6,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2003,2,7,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2003,2,8,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2003,2,9,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,2,10,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2003,2,11,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2003,2,12,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2003,2,13,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2003,2,14,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2003,2,15,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2003,2,16,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2003,2,17,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2003,2,18,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2003,2,19,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2003,2,20,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2003,2,21,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2003,2,22,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2003,2,23,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2003,2,24,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2003,2,25,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2003,2,26,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2003,2,27,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2003,2,28,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2003,3,1,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2003,3,2,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2003,3,3,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2003,3,4,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2003,3,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2003,3,6,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2003,3,7,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2003,3,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2003,3,9,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2003,3,10,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2003,3,11,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2003,3,12,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2003,3,13,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2003,3,14,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2003,3,15,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2003,3,16,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2003,3,17,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2003,3,18,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2003,3,19,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2003,3,20,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2003,3,21,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2003,3,22,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2003,3,23,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2003,3,24,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2003,3,25,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2003,3,26,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2003,3,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,3,28,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,3,29,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2003,3,30,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2003,3,31,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2003,4,1,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2003,4,2,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2003,4,3,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,4,4,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2003,4,5,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2003,4,6,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2003,4,7,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2003,4,8,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2003,4,9,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2003,4,10,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2003,4,11,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2003,4,12,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2003,4,13,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2003,4,14,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2003,4,15,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2003,4,16,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2003,4,17,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2003,4,18,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2003,4,19,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2003,4,20,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2003,4,21,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2003,4,22,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2003,4,23,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2003,4,24,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2003,4,25,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2003,4,26,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2003,4,27,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2003,4,28,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2003,4,29,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2003,4,30,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2003,5,1,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2003,5,2,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2003,5,3,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2003,5,4,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2003,5,5,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2003,5,6,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2003,5,7,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2003,5,8,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2003,5,9,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2003,5,10,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2003,5,11,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2003,5,12,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,5,13,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2003,5,14,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2003,5,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2003,5,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2003,5,17,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2003,5,18,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2003,5,19,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2003,5,20,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2003,5,21,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2003,5,22,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2003,5,23,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2003,5,24,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2003,5,25,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2003,5,26,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2003,5,27,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2003,5,28,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2003,5,29,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2003,5,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2003,5,31,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2003,6,1,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2003,6,2,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2003,6,3,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2003,6,4,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2003,6,5,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2003,6,6,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2003,6,7,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2003,6,8,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2003,6,9,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2003,6,10,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2003,6,11,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,6,12,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2003,6,13,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2003,6,14,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2003,6,15,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2003,6,16,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2003,6,17,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2003,6,18,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2003,6,19,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2003,6,20,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2003,6,21,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2003,6,22,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2003,6,23,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2003,6,24,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2003,6,25,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2003,6,26,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2003,6,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2003,6,28,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2003,6,29,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2003,6,30,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2003,7,1,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2003,7,2,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2003,7,3,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2003,7,4,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2003,7,5,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2003,7,6,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2003,7,7,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2003,7,8,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2003,7,9,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2003,7,10,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2003,7,11,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2003,7,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2003,7,13,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2003,7,14,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2003,7,15,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2003,7,16,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2003,7,17,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2003,7,18,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2003,7,19,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2003,7,20,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2003,7,21,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2003,7,22,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2003,7,23,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2003,7,24,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2003,7,25,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2003,7,26,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",2003,7,27,0.664,0.257262780269058,57.3696,0.762211315332639
+"FLOW_236212",2003,7,28,0.629,0.243702242152466,54.3456,0.754000970691041
+"FLOW_236212",2003,7,29,0.71,0.275085201793722,61.344,0.772491030779259
+"FLOW_236212",2003,7,30,2.493,0.965897757847534,215.3952,0.993084564218775
+"FLOW_236212",2003,7,31,2.683,1.03951210762332,231.8112,1.00778040643636
+"FLOW_236212",2003,8,1,1.57,0.608286995515695,135.648,0.905360841140012
+"FLOW_236212",2003,8,2,0.914,0.354123766816144,78.9696,0.812514303491783
+"FLOW_236212",2003,8,3,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",2003,8,4,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2003,8,5,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",2003,8,6,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2003,8,7,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2003,8,8,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2003,8,9,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2003,8,10,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2003,8,11,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2003,8,12,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2003,8,13,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2003,8,14,1.474,0.571092376681614,127.3536,0.89400772726163
+"FLOW_236212",2003,8,15,1.48,0.573417040358744,127.872,0.894734366850894
+"FLOW_236212",2003,8,16,0.903,0.349861883408072,78.0192,0.810549096360054
+"FLOW_236212",2003,8,17,0.604,0.234016143497758,52.1856,0.74790969617129
+"FLOW_236212",2003,8,18,0.483,0.187135426008969,41.7312,0.715206087040821
+"FLOW_236212",2003,8,19,1.214,0.470356950672646,104.8896,0.859974380235159
+"FLOW_236212",2003,8,20,1.326,0.51375067264574,114.5664,0.875287004925656
+"FLOW_236212",2003,8,21,0.826,0.320028699551569,71.3664,0.796228622466257
+"FLOW_236212",2003,8,22,0.585,0.226654708520179,50.544,0.743143954430343
+"FLOW_236212",2003,8,23,0.508,0.196821524663677,43.8912,0.722461183707755
+"FLOW_236212",2003,8,24,1.811,0.701660986547085,156.4704,0.931591389840616
+"FLOW_236212",2003,8,25,5.685,2.20261883408072,491.184,1.17108352068311
+"FLOW_236212",2003,8,26,4.596,1.78069237668161,397.0944,1.12232278497196
+"FLOW_236212",2003,8,27,3.477,1.34714260089686,300.4128,1.06140887340845
+"FLOW_236212",2003,8,28,2.87,1.11196412556054,247.968,1.0214524518637
+"FLOW_236212",2003,8,29,1.802,0.69817399103139,155.6928,0.930663610804595
+"FLOW_236212",2003,8,30,1.162,0.450209865470852,100.3968,0.852477649772266
+"FLOW_236212",2003,8,31,2.228,0.863225112107623,192.4992,0.9710124832308
+"FLOW_236212",2003,9,1,5.001,1.93760717488789,432.0864,1.14144015542869
+"FLOW_236212",2003,9,2,2.997,1.16116950672646,258.9408,1.03033659336984
+"FLOW_236212",2003,9,3,1.834,0.710572197309417,158.4576,0.933945732804954
+"FLOW_236212",2003,9,4,1.24,0.480430493273543,107.136,0.863626804085617
+"FLOW_236212",2003,9,5,0.849,0.328939910313901,73.3536,0.8006142466112
+"FLOW_236212",2003,9,6,0.659,0.255325560538117,56.9376,0.761059933464859
+"FLOW_236212",2003,9,7,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",2003,9,8,0.491,0.190234977578475,42.4224,0.717559757309554
+"FLOW_236212",2003,9,9,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2003,9,10,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",2003,9,11,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",2003,9,12,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2003,9,13,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",2003,9,14,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",2003,9,15,0.699,0.27082331838565,60.3936,0.77008242340526
+"FLOW_236212",2003,9,16,2.732,1.05849686098655,236.0448,1.01143485298729
+"FLOW_236212",2003,9,17,4.972,1.92637130044843,429.5808,1.14011326826701
+"FLOW_236212",2003,9,18,3.073,1.19061524663677,265.5072,1.03550997775966
+"FLOW_236212",2003,9,19,2.697,1.04493632286996,233.0208,1.00882994569687
+"FLOW_236212",2003,9,20,1.84,0.712896860986547,158.976,0.934556022412971
+"FLOW_236212",2003,9,21,1.217,0.471519282511211,105.1488,0.860398989253969
+"FLOW_236212",2003,9,22,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",2003,9,23,0.61,0.236340807174888,52.704,0.749389740131935
+"FLOW_236212",2003,9,24,0.494,0.19139730941704,42.6816,0.718434477206278
+"FLOW_236212",2003,9,25,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2003,9,26,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",2003,9,27,0.491,0.190234977578475,42.4224,0.717559757309554
+"FLOW_236212",2003,9,28,1.466,0.567992825112108,126.6624,0.893035184597104
+"FLOW_236212",2003,9,29,1.954,0.757065470852018,168.8256,0.945859625457982
+"FLOW_236212",2003,9,30,1.515,0.586977578475336,130.896,0.898926746326958
+"FLOW_236212",2003,10,1,1.05,0.406816143497758,90.72,0.835371463743786
+"FLOW_236212",2003,10,2,0.778,0.301431390134529,67.2192,0.786751709567792
+"FLOW_236212",2003,10,3,0.697,0.270048430493274,60.2208,0.769641241549598
+"FLOW_236212",2003,10,4,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2003,10,5,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",2003,10,6,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",2003,10,7,0.423,0.163888789237668,36.5472,0.696481923722871
+"FLOW_236212",2003,10,8,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2003,10,9,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2003,10,10,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2003,10,11,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2003,10,12,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2003,10,13,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2003,10,14,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2003,10,15,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2003,10,16,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2003,10,17,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2003,10,18,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2003,10,19,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2003,10,20,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2003,10,21,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2003,10,22,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2003,10,23,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2003,10,24,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2003,10,25,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2003,10,26,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2003,10,27,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2003,10,28,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2003,10,29,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2003,10,30,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2003,10,31,0.536,0.207669955156951,46.3104,0.73025532719464
+"FLOW_236212",2003,11,1,3.332,1.29096322869955,287.8848,1.052404689028
+"FLOW_236212",2003,11,2,4.357,1.6880932735426,376.4448,1.11039957752246
+"FLOW_236212",2003,11,3,3.211,1.24408251121076,277.4304,1.0446476629172
+"FLOW_236212",2003,11,4,1.855,0.718708520179372,160.272,0.936074810530359
+"FLOW_236212",2003,11,5,1.078,0.417664573991031,93.1392,0.839780001324386
+"FLOW_236212",2003,11,6,0.695,0.269273542600897,60.048,0.769199045772625
+"FLOW_236212",2003,11,7,0.497,0.192559641255605,42.9408,0.71930495772163
+"FLOW_236212",2003,11,8,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2003,11,9,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2003,11,10,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2003,11,11,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2003,11,12,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2003,11,13,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2003,11,14,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2003,11,15,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2003,11,16,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2003,11,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2003,11,18,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2003,11,19,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2003,11,20,0.625,0.242152466367713,54,0.753039538208907
+"FLOW_236212",2003,11,21,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2003,11,22,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2003,11,23,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2003,11,24,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2003,11,25,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2003,11,26,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2003,11,27,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2003,11,28,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2003,11,29,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2003,11,30,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2003,12,1,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,12,2,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,12,3,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2003,12,4,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2003,12,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2003,12,6,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,12,7,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2003,12,8,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2003,12,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2003,12,10,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2003,12,11,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2003,12,12,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2003,12,13,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2003,12,14,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,12,15,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2003,12,16,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2003,12,17,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2003,12,18,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2003,12,19,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2003,12,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2003,12,21,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2003,12,22,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2003,12,23,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2003,12,24,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2003,12,25,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2003,12,26,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2003,12,27,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2003,12,28,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2003,12,29,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2003,12,30,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2003,12,31,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2004,1,1,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2004,1,2,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2004,1,3,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2004,1,4,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2004,1,5,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2004,1,6,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2004,1,7,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2004,1,8,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,1,9,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2004,1,10,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2004,1,11,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2004,1,12,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2004,1,13,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2004,1,14,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2004,1,15,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2004,1,16,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2004,1,17,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2004,1,18,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2004,1,19,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2004,1,20,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2004,1,21,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2004,1,22,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2004,1,23,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2004,1,24,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2004,1,25,0.09,0.0348699551569507,7.776,0.51108163770667
+"FLOW_236212",2004,1,26,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2004,1,27,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2004,1,28,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2004,1,29,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2004,1,30,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2004,1,31,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2004,2,1,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2004,2,2,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2004,2,3,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2004,2,4,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2004,2,5,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",2004,2,6,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2004,2,7,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2004,2,8,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2004,2,9,0.093,0.0360322869955157,8.0352,0.514444307058102
+"FLOW_236212",2004,2,10,0.089,0.0344825112107623,7.6896,0.50994081909868
+"FLOW_236212",2004,2,11,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2004,2,12,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2004,2,13,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2004,2,14,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2004,2,15,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2004,2,16,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2004,2,17,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2004,2,18,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2004,2,19,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2004,2,20,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2004,2,21,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2004,2,22,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2004,2,23,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2004,2,24,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2004,2,25,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2004,2,26,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2004,2,27,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2004,2,28,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2004,2,29,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2004,3,1,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2004,3,2,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2004,3,3,0.09,0.0348699551569507,7.776,0.51108163770667
+"FLOW_236212",2004,3,4,0.08,0.0309955156950673,6.912,0.499182984526472
+"FLOW_236212",2004,3,5,0.08,0.0309955156950673,6.912,0.499182984526472
+"FLOW_236212",2004,3,6,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",2004,3,7,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2004,3,8,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2004,3,9,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2004,3,10,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2004,3,11,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2004,3,12,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2004,3,13,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2004,3,14,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2004,3,15,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2004,3,16,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2004,3,17,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2004,3,18,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2004,3,19,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2004,3,20,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2004,3,21,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2004,3,22,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2004,3,23,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2004,3,24,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2004,3,25,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2004,3,26,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2004,3,27,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2004,3,28,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2004,3,29,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2004,3,30,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2004,3,31,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2004,4,1,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2004,4,2,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2004,4,3,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2004,4,4,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2004,4,5,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2004,4,6,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2004,4,7,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2004,4,8,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2004,4,9,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2004,4,10,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2004,4,11,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2004,4,12,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2004,4,13,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2004,4,14,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2004,4,15,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2004,4,16,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2004,4,17,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2004,4,18,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2004,4,19,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2004,4,20,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2004,4,21,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2004,4,22,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2004,4,23,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2004,4,24,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2004,4,25,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2004,4,26,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2004,4,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2004,4,28,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2004,4,29,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2004,4,30,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2004,5,1,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2004,5,2,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2004,5,3,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2004,5,4,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2004,5,5,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2004,5,6,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2004,5,7,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2004,5,8,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2004,5,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2004,5,10,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2004,5,11,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2004,5,12,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2004,5,13,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2004,5,14,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2004,5,15,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2004,5,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2004,5,17,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2004,5,18,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2004,5,19,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2004,5,20,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2004,5,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2004,5,22,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2004,5,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2004,5,24,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2004,5,25,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2004,5,26,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2004,5,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2004,5,28,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2004,5,29,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2004,5,30,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2004,5,31,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2004,6,1,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2004,6,2,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2004,6,3,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2004,6,4,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,6,5,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,6,6,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,6,7,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2004,6,8,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2004,6,9,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2004,6,10,0.458,0.17744932735426,39.5712,0.707644069704618
+"FLOW_236212",2004,6,11,0.537,0.208057399103139,46.3968,0.730527607407242
+"FLOW_236212",2004,6,12,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2004,6,13,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2004,6,14,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2004,6,15,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2004,6,16,0.489,0.189460089686099,42.2496,0.716974232451725
+"FLOW_236212",2004,6,17,0.669,0.2592,57.8016,0.763355781923635
+"FLOW_236212",2004,6,18,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2004,6,19,1.131,0.438199103139013,97.7184,0.847879815954419
+"FLOW_236212",2004,6,20,3.509,1.35954080717489,303.1776,1.06335542118819
+"FLOW_236212",2004,6,21,4.326,1.67608251121076,373.7664,1.10881496548883
+"FLOW_236212",2004,6,22,3.031,1.17434260089686,261.8784,1.03266382390297
+"FLOW_236212",2004,6,23,2.04,0.790385650224215,176.256,0.954042692431437
+"FLOW_236212",2004,6,24,1.223,0.473843946188341,105.6672,0.861245701395927
+"FLOW_236212",2004,6,25,1.349,0.522661883408072,116.5536,0.878302594830664
+"FLOW_236212",2004,6,26,5.174,2.00463497757848,447.0336,1.14923028124406
+"FLOW_236212",2004,6,27,5.687,2.20339372197309,491.3568,1.17116590722729
+"FLOW_236212",2004,6,28,2.538,0.983332735426009,219.2832,0.996644098396138
+"FLOW_236212",2004,6,29,1.746,0.676477130044843,150.8544,0.924805968139747
+"FLOW_236212",2004,6,30,1.628,0.630758744394619,140.6592,0.911953419233601
+"FLOW_236212",2004,7,1,5.428,2.10304573991031,468.9792,1.16029853441804
+"FLOW_236212",2004,7,2,7.225,2.79928251121076,624.24,1.22859670480337
+"FLOW_236212",2004,7,3,3.144,1.21812376681614,271.6416,1.04025133870114
+"FLOW_236212",2004,7,4,1.991,0.771400896860987,172.0224,0.949414866534889
+"FLOW_236212",2004,7,5,1.625,0.629596412556054,140.4,0.911617070450449
+"FLOW_236212",2004,7,6,2.681,1.03873721973094,231.6384,1.00763011481735
+"FLOW_236212",2004,7,7,5.813,2.25221165919283,502.2432,1.17631013931395
+"FLOW_236212",2004,7,8,5.494,2.12861704035874,474.6816,1.16310656460347
+"FLOW_236212",2004,7,9,2.815,1.09065470852018,243.216,1.01750711728853
+"FLOW_236212",2004,7,10,1.783,0.690812556053812,154.0512,0.928692727236085
+"FLOW_236212",2004,7,11,1.205,0.466869955156951,104.112,0.85869549661307
+"FLOW_236212",2004,7,12,1.683,0.652068161434978,145.4112,0.918033647403566
+"FLOW_236212",2004,7,13,4.083,1.581933632287,352.7712,1.09606838684703
+"FLOW_236212",2004,7,14,3.291,1.27507802690583,284.3424,1.04980189491817
+"FLOW_236212",2004,7,15,2.442,0.946138116591928,210.9888,0.98898773980606
+"FLOW_236212",2004,7,16,2.119,0.820993721973094,183.0816,0.961319976634466
+"FLOW_236212",2004,7,17,4.284,1.65980986547085,370.1376,1.10665351636511
+"FLOW_236212",2004,7,18,6.318,2.44787085201794,545.8752,1.19607303694776
+"FLOW_236212",2004,7,19,8.496,3.29172376681614,734.0544,1.26906736670084
+"FLOW_236212",2004,7,20,5.617,2.17627264573991,485.3088,1.16826848300801
+"FLOW_236212",2004,7,21,3.282,1.27159103139013,283.5648,1.04922708061379
+"FLOW_236212",2004,7,22,2.772,1.07399461883408,239.5008,1.01437940021491
+"FLOW_236212",2004,7,23,2.296,0.889571300448431,198.3744,0.976868605756509
+"FLOW_236212",2004,7,24,2.368,0.917467264573991,204.5952,0.982919872162373
+"FLOW_236212",2004,7,25,2.799,1.08445560538117,241.8336,1.01634780972116
+"FLOW_236212",2004,7,26,2.91,1.12746188340807,251.424,1.02428396434673
+"FLOW_236212",2004,7,27,3.14,1.21657399103139,271.296,1.03998650889626
+"FLOW_236212",2004,7,28,6.403,2.48080358744395,553.2192,1.1992741598269
+"FLOW_236212",2004,7,29,10.926,4.23321255605381,944.0064,1.33454743564854
+"FLOW_236212",2004,7,30,4.402,1.70552825112108,380.3328,1.11268384739552
+"FLOW_236212",2004,7,31,2.546,0.986432286995516,219.9744,0.997271609737766
+"FLOW_236212",2004,8,1,1.837,0.711734529147982,158.7168,0.934251076941538
+"FLOW_236212",2004,8,2,1.344,0.52072466367713,116.1216,0.877650550611411
+"FLOW_236212",2004,8,3,1.07,0.414565022421525,92.448,0.838529858077405
+"FLOW_236212",2004,8,4,1.104,0.427738116591928,95.3856,0.843792363419142
+"FLOW_236212",2004,8,5,1.968,0.762489686098655,170.0352,0.947211134826159
+"FLOW_236212",2004,8,6,4.931,1.91048609865471,426.0384,1.13822671970828
+"FLOW_236212",2004,8,7,6.12,2.37115695067265,528.768,1.18848049967593
+"FLOW_236212",2004,8,8,3.379,1.3091730941704,291.9456,1.05535704654473
+"FLOW_236212",2004,8,9,2.789,1.08058116591928,240.9696,1.01562054757383
+"FLOW_236212",2004,8,10,2.967,1.14954618834081,256.3488,1.02826554828922
+"FLOW_236212",2004,8,11,3.371,1.3060735426009,291.2544,1.05485684747094
+"FLOW_236212",2004,8,12,3.295,1.27662780269058,284.688,1.05005696435116
+"FLOW_236212",2004,8,13,2.465,0.95504932735426,212.976,0.990843718761982
+"FLOW_236212",2004,8,14,5.346,2.07127533632287,461.8944,1.1567714643991
+"FLOW_236212",2004,8,15,10.634,4.12007892376682,918.7776,1.32733671393951
+"FLOW_236212",2004,8,16,5.136,1.98991210762332,443.7504,1.14753721539967
+"FLOW_236212",2004,8,17,2.741,1.06198385650224,236.8224,1.01210036852733
+"FLOW_236212",2004,8,18,2.007,0.7776,173.4048,0.950935915476668
+"FLOW_236212",2004,8,19,1.482,0.574191928251121,128.0448,0.894976056342359
+"FLOW_236212",2004,8,20,1.113,0.431225112107623,96.1632,0.845163647520222
+"FLOW_236212",2004,8,21,0.877,0.339788340807175,75.7728,0.805826776995879
+"FLOW_236212",2004,8,22,0.756,0.292907623318386,65.3184,0.782251014377559
+"FLOW_236212",2004,8,23,0.734,0.284383856502242,63.4176,0.777644290738638
+"FLOW_236212",2004,8,24,0.688,0.266561434977578,59.4432,0.767643299624964
+"FLOW_236212",2004,8,25,0.61,0.236340807174888,52.704,0.749389740131935
+"FLOW_236212",2004,8,26,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2004,8,27,0.48,0.185973094170404,41.472,0.714315416671224
+"FLOW_236212",2004,8,28,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",2004,8,29,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",2004,8,30,0.756,0.292907623318386,65.3184,0.782251014377559
+"FLOW_236212",2004,8,31,3.63,1.40642152466368,313.632,1.07058979888809
+"FLOW_236212",2004,9,1,3.618,1.40177219730942,312.5952,1.06988103309024
+"FLOW_236212",2004,9,2,1.995,0.77295067264574,172.368,0.949796042956668
+"FLOW_236212",2004,9,3,1.345,0.521112107623318,116.208,0.877781114519393
+"FLOW_236212",2004,9,4,2.345,0.908556053811659,202.608,0.981003022138394
+"FLOW_236212",2004,9,5,3.661,1.41843228699552,316.3104,1.07241213997766
+"FLOW_236212",2004,9,6,2.609,1.01084125560538,225.4176,1.00215890919663
+"FLOW_236212",2004,9,7,1.772,0.68655067264574,153.1008,0.927543997754614
+"FLOW_236212",2004,9,8,1.285,0.497865470852018,111.024,0.869806004561526
+"FLOW_236212",2004,9,9,1.108,0.429287892376682,95.7312,0.844402922931941
+"FLOW_236212",2004,9,10,1.088,0.421539013452915,94.0032,0.841332285491912
+"FLOW_236212",2004,9,11,6.048,2.34326098654709,522.5472,1.18567082166163
+"FLOW_236212",2004,9,12,16.301,6.31572376681614,1408.4064,1.44572145241361
+"FLOW_236212",2004,9,13,10.89,4.21926457399103,940.896,1.33366683611165
+"FLOW_236212",2004,9,14,6.91,2.67723766816143,597.024,1.21769181086726
+"FLOW_236212",2004,9,15,4.241,1.64314977578475,366.4224,1.10442296943366
+"FLOW_236212",2004,9,16,2.716,1.05229775784753,234.6624,1.01024737116403
+"FLOW_236212",2004,9,17,2.016,0.781086995515695,174.1824,0.951787247114192
+"FLOW_236212",2004,9,18,1.495,0.57922869955157,129.168,0.89654070935356
+"FLOW_236212",2004,9,19,1.133,0.43897399103139,97.8912,0.848179473147465
+"FLOW_236212",2004,9,20,0.89,0.344825112107623,76.896,0.808201732747543
+"FLOW_236212",2004,9,21,0.708,0.274310313901345,61.1712,0.772055333361098
+"FLOW_236212",2004,9,22,0.594,0.230141704035874,51.3216,0.745416608355256
+"FLOW_236212",2004,9,23,0.564,0.218518385650224,48.7296,0.737730258466806
+"FLOW_236212",2004,9,24,0.598,0.231691479820628,51.6672,0.746417843138657
+"FLOW_236212",2004,9,25,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2004,9,26,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2004,9,27,0.631,0.244477130044843,54.5184,0.754479853878591
+"FLOW_236212",2004,9,28,0.57,0.220843049327354,49.248,0.739293260319341
+"FLOW_236212",2004,9,29,0.506,0.1960466367713,43.7184,0.721891418658271
+"FLOW_236212",2004,9,30,0.449,0.173962331838565,38.7936,0.70484081402273
+"FLOW_236212",2004,10,1,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",2004,10,2,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2004,10,3,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",2004,10,4,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2004,10,5,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2004,10,6,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2004,10,7,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2004,10,8,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2004,10,9,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2004,10,10,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2004,10,11,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2004,10,12,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2004,10,13,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2004,10,14,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2004,10,15,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",2004,10,16,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",2004,10,17,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2004,10,18,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",2004,10,19,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2004,10,20,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2004,10,21,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2004,10,22,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2004,10,23,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2004,10,24,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2004,10,25,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2004,10,26,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2004,10,27,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",2004,10,28,0.617,0.239052914798206,53.3088,0.75110181026194
+"FLOW_236212",2004,10,29,0.588,0.227817040358744,50.8032,0.743904594666505
+"FLOW_236212",2004,10,30,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",2004,10,31,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",2004,11,1,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2004,11,2,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2004,11,3,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",2004,11,4,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2004,11,5,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2004,11,6,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",2004,11,7,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",2004,11,8,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",2004,11,9,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2004,11,10,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2004,11,11,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2004,11,12,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2004,11,13,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2004,11,14,0.691,0.267723766816143,59.7024,0.768311591380086
+"FLOW_236212",2004,11,15,1,0.387443946188341,86.4,0.827259524225765
+"FLOW_236212",2004,11,16,0.884,0.342500448430493,76.3776,0.807109072059093
+"FLOW_236212",2004,11,17,0.606,0.234791031390135,52.3584,0.748404345873839
+"FLOW_236212",2004,11,18,0.438,0.169700448430493,37.8432,0.701352901466612
+"FLOW_236212",2004,11,19,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2004,11,20,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2004,11,21,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2004,11,22,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2004,11,23,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2004,11,24,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2004,11,25,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2004,11,26,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2004,11,27,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2004,11,28,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2004,11,29,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2004,11,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2004,12,1,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2004,12,2,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2004,12,3,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2004,12,4,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2004,12,5,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2004,12,6,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2004,12,7,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2004,12,8,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2004,12,9,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2004,12,10,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2004,12,11,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",2004,12,12,0.603,0.23362869955157,52.0992,0.747661879782813
+"FLOW_236212",2004,12,13,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",2004,12,14,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2004,12,15,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2004,12,16,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2004,12,17,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2004,12,18,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2004,12,19,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2004,12,20,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2004,12,21,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2004,12,22,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,12,23,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2004,12,24,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2004,12,25,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2004,12,26,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2004,12,27,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2004,12,28,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2004,12,29,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2004,12,30,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2004,12,31,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2005,1,1,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2005,1,2,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2005,1,3,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2005,1,4,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2005,1,5,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2005,1,6,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2005,1,7,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2005,1,8,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2005,1,9,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2005,1,10,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2005,1,11,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,1,12,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2005,1,13,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2005,1,14,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2005,1,15,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2005,1,16,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2005,1,17,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2005,1,18,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2005,1,19,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2005,1,20,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2005,1,21,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2005,1,22,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2005,1,23,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2005,1,24,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2005,1,25,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2005,1,26,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2005,1,27,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2005,1,28,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2005,1,29,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2005,1,30,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2005,1,31,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2005,2,1,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2005,2,2,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2005,2,3,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",2005,2,4,1.029,0.398679820627803,88.9056,0.832002920658999
+"FLOW_236212",2005,2,5,0.481,0.186360538116592,41.5584,0.714612800378232
+"FLOW_236212",2005,2,6,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2005,2,7,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2005,2,8,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2005,2,9,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2005,2,10,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2005,2,11,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2005,2,12,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2005,2,13,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2005,2,14,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2005,2,15,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2005,2,16,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2005,2,17,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2005,2,18,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2005,2,19,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,2,20,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2005,2,21,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2005,2,22,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2005,2,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,2,24,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2005,2,25,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2005,2,26,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,2,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,2,28,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,3,1,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2005,3,2,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2005,3,3,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2005,3,4,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2005,3,5,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2005,3,6,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2005,3,7,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2005,3,8,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2005,3,9,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2005,3,10,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2005,3,11,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2005,3,12,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2005,3,13,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,3,14,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,3,15,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2005,3,16,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2005,3,17,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2005,3,18,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2005,3,19,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2005,3,20,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2005,3,21,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2005,3,22,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2005,3,23,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2005,3,24,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2005,3,25,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2005,3,26,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2005,3,27,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2005,3,28,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2005,3,29,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2005,3,30,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2005,3,31,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2005,4,1,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2005,4,2,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2005,4,3,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2005,4,4,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2005,4,5,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2005,4,6,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2005,4,7,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2005,4,8,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2005,4,9,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2005,4,10,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2005,4,11,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2005,4,12,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2005,4,13,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2005,4,14,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2005,4,15,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2005,4,16,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,4,17,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2005,4,18,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,4,19,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2005,4,20,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,4,21,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2005,4,22,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2005,4,23,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2005,4,24,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2005,4,25,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2005,4,26,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2005,4,27,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2005,4,28,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2005,4,29,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2005,4,30,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2005,5,1,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2005,5,2,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2005,5,3,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2005,5,4,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2005,5,5,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,6,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2005,5,7,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,8,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,9,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,10,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2005,5,11,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2005,5,12,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2005,5,13,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2005,5,14,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2005,5,15,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2005,5,16,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,17,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2005,5,19,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2005,5,20,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2005,5,21,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,22,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,23,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2005,5,24,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2005,5,25,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2005,5,26,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2005,5,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2005,5,28,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,5,29,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2005,5,30,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2005,5,31,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2005,6,1,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2005,6,2,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2005,6,3,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2005,6,4,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2005,6,5,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2005,6,6,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2005,6,7,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2005,6,8,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2005,6,9,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2005,6,10,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2005,6,11,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2005,6,12,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2005,6,13,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2005,6,14,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2005,6,15,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2005,6,16,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2005,6,17,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2005,6,18,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2005,6,19,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2005,6,20,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2005,6,21,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",2005,6,22,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",2005,6,23,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2005,6,24,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2005,6,25,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2005,6,26,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2005,6,27,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2005,6,28,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2005,6,29,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2005,6,30,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2005,7,1,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2005,7,2,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2005,7,3,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2005,7,4,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2005,7,5,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2005,7,6,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2005,7,7,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2005,7,8,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2005,7,9,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2005,7,10,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2005,7,11,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2005,7,12,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2005,7,13,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2005,7,14,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2005,7,15,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2005,7,16,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2005,7,17,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2005,7,18,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2005,7,19,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2005,7,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2005,7,21,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2005,7,22,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2005,7,23,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2005,7,24,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2005,7,25,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2005,7,26,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2005,7,27,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2005,7,28,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2005,7,29,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2005,7,30,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2005,7,31,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2005,8,1,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2005,8,2,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2005,8,3,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2005,8,4,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2005,8,5,0.704,0.272760538116592,60.8256,0.771180976266069
+"FLOW_236212",2005,8,6,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",2005,8,7,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2005,8,8,0.428,0.16582600896861,36.9792,0.698120722844581
+"FLOW_236212",2005,8,9,0.433,0.167763228699552,37.4112,0.699744276950525
+"FLOW_236212",2005,8,10,0.489,0.189460089686099,42.2496,0.716974232451725
+"FLOW_236212",2005,8,11,1.203,0.466095067264574,103.9392,0.858410263035213
+"FLOW_236212",2005,8,12,1.662,0.643931838565022,143.5968,0.915731133436516
+"FLOW_236212",2005,8,13,1.25,0.484304932735426,108,0.865015278788298
+"FLOW_236212",2005,8,14,0.995,0.385506726457399,85.968,0.826430605201494
+"FLOW_236212",2005,8,15,0.76,0.294457399103139,65.664,0.783077046856952
+"FLOW_236212",2005,8,16,0.739,0.286321076233184,63.8496,0.778700876553795
+"FLOW_236212",2005,8,17,0.772,0.299106726457399,66.7008,0.785534449930101
+"FLOW_236212",2005,8,18,0.604,0.234016143497758,52.1856,0.74790969617129
+"FLOW_236212",2005,8,19,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",2005,8,20,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",2005,8,21,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2005,8,22,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",2005,8,23,0.568,0.220068161434978,49.0752,0.738773728351126
+"FLOW_236212",2005,8,24,0.577,0.223555156950673,49.8528,0.741100213733267
+"FLOW_236212",2005,8,25,0.478,0.185198206278027,41.2992,0.713719159231734
+"FLOW_236212",2005,8,26,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2005,8,27,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",2005,8,28,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2005,8,29,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2005,8,30,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2005,8,31,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2005,9,1,0.872,0.337851121076233,75.3408,0.804905829700763
+"FLOW_236212",2005,9,2,1.427,0.552882511210762,123.2928,0.888232317472297
+"FLOW_236212",2005,9,3,0.882,0.341725560538117,76.2048,0.806743533380091
+"FLOW_236212",2005,9,4,0.603,0.23362869955157,52.0992,0.747661879782813
+"FLOW_236212",2005,9,5,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2005,9,6,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2005,9,7,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2005,9,8,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2005,9,9,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2005,9,10,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2005,9,11,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2005,9,12,0.477,0.184810762331839,41.2128,0.713420281757027
+"FLOW_236212",2005,9,13,1.492,0.578066367713005,128.9088,0.896180604520774
+"FLOW_236212",2005,9,14,1.829,0.708634977578475,158.0256,0.9334359367954
+"FLOW_236212",2005,9,15,1.269,0.491666367713004,109.6416,0.867629081264516
+"FLOW_236212",2005,9,16,0.912,0.353348878923767,78.7968,0.812158405718966
+"FLOW_236212",2005,9,17,0.813,0.314991928251121,70.2432,0.793706405080409
+"FLOW_236212",2005,9,18,1.515,0.586977578475336,130.896,0.898926746326958
+"FLOW_236212",2005,9,19,1.707,0.661366816143498,147.4848,0.920637116791477
+"FLOW_236212",2005,9,20,1.246,0.482755156950673,107.6544,0.864460959025751
+"FLOW_236212",2005,9,21,1.182,0.457958744394619,102.1248,0.855392171291923
+"FLOW_236212",2005,9,22,0.932,0.361097757847534,80.5248,0.815689662443497
+"FLOW_236212",2005,9,23,0.671,0.259974887892377,57.9744,0.763811653124739
+"FLOW_236212",2005,9,24,0.573,0.222005381165919,49.5072,0.740069830582908
+"FLOW_236212",2005,9,25,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2005,9,26,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",2005,9,27,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2005,9,28,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",2005,9,29,0.453,0.175512107623318,39.1392,0.706092203731679
+"FLOW_236212",2005,9,30,1.005,0.389381165919282,86.832,0.828085134177192
+"FLOW_236212",2005,10,1,1.03,0.399067264573991,88.992,0.832164568800337
+"FLOW_236212",2005,10,2,0.79,0.306080717488789,68.256,0.789163870461388
+"FLOW_236212",2005,10,3,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",2005,10,4,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",2005,10,5,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2005,10,6,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",2005,10,7,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",2005,10,8,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",2005,10,9,0.996,0.385894170403587,86.0544,0.826596655165714
+"FLOW_236212",2005,10,10,1.26,0.488179372197309,108.864,0.866394895496418
+"FLOW_236212",2005,10,11,1.062,0.411465470852018,91.7568,0.83727221483274
+"FLOW_236212",2005,10,12,1.025,0.397130044843049,88.56,0.8313550686598
+"FLOW_236212",2005,10,13,0.947,0.366909417040359,81.8208,0.818298531283392
+"FLOW_236212",2005,10,14,0.663,0.25687533632287,57.2832,0.761981595183807
+"FLOW_236212",2005,10,15,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",2005,10,16,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2005,10,17,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2005,10,18,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2005,10,19,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2005,10,20,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2005,10,21,0.621,0.24060269058296,53.6544,0.752073170521953
+"FLOW_236212",2005,10,22,0.633,0.24525201793722,54.6912,0.75495752431767
+"FLOW_236212",2005,10,23,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",2005,10,24,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",2005,10,25,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",2005,10,26,0.514,0.199146188340807,44.4096,0.724159778915107
+"FLOW_236212",2005,10,27,0.49,0.189847533632287,42.336,0.717267233870557
+"FLOW_236212",2005,10,28,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",2005,10,29,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",2005,10,30,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2005,10,31,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2005,11,1,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2005,11,2,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2005,11,3,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2005,11,4,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2005,11,5,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2005,11,6,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2005,11,7,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2005,11,8,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2005,11,9,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2005,11,10,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2005,11,11,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2005,11,12,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2005,11,13,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2005,11,14,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2005,11,15,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2005,11,16,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2005,11,17,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2005,11,18,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2005,11,19,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2005,11,20,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2005,11,21,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2005,11,22,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2005,11,23,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2005,11,24,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2005,11,25,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2005,11,26,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2005,11,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2005,11,28,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2005,11,29,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2005,11,30,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2005,12,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2005,12,2,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2005,12,3,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2005,12,4,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2005,12,5,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2005,12,6,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2005,12,7,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2005,12,8,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2005,12,9,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2005,12,10,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2005,12,11,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2005,12,12,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2005,12,13,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2005,12,14,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2005,12,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2005,12,16,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2005,12,17,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2005,12,18,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2005,12,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2005,12,20,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2005,12,21,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2005,12,22,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2005,12,23,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2005,12,24,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2005,12,25,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2005,12,26,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2005,12,27,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2005,12,28,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2005,12,29,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2005,12,30,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2005,12,31,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2006,1,1,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2006,1,2,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2006,1,3,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2006,1,4,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2006,1,5,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2006,1,6,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2006,1,7,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2006,1,8,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2006,1,9,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2006,1,10,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2006,1,11,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2006,1,12,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2006,1,13,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2006,1,14,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2006,1,15,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2006,1,16,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2006,1,17,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2006,1,18,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",2006,1,19,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2006,1,20,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2006,1,21,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2006,1,22,0.09,0.0348699551569507,7.776,0.51108163770667
+"FLOW_236212",2006,1,23,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2006,1,24,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2006,1,25,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2006,1,26,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2006,1,27,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2006,1,28,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2006,1,29,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2006,1,30,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,1,31,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2006,2,1,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2006,2,2,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2006,2,3,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,2,4,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2006,2,5,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2006,2,6,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2006,2,7,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2006,2,8,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2006,2,9,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2006,2,10,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2006,2,11,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2006,2,12,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,2,13,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2006,2,14,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,2,15,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2006,2,16,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,2,17,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2006,2,18,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2006,2,19,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2006,2,20,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2006,2,21,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,2,22,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2006,2,23,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,2,24,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2006,2,25,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,2,26,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2006,2,27,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2006,2,28,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2006,3,1,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2006,3,2,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2006,3,3,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2006,3,4,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2006,3,5,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2006,3,6,0.094,0.036419730941704,8.1216,0.51554591095497
+"FLOW_236212",2006,3,7,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2006,3,8,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2006,3,9,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2006,3,10,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2006,3,11,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2006,3,12,0.083,0.0321578475336323,7.1712,0.502871930131015
+"FLOW_236212",2006,3,13,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2006,3,14,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2006,3,15,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2006,3,16,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2006,3,17,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2006,3,18,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2006,3,19,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2006,3,20,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2006,3,21,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2006,3,22,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2006,3,23,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2006,3,24,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2006,3,25,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2006,3,26,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2006,3,27,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2006,3,28,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2006,3,29,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2006,3,30,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2006,3,31,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2006,4,1,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2006,4,2,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2006,4,3,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2006,4,4,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,4,5,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,4,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2006,4,7,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2006,4,8,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2006,4,9,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2006,4,10,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2006,4,11,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2006,4,12,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2006,4,13,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2006,4,14,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2006,4,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2006,4,16,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2006,4,17,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2006,4,18,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2006,4,19,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2006,4,20,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2006,4,21,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2006,4,22,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2006,4,23,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2006,4,24,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,4,25,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2006,4,26,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2006,4,27,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2006,4,28,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2006,4,29,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2006,4,30,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2006,5,1,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2006,5,2,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2006,5,3,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2006,5,4,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2006,5,5,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2006,5,6,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2006,5,7,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2006,5,8,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2006,5,9,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2006,5,10,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2006,5,11,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2006,5,12,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2006,5,13,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2006,5,14,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2006,5,15,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2006,5,16,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2006,5,17,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2006,5,18,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2006,5,19,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2006,5,20,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2006,5,21,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2006,5,22,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2006,5,23,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,5,24,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,5,25,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2006,5,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2006,5,27,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2006,5,28,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,5,29,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2006,5,30,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,5,31,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2006,6,1,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2006,6,2,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2006,6,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2006,6,4,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2006,6,5,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2006,6,6,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2006,6,7,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2006,6,8,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2006,6,9,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2006,6,10,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2006,6,11,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2006,6,12,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2006,6,13,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2006,6,14,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2006,6,15,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2006,6,16,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2006,6,17,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2006,6,18,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2006,6,19,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2006,6,20,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2006,6,21,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2006,6,22,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2006,6,23,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2006,6,24,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2006,6,25,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2006,6,26,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2006,6,27,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2006,6,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2006,6,29,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2006,6,30,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2006,7,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2006,7,2,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2006,7,3,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2006,7,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2006,7,5,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2006,7,6,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2006,7,7,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2006,7,8,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2006,7,9,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2006,7,10,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2006,7,11,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2006,7,12,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2006,7,13,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2006,7,14,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2006,7,15,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2006,7,16,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2006,7,17,0.389,0.150715695067265,33.6096,0.68490714393843
+"FLOW_236212",2006,7,18,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",2006,7,19,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2006,7,20,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2006,7,21,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2006,7,22,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2006,7,23,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2006,7,24,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2006,7,25,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2006,7,26,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2006,7,27,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2006,7,28,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2006,7,29,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2006,7,30,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2006,7,31,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2006,8,1,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2006,8,2,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",2006,8,3,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2006,8,4,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2006,8,5,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2006,8,6,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2006,8,7,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2006,8,8,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2006,8,9,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2006,8,10,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2006,8,11,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2006,8,12,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2006,8,13,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2006,8,14,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2006,8,15,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2006,8,16,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2006,8,17,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2006,8,18,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2006,8,19,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2006,8,20,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2006,8,21,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2006,8,22,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2006,8,23,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2006,8,24,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2006,8,25,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2006,8,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2006,8,27,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2006,8,28,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,8,29,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2006,8,30,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2006,8,31,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2006,9,1,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2006,9,2,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,9,3,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2006,9,4,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,9,5,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2006,9,6,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2006,9,7,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2006,9,8,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2006,9,9,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2006,9,10,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2006,9,11,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2006,9,12,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,9,13,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2006,9,14,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2006,9,15,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2006,9,16,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2006,9,17,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,9,18,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2006,9,19,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2006,9,20,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2006,9,21,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2006,9,22,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2006,9,23,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2006,9,24,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2006,9,25,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2006,9,26,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2006,9,27,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2006,9,28,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2006,9,29,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2006,9,30,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2006,10,1,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2006,10,2,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2006,10,3,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2006,10,4,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2006,10,5,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2006,10,6,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2006,10,7,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2006,10,8,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2006,10,9,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2006,10,10,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2006,10,11,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2006,10,12,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2006,10,13,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,10,14,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2006,10,15,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2006,10,16,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2006,10,17,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2006,10,18,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2006,10,19,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2006,10,20,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2006,10,21,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2006,10,22,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2006,10,23,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,10,24,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,10,25,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2006,10,26,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2006,10,27,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2006,10,28,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2006,10,29,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2006,10,30,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,10,31,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2006,11,1,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2006,11,2,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2006,11,3,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2006,11,4,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2006,11,5,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2006,11,6,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2006,11,7,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2006,11,8,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2006,11,9,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2006,11,10,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2006,11,11,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2006,11,12,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,11,13,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2006,11,14,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2006,11,15,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2006,11,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2006,11,17,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2006,11,18,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2006,11,19,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2006,11,20,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2006,11,21,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2006,11,22,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2006,11,23,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2006,11,24,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,11,25,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2006,11,26,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2006,11,27,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2006,11,28,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2006,11,29,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2006,11,30,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2006,12,1,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2006,12,2,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2006,12,3,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2006,12,4,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2006,12,5,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2006,12,6,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2006,12,7,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2006,12,8,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2006,12,9,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2006,12,10,0.091,0.035257399103139,7.8624,0.512212360351396
+"FLOW_236212",2006,12,11,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2006,12,12,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2006,12,13,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2006,12,14,0.093,0.0360322869955157,8.0352,0.514444307058102
+"FLOW_236212",2006,12,15,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2006,12,16,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2006,12,17,0.074,0.0286708520179372,6.3936,0.491459936081186
+"FLOW_236212",2006,12,18,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2006,12,19,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2006,12,20,0.092,0.0356448430493274,7.9488,0.513333185865999
+"FLOW_236212",2006,12,21,0.093,0.0360322869955157,8.0352,0.514444307058102
+"FLOW_236212",2006,12,22,0.088,0.034095067264574,7.6032,0.50878969941032
+"FLOW_236212",2006,12,23,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2006,12,24,0.088,0.034095067264574,7.6032,0.50878969941032
+"FLOW_236212",2006,12,25,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2006,12,26,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,12,27,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2006,12,28,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2006,12,29,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2006,12,30,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2006,12,31,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2007,1,1,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2007,1,2,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2007,1,3,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2007,1,4,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2007,1,5,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,1,6,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2007,1,7,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,1,8,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2007,1,9,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2007,1,10,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2007,1,11,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2007,1,12,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2007,1,13,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2007,1,14,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2007,1,15,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2007,1,16,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,1,17,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2007,1,18,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2007,1,19,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2007,1,20,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2007,1,21,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2007,1,22,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",2007,1,23,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2007,1,24,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2007,1,25,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2007,1,26,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2007,1,27,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2007,1,28,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2007,1,29,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2007,1,30,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2007,1,31,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2007,2,1,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2007,2,2,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2007,2,3,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2007,2,4,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2007,2,5,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2007,2,6,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2007,2,7,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2007,2,8,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2007,2,9,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2007,2,10,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2007,2,11,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2007,2,12,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2007,2,13,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2007,2,14,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2007,2,15,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2007,2,16,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2007,2,17,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,2,18,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2007,2,19,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2007,2,20,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2007,2,21,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2007,2,22,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2007,2,23,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2007,2,24,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2007,2,25,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2007,2,26,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2007,2,27,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2007,2,28,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2007,3,1,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",2007,3,2,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2007,3,3,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2007,3,4,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2007,3,5,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2007,3,6,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2007,3,7,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,3,8,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2007,3,9,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2007,3,10,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2007,3,11,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2007,3,12,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2007,3,13,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2007,3,14,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2007,3,15,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2007,3,16,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2007,3,17,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2007,3,18,0.094,0.036419730941704,8.1216,0.51554591095497
+"FLOW_236212",2007,3,19,0.091,0.035257399103139,7.8624,0.512212360351396
+"FLOW_236212",2007,3,20,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",2007,3,21,0.094,0.036419730941704,8.1216,0.51554591095497
+"FLOW_236212",2007,3,22,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2007,3,23,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2007,3,24,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2007,3,25,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2007,3,26,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2007,3,27,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2007,3,28,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2007,3,29,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2007,3,30,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2007,3,31,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2007,4,1,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2007,4,2,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2007,4,3,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2007,4,4,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2007,4,5,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2007,4,6,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2007,4,7,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2007,4,8,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2007,4,9,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2007,4,10,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2007,4,11,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2007,4,12,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2007,4,13,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2007,4,14,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2007,4,15,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2007,4,16,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2007,4,17,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2007,4,18,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2007,4,19,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2007,4,20,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2007,4,21,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2007,4,22,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2007,4,23,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2007,4,24,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2007,4,25,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2007,4,26,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2007,4,27,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2007,4,28,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2007,4,29,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2007,4,30,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2007,5,1,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,5,2,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2007,5,3,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2007,5,4,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2007,5,5,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2007,5,6,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2007,5,7,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2007,5,8,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2007,5,9,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2007,5,10,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2007,5,11,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2007,5,12,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2007,5,13,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2007,5,14,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2007,5,15,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2007,5,16,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2007,5,17,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2007,5,18,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2007,5,19,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2007,5,20,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2007,5,21,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2007,5,22,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2007,5,23,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2007,5,24,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2007,5,25,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2007,5,26,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2007,5,27,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2007,5,28,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2007,5,29,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2007,5,30,0.437,0.169313004484305,37.7568,0.701032356130901
+"FLOW_236212",2007,5,31,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2007,6,1,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2007,6,2,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2007,6,3,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",2007,6,4,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2007,6,5,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2007,6,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2007,6,7,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2007,6,8,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2007,6,9,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2007,6,10,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2007,6,11,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2007,6,12,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2007,6,13,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2007,6,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2007,6,15,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2007,6,16,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2007,6,17,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2007,6,18,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2007,6,19,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2007,6,20,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2007,6,21,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",2007,6,22,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2007,6,23,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2007,6,24,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2007,6,25,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2007,6,26,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2007,6,27,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2007,6,28,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2007,6,29,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2007,6,30,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2007,7,1,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2007,7,2,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2007,7,3,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",2007,7,4,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2007,7,5,0.954,0.369621524663677,82.4256,0.819504704075818
+"FLOW_236212",2007,7,6,2.236,0.86632466367713,193.1904,0.971708799818491
+"FLOW_236212",2007,7,7,1.201,0.465320179372197,103.7664,0.858124649841692
+"FLOW_236212",2007,7,8,0.839,0.325065470852018,72.4896,0.798719280392083
+"FLOW_236212",2007,7,9,0.727,0.281671748878924,62.8128,0.776155354747128
+"FLOW_236212",2007,7,10,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",2007,7,11,0.411,0.159239461883408,35.5104,0.692484632829216
+"FLOW_236212",2007,7,12,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",2007,7,13,0.624,0.241765022421525,53.9136,0.752798411185963
+"FLOW_236212",2007,7,14,1.014,0.392868161434978,87.6096,0.829562987367691
+"FLOW_236212",2007,7,15,1.024,0.396742600896861,88.4736,0.831192789695907
+"FLOW_236212",2007,7,16,0.663,0.25687533632287,57.2832,0.761981595183807
+"FLOW_236212",2007,7,17,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",2007,7,18,3.018,1.16930582959641,260.7552,1.03177647843773
+"FLOW_236212",2007,7,19,3.098,1.20030134529148,267.6672,1.0371893735116
+"FLOW_236212",2007,7,20,1.742,0.67492735426009,150.5088,0.924381842316658
+"FLOW_236212",2007,7,21,1.05,0.406816143497758,90.72,0.835371463743786
+"FLOW_236212",2007,7,22,0.717,0.27779730941704,61.9488,0.774008280749771
+"FLOW_236212",2007,7,23,0.55,0.213094170403587,47.52,0.734030851465829
+"FLOW_236212",2007,7,24,0.455,0.176286995515695,39.312,0.706714586543881
+"FLOW_236212",2007,7,25,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",2007,7,26,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2007,7,27,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2007,7,28,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2007,7,29,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2007,7,30,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2007,7,31,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",2007,8,1,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2007,8,2,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2007,8,3,0.925,0.358385650224215,79.92,0.814460679752971
+"FLOW_236212",2007,8,4,1.941,0.75202869955157,167.7024,0.944597698227476
+"FLOW_236212",2007,8,5,3.14,1.21657399103139,271.296,1.03998650889626
+"FLOW_236212",2007,8,6,3.535,1.36961434977578,305.424,1.06492656181301
+"FLOW_236212",2007,8,7,2.258,0.874848430493274,195.0912,0.97361344679819
+"FLOW_236212",2007,8,8,1.439,0.557531838565023,124.3296,0.889721191289285
+"FLOW_236212",2007,8,9,0.914,0.354123766816144,78.9696,0.812514303491783
+"FLOW_236212",2007,8,10,0.691,0.267723766816143,59.7024,0.768311591380086
+"FLOW_236212",2007,8,11,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2007,8,12,1.498,0.580391031390134,129.4272,0.896900236554862
+"FLOW_236212",2007,8,13,1.964,0.760939910313901,169.6896,0.946825776222235
+"FLOW_236212",2007,8,14,1.513,0.58620269058296,130.7232,0.898689280504265
+"FLOW_236212",2007,8,15,1.021,0.395580269058296,88.2144,0.830705190930593
+"FLOW_236212",2007,8,16,0.706,0.273535426008969,60.9984,0.771618650200755
+"FLOW_236212",2007,8,17,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",2007,8,18,1.718,0.66562869955157,148.4352,0.921820597185685
+"FLOW_236212",2007,8,19,2.012,0.779537219730942,173.8368,0.951409253648185
+"FLOW_236212",2007,8,20,1.276,0.494378475336323,110.2464,0.868584171417039
+"FLOW_236212",2007,8,21,0.832,0.3223533632287,71.8848,0.797382024631839
+"FLOW_236212",2007,8,22,0.641,0.248351569506726,55.3824,0.756856215452741
+"FLOW_236212",2007,8,23,0.535,0.207282511210762,46.224,0.729982640289904
+"FLOW_236212",2007,8,24,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",2007,8,25,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",2007,8,26,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2007,8,27,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",2007,8,28,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",2007,8,29,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2007,8,30,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2007,8,31,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2007,9,1,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2007,9,2,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2007,9,3,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2007,9,4,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2007,9,5,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2007,9,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2007,9,7,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2007,9,8,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2007,9,9,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2007,9,10,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2007,9,11,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2007,9,12,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2007,9,13,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2007,9,14,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2007,9,15,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",2007,9,16,0.906,0.351024215246637,78.2784,0.811086952902811
+"FLOW_236212",2007,9,17,0.785,0.304143497757848,67.824,0.78816239024069
+"FLOW_236212",2007,9,18,0.911,0.352961434977578,78.7104,0.811980222637809
+"FLOW_236212",2007,9,19,0.913,0.353736322869955,78.8832,0.812336432567664
+"FLOW_236212",2007,9,20,0.598,0.231691479820628,51.6672,0.746417843138657
+"FLOW_236212",2007,9,21,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",2007,9,22,0.625,0.242152466367713,54,0.753039538208907
+"FLOW_236212",2007,9,23,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",2007,9,24,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",2007,9,25,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2007,9,26,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2007,9,27,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2007,9,28,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2007,9,29,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2007,9,30,0.512,0.198371300448431,44.2368,0.723595351277449
+"FLOW_236212",2007,10,1,1.684,0.652455605381166,145.4976,0.918142716391724
+"FLOW_236212",2007,10,2,2.161,0.837266367713005,186.7104,0.965100919204118
+"FLOW_236212",2007,10,3,1.223,0.473843946188341,105.6672,0.861245701395927
+"FLOW_236212",2007,10,4,0.709,0.274697757847534,61.2576,0.772273304974995
+"FLOW_236212",2007,10,5,0.644,0.249513901345292,55.6416,0.757563338656869
+"FLOW_236212",2007,10,6,1.085,0.42037668161435,93.744,0.840867802901649
+"FLOW_236212",2007,10,7,1.706,0.66097937219731,147.3984,0.920529225411883
+"FLOW_236212",2007,10,8,1.236,0.478880717488789,106.7904,0.863068905167582
+"FLOW_236212",2007,10,9,0.743,0.287870852017937,64.1952,0.779542035282204
+"FLOW_236212",2007,10,10,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",2007,10,11,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2007,10,12,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",2007,10,13,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2007,10,14,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2007,10,15,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2007,10,16,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2007,10,17,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2007,10,18,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2007,10,19,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2007,10,20,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2007,10,21,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2007,10,22,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2007,10,23,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2007,10,24,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2007,10,25,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2007,10,26,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,10,27,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,10,28,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2007,10,29,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2007,10,30,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,10,31,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2007,11,1,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2007,11,2,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2007,11,3,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2007,11,4,3.466,1.34288071748879,299.4624,1.06073643736729
+"FLOW_236212",2007,11,5,4.538,1.75822062780269,392.0832,1.11947570832803
+"FLOW_236212",2007,11,6,2.191,0.848889686098655,189.3024,0.967765757534136
+"FLOW_236212",2007,11,7,1.013,0.392480717488789,87.5232,0.829399300893348
+"FLOW_236212",2007,11,8,0.593,0.229754260089686,51.2352,0.745165457149857
+"FLOW_236212",2007,11,9,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",2007,11,10,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2007,11,11,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2007,11,12,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2007,11,13,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2007,11,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2007,11,15,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2007,11,16,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2007,11,17,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2007,11,18,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2007,11,19,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2007,11,20,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2007,11,21,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2007,11,22,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2007,11,23,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2007,11,24,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2007,11,25,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2007,11,26,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,11,27,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2007,11,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2007,11,29,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2007,11,30,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2007,12,1,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2007,12,2,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2007,12,3,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2007,12,4,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2007,12,5,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2007,12,6,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2007,12,7,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2007,12,8,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2007,12,9,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2007,12,10,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2007,12,11,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2007,12,12,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2007,12,13,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2007,12,14,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2007,12,15,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2007,12,16,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2007,12,17,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2007,12,18,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2007,12,19,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2007,12,20,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2007,12,21,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2007,12,22,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2007,12,23,0.649,0.251451121076233,56.0736,0.758736042939418
+"FLOW_236212",2007,12,24,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2007,12,25,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2007,12,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2007,12,27,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2007,12,28,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2007,12,29,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2007,12,30,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2007,12,31,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2008,1,1,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2008,1,2,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,1,3,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2008,1,4,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2008,1,5,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2008,1,6,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2008,1,7,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2008,1,8,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2008,1,9,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2008,1,10,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2008,1,11,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2008,1,12,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2008,1,13,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2008,1,14,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2008,1,15,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2008,1,16,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2008,1,17,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2008,1,18,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2008,1,19,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2008,1,20,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,1,21,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2008,1,22,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,1,23,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2008,1,24,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2008,1,25,0.116,0.0449434977578475,10.0224,0.537691750917933
+"FLOW_236212",2008,1,26,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2008,1,27,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2008,1,28,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2008,1,29,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2008,1,30,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2008,1,31,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2008,2,1,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2008,2,2,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2008,2,3,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2008,2,4,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2008,2,5,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,2,6,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,2,7,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2008,2,8,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2008,2,9,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2008,2,10,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2008,2,11,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2008,2,12,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2008,2,13,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,2,14,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2008,2,15,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2008,2,16,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2008,2,17,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2008,2,18,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2008,2,19,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2008,2,20,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2008,2,21,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,2,22,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,2,23,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2008,2,24,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2008,2,25,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2008,2,26,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2008,2,27,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2008,2,28,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2008,2,29,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2008,3,1,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2008,3,2,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2008,3,3,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2008,3,4,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2008,3,5,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2008,3,6,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2008,3,7,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2008,3,8,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2008,3,9,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2008,3,10,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2008,3,11,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2008,3,12,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2008,3,13,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2008,3,14,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2008,3,15,0.09,0.0348699551569507,7.776,0.51108163770667
+"FLOW_236212",2008,3,16,0.091,0.035257399103139,7.8624,0.512212360351396
+"FLOW_236212",2008,3,17,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2008,3,18,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",2008,3,19,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",2008,3,20,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2008,3,21,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2008,3,22,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2008,3,23,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2008,3,24,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2008,3,25,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2008,3,26,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2008,3,27,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,3,28,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,3,29,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2008,3,30,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,3,31,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2008,4,1,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2008,4,2,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2008,4,3,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,4,4,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2008,4,5,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2008,4,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2008,4,7,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2008,4,8,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2008,4,9,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2008,4,10,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,4,11,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,4,12,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,4,13,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2008,4,14,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2008,4,15,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2008,4,16,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2008,4,17,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2008,4,18,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2008,4,19,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2008,4,20,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,4,21,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2008,4,22,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2008,4,23,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2008,4,24,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2008,4,25,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,4,26,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2008,4,27,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2008,4,28,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2008,4,29,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2008,4,30,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2008,5,1,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,5,2,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2008,5,3,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2008,5,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2008,5,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2008,5,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,5,7,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,5,8,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,5,9,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,5,10,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,5,11,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,5,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2008,5,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2008,5,14,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2008,5,15,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2008,5,16,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2008,5,17,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2008,5,18,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2008,5,19,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2008,5,20,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2008,5,21,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2008,5,22,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2008,5,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2008,5,24,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,5,25,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,5,26,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2008,5,27,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2008,5,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2008,5,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2008,5,30,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2008,5,31,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,6,1,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,6,2,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,6,3,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,6,4,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,6,5,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2008,6,6,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2008,6,7,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2008,6,8,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2008,6,9,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2008,6,10,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,6,11,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,6,12,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2008,6,13,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2008,6,14,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2008,6,15,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2008,6,16,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2008,6,17,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2008,6,18,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,6,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2008,6,20,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2008,6,21,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,6,22,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,6,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2008,6,24,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2008,6,25,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2008,6,26,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2008,6,27,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2008,6,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2008,6,29,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,6,30,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2008,7,1,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2008,7,2,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2008,7,3,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2008,7,4,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2008,7,5,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2008,7,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,7,7,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,7,8,2.247,0.870586547085202,194.1408,0.972662988124286
+"FLOW_236212",2008,7,9,3.209,1.24330762331839,277.2576,1.04451749685959
+"FLOW_236212",2008,7,10,1.297,0.502514798206278,112.0608,0.871424510417836
+"FLOW_236212",2008,7,11,2.323,0.900032286995516,200.7072,0.979155387539898
+"FLOW_236212",2008,7,12,1.894,0.733818834080718,163.6416,0.939978176781722
+"FLOW_236212",2008,7,13,1.089,0.421926457399103,94.0896,0.841486885334306
+"FLOW_236212",2008,7,14,0.63,0.244089686098655,54.432,0.754240564311294
+"FLOW_236212",2008,7,15,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",2008,7,16,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2008,7,17,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2008,7,18,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2008,7,19,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",2008,7,20,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2008,7,21,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",2008,7,22,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",2008,7,23,0.517,0.200308520179372,44.6688,0.725003134981235
+"FLOW_236212",2008,7,24,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",2008,7,25,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",2008,7,26,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2008,7,27,0.857,0.332039461883408,74.0448,0.802117405279478
+"FLOW_236212",2008,7,28,1.588,0.615260986547085,137.2032,0.907427372303745
+"FLOW_236212",2008,7,29,1.221,0.473069058295964,105.4944,0.860963833981127
+"FLOW_236212",2008,7,30,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",2008,7,31,0.55,0.213094170403587,47.52,0.734030851465829
+"FLOW_236212",2008,8,1,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",2008,8,2,1.345,0.521112107623318,116.208,0.877781114519393
+"FLOW_236212",2008,8,3,2.814,1.09026726457399,243.1296,1.01743481521167
+"FLOW_236212",2008,8,4,2.003,0.776050224215247,173.0592,0.950556565235041
+"FLOW_236212",2008,8,5,1.101,0.426575784753363,95.1264,0.843333281339109
+"FLOW_236212",2008,8,6,0.701,0.271598206278027,60.5664,0.770522596555566
+"FLOW_236212",2008,8,7,0.555,0.215031390134529,47.952,0.735360626221547
+"FLOW_236212",2008,8,8,0.499,0.193334529147982,43.1136,0.719882945577168
+"FLOW_236212",2008,8,9,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",2008,8,10,1.172,0.454084304932735,101.2608,0.853939884183484
+"FLOW_236212",2008,8,11,3.991,1.54628878923767,344.8224,1.09108383482337
+"FLOW_236212",2008,8,12,5.106,1.97828878923767,441.1584,1.14619349131987
+"FLOW_236212",2008,8,13,3.385,1.31149775784753,292.464,1.05573157448428
+"FLOW_236212",2008,8,14,3.148,1.2196735426009,271.9872,1.04051589909617
+"FLOW_236212",2008,8,15,4.452,1.72490044843049,384.6528,1.11520011880061
+"FLOW_236212",2008,8,16,5.364,2.07824932735426,463.4496,1.15754938807788
+"FLOW_236212",2008,8,17,4.657,1.8043264573991,402.3648,1.12528627918396
+"FLOW_236212",2008,8,18,3.638,1.40952107623318,314.3232,1.07106126876106
+"FLOW_236212",2008,8,19,3.313,1.28360179372197,286.2432,1.05120172059014
+"FLOW_236212",2008,8,20,2.568,0.994956053811659,221.8752,0.998989169269447
+"FLOW_236212",2008,8,21,3.785,1.46647533632287,327.024,1.07958030405027
+"FLOW_236212",2008,8,22,6.555,2.53969506726457,566.352,1.2049147340483
+"FLOW_236212",2008,8,23,4.823,1.86864215246637,416.7072,1.13319650987756
+"FLOW_236212",2008,8,24,3.106,1.20340089686099,268.3584,1.03772449020219
+"FLOW_236212",2008,8,25,2.365,0.916304932735426,204.336,0.982670695214972
+"FLOW_236212",2008,8,26,1.757,0.680739013452915,151.8048,0.925968319347403
+"FLOW_236212",2008,8,27,1.296,0.50212735426009,111.9744,0.871290093550353
+"FLOW_236212",2008,8,28,1.043,0.404104035874439,90.1152,0.834254652979247
+"FLOW_236212",2008,8,29,0.875,0.339013452914798,75.6,0.805458903399461
+"FLOW_236212",2008,8,30,0.726,0.281284304932735,62.7264,0.775941714374881
+"FLOW_236212",2008,8,31,0.69,0.267336322869955,59.616,0.768089085813831
+"FLOW_236212",2008,9,1,1.126,0.436261883408072,97.2864,0.847128814052798
+"FLOW_236212",2008,9,2,2.64,1.02285201793722,228.096,1.00452919021852
+"FLOW_236212",2008,9,3,2.376,0.920566816143498,205.2864,0.98358311154626
+"FLOW_236212",2008,9,4,1.506,0.583490582959641,130.1184,0.897856167568092
+"FLOW_236212",2008,9,5,0.983,0.380857399103139,84.9312,0.824427518163596
+"FLOW_236212",2008,9,6,0.735,0.284771300448431,63.504,0.777856067537677
+"FLOW_236212",2008,9,7,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",2008,9,8,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",2008,9,9,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",2008,9,10,0.416,0.16117668161435,35.9424,0.694161370705451
+"FLOW_236212",2008,9,11,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",2008,9,12,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",2008,9,13,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2008,9,14,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2008,9,15,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2008,9,16,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",2008,9,17,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",2008,9,18,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",2008,9,19,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2008,9,20,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2008,9,21,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2008,9,22,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2008,9,23,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",2008,9,24,0.503,0.194884304932735,43.4592,0.721033383642924
+"FLOW_236212",2008,9,25,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2008,9,26,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",2008,9,27,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",2008,9,28,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2008,9,29,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2008,9,30,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2008,10,1,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2008,10,2,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2008,10,3,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2008,10,4,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2008,10,5,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",2008,10,6,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2008,10,7,0.45,0.174349775784753,38.88,0.705154494972067
+"FLOW_236212",2008,10,8,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2008,10,9,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2008,10,10,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",2008,10,11,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2008,10,12,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2008,10,13,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2008,10,14,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2008,10,15,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2008,10,16,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2008,10,17,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2008,10,18,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2008,10,19,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2008,10,20,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2008,10,21,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2008,10,22,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2008,10,23,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2008,10,24,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2008,10,25,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2008,10,26,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2008,10,27,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2008,10,28,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2008,10,29,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2008,10,30,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2008,10,31,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2008,11,1,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2008,11,2,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2008,11,3,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2008,11,4,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2008,11,5,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2008,11,6,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2008,11,7,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2008,11,8,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2008,11,9,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2008,11,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2008,11,11,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2008,11,12,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2008,11,13,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2008,11,14,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,11,15,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2008,11,16,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2008,11,17,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2008,11,18,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2008,11,19,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2008,11,20,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2008,11,21,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2008,11,22,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2008,11,23,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2008,11,24,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2008,11,25,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2008,11,26,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2008,11,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2008,11,28,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2008,11,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2008,11,30,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2008,12,1,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2008,12,2,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,12,3,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2008,12,4,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2008,12,5,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2008,12,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2008,12,7,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2008,12,8,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2008,12,9,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2008,12,10,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2008,12,11,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2008,12,12,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2008,12,13,0.612,0.237115695067265,52.8768,0.749880500042019
+"FLOW_236212",2008,12,14,1.245,0.482367713004484,107.568,0.86432215668092
+"FLOW_236212",2008,12,15,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",2008,12,16,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2008,12,17,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2008,12,18,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2008,12,19,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2008,12,20,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2008,12,21,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2008,12,22,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2008,12,23,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2008,12,24,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2008,12,25,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2008,12,26,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2008,12,27,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2008,12,28,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2008,12,29,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2008,12,30,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2008,12,31,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2009,1,1,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2009,1,2,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2009,1,3,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2009,1,4,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2009,1,5,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2009,1,6,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2009,1,7,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2009,1,8,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2009,1,9,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2009,1,10,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2009,1,11,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2009,1,12,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2009,1,13,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2009,1,14,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2009,1,15,0.11,0.0426188340807175,9.504,0.532010633654456
+"FLOW_236212",2009,1,16,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2009,1,17,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2009,1,18,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2009,1,19,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2009,1,20,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2009,1,21,0.098,0.0379695067264574,8.4672,0.519860704531733
+"FLOW_236212",2009,1,22,0.094,0.036419730941704,8.1216,0.51554591095497
+"FLOW_236212",2009,1,23,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,1,24,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,1,25,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2009,1,26,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2009,1,27,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2009,1,28,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,1,29,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2009,1,30,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,1,31,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2009,2,1,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,2,2,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,2,3,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,2,4,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2009,2,5,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2009,2,6,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2009,2,7,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2009,2,8,0.089,0.0344825112107623,7.6896,0.50994081909868
+"FLOW_236212",2009,2,9,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,2,10,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2009,2,11,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,2,12,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2009,2,13,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,2,14,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2009,2,15,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2009,2,16,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2009,2,17,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2009,2,18,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2009,2,19,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",2009,2,20,0.096,0.0371946188340807,8.2944,0.517721287454763
+"FLOW_236212",2009,2,21,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2009,2,22,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2009,2,23,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2009,2,24,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2009,2,25,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2009,2,26,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2009,2,27,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2009,2,28,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2009,3,1,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2009,3,2,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2009,3,3,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2009,3,4,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2009,3,5,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2009,3,6,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2009,3,7,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2009,3,8,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2009,3,9,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2009,3,10,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2009,3,11,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2009,3,12,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2009,3,13,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2009,3,14,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2009,3,15,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2009,3,16,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2009,3,17,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2009,3,18,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2009,3,19,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2009,3,20,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2009,3,21,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2009,3,22,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2009,3,23,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2009,3,24,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2009,3,25,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2009,3,26,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2009,3,27,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2009,3,28,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2009,3,29,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2009,3,30,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2009,3,31,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2009,4,1,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2009,4,2,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2009,4,3,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2009,4,4,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2009,4,5,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2009,4,6,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2009,4,7,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2009,4,8,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2009,4,9,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2009,4,10,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2009,4,11,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2009,4,12,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2009,4,13,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2009,4,14,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2009,4,15,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2009,4,16,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2009,4,17,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2009,4,18,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2009,4,19,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2009,4,20,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2009,4,21,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2009,4,22,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2009,4,23,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2009,4,24,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2009,4,25,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2009,4,26,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2009,4,27,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2009,4,28,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2009,4,29,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2009,4,30,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2009,5,1,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2009,5,2,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2009,5,3,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,5,4,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,5,5,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,5,6,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,5,7,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2009,5,8,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2009,5,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2009,5,10,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2009,5,11,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2009,5,12,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2009,5,13,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2009,5,14,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2009,5,15,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2009,5,16,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2009,5,17,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2009,5,18,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2009,5,19,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2009,5,20,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2009,5,21,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2009,5,22,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2009,5,23,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2009,5,24,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2009,5,25,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2009,5,26,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2009,5,27,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2009,5,28,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2009,5,29,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2009,5,30,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2009,5,31,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2009,6,1,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2009,6,2,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2009,6,3,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2009,6,4,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2009,6,5,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2009,6,6,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2009,6,7,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2009,6,8,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",2009,6,9,0.962,0.372721076233184,83.1168,0.8208745482543
+"FLOW_236212",2009,6,10,0.797,0.308792825112108,68.8608,0.790557458129397
+"FLOW_236212",2009,6,11,0.56,0.216968609865471,48.384,0.736680851288836
+"FLOW_236212",2009,6,12,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",2009,6,13,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2009,6,14,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2009,6,15,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2009,6,16,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2009,6,17,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",2009,6,18,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2009,6,19,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2009,6,20,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2009,6,21,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2009,6,22,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2009,6,23,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2009,6,24,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2009,6,25,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2009,6,26,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2009,6,27,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2009,6,28,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2009,6,29,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2009,6,30,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2009,7,1,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2009,7,2,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",2009,7,3,1.745,0.676089686098655,150.768,0.924700009616326
+"FLOW_236212",2009,7,4,1.931,0.748154260089686,166.8384,0.943622375859525
+"FLOW_236212",2009,7,5,1.528,0.592014349775785,132.0192,0.900464190875174
+"FLOW_236212",2009,7,6,1.071,0.414952466367713,92.5344,0.838686534070795
+"FLOW_236212",2009,7,7,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",2009,7,8,0.624,0.241765022421525,53.9136,0.752798411185963
+"FLOW_236212",2009,7,9,0.468,0.181323766816144,40.4352,0.710707578523312
+"FLOW_236212",2009,7,10,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2009,7,11,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2009,7,12,0.388,0.150328251121076,33.5232,0.684554643934892
+"FLOW_236212",2009,7,13,1.296,0.50212735426009,111.9744,0.871290093550353
+"FLOW_236212",2009,7,14,1.774,0.687325560538117,153.2736,0.927753281168916
+"FLOW_236212",2009,7,15,1.225,0.474618834080718,105.84,0.8615272002962
+"FLOW_236212",2009,7,16,0.928,0.35954798206278,80.1792,0.814988294542294
+"FLOW_236212",2009,7,17,0.733,0.283996412556054,63.3312,0.777432282994267
+"FLOW_236212",2009,7,18,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",2009,7,19,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2009,7,20,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",2009,7,21,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",2009,7,22,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",2009,7,23,0.576,0.223167713004484,49.7664,0.740843154977772
+"FLOW_236212",2009,7,24,1.198,0.464157847533632,103.5072,0.857695515779771
+"FLOW_236212",2009,7,25,1.216,0.471131838565022,105.0624,0.860257546045723
+"FLOW_236212",2009,7,26,0.803,0.311117488789238,69.3792,0.791744189705586
+"FLOW_236212",2009,7,27,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",2009,7,28,0.706,0.273535426008969,60.9984,0.771618650200755
+"FLOW_236212",2009,7,29,0.741,0.287095964125561,64.0224,0.779121909985385
+"FLOW_236212",2009,7,30,0.748,0.289808071748879,64.6272,0.780588404198245
+"FLOW_236212",2009,7,31,2.015,0.780699551569507,174.096,0.951692805035818
+"FLOW_236212",2009,8,1,3.296,1.27701524663677,284.7744,1.05012069300153
+"FLOW_236212",2009,8,2,2.192,0.849277130044843,189.3888,0.967854081507272
+"FLOW_236212",2009,8,3,1.535,0.594726457399103,132.624,0.901287715813186
+"FLOW_236212",2009,8,4,1.183,0.458346188340807,102.2112,0.855536858744601
+"FLOW_236212",2009,8,5,0.953,0.369234080717489,82.3392,0.819332828074103
+"FLOW_236212",2009,8,6,0.727,0.281671748878924,62.8128,0.776155354747128
+"FLOW_236212",2009,8,7,0.612,0.237115695067265,52.8768,0.749880500042019
+"FLOW_236212",2009,8,8,0.573,0.222005381165919,49.5072,0.740069830582908
+"FLOW_236212",2009,8,9,0.571,0.221230493273543,49.3344,0.739552479618804
+"FLOW_236212",2009,8,10,0.539,0.208832286995516,46.5696,0.731070953197764
+"FLOW_236212",2009,8,11,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",2009,8,12,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2009,8,13,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",2009,8,14,0.56,0.216968609865471,48.384,0.736680851288836
+"FLOW_236212",2009,8,15,0.685,0.265399103139013,59.184,0.766972672538289
+"FLOW_236212",2009,8,16,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",2009,8,17,2.454,0.950787443946188,212.0256,0.989957813020296
+"FLOW_236212",2009,8,18,6.217,2.40873901345291,537.1488,1.19222424497323
+"FLOW_236212",2009,8,19,2.908,1.1266869955157,251.2512,1.02414313057758
+"FLOW_236212",2009,8,20,1.613,0.624947085201794,139.3632,0.910266687425038
+"FLOW_236212",2009,8,21,1.223,0.473843946188341,105.6672,0.861245701395927
+"FLOW_236212",2009,8,22,2.56,0.991856502242153,221.184,0.998365969052944
+"FLOW_236212",2009,8,23,2.443,0.946525560538117,211.0752,0.989068724718693
+"FLOW_236212",2009,8,24,2.288,0.886471748878924,197.6832,0.976186910322096
+"FLOW_236212",2009,8,25,7.689,2.97905650224215,664.3296,1.24398673127194
+"FLOW_236212",2009,8,26,14.392,5.5760932735426,1243.4688,1.41015225411119
+"FLOW_236212",2009,8,27,9.629,3.73069775784753,831.9456,1.3012418224171
+"FLOW_236212",2009,8,28,5.447,2.11040717488789,470.6208,1.16110969394763
+"FLOW_236212",2009,8,29,5.964,2.31071569506726,515.2896,1.18235884023574
+"FLOW_236212",2009,8,30,14.135,5.4765201793722,1221.264,1.40507963247692
+"FLOW_236212",2009,8,31,13.738,5.32270493273543,1186.9632,1.3970967368728
+"FLOW_236212",2009,9,1,7.652,2.96472107623318,661.1328,1.24278719008998
+"FLOW_236212",2009,9,2,3.351,1.29832466367713,289.5264,1.05360218219127
+"FLOW_236212",2009,9,3,2.172,0.841528251121076,187.6608,0.966081443081125
+"FLOW_236212",2009,9,4,1.547,0.599375784753363,133.6608,0.902692509305691
+"FLOW_236212",2009,9,5,1.187,0.459895964125561,102.5568,0.856114631926341
+"FLOW_236212",2009,9,6,0.963,0.373108520179372,83.2032,0.821045137327274
+"FLOW_236212",2009,9,7,0.955,0.370008968609865,82.512,0.819676436007093
+"FLOW_236212",2009,9,8,1.751,0.678414349775785,151.2864,0.925335033685116
+"FLOW_236212",2009,9,9,2.966,1.14915874439462,256.2624,1.02819622545785
+"FLOW_236212",2009,9,10,2.108,0.816731838565022,182.1312,0.960319830730109
+"FLOW_236212",2009,9,11,1.419,0.549782959641256,122.6016,0.887234160919858
+"FLOW_236212",2009,9,12,0.978,0.378920179372197,84.4992,0.823587121392559
+"FLOW_236212",2009,9,13,0.742,0.287483408071749,64.1088,0.779332085875151
+"FLOW_236212",2009,9,14,0.667,0.258425112107623,57.6288,0.762898819140526
+"FLOW_236212",2009,9,15,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",2009,9,16,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",2009,9,17,1.107,0.428900448430493,95.6448,0.844250448576939
+"FLOW_236212",2009,9,18,5.011,1.94148161434978,432.9504,1.14189627551655
+"FLOW_236212",2009,9,19,3.256,1.26151748878924,281.3184,1.04755939250756
+"FLOW_236212",2009,9,20,1.896,0.734593721973094,163.8144,0.940176610022287
+"FLOW_236212",2009,9,21,1.233,0.477718385650224,106.5312,0.862649532323646
+"FLOW_236212",2009,9,22,1.062,0.411465470852018,91.7568,0.83727221483274
+"FLOW_236212",2009,9,23,1.156,0.447885201793722,99.8784,0.851595470251863
+"FLOW_236212",2009,9,24,1.111,0.430450224215247,95.9904,0.844859686410279
+"FLOW_236212",2009,9,25,1.084,0.419989237668161,93.6576,0.840712747054315
+"FLOW_236212",2009,9,26,3.425,1.32699551569507,295.92,1.05821494583126
+"FLOW_236212",2009,9,27,8.196,3.17549058295964,708.1344,1.25997568977174
+"FLOW_236212",2009,9,28,5.093,1.97325201793722,440.0352,1.1456092487253
+"FLOW_236212",2009,9,29,2.59,1.0034798206278,223.776,1.00069499741113
+"FLOW_236212",2009,9,30,1.698,0.657879820627803,146.7072,0.919664268069476
+"FLOW_236212",2009,10,1,1.13,0.437811659192825,97.632,0.847729828360393
+"FLOW_236212",2009,10,2,0.799,0.309567713004484,69.0336,0.790953827071735
+"FLOW_236212",2009,10,3,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",2009,10,4,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2009,10,5,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2009,10,6,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",2009,10,7,0.434,0.16815067264574,37.4976,0.700067186306734
+"FLOW_236212",2009,10,8,0.434,0.16815067264574,37.4976,0.700067186306734
+"FLOW_236212",2009,10,9,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",2009,10,10,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2009,10,11,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2009,10,12,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2009,10,13,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2009,10,14,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",2009,10,15,0.678,0.262686995515695,58.5792,0.765398690135488
+"FLOW_236212",2009,10,16,0.915,0.354511210762332,79.056,0.812692018798262
+"FLOW_236212",2009,10,17,1.182,0.457958744394619,102.1248,0.855392171291923
+"FLOW_236212",2009,10,18,1.145,0.44362331838565,98.928,0.849968582684275
+"FLOW_236212",2009,10,19,0.801,0.310342600896861,69.2064,0.79134940307706
+"FLOW_236212",2009,10,20,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2009,10,21,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",2009,10,22,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",2009,10,23,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2009,10,24,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2009,10,25,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2009,10,26,0.307,0.118945291479821,26.5248,0.653235007543919
+"FLOW_236212",2009,10,27,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2009,10,28,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2009,10,29,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2009,10,30,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",2009,10,31,1.278,0.4951533632287,110.4192,0.868856284306101
+"FLOW_236212",2009,11,1,1.758,0.681126457399103,151.8912,0.92607369869682
+"FLOW_236212",2009,11,2,0.767,0.297169506726457,66.2688,0.78451427187624
+"FLOW_236212",2009,11,3,0.481,0.186360538116592,41.5584,0.714612800378232
+"FLOW_236212",2009,11,4,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",2009,11,5,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",2009,11,6,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",2009,11,7,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2009,11,8,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2009,11,9,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2009,11,10,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2009,11,11,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2009,11,12,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2009,11,13,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2009,11,14,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2009,11,15,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2009,11,16,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2009,11,17,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2009,11,18,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2009,11,19,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2009,11,20,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2009,11,21,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2009,11,22,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2009,11,23,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2009,11,24,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2009,11,25,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2009,11,26,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2009,11,27,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2009,11,28,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2009,11,29,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2009,11,30,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2009,12,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2009,12,2,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2009,12,3,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2009,12,4,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2009,12,5,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2009,12,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2009,12,7,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2009,12,8,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2009,12,9,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2009,12,10,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2009,12,11,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2009,12,12,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2009,12,13,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2009,12,14,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2009,12,15,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2009,12,16,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2009,12,17,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2009,12,18,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2009,12,19,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",2009,12,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2009,12,21,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2009,12,22,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2009,12,23,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2009,12,24,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2009,12,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2009,12,26,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2009,12,27,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,12,28,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2009,12,29,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2009,12,30,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2009,12,31,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2010,1,1,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2010,1,2,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2010,1,3,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2010,1,4,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2010,1,5,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2010,1,6,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2010,1,7,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2010,1,8,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2010,1,9,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2010,1,10,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2010,1,11,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2010,1,12,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2010,1,13,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2010,1,14,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2010,1,15,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2010,1,16,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2010,1,17,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2010,1,18,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2010,1,19,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2010,1,20,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2010,1,21,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2010,1,22,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2010,1,23,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2010,1,24,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2010,1,25,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2010,1,26,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2010,1,27,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2010,1,28,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2010,1,29,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2010,1,30,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2010,1,31,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2010,2,1,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2010,2,2,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2010,2,3,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2010,2,4,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2010,2,5,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2010,2,6,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2010,2,7,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2010,2,8,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2010,2,9,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2010,2,10,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2010,2,11,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2010,2,12,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2010,2,13,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2010,2,14,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,2,15,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2010,2,16,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2010,2,17,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2010,2,18,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2010,2,19,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2010,2,20,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2010,2,21,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2010,2,22,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2010,2,23,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2010,2,24,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2010,2,25,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2010,2,26,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2010,2,27,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2010,2,28,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2010,3,1,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2010,3,2,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2010,3,3,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2010,3,4,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2010,3,5,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2010,3,6,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2010,3,7,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,3,8,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,3,9,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2010,3,10,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2010,3,11,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2010,3,12,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2010,3,13,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2010,3,14,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2010,3,15,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2010,3,16,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2010,3,17,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2010,3,18,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2010,3,19,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2010,3,20,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2010,3,21,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2010,3,22,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2010,3,23,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2010,3,24,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2010,3,25,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2010,3,26,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2010,3,27,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2010,3,28,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2010,3,29,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2010,3,30,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2010,3,31,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2010,4,1,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2010,4,2,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2010,4,3,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2010,4,4,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2010,4,5,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2010,4,6,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2010,4,7,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2010,4,8,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2010,4,9,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,4,10,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2010,4,11,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2010,4,12,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2010,4,13,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2010,4,14,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2010,4,15,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2010,4,16,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2010,4,17,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,4,18,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2010,4,19,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2010,4,20,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2010,4,21,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2010,4,22,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2010,4,23,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2010,4,24,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2010,4,25,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2010,4,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2010,4,27,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2010,4,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2010,4,29,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2010,4,30,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2010,5,1,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2010,5,2,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2010,5,3,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2010,5,4,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2010,5,5,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2010,5,6,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2010,5,7,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2010,5,8,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2010,5,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2010,5,10,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2010,5,11,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2010,5,12,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2010,5,13,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2010,5,14,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2010,5,15,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2010,5,16,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2010,5,17,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2010,5,18,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2010,5,19,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2010,5,20,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2010,5,21,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2010,5,22,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2010,5,23,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2010,5,24,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2010,5,25,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2010,5,26,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2010,5,27,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2010,5,28,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2010,5,29,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2010,5,30,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2010,5,31,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2010,6,1,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2010,6,2,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2010,6,3,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2010,6,4,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2010,6,5,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2010,6,6,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2010,6,7,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2010,6,8,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2010,6,9,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2010,6,10,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2010,6,11,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2010,6,12,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2010,6,13,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2010,6,14,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2010,6,15,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2010,6,16,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2010,6,17,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2010,6,18,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2010,6,19,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2010,6,20,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2010,6,21,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2010,6,22,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2010,6,23,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2010,6,24,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2010,6,25,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2010,6,26,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2010,6,27,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2010,6,28,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2010,6,29,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2010,6,30,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2010,7,1,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2010,7,2,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2010,7,3,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",2010,7,4,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2010,7,5,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2010,7,6,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2010,7,7,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2010,7,8,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2010,7,9,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2010,7,10,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2010,7,11,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2010,7,12,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2010,7,13,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2010,7,14,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2010,7,15,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",2010,7,16,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2010,7,17,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2010,7,18,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2010,7,19,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2010,7,20,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2010,7,21,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2010,7,22,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2010,7,23,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2010,7,24,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2010,7,25,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2010,7,26,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2010,7,27,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2010,7,28,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2010,7,29,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2010,7,30,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2010,7,31,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2010,8,1,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",2010,8,2,4.301,1.66639641255605,371.6064,1.10753042199548
+"FLOW_236212",2010,8,3,5.009,1.9407067264574,432.7776,1.14180510979143
+"FLOW_236212",2010,8,4,2.171,0.841140807174888,187.5744,0.965992468918345
+"FLOW_236212",2010,8,5,2.366,0.916692376681614,204.4224,0.982753782279254
+"FLOW_236212",2010,8,6,3.261,1.26345470852018,281.7504,1.04788092706553
+"FLOW_236212",2010,8,7,2.795,1.08290582959641,241.488,1.01605715469121
+"FLOW_236212",2010,8,8,1.75,0.678026905829596,151.2,0.925229317352677
+"FLOW_236212",2010,8,9,1.065,0.412627802690583,92.016,0.837744716383519
+"FLOW_236212",2010,8,10,0.748,0.289808071748879,64.6272,0.780588404198245
+"FLOW_236212",2010,8,11,15.221,5.89728430493274,1315.0944,1.42603574960842
+"FLOW_236212",2010,8,12,38.824,15.0421237668161,3354.3936,1.71973619367296
+"FLOW_236212",2010,8,13,21.319,8.25991748878924,1841.9616,1.52544004059522
+"FLOW_236212",2010,8,14,12.875,4.98834080717489,1112.4,1.37908559907414
+"FLOW_236212",2010,8,15,5.964,2.31071569506726,515.2896,1.18235884023574
+"FLOW_236212",2010,8,16,4.079,1.58038385650224,352.4256,1.09585354518072
+"FLOW_236212",2010,8,17,5.501,2.13132914798206,475.2864,1.16340280048647
+"FLOW_236212",2010,8,18,3.169,1.22780986547085,273.8016,1.04190044549125
+"FLOW_236212",2010,8,19,2.261,0.876010762331839,195.3504,0.97387201981097
+"FLOW_236212",2010,8,20,2.035,0.788448430493274,175.824,0.953574565271504
+"FLOW_236212",2010,8,21,2.146,0.831454708520179,185.4144,0.96375738603124
+"FLOW_236212",2010,8,22,2.903,1.12474977578475,250.8192,1.02379070684283
+"FLOW_236212",2010,8,23,2.356,0.912817937219731,203.5584,0.981921644097978
+"FLOW_236212",2010,8,24,1.699,0.658267264573991,146.7936,0.919772565795992
+"FLOW_236212",2010,8,25,2.423,0.93877668161435,209.3472,0.987443962390711
+"FLOW_236212",2010,8,26,9.926,3.84576860986547,857.6064,1.30917176434052
+"FLOW_236212",2010,8,27,14.4,5.57919282511211,1244.16,1.41030898994413
+"FLOW_236212",2010,8,28,8.718,3.37773632286996,753.2352,1.27563124681629
+"FLOW_236212",2010,8,29,3.913,1.51606816143498,338.0832,1.08678527498741
+"FLOW_236212",2010,8,30,2.49,0.964735426008969,215.136,0.992845439566863
+"FLOW_236212",2010,8,31,1.841,0.713284304932735,159.0624,0.934657582513437
+"FLOW_236212",2010,9,1,2.536,0.982557847533632,219.1104,0.996486973353107
+"FLOW_236212",2010,9,2,5.691,2.20494349775785,491.7024,1.17133061080336
+"FLOW_236212",2010,9,3,2.981,1.15497040358744,257.5584,1.02923411070657
+"FLOW_236212",2010,9,4,3.384,1.31111031390135,292.3776,1.05566919006179
+"FLOW_236212",2010,9,5,13.312,5.15765381165919,1150.1568,1.3883227414109
+"FLOW_236212",2010,9,6,15.746,6.10069237668161,1360.4544,1.43574008339538
+"FLOW_236212",2010,9,7,15.644,6.0611730941704,1351.6416,1.43387514715765
+"FLOW_236212",2010,9,8,8.655,3.35332735426009,747.792,1.27378224289382
+"FLOW_236212",2010,9,9,3.887,1.50599461883408,335.8368,1.08533718819652
+"FLOW_236212",2010,9,10,4.882,1.89150134529148,421.8048,1.13595553211079
+"FLOW_236212",2010,9,11,4.808,1.86283049327354,415.4112,1.13249076103644
+"FLOW_236212",2010,9,12,2.798,1.08406816143498,241.7472,1.016275177132
+"FLOW_236212",2010,9,13,2.079,0.805495964125561,179.6256,0.957662923457711
+"FLOW_236212",2010,9,14,1.705,0.660591928251121,147.312,0.920421283426572
+"FLOW_236212",2010,9,15,1.354,0.524599103139014,116.9856,0.878952708492318
+"FLOW_236212",2010,9,16,1.183,0.458346188340807,102.2112,0.855536858744601
+"FLOW_236212",2010,9,17,1.066,0.413015246636771,92.1024,0.837901980279787
+"FLOW_236212",2010,9,18,0.907,0.351411659192825,78.3648,0.811265921797753
+"FLOW_236212",2010,9,19,0.801,0.310342600896861,69.2064,0.79134940307706
+"FLOW_236212",2010,9,20,0.78,0.302206278026906,67.392,0.787155793863912
+"FLOW_236212",2010,9,21,0.824,0.319253811659193,71.1936,0.7958426656089
+"FLOW_236212",2010,9,22,0.829,0.321191031390135,71.6256,0.796806158341485
+"FLOW_236212",2010,9,23,0.768,0.297556950672646,66.3552,0.784718732230276
+"FLOW_236212",2010,9,24,0.688,0.266561434977578,59.4432,0.767643299624964
+"FLOW_236212",2010,9,25,0.622,0.240990134529148,53.7408,0.752315228238822
+"FLOW_236212",2010,9,26,0.574,0.222392825112108,49.5936,0.740327964524528
+"FLOW_236212",2010,9,27,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2010,9,28,0.536,0.207669955156951,46.3104,0.73025532719464
+"FLOW_236212",2010,9,29,0.547,0.211931838565022,47.2608,0.73322833768245
+"FLOW_236212",2010,9,30,0.524,0.203020627802691,45.2736,0.726955845822797
+"FLOW_236212",2010,10,1,0.505,0.195659192825112,43.632,0.721605860254545
+"FLOW_236212",2010,10,2,0.483,0.187135426008969,41.7312,0.715206087040821
+"FLOW_236212",2010,10,3,0.446,0.1728,38.5344,0.703896405787105
+"FLOW_236212",2010,10,4,0.416,0.16117668161435,35.9424,0.694161370705451
+"FLOW_236212",2010,10,5,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2010,10,6,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",2010,10,7,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2010,10,8,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2010,10,9,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",2010,10,10,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",2010,10,11,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2010,10,12,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2010,10,13,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2010,10,14,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2010,10,15,0.857,0.332039461883408,74.0448,0.802117405279478
+"FLOW_236212",2010,10,16,3.214,1.24524484304933,277.6896,1.04484279044397
+"FLOW_236212",2010,10,17,2.264,0.877173094170404,195.6096,0.974130318499895
+"FLOW_236212",2010,10,18,1.674,0.648581165919282,144.6336,0.917049686248389
+"FLOW_236212",2010,10,19,1.236,0.478880717488789,106.7904,0.863068905167582
+"FLOW_236212",2010,10,20,0.872,0.337851121076233,75.3408,0.804905829700763
+"FLOW_236212",2010,10,21,0.643,0.249126457399103,55.5552,0.757327924273715
+"FLOW_236212",2010,10,22,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",2010,10,23,0.444,0.172025112107623,38.3616,0.703263973134143
+"FLOW_236212",2010,10,24,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2010,10,25,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2010,10,26,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2010,10,27,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",2010,10,28,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2010,10,29,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2010,10,30,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",2010,10,31,1.3,0.503677130044843,112.32,0.871827264176557
+"FLOW_236212",2010,11,1,4.889,1.8942134529148,422.4096,1.13628110081497
+"FLOW_236212",2010,11,2,3.061,1.18596591928251,264.4704,1.03469998265208
+"FLOW_236212",2010,11,3,2.039,0.789998206278027,176.1696,0.953949140488823
+"FLOW_236212",2010,11,4,1.445,0.559856502242153,124.848,0.890461906502913
+"FLOW_236212",2010,11,5,1.019,0.394805381165919,88.0416,0.830379487952203
+"FLOW_236212",2010,11,6,0.734,0.284383856502242,63.4176,0.777644290738638
+"FLOW_236212",2010,11,7,0.559,0.216581165919283,48.2976,0.736417562854411
+"FLOW_236212",2010,11,8,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",2010,11,9,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",2010,11,10,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",2010,11,11,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",2010,11,12,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2010,11,13,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2010,11,14,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2010,11,15,0.463,0.179386547085202,40.0032,0.709182440902584
+"FLOW_236212",2010,11,16,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2010,11,17,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2010,11,18,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",2010,11,19,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2010,11,20,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2010,11,21,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2010,11,22,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2010,11,23,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2010,11,24,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2010,11,25,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",2010,11,26,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",2010,11,27,0.39,0.151103139013453,33.696,0.685258919750036
+"FLOW_236212",2010,11,28,0.418,0.161951569506726,36.1152,0.694827553669708
+"FLOW_236212",2010,11,29,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2010,11,30,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",2010,12,1,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2010,12,2,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2010,12,3,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2010,12,4,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",2010,12,5,0.981,0.380082511210762,84.7584,0.824091770742832
+"FLOW_236212",2010,12,6,0.584,0.226267264573991,50.4576,0.742889714222194
+"FLOW_236212",2010,12,7,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",2010,12,8,32.023,12.4071174887892,2766.7872,1.65475681721605
+"FLOW_236212",2010,12,9,18.664,7.23125381165919,1612.5696,1.48539761082625
+"FLOW_236212",2010,12,10,8.909,3.45173811659193,769.7376,1.28117238078362
+"FLOW_236212",2010,12,11,3.822,1.48081076233184,330.2208,1.0816827712301
+"FLOW_236212",2010,12,12,2.534,0.981782959641256,218.9376,0.996329749146264
+"FLOW_236212",2010,12,13,2.717,1.05268520179372,234.7488,1.01032175250567
+"FLOW_236212",2010,12,14,1.959,0.75900269058296,169.2576,0.946343194026375
+"FLOW_236212",2010,12,15,1.298,0.502902242152466,112.1472,0.871558844401469
+"FLOW_236212",2010,12,16,0.887,0.343662780269058,76.6368,0.807656141521427
+"FLOW_236212",2010,12,17,0.672,0.260362331838565,58.0608,0.764039181211917
+"FLOW_236212",2010,12,18,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",2010,12,19,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",2010,12,20,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",2010,12,21,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",2010,12,22,0.476,0.18442331838565,41.1264,0.713120902599212
+"FLOW_236212",2010,12,23,0.425,0.164663677130045,36.72,0.697139293391625
+"FLOW_236212",2010,12,24,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2010,12,25,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2010,12,26,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",2010,12,27,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2010,12,28,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2010,12,29,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",2010,12,30,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2010,12,31,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2011,1,1,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2011,1,2,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2011,1,3,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2011,1,4,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2011,1,5,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2011,1,6,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2011,1,7,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2011,1,8,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2011,1,9,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2011,1,10,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2011,1,11,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2011,1,12,0.91,0.35257399103139,78.624,0.811801883015366
+"FLOW_236212",2011,1,13,1.274,0.493603587443946,110.0736,0.868311717106384
+"FLOW_236212",2011,1,14,5.123,1.98487533632287,442.6272,1.14695570797534
+"FLOW_236212",2011,1,15,10.22,3.95967713004484,883.008,1.31683680097164
+"FLOW_236212",2011,1,16,6.218,2.4091264573991,537.2352,1.19226259618909
+"FLOW_236212",2011,1,17,39.359,15.2494062780269,3400.6176,1.72444992218985
+"FLOW_236212",2011,1,18,13.083,5.06892914798206,1130.3712,1.38351299960815
+"FLOW_236212",2011,1,19,1.52,0.588914798206278,131.328,0.899519315560516
+"FLOW_236212",2011,1,20,0.619,0.239827802690583,53.4816,0.751588118089578
+"FLOW_236212",2011,1,21,0.484,0.187522869955157,41.8176,0.715501993673772
+"FLOW_236212",2011,1,22,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",2011,1,23,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",2011,1,24,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2011,1,25,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",2011,1,26,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2011,1,27,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2011,1,28,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2011,1,29,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2011,1,30,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2011,1,31,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2011,2,1,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2011,2,2,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2011,2,3,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2011,2,4,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2011,2,5,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2011,2,6,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2011,2,7,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2011,2,8,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2011,2,9,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2011,2,10,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2011,2,11,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2011,2,12,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2011,2,13,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2011,2,14,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2011,2,15,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2011,2,16,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2011,2,17,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2011,2,18,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2011,2,19,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",2011,2,20,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",2011,2,21,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2011,2,22,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2011,2,23,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2011,2,24,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2011,2,25,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2011,2,26,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2011,2,27,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2011,2,28,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2011,3,1,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2011,3,2,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2011,3,3,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2011,3,4,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2011,3,5,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2011,3,6,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2011,3,7,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2011,3,8,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2011,3,9,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2011,3,10,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",2011,3,11,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2011,3,12,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2011,3,13,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2011,3,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2011,3,15,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2011,3,16,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2011,3,17,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2011,3,18,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2011,3,19,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2011,3,20,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2011,3,21,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2011,3,22,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",2011,3,23,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",2011,3,24,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",2011,3,25,0.538,0.208444843049327,46.4832,0.730799482289912
+"FLOW_236212",2011,3,26,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",2011,3,27,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2011,3,28,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",2011,3,29,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",2011,3,30,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2011,3,31,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2011,4,1,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2011,4,2,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2011,4,3,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2011,4,4,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2011,4,5,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2011,4,6,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2011,4,7,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2011,4,8,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2011,4,9,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2011,4,10,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2011,4,11,1.363,0.528086098654709,117.7632,0.880118088894593
+"FLOW_236212",2011,4,12,0.961,0.372333632286996,83.0304,0.820703817260273
+"FLOW_236212",2011,4,13,0.726,0.281284304932735,62.7264,0.775941714374881
+"FLOW_236212",2011,4,14,0.732,0.283608968609866,63.2448,0.77722004373713
+"FLOW_236212",2011,4,15,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",2011,4,16,0.523,0.202633183856502,45.1872,0.726678169712029
+"FLOW_236212",2011,4,17,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",2011,4,18,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",2011,4,19,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2011,4,20,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",2011,4,21,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2011,4,22,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2011,4,23,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2011,4,24,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",2011,4,25,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2011,4,26,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2011,4,27,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",2011,4,28,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",2011,4,29,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2011,4,30,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2011,5,1,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2011,5,2,0.392,0.15187802690583,33.8688,0.685960312129592
+"FLOW_236212",2011,5,3,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",2011,5,4,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2011,5,5,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",2011,5,6,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2011,5,7,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2011,5,8,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2011,5,9,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2011,5,10,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2011,5,11,0.367,0.142191928251121,31.7088,0.676978675346536
+"FLOW_236212",2011,5,12,0.529,0.204957847533632,45.7056,0.728337901138057
+"FLOW_236212",2011,5,13,1.036,0.401391928251121,89.5104,0.833131829725643
+"FLOW_236212",2011,5,14,1.589,0.615648430493274,137.2896,0.907541629090584
+"FLOW_236212",2011,5,15,1.408,0.545521076233184,121.6512,0.88585431884184
+"FLOW_236212",2011,5,16,0.998,0.386669058295964,86.2272,0.826928355374913
+"FLOW_236212",2011,5,17,0.73,0.282834080717489,63.072,0.776794868403954
+"FLOW_236212",2011,5,18,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",2011,5,19,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",2011,5,20,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",2011,5,21,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2011,5,22,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2011,5,23,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",2011,5,24,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2011,5,25,0.733,0.283996412556054,63.3312,0.777432282994267
+"FLOW_236212",2011,5,26,0.911,0.352961434977578,78.7104,0.811980222637809
+"FLOW_236212",2011,5,27,1.385,0.536609865470852,119.664,0.882941094541318
+"FLOW_236212",2011,5,28,1.473,0.570704932735426,127.2672,0.893886390703514
+"FLOW_236212",2011,5,29,1.441,0.558306726457399,124.5024,0.889968370433224
+"FLOW_236212",2011,5,30,1.06,0.410690582959641,91.584,0.836956620240658
+"FLOW_236212",2011,5,31,0.8,0.309955156950673,69.12,0.791151713968427
+"FLOW_236212",2011,6,1,0.651,0.25222600896861,56.2464,0.759203101491453
+"FLOW_236212",2011,6,2,0.568,0.220068161434978,49.0752,0.738773728351126
+"FLOW_236212",2011,6,3,0.514,0.199146188340807,44.4096,0.724159778915107
+"FLOW_236212",2011,6,4,0.477,0.184810762331839,41.2128,0.713420281757027
+"FLOW_236212",2011,6,5,0.458,0.17744932735426,39.5712,0.707644069704618
+"FLOW_236212",2011,6,6,0.625,0.242152466367713,54,0.753039538208907
+"FLOW_236212",2011,6,7,1.085,0.42037668161435,93.744,0.840867802901649
+"FLOW_236212",2011,6,8,2.052,0.795034977578475,177.2928,0.955162463933159
+"FLOW_236212",2011,6,9,2.346,0.908943497757848,202.6944,0.98108667550553
+"FLOW_236212",2011,6,10,2.149,0.832617040358745,185.6736,0.964026692347331
+"FLOW_236212",2011,6,11,1.622,0.628434080717489,140.1408,0.911280224538602
+"FLOW_236212",2011,6,12,1.158,0.448660089686099,100.0512,0.851889936252506
+"FLOW_236212",2011,6,13,0.878,0.340175784753363,75.8592,0.806010462170424
+"FLOW_236212",2011,6,14,0.724,0.280509417040359,62.5536,0.77551372640652
+"FLOW_236212",2011,6,15,0.629,0.243702242152466,54.3456,0.754000970691041
+"FLOW_236212",2011,6,16,0.57,0.220843049327354,49.248,0.739293260319341
+"FLOW_236212",2011,6,17,0.546,0.211544394618834,47.1744,0.732960050622791
+"FLOW_236212",2011,6,18,0.613,0.237503139013453,52.9632,0.750125399016538
+"FLOW_236212",2011,6,19,0.992,0.384344394618834,85.7088,0.82593165297296
+"FLOW_236212",2011,6,20,2.361,0.914755156950673,203.9904,0.982338065640481
+"FLOW_236212",2011,6,21,13.806,5.34905112107623,1192.8384,1.398477069356
+"FLOW_236212",2011,6,22,27.233,10.5512609865471,2352.9312,1.60199395545391
+"FLOW_236212",2011,6,23,20.022,7.75740269058296,1729.9008,1.50641026812232
+"FLOW_236212",2011,6,24,13.137,5.08985112107623,1135.0368,1.38465320691587
+"FLOW_236212",2011,6,25,6.977,2.70319641255605,602.8128,1.22004407587797
+"FLOW_236212",2011,6,26,3.107,1.20378834080718,268.4448,1.03779130223533
+"FLOW_236212",2011,6,27,2.136,0.827580269058296,184.5504,0.962857517621114
+"FLOW_236212",2011,6,28,1.656,0.641607174887892,143.0784,0.915068998880249
+"FLOW_236212",2011,6,29,1.324,0.512975784753363,114.3936,0.875022807170014
+"FLOW_236212",2011,6,30,1.067,0.41340269058296,92.1888,0.83805912619862
+"FLOW_236212",2011,7,1,0.91,0.35257399103139,78.624,0.811801883015366
+"FLOW_236212",2011,7,2,0.796,0.308405381165919,68.7744,0.790358975187442
+"FLOW_236212",2011,7,3,0.749,0.290195515695067,64.7136,0.780797006153262
+"FLOW_236212",2011,7,4,0.806,0.312279820627803,69.6384,0.792334897293856
+"FLOW_236212",2011,7,5,1.882,0.729169506726457,162.6048,0.938784044392072
+"FLOW_236212",2011,7,6,7.764,3.00811479820628,670.8096,1.24640413530902
+"FLOW_236212",2011,7,7,13.748,5.32657937219731,1187.8272,1.3973000692547
+"FLOW_236212",2011,7,8,10.548,4.08675874439462,911.3472,1.32518282965993
+"FLOW_236212",2011,7,9,5.258,2.0371802690583,454.2912,1.15293783750158
+"FLOW_236212",2011,7,10,4.188,1.62261524663677,361.8432,1.1016486598916
+"FLOW_236212",2011,7,11,8.127,3.14875695067265,702.1728,1.25784702762744
+"FLOW_236212",2011,7,12,4.879,1.89033901345291,421.5456,1.13581588833521
+"FLOW_236212",2011,7,13,3.212,1.24446995515695,277.5168,1.044712721625
+"FLOW_236212",2011,7,14,3.806,1.47461165919283,328.8384,1.08077560335633
+"FLOW_236212",2011,7,15,2.892,1.12048789237668,249.8688,1.02301366201049
+"FLOW_236212",2011,7,16,2.204,0.853926457399103,190.4256,0.968911462851727
+"FLOW_236212",2011,7,17,1.804,0.698948878923767,155.8656,0.930870103749442
+"FLOW_236212",2011,7,18,1.842,0.713671748878924,159.1488,0.934759098490907
+"FLOW_236212",2011,7,19,2.408,0.932965022421525,208.0512,0.986218335158434
+"FLOW_236212",2011,7,20,2.571,0.996118385650224,222.1344,0.999222468960765
+"FLOW_236212",2011,7,21,2.039,0.789998206278027,176.1696,0.953949140488823
+"FLOW_236212",2011,7,22,1.618,0.626884304932736,139.7952,0.910830320480894
+"FLOW_236212",2011,7,23,1.326,0.51375067264574,114.5664,0.875287004925656
+"FLOW_236212",2011,7,24,1.203,0.466095067264574,103.9392,0.858410263035213
+"FLOW_236212",2011,7,25,1.26,0.488179372197309,108.864,0.866394895496418
+"FLOW_236212",2011,7,26,1.334,0.516850224215247,115.2576,0.876340618575902
+"FLOW_236212",2011,7,27,1.357,0.525761434977579,117.2448,0.879341855357576
+"FLOW_236212",2011,7,28,1.22,0.472681614349776,105.408,0.860822761741209
+"FLOW_236212",2011,7,29,1.001,0.387831390134529,86.4864,0.827424909989528
+"FLOW_236212",2011,7,30,0.845,0.327390134529148,73.008,0.799858414017954
+"FLOW_236212",2011,7,31,0.746,0.289033183856502,64.4544,0.780170530080621
+"FLOW_236212",2011,8,1,0.672,0.260362331838565,58.0608,0.764039181211917
+"FLOW_236212",2011,8,2,0.611,0.236728251121076,52.7904,0.749635280728596
+"FLOW_236212",2011,8,3,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",2011,8,4,0.536,0.207669955156951,46.3104,0.73025532719464
+"FLOW_236212",2011,8,5,0.706,0.273535426008969,60.9984,0.771618650200755
+"FLOW_236212",2011,8,6,1.918,0.743117488789238,165.7152,0.942348397706438
+"FLOW_236212",2011,8,7,2.052,0.795034977578475,177.2928,0.955162463933159
+"FLOW_236212",2011,8,8,1.58,0.612161434977579,136.512,0.90651123982209
+"FLOW_236212",2011,8,9,1.202,0.465707623318386,103.8528,0.858267503961453
+"FLOW_236212",2011,8,10,1.069,0.414177578475336,92.3616,0.83837306489926
+"FLOW_236212",2011,8,11,1.011,0.391705829596413,87.3504,0.829071539755236
+"FLOW_236212",2011,8,12,0.884,0.342500448430493,76.3776,0.807109072059093
+"FLOW_236212",2011,8,13,0.748,0.289808071748879,64.6272,0.780588404198245
+"FLOW_236212",2011,8,14,0.656,0.254163228699552,56.6784,0.760365745687031
+"FLOW_236212",2011,8,15,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",2011,8,16,0.671,0.259974887892377,57.9744,0.763811653124739
+"FLOW_236212",2011,8,17,0.761,0.294844843049327,65.7504,0.783283011390142
+"FLOW_236212",2011,8,18,1.29,0.49980269058296,111.456,0.870481845318645
+"FLOW_236212",2011,8,19,2.888,1.11893811659193,249.5232,1.02273051398426
+"FLOW_236212",2011,8,20,3.494,1.35372914798206,301.8816,1.06244475291956
+"FLOW_236212",2011,8,21,2.175,0.842690582959641,187.92,0.966348169086189
+"FLOW_236212",2011,8,22,1.433,0.555207174887892,123.8112,0.888978001173686
+"FLOW_236212",2011,8,23,0.998,0.386669058295964,86.2272,0.826928355374913
+"FLOW_236212",2011,8,24,0.775,0.300269058295964,66.96,0.786144022146046
+"FLOW_236212",2011,8,25,0.643,0.249126457399103,55.5552,0.757327924273715
+"FLOW_236212",2011,8,26,0.571,0.221230493273543,49.3344,0.739552479618804
+"FLOW_236212",2011,8,27,0.521,0.201858295964126,45.0144,0.726121540816752
+"FLOW_236212",2011,8,28,0.481,0.186360538116592,41.5584,0.714612800378232
+"FLOW_236212",2011,8,29,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",2011,8,30,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",2011,8,31,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",2011,9,1,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",2011,9,2,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",2011,9,3,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",2011,9,4,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",2011,9,5,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",2011,9,6,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",2011,9,7,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",2011,9,8,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",2011,9,9,0.531,0.205732735426009,45.8784,0.728887798254835
+"FLOW_236212",2011,9,10,0.7,0.271210762331839,60.48,0.770302635744216
+"FLOW_236212",2011,9,11,0.821,0.318091479820628,70.9344,0.795262322746253
+"FLOW_236212",2011,9,12,1.657,0.641994618834081,143.1648,0.915179487774907
+"FLOW_236212",2011,9,13,1.048,0.406041255605381,90.5472,0.835052984252125
+"FLOW_236212",2011,9,14,0.725,0.280896860986547,62.64,0.775727838456387
+"FLOW_236212",2011,9,15,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",2011,9,16,0.511,0.197983856502242,44.1504,0.723312475760448
+"FLOW_236212",2011,9,17,0.467,0.180936322869955,40.3488,0.710403597411843
+"FLOW_236212",2011,9,18,0.431,0.166988340807175,37.2384,0.699096664293204
+"FLOW_236212",2011,9,19,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",2011,9,20,0.384,0.148778475336323,33.1776,0.683137334372087
+"FLOW_236212",2011,9,21,0.397,0.153815246636771,34.3008,0.687701350552893
+"FLOW_236212",2011,9,22,0.366,0.141804484304933,31.6224,0.676609346880397
+"FLOW_236212",2011,9,23,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2011,9,24,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2011,9,25,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2011,9,26,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2011,9,27,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2011,9,28,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",2011,9,29,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2011,9,30,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",2011,10,1,1.406,0.544746188340807,121.4784,0.885602512570659
+"FLOW_236212",2011,10,2,1.823,0.706310313901345,157.5072,0.932822707795427
+"FLOW_236212",2011,10,3,1.074,0.416114798206278,92.7936,0.839155860908619
+"FLOW_236212",2011,10,4,0.686,0.265786547085202,59.2704,0.767196475515567
+"FLOW_236212",2011,10,5,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",2011,10,6,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",2011,10,7,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2011,10,8,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2011,10,9,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2011,10,10,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2011,10,11,0.363,0.140642152466368,31.3632,0.675496496534013
+"FLOW_236212",2011,10,12,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2011,10,13,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2011,10,14,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2011,10,15,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2011,10,16,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2011,10,17,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2011,10,18,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2011,10,19,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2011,10,20,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2011,10,21,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2011,10,22,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2011,10,23,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",2011,10,24,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2011,10,25,0.381,0.147616143497758,32.9184,0.682066580933824
+"FLOW_236212",2011,10,26,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2011,10,27,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2011,10,28,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2011,10,29,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2011,10,30,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2011,10,31,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2011,11,1,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2011,11,2,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2011,11,3,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2011,11,4,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2011,11,5,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2011,11,6,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2011,11,7,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2011,11,8,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2011,11,9,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2011,11,10,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",2011,11,11,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2011,11,12,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2011,11,13,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2011,11,14,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2011,11,15,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2011,11,16,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2011,11,17,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2011,11,18,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2011,11,19,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2011,11,20,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2011,11,21,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2011,11,22,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2011,11,23,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2011,11,24,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2011,11,25,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2011,11,26,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2011,11,27,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2011,11,28,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2011,11,29,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2011,11,30,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2011,12,1,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2011,12,2,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2011,12,3,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2011,12,4,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2011,12,5,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2011,12,6,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2011,12,7,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2011,12,8,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2011,12,9,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2011,12,10,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2011,12,11,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2011,12,12,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2011,12,13,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2011,12,14,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2011,12,15,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2011,12,16,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2011,12,17,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2011,12,18,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2011,12,19,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2011,12,20,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2011,12,21,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2011,12,22,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2011,12,23,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2011,12,24,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2011,12,25,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2011,12,26,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2011,12,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2011,12,28,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2011,12,29,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2011,12,30,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2011,12,31,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2012,1,1,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2012,1,2,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2012,1,3,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2012,1,4,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2012,1,5,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2012,1,6,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2012,1,7,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2012,1,8,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,1,9,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2012,1,10,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2012,1,11,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2012,1,12,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2012,1,13,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2012,1,14,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,1,15,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2012,1,16,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2012,1,17,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2012,1,18,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2012,1,19,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,1,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2012,1,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2012,1,22,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2012,1,23,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2012,1,24,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,1,25,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2012,1,26,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2012,1,27,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2012,1,28,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2012,1,29,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2012,1,30,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2012,1,31,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2012,2,1,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2012,2,2,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2012,2,3,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2012,2,4,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2012,2,5,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2012,2,6,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2012,2,7,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2012,2,8,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,2,9,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2012,2,10,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2012,2,11,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2012,2,12,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2012,2,13,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2012,2,14,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,2,15,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2012,2,16,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2012,2,17,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2012,2,18,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2012,2,19,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2012,2,20,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2012,2,21,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2012,2,22,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2012,2,23,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2012,2,24,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2012,2,25,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2012,2,26,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2012,2,27,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2012,2,28,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2012,2,29,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2012,3,1,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2012,3,2,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2012,3,3,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2012,3,4,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2012,3,5,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2012,3,6,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2012,3,7,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2012,3,8,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2012,3,9,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2012,3,10,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2012,3,11,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2012,3,12,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2012,3,13,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2012,3,14,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2012,3,15,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2012,3,16,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2012,3,17,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,3,18,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2012,3,19,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2012,3,20,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2012,3,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,3,22,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2012,3,23,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2012,3,24,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2012,3,25,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2012,3,26,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2012,3,27,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2012,3,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2012,3,29,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2012,3,30,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2012,3,31,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2012,4,1,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2012,4,2,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2012,4,3,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2012,4,4,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2012,4,5,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2012,4,6,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2012,4,7,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2012,4,8,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2012,4,9,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2012,4,10,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2012,4,11,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2012,4,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2012,4,13,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2012,4,14,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2012,4,15,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2012,4,16,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,4,17,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,4,18,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2012,4,19,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2012,4,20,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2012,4,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,4,22,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2012,4,23,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2012,4,24,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2012,4,25,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2012,4,26,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2012,4,27,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2012,4,28,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2012,4,29,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,4,30,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2012,5,1,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2012,5,2,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2012,5,3,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2012,5,4,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2012,5,5,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2012,5,6,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2012,5,7,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2012,5,8,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2012,5,9,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,5,10,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2012,5,11,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2012,5,12,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2012,5,13,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2012,5,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2012,5,15,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2012,5,16,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2012,5,17,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2012,5,18,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2012,5,19,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2012,5,20,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2012,5,21,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2012,5,22,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2012,5,23,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2012,5,24,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2012,5,25,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2012,5,26,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2012,5,27,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",2012,5,28,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2012,5,29,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2012,5,30,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2012,5,31,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2012,6,1,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2012,6,2,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2012,6,3,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2012,6,4,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2012,6,5,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",2012,6,6,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2012,6,7,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2012,6,8,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2012,6,9,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2012,6,10,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2012,6,11,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2012,6,12,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2012,6,13,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2012,6,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2012,6,15,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2012,6,16,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2012,6,17,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2012,6,18,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2012,6,19,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2012,6,20,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2012,6,21,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2012,6,22,0.413,0.160014349775785,35.6832,0.693157275800497
+"FLOW_236212",2012,6,23,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",2012,6,24,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2012,6,25,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2012,6,26,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2012,6,27,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2012,6,28,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2012,6,29,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2012,6,30,0.288,0.111583856502242,24.8832,0.644941425879977
+"FLOW_236212",2012,7,1,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",2012,7,2,0.685,0.265399103139013,59.184,0.766972672538289
+"FLOW_236212",2012,7,3,1.271,0.492441255605381,109.8144,0.867902393361989
+"FLOW_236212",2012,7,4,1.009,0.390930941704036,87.1776,0.828743259493989
+"FLOW_236212",2012,7,5,0.695,0.269273542600897,60.048,0.769199045772625
+"FLOW_236212",2012,7,6,0.596,0.230916591928251,51.4944,0.745917897717971
+"FLOW_236212",2012,7,7,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",2012,7,8,0.631,0.244477130044843,54.5184,0.754479853878591
+"FLOW_236212",2012,7,9,0.618,0.239440358744395,53.3952,0.751345121557008
+"FLOW_236212",2012,7,10,0.68,0.263461883408072,58.752,0.765849720908347
+"FLOW_236212",2012,7,11,0.77,0.298331838565022,66.528,0.785127014830157
+"FLOW_236212",2012,7,12,0.797,0.308792825112108,68.8608,0.790557458129397
+"FLOW_236212",2012,7,13,0.807,0.312667264573991,69.7248,0.792531408949302
+"FLOW_236212",2012,7,14,0.834,0.323128251121076,72.0576,0.797765013292957
+"FLOW_236212",2012,7,15,0.864,0.334751569506726,74.6496,0.803423488458436
+"FLOW_236212",2012,7,16,0.911,0.352961434977578,78.7104,0.811980222637809
+"FLOW_236212",2012,7,17,0.937,0.363034977578475,80.9568,0.81656299384771
+"FLOW_236212",2012,7,18,0.871,0.337463677130045,75.2544,0.804721133529475
+"FLOW_236212",2012,7,19,0.874,0.33862600896861,75.5136,0.805274714287262
+"FLOW_236212",2012,7,20,0.861,0.333589237668161,74.3904,0.802864778955102
+"FLOW_236212",2012,7,21,0.812,0.314604484304933,70.1568,0.793511055215469
+"FLOW_236212",2012,7,22,0.757,0.293295067264574,65.4048,0.78245784968262
+"FLOW_236212",2012,7,23,0.719,0.278572197309417,62.1216,0.77443960354795
+"FLOW_236212",2012,7,24,0.678,0.262686995515695,58.5792,0.765398690135488
+"FLOW_236212",2012,7,25,0.647,0.250676233183857,55.9008,0.758267831510243
+"FLOW_236212",2012,7,26,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",2012,7,27,1.001,0.387831390134529,86.4864,0.827424909989528
+"FLOW_236212",2012,7,28,2,0.774887892376682,172.8,0.950271654633766
+"FLOW_236212",2012,7,29,1.946,0.753965919282511,168.1344,0.945083852730057
+"FLOW_236212",2012,7,30,1.612,0.624559641255605,139.2768,0.910153793128197
+"FLOW_236212",2012,7,31,1.323,0.512588340807175,114.3072,0.874890588546997
+"FLOW_236212",2012,8,1,1.087,0.421151569506726,93.9168,0.841177571931339
+"FLOW_236212",2012,8,2,0.904,0.35024932735426,78.1056,0.810728540499041
+"FLOW_236212",2012,8,3,0.769,0.297944394618834,66.4416,0.784922979715581
+"FLOW_236212",2012,8,4,0.699,0.27082331838565,60.3936,0.77008242340526
+"FLOW_236212",2012,8,5,0.712,0.275860089686099,61.5168,0.772925747447752
+"FLOW_236212",2012,8,6,0.752,0.291357847533632,64.9728,0.781421478031143
+"FLOW_236212",2012,8,7,0.931,0.360710313901345,80.4384,0.815514546570238
+"FLOW_236212",2012,8,8,0.995,0.385506726457399,85.968,0.826430605201494
+"FLOW_236212",2012,8,9,0.965,0.373883408071749,83.376,0.821385890769942
+"FLOW_236212",2012,8,10,1.382,0.535447533632287,119.4048,0.882558261143458
+"FLOW_236212",2012,8,11,1.582,0.612936322869955,136.6848,0.906740620225909
+"FLOW_236212",2012,8,12,1.378,0.533897757847534,119.0592,0.882046781077295
+"FLOW_236212",2012,8,13,1.149,0.445173094170404,99.2736,0.850561619077045
+"FLOW_236212",2012,8,14,0.975,0.377757847533632,84.24,0.823081232115489
+"FLOW_236212",2012,8,15,0.834,0.323128251121076,72.0576,0.797765013292957
+"FLOW_236212",2012,8,16,0.736,0.285158744394619,63.5904,0.778067613956656
+"FLOW_236212",2012,8,17,0.835,0.323515695067265,72.144,0.797956232157998
+"FLOW_236212",2012,8,18,2.787,1.07980627802691,240.7968,1.01547484488544
+"FLOW_236212",2012,8,19,2.701,1.04648609865471,233.3664,1.00912901339804
+"FLOW_236212",2012,8,20,1.557,0.603250224215247,134.5248,0.903856526725883
+"FLOW_236212",2012,8,21,1.163,0.45059730941704,100.4832,0.852624325219658
+"FLOW_236212",2012,8,22,0.935,0.362260089686099,80.784,0.816214109723788
+"FLOW_236212",2012,8,23,0.793,0.307243049327354,68.5152,0.789762327067395
+"FLOW_236212",2012,8,24,0.728,0.282059192825112,62.8992,0.776368760156004
+"FLOW_236212",2012,8,25,1.067,0.41340269058296,92.1888,0.83805912619862
+"FLOW_236212",2012,8,26,2.191,0.848889686098655,189.3024,0.967765757534136
+"FLOW_236212",2012,8,27,1.737,0.672990134529148,150.0768,0.92385058803325
+"FLOW_236212",2012,8,28,1.247,0.483142600896861,107.7408,0.864599672280491
+"FLOW_236212",2012,8,29,0.952,0.3688466367713,82.2528,0.819160807729716
+"FLOW_236212",2012,8,30,0.883,0.342113004484305,76.2912,0.806926385514329
+"FLOW_236212",2012,8,31,1.905,0.738080717488789,164.592,0.941067492805833
+"FLOW_236212",2012,9,1,4.061,1.57340986547085,350.8704,1.09488466681415
+"FLOW_236212",2012,9,2,2.509,0.972096860986547,216.7776,0.994356021775446
+"FLOW_236212",2012,9,3,1.654,0.640832286995516,142.9056,0.91484786086532
+"FLOW_236212",2012,9,4,1.361,0.527311210762332,117.5904,0.879859648653601
+"FLOW_236212",2012,9,5,1.031,0.399454708520179,89.0784,0.832326091438438
+"FLOW_236212",2012,9,6,0.844,0.32700269058296,72.9216,0.799669008736464
+"FLOW_236212",2012,9,7,1.219,0.472294170403588,105.3216,0.860681596964554
+"FLOW_236212",2012,9,8,2.484,0.962410762331839,214.6176,0.992366497983769
+"FLOW_236212",2012,9,9,3.021,1.17046816143498,261.0144,1.03198152147428
+"FLOW_236212",2012,9,10,2.515,0.974421524663677,217.296,0.994831146315885
+"FLOW_236212",2012,9,11,1.885,0.730331838565022,162.864,0.939083147270668
+"FLOW_236212",2012,9,12,1.405,0.544358744394619,121.392,0.885476501963313
+"FLOW_236212",2012,9,13,1.131,0.438199103139013,97.7184,0.847879815954419
+"FLOW_236212",2012,9,14,0.973,0.376982959641256,84.0672,0.822743280356222
+"FLOW_236212",2012,9,15,0.89,0.344825112107623,76.896,0.808201732747543
+"FLOW_236212",2012,9,16,0.823,0.318866367713005,71.1072,0.795649406075272
+"FLOW_236212",2012,9,17,0.775,0.300269058295964,66.96,0.786144022146046
+"FLOW_236212",2012,9,18,0.75,0.290582959641256,64.8,0.781005385421307
+"FLOW_236212",2012,9,19,0.721,0.279347085201794,62.2944,0.774869967583366
+"FLOW_236212",2012,9,20,0.668,0.258812556053812,57.7152,0.76312743734734
+"FLOW_236212",2012,9,21,0.633,0.24525201793722,54.6912,0.75495752431767
+"FLOW_236212",2012,9,22,0.609,0.2359533632287,52.6176,0.749143877303363
+"FLOW_236212",2012,9,23,0.567,0.219680717488789,48.9888,0.738513413371622
+"FLOW_236212",2012,9,24,0.543,0.210382062780269,46.9152,0.732152823938167
+"FLOW_236212",2012,9,25,0.539,0.208832286995516,46.5696,0.731070953197764
+"FLOW_236212",2012,9,26,0.521,0.201858295964126,45.0144,0.726121540816752
+"FLOW_236212",2012,9,27,0.475,0.184035874439462,41.04,0.712821019859571
+"FLOW_236212",2012,9,28,0.441,0.170862780269058,38.1024,0.702311037419543
+"FLOW_236212",2012,9,29,0.52,0.201470852017937,44.928,0.725842585090593
+"FLOW_236212",2012,9,30,1.044,0.404491479820628,90.2016,0.834414563792795
+"FLOW_236212",2012,10,1,2.673,1.03563766816143,230.9472,1.00702804993752
+"FLOW_236212",2012,10,2,2.105,0.815569506726457,181.872,0.960046339182778
+"FLOW_236212",2012,10,3,1.499,0.580778475336323,129.5136,0.897019950952688
+"FLOW_236212",2012,10,4,1.054,0.408365919282511,91.0656,0.836006969586535
+"FLOW_236212",2012,10,5,0.793,0.307243049327354,68.5152,0.789762327067395
+"FLOW_236212",2012,10,6,0.653,0.253000896860987,56.4192,0.759669013533898
+"FLOW_236212",2012,10,7,0.596,0.230916591928251,51.4944,0.745917897717971
+"FLOW_236212",2012,10,8,0.55,0.213094170403587,47.52,0.734030851465829
+"FLOW_236212",2012,10,9,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",2012,10,10,0.593,0.229754260089686,51.2352,0.745165457149857
+"FLOW_236212",2012,10,11,0.732,0.283608968609866,63.2448,0.77722004373713
+"FLOW_236212",2012,10,12,2.164,0.83842869955157,186.9696,0.965368730063747
+"FLOW_236212",2012,10,13,2.438,0.944588340807175,210.6432,0.988663534609404
+"FLOW_236212",2012,10,14,2.309,0.894608071748879,199.4976,0.977972318973896
+"FLOW_236212",2012,10,15,1.71,0.662529147982063,147.744,0.920960487829032
+"FLOW_236212",2012,10,16,1.169,0.45292197309417,101.0016,0.853502265290676
+"FLOW_236212",2012,10,17,0.857,0.332039461883408,74.0448,0.802117405279478
+"FLOW_236212",2012,10,18,0.728,0.282059192825112,62.8992,0.776368760156004
+"FLOW_236212",2012,10,19,0.639,0.24757668161435,55.2096,0.756383327726054
+"FLOW_236212",2012,10,20,0.558,0.216193721973094,48.2112,0.736153897350762
+"FLOW_236212",2012,10,21,0.494,0.19139730941704,42.6816,0.718434477206278
+"FLOW_236212",2012,10,22,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2012,10,23,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",2012,10,24,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2012,10,25,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2012,10,26,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",2012,10,27,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2012,10,28,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2012,10,29,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2012,10,30,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2012,10,31,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",2012,11,1,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2012,11,2,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2012,11,3,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2012,11,4,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2012,11,5,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2012,11,6,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2012,11,7,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2012,11,8,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2012,11,9,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2012,11,10,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2012,11,11,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2012,11,12,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2012,11,13,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2012,11,14,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2012,11,15,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2012,11,16,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2012,11,17,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2012,11,18,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2012,11,19,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2012,11,20,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2012,11,21,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2012,11,22,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2012,11,23,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,11,24,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2012,11,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2012,11,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2012,11,27,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2012,11,28,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2012,11,29,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2012,11,30,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2012,12,1,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2012,12,2,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2012,12,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2012,12,4,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,12,5,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2012,12,6,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2012,12,7,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2012,12,8,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2012,12,9,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2012,12,10,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2012,12,11,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2012,12,12,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2012,12,13,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2012,12,14,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2012,12,15,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2012,12,16,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2012,12,17,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2012,12,18,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2012,12,19,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2012,12,20,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2012,12,21,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2012,12,22,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2012,12,23,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2012,12,24,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2012,12,25,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2012,12,26,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2012,12,27,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2012,12,28,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2012,12,29,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2012,12,30,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2012,12,31,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,1,1,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2013,1,2,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2013,1,3,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2013,1,4,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2013,1,5,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2013,1,6,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2013,1,7,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2013,1,8,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2013,1,9,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2013,1,10,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2013,1,11,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2013,1,12,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2013,1,13,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2013,1,14,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2013,1,15,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2013,1,16,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2013,1,17,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2013,1,18,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2013,1,19,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2013,1,20,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2013,1,21,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2013,1,22,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2013,1,23,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2013,1,24,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2013,1,25,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2013,1,26,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2013,1,27,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2013,1,28,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2013,1,29,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2013,1,30,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2013,1,31,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2013,2,1,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2013,2,2,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,2,3,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2013,2,4,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2013,2,5,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2013,2,6,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2013,2,7,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2013,2,8,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2013,2,9,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2013,2,10,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2013,2,11,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2013,2,12,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2013,2,13,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2013,2,14,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2013,2,15,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2013,2,16,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2013,2,17,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2013,2,18,0.127,0.0492053811659193,10.9728,0.547523192403278
+"FLOW_236212",2013,2,19,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2013,2,20,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2013,2,21,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2013,2,22,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2013,2,23,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2013,2,24,0.099,0.0383569506726457,8.5536,0.520917340701386
+"FLOW_236212",2013,2,25,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2013,2,26,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2013,2,27,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2013,2,28,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2013,3,1,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2013,3,2,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2013,3,3,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2013,3,4,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2013,3,5,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2013,3,6,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2013,3,7,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2013,3,8,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2013,3,9,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2013,3,10,0.125,0.0484304932735426,10.8,0.545787743239059
+"FLOW_236212",2013,3,11,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2013,3,12,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2013,3,13,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2013,3,14,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2013,3,15,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2013,3,16,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2013,3,17,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2013,3,18,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,3,19,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,3,20,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2013,3,21,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2013,3,22,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2013,3,23,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2013,3,24,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2013,3,25,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2013,3,26,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2013,3,27,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2013,3,28,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2013,3,29,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2013,3,30,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2013,3,31,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2013,4,1,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,4,2,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2013,4,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2013,4,4,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2013,4,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2013,4,6,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,4,7,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,4,8,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2013,4,9,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2013,4,10,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2013,4,11,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2013,4,12,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,4,13,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,4,14,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,4,15,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2013,4,16,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2013,4,17,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2013,4,18,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,4,19,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2013,4,20,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2013,4,21,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2013,4,22,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2013,4,23,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2013,4,24,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2013,4,25,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2013,4,26,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2013,4,27,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2013,4,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2013,4,29,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2013,4,30,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2013,5,1,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2013,5,2,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2013,5,3,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2013,5,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2013,5,5,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2013,5,6,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2013,5,7,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2013,5,8,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2013,5,9,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2013,5,10,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2013,5,11,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2013,5,12,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2013,5,13,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2013,5,14,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2013,5,15,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2013,5,16,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",2013,5,17,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2013,5,18,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2013,5,19,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2013,5,20,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2013,5,21,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2013,5,22,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2013,5,23,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2013,5,24,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2013,5,25,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2013,5,26,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,5,27,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2013,5,28,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2013,5,29,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2013,5,30,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2013,5,31,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2013,6,1,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2013,6,2,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2013,6,3,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2013,6,4,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2013,6,5,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2013,6,6,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2013,6,7,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2013,6,8,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2013,6,9,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2013,6,10,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2013,6,11,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2013,6,12,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2013,6,13,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2013,6,14,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2013,6,15,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2013,6,16,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2013,6,17,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2013,6,18,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2013,6,19,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2013,6,20,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2013,6,21,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2013,6,22,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2013,6,23,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2013,6,24,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,6,25,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,6,26,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2013,6,27,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2013,6,28,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2013,6,29,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2013,6,30,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,7,1,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2013,7,2,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2013,7,3,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2013,7,4,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2013,7,5,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2013,7,6,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2013,7,7,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2013,7,8,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2013,7,9,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2013,7,10,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2013,7,11,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2013,7,12,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2013,7,13,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2013,7,14,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2013,7,15,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",2013,7,16,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2013,7,17,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2013,7,18,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2013,7,19,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2013,7,20,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2013,7,21,0.492,0.190622421524664,42.5088,0.717851804519562
+"FLOW_236212",2013,7,22,0.484,0.187522869955157,41.8176,0.715501993673772
+"FLOW_236212",2013,7,23,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2013,7,24,0.396,0.153427802690583,34.2144,0.687354551914258
+"FLOW_236212",2013,7,25,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2013,7,26,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2013,7,27,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2013,7,28,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2013,7,29,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2013,7,30,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2013,7,31,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2013,8,1,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2013,8,2,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2013,8,3,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",2013,8,4,1.283,0.497090582959641,110.8512,0.8695350791065
+"FLOW_236212",2013,8,5,2.704,1.04764843049327,233.6256,1.00935308173781
+"FLOW_236212",2013,8,6,2.228,0.863225112107623,192.4992,0.9710124832308
+"FLOW_236212",2013,8,7,7.379,2.85894887892377,637.5456,1.23379009101176
+"FLOW_236212",2013,8,8,12.433,4.81709058295964,1074.2112,1.3694839998732
+"FLOW_236212",2013,8,9,8.622,3.34054170403587,744.9408,1.27280941632827
+"FLOW_236212",2013,8,10,5.33,2.06507623318386,460.512,1.15607821558911
+"FLOW_236212",2013,8,11,3.096,1.1995264573991,267.4944,1.03705542163143
+"FLOW_236212",2013,8,12,2.26,0.87562331838565,195.264,0.973785859319503
+"FLOW_236212",2013,8,13,8.96,3.47149775784753,774.144,1.28263586011793
+"FLOW_236212",2013,8,14,8.564,3.31806995515695,739.9296,1.2710923577846
+"FLOW_236212",2013,8,15,4.827,1.87019192825112,417.0528,1.13338441296958
+"FLOW_236212",2013,8,16,2.843,1.10150313901345,245.6352,1.01952328194646
+"FLOW_236212",2013,8,17,1.93,0.747766816143498,166.752,0.943524621545207
+"FLOW_236212",2013,8,18,1.64,0.635408071748879,141.696,0.913293875907204
+"FLOW_236212",2013,8,19,2.666,1.03292556053812,230.3424,1.00650005950788
+"FLOW_236212",2013,8,20,8.407,3.25724125560538,726.3648,1.26639732753909
+"FLOW_236212",2013,8,21,10.315,3.99648430493274,891.216,1.31927587981088
+"FLOW_236212",2013,8,22,7.43,2.87870852017937,641.952,1.23549086495605
+"FLOW_236212",2013,8,23,10.976,4.25258475336323,948.3264,1.33576664783776
+"FLOW_236212",2013,8,24,11.569,4.48233901345292,999.5616,1.34989795712591
+"FLOW_236212",2013,8,25,6.495,2.51644843049327,561.168,1.20270081971256
+"FLOW_236212",2013,8,26,3.188,1.23517130044843,275.4432,1.04314681992225
+"FLOW_236212",2013,8,27,2.197,0.851214349775785,189.8208,0.968295218464221
+"FLOW_236212",2013,8,28,1.675,0.648968609865471,144.72,0.917159223958902
+"FLOW_236212",2013,8,29,1.313,0.508713901345291,113.4432,0.87356398564378
+"FLOW_236212",2013,8,30,1.104,0.427738116591928,95.3856,0.843792363419142
+"FLOW_236212",2013,8,31,1.297,0.502514798206278,112.0608,0.871424510417836
+"FLOW_236212",2013,9,1,1.345,0.521112107623318,116.208,0.877781114519393
+"FLOW_236212",2013,9,2,0.929,0.359935426008969,80.2656,0.81516386291157
+"FLOW_236212",2013,9,3,0.687,0.26617399103139,59.3568,0.767420017650114
+"FLOW_236212",2013,9,4,0.581,0.225104932735426,50.1984,0.742124898206603
+"FLOW_236212",2013,9,5,0.522,0.202245739910314,45.1008,0.726400068532307
+"FLOW_236212",2013,9,6,0.515,0.199533632286996,44.496,0.724441334124209
+"FLOW_236212",2013,9,7,0.483,0.187135426008969,41.7312,0.715206087040821
+"FLOW_236212",2013,9,8,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",2013,9,9,0.457,0.177061883408072,39.4848,0.70733478459093
+"FLOW_236212",2013,9,10,0.462,0.178999103139013,39.9168,0.708875833593429
+"FLOW_236212",2013,9,11,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",2013,9,12,0.681,0.26384932735426,58.8384,0.766074838443101
+"FLOW_236212",2013,9,13,0.691,0.267723766816143,59.7024,0.768311591380086
+"FLOW_236212",2013,9,14,0.61,0.236340807174888,52.704,0.749389740131935
+"FLOW_236212",2013,9,15,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2013,9,16,0.474,0.183648430493274,40.9536,0.712520631628183
+"FLOW_236212",2013,9,17,0.619,0.239827802690583,53.4816,0.751588118089578
+"FLOW_236212",2013,9,18,1.412,0.547070852017937,121.9968,0.88635707417064
+"FLOW_236212",2013,9,19,5.539,2.14605201793722,478.5696,1.16500569647075
+"FLOW_236212",2013,9,20,7.247,2.80780627802691,626.1408,1.22934400629083
+"FLOW_236212",2013,9,21,3.86,1.495533632287,333.504,1.08382518066818
+"FLOW_236212",2013,9,22,2.181,0.845015246636771,188.4384,0.966880739356755
+"FLOW_236212",2013,9,23,1.532,0.593564125560538,132.3648,0.900935145207484
+"FLOW_236212",2013,9,24,1.072,0.415339910313901,92.6208,0.838843093076304
+"FLOW_236212",2013,9,25,0.764,0.296007174887892,66.0096,0.783899608594372
+"FLOW_236212",2013,9,26,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",2013,9,27,0.56,0.216968609865471,48.384,0.736680851288836
+"FLOW_236212",2013,9,28,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2013,9,29,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",2013,9,30,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",2013,10,1,1.228,0.475781165919283,106.0992,0.861948760047993
+"FLOW_236212",2013,10,2,6.666,2.58270134529148,575.9424,1.20896808912539
+"FLOW_236212",2013,10,3,5.101,1.97635156950673,440.7264,1.14596892361233
+"FLOW_236212",2013,10,4,3.81,1.47616143497758,329.184,1.08100268092724
+"FLOW_236212",2013,10,5,2.233,0.865162331838565,192.9312,0.971447915012712
+"FLOW_236212",2013,10,6,1.443,0.559081614349776,124.6752,0.890215275276452
+"FLOW_236212",2013,10,7,0.955,0.370008968609865,82.512,0.819676436007093
+"FLOW_236212",2013,10,8,0.692,0.268111210762332,59.7888,0.768533839491147
+"FLOW_236212",2013,10,9,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",2013,10,10,0.464,0.17977399103139,40.0896,0.709488518893542
+"FLOW_236212",2013,10,11,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",2013,10,12,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2013,10,13,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",2013,10,14,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2013,10,15,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2013,10,16,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2013,10,17,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",2013,10,18,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2013,10,19,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",2013,10,20,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2013,10,21,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2013,10,22,0.758,0.293682511210762,65.4912,0.782664466518846
+"FLOW_236212",2013,10,23,11.91,4.61445739910314,1029.024,1.35776349557337
+"FLOW_236212",2013,10,24,13.783,5.3401399103139,1190.8512,1.39801080210928
+"FLOW_236212",2013,10,25,9.498,3.67994260089686,820.6272,1.29768178578493
+"FLOW_236212",2013,10,26,5.609,2.1731730941704,484.6176,1.16793551249311
+"FLOW_236212",2013,10,27,3.074,1.19100269058296,265.5936,1.03557736306495
+"FLOW_236212",2013,10,28,2.08,0.805883408071749,179.712,0.957755033002885
+"FLOW_236212",2013,10,29,1.586,0.614486098654709,137.0304,0.907198685940947
+"FLOW_236212",2013,10,30,1.181,0.457571300448431,102.0384,0.855247385878871
+"FLOW_236212",2013,10,31,0.897,0.347537219730942,77.5008,0.809469080402145
+"FLOW_236212",2013,11,1,0.712,0.275860089686099,61.5168,0.772925747447752
+"FLOW_236212",2013,11,2,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",2013,11,3,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2013,11,4,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",2013,11,5,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2013,11,6,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",2013,11,7,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",2013,11,8,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2013,11,9,0.553,0.214256502242153,47.7792,0.734829870976035
+"FLOW_236212",2013,11,10,0.519,0.201083408071749,44.8416,0.725563199871113
+"FLOW_236212",2013,11,11,0.475,0.184035874439462,41.04,0.712821019859571
+"FLOW_236212",2013,11,12,0.416,0.16117668161435,35.9424,0.694161370705451
+"FLOW_236212",2013,11,13,0.391,0.151490582959641,33.7824,0.685609974708717
+"FLOW_236212",2013,11,14,0.42,0.162726457399103,36.288,0.695491191511887
+"FLOW_236212",2013,11,15,0.456,0.176674439461883,39.3984,0.707024957584328
+"FLOW_236212",2013,11,16,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",2013,11,17,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",2013,11,18,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",2013,11,19,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2013,11,20,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2013,11,21,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2013,11,22,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",2013,11,23,0.494,0.19139730941704,42.6816,0.718434477206278
+"FLOW_236212",2013,11,24,0.966,0.374270852017937,83.4624,0.821556055667657
+"FLOW_236212",2013,11,25,1.117,0.432774887892377,96.5088,0.845770261206206
+"FLOW_236212",2013,11,26,0.761,0.294844843049327,65.7504,0.783283011390142
+"FLOW_236212",2013,11,27,0.532,0.206120179372197,45.9648,0.729162125693567
+"FLOW_236212",2013,11,28,0.426,0.165051121076233,36.8064,0.697467050609071
+"FLOW_236212",2013,11,29,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",2013,11,30,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2013,12,1,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2013,12,2,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",2013,12,3,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2013,12,4,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2013,12,5,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2013,12,6,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2013,12,7,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2013,12,8,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2013,12,9,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2013,12,10,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2013,12,11,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2013,12,12,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2013,12,13,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",2013,12,14,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2013,12,15,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2013,12,16,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2013,12,17,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2013,12,18,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2013,12,19,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2013,12,20,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2013,12,21,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2013,12,22,0.336,0.130181165919283,29.0304,0.665134739584344
+"FLOW_236212",2013,12,23,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2013,12,24,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2013,12,25,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2013,12,26,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2013,12,27,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2013,12,28,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2013,12,29,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2013,12,30,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2013,12,31,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2014,1,1,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2014,1,2,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2014,1,3,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2014,1,4,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2014,1,5,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2014,1,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2014,1,7,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2014,1,8,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2014,1,9,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2014,1,10,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2014,1,11,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,1,12,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2014,1,13,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2014,1,14,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2014,1,15,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2014,1,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2014,1,17,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2014,1,18,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2014,1,19,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2014,1,20,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2014,1,21,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2014,1,22,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2014,1,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2014,1,24,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2014,1,25,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2014,1,26,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2014,1,27,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2014,1,28,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2014,1,29,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2014,1,30,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2014,1,31,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2014,2,1,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2014,2,2,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2014,2,3,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2014,2,4,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2014,2,5,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2014,2,6,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2014,2,7,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2014,2,8,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2014,2,9,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2014,2,10,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2014,2,11,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2014,2,12,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2014,2,13,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2014,2,14,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2014,2,15,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2014,2,16,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2014,2,17,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2014,2,18,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2014,2,19,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2014,2,20,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2014,2,21,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2014,2,22,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2014,2,23,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2014,2,24,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2014,2,25,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2014,2,26,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2014,2,27,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2014,2,28,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2014,3,1,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2014,3,2,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2014,3,3,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2014,3,4,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2014,3,5,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2014,3,6,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2014,3,7,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2014,3,8,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2014,3,9,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2014,3,10,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2014,3,11,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2014,3,12,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2014,3,13,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2014,3,14,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2014,3,15,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2014,3,16,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2014,3,17,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2014,3,18,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2014,3,19,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2014,3,20,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2014,3,21,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2014,3,22,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2014,3,23,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2014,3,24,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2014,3,25,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2014,3,26,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2014,3,27,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2014,3,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2014,3,29,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2014,3,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2014,3,31,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2014,4,1,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2014,4,2,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2014,4,3,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2014,4,4,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2014,4,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2014,4,6,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2014,4,7,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2014,4,8,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2014,4,9,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2014,4,10,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2014,4,11,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2014,4,12,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2014,4,13,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2014,4,14,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2014,4,15,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2014,4,16,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2014,4,17,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,4,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,4,19,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2014,4,20,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2014,4,21,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,4,22,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2014,4,23,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2014,4,24,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2014,4,25,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2014,4,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2014,4,27,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2014,4,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2014,4,29,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2014,4,30,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2014,5,1,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2014,5,2,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2014,5,3,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2014,5,4,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2014,5,5,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2014,5,6,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2014,5,7,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2014,5,8,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2014,5,9,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2014,5,10,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2014,5,11,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2014,5,12,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2014,5,13,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2014,5,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2014,5,15,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2014,5,16,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2014,5,17,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2014,5,18,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2014,5,19,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2014,5,20,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2014,5,21,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2014,5,22,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2014,5,23,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2014,5,24,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2014,5,25,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2014,5,26,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2014,5,27,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2014,5,28,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2014,5,29,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2014,5,30,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2014,5,31,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2014,6,1,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2014,6,2,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2014,6,3,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2014,6,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2014,6,5,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2014,6,6,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2014,6,7,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2014,6,8,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2014,6,9,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2014,6,10,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2014,6,11,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2014,6,12,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2014,6,13,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2014,6,14,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2014,6,15,0.377,0.146066367713004,32.5728,0.680628367644227
+"FLOW_236212",2014,6,16,0.341,0.132118385650224,29.4624,0.667102628243704
+"FLOW_236212",2014,6,17,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2014,6,18,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2014,6,19,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2014,6,20,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2014,6,21,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2014,6,22,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2014,6,23,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2014,6,24,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2014,6,25,0.733,0.283996412556054,63.3312,0.777432282994267
+"FLOW_236212",2014,6,26,0.924,0.357998206278027,79.8336,0.814284503945924
+"FLOW_236212",2014,6,27,0.821,0.318091479820628,70.9344,0.795262322746253
+"FLOW_236212",2014,6,28,0.693,0.26849865470852,59.8752,0.768755830816311
+"FLOW_236212",2014,6,29,1.523,0.590077130044843,131.5872,0.899874108984938
+"FLOW_236212",2014,6,30,3.796,1.47073721973094,327.9744,1.08020707300301
+"FLOW_236212",2014,7,1,4.282,1.65903497757848,369.9648,1.10655016807079
+"FLOW_236212",2014,7,2,2.317,0.897707623318386,200.1888,0.978649058532919
+"FLOW_236212",2014,7,3,1.332,0.51607533632287,115.0848,0.876077690050138
+"FLOW_236212",2014,7,4,0.86,0.333201793721973,74.304,0.802678196369537
+"FLOW_236212",2014,7,5,0.687,0.26617399103139,59.3568,0.767420017650114
+"FLOW_236212",2014,7,6,1.66,0.643156950672646,143.424,0.915510634703158
+"FLOW_236212",2014,7,7,11.595,4.49241255605381,1001.808,1.35050416057406
+"FLOW_236212",2014,7,8,7.918,3.06778116591928,684.1152,1.25130988495604
+"FLOW_236212",2014,7,9,3.646,1.41262062780269,315.0144,1.07153190994849
+"FLOW_236212",2014,7,10,6.049,2.34364843049327,522.6336,1.18571002775991
+"FLOW_236212",2014,7,11,12.298,4.76478565022421,1062.5472,1.36649697182603
+"FLOW_236212",2014,7,12,10.285,3.98486098654709,888.624,1.31850759284112
+"FLOW_236212",2014,7,13,7.847,3.04027264573991,677.9808,1.24905771556961
+"FLOW_236212",2014,7,14,4.015,1.55558744394619,346.896,1.09239294282637
+"FLOW_236212",2014,7,15,2.601,1.00774170403587,224.7264,1.00154356824025
+"FLOW_236212",2014,7,16,1.952,0.756290582959641,168.6528,0.945665920828695
+"FLOW_236212",2014,7,17,1.764,0.683451121076233,152.4096,0.926704969698206
+"FLOW_236212",2014,7,18,5.275,2.0437668161435,455.76,1.15368240366101
+"FLOW_236212",2014,7,19,4.912,1.90312466367713,424.3968,1.13734820756421
+"FLOW_236212",2014,7,20,2.739,1.06120896860987,236.6496,1.01195262742643
+"FLOW_236212",2014,7,21,2.171,0.841140807174888,187.5744,0.965992468918345
+"FLOW_236212",2014,7,22,1.757,0.680739013452915,151.8048,0.925968319347403
+"FLOW_236212",2014,7,23,1.396,0.540871748878924,120.6144,0.884339165752444
+"FLOW_236212",2014,7,24,1.137,0.440523766816144,98.2368,0.848777520259195
+"FLOW_236212",2014,7,25,1.16,0.449434977578475,100.224,0.852183995672329
+"FLOW_236212",2014,7,26,1.557,0.603250224215247,134.5248,0.903856526725883
+"FLOW_236212",2014,7,27,4.497,1.74233542600897,388.5408,1.11744550635416
+"FLOW_236212",2014,7,28,3.133,1.21386188340807,270.6912,1.03952240660999
+"FLOW_236212",2014,7,29,2.047,0.793097757847534,176.8608,0.954696530802128
+"FLOW_236212",2014,7,30,1.684,0.652455605381166,145.4976,0.918142716391724
+"FLOW_236212",2014,7,31,1.663,0.644319282511211,143.6832,0.91584130321033
+"FLOW_236212",2014,8,1,3.977,1.54086457399103,343.6128,1.09031727745034
+"FLOW_236212",2014,8,2,9.853,3.81748520179372,851.2992,1.30724043390885
+"FLOW_236212",2014,8,3,6.442,2.49591390134529,556.5888,1.20073154400074
+"FLOW_236212",2014,8,4,3.229,1.25105650224215,278.9856,1.04581624814909
+"FLOW_236212",2014,8,5,1.976,0.765589237668161,170.7264,0.947979976045672
+"FLOW_236212",2014,8,6,1.52,0.588914798206278,131.328,0.899519315560516
+"FLOW_236212",2014,8,7,1.284,0.49747802690583,110.9376,0.869670584034244
+"FLOW_236212",2014,8,8,1.548,0.599763228699552,133.7472,0.902809181470543
+"FLOW_236212",2014,8,9,1.514,0.586590134529148,130.8096,0.898808044784943
+"FLOW_236212",2014,8,10,1.323,0.512588340807175,114.3072,0.874890588546997
+"FLOW_236212",2014,8,11,1.587,0.614873542600897,137.1168,0.907313057942306
+"FLOW_236212",2014,8,12,1.779,0.689262780269058,153.7056,0.928275665104318
+"FLOW_236212",2014,8,13,1.74,0.674152466367713,150.336,0.924169487173103
+"FLOW_236212",2014,8,14,1.446,0.560243946188341,124.9344,0.890585119722866
+"FLOW_236212",2014,8,15,1.182,0.457958744394619,102.1248,0.855392171291923
+"FLOW_236212",2014,8,16,0.98,0.379695067264574,84.672,0.823923691640382
+"FLOW_236212",2014,8,17,0.837,0.324290582959641,72.3168,0.798338120934069
+"FLOW_236212",2014,8,18,0.72,0.278959641255605,62.208,0.774654905111265
+"FLOW_236212",2014,8,19,0.62,0.240215246636771,53.568,0.751830800774365
+"FLOW_236212",2014,8,20,0.553,0.214256502242153,47.7792,0.734829870976035
+"FLOW_236212",2014,8,21,0.51,0.197596412556054,44.064,0.723029157038257
+"FLOW_236212",2014,8,22,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2014,8,23,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",2014,8,24,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2014,8,25,0.554,0.214643946188341,47.8656,0.735095440207295
+"FLOW_236212",2014,8,26,1.532,0.593564125560538,132.3648,0.900935145207484
+"FLOW_236212",2014,8,27,1.206,0.467257399103139,104.1984,0.858837971400508
+"FLOW_236212",2014,8,28,0.848,0.328552466367713,73.2672,0.800425555982974
+"FLOW_236212",2014,8,29,0.649,0.251451121076233,56.0736,0.758736042939418
+"FLOW_236212",2014,8,30,0.542,0.209994618834081,46.8288,0.731882955518366
+"FLOW_236212",2014,8,31,0.479,0.185585650224215,41.3856,0.714017536910939
+"FLOW_236212",2014,9,1,0.446,0.1728,38.5344,0.703896405787105
+"FLOW_236212",2014,9,2,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",2014,9,3,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",2014,9,4,0.528,0.204570403587444,45.6192,0.728062328641006
+"FLOW_236212",2014,9,5,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",2014,9,6,0.461,0.178611659192825,39.8304,0.708568694902007
+"FLOW_236212",2014,9,7,0.412,0.159626905829596,35.5968,0.692821280840917
+"FLOW_236212",2014,9,8,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2014,9,9,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",2014,9,10,0.349,0.135217937219731,30.1536,0.670203761972482
+"FLOW_236212",2014,9,11,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2014,9,12,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2014,9,13,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2014,9,14,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2014,9,15,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",2014,9,16,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2014,9,17,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2014,9,18,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2014,9,19,0.364,0.141029596412556,31.4496,0.675868261479323
+"FLOW_236212",2014,9,20,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",2014,9,21,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2014,9,22,0.324,0.125531838565022,27.9936,0.660314414515333
+"FLOW_236212",2014,9,23,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2014,9,24,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2014,9,25,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2014,9,26,0.352,0.136380269058296,30.4128,0.671352033291681
+"FLOW_236212",2014,9,27,0.34,0.131730941704036,29.376,0.666710905936941
+"FLOW_236212",2014,9,28,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2014,9,29,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2014,9,30,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",2014,10,1,0.321,0.124369506726457,27.7344,0.659087055813739
+"FLOW_236212",2014,10,2,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2014,10,3,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2014,10,4,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",2014,10,5,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2014,10,6,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2014,10,7,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2014,10,8,0.304,0.117782959641256,26.2656,0.651953307003542
+"FLOW_236212",2014,10,9,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2014,10,10,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2014,10,11,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2014,10,12,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2014,10,13,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2014,10,14,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2014,10,15,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2014,10,16,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2014,10,17,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2014,10,18,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2014,10,19,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2014,10,20,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2014,10,21,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2014,10,22,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2014,10,23,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2014,10,24,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2014,10,25,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2014,10,26,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2014,10,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2014,10,28,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2014,10,29,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2014,10,30,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2014,10,31,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2014,11,1,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2014,11,2,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2014,11,3,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2014,11,4,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2014,11,5,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,11,6,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2014,11,7,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2014,11,8,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2014,11,9,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2014,11,10,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2014,11,11,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2014,11,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2014,11,13,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2014,11,14,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2014,11,15,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2014,11,16,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2014,11,17,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2014,11,18,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2014,11,19,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2014,11,20,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2014,11,21,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2014,11,22,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2014,11,23,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2014,11,24,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2014,11,25,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2014,11,26,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2014,11,27,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2014,11,28,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2014,11,29,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2014,11,30,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2014,12,1,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2014,12,2,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2014,12,3,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2014,12,4,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2014,12,5,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2014,12,6,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2014,12,7,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2014,12,8,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2014,12,9,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2014,12,10,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2014,12,11,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2014,12,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2014,12,13,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2014,12,14,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2014,12,15,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2014,12,16,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2014,12,17,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2014,12,18,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2014,12,19,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2014,12,20,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2014,12,21,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2014,12,22,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2014,12,23,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2014,12,24,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2014,12,25,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2014,12,26,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2014,12,27,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2014,12,28,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2014,12,29,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2014,12,30,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2014,12,31,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2015,1,1,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2015,1,2,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2015,1,3,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2015,1,4,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2015,1,5,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,1,6,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2015,1,7,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2015,1,8,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,1,9,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2015,1,10,0.197,0.0763264573991031,17.0208,0.5977712464837
+"FLOW_236212",2015,1,11,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2015,1,12,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2015,1,13,0.298,0.115458295964126,25.7472,0.649359246031041
+"FLOW_236212",2015,1,14,0.561,0.217356053811659,48.4704,0.73694376386518
+"FLOW_236212",2015,1,15,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2015,1,16,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2015,1,17,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2015,1,18,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2015,1,19,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2015,1,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2015,1,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2015,1,22,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,1,23,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2015,1,24,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2015,1,25,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,1,26,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2015,1,27,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2015,1,28,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2015,1,29,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2015,1,30,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2015,1,31,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,2,1,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2015,2,2,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,2,3,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,2,4,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2015,2,5,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2015,2,6,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2015,2,7,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2015,2,8,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2015,2,9,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2015,2,10,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2015,2,11,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2015,2,12,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2015,2,13,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,2,14,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2015,2,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,2,16,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2015,2,17,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2015,2,18,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,2,19,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2015,2,20,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2015,2,21,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2015,2,22,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2015,2,23,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2015,2,24,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2015,2,25,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2015,2,26,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,2,27,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2015,2,28,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2015,3,1,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,3,2,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2015,3,3,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2015,3,4,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2015,3,5,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2015,3,6,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2015,3,7,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2015,3,8,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2015,3,9,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2015,3,10,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2015,3,11,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2015,3,12,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2015,3,13,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2015,3,14,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2015,3,15,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,3,16,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2015,3,17,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2015,3,18,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,3,19,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2015,3,20,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2015,3,21,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2015,3,22,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2015,3,23,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,3,24,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2015,3,25,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2015,3,26,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2015,3,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,3,28,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2015,3,29,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2015,3,30,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2015,3,31,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2015,4,1,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2015,4,2,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2015,4,3,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2015,4,4,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2015,4,5,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2015,4,6,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2015,4,7,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2015,4,8,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,4,9,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2015,4,10,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2015,4,11,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2015,4,12,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2015,4,13,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2015,4,14,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2015,4,15,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2015,4,16,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2015,4,17,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2015,4,18,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2015,4,19,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2015,4,20,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2015,4,21,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2015,4,22,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2015,4,23,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2015,4,24,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2015,4,25,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2015,4,26,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2015,4,27,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2015,4,28,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,4,29,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2015,4,30,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2015,5,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2015,5,2,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2015,5,3,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2015,5,4,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2015,5,5,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2015,5,6,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2015,5,7,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2015,5,8,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2015,5,9,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2015,5,10,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2015,5,11,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2015,5,12,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2015,5,13,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2015,5,14,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2015,5,15,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2015,5,16,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2015,5,17,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2015,5,18,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2015,5,19,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2015,5,20,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2015,5,21,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2015,5,22,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",2015,5,23,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2015,5,24,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2015,5,25,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2015,5,26,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2015,5,27,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2015,5,28,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2015,5,29,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2015,5,30,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2015,5,31,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2015,6,1,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2015,6,2,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2015,6,3,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2015,6,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2015,6,5,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2015,6,6,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2015,6,7,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2015,6,8,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2015,6,9,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2015,6,10,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2015,6,11,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2015,6,12,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2015,6,13,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2015,6,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2015,6,15,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2015,6,16,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2015,6,17,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2015,6,18,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2015,6,19,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2015,6,20,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2015,6,21,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2015,6,22,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2015,6,23,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2015,6,24,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2015,6,25,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2015,6,26,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2015,6,27,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2015,6,28,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2015,6,29,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2015,6,30,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2015,7,1,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2015,7,2,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2015,7,3,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2015,7,4,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2015,7,5,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2015,7,6,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2015,7,7,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2015,7,8,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2015,7,9,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2015,7,10,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2015,7,11,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2015,7,12,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2015,7,13,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2015,7,14,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2015,7,15,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2015,7,16,0.427,0.165438565022422,36.8928,0.697794192896794
+"FLOW_236212",2015,7,17,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2015,7,18,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2015,7,19,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2015,7,20,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2015,7,21,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2015,7,22,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2015,7,23,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2015,7,24,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2015,7,25,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2015,7,26,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2015,7,27,0.443,0.171637668161435,38.2752,0.702946901781976
+"FLOW_236212",2015,7,28,0.447,0.173187443946188,38.6208,0.704211771686885
+"FLOW_236212",2015,7,29,0.466,0.180548878923767,40.2624,0.710099095115012
+"FLOW_236212",2015,7,30,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",2015,7,31,0.376,0.145678923766816,32.4864,0.680266907871701
+"FLOW_236212",2015,8,1,0.368,0.142579372197309,31.7952,0.677347199612438
+"FLOW_236212",2015,8,2,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2015,8,3,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",2015,8,4,0.716,0.277409865470852,61.8624,0.773792258310098
+"FLOW_236212",2015,8,5,0.828,0.320803587443946,71.5392,0.796613832430021
+"FLOW_236212",2015,8,6,1.21,0.468807174887892,104.544,0.859406927163996
+"FLOW_236212",2015,8,7,1.453,0.562956053811659,125.5392,0.891445708827626
+"FLOW_236212",2015,8,8,1.433,0.555207174887892,123.8112,0.888978001173686
+"FLOW_236212",2015,8,9,1.027,0.397904932735426,88.7328,0.831679246987517
+"FLOW_236212",2015,8,10,0.746,0.289033183856502,64.4544,0.780170530080621
+"FLOW_236212",2015,8,11,0.597,0.231304035874439,51.5808,0.746168037914302
+"FLOW_236212",2015,8,12,0.504,0.195271748878924,43.5456,0.721319849122154
+"FLOW_236212",2015,8,13,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",2015,8,14,0.459,0.177836771300448,39.6576,0.707952815053246
+"FLOW_236212",2015,8,15,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",2015,8,16,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",2015,8,17,0.422,0.16350134529148,36.4608,0.696152306005982
+"FLOW_236212",2015,8,18,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2015,8,19,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2015,8,20,0.511,0.197983856502242,44.1504,0.723312475760448
+"FLOW_236212",2015,8,21,0.44,0.17047533632287,38.016,0.701992239722355
+"FLOW_236212",2015,8,22,0.386,0.1495533632287,33.3504,0.68384745787516
+"FLOW_236212",2015,8,23,0.362,0.140254708520179,31.2768,0.675123911367072
+"FLOW_236212",2015,8,24,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2015,8,25,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2015,8,26,0.317,0.122819730941704,27.3888,0.657436222160782
+"FLOW_236212",2015,8,27,0.316,0.122432286995516,27.3024,0.657020911518305
+"FLOW_236212",2015,8,28,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2015,8,29,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2015,8,30,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2015,8,31,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2015,9,1,0.308,0.119332735426009,26.6112,0.653660014414706
+"FLOW_236212",2015,9,2,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2015,9,3,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2015,9,4,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2015,9,5,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2015,9,6,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",2015,9,7,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",2015,9,8,1.35,0.52304932735426,116.64,0.878432771595658
+"FLOW_236212",2015,9,9,1.863,0.721808071748879,160.9632,0.936880817373933
+"FLOW_236212",2015,9,10,1.323,0.512588340807175,114.3072,0.874890588546997
+"FLOW_236212",2015,9,11,0.865,0.335139013452915,74.736,0.803609380076777
+"FLOW_236212",2015,9,12,0.618,0.239440358744395,53.3952,0.751345121557008
+"FLOW_236212",2015,9,13,0.487,0.188685201793722,42.0768,0.716386788620938
+"FLOW_236212",2015,9,14,0.401,0.155365022421525,34.6464,0.689081595179898
+"FLOW_236212",2015,9,15,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2015,9,16,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2015,9,17,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2015,9,18,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2015,9,19,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2015,9,20,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2015,9,21,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2015,9,22,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2015,9,23,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2015,9,24,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2015,9,25,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2015,9,26,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2015,9,27,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2015,9,28,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2015,9,29,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2015,9,30,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2015,10,1,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2015,10,2,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2015,10,3,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2015,10,4,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2015,10,5,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2015,10,6,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2015,10,7,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2015,10,8,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2015,10,9,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2015,10,10,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2015,10,11,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2015,10,12,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2015,10,13,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2015,10,14,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2015,10,15,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,10,16,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2015,10,17,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2015,10,18,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,10,19,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2015,10,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2015,10,21,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2015,10,22,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2015,10,23,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2015,10,24,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2015,10,25,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2015,10,26,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2015,10,27,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2015,10,28,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2015,10,29,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2015,10,30,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2015,10,31,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2015,11,1,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2015,11,2,0.235,0.0910493273542601,20.304,0.61923479048723
+"FLOW_236212",2015,11,3,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2015,11,4,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2015,11,5,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2015,11,6,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2015,11,7,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2015,11,8,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2015,11,9,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2015,11,10,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2015,11,11,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2015,11,12,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2015,11,13,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2015,11,14,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2015,11,15,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2015,11,16,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2015,11,17,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2015,11,18,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2015,11,19,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2015,11,20,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2015,11,21,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2015,11,22,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2015,11,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,11,24,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2015,11,25,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2015,11,26,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2015,11,27,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2015,11,28,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2015,11,29,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2015,11,30,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2015,12,1,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2015,12,2,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2015,12,3,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2015,12,4,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2015,12,5,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,12,6,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2015,12,7,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,12,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2015,12,9,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2015,12,10,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2015,12,11,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2015,12,12,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2015,12,13,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2015,12,14,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2015,12,15,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2015,12,16,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2015,12,17,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2015,12,18,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2015,12,19,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2015,12,20,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2015,12,21,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2015,12,22,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2015,12,23,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2015,12,24,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2015,12,25,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2015,12,26,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2015,12,27,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2015,12,28,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2015,12,29,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2015,12,30,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2015,12,31,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2016,1,1,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2016,1,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2016,1,3,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2016,1,4,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2016,1,5,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2016,1,6,0.091,0.035257399103139,7.8624,0.512212360351396
+"FLOW_236212",2016,1,7,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2016,1,8,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2016,1,9,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2016,1,10,0.106,0.0410690582959641,9.1584,0.528083926552163
+"FLOW_236212",2016,1,11,0.097,0.0375820627802691,8.3808,0.518795407246872
+"FLOW_236212",2016,1,12,0.089,0.0344825112107623,7.6896,0.50994081909868
+"FLOW_236212",2016,1,13,0.102,0.0395192825112108,8.8128,0.524036829267355
+"FLOW_236212",2016,1,14,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2016,1,15,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2016,1,16,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2016,1,17,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2016,1,18,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2016,1,19,0.089,0.0344825112107623,7.6896,0.50994081909868
+"FLOW_236212",2016,1,20,0.088,0.034095067264574,7.6032,0.50878969941032
+"FLOW_236212",2016,1,21,0.103,0.0399067264573991,8.8992,0.525060346500765
+"FLOW_236212",2016,1,22,0.092,0.0356448430493274,7.9488,0.513333185865999
+"FLOW_236212",2016,1,23,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2016,1,24,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2016,1,25,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2016,1,26,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2016,1,27,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2016,1,28,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2016,1,29,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2016,1,30,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2016,1,31,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2016,2,1,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2016,2,2,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2016,2,3,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2016,2,4,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2016,2,5,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2016,2,6,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2016,2,7,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2016,2,8,0.111,0.0430062780269058,9.5904,0.532974427354672
+"FLOW_236212",2016,2,9,0.095,0.0368071748878924,8.208,0.516638179036212
+"FLOW_236212",2016,2,10,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2016,2,11,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2016,2,12,0.118,0.0457183856502242,10.1952,0.539533204144967
+"FLOW_236212",2016,2,13,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2016,2,14,0.104,0.0402941704035874,8.9856,0.526075944704909
+"FLOW_236212",2016,2,15,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2016,2,16,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2016,2,17,0.112,0.0433937219730942,9.6768,0.533931299634921
+"FLOW_236212",2016,2,18,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2016,2,19,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2016,2,20,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2016,2,21,0.137,0.0530798206278027,11.8368,0.555886195652285
+"FLOW_236212",2016,2,22,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2016,2,23,0.105,0.0406816143497758,9.072,0.527083760418311
+"FLOW_236212",2016,2,24,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2016,2,25,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2016,2,26,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2016,2,27,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2016,2,28,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2016,2,29,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2016,3,1,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2016,3,2,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2016,3,3,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2016,3,4,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2016,3,5,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2016,3,6,0.101,0.0391318385650224,8.7264,0.523005252703143
+"FLOW_236212",2016,3,7,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2016,3,8,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2016,3,9,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2016,3,10,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2016,3,11,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2016,3,12,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2016,3,13,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2016,3,14,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2016,3,15,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2016,3,16,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2016,3,17,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2016,3,18,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2016,3,19,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2016,3,20,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2016,3,21,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2016,3,22,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2016,3,23,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2016,3,24,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2016,3,25,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2016,3,26,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2016,3,27,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2016,3,28,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2016,3,29,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2016,3,30,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2016,3,31,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2016,4,1,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2016,4,2,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2016,4,3,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2016,4,4,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2016,4,5,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2016,4,6,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2016,4,7,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2016,4,8,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2016,4,9,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2016,4,10,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2016,4,11,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2016,4,12,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2016,4,13,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2016,4,14,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2016,4,15,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2016,4,16,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2016,4,17,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2016,4,18,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2016,4,19,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2016,4,20,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2016,4,21,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2016,4,22,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2016,4,23,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2016,4,24,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2016,4,25,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2016,4,26,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2016,4,27,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2016,4,28,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2016,4,29,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2016,4,30,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2016,5,1,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2016,5,2,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2016,5,3,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2016,5,4,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2016,5,5,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2016,5,6,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2016,5,7,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2016,5,8,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2016,5,9,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2016,5,10,0.311,0.120495067264574,26.8704,0.654928445719558
+"FLOW_236212",2016,5,11,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2016,5,12,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2016,5,13,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2016,5,14,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2016,5,15,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2016,5,16,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2016,5,17,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2016,5,18,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2016,5,19,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2016,5,20,0.216,0.0836878923766816,18.6624,0.608881145690013
+"FLOW_236212",2016,5,21,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2016,5,22,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2016,5,23,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2016,5,24,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2016,5,25,0.309,0.119720179372197,26.6976,0.654083918801248
+"FLOW_236212",2016,5,26,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2016,5,27,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",2016,5,28,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2016,5,29,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2016,5,30,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2016,5,31,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2016,6,1,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2016,6,2,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2016,6,3,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2016,6,4,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2016,6,5,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2016,6,6,0.228,0.0883372197309417,19.6992,0.615500975089455
+"FLOW_236212",2016,6,7,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2016,6,8,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2016,6,9,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",2016,6,10,0.685,0.265399103139013,59.184,0.766972672538289
+"FLOW_236212",2016,6,11,1.011,0.391705829596413,87.3504,0.829071539755236
+"FLOW_236212",2016,6,12,1.795,0.695461883408072,155.088,0.92993943859832
+"FLOW_236212",2016,6,13,1.193,0.462220627802691,103.0752,0.856978378057773
+"FLOW_236212",2016,6,14,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",2016,6,15,0.432,0.167375784753363,37.3248,0.699420770442828
+"FLOW_236212",2016,6,16,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2016,6,17,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",2016,6,18,0.318,0.123207174887892,27.4752,0.657850486019678
+"FLOW_236212",2016,6,19,0.31,0.120107623318386,26.784,0.6545067271175
+"FLOW_236212",2016,6,20,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2016,6,21,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2016,6,22,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2016,6,23,0.403,0.156139910313901,34.8192,0.689767591158343
+"FLOW_236212",2016,6,24,0.681,0.26384932735426,58.8384,0.766074838443101
+"FLOW_236212",2016,6,25,1.91,0.740017937219731,165.024,0.94156097367118
+"FLOW_236212",2016,6,26,1.829,0.708634977578475,158.0256,0.9334359367954
+"FLOW_236212",2016,6,27,1.032,0.399842152466368,89.1648,0.832487488792331
+"FLOW_236212",2016,6,28,0.633,0.24525201793722,54.6912,0.75495752431767
+"FLOW_236212",2016,6,29,0.482,0.18674798206278,41.6448,0.714909689886756
+"FLOW_236212",2016,6,30,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",2016,7,1,0.498,0.192947085201794,43.0272,0.719594183773205
+"FLOW_236212",2016,7,2,1.039,0.402554260089686,89.7696,0.833613780585938
+"FLOW_236212",2016,7,3,1.273,0.493216143497758,109.9872,0.868175361616444
+"FLOW_236212",2016,7,4,0.948,0.367296860986547,81.9072,0.818471277452742
+"FLOW_236212",2016,7,5,0.955,0.370008968609865,82.512,0.819676436007093
+"FLOW_236212",2016,7,6,1.592,0.616810762331839,137.5488,0.907884054654957
+"FLOW_236212",2016,7,7,1.712,0.663304035874439,147.9168,0.921175816467916
+"FLOW_236212",2016,7,8,1.382,0.535447533632287,119.4048,0.882558261143458
+"FLOW_236212",2016,7,9,1.09,0.422313901345292,94.176,0.841641371646414
+"FLOW_236212",2016,7,10,0.918,0.355673542600897,79.3152,0.813224234067184
+"FLOW_236212",2016,7,11,0.888,0.344050224215247,76.7232,0.807838169067869
+"FLOW_236212",2016,7,12,1.36,0.526923766816144,117.504,0.879730314582357
+"FLOW_236212",2016,7,13,6.879,2.6652269058296,594.3456,1.21659727052968
+"FLOW_236212",2016,7,14,8.362,3.23980627802691,722.4768,1.26503869096756
+"FLOW_236212",2016,7,15,4.649,1.8012269058296,401.6736,1.1248993999729
+"FLOW_236212",2016,7,16,2.529,0.979845739910314,218.5056,0.995936253861894
+"FLOW_236212",2016,7,17,1.429,0.553657399103139,123.4656,0.888481156933532
+"FLOW_236212",2016,7,18,0.917,0.355286098654709,79.2288,0.813046983781364
+"FLOW_236212",2016,7,19,0.687,0.26617399103139,59.3568,0.767420017650114
+"FLOW_236212",2016,7,20,0.58,0.224717488789238,50.112,0.741869257464488
+"FLOW_236212",2016,7,21,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",2016,7,22,1.197,0.463770403587444,103.4208,0.857552280047703
+"FLOW_236212",2016,7,23,6.66,2.58037668161435,575.424,1.2087503747066
+"FLOW_236212",2016,7,24,9.257,3.58656860986547,799.8048,1.29102849710294
+"FLOW_236212",2016,7,25,6.736,2.60982242152466,581.9904,1.21149657749739
+"FLOW_236212",2016,7,26,14.197,5.50054170403587,1226.6208,1.40631008870892
+"FLOW_236212",2016,7,27,10.41,4.03329147982063,899.424,1.32169705342345
+"FLOW_236212",2016,7,28,5.215,2.0205201793722,450.576,1.1510458899275
+"FLOW_236212",2016,7,29,3.165,1.2262600896861,273.456,1.04163728944442
+"FLOW_236212",2016,7,30,2.63,1.01897757847534,227.232,1.00376702758786
+"FLOW_236212",2016,7,31,2.162,0.837653811659193,186.7968,0.965190222520092
+"FLOW_236212",2016,8,1,1.643,0.636570403587444,141.9552,0.91362776359426
+"FLOW_236212",2016,8,2,1.398,0.5416466367713,120.7872,0.884592412977752
+"FLOW_236212",2016,8,3,1.211,0.469194618834081,104.6304,0.859548930960111
+"FLOW_236212",2016,8,4,1.047,0.405653811659193,90.4608,0.834893562135835
+"FLOW_236212",2016,8,5,0.898,0.34792466367713,77.5872,0.809649483602681
+"FLOW_236212",2016,8,6,0.731,0.283221524663677,63.1584,0.777007572397622
+"FLOW_236212",2016,8,7,0.621,0.24060269058296,53.6544,0.752073170521953
+"FLOW_236212",2016,8,8,0.546,0.211544394618834,47.1744,0.732960050622791
+"FLOW_236212",2016,8,9,0.496,0.192172197309417,42.8544,0.71901526573971
+"FLOW_236212",2016,8,10,0.49,0.189847533632287,42.336,0.717267233870557
+"FLOW_236212",2016,8,11,0.622,0.240990134529148,53.7408,0.752315228238822
+"FLOW_236212",2016,8,12,0.974,0.377370403587444,84.1536,0.82291232563048
+"FLOW_236212",2016,8,13,1.088,0.421539013452915,94.0032,0.841332285491912
+"FLOW_236212",2016,8,14,1.162,0.450209865470852,100.3968,0.852477649772266
+"FLOW_236212",2016,8,15,1.005,0.389381165919282,86.832,0.828085134177192
+"FLOW_236212",2016,8,16,0.8,0.309955156950673,69.12,0.791151713968427
+"FLOW_236212",2016,8,17,0.639,0.24757668161435,55.2096,0.756383327726054
+"FLOW_236212",2016,8,18,0.686,0.265786547085202,59.2704,0.767196475515567
+"FLOW_236212",2016,8,19,1.408,0.545521076233184,121.6512,0.88585431884184
+"FLOW_236212",2016,8,20,6.355,2.46220627802691,549.072,1.19747067537472
+"FLOW_236212",2016,8,21,6.179,2.39401614349776,533.8656,1.19076322857595
+"FLOW_236212",2016,8,22,13.998,5.4234403587444,1209.4272,1.40234533368835
+"FLOW_236212",2016,8,23,10.751,4.16540986547085,928.8864,1.33024473365211
+"FLOW_236212",2016,8,24,4.625,1.79192825112108,399.6,1.12373555794904
+"FLOW_236212",2016,8,25,2.943,1.14024753363229,254.2752,1.02659661589688
+"FLOW_236212",2016,8,26,2.692,1.04299910313901,232.5888,1.00845561166215
+"FLOW_236212",2016,8,27,2.145,0.831067264573991,185.328,0.963667550330011
+"FLOW_236212",2016,8,28,1.627,0.63037130044843,140.5728,0.911841358107184
+"FLOW_236212",2016,8,29,1.188,0.460283408071749,102.6432,0.856258831805151
+"FLOW_236212",2016,8,30,0.889,0.344437668161435,76.8096,0.808020032699328
+"FLOW_236212",2016,8,31,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",2016,9,1,0.686,0.265786547085202,59.2704,0.767196475515567
+"FLOW_236212",2016,9,2,0.648,0.251063677130045,55.9872,0.758502081734593
+"FLOW_236212",2016,9,3,0.616,0.238665470852018,53.2224,0.75085818328551
+"FLOW_236212",2016,9,4,0.622,0.240990134529148,53.7408,0.752315228238822
+"FLOW_236212",2016,9,5,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",2016,9,6,0.551,0.213481614349776,47.6064,0.734297577863094
+"FLOW_236212",2016,9,7,0.488,0.18907264573991,42.1632,0.716680751292182
+"FLOW_236212",2016,9,8,0.469,0.181711210762332,40.5216,0.711011040452268
+"FLOW_236212",2016,9,9,6.065,2.34984753363229,524.016,1.18633662124795
+"FLOW_236212",2016,9,10,14.74,5.71092376681614,1273.536,1.41690676094459
+"FLOW_236212",2016,9,11,7.982,3.09257757847534,689.6448,1.25332620622546
+"FLOW_236212",2016,9,12,4.796,1.85818116591928,414.3744,1.13192489261872
+"FLOW_236212",2016,9,13,3.54,1.37155156950673,305.856,1.06522764372763
+"FLOW_236212",2016,9,14,17.637,6.83334887892377,1523.8368,1.46867843774758
+"FLOW_236212",2016,9,15,22.337,8.65433542600897,1929.9168,1.53973770446053
+"FLOW_236212",2016,9,16,13.942,5.40174349775785,1204.5888,1.40122149729351
+"FLOW_236212",2016,9,17,7.504,2.90737937219731,648.3456,1.23794212355097
+"FLOW_236212",2016,9,18,3.794,1.46996233183857,327.8016,1.0800932231825
+"FLOW_236212",2016,9,19,2.221,0.860513004484305,191.8944,0.970401563514332
+"FLOW_236212",2016,9,20,1.564,0.605962331838565,135.1296,0.904667785332956
+"FLOW_236212",2016,9,21,1.16,0.449434977578475,100.224,0.852183995672329
+"FLOW_236212",2016,9,22,0.968,0.375045739910314,83.6352,0.821895963130161
+"FLOW_236212",2016,9,23,0.846,0.327777578475336,73.0944,0.800047640065633
+"FLOW_236212",2016,9,24,0.725,0.280896860986547,62.64,0.775727838456387
+"FLOW_236212",2016,9,25,0.801,0.310342600896861,69.2064,0.79134940307706
+"FLOW_236212",2016,9,26,0.831,0.321965919282511,71.7984,0.797190254040957
+"FLOW_236212",2016,9,27,0.867,0.335913901345291,74.9088,0.803980648140094
+"FLOW_236212",2016,9,28,1.101,0.426575784753363,95.1264,0.843333281339109
+"FLOW_236212",2016,9,29,1.789,0.693137219730942,154.5696,0.929316919170229
+"FLOW_236212",2016,9,30,2.515,0.974421524663677,217.296,0.994831146315885
+"FLOW_236212",2016,10,1,2.722,1.05462242152466,235.1808,1.01069333103666
+"FLOW_236212",2016,10,2,4.581,1.77488071748879,395.7984,1.12158924004113
+"FLOW_236212",2016,10,3,5.508,2.13404125560538,475.8912,1.16369873495564
+"FLOW_236212",2016,10,4,4.52,1.7512466367713,390.528,1.11858621471969
+"FLOW_236212",2016,10,5,3.568,1.3824,308.2752,1.06690744335864
+"FLOW_236212",2016,10,6,2.399,0.92947802690583,207.2736,0.98548002414786
+"FLOW_236212",2016,10,7,1.675,0.648968609865471,144.72,0.917159223958902
+"FLOW_236212",2016,10,8,1.153,0.446722869955157,99.6192,0.851153006138517
+"FLOW_236212",2016,10,9,0.822,0.318478923766816,71.0208,0.795455958591646
+"FLOW_236212",2016,10,10,0.657,0.25455067264574,56.7648,0.760597423288142
+"FLOW_236212",2016,10,11,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",2016,10,12,0.838,0.32467802690583,72.4032,0.798528791631944
+"FLOW_236212",2016,10,13,1.422,0.550945291479821,122.8608,0.887608995933618
+"FLOW_236212",2016,10,14,1.315,0.509488789237668,113.616,0.873829951284691
+"FLOW_236212",2016,10,15,0.922,0.35722331838565,79.6608,0.813931694236331
+"FLOW_236212",2016,10,16,0.715,0.277022421524664,61.776,0.773575994369569
+"FLOW_236212",2016,10,17,0.809,0.313442152466368,69.8976,0.792923848561831
+"FLOW_236212",2016,10,18,1.082,0.419214349775785,93.4848,0.840402291745459
+"FLOW_236212",2016,10,19,1.43,0.554044843049327,123.552,0.88860547219752
+"FLOW_236212",2016,10,20,1.533,0.593951569506726,132.4512,0.901052730064688
+"FLOW_236212",2016,10,21,2.093,0.810920179372197,180.8352,0.958949244984481
+"FLOW_236212",2016,10,22,7.98,3.09180269058296,689.472,1.25326339230212
+"FLOW_236212",2016,10,23,6.027,2.33512466367713,520.7328,1.18484629317148
+"FLOW_236212",2016,10,24,3.289,1.27430313901345,284.1696,1.04967426718418
+"FLOW_236212",2016,10,25,2.034,0.788060986547085,175.7376,0.953480829442968
+"FLOW_236212",2016,10,26,1.322,0.512200896860987,114.2208,0.874758289948999
+"FLOW_236212",2016,10,27,0.951,0.368459192825112,82.1664,0.818988642769627
+"FLOW_236212",2016,10,28,0.755,0.292520179372197,65.232,0.782043960083223
+"FLOW_236212",2016,10,29,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",2016,10,30,0.5,0.19372197309417,43.2,0.720171244806823
+"FLOW_236212",2016,10,31,0.512,0.198371300448431,44.2368,0.723595351277449
+"FLOW_236212",2016,11,1,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",2016,11,2,0.545,0.211156950672646,47.088,0.732691370180108
+"FLOW_236212",2016,11,3,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2016,11,4,0.436,0.168925560538117,37.6704,0.700711223446334
+"FLOW_236212",2016,11,5,0.406,0.157302242152466,35.0784,0.690791496099528
+"FLOW_236212",2016,11,6,0.393,0.152265470852018,33.9552,0.686309935304125
+"FLOW_236212",2016,11,7,0.382,0.148003587443946,33.0048,0.682424245829442
+"FLOW_236212",2016,11,8,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2016,11,9,0.37,0.143354260089686,31.968,0.678081851231046
+"FLOW_236212",2016,11,10,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",2016,11,11,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2016,11,12,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2016,11,13,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2016,11,14,0.683,0.264624215246637,59.0112,0.766524281309425
+"FLOW_236212",2016,11,15,0.589,0.228204484304933,50.8896,0.744157451487216
+"FLOW_236212",2016,11,16,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2016,11,17,0.4,0.154977578475336,34.56,0.688737570247908
+"FLOW_236212",2016,11,18,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",2016,11,19,0.356,0.137930044843049,30.7584,0.672870944826718
+"FLOW_236212",2016,11,20,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2016,11,21,0.312,0.120882511210762,26.9568,0.655349080906367
+"FLOW_236212",2016,11,22,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2016,11,23,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2016,11,24,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2016,11,25,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2016,11,26,0.292,0.113133632286996,25.2288,0.646723059183028
+"FLOW_236212",2016,11,27,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2016,11,28,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2016,11,29,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2016,11,30,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2016,12,1,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2016,12,2,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2016,12,3,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2016,12,4,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2016,12,5,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2016,12,6,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2016,12,7,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2016,12,8,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2016,12,9,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2016,12,10,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2016,12,11,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2016,12,12,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2016,12,13,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2016,12,14,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2016,12,15,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2016,12,16,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2016,12,17,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2016,12,18,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2016,12,19,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2016,12,20,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2016,12,21,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2016,12,22,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2016,12,23,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2016,12,24,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2016,12,25,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2016,12,26,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2016,12,27,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2016,12,28,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2016,12,29,0.322,0.124756950672646,27.8208,0.65949719120023
+"FLOW_236212",2016,12,30,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2016,12,31,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2017,1,1,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2017,1,2,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2017,1,3,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2017,1,4,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2017,1,5,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2017,1,6,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2017,1,7,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2017,1,8,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2017,1,9,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2017,1,10,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2017,1,11,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2017,1,12,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2017,1,13,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2017,1,14,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2017,1,15,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2017,1,16,0.183,0.0709022421524664,15.8112,0.589022648058152
+"FLOW_236212",2017,1,17,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2017,1,18,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,1,19,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2017,1,20,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2017,1,21,0.303,0.117395515695067,26.1792,0.651523824873738
+"FLOW_236212",2017,1,22,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2017,1,23,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2017,1,24,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2017,1,25,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2017,1,26,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2017,1,27,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2017,1,28,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2017,1,29,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2017,1,30,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2017,1,31,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2017,2,1,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2017,2,2,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2017,2,3,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2017,2,4,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2017,2,5,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,2,6,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2017,2,7,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2017,2,8,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2017,2,9,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2017,2,10,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,2,11,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2017,2,12,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2017,2,13,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2017,2,14,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2017,2,15,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2017,2,16,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2017,2,17,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2017,2,18,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2017,2,19,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2017,2,20,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2017,2,21,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2017,2,22,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2017,2,23,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2017,2,24,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2017,2,25,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2017,2,26,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2017,2,27,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2017,2,28,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2017,3,1,0.139,0.0538547085201794,12.0096,0.557499825696288
+"FLOW_236212",2017,3,2,0.108,0.0418439461883408,9.3312,0.53006182436083
+"FLOW_236212",2017,3,3,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2017,3,4,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2017,3,5,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2017,3,6,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2017,3,7,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2017,3,8,0.128,0.0495928251121076,11.0592,0.54838273069057
+"FLOW_236212",2017,3,9,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2017,3,10,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2017,3,11,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2017,3,12,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2017,3,13,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2017,3,14,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,3,15,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2017,3,16,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2017,3,17,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2017,3,18,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2017,3,19,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2017,3,20,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2017,3,21,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2017,3,22,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2017,3,23,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2017,3,24,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2017,3,25,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2017,3,26,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2017,3,27,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2017,3,28,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2017,3,29,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2017,3,30,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2017,3,31,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2017,4,1,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2017,4,2,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2017,4,3,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2017,4,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2017,4,5,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2017,4,6,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2017,4,7,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2017,4,8,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2017,4,9,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2017,4,10,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2017,4,11,0.353,0.136767713004484,30.4992,0.671733050582062
+"FLOW_236212",2017,4,12,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2017,4,13,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2017,4,14,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2017,4,15,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2017,4,16,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2017,4,17,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2017,4,18,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2017,4,19,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2017,4,20,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2017,4,21,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2017,4,22,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2017,4,23,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2017,4,24,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2017,4,25,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",2017,4,26,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",2017,4,27,0.417,0.161564125560538,36.0288,0.694494781700124
+"FLOW_236212",2017,4,28,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2017,4,29,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2017,4,30,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2017,5,1,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2017,5,2,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2017,5,3,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2017,5,4,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2017,5,5,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2017,5,6,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2017,5,7,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2017,5,8,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2017,5,9,0.251,0.0972484304932736,21.6864,0.627446238652902
+"FLOW_236212",2017,5,10,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2017,5,11,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2017,5,12,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2017,5,13,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2017,5,14,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2017,5,15,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2017,5,16,0.242,0.0937614349775785,20.9088,0.622880663632203
+"FLOW_236212",2017,5,17,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2017,5,18,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2017,5,19,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2017,5,20,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2017,5,21,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2017,5,22,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2017,5,23,0.236,0.0914367713004484,20.3904,0.619760904067603
+"FLOW_236212",2017,5,24,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2017,5,25,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2017,5,26,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2017,5,27,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2017,5,28,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2017,5,29,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2017,5,30,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2017,5,31,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2017,6,1,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2017,6,2,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",2017,6,3,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2017,6,4,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2017,6,5,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2017,6,6,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2017,6,7,0.295,0.114295964125561,25.488,0.648046514442812
+"FLOW_236212",2017,6,8,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2017,6,9,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2017,6,10,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2017,6,11,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2017,6,12,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2017,6,13,0.274,0.106159641255605,23.6736,0.638545558511339
+"FLOW_236212",2017,6,14,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2017,6,15,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2017,6,16,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2017,6,17,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2017,6,18,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2017,6,19,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2017,6,20,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2017,6,21,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2017,6,22,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2017,6,23,0.266,0.103060089686099,22.9824,0.634772499256734
+"FLOW_236212",2017,6,24,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2017,6,25,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2017,6,26,0.285,0.110421524663677,24.624,0.64359216421203
+"FLOW_236212",2017,6,27,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2017,6,28,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2017,6,29,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2017,6,30,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2017,7,1,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2017,7,2,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2017,7,3,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2017,7,4,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2017,7,5,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2017,7,6,0.344,0.133280717488789,29.7216,0.668272306899008
+"FLOW_236212",2017,7,7,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2017,7,8,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2017,7,9,0.335,0.129793721973094,28.944,0.66473835387983
+"FLOW_236212",2017,7,10,0.442,0.171250224215247,38.1888,0.702629257322456
+"FLOW_236212",2017,7,11,0.408,0.158077130044843,35.2512,0.69147074175462
+"FLOW_236212",2017,7,12,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",2017,7,13,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",2017,7,14,0.358,0.138704932735426,30.9312,0.673625286346036
+"FLOW_236212",2017,7,15,0.407,0.157689686098655,35.1648,0.691131452709085
+"FLOW_236212",2017,7,16,0.477,0.184810762331839,41.2128,0.713420281757027
+"FLOW_236212",2017,7,17,0.46,0.178224215246637,39.744,0.708261022751702
+"FLOW_236212",2017,7,18,0.654,0.253388340807175,56.5056,0.759901541588952
+"FLOW_236212",2017,7,19,0.969,0.375433183856502,83.7216,0.822065706218397
+"FLOW_236212",2017,7,20,1.527,0.591626905829596,131.9328,0.900346298204089
+"FLOW_236212",2017,7,21,1.257,0.487017040358744,108.6048,0.865981933013452
+"FLOW_236212",2017,7,22,0.811,0.314217040358744,70.0704,0.793315512792699
+"FLOW_236212",2017,7,23,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",2017,7,24,0.752,0.291357847533632,64.9728,0.781421478031143
+"FLOW_236212",2017,7,25,0.924,0.357998206278027,79.8336,0.814284503945924
+"FLOW_236212",2017,7,26,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",2017,7,27,0.782,0.302981165919283,67.5648,0.787559050117462
+"FLOW_236212",2017,7,28,0.958,0.371171300448431,82.7712,0.820190770088631
+"FLOW_236212",2017,7,29,1.415,0.548233183856502,122.256,0.886733393471652
+"FLOW_236212",2017,7,30,1.164,0.450984753363229,100.5696,0.852770899807179
+"FLOW_236212",2017,7,31,0.969,0.375433183856502,83.7216,0.822065706218397
+"FLOW_236212",2017,8,1,0.835,0.323515695067265,72.144,0.797956232157998
+"FLOW_236212",2017,8,2,0.662,0.256487892376682,57.1968,0.761751597678812
+"FLOW_236212",2017,8,3,0.533,0.206507623318386,46.0512,0.729436040919506
+"FLOW_236212",2017,8,4,0.478,0.185198206278027,41.2992,0.713719159231734
+"FLOW_236212",2017,8,5,0.47,0.18209865470852,40.608,0.711313985189615
+"FLOW_236212",2017,8,6,0.545,0.211156950672646,47.088,0.732691370180108
+"FLOW_236212",2017,8,7,0.989,0.383182062780269,85.4496,0.825431492137924
+"FLOW_236212",2017,8,8,2.537,0.982945291479821,219.1968,0.996565548261304
+"FLOW_236212",2017,8,9,2.551,0.988369506726458,220.4064,0.997663003739686
+"FLOW_236212",2017,8,10,1.823,0.706310313901345,157.5072,0.932822707795427
+"FLOW_236212",2017,8,11,1.146,0.444010762331839,99.0144,0.850116996973521
+"FLOW_236212",2017,8,12,0.786,0.304530941704036,67.9104,0.78836309370197
+"FLOW_236212",2017,8,13,0.626,0.242539910313901,54.0864,0.753280356786524
+"FLOW_236212",2017,8,14,0.534,0.206895067264574,46.1376,0.7297095453237
+"FLOW_236212",2017,8,15,0.483,0.187135426008969,41.7312,0.715206087040821
+"FLOW_236212",2017,8,16,0.79,0.306080717488789,68.256,0.789163870461388
+"FLOW_236212",2017,8,17,1.726,0.668728251121076,149.1264,0.922677508354255
+"FLOW_236212",2017,8,18,2.86,1.10808968609865,247.104,1.02073964415465
+"FLOW_236212",2017,8,19,6.423,2.48855246636771,554.9472,1.20002242068684
+"FLOW_236212",2017,8,20,4.851,1.87949058295964,419.1264,1.13450922293803
+"FLOW_236212",2017,8,21,3.086,1.19565201793722,266.6304,1.03638462196151
+"FLOW_236212",2017,8,22,2.259,0.875235874439462,195.1776,0.973699668323346
+"FLOW_236212",2017,8,23,1.752,0.678801793721973,151.3728,0.925440701728717
+"FLOW_236212",2017,8,24,2.05,0.794260089686099,177.12,0.954976199787959
+"FLOW_236212",2017,8,25,3.466,1.34288071748879,299.4624,1.06073643736729
+"FLOW_236212",2017,8,26,2.332,0.903519282511211,201.4848,0.979912922916254
+"FLOW_236212",2017,8,27,1.831,0.709409865470852,158.1984,0.933639988828627
+"FLOW_236212",2017,8,28,1.771,0.686163228699552,153.0144,0.927439285175996
+"FLOW_236212",2017,8,29,1.703,0.659817040358744,147.1392,0.920205247424964
+"FLOW_236212",2017,8,30,1.576,0.610611659192825,136.1664,0.906051781278914
+"FLOW_236212",2017,8,31,1.471,0.569930044843049,127.0944,0.893643519755898
+"FLOW_236212",2017,9,1,1.112,0.430837668161435,96.0768,0.845011721634523
+"FLOW_236212",2017,9,2,0.846,0.327777578475336,73.0944,0.800047640065633
+"FLOW_236212",2017,9,3,0.704,0.272760538116592,60.8256,0.771180976266069
+"FLOW_236212",2017,9,4,1.014,0.392868161434978,87.6096,0.829562987367691
+"FLOW_236212",2017,9,5,8.48,3.28552466367713,732.672,1.26858901474932
+"FLOW_236212",2017,9,6,28.335,10.9782242152466,2448.144,1.61475418683233
+"FLOW_236212",2017,9,7,19.107,7.40289147982063,1650.8448,1.49238294273642
+"FLOW_236212",2017,9,8,17.49,6.77639461883408,1511.136,1.4662220208307
+"FLOW_236212",2017,9,9,10.315,3.99648430493274,891.216,1.31927587981088
+"FLOW_236212",2017,9,10,5.716,2.21462959641256,493.8624,1.17235791514645
+"FLOW_236212",2017,9,11,3.318,1.28553901345291,286.6752,1.05151882523927
+"FLOW_236212",2017,9,12,2.542,0.984882511210762,219.6288,0.996958051552907
+"FLOW_236212",2017,9,13,3.572,1.38394977578475,308.6208,1.06714655307413
+"FLOW_236212",2017,9,14,9.798,3.79617578475336,846.5472,1.30577774641025
+"FLOW_236212",2017,9,15,10.365,4.01585650224215,895.536,1.32055239490415
+"FLOW_236212",2017,9,16,30.291,11.736064573991,2617.1424,1.63645662293137
+"FLOW_236212",2017,9,17,18.637,7.22079282511211,1610.2368,1.48496759622055
+"FLOW_236212",2017,9,18,8.767,3.39672107623318,757.4688,1.2770619849656
+"FLOW_236212",2017,9,19,4.869,1.88646457399103,420.6816,1.13534991242926
+"FLOW_236212",2017,9,20,3.329,1.28980089686099,287.6256,1.05221511209321
+"FLOW_236212",2017,9,21,2.353,0.911655605381166,203.2992,0.981671451698213
+"FLOW_236212",2017,9,22,1.768,0.685000896860987,152.7552,0.927124863377464
+"FLOW_236212",2017,9,23,1.339,0.518787443946188,115.6896,0.876996562887401
+"FLOW_236212",2017,9,24,1.114,0.431612556053812,96.2496,0.845315464244141
+"FLOW_236212",2017,9,25,1.5,0.581165919282511,129.6,0.89713960147728
+"FLOW_236212",2017,9,26,3.368,1.30491121076233,290.9952,1.05466902797168
+"FLOW_236212",2017,9,27,2.793,1.08213094170404,241.3152,1.01591170236907
+"FLOW_236212",2017,9,28,2.025,0.78457399103139,174.96,0.952635543690447
+"FLOW_236212",2017,9,29,1.395,0.540484304932735,120.528,0.884212433278658
+"FLOW_236212",2017,9,30,1.106,0.428513004484305,95.5584,0.844097863992855
+"FLOW_236212",2017,10,1,1.159,0.449047533632287,100.1376,0.852037016706069
+"FLOW_236212",2017,10,2,1.068,0.413790134529148,92.2752,0.838216154338968
+"FLOW_236212",2017,10,3,0.922,0.35722331838565,79.6608,0.813931694236331
+"FLOW_236212",2017,10,4,0.758,0.293682511210762,65.4912,0.782664466518846
+"FLOW_236212",2017,10,5,0.64,0.247964125560538,55.296,0.756619919366877
+"FLOW_236212",2017,10,6,0.562,0.217743497757848,48.5568,0.737206301788547
+"FLOW_236212",2017,10,7,0.495,0.191784753363229,42.768,0.7187251061352
+"FLOW_236212",2017,10,8,0.444,0.172025112107623,38.3616,0.703263973134143
+"FLOW_236212",2017,10,9,0.41,0.15885201793722,35.424,0.692147328902521
+"FLOW_236212",2017,10,10,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",2017,10,11,0.476,0.18442331838565,41.1264,0.713120902599212
+"FLOW_236212",2017,10,12,1.891,0.732656502242153,163.3824,0.93968021238008
+"FLOW_236212",2017,10,13,1.904,0.737693273542601,164.5056,0.940968672317167
+"FLOW_236212",2017,10,14,1.33,0.515300448430493,114.912,0.875814445504383
+"FLOW_236212",2017,10,15,0.863,0.334364125560538,74.5632,0.803237424638461
+"FLOW_236212",2017,10,16,0.619,0.239827802690583,53.4816,0.751588118089578
+"FLOW_236212",2017,10,17,0.481,0.186360538116592,41.5584,0.714612800378232
+"FLOW_236212",2017,10,18,0.38,0.14722869955157,32.832,0.681708164245585
+"FLOW_236212",2017,10,19,0.369,0.142966816143498,31.8816,0.677714923607427
+"FLOW_236212",2017,10,20,0.402,0.155752466367713,34.7328,0.689424934461032
+"FLOW_236212",2017,10,21,0.347,0.134443049327354,29.9808,0.669433853262682
+"FLOW_236212",2017,10,22,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2017,10,23,0.345,0.133668161434978,29.808,0.668660386316836
+"FLOW_236212",2017,10,24,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2017,10,25,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2017,10,26,0.343,0.132893273542601,29.6352,0.667883323918878
+"FLOW_236212",2017,10,27,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2017,10,28,0.319,0.123594618834081,27.5616,0.658263709012757
+"FLOW_236212",2017,10,29,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2017,10,30,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2017,10,31,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2017,11,1,0.331,0.128243946188341,28.5984,0.663143282451012
+"FLOW_236212",2017,11,2,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",2017,11,3,0.33,0.127856502242152,28.512,0.662742106547812
+"FLOW_236212",2017,11,4,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2017,11,5,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2017,11,6,0.299,0.115845739910314,25.8336,0.649794473798635
+"FLOW_236212",2017,11,7,0.286,0.110808968609865,24.7104,0.644043175281478
+"FLOW_236212",2017,11,8,0.282,0.109259192825112,24.3648,0.642231492068873
+"FLOW_236212",2017,11,9,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2017,11,10,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2017,11,11,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2017,11,12,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2017,11,13,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2017,11,14,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2017,11,15,0.259,0.10034798206278,22.3776,0.631395858201139
+"FLOW_236212",2017,11,16,0.765,0.296394618834081,66.096,0.784104710560857
+"FLOW_236212",2017,11,17,0.776,0.300656502242153,67.0464,0.786346793393592
+"FLOW_236212",2017,11,18,0.513,0.198758744394619,44.3232,0.72387778514619
+"FLOW_236212",2017,11,19,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2017,11,20,0.306,0.118557847533632,26.4384,0.652808891716359
+"FLOW_236212",2017,11,21,0.294,0.113908520179372,25.4016,0.647606563925501
+"FLOW_236212",2017,11,22,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2017,11,23,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2017,11,24,0.243,0.0941488789237668,20.9952,0.623394592271931
+"FLOW_236212",2017,11,25,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2017,11,26,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2017,11,27,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2017,11,28,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2017,11,29,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2017,11,30,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2017,12,1,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2017,12,2,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2017,12,3,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2017,12,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2017,12,5,0.241,0.0933739910313901,20.8224,0.622365033236773
+"FLOW_236212",2017,12,6,0.222,0.0860125560538117,19.1808,0.612226847957798
+"FLOW_236212",2017,12,7,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2017,12,8,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2017,12,9,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2017,12,10,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2017,12,11,0.234,0.0906618834080718,20.2176,0.618706882822294
+"FLOW_236212",2017,12,12,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2017,12,13,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2017,12,14,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2017,12,15,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2017,12,16,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2017,12,17,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2017,12,18,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2017,12,19,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2017,12,20,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2017,12,21,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2017,12,22,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,12,23,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2017,12,24,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2017,12,25,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2017,12,26,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2017,12,27,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2017,12,28,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2017,12,29,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2017,12,30,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2017,12,31,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2018,1,1,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2018,1,2,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2018,1,3,0.166,0.0643156950672646,14.3424,0.577648158915681
+"FLOW_236212",2018,1,4,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2018,1,5,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2018,1,6,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2018,1,7,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2018,1,8,0.114,0.0441686098654709,9.8496,0.535824720573439
+"FLOW_236212",2018,1,9,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2018,1,10,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2018,1,11,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2018,1,12,0.131,0.0507551569506726,11.3184,0.550929509317596
+"FLOW_236212",2018,1,13,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2018,1,14,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2018,1,15,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2018,1,16,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2018,1,17,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2018,1,18,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2018,1,19,0.148,0.0573417040358744,12.7872,0.564539220123407
+"FLOW_236212",2018,1,20,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2018,1,21,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2018,1,22,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2018,1,23,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2018,1,24,0.177,0.0685775784753363,15.2928,0.585108529519062
+"FLOW_236212",2018,1,25,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2018,1,26,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2018,1,27,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2018,1,28,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2018,1,29,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2018,1,30,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2018,1,31,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2018,2,1,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2018,2,2,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2018,2,3,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2018,2,4,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2018,2,5,0.142,0.0550170403587444,12.2688,0.559885789482227
+"FLOW_236212",2018,2,6,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2018,2,7,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2018,2,8,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2018,2,9,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2018,2,10,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2018,2,11,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2018,2,12,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2018,2,13,0.147,0.0569542600896861,12.7008,0.563774259019612
+"FLOW_236212",2018,2,14,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2018,2,15,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2018,2,16,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2018,2,17,0.171,0.0662529147982063,14.7744,0.581086783771789
+"FLOW_236212",2018,2,18,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2018,2,19,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2018,2,20,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2018,2,21,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2018,2,22,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2018,2,23,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2018,2,24,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2018,2,25,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2018,2,26,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2018,2,27,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2018,2,28,0.152,0.0588914798206278,13.1328,0.567558318654704
+"FLOW_236212",2018,3,1,0.169,0.0654780269058296,14.6016,0.579721112301707
+"FLOW_236212",2018,3,2,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2018,3,3,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2018,3,4,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2018,3,5,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2018,3,6,0.155,0.0600538116591928,13.392,0.569781199973242
+"FLOW_236212",2018,3,7,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2018,3,8,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2018,3,9,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2018,3,10,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2018,3,11,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2018,3,12,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2018,3,13,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2018,3,14,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2018,3,15,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2018,3,16,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2018,3,17,0.168,0.0650905829596413,14.5152,0.579033422212972
+"FLOW_236212",2018,3,18,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2018,3,19,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2018,3,20,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2018,3,21,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2018,3,22,0.176,0.068190134529148,15.2064,0.584445890752071
+"FLOW_236212",2018,3,23,0.174,0.0674152466367713,15.0336,0.583111525476363
+"FLOW_236212",2018,3,24,0.181,0.0701273542600897,15.6384,0.587729501335287
+"FLOW_236212",2018,3,25,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2018,3,26,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2018,3,27,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2018,3,28,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2018,3,29,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2018,3,30,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2018,3,31,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2018,4,1,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2018,4,2,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2018,4,3,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2018,4,4,0.21,0.0813632286995516,18.144,0.605460248538165
+"FLOW_236212",2018,4,5,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2018,4,6,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2018,4,7,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2018,4,8,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2018,4,9,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2018,4,10,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2018,4,11,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2018,4,12,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2018,4,13,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2018,4,14,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2018,4,15,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2018,4,16,0.354,0.137155156950673,30.5856,0.672113205353281
+"FLOW_236212",2018,4,17,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2018,4,18,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2018,4,19,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2018,4,20,0.225,0.0871748878923767,19.44,0.613872642808727
+"FLOW_236212",2018,4,21,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2018,4,22,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2018,4,23,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2018,4,24,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2018,4,25,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2018,4,26,0.23,0.0891121076233184,19.872,0.616577032317183
+"FLOW_236212",2018,4,27,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2018,4,28,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2018,4,29,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2018,4,30,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2018,5,1,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2018,5,2,0.221,0.0856251121076233,19.0944,0.611674295750401
+"FLOW_236212",2018,5,3,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2018,5,4,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2018,5,5,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2018,5,6,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2018,5,7,0.244,0.0945363228699552,21.0816,0.623906831740899
+"FLOW_236212",2018,5,8,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2018,5,9,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2018,5,10,0.379,0.146841255605381,32.7456,0.681348992199842
+"FLOW_236212",2018,5,11,0.615,0.23827802690583,53.136,0.750614239704673
+"FLOW_236212",2018,5,12,0.564,0.218518385650224,48.7296,0.737730258466806
+"FLOW_236212",2018,5,13,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2018,5,14,0.293,0.113521076233184,25.3152,0.647165414630438
+"FLOW_236212",2018,5,15,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2018,5,16,0.265,0.10267264573991,22.896,0.634294507374659
+"FLOW_236212",2018,5,17,0.261,0.101122869955157,22.5504,0.632367988839143
+"FLOW_236212",2018,5,18,0.415,0.160789237668161,35.856,0.693827317918013
+"FLOW_236212",2018,5,19,0.507,0.196434080717489,43.8048,0.722176525942559
+"FLOW_236212",2018,5,20,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",2018,5,21,0.601,0.232853811659193,51.9264,0.747165259032401
+"FLOW_236212",2018,5,22,1.6,0.619910313901345,138.24,0.908794672388616
+"FLOW_236212",2018,5,23,0.851,0.329714798206278,73.5264,0.800991095093804
+"FLOW_236212",2018,5,24,0.608,0.235565919282511,52.5312,0.748897691289846
+"FLOW_236212",2018,5,25,0.486,0.188297757847534,41.9904,0.716092342656814
+"FLOW_236212",2018,5,26,0.419,0.162339013452915,36.2016,0.695159689363362
+"FLOW_236212",2018,5,27,0.371,0.143741704035874,32.0544,0.678447986353356
+"FLOW_236212",2018,5,28,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2018,5,29,0.36,0.139479820627803,31.104,0.674376264004717
+"FLOW_236212",2018,5,30,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",2018,5,31,0.435,0.168538116591928,37.584,0.700389500985845
+"FLOW_236212",2018,6,1,0.399,0.154590134529148,34.4736,0.68839285657654
+"FLOW_236212",2018,6,2,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2018,6,3,0.365,0.141417040358744,31.536,0.676239210254601
+"FLOW_236212",2018,6,4,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2018,6,5,0.301,0.116620627802691,26.0064,0.650661447495414
+"FLOW_236212",2018,6,6,0.291,0.112746188340807,25.1424,0.64627949013786
+"FLOW_236212",2018,6,7,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2018,6,8,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2018,6,9,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2018,6,10,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2018,6,11,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2018,6,12,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2018,6,13,0.351,0.135992825112108,30.3264,0.6709701490665
+"FLOW_236212",2018,6,14,0.475,0.184035874439462,41.04,0.712821019859571
+"FLOW_236212",2018,6,15,0.729,0.2824466367713,62.9856,0.776581931182144
+"FLOW_236212",2018,6,16,7.015,2.71791928251121,606.096,1.22137017594285
+"FLOW_236212",2018,6,17,21.52,8.33779372197309,1859.328,1.52830568739111
+"FLOW_236212",2018,6,18,18.232,7.06387802690583,1575.2448,1.47845679730622
+"FLOW_236212",2018,6,19,9,3.48699551569507,777.6,1.28377903114908
+"FLOW_236212",2018,6,20,3.615,1.40060986547085,312.336,1.06970354783941
+"FLOW_236212",2018,6,21,1.733,0.671440358744395,149.7312,0.923424703058821
+"FLOW_236212",2018,6,22,1.089,0.421926457399103,94.0896,0.841486885334306
+"FLOW_236212",2018,6,23,0.803,0.311117488789238,69.3792,0.791744189705586
+"FLOW_236212",2018,6,24,0.694,0.268886098654709,59.9616,0.768977566022169
+"FLOW_236212",2018,6,25,0.611,0.236728251121076,52.7904,0.749635280728596
+"FLOW_236212",2018,6,26,0.56,0.216968609865471,48.384,0.736680851288836
+"FLOW_236212",2018,6,27,0.525,0.203408071748879,45.36,0.727233098323661
+"FLOW_236212",2018,6,28,0.492,0.190622421524664,42.5088,0.717851804519562
+"FLOW_236212",2018,6,29,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",2018,6,30,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",2018,7,1,0.501,0.194109417040359,43.2864,0.720459083126107
+"FLOW_236212",2018,7,2,0.566,0.219293273542601,48.9024,0.738252730845171
+"FLOW_236212",2018,7,3,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",2018,7,4,0.493,0.191009865470852,42.5952,0.718143377241467
+"FLOW_236212",2018,7,5,0.454,0.175899551569507,39.2256,0.70640366931547
+"FLOW_236212",2018,7,6,0.503,0.194884304932735,43.4592,0.721033383642924
+"FLOW_236212",2018,7,7,0.95,0.368071748878924,82.08,0.818816332919998
+"FLOW_236212",2018,7,8,3.221,1.24795695067265,278.2944,1.04529752199727
+"FLOW_236212",2018,7,9,3.254,1.26074260089686,281.1456,1.04743066807599
+"FLOW_236212",2018,7,10,2.516,0.974808968609866,217.3824,0.99491024555727
+"FLOW_236212",2018,7,11,1.75,0.678026905829596,151.2,0.925229317352677
+"FLOW_236212",2018,7,12,1.483,0.574579372197309,128.1312,0.89509680325128
+"FLOW_236212",2018,7,13,1.4,0.542421524663677,120.96,0.884845370529262
+"FLOW_236212",2018,7,14,1.083,0.419601793721973,93.5712,0.840557576732363
+"FLOW_236212",2018,7,15,0.84,0.325452914798206,72.576,0.798909587604632
+"FLOW_236212",2018,7,16,0.682,0.264236771300448,58.9248,0.766299691677741
+"FLOW_236212",2018,7,17,0.584,0.226267264573991,50.4576,0.742889714222194
+"FLOW_236212",2018,7,18,0.657,0.25455067264574,56.7648,0.760597423288142
+"FLOW_236212",2018,7,19,0.884,0.342500448430493,76.3776,0.807109072059093
+"FLOW_236212",2018,7,20,1.345,0.521112107623318,116.208,0.877781114519393
+"FLOW_236212",2018,7,21,6.625,2.56681614349776,572.4,1.20747723726962
+"FLOW_236212",2018,7,22,5.533,2.14372735426009,478.0512,1.16475319367713
+"FLOW_236212",2018,7,23,2.722,1.05462242152466,235.1808,1.01069333103666
+"FLOW_236212",2018,7,24,1.706,0.66097937219731,147.3984,0.920529225411883
+"FLOW_236212",2018,7,25,1.421,0.550557847533632,122.7744,0.887484121280963
+"FLOW_236212",2018,7,26,1.317,0.510263677130045,113.7888,0.874095593513835
+"FLOW_236212",2018,7,27,1.083,0.419601793721973,93.5712,0.840557576732363
+"FLOW_236212",2018,7,28,0.878,0.340175784753363,75.8592,0.806010462170424
+"FLOW_236212",2018,7,29,0.815,0.315766816143498,70.416,0.794096528839807
+"FLOW_236212",2018,7,30,2.107,0.816344394618834,182.0448,0.960228701497994
+"FLOW_236212",2018,7,31,3.207,1.24253273542601,277.0848,1.0443872658853
+"FLOW_236212",2018,8,1,4.724,1.83028520179372,408.1536,1.12850569133501
+"FLOW_236212",2018,8,2,6.617,2.56371659192825,571.7088,1.2071854791668
+"FLOW_236212",2018,8,3,3.544,1.37310134529148,306.2016,1.0654682644081
+"FLOW_236212",2018,8,4,4.918,1.90544932735426,424.9152,1.13762592570108
+"FLOW_236212",2018,8,5,5.43,2.10382062780269,469.152,1.16038402650078
+"FLOW_236212",2018,8,6,3.107,1.20378834080718,268.4448,1.03779130223533
+"FLOW_236212",2018,8,7,2.927,1.13404843049327,252.8928,1.02547793545798
+"FLOW_236212",2018,8,8,4.506,1.74582242152466,389.3184,1.1178924251105
+"FLOW_236212",2018,8,9,4.759,1.84384573991031,411.1776,1.13017297168532
+"FLOW_236212",2018,8,10,3.035,1.17589237668161,262.224,1.03293624069849
+"FLOW_236212",2018,8,11,6.147,2.38161793721973,531.1008,1.18952731324868
+"FLOW_236212",2018,8,12,9.941,3.8515802690583,858.9024,1.30956720493754
+"FLOW_236212",2018,8,13,11.367,4.40407533632287,982.1088,1.34515071832627
+"FLOW_236212",2018,8,14,9.456,3.66366995515695,816.9984,1.29653208489047
+"FLOW_236212",2018,8,15,4.54,1.75899551569507,392.256,1.11957436662764
+"FLOW_236212",2018,8,16,2.92,1.13133632286996,252.288,1.02498697390681
+"FLOW_236212",2018,8,17,2.812,1.08949237668161,242.9568,1.01729014937116
+"FLOW_236212",2018,8,18,2.905,1.12552466367713,250.992,1.02393173456453
+"FLOW_236212",2018,8,19,6.944,2.69041076233184,599.9616,1.21888776676892
+"FLOW_236212",2018,8,20,9.631,3.73147264573991,832.1184,1.3012958730444
+"FLOW_236212",2018,8,21,7.915,3.06661883408072,683.856,1.25121505043457
+"FLOW_236212",2018,8,22,7.069,2.73884125560538,610.7616,1.22324478299745
+"FLOW_236212",2018,8,23,4.438,1.71947623318386,383.4432,1.11449785116613
+"FLOW_236212",2018,8,24,2.649,1.02633901345291,228.8736,1.00521316442732
+"FLOW_236212",2018,8,25,1.93,0.747766816143498,166.752,0.943524621545207
+"FLOW_236212",2018,8,26,1.491,0.577678923766816,128.8224,0.896060440852666
+"FLOW_236212",2018,8,27,1.17,0.453309417040359,101.088,0.853648237980127
+"FLOW_236212",2018,8,28,0.932,0.361097757847534,80.5248,0.815689662443497
+"FLOW_236212",2018,8,29,0.777,0.301043946188341,67.1328,0.78654935570625
+"FLOW_236212",2018,8,30,0.672,0.260362331838565,58.0608,0.764039181211917
+"FLOW_236212",2018,8,31,0.669,0.2592,57.8016,0.763355781923635
+"FLOW_236212",2018,9,1,0.743,0.287870852017937,64.1952,0.779542035282204
+"FLOW_236212",2018,9,2,0.814,0.315379372197309,70.3296,0.793901562813638
+"FLOW_236212",2018,9,3,0.874,0.33862600896861,75.5136,0.805274714287262
+"FLOW_236212",2018,9,4,0.813,0.314991928251121,70.2432,0.793706405080409
+"FLOW_236212",2018,9,5,0.682,0.264236771300448,58.9248,0.766299691677741
+"FLOW_236212",2018,9,6,0.582,0.225492376681614,50.2848,0.742380187189683
+"FLOW_236212",2018,9,7,0.549,0.212706726457399,47.4336,0.733763736820302
+"FLOW_236212",2018,9,8,0.526,0.203795515695067,45.4464,0.727509928665892
+"FLOW_236212",2018,9,9,0.474,0.183648430493274,40.9536,0.712520631628183
+"FLOW_236212",2018,9,10,0.445,0.172412556053812,38.448,0.703580473700277
+"FLOW_236212",2018,9,11,0.43,0.166600896860987,37.152,0.698771955995017
+"FLOW_236212",2018,9,12,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2018,9,13,0.394,0.152652914798206,34.0416,0.686658847500353
+"FLOW_236212",2018,9,14,0.383,0.148391031390135,33.0912,0.682781162471217
+"FLOW_236212",2018,9,15,0.372,0.144129147982063,32.1408,0.678813332815245
+"FLOW_236212",2018,9,16,0.414,0.160401793721973,35.7696,0.693492620551439
+"FLOW_236212",2018,9,17,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2018,9,18,0.387,0.149940807174888,33.4368,0.684201416376297
+"FLOW_236212",2018,9,19,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",2018,9,20,0.448,0.173574887892377,38.7072,0.704526573677531
+"FLOW_236212",2018,9,21,0.452,0.17512466367713,39.0528,0.705780187611685
+"FLOW_236212",2018,9,22,0.395,0.153040358744395,34.128,0.687007051963111
+"FLOW_236212",2018,9,23,0.374,0.144904035874439,32.3136,0.679541674977205
+"FLOW_236212",2018,9,24,0.357,0.138317488789238,30.8448,0.673248538187265
+"FLOW_236212",2018,9,25,0.342,0.132505829596413,29.5488,0.667493432629172
+"FLOW_236212",2018,9,26,0.332,0.128631390134529,28.6848,0.663543489913508
+"FLOW_236212",2018,9,27,0.325,0.125919282511211,28.08,0.660721513723923
+"FLOW_236212",2018,9,28,0.326,0.126306726457399,28.1664,0.661127612072275
+"FLOW_236212",2018,9,29,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2018,9,30,0.359,0.139092376681614,31.0176,0.674001193547915
+"FLOW_236212",2018,10,1,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",2018,10,2,0.327,0.126694170403587,28.2528,0.661532715079855
+"FLOW_236212",2018,10,3,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2018,10,4,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2018,10,5,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2018,10,6,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2018,10,7,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2018,10,8,0.302,0.117008071748879,26.0928,0.651093207296549
+"FLOW_236212",2018,10,9,0.32,0.123982062780269,27.648,0.658675897005903
+"FLOW_236212",2018,10,10,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2018,10,11,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2018,10,12,0.296,0.114683408071749,25.5744,0.648485273487067
+"FLOW_236212",2018,10,13,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2018,10,14,0.283,0.1096466367713,24.4512,0.642686330692165
+"FLOW_236212",2018,10,15,0.284,0.110034080717489,24.5376,0.643139885364451
+"FLOW_236212",2018,10,16,0.28,0.108484304932735,24.192,0.641317930058964
+"FLOW_236212",2018,10,17,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2018,10,18,0.355,0.137542600896861,30.672,0.672492501986087
+"FLOW_236212",2018,10,19,0.348,0.134830493273543,30.0672,0.66981925009451
+"FLOW_236212",2018,10,20,0.315,0.122044843049327,27.216,0.656604548121983
+"FLOW_236212",2018,10,21,0.289,0.11197130044843,24.9696,0.645388681112981
+"FLOW_236212",2018,10,22,0.281,0.108871748878924,24.2784,0.641775361287472
+"FLOW_236212",2018,10,23,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2018,10,24,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2018,10,25,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2018,10,26,0.267,0.103447533632287,23.0688,0.635249055724106
+"FLOW_236212",2018,10,27,0.268,0.103834977578475,23.1552,0.635724186439289
+"FLOW_236212",2018,10,28,0.263,0.101897757847534,22.7232,0.633334178203615
+"FLOW_236212",2018,10,29,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2018,10,30,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2018,10,31,0.25,0.0968609865470852,21.6,0.626945482836251
+"FLOW_236212",2018,11,1,0.255,0.0987982062780269,22.032,0.629433439939176
+"FLOW_236212",2018,11,2,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2018,11,3,0.248,0.0960860986547085,21.4272,0.625939144608217
+"FLOW_236212",2018,11,4,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2018,11,5,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2018,11,6,0.278,0.107709417040359,24.0192,0.640399132688459
+"FLOW_236212",2018,11,7,0.273,0.105772197309417,23.5872,0.638078784943226
+"FLOW_236212",2018,11,8,0.26,0.100735426008969,22.464,0.63188267131493
+"FLOW_236212",2018,11,9,0.257,0.0995730941704036,22.2048,0.630417703450943
+"FLOW_236212",2018,11,10,0.276,0.106934529147982,23.8464,0.639475031964678
+"FLOW_236212",2018,11,11,0.272,0.105384753363229,23.5008,0.637610641530074
+"FLOW_236212",2018,11,12,0.256,0.0991856502242153,22.1184,0.62992634065304
+"FLOW_236212",2018,11,13,0.253,0.0980233183856502,21.8592,0.628442981151596
+"FLOW_236212",2018,11,14,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2018,11,15,0.249,0.0964735426008969,21.5136,0.626443122028378
+"FLOW_236212",2018,11,16,0.239,0.0925991031390135,20.6496,0.621328616104422
+"FLOW_236212",2018,11,17,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2018,11,18,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2018,11,19,0.227,0.0879497757847534,19.6128,0.614960112237031
+"FLOW_236212",2018,11,20,0.224,0.0867874439461883,19.3536,0.613326005572063
+"FLOW_236212",2018,11,21,0.237,0.0918242152466368,20.4768,0.620285237224025
+"FLOW_236212",2018,11,22,0.262,0.101510313901345,22.6368,0.632851821072446
+"FLOW_236212",2018,11,23,0.271,0.10499730941704,23.4144,0.637141119193346
+"FLOW_236212",2018,11,24,0.29,0.112358744394619,25.056,0.645834699977787
+"FLOW_236212",2018,11,25,0.3,0.116233183856502,25.92,0.650228538630645
+"FLOW_236212",2018,11,26,0.264,0.102285201793722,22.8096,0.633815070313115
+"FLOW_236212",2018,11,27,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2018,11,28,0.245,0.0949237668161435,21.168,0.624417394479866
+"FLOW_236212",2018,11,29,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2018,11,30,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2018,12,1,0.229,0.0887246636771301,19.7856,0.616039943494517
+"FLOW_236212",2018,12,2,0.231,0.0894995515695067,19.9584,0.617112256241769
+"FLOW_236212",2018,12,3,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2018,12,4,0.258,0.0999605381165919,22.2912,0.630907539087588
+"FLOW_236212",2018,12,5,0.232,0.0898869955156951,20.0448,0.617645629774905
+"FLOW_236212",2018,12,6,0.22,0.085237668161435,19.008,0.611119739719804
+"FLOW_236212",2018,12,7,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2018,12,8,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2018,12,9,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2018,12,10,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2018,12,11,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2018,12,12,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2018,12,13,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2018,12,14,0.233,0.0902744394618834,20.1312,0.618177167248442
+"FLOW_236212",2018,12,15,0.252,0.0976358744394619,21.7728,0.627945400969966
+"FLOW_236212",2018,12,16,0.24,0.0929865470852018,20.736,0.62184768835424
+"FLOW_236212",2018,12,17,0.217,0.08407533632287,18.7488,0.60944388338446
+"FLOW_236212",2018,12,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2018,12,19,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2018,12,20,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2018,12,21,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2018,12,22,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2018,12,23,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2018,12,24,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2018,12,25,0.172,0.0666403587443946,14.8608,0.581764833206132
+"FLOW_236212",2018,12,26,0.17,0.065865470852018,14.688,0.580405554719073
+"FLOW_236212",2018,12,27,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2018,12,28,0.184,0.0712896860986547,15.8976,0.58966498616966
+"FLOW_236212",2018,12,29,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2018,12,30,0.186,0.0720645739910314,16.0704,0.590941329255231
+"FLOW_236212",2018,12,31,0.158,0.0612161434977578,13.6512,0.571969924619594
+"FLOW_236212",2019,1,1,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2019,1,2,0.18,0.0697399103139013,15.552,0.587078636502842
+"FLOW_236212",2019,1,3,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2019,1,4,0.145,0.0561793721973094,12.528,0.562231761861846
+"FLOW_236212",2019,1,5,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2019,1,6,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2019,1,7,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2019,1,8,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2019,1,9,0.14,0.0542421524663677,12.096,0.558299685264736
+"FLOW_236212",2019,1,10,0.12,0.0464932735426009,10.368,0.541349855381176
+"FLOW_236212",2019,1,11,0.134,0.0519174887892377,11.5776,0.553430048605693
+"FLOW_236212",2019,1,12,0.141,0.0546295964125561,12.1824,0.559094987187068
+"FLOW_236212",2019,1,13,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2019,1,14,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2019,1,15,0.133,0.0515300448430493,11.4912,0.552601556792838
+"FLOW_236212",2019,1,16,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2019,1,17,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2019,1,18,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2019,1,19,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2019,1,20,0.156,0.0604412556053812,13.4784,0.570514511538635
+"FLOW_236212",2019,1,21,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2019,1,22,0.15,0.0581165919282511,12.96,0.566056820576124
+"FLOW_236212",2019,1,23,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2019,1,24,0.157,0.0608286995515695,13.5648,0.571244072122056
+"FLOW_236212",2019,1,25,0.151,0.0585040358744395,13.0464,0.566809558370291
+"FLOW_236212",2019,1,26,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2019,1,27,0.109,0.0422313901345292,9.4176,0.531039804858689
+"FLOW_236212",2019,1,28,0.1,0.0387443946188341,8.64,0.521965472601436
+"FLOW_236212",2019,1,29,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2019,1,30,0.143,0.0554044843049327,12.3552,0.560672149028315
+"FLOW_236212",2019,1,31,0.107,0.0414565022421525,9.2448,0.529076572519873
+"FLOW_236212",2019,2,1,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2019,2,2,0.126,0.0488179372197309,10.8864,0.546658222533615
+"FLOW_236212",2019,2,3,0.135,0.052304932735426,11.664,0.554253608870373
+"FLOW_236212",2019,2,4,0.115,0.0445560538116592,9.936,0.536761482799176
+"FLOW_236212",2019,2,5,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2019,2,6,0.164,0.0635408071748879,14.1696,0.576249478672433
+"FLOW_236212",2019,2,7,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2019,2,8,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2019,2,9,0.173,0.067027802690583,14.9472,0.582439736220287
+"FLOW_236212",2019,2,10,0.175,0.0678026905829596,15.12,0.583780233112067
+"FLOW_236212",2019,2,11,0.178,0.0689650224215247,15.3792,0.585768180044495
+"FLOW_236212",2019,2,12,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2019,2,13,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2019,2,14,0.223,0.0864,19.2672,0.612777412560081
+"FLOW_236212",2019,2,15,0.207,0.0802008968609866,17.8848,0.603720391462761
+"FLOW_236212",2019,2,16,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2019,2,17,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2019,2,18,0.185,0.0716771300448431,15.984,0.590304537550066
+"FLOW_236212",2019,2,19,0.179,0.069352466367713,15.4656,0.586424872479054
+"FLOW_236212",2019,2,20,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2019,2,21,0.149,0.0577291479820628,12.8736,0.565300057413869
+"FLOW_236212",2019,2,22,0.167,0.0647031390134529,14.4288,0.578342449572995
+"FLOW_236212",2019,2,23,0.165,0.0639282511210762,14.256,0.576950514175258
+"FLOW_236212",2019,2,24,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2019,2,25,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2019,2,26,0.144,0.0557919282511211,12.4416,0.56145412159282
+"FLOW_236212",2019,2,27,0.13,0.0503677130044843,11.232,0.550085815450272
+"FLOW_236212",2019,2,28,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2019,3,1,0.129,0.049980269058296,11.1456,0.549236913540471
+"FLOW_236212",2019,3,2,0.117,0.0453309417040359,10.1088,0.538615625356137
+"FLOW_236212",2019,3,3,0.113,0.0437811659192825,9.7632,0.534881361338924
+"FLOW_236212",2019,3,4,0.121,0.0468807174887892,10.4544,0.542249112591278
+"FLOW_236212",2019,3,5,0.123,0.0476556053811659,10.6272,0.544029936204842
+"FLOW_236212",2019,3,6,0.122,0.0472681614349776,10.5408,0.543142443816339
+"FLOW_236212",2019,3,7,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2019,3,8,0.132,0.051142600896861,11.4048,0.551768066486655
+"FLOW_236212",2019,3,9,0.136,0.0526923766816144,11.7504,0.555072303147609
+"FLOW_236212",2019,3,10,0.119,0.0461058295964126,10.2816,0.540444582997237
+"FLOW_236212",2019,3,11,0.146,0.0565668161434978,12.6144,0.563005123468378
+"FLOW_236212",2019,3,12,0.159,0.0616035874439462,13.7376,0.57269211116906
+"FLOW_236212",2019,3,13,0.161,0.0623784753363229,13.9104,0.574125651291572
+"FLOW_236212",2019,3,14,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2019,3,15,0.16,0.0619910313901345,13.824,0.573410673168069
+"FLOW_236212",2019,3,16,0.162,0.0627659192825112,13.9968,0.574837085508873
+"FLOW_236212",2019,3,17,0.138,0.053467264573991,11.9232,0.556695349290657
+"FLOW_236212",2019,3,18,0.124,0.0480430493273543,10.7136,0.544911674927777
+"FLOW_236212",2019,3,19,0.153,0.0592789237668161,13.2192,0.568303148408395
+"FLOW_236212",2019,3,20,0.154,0.0596663677130045,13.3056,0.569044093752875
+"FLOW_236212",2019,3,21,0.163,0.0631533632286996,14.0832,0.575545015100141
+"FLOW_236212",2019,3,22,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2019,3,23,0.187,0.0724520179372197,16.1568,0.591575387934597
+"FLOW_236212",2019,3,24,0.182,0.070514798206278,15.7248,0.588377495744797
+"FLOW_236212",2019,3,25,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,3,26,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2019,3,27,0.215,0.0833004484304933,18.576,0.608316319906996
+"FLOW_236212",2019,3,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2019,3,29,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2019,3,30,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2019,3,31,0.27,0.104609865470852,23.328,0.636670208760567
+"FLOW_236212",2019,4,1,0.269,0.104222421524664,23.2416,0.636197900963999
+"FLOW_236212",2019,4,2,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2019,4,3,0.2,0.0774887892376682,17.28,0.59958087974252
+"FLOW_236212",2019,4,4,0.194,0.0751641255605381,16.7616,0.595939430884498
+"FLOW_236212",2019,4,5,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2019,4,6,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,4,7,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2019,4,8,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2019,4,9,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2019,4,10,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2019,4,11,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2019,4,12,0.206,0.0798134529147982,17.7984,0.603135956299603
+"FLOW_236212",2019,4,13,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2019,4,14,0.204,0.0790385650224215,17.6256,0.601960243737273
+"FLOW_236212",2019,4,15,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2019,4,16,0.198,0.0767139013452915,17.1072,0.598376892353112
+"FLOW_236212",2019,4,17,0.192,0.0743892376681615,16.5888,0.594705591246233
+"FLOW_236212",2019,4,18,0.191,0.0740017937219731,16.5024,0.594084811613753
+"FLOW_236212",2019,4,19,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,4,20,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,4,21,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,4,22,0.188,0.0728394618834081,16.2432,0.592206739839422
+"FLOW_236212",2019,4,23,0.189,0.0732269058295964,16.3296,0.592835410830815
+"FLOW_236212",2019,4,24,0.19,0.0736143497757848,16.416,0.59346142638756
+"FLOW_236212",2019,4,25,0.193,0.0747766816143498,16.6752,0.595323789661843
+"FLOW_236212",2019,4,26,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2019,4,27,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2019,4,28,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2019,4,29,0.203,0.0786511210762332,17.5392,0.601368926049617
+"FLOW_236212",2019,4,30,0.202,0.0782636771300449,17.4528,0.600775273434909
+"FLOW_236212",2019,5,1,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2019,5,2,0.208,0.0805883408071749,17.9712,0.60430257228604
+"FLOW_236212",2019,5,3,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2019,5,4,0.201,0.0778762331838565,17.3664,0.600179265045445
+"FLOW_236212",2019,5,5,0.195,0.0755515695067265,16.848,0.596552538592087
+"FLOW_236212",2019,5,6,0.196,0.0759390134529148,16.9344,0.597163136123202
+"FLOW_236212",2019,5,7,0.199,0.0771013452914798,17.1936,0.598980096090894
+"FLOW_236212",2019,5,8,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2019,5,9,0.247,0.0956986547085202,21.3408,0.625433538823281
+"FLOW_236212",2019,5,10,0.439,0.170087892376682,37.9296,0.701672861865032
+"FLOW_236212",2019,5,11,0.487,0.188685201793722,42.0768,0.716386788620938
+"FLOW_236212",2019,5,12,0.287,0.111196412556054,24.7968,0.644492926540701
+"FLOW_236212",2019,5,13,0.254,0.0984107623318386,21.9456,0.628938990436371
+"FLOW_236212",2019,5,14,0.238,0.0922116591928251,20.5632,0.620807803455985
+"FLOW_236212",2019,5,15,0.226,0.087562331838565,19.5264,0.614417339888596
+"FLOW_236212",2019,5,16,0.219,0.0848502242152466,18.9216,0.61056316344113
+"FLOW_236212",2019,5,17,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2019,5,18,0.209,0.0809757847533632,18.0576,0.604882518240833
+"FLOW_236212",2019,5,19,0.205,0.0794260089686099,17.712,0.602549247059997
+"FLOW_236212",2019,5,20,0.211,0.0817506726457399,18.2304,0.606035782133403
+"FLOW_236212",2019,5,21,0.213,0.0825255605381166,18.4032,0.607180333788213
+"FLOW_236212",2019,5,22,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2019,5,23,0.212,0.0821381165919283,18.3168,0.606609137730845
+"FLOW_236212",2019,5,24,0.214,0.0829130044843049,18.4896,0.607749388521048
+"FLOW_236212",2019,5,25,0.218,0.0844627802690583,18.8352,0.610004550279122
+"FLOW_236212",2019,5,26,0.246,0.0953112107623318,21.2544,0.624926292787644
+"FLOW_236212",2019,5,27,0.333,0.129018834080718,28.7712,0.663942734179578
+"FLOW_236212",2019,5,28,0.329,0.127469058295964,28.4256,0.66233995691508
+"FLOW_236212",2019,5,29,0.361,0.139867264573991,31.1904,0.674750501895532
+"FLOW_236212",2019,5,30,0.476,0.18442331838565,41.1264,0.713120902599212
+"FLOW_236212",2019,5,31,0.472,0.182873542600897,40.7808,0.711918330993931
+"FLOW_236212",2019,6,1,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2019,6,2,0.314,0.121657399103139,27.1296,0.656187125948414
+"FLOW_236212",2019,6,3,0.398,0.15420269058296,34.3872,0.688047451055575
+"FLOW_236212",2019,6,4,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",2019,6,5,0.404,0.15652735426009,34.9056,0.690109568317574
+"FLOW_236212",2019,6,6,0.323,0.125144394618834,27.9072,0.659906308879283
+"FLOW_236212",2019,6,7,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2019,6,8,0.297,0.115070852017937,25.6608,0.648922848293954
+"FLOW_236212",2019,6,9,0.277,0.10732197309417,23.9328,0.639937749548873
+"FLOW_236212",2019,6,10,0.275,0.106547085201794,23.76,0.639010971220311
+"FLOW_236212",2019,6,11,0.279,0.108096860986547,24.1056,0.640859190011343
+"FLOW_236212",2019,6,12,0.305,0.118170403587444,26.352,0.652381660400192
+"FLOW_236212",2019,6,13,0.592,0.229366816143498,51.1488,0.744913966894396
+"FLOW_236212",2019,6,14,0.579,0.224330044843049,50.0256,0.741613263870918
+"FLOW_236212",2019,6,15,0.475,0.184035874439462,41.04,0.712821019859571
+"FLOW_236212",2019,6,16,0.451,0.174737219730942,38.9664,0.705467618761141
+"FLOW_236212",2019,6,17,0.378,0.146453811659193,32.6592,0.680989061205348
+"FLOW_236212",2019,6,18,0.339,0.131343497757848,29.2896,0.666318260843565
+"FLOW_236212",2019,6,19,0.334,0.129406278026906,28.8576,0.664341020449455
+"FLOW_236212",2019,6,20,0.337,0.130568609865471,29.1168,0.665530182634077
+"FLOW_236212",2019,6,21,0.346,0.134055605381166,29.8944,0.669047566881151
+"FLOW_236212",2019,6,22,0.446,0.1728,38.5344,0.703896405787105
+"FLOW_236212",2019,6,23,0.465,0.180161434977578,40.176,0.709794069617924
+"FLOW_236212",2019,6,24,0.45,0.174349775784753,38.88,0.705154494972067
+"FLOW_236212",2019,6,25,0.385,0.149165919282511,33.264,0.683492765019398
+"FLOW_236212",2019,6,26,0.35,0.135605381165919,30.24,0.670587393455617
+"FLOW_236212",2019,6,27,0.328,0.127081614349776,28.3392,0.661936828218923
+"FLOW_236212",2019,6,28,0.313,0.121269955156951,27.0432,0.655768638920414
+"FLOW_236212",2019,6,29,0.338,0.130956053811659,29.2032,0.665924688058023
+"FLOW_236212",2019,6,30,1.514,0.586590134529148,130.8096,0.898808044784943
+"FLOW_236212",2019,7,1,2.312,0.895770403587444,199.7568,0.978226315801242
+"FLOW_236212",2019,7,2,1.222,0.473456502242153,105.5808,0.861104813820679
+"FLOW_236212",2019,7,3,0.721,0.279347085201794,62.2944,0.774869967583366
+"FLOW_236212",2019,7,4,0.565,0.218905829596413,48.816,0.737991679602067
+"FLOW_236212",2019,7,5,0.473,0.183260986547085,40.8672,0.712219735983835
+"FLOW_236212",2019,7,6,0.409,0.158464573991031,35.3376,0.691809366178387
+"FLOW_236212",2019,7,7,0.373,0.144516591928251,32.2272,0.67917789442872
+"FLOW_236212",2019,7,8,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2019,7,9,0.375,0.145291479820628,32.4,0.679904678215825
+"FLOW_236212",2019,7,10,0.502,0.194496860986547,43.3728,0.720746462189665
+"FLOW_236212",2019,7,11,5.62,2.17743497757848,485.568,1.1683932491441
+"FLOW_236212",2019,7,12,7.776,3.01276412556054,671.8464,1.24678918454386
+"FLOW_236212",2019,7,13,10.946,4.24096143497758,945.7344,1.33503565507155
+"FLOW_236212",2019,7,14,13.338,5.16772735426009,1152.4032,1.38886463179511
+"FLOW_236212",2019,7,15,8.395,3.25259192825112,725.328,1.2660355944118
+"FLOW_236212",2019,7,16,6.145,2.38084304932735,530.928,1.18944989778395
+"FLOW_236212",2019,7,17,6.656,2.5788269058296,575.0784,1.20860514457208
+"FLOW_236212",2019,7,18,9.133,3.53852556053812,789.0912,1.2875510798155
+"FLOW_236212",2019,7,19,6.211,2.40641434977578,536.6304,1.1919940339861
+"FLOW_236212",2019,7,20,4.041,1.56566098654709,349.1424,1.09380409755607
+"FLOW_236212",2019,7,21,2.447,0.94807533632287,211.4208,0.989392399409532
+"FLOW_236212",2019,7,22,1.629,0.631146188340807,140.7456,0.912065425306647
+"FLOW_236212",2019,7,23,1.397,0.541259192825112,120.7008,0.884465825620968
+"FLOW_236212",2019,7,24,4.565,1.76868161434978,394.416,1.12080467109919
+"FLOW_236212",2019,7,25,4.329,1.67724484304933,374.0256,1.10896871133022
+"FLOW_236212",2019,7,26,2.699,1.04571121076233,233.1936,1.0089795238702
+"FLOW_236212",2019,7,27,1.829,0.708634977578475,158.0256,0.9334359367954
+"FLOW_236212",2019,7,28,1.305,0.505614349775785,112.752,0.87249687115994
+"FLOW_236212",2019,7,29,0.995,0.385506726457399,85.968,0.826430605201494
+"FLOW_236212",2019,7,30,0.841,0.325840358744395,72.6624,0.799099713658437
+"FLOW_236212",2019,7,31,0.714,0.276634977578475,61.6896,0.773359488319869
+"FLOW_236212",2019,8,1,0.624,0.241765022421525,53.9136,0.752798411185963
+"FLOW_236212",2019,8,2,0.602,0.233241255605381,52.0128,0.747413734397971
+"FLOW_236212",2019,8,3,0.577,0.223555156950673,49.8528,0.741100213733267
+"FLOW_236212",2019,8,4,0.59,0.228591928251121,50.976,0.74440996510202
+"FLOW_236212",2019,8,5,0.627,0.24292735426009,54.1728,0.753520867805153
+"FLOW_236212",2019,8,6,0.59,0.228591928251121,50.976,0.74440996510202
+"FLOW_236212",2019,8,7,0.587,0.227429596412556,50.7168,0.743651393588541
+"FLOW_236212",2019,8,8,0.618,0.239440358744395,53.3952,0.751345121557008
+"FLOW_236212",2019,8,9,0.792,0.306855605381166,68.4288,0.789563043083632
+"FLOW_236212",2019,8,10,1.49,0.577291479820628,128.736,0.895940212693118
+"FLOW_236212",2019,8,11,4.532,1.75589596412556,391.5648,1.11917952457327
+"FLOW_236212",2019,8,12,8.074,3.12822242152466,697.5936,1.25620212840406
+"FLOW_236212",2019,8,13,6.162,2.38742959641256,532.3968,1.19010728783604
+"FLOW_236212",2019,8,14,4.309,1.66949596412556,372.2976,1.10794212432456
+"FLOW_236212",2019,8,15,2.436,0.943813452914798,210.4704,0.988501272408867
+"FLOW_236212",2019,8,16,1.632,0.632308520179372,141.0048,0.912401113813222
+"FLOW_236212",2019,8,17,1.398,0.5416466367713,120.7872,0.884592412977752
+"FLOW_236212",2019,8,18,1.271,0.492441255605381,109.8144,0.867902393361989
+"FLOW_236212",2019,8,19,1.944,0.753191031390135,167.9616,0.94488951097956
+"FLOW_236212",2019,8,20,4.383,1.6981668161435,378.6912,1.11172166713284
+"FLOW_236212",2019,8,21,4.183,1.62067802690583,361.4112,1.10138548531308
+"FLOW_236212",2019,8,22,4.831,1.87174170403587,417.3984,1.13357219153483
+"FLOW_236212",2019,8,23,4.14,1.60401793721973,357.696,1.09911173333399
+"FLOW_236212",2019,8,24,2.537,0.982945291479821,219.1968,0.996565548261304
+"FLOW_236212",2019,8,25,1.823,0.706310313901345,157.5072,0.932822707795427
+"FLOW_236212",2019,8,26,1.577,0.610999103139014,136.2528,0.906166733297494
+"FLOW_236212",2019,8,27,1.362,0.52769865470852,117.6768,0.879988906724209
+"FLOW_236212",2019,8,28,1.125,0.435874439461883,97.2,0.846978293642786
+"FLOW_236212",2019,8,29,1.183,0.458346188340807,102.2112,0.855536858744601
+"FLOW_236212",2019,8,30,1.202,0.465707623318386,103.8528,0.858267503961453
+"FLOW_236212",2019,8,31,0.969,0.375433183856502,83.7216,0.822065706218397
+"FLOW_236212",2019,9,1,0.82,0.317704035874439,70.848,0.795068498125917
+"FLOW_236212",2019,9,2,1.022,0.395967713004484,88.3008,0.830867851054859
+"FLOW_236212",2019,9,3,0.943,0.365359641255605,81.4752,0.817606083892927
+"FLOW_236212",2019,9,4,0.845,0.327390134529148,73.008,0.799858414017954
+"FLOW_236212",2019,9,5,0.743,0.287870852017937,64.1952,0.779542035282204
+"FLOW_236212",2019,9,6,1.053,0.407978475336323,90.9792,0.835848274250574
+"FLOW_236212",2019,9,7,2.444,0.946913004484305,211.1616,0.989149683115842
+"FLOW_236212",2019,9,8,2.884,1.11738834080717,249.1776,1.02244705204659
+"FLOW_236212",2019,9,9,3.665,1.41998206278027,316.656,1.0726463806247
+"FLOW_236212",2019,9,10,3.778,1.46376322869955,326.4192,1.07918069196209
+"FLOW_236212",2019,9,11,2.577,0.998443049327354,222.6528,0.99968841575648
+"FLOW_236212",2019,9,12,1.761,0.682288789237668,152.1504,0.926389549281442
+"FLOW_236212",2019,9,13,1.238,0.479655605381166,106.9632,0.863348034884816
+"FLOW_236212",2019,9,14,0.916,0.35489865470852,79.1424,0.812869578793101
+"FLOW_236212",2019,9,15,0.747,0.289420627802691,64.5408,0.780379579020089
+"FLOW_236212",2019,9,16,2.213,0.857413452914798,191.2032,0.969701480495839
+"FLOW_236212",2019,9,17,3.132,1.21347443946188,270.6048,1.03945603858247
+"FLOW_236212",2019,9,18,2.038,0.789610762331839,176.0832,0.953855551833976
+"FLOW_236212",2019,9,19,1.376,0.533122869955157,118.8864,0.881790595503693
+"FLOW_236212",2019,9,20,0.947,0.366909417040359,81.8208,0.818298531283392
+"FLOW_236212",2019,9,21,0.773,0.299494170403588,66.7872,0.78573785090246
+"FLOW_236212",2019,9,22,1.567,0.60712466367713,135.3888,0.905014578605826
+"FLOW_236212",2019,9,23,2.125,0.823318385650224,183.6,0.961863761262538
+"FLOW_236212",2019,9,24,2.284,0.88492197309417,197.3376,0.975845347288721
+"FLOW_236212",2019,9,25,1.836,0.711347085201794,158.6304,0.934149339916698
+"FLOW_236212",2019,9,26,1.324,0.512975784753363,114.3936,0.875022807170014
+"FLOW_236212",2019,9,27,0.997,0.386281614349776,86.1408,0.826762571809987
+"FLOW_236212",2019,9,28,0.842,0.326227802690583,72.7488,0.799289658941048
+"FLOW_236212",2019,9,29,0.736,0.285158744394619,63.5904,0.778067613956656
+"FLOW_236212",2019,9,30,0.625,0.242152466367713,54,0.753039538208907
+"FLOW_236212",2019,10,1,0.569,0.220455605381166,49.1616,0.739033676947626
+"FLOW_236212",2019,10,2,0.53,0.205345291479821,45.792,0.728613057204925
+"FLOW_236212",2019,10,3,0.468,0.181323766816144,40.4352,0.710707578523312
diff --git a/algorithms/models/TransferNoise/Example_model/Brucknell/obsHead_bore_WRK961324.csv b/algorithms/models/TransferNoise/Example_model/Brucknell/obsHead_bore_WRK961324.csv
new file mode 100644
index 0000000..39f884b
--- /dev/null
+++ b/algorithms/models/TransferNoise/Example_model/Brucknell/obsHead_bore_WRK961324.csv
@@ -0,0 +1,152 @@
+BoreID,Year,Month,Day,Hour,Minute,Obs_Head 
+bore_WRK961324,2003,11,11,0,0,37.15
+bore_WRK961324,2003,12,11,0,0,36.75
+bore_WRK961324,2004,1,22,0,0,36.43
+bore_WRK961324,2004,2,26,0,0,36.31
+bore_WRK961324,2004,3,30,0,0,36.37
+bore_WRK961324,2004,4,22,0,0,36.37
+bore_WRK961324,2004,5,12,0,0,36.52
+bore_WRK961324,2004,6,8,0,0,36.53
+bore_WRK961324,2004,7,12,0,0,37.55
+bore_WRK961324,2004,8,25,0,0,37.79
+bore_WRK961324,2004,9,23,0,0,37.88
+bore_WRK961324,2004,10,27,0,0,37.4
+bore_WRK961324,2004,11,24,0,0,37.26
+bore_WRK961324,2004,12,8,0,0,37.12
+bore_WRK961324,2005,1,26,0,0,36.6
+bore_WRK961324,2005,2,21,0,0,36.73
+bore_WRK961324,2005,3,23,0,0,36.61
+bore_WRK961324,2005,4,14,0,0,36.49
+bore_WRK961324,2005,5,25,0,0,36.54
+bore_WRK961324,2005,6,29,0,0,36.68
+bore_WRK961324,2005,7,26,0,0,36.79
+bore_WRK961324,2005,8,30,0,0,37.16
+bore_WRK961324,2005,9,26,0,0,37.25
+bore_WRK961324,2005,10,20,0,0,37.33
+bore_WRK961324,2005,11,22,0,0,36.99
+bore_WRK961324,2005,12,13,0,0,36.73
+bore_WRK961324,2006,1,24,0,0,36.37
+bore_WRK961324,2006,2,24,0,0,36.34
+bore_WRK961324,2006,3,28,0,0,36.3
+bore_WRK961324,2006,4,22,0,0,36.43
+bore_WRK961324,2006,5,24,0,0,36.6
+bore_WRK961324,2006,6,16,0,0,36.6
+bore_WRK961324,2006,8,14,0,0,36.82
+bore_WRK961324,2006,9,23,0,0,36.72
+bore_WRK961324,2006,10,22,0,0,36.49
+bore_WRK961324,2006,11,9,0,0,36.42
+bore_WRK961324,2006,12,18,0,0,36.13
+bore_WRK961324,2007,1,21,0,0,36.1
+bore_WRK961324,2007,2,14,0,0,36.13
+bore_WRK961324,2007,3,23,0,0,35.97
+bore_WRK961324,2007,4,25,0,0,36.05
+bore_WRK961324,2007,5,9,0,0,36.18
+bore_WRK961324,2007,6,11,0,0,36.43
+bore_WRK961324,2007,7,14,0,0,37.05
+bore_WRK961324,2007,8,14,0,0,37.47
+bore_WRK961324,2007,9,12,0,0,37.07
+bore_WRK961324,2007,10,10,0,0,37.28
+bore_WRK961324,2007,11,14,0,0,37.13
+bore_WRK961324,2007,12,19,0,0,36.58
+bore_WRK961324,2008,1,17,0,0,36.39
+bore_WRK961324,2008,2,12,0,0,36.35
+bore_WRK961324,2008,3,13,0,0,36.19
+bore_WRK961324,2008,4,17,0,0,36.33
+bore_WRK961324,2008,5,14,0,0,36.39
+bore_WRK961324,2008,6,13,0,0,36.44
+bore_WRK961324,2008,7,10,0,0,36.9
+bore_WRK961324,2008,8,11,0,0,37.62
+bore_WRK961324,2008,9,10,0,0,37.67
+bore_WRK961324,2008,10,13,0,0,37.38
+bore_WRK961324,2008,11,12,0,0,37.04
+bore_WRK961324,2008,12,11,0,0,36.8
+bore_WRK961324,2009,1,13,0,0,36.71
+bore_WRK961324,2009,2,11,0,0,36.4
+bore_WRK961324,2009,3,10,0,0,36.3
+bore_WRK961324,2009,4,15,0,0,36.31
+bore_WRK961324,2009,5,15,0,0,36.56
+bore_WRK961324,2009,6,11,0,0,36.9
+bore_WRK961324,2009,7,17,0,0,37.27
+bore_WRK961324,2009,8,12,0,0,37.43
+bore_WRK961324,2009,9,10,0,0,37.98
+bore_WRK961324,2009,10,16,0,0,37.66
+bore_WRK961324,2009,11,13,0,0,37.26
+bore_WRK961324,2009,12,17,0,0,37.03
+bore_WRK961324,2010,1,19,0,0,36.67
+bore_WRK961324,2010,2,10,0,0,36.52
+bore_WRK961324,2010,3,17,0,0,36.52
+bore_WRK961324,2010,4,16,0,0,36.57
+bore_WRK961324,2010,5,20,0,0,36.67
+bore_WRK961324,2010,6,18,0,0,36.74
+bore_WRK961324,2010,7,21,0,0,36.83
+bore_WRK961324,2010,8,12,0,0,37.88
+bore_WRK961324,2010,9,22,0,0,37.86
+bore_WRK961324,2010,10,22,0,0,37.65
+bore_WRK961324,2010,11,9,0,0,37.66
+bore_WRK961324,2010,12,16,0,0,37.81
+bore_WRK961324,2011,1,19,0,0,37.7
+bore_WRK961324,2011,2,11,0,0,37.31
+bore_WRK961324,2011,3,10,0,0,37.2
+bore_WRK961324,2011,4,12,0,0,37.39
+bore_WRK961324,2011,5,10,0,0,37.41
+bore_WRK961324,2011,6,16,0,0,37.85
+bore_WRK961324,2011,7,13,0,0,38.29
+bore_WRK961324,2011,8,24,0,0,37.97
+bore_WRK961324,2011,9,30,0,0,37.68
+bore_WRK961324,2011,10,24,0,0,37.58
+bore_WRK961324,2011,11,22,0,0,37.31
+bore_WRK961324,2011,12,20,0,0,37.09
+bore_WRK961324,2012,1,23,0,0,36.78
+bore_WRK961324,2012,2,28,0,0,36.63
+bore_WRK961324,2012,3,29,0,0,36.69
+bore_WRK961324,2012,4,24,0,0,36.68
+bore_WRK961324,2012,5,22,0,0,36.73
+bore_WRK961324,2012,6,25,0,0,36.95
+bore_WRK961324,2012,7,26,0,0,37.47
+bore_WRK961324,2012,8,27,0,0,37.84
+bore_WRK961324,2012,9,28,0,0,37.65
+bore_WRK961324,2012,10,26,0,0,37.51
+bore_WRK961324,2012,11,27,0,0,37.13
+bore_WRK961324,2012,12,28,0,0,36.87
+bore_WRK961324,2013,1,23,0,0,36.68
+bore_WRK961324,2013,2,27,0,0,36.58
+bore_WRK961324,2013,3,25,0,0,36.53
+bore_WRK961324,2013,4,25,0,0,36.56
+bore_WRK961324,2013,5,27,0,0,36.73
+bore_WRK961324,2013,6,24,0,0,36.82
+bore_WRK961324,2013,7,26,0,0,37.13
+bore_WRK961324,2013,8,29,0,0,38.06
+bore_WRK961324,2013,9,26,0,0,37.84
+bore_WRK961324,2013,10,24,0,0,38.24
+bore_WRK961324,2013,11,26,0,0,37.58
+bore_WRK961324,2013,12,23,0,0,37.34
+bore_WRK961324,2014,1,31,0,0,36.85
+bore_WRK961324,2014,2,26,0,0,36.72
+bore_WRK961324,2014,3,26,0,0,36.72
+bore_WRK961324,2014,4,22,0,0,36.72
+bore_WRK961324,2014,5,28,0,0,36.9
+bore_WRK961324,2014,6,23,0,0,37
+bore_WRK961324,2014,7,22,0,0,38.11
+bore_WRK961324,2014,8,28,0,0,37.79
+bore_WRK961324,2014,9,23,0,0,37.54
+bore_WRK961324,2014,10,31,0,0,37.26
+bore_WRK961324,2014,11,26,0,0,37.21
+bore_WRK961324,2014,12,22,0,0,36.83
+bore_WRK961324,2015,1,27,0,0,36.75
+bore_WRK961324,2015,2,23,0,0,36.59
+bore_WRK961324,2015,3,24,0,0,36.56
+bore_WRK961324,2015,4,27,0,0,36.66
+bore_WRK961324,2015,5,25,0,0,36.62
+bore_WRK961324,2015,6,24,0,0,36.82
+bore_WRK961324,2015,7,27,0,0,37.09
+bore_WRK961324,2015,8,25,0,0,37.34
+bore_WRK961324,2015,9,25,0,0,37.28
+bore_WRK961324,2015,10,29,0,0,36.88
+bore_WRK961324,2015,11,26,0,0,36.71
+bore_WRK961324,2015,12,21,0,0,36.56
+bore_WRK961324,2016,1,29,0,0,36.39
+bore_WRK961324,2016,2,25,0,0,36.45
+bore_WRK961324,2016,3,23,0,0,36.36
+bore_WRK961324,2016,4,29,0,0,36.37
+bore_WRK961324,2016,5,24,0,0,36.71
+bore_WRK961324,2016,6,27,0,0,37.42
diff --git a/algorithms/models/TransferNoise/Example_model/List of Scenarios.xlsx b/algorithms/models/TransferNoise/Example_model/List of Scenarios.xlsx
new file mode 100644
index 0000000..e231edb
Binary files /dev/null and b/algorithms/models/TransferNoise/Example_model/List of Scenarios.xlsx differ
diff --git a/algorithms/models/TransferNoise/Example_model/example_TFN_SW_GW.m b/algorithms/models/TransferNoise/Example_model/example_TFN_SW_GW.m
new file mode 100644
index 0000000..bc8c6d1
--- /dev/null
+++ b/algorithms/models/TransferNoise/Example_model/example_TFN_SW_GW.m
@@ -0,0 +1,425 @@
+% example_TFN_SW_GW_model:
+
+
+tic % start timer
+
+% Add Paths
+%    addpath(pwd);
+addpath(genpath([pwd, filesep, 'algorithms']));
+addpath(genpath([pwd, filesep, 'dataPreparationAnalysis']));
+addpath(genpath([pwd, filesep, 'Examples']));
+addpath(genpath([pwd, filesep, 'documentation']));
+addpath(genpath([pwd, filesep, 'GUI']));
+
+
+% Description
+%   This example builds and calibrates a nonlinear transfer-function noise
+%   model for the joint calibration and simulation of GW head and streamflow time-series.
+
+%   The model requires the following three data files: obsHead_bore_WRK961324.csv,
+%   obsFlow_Brucknell.csv, and climate_Brucknell_Catchment_ETMortonCRAE.csv, 
+%   List of Scenarios.csv.
+%
+%
+% References:
+%
+%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
+%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
+%   - Bonotto, Peterson, Fowler, & Western (2022), HydroSight SW-GW: lumped rainfall-runoff model for the joint simulation of streamflow and groundwater in a drying climate, Geoscientific Model Development , , –', ...
+%   - Bonotto, Peterson, Fowler, & Western (2022), Can the joint simulation of daily streamflow and groundwater head help to explain the Millennium Drought hydrology?, Water Resour. Res., , –\', ...
+%
+% Author:
+%   Giancarlo Bonotto, The Department of Infrastructure Engineering,
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering,
+%   The University of Melbourne.
+%
+% Date:
+%   May 2022
+%
+
+
+testing_only = 0; % 1-flag so we run model in diagnosis mode
+
+
+% Setting pos-processing and diagnostics parameters for model_TFN_SW_GW
+if testing_only ==1
+    % hydrograph diagnosis plots are generated but not saved for each AMALGAM iteration
+    model_object.inputData.plot_hydrographs = true;
+    model_object.inputData.save_output_data = false;
+else
+    % hydrograph diagnosis plots are not generated and not saved for each AMALGAM iteration
+    model_object.inputData.plot_hydrographs = false;
+    model_object.inputData.save_output_data = false;
+
+end
+
+
+% Example scenario to be run
+scenario="S1"
+
+% Scenario	Catchment	Bore_ID	        Baseflow method	  Interflow Hypothesis
+% S1	    Brucknell	bore_WRK961324	baseflow_m9	      IF1
+
+% Load the list of scenarios to run and their inputs
+opts = spreadsheetImportOptions("NumVariables", 5);
+opts.Sheet = "Sheet1";
+opts.DataRange = "B6:F06";
+opts.VariableNames = ["A1", "A2", "A3", "A4", "A5"];
+list_of_scenarios = readtable("List of Scenarios.xlsx", opts, "UseExcel", false);
+
+
+%find the scenario in the list of scenarios
+index = list_of_scenarios.A1==scenario;
+
+% check if scenario input is valid
+check_scenario = list_of_scenarios.A1(index);
+if check_scenario ~= scenario
+    error('Scenario input is not valid... Check spelling or type of variable used as input..')
+end
+
+% get the parameter flags for the scenario
+catchment = cell2mat(list_of_scenarios.A2(index));
+bore_ID = cell2mat(list_of_scenarios.A3(index));
+baseflow_method = cell2mat(list_of_scenarios.A4(index));
+interflow_hypothesis = cell2mat(list_of_scenarios.A5(index));
+
+A1 = scenario;
+A2 = catchment;
+A3 = bore_ID;
+A4 = baseflow_method;
+A5 = interflow_hypothesis;
+formatSpec = '%1$s %2$s %3$s %4$s %5$s';
+modelLabel = sprintf(formatSpec,A1,A2,A3,A4,A5)
+
+
+
+%   -------- CHANGE THE CACTHMENT ACCORDINGLY  -----------
+
+% Read in file with obs flow.
+% Note, eventually site_IDs needs to be chnaged from a single
+% string with the bore ID to a vector of stream and bore IDs.
+% Maybe the first could be the stream ID.
+
+if catchment=="Brucknell"
+    obsDataFlow = readtable('obsFlow_Brucknell.csv'); % read in the obs flow data. Choose from obsFlow_"Catchment".csv
+    obsDataFlow = obsDataFlow(:,[2:4 6]); % FLOW UNIT -> columns -> [5] = daily average [m3/s], [6] = [mm/day], [7] = [ML/day], [8] = [mm/day]^(1/5),
+    obsDataFlow = table2array(obsDataFlow);
+elseif catchment=="Sunday"
+    obsDataFlow = readtable('obsFlow_Sunday.csv'); % read in the obs flow data. Choose from obsFlow_"Catchment".csv
+    obsDataFlow = obsDataFlow(:,[2:4 6]); % FLOW UNIT -> columns -> [5] = daily average [m3/s], [6] = [mm/day], [7] = [ML/day], [8] = [mm/day]^(1/5),
+    obsDataFlow = table2array(obsDataFlow);
+elseif catchment=="Ford"
+    obsDataFlow = readtable('obsFlow_Ford.csv'); % read in the obs flow data. Choose from obsFlow_"Catchment".csv
+    obsDataFlow = obsDataFlow(:,[2:4 6]); % FLOW UNIT -> columns -> [5] = daily average [m3/s], [6] = [mm/day], [7] = [ML/day], [8] = [mm/day]^(1/5),
+    obsDataFlow = table2array(obsDataFlow);
+end
+
+% List of bores in the study area
+if catchment=="Brucknell"
+    list_bores = {'bore_WRK961324', 'bore_141234','bore_141243' ,'bore_WRK961325' , 'bore_WRK961326'} ; %  ----- Brucknell
+elseif catchment=="Sunday"
+    list_bores = {'bore_2091', 'bore_WRK958154', 'bore_WRK958156', 'bore_WRK958155', 'bore_2092'} ; %  --------- Sunday
+elseif catchment=="Ford"
+    list_bores = {'bore_118946', 'bore_118947'} ; %  ----------------------------------------------------------- Ford
+end
+
+% check if boreID is valid
+index = find(strcmp(list_bores, bore_ID));
+if index <= 0;
+    error('bore_ID is not valid... Check spelling or type of variable used as input..')
+end
+
+% get the bore data to be used
+LoadBoreDataWL = readtable('obsHead_bore_WRK961324.csv');
+boreDataWL = LoadBoreDataWL(strcmp(LoadBoreDataWL.BoreID, bore_ID), :);
+boreDataWL = boreDataWL(:,2:end);
+boreDataWL = table2array(boreDataWL);
+
+
+% baseflow options
+baseflow_options = {'baseflow_v1'; 'baseflow_v2'; 'baseflow_m1';'baseflow_m2';
+    'baseflow_m3'; 'baseflow_m4'; 'baseflow_m5'; 'baseflow_m6';
+    'baseflow_m7'; 'baseflow_m8'; 'baseflow_m9'; 'baseflow_bi_1';
+    'baseflow_bi_2'; 'baseflow_bi_3'; 'baseflow_bi_4'};
+
+% check if baseflow_method is valid
+index = find(strcmp(baseflow_options, baseflow_method));
+if index <= 0;
+    error('baseflow_method is not valid... Check spelling or type of variable used as input..')
+end
+
+
+
+% read the forcging data time-series in the study area
+if catchment=="Brucknell"
+    forcingData = readtable('climate_Brucknell_Catchment_ETMortonCRAE.csv');
+elseif catchment=="Sunday"
+    forcingData = readtable('climate_Sunday_Catchment_ETMortonCRAE.csv');
+elseif catchment=="Ford"
+    forcingData = readtable('climate_Ford_Catchment_ETMortonCRAE.csv');
+end
+forcingData = forcingData(:,[3:6 12]);
+forcingData = table2array(forcingData);
+
+
+% Reformat the matric of forcing data to a structure variable containing the column names.
+forcingDataStruct.data = forcingData;
+forcingDataStruct.colnames = {'YEAR','MONTH','DAY','PRECIP','APET'};
+
+% To increase performance, we can reduce the length of the climate record.
+% This may cause longer time scales to be less reliably estimated.
+yearsOfPriorForcing = 50;
+forcingData_thresholddate  = datenum( boreDataWL(1,1)- yearsOfPriorForcing, boreDataWL(1,2), boreDataWL(1,3));
+filt = datenum(forcingDataStruct.data(:,1), forcingDataStruct.data(:,2), forcingDataStruct.data(:,3)) >= forcingData_thresholddate;
+forcingDataStruct.data = forcingDataStruct.data(filt,:);
+
+% Define the bore ID and create dummy site coordinates. This must be
+% for the bore and each column in the forcing file.
+siteCoordinates = {bore_ID, 100, 100;...
+    'PRECIP', 100, 100;...
+    'APET', 100, 100};
+
+% Define the soil moisture model according to the ---- INTERFLOW Hypothesis ----
+
+if interflow_hypothesis=="IF1"
+
+    %---- Hypothesis 1 ---- NO INTERFLOW ----%
+    % using 1-layer soil model "climateTransform_soilMoistureModels_v2"
+    %----- Alpha, beta, gamma, EPS are calibrated  ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_v2'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed';'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.0,'fixed'}}; % withouth ('k_infilt',inf,'fixed') k_infilt is set to 1 automatically..
+
+elseif interflow_hypothesis=="IF2"
+
+    %---- Hypothesis 2 ---- INTERFLOW, NO STORAGE ----%
+    % using 1-layer soil model "climateTransform_soilMoistureModels_v2"
+    % ---- Alpha, beta, gamma, EPS, inteflow_frac are calibrated  ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_v2'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed'; 'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.5,''}}; % withouth ('k_infilt',inf,'fixed') k_infilt is set to 1 automatically..
+
+elseif interflow_hypothesis=="IF3"
+
+    %---- Hypothesis 3 ---- INTERFLOW, INFINITE STORAGE ----%
+    % using 2-layer soil model "climateTransform_soilMoistureModels_interflow"
+    %-----  Alpha, beta, gamma, EPS, inteflow_frac are calibrated. alpha_interflow=0 and beta_sat_interflow=1 fixed ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_interflow'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed';'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.5,'';'SMSC_interflow',1,'fixed';'alpha_interflow',0,'fixed';'eps_interflow',0,'fixed';...
+        'beta_interflow',1,'fixed';'k_sat_interflow',1,'';...
+        'PET_scaler_interflow',0,'fixed'; 'gamma_interflow',0,'fixed'}};
+
+elseif interflow_hypothesis=="IF4"
+    %---- Hypothesis 4 ---- INTERFLOW, FINITE STORAGE, NO ET LOSSES ----%
+    %using 2-layer soil model "climateTransform_soilMoistureModels_interflow"
+    %---- Alpha, beta, gamma, EPS, inteflow_frac, alpha_interflow, beta_sat_interflow are calibrated. gamma_interflow= -999999999 fixed ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_interflow'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed';'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.5,'';'SMSC_interflow',2,'';'alpha_interflow',1,'';'eps_interflow',0,'fixed';...
+        'beta_interflow',1,'';'k_sat_interflow',1,'';...
+        'PET_scaler_interflow',0,'fixed'; 'gamma_interflow',0,'fixed'}};
+
+elseif interflow_hypothesis=="IF5"
+
+    %---- Hypothesis 5 ---- INTERFLOW, FINITE STORAGE WITH ET LOSSES ----%
+    %using 2-layer soil model "climateTransform_soilMoistureModels_interflow"
+    %---- Alpha, beta, gamma, EPS, inteflow_frac are calibrated  ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_interflow'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed';'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.5,'';'SMSC_interflow',2,'';'alpha_interflow',1,'';'eps_interflow',0,'fixed';...
+        'beta_interflow',1,'';'k_sat_interflow',1,'';...
+        'PET_scaler_interflow',1,'fixed'; 'gamma_interflow',NaN,'fixed'}};
+
+elseif interflow_hypothesis=="IF6"
+
+    %---- Hypothesis 6 ---- INTERFLOW, FINITE STORAGE AND ET LOSSES ----- INTERFLOW STORE SAME PARAMETERS AS SHALLOW SOIL STORE ----%
+    %using 2-layer soil model "climateTransform_soilMoistureModels_interflow"
+    %---- Alpha, beta, gamma, EPS, inteflow_frac are calibrated  ----%
+    forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels_interflow'; ...
+        'forcingdata', {'precip','PRECIP';'et','APET'}; ...
+        'outputdata', 'drainage'; ...
+        'options', {'SMSC',2,[];'k_infilt',Inf,'fixed';'beta',0,'';'k_sat',1,'';'alpha',0,'';'eps',0.0,'';'gamma',0.0,'';...
+        'interflow_frac',0.5,'';'SMSC_interflow',2,'';'alpha_interflow',NaN,'fixed';'eps_interflow',0,'fixed';...
+        'beta_interflow',NaN,'fixed';'k_sat_interflow',NaN,'fixed';...
+        'PET_scaler_interflow',1,'fixed'; 'gamma_interflow',NaN,'fixed'}};
+end
+
+
+% using only the the transformed PRECIP from the chosen soil model
+modelOptions_7params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
+    'precip','forcingdata',forcingTransform_Precip};
+
+
+% If using ET for the GW convolution, set the transformation of the ET.
+% However because we've already defined the soil model, we only need to specify the output we require.
+% Here we're selecting  'evap_gw_potential', which is the potential ET - actual soil ET.
+
+% % using 1-layer soil model "climateTransform_soilMoistureModels"
+% forcingTransform_ET = {'transformfunction', 'climateTransform_soilMoistureModels'; ...
+%                'outputdata', 'evap_gw_potential'};
+
+% using 2-layer soil model "climateTransform_soilMoistureModels_2layer_v2"
+% forcingTransform_ET = {'transformfunction', 'climateTransform_soilMoistureModels_2layer_v2'; ...
+%                'outputdata', 'evap_gw_potential'};
+
+
+% Next we create a cell array for all of the model options. The column format is:
+% the forcing name (can be anything), the setting we want to define (ie
+% 'weightingfunction' or 'forcingdata'); and the setting we want to apply.
+% Note 'responseFunction_Pearsons' is the name of a function.
+
+
+% using the transformed PRECIP and ET from the soil model
+% modelOptions_7params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
+%                         'precip','forcingdata',forcingTransform_Precip; ...
+%                         'et','weightingfunction','derivedweighting_PearsonsNegativeRescaled'; ...
+%                         'et','inputcomponent','precip'; ...
+%                         'et','forcingdata',forcingTransform_ET};
+
+
+% Set the maximum frequency of water level obs
+maxObsFreq = 1;
+
+
+% Build the 7 parameter model for model_TFN_SW_GW.
+model_7params = HydroSightModel(modelLabel, bore_ID, 'model_TFN_SW_GW', boreDataWL, obsDataFlow, maxObsFreq, forcingDataStruct, siteCoordinates, modelOptions_7params);
+
+% getting the parameters necessary for running the transfer functions
+[params, param_names] = getParameters(model_7params.model);
+t = datenum(boreDataWL(:,1),boreDataWL(:,2),boreDataWL(:,3));
+
+t_start = 0;
+t_end  = inf;
+
+%%%% Creating the model structure required to calculate ObjFun for head in model_TFN
+[params_initial, time_points_head, time_points_streamflow] = calibration_initialise_joint(model_7params.model, t_start, t_end);
+
+
+% ----------------------------------------------------------------------------------------- %
+% ADJUSTING AND CALLING AMALGAM FOR THE JOINT CALIBRATION
+% ----------------------------------------------------------------------------------------- %
+
+% Define which algorithms to use in AMALGAM
+Extra.Alg = {'GA','PSO','AMS','DE'};
+% Define the number of algorithms
+AMALGAMPar.q = size(Extra.Alg,2);
+
+AMALGAMPar.n = length(params_initial);  % Dimension of the problem
+AMALGAMPar.N = 20;                     % Size of the population
+AMALGAMPar.nobj = 2;                    % Number of objectives
+AMALGAMPar.ndraw = 60;               % Maximum number of function evaluations
+
+% Getting the parameter ranges (minimum and maximum values)
+[params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(model_7params.model);
+
+% set baseflow head_threshold parameter bounds to be +5m within
+% observed GW head data.
+params_upperLimit(end,:) = max(boreDataWL(:,6)) + 5;
+params_lowerLimit(end,:) = min(boreDataWL(:,6)) - 5;
+
+% Setting the parameter ranges (minimum and maximum values)
+ParRange.minn = params_lowerLimit';
+ParRange.maxn = params_upperLimit';
+
+% Initial Sample of Parameters Created with Latin Hypercube sampling
+Extra.InitPopulation = 'LHS';
+
+% Define the timepoints for the obs. head and streamflow data
+Measurement.time_points_head = time_points_head; Measurement.time_points_streamflow = time_points_streamflow;
+Measurement.Sigma = []; Measurement.N = size(Measurement.time_points_streamflow,1);
+
+% Define ModelName
+model_object = model_7params.model; % hydrosight model as input
+ModelName = modelLabel; % Scenario that is running
+
+% Define the boundary handling
+Extra.BoundHandling = 'Bound';
+
+% True Pareto front is not available -- real world problem
+Fpareto = [];
+
+% Store example number in structure Extra
+Extra.m = AMALGAMPar.n;
+
+
+% Run the AMALGAM calibration and obtain the solution set
+[output,ParGen,ObjVals,ParSet,allOriginalObjVals_Flow] = AMALGAM(AMALGAMPar,ModelName,ParRange,Measurement,Extra,Fpareto,model_object);
+
+
+
+if testing_only ==1
+    % continue
+
+    % Don't save any data from the AMALGAM Run
+
+else
+
+    % Path used to store the hydrographs
+    folder = 'C:\Users\bonottog\OneDrive - The University of Melbourne (1)\1 - UNIMELB\5 - HydroSight\10 - Run Results\Interflow_Scenarios\500k';
+
+
+    %--------------AUTOMATICALLY GENERATE HYDROGRAPH PLOTS FOR THE SWEET-SPOT IN THE LAST PARETO FRONT -------------------------------- %
+
+    % find the parameter set that represents the sweet-stop in the last Pareto Front
+    final_pareto_points = ParSet(end-AMALGAMPar.N+1:end,end-1:end);
+    sweet_spot = [min(final_pareto_points(:,1)) min(final_pareto_points(:,2))];
+    [k,dist] = dsearchn(final_pareto_points,sweet_spot);
+    final_pareto_params_points = ParSet(end-AMALGAMPar.N+1:end,:);
+    optimal_parameters = final_pareto_params_points(k, 1:end-3);
+    optimal_parameters_ObjFun = final_pareto_params_points(k, :);
+
+    % diagnosis plot to find the sweet-spot in the pareto front
+    figure(1);
+    scatter(final_pareto_points(:,1),final_pareto_points(:,2))
+    %xlim([0 5])
+    %ylim([0 5])
+    hold on
+    scatter(optimal_parameters_ObjFun(:,end-1),optimal_parameters_ObjFun(:,end),'red')
+    scatter(sweet_spot(:,end-1),sweet_spot(:,end),'red')
+    plot( [sweet_spot(:,end-1);optimal_parameters_ObjFun(:,end-1)], [sweet_spot(:,end);optimal_parameters_ObjFun(:,end)] )
+
+    % Save the Pareto Front diagnosis plot
+    f = figure(1);
+    %     f = figure(2);
+    set(f, 'Color', 'w');
+    A1 = 'DIAGNOSIS_Pareto_Front_Final Generation';
+    A2 = ModelName;
+    formatSpec = '%1$s %2$s';
+    Filename = sprintf(formatSpec,A2,A1);
+    saveas(f, fullfile(folder, Filename), 'png');
+
+
+    % save the parameter set and objfun sweet-spot as a separate
+    % CSV file
+    A1 = ModelName;
+    A2 = 'ParetoFront_Optimal_Point';
+    A3 = '.csv';
+    formatSpec = '%1$s %2$s %3$s';
+    Filename = sprintf(formatSpec,A1,A2,A3);
+    path =fullfile(folder, Filename);
+    csvwrite(path,optimal_parameters_ObjFun)
+
+    % set model_TFN_SW_GW to plot and save the plots
+    model_object.inputData.plot_hydrographs = true;
+    model_7params.model.inputData.save_output_data = true;
+
+    % run model_TFN_SW_GW, plotting and saving hydrographs for optimal
+    % point in the Pareto Front
+    [ObjVals_prime, ~, ~, objFn_flow_NSE, objFn_flow_NNSE, objFn_flow_RMSE, objFn_flow_SSE, objFn_flow_bias, ~, ~,~] = objectiveFunction_joint(optimal_parameters', time_points_head, time_points_streamflow, model_7params.model,{});
+
+end
+
+toc % stop timer
+
+
diff --git a/algorithms/models/TransferNoise/Example_model/example_TFN_model.m b/algorithms/models/TransferNoise/Example_model/example_TFN_model.m
deleted file mode 100644
index 6272365..0000000
--- a/algorithms/models/TransferNoise/Example_model/example_TFN_model.m
+++ /dev/null
@@ -1,159 +0,0 @@
-% example_TFN_model:
-%
-% Description
-%   This example builds and calibrates a nonlinear transfer-function noise
-%   model. The example is taken from Peterson & Western (2014). The model
-%   requires the following three data files: 124705_boreData.mat,
-%   124676_boreData.mat and 124705_forcingData.mat.
-%
-%   By default the example models bore 124676. By commenting out line 29
-%   and un-commenting line 28, bore 124705 can be modelled.
-%
-%   Also, logical variables at line 112 and 113 also define which of 
-%   two model structures are to be calibrated.
-%
-% References:
-%
-%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
-%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-%
-
-%clear all
-
-% Comment out the one bore ID that you DO NOT want to model.
-bore_ID = 'ID124705';
-
-if strcmp(bore_ID,'ID124705')
-    load('124705_boreData.mat');
-else
-    load('124676_boreData.mat');
-end
-
-load('124705_forcingData.mat');
-
-
-
-% Reformat the matric of forcing data to a sturctire variable containing
-% the column names.
-forcingDataStruct.data = forcingData;
-forcingDataStruct.colnames = {'YEAR','MONTH','DAY','PRECIP','APET','RevegFrac'};
-
-% To increase performance, we can reduce the length of the climate record.
-% This may cause longer time scales to be less reliably estimated.
-yearsOfPriorForcing = 100;
-forcingData_thresholddate  = datenum( boreDataWL(1,1)- yearsOfPriorForcing, boreDataWL(1,2), boreDataWL(1,3)); 
-filt = datenum(forcingDataStruct.data(:,1), forcingDataStruct.data(:,2), forcingDataStruct.data(:,3)) >= forcingData_thresholddate;
-forcingDataStruct.data = forcingDataStruct.data(filt,:);
-
-% Define the bore ID and create sume dummy site coordinates. This must be
-% for the bore and each column in the forcing file.
-siteCoordinates = {bore_ID, 100, 100; 'PRECIP', 100, 100; 'APET', 100, 100; 'RevegFrac',602, 100};
-
-% Define the way in which the precipitation is transformed. In this case it
-% is transformed using the 'climateTransform_soilMoistureModels' soil
-% model. 
-% Next, the soil ODE needs inputs data 'precip' and 'et' and the forcing
-% data input columns are 'PRECIP' and 'ET'.
-% Next, the 'outputdata' that is to be taken from the soil model is
-% defined. Each model has fixed options and here we're taking
-% 'drainage_normalised'.
-% Lastly, we can set 'options' for the soil model. In this case we are
-% defining the initial values for three parameters (SMSC, beta, ksat) and
-% fixing alpha to zero.
-forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels'; ...
-               'forcingdata', {'precip','PRECIP';'et','APET'}; ...
-               'outputdata', 'drainage_normalised'; ...
-               'options', {'SMSC',2,[];'beta',0,'';'k_sat',-inf,'fixed';'alpha',0,'fixed'}};
-           
-% The transformation of the ET is then defined. However because we've already 
-% defined the soil model, we only need to specify the output we require.
-% Here we're selecting  'evap_gw_potential', which is the potential ET -
-% actual soil ET.
-forcingTransform_ET = {'transformfunction', 'climateTransform_soilMoistureModels'; ...
-               'outputdata', 'evap_gw_potential'};
-
-% Next we create a cell array for all of the model options. The column format is:            
-% the forcing name (can be anything), the setting we want to define (ie
-% 'weightingfunction' or 'forcingdata'); and the setting we want to apply.
-% Note 'responseFunction_Pearsons' is the name of a function.
-modelOptions_9params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
-                        'precip','forcingdata',forcingTransform_Precip; ...
-                        'et','weightingfunction','responseFunction_PearsonsNegative'; ...
-                        'et','forcingdata',forcingTransform_ET};
-
-% Alternatively, we can create a cell array for the model options where the
-% ET weighting function inherits the shape from the precipitation. The column format is:            
-% the forcing name (can be anything), the setting we want to define (ie
-% 'weightingfunction' or 'forcingdata'); and the setting we want to apply.
-% Note 'responseFunction_Pearsons' is the name of a function.
-modelOptions_7params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
-                        'precip','forcingdata',forcingTransform_Precip; ...
-                        'et','weightingfunction','derivedweighting_PearsonsNegativeRescaled'; ...
-                        'et','inputcomponent','precip'; ...
-                        'et','forcingdata',forcingTransform_ET};                    
-                    
-% Set the maximum frequency of water level obs
-maxObsFreq = 7;
-
-% Select which model structures to build and calibrate.
-run7paramModel = true;
-run9paramModel = false;
-
-% Define a model lable
-modelLabel = 'Great Western Catchment - no landuse change';
-
-if run7paramModel
-    % Build the 7 parameter model.
-    model_7params = HydroSightModel(modelLabel, bore_ID, 'model_TFN', boreDataWL, maxObsFreq, forcingDataStruct, siteCoordinates, modelOptions_7params);
-
-    % Set the number of SP-UCI calibration clusters per parameter
-    SchemeSetting.ngs = 2*7;    
-    
-    % Calibrate the 7 parameter model.
-    calibrateModel(model_7params, [], 0, inf, 'SP-UCI', SchemeSetting);
-    
-    % Plot the calibration results.    
-    calibrateModelPlotResults(model_7params,[]);
-   
-    % Plot the simulation results. 
-    time_points = model_7params.model.variables.time_points;
-    newForcingData = [];
-    simulationLabel = 'default simulation';
-    doKrigingOnResiduals = false;    
-    solveModel(model_7params, time_points, newForcingData, simulationLabel, doKrigingOnResiduals);    
-    solveModelPlotResults(model_7params, simulationLabel, []);    
-end
-
-if run9paramModel
-    % Build the 9 parameter model.
-    model_9params = HydroSightModel(modelLabel, bore_ID, 'model_TFN', boreDataWL, maxObsFreq, forcingDataStruct, siteCoordinates, modelOptions_9params);
-
-
-    % Set the number of SP-UCI calibration clusters per parameter
-    SchemeSetting.ngs = 4*9;    
-    
-    % Calibrate the 7 parameter model.
-    calibrateModel(model_9params, [],  0, inf, 'SP-UCI', SchemeSetting);
-    
-    % Plot the calibration results.
-    sTime = tic;
-    calibrateModelPlotResults(model_9params,[]);
-    eTime = tic;
-    eTime = toc(eTime);
-    display(['Calibration time = ',num2str(eTime-sTime),'  sec']); 
-   
-    % Plot the simulation results. 
-    time_points = model_9params.model.variables.time_points;
-    newForcingData = [];
-    simulationLabel = 'default simulation';
-    doKrigingOnResiduals = false;    
-    solveModel(model_9params, time_points, newForcingData, simulationLabel, doKrigingOnResiduals);    
-    solveModelPlotResults(model_9params, simulationLabel, []);    
-end
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow.m
new file mode 100644
index 0000000..e4653fb
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow.m
@@ -0,0 +1,251 @@
+classdef baseflow < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head. 
+    
+    %   Detailed explanation goes here
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_threshold   % GW head threshold that defines if have positive or negative baseflow. Below this threshold, baseflow recharges from the river.
+        head_to_baseflow % controls the smoothening of the baseflow response due to the rise of GW head. 
+       % log these parameters? 
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+                                    
+            variable_names ={'baseflow'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow class
+    
+    methods
+        function obj = baseflow_v1(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
+            % Constructor of the Baseflow class 
+            %   Detailed explanation goes here
+            obj.head_threshold = 200;
+            obj.head_to_baseflow = 1;
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_threshold; obj.head_to_baseflow];
+           param_names = {'head_threshold'; 'head_to_baseflow'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_threshold'; 'head_to_baseflow'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0;0];
+            params_upperLimit = [inf;inf];
+        end
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0;0];
+            params_upperLimit = [1000;1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+%             if any(delta_t ~=1)
+%                 error('forcing data must have no gaps and be a daily
+%                 time-series ') 
+%             end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+        
+            
+            % get simulated head to use to estimate baseflow
+            
+%             [head, colnames, noise] = solve(obj, time_points); % just the deterministic . is it at the same time step (daily)?
+            
+            % calculate the baseflow
+            obj.variables.baseFlow = max(0,obj.variables.head - obj.head_threshold) .* obj.head_to_baseflow;
+            
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_1.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_1.m
new file mode 100644
index 0000000..38c65f0
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_1.m
@@ -0,0 +1,268 @@
+classdef baseflow_bi_1 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Linear outflow - inflow from a reservoir if a storage threshold is exceeded
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_scaler   % - time coefficient [d-1]
+        head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_bi_1'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'baseflow_bi_1', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_bi_1 class
+    
+    methods
+        function obj = baseflow_bi_1(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_bi_1 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_scaler; obj.head_threshold];
+           param_names = {'linear_scaler'; 'head_threshold'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'linear_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_20 in MaRRMOT (GSFB, Nathan&McMahon 1990)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 1]; 
+            params_upperLimit = [10; 1000];
+        end
+        % 0, 1;           % b, Fraction of subsurface flow that is baseflow [-]
+        %   0, 1;         % dpf, Baseflow time coefficient [d-1]
+        % linear_scaler = b*dpf in GSFB cause it assumes 2 linear storages (deep percolation, and baseflow above threshold)
+        %  1, 300];       % sdrmax, Threshold before baseflow can occur [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 1];
+            params_upperLimit = [1; 1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+            % calculate the baseflow 
+            obj.variables.baseFlow = obj.linear_scaler .* (obj.variables.head - obj.head_threshold);
+            plot(obj.variables.baseFlow)
+            
+            % Description:  Linear outflow and inflow from a reservoir according to a GW head threshold, which represents the catchment average 
+            % GW head controlling whether the stream is mostly loosing or gaining at the catchment scale 
+            % Constraints:  -
+            % @(Inputs):    p1   - linear time coefficient [day-1]
+            %               p2   - storage threshold for flow generation [mAHD]
+            %               S    - current storage [mAHD]
+            % func = p1 .* (S-p2);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_2.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_2.m
new file mode 100644
index 0000000..9873f85
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_2.m
@@ -0,0 +1,282 @@
+classdef baseflow_bi_2 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Linear outflow - inflow from a reservoir if a storage
+    % threshold is exceeded, but with different slopes for gaining/losing
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_gaining_scaler   % - time coefficient [d-1]
+        linear_losing_scaler   % - time coefficient [d-1]
+        head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_bi_2'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'baseflow_bi_2', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_bi_2 class
+    
+    methods
+        function obj = baseflow_bi_2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_bi_2 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_gaining_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.linear_losing_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_gaining_scaler; obj.linear_losing_scaler;  obj.head_threshold];
+           param_names = {'linear_gaining_scaler';'linear_losing_scaler'; 'head_threshold'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'linear_gaining_scaler'; 'linear_losing_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_20 in MaRRMOT (GSFB, Nathan&McMahon 1990)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 0 ; 1]; 
+            params_upperLimit = [10; 10; 1000];
+        end
+        % 0, 1;           % b, Fraction of subsurface flow that is baseflow [-]
+        %   0, 1;         % dpf, Baseflow time coefficient [d-1]
+        % linear_scaler = b*dpf in GSFB cause it assumes 2 linear storages (deep percolation, and baseflow above threshold)
+        %  1, 300];       % sdrmax, Threshold before baseflow can occur [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 0 ; 1];
+            params_upperLimit = [1; 1; 1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+                      
+            % calculate the baseflow 
+            for i=1:length(obj.variables.head)
+                % gaining river
+                if (obj.variables.head(i) - obj.head_threshold) > 0
+                obj.variables.baseFlow(i) = obj.linear_gaining_scaler .* (obj.variables.head(i) - obj.head_threshold);
+                end
+                % losing river
+                if (obj.variables.head(i) - obj.head_threshold) < 0
+                obj.variables.baseFlow(i) = obj.linear_losing_scaler .* (obj.variables.head(i) - obj.head_threshold);
+                end                
+            end
+            plot(obj.variables.baseFlow)
+            
+
+            % Description:  Linear outflow and inflow from a reservoir according to a GW head threshold, which represents the catchment average 
+            % GW head controlling whether the stream is mostly loosing or gaining at the catchment scale 
+            % Constraints:  -
+            % @(Inputs):    p1   - linear time coefficient [day-1]
+            %               p2   - storage threshold for flow generation [mAHD]
+            %               S    - current storage [mAHD]
+            % func = p1 .* (S-p2);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_3.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_3.m
new file mode 100644
index 0000000..3033be0
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_3.m
@@ -0,0 +1,272 @@
+classdef baseflow_bi_3 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  nonLinear outflow - inflow from a reservoir if a
+    % storage threshold is exceeded, with same asymptotes for
+    % gaining/losing
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_scaler   % - time coefficient [d-1]
+        exponential_scaler   % - time coefficient [d-1]
+        head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_bi_3'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'baseflow_bi_3', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_bi_3 class
+    
+    methods
+        function obj = baseflow_bi_3(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_bi_3 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.exponential_scaler = 0;  %initial guess for - time coefficient [d-1]
+            obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_scaler; obj.exponential_scaler; obj.head_threshold];
+           param_names = {'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_20 in MaRRMOT (GSFB, Nathan&McMahon 1990)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 0 ; 1]; 
+            params_upperLimit = [10; 10; 1000];
+        end
+        % 0, 1;           % b, Fraction of subsurface flow that is baseflow [-]
+        %   0, 1;         % dpf, Baseflow time coefficient [d-1]
+        % linear_scaler = b*dpf in GSFB cause it assumes 2 linear storages (deep percolation, and baseflow above threshold)
+        %  1, 300];       % sdrmax, Threshold before baseflow can occur [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 0 ;  1];
+            params_upperLimit = [1; 5; 1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+            % calculate the baseflow 
+            obj.variables.baseFlow = obj.linear_scaler .* (1 - exp( - obj.exponential_scaler .* (obj.variables.head - obj.head_threshold))) ;
+            plot(obj.variables.baseFlow)
+            
+            % Description:  nonlinear outflow and inflow from a reservoir according to a GW head threshold, which represents the catchment average 
+            % GW head controlling whether the stream is mostly loosing or gaining at the catchment scale
+            % Constraints:  -
+            % @(Inputs):    K2   - linear time coefficient [day-1]
+            %               K3   - storage threshold for flow generation [mAHD]
+            %               S    - threshold storage [mAHD]
+            % func =  K2 [1 - exp(- K3· Delta_H)] - This relationship permits a rapid increase in the flow for small head changes when the head difference is small, but postulates maximum flows that cannot be exceeded as long as the head differ- ence becomes larger
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_4.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_4.m
new file mode 100644
index 0000000..260ea1c
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_bi_4.m
@@ -0,0 +1,288 @@
+classdef baseflow_bi_4 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  nonLinear outflow - inflow from a reservoir if a
+    % storage threshold is exceeded, with a asymptote for losing stream but
+    % a unlimited linear storage gaining stream 
+    
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_storage_constant   % - time coefficient [d-1]
+        linear_scaler   % - time coefficient [d-1]
+        exponential_scaler   % - time coefficient [d-1]
+        head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_bi_4'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'baseflow_bi_4', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_bi_4 class
+    
+    methods
+        function obj = baseflow_bi_4(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_bi_4 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_storage_constant = 0; % initial guess for - time coefficient [d-1]
+            obj.linear_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.exponential_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_storage_constant; obj.linear_scaler; obj.exponential_scaler; obj.head_threshold];
+           param_names = {'linear_storage_constant'; 'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'linear_storage_constant'; 'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_20 in MaRRMOT (GSFB, Nathan&McMahon 1990)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 0 ; 0 ; 1]; 
+            params_upperLimit = [10; 10; 10; 1000];
+        end
+        % 0, 1;           % b, Fraction of subsurface flow that is baseflow [-]
+        %   0, 1;         % dpf, Baseflow time coefficient [d-1]
+        % linear_scaler = b*dpf in GSFB cause it assumes 2 linear storages (deep percolation, and baseflow above threshold)
+        %  1, 300];       % sdrmax, Threshold before baseflow can occur [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 0 ; 0 ;  1];
+            params_upperLimit = [1; 1; 5; 1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+                      
+        
+            % calculate the baseflow 
+            for i=1:length(obj.variables.head)
+                % gaining river
+                if (obj.variables.head(i) - obj.head_threshold) > 0
+                obj.variables.baseFlow(i) = (obj.linear_storage_constant .* (obj.variables.head(i) - obj.head_threshold)) + (obj.linear_scaler .* (1 - exp( - obj.exponential_scaler .* (obj.variables.head(i) - obj.head_threshold))));
+                end
+                % losing river
+                if (obj.variables.head(i) - obj.head_threshold) < 0
+                obj.variables.baseFlow(i) = obj.linear_scaler .* (1 - exp( - obj.exponential_scaler .* (obj.variables.head(i) - obj.head_threshold))) ;
+                end             
+            end
+            plot(obj.variables.baseFlow)
+            
+            % Description:  nonlinear outflow and inflow from a reservoir according to a GW head threshold, which represents the catchment average 
+            % GW head controlling whether the stream is mostly loosing or gaining at the catchment scale
+            % Constraints:  -
+            % @(Inputs):    K2   - linear time coefficient [day-1]
+            %               K3   - storage threshold for flow generation [mAHD]
+            %               S    - threshold storage [mAHD]
+            % This relationship permits discharge function asymptotes to a linear storage model, and that the recharge function asymptotes to a constant.
+            % discharge = (K1 * Delta_H) + K2[1 - exp(-K3 * Delta_H)]
+            % recharge = K2 [1 - exp(- K3 * Delta_H)] (5)
+            
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m1.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m1.m
new file mode 100644
index 0000000..9f6d3e7
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m1.m
@@ -0,0 +1,260 @@
+classdef baseflow_m1 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    %   Detailed explanation goes here
+    % Description:  Outflow from a linear reservoir
+
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_scaler   % time scale parameter [d-1]
+        % log these parameters? 
+       
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m1'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m1', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m1 class
+    
+    methods
+        function obj = baseflow_m1(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m1 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.head_scaler = 0; % initial guess for the time scale parameter [d-1]
+  
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_scaler];
+           param_names = {'head_scaler'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_scaler'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        % as per range of parameters for model_46 in MaRRMOT 
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0];
+            params_upperLimit = [1];
+        end
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0];
+            params_upperLimit = [1];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+                   
+           % calculate the baseflow 
+            obj.variables.baseFlow = obj.variables.head .* obj.head_scaler;
+            % Description:  Outflow from a linear reservoir
+            % @(Inputs):    p1   - time scale parameter [d-1]
+            %               S    - current storage [mm]
+            % func = @(p1,S) p1.*S;
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m2.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m2.m
new file mode 100644
index 0000000..c392c0e
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m2.m
@@ -0,0 +1,268 @@
+classdef baseflow_m2 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Non-linear outflow from a reservoir
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_scaler   % time scale parameter [d]
+        exponential_scaler % exponential scaling parameter [-]
+        % log these parameters? 
+        
+                
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m2'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m2', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m2 class
+    
+    methods
+        function obj = baseflow_m2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m2 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.head_scaler = 0; % initial guess for the time scale parameter [d-1]
+            obj.exponential_scaler = 1; % initial guess for the exponential scaling parameter [-]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_scaler; obj.exponential_scaler];
+           param_names = {'head_scaler'; 'exponential_scaler'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_scaler'; 'exponential_scaler'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        % as per range of parameters for model_9 in MaRRMOT 
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [1 ; 0.2]; 
+            params_upperLimit = [50; 1];
+        end
+
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [1 ; 0.2];
+            params_upperLimit = [50; 1];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+                
+                delta_t = diff(obj.variables.t); % dt=1 as we use daily head as input
+                delta_t(end+1,1) = delta_t(end,1); % duplicate last point to match head/delta_t matrixes
+   
+                
+           % calculate the baseflow 
+            obj.variables.baseFlow = min(1 ./ obj.head_scaler .* max(obj.variables.head, 0) .^(1./obj.exponential_scaler), max(obj.variables.head,0)./delta_t); 
+            % Description:  Non-linear outflow from a reservoir
+            % Constraints:  f <= S/dt
+            %               S >= 0       prevents numerical issues with complex numbers
+            % @(Inputs):    S    - current storage [mm]
+            %               p1   - time coefficient [d]
+            %               p2   - exponential scaling parameter [-]
+            %               dt   - time step size [d]
+            %func = @(S,p1,p2,dt) min((1./p1*max(S,0)).^(1./p2),max(S,0)/dt);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m3.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m3.m
new file mode 100644
index 0000000..d1a97ad
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m3.m
@@ -0,0 +1,262 @@
+classdef baseflow_m3 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Empirical non-linear outflow from a reservoir
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_max   % - maximum contributing storage [mm]
+        % log these parameters? 
+        
+        
+                       
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m3'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m3', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m3 class
+    
+    methods
+        function obj = baseflow_m3(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m3 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.head_max = 1000; % initial guess for the - maximum contributing storage [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_max];
+           param_names = {'head_max'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_max'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        % as per range of parameters for model_7 in MaRRMOT (GR4J)    
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [1]; 
+            params_upperLimit = [300];
+        end
+        %(1   , 300)     % x3 [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [1];
+            params_upperLimit = [300];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+                   
+           % calculate the baseflow 
+            obj.variables.baseFlow = ((obj.head_max ^ (-4)) / 4) .* (obj.variables.head .^ 5);
+            % Description:  Empirical non-linear outflow from a reservoir
+            % Constraints:  None specified
+            % @(Inputs):    S    - current storage [mm]
+            %               Smax - maximum contributing storage [mm]
+            % func = @(S,Smax) Smax^(-4)/4*(S^5);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m4.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m4.m
new file mode 100644
index 0000000..0894dce
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m4.m
@@ -0,0 +1,268 @@
+classdef baseflow_m4 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Exponential outflow from deficit store, therefore, uses
+    % 1/head instead of head. 
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        base_rate   % - base outflow rate [mm/d]
+        exponential_scaler % exponential scaling parameter [-]
+        % log these parameters? 
+        
+        
+
+                
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m4'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m4', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m4 class
+    
+    methods
+        function obj = baseflow_m4(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m4 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.base_rate = 0; % initial guess for - base outflow rate [mm/d]
+            obj.exponential_scaler = 1; % initial guess for - exponential scaling parameter [-]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.base_rate; obj.exponential_scaler];
+           param_names = {'base_rate'; 'exponential_scaler'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'base_rate'; 'exponential_scaler'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_14 in MaRRMOT (TOPMODEL)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0.1 ; 0]; 
+            params_upperLimit = [200; 1];
+        end
+        %0.1,200;    % q0, Zero deficit base flow speed [mm/d]
+        %0,1;        % m, Baseflow coefficient [mm-1]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0.1 ; 0];
+            params_upperLimit = [200; 1];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+                
+           % calculate the baseflow 
+            obj.variables.baseFlow = obj.base_rate .* exp(-1 .* obj.exponential_scaler .* (1./obj.variables.head));
+            % Description:  Exponential outflow from deficit store
+            % Constraints:  -
+            % @(Inputs):    p1   - base outflow rate [mm/d]
+            %               p2   - exponent parameter [mm-1]
+            %               S    - current storage [mm]
+            %func = @(p1,p2,S) p1*exp(-1*p2*S);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m5.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m5.m
new file mode 100644
index 0000000..6e565e7
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m5.m
@@ -0,0 +1,278 @@
+classdef baseflow_m5 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Non-linear scaled outflow from a reservoir
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        base_rate   % - base outflow rate [mm/d]
+        exponential_scaler % - exponential scaling parameter [-]
+        head_max % - maximum contributing storage [mm]
+        % log these parameters? 
+        
+
+                
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m5'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m5', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m5 class
+    
+    methods
+        function obj = baseflow_m5(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m5 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.base_rate = 0; % initial guess for - base outflow rate [mm/d]
+            obj.exponential_scaler = 1; % initial guess for - exponential scaling parameter [-]
+            obj.head_max = 200;% initial guess for - maximum contributing storage [mm]
+
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.base_rate; obj.exponential_scaler; obj.head_max];
+           param_names = {'base_rate'; 'exponential_scaler'; 'head_max'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'base_rate'; 'exponential_scaler'; 'head_max'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_22 in MaRRMOT (VIC)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 1; 1]; 
+            params_upperLimit = [1; 5; 2000];
+        end
+        % 0   ,1;         % k2,       Baseflow time parameter [d-1]
+        % 1   , 5];       % c2,       Baseflow non-linearity parameter
+        % 1   , 2000;     % stot,    Maximum soil moisture capacity [mm]
+        % 0.01, 0.99;     % fsm, Fraction of stot that constitutes maximum soil moisture smmax [-]
+        % gwmax   = (1-fsm)*stot; % Maximum groundwater storage [mm]
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 1; 1];
+            params_upperLimit = [1; 5; 2000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+                % delta_t in input head to the baseflow obj
+                delta_t = diff(obj.variables.t);
+                delta_t(end+1,1) = delta_t(end,1); % duplicate last point to match head/delta_t matrixes
+
+                                
+           % calculate the baseflow 
+            obj.variables.baseFlow = min(obj.variables.head ./delta_t, obj.base_rate.*((max(obj.variables.head,0)./ obj.head_max).^obj.exponential_scaler));
+            % Description:  Non-linear scaled outflow from a reservoir
+            % Constraints:  f <= S/dt
+            % @(Inputs):    p1   - base outflow rate [mm/d]
+            %               p2   - exponential scaling parameter [-]
+            %               S    - current storage [mm]
+            %               Smax - maximum contributing storage [mm]
+            %               dt   - time step size [d]
+            % func = @(p1,p2,S,Smax,dt) min(S/dt,p1*((max(S,0)/Smax)^p2));
+            
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m6.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m6.m
new file mode 100644
index 0000000..267bcc8
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m6.m
@@ -0,0 +1,276 @@
+classdef baseflow_m6 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Quadratic outflow from a reservoir if a storage threshold is exceeded
+
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_scaler   % - linear scaling parameter [mm-1 d-1]
+        head_threshold % - threshold that must be exceeded for flow to occur [mm]
+        % log these parameters? 
+
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m6'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m6', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m6 class
+    
+    methods
+        function obj = baseflow_m6(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m6 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_scaler = 1; % initial guess for - linear scaling parameter [mm-1 d-1]
+            obj.head_threshold = 200; % initial guess for - threshold that must be exceeded for flow to occur [mm]
+            
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_scaler; obj.head_threshold];
+           param_names = {'linear_scaler'; 'head_threshold'};
+        end
+        
+               
+        function setParameters(obj, params)
+            param_names = {'linear_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_25 in MaRRMOT (TCM)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 0]; 
+            params_upperLimit = [1; 1000];
+        end
+        %0, 1];          % k2, Runoff coefficient [mm-1 d-1]
+        %not specified   % threshold that must be exceeded for flow to occur [mm]
+                  
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 0];
+            params_upperLimit = [1; 1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+                
+                % delta_t in input head to the baseflow obj
+                delta_t = diff(obj.variables.t);
+                delta_t(end+1,1) = delta_t(end,1); % duplicate last point to match head/delta_t matrixes
+
+                
+                r = 0.01;
+                e = 5.00;
+                % Calculate smoothThreshold_storage_logistic
+                if obj.head_threshold == 0
+                    smoothThreshold = 1 ./ (1+exp((obj.variables.head - obj.head_threshold + r*e* obj.head_threshold)/(r)));
+                else
+                    smoothThreshold = 1 ./ (1+exp((obj.variables.head - obj.head_threshold + r*e* obj.head_threshold)/(r*obj.head_threshold)));
+                end
+                
+           % calculate the baseflow 
+            obj.variables.baseFlow = min(obj.variables.head./delta_t, obj.linear_scaler.*obj.variables.head.^2).*(1 - smoothThreshold);
+            % Description:  Quadratic outflow from a reservoir if a storage threshold is exceeded
+            % Constraints:  f <= S/dt
+            % @(Inputs):    p1   - linear scaling parameter [mm-1 d-1]
+            %               p2   - threshold that must be exceeded for flow to occur [mm]
+            %               S    - current storage [mm]
+            % func = @(p1,p2,S,dt) min(S/dt,p1.*S.^2).*(1-smoothThreshold_storage_logistic(S,p2));
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m7.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m7.m
new file mode 100644
index 0000000..be87ad7
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m7.m
@@ -0,0 +1,273 @@
+classdef baseflow_m7 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Non-linear outflow from a reservoir
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_scaler   % - linear time coefficient [d-1]
+        exponential_scaler % - exponential scaling parameter [-]
+		head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+        
+                
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m7'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m7', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m7 class
+    
+    methods
+        function obj = baseflow_m7(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m7 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_scaler = 0; % initial guess for - linear time coefficient [d-1]
+            obj.exponential_scaler = 1; % initial guess for - exponential scaling parameter [-]
+			obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+
+           
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_scaler; obj.exponential_scaler; obj.head_threshold];
+           param_names = {'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+        end
+        
+          
+        function setParameters(obj, params)
+            param_names = {'linear_scaler'; 'exponential_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_42 in MaRRMOT (hycymodel)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 1; 1]; 
+            params_upperLimit = [1; 5; 1000];
+        end
+        %  0, 1;           % kb, Baseflow runoff coefficient [d-1]
+        %  1, 5;           % pb, Baseflow non-linearity [-]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 1; 1];
+            params_upperLimit = [1; 5; 400];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+                % delta_t in input head to the baseflow obj
+                delta_t = diff(obj.variables.t);
+                delta_t(end+1,1) = delta_t(end,1); % duplicate last point to match head/delta_t matrixes
+                
+           % calculate the baseflow 
+            obj.variables.baseFlow = min( max(0, obj.variables.head - obj.head_threshold )./delta_t, obj.linear_scaler.* max(0, obj.variables.head - obj.head_threshold).^obj.exponential_scaler);
+            % Description:  Non-linear outflow from a reservoir
+            % Constraints:  f <= S/dt
+            %               S >= 0
+            % @(Inputs):    p1   - time coefficient [d-1]
+            %               p2   - exponential scaling parameter [-]
+            %               S    - current storage [mm]
+            %               dt   - time step size [d]
+            %func = @(p1,p2,S,dt) min( max(0, S-p3)./dt , p1.*max(0, S-p3).^p2);
+			% modified to include:  p3 - storage threshold for baseflow generation [mm]
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m8.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m8.m
new file mode 100644
index 0000000..69e1a0f
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m8.m
@@ -0,0 +1,275 @@
+classdef baseflow_m8 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Exponential scaled outflow from a deficit store
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        base_rate   % - base outflow rate [mm/d]
+        exponential_scaler % exponential scaling parameter [-]
+        head_max % - maximum contributing storage [mm]
+        % log these parameters? 
+        
+
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m8'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_m8', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m8 class
+    
+    methods
+        function obj = baseflow_m8(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m8 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.base_rate = 0; % initial guess for - base outflow rate [mm/d]
+            obj.exponential_scaler = 1; % initial guess for - exponential scaling parameter [-]
+            obj.head_max = 200;  % initial guess for - maximum contributing storage [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.base_rate; obj.exponential_scaler; obj.head_max];
+           param_names = {'base_rate'; 'exponential_scaler'; 'head_max'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'base_rate'; 'exponential_scaler'; 'head_max'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_23 in MaRRMOT (LASCAM)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0.01 ; 0; 1]; 
+            params_upperLimit = [200; 1; 2000];
+        end
+
+        %0.01, 200;      % bb, Groundwater flow base rate [mm/d]
+        %0, 1;           % ab, Groundwater flow scaling [-]
+        
+        %1, 2000;        % stot, Total catchment storage [mm]
+        %0.01, 0.99;     % xa, Fraction of Stot that is Amax [-]
+        %0.01, 0.99;     % xf, Fraction of Stot-Amx that is depth Fmax [-]
+        %amax = xa*stot;             % Maximum contributing area depth [mm]
+        %fmax = xf*(stot-amax);      % Infiltration depth scaling [mm]
+        %bmax = (1-xf)*(stot-amax);  % Groundwater depth scaling [mm]
+
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0.01 ; 0; 1];
+            params_upperLimit = [200; 1; 2000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+                
+           % calculate the baseflow 
+            obj.variables.baseFlow = obj.base_rate .* (exp(obj.exponential_scaler .* min(1, max(obj.variables.head,0)./ obj.head_max))-1);
+            % Description:  Exponential scaled outflow from a deficit store
+            % Constraints:  S <= Smax
+            % @(Inputs):    p1   - base outflow rate [mm/d]
+            %               p2   - exponential scaling parameter [-]
+            %               S    - current storage [mm]
+            %               Smax - maximum contributing storage [mm]
+            % func = @(p1,p2,S,Smax) p1.*(exp(p2.*min(1,max(S,0)./Smax))-1);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m9.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m9.m
new file mode 100644
index 0000000..98a1d16
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_m9.m
@@ -0,0 +1,265 @@
+classdef baseflow_m9 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    % Detailed explanation goes here
+    % Description:  Linear outflow from a reservoir if a storage threshold is exceeded
+        
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        linear_scaler   % - time coefficient [d-1]
+        head_threshold  % - storage threshold for flow generation [mm]
+        % log these parameters? 
+        
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow_m9'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'baseflow_m9', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_m9 class
+    
+    methods
+        function obj = baseflow_m9(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_m9 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.linear_scaler = 0; % initial guess for - time coefficient [d-1]
+            obj.head_threshold = 1; % initial guess for - storage threshold for flow generation [mm]
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.linear_scaler; obj.head_threshold];
+           param_names = {'linear_scaler'; 'head_threshold'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'linear_scaler'; 'head_threshold'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+         
+        % as per range of parameters for model_20 in MaRRMOT (GSFB, Nathan&McMahon 1990)
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0 ; 1]; 
+            params_upperLimit = [1; 1000];
+        end
+        % 0, 1;           % b, Fraction of subsurface flow that is baseflow [-]
+        %   0, 1;         % dpf, Baseflow time coefficient [d-1]
+        % linear_scaler = b*dpf in GSFB cause it assumes 2 linear storages (deep percolation, and baseflow above threshold)
+        %  1, 300];       % sdrmax, Threshold before baseflow can occur [mm]
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0 ; 1];
+            params_upperLimit = [1; 400];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+
+            % calculate the baseflow 
+            obj.variables.baseFlow = obj.linear_scaler .* max(0, obj.variables.head - obj.head_threshold);
+            % Description:  Linear outflow from a reservoir if a storage threshold is exceeded
+            % Constraints:  -
+            % @(Inputs):    p1   - time coefficient [d-1]
+            %               p2   - storage threshold for flow generation [mm]
+            %               S    - current storage [mm]
+            % func = @(p1,p2,S) p1.*max(0,S-p2);
+
+
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v1.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v1.m
new file mode 100644
index 0000000..d42a41a
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v1.m
@@ -0,0 +1,251 @@
+classdef baseflow_v1 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head. 
+    
+    %   Detailed explanation goes here
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_threshold   % GW head threshold that defines if have positive or negative baseflow. Below this threshold, baseflow recharges from the river.
+        head_to_baseflow % controls the smoothening of the baseflow response due to the rise of GW head. 
+       % log these parameters? 
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+                                    
+            variable_names ={'baseflow'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_v1', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow class
+    
+    methods
+        function obj = baseflow_v1(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
+            % Constructor of the Baseflow class 
+            %   Detailed explanation goes here
+            obj.head_threshold = 200;
+            obj.head_to_baseflow = 1;
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_threshold; obj.head_to_baseflow];
+           param_names = {'head_threshold'; 'head_to_baseflow'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_threshold'; 'head_to_baseflow'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0;0];
+            params_upperLimit = [inf;inf];
+        end
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0;0];
+            params_upperLimit = [1000;1000];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+%             if any(delta_t ~=1)
+%                 error('forcing data must have no gaps and be a daily
+%                 time-series ') 
+%             end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+        
+            
+            % get simulated head to use to estimate baseflow
+            
+%             [head, colnames, noise] = solve(obj, time_points); % just the deterministic . is it at the same time step (daily)?
+            
+            % calculate the baseflow
+            obj.variables.baseFlow = max(0,obj.variables.head - obj.head_threshold) .* obj.head_to_baseflow;
+            
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v2.m b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v2.m
new file mode 100644
index 0000000..5eb37d3
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/baseflow_v2.m
@@ -0,0 +1,273 @@
+classdef baseflow_v2 < forcingTransform_abstract
+    % Defines the behaviour of baseflow according to the GW head and a scaled weighted rate. 
+    
+    %   Detailed explanation goes here
+    
+    properties (GetAccess=public, SetAccess=protected)
+        
+        % Model Parameters
+        %----------------------------------------------------------------
+        head_threshold   % GW head threshold that defines if baseflow is negative or positive. Below this threshold, aquifer recharges from the river.
+        decayRate; % controls the smoothening of the baseflow response due to the rise of GW head. --- COULD we get somewhere else from model_TFN?
+        riseRate; % controls the time for free drainage to produce a head response, i.e. some metres below the root zone --- COULD we get somewhere else from model_TFN?
+        totalWeigthScaler; % weighting scaling term  
+       % log these parameters? 
+       
+        %----------------------------------------------------------------        
+    end
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'head'};
+            isOptionalInput = [true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'baseflow'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { };
+
+        
+            colNames = {};
+            colFormats = {};
+            colEdits = logical([0]);
+
+            toolTip = sprintf([ 'there are no user options available for this module']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: baseflow_v2', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+          
+    %% Constructor of the baseflow_v2 class
+    
+    methods
+        function obj = baseflow_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            
+            % Constructor of the baseflow_v2 class 
+            %   Detailed explanation goes here
+            
+            % Use sub-class constructor to inherit the structure of the object "baseflow"
+%             obj = obj@baseflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+            
+            
+            % initializing the parameters of the object
+            obj.head_threshold = 200; % initial guess for the head_threshold, maybe set as mean ObsHead?
+            obj.decayRate = -0.1; % initial guess 
+            obj.riseRate = -1.5; % initial guess 
+            obj.totalWeigthScaler = 0.5; % initial guess 
+            
+            obj.variables.baseFlow = [];
+            obj.variables.head = [];
+            obj.variables.t = [];
+            obj.variables.isNewParameters = false;
+            obj.settings.forcingData_colnames = {""};
+            obj.settings.forcingData = [];
+            obj.settings.siteCoordinates = siteCoordinates;
+                 
+            
+        end
+ 
+        function [params, param_names] = getParameters(obj)            
+           params = [ obj.head_threshold; obj.decayRate; obj.riseRate; obj.totalWeigthScaler];
+           param_names = {'head_threshold'; 'decayRate'; 'riseRate'; 'totalWeigthScaler'};
+        end
+        
+        
+        
+        function setParameters(obj, params)
+            param_names = {'head_threshold'; 'decayRate'; 'riseRate'; 'totalWeigthScaler'};
+            for i=1: length(param_names)
+                obj.(param_names{i}) = params(i,:);
+            end
+        end
+        
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_lowerLimit = [0;-inf;-inf;0];
+            params_upperLimit = [inf;inf;inf;inf];
+        end
+        
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_lowerLimit = [0;-100;-100;0];
+            params_upperLimit = [1000;0.0009;0.0009;100];
+        end
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+           % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));  
+            
+        end
+        
+        function  isNewParameters = detectParameterChange(obj, params)
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end 
+        end
+        
+        
+        % Set the input forcing data
+        function setForcingData(obj, forcingData, forcingData_colnames)
+        
+            
+             % check if lenght of forcingData_colnames is 1
+            if size(forcingData_colnames,1) ~= 1
+                error('The number of column name is not 1.');
+            end
+            
+            % check if forcingData_colnames is "head" (simulated head)
+            if ~strcmp('head', forcingData_colnames)
+                error('forcing data col. name is not "head"')
+            end
+            
+             
+            % check if the "head" is daily without gaps
+            t = forcingData(:,1);
+            delta_t = diff(t);
+            %%%%%%% need to change antecedent code so we have a daily time-seires of GW for the period where we have GW obs.data and match it with streamflow time-series
+            if any(delta_t ~=1)
+                error('forcing data must have no gaps and be a daily time-series') 
+            end
+                
+            
+            % place the forcingData into "obj"
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData = forcingData;
+            
+        end
+        
+        function setTransformedForcing(obj, t, forceRecalculation)        
+
+            % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+%                 filt = strcmp(obj.settings.forcingData_cols(:,1),'head');
+                filt = strcmp(obj.settings.forcingData_colnames,'head');
+%                 head_col = obj.settings.forcingData_cols{filt,2};
+%                 head_col = obj.settings.forcingData_colnames(filt,:);
+                obj.variables.head = obj.settings.forcingData(filt_time, 2 ); % columns in the input data have no name 
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+            
+        
+            
+            % get simulated head to use to estimate baseflow
+            
+%             [head, colnames, noise] = solve(obj, time_points); % just the deterministic . is it at the same time step (daily)?
+            
+            % calculate the baseflow 
+            
+            
+            decayWeight = exp(obj.decayRate.*t);
+            
+            riseWeight = -exp(obj.riseRate.*t);
+            
+            totalWeight = decayWeight + riseWeight;
+            
+            totalWeightScaled = totalWeight .* obj.totalWeigthScaler;
+            
+            
+            obj.variables.baseFlow = (obj.variables.head - obj.head_threshold)  .* totalWeightScaled;
+%             obj.variables.baseFlow = max(0,obj.variables.head - obj.head_threshold) .* obj.head_to_baseflow;
+            
+        end
+      
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)
+            
+            forcingData = obj.variables.baseFlow;
+            
+            isDailyIntegralFlux = true ;
+                      
+            
+        end
+        
+        
+        
+         % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+        
+  
+        
+          
+        
+        
+        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels.m b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels.m
index 455486c..d43e722 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels.m
@@ -1,1560 +1,1641 @@
-classdef climateTransform_soilMoistureModels < forcingTransform_abstract 
-% Class definition for soil moisture transformation of climate forcing.
-%
-% Description        
-%   climateTransform_soilMoistureModels is a generalised vertically lumped 1-D
-%   soil moisture model. It is used to transform the daily climate data
-%   for use in the transfer noise function groundwater time series model.
-%   Importantly, a user of the groundwater time series model has no need to
-%   run any of these soil moisture model methods. All of the methods are
-%   automatically called by the function "model_TFN.m".
-%   
-%   The soil moisture model is defined by the following ordinary
-%   diffenential equation (Kavetski et al. 2006):
-%   
-%   DS/dt = P_inf (1 - S/SMSC)^alpha - k_sat (S/SMSC)^beta - PET (S/SMSC)^gamma
-% 
-%   where:
-%       S       - is the soil moisture storage state variable [L];
-%       t       - is time in units of days [T];
-%       P_inf   - is the precipitation available for infiltration. This can
-%                 be limited to a maximum infiltration rate "k_infilt". If
-%                 this parameter is not defined then P_inf equals the input
-%                 daily precipitation rate.
-%       SMSC    - is a parameter for the soil moisture storage capacity [L].
-%                 It is in same units as the input precipitation.
-%       alpha   - is a dimensionless parameter to transform the rate at which
-%                 the soil storage fills and limits infiltration. This 
-%                 allows simulation of a portion of the catchment saturating
-%                 and not longer allowing infiltration.
-%       k_sat   - is a parameter for the saturated vertical soil water
-%                 conductivity [L T^-1]. 
-%       beta    - is a dimensionless parameter to transform the rate at
-%                 which vertical free drainage occurs with filling of
-%                 the soil layer.
-%       interflow_frac - fraction of free drainage going to interflow.
-%       PET     - is the input daily potential evapotranspiration [L T^-1].
-%       gamma   - is a dimensionless parameter to transform the rate at
-%                 which soil water evaporation occurs with filling of the 
-%                 soil layer.
-%       S_initialfrac - the initial soil moisture.
-%
-%	The soil moisture model can also be used to simulate the impacts from
-%	different vegetation; for example, trees and pastures. This is achieved
-%	by simulating a soil store for upto two land types and then weighting
-%	required flux from each soil model by an input time series of the
-%	fraction of the second land type. A challange with the input time series 
-%	of land cover is, however, that while the fraction of, say, land
-%	data clearing over time may be known the fraction of the catchment area
-%	cleared that influences a bore hydrograph is unknown. To address this,
-%	the modelling also include a parameter 'treeArea_frac' for the fraction
-%	of the second land cover (notially trees) that is influencing the bore.
-%	In summary, the simulation of tree cover requires the following two
-%	parameters:
-%
-%       SMSC_trees - is a parameter for the soil moisture storage capacity [L]
-%                  of the second soil model (notionaly trees). This is the
-%                  only other parameter required for the simulation of a
-%                  second soil layer (other parameters are taken from the
-%                  first soil model). Also, the soil storage parameter has 
-%                  the same units as the input precipitation.
-%       treeArea_frac - is a parameter from 0 to 1 that simulates the
-%                   weight to be applied to the tree cover soil model flux.       
-%   
-%   Additionally, in parametrizing the model, many of the parameters were
-%   transformed to a parameter space more amenable to efficient
-%   calibration. Details of the transformations are as follows:
-%
-%       SMSC          - log10(Soil moisture capacity as water depth).
-%       SMSC_trees    - log10(Soil moisture capacity as water depth).
-%       S_initialfrac - Initial soil moisture fraction (0-1).
-%       k_infilt      - log10(Soil infiltration capacity as water depth).
-%       k_sat         - log10(Maximum vertical infiltration rate).
-%       bypass_frac   - Fraction of runoff to bypass drainage (0-1).
-%       interflow_frac- Fraction of free drainage going to interflow (0-1).
-%       beta          - log10(Power term for dainage rate).
-%       gamma         - log10(Power term for soil evap. rate).
-%
-%   The soil moisture model is an adaption of VIC Model (Wood et al. 1992)
-%   by Kavetski et al. (2006). It is a soil moisture model that is without
-%   any discontinuities (thus able to produce a first-order smooth
-%   calibration response surface) and therefore amenable to gradient based
-%   calibration. The model, as implemented here, is very flexible in that a
-%   wide range of models can be implemented by simply turning componants of
-%   the differential equation on or off. For example, a one parameter
-%   model (comprising only of infiltration and evaporation) can be derived
-%   by turning off the drainage term and fixing alpha and gamma to one. For
-%   details on how to build various types of models see the documentation
-%   for the class constructor (i.e. "climateTransform_soilMoistureModels"
-%   below);
-%   
-%   Considerable effort has been put into efficiently solving the soil 
-%   moisture model while producing a smooth response surface. The
-%   differential equation is solved using a fixed-time step solver. For each
-%   time step, an initial estimate is made using the explicit Huen
-%   algorithm. This produces an O(h^2) error. The solution error is then
-%   refined near to machine precision using an implicit Huen Newton's
-%   algorithm. To achieve efficient computation, the differential equation
-%   and solver are implemeneted within MatLab's C-MEX language. This
-%   requires compilation of the file "forcingTransform_soilMoisture.c" and
-%   can be achieved by executing the following command within MatLab: 
-%   mex forcingTransform_soilMoisture.c
-%
-%   Once the soil moisture model has been ran a range of fluxes can be
-%   derived from it for input to the transfer function noise model in place
-%   of, say, precipitation. These fluxes include standard fluxes such as
-%   free drainage or actual ET but also derived fluxes such as the bypass 
-%   drainage (runoff plus free drainage) and the groundwater potential
-%   evaporation ( potential ET minus soil ET). For a full list see
-%   getTransformedForcing().
-%
-% See also
-%   climateTransform_soilMoistureModels: model_construction;
-%   setParameters: set_calibration_parameters_values;
-%   getParameters: get_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies
-%   forcingTransform_soilMoisture.c
-%
-% References:
-%   Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of
-%   input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res.,
-%   42, W03407, doi:10.1029/2005WR004368. 
-%
-%   Wood, E. F., D. P. Lettenmaier, and V. G. Zartarian (1992), A
-%   Land-Surface Hydrology Parameterization With Subgrid Variability for 
-%   General Circulation Models, J. Geophys. Res., 97(D3), 2717–2728, 
-%   doi:10.1029/91JD01786. 
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012   
-    
-    properties(GetAccess=public, SetAccess=protected)
-                        
-        % Model Parameters
-        %----------------------------------------------------------------
-        SMSC            % Soil moisture capacity parameter (as water depth)
-        SMSC_trees      % Trees soil moisture capacity parameter (as water depth)       
-        treeArea_frac   % Scaler for the tree fraction data (optional)
-        S_initialfrac   % Initial soil moisture 
-        k_infilt        % Maximum infiltration rate.
-        k_sat           % Maximum vertical conductivity.
-        bypass_frac     % Fraction of runoff that goes to bypass drainage
-        interflow_frac  % Fraction of free drainage going to interflow (0-1).        
-        alpha           % Power term for infiltration.        
-        beta            % Power term for dainage rate (eg Brook-Corey pore index power term)
-        gamma           % Power term for soil evaporation rate.        
-        %----------------------------------------------------------------        
-    end
-
-
-    
-%%  STATIC METHODS        
-% Static methods used to inform the
-% user of the available model types. 
-    methods(Static)
-        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            variable_names = {'precip';'et';'TreeFraction'};
-            isOptionalInput = [false; false; true];
-        end
-        
-        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            variable_names = {'effectivePrecip'; 
-                              'drainage';       'infiltration';             'evap_soil';        'evap_gw_potential';        'runoff';       'SMS'; ...
-                              'drainage_tree';  'infiltration_tree';        'evap_soil_tree';   'evap_gw_potential_tree';   'runoff_tree';  'SMS_tree'; ...
-                              'drainage_nontree';'infiltration_nontree';    'evap_soil_nontree';'evap_gw_potential_nontree';'runoff_nontree';'SMS_nontree'; ...
-                              'mass_balance_error'};
-        end
-        
-        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
-           
-            options = { 'SMSC'          ,   2, 'Calib.';...
-                        'SMSC_trees'    ,   2, 'Fixed';...
-                        'treeArea_frac' , 0.5, 'Fixed'; ...
-                        'S_initialfrac' , 0.5, 'Fixed'  ; ...
-                        'k_infilt'      , inf,'Fixed'   ; ...
-                        'k_sat'         ,   1, 'Calib.'   ; ...
-                        'bypass_frac'   ,   0, 'Fixed'    ; ...
-                        'interflow_frac',   0, 'Fixed'    ; ...
-                        'alpha'         ,   0, 'Fixed'    ; ...
-                        'beta'          , 0.5,'Calib.' ; ...
-                        'gamma'         ,   0,  'Fixed'};
-
-        
-            colNames = {'Parameter', 'Initial Value','Fixed or Calibrated?'};
-            colFormats = {'char', 'char', {'Calib.' 'Fixed'}};
-            colEdits = logical([0 1 1]);
-
-            toolTip = sprintf([ 'Use this table to define the type of soil moisture model. \n', ...
-                                'Each parameter (except the soil moisture capacity) can be \n', ...
-                                'set to a fixed value or calibrated. Below is a summary: \n \n' , ...
-                                '   SMSC          : log10(Soil moisture capacity as water depth).\n', ...
-                                '   SMSC_trees    : log10(Tree soil moisture capacity as water depth).\n', ...
-                                '   treeArea_frac : Scaler applied to the tree fraction input data.\n', ...
-                                '   S_initialfrac : Initial soil moisture fraction (0-1).\n', ...
-                                '   k_infilt      : log10(Soil infiltration capacity as water depth).\n', ...
-                                '   k_sat         : log10(Maximum vertical infiltration rate).\n', ...
-                                '   bypass_frac   : Fraction of runoff to bypass drainage.\n', ...
-                                '   interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
-                                '   alpha         : Power term for infiltration rate.\n', ...
-                                '   beta          : log10(Power term for dainage rate).\n', ...
-                                '   gamma         : log10(Power term for soil evap. rate).']);                               
-            
-        end
-        
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: climateTransform_soilMoistureModels', ...
-                               '', ...
-                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
-                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
-                               '', ...                               
-                               'Number of parameters: 1 to 8', ...
-                               '', ...                               
-                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
-                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
-                               '', ...                               
-                               'Comments: Below is a summary of the model parameters:' , ...
-                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
-                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
-                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
-                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
-                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
-                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
-                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
-                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
-                                'alpha         : Power term for infiltration rate.', ...
-                                'beta          : log10(Power term for dainage rate).', ...
-                                'gamma         : log10(Power term for soil evap. rate).', ...
-                               '', ...               
-                               'References: ', ...
-                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
-        end        
-           
-    end
-        
-    
-    methods       
-%% Construct the model
-        function obj = climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
-% climateTransform_soilMoistureModels constructs the soil model object.
-%
-% Syntax:
-%   soilModel = climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
-%
-% Description:
-%   Builds the form of the soil moisture differential equation for the user
-%   input model options. This constructor also checks that the required 
-%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
-%   model options are two or three columns wide.
-%
-% Input:
-%
-%   bore_ID - string for the bore ID. The bore ID must be listed in the site
-%   coordinates array.
-%
-%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
-%   must comprise of the following columns: year, month, day and
-%   observation data. The observation data must include precipitation &
-%   PET. The record must be continuous (no skipped days) and cannot contain  
-%   blank observations.
-%
-%   forcingData_colnames - 1 x N cell array of the column names within the
-%   input numerical array "forcingData_data".  Importantly, each forcing 
-%   data column name must be listed within the input "siteCoordinates". 
-%
-%   siteCoordinates - Mx3 cell matrix containing the following columns :
-%   site ID, projected easting, projected northing. Importantly,
-%   the input 'bore_ID' must be listed and all columns within
-%   the forcing data (excluding the year, month,day). 
-%
-%   modelOptions - cell matrix defining the soil model componants, ie how the
-%   model should be constructed. The cell matrix can be two or three
-%   columns wide and at least three rows long (for the simplest of models).
-%   The first column defines the model parameter name or user option name
-%   to be defined. The second column defines the initial value for the
-%   parameter or the setting for the user option. A third column can be
-%   input if a parameter is to be fixed, and thus not modified during
-%   calibration. This third column can contain the term 'fixed'. 
-%
-%   Below are some examples of model options for various types of models:
-%
-%       - 1 parameter with linear scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100; ...
-%                               'alpha', 1, 'fixed'};};
-%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100, '' ;
-%                               'alpha', 0, 'fixed'};
-%       - 2 parameter with linear scaling of infiltration by the soil 
-%       moisture and linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'k_sat', 10 };
-%       - 3 parameter with linear scaling of infiltration by the soil
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = {  'SMSC', 100 ; 
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%       - 4 parameter with non-linear scaling of infiltration by the soil 
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'alpha', 0.5 ;
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%
-% Output:
-%   obj - climateTransform_soilMoistureModels class object 
-%
-% See also
-%   climateTransform_soilMoistureModels: class_definition;
-%   setParameters: set_calibration_parameters_values;
-%   getParameters: get_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012   
-
-            % Check the MEX 'C' code for the soil moisture model is
-            % compiled for the current operating system.                                                
-            if exist(['forcingTransform_soilMoisture.',mexext()],'file')~=3
-                errorMessage = ['Missing compiled soil moisture "forcingTransform_soilMoisture.',mexext(),'"'];
-                error(errorMessage);
-            end            
-            
-            % Check there are at least two columns in the model options.
-            ncols_modelOptions = size(modelOptions,2);
-            if ncols_modelOptions < 2 || ncols_modelOptions > 3
-                errorMessage = ['The soil moisture model options must be at least two columns, where:' char(13), ...
-                                '  - column 1 is the parameter name.', char(13), ...
-                                '  - column 2 is the parameter value.', char(13), ...
-                                'Note: A third column can be given. This column can contain the input "fixed".', char(13), ...
-                                '      This will make the parameter a constant and it will not be adjusted in the calibration.'];
-                error(errorMessage);
-            end            
-                               
-            % Get a list of required forcing inputs and (again) check that
-            % each of the required inputs is provided.
-            %--------------------------------------------------------------
-            obj.settings.simulateLandCover = false;
-            requiredFocingInputs = climateTransform_soilMoistureModels.inputForcingData_required();
-            for j=1:size(requiredFocingInputs,1)
-                filt = strcmpi(forcingData_reqCols(:,1), requiredFocingInputs(j));                                    
-                if ~any(filt) && ~strcmpi(requiredFocingInputs(j),'TreeFraction')
-                    error(['An unexpected error occured. When transforming forcing data, the input cell array for the transformation must contain a row (in 1st column) labelled "forcingdata" that its self contains a cell array in which the forcing data column is defined for the input:', requiredFocingInputs{j}]);
-                elseif any(filt) && strcmpi(requiredFocingInputs(j),'TreeFraction')                    
-                    % Do land cover simulation if the data has any tree
-                    % fraction > 0;
-                    filt_LC = find(strcmpi(forcingData_reqCols(:,1), 'TreeFraction'))+1; 
-                    if any(forcingData_data(:,filt_LC)<0) || any(forcingData_data(:,filt_LC)>1)
-                       error('The tree fraction input data must be between 0 and 1.'); 
-                    end
-                    if sum(forcingData_data(:,filt_LC))>0
-                        obj.settings.simulateLandCover = true;
-                    end
-                end
-            end
-             
-            % Assign the input forcing data to obj.settings.
-            obj.settings.forcingData = forcingData_data;
-            obj.settings.forcingData_colnames = forcingData_colnames;
-            obj.settings.forcingData_cols = forcingData_reqCols;
-            obj.settings.siteCoordinates = siteCoordinates;
-                              
-            % Get list of model parameters and exclude settings, variables
-            % and the model 'type';
-            all_parameter_names = properties(obj);
-            ind = true(length(all_parameter_names),1);
-            for i=1:length(all_parameter_names)
-                switch all_parameter_names{i,1}
-                    case {'settings', 'variables'}
-                        ind(i) = false;                    
-                end
-            end
-            all_parameter_names = all_parameter_names(ind);
-            
-            % Assign model parameters.
-            % Importantly, if the parameter is not listed then that 
-            % feature of the soil moisture model is turned off.
-            %--------------------------------------------------------------
-            nparams = 0;
-            
-            for i=1:length(all_parameter_names)
-                
-                % Find the required parameter within the input model
-                % options.
-                ind = [];
-                for j=1:length(modelOptions(:,1))
-                    if strcmp(modelOptions(j,1), all_parameter_names{i} )                        
-                        ind = j;
-                        break;
-                    end
-                end
-            
-                % Record the parameter as 'Active'. That is, it can be
-                % calibrated within the time series model,
-                if ~isempty(ind)
-                    if ncols_modelOptions ==3 && strcmp(lower(modelOptions{ind,3}),'fixed')
-                        obj.settings.fixedParameters.(all_parameter_names{i})=true;
-                        obj.settings.activeParameters.(all_parameter_names{i})=false;
-                        obj.(all_parameter_names{i}) = modelOptions{ind,2};
-                    else
-                        nparams = nparams+1;
-                        obj.settings.fixedParameters.(all_parameter_names{i})=false;
-                        obj.settings.activeParameters.(all_parameter_names{i})=true;
-                        paramsInitial(nparams,1) = modelOptions{ind,2};
-                    end
-                else
-                    obj.settings.fixedParameters.(all_parameter_names{i})=true;
-                    obj.settings.activeParameters.(all_parameter_names{i})=false;                    
-                    if strcmp(all_parameter_names{i}, 'alpha')         
-                        obj.(all_parameter_names{i}) = 1;
-                    elseif strcmp(all_parameter_names{i}, 'beta') || strcmp(all_parameter_names{i}, 'gamma')      
-                        % Note, beta is transformed in the soil model to
-                        % 10^beta.
-                        obj.(all_parameter_names{i}) = 0;
-                    elseif strcmp(all_parameter_names{i}, 'k_sat')
-                        % Note, k_sat is transformed in the soil model to
-                        % 10^k_sat = 0 m/d.
-                        obj.(all_parameter_names{i}) = -inf;
-                    elseif strcmp(all_parameter_names{i}, 'interflow_frac')
-                        obj.(all_parameter_names{i}) = 0;                        
-                    elseif strcmp(all_parameter_names{i}, 'S_initialfrac')
-                        obj.(all_parameter_names{i}) = 0.5;  
-                    else
-                        obj.(all_parameter_names{i}) = 0;
-                    end
-                end
-            end
-            
-            % Check that the soil moisture capacity parameter is active.
-            % This is the simplest model able to be simulated
-            if ~obj.settings.activeParameters.SMSC
-                error('The soil moisture model options must include the soil moisture capacity parameter.');
-            end
-            
-            % Check the SMSM_trees parameter is active if and only if there
-            % is land cover input data.
-            if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
-               if ~obj.settings.activeParameters.SMSC_trees 
-                   error('The trees soil moisture model options must include the soil moisture capacity parameter when land cover data is input.');
-               end
-            else
-                obj.settings.activeParameters.SMSC_trees = false; 
-                obj.settings.fixedParameters.SMSC_trees = true;
-                obj.settings.fixedParameters.treeArea_frac = true;
-                obj.settings.activeParameters.treeArea_frac = false;
-            end
-            
-            % Set a constant for smoothing the soil moisture capacity
-            % thresholds and infiltration excess threshold            
-            obj.settings.lambda_p = 0.2;                        
-            
-            % Set parameters for transfer function.
-            setParameters(obj, paramsInitial)                             
-
-            % Initialise soil moisture variables
-            obj.variables.t = [];
-            obj.variables.SMS = [];           
-            obj.variables.nDailySubSteps = getNumDailySubsteps(obj);          
-        end
-        
-%% Set parameters
-        function setParameters(obj, params)
-% setParameters returns the soil model parameters.
-%
-% Syntax:
-%   setParameters(obj, params)  
-%
-% Description:  
-%   This method sets the soil moisture model parameters to user input values.
-%   Only parameters that are to be calibrated (i.e. non-fixed parameters)
-%   can be set. Also the vector of input parameter values must be in the
-%   same order as the parameter names returned by the method
-%   'getParameters'.
-%
-% Input:
-%   obj     - soil moisture model object.
-%   params  - vector (Nx1) of parameter values.
-%
-% Outputs:
-%   (none)
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   getParameters: get_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012  
-
-            % Get the active parameter names
-            param_names = getActiveParameters(obj);
-           
-            % Check if the parameters have changed since the last call to
-            % setTransformedForcing. Note, this must be done prior to the
-            % updating of the parameters.
-            detectParameterChange(obj, params);             
-            
-            % Cycle through each parameter and assign the parameter value.
-            for i=1: length(param_names)
-               obj.(param_names{i}) = params(i,:); 
-            end
-                      
-        end
-        
-        function setForcingData(obj, forcingData, forcingData_colnames)
-% setForcingData sets the forcing data.
-%
-% Syntax:
-%   setForcingData(obj, forcingData, forcingData_colnames)
-%
-% Description:  
-%   This method set the climate forcing data. It is used to update the
-%   forcing data for model simulations (primarily from the GUI).
-%
-% Input:
-%   obj         - soil moisture model object.
-%
-%   forcingData - nxm matrix of focrinf data with column 1 being the
-%                 date/time.
-%
-%   forcingData_colnames - 1xm cell array of column names within the above
-%                 data.
-%
-% Outputs:
-%   (none)
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   21 Dec 2015              
-            
-            %obj.settings.forcingData_colnames = forcingData_colnames;
-            %obj.settings.forcingData = forcingData;
-            if length(forcingData_colnames) < length(obj.settings.forcingData_colnames)
-                error('The number of column name to be set is less than that used to build the object.');
-            end
-            forcingDataNew = nan(size(forcingData,1),length(obj.settings.forcingData_colnames));
-            for i=1:length(forcingData_colnames)               
-                filt  = strcmp(obj.settings.forcingData_colnames, forcingData_colnames{i});
-                if ~isempty(filt)
-                    forcingDataNew(:,filt) = forcingData(:,i);
-                end
-            end
-            obj.settings.forcingData = forcingDataNew;
-            obj.variables.isNewParameters = true;
-        end                
-        
-%% Get model parameters
-        function [params, param_names] = getParameters(obj)            
-% getParameters sets the soil model parameters.
-%
-% Syntax:
-%   setParameters(obj, params)  
-%
-% Description:  
-%   This method gets the soil moisture model parameters. Only parameters 
-%   that are to be calibrated (i.e. non-fixed parameters) are returned. The
-%   method also returns the parameter names and the order of the parameter
-%   names correspondes to the returned order of the parameter values.
-%
-% Input:
-%   obj         - soil moisture model object.
-%
-% Outputs:
-%   params      - a vector (Nx1) of soil moisture model parameter values.
-%
-%   param_names - a vector (Nx1) of parameter names.   
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   setParameters: set_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012              
-            
-            % Get the active parameter names
-            param_names = getActiveParameters(obj);
-            
-            % Cycle through each parameter and get the parameter value.
-            params = zeros(length(param_names),size( obj.(param_names{1}),2));
-            for i=1: length(param_names)
-                if isempty(obj.(param_names{i}))
-                    params(i,:) = NaN;
-                else
-                    params(i,:) = obj.(param_names{i});
-                end
-            end
-        end   
-        
-%% Assess if matrix of parameters is valid.
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-% getParameterValidity returns a logical vector for the validity or each parameter.
-%
-% Syntax:
-%   isValidParameter = getParameterValidity(obj, params, param_names)
-%
-% Description:
-%   Cycles though all active soil model parameters and returns a logical 
-%   vector denoting if each parameter is valid ie within the physical 
-%   parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-%   params - vector of model parameters
-%
-%   param_names - cell array of the parameter names.
-%
-% Outputs:
-%   isValidParameter - column vector of the parameter validity.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   model_TFN: model_construction;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Check parameters are within bounds.
-            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
-    		params <= params_upperLimit(:,ones(1,size(params,2)));   
-        end   
-
-%% Return fixed upper and lower bounds to the parameters.
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
-% getParameters_physicalLimit returns the physical limits to each soil model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
-%
-% Description:
-%   Cycles through all active soil model componants and parameters and returns
-%    a vector of the physical upper and lower parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter bounds
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   model_TFN: model_construction;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-
-            [params, param_names] = getParameters(obj);
-            
-            params_lowerLimit = repmat(0,size(params,1),1);
-            params_upperLimit = repmat(inf,size(params,1),1);         
-
-            if obj.settings.activeParameters.k_infilt
-                ind = cellfun(@(x)(strcmp(x,'k_infilt')),param_names);
-                params_lowerLimit(ind,1) = -inf;                    
-            end       
-            
-            % Upper and lower bounds taken from Rawls et al 1982 Estimation
-            % of Soil Properties. The values are for sand and clay
-            % respectively and transformed from units of cm/h to the assumed
-            % input units of mm/d.
-            if obj.settings.activeParameters.k_sat
-                ind = cellfun(@(x)(strcmp(x,'k_sat')),param_names);
-                %params_lowerLimit(ind,1) = log10(10);                    
-                %params_upperLimit(ind,1) = log10(100);
-                params_lowerLimit(ind,1) = floor(log10(0.06*24*10));
-                params_upperLimit(ind,1) = ceil(log10(21*24*10));
-            end             
-            
-            if obj.settings.activeParameters.bypass_frac
-                ind = cellfun(@(x)(strcmp(x,'bypass_frac')),param_names);
-                params_lowerLimit(ind,1) = 0;         
-                params_upperLimit(ind,1) = 1;
-            end    
-            
-            if isfield(obj.settings.activeParameters,'treeArea_frac') && obj.settings.activeParameters.treeArea_frac
-                ind = cellfun(@(x)(strcmp(x,'treeArea_frac')),param_names);
-                params_lowerLimit(ind,1) = 0;         
-                params_upperLimit(ind,1) = 1;
-            end            
-            
-            if isfield(obj.settings.activeParameters,'interflow_frac') && obj.settings.activeParameters.interflow_frac
-                ind = cellfun(@(x)(strcmp(x,'interflow_frac')),param_names);
-                params_lowerLimit(ind,1) = 0;         
-                params_upperLimit(ind,1) = 1;
-            end                
-            
-            if obj.settings.activeParameters.S_initialfrac
-                ind = cellfun(@(x)(strcmp(x,'S_initialfrac')),param_names);
-                params_lowerLimit(ind,1) = 0;                                    
-                params_upperLimit(ind,1) = 1;                                    
-            end    
-                   
-            if obj.settings.activeParameters.beta
-                ind = cellfun(@(x)(strcmp(x,'gamma')),param_names);
-                params_lowerLimit(ind,1) = log10(0.01);
-                params_upperLimit(ind,1) = log10(100);
-            end                  
-            
-            if obj.settings.activeParameters.SMSC
-                ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
-                params_lowerLimit(ind,1) = log10(10);
-                %params_upperLimit(ind,1) = Inf;
-                params_upperLimit(ind,1) = log10(1000);
-            end    
-            
-            if obj.settings.activeParameters.SMSC_trees
-                ind = cellfun(@(x)(strcmp(x,'SMSC_trees')),param_names);
-                params_lowerLimit(ind,1) = log10(10);
-                params_upperLimit(ind,1) = log10(2000);
-            end              
-     
-            if obj.settings.activeParameters.beta
-                ind = cellfun(@(x)(strcmp(x,'beta')),param_names);
-                params_lowerLimit(ind,1) = log10(1);
-                params_upperLimit(ind,1) = log10(10);
-            end                
-        end  
-        
-%% Return fixed upper and lower plausible parameter ranges. 
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-% getParameters_plausibleLimit returns the plausible limits to each soil model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-%
-% Description:
-%   Cycles through all active soil model componants and parameters and returns
-%    a vector of the plausible upper and lower parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter bounds
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   model_TFN: model_construction;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014              
-            
-        % Return fixed upper and lower plausible parameter ranges. 
-        % This is used to define reasonable range for the initial parameter sets
-        % for the calibration. These parameter ranges are only used in the 
-        % calibration if the user does not input parameter ranges.
-            
-            % get parameyer names
-            [params, param_names] = getParameters(obj);
-            
-            % Set default plausible parameter range
-            params_lowerLimit = zeros(length(param_names),1);  
-            params_upperLimit = 5.*ones(length(param_names),1);  
-            
-            % Modify plausible range for specific parameters
-            if obj.settings.activeParameters.S_initialfrac
-                ind = cellfun(@(x)(strcmp(x,'S_initialfrac')),param_names);
-                params_lowerLimit(ind,1) = 0;
-                params_upperLimit(ind,1) = 1;
-            end  
-
-            if obj.settings.activeParameters.bypass_frac
-                ind = cellfun(@(x)(strcmp(x,'bypass_frac')),param_names);                
-                params_lowerLimit(ind,1) = 0;
-                params_upperLimit(ind,1) = 1;
-            end              
-            
-            if isfield(obj.settings.activeParameters,'treeArea_frac') && obj.settings.activeParameters.treeArea_frac
-                ind = cellfun(@(x)(strcmp(x,'treeArea_frac')),param_names);                
-                params_lowerLimit(ind,1) = 0;
-                params_upperLimit(ind,1) = 1;
-            end                                                  
-            
-            if  isfield(obj.settings.activeParameters,'interflow_frac') && obj.settings.activeParameters.interflow_frac
-                ind = cellfun(@(x)(strcmp(x,'interflow_frac')),param_names);                
-                params_lowerLimit(ind,1) = 0;
-                params_upperLimit(ind,1) = 1;
-            end                
-            
-%             if obj.settings.activeParameters.SMSC
-%                 ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
-%                 params_lowerLimit(ind,1) = log10(10);
-%                 params_upperLimit(ind,1) = log10(1000);
-%             end  
-            if obj.settings.activeParameters.SMSC
-                ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
-                params_lowerLimit(ind,1) = log10(50);
-                params_upperLimit(ind,1) = log10(500);
-            end
-
-            if obj.settings.activeParameters.SMSC_trees
-                ind = cellfun(@(x)(strcmp(x,'SMSC_trees')),param_names);
-                params_lowerLimit(ind,1) = log10(50);
-                params_upperLimit(ind,1) = log10(1000);
-            end  
-                        
-            if obj.settings.activeParameters.k_infilt
-                ind = cellfun(@(x)(strcmp(x,'k_infilt')),param_names);
-                params_lowerLimit(ind,1) = log10(10);                    
-                params_upperLimit(ind,1) = log10(100);
-            end  
-            
-            % Upper and lower bounds taken from Rawls et al 1982 Estimation
-            % of Soil Properties. The values are for sand loam and silty clay
-            % respectively and transformed from units of cm/h to the assumed
-            % input units of mm/d.
-            if obj.settings.activeParameters.k_sat
-                ind = cellfun(@(x)(strcmp(x,'k_sat')),param_names);
-                params_lowerLimit(ind,1) = log10(0.09*24*10);
-                params_upperLimit(ind,1) = log10(6.11*24*10);
-            end 
-            
-            if obj.settings.activeParameters.beta
-                ind = cellfun(@(x)(strcmp(x,'beta')),param_names);
-                params_lowerLimit(ind,1) = log10(1);
-                params_upperLimit(ind,1) = log10(10);
-            end      
-            
-            if obj.settings.activeParameters.beta
-                ind = cellfun(@(x)(strcmp(x,'gamma')),param_names);
-                params_lowerLimit(ind,1) = log10(0.1);
-                params_upperLimit(ind,1) = log10(10);
-            end                  
-        end        
-        
-%% Check if the model parameters have chanaged since the last calculated.
-        function detectParameterChange(obj, params)
-% detectParameterChange detects if the soil model parameters have changed.            
-%
-% Syntax:
-%   detectParameterChange(obj, params)  
-%
-% Description:  
-%   This method assesses if the user input parameters are different from
-%   those used in the most recent solution to the soil moisture model. This
-%   method allows the differential equation to be only solved when
-%   required. The result of this assessment is stored within the object
-%   variable: obj.variables.isNewParameters .
-%
-% Input:
-%   obj         - soil moisture model object.
-%   params      - a vector (Nx1) of new soil moisture model parameter values.
-%
-% Outputs:
-%   (none)
-%
-% Example:
-%   Create a cell matrix of options for a two parameter soil model:
-%   >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
-%                           'gainingOption', 'soil_infiltration' ;
-%                           'SMSC', 100 ; 
-%                           'k_sat', 10 };
-%   Build the soil model:
-%   >> soilModel = climateTransform_soilMoistureModels(soilModelOptions);
-%
-%   Check if the new parameter values are different from those already in 
-%   the model. This would set obj.variables.isNewParameters = false:
-%   >> detectParameterChange(soilModel, [100; 10])
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   setParameters: set_calibration_parameters_values;
-%   getParameters: get_calibration_parameters_values;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012  
-
-            % Get current parameters
-            set_params = getParameters(obj);
-
-            % Check if there are any changes to the parameters.
-            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
-                obj.variables.isNewParameters = true;
-            else
-                obj.variables.isNewParameters = false;
-            end                        
-            
-        end
-                    
-%% Solve the soil moisture differential equation
-        function setTransformedForcing(obj, t, forceRecalculation)
-% setTransformedForcing solves the soil moisture ODE model
-%
-% Syntax:
-%   setTransformedForcing(obj, climateData, forceRecalculation)
-%
-% Description:  
-%   This method solves the soil moisture differential equation for all time
-%   points within the input daily climate data. 
-%
-%   Importantly, if the model parameters have not changed since the last
-%   simulations, then a solution is not re-derived. Also, this method
-%   requires prior compilation of the C-MEX function
-%   "forcingTransform_soilMoisture.c".
-%
-% Input:
-%   obj         - soil moisture model object.
-%
-%   t           - a vector (Nx1) of time points for simulation.
-%
-%   forceRecalculation - is a logical scalar input (i.e. true of false) to
-%                 force re-calculation of the model and thus ingore if the
-%                 parameters have or have not changed.
-% Outputs:
-%   (none)
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   forcingTransform_soilMoisture.c
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012  
-            if nargin==2
-                forceRecalculation=false;
-            end               
-            if obj.variables.isNewParameters || forceRecalculation || ~isfield(obj.variables,'t') || ...
-            (isfield(obj.variables,'t') && obj.variables.t(end) ~= t(end))
-
-                % back transform the parameters
-                [params, param_names] = getDerivedParameters(obj);
-
-                % Assign each param to a variable for efficient access.
-                SMSC = params(1,:);
-                SMSC_trees = params(2,:);
-                treeArea_frac = params(3,:);
-                S_initialfrac = params(4,:);
-                k_infilt = params(5,:);
-                k_sat = params(6,:);
-                bypass_frac = params(7,:);            
-                interflow_frac = params(8,:);
-                alpha = params(9,:);
-                beta = params(10,:);
-                gamma = params(11,:);                 
-                
-                % Filter the forcing data to input t.
-                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
-                
-                % Get the required forcing data
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
-                precip_col = obj.settings.forcingData_cols{filt,2};
-                obj.variables.precip = obj.settings.forcingData(filt_time, precip_col );
-
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
-                evap_col = obj.settings.forcingData_cols{filt,2};
-                obj.variables.evap = obj.settings.forcingData(filt_time, evap_col );
-                
-                % Store the time points
-                obj.variables.t = obj.settings.forcingData(filt_time,1);
-                 
-                if isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
-                    filt = strcmp(obj.settings.forcingData_cols(:,1),'TreeFraction');
-                    tree_col = obj.settings.forcingData_cols{filt,2};
-                    obj.variables.treeFrac = obj.settings.forcingData(filt_time, tree_col );                    
-                end
-                
-                % Filter percip by max infiltration rate, k_infilt.  
-%                 effectivePrecip = obj.variables.precip;
-%                 if k_infilt < inf && (obj.settings.activeParameters.k_infilt || obj.settings.fixedParameters.k_infilt)
-%                     lambda_p = obj.settings.lambda_p .* k_infilt;
-%                     effectivePrecip= (effectivePrecip>0).*(effectivePrecip+ lambda_p * log( 1./(1 + exp( (effectivePrecip - k_infilt)./lambda_p))));
-%                     effectivePrecip(isinf(effectivePrecip)) = k_infilt;
-%                 end
-                effectivePrecip = getTransformedForcing(obj, 'effectivePrecip',1);
-
-                % Set the initial soil moisture.
-                if isempty(S_initialfrac)
-                    S_initial = 0.5.*SMSC;
-                else
-                    S_initial = S_initialfrac * SMSC;
-                end
-                
-                % Store the time points
-                %obj.variables.t = t(filt_time);
-                
-                % Call MEX function containing soil moisture model.
-%                 if ~doSubDailyEst
-%                     obj.variables.SMS = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, SMSC, k_sat, alpha, beta, gamma);
-%                     
-%                     % Empty sub daily data to ensure there is no
-%                     % inconistency.
-%                     obj.variables.t_subdaily = [];
-%                     obj.variables.SMS_subdaily = [];
-%                     
-%                     % Run soil model again if tree cover is to be simulated
-%                     if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
-%                         if isempty(S_initialfrac)
-%                             S_initial = 0.5.*SMSC_trees;
-%                         else
-%                             S_initial = S_initialfrac * SMSC_trees;
-%                         end
-%                         
-%                         obj.variables.SMS_trees = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, ...
-%                             SMSC_trees, k_sat, alpha, beta, gamma);                        
-%                     end
-%                     
-%                 else 
-
-                    % Get subset size
-                    nSubSteps = getNumDailySubsteps(obj);
-
-                    % Define daily sub-steps.
-                    t_substeps = linspace(1,0,nSubSteps+1);
-                    
-                    % Get number of days
-                    nDays = length(effectivePrecip);
-                    
-                    % Build the timesteps for sub-daily analysis.
-                    obj.variables.t = bsxfun(@minus,obj.variables.t, t_substeps(2:end)); 
-                    obj.variables.t = reshape(obj.variables.t', nDays * nSubSteps,1);
-                    
-                    % Add the initial time step
-                    obj.variables.t = [floor(obj.variables.t(1)); obj.variables.t];
-                    
-                    % Scale ksat from units of 'per day' to 'per sub daily
-                    % time step'
-                    k_sat = k_sat./nSubSteps;
-                    
-                    % Expand input forcing data to have the required number of days and
-                    % scale the forcing data by the number of time steps.
-                    effectivePrecip = getSubDailyForcing(obj,effectivePrecip);
-                    evap = getSubDailyForcing(obj,obj.variables.evap);
-                    
-                    % Run the soil models using the sub-steps.
-                    obj.variables.SMS = forcingTransform_soilMoisture(S_initial, effectivePrecip, evap, SMSC, k_sat, alpha, beta, gamma);
-                    
-                    % Run soil model again if tree cover is to be simulated
-                    if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
-                        if isempty(S_initialfrac)
-                            S_initial = 0.5.*SMSC_trees;
-                        else
-                            S_initial = S_initialfrac * SMSC_trees;
-                        end
-                        
-                        obj.variables.SMS_trees = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, ...
-                            SMSC_trees, k_sat, alpha, beta, gamma);
-                    end
-%                 end                                                 
-            end
-        end
-        
-%% Return the transformed forcing data
-        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName, SMSnumber, doSubstepIntegration) 
-% getTransformedForcing returns the required flux from the soil model.
-%
-% Syntax:
-%   [precip_Forcing, et_Forcing] = getTransformedForcing(obj, variableName)
-%
-% Description:  
-%   This method returns the requested flux/data from the soil moisture 
-%   differential equation. The available fluxes/data are as follows:
-%
-%   * drainage: soil free drainage ranging (0 to k_sat) at the end of the day.
-%   * drainage_bypassFlow: free drainage plus a parameter set fraction of runoff;
-%   * drainage_normalised: normalised free drainage (0 to 1) at the end of the day.
-%   * evap_soil: actual soil ET at the end of the day.    
-%   * infiltration: daily total infiltration rate.
-%   * evap_gw_potential: groundwater evaporative potential (PET - soil ET)
-%   * runoff: daily total runoff.
-%   * SMS: soil moisture storage at the end of each day.
-%
-% Input:
-%   obj - soil moisture model object.
-%
-%   variableName - a string for the variable name to return.
-%
-% Outputs:
-%   forcingData  - a vector (Nx1) of the forcing data output to
-%                     be input to the groundwater time series model.
-%
-%   isDailyIntegralFlux - logical scaler denoting if the flux is a daily
-%   integral or an instantaneous value. This is used to inform the type of
-%   numerical integration within modelTFN.get_h_star().
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   setParameters: set_calibration_parameters_values;
-%   getParameters: get_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: set_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012  
-        
-            % back transform the parameters
-            [params, param_names] = getDerivedParameters(obj);
-            
-            if ischar(variableName)
-                variableNametmp{1}=variableName;
-                variableName = variableNametmp;
-                clear variableNametmp;
-            end            
-            
-            % Assign each param to a variable for efficient access.
-            SMSC = params(1,:);
-            SMSC_trees = params(2,:);
-            treeArea_frac = params(3,:);
-            S_initialfrac = params(4,:);
-            k_infilt = params(5,:);
-            k_sat = params(6,:);
-            bypass_frac = params(7,:);            
-            interflow_frac = params(8,:);
-            alpha = params(9,:);
-            beta = params(10,:);
-            gamma = params(11,:);            
-             
-            % Set if the subdaily steps should be integrated.
-            if nargin < 4
-                doSubstepIntegration = true;
-            end
-            
-            % Get the soil moisture store for the required soil unit
-            if nargin==2 || SMSnumber==1
-                SMS = obj.variables.SMS;
-                SMSnumber = 1;
-            elseif SMSnumber==2
-                SMS = obj.variables.SMS_trees;
-                SMSC = SMSC_trees;
-            else
-                error('The soil moisture unit number is unknown')
-            end            
-            
-            % Calculate the required fluxes.
-            try 
-                if doSubstepIntegration
-                    nrows = size(obj.variables.precip,1);
-                else
-                    nrows = size(getSubDailyForcing(obj,obj.variables.precip),1);
-                end
-                forcingData = zeros(nrows , length(variableName));
-                
-                for i=1:length(variableName)
-                    switch variableName{i}                    
-                        case 'drainage'
-                            runoff = 0;
-                            if bypass_frac~=0
-                                % Get runoff
-                                runoff = getTransformedForcing(obj, 'runoff',SMSnumber, false);
-                                
-                                % Re-scale runoff from that going to the stream to that going to recharge plus the stream.
-                                runoff  = runoff ./ (1-bypass_frac);
-                            end            
-                            
-                            drainage = (1-interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(SMS/SMSC).^beta;
-                            drainage  = drainage + bypass_frac.*runoff;
-                            if doSubstepIntegration
-                                forcingData(:,i) = dailyIntegration(obj, drainage);
-                                isDailyIntegralFlux(i) = true;
-                            else
-                               forcingData(:,i) = drainage;  
-                               isDailyIntegralFlux(i) = false;
-                            end
-                            
-                        case 'evap_soil'    
-                            % Expand input forcing data to have the required number of substeps.
-                            evap = getSubDailyForcing(obj,obj.variables.evap);
-                                                        
-                            % Est ET
-                            evap = evap .* (SMS/SMSC).^gamma;         
-                            if doSubstepIntegration
-                                forcingData(:,i) = dailyIntegration(obj, evap);
-                                isDailyIntegralFlux(i) = true;
-                            else
-                                forcingData(:,i) = evap;
-                                isDailyIntegralFlux(i) = false;
-                            end
-                            
-                            
-                        case 'infiltration'                       
-                            % Calculate max. infiltration assuming none
-                            % goes to SATURATED runoff.
-                            effectivePrecip_daily = getTransformedForcing(obj, 'effectivePrecip',SMSnumber); 
-                            effectivePrecip = getSubDailyForcing(obj,effectivePrecip_daily);
-                            if alpha==0
-                                infiltration_daily =  effectivePrecip_daily;
-                                infiltration =  effectivePrecip;                                
-                            else
-                                infiltration =  effectivePrecip .* (1-SMS/SMSC).^alpha;
-                            end
-                            
-                            % Calculatre when the soil is probably
-                            % saturated. 
-                            drainage = getTransformedForcing(obj, 'drainage',SMSnumber, false);
-                            evap = getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
-                            Infilt2Runoff = [0;(SMS(1:end-1) + infiltration(2:end) - evap(2:end) - drainage(2:end)) - SMSC];
-                            Infilt2Runoff(Infilt2Runoff<0) = 0;
-                            
-                            % Subtract estimated satruated excess runoff
-                            % from the infiltration and then integrate.
-                            if alpha==0
-                                % Integrate ONLY saturated runoff
-                                if doSubstepIntegration
-                                    Infilt2Runoff = dailyIntegration(obj, Infilt2Runoff);
-                                    
-                                    % Subtract from precip.
-                                    forcingData(:,i) = infiltration_daily - Infilt2Runoff;
-                                else
-                                    forcingData(:,i) = infiltration - Infilt2Runoff;
-                                end
-                            else
-                                infiltration = infiltration - Infilt2Runoff;
-                                if doSubstepIntegration
-                                    forcingData(:,i) = dailyIntegration(obj, infiltration);
-                                else
-                                    forcingData(:,i) = infiltration;
-                                end                                
-                            end
-                            
-                            if doSubstepIntegration
-                                isDailyIntegralFlux(i) = true;
-                            else
-                                isDailyIntegralFlux(i) = false;
-                            end
-
-                        case 'evap_gw_potential'
-                            evap = getSubDailyForcing(obj,obj.variables.evap) - getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
-                            if doSubstepIntegration
-                                forcingData(:,i) = dailyIntegration(obj, evap);
-                                isDailyIntegralFlux(i) = true;
-                            else
-                                forcingData(:,i) = evap;
-                                isDailyIntegralFlux(i) = false;
-                            end                            
-                          
-                        case 'runoff'
-                            
-                            % Calculate infiltration.
-                            infiltration = getTransformedForcing(obj, 'infiltration',SMSnumber, false);
-                            
-                            % Calc sub daily interflow
-                            %interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
-                                
-                            % Calc sub daily runoff
-                            runoff = max(0,getSubDailyForcing(obj,obj.variables.precip) - infiltration);
-                            runoff = runoff * (1-bypass_frac);
-                            
-                            % Integreate to daily.
-                            if doSubstepIntegration
-                                forcingData(:,i) = dailyIntegration(obj, runoff);
-                                isDailyIntegralFlux(i) = true;
-                            else
-                                forcingData(:,i) = runoff;
-                                isDailyIntegralFlux(i) = false;
-                            end
-                       
-                        case'SMS'
-                            forcingData(:,i) = SMS((1+obj.variables.nDailySubSteps):obj.variables.nDailySubSteps:end);
-                            isDailyIntegralFlux(i) = false;
-
-                        case'effectivePrecip'
-                            effectivePrecip = obj.variables.precip;
-                            if k_infilt < inf && (obj.settings.activeParameters.k_infilt || obj.settings.fixedParameters.k_infilt)
-                                lambda_p = obj.settings.lambda_p .* k_infilt;
-                                effectivePrecip= (effectivePrecip>0).*(effectivePrecip+ lambda_p * log( 1./(1 + exp( (effectivePrecip - k_infilt)./lambda_p))));
-                                effectivePrecip(isinf(effectivePrecip)) = k_infilt;
-                            end
-                            forcingData(:,i) = effectivePrecip;
-                            isDailyIntegralFlux(i) = true;
-                        case 'mass_balance_error'
-                            precip = getSubDailyForcing(obj,obj.variables.precip);
-                            runoff = getTransformedForcing(obj, 'runoff',SMSnumber, false);
-                            AET = getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
-                            drainage = getTransformedForcing(obj, 'drainage',SMSnumber, false);
-                            
-                            fluxEstError = [0;precip(2:end) - diff(SMS) -  runoff(2:end) - AET(2:end) - drainage(2:end)];
-                            
-                            % Integreate to daily.
-                            if doSubstepIntegration
-                                forcingData(:,i) = dailyIntegration(obj, fluxEstError);
-                                isDailyIntegralFlux(i) = true;
-                            else
-                                forcingData(:,i) = fluxEstError;
-                                isDailyIntegralFlux(i) = false;
-                            end
-                             
-                        otherwise
-                            if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover && nargin==2 
-                                if isempty(strfind(variableName{i}, '_nontree')) && isempty(strfind(variableName{i}, '_tree'))
-                                    error('The requested transformed forcing variable is not known.');
-                                end
-                            else                            
-                                error('The requested transformed forcing variable is not known.');
-                            end
-                    end
-
-                    % Get flixes for tree soil unit (if required) and weight the
-                    % flux from the two units
-                    if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover && nargin==2
-                        if ~isempty(strfind(variableName{i}, '_nontree'))
-                            % Get flux for non-tree componant
-                            ind = strfind(variableName{i}, '_nontree');
-                            variableName{i} = variableName{i}(1:ind-1);
-                            [forcingData(:,i),isDailyIntegralFlux(i)]  = getTransformedForcing(obj, variableName{i}, 1) ;
-                            forcingData(:,i) =  (1-treeArea_frac .* obj.variables.treeFrac) .* forcingData(:,i);
-                        elseif ~isempty(strfind(variableName{i}, '_tree'))
-                            % Get flux for non-tree componant
-                            ind = strfind(variableName{i}, '_tree');
-                            variableName{i} = variableName{i}(1:ind-1);
-                            [forcingData(:,i),isDailyIntegralFlux(i)] = getTransformedForcing(obj, variableName{i}, 2);                
-                            forcingData(:,i) = treeArea_frac .* obj.variables.treeFrac .* forcingData(:,i);
-                        else
-                            % Get flux for tree SMS
-                            forcingData_trees = getTransformedForcing(obj, variableName{i}, 2);                    
-
-                            % Do weighting
-                            forcingData(:,i) = (1-treeArea_frac .* obj.variables.treeFrac) .* forcingData(:,i) + ...
-                                          treeArea_frac .* obj.variables.treeFrac .* forcingData_trees;
-                        end
-                    end                    
-                end
-            catch ME
-                error(ME.message)
-            end
-            
-
-        end
-
-        
-        % Return the derived variables. This is used by this class to get
-        % the back transformed parameters.
-        function [params, param_names] = getDerivedParameters(obj)
-            
-            param_names = {'SMSC: back transformed soil moisture storage capacity (in rainfall units)'; ...                  
-                           'SMSC_trees: back transformed soil moisture storage capacity in trees unit (in rainfall units)'; ...
-                           'treeArea_frac: fractional area of the tree units (-)'; ...
-                           'S_initialfrac: fractional initial soil moisture (-)'; ... 
-                           'k_infilt : back transformed maximum soil infiltration rate (in rainfall units)'; ...
-                           'k_sat : back transformed maximum vertical conductivity (in rainfall units/day)'; ...
-                           'bypass_frac : fraction of runoff that goes to bypass drainage (-)'; ...
-                           'interflow_frac : fraction of free drainage going to interflow (-)'; ...        
-                           'alpha : power term for infiltration rate (-)'; ...       
-                           'beta : back transformed power term for dainage rate (eg approx. Brook-Corey pore index power term)'; ...
-                           'gamma : back transformed power term for soil evaporation rate (-)'};    
-        
-            params = [  10.^(obj.SMSC); ...
-                        10.^(obj.SMSC_trees); ...
-                        obj.treeArea_frac; ...
-                        obj.S_initialfrac; ...
-                        10.^obj.k_infilt; ...
-                        10.^obj.k_sat; ...
-                        obj.bypass_frac; ...
-                        obj.interflow_frac; ...
-                        obj.alpha; ...
-                        10.^(obj.beta); ...
-                        10.^(obj.gamma)];
-        end
-
-        % Return coordinates for forcing variable
-        function coordinates = getCoordinates(obj, variableName)
-
-            if ~iscell(variableName)
-                variableNameTmp{1}=variableName;
-                variableName = variableNameTmp;
-                clear variableNameTmp;
-            end
-                
-            coordinates = cell(length(variableName),3);
-            for i=1:length(variableName)
-                % Find row within the list of required containing variabeName
-                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
-
-                % If empty, then it is likely to be a model output variable os use the precip coordinate.
-                if ~any(filt)
-                    filt = strcmp(obj.settings.forcingData_cols(:,1), 'precip');
-                end
-                sourceColNumber = obj.settings.forcingData_cols{filt,2};
-                sourceColName = obj.settings.forcingData_colnames{sourceColNumber};
-
-                % Get coordinates
-                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceColName);
-                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
-                coordinates{i,1} = variableName{i};
-            end    
-        end        
-        
-        function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-            propNames = properties(obj);
-            for i=1:length(propNames)
-               if isempty(obj.(propNames{i}))
-                   continue;
-               end                
-               if isobject(obj.(propNames{i}))
-                delete(obj.(propNames{i}));
-               else               
-                obj.(propNames{i}) = []; 
-               end
-            end
-        end            
-    end
-    
-    methods(Access=protected, Hidden=true)
-        
-        % Get the names of the active parameters
-        function param_names = getActiveParameters(obj)
-            
-            % Get field array value for if the parameter is active.
-            ind=structfun(@(x) (x),  obj.settings.activeParameters);
-            
-            % Get list of all parameter names.
-            all_parameter_names = fieldnames(obj.settings.activeParameters);
-            
-            % Return only those parameter names that are active
-            param_names=all_parameter_names(ind,1);
-            
-        end
-        
-        function data_daily = dailyIntegration(obj, data)
-                       
-            % reshape data so sun-daily steps are on one row     
-            nSubSteps = obj.variables.nDailySubSteps;
-            nDays = floor(length(obj.variables.t)./nSubSteps);
-            data_reshaped = zeros(nDays , nSubSteps+1);
-                        
-            for (i=1:(nSubSteps+1))
-                data_reshaped(:,i) = data(i:nSubSteps :(end- (nSubSteps+1)+i ));
-            end
-            
-            % To trapazoidal integration
-            data_daily = trapz(0:nSubSteps, data_reshaped, 2);
-            
-        end
-        
-        function nDailySubSteps = getNumDailySubsteps(obj)
-
-            if isfield(obj.variables,'nDailySubSteps')
-                nDailySubSteps = obj.variables.nDailySubSteps;
-            else
-                nDailySubSteps = 3;
-                obj.variables.nDailySubSteps = nDailySubSteps;
-            end
-        end
-        
-        function data = getSubDailyForcing(obj,data)
-            % Get subset size
-            nSubSteps = getNumDailySubsteps(obj);
-            
-            % Get number of days
-            %nDays = (length(obj.variables.precip)-1)./nSubSteps;
-            nDays = length(obj.variables.precip);
-            
-            % Get the forcing rate PER substep.
-            data = data./nSubSteps;
-            
-            % Build daily subseteps
-            data = reshape(repmat(data,1,nSubSteps)',nDays * nSubSteps,1);
-            
-            % Add dummy row of data to account for the initial
-            % condition being added to to t.
-            data = [0;data];
-        end
-    end
-    
-end
-
-
+classdef climateTransform_soilMoistureModels < forcingTransform_abstract 
+% Class definition for soil moisture transformation of climate forcing.
+%
+% Description        
+%   climateTransform_soilMoistureModels is a generalised vertically lumped 1-D
+%   soil moisture model. It is used to transform the daily climate data
+%   for use in the transfer noise function groundwater time series model.
+%   Importantly, a user of the groundwater time series model has no need to
+%   run any of these soil moisture model methods. All of the methods are
+%   automatically called by the function "model_TFN.m".
+%   
+%   The soil moisture model is defined by the following ordinary
+%   diffenential equation (Kavetski et al. 2003, 2006):
+%   
+%   DS/dt = P_inf (1 - S/SMSC)^alpha - k_sat (S/SMSC)^beta - PET (S/SMSC)^gamma -- (Kavestki 2006)
+%
+%   DS/dt = P_inf [(SMSC-S)/(SMSC(1-eps))]^alpha - k_sat (S/SMSC)^beta - PET (S/SMSC)^gamma  -- (Kavestki 2003, 2006)
+%
+% 
+%   where:
+%       S       - is the soil moisture storage state variable [L];
+%       t       - is time in units of days [T];
+%       P_inf   - is the precipitation available for infiltration. This can
+%                 be limited to a maximum infiltration rate "k_infilt". If
+%                 this parameter is not defined then P_inf equals the input
+%                 daily precipitation rate.
+%       SMSC    - is a parameter for the soil moisture storage capacity [L].
+%                 It is in same units as the input precipitation.
+%       alpha   - is a dimensionless parameter to transform the rate at which
+%                 the soil storage fills and limits infiltration. This 
+%                 allows simulation of a portion of the catchment saturating
+%                 and not longer allowing infiltration.
+%       k_sat   - is a parameter for the saturated vertical soil water
+%                 conductivity [L T^-1]. 
+%       beta    - is a dimensionless parameter to transform the rate at
+%                 which vertical free drainage occurs with filling of
+%                 the soil layer.
+%       interflow_frac - fraction of free drainage going to interflow.
+%       PET     - is the input daily potential evapotranspiration [L T^-1].
+%       gamma   - is a dimensionless parameter to transform the rate at
+%                 which soil water evaporation occurs with filling of the 
+%                 soil layer.
+%       eps      - ratio of S_min/SMSC. If eps=0, S_min=zero
+%       S_initialfrac - the initial soil moisture.
+%
+%	The soil moisture model can also be used to simulate the impacts from
+%	different vegetation; for example, trees and pastures. This is achieved
+%	by simulating a soil store for upto two land types and then weighting
+%	required flux from each soil model by an input time series of the
+%	fraction of the second land type. A challange with the input time series 
+%	of land cover is, however, that while the fraction of, say, land
+%	data clearing over time may be known the fraction of the catchment area
+%	cleared that influences a bore hydrograph is unknown. To address this,
+%	the modelling also include a parameter 'treeArea_frac' for the fraction
+%	of the second land cover (notially trees) that is influencing the bore.
+%	In summary, the simulation of tree cover requires the following two
+%	parameters:
+%
+%       SMSC_trees - is a parameter for the soil moisture storage capacity [L]
+%                  of the second soil model (notionaly trees). This is the
+%                  only other parameter required for the simulation of a
+%                  second soil layer (other parameters are taken from the
+%                  first soil model). Also, the soil storage parameter has 
+%                  the same units as the input precipitation.
+%       treeArea_frac - is a parameter from 0 to 1 that simulates the
+%                   weight to be applied to the tree cover soil model flux.       
+%   
+%   Additionally, in parametrizing the model, many of the parameters were
+%   transformed to a parameter space more amenable to efficient
+%   calibration. Details of the transformations are as follows:
+%
+%       SMSC          - log10(Soil moisture capacity as water depth).
+%       SMSC_trees    - log10(Soil moisture capacity as water depth).
+%       S_initialfrac - Initial soil moisture fraction (0-1).
+%       k_infilt      - log10(Soil infiltration capacity as water depth).
+%       k_sat         - log10(Maximum vertical infiltration rate).
+%       bypass_frac   - Fraction of runoff to bypass drainage (0-1).
+%       interflow_frac- Fraction of free drainage going to interflow (0-1).
+%       beta          - log10(Power term for dainage rate).
+%       gamma         - log10(Power term for soil evap. rate).
+%       eps           - ratio of S_min/SMSC. If eps=0, S_min=zero
+%
+%   The soil moisture model is an adaption of VIC Model (Wood et al. 1992)
+%   by Kavetski et al. (2006). It is a soil moisture model that is without
+%   any discontinuities (thus able to produce a first-order smooth
+%   calibration response surface) and therefore amenable to gradient based
+%   calibration. The model, as implemented here, is very flexible in that a
+%   wide range of models can be implemented by simply turning componants of
+%   the differential equation on or off. For example, a one parameter
+%   model (comprising only of infiltration and evaporation) can be derived
+%   by turning off the drainage term and fixing alpha and gamma to one. For
+%   details on how to build various types of models see the documentation
+%   for the class constructor (i.e. "climateTransform_soilMoistureModels"
+%   below);
+%
+%   The current infiltration term of the soil moisture model is an adaption of VIC Model (Wood et al. 1992) with a 
+%   S_min threshold as per Kalma et al. (1995) and Kavestki et al. (2003).
+%   This S_min term adds discontinuity to the infiltration term but the
+%   evapotranspiration and drainage terms are kept as in Kavetski et al.
+%   (2006), so the model has limited discontinuities (thus able to produce a first-order smooth
+%   calibration response surface) and therefore amenable to gradient based
+%   calibration. The S_min term is included as a ration of S_min/SMSC =
+%   eps, thus if eps = 0 we have the original soil moisture model given by
+%   Kavetski et al. (2006) 
+%
+%   Considerable effort has been put into efficiently solving the soil 
+%   moisture model while producing a smooth response surface. The
+%   differential equation is solved using a fixed-time step solver. For each
+%   time step, an initial estimate is made using the explicit Huen
+%   algorithm. This produces an O(h^2) error. The solution error is then
+%   refined near to machine precision using an implicit Huen Newton's
+%   algorithm. To achieve efficient computation, the differential equation
+%   and solver are implemeneted within MatLab's C-MEX language. This
+%   requires compilation of the file "forcingTransform_soilMoisture.c" and
+%   can be achieved by executing the following command within MatLab: 
+%   mex forcingTransform_soilMoisture.c
+%
+%   Once the soil moisture model has been ran a range of fluxes can be
+%   derived from it for input to the transfer function noise model in place
+%   of, say, precipitation. These fluxes include standard fluxes such as
+%   free drainage or actual ET but also derived fluxes such as the bypass 
+%   drainage (runoff plus free drainage) and the groundwater potential
+%   evaporation ( potential ET minus soil ET). For a full list see
+%   getTransformedForcing().
+%
+% See also
+%   climateTransform_soilMoistureModels: model_construction;
+%   setParameters: set_calibration_parameters_values;
+%   getParameters: get_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies
+%   forcingTransform_soilMoisture.c
+%
+% References:
+%   Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of
+%   input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res.,
+%   42, W03407, doi:10.1029/2005WR004368. 
+%
+%   Wood, E. F., D. P. Lettenmaier, and V. G. Zartarian (1992), A
+%   Land-Surface Hydrology Parameterization With Subgrid Variability for 
+%   General Circulation Models, J. Geophys. Res., 97(D3), 2717–2728, 
+%   doi:10.1029/91JD01786. 
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012   
+    
+    properties(GetAccess=public, SetAccess=protected)
+                        
+        % Model Parameters
+        %----------------------------------------------------------------
+        SMSC            % Soil moisture capacity parameter (as water depth)
+        SMSC_trees      % Trees soil moisture capacity parameter (as water depth)       
+        treeArea_frac   % Scaler for the tree fraction data (optional)
+        S_initialfrac   % Initial soil moisture 
+        k_infilt        % Maximum infiltration rate.
+        k_sat           % Maximum vertical conductivity.
+        bypass_frac     % Fraction of runoff that goes to bypass drainage
+        interflow_frac  % Fraction of free drainage going to interflow (0-1).        
+        alpha           % Power term for infiltration.        
+        beta            % Power term for dainage rate (eg Brook-Corey pore index power term)
+        gamma           % Power term for soil evaporation rate.  
+        eps             % Scaler defining S_min/SMSC ratio. S_min is the minimum soil moisture threshold for having runoff produced. If eps=0, runoff and infiltration terms are as Kavestki 2006. 
+        %----------------------------------------------------------------        
+    end
+
+
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'precip';'et';'TreeFraction'};
+            isOptionalInput = [false; false; true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'effectivePrecip'; 
+                              'drainage';       'infiltration';            'evap_soil';        'evap_gw_potential';        'runoff';       'SMS'; ...
+                              'drainage_tree';  'infiltration_tree';        'evap_soil_tree';   'evap_gw_potential_tree';   'runoff_tree';  'SMS_tree'; ...
+                              'drainage_nontree';'infiltration_nontree';    'evap_soil_nontree';'evap_gw_potential_nontree';'runoff_nontree';'SMS_nontree'; ...
+                              'infiltration_fractional_capacity'; 'interflow';  'mass_balance_error'};
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { 'SMSC'          ,   2, 'Calib.';...
+                        'SMSC_trees'    ,   2, 'Fixed';...
+                        'treeArea_frac' , 0.5, 'Fixed'; ...
+                        'S_initialfrac' , 0.5, 'Fixed'  ; ...
+                        'k_infilt'      , inf,'Fixed'   ; ...
+                        'k_sat'         ,   1, 'Calib.'   ; ...
+                        'bypass_frac'   ,   0, 'Fixed'    ; ...
+                        'interflow_frac',   0, 'Fixed'    ; ...
+                        'alpha'         ,   0, 'Fixed'    ; ...
+                        'beta'          , 0.5,'Calib.' ; ...
+                        'gamma'         ,   0,  'Fixed' ; ...
+                        'eps'           ,   0,  'Fixed'};
+
+        
+            colNames = {'Parameter', 'Initial Value','Fixed or Calibrated?'};
+            colFormats = {'char', 'char', {'Calib.' 'Fixed'}};
+            colEdits = logical([0 1 1]);
+
+            toolTip = sprintf([ 'Use this table to define the type of soil moisture model. \n', ...
+                                'Each parameter (except the soil moisture capacity) can be \n', ...
+                                'set to a fixed value or calibrated. Below is a summary: \n \n' , ...
+                                '   SMSC          : log10(Soil moisture capacity as water depth).\n', ...
+                                '   SMSC_trees    : log10(Tree soil moisture capacity as water depth).\n', ...
+                                '   treeArea_frac : Scaler applied to the tree fraction input data.\n', ...
+                                '   S_initialfrac : Initial soil moisture fraction (0-1).\n', ...
+                                '   k_infilt      : log10(Soil infiltration capacity as water depth).\n', ...
+                                '   k_sat         : log10(Maximum vertical infiltration rate).\n', ...
+                                '   bypass_frac   : Fraction of runoff to bypass drainage.\n', ...
+                                '   interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                '   alpha         : Power term for infiltration rate.\n', ...
+                                '   beta          : log10(Power term for dainage rate).\n', ...
+                                '   gamma         : log10(Power term for soil evap. rate).\n',...
+                                '   eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: climateTransform_soilMoistureModels', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage) ', ...
+                               'using a highly flexible single layer soil moisture model. Two types of land cover can be simulated using two parrallel soil models.', ...
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                                'eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
+                                '', ...               
+                               'References: ', ...
+                               '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330-8355'};
+        end        
+           
+    end
+        
+    
+    methods       
+%% Construct the model
+        function obj = climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
+% climateTransform_soilMoistureModels constructs the soil model object.
+%
+% Syntax:
+%   soilModel = climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
+%
+% Description:
+%   Builds the form of the soil moisture differential equation for the user
+%   input model options. This constructor also checks that the required 
+%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
+%   model options are two or three columns wide.
+%
+% Input:
+%
+%   bore_ID - string for the bore ID. The bore ID must be listed in the site
+%   coordinates array.
+%
+%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
+%   must comprise of the following columns: year, month, day and
+%   observation data. The observation data must include precipitation &
+%   PET. The record must be continuous (no skipped days) and cannot contain  
+%   blank observations.
+%
+%   forcingData_colnames - 1 x N cell array of the column names within the
+%   input numerical array "forcingData_data".  Importantly, each forcing 
+%   data column name must be listed within the input "siteCoordinates". 
+%
+%   siteCoordinates - Mx3 cell matrix containing the following columns :
+%   site ID, projected easting, projected northing. Importantly,
+%   the input 'bore_ID' must be listed and all columns within
+%   the forcing data (excluding the year, month,day). 
+%
+%   modelOptions - cell matrix defining the soil model componants, ie how the
+%   model should be constructed. The cell matrix can be two or three
+%   columns wide and at least three rows long (for the simplest of models).
+%   The first column defines the model parameter name or user option name
+%   to be defined. The second column defines the initial value for the
+%   parameter or the setting for the user option. A third column can be
+%   input if a parameter is to be fixed, and thus not modified during
+%   calibration. This third column can contain the term 'fixed'. 
+%
+%   Below are some examples of model options for various types of models:
+%
+%       - 1 parameter with linear scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100; ...
+%                               'alpha', 1, 'fixed'};};
+%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100, '' ;
+%                               'alpha', 0, 'fixed'};
+%       - 2 parameter with linear scaling of infiltration by the soil 
+%       moisture and linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'k_sat', 10 };
+%       - 3 parameter with linear scaling of infiltration by the soil
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = {  'SMSC', 100 ; 
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%       - 4 parameter with non-linear scaling of infiltration by the soil 
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'alpha', 0.5 ;
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%
+% Output:
+%   obj - climateTransform_soilMoistureModels class object 
+%
+% See also
+%   climateTransform_soilMoistureModels: class_definition;
+%   setParameters: set_calibration_parameters_values;
+%   getParameters: get_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012   
+
+            % Check the MEX 'C' code for the soil moisture model is
+            % compiled for the current operating system.                                                
+            if exist(['forcingTransform_soilMoisture.',mexext()],'file')~=3
+                errorMessage = ['Missing compiled soil moisture "forcingTransform_soilMoisture.',mexext(),'"'];
+                error(errorMessage);
+            end            
+            
+            % Check there are at least two columns in the model options.
+            ncols_modelOptions = size(modelOptions,2);
+            if ncols_modelOptions < 2 || ncols_modelOptions > 3
+                errorMessage = ['The soil moisture model options must be at least two columns, where:' char(13), ...
+                                '  - column 1 is the parameter name.', char(13), ...
+                                '  - column 2 is the parameter value.', char(13), ...
+                                'Note: A third column can be given. This column can contain the input "fixed".', char(13), ...
+                                '      This will make the parameter a constant and it will not be adjusted in the calibration.'];
+                error(errorMessage);
+            end            
+                               
+            % Get a list of required forcing inputs and (again) check that
+            % each of the required inputs is provided.
+            %--------------------------------------------------------------
+            obj.settings.simulateLandCover = false;
+            requiredFocingInputs = climateTransform_soilMoistureModels.inputForcingData_required();
+            for j=1:size(requiredFocingInputs,1)
+                filt = strcmpi(forcingData_reqCols(:,1), requiredFocingInputs(j));                                    
+                if ~any(filt) && ~strcmpi(requiredFocingInputs(j),'TreeFraction')
+                    error(['An unexpected error occured. When transforming forcing data, the input cell array for the transformation must contain a row (in 1st column) labelled "forcingdata" that its self contains a cell array in which the forcing data column is defined for the input:', requiredFocingInputs{j}]);
+                elseif any(filt) && strcmpi(requiredFocingInputs(j),'TreeFraction')                    
+                    % Do land cover simulation if the data has any tree
+                    % fraction > 0;
+                    filt_LC = find(strcmpi(forcingData_reqCols(:,1), 'TreeFraction'))+1; 
+                    if any(forcingData_data(:,filt_LC)<0) || any(forcingData_data(:,filt_LC)>1)
+                       error('The tree fraction input data must be between 0 and 1.'); 
+                    end
+                    if sum(forcingData_data(:,filt_LC))>0
+                        obj.settings.simulateLandCover = true;
+                    end
+                end
+            end
+             
+            % Assign the input forcing data to obj.settings.
+            obj.settings.forcingData = forcingData_data;
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData_cols = forcingData_reqCols;
+            obj.settings.siteCoordinates = siteCoordinates;
+                              
+            % Get list of model parameters and exclude settings, variables
+            % and the model 'type';
+            all_parameter_names = properties(obj);
+            ind = true(length(all_parameter_names),1);
+            for i=1:length(all_parameter_names)
+                switch all_parameter_names{i,1}
+                    case {'settings', 'variables'}
+                        ind(i) = false;                    
+                end
+            end
+            all_parameter_names = all_parameter_names(ind);
+            
+            % Assign model parameters.
+            % Importantly, if the parameter is not listed then that 
+            % feature of the soil moisture model is turned off.
+            %--------------------------------------------------------------
+            nparams = 0;
+            
+            for i=1:length(all_parameter_names)
+                
+                % Find the required parameter within the input model
+                % options.
+                ind = [];
+                for j=1:length(modelOptions(:,1))
+                    if strcmp(modelOptions(j,1), all_parameter_names{i} )                        
+                        ind = j;
+                        break;
+                    end
+                end
+            
+                % Record the parameter as 'Active'. That is, it can be
+                % calibrated within the time series model,
+                if ~isempty(ind)
+                    if ncols_modelOptions ==3 && strcmp(lower(modelOptions{ind,3}),'fixed')
+                        obj.settings.fixedParameters.(all_parameter_names{i})=true;
+                        obj.settings.activeParameters.(all_parameter_names{i})=false;
+                        obj.(all_parameter_names{i}) = modelOptions{ind,2};
+                    else
+                        nparams = nparams+1;
+                        obj.settings.fixedParameters.(all_parameter_names{i})=false;
+                        obj.settings.activeParameters.(all_parameter_names{i})=true;
+                        paramsInitial(nparams,1) = modelOptions{ind,2};
+                    end
+                else
+                    obj.settings.fixedParameters.(all_parameter_names{i})=true;
+                    obj.settings.activeParameters.(all_parameter_names{i})=false;                    
+                    if strcmp(all_parameter_names{i}, 'alpha')         
+                        obj.(all_parameter_names{i}) = 1;
+                    elseif strcmp(all_parameter_names{i}, 'beta') || strcmp(all_parameter_names{i}, 'gamma')      
+                        % Note, beta is transformed in the soil model to
+                        % 10^beta.
+                        obj.(all_parameter_names{i}) = 0;
+                    elseif strcmp(all_parameter_names{i}, 'k_sat')
+                        % Note, k_sat is transformed in the soil model to
+                        % 10^k_sat = 0 m/d.
+                        obj.(all_parameter_names{i}) = -inf;
+                    elseif strcmp(all_parameter_names{i}, 'interflow_frac')
+                        obj.(all_parameter_names{i}) = 0;                        
+                    elseif strcmp(all_parameter_names{i}, 'S_initialfrac')
+                        obj.(all_parameter_names{i}) = 0.5;  
+                    elseif strcmp(all_parameter_names{i}, 'eps')
+                        obj.(all_parameter_names{i}) = 0;
+                    else
+                        obj.(all_parameter_names{i}) = 0;
+                    end
+                end
+            end
+            
+            % Check that the soil moisture capacity parameter is active.
+            % This is the simplest model able to be simulated
+            if ~obj.settings.activeParameters.SMSC
+                error('The soil moisture model options must include the soil moisture capacity parameter.');
+            end
+            
+            % Check the SMSM_trees parameter is active if and only if there
+            % is land cover input data.
+            if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+               if ~obj.settings.activeParameters.SMSC_trees 
+                   error('The trees soil moisture model options must include the soil moisture capacity parameter when land cover data is input.');
+               end
+            else
+                obj.settings.activeParameters.SMSC_trees = false; 
+                obj.settings.fixedParameters.SMSC_trees = true;
+                obj.settings.fixedParameters.treeArea_frac = true;
+                obj.settings.activeParameters.treeArea_frac = false;
+            end
+            
+            % Set a constant for smoothing the soil moisture capacity
+            % thresholds and infiltration excess threshold            
+            obj.settings.lambda_p = 0.2;                        
+            
+            % Set parameters for transfer function.
+            setParameters(obj, paramsInitial)                             
+
+            % Initialise soil moisture variables
+            obj.variables.t = [];
+            obj.variables.SMS = [];           
+            obj.variables.nDailySubSteps = getNumDailySubsteps(obj);          
+        end
+        
+%% Set parameters
+        function setParameters(obj, params)
+% setParameters returns the soil model parameters.
+%
+% Syntax:
+%   setParameters(obj, params)  
+%
+% Description:  
+%   This method sets the soil moisture model parameters to user input values.
+%   Only parameters that are to be calibrated (i.e. non-fixed parameters)
+%   can be set. Also the vector of input parameter values must be in the
+%   same order as the parameter names returned by the method
+%   'getParameters'.
+%
+% Input:
+%   obj     - soil moisture model object.
+%   params  - vector (Nx1) of parameter values.
+%
+% Outputs:
+%   (none)
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   getParameters: get_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012  
+
+            % Get the active parameter names
+            param_names = getActiveParameters(obj);
+           
+            % Check if the parameters have changed since the last call to
+            % setTransformedForcing. Note, this must be done prior to the
+            % updating of the parameters.
+            detectParameterChange(obj, params);             
+            
+            % Cycle through each parameter and assign the parameter value.
+            for i=1: length(param_names)
+               obj.(param_names{i}) = params(i,:); 
+            end
+                      
+        end
+        
+        function setForcingData(obj, forcingData, forcingData_colnames)
+% setForcingData sets the forcing data.
+%
+% Syntax:
+%   setForcingData(obj, forcingData, forcingData_colnames)
+%
+% Description:  
+%   This method set the climate forcing data. It is used to update the
+%   forcing data for model simulations (primarily from the GUI).
+%
+% Input:
+%   obj         - soil moisture model object.
+%
+%   forcingData - nxm matrix of focrinf data with column 1 being the
+%                 date/time.
+%
+%   forcingData_colnames - 1xm cell array of column names within the above
+%                 data.
+%
+% Outputs:
+%   (none)
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   21 Dec 2015              
+            
+            %obj.settings.forcingData_colnames = forcingData_colnames;
+            %obj.settings.forcingData = forcingData;
+            if length(forcingData_colnames) < length(obj.settings.forcingData_colnames)
+                error('The number of column name to be set is less than that used to build the object.');
+            end
+            forcingDataNew = nan(size(forcingData,1),length(obj.settings.forcingData_colnames));
+            for i=1:length(forcingData_colnames)               
+                filt  = strcmp(obj.settings.forcingData_colnames, forcingData_colnames{i});
+                if ~isempty(filt)
+                    forcingDataNew(:,filt) = forcingData(:,i);
+                end
+            end
+            obj.settings.forcingData = forcingDataNew;
+            obj.variables.isNewParameters = true;
+        end                
+        
+%% Get model parameters
+        function [params, param_names] = getParameters(obj)            
+% getParameters sets the soil model parameters.
+%
+% Syntax:
+%   setParameters(obj, params)  
+%
+% Description:  
+%   This method gets the soil moisture model parameters. Only parameters 
+%   that are to be calibrated (i.e. non-fixed parameters) are returned. The
+%   method also returns the parameter names and the order of the parameter
+%   names correspondes to the returned order of the parameter values.
+%
+% Input:
+%   obj         - soil moisture model object.
+%
+% Outputs:
+%   params      - a vector (Nx1) of soil moisture model parameter values.
+%
+%   param_names - a vector (Nx1) of parameter names.   
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   setParameters: set_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012              
+            
+            % Get the active parameter names
+            param_names = getActiveParameters(obj);
+            
+            % Cycle through each parameter and get the parameter value.
+            params = zeros(length(param_names),size( obj.(param_names{1}),2));
+            for i=1: length(param_names)
+                if isempty(obj.(param_names{i}))
+                    params(i,:) = NaN;
+                else
+                    params(i,:) = obj.(param_names{i});
+                end
+            end
+        end   
+        
+%% Assess if matrix of parameters is valid.
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+% getParameterValidity returns a logical vector for the validity or each parameter.
+%
+% Syntax:
+%   isValidParameter = getParameterValidity(obj, params, param_names)
+%
+% Description:
+%   Cycles though all active soil model parameters and returns a logical 
+%   vector denoting if each parameter is valid ie within the physical 
+%   parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+%   params - vector of model parameters
+%
+%   param_names - cell array of the parameter names.
+%
+% Outputs:
+%   isValidParameter - column vector of the parameter validity.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   model_TFN: model_construction;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds.
+            isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));   
+        end   
+
+%% Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
+% getParameters_physicalLimit returns the physical limits to each soil model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
+%
+% Description:
+%   Cycles through all active soil model componants and parameters and returns
+%    a vector of the physical upper and lower parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter bounds
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   model_TFN: model_construction;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+
+            [params, param_names] = getParameters(obj);
+            
+            params_lowerLimit = repmat(0,size(params,1),1);
+            params_upperLimit = repmat(inf,size(params,1),1);         
+
+            if obj.settings.activeParameters.k_infilt
+                ind = cellfun(@(x)(strcmp(x,'k_infilt')),param_names);
+                params_lowerLimit(ind,1) = -inf;                    
+            end       
+            
+            % Upper and lower bounds taken from Rawls et al 1982 Estimation
+            % of Soil Properties. The values are for sand and clay
+            % respectively and transformed from units of cm/h to the assumed
+            % input units of mm/d.
+            if obj.settings.activeParameters.k_sat
+                ind = cellfun(@(x)(strcmp(x,'k_sat')),param_names);
+                %params_lowerLimit(ind,1) = log10(10);                    
+                %params_upperLimit(ind,1) = log10(100);
+                params_lowerLimit(ind,1) = floor(log10(0.06*24*10));
+                params_upperLimit(ind,1) = ceil(log10(21*24*10));
+            end             
+            
+            if obj.settings.activeParameters.bypass_frac
+                ind = cellfun(@(x)(strcmp(x,'bypass_frac')),param_names);
+                params_lowerLimit(ind,1) = 0;         
+                params_upperLimit(ind,1) = 1;
+            end    
+            
+            if isfield(obj.settings.activeParameters,'treeArea_frac') && obj.settings.activeParameters.treeArea_frac
+                ind = cellfun(@(x)(strcmp(x,'treeArea_frac')),param_names);
+                params_lowerLimit(ind,1) = 0;         
+                params_upperLimit(ind,1) = 1;
+            end            
+            
+            if isfield(obj.settings.activeParameters,'interflow_frac') && obj.settings.activeParameters.interflow_frac
+                ind = cellfun(@(x)(strcmp(x,'interflow_frac')),param_names);
+                params_lowerLimit(ind,1) = 0;         
+                params_upperLimit(ind,1) = 1;
+            end                
+            
+            if obj.settings.activeParameters.S_initialfrac
+                ind = cellfun(@(x)(strcmp(x,'S_initialfrac')),param_names);
+                params_lowerLimit(ind,1) = 0;                                    
+                params_upperLimit(ind,1) = 1;                                    
+            end    
+                   
+            % if obj.settings.activeParameters.beta % TODO: is it a bug?
+			if obj.settings.activeParameters.gamma 
+                ind = cellfun(@(x)(strcmp(x,'gamma')),param_names); 
+                params_lowerLimit(ind,1) = log10(0.01);
+                params_upperLimit(ind,1) = log10(100);
+            end                  
+            
+            if obj.settings.activeParameters.SMSC
+                ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
+                params_lowerLimit(ind,1) = log10(10);
+                %params_upperLimit(ind,1) = Inf;
+                params_upperLimit(ind,1) = log10(1000);
+            end    
+            
+            if obj.settings.activeParameters.SMSC_trees
+                ind = cellfun(@(x)(strcmp(x,'SMSC_trees')),param_names);
+                params_lowerLimit(ind,1) = log10(10);
+                params_upperLimit(ind,1) = log10(2000);
+            end              
+     
+            if obj.settings.activeParameters.beta
+                ind = cellfun(@(x)(strcmp(x,'beta')),param_names);
+                params_lowerLimit(ind,1) = log10(1);
+                params_upperLimit(ind,1) = log10(10);
+            end                
+            
+            if obj.settings.activeParameters.eps
+                ind = cellfun(@(x)(strcmp(x,'eps')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end  
+        end  
+        
+%% Return fixed upper and lower plausible parameter ranges. 
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+% getParameters_plausibleLimit returns the plausible limits to each soil model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+%
+% Description:
+%   Cycles through all active soil model componants and parameters and returns
+%    a vector of the plausible upper and lower parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter bounds
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   model_TFN: model_construction;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014              
+            
+        % Return fixed upper and lower plausible parameter ranges. 
+        % This is used to define reasonable range for the initial parameter sets
+        % for the calibration. These parameter ranges are only used in the 
+        % calibration if the user does not input parameter ranges.
+            
+            % get parameyer names
+            [params, param_names] = getParameters(obj);
+            
+            % Set default plausible parameter range
+            params_lowerLimit = zeros(length(param_names),1);  
+            params_upperLimit = 5.*ones(length(param_names),1);  
+            
+            % Modify plausible range for specific parameters
+            if obj.settings.activeParameters.S_initialfrac
+                ind = cellfun(@(x)(strcmp(x,'S_initialfrac')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end  
+
+            if obj.settings.activeParameters.bypass_frac
+                ind = cellfun(@(x)(strcmp(x,'bypass_frac')),param_names);                
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end              
+            
+            if isfield(obj.settings.activeParameters,'treeArea_frac') && obj.settings.activeParameters.treeArea_frac
+                ind = cellfun(@(x)(strcmp(x,'treeArea_frac')),param_names);                
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end                                                  
+            
+            if  isfield(obj.settings.activeParameters,'interflow_frac') && obj.settings.activeParameters.interflow_frac
+                ind = cellfun(@(x)(strcmp(x,'interflow_frac')),param_names);                
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end                
+            
+%             if obj.settings.activeParameters.SMSC
+%                 ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
+%                 params_lowerLimit(ind,1) = log10(10);
+%                 params_upperLimit(ind,1) = log10(1000);
+%             end  
+            if obj.settings.activeParameters.SMSC
+                ind = cellfun(@(x)(strcmp(x,'SMSC')),param_names);
+                params_lowerLimit(ind,1) = log10(50);
+                params_upperLimit(ind,1) = log10(500);
+            end
+
+            if obj.settings.activeParameters.SMSC_trees
+                ind = cellfun(@(x)(strcmp(x,'SMSC_trees')),param_names);
+                params_lowerLimit(ind,1) = log10(50);
+                params_upperLimit(ind,1) = log10(1000);
+            end  
+                        
+            if obj.settings.activeParameters.k_infilt
+                ind = cellfun(@(x)(strcmp(x,'k_infilt')),param_names);
+                params_lowerLimit(ind,1) = log10(10);                    
+                params_upperLimit(ind,1) = log10(100);
+            end  
+            
+            % Upper and lower bounds taken from Rawls et al 1982 Estimation
+            % of Soil Properties. The values are for sand loam and silty clay
+            % respectively and transformed from units of cm/h to the assumed
+            % input units of mm/d.
+            if obj.settings.activeParameters.k_sat
+                ind = cellfun(@(x)(strcmp(x,'k_sat')),param_names);
+                params_lowerLimit(ind,1) = log10(0.09*24*10);
+                params_upperLimit(ind,1) = log10(6.11*24*10);
+            end 
+            
+            if obj.settings.activeParameters.beta
+                ind = cellfun(@(x)(strcmp(x,'beta')),param_names);
+                params_lowerLimit(ind,1) = log10(1);
+                params_upperLimit(ind,1) = log10(10);
+            end      
+            
+            % if obj.settings.activeParameters.beta   % TODO: is it a bug?
+			if obj.settings.activeParameters.gamma  
+                ind = cellfun(@(x)(strcmp(x,'gamma')),param_names);
+                params_lowerLimit(ind,1) = log10(0.1);
+                params_upperLimit(ind,1) = log10(10);
+            end                  
+            
+             if obj.settings.activeParameters.eps
+                ind = cellfun(@(x)(strcmp(x,'eps')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end   
+        end        
+        
+%% Check if the model parameters have chanaged since the last calculated.
+        function detectParameterChange(obj, params)
+% detectParameterChange detects if the soil model parameters have changed.            
+%
+% Syntax:
+%   detectParameterChange(obj, params)  
+%
+% Description:  
+%   This method assesses if the user input parameters are different from
+%   those used in the most recent solution to the soil moisture model. This
+%   method allows the differential equation to be only solved when
+%   required. The result of this assessment is stored within the object
+%   variable: obj.variables.isNewParameters .
+%
+% Input:
+%   obj         - soil moisture model object.
+%   params      - a vector (Nx1) of new soil moisture model parameter values.
+%
+% Outputs:
+%   (none)
+%
+% Example:
+%   Create a cell matrix of options for a two parameter soil model:
+%   >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+%                           'gainingOption', 'soil_infiltration' ;
+%                           'SMSC', 100 ; 
+%                           'k_sat', 10 };
+%   Build the soil model:
+%   >> soilModel = climateTransform_soilMoistureModels(soilModelOptions);
+%
+%   Check if the new parameter values are different from those already in 
+%   the model. This would set obj.variables.isNewParameters = false:
+%   >> detectParameterChange(soilModel, [100; 10])
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   setParameters: set_calibration_parameters_values;
+%   getParameters: get_calibration_parameters_values;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012  
+
+            % Get current parameters
+            set_params = getParameters(obj);
+
+            % Check if there are any changes to the parameters.
+            if isempty(set_params) || max(abs(set_params - params)) ~= 0            
+                obj.variables.isNewParameters = true;
+            else
+                obj.variables.isNewParameters = false;
+            end                        
+            
+        end
+                    
+%% Solve the soil moisture differential equation
+        function setTransformedForcing(obj, t, forceRecalculation)
+% setTransformedForcing solves the soil moisture ODE model
+%
+% Syntax:
+%   setTransformedForcing(obj, climateData, forceRecalculation)
+%
+% Description:  
+%   This method solves the soil moisture differential equation for all time
+%   points within the input daily climate data. 
+%
+%   Importantly, if the model parameters have not changed since the last
+%   simulations, then a solution is not re-derived. Also, this method
+%   requires prior compilation of the C-MEX function
+%   "forcingTransform_soilMoisture.c".
+%
+% Input:
+%   obj         - soil moisture model object.
+%
+%   t           - a vector (Nx1) of time points for simulation.
+%
+%   forceRecalculation - is a logical scalar input (i.e. true of false) to
+%                 force re-calculation of the model and thus ingore if the
+%                 parameters have or have not changed.
+% Outputs:
+%   (none)
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   forcingTransform_soilMoisture.c
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012  
+            if nargin==2
+                forceRecalculation=false;
+            end               
+            if obj.variables.isNewParameters || forceRecalculation || ~isfield(obj.variables,'t') || ...
+            (isfield(obj.variables,'t') && obj.variables.t(end) ~= t(end))
+
+                % back transform the parameters
+                [params, param_names] = getDerivedParameters(obj);
+
+                % Assign each param to a variable for efficient access.
+                SMSC = params(1,:);
+                SMSC_trees = params(2,:);
+                treeArea_frac = params(3,:);
+                S_initialfrac = params(4,:);
+                k_infilt = params(5,:);
+                k_sat = params(6,:);
+                bypass_frac = params(7,:);            
+                interflow_frac = params(8,:);
+                alpha = params(9,:);
+                beta = params(10,:);
+                gamma = params(11,:);
+                eps = params(12,:);
+                
+                % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+                
+                % Get the required forcing data
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
+                precip_col = obj.settings.forcingData_cols{filt,2};
+                obj.variables.precip = obj.settings.forcingData(filt_time, precip_col );
+
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
+                evap_col = obj.settings.forcingData_cols{filt,2};
+                obj.variables.evap = obj.settings.forcingData(filt_time, evap_col );
+                
+                % Store the time points
+                obj.variables.t = obj.settings.forcingData(filt_time,1);
+                 
+                if isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+                    filt = strcmp(obj.settings.forcingData_cols(:,1),'TreeFraction');
+                    tree_col = obj.settings.forcingData_cols{filt,2};
+                    obj.variables.treeFrac = obj.settings.forcingData(filt_time, tree_col );                    
+                end
+                
+                % Filter percip by max infiltration rate, k_infilt.  
+%                 effectivePrecip = obj.variables.precip;
+%                 if k_infilt < inf && (obj.settings.activeParameters.k_infilt || obj.settings.fixedParameters.k_infilt)
+%                     lambda_p = obj.settings.lambda_p .* k_infilt;
+%                     effectivePrecip= (effectivePrecip>0).*(effectivePrecip+ lambda_p * log( 1./(1 + exp( (effectivePrecip - k_infilt)./lambda_p))));
+%                     effectivePrecip(isinf(effectivePrecip)) = k_infilt;
+%                 end
+                effectivePrecip = getTransformedForcing(obj, 'effectivePrecip',1);
+
+                % Set the initial soil moisture.
+                if isempty(S_initialfrac)
+                    S_initial = 0.5.*SMSC;
+                else
+                    S_initial = S_initialfrac * SMSC;
+                end
+                
+                % Store the time points
+                %obj.variables.t = t(filt_time);
+                
+                % Call MEX function containing soil moisture model.
+%                 if ~doSubDailyEst
+%                     obj.variables.SMS = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, SMSC, k_sat, alpha, beta, gamma);
+%                     
+%                     % Empty sub daily data to ensure there is no
+%                     % inconistency.
+%                     obj.variables.t_subdaily = [];
+%                     obj.variables.SMS_subdaily = [];
+%                     
+%                     % Run soil model again if tree cover is to be simulated
+%                     if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+%                         if isempty(S_initialfrac)
+%                             S_initial = 0.5.*SMSC_trees;
+%                         else
+%                             S_initial = S_initialfrac * SMSC_trees;
+%                         end
+%                         
+%                         obj.variables.SMS_trees = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, ...
+%                             SMSC_trees, k_sat, alpha, beta, gamma);                        
+%                     end
+%                     
+%                 else 
+
+                    % Get subset size
+                    nSubSteps = getNumDailySubsteps(obj);
+
+                    % Define daily sub-steps.
+                    t_substeps = linspace(1,0,nSubSteps+1);
+                    
+                    % Get number of days
+                    nDays = length(effectivePrecip);
+                    
+                    % Build the timesteps for sub-daily analysis.
+                    obj.variables.t = bsxfun(@minus,obj.variables.t, t_substeps(2:end)); 
+                    obj.variables.t = reshape(obj.variables.t', nDays * nSubSteps,1);
+                    
+                    % Add the initial time step
+                    obj.variables.t = [floor(obj.variables.t(1)); obj.variables.t];
+                    
+                    % Scale ksat from units of 'per day' to 'per sub daily
+                    % time step'
+                    k_sat = k_sat./nSubSteps;
+                    
+                    % Expand input forcing data to have the required number of days and
+                    % scale the forcing data by the number of time steps.
+                    effectivePrecip = getSubDailyForcing(obj,effectivePrecip);
+                    evap = getSubDailyForcing(obj,obj.variables.evap);
+                    
+                    % Run the soil models using the sub-steps.
+                    obj.variables.SMS = forcingTransform_soilMoisture(S_initial, effectivePrecip, evap, SMSC, k_sat, alpha, beta, gamma, eps);
+                    
+                    % Run soil model again if tree cover is to be simulated
+                    if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+                        if isempty(S_initialfrac)
+                            S_initial = 0.5.*SMSC_trees;
+                        else
+                            S_initial = S_initialfrac * SMSC_trees;
+                        end
+                        
+                        obj.variables.SMS_trees = forcingTransform_soilMoisture(S_initial, effectivePrecip, obj.variables.evap, ...
+                            SMSC_trees, k_sat, alpha, beta, gamma, eps);
+                    end
+%                 end                                                 
+            end
+        end
+        
+%% Return the transformed forcing data
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName, SMSnumber, doSubstepIntegration) 
+% getTransformedForcing returns the required flux from the soil model.
+%
+% Syntax:
+%   [precip_Forcing, et_Forcing] = getTransformedForcing(obj, variableName)
+%
+% Description:  
+%   This method returns the requested flux/data from the soil moisture 
+%   differential equation. The available fluxes/data are as follows:
+%
+%   * drainage: soil free drainage ranging (0 to k_sat) at the end of the day.
+%   * drainage_bypassFlow: free drainage plus a parameter set fraction of runoff;
+%   * drainage_normalised: normalised free drainage (0 to 1) at the end of the day.
+%   * evap_soil: actual soil ET at the end of the day.    
+%   * infiltration: daily total infiltration rate.
+%   * evap_gw_potential: groundwater evaporative potential (PET - soil ET)
+%   * runoff: daily total runoff.
+%   * SMS: soil moisture storage at the end of each day.
+%
+% Input:
+%   obj - soil moisture model object.
+%
+%   variableName - a string for the variable name to return.
+%
+% Outputs:
+%   forcingData  - a vector (Nx1) of the forcing data output to
+%                     be input to the groundwater time series model.
+%
+%   isDailyIntegralFlux - logical scaler denoting if the flux is a daily
+%   integral or an instantaneous value. This is used to inform the type of
+%   numerical integration within modelTFN.get_h_star().
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   setParameters: set_calibration_parameters_values;
+%   getParameters: get_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: set_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012  
+        
+            % back transform the parameters
+            [params, param_names] = getDerivedParameters(obj);
+            
+            if ischar(variableName)
+                variableNametmp{1}=variableName;
+                variableName = variableNametmp;
+                clear variableNametmp;
+            end            
+            
+            % Assign each param to a variable for efficient access.
+            SMSC = params(1,:);
+            SMSC_trees = params(2,:);
+            treeArea_frac = params(3,:);
+            S_initialfrac = params(4,:);
+            k_infilt = params(5,:);
+            k_sat = params(6,:);
+            bypass_frac = params(7,:);            
+            interflow_frac = params(8,:);
+            alpha = params(9,:);
+            beta = params(10,:);
+            gamma = params(11,:); 
+            eps = params(12,:);
+                            
+             
+            % Set if the subdaily steps should be integrated.
+            if nargin < 4
+                doSubstepIntegration = true;
+            end
+            
+            % Get the soil moisture store for the required soil unit
+            if nargin==2 || SMSnumber==1
+                SMS = obj.variables.SMS;
+                SMSnumber = 1;
+            elseif SMSnumber==2
+                SMS = obj.variables.SMS_trees;
+                SMSC = SMSC_trees;
+            else
+                error('The soil moisture unit number is unknown')
+            end            
+            
+            % Calculate the required fluxes.
+            try 
+                if doSubstepIntegration
+                    nrows = size(obj.variables.precip,1);
+                else
+                    nrows = size(getSubDailyForcing(obj,obj.variables.precip),1);
+                end
+                forcingData = zeros(nrows , length(variableName));
+                
+                for i=1:length(variableName)
+                    switch variableName{i}                    
+                        case 'drainage'
+                            runoff = 0;
+                            if bypass_frac~=0
+                                % Get runoff
+                                runoff = getTransformedForcing(obj, 'runoff',SMSnumber, false);
+                                
+                                % Re-scale runoff from that going to the stream to that going to recharge plus the stream.
+                                runoff  = runoff ./ (1-bypass_frac);
+                            end            
+                            
+                            drainage = (1-interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(SMS/SMSC).^beta;
+                            drainage  = drainage + bypass_frac.*runoff;
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, drainage);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                               forcingData(:,i) = drainage;  
+                               isDailyIntegralFlux(i) = false;
+                            end
+                            
+                        case 'evap_soil'    
+                            % Expand input forcing data to have the required number of substeps.
+                            evap = getSubDailyForcing(obj,obj.variables.evap);
+                                                        
+                            % Est ET
+                            evap = evap .* (SMS/SMSC).^gamma;         
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, evap);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = evap;
+                                isDailyIntegralFlux(i) = false;
+                            end
+                            
+                        case 'infiltration_fractional_capacity'     
+						
+                            % CalculateS infiltration fractional capacity, representing the fraction of rainfall that is infiltrated 
+                            infiltration_fractional_capacity = min(1, ((SMSC - SMS)/(SMSC*(1-eps))).^alpha);
+                             
+                            if doSubstepIntegration
+                                % forcingData(:,i) = dailyIntegration(obj, infiltration_fractional_capacity./obj.variables.nDailySubSteps); % TODO: this was incorrect, right?
+								forcingData(:,i) = dailyIntegration(obj, infiltration_fractional_capacity);
+                            else
+                                forcingData(:,i) = infiltration_fractional_capacity;
+                            end
+                                                        
+                            if doSubstepIntegration
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                isDailyIntegralFlux(i) = false;
+                            end
+                                                    
+                            
+                        case 'infiltration'                       
+                            % Calculate max. infiltration assuming none
+                            % goes to SATURATED runoff.
+                            effectivePrecip_daily = getTransformedForcing(obj, 'effectivePrecip',SMSnumber); 
+                            effectivePrecip = getSubDailyForcing(obj,effectivePrecip_daily);
+                            
+							% Tim's implemetation
+							% infiltration_fractional_capacity_daily = getTransformedForcing(obj, 'infiltration_fractional_capacity',SMSnumber); 
+                            
+                            if alpha==0
+                                infiltration_daily =  effectivePrecip_daily;
+                                infiltration =  effectivePrecip;                                
+                            else
+                                % Tim's implemetation
+								% infiltration =  effectivePrecip .* (1-SMS/SMSC).^alpha;
+                                infiltration_fractional_capacity = getTransformedForcing(obj, 'infiltration_fractional_capacity', SMSnumber, false);
+                                infiltration =  effectivePrecip .* infiltration_fractional_capacity;                                                                
+                            end
+                                                                             
+                                                        
+                            % Calculate when the soil is probably
+                            % saturated. 
+                            drainage = getTransformedForcing(obj, 'drainage',SMSnumber, false);
+                            evap = getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
+                            Infilt2Runoff = [0;(SMS(1:end-1) + infiltration(2:end) - evap(2:end) - drainage(2:end)) - SMSC];
+                            Infilt2Runoff(Infilt2Runoff<0) = 0;
+                            
+                            % Subtract estimated satruated excess runoff
+                            % from the infiltration and then integrate.
+                            if alpha==0
+                                % Integrate ONLY saturated runoff
+                                if doSubstepIntegration
+                                    Infilt2Runoff = dailyIntegration(obj, Infilt2Runoff);
+                                    
+                                    % Subtract from precip.
+                                    forcingData(:,i) = infiltration_daily - Infilt2Runoff;
+                                else
+                                    forcingData(:,i) = infiltration - Infilt2Runoff;
+                                end
+                            else
+                                infiltration = infiltration - Infilt2Runoff;
+                                if doSubstepIntegration
+                                    forcingData(:,i) = dailyIntegration(obj, infiltration);
+                                else
+                                    forcingData(:,i) = infiltration;
+                                end                                
+                            end
+                            
+                            if doSubstepIntegration
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'evap_gw_potential'
+                            evap = getSubDailyForcing(obj,obj.variables.evap) - getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, evap);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = evap;
+                                isDailyIntegralFlux(i) = false;
+                            end                            
+                          
+						case 'interflow'
+                            
+                            % Calculate subdaily interflow.                        							
+							interflow = (interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(SMS/SMSC).^beta;
+                            
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, interflow);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                               forcingData(:,i) = interflow;  
+                               isDailyIntegralFlux(i) = false;
+                            end           
+                            
+						
+                        case 'runoff'
+                            
+                            % Calculate infiltration.
+                            infiltration = getTransformedForcing(obj, 'infiltration',SMSnumber, false);
+                            
+                            % Calc sub daily interflow
+                            interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+                                
+                            % Calc sub daily runoff
+                            runoff = max(0,getSubDailyForcing(obj,obj.variables.precip) - infiltration);
+                            % runoff = runoff * (1-bypass_frac);
+							runoff = interflow + (runoff * (1-bypass_frac)); % only the runoff can be redirected to free drainage from shallow soil moisture
+                            
+                            % Integrate to daily.
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, runoff);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = runoff;
+                                isDailyIntegralFlux(i) = false;
+                            end
+                       
+                        case'SMS'
+                            forcingData(:,i) = SMS((1+obj.variables.nDailySubSteps):obj.variables.nDailySubSteps:end);
+                            isDailyIntegralFlux(i) = false;
+
+                        case'effectivePrecip'
+                            effectivePrecip = obj.variables.precip;
+                            if k_infilt < inf && (obj.settings.activeParameters.k_infilt || obj.settings.fixedParameters.k_infilt)
+                                lambda_p = obj.settings.lambda_p .* k_infilt;
+                                effectivePrecip= (effectivePrecip>0).*(effectivePrecip+ lambda_p * log( 1./(1 + exp( (effectivePrecip - k_infilt)./lambda_p))));
+                                effectivePrecip(isinf(effectivePrecip)) = k_infilt;
+                            end
+                            forcingData(:,i) = effectivePrecip;
+                            isDailyIntegralFlux(i) = true;
+                        case 'mass_balance_error'
+                            precip = getSubDailyForcing(obj,obj.variables.precip);
+                            runoff = getTransformedForcing(obj, 'runoff',SMSnumber, false);
+                            AET = getTransformedForcing(obj, 'evap_soil',SMSnumber, false);
+                            drainage = getTransformedForcing(obj, 'drainage',SMSnumber, false);
+                            
+                            fluxEstError = [0;precip(2:end) - diff(SMS) -  runoff(2:end) - AET(2:end) - drainage(2:end)];
+                            
+                            % Integreate to daily.
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, fluxEstError);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = fluxEstError;
+                                isDailyIntegralFlux(i) = false;
+                            end
+                             
+                        otherwise
+                            if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover && nargin==2 
+                                if isempty(strfind(variableName{i}, '_nontree')) && isempty(strfind(variableName{i}, '_tree'))
+                                    error('The requested transformed forcing variable is not known.');
+                                end
+                            else                            
+                                error('The requested transformed forcing variable is not known.');
+                            end
+                    end
+
+                    % Get flixes for tree soil unit (if required) and weight the
+                    % flux from the two units
+                    if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover && nargin==2
+                        if ~isempty(strfind(variableName{i}, '_nontree'))
+                            % Get flux for non-tree componant
+                            ind = strfind(variableName{i}, '_nontree');
+                            variableName{i} = variableName{i}(1:ind-1);
+                            [forcingData(:,i),isDailyIntegralFlux(i)]  = getTransformedForcing(obj, variableName{i}, 1) ;
+                            forcingData(:,i) =  (1-treeArea_frac .* obj.variables.treeFrac) .* forcingData(:,i);
+                        elseif ~isempty(strfind(variableName{i}, '_tree'))
+                            % Get flux for non-tree componant
+                            ind = strfind(variableName{i}, '_tree');
+                            variableName{i} = variableName{i}(1:ind-1);
+                            [forcingData(:,i),isDailyIntegralFlux(i)] = getTransformedForcing(obj, variableName{i}, 2);                
+                            forcingData(:,i) = treeArea_frac .* obj.variables.treeFrac .* forcingData(:,i);
+                        else
+                            % Get flux for tree SMS
+                            forcingData_trees = getTransformedForcing(obj, variableName{i}, 2);                    
+
+                            % Do weighting
+                            forcingData(:,i) = (1-treeArea_frac .* obj.variables.treeFrac) .* forcingData(:,i) + ...
+                                          treeArea_frac .* obj.variables.treeFrac .* forcingData_trees;
+                        end
+                    end                    
+                end
+            catch ME
+                error(ME.message)
+            end
+            
+
+        end
+
+        
+        % Return the derived variables. This is used by this class to get
+        % the back transformed parameters.
+        function [params, param_names] = getDerivedParameters(obj)
+            
+            param_names = {'SMSC: back transformed soil moisture storage capacity (in rainfall units)'; ...                  
+                           'SMSC_trees: back transformed soil moisture storage capacity in trees unit (in rainfall units)'; ...
+                           'treeArea_frac: fractional area of the tree units (-)'; ...
+                           'S_initialfrac: fractional initial soil moisture (-)'; ... 
+                           'k_infilt : back transformed maximum soil infiltration rate (in rainfall units)'; ...
+                           'k_sat : back transformed maximum vertical conductivity (in rainfall units/day)'; ...
+                           'bypass_frac : fraction of runoff that goes to bypass drainage (-)'; ...
+                           'interflow_frac : fraction of free drainage going to interflow (-)'; ...        
+                           'alpha : power term for infiltration rate (-)'; ...       
+                           'beta : back transformed power term for dainage rate (eg approx. Brook-Corey pore index power term)'; ...
+                           'gamma : back transformed power term for soil evaporation rate (-)';...
+                           'eps: S_min/SMSC ratio. S_min is the minimum soil moisture threshold.(-)'};    
+        
+            params = [  10.^(obj.SMSC); ...
+                        10.^(obj.SMSC_trees); ...
+                        obj.treeArea_frac; ...
+                        obj.S_initialfrac; ...
+                        10.^obj.k_infilt; ...
+                        10.^obj.k_sat; ...
+                        obj.bypass_frac; ...
+                        obj.interflow_frac; ...
+                        obj.alpha; ...
+                        10.^(obj.beta); ...
+                        10.^(obj.gamma);...
+                        obj.eps];
+        end
+
+        % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % If empty, then it is likely to be a model output variable os use the precip coordinate.
+                if ~any(filt)
+                    filt = strcmp(obj.settings.forcingData_cols(:,1), 'precip');
+                end
+                sourceColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceColName = obj.settings.forcingData_colnames{sourceColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end    
+        end        
+        
+        function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+            propNames = properties(obj);
+            for i=1:length(propNames)
+               if isempty(obj.(propNames{i}))
+                   continue;
+               end                
+               if isobject(obj.(propNames{i}))
+                delete(obj.(propNames{i}));
+               else               
+                obj.(propNames{i}) = []; 
+               end
+            end
+        end            
+    end
+    
+    methods(Access=protected, Hidden=true)
+        
+        % Get the names of the active parameters
+        function param_names = getActiveParameters(obj)
+            
+            % Get field array value for if the parameter is active.
+            ind=structfun(@(x) (x),  obj.settings.activeParameters);
+            
+            % Get list of all parameter names.
+            all_parameter_names = fieldnames(obj.settings.activeParameters);
+            
+            % Return only those parameter names that are active
+            param_names=all_parameter_names(ind,1);
+            
+        end
+        
+        function data_daily = dailyIntegration(obj, data)
+                       
+            % reshape data so sun-daily steps are on one row     
+            nSubSteps = obj.variables.nDailySubSteps;
+            nDays = floor(length(obj.variables.t)./nSubSteps);
+            data_reshaped = zeros(nDays , nSubSteps+1);
+                        
+            for (i=1:(nSubSteps+1))
+                data_reshaped(:,i) = data(i:nSubSteps :(end- (nSubSteps+1)+i ));
+            end
+            
+            % To trapazoidal integration
+            data_daily = trapz(0:nSubSteps, data_reshaped, 2);
+            
+        end
+        
+        function nDailySubSteps = getNumDailySubsteps(obj)
+
+            if isfield(obj.variables,'nDailySubSteps')
+                nDailySubSteps = obj.variables.nDailySubSteps;
+            else
+                nDailySubSteps = 3;
+                obj.variables.nDailySubSteps = nDailySubSteps;
+            end
+        end
+        
+        function data = getSubDailyForcing(obj,data)
+            % Get subset size
+            nSubSteps = getNumDailySubsteps(obj);
+            
+            % Get number of days
+            %nDays = (length(obj.variables.precip)-1)./nSubSteps;
+            nDays = length(obj.variables.precip);
+            
+            % Get the forcing rate PER substep.
+            data = data./nSubSteps;
+            
+            % Build daily subseteps
+            data = reshape(repmat(data,1,nSubSteps)',nDays * nSubSteps,1);
+            
+            % Add dummy row of data to account for the initial
+            % condition being added to to t.
+            data = [0;data];
+        end
+    end
+    
+end
+
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer.m b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer.m
index 06466d3..3949be6 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer.m
@@ -207,10 +207,11 @@
                         'alpha'         , 0, 'Fixed'    ; ...
                         'beta'          ,  0.5,'Calib.' ; ...
                         'gamma'         ,  0,  'Fixed'  ; ...
-                        'SMSC_deep'     ,  2, 'Calib.'   ;
+                        'eps'           ,   0,  'Fixed'; ...
+                        'SMSC_deep'     ,  2, 'Calib.'   ;...
                         'SMSC_deep_trees',   2, 'Fixed';...
                         'S_initialfrac_deep', 0.5,'Fixed'; ...
-                        'k_sat_deep'     , 1, 'Calib.'   ;
+                        'k_sat_deep'     , 1, 'Calib.'   ;...
                         'beta_deep'     ,  0.5, 'Calib.'};
 
         
@@ -231,6 +232,7 @@
                                 '   alpha        : Power term for infiltration rate.\n', ...
                                 '   beta         : log10(Power term for dainage rate).\n', ...
                                 '   gamma        : log10(Power term for soil evap. rate).\n', ...
+                                '   eps          : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
                                 '   SMSC_deep    : log10(Deep layer soil moisture capacity as water depth).\n', ...
                                 '   SMSC_deep_trees: log10(Deep layer tree soil moisture capacity as water depth).\n', ...
                                 '   S_initialfrac_deep: Initial deep soil moisture fraction (0-1).\n', ...
@@ -262,6 +264,7 @@
                                 'alpha        : Power term for infiltration rate.', ...
                                 'beta         : log10(Power term for dainage rate).', ...
                                 'gamma        : log10(Power term for soil evap. rate).', ...
+                                'eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
                                 'SMSC_deep    : log10(Deep layer soil moisture capacity as water depth). ', ...
                                 '               Input an empty value and "fixed" to for it to equal SMSC.', ...
                                 'SMSC_deep_tree : log10(Tree deep layer soil moisture capacity as water depth).', ... 
@@ -292,7 +295,7 @@
 %
 % Description:
 %   Builds the form of the soil moisture differential equations using user
-%   input model options. The way in which the soil moisture model is used
+%   input mSure, odel options. The way in which the soil moisture model is used
 %   to transform the precipitation and potential evapotranspiration is also
 %   defined within the model options.
 %
@@ -488,7 +491,8 @@
             if isnan(obj.SMSC_deep_trees) && obj.settings.activeParameters.SMSC_deep_trees
                 error('"SMSC_deep_trees" can only be initialsied to Nan if it is "Fixed".');
             end                
-            if isnan(obj.SMSC_deep) && obj.settings.activeParameters.SMSC_deep_trees
+            % if isnan(obj.SMSC_deep) && obj.settings.activeParameters.SMSC_deep_trees % TODO: is it a bug?
+			if isnan(obj.SMSC_deep) && obj.settings.activeParameters.SMSC_deep 
                 error('"SMSC_deep" can only be initialsied to Nan if it is "Fixed".');
             end                
             
@@ -709,6 +713,7 @@ function setTransformedForcing(obj, t, forceRecalculation)
                     beta = 10.^(obj.beta);
                     gamma = 10.^(obj.gamma);
                     k_sat = 10.^obj.k_sat;
+                    % eps_deep=0;
                     interflow_frac = obj.interflow_frac;
                     drainage = (1-interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(obj.variables.SMS/SMSC).^beta;                  
                     
@@ -720,7 +725,7 @@ function setTransformedForcing(obj, t, forceRecalculation)
                     
                     % Call MEX soil model
                     obj.variables.SMS_deep = forcingTransform_soilMoisture(S_deep_initial, drainage, PET, SMSC_deep, k_sat_deep/nDailySubSteps, ...
-                        0, beta_deep, 10.^obj.gamma);
+                        0, beta_deep, 10.^obj.gamma, 0);
                     
                     % Run soil model again if tree cover is to be simulated
                     if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
@@ -745,7 +750,7 @@ function setTransformedForcing(obj, t, forceRecalculation)
                         
                         % Call MEX function for DEEP soil moisture model.
                         obj.variables.SMS_deep_trees = forcingTransform_soilMoisture(S_deep_initial, drainage, PET, SMSC_deep_trees, ...
-                            k_sat_deep, 0, beta_deep, 10.^obj.gamma);
+                            k_sat_deep, 0, beta_deep, 10.^obj.gamma, 0);
                     end
 %                 else
 %                     % Define the number of daily sub-steps.
@@ -840,11 +845,16 @@ function setTransformedForcing(obj, t, forceRecalculation)
             interflow_frac = params(8,:);
             beta = params(10,:);
             gamma = params(11,:);             
-            SMSC_deep = params(end-4,:);
-            SMSC_deep_trees = params(end-3,:);
-            S_deep_initial = params(end-2,:);
-            k_sat_deep = params(end-1,:);
-            beta_deep = params(end,:);
+%             SMSC_deep = params(end-4,:);
+            SMSC_deep = params(13,:);
+%             SMSC_deep_trees = params(end-3,:);
+            SMSC_deep_trees = params(14,:);
+%             S_deep_initial = params(end-2,:);
+            S_deep_initial = params(15,:);
+%             k_sat_deep = params(end-1,:);
+            k_sat_deep = params(16,:);
+%             beta_deep = params(end,:);
+            beta_deep = params(17,:);
             
             % Set if the subdaily steps should be integrated.
             if nargin < 4
@@ -1030,7 +1040,7 @@ function setTransformedForcing(obj, t, forceRecalculation)
             param_names(size(param_names,1)+1:size(param_names,1)+5) = {
                            'SMSC_deep: back transformed soil moisture deep layer storage capacity (in rainfall units)'; ...                  
                            'SMSC_deep_trees: back transformed soil moisture deep layer storage capacity in trees unit (in rainfall units)'; ...
-                           'S_initialfrac_deep: fractional initial deep layer soil moisture (-)'; ... 
+                           'S_deep_initial: fractional initial deep layer soil moisture (-)'; ... 
                            'k_sat_deep : back transformed deep layer maximum vertical conductivity (in rainfall units/day)'; ...
                            'beta_deep : back transformed power term for dainage rate of deep layer (eg approx. Brook-Corey pore index power term)'};    
         
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer_v2.m b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer_v2.m
index c86a185..29f1adf 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer_v2.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_2layer_v2.m
@@ -1,268 +1,269 @@
-classdef climateTransform_soilMoistureModels_2layer_v2 < climateTransform_soilMoistureModels_2layer  
-% Class definition for soil moisture transformation of climate forcing.
-%
-% Description        
-%   climateTransform_soilMoistureModels_2layer_v2 is a generalised vertically lumped 1-D
-%   soil moisture model. It is identical to climateTransform_soilMoistureModels_2layer ()
-%   except that the simulated average actual soil evaporation is constrained to be 
-%   between the 5st and 95th perceililes of the actual ET as defined from Greve et 
-%   al. 2015. This is achievd by rejecting parameter sets that produce actual 
-%   ET values outsde these bounds.
-%
-% See also
-%   climateTransform_soilMoistureModels_2layer : parent model;
-%   climateTransform_soilMoistureModels_2layer_v2: model_construction;
-%
-% Dependencies
-%   forcingTransform_soilMoisture.c
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   1 September 2017
-    
-    properties(GetAccess=public, SetAccess=protected)
-    end
-
-
-    
-%%  STATIC METHODS        
-% Static methods used to inform the
-% user of the available model types. 
-    methods(Static)
-
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: climateTransform_soilMoistureModels_2layer_v2', ...
-                               '', ...
-                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage). ', ...
-                               'It is identical to climateTransform_soilMoistureModels_2layer() except that the simulated average actual soil evaporation is', ...
-                               'constrained to be between the 5th and 95th percentiles of the actual ET as defined from Greve et al. 2015. This is achievd by', ...
-                               'rejecting parameter sets that produce actual ET values outsde these bounds.', ...                                                              
-                                                              '', ...                               
-                               'Number of parameters: 2 to 10', ...
-                               '', ...                               
-                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
-                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...                               
-                               '', ...                               
-                               'Comments: Below is a summary of the model parameters:' , ...
-                                'SMSC         : log10(Soil moisture capacity as water depth).', ...
-                                'SMSC_trees   : log10(Tree soil moisture capacity as water depth).', ...
-                                'treeArea_frac: Scaler applied to the tree fraction input data.', ...                                                                
-                                'S_initialfrac: Initial soil moisture fraction (0-1).', ...
-                                'k_infilt     : log10(Soil infiltration capacity as water depth).', ...
-                                'k_sat        : log10(Maximum vertical infiltration rate).', ...
-                                'bypass_frac  : Fraction of runoff to bypass drainage.', ...
-                                'alpha        : Power term for infiltration rate.', ...
-                                'beta         : log10(Power term for dainage rate).', ...
-                                'gamma        : log10(Power term for soil evap. rate).', ...
-                                'SMSC_deep    : log10(Deep layer soil moisture capacity as water depth). ', ...
-                                '               Input an empty value and "fixed" to for it to equal SMSC.', ...
-                                'SMSC_deep_tree : log10(Tree deep layer soil moisture capacity as water depth).', ... 
-                                '               Input an empty value and "fixed" to for it to SMSC_tree', ...
-                                'S_initialfrac_deep: Initial deep soil moisture fraction (0-1).\n', ...      
-                                '               Input an empty value and "fixed" to for it to equal S_initialfrac', ...                                
-                                'k_sat_deep   : log10(Maximum vertical infiltration rate).', ...
-                                '               Input an empty value and "fixed" to for it to equal k_sat.', ...
-                                'beta_deep    : log10(Deep layer power term for dainage rate).', ...
-                                '               Input an empty value and "fixed" to for it to beta.', ...                                
-                               '', ...               
-                               'References: ', ...
-                               'Greve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne (2015), Introducing a probabilistic Budyko ', ...
-                               'framework. Geophys. Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449'};
-        end        
-           
-    end
-        
-    
-    methods       
-%% Construct the model
-        function obj = climateTransform_soilMoistureModels_2layer_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
-% climateTransform_soilMoistureModels_2layer_v2 constructs the soil model object.
-%
-% Syntax:
-%   soilModel = climateTransform_soilMoistureModels_2layer_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
-%
-% Description:
-%   Builds the form of the soil moisture differential equation for the user
-%   input model options. This constructor also checks that the required 
-%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
-%   model options are two or three columns wide.
-%
-% Input:
-%
-%   bore_ID - string for the bore ID. The bore ID must be listed in the site
-%   coordinates array.
-%
-%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
-%   must comprise of the following columns: year, month, day and
-%   observation data. The observation data must include precipitation &
-%   PET. The record must be continuous (no skipped days) and cannot contain  
-%   blank observations.
-%
-%   forcingData_colnames - 1 x N cell array of the column names within the
-%   input numerical array "forcingData_data".  Importantly, each forcing 
-%   data column name must be listed within the input "siteCoordinates". 
-%
-%   siteCoordinates - Mx3 cell matrix containing the following columns :
-%   site ID, projected easting, projected northing. Importantly,
-%   the input 'bore_ID' must be listed and all columns within
-%   the forcing data (excluding the year, month,day). 
-%
-%   modelOptions - cell matrix defining the soil model componants, ie how the
-%   model should be constructed. The cell matrix can be two or three
-%   columns wide and at least three rows long (for the simplest of models).
-%   The first column defines the model parameter name or user option name
-%   to be defined. The second column defines the initial value for the
-%   parameter or the setting for the user option. A third column can be
-%   input if a parameter is to be fixed, and thus not modified during
-%   calibration. This third column can contain the term 'fixed'. 
-%
-%   Below are some examples of model options for various types of models:
-%
-%       - 1 parameter with linear scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100; ...
-%                               'alpha', 1, 'fixed'};};
-%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100, '' ;
-%                               'alpha', 0, 'fixed'};
-%       - 2 parameter with linear scaling of infiltration by the soil 
-%       moisture and linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'k_sat', 10 };
-%       - 3 parameter with linear scaling of infiltration by the soil
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = {  'SMSC', 100 ; 
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%       - 4 parameter with non-linear scaling of infiltration by the soil 
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'alpha', 0.5 ;
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%
-% Output:
-%   obj - climateTransform_soilMoistureModels class object 
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   1 September 2017
-
-            % Build soil model
-            obj = obj@climateTransform_soilMoistureModels_2layer(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);                     
-
-            
-            % Sample Buyko w values. Approach taken from
-            % reve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne
-            % (2015), Introducing a probabilistic Budyko framework. Geophys. 
-            % Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449. 
-            %-----------------
-            
-            % build gamma distribution.
-            pd = makedist('Gamma','a',4.54,'b',0.37);
-            
-            % Sample w values from distribution and add one (Bufyko must be >=1)
-            nsamples = 10000;
-            obj.settings.Budyko_omega = random(pd,nsamples,1)+1;            
-            
-        end
-
-        
-%% Assess if matrix of parameters is valid.
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-% getParameterValidity returns a logical vector for the validity or each parameter.
-%
-% Syntax:
-%   isValidParameter = getParameterValidity(obj, params, param_names)
-%
-% Description:
-%   Cycles though all active soil model parameters and returns a logical 
-%   vector denoting if each parameter is valid ie within the physical 
-%   parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-%   params - vector of model parameters
-%
-%   param_names - cell array of the parameter names.
-%
-% Outputs:
-%   isValidParameter - column vector of the parameter validity.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   model_TFN: model_construction;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Get the filetred P and potential ET is already stored in the
-            % model object.
-            if isfield(obj.variables,'precip') && isfield(obj.variables,'evap')
-                P = obj.variables.precip;
-                PET = obj.variables.evap;
-                t = obj.variables.t;
-            else
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
-                precip_col = obj.settings.forcingData_cols{filt,2};
-                P = obj.settings.forcingData(:, precip_col );
-
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
-                evap_col = obj.settings.forcingData_cols{filt,2};
-                PET = obj.settings.forcingData(:, evap_col );                
-                
-                t = obj.settings.forcingData(:, 1);
-            end
-
-            % Calculate mean aridity index
-            P = mean(P);
-            PET = mean(PET);
-            PET_on_P = PET/P;
-            
-            % Calculate Budyko samples of E/P using prior sampled values of 
-            % omega at PET_on_P.
-            AET_on_P = 1 + PET_on_P -(1+PET_on_P.^obj.settings.Budyko_omega).^(1./obj.settings.Budyko_omega);
-            
-            % Calculate the 1st and 99th percentiles
-            AET_on_P = prctile(AET_on_P, [10 90],1);
-            
-            % Initialise output
-            isValidParameter = true(size(params));
-            
-            % Calculate the soil moisture estimate of actual ET.
-            for i=1:size(params,2)
-                setParameters(obj, params(:,i));
-                setTransformedForcing(obj, t, true);
-                AET = mean(getTransformedForcing(obj, 'evap_soil_total'));
-
-                % Ceck if the AET is within the Budyko bounds
-                if AET/P < AET_on_P(1) || AET/P > AET_on_P(2)
-                    isValidParameter(:,i) = false;
-                end
-            end
-            setParameters(obj, params);
-
-            % Check parameters are within bounds.
-            isValidParameter = isValidParameter & params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
-    		params <= params_upperLimit(:,ones(1,size(params,2)));   
-        end 
-    end
-    
-end
-
-
+classdef climateTransform_soilMoistureModels_2layer_v2 < climateTransform_soilMoistureModels_2layer  
+% Class definition for soil moisture transformation of climate forcing.
+%
+% Description        
+%   climateTransform_soilMoistureModels_2layer_v2 is a generalised vertically lumped 1-D
+%   soil moisture model. It is identical to climateTransform_soilMoistureModels_2layer ()
+%   except that the simulated average actual soil evaporation is constrained to be 
+%   between the 5st and 95th perceililes of the actual ET as defined from Greve et 
+%   al. 2015. This is achievd by rejecting parameter sets that produce actual 
+%   ET values outsde these bounds.
+%
+% See also
+%   climateTransform_soilMoistureModels_2layer : parent model;
+%   climateTransform_soilMoistureModels_2layer_v2: model_construction;
+%
+% Dependencies
+%   forcingTransform_soilMoisture.c
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   1 September 2017
+    
+    properties(GetAccess=public, SetAccess=protected)
+    end
+
+
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: climateTransform_soilMoistureModels_2layer_v2', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage). ', ...
+                               'It is identical to climateTransform_soilMoistureModels_2layer() except that the simulated average actual soil evaporation is', ...
+                               'constrained to be between the 5th and 95th percentiles of the actual ET as defined from Greve et al. 2015. This is achievd by', ...
+                               'rejecting parameter sets that produce actual ET values outsde these bounds.', ...                                                              
+                                                              '', ...                               
+                               'Number of parameters: 2 to 10', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...                               
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC         : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees   : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac: Scaler applied to the tree fraction input data.', ...                                                                
+                                'S_initialfrac: Initial soil moisture fraction (0-1).', ...
+                                'k_infilt     : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat        : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac  : Fraction of runoff to bypass drainage.', ...
+                                'alpha        : Power term for infiltration rate.', ...
+                                'beta         : log10(Power term for dainage rate).', ...
+                                'gamma        : log10(Power term for soil evap. rate).', ...
+                                'eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
+                                'SMSC_deep    : log10(Deep layer soil moisture capacity as water depth). ', ...
+                                '               Input an empty value and "fixed" to for it to equal SMSC.', ...
+                                'SMSC_deep_tree : log10(Tree deep layer soil moisture capacity as water depth).', ... 
+                                '               Input an empty value and "fixed" to for it to SMSC_tree', ...
+                                'S_initialfrac_deep: Initial deep soil moisture fraction (0-1).\n', ...      
+                                '               Input an empty value and "fixed" to for it to equal S_initialfrac', ...                                
+                                'k_sat_deep   : log10(Maximum vertical infiltration rate).', ...
+                                '               Input an empty value and "fixed" to for it to equal k_sat.', ...
+                                'beta_deep    : log10(Deep layer power term for dainage rate).', ...
+                                '               Input an empty value and "fixed" to for it to beta.', ...                                
+                               '', ...               
+                               'References: ', ...
+                               'Greve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne (2015), Introducing a probabilistic Budyko ', ...
+                               'framework. Geophys. Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449'};
+        end        
+           
+    end
+        
+    
+    methods       
+%% Construct the model
+        function obj = climateTransform_soilMoistureModels_2layer_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
+% climateTransform_soilMoistureModels_2layer_v2 constructs the soil model object.
+%
+% Syntax:
+%   soilModel = climateTransform_soilMoistureModels_2layer_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
+%
+% Description:
+%   Builds the form of the soil moisture differential equation for the user
+%   input model options. This constructor also checks that the required 
+%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
+%   model options are two or three columns wide.
+%
+% Input:
+%
+%   bore_ID - string for the bore ID. The bore ID must be listed in the site
+%   coordinates array.
+%
+%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
+%   must comprise of the following columns: year, month, day and
+%   observation data. The observation data must include precipitation &
+%   PET. The record must be continuous (no skipped days) and cannot contain  
+%   blank observations.
+%
+%   forcingData_colnames - 1 x N cell array of the column names within the
+%   input numerical array "forcingData_data".  Importantly, each forcing 
+%   data column name must be listed within the input "siteCoordinates". 
+%
+%   siteCoordinates - Mx3 cell matrix containing the following columns :
+%   site ID, projected easting, projected northing. Importantly,
+%   the input 'bore_ID' must be listed and all columns within
+%   the forcing data (excluding the year, month,day). 
+%
+%   modelOptions - cell matrix defining the soil model componants, ie how the
+%   model should be constructed. The cell matrix can be two or three
+%   columns wide and at least three rows long (for the simplest of models).
+%   The first column defines the model parameter name or user option name
+%   to be defined. The second column defines the initial value for the
+%   parameter or the setting for the user option. A third column can be
+%   input if a parameter is to be fixed, and thus not modified during
+%   calibration. This third column can contain the term 'fixed'. 
+%
+%   Below are some examples of model options for various types of models:
+%
+%       - 1 parameter with linear scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100; ...
+%                               'alpha', 1, 'fixed'};};
+%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100, '' ;
+%                               'alpha', 0, 'fixed'};
+%       - 2 parameter with linear scaling of infiltration by the soil 
+%       moisture and linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'k_sat', 10 };
+%       - 3 parameter with linear scaling of infiltration by the soil
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = {  'SMSC', 100 ; 
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%       - 4 parameter with non-linear scaling of infiltration by the soil 
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'alpha', 0.5 ;
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%
+% Output:
+%   obj - climateTransform_soilMoistureModels class object 
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   1 September 2017
+
+            % Build soil model
+            obj = obj@climateTransform_soilMoistureModels_2layer(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);                     
+
+            
+            % Sample Buyko w values. Approach taken from
+            % reve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne
+            % (2015), Introducing a probabilistic Budyko framework. Geophys. 
+            % Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449. 
+            %-----------------
+            
+            % build gamma distribution.
+            pd = makedist('Gamma','a',4.54,'b',0.37);
+            
+            % Sample w values from distribution and add one (Bufyko must be >=1)
+            nsamples = 10000;
+            obj.settings.Budyko_omega = random(pd,nsamples,1)+1;            
+            
+        end
+
+        
+%% Assess if matrix of parameters is valid.
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+% getParameterValidity returns a logical vector for the validity or each parameter.
+%
+% Syntax:
+%   isValidParameter = getParameterValidity(obj, params, param_names)
+%
+% Description:
+%   Cycles though all active soil model parameters and returns a logical 
+%   vector denoting if each parameter is valid ie within the physical 
+%   parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+%   params - vector of model parameters
+%
+%   param_names - cell array of the parameter names.
+%
+% Outputs:
+%   isValidParameter - column vector of the parameter validity.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   model_TFN: model_construction;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Get the filetred P and potential ET is already stored in the
+            % model object.
+            if isfield(obj.variables,'precip') && isfield(obj.variables,'evap')
+                P = obj.variables.precip;
+                PET = obj.variables.evap;
+                t = obj.variables.t;
+            else
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
+                precip_col = obj.settings.forcingData_cols{filt,2};
+                P = obj.settings.forcingData(:, precip_col );
+
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
+                evap_col = obj.settings.forcingData_cols{filt,2};
+                PET = obj.settings.forcingData(:, evap_col );                
+                
+                t = obj.settings.forcingData(:, 1);
+            end
+
+            % Calculate mean aridity index
+            P = mean(P);
+            PET = mean(PET);
+            PET_on_P = PET/P;
+            
+            % Calculate Budyko samples of E/P using prior sampled values of 
+            % omega at PET_on_P.
+            AET_on_P = 1 + PET_on_P -(1+PET_on_P.^obj.settings.Budyko_omega).^(1./obj.settings.Budyko_omega);
+            
+            % Calculate the 1st and 99th percentiles
+            AET_on_P = prctile(AET_on_P, [10 90],1);
+            
+            % Initialise output
+            isValidParameter = true(size(params));
+            
+            % Calculate the soil moisture estimate of actual ET.
+            for i=1:size(params,2)
+                setParameters(obj, params(:,i));
+                setTransformedForcing(obj, t, true);
+                AET = mean(getTransformedForcing(obj, 'evap_soil_total'));
+
+                % Ceck if the AET is within the Budyko bounds
+                if AET/P < AET_on_P(1) || AET/P > AET_on_P(2)
+                    isValidParameter(:,i) = false;
+                end
+            end
+            setParameters(obj, params);
+
+            % Check parameters are within bounds.
+            isValidParameter = isValidParameter & params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));   
+        end 
+    end
+    
+end
+
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_interflow.m b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_interflow.m
new file mode 100644
index 0000000..662d26c
--- /dev/null
+++ b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_interflow.m
@@ -0,0 +1,1357 @@
+classdef climateTransform_soilMoistureModels_interflow < climateTransform_soilMoistureModels_v2
+    % Class definition for building a time-series model of interflow
+    %
+    % Description
+    %   climateTransform_soilMoistureModels_interflow is a generalised and flexible 1-layer 1-D
+    %   soil moisture model. It is used to transform part of the daily free drainage from
+    %   the shallow soil layer into interflow.
+    %   Hence, inflow to the interflow layer (which can have zero to infinite storage)
+    %   occurs by free-drainage from the shallow soil layer.
+    %   Also, evapotranspiration from the interflow layer can be set to zero or as a complement to
+    %   evapotranspiration not met by the shallow soil layer.
+    %
+    %   Importantly, a user of the groundwater time series model has no need to
+    %   run any of these soil moisture model methods. All of the methods are
+    %   automatically called by the function "model_IRF.m".
+    %
+    %   The precipitation can be transformed into a daily infiltration rate
+    %   or daily free-draiage rate. The potential evapotranspiration can be
+    %   transformed into a daily actual evapotranspiration rate or a daily
+    %   remaining potential evapotranspiration.
+    %
+    %   The top soil moisture layer is defined by the following ordinary
+    %   diffenential equation (Kavetski et al. 2006):
+    %
+    %   DS_1/dt = max(P, P(SMSC - S_1/(SMSC-e))^alpha) - k_sat(S_1/SMSC)^beta - PET(S_1/SMSC)^gamma
+    %
+    %   And the bottom interflow layer is defined by the following ordinary
+    %   diffenential equation:
+    %
+    %   DS_interflow/dt = I_1 (1- S_interflow/SMSC_interflow)^alpha_interflow - (PET- E_1)(S_interflow/SMSC_interflow)^gamma - k_interflow (S_interflow/SMSC_interflow)^beta_interflow,
+    %
+    %	where:
+    %			I_1 = (1- interflow_frac) k_sat (S_1/SMSC)^beta
+    %			E_1 = PET(S_1/SMSC)^gamma
+    %
+    %       S_1     - is the top layer soil moisture storage state variable [L];
+    %       S_interflow     - is the bottom interflow layer soil moisture storage state variable [L];
+    %       t       - is time in units of days [T];
+    %       P_inf   - is the precipitation available for infiltration. This can
+    %                 be limited to a maximum infiltration rate "k_infilt". Of
+    %                 this parameteris not defined then P_inf equals the input
+    %                 daily precipitation rate.
+    %       SMSC    - is a parameter for the top layer soil moisture storage
+    %                 capacity [L]. It is in same units as the input precipitation.
+    %       SMSC_interflow  - is a parameter for the bottom layer interflow soil moisture storage
+    %                 capacity [L]. It is in same units as the input
+    %                 precipitation. Note, if equal to NULL, then SMSC_interflow is
+    %                 set to SMSC.
+    %       alpha   - is a dimensionless parameter to transform the rate at which
+    %                 the soil storage fills and limits infiltration. This
+    %                 allows simulation of a portion of the catchment saturating
+    %                 and not longer allowing infiltration.
+    %       k_sat   - is a parameter for the saturated vertical soil water
+    %                 conductivity from the top layer [L T^-1].
+    %       k_interflow -is a parameter for the saturated vertical soil water
+    %                 conductivity from the bottom interflow layer [L T^-1].
+    %                 Note, if equal to NULL, then k_interflow is set to k_sat.
+    %       beta    - is a dimensionless parameter to transform the rate at
+    %                 which vertical free drainage occurs with filling of
+    %                 the top soil layer.
+    %       beta_interflow - is a dimensionless parameter to transform the rate at
+    %                 which vertical free drainage occurs with filling of
+    %                 the bottom interflow soil layer. Note, if equal to NULL, then beta_interflow
+    %                 is set to k_sat.
+    %       PET     - is the input daily potential evapotranspiration [L T^-1].
+    %       gamma   - is a dimensionless parameter to transform the rate at
+    %                 which soil water evaporation occurs with filling of the
+    %                 soil layer. This parameters is assumed as the same for both
+    %				  shallow soil store and bottom interflow soil store.
+    %
+    %
+    %   Additionally, in parametrizing the model, many of the parameters were
+    %   transformed to a parameter space more amenable to efficient
+    %   calibration. Details of the transformations are as follows:
+    %
+    %       SMSC          - log10(Top layer soil moisture capacity as water depth).
+    %       SMSC_trees    - log10(Trees top layer soil moisture capacity as water depth).
+    %       SMSC_interflow  - log10(Bottom layer soil moisture capacity as water depth).
+    %       S_initialfrac - Initial soil moisture fraction (0-1).
+    %       k_infilt      - log10(Soil infiltration capacity as water depth).
+    %       k_sat         - log10(Maximum vertical infiltration rate).
+    %       k_inteflow    - log10(Maximum vertical interflow rate).
+    %       bypass_frac   - Fraction of runoff to bypass drainage (0-1).
+    %       interflow_frac- Fraction of free drainage going to interflow (0-1).
+    %       alpha         - log10(Power term for infiltration rate).
+    %       beta          - log10(Power term for drainage rate).
+    %       gamma         - log10(Power term for soil evap. rate).
+    %
+    %   The soil moisture model is an adaption of VIC Model (Wood et al. 1992)
+    %   by Kavetski et al. (2006). It is a soil moisture model that is without
+    %   any discontinuities (thus able to produce a first-order smooth
+    %   calibration response surface) and therefore amenable to gradient based
+    %   calibration. The model, as implemented here, is very flexible in that a
+    %   wide range of models can be implemented by simply turning componants of
+    %   the differential equation on or off. For example, a one parameter
+    %   model (comprising only of infiltration and evaporation) can be derived
+    %   by turning off the drainage term and fixing alpha and gamma to one. For
+    %   details on how to build various types of models see the documentation
+    %   for the class constructor (i.e. "climateTransform_soilMoistureModels"
+    %   below);
+    %
+    %   Considerable effort has been put into efficiently solving the soil
+    %   moisture model while producing a smooth response surface. The
+    %   differential equation is solved using a fixed-time step solver. For each
+    %   time step, an initial estimate is made using the explicit Huen
+    %   algorithm. This produces an O(h^2) error. The solution error is then
+    %   refined near to machine precision using an implicit Huen Newton's
+    %   algorithm. To achieve efficient computation, the differential equation
+    %   and solver are implemeneted within MatLab's C-MEX language. This
+    %   requires compilation of the file "forcingTransform_soilMoisture.c" and
+    %   can be achieved by executing the following command within MatLab:
+    %   mex forcingTransform_soilMoisture.c
+    %
+    % See also
+    %   climateTransform_soilMoistureModels: model_construction;
+    %   setParameters: set_calibration_parameters_values;
+    %   getParameters: get_calibration_parameters_values;
+    %   detectParameterChange: assesst_if_parameters_have_changed_recently;
+    %   setTransformedForcing: run_model_and_store_simulation_results;
+    %   getTransformedForcing: get_outputs_for_timeseries_model.
+    %
+    % Dependencies
+    %   forcingTransform_soilMoisture.c
+    %   evapOptions.m
+    %   rechargeOptions.m
+    %
+    % References:
+    %   Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of
+    %   input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res.,
+    %   42, W03407, doi:10.1029/2005WR004368.
+    %
+    %   Wood, E. F., D. P. Lettenmaier, and V. G. Zartarian (1992), A
+    %   Land-Surface Hydrology Parameterization With Subgrid Variability for
+    %   General Circulation Models, J. Geophys. Res., 97(D3), 2717–2728,
+    %   doi:10.1029/91JD01786.
+    %
+    % Author:
+    %   Dr. Tim Peterson, The Department of Infrastructure Engineering,
+    %   Giancarlo Bonotto, The Department of Infrastructure Engineering,
+    %   The University of Melbourne.
+    %
+    % Date:
+    %   March 2022
+
+    properties
+
+        % Additional Model Parameters to that of sub-class.
+        %----------------------------------------------------------------
+        SMSC_interflow       % Interflow Layer 2 soil moisture capacity parameter
+        S_initialfrac_interflow % Interflow Layer 2 Fractional initial  soil moisture
+        k_sat_interflow      % Interflow Layer 2 maximum vertical conductivity.
+        alpha_interflow       % Interflow Layer 2 power term for infiltration
+        beta_interflow       % Layer 2 power term for dainage rate (eg Brook-Corey pore index power term)
+        gamma_interflow    % Power term for interflow soil evaporation rate.
+        PET_scaler_interflow           % Scaling term for the ET from the interflow soil evaporation rate.
+        eps_interflow   %  Scaler defining S_min/SMSC_interflow ratio. S_min is the minimum soil moisture threshold for having runoff produced from interflow store. If eps=0, runoff and infiltration terms are as Kavestki 2006.
+        %----------------------------------------------------------------
+    end
+
+
+    %%  STATIC METHODS
+    % Static methods used to inform the
+    % user of the available model types.
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'precip';'et'};
+            isOptionalInput = [false; false];
+        end
+
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+
+            variable_names = climateTransform_soilMoistureModels.outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+
+            variable_names_interflow = {'infiltration_fractional_capacity_interflow';'infiltration_interflow';'interflow_slow';'evap_soil_interflow'; 'evap_soil_total';'runoff_interflow';'runoff_total';'SMS_interflow';'mass_balance_error'};
+
+            variable_names = {variable_names{:}, variable_names_interflow{:}};
+            variable_names = unique(variable_names);
+        end
+
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+
+            options = { 'SMSC'           ,    2, 'Calib.';...
+                'SMSC_trees'    ,   2, 'Fixed';...
+                'treeArea_frac' , 0.5, 'Fixed'; ...
+                'S_initialfrac' , 0.5, 'Fixed'  ; ...
+                'k_infilt'      , inf,'Fixed'   ; ...
+                'k_sat'         , 1, 'Calib.'   ; ...
+                'bypass_frac'   , 0, 'Fixed'    ; ...
+                'alpha'         , 0, 'Fixed'    ; ...
+                'beta'          ,  0.5,'Calib.' ; ...
+                'gamma'         ,  0,  'Fixed'  ; ...
+                'eps'           ,   0,  'Fixed'; ...
+                'SMSC_interflow'     ,  2, 'Calib.'   ;...
+                'S_initialfrac_interflow', 0.5,'Fixed'; ...
+                'k_sat_interflow'     , NaN, 'Fixed.'   ;...
+                'alpha_interflow'     , NaN, 'Fixed'   ;...
+                'beta_interflow'     ,  NaN, 'Fixed.';...
+                'gamma_interflow'     ,  NaN, 'Fixed';...
+                'PET_scaler_interflow' ,  1, 'Fixed';...
+                'eps_interflow' ,  0, 'Fixed'};
+
+
+            colNames = {'Parameter', 'Initial Value','Fixed or Calibrated?'};
+            colFormats = {'char', 'char', {'Calib.' 'Fixed'}};
+            colEdits = logical([0 1 1]);
+
+            toolTip = sprintf([ 'Use this table to define the type of soil moisture model. \n', ...
+                'Each parameter (except the soil moisture capacity) can be \n', ...
+                'set to a fixed value or calibrated. Below is a summary: \n \n' , ...
+                '   SMSC         : log10(Soil moisture capacity as water depth).\n', ...
+                '   SMSC_trees    : log10(Tree soil moisture capacity as water depth).\n', ...
+                '   treeArea_frac : Scaler applied to the tree fraction input data.\n', ...
+                '   S_initialfrac: Initial soil moisture fraction (0-1).\n', ...
+                '   k_infilt     : log10(Soil infiltration capacity as water depth).\n', ...
+                '   k_sat        : log10(Maximum vertical infiltration rate).\n', ...
+                '   bypass_frac  : Fraction of runoff to bypass drainage.\n', ...
+                '   alpha        : Power term for infiltration rate.\n', ...
+                '   beta         : log10(Power term for dainage rate).\n', ...
+                '   gamma        : log10(Power term for soil evap. rate).\n', ...
+                '   eps          : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
+                '   SMSC_interflow    : log10(Interflow layer soil moisture capacity as water depth).\n', ...
+                '   S_initialfrac_interflow: Initial interflow soil moisture fraction (0-1).\n', ...
+                '   k_sat_interflow   : log10(Interflow layer maximum vertical infiltration rate).\n', ...
+                '   alpha_interflow   : Power term for interflow layer infiltration rate.\n', ...
+                '   beta_interflow    : log10(Interflow layer power term for drainage rate).',...
+                '   gamma_interflow    : log10(Power term for interflow soil evap. rate).\n',...
+                '   PET_scaler_interflow    :  % Scaling term for the ET from the interflow soil evaporation rate (0-1).',...
+                '   eps_interflow          : eps_interflow = (S_min/SMSC ratio). S_min is the minimum soil moisture threshold for interflow store.']);
+
+        end
+
+        function modelDescription = modelDescription()
+            modelDescription = {'Name: climateTransform_soilMoistureModels_interflow', ...
+                '', ...
+                'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage, runoff, interflow, baseflow) ', ...
+                'using a highly flexible two layer soil moisture model. Note, the top layer free-drains into to deeper layer or interflow layer.', ...
+                'Also, two types of land cover can be simulated using two parallel soil models (not when using interflow layer).', ...
+                '', ...
+                'Number of parameters: 2 to 10', ...
+                '', ...
+                'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                '', ...
+                'Comments: Below is a summary of the model parameters:' , ...
+                'SMSC         : log10(Soil moisture capacity as water depth).', ...
+                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                'treeArea_frac : Scaler applied to the tree fraction input data.', ...
+                'S_initialfrac: Initial soil moisture fraction (0-1).', ...
+                'k_infilt     : log10(Soil infiltration capacity as water depth).', ...
+                'k_sat        : log10(Maximum vertical infiltration rate).', ...
+                'bypass_frac  : Fraction of runoff to bypass drainage.', ...
+                'alpha        : Power term for infiltration rate.', ...
+                'beta         : log10(Power term for dainage rate).', ...
+                'gamma        : log10(Power term for soil evap. rate).', ...
+                'eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
+                'SMSC_interflow    : log10(Deep layer soil moisture capacity as water depth). ', ...
+                '               Input an NaN value and "fixed" to for it to equal SMSC.', ...
+                'S_initialfrac_interflow: Initial interflow soil moisture fraction (0-1).\n', ...
+                '               Input an NaN value and "fixed" to for it to S_initialfrac', ...
+                'k_sat_interflow   : log10(interflow layer maximum vertical infiltration rate).', ...
+                '               Input an NaN value and "fixed" to for it to equal k_sat.', ...
+                'alpha_interflow       : Power term for interflow layer infiltration rate.', ...
+                'beta_interflow    : log10(interflow layer power term for dainage rate).', ...
+                '               Input an NaN value and "fixed" to for it to equal beta.', ...
+                'gamma_interflow    : log10(Power term for interflow soil evap. rate)', ...
+                '               Input an NaN value and "fixed" to for it to equal gamma.', ...
+                'PET_scaler_interflow    :  Scaling term for the ET from the interflow soil evaporation rate (0-1).', ...
+                '               Input an NaN value and "fixed" to for it to equal zero. Fixed to zero as default', ...
+                'eps_interflow         : S_min/SMSC ratio. S_min is the minimum interflow soil moisture threshold.',...
+                '', ...
+                'References: ', ...
+                '1. Peterson & Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355', ...
+                '2. Bonotto, Peterson, Fowler, & Western (2022), HydroSight SW-GW: lumped rainfall-runoff model for the joint simulation of streamflow and groundwater in a drying climate, Geoscientific Model Development , , –', ...
+                '3. Bonotto, Peterson, Fowler, & Western (2022), Can the joint simulation of daily streamflow and groundwater head help to explain the Millennium Drought hydrology?, Water Resour. Res., , –\'};
+        end
+
+    end
+
+
+
+
+    methods
+        %% Construct the model
+        function obj = climateTransform_soilMoistureModels_interflow(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)
+            % Model construction.
+            %
+            % Syntax:
+            %   soilModel = climateTransform_soilMoistureModels_2layer(modelOptions)
+            %
+            % Description:
+            %   Builds the form of the soil moisture differential equations using user
+            %   input mSure, odel options. The way in which the soil moisture model is used
+            %   to transform the precipitation and potential evapotranspiration is also
+            %   defined within the model options.
+            %
+            %   Importantly, the parameters for the deep layer do not need to be
+            %   specified within the model options input. If they are not specified,
+            %   then their initial values are taken from those for the top soil layer.
+            %
+            %   This constructor also checks that the required compiled code exists (ie
+            %   forcingTransform_soilMoisture.c), the input model options are two or
+            %   three columns wide and that the way in which the transformations are to
+            %   be undertaken is specified.
+            %
+            % Input:
+            %   modelOptions - cell matrix defining the soil model componants, ie how the
+            %   model should be constructed. The cell matrix can be two or three
+            %   columns wide and at least three rows long (for the simplest of models).
+            %   The first column defines the model parameter name or user option name
+            %   to be defined. The second column defines the initial value for the
+            %   parameter or the setting for the user option. A third column can be
+            %   input if a parameter is to be fixed, and thus not modified during
+            %   calibration. This third column can contain the term 'fixed'.
+            %
+            %   The required user options, and option choices, are as follows:
+            %       'SMSC_interflow'
+            %       This is for setting the bottom layer soil moisture storage capacity parameter.
+            %       The value for the second column is the initial value for this
+            %       parameter. This model option is required because all model variants
+            %       require this parameter to be set.
+            %
+            %       'k_sat'
+            %       This is for setting the drainage rate from the top layer. This
+            %       model option is required to estimated drainage into the deep layer.
+            %
+            %   The optional user options are as follows. For the model parameter
+            %   options the second column defines the initial value. A third column can
+            %   also be input to make the parameter a constant:
+            %
+            %       'k_infilt'          - layer 1 parameter for the maximum infiltration rate.
+            %       'alpha'             - layer 1 parameter for the infiltration rate power term.
+            %       'beta'            - layer 1 parameter for the drainage rate power term.
+            %       'k_sat'           - layer 1 parameter for the maximum vertical saturated
+            %                             conductivity.
+            %       'gamma'             - layer 1 parameter for the evaporation rate power term.
+            %       'beta_interflow'            - layer 2 parameter for the drainage rate power term.
+            %       'k_sat_interflow'           - layer 2 parameter for the maximum vertical saturated conductivity.
+            %       'gamma_interflow'           - layer 2 parameter for the soil evapotranspiration rate
+            %
+            %       'numDailySubsteps'  - a user option to define the number of
+            %                             sub-daily time steps when solving the
+            %                             differential equation. Having more than one
+            %                             step per day will not notably improve the
+            %                             estimate of soil moisture. It will however
+            %                             result in better estimation of infiltration
+            %                             when the soil layer saturates.
+            %
+            %   Below are some examples of model options for various types of models:
+            %
+            %       - 1 parameter with linear scaling of infiltration by the soil moisture:
+            %       >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                               'gainingOption', 'soil_infiltration' ;
+            %                               'SMSC', 100};
+            %       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
+            %       >> soilModelOptions = { 'lossingOption', 'evap_deficit', '' ;
+            %                               'gainingOption', 'soil_infiltration', '' ;
+            %                               'SMSC', 100, '' ;
+            %                               'alpha', 0, 'fixed'};
+            %       - 2 parameter with linear scaling of infiltration by the soil
+            %       moisture and linear vertical drainage:
+            %       >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                               'gainingOption', 'soil_infiltration' ;
+            %                               'SMSC', 100 ;
+            %                               'k_sat', 10 };
+            %       - 3 parameter with linear scaling of infiltration by the soil
+            %       moisture and non-linear vertical drainage:
+            %       >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                               'gainingOption', 'soil_infiltration' ;
+            %                               'SMSC', 100 ;
+            %                               'beta', 2 ;
+            %                               'k_sat', 10 };
+            %       - 4 parameter with non-linear scaling of infiltration by the soil
+            %       moisture and non-linear vertical drainage:
+            %       >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                               'gainingOption', 'soil_infiltration' ;
+            %                               'SMSC', 100 ;
+            %                               'alpha', 0.5 ;
+            %                               'beta', 2 ;
+            %                               'k_sat', 10 };
+            %
+            % Output:
+            %   soilModel - climateTransform_soilMoistureModels_2layer class object
+            %
+            % Example:
+            %   Create a cell matrix of options for a two parameter soil model:
+            %   >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                           'gainingOption', 'soil_infiltration' ;
+            %                           'SMSC', 100 ;
+            %                           'k_sat', 10 };
+            %   Build the soil model:
+            %   >> soilModel = climateTransform_soilMoistureModels_2layer(soilModelOptions);
+            %
+            % See also
+            %   climateTransform_soilMoistureModels_2layer: class_definition;
+            %   setParameters: set_calibration_parameters_values;
+            %   getParameters: get_calibration_parameters_values;
+            %   detectParameterChange: assesst_if_parameters_have_changed_recently;
+            %   setTransformedForcing: run_model_and_store_simulation_results;
+            %   getTransformedForcing: get_outputs_for_timeseries_model.
+            %
+            % Dependencies
+            %   evapOptions.m
+            %   rechargeOptions.m
+            %
+            % Author:
+            %   Dr. Tim Peterson, The Department of Infrastructure Engineering,
+            %   The University of Melbourne.
+            %
+            % Date:
+            %   11 April 2012
+
+            % Use sub-class constructor.
+            obj = obj@climateTransform_soilMoistureModels_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);
+
+            % Get list of model parameters and exclude settings, variables
+            % and the model 'type';
+            all_parameter_names = properties(obj);
+            ind = true(length(all_parameter_names),1);
+            for i=1:length(all_parameter_names)
+                switch all_parameter_names{i,1}
+                    case {'settings', 'variables'}
+                        ind(i) = false;
+                end
+            end
+            all_parameter_names = all_parameter_names(ind);
+
+            % Assign model parameters.
+            % Importantly, if the parameter is not listed then that
+            % feature of the soil moisture model is turned off.
+            %--------------------------------------------------------------
+            for i=1:length(all_parameter_names)
+
+                % Find the required parameter within the input model
+                % options.
+                ind = [];
+                for j=1:length(modelOptions(:,1))
+                    if strcmp(modelOptions(j,1), all_parameter_names{i} )
+                        ind = j;
+                        break;
+                    end
+                end
+
+                % Record the deep parameters not listed as 'Fixed'.
+                if isempty(ind)
+                    obj.settings.fixedParameters.(all_parameter_names{i})=false;
+                    obj.settings.activeParameters.(all_parameter_names{i})=false;
+                    if strcmp(all_parameter_names{i}, 'beta_interflow')
+                        % Note, beta is transformed in the soil model to 10^beta.
+                        obj.(all_parameter_names{i}) = 0;
+
+                    elseif strcmp(all_parameter_names{i}, 'k_sat_interflow')
+                        % Note, k_sat is transformed in the soil model to
+                        % 10^k_sat = 0 m/d.
+                        obj.(all_parameter_names{i}) = -inf;
+
+                    elseif strcmp(all_parameter_names{i}, 'alpha_interflow')
+                        obj.(all_parameter_names{i}) = 0;
+
+                    elseif strcmp(all_parameter_names{i}, 'gamma_interflow')
+                        obj.(all_parameter_names{i}) = 0;
+
+                    elseif strcmp(all_parameter_names{i}, 'S_initialfrac_interflow')
+                        obj.(all_parameter_names{i}) = 0.5;
+
+                    elseif strcmp(all_parameter_names{i}, 'PET_scaler_interflow')
+                        obj.(all_parameter_names{i}) = 1;
+
+                    elseif strcmp(all_parameter_names{i}, 'eps_interflow')
+                        obj.(all_parameter_names{i}) = 0;
+
+                    else
+                        obj.(all_parameter_names{i}) = 0;
+                    end
+                end
+            end
+
+            % Check the SMSM_trees parameter is active if and only if there
+            % is land cover input data.
+            % if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+            % if ~obj.settings.activeParameters.SMSC_interflow_trees
+            % error('The trees deep soil moisture model options must include the soil moisture capacity parameter when land cover data is input.');
+            % end
+            % else
+            % obj.settings.activeParameters.SMSC_interflow_trees = false;
+            % obj.settings.fixedParameters.SMSC_interflow_trees = true;
+            % end
+
+            % Check that deep parameters set to NaN are not active.
+            if isnan(obj.beta_interflow) && obj.settings.activeParameters.beta_interflow
+                error('"beta_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            if isnan(obj.k_sat_interflow) && obj.settings.activeParameters.k_sat_interflow
+                error('"k_sat_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            if isnan(obj.alpha_interflow) && obj.settings.activeParameters.alpha_interflow
+                error('"alpha_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            if isnan(obj.gamma_interflow) && obj.settings.activeParameters.gamma_interflow
+                error('"gamma_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            if isnan(obj.S_initialfrac_interflow) && obj.settings.activeParameters.S_initialfrac_interflow
+                error('"S_initialfrac_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            % if isnan(obj.SMSC_interflow_trees) && obj.settings.activeParameters.SMSC_interflow_trees
+            % error('"SMSC_interflow_trees" can only be initialsied to Nan if it is "Fixed".');
+            % end
+            if isnan(obj.SMSC_interflow) && obj.settings.activeParameters.SMSC_interflow
+                error('"SMSC_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+            if isnan(obj.eps_interflow) && obj.settings.activeParameters.eps_interflow
+                error('"eps_interflow" can only be initialsied to Nan if it is "Fixed".');
+            end
+
+
+            % Initialise interflow soil moisture variables
+            obj.variables.SMS_interflow = [];
+            obj.variables.SMS_interflow_subdaily = [];
+        end
+
+        %% Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+
+            % Get the parameter names.
+            [params, param_names] = getParameters(obj);
+
+            % Get the bounds from the original soil model
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit@climateTransform_soilMoistureModels(obj);
+
+            % Upper and lower bounds of SMSC.
+            if obj.settings.activeParameters.SMSC_interflow
+                ind = cellfun(@(x)(strcmp(x,'SMSC_interflow')),param_names);
+                params_lowerLimit(ind,1) = log10(10);
+                %params_upperLimit(ind,1) = Inf;
+                params_upperLimit(ind,1) = log10(1000);
+            end
+
+            % Upper and lower bounds of SMSC_interflow_trees.
+            % if obj.settings.activeParameters.SMSC_interflow_trees
+            % ind = cellfun(@(x)(strcmp(x,'SMSC_interflow_trees')),param_names);
+            % params_lowerLimit(ind,1) = log10(10);
+            % %params_upperLimit(ind,1) = Inf;
+            % params_upperLimit(ind,1) = log10(2000);
+            % end
+
+            % Upper and lower bounds of k_sat_interflow.
+            if obj.settings.activeParameters.k_sat_interflow
+                ind = cellfun(@(x)(strcmp(x,'k_sat_interflow')),param_names);
+                % Upper and lower bounds taken from Rawls et al 1982 Estimation
+                % of Soil Properties. The values are for sand loam and silty clay
+                % respectively and transformed from units of cm/h to the assumed
+                % input units of mm/d.
+                params_lowerLimit(ind,1) = floor(log10(0.06*24*10));
+                params_upperLimit(ind,1) = ceil(log10(21*24*10));
+            end
+
+            % Upper and lower bounds of beta_interflow.
+            if obj.settings.activeParameters.beta_interflow
+                ind = cellfun(@(x)(strcmp(x,'beta_interflow')),param_names);
+                % Note, To make the parameter range that is explored
+                % more compact, the beta parameter was converted to the
+                % log10 space. Prior to this transformation, the lower
+                % and upper boundaries were 1 and 5. Calibration trials
+                % for the Great Western Catchments, Victoria, Australia
+                % found that very often this non-transformed value
+                % would be >100.
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = Inf;
+            end
+
+            % Upper and lower bounds of alpha_interflow.
+            if obj.settings.activeParameters.alpha_interflow
+                ind = cellfun(@(x)(strcmp(x,'alpha_interflow')),param_names);
+                % Note, To make the parameter range that is explored
+                % more compact, the alpha parameter was converted to the
+                % log10 space. Prior to this transformation, the lower
+                % and upper boundaries were 1 and 5. Calibration trials
+                % for the Great Western Catchments, Victoria, Australia
+                % found that very often this non-transformed value
+                % would be >100.
+                params_lowerLimit(ind,1) = 0; % TODO: reasonable??
+                params_upperLimit(ind,1) = Inf; % TODO: reasonable??
+            end
+
+
+            if obj.settings.activeParameters.gamma_interflow
+                ind = cellfun(@(x)(strcmp(x,'gamma_interflow')),param_names);
+                params_lowerLimit(ind,1) = log10(0.01);
+                params_upperLimit(ind,1) = log10(100);
+            end
+
+            if obj.settings.activeParameters.PET_scaler_interflow
+                ind = cellfun(@(x)(strcmp(x,'PET_scaler_interflow')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end
+
+            if obj.settings.activeParameters.eps_interflow
+                ind = cellfun(@(x)(strcmp(x,'eps_interflow')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end
+
+        end
+
+
+        %% Return fixed upper and lower plausible parameter ranges.
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            % Return fixed upper and lower plausible parameter ranges.
+            % This is used to define reasonable range for the initial parameter sets
+            % for the calibration. These parameter ranges are only used in the
+            % calibration if the user does not input parameter ranges.
+
+            % Get the parameter names.
+            [params, param_names] = getParameters(obj);
+
+            % Get the bounds from the original soil model
+            [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit@climateTransform_soilMoistureModels(obj);
+
+            % Upper and lower bounds of SMSC_interflow.
+            if obj.settings.activeParameters.SMSC_interflow
+                ind = cellfun(@(x)(strcmp(x,'SMSC_interflow')),param_names);
+                params_lowerLimit(ind,1) = log10(50);
+                params_upperLimit(ind,1) = log10(500);
+            end
+
+            % Upper and lower bounds of SMSC_interflow_trees.
+            % if obj.settings.activeParameters.SMSC_interflow_trees
+            % ind = cellfun(@(x)(strcmp(x,'SMSC_interflow_trees')),param_names);
+            % params_lowerLimit(ind,1) = log10(50);
+            % params_upperLimit(ind,1) = log10(1000);
+            % end
+
+            % Upper and lower bounds of k_sat_interflow.
+            if obj.settings.activeParameters.k_sat_interflow
+                ind = cellfun(@(x)(strcmp(x,'k_sat_interflow')),param_names);
+                % Upper and lower bounds taken from Rawls et al 1982 Estimation
+                % of Soil Properties. The values are for sand loam and silty clay
+                % respectively and transformed from units of cm/h to the assumed
+                % input units of mm/d.
+                params_lowerLimit(ind,1) = log10(0.09*24*10);
+                params_upperLimit(ind,1) = log10(6.11*24*10);
+            end
+
+            % Upper and lower bounds of beta_interflow.
+            if obj.settings.activeParameters.beta_interflow
+                ind = cellfun(@(x)(strcmp(x,'beta_interflow')),param_names);
+                % Note, To make the parameter range that is explored
+                % more compact, the beta parameter was converted to the
+                % log10 space. Prior to this transformation, the lower
+                % and upper boundaries were 1 and 5. Calibration trials
+                % for the Great Western Catchments, Victoria, Australia
+                % found that very often this non-transformed value
+                % would be >100.
+                params_lowerLimit(ind,1) = log10(1);
+                params_upperLimit(ind,1) = log10(5);
+            end
+
+
+            if obj.settings.activeParameters.gamma_interflow
+                ind = cellfun(@(x)(strcmp(x,'gamma_interflow')),param_names);
+                params_lowerLimit(ind,1) = log10(0.1);
+                params_upperLimit(ind,1) = log10(10);
+            end
+
+            if obj.settings.activeParameters.PET_scaler_interflow
+                ind = cellfun(@(x)(strcmp(x,'PET_scaler_interflow')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end
+
+            if obj.settings.activeParameters.eps_interflow
+                ind = cellfun(@(x)(strcmp(x,'eps_interflow')),param_names);
+                params_lowerLimit(ind,1) = 0;
+                params_upperLimit(ind,1) = 1;
+            end
+
+        end
+
+        %% Solve the soil moisture differential equation
+        function setTransformedForcing(obj, t, forceRecalculation)
+            % Syntax:
+            %   setTransformedForcing(obj, climateData, forceRecalculation)
+            %
+            % Description:
+            %   This method solves the soil moisture differential equations for all time
+            %   points within the input daily climate data. The resulting soil moisture
+            %   fluxes are then transformed to the required form (as defined within the
+            %   input model options). The results from this are stored within the
+            %   object variables "obj.variables.evap_Forcing" and
+            %   "obj.variables.precip_Forcing".
+            %
+            %   Importantly, if the model parameters have not changed since the last
+            %   simulations, then a solution is not re-derived. Also, this method
+            %   requires prior compilation of the C-MEX function
+            %   "forcingTransform_soilMoisture.c".
+            %
+            % Input:
+            %   obj         - soil moisture model object.
+            %   climateData - a matrix (Nx3) of daily climate data where: column 1 is
+            %                 the date (as a floating point), column 2 is the daily
+            %                 precipitation, and column 3 is the daily potential
+            %                 evaporation.
+            %   forceRecalculation - is a logical scalar input (i.e. true of false) to
+            %                 force re-calculation of the model and thus ingore if the
+            %                 parameters have or have not changed.
+            % Outputs:
+            %   (none)
+            %
+            % Example:
+            %   Create a cell matrix of options for a two parameter soil model:
+            %   >> soilModelOptions = { 'lossingOption', 'evap_deficit' ;
+            %                           'gainingOption', 'soil_infiltration' ;
+            %                           'SMSC', 100 ;
+            %                           'k_sat', 10 };
+            %   Build the soil model:
+            %   >> soilModel = climateTransform_soilMoistureModels_2layer(soilModelOptions);
+            %
+            %   Solve the differential equation:
+            %   >> setTransformedForcing(soilModel, climateData, true)
+            %
+            % See also:
+            %   climateTransform_soilMoistureModels_2layer: class_definition;
+            %   setParameters: set_calibration_parameters_values;
+            %   getParameters: get_calibration_parameters_values;
+            %   detectParameterChange: assesst_if_parameters_have_changed_recently;
+            %   getTransformedForcing: get_outputs_for_timeseries_model.
+            %
+            % Dependencies:
+            %   forcingTransform_soilMoisture.c
+            %
+            % Author:
+            %   Dr. Tim Peterson, The Department of Infrastructure Engineering,
+            %   The University of Melbourne.
+            %
+            % Date:
+            %   11 April 2012
+            if nargin==2
+                forceRecalculation=false;
+            end
+            if obj.variables.isNewParameters || forceRecalculation || ~isfield(obj.variables,'t') || ...
+                    (isfield(obj.variables,'t') && obj.variables.t(end) ~= t(end))
+
+                % Run the top layer model
+                setTransformedForcing@climateTransform_soilMoistureModels(obj, t, forceRecalculation);
+
+                % Handle deep soil layer parameters taken from the shallow
+                % layer.
+                if isnan(obj.SMSC_interflow)
+                    SMSC_interflow = 10^(obj.SMSC);
+                else
+                    SMSC_interflow = 10^obj.SMSC_interflow;
+                end
+                if isnan(obj.k_sat_interflow)
+                    k_sat_interflow = 10^(obj.k_sat);
+                else
+                    k_sat_interflow = 10^(obj.k_sat_interflow);
+                end
+                if isnan(obj.beta_interflow)
+                    beta_interflow = 10^(obj.beta);
+                else
+                    beta_interflow = 10^(obj.beta_interflow);
+                end
+                if isnan(obj.alpha_interflow)
+                    alpha_interflow = obj.alpha;
+                else
+                    alpha_interflow = obj.alpha_interflow;
+                end
+                if isnan(obj.gamma_interflow)
+                    gamma_interflow = 10^(obj.gamma);
+                else
+                    gamma_interflow = 10^(obj.gamma_interflow);
+                end
+                if isnan(obj.eps_interflow)
+                    eps_interflow = obj.eps;
+                else
+                    eps_interflow = obj.eps_interflow;
+                end
+
+                % Set interflow initial soil moisture.
+                if isnan(obj.S_initialfrac_interflow)
+                    S_interflow_initial = obj.S_initialfrac.*SMSC_interflow;
+                else
+                    S_interflow_initial = obj.S_initialfrac_interflow.*SMSC_interflow;
+                end
+
+
+
+                % Call MEX function for DEEP soil moisture model.
+                %                 if ~doSubDailyEst
+                % Get free drainage from the shallow layer
+                %drainage = getTransformedForcing(obj, 'drainage',1);
+
+                % Calculate remaining PET after shallow ET
+                %PET = max(0,obj.variables.evap - getTransformedForcing(obj, 'evap_soil',1));
+
+                % Calculate the interflow from the shallow layer to the interflow layer.
+                nDays = length(obj.variables.evap);
+                nDailySubSteps = getNumDailySubsteps(obj);
+                SMSC = 10^(obj.SMSC);
+                beta = 10.^(obj.beta);
+                gamma = 10.^(obj.gamma);
+                k_sat = 10.^obj.k_sat;
+                interflow_frac = obj.interflow_frac;
+                interflow = (interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(obj.variables.SMS/SMSC).^beta;
+
+                % Calc potential ET for interflow store
+                if obj.PET_scaler_interflow==0
+                    PET = zeros(size(interflow));
+                else
+                    % Expand input forcing data to have the required number of substeps.
+                    evap = getSubDailyForcing(obj,obj.variables.evap);
+
+                    % Calc potential ET for interflow layer
+                    if obj.PET_scaler_interflow==1
+
+                        PET = evap - getTransformedForcing(obj, 'evap_soil',1, false);
+                    else
+                        PET = obj.PET_scaler_interflow .* (evap - getTransformedForcing(obj, 'evap_soil',1, false));
+                    end
+                end
+
+                % Call MEX soil model
+                obj.variables.SMS_interflow = forcingTransform_soilMoisture(S_interflow_initial, interflow, PET, SMSC_interflow, k_sat_interflow/nDailySubSteps, ...
+                    alpha_interflow, beta_interflow, gamma_interflow, eps_interflow); % TODO: gamma_interflow checked?, eps_interflow=0 indeed by default?;
+
+                % Run soil model again if tree cover is to be simulated
+                % if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover
+
+                % if isempty(obj.SMSC_interflow_trees)
+                % SMSC_interflow_trees = 10^(obj.SMSC_trees);
+                % else
+                % SMSC_interflow_trees = obj.SMSC_interflow_trees;
+                % end
+
+                % if isempty(obj.S_initialfrac)
+                % S_interflow_initial = obj.S_initialfrac.*SMSC_interflow_trees;
+                % else
+                % S_interflow_initial = obj.S_initialfrac_interflow * SMSC_interflow_trees;
+                % end
+
+                % % Get free drainage from the shallow layer
+                % drainage = (1-interflow_frac) .* k_sat/obj.variables.nDailySubSteps .*(obj.variables.SMS_trees/SMSC_trees).^beta;
+
+                % % Calculate remaining PET after shallow ET
+                % PET =  evap .*( 1 - (obj.variables.SMS_trees/SMSC_trees).^gamma);
+
+                % % Call MEX function for DEEP soil moisture model.
+                % obj.variables.SMS_interflow_trees = forcingTransform_soilMoisture(S_interflow_initial, drainage, PET, SMSC_interflow_trees, ...
+                % k_sat_interflow, 0, beta_interflow, 10.^obj.gamma, 0);
+                % end
+                % else
+                % % Define the number of daily sub-steps.
+                % nSubSteps = obj.variables.nDailySubSteps;
+                % t_substeps = linspace(0,1,nSubSteps+1);
+
+                % % Get number of days
+                % nDays = length(effectivePrecip);
+
+                % % Scale the forcing data by the number of time steps.
+                % effectivePrecip = effectivePrecip./nSubSteps;
+                % evap = obj.variables.evap./nSubSteps;
+
+                % % Scale ksat from units of 'per day' to 'per sub daily
+                % % time step'
+                % k_sat = k_sat./nSubSteps;
+
+                % % Expand input forcing data to have the required number of days.
+                % effectivePrecip = reshape(repmat(effectivePrecip,1,nSubSteps)',nDays * nSubSteps,1);
+                % evap = reshape(repmat(evap,1,nSubSteps)',nDays * nSubSteps,1);
+
+                % % Run the soil models using the sub-steps.
+                % obj.variables.SMS_subdaily = forcingTransform_soilMoisture(S_initial, effectivePrecip, evap, SMSC, k_sat, alpha, beta, gamma);
+                % end
+
+            end
+        end
+
+        %% Return the transformed forcing data
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName, SMSnumber, doSubstepIntegration)
+            % getTransformedForcing returns the required flux from the soil model.
+            %
+            % Syntax:
+            %   [precip_Forcing, et_Forcing] = getTransformedForcing(obj, variableName)
+            %
+            % Description:
+            %   This method returns the requested flux/data from the soil moisture
+            %   differential equation. The available fluxes/data are as follows:
+            %
+            %   * drainage: soil free drainage ranging (0 to k_sat) at the end of the day.
+            %   * drainage_bypassFlow: free drainage plus a parameter set fraction of runoff;
+            %   * drainage_normalised: normalised free drainage (0 to 1) at the end of the day.
+            %   * evap_soil: actual soil ET at the end of the day.
+            %   * infiltration: daily total infiltration rate.
+            %   * evap_gw_potential: groundwater evaporative potential (PET - soil ET)
+            %   * runoff: daily total runoff.
+            %   * SMS: soil moisture storage at the end of each day.
+            %
+            % Input:
+            %   obj - soil moisture model object.
+            %
+            %   variableName - a string for the variable name to return.
+            %
+            % Outputs:
+            %   forcingData  - a vector (Nx1) of the forcing data output to
+            %                     be input to the groundwater time series model.
+            %
+            %   isDailyIntegralFlux - logical scaler denoting if the flux is a daily
+            %   integral or an instantaneous value. This is used to inform the type of
+            %   numerical integration within modelTFN.get_h_star().
+            %
+            % See also:
+            %   climateTransform_soilMoistureModels_2layer: class_definition;
+            %   setParameters: set_calibration_parameters_values;
+            %   getParameters: get_calibration_parameters_values;
+            %   detectParameterChange: assesst_if_parameters_have_changed_recently;
+            %   setTransformedForcing: set_outputs_for_timeseries_model.
+            %
+            % Dependencies:
+            %   (none)
+            %
+            % Author:
+            %   Dr. Tim Peterson, The Department of Infrastructure Engineering,
+            %   The University of Melbourne.
+            %
+            % Date:
+            %   11 April 2012
+
+            if ischar(variableName)
+                variableNametmp{1}=variableName;
+                variableName = variableNametmp;
+                clear variableNametmp;
+            end
+
+            % Get back transformed parameters and assign each param to a variable for efficient access.
+            [params, param_names] = getDerivedParameters(obj);
+            SMSC = params(1,:);
+            SMSC_trees = params(2,:);
+            treeArea_frac = params(3,:);
+            k_sat = params(6,:);
+            bypass_frac = params(7,:);
+            interflow_frac = params(8,:);
+            beta = params(10,:);
+            gamma = params(11,:);
+            SMSC_interflow = params(13,:);
+            % SMSC_interflow_trees = params(14,:);
+            S_interflow_initial = params(14,:);
+            k_sat_interflow = params(15,:);
+            beta_interflow = params(16,:);
+            alpha_interflow = params(17,:);
+            gamma_interflow = params(18,:);
+            PET_scaler_interflow = params(19,:);
+            eps_interflow = params(20,:); % zero by default
+
+
+            % Set if the subdaily steps should be integrated.
+            if nargin < 4
+                doSubstepIntegration = true;
+            end
+
+            try
+                if doSubstepIntegration
+                    nrows = size(obj.variables.precip,1);
+                else
+                    nrows = size(getSubDailyForcing(obj,obj.variables.precip),1);
+                end
+                forcingData = zeros(nrows , length(variableName));
+                for i=1:length(variableName)
+                    % Test if the flux can be derived from the parent class.
+                    if ~any(strcmp({'infiltration_fractional_capacity_interflow', 'infiltration_interflow', 'evap_soil_interflow','evap_soil_total', 'evap_gw_potential', ...
+                            'interflow_slow','runoff_interflow', 'runoff_total','SMS_interflow','mass_balance_error'}, ...
+                            variableName{i}))
+
+                        if nargin==2
+                            [forcingData(:,i), isDailyIntegralFlux(i)] = getTransformedForcing@climateTransform_soilMoistureModels(obj, variableName{i});
+                        else
+                            [forcingData(:,i), isDailyIntegralFlux(i)] = getTransformedForcing@climateTransform_soilMoistureModels(obj, variableName{i}, SMSnumber, doSubstepIntegration);
+                        end
+                        continue
+                    end
+
+                    % Get the soil moisture store for the required soil unit
+                    if nargin==2 || SMSnumber==1
+                        SMS = obj.variables.SMS;
+                        SMS_interflow = obj.variables.SMS_interflow;
+                        SMSnumber = 1;
+                    elseif SMSnumber==2
+                        SMS = obj.variables.SMS_trees;
+                        SMSC_interflow = SMSC_interflow_trees;
+                        SMS_interflow = obj.variables.SMS_interflow_trees;
+                    else
+                        error('The soil moisture unit number is unknown')
+                    end
+
+                    switch variableName{i}
+                        case 'infiltration_fractional_capacity_interflow'
+
+                            % CalculateS infiltration fractional capacity, representing the fraction of rainfall that is infiltrated
+                            infiltration_fractional_capacity_interflow = min(1, ((SMSC_interflow - SMS_interflow)/(SMSC_interflow*(1-eps_interflow))).^alpha_interflow);
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, infiltration_fractional_capacity_interflow./obj.variables.nDailySubSteps);
+                            else
+                                forcingData(:,i) = infiltration_fractional_capacity_interflow;
+                            end
+
+                            if doSubstepIntegration
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'infiltration_interflow'
+                            % input to interflow store is the interflow_fraction taken from the free drainage of the shallow soil store
+                            interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+
+                            if alpha_interflow==0
+                                infiltration_interflow =  interflow;
+                            else
+                                infiltration_fractional_capacity_interflow = getTransformedForcing(obj, 'infiltration_fractional_capacity_interflow', SMSnumber, false);
+                                infiltration_interflow =  interflow .* infiltration_fractional_capacity_interflow;
+                            end
+
+                            % Calculate when the soil is probably
+                            % saturated.
+                            interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+                            evap_soil_interflow = getTransformedForcing(obj, 'evap_soil_interflow',SMSnumber, false);
+                            Infilt2Runoff = [0;(SMS_interflow(1:end-1) + infiltration_interflow(2:end) - evap_soil_interflow(2:end) - interflow(2:end)) - SMSC_interflow];
+                            Infilt2Runoff(Infilt2Runoff<0) = 0;
+
+                            % Subtract estimated saturated excess runoff
+                            % from the infiltration and then integrate.
+                            infiltration_interflow = infiltration_interflow - Infilt2Runoff;
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, infiltration_interflow);
+                            else
+                                forcingData(:,i) = infiltration_interflow;
+                            end
+
+                        case 'evap_soil_interflow'
+
+                            % Calc potential ET for interflow store
+                            if PET_scaler_interflow==0
+                                interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+                                evap = zeros(size(interflow));
+                            else
+                                % Expand input forcing data to have the required number of substeps.
+                                evap = getSubDailyForcing(obj,obj.variables.evap);
+
+                                %Subtract evap from shallow layer considering the EVAP_scaler for the input PET into interflow store
+                                evap = PET_scaler_interflow .* (evap - getTransformedForcing(obj, 'evap_soil',SMSnumber, false));
+
+                                % Est ET for interflow store
+                                evap = evap .* (SMS_interflow/SMSC_interflow).^gamma_interflow;
+                            end
+
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, evap);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = evap;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'evap_soil_total'
+
+                            evap = getTransformedForcing(obj, 'evap_soil',SMSnumber, false) + ...
+                                getTransformedForcing(obj, 'evap_soil_interflow',SMSnumber, false);
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, evap);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = evap;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'evap_gw_potential'
+                            evap = getSubDailyForcing(obj,obj.variables.evap) - getTransformedForcing(obj, 'evap_soil_total',SMSnumber, false);
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, evap);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = evap;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'interflow_slow'
+
+                            interflow_slow =  k_sat_interflow/obj.variables.nDailySubSteps .*(SMS_interflow/SMSC_interflow).^beta_interflow;
+
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, interflow_slow);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = interflow_slow;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+
+                        case 'runoff_interflow'
+
+                            % Calc sub daily runoff from interflow store
+                            interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+                            infiltration_interflow = getTransformedForcing(obj, 'infiltration_interflow',SMSnumber, false);
+                            runoff_interflow = max(0,interflow - infiltration_interflow);
+
+                        case 'runoff_total'
+                            %Calculate the runoff from the shallow soil store
+                            runoff = getTransformedForcing(obj, 'runoff',SMSnumber, false);
+                            % subtract interflow (which is a simple fraction of the free drainage from shallow soil store) to avoid double-counting as interflow from shallow store is the input to interflow store
+                            interflow = getTransformedForcing(obj, 'interflow',SMSnumber, false);
+                            runoff = runoff - interflow ;
+
+                            % Calculate the saturation excess runoff out of the interflow store.
+                            runoff_interflow = getTransformedForcing(obj, 'runoff_interflow',SMSnumber, false);
+
+                            % Calculate the interflow_slow as free drainage out of interflow store
+                            interflow_slow = getTransformedForcing(obj, 'interflow_slow',SMSnumber, false);
+
+
+                            % Sum runoff from the shallow layer (without its interflow), interflow_slow, and runoff_interflow
+                            % NOTE: runoff_interflow can't be captured back to free drainage to feed groundwater (bypass_frac_interflow=0);
+                            runoff_total  = runoff  + runoff_interflow + interflow_slow; % TODO: is it correct?
+
+                            % Integrate to daily.
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, runoff);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = runoff;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        case 'SMS_interflow'
+                            forcingData(:,i) = SMS_interflow((1+obj.variables.nDailySubSteps):obj.variables.nDailySubSteps:end);
+                            isDailyIntegralFlux(i) = true;
+
+                        case 'mass_balance_error'
+                            precip = getSubDailyForcing(obj,obj.variables.precip);
+                            runoff = getTransformedForcing(obj, 'runoff_total',SMSnumber, false);
+                            AET = getTransformedForcing(obj, 'evap_soil_total',SMSnumber, false);
+                            drainage = getTransformedForcing(obj, 'drainage',SMSnumber, false); % TODO: this is correct as interflow is already in runoff total?
+
+                            fluxEstError = [0;precip(2:end) - diff(SMS + SMS_interflow) -  runoff(2:end) - AET(2:end) - drainage(2:end)];
+
+                            % Integreate to daily.
+                            if doSubstepIntegration
+                                forcingData(:,i) = dailyIntegration(obj, fluxEstError);
+                                isDailyIntegralFlux(i) = true;
+                            else
+                                forcingData(:,i) = fluxEstError;
+                                isDailyIntegralFlux(i) = false;
+                            end
+
+                        otherwise
+                            error('The requested transformed forcing variable is not known.');
+                    end
+
+                    % Get flixes for tree soil unit (if required) and weight the
+                    % flux from the two units
+                    % if  isfield(obj.settings,'simulateLandCover') && obj.settings.simulateLandCover && nargin==2
+                    % % Get flux for tree SMS
+                    % forcingData_trees = getTransformedForcing(obj, variableName{i}, 2) ;
+
+                    % % Do weighting
+                    % forcingData(:,i) = (1-treeArea_frac .* obj.variables.treeFrac) .* forcingData(:,i) + ...
+                    % treeArea_frac .* obj.variables.treeFrac .* forcingData_trees;
+                    % end
+                end
+            catch ME
+                error(ME.message)
+            end
+
+
+
+        end
+
+        function [params, param_names] = getDerivedParameters(obj)
+
+
+            [params, param_names] = getDerivedParameters@climateTransform_soilMoistureModels(obj);
+
+            param_names(size(param_names,1)+1:size(param_names,1)+8) = {
+                'SMSC_interflow: back transformed soil moisture interflow layer storage capacity (in rainfall units)'; ...
+                % 'SMSC_interflow_trees: back transformed soil moisture interflow layer storage capacity in trees unit (in rainfall units)'; ...
+                'S_interflow_initial: fractional initial interflow layer soil moisture (-)'; ...
+                'k_sat_interflow : back transformed interflow layer maximum vertical conductivity (in rainfall units/day)'; ...
+                'beta_interflow : back transformed power term for drainage rate of interflow layer (eg approx. Brook-Corey pore index power term)'; ...
+                'alpha_interflow : back transformed power term for infiltration rate of interflow layer';...
+                'gamma_interflow : back transformed power term for soil evaporation rate (-)';...
+                'PET_scaler_interflow : Scaling term for the ET from the interflow soil evaporation rate (-)';...
+                'eps_interflow : equal to S_min/SMSC ratio. S_min is the minimum soil moisture threshold for runoff to occur.(-)'};
+
+
+
+            % Handle interflow soil layer parameters taken from the shallow
+            % layer.
+            if isnan(obj.SMSC_interflow)
+                SMSC_interflow = 10^(obj.SMSC);
+            else
+                SMSC_interflow = 10^obj.SMSC_interflow;
+            end
+            %   if isnan(obj.SMSC_interflow_trees)
+            %       SMSC_interflow_trees = 10^(obj.SMSC_trees);
+            %   else
+            %       SMSC_interflow_trees = 10^obj.SMSC_interflow_trees;
+            %   end
+            if isnan(obj.k_sat_interflow)
+                k_sat_interflow = 10^(obj.k_sat);
+            else
+                k_sat_interflow = 10^(obj.k_sat_interflow);
+            end
+            if isnan(obj.beta_interflow)
+                beta_interflow = 10^(obj.beta);
+            else
+                beta_interflow = 10^(obj.beta_interflow);
+            end
+
+            if isnan(obj.alpha_interflow)
+                alpha_interflow = obj.alpha;
+            else
+                alpha_interflow = obj.alpha_interflow;
+            end
+
+            if isnan(obj.S_initialfrac_interflow)
+                S_interflow_initial = obj.S_initialfrac.*SMSC_interflow;
+            else
+                S_interflow_initial = obj.S_initialfrac_interflow.*SMSC_interflow;
+            end
+
+            if isnan(obj.gamma_interflow)
+                gamma_interflow = 10^(obj.gamma);
+            else
+                gamma_interflow = 10^(obj.gamma_interflow);
+            end
+
+            if isnan(obj.eps_interflow)
+                eps_interflow = obj.eps;
+            else
+                eps_interflow = obj.eps_interflow;
+            end
+
+
+
+            params = [  params;  ...
+                SMSC_interflow; ...
+                % SMSC_interflow_trees; ...
+                S_interflow_initial; ...
+                k_sat_interflow; ...
+                beta_interflow; ...
+                alpha_interflow;...
+                gamma_interflow;...
+                obj.PET_scaler_interflow;...
+                eps_interflow];
+
+
+        end
+
+
+
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+            % getParameterValidity returns a logical vector for the validity or each parameter.
+            %
+            % Syntax:
+            %   isValidParameter = getParameterValidity(obj, params, param_names)
+            %
+            % Description:
+            %   Cycles though all active soil model parameters and returns a logical
+            %   vector denoting if each parameter is valid, ie within the physical
+            %   parameter bounds including reasonable mass-balance of the free-drainage (GW-recharge) and baseflow components.
+            %
+            % Input:
+            %   obj -  model object.
+            %
+            %   params - vector of model parameters
+            %
+            %   param_names - cell array of the parameter names.
+            %
+            % Outputs:
+            %   isValidParameter - column vector of the parameter validity.
+            %
+            % Example:
+            %   see HydroSight: time_series_model_calibration_and_construction;
+            %
+            % See also:
+            %   model_TFN_SW_GW: model_construction;
+            %
+            % Author:
+            %   Dr. Tim Peterson, Monash University
+            %   Giancarlo Bonotto, The University of Melbourne.
+            %
+            % Date:
+            %   April 2022
+
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Get the filetred P and potential ET is already stored in the
+            % model object.
+            if isfield(obj.variables,'precip') && isfield(obj.variables,'evap')
+                P = obj.variables.precip;
+                PET = obj.variables.evap;
+                t = obj.variables.t;
+            else
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
+                precip_col = obj.settings.forcingData_cols{filt,2};
+                P = obj.settings.forcingData(:, precip_col );
+
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
+                evap_col = obj.settings.forcingData_cols{filt,2};
+                PET = obj.settings.forcingData(:, evap_col );
+
+                t = obj.settings.forcingData(:, 1);
+            end
+
+            % Calculate mean aridity index
+            P = mean(P);
+            PET = mean(PET);
+            PET_on_P = PET/P;
+
+            % Calculate Budyko samples of E/P using prior sampled values of
+            % omega at PET_on_P.
+            AET_on_P = 1 + PET_on_P -(1+PET_on_P.^obj.settings.Budyko_omega).^(1./obj.settings.Budyko_omega);
+
+            % Calculate the 1st and 99th percentiles
+            AET_on_P = prctile(AET_on_P, [10 90],1);
+
+            % Initialise output
+            isValidParameter = true(size(params));
+
+            % Calculate the soil moisture estimate of actual ET.
+            for i=1:size(params,2)
+                setParameters(obj, params(:,i));
+                setTransformedForcing(obj, t, true);
+                AET = mean(getTransformedForcing(obj, 'evap_soil_total'));
+
+                % Ceck if the AET is within the Budyko bounds
+                if AET/P < AET_on_P(1) || AET/P > AET_on_P(2)
+                    isValidParameter(:,i) = false;
+                end
+            end
+
+            setParameters(obj, params);
+
+            % Check parameters are within bounds.
+            isValidParameter = isValidParameter & params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+                params <= params_upperLimit(:,ones(1,size(params,2)));
+        end
+
+    end
+
+
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_v2.m b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_v2.m
index 002ec6f..f66b92f 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_v2.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/climateTransform_soilMoistureModels_v2.m
@@ -1,303 +1,306 @@
-classdef climateTransform_soilMoistureModels_v2 < climateTransform_soilMoistureModels
-% Class definition for soil moisture transformation of climate forcing.
-%
-% Description        
-%   climateTransform_soilMoistureModels_v2 is a generalised vertically lumped 1-D
-%   soil moisture model. It is identical to climateTransform_soilMoistureModels()
-%   except that the simulated average actual soil evaporation is constrained to be 
-%   between the 5th and 95th perceililes of the actual ET as defined from Greve et 
-%   al. 2015. This is achievd by rejecting parameter sets that produce actual 
-%   ET values outsde these bounds.
-%
-% See also
-%   climateTransform_soilMoistureModels: parent model;
-%   climateTransform_soilMoistureModels_v2: model_construction;
-%
-% Dependencies
-%   forcingTransform_soilMoisture.c
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   1 September 2017
-    
-    properties(GetAccess=public, SetAccess=protected)
-    end
-
-
-    
-%%  STATIC METHODS        
-% Static methods used to inform the
-% user of the available model types. 
-    methods(Static)
-        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            variable_names = {'precip';'et';'TreeFraction'};
-            isOptionalInput = [false; false; true];
-        end
-        
-        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            variable_names = climateTransform_soilMoistureModels.outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-        end
-        
-        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
-           
-            options = { 'SMSC'          ,   2, 'Calib.';...
-                        'SMSC_trees'    ,   2, 'Fixed';...
-                        'treeArea_frac' , 0.5, 'Fixed'; ...
-                        'S_initialfrac' , 0.5, 'Fixed'  ; ...
-                        'k_infilt'      , inf,'Fixed'   ; ...
-                        'k_sat'         ,   0.5, 'Calib.'   ; ...
-                        'bypass_frac'   ,   0, 'Fixed'    ; ...
-                        'interflow_frac',   0, 'Fixed'    ; ...
-                        'alpha'         ,   0, 'Fixed'    ; ...
-                        'beta'          , 0.5,'Calib.' ; ...
-                        'gamma'         ,   0,  'Fixed'};
-
-        
-            colNames = {'Parameter', 'Initial Value','Fixed or Calibrated?'};
-            colFormats = {'char', 'char', {'Calib.' 'Fixed'}};
-            colEdits = logical([0 1 1]);
-
-            toolTip = sprintf([ 'Use this table to define the type of soil moisture model. \n', ...
-                                'Each parameter (except the soil moisture capacity) can be \n', ...
-                                'set to a fixed value or calibrated. Below is a summary: \n \n' , ...
-                                '   SMSC          : log10(Soil moisture capacity as water depth).\n', ...
-                                '   SMSC_trees    : log10(Tree soil moisture capacity as water depth).\n', ...
-                                '   treeArea_frac : Scaler applied to the tree fraction input data.\n', ...
-                                '   S_initialfrac : Initial soil moisture fraction (0-1).\n', ...
-                                '   k_infilt      : log10(Soil infiltration capacity as water depth).\n', ...
-                                '   k_sat         : log10(Maximum vertical infiltration rate).', ...
-                                '   bypass_frac   : Fraction of runoff to bypass drainage.\n', ...
-                                '   interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
-                                '   alpha         : Power term for infiltration rate.\n', ...
-                                '   beta          : log10(Power term for dainage rate).\n', ...
-                                '   gamma         : log10(Power term for soil evap. rate).']);                               
-            
-        end
-        
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: climateTransform_soilMoistureModels_v2', ...
-                               '', ...
-                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage). ', ...
-                               'It is identical to climateTransform_soilMoistureModels() except that the simulated average actual soil evaporation is', ...
-                               'constrained to be between the 5th and 95th percentiles of the actual ET as defined from Greve et al. 2015. This is achievd by', ...
-                               'rejecting parameter sets that produce actual ET values outsde these bounds.', ...                                                              
-                               '', ...                               
-                               'Number of parameters: 1 to 8', ...
-                               '', ...                               
-                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
-                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
-                               '', ...                               
-                               'Comments: Below is a summary of the model parameters:' , ...
-                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
-                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
-                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
-                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
-                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
-                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
-                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
-                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
-                                'alpha         : Power term for infiltration rate.', ...
-                                'beta          : log10(Power term for dainage rate).', ...
-                                'gamma         : log10(Power term for soil evap. rate).', ...
-                               '', ...
-                               'References:', ...
-                               'Greve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne (2015), Introducing a probabilistic Budyko ', ...
-                               'framework. Geophys. Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449', };
-        end        
-           
-    end
-        
-    
-    methods       
-%% Construct the model
-        function obj = climateTransform_soilMoistureModels_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
-% climateTransform_soilMoistureModels_v2 constructs the soil model object.
-%
-% Syntax:
-%   soilModel = climateTransform_soilMoistureModels_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
-%
-% Description:
-%   Builds the form of the soil moisture differential equation for the user
-%   input model options. This constructor also checks that the required 
-%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
-%   model options are two or three columns wide.
-%
-% Input:
-%
-%   bore_ID - string for the bore ID. The bore ID must be listed in the site
-%   coordinates array.
-%
-%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
-%   must comprise of the following columns: year, month, day and
-%   observation data. The observation data must include precipitation &
-%   PET. The record must be continuous (no skipped days) and cannot contain  
-%   blank observations.
-%
-%   forcingData_colnames - 1 x N cell array of the column names within the
-%   input numerical array "forcingData_data".  Importantly, each forcing 
-%   data column name must be listed within the input "siteCoordinates". 
-%
-%   siteCoordinates - Mx3 cell matrix containing the following columns :
-%   site ID, projected easting, projected northing. Importantly,
-%   the input 'bore_ID' must be listed and all columns within
-%   the forcing data (excluding the year, month,day). 
-%
-%   modelOptions - cell matrix defining the soil model componants, ie how the
-%   model should be constructed. The cell matrix can be two or three
-%   columns wide and at least three rows long (for the simplest of models).
-%   The first column defines the model parameter name or user option name
-%   to be defined. The second column defines the initial value for the
-%   parameter or the setting for the user option. A third column can be
-%   input if a parameter is to be fixed, and thus not modified during
-%   calibration. This third column can contain the term 'fixed'. 
-%
-%   Below are some examples of model options for various types of models:
-%
-%       - 1 parameter with linear scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100; ...
-%                               'alpha', 1, 'fixed'};};
-%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
-%       >> soilModelOptions = { 'SMSC', 100, '' ;
-%                               'alpha', 0, 'fixed'};
-%       - 2 parameter with linear scaling of infiltration by the soil 
-%       moisture and linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'k_sat', 10 };
-%       - 3 parameter with linear scaling of infiltration by the soil
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = {  'SMSC', 100 ; 
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%       - 4 parameter with non-linear scaling of infiltration by the soil 
-%       moisture and non-linear vertical drainage:
-%       >> soilModelOptions = { 'SMSC', 100 ; 
-%                               'alpha', 0.5 ;
-%                               'beta', 2 ;
-%                               'k_sat', 10 };
-%
-% Output:
-%   obj - climateTransform_soilMoistureModels class object 
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   1 September 2017
-
-            % Build soil model
-            obj = obj@climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);                     
-
-            
-            % Sample Buyko w values. Approach taken from
-            % reve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne
-            % (2015), Introducing a probabilistic Budyko framework. Geophys. 
-            % Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449. 
-            %-----------------
-            
-            % build gamma distribution.
-            pd = makedist('Gamma','a',4.54,'b',0.37);
-            
-            % Sample w values from distribution and add one (Bufyko must be >=1)
-            nsamples = 10000;
-            obj.settings.Budyko_omega = random(pd,nsamples,1)+1;            
-            
-        end
-
-        
-%% Assess if matrix of parameters is valid.
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-% getParameterValidity returns a logical vector for the validity or each parameter.
-%
-% Syntax:
-%   isValidParameter = getParameterValidity(obj, params, param_names)
-%
-% Description:
-%   Cycles though all active soil model parameters and returns a logical 
-%   vector denoting if each parameter is valid ie within the physical 
-%   parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-%   params - vector of model parameters
-%
-%   param_names - cell array of the parameter names.
-%
-% Outputs:
-%   isValidParameter - column vector of the parameter validity.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   model_TFN: model_construction;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Get the filetred P and potential ET is already stored in the
-            % model object.
-            if isfield(obj.variables,'precip') && isfield(obj.variables,'evap')
-                P = obj.variables.precip;
-                PET = obj.variables.evap;
-                t = obj.variables.t;
-            else
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
-                precip_col = obj.settings.forcingData_cols{filt,2};
-                P = obj.settings.forcingData(:, precip_col );
-
-                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
-                evap_col = obj.settings.forcingData_cols{filt,2};
-                PET = obj.settings.forcingData(:, evap_col );                
-                
-                t = obj.settings.forcingData(:, 1);
-            end
-
-            % Calculate mean aridity index
-            P = mean(P);
-            PET = mean(PET);
-            PET_on_P = PET/P;
-            
-            % Calculate Budyko samples of E/P using prior sampled values of 
-            % omega at PET_on_P.
-            AET_on_P = 1 + PET_on_P -(1+PET_on_P.^obj.settings.Budyko_omega).^(1./obj.settings.Budyko_omega);
-            
-            % Calculate the 1st and 99th percentiles
-            AET_on_P = prctile(AET_on_P, [10 90],1);
-            
-            % Initialise output
-            isValidParameter = true(size(params));
-            
-            % Calculate the soil moisture estimate of actual ET.
-            for i=1:size(params,2)
-                setParameters(obj, params(:,i));
-                setTransformedForcing(obj, t, true);
-                AET = mean(getTransformedForcing(obj, 'evap_soil'));
-
-                % Ceck if the AET is within the Budyko bounds
-                if AET/P < AET_on_P(1) || AET/P > AET_on_P(2)
-                    isValidParameter(:,i) = false;
-                end
-            end
-            setParameters(obj, params);
-
-            % Check parameters are within bounds.
-            isValidParameter = isValidParameter & params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
-    		params <= params_upperLimit(:,ones(1,size(params,2)));   
-        end             
-    end
-    
-end
-
-
+classdef climateTransform_soilMoistureModels_v2 < climateTransform_soilMoistureModels
+% Class definition for soil moisture transformation of climate forcing.
+%
+% Description        
+%   climateTransform_soilMoistureModels_v2 is a generalised vertically lumped 1-D
+%   soil moisture model. It is identical to climateTransform_soilMoistureModels()
+%   except that the simulated average actual soil evaporation is constrained to be 
+%   between the 5th and 95th perceililes of the actual ET as defined from Greve et 
+%   al. 2015. This is achievd by rejecting parameter sets that produce actual 
+%   ET values outsde these bounds.
+%
+% See also
+%   climateTransform_soilMoistureModels: parent model;
+%   climateTransform_soilMoistureModels_v2: model_construction;
+%
+% Dependencies
+%   forcingTransform_soilMoisture.c
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   1 September 2017
+    
+    properties(GetAccess=public, SetAccess=protected)
+    end
+
+
+    
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = {'precip';'et';'TreeFraction'};
+            isOptionalInput = [false; false; true];
+        end
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            variable_names = climateTransform_soilMoistureModels.outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+           
+            options = { 'SMSC'          ,   2, 'Calib.';...
+                        'SMSC_trees'    ,   2, 'Fixed';...
+                        'treeArea_frac' , 0.5, 'Fixed'; ...
+                        'S_initialfrac' , 0.5, 'Fixed'  ; ...
+                        'k_infilt'      , inf,'Fixed'   ; ...
+                        'k_sat'         ,   0.5, 'Calib.'   ; ...
+                        'bypass_frac'   ,   0, 'Fixed'    ; ...
+                        'interflow_frac',   0, 'Fixed'    ; ...
+                        'alpha'         ,   0, 'Fixed'    ; ...
+                        'beta'          , 0.5,'Calib.' ; ...
+                        'gamma'         ,   0,  'Fixed'; ...
+                        'eps'           ,   0,  'Fixed'};
+
+        
+            colNames = {'Parameter', 'Initial Value','Fixed or Calibrated?'};
+            colFormats = {'char', 'char', {'Calib.' 'Fixed'}};
+            colEdits = logical([0 1 1]);
+
+            toolTip = sprintf([ 'Use this table to define the type of soil moisture model. \n', ...
+                                'Each parameter (except the soil moisture capacity) can be \n', ...
+                                'set to a fixed value or calibrated. Below is a summary: \n \n' , ...
+                                '   SMSC          : log10(Soil moisture capacity as water depth).\n', ...
+                                '   SMSC_trees    : log10(Tree soil moisture capacity as water depth).\n', ...
+                                '   treeArea_frac : Scaler applied to the tree fraction input data.\n', ...
+                                '   S_initialfrac : Initial soil moisture fraction (0-1).\n', ...
+                                '   k_infilt      : log10(Soil infiltration capacity as water depth).\n', ...
+                                '   k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                '   bypass_frac   : Fraction of runoff to bypass drainage.\n', ...
+                                '   interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                '   alpha         : Power term for infiltration rate.\n', ...
+                                '   beta          : log10(Power term for dainage rate).\n', ...
+                                '   gamma         : log10(Power term for soil evap. rate).',...
+                                '   eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.']);                               
+            
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: climateTransform_soilMoistureModels_v2', ...
+                               '', ...
+                               'Purpose: nonlinear transformation of rainfall and areal potential evaporation to a range of forcing data (eg free-drainage). ', ...
+                               'It is identical to climateTransform_soilMoistureModels() except that the simulated average actual soil evaporation is', ...
+                               'constrained to be between the 5th and 95th percentiles of the actual ET as defined from Greve et al. 2015. This is achievd by', ...
+                               'rejecting parameter sets that produce actual ET values outsde these bounds.', ...                                                              
+                               '', ...                               
+                               'Number of parameters: 1 to 8', ...
+                               '', ...                               
+                               'Options: each model parameter (excluding the soil moisture capacity) can be set to a fixed value (ie not calibrated) or calibrated.', ...
+                               'Also, the input forcing data field "TreeFraction" is optional and only required if the soil model is to simulate land cover change.', ...
+                               '', ...                               
+                               'Comments: Below is a summary of the model parameters:' , ...
+                                'SMSC          : log10(Soil moisture capacity as water depth).', ...
+                                'SMSC_trees    : log10(Tree soil moisture capacity as water depth).', ...
+                                'treeArea_frac : Scaler applied to the tree fraction input data.', ...                                
+                                'S_initialfrac : Initial soil moisture fraction (0-1).', ...
+                                'k_infilt      : log10(Soil infiltration capacity as water depth).', ...
+                                'k_sat         : log10(Maximum vertical infiltration rate).', ...
+                                'bypass_frac   : Fraction of runoff to bypass drainage.', ...
+                                'interflow_frac: Fraction of free drainage going to interflow (0-1).', ...
+                                'alpha         : Power term for infiltration rate.', ...
+                                'beta          : log10(Power term for dainage rate).', ...
+                                'gamma         : log10(Power term for soil evap. rate).', ...
+                                'eps           : S_min/SMSC ratio. S_min is the minimum soil moisture threshold.',...
+                               '', ...
+                               'References:', ...
+                               'Greve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne (2015), Introducing a probabilistic Budyko ', ...
+                               'framework. Geophys. Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449', };
+        end        
+           
+    end
+        
+    
+    methods       
+%% Construct the model
+        function obj = climateTransform_soilMoistureModels_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)            
+% climateTransform_soilMoistureModels_v2 constructs the soil model object.
+%
+% Syntax:
+%   soilModel = climateTransform_soilMoistureModels_v2(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)   
+%
+% Description:
+%   Builds the form of the soil moisture differential equation for the user
+%   input model options. This constructor also checks that the required 
+%   compiled code exists (ie forcingTransform_soilMoisture.c)and that the input 
+%   model options are two or three columns wide.
+%
+% Input:
+%
+%   bore_ID - string for the bore ID. The bore ID must be listed in the site
+%   coordinates array.
+%
+%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
+%   must comprise of the following columns: year, month, day and
+%   observation data. The observation data must include precipitation &
+%   PET. The record must be continuous (no skipped days) and cannot contain  
+%   blank observations.
+%
+%   forcingData_colnames - 1 x N cell array of the column names within the
+%   input numerical array "forcingData_data".  Importantly, each forcing 
+%   data column name must be listed within the input "siteCoordinates". 
+%
+%   siteCoordinates - Mx3 cell matrix containing the following columns :
+%   site ID, projected easting, projected northing. Importantly,
+%   the input 'bore_ID' must be listed and all columns within
+%   the forcing data (excluding the year, month,day). 
+%
+%   modelOptions - cell matrix defining the soil model componants, ie how the
+%   model should be constructed. The cell matrix can be two or three
+%   columns wide and at least three rows long (for the simplest of models).
+%   The first column defines the model parameter name or user option name
+%   to be defined. The second column defines the initial value for the
+%   parameter or the setting for the user option. A third column can be
+%   input if a parameter is to be fixed, and thus not modified during
+%   calibration. This third column can contain the term 'fixed'. 
+%
+%   Below are some examples of model options for various types of models:
+%
+%       - 1 parameter with linear scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100; ...
+%                               'alpha', 1, 'fixed'};};
+%       - 1 parameter WITHOUT scaling of infiltration by the soil moisture:
+%       >> soilModelOptions = { 'SMSC', 100, '' ;
+%                               'alpha', 0, 'fixed'};
+%       - 2 parameter with linear scaling of infiltration by the soil 
+%       moisture and linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'k_sat', 10 };
+%       - 3 parameter with linear scaling of infiltration by the soil
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = {  'SMSC', 100 ; 
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%       - 4 parameter with non-linear scaling of infiltration by the soil 
+%       moisture and non-linear vertical drainage:
+%       >> soilModelOptions = { 'SMSC', 100 ; 
+%                               'alpha', 0.5 ;
+%                               'beta', 2 ;
+%                               'k_sat', 10 };
+%
+% Output:
+%   obj - climateTransform_soilMoistureModels class object 
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   1 September 2017
+
+            % Build soil model
+            obj = obj@climateTransform_soilMoistureModels(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions);                     
+
+            
+            % Sample Buyko w values. Approach taken from
+            % reve, P., L. Gudmundsson, B. Orlowsky, and S. I. Seneviratne
+            % (2015), Introducing a probabilistic Budyko framework. Geophys. 
+            % Res. Lett., 42, 2261–2269. doi: 10.1002/2015GL063449. 
+            %-----------------
+            
+            % build gamma distribution.
+            pd = makedist('Gamma','a',4.54,'b',0.37);
+            
+            % Sample w values from distribution and add one (Bufyko must be >=1)
+            nsamples = 10000;
+            obj.settings.Budyko_omega = random(pd,nsamples,1)+1;            
+            
+        end
+
+        
+%% Assess if matrix of parameters is valid.
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+% getParameterValidity returns a logical vector for the validity or each parameter.
+%
+% Syntax:
+%   isValidParameter = getParameterValidity(obj, params, param_names)
+%
+% Description:
+%   Cycles though all active soil model parameters and returns a logical 
+%   vector denoting if each parameter is valid ie within the physical 
+%   parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+%   params - vector of model parameters
+%
+%   param_names - cell array of the parameter names.
+%
+% Outputs:
+%   isValidParameter - column vector of the parameter validity.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   model_TFN: model_construction;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Get the filetred P and potential ET is already stored in the
+            % model object.
+            if isfield(obj.variables,'precip') && isfield(obj.variables,'evap')
+                P = obj.variables.precip;
+                PET = obj.variables.evap;
+                t = obj.variables.t;
+            else
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'precip');
+                precip_col = obj.settings.forcingData_cols{filt,2};
+                P = obj.settings.forcingData(:, precip_col );
+
+                filt = strcmp(obj.settings.forcingData_cols(:,1),'et');
+                evap_col = obj.settings.forcingData_cols{filt,2};
+                PET = obj.settings.forcingData(:, evap_col );                
+                
+                t = obj.settings.forcingData(:, 1);
+            end
+
+            % Calculate mean aridity index
+            P = mean(P);
+            PET = mean(PET);
+            PET_on_P = PET/P;
+            
+            % Calculate Budyko samples of E/P using prior sampled values of 
+            % omega at PET_on_P.
+            AET_on_P = 1 + PET_on_P -(1+PET_on_P.^obj.settings.Budyko_omega).^(1./obj.settings.Budyko_omega);
+            
+            % Calculate the 1st and 99th percentiles
+            AET_on_P = prctile(AET_on_P, [10 90],1);
+            
+            % Initialise output
+            isValidParameter = true(size(params));
+            
+            % Calculate the soil moisture estimate of actual ET.
+            for i=1:size(params,2)
+                setParameters(obj, params(:,i));
+                setTransformedForcing(obj, t, true);
+                AET = mean(getTransformedForcing(obj, 'evap_soil'));
+
+                % Ceck if the AET is within the Budyko bounds
+                if AET/P < AET_on_P(1) || AET/P > AET_on_P(2)
+                    isValidParameter(:,i) = false;
+                end
+            end
+            setParameters(obj, params);
+
+            % Check parameters are within bounds.
+            isValidParameter = isValidParameter & params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+    		params <= params_upperLimit(:,ones(1,size(params,2)));   
+        end             
+    end
+    
+end
+
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/derivedTransformations/derivedForcingTransform_abstract.m b/algorithms/models/TransferNoise/ForcingTransformation/derivedTransformations/derivedForcingTransform_abstract.m
index 33f5510..1e72c81 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/derivedTransformations/derivedForcingTransform_abstract.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/derivedTransformations/derivedForcingTransform_abstract.m
@@ -1,55 +1,55 @@
-classdef derivedForcingTransform_abstract < handle
-    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-        
-        % Vectors for the tranformed forcings and model state variable(s)
-        settings
-        variables
-        
-    end
-    
-    methods(Static, Abstract=true)
-        [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-        [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-        [options, colNames, colFormats, colEdits, toolTip] = modelOptions(sourceForcingTransformName)
-        modelDescription = modelDescription()
-    end
-    
-    methods(Abstract)
-        % Set parameters
-        setParameters(obj, params)
-        
-        % Get model parameters
-        [params, param_names] = getParameters(obj)
-        
-        % Return pre-set physical limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-        
-        % Return pre-set plausible limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)        
-        
-        % Assess if matrix of parameters is valid. This is used by the calibration ansl allows for 
-        % complex parameter constraints to be met.
-        isValidParameter = getParameterValidity(obj, params, param_names)
-
-        % Check if the model parameters have chnaged since the last
-        % estimation of the transformed forcing calculation
-        isNewParameters = detectParameterChange(obj, params)
-        
-        % Calculate and set tranformed forcing from input climate data
-        setTransformedForcing(obj, t, forceRecalculation)        
-        
-        % Get tranformed forcing data
-        [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, outputVariableName)        
-
-        % Set the input forcing data
-        setForcingData(obj, forcingData, forcingData_colnames)        
-        
-        % Get the spatial coordinates for the input forcing variables.
-        coordinates = getCoordinates(obj, variableName)         
-        
-    end
-    
-end
+classdef derivedForcingTransform_abstract < handle
+    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+        
+        % Vectors for the tranformed forcings and model state variable(s)
+        settings
+        variables
+        
+    end
+    
+    methods(Static, Abstract=true)
+        [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+        [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+        [options, colNames, colFormats, colEdits, toolTip] = modelOptions(sourceForcingTransformName)
+        modelDescription = modelDescription()
+    end
+    
+    methods(Abstract)
+        % Set parameters
+        setParameters(obj, params)
+        
+        % Get model parameters
+        [params, param_names] = getParameters(obj)
+        
+        % Return pre-set physical limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+        
+        % Return pre-set plausible limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)        
+        
+        % Assess if matrix of parameters is valid. This is used by the calibration ansl allows for 
+        % complex parameter constraints to be met.
+        isValidParameter = getParameterValidity(obj, params, param_names)
+
+        % Check if the model parameters have chnaged since the last
+        % estimation of the transformed forcing calculation
+        isNewParameters = detectParameterChange(obj, params)
+        
+        % Calculate and set tranformed forcing from input climate data
+        setTransformedForcing(obj, t, forceRecalculation)        
+        
+        % Get tranformed forcing data
+        [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, outputVariableName)        
+
+        % Set the input forcing data
+        setForcingData(obj, forcingData, forcingData_colnames)        
+        
+        % Get the spatial coordinates for the input forcing variables.
+        coordinates = getCoordinates(obj, variableName)         
+        
+    end
+    
+end
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_abstract.m b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_abstract.m
index 5483f3c..04d0d84 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_abstract.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_abstract.m
@@ -1,54 +1,54 @@
-classdef forcingTransform_abstract < dynamicprops
-    % forcingTransform_abstract abstract for forcing transformations..
-    
-    properties
-        
-        % Vectors for the tranformed forcings and model state variable(s)
-        settings
-        variables
-        
-    end
-    
-    methods(Static, Abstract=true)
-        [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-        [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-        [options, colNames, colFormats, colEdits, toolTip] = modelOptions(sourceForcingTransformName)
-        modelDescription = modelDescription()
-    end  
-    
-    methods(Abstract)
-        % Set parameters
-        setParameters(obj, params)
-        
-        % Get model parameters
-        [params, param_names] = getParameters(obj)
-
-        % Return pre-set physical limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)        
-        
-        % Return pre-set plausible limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)                
-        
-        % Assess if matrix of parameters is valid. This is used by the calibration ansl allows for 
-        % complex parameter constraints to be met.
-        isValidParameter = getParameterValidity(obj, params, param_names)
-
-        % Check if the model parameters have chnaged since the last
-        % estimation of the transformed forcing calculation
-        isNewParameters = detectParameterChange(obj, params)
-        
-        % Calculate and set tranformed forcing from input climate data
-        setTransformedForcing(obj, t, forceRecalculation)        
-        
-        % Get tranformed forcing data
-        [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)        
-        
-        % Set the input forcing data
-        setForcingData(obj, forcingData, forcingData_colnames)        
-        
-        % Get the spatial coordinates for the input forcing variables.
-        coordinates = getCoordinates(obj, variableName)                
-    end
-    
-end
-
+classdef forcingTransform_abstract < dynamicprops
+    % forcingTransform_abstract abstract for forcing transformations..
+    
+    properties
+        
+        % Vectors for the tranformed forcings and model state variable(s)
+        settings
+        variables
+        
+    end
+    
+    methods(Static, Abstract=true)
+        [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+        [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+        [options, colNames, colFormats, colEdits, toolTip] = modelOptions(sourceForcingTransformName)
+        modelDescription = modelDescription()
+    end  
+    
+    methods(Abstract)
+        % Set parameters
+        setParameters(obj, params)
+        
+        % Get model parameters
+        [params, param_names] = getParameters(obj)
+
+        % Return pre-set physical limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)        
+        
+        % Return pre-set plausible limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)                
+        
+        % Assess if matrix of parameters is valid. This is used by the calibration ansl allows for 
+        % complex parameter constraints to be met.
+        isValidParameter = getParameterValidity(obj, params, param_names)
+
+        % Check if the model parameters have chnaged since the last
+        % estimation of the transformed forcing calculation
+        isNewParameters = detectParameterChange(obj, params)
+        
+        % Calculate and set tranformed forcing from input climate data
+        setTransformedForcing(obj, t, forceRecalculation)        
+        
+        % Get tranformed forcing data
+        [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, t)        
+        
+        % Set the input forcing data
+        setForcingData(obj, forcingData, forcingData_colnames)        
+        
+        % Get the spatial coordinates for the input forcing variables.
+        coordinates = getCoordinates(obj, variableName)                
+    end
+    
+end
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c
index 94241cf..462473c 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c
+++ b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.c
@@ -12,11 +12,12 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
                   Ksat = mxGetScalar( prhs[4] ),
                   alpha = mxGetScalar( prhs[5] ),
                   beta = mxGetScalar( prhs[6] ),
-                  gamma = mxGetScalar( prhs[7] );        
+                  gamma = mxGetScalar( prhs[7] ),
+				  eps = mxGetScalar( prhs[8] );        
     
     /* Declare input data */
     const double *precip= mxGetPr( prhs[1] ), *et= mxGetPr( prhs[2] );
-    
+     
     /* Declare output data */    
     double *soilMoisture;
         
@@ -67,13 +68,22 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
         if (noPrecip == 1) 
             dSdt_precip = 0.0;
         else     
-            if (alpha == 1.0) 
-                dSdt_precip = precip[iDay] * (1.0 - soilMoisture_frac);                
-            else if (alpha == 0.0) 
-                dSdt_precip = precip[iDay];
-            else
-                dSdt_precip = precip[iDay] * pow(1.0 - soilMoisture_frac,alpha);
-
+			if (eps==0.0)
+				if (alpha == 1.0) 
+					dSdt_precip = precip[iDay] * (1.0 - soilMoisture_frac);                
+				else if (alpha == 0.0) 
+					dSdt_precip = precip[iDay];
+				else
+					dSdt_precip = precip[iDay] * pow(1.0 - soilMoisture_frac,alpha);
+			else 
+				if (alpha == 1.0) 
+					dSdt_precip = precip[iDay] * MIN(1.0, ((S_cap - soilMoisture[iDay-1])/(S_cap*(1-eps))));                
+				else if (alpha == 0.0) 
+					dSdt_precip = precip[iDay];
+				else
+					dSdt_precip = precip[iDay] * MIN(1.0, pow(((S_cap - soilMoisture[iDay-1])/(S_cap*(1-eps))),alpha));
+		
+		
         if (beta == 0.0 || Ksat==0.0 )
             dSdt_drain = 0.0;
         else if (beta==1.0)
@@ -112,13 +122,22 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
             if (noPrecip == 1) 
                 dSdt_precip = 0.0;
             else          
-                if (alpha == 1.0) 
-                    dSdt_precip = precip[iDay] * (1.0 - soilMoisture_frac);                
-                else if (alpha == 0.0) 
-                    dSdt_precip = precip[iDay];
-                else
-                    dSdt_precip = precip[iDay] * pow(1.0 - soilMoisture_frac,alpha);                    
-
+				if (eps==0.0)
+					if (alpha == 1.0) 
+						dSdt_precip = precip[iDay] * (1.0 - soilMoisture_frac);                
+					else if (alpha == 0.0) 
+						dSdt_precip = precip[iDay];
+					else
+						dSdt_precip = precip[iDay] * pow(1.0 - soilMoisture_frac,alpha);
+				else 
+					if (alpha == 1.0) 
+						dSdt_precip = precip[iDay] * MIN(1.0, ((S_cap - soilMoisture[iDay])/(S_cap*(1-eps))));                
+					else if (alpha == 0.0) 
+						dSdt_precip = precip[iDay];
+					else
+						dSdt_precip = precip[iDay] * MIN(1.0, pow(((S_cap - soilMoisture[iDay])/(S_cap*(1-eps))),alpha));                 
+
+		
             if (beta == 0.0  || Ksat==0.0 )
                 dSdt_drain = 0.0;
             else if (beta == 1.0)
@@ -144,14 +163,26 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
                 d2Sdt2_precip = 0.0;
             }
             else {
-                if (alpha == 1.0)
-                    d2Sdt2_precip = -precip[iDay] / S_cap;
-                else if (alpha == 0.0) 
-                    d2Sdt2_precip = 0.0;
-                else if (alpha == 2.0)
-                    d2Sdt2_precip = -precip[iDay] * alpha / S_cap * (1.0 - soilMoisture_frac);
-                else
-                    d2Sdt2_precip = -precip[iDay] * alpha / S_cap * pow(1.0 - soilMoisture_frac,alpha-1.0);
+                if (eps==0.0)					
+					if (alpha == 1.0)
+						d2Sdt2_precip = -precip[iDay] / S_cap;
+					else if (alpha == 0.0) 
+						d2Sdt2_precip = 0.0;
+					else if (alpha == 2.0)
+						d2Sdt2_precip = -precip[iDay] * alpha / S_cap * (1.0 - soilMoisture_frac);
+					else
+						d2Sdt2_precip = -precip[iDay] * alpha / S_cap * pow(1.0 - soilMoisture_frac,alpha-1.0);
+				else 
+					if (soilMoisture[iDay] < (S_cap*eps))
+						d2Sdt2_precip = 0;
+					else if (alpha == 1.0)
+						d2Sdt2_precip = -precip[iDay] / (S_cap*(1-eps));
+					else if (alpha == 0.0) 
+						d2Sdt2_precip = 0.0;
+					else if (alpha == 2.0)
+						d2Sdt2_precip = -precip[iDay] * alpha / (S_cap*(1-eps)) * ((S_cap - soilMoisture[iDay])/(S_cap*(1-eps)));
+					else
+						d2Sdt2_precip = -precip[iDay] * alpha / (S_cap*(1-eps)) * pow(((S_cap - soilMoisture[iDay])/(S_cap*(1-eps))),alpha-1.0);
 
                 df = 1.0-dt * 0.5 * (d2Sdt2_precip + (beta * dSdt_drain + gamma * dSdt_et)/soilMoisture[iDay]); 
             }
@@ -186,23 +217,42 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
                     fb = soilMoisture_iDay_upper - soilMoisture[iDay-1] - dt*dSdt;
                 }
                 else {
-                    soilMoisture[iDay] = 0.5*S_cap;                                                                                                
-                    dSdt = 0.5*(precip[iDay] * pow(0.5,alpha)- Ksat * pow(0.5, beta) - et[iDay] * pow(0.5, gamma) +
-                                dSdt_iprevDay);
-                    f = soilMoisture[iDay] - soilMoisture[iDay-1] - dt*dSdt;
-
-                    soilMoisture_iDay_lower = 0.0;
-                    dSdt = 0.5*(precip[iDay] + dSdt_iprevDay);
-                    fa = soilMoisture_iDay_lower - soilMoisture[iDay-1] - dt*dSdt;
-
-                    soilMoisture_iDay_upper = S_cap;                        
-                    dSdt = precip[iDay] * pow(0.0,alpha)- Ksat - et[iDay];
-                    /* Set to zero if dS/dt would overfill soil store. 
-                    if (dSdt > 0.0)
-                        dSdt = 0.0;  */                  
-                    dSdt =  0.5*(dSdt + dSdt_iprevDay);                    
-                    fb = soilMoisture_iDay_upper - soilMoisture[iDay-1] - dt*dSdt;                            
-                            
+					if (eps==0.0) {
+						soilMoisture[iDay] = 0.5*S_cap;                                                                                                
+						dSdt = 0.5*(precip[iDay] * pow(0.5,alpha)- Ksat * pow(0.5, beta) - et[iDay] * pow(0.5, gamma) +
+									dSdt_iprevDay);
+						f = soilMoisture[iDay] - soilMoisture[iDay-1] - dt*dSdt;
+
+						soilMoisture_iDay_lower = 0.0;
+						dSdt = 0.5*(precip[iDay] + dSdt_iprevDay);
+						fa = soilMoisture_iDay_lower - soilMoisture[iDay-1] - dt*dSdt;
+
+						soilMoisture_iDay_upper = S_cap;                        
+						dSdt = precip[iDay] * pow(0.0,alpha)- Ksat - et[iDay];
+						/* Set to zero if dS/dt would overfill soil store. 
+						if (dSdt > 0.0)
+							dSdt = 0.0;  */                  
+						dSdt =  0.5*(dSdt + dSdt_iprevDay);                    
+						fb = soilMoisture_iDay_upper - soilMoisture[iDay-1] - dt*dSdt;   
+					}
+                    else {
+						soilMoisture[iDay] = 0.5*S_cap;                                                                                                
+						dSdt = 0.5*(precip[iDay] * MIN(1.0, pow((0.5/(S_cap*(1-eps))),alpha)) - Ksat * pow(0.5, beta) - et[iDay] * pow(0.5, gamma) +
+									dSdt_iprevDay);
+						f = soilMoisture[iDay] - soilMoisture[iDay-1] - dt*dSdt;
+
+						soilMoisture_iDay_lower = 0.0;
+						dSdt = 0.5*(precip[iDay] * MIN(1.0, pow((S_cap/(S_cap*(1-eps))),alpha)) + dSdt_iprevDay);
+						fa = soilMoisture_iDay_lower - soilMoisture[iDay-1] - dt*dSdt;
+
+						soilMoisture_iDay_upper = S_cap;                        
+						dSdt = precip[iDay] * pow(0.0,alpha)- Ksat - et[iDay];
+						/* Set to zero if dS/dt would overfill soil store. 
+						if (dSdt > 0.0)
+							dSdt = 0.0;  */                  
+						dSdt =  0.5*(dSdt + dSdt_iprevDay);                    
+						fb = soilMoisture_iDay_upper - soilMoisture[iDay-1] - dt*dSdt; 
+					}	
                 }
                
                 /* Reset error ests.*/
@@ -250,8 +300,13 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
                     if (noPrecip==1)
                         dSdt = 0.5*(- Ksat * pow(soilMoisture_frac, beta) - et[iDay] * pow(soilMoisture_frac, gamma) + dSdt_iprevDay);
                     else 
-                        dSdt = 0.5*(precip[iDay] * pow(1.0-soilMoisture_frac,alpha) - Ksat * pow(soilMoisture_frac, beta) - et[iDay] * pow(soilMoisture_frac, gamma) + dSdt_iprevDay);
-
+                        if (eps==0.0)
+							dSdt = 0.5*(precip[iDay] * pow(1.0-soilMoisture_frac,alpha) - Ksat * pow(soilMoisture_frac, beta) - et[iDay] * pow(soilMoisture_frac, gamma) + dSdt_iprevDay);
+						else if (soilMoisture[iDay] < (S_cap*eps))
+							dSdt = 0.5*(precip[iDay] - Ksat * pow(soilMoisture_frac, beta) - et[iDay] * pow(soilMoisture_frac, gamma) + dSdt_iprevDay);
+						else
+							dSdt = 0.5*(precip[iDay] * MIN(1.0, pow(((S_cap - soilMoisture[iDay])/(S_cap*(1-eps))),alpha)) - Ksat * pow(soilMoisture_frac, beta) - et[iDay] * pow(soilMoisture_frac, gamma) + dSdt_iprevDay);
+						
                     /* Recalculate f using new dS/dt value*/
                     f = soilMoisture[iDay] - soilMoisture[iDay-1] - dt*dSdt;
 
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.mexw64 b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.mexw64
index 0f936bd..f5293d5 100644
Binary files a/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.mexw64 and b/algorithms/models/TransferNoise/ForcingTransformation/forcingTransform_soilMoisture.mexw64 differ
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diff --git a/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/pumpingRate_SAestimation.m b/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/pumpingRate_SAestimation.m
index c7034e5..95aa5f3 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/pumpingRate_SAestimation.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/pumpingRate_SAestimation.m
@@ -1,1522 +1,1522 @@
-classdef pumpingRate_SAestimation < stochForcingTransform_abstract & dynamicprops
-% Class definition for downscaling groundwater pumping rates.
-%
-% Description          
-%   This class allows the downscaling of infrequent (>daily) groundwater pumping
-%   data to daily, weeekly or monthly rates. The downscaling is required when
-%   pumping is metered, say, only annually. The downscaling is undertaken by
-%   simulating the pump as being either on or off for each user-set downscaling
-%   time step. When the period is metered then the number of days pumping is
-%   used to estimate the average daily pumping rate. The average of all
-%   metered periods is then applied to non-metered periods. This approach 
-%   requires no assumption of the behaviour of groudnwater users or their 
-%   response to climatic conditions.
-%
-%   Importantly, the number of combinations of pump state over, say, a decade
-%   to assess against the observed head far exceeds the largest number possible
-%   on a 64 bit machine. The implications are that the calibration cannot 
-%   assess all combinations and that if an identical model is re-calibrated
-%   an alternate optima may be identified. To manage this challenge the approach
-%   uses simulated annealing (SA); note SA comes from annealing in metallurgy,
-%   a technique involving heating and controlled cooling of a material to 
-%   increase the size of its crystals and reduce their defects. Further
-%   details are availabe in Peterson & Fulton (in-prep).
-%   
-%   The class allows the downscaling of infrequently metered periods and
-%   also the estimation of pumping during periods that were not metered. It
-%   is important the >=1 period is however metered so that the
-%   calibration can constrain the draw-down parameters (eg T and S). The
-%   different metering scenarios are defined from the TFN input forcing
-%   data file. Any period that was infrequently metered is defined by -999
-%   values in the forcind ata file followed by the total extraction volume
-%   over the preceeding perids of -999. Within the same file, and period
-%   with no metering is defined by a period of -999 values ending with a
-%   zero pumping rate.
-%
-%   Lastly, the class development of this class required a number of
-%   chnages to HydroSight. Please note the following:
-%      1. pumpingRate_SAestimation() required considerable changes to the 
-%         calibration scheme and is only operational with the SP-UCI. Other
-%         calibration schemes will be edited in the future.
-%      2. Calibration with an evaluation period required downscaling will 
-%         crash when using this componant. Future releases will use the 
-%         stochastically derived forcing tiem-series to build a predictive 
-%         model for the estimation during evaluation periods.
-%      3. This extension to HydroSight was funded by the Department of 
-%         Environment, Land, Water and Planning, Victoria.
-%         https://delwp.vic.gov.au/
-%               
-%References: 
-%   Peterson & Fulton (in-prep), Estimating aquifer hydraulic properties 
-%   from existing groundwater monitoring data.};  
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   July 2017
-%
- 
-    properties(GetAccess=public, SetAccess=protected)                        
-        % NOTE: All parameters are dynamically added
-    end
-
-%%  STATIC METHODS        
-% Static methods used to inform the
-% user of the available model types. 
-    methods(Static)
-        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            %variable_names = cell(75,1);
-            %variable_names(1:75) = strrep(strrep(strcat({'Pump '},num2str([1:75]')),' ','_'),'__','_');            
-            %isOptionalInput = true(75,1);
-            [variable_names] = pumpingRate_SAestimation.outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-            isOptionalInput = true(length(variable_names),1);     
-            
-            global pumpingRate_SAestimation_wizardResults
-            pumpingRate_SAestimation_wizardResults={};
-        end
-        
-        function [variable_names, isOptionalInput] = dataWizard(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            
-            dlg_title = 'Data Wizard: Set criteria for pumping selection ...';
-            prompt = {'Maximum radius of selected pumps from the obs. bore (in units of coordinates):', ...
-                      'Maximum number of selected pumps:'};            
-            num_lines = 1;
-            defaultans = {'5000','10'};
-            answer = inputdlg(prompt,dlg_title,num_lines,defaultans);
-            
-            if isempty(answer) 
-                global pumpingRate_SAestimation_wizardResults
-                pumpingRate_SAestimation_wizardResults = {};
-                [variable_names, isOptionalInput] = pumpingRate_SAestimation.inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-            else
-                % Check inputs
-                try 
-                    maxDist = str2double(answer{1});
-                    if maxDist <0
-                        warndlg('The maximum distance must be >>0. It will be set to Inf.','Input data error ...')
-                        maxDist = Inf;
-                    end
-                    maxNum = str2double(answer{2});
-                    if maxNum<0
-                        warndlg('The maximum number of pumps must be >0. It will be set to Inf.','Input data error ...')
-                        maxNum = Inf;
-                    end                    
-                catch
-                    warndlg('Both inputs must be scalar numbers >0. Both will be set to Inf.','Input data error ...')
-                    maxDist = Inf;
-                    maxNum = Inf;
-                end
-                
-                % Get coordinate of the obs bore
-                filt = cellfun(@(x) strcmp(x, bore_ID),table2cell(siteCoordinates(:,1)));
-                boreEN = table2array(siteCoordinates(filt,2:3));
-                
-                % Find those forcing data columns with any infilling. These
-                % are most likely pumping bores.
-                filt = false(size(forcingData_data,2),1);
-                for i=1:size(forcingData_data,2)
-                    filt(i) = any(forcingData_data{:,i} == -999);
-                end
-                forcingData_colnames = forcingData_colnames(filt);
-                
-                % Find the coordinates for the identified pumping bores.
-                filt = cellfun(@(x) any(strcmp(forcingData_colnames,x)), table2cell(siteCoordinates(:,1)));
-                pumpEN = table2array(siteCoordinates(filt,2:3));
-                pumpEN = bsxfun(@minus, pumpEN, boreEN);
-                
-                % Calculate distance to pump
-                dist = sqrt(pumpEN(:,1).^2 + pumpEN(:,2).^2);
-                
-                % Create table of pump IDs and diatances
-                pumpTable = table(siteCoordinates{filt,1},dist);
-                pumpTable = sortrows(pumpTable,2);
-                
-                % Filter data based on user input criteria.
-                filt = table2array(pumpTable(:,2)) <= maxDist;
-                pumpTable = pumpTable(filt,1:2);
-                if size(pumpTable,1)>maxNum                    
-                    pumpTable = pumpTable(1:maxNum,:);
-                end
-                
-                nrows = size(pumpTable,1);
-                variable_names = cell(nrows ,2);
-                %variable_names(1:nrows ,1) = strrep(strrep(strcat({'Pump '},num2str([1:nrows]')),' ','_'),'__','_');            
-                variable_names(1:nrows ,1) = strcat(table2cell(pumpTable(:,1)),'_infilled');
-                variable_names(1:size(pumpTable,1),2) = table2cell(pumpTable(:,1));
-                isOptionalInput = true(nrows ,1);
-                
-                % Set a global variable that stores the wizard results.
-                % This is an unfortunate requirement to allow the efficient
-                % selection of input data within the GUI TFN weighting
-                % function box.
-                global pumpingRate_SAestimation_wizardResults
-                pumpingRate_SAestimation_wizardResults = variable_names(1:nrows ,1);
-                
-                
-            end
-        end        
-        
-        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
-            %variable_names = strrep(strrep(strcat({'Pump '},num2str([1:75]')),' ','_'),'__','_');
-            
-            global pumpingRate_SAestimation_wizardResults
-            if ~isempty(pumpingRate_SAestimation_wizardResults)
-                variable_names = pumpingRate_SAestimation_wizardResults;
-            else            
-                % Find those forcing data columns with any infilling. These
-                % are most likely pumping bores.
-                filt = false(size(forcingData_data,2),1);
-                if iscell(forcingData_data) || istable(forcingData_data)
-                    for i=1:size(forcingData_data,2)
-                        filt(i) = any(forcingData_data{:,i} == -999);
-                    end                
-                else
-                    for i=1:size(forcingData_data,2)
-                        filt(i) = any(forcingData_data(:,i) == -999);
-                    end                
-                end
-                variable_names = strcat(forcingData_colnames(filt),'_infilled');
-                variable_names = variable_names';
-            end
-        end
-        
-        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
-            
-            options = cell(3,2);
-            colNames = {'Setting name','Setting value'};
-            colFormats = {'char','numeric'};
-            colEdits = logical([0 1]);
-
-            options{1,1} = 'Downscaling time-step (none=0, monthly=1, weekly=2, daily=3):';
-            options{1,2} = 3;
-
-            options{2,1} = 'Minimum number of bore meter readings:';
-            options{2,2} = 2;
-                     
-            options{3,1} = 'Minimum days to downscale:';
-            options{3,2} = 7;
-                                                
-            toolTip = sprintf([ 'Use this table to define the method for downscaling time-step. \n', ...
-                                'Note, pumps have less than the min. number of meter readings \n', ...
-                                'will be omitted from analysis.']);
-        end
-        
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: pumpingRate_SAestimation', ...
-                               '', ...
-                               'Purpose: downscaling of infrequent (>daily) groundwater pumping data to daily, weeekly or monthly rates. The downscaling', ...
-                               'is required when pumping is metered, say, only annually. The downscaling is undertaken by simulating the pump as being', ...
-                               'either on or off for each user-set downscaling time step. When the period is metered then the number of days pumping is', ...
-                               'used to estimate the average daily pumping rate. The average of all metered periods is then applied to non-metered periods.', ...
-                               'This approach requires no assumption of the behaviour of groudnwater users or their response to climatic conditions', ...
-                               '', ...
-                               'Importantly, the number of combinations of pump state over, say, a decade to assess against the observed head far exceeds', ...
-                               'the largest number possible on a 64 bit machine. The implications are that the calibration cannot assess all combinations', ...
-                               'and that if an identical model is re-calibrated an alternate optima may be identified. To manage this challenge the approach', ...
-                               'uses simulated annealing (SA); note SA comes from annealing in metallurgy, a technique involving heating and controlled ', ...
-                               'cooling of a material to increase the size of its crystals and reduce their defects. For details see the help documentation.', ...
-                               '', ...                               
-                               'Number of parameters: 0', ...
-                               '', ...                               
-                               'Options: For each pump the user can set the "Downscaling time-step","Initial Temperature","Cooling rate multipler". Note, a',  ...
-                               'daily time step can require >1M model runs. Also, the probability of the claibration findings the best down-scaled forcing', ...
-                               'increases as the SA initial temperature is increased and the SA cooling rate is decreased.', ...
-                               '', ...                               
-                               'Comments: ', ...
-                               '1. pumpingRate_SAestimation() required considerable changes to the calibration scheme and is only operational with the' , ...
-                               'SP-UCI. Other calibration schemes will be edited in the future.', ...
-                               '2. Calibration with an evaluation period required downscaling will crash when using this componant. Future releases will', ...
-                               'use the stochastically derived forcing tiem-series to build a predictive model for the estimation during evaluation periods.', ...
-                               '3. This extension to HydroSight was funded by the Department of Environment, Land, Water and Planning, Victoria.', ...
-                               'https://delwp.vic.gov.au/', ...
-                               '', ...               
-                               'References: ', ...
-                               '1. Peterson & Fulton (in-prep), Estimating aquifer hydraulic properties from existing groundwater monitoring data.'};
-        end        
-           
-    end
-        
-    
-    methods       
-%% Construct the model
-        function obj = pumpingRate_SAestimation(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)                        
-
-            % Clear the global variable for the required forcing columns.
-            global pumpingRate_SAestimation_wizardResults
-            clear pumpingRate_SAestimation_wizardResults
-            
-            % Set the calibration state as false
-            obj.variables.doingCalibration = false;
-            
-            % Get a list of required forcing inputs and (again) check that
-            % each of the required inputs is provided.
-            %--------------------------------------------------------------
-            [requiredFocingInputs, isOptionalInput] = pumpingRate_SAestimation.inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-            requiredFocingInputs = requiredFocingInputs(~isOptionalInput);
-            for j=1:numel(requiredFocingInputs)
-                filt = strcmpi(forcingData_reqCols(:,1), requiredFocingInputs(j));                                    
-                if ~any(filt)
-                    error(['An unexpected error occured. When transforming forcing data, the input cell array for the transformation must contain a row (in 1st column) labelled "forcingdata" that its self contains a cell array in which the forcing data column is defined for the input:', requiredFocingInputs{j}]);
-                end
-            end                         
-            
-            % Assign the input forcing data to obj.settings.
-            obj.settings.forcingData = forcingData_data;
-            obj.settings.forcingData_colnames = forcingData_colnames;
-            obj.settings.forcingData_cols = forcingData_reqCols;
-            obj.settings.siteCoordinates = siteCoordinates;
-            obj.settings.downscalingTimeStepFinal = modelOptions{1,1};
-            switch modelOptions{1,2}
-                case 0
-                   obj.settings.downscalingTimeStepFinal = '(none)';
-                case 1
-                   obj.settings.downscalingTimeStepFinal = 'monthly';
-                case 2
-                   obj.settings.downscalingTimeStepFinal = 'weekly'; 
-                case 3
-                   obj.settings.downscalingTimeStepFinal = 'daily';
-                otherwise
-                    error('pumpingRate_SAestimation: unknown timestep setting.')
-            end
-            obj.settings.downscalingTimeStepCurrent = '(none)';
-            obj.settings.minObsPeriodsPerPump = max(0,modelOptions{2,2});                     
-            obj.settings.minDays2Downscale = max(0,modelOptions{3,2}); 
-                        
-            % Get the forcing data time points
-            obsTime = obj.settings.forcingData(:, 1);            
-            
-            % Initialise the calibration period
-            obj.variables.t_start_calib = -inf;
-            obj.variables.t_end_calib = inf;
-            
-            % Loop through each pumping bore. Any observation that
-            % is preceeded by -999 is assumed to be the total pumping for
-            % the preceeding period of -999 values. An period that ends in
-            % -999 is assumed to be a non-metered period. Any observations
-            % that are not preceeded by -999 and daily obs.
-            nonObsPeriods=[];
-            nNonObsPeriod=0;
-            obsDays=nan(0,3);
-            nObsDays=0;
-            pumpIDs_insufficientMetering=[];
-            for i = 1:size(obj.settings.forcingData_cols,1)
-                
-                % Get pumping data
-                pumpingColName = obj.settings.forcingData_cols{i,1};
-                pumpingColInd = obj.settings.forcingData_cols{i,2};                
-                pumping = obj.settings.forcingData(:,pumpingColInd);                
-                
-                % Initialise the  number of meter readings
-                nMeterReadings = 0;
-
-                % Add calibration parameter for the pump and
-                % initialise to 0.5.
-                parName = [pumpingColName, '_position'];                        
-                dynPropMetaData{i} = addprop(obj,parName);                
-                obj.(parName)=-0.5;
-                
-                % Loop through each time point
-                for j=1: size(pumping,1)    
-
-                    % Find the start and end dates of each non-obs period.
-                    % ie -999 periods ending with zero. Below, for each period 
-                    % of no observations add instance property and initialised using
-                    % the regression relationship and period's evap deficit.
-                    if pumping(j)==-999 && (j==1 || pumping(j-1)>=0)
-                        % First day of -999 periods.
-                        nNonObsPeriod = nNonObsPeriod+1;
-                        nonObsPeriods{nNonObsPeriod,1} = i;
-                        nonObsPeriods{nNonObsPeriod,2} = pumpingColName;
-                        nonObsPeriods{nNonObsPeriod,3} = obsTime(j);
-                        nonObsPeriods{nNonObsPeriod,4} = obsTime(j);
-                        nonObsPeriods{nNonObsPeriod,5} = NaN;
-                        nonObsPeriods{nNonObsPeriod,6} = obj.settings.downscalingTimeStepCurrent;
-                    elseif pumping(j)==-999 && (j==size(pumping,1) || pumping(j+1)>=0)
-                        % End day of -999 periods.
-                        if j>=length(pumping) || pumping(j+1)==0
-                            % Unmetered period because the -999 period ends
-                            % with a zero.
-                            nonObsPeriods{nNonObsPeriod,4} = obsTime(j);
-                            nonObsPeriods{nNonObsPeriod,5} = NaN;
-                        else
-                            % Unmetred period endds with a recorded total
-                            % volume.
-                            nonObsPeriods{nNonObsPeriod,4} = obsTime(j)+1;
-                            nonObsPeriods{nNonObsPeriod,5} = pumping(j+1);
-                            nMeterReadings = nMeterReadings + 1;
-                        end
-                        
-                        % Store time-step for stochastic forcing generation.
-                        nonObsPeriods{nNonObsPeriod,6} = obj.settings.downscalingTimeStepCurrent;
-                    elseif (j==1 && pumping(j)>0) || (pumping(j)>0 && pumping(j-1)~=-999)
-                        nObsDays = nObsDays +1;
-                        obsDays(nObsDays, :) = [i,  obsTime(j), pumping(j)];
-                        nMeterReadings = nMeterReadings + 1;
-                    end
-                end
-                
-                % Check there are at least the user set minimum number of metered periods for the pump.
-                if nMeterReadings < obj.settings.minObsPeriodsPerPump
-                    pumpIDs_insufficientMetering=[pumpIDs_insufficientMetering, i];
-                    %error(['Pump ',forcingData_colnames{pumpingColInd}, ' must have >=',num2str(obj.settings.minObsPeriodsPerPump),' periods of metered total extractions.']);
-                end
-                
-            end
-            
-            % Remove pumps with insufficient metering.
-            for i=1:length(pumpIDs_insufficientMetering)
-                filt = cell2mat(nonObsPeriods(:,1)) ~= pumpIDs_insufficientMetering(i);
-                nonObsPeriods = nonObsPeriods(filt,:);
-                delete(dynPropMetaData{pumpIDs_insufficientMetering(i)})
-            end
-            
-            % Remove unmetered periods that are less than the user set
-            % minimum duration.
-            filt= true(size(nonObsPeriods,1),1);
-            for i=1:size(nonObsPeriods,1)
-                if isnan(nonObsPeriods{i,5}) && ...
-                nonObsPeriods{i,4}-nonObsPeriods{i,3}<obj.settings.minDays2Downscale
-            
-                    filt(i) = false;
-            
-                     % Set period in forcing data to zero
-                    pumpingColInd = obj.settings.forcingData_cols{nonObsPeriods{i,1},2};                
-                    filt_time = obj.settings.forcingData(:,1) >= nonObsPeriods{i,3} & obj.settings.forcingData(:,1) <= nonObsPeriods{i,4};
-                    obj.settings.forcingData(filt_time ,pumpingColInd) = 0;
-                end
-            end
-            nonObsPeriods = nonObsPeriods(filt,:);
-            
-            % Add observed and non-obs periods to object
-            obj.variables.nonObsPeriods = nonObsPeriods;                       
-            obj.variables.obsDays = obsDays;                       
-                        
-            % Set the indexes to the unmetered data. The indexes are used
-            % to efficiently find the forcing date time points for a given
-            % time point of a given time-step.
-            setTimestepIndexes(obj,obj.settings.downscalingTimeStepCurrent)
-            
-            % Set the pump usage data. The following should be identical to
-            % updateStochForcingData(obj) but without the change to the
-            % pump state.
-            %----------            
-            % Get the mean daily pumping rate during the metered periods.
-            dailyRate = getDailyAveragePumpingRates(obj, -inf, inf);
-
-            % Filter out periods for which the daily rate could not be calculated.
-            dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
-
-            % Build a table of pump numbers and names
-            if isfield(obj.variables,'pumpNumTable')
-                obj.variables = rmfield(obj.variables,'pumpNumTable');
-            end
-            getPumpNumTable(obj);
-                   
-            % Update pumping rates based on new pump states
-            for i=1:size(obj.variables.pumpNumTable ,1)
-
-                % Get the current pump name and number.
-                pumpNum = obj.variables.pumpNumTable.pumpNum(i);
-                pumpName = obj.variables.pumpNumTable.pumpName{i};
-                
-                % Initialise daily pump rate to mean
-                filt = dailyRate(:,1) == obj.variables.pumpNumTable{i,1};
-                dailyRate_median = median(dailyRate(filt ,5));
-                obj.variables.(pumpName)(:,7)=dailyRate_median;
-
-                % Initialise daily pump volume to nan
-                obj.variables.(pumpName)(:,8)=nan;
-
-                % Assign the average daily pumping rate during each metered period
-                filt = find(dailyRate(:,1) ==pumpNum)';
-                for j=filt                        
-                    filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(j,2) & obj.variables.(pumpName)(:,1) <= dailyRate(j,3); 
-                    obj.variables.(pumpName)(filt_obsPeriods,7)=dailyRate(j,5);
-                end
-
-                % Multiple pump state by daily rate to create down-scaled pumping rate
-                obj.variables.(pumpName)(:,8)=obj.variables.(pumpName)(:,6).*obj.variables.(pumpName)(:,7);
-            end            
-            %----------
-                        
-        end
-        
-%% Set parameters
-        function setParameters(obj, params)
-% setParameters returns the soil model parameters.
-%
-% Syntax:
-%   setParameters(obj, params)  
-%
-% Description:  
-%   This method sets the soil moisture model parameters to user input values.
-%   Only parameters that are to be calibrated (i.e. non-fixed parameters)
-%   can be set. Also the vector of input parameter values must be in the
-%   same order as the parameter names returned by the method
-%   'getParameters'.
-%
-% Input:
-%   obj     - soil moisture model object.
-%   params  - vector (Nx1) of parameter values.
-%
-% Outputs:
-%   (none)
-%
-% See also:
-%   climateTransform_soilMoistureModels: class_definition;
-%   getParameters: get_calibration_parameters_values;
-%   detectParameterChange: assesst_if_parameters_have_changed_recently;
-%   setTransformedForcing: run_model_and_store_simulation_results;
-%   getTransformedForcing: get_outputs_for_timeseries_model.
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   11 April 2012  
-
-
-            % Get the parameter names
-            param_names = properties(obj);
-            filt = cellfun( @(x) ~strcmp(x,'settings') && ~strcmp(x,'variables'), param_names);
-            param_names = param_names(filt);
-            
-            % Cycle through each parameter and assign the parameter value.
-            for i=1: length(param_names)
-               obj.(param_names{i}) = params(i,:); 
-            end
-            
-            % Check if the parameters have changed since the last call to
-            % setTransformedForcing.
-            detectParameterChange(obj, params);   
-        end
-        
-        function setForcingData(obj, forcingData, forcingData_colnames)
-% setForcingData sets the forcing data.
-%
-% Syntax:
-%   setForcingData(obj, forcingData, forcingData_colnames)
-%
-% Description:  
-%   This method set the climate forcing data. It is used to update the
-%   forcing data for model simulations (primarily from the GUI).
-%
-% Input:
-%   obj         - soil moisture model object.
-%
-%   forcingData - nxm matrix of focrinf data with column 1 being the
-%                 date/time.
-%
-%   forcingData_colnames - 1xm cell array of column names within the above
-%                 data.
-%
-% Outputs:
-%   (none)
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   21 Dec 2015              
-
-
-            % Store only the columns listed in obj.settings.forcingData_colnames
-            % ie those needed and index within obj.settings.forcingData_cols
-            if length(forcingData_colnames) < length(obj.settings.forcingData_colnames)
-                error('The number of column name to be set is less than that used to build the object.');
-            end
-            forcingDataNew = nan(size(forcingData,1),length(obj.settings.forcingData_colnames));
-            for i=1:length(forcingData_colnames)               
-                filt  = strcmp(obj.settings.forcingData_colnames, forcingData_colnames{i});
-                if ~isempty(filt)
-                    forcingDataNew(:,filt) = forcingData(:,i);
-                end
-            end
-            obj.settings.forcingData = forcingDataNew;
-                        
-        end                
-
-        function forcingData = getStochForcingData(obj)        
-           
-            % Get pump names and numbers
-            getPumpNumTable(obj);    
-
-            % Check if there are sufficient metereed usage observations
-            % WITHIN the calibration at each bore. If there are not any
-            % metered usage values then an error should be thrown.
-            updateStochForcingData(obj);
-            
-            % Get pump states from each bore.
-            for i=1:size(obj.variables.pumpNumTable ,1)
-                pumpName = obj.variables.pumpNumTable.pumpName{i};
-                forcingData.(pumpName) = logical(obj.variables.(pumpName)(:,6));                                                            
-            end                
-        end
-        
-        function setStochForcingState(obj,doingCalibration, t_start_calib, t_end_calib)
-            obj.variables.doingCalibration = doingCalibration;
-
-            % Update the calibration period
-            obj.variables.t_start_calib = t_start_calib;
-            obj.variables.t_end_calib = t_end_calib;              
-            
-            % Build downscaling indexes using t_start_calib and
-            % t_end_calib.
-            if doingCalibration
-                % Re-initialise downscaling time stwp
-                obj.settings.downscalingTimeStepCurrent = '(none)';
-                
-                % Re-build lookup indexes for the starting downscaling time step.
-                setTimestepIndexes(obj, obj.settings.downscalingTimeStepCurrent, t_start_calib, t_end_calib)                
-            end
-            
-        end
-                
-        function finishedStochForcing = updateStochForcingData(obj, forcingData, refineStochForcingMethod)
-            
-            % Only update stochastic forcing if doing calibration
-            if isfield(obj.variables,'doingCalibration') && ~obj.variables.doingCalibration
-                finishedStochForcing = true;
-            else
-                % Initilaise output
-                finishedStochForcing = false;
-
-                % Estimate the mean daily pumping rate during the metered
-                % periods.
-                [dailyRate, ind_dailyRate] = getDailyAveragePumpingRates(obj,obj.variables.t_start_calib, obj.variables.t_end_calib);
-
-                % Filter out periods for which the daily rate could not be
-                % calculated.
-                dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
-
-                % Get pump names and numbers
-                pumpNumTable=getPumpNumTable(obj);
-
-                % Update the pump state if input.
-                if nargin>=2
-
-                    % Get names of pumps within the input forcing data.
-                    pumpsNames_input =  fieldnames(forcingData);
-
-                    % Loop through each input names and assign the forcing data
-                    % to the object.
-                    for i=1:length(pumpsNames_input)
-
-                        
-                        % Check the input field name is within the object
-                        % already.
-                        if ~any(strcmp(obj.variables.pumpNumTable.pumpName, pumpsNames_input{i}))
-                            error('A fieldname within the input "forcingData" is not listed within the pumpingRate_SAestimation object.');
-                        end
-
-                        % Check the input number of days is as expected.
-                        if size(forcingData.(pumpsNames_input{i}),1) ~= size(obj.variables.(pumpsNames_input{i}),1)
-                            error(['The number of rows within forcingData.',pumpsNames_input{i},' does not equal that within the corresponding  pumpingRate_SAestimation variable.']);
-                        end 
-
-                        % Assess if each metered period with pump rate >0 has >0 periods with
-                        % the pump on. That is, if a metered period to be downscaled has usage
-                        % >0 then the days when the pump is on must be >0.
-                        % In doing this assessment, periods of daily
-                        % metered usage are not assessed because they are
-                        % not to be downscaled.
-                        %-------------
-
-                        % Find the pump number for the cirrent value of i.
-                        filt = find(strcmp(obj.variables.pumpNumTable.pumpName, pumpsNames_input{i}));
-                        pumpNum = obj.variables.pumpNumTable.pumpNum(filt);
-                        pumpName = obj.variables.pumpNumTable.pumpName{filt};
-                        
-                        
-                        % Filter for any metered periods.
-                        filt = [find(dailyRate(:,1)==pumpNum)]';
-
-                        isValidPumpState = true;
-                        for j=filt                            
-                           filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(j,2) ...
-                                              & obj.variables.(pumpName)(:,1) <= dailyRate(j,3); 
-                           if  sum(filt_obsPeriods)>0 && sum(forcingData.(pumpName)(filt_obsPeriods)) <=0
-                               isValidPumpState = false;
-                           end
-                        end
-                        %-------------
-
-                        % Assign input forcing
-                        if isValidPumpState
-                            obj.variables.(pumpName)(:,6) = double(forcingData.(pumpName));
-                        end
-
-                    end
-                end
-
-                % Redefine the downscaling time step if the new AND if
-                % different to the current time step
-                if nargin==3 && refineStochForcingMethod
-
-                    % Check if the bore downscaling is already at the user specified
-                    % time-step. If so, exit if() and return that the time step
-                    % was not refined.
-                    if strcmp(lower(obj.settings.downscalingTimeStepCurrent), lower(obj.settings.downscalingTimeStepFinal))
-                        finishedStochForcing = true;
-                    else 
-                        switch lower(obj.settings.downscalingTimeStepCurrent)
-                            case 'daily'
-                                finishedStochForcing  = true;
-                            case 'weekly'
-                                obj.settings.downscalingTimeStepCurrent = 'daily';
-                                finishedStochForcing  = false;
-                            case 'monthly'
-                                obj.settings.downscalingTimeStepCurrent = 'weekly';
-                                finishedStochForcing  = false;
-                            case '(none)'
-                                obj.settings.downscalingTimeStepCurrent = 'monthly';
-                                finishedStochForcing  = false;
-                            otherwise
-                                error(['The following time-step for generation of forcing data is not recognised:',obj.settings.downscalingTimeStepCurrent])
-                        end                
-                    end                        
-
-                    % If the downscaling time step is not yet at the final
-                    % user set resolution, then re-build the time-step
-                    % indexes for the new time step size. Importantly,
-                    % setTimestepIndexes() preserves the pump states from
-                    % the prior time step and copies them to the new time
-                    % step.
-                    if ~finishedStochForcing
-                        setTimestepIndexes(obj, obj.settings.downscalingTimeStepCurrent)
-                    end
-
-                end
-
-                % Reculate the pump rates if the forcing state was input.
-                if nargin>1
-                    % Update the mean daily pumping rate during the metered
-                    % periods because the pump state changed.
-                    [dailyRate, ind_dailyRate] = getDailyAveragePumpingRates(obj,obj.variables.t_start_calib, obj.variables.t_end_calib);
-                    
-                    % Filter out periods for which the daily rate could not be
-                    % calculated.
-                    dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
-                end
-                % Update pumping rates based on new pump states
-                for i=1:size(pumpNumTable,1)
-
-                    % Get the current pump name and number.
-                    pumpNum = obj.variables.pumpNumTable.pumpNum(i);
-                    pumpName = obj.variables.pumpNumTable.pumpName{i};
-                    
-                    % Get indexes to the current pump number.
-                    filt = find(dailyRate(:,1) == pumpNum)';
-                    
-                    % If there are no metered usage rates within the calib 
-                    % period AND there are dates where the pumping needs to
-                    % be estimated (but not downscaled), then an estimate 
-                    % can not be made because the median rate cannot be
-                    % estimated.
-                    filt_calibPeriod = obj.variables.(pumpName)(:,1)<=obj.variables.t_end_calib;
-                    if isempty(filt) && any(~isfinite(obj.variables.(pumpName)(filt_calibPeriod ,4)))
-                        error(['Pump ',pumpName,' has no metered usage in calib. period.'])
-                    end
-                    
-                    % Calculate the median metered pump rate. 
-                    dailyRate_median = median(dailyRate(filt ,5));
-                    
-                    % Assign the average daily pumping rate during each
-                    % metered period. NOTE, this task was the most
-                    % computationally intesive steps in the evaluation of
-                    % the objective function. Initially, this was addressed
-                    % by using bsxfun commands for situations when there
-                    % are >5 periods to assign downscaled pumping. However
-                    % in trials for the Warrion management area (Victoria, Australia)
-                    % the bsxfun steps still used ~60% of CPU time. After
-                    % some trials, the computational demand was
-                    % significantly reduced by having getDailyAveragePumpingRates()
-                    % return indexes to each row of obj.variables.(pumpName)
-                    % and the associated downscaled pumping for the row.
-                    % This approach eliminated the need for the bsxfun
-                    % commands!!!
-                    %------------------------------------
-%                     filt=find(dailyRate(:,1) == pumpNum & (dailyRate(:,3)-dailyRate(:,2))>1)';
-%                     if length(filt)>5                           
-%                         filt_obsPeriods=bsxfun(@ge, obj.variables.(pumpName)(:,1), dailyRate(filt,2)') & bsxfun(@le, obj.variables.(pumpName)(:,1), dailyRate(filt,3)');
-%                         obj.variables.(pumpName)(:,7) = sum(bsxfun(@times, filt_obsPeriods, dailyRate(filt,5)'),2) + all(~filt_obsPeriods,2) * dailyRate_median;                        
-%                     else
-%                         obj.variables.(pumpName)(:,7)=dailyRate_median;
-%                         for j=filt                        
-%                             filt_obsPeriods = bsxfun(@ge, obj.variables.(pumpName)(:,1), dailyRate(j,2)') & bsxfun(@le, obj.variables.(pumpName)(:,1), dailyRate(j,3)');
-%                             obj.variables.(pumpName)(ind_obsPeriods,7)=dailyRate(j,5);
-%                         end                    
-%                     end
-                    % Add median pump rate to all time points.
-                    obj.variables.(pumpName)(:,7)=dailyRate_median;                   
-                    % Add metered pump rate (is any exists).
-                    if ~isempty(ind_dailyRate.(pumpName))
-                        obj.variables.(pumpName)(ind_dailyRate.(pumpName)(:,1),7)  = ind_dailyRate.(pumpName)(:,2);
-                    end
-                    %------------------------------------
-                    
-                    % Multiple pump state by daily rate to create down-scaled pumping rate
-                    obj.variables.(pumpName)(:,8)=obj.variables.(pumpName)(:,6).*obj.variables.(pumpName)(:,7);
-                end
-            end
-        end
-        
-%% Randomise the model parameters. 
-%  This function should be called at the end of an evolutionary loop to
-%  avoid the parameters converging to a local minimum
-        function updateStochForcingParameters(obj, forcingData)
-
-            % Get the parameter bounds
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-                        
-            % Cycle through each parameter and randoly assign the parameter value.
-            newparams = params_lowerLimit + (params_upperLimit - params_lowerLimit) .* rand(size(params_lowerLimit));
-            
-            % Set the new parameter 
-            setParameters(obj, newparams );            
-        end
-        
-%% Get model parameters
-        function [params, param_names] = getParameters(obj)            
-% getParameters sets the soil model parameters.
-%
-% Syntax:
-%   setParameters(obj, params)  
-%
-% Description:  
-%   This method gets model parameters. For this class, mno parameters are
-%   every returned.
-%
-% Input:
-%   obj         - model object.
-%
-% Outputs:
-%   params      - an empty vector.
-%
-%   param_names - an empry vector (Nx1) of parameter names.   
-%%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   June 2017
-            % Get the parameter names
-            param_names = properties(obj);
-            filt = cellfun( @(x) ~strcmp(x,'settings') && ~strcmp(x,'variables'), param_names);
-            param_names = param_names(filt);
-            
-            % Cycle through each parameter and assign the parameter value.
-            params=[];
-            for i=1: length(param_names)
-               params(i,:) = obj.(param_names{i}); 
-            end
-        end   
-        
-%% Assess if matrix of parameters is valid.
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-% getParameterValidity returns a logical vector for the validity or each parameter.
-%
-% Syntax:
-%   isValidParameter = getParameterValidity(obj, params, param_names)
-%
-% Description:
-%   Returns a logical vector denoting if each parameter is valid.
-%
-% Input:
-%   obj -  model object.
-%
-%   params - vector of model parameters
-%
-%   param_names - cell array of the parameter names.
-%
-% Outputs:
-%   isValidParameter - column vector of the parameter validity.
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   June 2017
-
-            % Get physical bounds.
-            isValidParameter = true(size(params));   
-        end   
-
-        function isNewParameters = detectParameterChange(obj, params)
-           
-            isNewParameters = true(size(params));
-            
-        end
-        
-%% Return fixed upper and lower bounds to the parameters.
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
-% getParameters_physicalLimit returns the physical limits to each model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
-%
-% Description:
-%   Cycles through all active soil model componants and parameters and returns
-%    a vector of the physical upper and lower parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter bounds
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   June 2017
-
-            [params, param_names] = getParameters(obj);            
-            params_lowerLimit = -1.*ones(size(params));
-            params_upperLimit = ones(size(params));
-        end  
-        
-%% Return fixed upper and lower plausible parameter ranges. 
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-% getParameters_plausibleLimit returns the plausible limits to each model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-%
-% Description:
-%   Cycles through all active soil model componants and parameters and returns
-%    a vector of the plausible upper and lower parameter bounds.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter bounds
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   June 2017
-            [params, param_names] = getParameters(obj);            
-            params_lowerLimit = -1.*ones(size(params));
-            params_upperLimit = ones(size(params));
-        end        
-        
-%% Solve the soil moisture differential equation
-        function setTransformedForcing(obj, t, forceRecalculation)
-% setTransformedForcing does nothing.
-%
-% Syntax:
-%   setTransformedForcing(obj, climateData, forceRecalculation)
-%
-% Description:  
-%   This function does nothing. Handling of the focring data is undertaken
-%   by updateStochForcingData().
-%
-% Input:
-%   obj         - model object.
-%
-%   t           - a vector (Nx1) of time points for simulation.
-%
-%   forceRecalculation - is a logical scalar input (i.e. true of false) to
-%                 force re-calculation of the model and thus ingore if the
-%                 parameters have or have not changed.
-% Outputs:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   June 2017
-
-            % Only change the stochatsic forcing if doing calibration.
-            if ~isfield(obj.variables,'doingCalibration') || obj.variables.doingCalibration
-
-                % Filter the forcing data to input t.
-                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
-
-                % Store the time points
-                obj.variables.t = t(filt_time);
-
-                % Get the start and end date for downscaled periods for
-                % each pump.
-                %------------                
-                % Build vector of from non-obs periods 'nonObsPeriods' of bore number, 
-                % start and end date of non-metered periods.
-                dailyRate = cell2mat(obj.variables.nonObsPeriods(:,[1,3,4]));
-                
-                % Filter out periods that do not have a metered reading
-                filt = isfinite(cell2mat(obj.variables.nonObsPeriods(:,5)));
-                dailyRate = dailyRate(filt,:);
-                
-                % Add the daily metered volumes. Since these readings are for a
-                % single day, the the start and end dates are equal.
-                dailyRate = [dailyRate; [obj.variables.obsDays(:,1), obj.variables.obsDays(:,2), obj.variables.obsDays(:,2)]];
-                %------------
-                
-                % Get the object parameters.
-                [params, param_names] = getParameters(obj);
-
-                % Get the table of pump numbers and names if it exists,
-                % else built it
-                pumpNumTable = getPumpNumTable(obj);
-                
-                % Loop through each PARAMETER, find the date to edit pumping state and 
-                % then update the state. Once updated, calculate the median
-                % pumping rate for each period using ONLY the periods with
-                % a metered total extraction volume. Then, apply the
-                % average pumping rate to all periods without metered total
-                % extractions.                
-                for i=1:size(params,1)
-
-                    % Get bore names.
-                    pumpName = param_names{i}(1:end-9);
-
-                    % Get pump number 
-                    pumpNum = pumpNumTable.pumpNum(strcmp(pumpNumTable.pumpName,pumpName));
-
-                    % Find the number of discrete time steps possible
-                    ind_calibDays = obj.variables.(pumpName)(:,3)>0;
-                    timestepMax = length((unique(obj.variables.(pumpName)(ind_calibDays,3))));
-
-                    % Find normalised time-period from all periods of non-daily pumping.                    
-                    timestepValNormalised = ceil(abs(params(i)) * timestepMax)/timestepMax;
-                    filt = obj.variables.(pumpName)(:,5)==timestepValNormalised;
-
-                    % Calc number of pumping days to change.
-                    nDaysToChange = sum(filt);                   
-
-                    % Find if the current time point is a metered period.
-                    filt_dailyRate = find(dailyRate(:,1) == pumpNum)';
-                    date2Chnage = obj.variables.(pumpName)(filt,1);
-                    dailyRate_index = nan;
-                    for j=filt_dailyRate                        
-                        if any(dailyRate(j,2) <= date2Chnage) && any(dailyRate(j,3) >=date2Chnage)
-                            dailyRate_index = j;
-                            break
-                        end
-                    end
-
-                    % Change state of pumping only if, over a metered period,
-                    % the number of days with pumping is greater than those
-                    % to be turned off.                        
-                    changePumpState = true;
-                    isValidPumpState = true;
-                    if isfinite(dailyRate_index) && max(obj.variables.(pumpName)(filt,6))==1 && params(i)<sqrt(eps())
-
-                        filt_periodPumpingDays = obj.variables.(pumpName)(:,1) >=dailyRate(dailyRate_index,2) ...
-                        & obj.variables.(pumpName)(:,1) <= dailyRate(dailyRate_index,3);
-
-                        nPeriodPumpingDays = sum(obj.variables.(pumpName)(filt_periodPumpingDays,6));
-
-                        if nPeriodPumpingDays <= nDaysToChange
-                            changePumpState = false;
-                            isValidPumpState=false;
-                        end
-                    end                                        
-                    if changePumpState 
-                        %obj.variables.(pumpName{i})(filt,6) = double(newPumpState);
-                        if params(i)>sqrt(eps())
-                            obj.variables.(pumpName)(filt,6) = 1;
-                        elseif params(i)<-sqrt(eps())
-                            obj.variables.(pumpName)(filt,6) = 0;
-                        end    
-                    else
-                        % do nothing
-                        changePumpState = false;
-                    end
-                end  
-                
-                % Update the average daily pump date over each time step
-                % and bore and then multiple by the pump state.
-                updateStochForcingData(obj);
-            end
-        end
-        
-%% Return the transformed forcing data
-        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName) 
-% getTransformedForcing returns the required flux from the soil model.
-%
-% Syntax:
-%   [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName)
-%
-% Description:  
-%   This method returns the requested flux/data from the apporiate pump.
-%
-% Input:
-%   obj - model object.
-%
-%   variableName - a string for the variable name to return.
-%
-% Outputs:
-%   forcingData  - a vector (Nx1) of the forcing data output to
-%                     be input to the groundwater time series model.
-%
-%   isDailyIntegralFlux - logical scaler denoting if the flux is a daily
-%   integral or an instantaneous value. This is used to inform the type of
-%   numerical integration within modelTFN.get_h_star().
-%
-% Dependencies:
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   July 2017 
-
-
-            if ischar(variableName)
-                variableNametmp{1}=variableName;
-                variableName = variableNametmp;
-                clear variableNametmp;
-            end
-
-            isDailyIntegralFlux=true;
-            try 
-                for i=1:length(variableName)
-                    % Initialise to input forcing data.
-                    colForcingData_filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
-                    colForcingData = obj.settings.forcingData_cols{colForcingData_filt,2};
-                    if i==1
-                        forcingData = obj.settings.forcingData(:,colForcingData);
-                    else
-                        forcingData(:,i) = obj.settings.forcingData(:,colForcingData);
-                    end
-                    
-                    % Skip if the pump was removed because if could not be
-                    % downscaled
-                    if (~isfield(obj.variables,variableName{i}))
-                        continue;
-                    end
-                    
-                    % Add stochastically generated forcing data
-                    filt_calibPeriod = obj.variables.(variableName{i})(:,2)>0;
-                    forcingData(obj.variables.(variableName{i})(filt_calibPeriod,2),i) = obj.variables.(variableName{i})(filt_calibPeriod ,8);                                                         
-                    
-                    % If there are any remaining -999s, then these should
-                    % be in evaluation periods. To handle this, the mean daily
-                    % extractions is calculated for each day of the year.
-                    % Also the mean pump state (on or off) is calculated. 
-                    % Importantly, the averages are calculated using only
-                    % data from the first non-zero value ie once the pump
-                    % was active. The former is then used for periods of
-                    % -999 without a metered volume for the period. The
-                    % latter is used for the metered periods and is used to
-                    % weight the metered volume to the -999 days.
-                    if ~obj.variables.doingCalibration
-                        
-                        % Find remaining periods of-999s.
-                        ind_999s = find(forcingData(:,i) == -999);
-
-                        % If there are any -999s then, firstly, calculate the
-                        % probability of the pumping being on for each calendar
-                        % day. This is done using data from the first >0 value
-                        % up to end of the calibration period?
-                        if ~isempty(ind_999s)
-
-                            % Build indexes to the start of pumping and the end
-                            % of the calibration period.
-                            obsTime = obj.settings.forcingData(:, 1);
-                            ind_startOfPumping = find(forcingData(:,i)>0,1,'first');                        
-                            if isfield(obj.variables,'t_end_calib') && isfinite(obj.variables.t_end_calib)
-                                ind_endOfCalib = find(obsTime>=obj.variables.t_end_calib,1,'first');
-                            else
-                                ind_endOfCalib = size(obsTime,1);
-                            end
-                            filt_obsTimes = obsTime>=obsTime(ind_startOfPumping) & obsTime<=obsTime(ind_endOfCalib) & forcingData(:,i)~=-999;
-
-                            % Calculate the probability that the pump is on
-                            % during each calander day.                        
-                            [monthsDays,~,ind] = unique(month(obsTime(filt_obsTimes))*100+day(obsTime(filt_obsTimes)),'rows');
-                            meanUsagePerDay = accumarray(ind, forcingData(filt_obsTimes,i), [], @mean);                
-                            meanUsagePerDay = [monthsDays, meanUsagePerDay];
-
-                            % Calculate the mean probability that the pump is on for
-                            % each day day of the year.
-                            meanProbPumpOn = accumarray(ind, forcingData(filt_obsTimes,i)>0, [], @mean);
-                            meanProbPumpOn = [monthsDays, meanProbPumpOn];                        
-
-                            % Find the periods of -999s with a meter reading
-                            % >0 immediatly after a -999.
-                            ind_999s_metered = ind_999s(forcingData(min(ind_999s+1,size(forcingData,1)),i)>0);
-
-                            % Loop through the -999 metered periods and
-                            % downscale the metered volume using the mean
-                            % probability of the pump being on.
-                            for j=1:length(ind_999s_metered)
-
-                                % Find the metered volume for the period.
-                                pumpingVol = forcingData(min(size(forcingData,1),ind_999s_metered(j)+1),i);                            
-
-                                % Find the first -999 for the metered period.
-                                k = ind_999s_metered(j);
-                                while forcingData(k,i) == -999
-                                    k = k-1;
-                                end
-                                k=k+1;
-
-                                % Set indexes to the -999s for the metered 
-                                % period. Note, the period includes the first 
-                                % day after the -999s because it to is to
-                                % be downscaled because the value
-                                % represents the total metered volume over the
-                                % preceeding period of -999s.
-                                ind_999s_metered_period  = k:(ind_999s_metered(j)+1);
-
-                                % Build index to the days and months for the period of
-                                % -999s.
-                                ind_monthsDays = zeros(length(ind_999s_metered_period),1);
-                                monthDay_period = month(obsTime(ind_999s_metered_period))*100+day(obsTime(ind_999s_metered_period));
-                                for el=1:length(ind_999s_metered_period)
-                                    ind_monthsDays(el,1) = find(monthsDays == monthDay_period(el),1,'first');    
-                                end
-                                    
-                                % Get the average pump state for the period.
-                                downscalingWeights = meanProbPumpOn(ind_monthsDays,2);
-                                downscalingWeights = downscalingWeights./sum(downscalingWeights);
-
-                                % Replace non-finite values by 0. 
-                                downscalingWeights(~isfinite(downscalingWeights))=0;
-                                
-                                % Down scale using the weighting.
-                                if sum(downscalingWeights)==0
-                                    pumpingVol = pumpingVol./(length(downscalingWeights).*ones(size(downscalingWeights)));
-                                else
-                                    pumpingVol = pumpingVol.*downscalingWeights;
-                                end
-
-                                % Assign downscaled weights to the forcing
-                                % data.
-                                forcingData(ind_999s_metered_period,i) = pumpingVol;
-                            end
-
-                            % Find the remaining -999s.
-                            ind_999s = find(forcingData(:,i) == -999);
-
-                            % Find the day and month of each -999 day
-                            monthDay_period = month(obsTime(ind_999s))*100+day(obsTime(ind_999s));
-
-                            % Loop though each periods of -999s and apply the
-                            % average usage for each day of the year.                         
-                            for j=1:length(monthDay_period)                           
-                                ind_monthsDays = find(monthsDays == monthDay_period(j),1,'first');
-                                forcingData(ind_999s(j),i) = meanUsagePerDay(ind_monthsDays ,2);
-                            end
-
-                        end     
-                    end
-                end
-            catch ME
-                error('The requested transformed forcing variable is not known.');
-            end
-        end
-        
-        % Return the derived variables.
-        function [params, param_names] = getDerivedParameters(obj)
-            params = [];
-            param_names = cell(0,2);
-        end
-
-        % Return coordinates for forcing variable
-        function coordinates = getCoordinates(obj, variableName)
-
-            if ~iscell(variableName)
-                variableNameTmp{1}=variableName;
-                variableName = variableNameTmp;
-                clear variableNameTmp;
-            end
-                
-            % Check each requested variable is within forcingData_cols
-            for i=1:length(variableName)
-                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
-                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
-                end
-            end
-            
-            coordinates = cell(length(variableName),3);
-            for i=1:length(variableName)
-                % Find row within the list of required containing variabeName
-                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
-
-                % Find input bore for requested output
-                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
-                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
-
-                % Get coordinates
-                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
-                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
-                coordinates{i,1} = variableName{i};
-            end
-        end
-                
-    end
-    
-    methods(Access=protected, Hidden=true)
-                
-        % Get/build a table of of pump numes and numbers
-        function pumpNumTable=getPumpNumTable(obj)            
-            if isfield(obj.variables,'pumpNumTable')
-                % Get the table of pump numbers and names if it exists.
-                pumpNumTable = obj.variables.pumpNumTable;
-            else
-                % Build table.
-                pumpNumTable = unique(cell2table(obj.variables.nonObsPeriods(:,1:2),'VariableNames',{'pumpNum','pumpName'}),'rows');
-                obj.variables.pumpNumTable = pumpNumTable;
-            end
-        end
-        
-        % Calculate the mean daily pumping rate for each period with metered volume. 
-        % Returns matrix of size(obj.variables.nonObsPeriods,1) with
-        % pump number, start date for period, end date for period, average
-        % daily pumping rate for the period.
-        function [dailyRate,ind_dailyRate] = getDailyAveragePumpingRates(obj, t_start_calib, t_end_calib)
-                  
-            % Get list of non-obs periods nonObsPeriods.
-            nonObsPeriods = obj.variables.nonObsPeriods;
-
-            pumpName_all = unique(nonObsPeriods(:,2));
-            for i=1:length(pumpName_all)
-                ind_dailyRate.(pumpName_all{i}) = [];
-            end
-            
-            % For each bore, calculate the average daily pumping rate
-            % during each petered period.
-            dailyRate = nan(size(nonObsPeriods,1),5);
-            for i=1:size(nonObsPeriods,1)
-                
-                dailyRate(i,1) = nonObsPeriods{i,1};
-                dailyRate(i,2) = nonObsPeriods{i,3};
-                dailyRate(i,3) = nonObsPeriods{i,4};
-                                                
-                % Check if the pumping was metered for the period.
-                if ~isfinite(nonObsPeriods{i,5})
-                    continue
-                end
-                
-                % Check the period of metered total extractions is within
-                % the calcibration time range t. That is, if the end
-                % date of the metered period is after the end calib period
-                % then do not use the metered rate.
-                if dailyRate(i,3) > t_end_calib
-                    continue
-                end
-                                
-                % get the pump names
-                pumpName = nonObsPeriods{i,2};
-                
-                % Find the number of days the pump is recorded as 'on'. 
-                [~,ind_from] = ismember(dailyRate(i,2),obj.variables.(pumpName)(:,1));
-                [~,ind_to] = ismember(dailyRate(i,3),obj.variables.(pumpName)(:,1));
-                ind = ind_from:ind_to;
-                
-                %filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(i,2) & obj.variables.(pumpName)(:,1) <= dailyRate(i,3); 
-                dailyRate(i,4) = sum(obj.variables.(pumpName)(ind,6));
-                
-                % Calculate average daily pumping rate for the period.
-                dailyRate(i,5) = nonObsPeriods{i,5}/dailyRate(i,4);                            
-                
-                ind_dailyRate.(pumpName) = [ind_dailyRate.(pumpName); [ind', repmat(dailyRate(i,5), length(ind),1)]];
-            end        
-            
-            % Add the daily metered volumes. Since these readings are for a
-            % single day, the the start and end dates are equal.
-            % Importantly, ONLY daily metered usage before the calibration
-            % end date is added.
-            filt = obj.variables.obsDays(:,2)<=t_end_calib;
-            if sum(filt)>0
-                dailyRate = [dailyRate; [obj.variables.obsDays(filt ,1), ...
-                    obj.variables.obsDays(filt ,2), obj.variables.obsDays(filt ,2), ones(sum(filt),1), obj.variables.obsDays(filt ,3)]];           
-            end
-        end
-        
-        function setTimestepIndexes(obj, updateTimeStep, t_start_calib, t_end_calib)
-           
-            % Loop through each pump bore and add one property time point at which the 
-            % forcing should be changed from zero to one, or vise versa.
-            if ~isempty(obj.variables.nonObsPeriods)
-                for i=1:size(obj.settings.forcingData_cols,1)
-                    % Get unobserved periods for the current bore
-                    filt = find(cellfun(@(x) x == i, obj.variables.nonObsPeriods(:,1)))';
-                    nNonObsPeriod = length(filt);
-
-                    % Add properties (ie parameters) for annual pumping data. For each
-                    % year without pumping data a parameter is added.
-                    if nNonObsPeriod>0
-
-                        % Get name of pumping bore
-                        pumpName = obj.settings.forcingData_cols{i,1};
-
-                        % Create a lookup conversion table for each production bore to
-                        % transform the date to alter pumping (propName) and the true
-                        % date. Each production bore has one numeric matrix
-                        % with the following format.
-                        %   - date (from obj.settings.forcingData) for each period without daily metering
-                        %   - row index to input forcing data
-                        %   - time-step integer for user-set downscaled time-step
-                        %   - time-step for only periods with metering normalised from 0 to 1.                        
-                        %   - time-step all periods without daily metering normalised from 0 to 1.                        
-                        %   - 0/1 for the pumping being on or off over the user set time period.
-                        %   - The pumping rate for the period (assigned in
-                        %   updateStochasticForcingData()).
-                        %   - The daily extraction rate (pump state * pump rate).
-                        %
-                        if isfield(obj.variables,pumpName) && size(obj.variables.(pumpName),1)>0
-                            updateExistingTable = true;
-                        else
-                            updateExistingTable = false;
-                            obj.variables.(pumpName) = zeros(0,8);
-                        end
-                        for j=filt
-                            
-                            % Get dates for non-obs period
-                            sDate = obj.variables.nonObsPeriods{j,3};
-                            eDate = obj.variables.nonObsPeriods{j,4};                                                        
-                                                        
-                            % Setup non-obs days
-                            nonObsDays = [sDate:eDate]';
-                            
-                            % Get index to non obs days
-                            nonObsDaysIndex = find(obj.settings.forcingData(:,1) >=sDate & obj.settings.forcingData(:,1) <=eDate);
-                            
-                            % Convert non-obs days increments for user set time-step
-                            switch lower(updateTimeStep)
-                                case 'daily'
-                                    nonObsSteps = year(nonObsDays) *1000 + day(datetime(nonObsDays,'ConvertFrom','datenum'), 'dayofyear');
-                                case 'weekly'
-                                    nonObsSteps = year(nonObsDays) *1000 + week(datetime(nonObsDays,'ConvertFrom','datenum'));
-                                case 'monthly'
-                                    nonObsSteps = year(nonObsDays) *1000 + month(nonObsDays);
-                                case '(none)'
-                                    nonObsSteps = ones(length(nonObsDays),1) * j;                                    
-                                otherwise
-                                    error(['The following time-step for generation of forcing data is not recognised:',updateTimeStep])
-                            end
-                            
-                            % Set junk data for normalised time-step value.
-                            nonObsStepsNormalised = zeros(size(nonObsSteps));
-                            
-                            % Build temporary vector to denote if the
-                            % total extractions for the periods were
-                            % metered.
-                            if isfinite(obj.variables.nonObsPeriods{j,5})
-                                isMeteredPeriod = ones(size(nonObsSteps));
-                            else
-                                isMeteredPeriod = zeros(size(nonObsSteps));
-                            end
-                            
-                            % Limit dates to only calibration periods.
-                            if nargin==4 && any(nonObsDays > t_end_calib)
-                                
-                                % If part of the period without daily metering is 
-                                % less than the end date of the
-                                % calibration then, for the
-                                % calibration, set the dates
-                                % <=t_end_calib to be unmetered.                                
-                                filt_calibDays = nonObsDays <= t_end_calib;
-                                if any(filt_calibDays)
-                                    isMeteredPeriod(filt_calibDays) = 0;                                    
-                                end
-                                
-                                % Change the nonObsSteps to dates to zero.
-                                % Below, nonObsSteps==0 are filtered from
-                                % the normalisation and so calibration will
-                                % not change the pump state after
-                                % t_end_calib.
-                                nonObsSteps(nonObsDays > t_end_calib) = 0;
-                                
-                                % Also, change the indexes to zero. This is
-                                % required so that within getTransformed
-                                % Forcing() only the transformted data for the 
-                                % calibration period is added to input
-                                % forcing data.
-                                nonObsDaysIndex(nonObsDays > t_end_calib) = 0;
-                            end
-                            
-                            
-                            % Set pump to 'on' for each time step
-                            nonObsPumpState = ones(size(nonObsSteps));
-                            
-                            if updateExistingTable
-                                % Find rows to be updated within existing
-                                % table.
-                                filt_existingTable = find(obj.variables.(pumpName)(:,1) >=sDate & obj.variables.(pumpName)(:,1) <=eDate);
-
-                                % Create matrix of new lookup data.
-                                newLookupTableRows = [nonObsDays nonObsDaysIndex nonObsSteps isMeteredPeriod nonObsStepsNormalised];
-                                                                
-                                % Update table rows BUT NOT THE PUMPING STATE.
-                                obj.variables.(pumpName)(filt_existingTable ,1:5) = newLookupTableRows;
-                            else                                
-                                % Create matrix of new lookup data.
-                                newLookupTableRows = [nonObsDays nonObsDaysIndex nonObsSteps isMeteredPeriod nonObsStepsNormalised nonObsPumpState];
-                                
-                                if isempty(obj.variables.(pumpName))
-                                    obj.variables.(pumpName) = newLookupTableRows;
-                                else
-                                    obj.variables.(pumpName) = [obj.variables.(pumpName); newLookupTableRows];
-                                end
-                            end
-                        end            
-                        
-                        % Normalise 'nonObsSteps' during periods a metered total volume
-                        % so that required time step at which the pump state to be changed can
-                        % be located from the pumping bore class parameter.
-                        ind_metered = find(obj.variables.(pumpName)(:,4)==1 & obj.variables.(pumpName)(:,3)>0);
-                        ind_notMetered = find(obj.variables.(pumpName)(:,4)==0 & obj.variables.(pumpName)(:,3)>0);
-                        nonObsStepsUnique = sort(unique(obj.variables.(pumpName)(ind_metered ,3)));
-                        for k=1:length(nonObsStepsUnique)
-                            filt = find(obj.variables.(pumpName)(ind_metered,3) == nonObsStepsUnique(k));
-                            obj.variables.(pumpName)(ind_metered(filt) ,4) = k;
-                        end                        
-                        obj.variables.(pumpName)(ind_metered,4) = obj.variables.(pumpName)(ind_metered,4)/length(nonObsStepsUnique);
-                        obj.variables.(pumpName)(ind_notMetered,4) = NaN;                        
-                        
-                        % Normalise 'nonObsSteps' during all periods
-                        % without daily metering so that required time
-                        % step at which the pump state to be changed can
-                        % be located from the pumping bore class parameter.
-                        ind_calibDays = find(obj.variables.(pumpName)(:,3)>0);
-                        nonObsStepsUnique = sort(unique(obj.variables.(pumpName)(ind_calibDays,3)));
-                        for k=1:length(nonObsStepsUnique)
-                            filt_timesteps = obj.variables.(pumpName)(:,3) == nonObsStepsUnique(k);
-                            obj.variables.(pumpName)(filt_timesteps ,5) = k;
-                        end                        
-                        obj.variables.(pumpName)(:,5) = obj.variables.(pumpName)(:,5)/length(nonObsStepsUnique);
-                    end                                                    
-                    
-                end                
-            end
-            
-            
-        end
-    end
-    
-end
-
-
+classdef pumpingRate_SAestimation < stochForcingTransform_abstract & dynamicprops
+% Class definition for downscaling groundwater pumping rates.
+%
+% Description          
+%   This class allows the downscaling of infrequent (>daily) groundwater pumping
+%   data to daily, weeekly or monthly rates. The downscaling is required when
+%   pumping is metered, say, only annually. The downscaling is undertaken by
+%   simulating the pump as being either on or off for each user-set downscaling
+%   time step. When the period is metered then the number of days pumping is
+%   used to estimate the average daily pumping rate. The average of all
+%   metered periods is then applied to non-metered periods. This approach 
+%   requires no assumption of the behaviour of groudnwater users or their 
+%   response to climatic conditions.
+%
+%   Importantly, the number of combinations of pump state over, say, a decade
+%   to assess against the observed head far exceeds the largest number possible
+%   on a 64 bit machine. The implications are that the calibration cannot 
+%   assess all combinations and that if an identical model is re-calibrated
+%   an alternate optima may be identified. To manage this challenge the approach
+%   uses simulated annealing (SA); note SA comes from annealing in metallurgy,
+%   a technique involving heating and controlled cooling of a material to 
+%   increase the size of its crystals and reduce their defects. Further
+%   details are availabe in Peterson & Fulton (in-prep).
+%   
+%   The class allows the downscaling of infrequently metered periods and
+%   also the estimation of pumping during periods that were not metered. It
+%   is important the >=1 period is however metered so that the
+%   calibration can constrain the draw-down parameters (eg T and S). The
+%   different metering scenarios are defined from the TFN input forcing
+%   data file. Any period that was infrequently metered is defined by -999
+%   values in the forcind ata file followed by the total extraction volume
+%   over the preceeding perids of -999. Within the same file, and period
+%   with no metering is defined by a period of -999 values ending with a
+%   zero pumping rate.
+%
+%   Lastly, the class development of this class required a number of
+%   chnages to HydroSight. Please note the following:
+%      1. pumpingRate_SAestimation() required considerable changes to the 
+%         calibration scheme and is only operational with the SP-UCI. Other
+%         calibration schemes will be edited in the future.
+%      2. Calibration with an evaluation period required downscaling will 
+%         crash when using this componant. Future releases will use the 
+%         stochastically derived forcing tiem-series to build a predictive 
+%         model for the estimation during evaluation periods.
+%      3. This extension to HydroSight was funded by the Department of 
+%         Environment, Land, Water and Planning, Victoria.
+%         https://delwp.vic.gov.au/
+%               
+%References: 
+%   Peterson & Fulton (in-prep), Estimating aquifer hydraulic properties 
+%   from existing groundwater monitoring data.};  
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   July 2017
+%
+ 
+    properties(GetAccess=public, SetAccess=protected)                        
+        % NOTE: All parameters are dynamically added
+    end
+
+%%  STATIC METHODS        
+% Static methods used to inform the
+% user of the available model types. 
+    methods(Static)
+        function [variable_names, isOptionalInput] = inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            %variable_names = cell(75,1);
+            %variable_names(1:75) = strrep(strrep(strcat({'Pump '},num2str([1:75]')),' ','_'),'__','_');            
+            %isOptionalInput = true(75,1);
+            [variable_names] = pumpingRate_SAestimation.outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+            isOptionalInput = true(length(variable_names),1);     
+            
+            global pumpingRate_SAestimation_wizardResults
+            pumpingRate_SAestimation_wizardResults={};
+        end
+        
+        function [variable_names, isOptionalInput] = dataWizard(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            
+            dlg_title = 'Data Wizard: Set criteria for pumping selection ...';
+            prompt = {'Maximum radius of selected pumps from the obs. bore (in units of coordinates):', ...
+                      'Maximum number of selected pumps:'};            
+            num_lines = 1;
+            defaultans = {'5000','10'};
+            answer = inputdlg(prompt,dlg_title,num_lines,defaultans);
+            
+            if isempty(answer) 
+                global pumpingRate_SAestimation_wizardResults
+                pumpingRate_SAestimation_wizardResults = {};
+                [variable_names, isOptionalInput] = pumpingRate_SAestimation.inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+            else
+                % Check inputs
+                try 
+                    maxDist = str2double(answer{1});
+                    if maxDist <0
+                        warndlg('The maximum distance must be >>0. It will be set to Inf.','Input data error ...')
+                        maxDist = Inf;
+                    end
+                    maxNum = str2double(answer{2});
+                    if maxNum<0
+                        warndlg('The maximum number of pumps must be >0. It will be set to Inf.','Input data error ...')
+                        maxNum = Inf;
+                    end                    
+                catch
+                    warndlg('Both inputs must be scalar numbers >0. Both will be set to Inf.','Input data error ...')
+                    maxDist = Inf;
+                    maxNum = Inf;
+                end
+                
+                % Get coordinate of the obs bore
+                filt = cellfun(@(x) strcmp(x, bore_ID),table2cell(siteCoordinates(:,1)));
+                boreEN = table2array(siteCoordinates(filt,2:3));
+                
+                % Find those forcing data columns with any infilling. These
+                % are most likely pumping bores.
+                filt = false(size(forcingData_data,2),1);
+                for i=1:size(forcingData_data,2)
+                    filt(i) = any(forcingData_data{:,i} == -999);
+                end
+                forcingData_colnames = forcingData_colnames(filt);
+                
+                % Find the coordinates for the identified pumping bores.
+                filt = cellfun(@(x) any(strcmp(forcingData_colnames,x)), table2cell(siteCoordinates(:,1)));
+                pumpEN = table2array(siteCoordinates(filt,2:3));
+                pumpEN = bsxfun(@minus, pumpEN, boreEN);
+                
+                % Calculate distance to pump
+                dist = sqrt(pumpEN(:,1).^2 + pumpEN(:,2).^2);
+                
+                % Create table of pump IDs and diatances
+                pumpTable = table(siteCoordinates{filt,1},dist);
+                pumpTable = sortrows(pumpTable,2);
+                
+                % Filter data based on user input criteria.
+                filt = table2array(pumpTable(:,2)) <= maxDist;
+                pumpTable = pumpTable(filt,1:2);
+                if size(pumpTable,1)>maxNum                    
+                    pumpTable = pumpTable(1:maxNum,:);
+                end
+                
+                nrows = size(pumpTable,1);
+                variable_names = cell(nrows ,2);
+                %variable_names(1:nrows ,1) = strrep(strrep(strcat({'Pump '},num2str([1:nrows]')),' ','_'),'__','_');            
+                variable_names(1:nrows ,1) = strcat(table2cell(pumpTable(:,1)),'_infilled');
+                variable_names(1:size(pumpTable,1),2) = table2cell(pumpTable(:,1));
+                isOptionalInput = true(nrows ,1);
+                
+                % Set a global variable that stores the wizard results.
+                % This is an unfortunate requirement to allow the efficient
+                % selection of input data within the GUI TFN weighting
+                % function box.
+                global pumpingRate_SAestimation_wizardResults
+                pumpingRate_SAestimation_wizardResults = variable_names(1:nrows ,1);
+                
+                
+            end
+        end        
+        
+        function [variable_names] = outputForcingdata_options(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates)
+            %variable_names = strrep(strrep(strcat({'Pump '},num2str([1:75]')),' ','_'),'__','_');
+            
+            global pumpingRate_SAestimation_wizardResults
+            if ~isempty(pumpingRate_SAestimation_wizardResults)
+                variable_names = pumpingRate_SAestimation_wizardResults;
+            else            
+                % Find those forcing data columns with any infilling. These
+                % are most likely pumping bores.
+                filt = false(size(forcingData_data,2),1);
+                if iscell(forcingData_data) || istable(forcingData_data)
+                    for i=1:size(forcingData_data,2)
+                        filt(i) = any(forcingData_data{:,i} == -999);
+                    end                
+                else
+                    for i=1:size(forcingData_data,2)
+                        filt(i) = any(forcingData_data(:,i) == -999);
+                    end                
+                end
+                variable_names = strcat(forcingData_colnames(filt),'_infilled');
+                variable_names = variable_names';
+            end
+        end
+        
+        function [options, colNames, colFormats, colEdits, toolTip] = modelOptions()
+            
+            options = cell(3,2);
+            colNames = {'Setting name','Setting value'};
+            colFormats = {'char','numeric'};
+            colEdits = logical([0 1]);
+
+            options{1,1} = 'Downscaling time-step (none=0, monthly=1, weekly=2, daily=3):';
+            options{1,2} = 3;
+
+            options{2,1} = 'Minimum number of bore meter readings:';
+            options{2,2} = 2;
+                     
+            options{3,1} = 'Minimum days to downscale:';
+            options{3,2} = 7;
+                                                
+            toolTip = sprintf([ 'Use this table to define the method for downscaling time-step. \n', ...
+                                'Note, pumps have less than the min. number of meter readings \n', ...
+                                'will be omitted from analysis.']);
+        end
+        
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: pumpingRate_SAestimation', ...
+                               '', ...
+                               'Purpose: downscaling of infrequent (>daily) groundwater pumping data to daily, weeekly or monthly rates. The downscaling', ...
+                               'is required when pumping is metered, say, only annually. The downscaling is undertaken by simulating the pump as being', ...
+                               'either on or off for each user-set downscaling time step. When the period is metered then the number of days pumping is', ...
+                               'used to estimate the average daily pumping rate. The average of all metered periods is then applied to non-metered periods.', ...
+                               'This approach requires no assumption of the behaviour of groudnwater users or their response to climatic conditions', ...
+                               '', ...
+                               'Importantly, the number of combinations of pump state over, say, a decade to assess against the observed head far exceeds', ...
+                               'the largest number possible on a 64 bit machine. The implications are that the calibration cannot assess all combinations', ...
+                               'and that if an identical model is re-calibrated an alternate optima may be identified. To manage this challenge the approach', ...
+                               'uses simulated annealing (SA); note SA comes from annealing in metallurgy, a technique involving heating and controlled ', ...
+                               'cooling of a material to increase the size of its crystals and reduce their defects. For details see the help documentation.', ...
+                               '', ...                               
+                               'Number of parameters: 0', ...
+                               '', ...                               
+                               'Options: For each pump the user can set the "Downscaling time-step","Initial Temperature","Cooling rate multipler". Note, a',  ...
+                               'daily time step can require >1M model runs. Also, the probability of the claibration findings the best down-scaled forcing', ...
+                               'increases as the SA initial temperature is increased and the SA cooling rate is decreased.', ...
+                               '', ...                               
+                               'Comments: ', ...
+                               '1. pumpingRate_SAestimation() required considerable changes to the calibration scheme and is only operational with the' , ...
+                               'SP-UCI. Other calibration schemes will be edited in the future.', ...
+                               '2. Calibration with an evaluation period required downscaling will crash when using this componant. Future releases will', ...
+                               'use the stochastically derived forcing tiem-series to build a predictive model for the estimation during evaluation periods.', ...
+                               '3. This extension to HydroSight was funded by the Department of Environment, Land, Water and Planning, Victoria.', ...
+                               'https://delwp.vic.gov.au/', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. Peterson & Fulton (in-prep), Estimating aquifer hydraulic properties from existing groundwater monitoring data.'};
+        end        
+           
+    end
+        
+    
+    methods       
+%% Construct the model
+        function obj = pumpingRate_SAestimation(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates, forcingData_reqCols, modelOptions)                        
+
+            % Clear the global variable for the required forcing columns.
+            global pumpingRate_SAestimation_wizardResults
+            clear pumpingRate_SAestimation_wizardResults
+            
+            % Set the calibration state as false
+            obj.variables.doingCalibration = false;
+            
+            % Get a list of required forcing inputs and (again) check that
+            % each of the required inputs is provided.
+            %--------------------------------------------------------------
+            [requiredFocingInputs, isOptionalInput] = pumpingRate_SAestimation.inputForcingData_required(bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+            requiredFocingInputs = requiredFocingInputs(~isOptionalInput);
+            for j=1:numel(requiredFocingInputs)
+                filt = strcmpi(forcingData_reqCols(:,1), requiredFocingInputs(j));                                    
+                if ~any(filt)
+                    error(['An unexpected error occured. When transforming forcing data, the input cell array for the transformation must contain a row (in 1st column) labelled "forcingdata" that its self contains a cell array in which the forcing data column is defined for the input:', requiredFocingInputs{j}]);
+                end
+            end                         
+            
+            % Assign the input forcing data to obj.settings.
+            obj.settings.forcingData = forcingData_data;
+            obj.settings.forcingData_colnames = forcingData_colnames;
+            obj.settings.forcingData_cols = forcingData_reqCols;
+            obj.settings.siteCoordinates = siteCoordinates;
+            obj.settings.downscalingTimeStepFinal = modelOptions{1,1};
+            switch modelOptions{1,2}
+                case 0
+                   obj.settings.downscalingTimeStepFinal = '(none)';
+                case 1
+                   obj.settings.downscalingTimeStepFinal = 'monthly';
+                case 2
+                   obj.settings.downscalingTimeStepFinal = 'weekly'; 
+                case 3
+                   obj.settings.downscalingTimeStepFinal = 'daily';
+                otherwise
+                    error('pumpingRate_SAestimation: unknown timestep setting.')
+            end
+            obj.settings.downscalingTimeStepCurrent = '(none)';
+            obj.settings.minObsPeriodsPerPump = max(0,modelOptions{2,2});                     
+            obj.settings.minDays2Downscale = max(0,modelOptions{3,2}); 
+                        
+            % Get the forcing data time points
+            obsTime = obj.settings.forcingData(:, 1);            
+            
+            % Initialise the calibration period
+            obj.variables.t_start_calib = -inf;
+            obj.variables.t_end_calib = inf;
+            
+            % Loop through each pumping bore. Any observation that
+            % is preceeded by -999 is assumed to be the total pumping for
+            % the preceeding period of -999 values. An period that ends in
+            % -999 is assumed to be a non-metered period. Any observations
+            % that are not preceeded by -999 and daily obs.
+            nonObsPeriods=[];
+            nNonObsPeriod=0;
+            obsDays=nan(0,3);
+            nObsDays=0;
+            pumpIDs_insufficientMetering=[];
+            for i = 1:size(obj.settings.forcingData_cols,1)
+                
+                % Get pumping data
+                pumpingColName = obj.settings.forcingData_cols{i,1};
+                pumpingColInd = obj.settings.forcingData_cols{i,2};                
+                pumping = obj.settings.forcingData(:,pumpingColInd);                
+                
+                % Initialise the  number of meter readings
+                nMeterReadings = 0;
+
+                % Add calibration parameter for the pump and
+                % initialise to 0.5.
+                parName = [pumpingColName, '_position'];                        
+                dynPropMetaData{i} = addprop(obj,parName);                
+                obj.(parName)=-0.5;
+                
+                % Loop through each time point
+                for j=1: size(pumping,1)    
+
+                    % Find the start and end dates of each non-obs period.
+                    % ie -999 periods ending with zero. Below, for each period 
+                    % of no observations add instance property and initialised using
+                    % the regression relationship and period's evap deficit.
+                    if pumping(j)==-999 && (j==1 || pumping(j-1)>=0)
+                        % First day of -999 periods.
+                        nNonObsPeriod = nNonObsPeriod+1;
+                        nonObsPeriods{nNonObsPeriod,1} = i;
+                        nonObsPeriods{nNonObsPeriod,2} = pumpingColName;
+                        nonObsPeriods{nNonObsPeriod,3} = obsTime(j);
+                        nonObsPeriods{nNonObsPeriod,4} = obsTime(j);
+                        nonObsPeriods{nNonObsPeriod,5} = NaN;
+                        nonObsPeriods{nNonObsPeriod,6} = obj.settings.downscalingTimeStepCurrent;
+                    elseif pumping(j)==-999 && (j==size(pumping,1) || pumping(j+1)>=0)
+                        % End day of -999 periods.
+                        if j>=length(pumping) || pumping(j+1)==0
+                            % Unmetered period because the -999 period ends
+                            % with a zero.
+                            nonObsPeriods{nNonObsPeriod,4} = obsTime(j);
+                            nonObsPeriods{nNonObsPeriod,5} = NaN;
+                        else
+                            % Unmetred period endds with a recorded total
+                            % volume.
+                            nonObsPeriods{nNonObsPeriod,4} = obsTime(j)+1;
+                            nonObsPeriods{nNonObsPeriod,5} = pumping(j+1);
+                            nMeterReadings = nMeterReadings + 1;
+                        end
+                        
+                        % Store time-step for stochastic forcing generation.
+                        nonObsPeriods{nNonObsPeriod,6} = obj.settings.downscalingTimeStepCurrent;
+                    elseif (j==1 && pumping(j)>0) || (pumping(j)>0 && pumping(j-1)~=-999)
+                        nObsDays = nObsDays +1;
+                        obsDays(nObsDays, :) = [i,  obsTime(j), pumping(j)];
+                        nMeterReadings = nMeterReadings + 1;
+                    end
+                end
+                
+                % Check there are at least the user set minimum number of metered periods for the pump.
+                if nMeterReadings < obj.settings.minObsPeriodsPerPump
+                    pumpIDs_insufficientMetering=[pumpIDs_insufficientMetering, i];
+                    %error(['Pump ',forcingData_colnames{pumpingColInd}, ' must have >=',num2str(obj.settings.minObsPeriodsPerPump),' periods of metered total extractions.']);
+                end
+                
+            end
+            
+            % Remove pumps with insufficient metering.
+            for i=1:length(pumpIDs_insufficientMetering)
+                filt = cell2mat(nonObsPeriods(:,1)) ~= pumpIDs_insufficientMetering(i);
+                nonObsPeriods = nonObsPeriods(filt,:);
+                delete(dynPropMetaData{pumpIDs_insufficientMetering(i)})
+            end
+            
+            % Remove unmetered periods that are less than the user set
+            % minimum duration.
+            filt= true(size(nonObsPeriods,1),1);
+            for i=1:size(nonObsPeriods,1)
+                if isnan(nonObsPeriods{i,5}) && ...
+                nonObsPeriods{i,4}-nonObsPeriods{i,3}<obj.settings.minDays2Downscale
+            
+                    filt(i) = false;
+            
+                     % Set period in forcing data to zero
+                    pumpingColInd = obj.settings.forcingData_cols{nonObsPeriods{i,1},2};                
+                    filt_time = obj.settings.forcingData(:,1) >= nonObsPeriods{i,3} & obj.settings.forcingData(:,1) <= nonObsPeriods{i,4};
+                    obj.settings.forcingData(filt_time ,pumpingColInd) = 0;
+                end
+            end
+            nonObsPeriods = nonObsPeriods(filt,:);
+            
+            % Add observed and non-obs periods to object
+            obj.variables.nonObsPeriods = nonObsPeriods;                       
+            obj.variables.obsDays = obsDays;                       
+                        
+            % Set the indexes to the unmetered data. The indexes are used
+            % to efficiently find the forcing date time points for a given
+            % time point of a given time-step.
+            setTimestepIndexes(obj,obj.settings.downscalingTimeStepCurrent)
+            
+            % Set the pump usage data. The following should be identical to
+            % updateStochForcingData(obj) but without the change to the
+            % pump state.
+            %----------            
+            % Get the mean daily pumping rate during the metered periods.
+            dailyRate = getDailyAveragePumpingRates(obj, -inf, inf);
+
+            % Filter out periods for which the daily rate could not be calculated.
+            dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
+
+            % Build a table of pump numbers and names
+            if isfield(obj.variables,'pumpNumTable')
+                obj.variables = rmfield(obj.variables,'pumpNumTable');
+            end
+            getPumpNumTable(obj);
+                   
+            % Update pumping rates based on new pump states
+            for i=1:size(obj.variables.pumpNumTable ,1)
+
+                % Get the current pump name and number.
+                pumpNum = obj.variables.pumpNumTable.pumpNum(i);
+                pumpName = obj.variables.pumpNumTable.pumpName{i};
+                
+                % Initialise daily pump rate to mean
+                filt = dailyRate(:,1) == obj.variables.pumpNumTable{i,1};
+                dailyRate_median = median(dailyRate(filt ,5));
+                obj.variables.(pumpName)(:,7)=dailyRate_median;
+
+                % Initialise daily pump volume to nan
+                obj.variables.(pumpName)(:,8)=nan;
+
+                % Assign the average daily pumping rate during each metered period
+                filt = find(dailyRate(:,1) ==pumpNum)';
+                for j=filt                        
+                    filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(j,2) & obj.variables.(pumpName)(:,1) <= dailyRate(j,3); 
+                    obj.variables.(pumpName)(filt_obsPeriods,7)=dailyRate(j,5);
+                end
+
+                % Multiple pump state by daily rate to create down-scaled pumping rate
+                obj.variables.(pumpName)(:,8)=obj.variables.(pumpName)(:,6).*obj.variables.(pumpName)(:,7);
+            end            
+            %----------
+                        
+        end
+        
+%% Set parameters
+        function setParameters(obj, params)
+% setParameters returns the soil model parameters.
+%
+% Syntax:
+%   setParameters(obj, params)  
+%
+% Description:  
+%   This method sets the soil moisture model parameters to user input values.
+%   Only parameters that are to be calibrated (i.e. non-fixed parameters)
+%   can be set. Also the vector of input parameter values must be in the
+%   same order as the parameter names returned by the method
+%   'getParameters'.
+%
+% Input:
+%   obj     - soil moisture model object.
+%   params  - vector (Nx1) of parameter values.
+%
+% Outputs:
+%   (none)
+%
+% See also:
+%   climateTransform_soilMoistureModels: class_definition;
+%   getParameters: get_calibration_parameters_values;
+%   detectParameterChange: assesst_if_parameters_have_changed_recently;
+%   setTransformedForcing: run_model_and_store_simulation_results;
+%   getTransformedForcing: get_outputs_for_timeseries_model.
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   11 April 2012  
+
+
+            % Get the parameter names
+            param_names = properties(obj);
+            filt = cellfun( @(x) ~strcmp(x,'settings') && ~strcmp(x,'variables'), param_names);
+            param_names = param_names(filt);
+            
+            % Cycle through each parameter and assign the parameter value.
+            for i=1: length(param_names)
+               obj.(param_names{i}) = params(i,:); 
+            end
+            
+            % Check if the parameters have changed since the last call to
+            % setTransformedForcing.
+            detectParameterChange(obj, params);   
+        end
+        
+        function setForcingData(obj, forcingData, forcingData_colnames)
+% setForcingData sets the forcing data.
+%
+% Syntax:
+%   setForcingData(obj, forcingData, forcingData_colnames)
+%
+% Description:  
+%   This method set the climate forcing data. It is used to update the
+%   forcing data for model simulations (primarily from the GUI).
+%
+% Input:
+%   obj         - soil moisture model object.
+%
+%   forcingData - nxm matrix of focrinf data with column 1 being the
+%                 date/time.
+%
+%   forcingData_colnames - 1xm cell array of column names within the above
+%                 data.
+%
+% Outputs:
+%   (none)
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   21 Dec 2015              
+
+
+            % Store only the columns listed in obj.settings.forcingData_colnames
+            % ie those needed and index within obj.settings.forcingData_cols
+            if length(forcingData_colnames) < length(obj.settings.forcingData_colnames)
+                error('The number of column name to be set is less than that used to build the object.');
+            end
+            forcingDataNew = nan(size(forcingData,1),length(obj.settings.forcingData_colnames));
+            for i=1:length(forcingData_colnames)               
+                filt  = strcmp(obj.settings.forcingData_colnames, forcingData_colnames{i});
+                if ~isempty(filt)
+                    forcingDataNew(:,filt) = forcingData(:,i);
+                end
+            end
+            obj.settings.forcingData = forcingDataNew;
+                        
+        end                
+
+        function forcingData = getStochForcingData(obj)        
+           
+            % Get pump names and numbers
+            getPumpNumTable(obj);    
+
+            % Check if there are sufficient metereed usage observations
+            % WITHIN the calibration at each bore. If there are not any
+            % metered usage values then an error should be thrown.
+            updateStochForcingData(obj);
+            
+            % Get pump states from each bore.
+            for i=1:size(obj.variables.pumpNumTable ,1)
+                pumpName = obj.variables.pumpNumTable.pumpName{i};
+                forcingData.(pumpName) = logical(obj.variables.(pumpName)(:,6));                                                            
+            end                
+        end
+        
+        function setStochForcingState(obj,doingCalibration, t_start_calib, t_end_calib)
+            obj.variables.doingCalibration = doingCalibration;
+
+            % Update the calibration period
+            obj.variables.t_start_calib = t_start_calib;
+            obj.variables.t_end_calib = t_end_calib;              
+            
+            % Build downscaling indexes using t_start_calib and
+            % t_end_calib.
+            if doingCalibration
+                % Re-initialise downscaling time stwp
+                obj.settings.downscalingTimeStepCurrent = '(none)';
+                
+                % Re-build lookup indexes for the starting downscaling time step.
+                setTimestepIndexes(obj, obj.settings.downscalingTimeStepCurrent, t_start_calib, t_end_calib)                
+            end
+            
+        end
+                
+        function finishedStochForcing = updateStochForcingData(obj, forcingData, refineStochForcingMethod)
+            
+            % Only update stochastic forcing if doing calibration
+            if isfield(obj.variables,'doingCalibration') && ~obj.variables.doingCalibration
+                finishedStochForcing = true;
+            else
+                % Initilaise output
+                finishedStochForcing = false;
+
+                % Estimate the mean daily pumping rate during the metered
+                % periods.
+                [dailyRate, ind_dailyRate] = getDailyAveragePumpingRates(obj,obj.variables.t_start_calib, obj.variables.t_end_calib);
+
+                % Filter out periods for which the daily rate could not be
+                % calculated.
+                dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
+
+                % Get pump names and numbers
+                pumpNumTable=getPumpNumTable(obj);
+
+                % Update the pump state if input.
+                if nargin>=2
+
+                    % Get names of pumps within the input forcing data.
+                    pumpsNames_input =  fieldnames(forcingData);
+
+                    % Loop through each input names and assign the forcing data
+                    % to the object.
+                    for i=1:length(pumpsNames_input)
+
+                        
+                        % Check the input field name is within the object
+                        % already.
+                        if ~any(strcmp(obj.variables.pumpNumTable.pumpName, pumpsNames_input{i}))
+                            error('A fieldname within the input "forcingData" is not listed within the pumpingRate_SAestimation object.');
+                        end
+
+                        % Check the input number of days is as expected.
+                        if size(forcingData.(pumpsNames_input{i}),1) ~= size(obj.variables.(pumpsNames_input{i}),1)
+                            error(['The number of rows within forcingData.',pumpsNames_input{i},' does not equal that within the corresponding  pumpingRate_SAestimation variable.']);
+                        end 
+
+                        % Assess if each metered period with pump rate >0 has >0 periods with
+                        % the pump on. That is, if a metered period to be downscaled has usage
+                        % >0 then the days when the pump is on must be >0.
+                        % In doing this assessment, periods of daily
+                        % metered usage are not assessed because they are
+                        % not to be downscaled.
+                        %-------------
+
+                        % Find the pump number for the cirrent value of i.
+                        filt = find(strcmp(obj.variables.pumpNumTable.pumpName, pumpsNames_input{i}));
+                        pumpNum = obj.variables.pumpNumTable.pumpNum(filt);
+                        pumpName = obj.variables.pumpNumTable.pumpName{filt};
+                        
+                        
+                        % Filter for any metered periods.
+                        filt = [find(dailyRate(:,1)==pumpNum)]';
+
+                        isValidPumpState = true;
+                        for j=filt                            
+                           filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(j,2) ...
+                                              & obj.variables.(pumpName)(:,1) <= dailyRate(j,3); 
+                           if  sum(filt_obsPeriods)>0 && sum(forcingData.(pumpName)(filt_obsPeriods)) <=0
+                               isValidPumpState = false;
+                           end
+                        end
+                        %-------------
+
+                        % Assign input forcing
+                        if isValidPumpState
+                            obj.variables.(pumpName)(:,6) = double(forcingData.(pumpName));
+                        end
+
+                    end
+                end
+
+                % Redefine the downscaling time step if the new AND if
+                % different to the current time step
+                if nargin==3 && refineStochForcingMethod
+
+                    % Check if the bore downscaling is already at the user specified
+                    % time-step. If so, exit if() and return that the time step
+                    % was not refined.
+                    if strcmp(lower(obj.settings.downscalingTimeStepCurrent), lower(obj.settings.downscalingTimeStepFinal))
+                        finishedStochForcing = true;
+                    else 
+                        switch lower(obj.settings.downscalingTimeStepCurrent)
+                            case 'daily'
+                                finishedStochForcing  = true;
+                            case 'weekly'
+                                obj.settings.downscalingTimeStepCurrent = 'daily';
+                                finishedStochForcing  = false;
+                            case 'monthly'
+                                obj.settings.downscalingTimeStepCurrent = 'weekly';
+                                finishedStochForcing  = false;
+                            case '(none)'
+                                obj.settings.downscalingTimeStepCurrent = 'monthly';
+                                finishedStochForcing  = false;
+                            otherwise
+                                error(['The following time-step for generation of forcing data is not recognised:',obj.settings.downscalingTimeStepCurrent])
+                        end                
+                    end                        
+
+                    % If the downscaling time step is not yet at the final
+                    % user set resolution, then re-build the time-step
+                    % indexes for the new time step size. Importantly,
+                    % setTimestepIndexes() preserves the pump states from
+                    % the prior time step and copies them to the new time
+                    % step.
+                    if ~finishedStochForcing
+                        setTimestepIndexes(obj, obj.settings.downscalingTimeStepCurrent)
+                    end
+
+                end
+
+                % Reculate the pump rates if the forcing state was input.
+                if nargin>1
+                    % Update the mean daily pumping rate during the metered
+                    % periods because the pump state changed.
+                    [dailyRate, ind_dailyRate] = getDailyAveragePumpingRates(obj,obj.variables.t_start_calib, obj.variables.t_end_calib);
+                    
+                    % Filter out periods for which the daily rate could not be
+                    % calculated.
+                    dailyRate = dailyRate(isfinite(dailyRate(:,end)),:);
+                end
+                % Update pumping rates based on new pump states
+                for i=1:size(pumpNumTable,1)
+
+                    % Get the current pump name and number.
+                    pumpNum = obj.variables.pumpNumTable.pumpNum(i);
+                    pumpName = obj.variables.pumpNumTable.pumpName{i};
+                    
+                    % Get indexes to the current pump number.
+                    filt = find(dailyRate(:,1) == pumpNum)';
+                    
+                    % If there are no metered usage rates within the calib 
+                    % period AND there are dates where the pumping needs to
+                    % be estimated (but not downscaled), then an estimate 
+                    % can not be made because the median rate cannot be
+                    % estimated.
+                    filt_calibPeriod = obj.variables.(pumpName)(:,1)<=obj.variables.t_end_calib;
+                    if isempty(filt) && any(~isfinite(obj.variables.(pumpName)(filt_calibPeriod ,4)))
+                        error(['Pump ',pumpName,' has no metered usage in calib. period.'])
+                    end
+                    
+                    % Calculate the median metered pump rate. 
+                    dailyRate_median = median(dailyRate(filt ,5));
+                    
+                    % Assign the average daily pumping rate during each
+                    % metered period. NOTE, this task was the most
+                    % computationally intesive steps in the evaluation of
+                    % the objective function. Initially, this was addressed
+                    % by using bsxfun commands for situations when there
+                    % are >5 periods to assign downscaled pumping. However
+                    % in trials for the Warrion management area (Victoria, Australia)
+                    % the bsxfun steps still used ~60% of CPU time. After
+                    % some trials, the computational demand was
+                    % significantly reduced by having getDailyAveragePumpingRates()
+                    % return indexes to each row of obj.variables.(pumpName)
+                    % and the associated downscaled pumping for the row.
+                    % This approach eliminated the need for the bsxfun
+                    % commands!!!
+                    %------------------------------------
+%                     filt=find(dailyRate(:,1) == pumpNum & (dailyRate(:,3)-dailyRate(:,2))>1)';
+%                     if length(filt)>5                           
+%                         filt_obsPeriods=bsxfun(@ge, obj.variables.(pumpName)(:,1), dailyRate(filt,2)') & bsxfun(@le, obj.variables.(pumpName)(:,1), dailyRate(filt,3)');
+%                         obj.variables.(pumpName)(:,7) = sum(bsxfun(@times, filt_obsPeriods, dailyRate(filt,5)'),2) + all(~filt_obsPeriods,2) * dailyRate_median;                        
+%                     else
+%                         obj.variables.(pumpName)(:,7)=dailyRate_median;
+%                         for j=filt                        
+%                             filt_obsPeriods = bsxfun(@ge, obj.variables.(pumpName)(:,1), dailyRate(j,2)') & bsxfun(@le, obj.variables.(pumpName)(:,1), dailyRate(j,3)');
+%                             obj.variables.(pumpName)(ind_obsPeriods,7)=dailyRate(j,5);
+%                         end                    
+%                     end
+                    % Add median pump rate to all time points.
+                    obj.variables.(pumpName)(:,7)=dailyRate_median;                   
+                    % Add metered pump rate (is any exists).
+                    if ~isempty(ind_dailyRate.(pumpName))
+                        obj.variables.(pumpName)(ind_dailyRate.(pumpName)(:,1),7)  = ind_dailyRate.(pumpName)(:,2);
+                    end
+                    %------------------------------------
+                    
+                    % Multiple pump state by daily rate to create down-scaled pumping rate
+                    obj.variables.(pumpName)(:,8)=obj.variables.(pumpName)(:,6).*obj.variables.(pumpName)(:,7);
+                end
+            end
+        end
+        
+%% Randomise the model parameters. 
+%  This function should be called at the end of an evolutionary loop to
+%  avoid the parameters converging to a local minimum
+        function updateStochForcingParameters(obj, forcingData)
+
+            % Get the parameter bounds
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+                        
+            % Cycle through each parameter and randoly assign the parameter value.
+            newparams = params_lowerLimit + (params_upperLimit - params_lowerLimit) .* rand(size(params_lowerLimit));
+            
+            % Set the new parameter 
+            setParameters(obj, newparams );            
+        end
+        
+%% Get model parameters
+        function [params, param_names] = getParameters(obj)            
+% getParameters sets the soil model parameters.
+%
+% Syntax:
+%   setParameters(obj, params)  
+%
+% Description:  
+%   This method gets model parameters. For this class, mno parameters are
+%   every returned.
+%
+% Input:
+%   obj         - model object.
+%
+% Outputs:
+%   params      - an empty vector.
+%
+%   param_names - an empry vector (Nx1) of parameter names.   
+%%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   June 2017
+            % Get the parameter names
+            param_names = properties(obj);
+            filt = cellfun( @(x) ~strcmp(x,'settings') && ~strcmp(x,'variables'), param_names);
+            param_names = param_names(filt);
+            
+            % Cycle through each parameter and assign the parameter value.
+            params=[];
+            for i=1: length(param_names)
+               params(i,:) = obj.(param_names{i}); 
+            end
+        end   
+        
+%% Assess if matrix of parameters is valid.
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+% getParameterValidity returns a logical vector for the validity or each parameter.
+%
+% Syntax:
+%   isValidParameter = getParameterValidity(obj, params, param_names)
+%
+% Description:
+%   Returns a logical vector denoting if each parameter is valid.
+%
+% Input:
+%   obj -  model object.
+%
+%   params - vector of model parameters
+%
+%   param_names - cell array of the parameter names.
+%
+% Outputs:
+%   isValidParameter - column vector of the parameter validity.
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   June 2017
+
+            % Get physical bounds.
+            isValidParameter = true(size(params));   
+        end   
+
+        function isNewParameters = detectParameterChange(obj, params)
+           
+            isNewParameters = true(size(params));
+            
+        end
+        
+%% Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
+% getParameters_physicalLimit returns the physical limits to each model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)  
+%
+% Description:
+%   Cycles through all active soil model componants and parameters and returns
+%    a vector of the physical upper and lower parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter bounds
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   June 2017
+
+            [params, param_names] = getParameters(obj);            
+            params_lowerLimit = -1.*ones(size(params));
+            params_upperLimit = ones(size(params));
+        end  
+        
+%% Return fixed upper and lower plausible parameter ranges. 
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+% getParameters_plausibleLimit returns the plausible limits to each model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+%
+% Description:
+%   Cycles through all active soil model componants and parameters and returns
+%    a vector of the plausible upper and lower parameter bounds.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter bounds
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   June 2017
+            [params, param_names] = getParameters(obj);            
+            params_lowerLimit = -1.*ones(size(params));
+            params_upperLimit = ones(size(params));
+        end        
+        
+%% Solve the soil moisture differential equation
+        function setTransformedForcing(obj, t, forceRecalculation)
+% setTransformedForcing does nothing.
+%
+% Syntax:
+%   setTransformedForcing(obj, climateData, forceRecalculation)
+%
+% Description:  
+%   This function does nothing. Handling of the focring data is undertaken
+%   by updateStochForcingData().
+%
+% Input:
+%   obj         - model object.
+%
+%   t           - a vector (Nx1) of time points for simulation.
+%
+%   forceRecalculation - is a logical scalar input (i.e. true of false) to
+%                 force re-calculation of the model and thus ingore if the
+%                 parameters have or have not changed.
+% Outputs:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   June 2017
+
+            % Only change the stochatsic forcing if doing calibration.
+            if ~isfield(obj.variables,'doingCalibration') || obj.variables.doingCalibration
+
+                % Filter the forcing data to input t.
+                filt_time = obj.settings.forcingData(:,1) >= t(1) & obj.settings.forcingData(:,1) <= t(end);
+
+                % Store the time points
+                obj.variables.t = t(filt_time);
+
+                % Get the start and end date for downscaled periods for
+                % each pump.
+                %------------                
+                % Build vector of from non-obs periods 'nonObsPeriods' of bore number, 
+                % start and end date of non-metered periods.
+                dailyRate = cell2mat(obj.variables.nonObsPeriods(:,[1,3,4]));
+                
+                % Filter out periods that do not have a metered reading
+                filt = isfinite(cell2mat(obj.variables.nonObsPeriods(:,5)));
+                dailyRate = dailyRate(filt,:);
+                
+                % Add the daily metered volumes. Since these readings are for a
+                % single day, the the start and end dates are equal.
+                dailyRate = [dailyRate; [obj.variables.obsDays(:,1), obj.variables.obsDays(:,2), obj.variables.obsDays(:,2)]];
+                %------------
+                
+                % Get the object parameters.
+                [params, param_names] = getParameters(obj);
+
+                % Get the table of pump numbers and names if it exists,
+                % else built it
+                pumpNumTable = getPumpNumTable(obj);
+                
+                % Loop through each PARAMETER, find the date to edit pumping state and 
+                % then update the state. Once updated, calculate the median
+                % pumping rate for each period using ONLY the periods with
+                % a metered total extraction volume. Then, apply the
+                % average pumping rate to all periods without metered total
+                % extractions.                
+                for i=1:size(params,1)
+
+                    % Get bore names.
+                    pumpName = param_names{i}(1:end-9);
+
+                    % Get pump number 
+                    pumpNum = pumpNumTable.pumpNum(strcmp(pumpNumTable.pumpName,pumpName));
+
+                    % Find the number of discrete time steps possible
+                    ind_calibDays = obj.variables.(pumpName)(:,3)>0;
+                    timestepMax = length((unique(obj.variables.(pumpName)(ind_calibDays,3))));
+
+                    % Find normalised time-period from all periods of non-daily pumping.                    
+                    timestepValNormalised = ceil(abs(params(i)) * timestepMax)/timestepMax;
+                    filt = obj.variables.(pumpName)(:,5)==timestepValNormalised;
+
+                    % Calc number of pumping days to change.
+                    nDaysToChange = sum(filt);                   
+
+                    % Find if the current time point is a metered period.
+                    filt_dailyRate = find(dailyRate(:,1) == pumpNum)';
+                    date2Chnage = obj.variables.(pumpName)(filt,1);
+                    dailyRate_index = nan;
+                    for j=filt_dailyRate                        
+                        if any(dailyRate(j,2) <= date2Chnage) && any(dailyRate(j,3) >=date2Chnage)
+                            dailyRate_index = j;
+                            break
+                        end
+                    end
+
+                    % Change state of pumping only if, over a metered period,
+                    % the number of days with pumping is greater than those
+                    % to be turned off.                        
+                    changePumpState = true;
+                    isValidPumpState = true;
+                    if isfinite(dailyRate_index) && max(obj.variables.(pumpName)(filt,6))==1 && params(i)<sqrt(eps())
+
+                        filt_periodPumpingDays = obj.variables.(pumpName)(:,1) >=dailyRate(dailyRate_index,2) ...
+                        & obj.variables.(pumpName)(:,1) <= dailyRate(dailyRate_index,3);
+
+                        nPeriodPumpingDays = sum(obj.variables.(pumpName)(filt_periodPumpingDays,6));
+
+                        if nPeriodPumpingDays <= nDaysToChange
+                            changePumpState = false;
+                            isValidPumpState=false;
+                        end
+                    end                                        
+                    if changePumpState 
+                        %obj.variables.(pumpName{i})(filt,6) = double(newPumpState);
+                        if params(i)>sqrt(eps())
+                            obj.variables.(pumpName)(filt,6) = 1;
+                        elseif params(i)<-sqrt(eps())
+                            obj.variables.(pumpName)(filt,6) = 0;
+                        end    
+                    else
+                        % do nothing
+                        changePumpState = false;
+                    end
+                end  
+                
+                % Update the average daily pump date over each time step
+                % and bore and then multiple by the pump state.
+                updateStochForcingData(obj);
+            end
+        end
+        
+%% Return the transformed forcing data
+        function [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName) 
+% getTransformedForcing returns the required flux from the soil model.
+%
+% Syntax:
+%   [forcingData, isDailyIntegralFlux] = getTransformedForcing(obj, variableName)
+%
+% Description:  
+%   This method returns the requested flux/data from the apporiate pump.
+%
+% Input:
+%   obj - model object.
+%
+%   variableName - a string for the variable name to return.
+%
+% Outputs:
+%   forcingData  - a vector (Nx1) of the forcing data output to
+%                     be input to the groundwater time series model.
+%
+%   isDailyIntegralFlux - logical scaler denoting if the flux is a daily
+%   integral or an instantaneous value. This is used to inform the type of
+%   numerical integration within modelTFN.get_h_star().
+%
+% Dependencies:
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   July 2017 
+
+
+            if ischar(variableName)
+                variableNametmp{1}=variableName;
+                variableName = variableNametmp;
+                clear variableNametmp;
+            end
+
+            isDailyIntegralFlux=true;
+            try 
+                for i=1:length(variableName)
+                    % Initialise to input forcing data.
+                    colForcingData_filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+                    colForcingData = obj.settings.forcingData_cols{colForcingData_filt,2};
+                    if i==1
+                        forcingData = obj.settings.forcingData(:,colForcingData);
+                    else
+                        forcingData(:,i) = obj.settings.forcingData(:,colForcingData);
+                    end
+                    
+                    % Skip if the pump was removed because if could not be
+                    % downscaled
+                    if (~isfield(obj.variables,variableName{i}))
+                        continue;
+                    end
+                    
+                    % Add stochastically generated forcing data
+                    filt_calibPeriod = obj.variables.(variableName{i})(:,2)>0;
+                    forcingData(obj.variables.(variableName{i})(filt_calibPeriod,2),i) = obj.variables.(variableName{i})(filt_calibPeriod ,8);                                                         
+                    
+                    % If there are any remaining -999s, then these should
+                    % be in evaluation periods. To handle this, the mean daily
+                    % extractions is calculated for each day of the year.
+                    % Also the mean pump state (on or off) is calculated. 
+                    % Importantly, the averages are calculated using only
+                    % data from the first non-zero value ie once the pump
+                    % was active. The former is then used for periods of
+                    % -999 without a metered volume for the period. The
+                    % latter is used for the metered periods and is used to
+                    % weight the metered volume to the -999 days.
+                    if ~obj.variables.doingCalibration
+                        
+                        % Find remaining periods of-999s.
+                        ind_999s = find(forcingData(:,i) == -999);
+
+                        % If there are any -999s then, firstly, calculate the
+                        % probability of the pumping being on for each calendar
+                        % day. This is done using data from the first >0 value
+                        % up to end of the calibration period?
+                        if ~isempty(ind_999s)
+
+                            % Build indexes to the start of pumping and the end
+                            % of the calibration period.
+                            obsTime = obj.settings.forcingData(:, 1);
+                            ind_startOfPumping = find(forcingData(:,i)>0,1,'first');                        
+                            if isfield(obj.variables,'t_end_calib') && isfinite(obj.variables.t_end_calib)
+                                ind_endOfCalib = find(obsTime>=obj.variables.t_end_calib,1,'first');
+                            else
+                                ind_endOfCalib = size(obsTime,1);
+                            end
+                            filt_obsTimes = obsTime>=obsTime(ind_startOfPumping) & obsTime<=obsTime(ind_endOfCalib) & forcingData(:,i)~=-999;
+
+                            % Calculate the probability that the pump is on
+                            % during each calander day.                        
+                            [monthsDays,~,ind] = unique(month(obsTime(filt_obsTimes))*100+day(obsTime(filt_obsTimes)),'rows');
+                            meanUsagePerDay = accumarray(ind, forcingData(filt_obsTimes,i), [], @mean);                
+                            meanUsagePerDay = [monthsDays, meanUsagePerDay];
+
+                            % Calculate the mean probability that the pump is on for
+                            % each day day of the year.
+                            meanProbPumpOn = accumarray(ind, forcingData(filt_obsTimes,i)>0, [], @mean);
+                            meanProbPumpOn = [monthsDays, meanProbPumpOn];                        
+
+                            % Find the periods of -999s with a meter reading
+                            % >0 immediatly after a -999.
+                            ind_999s_metered = ind_999s(forcingData(min(ind_999s+1,size(forcingData,1)),i)>0);
+
+                            % Loop through the -999 metered periods and
+                            % downscale the metered volume using the mean
+                            % probability of the pump being on.
+                            for j=1:length(ind_999s_metered)
+
+                                % Find the metered volume for the period.
+                                pumpingVol = forcingData(min(size(forcingData,1),ind_999s_metered(j)+1),i);                            
+
+                                % Find the first -999 for the metered period.
+                                k = ind_999s_metered(j);
+                                while forcingData(k,i) == -999
+                                    k = k-1;
+                                end
+                                k=k+1;
+
+                                % Set indexes to the -999s for the metered 
+                                % period. Note, the period includes the first 
+                                % day after the -999s because it to is to
+                                % be downscaled because the value
+                                % represents the total metered volume over the
+                                % preceeding period of -999s.
+                                ind_999s_metered_period  = k:(ind_999s_metered(j)+1);
+
+                                % Build index to the days and months for the period of
+                                % -999s.
+                                ind_monthsDays = zeros(length(ind_999s_metered_period),1);
+                                monthDay_period = month(obsTime(ind_999s_metered_period))*100+day(obsTime(ind_999s_metered_period));
+                                for el=1:length(ind_999s_metered_period)
+                                    ind_monthsDays(el,1) = find(monthsDays == monthDay_period(el),1,'first');    
+                                end
+                                    
+                                % Get the average pump state for the period.
+                                downscalingWeights = meanProbPumpOn(ind_monthsDays,2);
+                                downscalingWeights = downscalingWeights./sum(downscalingWeights);
+
+                                % Replace non-finite values by 0. 
+                                downscalingWeights(~isfinite(downscalingWeights))=0;
+                                
+                                % Down scale using the weighting.
+                                if sum(downscalingWeights)==0
+                                    pumpingVol = pumpingVol./(length(downscalingWeights).*ones(size(downscalingWeights)));
+                                else
+                                    pumpingVol = pumpingVol.*downscalingWeights;
+                                end
+
+                                % Assign downscaled weights to the forcing
+                                % data.
+                                forcingData(ind_999s_metered_period,i) = pumpingVol;
+                            end
+
+                            % Find the remaining -999s.
+                            ind_999s = find(forcingData(:,i) == -999);
+
+                            % Find the day and month of each -999 day
+                            monthDay_period = month(obsTime(ind_999s))*100+day(obsTime(ind_999s));
+
+                            % Loop though each periods of -999s and apply the
+                            % average usage for each day of the year.                         
+                            for j=1:length(monthDay_period)                           
+                                ind_monthsDays = find(monthsDays == monthDay_period(j),1,'first');
+                                forcingData(ind_999s(j),i) = meanUsagePerDay(ind_monthsDays ,2);
+                            end
+
+                        end     
+                    end
+                end
+            catch ME
+                error('The requested transformed forcing variable is not known.');
+            end
+        end
+        
+        % Return the derived variables.
+        function [params, param_names] = getDerivedParameters(obj)
+            params = [];
+            param_names = cell(0,2);
+        end
+
+        % Return coordinates for forcing variable
+        function coordinates = getCoordinates(obj, variableName)
+
+            if ~iscell(variableName)
+                variableNameTmp{1}=variableName;
+                variableName = variableNameTmp;
+                clear variableNameTmp;
+            end
+                
+            % Check each requested variable is within forcingData_cols
+            for i=1:length(variableName)
+                if ~any(strcmp(variableName{i}, obj.settings.forcingData_cols(:,1)))
+                    error(['pumpingRate_SAestiation: Inconsistency between selected downscaled pumps and weighting function input data. See ',variableName{i}])
+                end
+            end
+            
+            coordinates = cell(length(variableName),3);
+            for i=1:length(variableName)
+                % Find row within the list of required containing variabeName
+                filt = strcmp(obj.settings.forcingData_cols(:,1), variableName{i});
+
+                % Find input bore for requested output
+                sourceBoreColNumber = obj.settings.forcingData_cols{filt,2};
+                sourceBoreColName = obj.settings.forcingData_colnames{sourceBoreColNumber};
+
+                % Get coordinates
+                filt = strcmp(obj.settings.siteCoordinates(:,1), sourceBoreColName);
+                coordinates(i,:) = obj.settings.siteCoordinates(filt,:);
+                coordinates{i,1} = variableName{i};
+            end
+        end
+                
+    end
+    
+    methods(Access=protected, Hidden=true)
+                
+        % Get/build a table of of pump numes and numbers
+        function pumpNumTable=getPumpNumTable(obj)            
+            if isfield(obj.variables,'pumpNumTable')
+                % Get the table of pump numbers and names if it exists.
+                pumpNumTable = obj.variables.pumpNumTable;
+            else
+                % Build table.
+                pumpNumTable = unique(cell2table(obj.variables.nonObsPeriods(:,1:2),'VariableNames',{'pumpNum','pumpName'}),'rows');
+                obj.variables.pumpNumTable = pumpNumTable;
+            end
+        end
+        
+        % Calculate the mean daily pumping rate for each period with metered volume. 
+        % Returns matrix of size(obj.variables.nonObsPeriods,1) with
+        % pump number, start date for period, end date for period, average
+        % daily pumping rate for the period.
+        function [dailyRate,ind_dailyRate] = getDailyAveragePumpingRates(obj, t_start_calib, t_end_calib)
+                  
+            % Get list of non-obs periods nonObsPeriods.
+            nonObsPeriods = obj.variables.nonObsPeriods;
+
+            pumpName_all = unique(nonObsPeriods(:,2));
+            for i=1:length(pumpName_all)
+                ind_dailyRate.(pumpName_all{i}) = [];
+            end
+            
+            % For each bore, calculate the average daily pumping rate
+            % during each petered period.
+            dailyRate = nan(size(nonObsPeriods,1),5);
+            for i=1:size(nonObsPeriods,1)
+                
+                dailyRate(i,1) = nonObsPeriods{i,1};
+                dailyRate(i,2) = nonObsPeriods{i,3};
+                dailyRate(i,3) = nonObsPeriods{i,4};
+                                                
+                % Check if the pumping was metered for the period.
+                if ~isfinite(nonObsPeriods{i,5})
+                    continue
+                end
+                
+                % Check the period of metered total extractions is within
+                % the calcibration time range t. That is, if the end
+                % date of the metered period is after the end calib period
+                % then do not use the metered rate.
+                if dailyRate(i,3) > t_end_calib
+                    continue
+                end
+                                
+                % get the pump names
+                pumpName = nonObsPeriods{i,2};
+                
+                % Find the number of days the pump is recorded as 'on'. 
+                [~,ind_from] = ismember(dailyRate(i,2),obj.variables.(pumpName)(:,1));
+                [~,ind_to] = ismember(dailyRate(i,3),obj.variables.(pumpName)(:,1));
+                ind = ind_from:ind_to;
+                
+                %filt_obsPeriods =  obj.variables.(pumpName)(:,1) >= dailyRate(i,2) & obj.variables.(pumpName)(:,1) <= dailyRate(i,3); 
+                dailyRate(i,4) = sum(obj.variables.(pumpName)(ind,6));
+                
+                % Calculate average daily pumping rate for the period.
+                dailyRate(i,5) = nonObsPeriods{i,5}/dailyRate(i,4);                            
+                
+                ind_dailyRate.(pumpName) = [ind_dailyRate.(pumpName); [ind', repmat(dailyRate(i,5), length(ind),1)]];
+            end        
+            
+            % Add the daily metered volumes. Since these readings are for a
+            % single day, the the start and end dates are equal.
+            % Importantly, ONLY daily metered usage before the calibration
+            % end date is added.
+            filt = obj.variables.obsDays(:,2)<=t_end_calib;
+            if sum(filt)>0
+                dailyRate = [dailyRate; [obj.variables.obsDays(filt ,1), ...
+                    obj.variables.obsDays(filt ,2), obj.variables.obsDays(filt ,2), ones(sum(filt),1), obj.variables.obsDays(filt ,3)]];           
+            end
+        end
+        
+        function setTimestepIndexes(obj, updateTimeStep, t_start_calib, t_end_calib)
+           
+            % Loop through each pump bore and add one property time point at which the 
+            % forcing should be changed from zero to one, or vise versa.
+            if ~isempty(obj.variables.nonObsPeriods)
+                for i=1:size(obj.settings.forcingData_cols,1)
+                    % Get unobserved periods for the current bore
+                    filt = find(cellfun(@(x) x == i, obj.variables.nonObsPeriods(:,1)))';
+                    nNonObsPeriod = length(filt);
+
+                    % Add properties (ie parameters) for annual pumping data. For each
+                    % year without pumping data a parameter is added.
+                    if nNonObsPeriod>0
+
+                        % Get name of pumping bore
+                        pumpName = obj.settings.forcingData_cols{i,1};
+
+                        % Create a lookup conversion table for each production bore to
+                        % transform the date to alter pumping (propName) and the true
+                        % date. Each production bore has one numeric matrix
+                        % with the following format.
+                        %   - date (from obj.settings.forcingData) for each period without daily metering
+                        %   - row index to input forcing data
+                        %   - time-step integer for user-set downscaled time-step
+                        %   - time-step for only periods with metering normalised from 0 to 1.                        
+                        %   - time-step all periods without daily metering normalised from 0 to 1.                        
+                        %   - 0/1 for the pumping being on or off over the user set time period.
+                        %   - The pumping rate for the period (assigned in
+                        %   updateStochasticForcingData()).
+                        %   - The daily extraction rate (pump state * pump rate).
+                        %
+                        if isfield(obj.variables,pumpName) && size(obj.variables.(pumpName),1)>0
+                            updateExistingTable = true;
+                        else
+                            updateExistingTable = false;
+                            obj.variables.(pumpName) = zeros(0,8);
+                        end
+                        for j=filt
+                            
+                            % Get dates for non-obs period
+                            sDate = obj.variables.nonObsPeriods{j,3};
+                            eDate = obj.variables.nonObsPeriods{j,4};                                                        
+                                                        
+                            % Setup non-obs days
+                            nonObsDays = [sDate:eDate]';
+                            
+                            % Get index to non obs days
+                            nonObsDaysIndex = find(obj.settings.forcingData(:,1) >=sDate & obj.settings.forcingData(:,1) <=eDate);
+                            
+                            % Convert non-obs days increments for user set time-step
+                            switch lower(updateTimeStep)
+                                case 'daily'
+                                    nonObsSteps = year(nonObsDays) *1000 + day(datetime(nonObsDays,'ConvertFrom','datenum'), 'dayofyear');
+                                case 'weekly'
+                                    nonObsSteps = year(nonObsDays) *1000 + week(datetime(nonObsDays,'ConvertFrom','datenum'));
+                                case 'monthly'
+                                    nonObsSteps = year(nonObsDays) *1000 + month(nonObsDays);
+                                case '(none)'
+                                    nonObsSteps = ones(length(nonObsDays),1) * j;                                    
+                                otherwise
+                                    error(['The following time-step for generation of forcing data is not recognised:',updateTimeStep])
+                            end
+                            
+                            % Set junk data for normalised time-step value.
+                            nonObsStepsNormalised = zeros(size(nonObsSteps));
+                            
+                            % Build temporary vector to denote if the
+                            % total extractions for the periods were
+                            % metered.
+                            if isfinite(obj.variables.nonObsPeriods{j,5})
+                                isMeteredPeriod = ones(size(nonObsSteps));
+                            else
+                                isMeteredPeriod = zeros(size(nonObsSteps));
+                            end
+                            
+                            % Limit dates to only calibration periods.
+                            if nargin==4 && any(nonObsDays > t_end_calib)
+                                
+                                % If part of the period without daily metering is 
+                                % less than the end date of the
+                                % calibration then, for the
+                                % calibration, set the dates
+                                % <=t_end_calib to be unmetered.                                
+                                filt_calibDays = nonObsDays <= t_end_calib;
+                                if any(filt_calibDays)
+                                    isMeteredPeriod(filt_calibDays) = 0;                                    
+                                end
+                                
+                                % Change the nonObsSteps to dates to zero.
+                                % Below, nonObsSteps==0 are filtered from
+                                % the normalisation and so calibration will
+                                % not change the pump state after
+                                % t_end_calib.
+                                nonObsSteps(nonObsDays > t_end_calib) = 0;
+                                
+                                % Also, change the indexes to zero. This is
+                                % required so that within getTransformed
+                                % Forcing() only the transformted data for the 
+                                % calibration period is added to input
+                                % forcing data.
+                                nonObsDaysIndex(nonObsDays > t_end_calib) = 0;
+                            end
+                            
+                            
+                            % Set pump to 'on' for each time step
+                            nonObsPumpState = ones(size(nonObsSteps));
+                            
+                            if updateExistingTable
+                                % Find rows to be updated within existing
+                                % table.
+                                filt_existingTable = find(obj.variables.(pumpName)(:,1) >=sDate & obj.variables.(pumpName)(:,1) <=eDate);
+
+                                % Create matrix of new lookup data.
+                                newLookupTableRows = [nonObsDays nonObsDaysIndex nonObsSteps isMeteredPeriod nonObsStepsNormalised];
+                                                                
+                                % Update table rows BUT NOT THE PUMPING STATE.
+                                obj.variables.(pumpName)(filt_existingTable ,1:5) = newLookupTableRows;
+                            else                                
+                                % Create matrix of new lookup data.
+                                newLookupTableRows = [nonObsDays nonObsDaysIndex nonObsSteps isMeteredPeriod nonObsStepsNormalised nonObsPumpState];
+                                
+                                if isempty(obj.variables.(pumpName))
+                                    obj.variables.(pumpName) = newLookupTableRows;
+                                else
+                                    obj.variables.(pumpName) = [obj.variables.(pumpName); newLookupTableRows];
+                                end
+                            end
+                        end            
+                        
+                        % Normalise 'nonObsSteps' during periods a metered total volume
+                        % so that required time step at which the pump state to be changed can
+                        % be located from the pumping bore class parameter.
+                        ind_metered = find(obj.variables.(pumpName)(:,4)==1 & obj.variables.(pumpName)(:,3)>0);
+                        ind_notMetered = find(obj.variables.(pumpName)(:,4)==0 & obj.variables.(pumpName)(:,3)>0);
+                        nonObsStepsUnique = sort(unique(obj.variables.(pumpName)(ind_metered ,3)));
+                        for k=1:length(nonObsStepsUnique)
+                            filt = find(obj.variables.(pumpName)(ind_metered,3) == nonObsStepsUnique(k));
+                            obj.variables.(pumpName)(ind_metered(filt) ,4) = k;
+                        end                        
+                        obj.variables.(pumpName)(ind_metered,4) = obj.variables.(pumpName)(ind_metered,4)/length(nonObsStepsUnique);
+                        obj.variables.(pumpName)(ind_notMetered,4) = NaN;                        
+                        
+                        % Normalise 'nonObsSteps' during all periods
+                        % without daily metering so that required time
+                        % step at which the pump state to be changed can
+                        % be located from the pumping bore class parameter.
+                        ind_calibDays = find(obj.variables.(pumpName)(:,3)>0);
+                        nonObsStepsUnique = sort(unique(obj.variables.(pumpName)(ind_calibDays,3)));
+                        for k=1:length(nonObsStepsUnique)
+                            filt_timesteps = obj.variables.(pumpName)(:,3) == nonObsStepsUnique(k);
+                            obj.variables.(pumpName)(filt_timesteps ,5) = k;
+                        end                        
+                        obj.variables.(pumpName)(:,5) = obj.variables.(pumpName)(:,5)/length(nonObsStepsUnique);
+                    end                                                    
+                    
+                end                
+            end
+            
+            
+        end
+    end
+    
+end
+
+
diff --git a/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/stochForcingTransform_abstract.m b/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/stochForcingTransform_abstract.m
index 8ab05c3..428ed90 100644
--- a/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/stochForcingTransform_abstract.m
+++ b/algorithms/models/TransferNoise/ForcingTransformation/stochasticTransformations/stochForcingTransform_abstract.m
@@ -1,37 +1,37 @@
-classdef stochForcingTransform_abstract < forcingTransform_abstract
-% stochForcingTransform_abstract abstract for stochastic forcing.
-%
-% Description: 
-%   This abstract is for forcing data that is stochastically derived. Each
-%   of the additional methods listed below (relative to
-%   forcingTransform_abstract() are required by the calibration schemes.
-    
-    properties
-    end
-        
-    methods(Abstract)
-        
-        % Get a data structure (forcingData) containing the stochastic forcing data.
-        stochDerivedForcingData = getStochForcingData(obj)   
-
-        % Update the stochasticlly derived forcing data. The inputs should
-        % be in the same format at returned by getStochForcingData().
-        % The input 'refineStochForcingMethod' provides the flexibility to
-        % reduce the down-scaling time step at the end of calibration
-        % iterations.
-        stochDerivedForcingData = updateStochForcingData(obj, stochDerivedForcingData, refineStochForcingMethod)
-        
-        % Set if model calibration is being undertaken or not. This was
-        % required to all for model simulations without undertaking chnages
-        % to the stochastic forcing data. The method can also be used to
-        % set the start and end dates for calibrtion.
-        setStochForcingState(obj,doingCalibration, t_start_calib, t_end_calib);
-        
-        % Update the the model parameters. 
-        % This function should be called at the end of a, say, SP-UCI, evolutionary loop to
-        % avoid the parameters converging to a local minimum
-        updateStochForcingParameters(obj, forcingData);
-    end
-    
-end
-
+classdef stochForcingTransform_abstract < forcingTransform_abstract
+% stochForcingTransform_abstract abstract for stochastic forcing.
+%
+% Description: 
+%   This abstract is for forcing data that is stochastically derived. Each
+%   of the additional methods listed below (relative to
+%   forcingTransform_abstract() are required by the calibration schemes.
+    
+    properties
+    end
+        
+    methods(Abstract)
+        
+        % Get a data structure (forcingData) containing the stochastic forcing data.
+        stochDerivedForcingData = getStochForcingData(obj)   
+
+        % Update the stochasticlly derived forcing data. The inputs should
+        % be in the same format at returned by getStochForcingData().
+        % The input 'refineStochForcingMethod' provides the flexibility to
+        % reduce the down-scaling time step at the end of calibration
+        % iterations.
+        stochDerivedForcingData = updateStochForcingData(obj, stochDerivedForcingData, refineStochForcingMethod)
+        
+        % Set if model calibration is being undertaken or not. This was
+        % required to all for model simulations without undertaking chnages
+        % to the stochastic forcing data. The method can also be used to
+        % set the start and end dates for calibrtion.
+        setStochForcingState(obj,doingCalibration, t_start_calib, t_end_calib);
+        
+        % Update the the model parameters. 
+        % This function should be called at the end of a, say, SP-UCI, evolutionary loop to
+        % avoid the parameters converging to a local minimum
+        updateStochForcingParameters(obj, forcingData);
+    end
+    
+end
+
diff --git a/algorithms/models/TransferNoise/ResponseFunctions/DerivedFunctions/derivedResponseFunction_abstract.m b/algorithms/models/TransferNoise/ResponseFunctions/DerivedFunctions/derivedResponseFunction_abstract.m
index 667051e..6a254dc 100644
--- a/algorithms/models/TransferNoise/ResponseFunctions/DerivedFunctions/derivedResponseFunction_abstract.m
+++ b/algorithms/models/TransferNoise/ResponseFunctions/DerivedFunctions/derivedResponseFunction_abstract.m
@@ -1,54 +1,54 @@
-classdef derivedResponseFunction_abstract < handle
-    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-    end
-        
-    methods(Static, Abstract=true)
-        [modelSettings, colNames, colFormats, colEdits,tooltipString] = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
-        modelDescription = modelDescription()         
-    end
-    
-    methods(Abstract=true)
-                
-        % Set parameters
-        setParameters(obj, params)
-        
-        % Get model parameters
-        [params, param_names] = getParameters(obj)
-
-        % Return pre-set physical limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)
-
-        % Return pre-set physical limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj, param_name)
-       
-        % Check if each parameters is valid. This is primarily used to 
-        % check parameters are within the physical bounds and sensible (eg 0<specific yield<1)
-        isValidParameter = getParameterValidity(obj, params, param_names)
-
-        % Calculate impulse-response function.
-        result = theta(obj, t)
-        
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data set.
-        result = intTheta_upperTail2Inf(obj, t)           
-        
-        % Calculate integral of impulse-response function from 0 to 1.
-        % This is used handle rapidly chnageing fucntion in the range from 0 to 1.
-        result = intTheta_lowerTail(obj, t)     
-        
-        
-        % Transform the result of the response function multiplied by the
-        % forcing. This method was included so that the groundwater pumping 
-        % transfer function of Shapoori, Peterson, Western and Costelleo
-        % 2013 could be corrected from a confined aquifer to an unconfined
-        % aquifer. The method is called from model_IRF.get_h_star
-        % Peterson Feb 2013
-        result = transform_h_star(obj, h_star_est)
-        
-    end
-    
-end
-
+classdef derivedResponseFunction_abstract < handle
+    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+    end
+        
+    methods(Static, Abstract=true)
+        [modelSettings, colNames, colFormats, colEdits,tooltipString] = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
+        modelDescription = modelDescription()         
+    end
+    
+    methods(Abstract=true)
+                
+        % Set parameters
+        setParameters(obj, params)
+        
+        % Get model parameters
+        [params, param_names] = getParameters(obj)
+
+        % Return pre-set physical limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)
+
+        % Return pre-set physical limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj, param_name)
+       
+        % Check if each parameters is valid. This is primarily used to 
+        % check parameters are within the physical bounds and sensible (eg 0<specific yield<1)
+        isValidParameter = getParameterValidity(obj, params, param_names)
+
+        % Calculate impulse-response function.
+        result = theta(obj, t)
+        
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data set.
+        result = intTheta_upperTail2Inf(obj, t)           
+        
+        % Calculate integral of impulse-response function from 0 to 1.
+        % This is used handle rapidly chnageing fucntion in the range from 0 to 1.
+        result = intTheta_lowerTail(obj, t)     
+        
+        
+        % Transform the result of the response function multiplied by the
+        % forcing. This method was included so that the groundwater pumping 
+        % transfer function of Shapoori, Peterson, Western and Costelleo
+        % 2013 could be corrected from a confined aquifer to an unconfined
+        % aquifer. The method is called from model_IRF.get_h_star
+        % Peterson Feb 2013
+        result = transform_h_star(obj, h_star_est)
+        
+    end
+    
+end
+
diff --git a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Bruggeman.m b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Bruggeman.m
index 9f363df..0bafc99 100644
--- a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Bruggeman.m
+++ b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Bruggeman.m
@@ -1,247 +1,247 @@
-classdef responseFunction_Bruggeman < responseFunction_abstract
-% Pearson's type III impulse response transfer function class. 
-
-    properties(GetAccess=public, SetAccess=protected)          
-        alpha
-        beta
-        gamma  
-        settings
-    end
-
-%%  STATIC METHODS        
-% Static methods used by the Graphical User Interface to inform the
-% user of the available model options and their input format.
-    methods(Static)        
-        function [modelSettings, colNames, colFormats, colEdits,tooltipString] = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
-           modelSettings = {};
-           colNames = {};
-           colFormats = {};
-           colEdits = [];       
-           tooltipString='';
-        end
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: responseFunction_Bruggeman', ...
-                               '', ...               
-                               'Purpose: simulation of streamflow lateral flow influence on groundwater head.', ...
-                               '', ...               
-                               'Number of parameters: 3', ...
-                               '', ...               
-                               'Options: none', ...
-                               '', ...               
-                               'Comments: This function has not been rigerouslt tested within the framework. Use with caution!.', ...
-                               '', ...               
-                               'References: ', ...
-                               '1. von Asmuth J.R., Maas K., Bakker M., Petersen J.,(2008), Modeling time series of ground water', ...
-                               'head fluctuations subjected to multiple stresses, Ground Water, Jan-Feb;46(1):30-40'};
-        end                
-    end
-    
-%%  PUBLIC METHODS     
-    methods
-        % Constructor
-        function obj = responseFunction_Bruggeman(bore_ID, forcingDataSiteID, siteCoordinates, options, params)
-            
-            % Define default parameters 
-            if nargin==4
-                params=[10; 10; 10];
-            end
-                
-            % Set parameters for transfer function.
-            setParameters(obj, params);                 
-            
-            % No settings are required.
-            obj.settings=[];
-        end
-       
-         % Set parameters
-        function setParameters(obj, params)
-            obj.alpha = params(1,:);
-            obj.beta = params(2,:);
-            obj.gamma = params(3,:);            
-        end
-        
-        % Get model parameters
-        function [params, param_names] = getParameters(obj)
-            params(1,:) = obj.alpha;
-            params(2,:) = obj.beta;
-            params(3,:) = obj.gamma;       
-            param_names = {'alpha';'beta';'gamma'};
-        end        
-        
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-            
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Check parameters are within bounds and T>0 and 0<S<1.
-                isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
-            params <= params_upperLimit(:,ones(1,size(params,2)));   
-
-        end
-
-        % Return fixed upper and lower bounds to the parameters.
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-            params_upperLimit = inf(3,1);
-            params_lowerLimit = zeros(3,1);
-        end        
-        
-        % Return fixed upper and lower plausible parameter ranges. 
-        % This is used to define reasonable range for the initial parameter sets
-        % for the calibration. These parameter ranges are only used in the 
-        % calibration if the user does not input parameter ranges.
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-            params_upperLimit = [100; 100; 100];
-            params_lowerLimit = [ 0; 0; 0];
-        end        
-        
-        % Calculate impulse-response function.
-        function result = theta(obj, t)        
-           
-            result = - obj.gamma ./ sqrt( pi()*obj.beta^2/obj.alpha^2 .* t.^3 ).* exp( -obj.alpha^2 ./(obj.beta.^2 .* t)- obj.beta.^2.*t );
-            
-            % Set theta at first time point to zero. NOTE: the first time
-            % point is more accuratly estimated by intTheta_lowerTail().
-            result(t==0,:) = 0;
-        end    
-        
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data
-        % set.
-        % TODO: IMPLEMENTED integral of theta
-        function result = intTheta_upperTail2Inf(obj, t)           
-            
-            result = 0; 
-        end   
-
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data set.
-        % TODO: IMPLEMENTED integral of theta
-        function result = intTheta_lowerTail(obj, t)          
-            result = 0; 
-        end
-        
-        % Transform the estimate of the response function * the forcing.
-        function result = transform_h_star(obj, h_star_est)
-           result = h_star_est(:,end);
-        end     
-        
-        % Return the derived variables.
-        function [params, param_names] = getDerivedParameters(obj)
-            params = [];
-            param_names = cell(0,2);
-        end
-      
-
-        function derivedData_types = getDerivedDataTypes(obj)
-           
-            derivedData_types = 'weighting';
-            
-        end
-        
-        % Return the theta values for the GUI 
-        function [derivedData, derivedData_names] = getDerivedData(obj,derivedData_variable,t,axisHandle)
-           
-            [params, param_names] = getParameters(obj);
-            nparamSets = size(params,2);
-            setParameters(obj,params(:,1));
-            derivedData_tmp = theta(obj, t);            
-            if nparamSets >1
-                derivedData = zeros(size(derivedData_tmp,1), nparamSets );
-                derivedData(:,1) = derivedData_tmp;            
-                parfor i=2:nparamSets 
-                    setParameters(obj,params(:,i));
-                    derivedData(:,i) = theta(obj, t);
-                end
-                setParameters(obj,params);
-                
-                % Calculate percentiles
-                derivedData_prctiles = prctile( derivedData,[5 10 25 50 75 90 95],2);
-                
-                % Plot percentiles
-                XFill = [t' fliplr(t')];
-                YFill = [derivedData_prctiles(:,1)', fliplr(derivedData_prctiles(:,7)')];                   
-                fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
-                hold(axisHandle,'on');                    
-                YFill = [derivedData_prctiles(:,2)', fliplr(derivedData_prctiles(:,6)')];                   
-                fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
-                hold(axisHandle,'on');
-                YFill = [derivedData_prctiles(:,3)', fliplr(derivedData_prctiles(:,5)')];                   
-                fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
-                hold(axisHandle,'on');
-                clear XFill YFill     
-
-                % Plot median
-                plot(axisHandle,t, derivedData_prctiles(:,4),'-b');
-                hold(axisHandle,'off');                
-                
-                ind = find(abs(derivedData_prctiles(:,4)) > max(abs(derivedData_prctiles(:,4)))*0.01,1,'last');
-                if isempty(ind);
-                    ind = length(t);
-                end                
-                xlim(axisHandle, [1, t(ind)]);
-                
-                % Add legend
-                legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');   
-                
-                % Add data column names
-                derivedData =[t,derivedData];
-                derivedData_names = cell(nparamSets+1,1);
-                derivedData_names{1,1}='Time lag (days)';
-                derivedData_names(2:end,1) = strcat(repmat({'Weight-Parm. Set'},1,nparamSets )',num2str([1:nparamSets ]'));                
-            else
-                plot(axisHandle, t,derivedData_tmp,'-b');                                   
-                ind = find(abs(derivedData_tmp) > max(abs(derivedData_tmp))*0.05,1,'last');
-                if isempty(ind);
-                    ind = length(t);
-                end
-                xlim(axisHandle, [1, t(ind)]);
-                
-                derivedData_names = {'Time lag (days)','Weight'};                
-                derivedData =[t,derivedData_tmp ];
-            end
-
-            xlabel(axisHandle,'Time lag (days)');
-            ylabel(axisHandle,'Weight');            
-            box(axisHandle,'on');
-            
-        end
-        
-        
-        function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-            propNames = properties(obj);
-            for i=1:length(propNames)
-               if isempty(obj.(propNames{i}))
-                   continue;
-               end                
-               if isobject(obj.(propNames{i}))
-                delete(obj.(propNames{i}));
-               else               
-                obj.(propNames{i}) = []; 
-               end
-            end
-        end        
-    end
-end
-
+classdef responseFunction_Bruggeman < responseFunction_abstract
+% Pearson's type III impulse response transfer function class. 
+
+    properties(GetAccess=public, SetAccess=protected)          
+        alpha
+        beta
+        gamma  
+        settings
+    end
+
+%%  STATIC METHODS        
+% Static methods used by the Graphical User Interface to inform the
+% user of the available model options and their input format.
+    methods(Static)        
+        function [modelSettings, colNames, colFormats, colEdits,tooltipString] = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
+           modelSettings = {};
+           colNames = {};
+           colFormats = {};
+           colEdits = [];       
+           tooltipString='';
+        end
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: responseFunction_Bruggeman', ...
+                               '', ...               
+                               'Purpose: simulation of streamflow lateral flow influence on groundwater head.', ...
+                               '', ...               
+                               'Number of parameters: 3', ...
+                               '', ...               
+                               'Options: none', ...
+                               '', ...               
+                               'Comments: This function has not been rigerouslt tested within the framework. Use with caution!.', ...
+                               '', ...               
+                               'References: ', ...
+                               '1. von Asmuth J.R., Maas K., Bakker M., Petersen J.,(2008), Modeling time series of ground water', ...
+                               'head fluctuations subjected to multiple stresses, Ground Water, Jan-Feb;46(1):30-40'};
+        end                
+    end
+    
+%%  PUBLIC METHODS     
+    methods
+        % Constructor
+        function obj = responseFunction_Bruggeman(bore_ID, forcingDataSiteID, siteCoordinates, options, params)
+            
+            % Define default parameters 
+            if nargin==4
+                params=[10; 10; 10];
+            end
+                
+            % Set parameters for transfer function.
+            setParameters(obj, params);                 
+            
+            % No settings are required.
+            obj.settings=[];
+        end
+       
+         % Set parameters
+        function setParameters(obj, params)
+            obj.alpha = params(1,:);
+            obj.beta = params(2,:);
+            obj.gamma = params(3,:);            
+        end
+        
+        % Get model parameters
+        function [params, param_names] = getParameters(obj)
+            params(1,:) = obj.alpha;
+            params(2,:) = obj.beta;
+            params(3,:) = obj.gamma;       
+            param_names = {'alpha';'beta';'gamma'};
+        end        
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+            
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds and T>0 and 0<S<1.
+                isValidParameter = params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+            params <= params_upperLimit(:,ones(1,size(params,2)));   
+
+        end
+
+        % Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_upperLimit = inf(3,1);
+            params_lowerLimit = zeros(3,1);
+        end        
+        
+        % Return fixed upper and lower plausible parameter ranges. 
+        % This is used to define reasonable range for the initial parameter sets
+        % for the calibration. These parameter ranges are only used in the 
+        % calibration if the user does not input parameter ranges.
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_upperLimit = [100; 100; 100];
+            params_lowerLimit = [ 0; 0; 0];
+        end        
+        
+        % Calculate impulse-response function.
+        function result = theta(obj, t)        
+           
+            result = - obj.gamma ./ sqrt( pi()*obj.beta^2/obj.alpha^2 .* t.^3 ).* exp( -obj.alpha^2 ./(obj.beta.^2 .* t)- obj.beta.^2.*t );
+            
+            % Set theta at first time point to zero. NOTE: the first time
+            % point is more accuratly estimated by intTheta_lowerTail().
+            result(t==0,:) = 0;
+        end    
+        
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data
+        % set.
+        % TODO: IMPLEMENTED integral of theta
+        function result = intTheta_upperTail2Inf(obj, t)           
+            
+            result = 0; 
+        end   
+
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data set.
+        % TODO: IMPLEMENTED integral of theta
+        function result = intTheta_lowerTail(obj, t)          
+            result = 0; 
+        end
+        
+        % Transform the estimate of the response function * the forcing.
+        function result = transform_h_star(obj, h_star_est)
+           result = h_star_est(:,end);
+        end     
+        
+        % Return the derived variables.
+        function [params, param_names] = getDerivedParameters(obj)
+            params = [];
+            param_names = cell(0,2);
+        end
+      
+
+        function derivedData_types = getDerivedDataTypes(obj)
+           
+            derivedData_types = 'weighting';
+            
+        end
+        
+        % Return the theta values for the GUI 
+        function [derivedData, derivedData_names] = getDerivedData(obj,derivedData_variable,t,axisHandle)
+           
+            [params, param_names] = getParameters(obj);
+            nparamSets = size(params,2);
+            setParameters(obj,params(:,1));
+            derivedData_tmp = theta(obj, t);            
+            if nparamSets >1
+                derivedData = zeros(size(derivedData_tmp,1), nparamSets );
+                derivedData(:,1) = derivedData_tmp;            
+                parfor i=2:nparamSets 
+                    setParameters(obj,params(:,i));
+                    derivedData(:,i) = theta(obj, t);
+                end
+                setParameters(obj,params);
+                
+                % Calculate percentiles
+                derivedData_prctiles = prctile( derivedData,[5 10 25 50 75 90 95],2);
+                
+                % Plot percentiles
+                XFill = [t' fliplr(t')];
+                YFill = [derivedData_prctiles(:,1)', fliplr(derivedData_prctiles(:,7)')];                   
+                fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
+                hold(axisHandle,'on');                    
+                YFill = [derivedData_prctiles(:,2)', fliplr(derivedData_prctiles(:,6)')];                   
+                fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
+                hold(axisHandle,'on');
+                YFill = [derivedData_prctiles(:,3)', fliplr(derivedData_prctiles(:,5)')];                   
+                fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
+                hold(axisHandle,'on');
+                clear XFill YFill     
+
+                % Plot median
+                plot(axisHandle,t, derivedData_prctiles(:,4),'-b');
+                hold(axisHandle,'off');                
+                
+                ind = find(abs(derivedData_prctiles(:,4)) > max(abs(derivedData_prctiles(:,4)))*0.01,1,'last');
+                if isempty(ind);
+                    ind = length(t);
+                end                
+                xlim(axisHandle, [1, t(ind)]);
+                
+                % Add legend
+                legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');   
+                
+                % Add data column names
+                derivedData =[t,derivedData];
+                derivedData_names = cell(nparamSets+1,1);
+                derivedData_names{1,1}='Time lag (days)';
+                derivedData_names(2:end,1) = strcat(repmat({'Weight-Parm. Set'},1,nparamSets )',num2str([1:nparamSets ]'));                
+            else
+                plot(axisHandle, t,derivedData_tmp,'-b');                                   
+                ind = find(abs(derivedData_tmp) > max(abs(derivedData_tmp))*0.05,1,'last');
+                if isempty(ind);
+                    ind = length(t);
+                end
+                xlim(axisHandle, [1, t(ind)]);
+                
+                derivedData_names = {'Time lag (days)','Weight'};                
+                derivedData =[t,derivedData_tmp ];
+            end
+
+            xlabel(axisHandle,'Time lag (days)');
+            ylabel(axisHandle,'Weight');            
+            box(axisHandle,'on');
+            
+        end
+        
+        
+        function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+            propNames = properties(obj);
+            for i=1:length(propNames)
+               if isempty(obj.(propNames{i}))
+                   continue;
+               end                
+               if isobject(obj.(propNames{i}))
+                delete(obj.(propNames{i}));
+               else               
+                obj.(propNames{i}) = []; 
+               end
+            end
+        end        
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Hantush.m b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Hantush.m
index 87cbbe9..cb48ed4 100644
--- a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Hantush.m
+++ b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_Hantush.m
@@ -1,317 +1,317 @@
-classdef responseFunction_Hantush < responseFunction_FerrisKnowles
-% Pearson's type III impulse response transfer function class. 
-
-    properties(GetAccess=public, SetAccess=protected)
-        gamma
-    end
-
-    methods(Static)    
-
-        function modelDescription = modelDescription()
-           modelDescription = {'Name: responseFunction_FerrisKnowles', ...
-                               '', ...               
-                               'Purpose: simulation of pumping drawdown using the Hantush instantaneous drawdown function for a leaky aquifer.', ...
-                               '', ...                               
-                               'Number of parameters: 3', ...
-                               '', ...                               
-                               'Options: Recharge or no-flow boundary conditions.', ...
-                               '', ...                               
-                               'References: ', ...
-                               '1. Hantush MS (1956) Analysis of data from pumping tests in leaky aquifers. Transactions American Geophysical Union 37: 702-714', ...
-                               '2. Shapoori V., Peterson T.J., Western A.W., Costelloe J.F, (in-review) Decomposing groundwater head variations', ...
-                               'into climate and pumping components: a synthetic study, Hydrogeology Journal.'};
-
-        end           
-    end    
-
-%%  PUBLIC METHODS     
-    methods
-        % Constructor
-        function obj = responseFunction_Hantush(bore_ID, forcingDataSiteID, siteCoordinates, options, params)
-            
-            % Use inheritence to construct Ferris Knowles and Jacobs correction objects.
-            obj = obj@responseFunction_FerrisKnowles(bore_ID, forcingDataSiteID, siteCoordinates, options);
-            
-            % Define default parameters 
-            if nargin==4
-                obj.gamma=0.1;
-            end
-                
-            % Set parameters for transfer function.
-            %setParameters(obj, params)                 
-        end
-       
-        % Set parameters
-        function setParameters(obj, params)
-            if size(params,1)==3
-                setParameters@responseFunction_FerrisKnowles(obj, params(1:2,:));                        
-                obj.gamma = params(3,:);            
-            elseif size(params,1)==2
-                setParameters@responseFunction_FerrisKnowles(obj, params(1:2,:));                        
-            end
-        end
-        
-        % Get model parameters
-        function [params, param_names] = getParameters(obj)
-            [params, param_names] = getParameters@responseFunction_FerrisKnowles(obj);
-            params(3,:) = obj.gamma;       
-            param_names{3,1} = 'gamma';
-        end        
-        
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-            % Initialise output.
-            isValidParameter = true(size(params));
-            
-            alpha_filt =  strcmp('alpha',param_names);
-            beta_filt =  strcmp('beta',param_names);
-            gamma_filt =  strcmp('gamma',param_names);
-            
-            alpha = params(alpha_filt,:);
-            beta = params(beta_filt,:);
-            gamma = params(gamma_filt,:);
-            
-            % Calculate T, leakage and S.
-            T= 1./(4.*pi.*10.^alpha);
-            S= 4 .* 10.^beta .* T;    
-            Leakage = 1./(S .* 10.^gamma);            
-                        
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Check gamma is within bounds.
-            isValidParameter = repmat(S >=0 & S <1 & T>= 0,size(params,1),1) & ...
-            params >= params_lowerLimit(1:size(params,1),ones(1,size(params,2))) & ...
-            params <= params_upperLimit(1:size(params,1),ones(1,size(params,2)));       
-
-        end
-
-        % Return fixed upper and lower bounds to the parameters.
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit@responseFunction_FerrisKnowles(obj);
-            params_upperLimit(3,1) = inf;
-            params_lowerLimit(3,1) = -inf;
-        end        
-        
-        % Return fixed upper and lower plausible parameter ranges. 
-        % This is used to define reasonable range for the initial parameter sets
-        % for the calibration. These parameter ranges are only used in the 
-        % calibration if the user does not input parameter ranges.
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-            [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit@responseFunction_FerrisKnowles(obj);
-            params_upperLimit(3,1) = 10;
-            params_lowerLimit(3,1) = -10;                        
-        end        
-        
-        % Calculate impulse-response function.
-        function result = theta(obj, t)           
-            
-            % Loop though each production bore and, if image wells exist,
-            % account for them in the drawdown.
-            result = zeros(size(t,1),size(obj.settings.pumpingBores,1));
-            for i=1: size(obj.settings.pumpingBores,1)
-                % Calc. distance to obs well.
-                pumpDistancesSqr = (obj.settings.obsBore.Easting - obj.settings.pumpingBores{i,1}.Easting).^2 ...
-                    + (obj.settings.obsBore.Northing - obj.settings.pumpingBores{i,1}.Northing).^2;
-                
-                if isfield(obj.settings.pumpingBores{i,1},'imageBoreID')
-
-                    % Calculate the distance to each image bore.
-                    imageDistancesSqr = (obj.settings.obsBore.Easting - obj.settings.pumpingBores{i,1}.imageBoreEasting).^2 ...
-                    + (obj.settings.obsBore.Northing - obj.settings.pumpingBores{i,1}.imageBoreNorthing).^2;
-                    
-                    imageWellMultiplier=zeros(size(obj.settings.pumpingBores{i,1}.imageBoreType,1),1);
-                    
-                    % create filter for recharge image wells
-                    filt =  cellfun(@(x)strcmp(x,'Recharge'),obj.settings.pumpingBores{i,1}.imageBoreType);
-                    imageWellMultiplier(filt)= 1;
-                    
-                    % create filter for no flow image wells
-                    filt =  cellfun(@(x)strcmp(x,'No flow'),obj.settings.pumpingBores{i,1}.imageBoreType);
-                    imageWellMultiplier(filt)= -1;
-    
-                    % Calculate the drawdown from the production well plus
-                    % the influence from each image well.
-                    result(:,i) = bsxfun(@plus, - 10.^obj.alpha./t.*exp( -10.^obj.beta * (pumpDistancesSqr./t) - 10.^obj.gamma .* t ), ...
-                        sum(bsxfun(@times, imageWellMultiplier' , bsxfun(@times, 10^obj.alpha./t , exp( bsxfun(@plus, -10^obj.beta * bsxfun(@rdivide,imageDistancesSqr',t),-10.^obj.gamma.*t)))),2));
-                else                    
-                    result(:,i) = - 10.^obj.alpha./t.*exp( -10.^obj.beta * (pumpDistancesSqr./t) - 10.^obj.gamma .* t );
-                end
-            end  
-            
-            % Set theta at first time point to zero. NOTE: the first time
-            % point is more accuratly estimated by intTheta_lowerTail().
-            result(t==0,:) = 0;     
-            
-        end    
-        
-        
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data
-        % set.
-        % TODO: IMPLEMENTED integral of theta
-        function result = intTheta_upperTail2Inf(obj, t)                       
-            result = zeros(size(obj.settings.pumpingBores,1),1); 
-        end   
-
-        % Nuemrical integration of impulse-response function from 0 to 1.
-        % This is undertaken to ensure the first time step is accuratly
-        % estimated. This was found to be important for highly transmissive aquifers.
-        function result = intTheta_lowerTail(obj, t)     
-            
-            % Calculate Theta at 1 minute time steps.
-            delta_t = 1/(60*24);
-            t_0to1 = [eps():delta_t:t]';
-            theta_0to1 = theta(obj, t_0to1);
-            
-            % Undertake Simpson's 3/8 composite integratraion for 1 minute
-            % time steps.            
-            result = 3*delta_t/8 .* (theta_0to1(1,:) + sum(3.*theta_0to1([2:3:end-3],:) +3.*theta_0to1([3:3:end-2],:) + 2.*theta_0to1([4:3:end-1],:),1) + theta_0to1(end,:));
-            
-        end
-        
-        % Extract the estimates of aquifer properties from the values of
-        % alpha, beta and zeta.
-        function [T,S, Leakage] = get_AquiferProperties(obj)    
-
-            T= 1./(4.*pi.*10.^obj.alpha);
-            S= 4 .* 10.^obj.beta .* T;    
-            Leakage = 1./(S .* 10.^obj.gamma);            
-
-        end   
-        
-        function [params, param_names] = getDerivedParameters(obj)            
-            T= 1./(4.*pi.*10.^obj.alpha);
-            S= 4 .* 10.^obj.beta .* T;    
-            Leakage = 1./(S .* 10.^obj.gamma);       
-            
-            params = [T;S; Leakage];
-            param_names = {'T: Transmissivity (Head units^2/day)'; 'S: Storativity'; 'G : Leakage Param.'};
-        end             
-        
-
-        function derivedData_types = getDerivedDataTypes(obj)
-            derivedData_types = cell(size(obj.settings.pumpingBores,1),1);
-            for i=1:size(obj.settings.pumpingBores,1)
-                derivedData_types{i,1} = [obj.settings.pumpingBores{i,1}.BoreID,' weighting'];
-            end
-        end        
-        
-        % Return the theta values for the GUI 
-        function [derivedData, derivedData_names] = getDerivedData(obj,derivedData_variable,t,axisHandle)
-           
-            % Find which bore to extract data for.
-            ind = [];
-            for i=1:size(obj.settings.pumpingBores,1)
-                if strcmp([obj.settings.pumpingBores{i,1}.BoreID,' weighting'], derivedData_variable)
-                    ind = i;
-                    break;
-                end                
-            end            
-            if isempty(ind)
-                error(['The following derived variable could not be found:',derivedData_variable]);
-            end
-            
-            [params, param_names] = getParameters(obj);
-            nparamSets = size(params,2);
-            setParameters(obj,params(:,1));
-            derivedData_tmp = theta(obj, t);
-            if nparamSets >1
-                derivedData = zeros(size(derivedData_tmp,1), nparamSets );                
-                derivedData(:,1) = derivedData_tmp(:,ind);
-                for i=2:nparamSets 
-                    setParameters(obj,params(:,i));
-                    derivedData_tmp = theta(obj, t);
-                    derivedData(:,i) = derivedData_tmp(:,ind);
-                end
-                setParameters(obj,params);
-                
-                % Calculate percentiles
-                derivedData_prctiles = prctile( derivedData,[5 10 25 50 75 90 95],2);
-                
-                % Plot percentiles
-                XFill = [t' fliplr(t')];
-                YFill = [derivedData_prctiles(:,1)', fliplr(derivedData_prctiles(:,7)')];                   
-                fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
-                hold(axisHandle,'on');                    
-                YFill = [derivedData_prctiles(:,2)', fliplr(derivedData_prctiles(:,6)')];                   
-                fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
-                hold(axisHandle,'on');
-                YFill = [derivedData_prctiles(:,3)', fliplr(derivedData_prctiles(:,5)')];                   
-                fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
-                hold(axisHandle,'on');
-                clear XFill YFill     
-
-                % Plot median
-                plot(axisHandle,t, derivedData_prctiles(:,4),'-b');
-                hold(axisHandle,'off');                
-                
-                ind = find(abs(derivedData_prctiles(:,4)) > max(abs(derivedData_prctiles(:,4)))*0.05,1,'last');
-                if isempty(ind);
-                    ind = length(t);
-                end                
-                xlim(axisHandle, [1, t(ind)]);
-                                                
-                % Add legend
-                legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');   
-                
-                % Add data column names
-                derivedData_names = cell(nparamSets+1,1);
-                derivedData_names{1,1}='Time lag (days)';
-                derivedData_names(2:end,1) = strcat(repmat({'Weight-Parm. Set '},1,nparamSets )',num2str([1:nparamSets ]'));                
-                
-                derivedData =[t,derivedData];
-            else
-                plot(axisHandle, t,derivedData_tmp(:,ind),'-b');      
-                t_ind = find(abs(derivedData_tmp(:,ind)) > max(abs(derivedData_tmp(:,ind)))*0.05,1,'last');
-                if isempty(ind);
-                    ind = length(t);
-                end
-                xlim(axisHandle, [1, t(ind)]);
-                
-                derivedData_names = {'Time lag (days)','Weight'};
-                derivedData =[t,derivedData_tmp(:,ind) ];
-            end
-            xlabel(axisHandle,'Time lag (days)');
-            ylabel(axisHandle,'Weight');
-            box(axisHandle,'on');
-        end      
-                
-        
-        function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-            propNames = properties(obj);
-            for i=1:length(propNames)
-               if isempty(obj.(propNames{i}))
-                   continue;
-               end                
-               if isobject(obj.(propNames{i}))
-                delete(obj.(propNames{i}));
-               else               
-                obj.(propNames{i}) = []; 
-               end
-            end
-        end            
-    end
-end
-
+classdef responseFunction_Hantush < responseFunction_FerrisKnowles
+% Pearson's type III impulse response transfer function class. 
+
+    properties(GetAccess=public, SetAccess=protected)
+        gamma
+    end
+
+    methods(Static)    
+
+        function modelDescription = modelDescription()
+           modelDescription = {'Name: responseFunction_FerrisKnowles', ...
+                               '', ...               
+                               'Purpose: simulation of pumping drawdown using the Hantush instantaneous drawdown function for a leaky aquifer.', ...
+                               '', ...                               
+                               'Number of parameters: 3', ...
+                               '', ...                               
+                               'Options: Recharge or no-flow boundary conditions.', ...
+                               '', ...                               
+                               'References: ', ...
+                               '1. Hantush MS (1956) Analysis of data from pumping tests in leaky aquifers. Transactions American Geophysical Union 37: 702-714', ...
+                               '2. Shapoori V., Peterson T.J., Western A.W., Costelloe J.F, (in-review) Decomposing groundwater head variations', ...
+                               'into climate and pumping components: a synthetic study, Hydrogeology Journal.'};
+
+        end           
+    end    
+
+%%  PUBLIC METHODS     
+    methods
+        % Constructor
+        function obj = responseFunction_Hantush(bore_ID, forcingDataSiteID, siteCoordinates, options, params)
+            
+            % Use inheritence to construct Ferris Knowles and Jacobs correction objects.
+            obj = obj@responseFunction_FerrisKnowles(bore_ID, forcingDataSiteID, siteCoordinates, options);
+            
+            % Define default parameters 
+            if nargin==4
+                obj.gamma=0.1;
+            end
+                
+            % Set parameters for transfer function.
+            %setParameters(obj, params)                 
+        end
+       
+        % Set parameters
+        function setParameters(obj, params)
+            if size(params,1)==3
+                setParameters@responseFunction_FerrisKnowles(obj, params(1:2,:));                        
+                obj.gamma = params(3,:);            
+            elseif size(params,1)==2
+                setParameters@responseFunction_FerrisKnowles(obj, params(1:2,:));                        
+            end
+        end
+        
+        % Get model parameters
+        function [params, param_names] = getParameters(obj)
+            [params, param_names] = getParameters@responseFunction_FerrisKnowles(obj);
+            params(3,:) = obj.gamma;       
+            param_names{3,1} = 'gamma';
+        end        
+        
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+            % Initialise output.
+            isValidParameter = true(size(params));
+            
+            alpha_filt =  strcmp('alpha',param_names);
+            beta_filt =  strcmp('beta',param_names);
+            gamma_filt =  strcmp('gamma',param_names);
+            
+            alpha = params(alpha_filt,:);
+            beta = params(beta_filt,:);
+            gamma = params(gamma_filt,:);
+            
+            % Calculate T, leakage and S.
+            T= 1./(4.*pi.*10.^alpha);
+            S= 4 .* 10.^beta .* T;    
+            Leakage = 1./(S .* 10.^gamma);            
+                        
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check gamma is within bounds.
+            isValidParameter = repmat(S >=0 & S <1 & T>= 0,size(params,1),1) & ...
+            params >= params_lowerLimit(1:size(params,1),ones(1,size(params,2))) & ...
+            params <= params_upperLimit(1:size(params,1),ones(1,size(params,2)));       
+
+        end
+
+        % Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit@responseFunction_FerrisKnowles(obj);
+            params_upperLimit(3,1) = inf;
+            params_lowerLimit(3,1) = -inf;
+        end        
+        
+        % Return fixed upper and lower plausible parameter ranges. 
+        % This is used to define reasonable range for the initial parameter sets
+        % for the calibration. These parameter ranges are only used in the 
+        % calibration if the user does not input parameter ranges.
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit@responseFunction_FerrisKnowles(obj);
+            params_upperLimit(3,1) = 10;
+            params_lowerLimit(3,1) = -10;                        
+        end        
+        
+        % Calculate impulse-response function.
+        function result = theta(obj, t)           
+            
+            % Loop though each production bore and, if image wells exist,
+            % account for them in the drawdown.
+            result = zeros(size(t,1),size(obj.settings.pumpingBores,1));
+            for i=1: size(obj.settings.pumpingBores,1)
+                % Calc. distance to obs well.
+                pumpDistancesSqr = (obj.settings.obsBore.Easting - obj.settings.pumpingBores{i,1}.Easting).^2 ...
+                    + (obj.settings.obsBore.Northing - obj.settings.pumpingBores{i,1}.Northing).^2;
+                
+                if isfield(obj.settings.pumpingBores{i,1},'imageBoreID')
+
+                    % Calculate the distance to each image bore.
+                    imageDistancesSqr = (obj.settings.obsBore.Easting - obj.settings.pumpingBores{i,1}.imageBoreEasting).^2 ...
+                    + (obj.settings.obsBore.Northing - obj.settings.pumpingBores{i,1}.imageBoreNorthing).^2;
+                    
+                    imageWellMultiplier=zeros(size(obj.settings.pumpingBores{i,1}.imageBoreType,1),1);
+                    
+                    % create filter for recharge image wells
+                    filt =  cellfun(@(x)strcmp(x,'Recharge'),obj.settings.pumpingBores{i,1}.imageBoreType);
+                    imageWellMultiplier(filt)= 1;
+                    
+                    % create filter for no flow image wells
+                    filt =  cellfun(@(x)strcmp(x,'No flow'),obj.settings.pumpingBores{i,1}.imageBoreType);
+                    imageWellMultiplier(filt)= -1;
+    
+                    % Calculate the drawdown from the production well plus
+                    % the influence from each image well.
+                    result(:,i) = bsxfun(@plus, - 10.^obj.alpha./t.*exp( -10.^obj.beta * (pumpDistancesSqr./t) - 10.^obj.gamma .* t ), ...
+                        sum(bsxfun(@times, imageWellMultiplier' , bsxfun(@times, 10^obj.alpha./t , exp( bsxfun(@plus, -10^obj.beta * bsxfun(@rdivide,imageDistancesSqr',t),-10.^obj.gamma.*t)))),2));
+                else                    
+                    result(:,i) = - 10.^obj.alpha./t.*exp( -10.^obj.beta * (pumpDistancesSqr./t) - 10.^obj.gamma .* t );
+                end
+            end  
+            
+            % Set theta at first time point to zero. NOTE: the first time
+            % point is more accuratly estimated by intTheta_lowerTail().
+            result(t==0,:) = 0;     
+            
+        end    
+        
+        
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data
+        % set.
+        % TODO: IMPLEMENTED integral of theta
+        function result = intTheta_upperTail2Inf(obj, t)                       
+            result = zeros(size(obj.settings.pumpingBores,1),1); 
+        end   
+
+        % Nuemrical integration of impulse-response function from 0 to 1.
+        % This is undertaken to ensure the first time step is accuratly
+        % estimated. This was found to be important for highly transmissive aquifers.
+        function result = intTheta_lowerTail(obj, t)     
+            
+            % Calculate Theta at 1 minute time steps.
+            delta_t = 1/(60*24);
+            t_0to1 = [eps():delta_t:t]';
+            theta_0to1 = theta(obj, t_0to1);
+            
+            % Undertake Simpson's 3/8 composite integratraion for 1 minute
+            % time steps.            
+            result = 3*delta_t/8 .* (theta_0to1(1,:) + sum(3.*theta_0to1([2:3:end-3],:) +3.*theta_0to1([3:3:end-2],:) + 2.*theta_0to1([4:3:end-1],:),1) + theta_0to1(end,:));
+            
+        end
+        
+        % Extract the estimates of aquifer properties from the values of
+        % alpha, beta and zeta.
+        function [T,S, Leakage] = get_AquiferProperties(obj)    
+
+            T= 1./(4.*pi.*10.^obj.alpha);
+            S= 4 .* 10.^obj.beta .* T;    
+            Leakage = 1./(S .* 10.^obj.gamma);            
+
+        end   
+        
+        function [params, param_names] = getDerivedParameters(obj)            
+            T= 1./(4.*pi.*10.^obj.alpha);
+            S= 4 .* 10.^obj.beta .* T;    
+            Leakage = 1./(S .* 10.^obj.gamma);       
+            
+            params = [T;S; Leakage];
+            param_names = {'T: Transmissivity (Head units^2/day)'; 'S: Storativity'; 'G : Leakage Param.'};
+        end             
+        
+
+        function derivedData_types = getDerivedDataTypes(obj)
+            derivedData_types = cell(size(obj.settings.pumpingBores,1),1);
+            for i=1:size(obj.settings.pumpingBores,1)
+                derivedData_types{i,1} = [obj.settings.pumpingBores{i,1}.BoreID,' weighting'];
+            end
+        end        
+        
+        % Return the theta values for the GUI 
+        function [derivedData, derivedData_names] = getDerivedData(obj,derivedData_variable,t,axisHandle)
+           
+            % Find which bore to extract data for.
+            ind = [];
+            for i=1:size(obj.settings.pumpingBores,1)
+                if strcmp([obj.settings.pumpingBores{i,1}.BoreID,' weighting'], derivedData_variable)
+                    ind = i;
+                    break;
+                end                
+            end            
+            if isempty(ind)
+                error(['The following derived variable could not be found:',derivedData_variable]);
+            end
+            
+            [params, param_names] = getParameters(obj);
+            nparamSets = size(params,2);
+            setParameters(obj,params(:,1));
+            derivedData_tmp = theta(obj, t);
+            if nparamSets >1
+                derivedData = zeros(size(derivedData_tmp,1), nparamSets );                
+                derivedData(:,1) = derivedData_tmp(:,ind);
+                for i=2:nparamSets 
+                    setParameters(obj,params(:,i));
+                    derivedData_tmp = theta(obj, t);
+                    derivedData(:,i) = derivedData_tmp(:,ind);
+                end
+                setParameters(obj,params);
+                
+                % Calculate percentiles
+                derivedData_prctiles = prctile( derivedData,[5 10 25 50 75 90 95],2);
+                
+                % Plot percentiles
+                XFill = [t' fliplr(t')];
+                YFill = [derivedData_prctiles(:,1)', fliplr(derivedData_prctiles(:,7)')];                   
+                fill(XFill, YFill,[0.8 0.8 0.8],'Parent',axisHandle);
+                hold(axisHandle,'on');                    
+                YFill = [derivedData_prctiles(:,2)', fliplr(derivedData_prctiles(:,6)')];                   
+                fill(XFill, YFill,[0.6 0.6 0.6],'Parent',axisHandle);                    
+                hold(axisHandle,'on');
+                YFill = [derivedData_prctiles(:,3)', fliplr(derivedData_prctiles(:,5)')];                   
+                fill(XFill, YFill,[0.4 0.4 0.4],'Parent',axisHandle);                    
+                hold(axisHandle,'on');
+                clear XFill YFill     
+
+                % Plot median
+                plot(axisHandle,t, derivedData_prctiles(:,4),'-b');
+                hold(axisHandle,'off');                
+                
+                ind = find(abs(derivedData_prctiles(:,4)) > max(abs(derivedData_prctiles(:,4)))*0.05,1,'last');
+                if isempty(ind);
+                    ind = length(t);
+                end                
+                xlim(axisHandle, [1, t(ind)]);
+                                                
+                % Add legend
+                legend(axisHandle, '5-95th%ile','10-90th%ile','25-75th%ile','median','Location', 'northeastoutside');   
+                
+                % Add data column names
+                derivedData_names = cell(nparamSets+1,1);
+                derivedData_names{1,1}='Time lag (days)';
+                derivedData_names(2:end,1) = strcat(repmat({'Weight-Parm. Set '},1,nparamSets )',num2str([1:nparamSets ]'));                
+                
+                derivedData =[t,derivedData];
+            else
+                plot(axisHandle, t,derivedData_tmp(:,ind),'-b');      
+                t_ind = find(abs(derivedData_tmp(:,ind)) > max(abs(derivedData_tmp(:,ind)))*0.05,1,'last');
+                if isempty(ind);
+                    ind = length(t);
+                end
+                xlim(axisHandle, [1, t(ind)]);
+                
+                derivedData_names = {'Time lag (days)','Weight'};
+                derivedData =[t,derivedData_tmp(:,ind) ];
+            end
+            xlabel(axisHandle,'Time lag (days)');
+            ylabel(axisHandle,'Weight');
+            box(axisHandle,'on');
+        end      
+                
+        
+        function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+            propNames = properties(obj);
+            for i=1:length(propNames)
+               if isempty(obj.(propNames{i}))
+                   continue;
+               end                
+               if isobject(obj.(propNames{i}))
+                delete(obj.(propNames{i}));
+               else               
+                obj.(propNames{i}) = []; 
+               end
+            end
+        end            
+    end
+end
+
diff --git a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_JacobsCorrection.m b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_JacobsCorrection.m
index 33f630a..6bc7f18 100644
--- a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_JacobsCorrection.m
+++ b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_JacobsCorrection.m
@@ -1,115 +1,115 @@
-classdef responseFunction_JacobsCorrection < handle
-    %RESPONSEFUNCTION_JACOBSCORRECTION Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties(GetAccess=public, SetAccess=protected)
-        zeta
-    end
-    
-    methods
-        % Constructor
-        function obj = responseFunction_JacobsCorrection(params)
-           
-            if nargin==0
-                params = log10(1);
-            end
-            
-            % Set parameters for transfer function.
-            setParameters(obj, params)                 
-        end
-                  
-        % Set parameters
-        function setParameters(obj, params)
-            obj.zeta = params(1,:);
-        end
-        
-        % Get model parameters
-        function [params, param_names] = getParameters(obj)
-            params(1,:) = obj.zeta;
-            param_names = {'zeta'};
-        end
-        
-        % Return fixed upper and lower bounds to the parameters.
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-            params_upperLimit = inf;
-            params_lowerLimit = log10(eps());
-        end        
-        
-        % Return fixed upper and lower plausible parameter ranges. 
-        % This is used to define reasonable range for the initial parameter sets
-        % for the calibration. These parameter ranges are only used in the 
-        % calibration if the user does not input parameter ranges.
-        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-            params_upperLimit = log10(100);
-            params_lowerLimit = log10(sqrt(eps()))-5;
-        end        
-                
-        function isValidParameter = getParameterValidity(obj, params, param_names)
-                        
-            zeta_filt =  strcmp('zeta',param_names);
-            
-            zeta = params(zeta_filt,:);
-            
-            % Back transform to get Sat. Thickness.
-            SatThickness = 10.^zeta;
-
-            % Get physical bounds.
-            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
-
-            % Check parameters are within bounds and T>0 and 0<S<1.
-            isValidParameter = SatThickness>0 & ...
-            params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
-            params <= params_upperLimit(:,ones(1,size(params,2)));            
-        end        
-        
-        % Transform the estimate of the response function * the forcing.
-        % This undertakes the Jacob's correction for an unconfined aquifer.
-        % If, in solution to the quadratic equation, a complex number is
-        % produced, then the input h_star value is returned. Peterson Feb
-        % 2013.
-        function result = transform_h_star(obj, h_star_est)
-           filt = (1-2.*h_star_est(:,2)./10.^obj.zeta)>=0;
-           result = h_star_est(:,2);
-           result(filt) = -10.^obj.zeta .* (-1 + sqrt(1-2.*h_star_est(filt,2)./10.^obj.zeta));           
-        end 
-
-        function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-            propNames = properties(obj);
-            for i=1:length(propNames)
-               if isempty(obj.(propNames{i}))
-                   continue;
-               end                
-               if isobject(obj.(propNames{i}))
-                delete(obj.(propNames{i}));
-               else               
-                obj.(propNames{i}) = []; 
-               end
-            end
-        end            
-        
-    end
-    
-end
-
+classdef responseFunction_JacobsCorrection < handle
+    %RESPONSEFUNCTION_JACOBSCORRECTION Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties(GetAccess=public, SetAccess=protected)
+        zeta
+    end
+    
+    methods
+        % Constructor
+        function obj = responseFunction_JacobsCorrection(params)
+           
+            if nargin==0
+                params = log10(1);
+            end
+            
+            % Set parameters for transfer function.
+            setParameters(obj, params)                 
+        end
+                  
+        % Set parameters
+        function setParameters(obj, params)
+            obj.zeta = params(1,:);
+        end
+        
+        % Get model parameters
+        function [params, param_names] = getParameters(obj)
+            params(1,:) = obj.zeta;
+            param_names = {'zeta'};
+        end
+        
+        % Return fixed upper and lower bounds to the parameters.
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+            params_upperLimit = inf;
+            params_lowerLimit = log10(eps());
+        end        
+        
+        % Return fixed upper and lower plausible parameter ranges. 
+        % This is used to define reasonable range for the initial parameter sets
+        % for the calibration. These parameter ranges are only used in the 
+        % calibration if the user does not input parameter ranges.
+        function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+            params_upperLimit = log10(100);
+            params_lowerLimit = log10(sqrt(eps()))-5;
+        end        
+                
+        function isValidParameter = getParameterValidity(obj, params, param_names)
+                        
+            zeta_filt =  strcmp('zeta',param_names);
+            
+            zeta = params(zeta_filt,:);
+            
+            % Back transform to get Sat. Thickness.
+            SatThickness = 10.^zeta;
+
+            % Get physical bounds.
+            [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj);
+
+            % Check parameters are within bounds and T>0 and 0<S<1.
+            isValidParameter = SatThickness>0 & ...
+            params >= params_lowerLimit(:,ones(1,size(params,2))) & ...
+            params <= params_upperLimit(:,ones(1,size(params,2)));            
+        end        
+        
+        % Transform the estimate of the response function * the forcing.
+        % This undertakes the Jacob's correction for an unconfined aquifer.
+        % If, in solution to the quadratic equation, a complex number is
+        % produced, then the input h_star value is returned. Peterson Feb
+        % 2013.
+        function result = transform_h_star(obj, h_star_est)
+           filt = (1-2.*h_star_est(:,2)./10.^obj.zeta)>=0;
+           result = h_star_est(:,2);
+           result(filt) = -10.^obj.zeta .* (-1 + sqrt(1-2.*h_star_est(filt,2)./10.^obj.zeta));           
+        end 
+
+        function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+            propNames = properties(obj);
+            for i=1:length(propNames)
+               if isempty(obj.(propNames{i}))
+                   continue;
+               end                
+               if isobject(obj.(propNames{i}))
+                delete(obj.(propNames{i}));
+               else               
+                obj.(propNames{i}) = []; 
+               end
+            end
+        end            
+        
+    end
+    
+end
+
diff --git a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_abstract.m b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_abstract.m
index 117d0ea..c1149da 100644
--- a/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_abstract.m
+++ b/algorithms/models/TransferNoise/ResponseFunctions/responseFunction_abstract.m
@@ -1,54 +1,54 @@
-classdef responseFunction_abstract < handle
-    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
-    %   Detailed explanation goes here
-    
-    properties
-    end
-    
-    % Static methods
-    methods(Static, Abstract=true)
-        % Options for GUI table. modelOptions must be a modelOptions data
-        % dype object or cell array of modelOptions         
-        modelOptions = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
-    end
-    
-    methods(Abstract=true)
-        % Static methods
-        %[types] = responseFunction_optionsFormat()  
-                
-        % Set parameters
-        setParameters(obj, params)
-        
-        % Get model parameters
-        [params, param_names] = getParameters(obj)
-        
-        % Check if each parameters is valid. This is primarily used to 
-        % check parameters are within the physical bounds and sensible (eg 0<specific yield<1)
-        isValidParameter = getParameterValidity(obj, params, param_names)
-
-        % Calculate impulse-response function.
-        result = theta(obj, t)
-        
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data set.
-        result = intTheta_upperTail2Inf(obj, t)           
-
-        % Calculate integral of impulse-response function from t to inf.
-        % This is used to minimise the impact from a finit forcign data set.
-        result = intTheta_lowerTail(obj, t)           
-                
-        % Return pre-set physical limits to the function parameters.
-        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)
-        
-        % Transform the result of the response function multiplied by the
-        % forcing. This method was included so that the groundwater pumping 
-        % transfer function of Shapoori, Peterson, Western and Costelleo
-        % 2013 could be corrected from a confined aquifer to an unconfined
-        % aquifer. The method is called from model_IRF.get_h_star
-        % Peterson Feb 2013
-        result = transform_h_star(obj, h_star_est)
-        
-    end
-    
-end
-
+classdef responseFunction_abstract < handle
+    %RESPONSEFUNCTION_ABSTRACT Summary of this class goes here
+    %   Detailed explanation goes here
+    
+    properties
+    end
+    
+    % Static methods
+    methods(Static, Abstract=true)
+        % Options for GUI table. modelOptions must be a modelOptions data
+        % dype object or cell array of modelOptions         
+        modelOptions = modelOptions(bore_ID, forcingDataSiteID, siteCoordinates)
+    end
+    
+    methods(Abstract=true)
+        % Static methods
+        %[types] = responseFunction_optionsFormat()  
+                
+        % Set parameters
+        setParameters(obj, params)
+        
+        % Get model parameters
+        [params, param_names] = getParameters(obj)
+        
+        % Check if each parameters is valid. This is primarily used to 
+        % check parameters are within the physical bounds and sensible (eg 0<specific yield<1)
+        isValidParameter = getParameterValidity(obj, params, param_names)
+
+        % Calculate impulse-response function.
+        result = theta(obj, t)
+        
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data set.
+        result = intTheta_upperTail2Inf(obj, t)           
+
+        % Calculate integral of impulse-response function from t to inf.
+        % This is used to minimise the impact from a finit forcign data set.
+        result = intTheta_lowerTail(obj, t)           
+                
+        % Return pre-set physical limits to the function parameters.
+        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj, param_name)
+        
+        % Transform the result of the response function multiplied by the
+        % forcing. This method was included so that the groundwater pumping 
+        % transfer function of Shapoori, Peterson, Western and Costelleo
+        % 2013 could be corrected from a confined aquifer to an unconfined
+        % aquifer. The method is called from model_IRF.get_h_star
+        % Peterson Feb 2013
+        result = transform_h_star(obj, h_star_est)
+        
+    end
+    
+end
+
diff --git a/algorithms/models/TransferNoise/doIRFconvolution.c b/algorithms/models/TransferNoise/doIRFconvolution.c
index 7b3d601..3632491 100644
--- a/algorithms/models/TransferNoise/doIRFconvolution.c
+++ b/algorithms/models/TransferNoise/doIRFconvolution.c
@@ -1,410 +1,410 @@
-/* doIRFconvolution - is a very computationally efficient numerical integration routine.
- * It can be compiled to use Intel Xeon Phi Coprocessors with the commands below.
- * Importantly, the paths have been defined. Edit for your versions of the intel compiler
- * (which must be >=2013.1) and mtalab.
- * 
- *
- * Load compiled (only required on a PBS cluster where code is to be compiled)
- * Also, edit for the required version of intel icc compiler
-      module load icc      
-      module load MATLAB/2016a 
-      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/bin/compilervars.sh intel64      
-
- * Intel compiler commands for compiling with offload
-      cp doIRFconvolution.c doIRFconvolutionPhi.c
-      icc -c -qoffload -restrict -I/usr/local/easybuild/software/MATLAB/2016a/extern/include -I/usr/local/easybuild/software/MATLAB/2016a/simulink/include -DMATLAB_MEX_FILE -ansi -D_GNU_SOURCE  -fexceptions -fPIC -fno-omit-frame-pointer -pthread -std=c99 -fopenmp  -DMX_COMPAT_32 -O3 -DNDEBUG  "doIRFconvolutionPhi.c"
-      icc -O3 -pthread -shared -Wl,--version-script,/usr/local/easybuild/software/MATLAB/2016a/extern/lib/glnxa64/mexFunction.map -Wl,--no-undefined -fopenmp -o  "doIRFconvolutionPhi.mexa64"  doIRFconvolutionPhi.o  -Wl,-rpath-link,/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -L/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -lmx -lmex -lmat -lm -lstdc++ -Wl,-rpath,/usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/intel64 -lintlc
-      rm doIRFconvolutionPhi.c
-
- * Intel compiler commands for compiling with NO offload
-      icc -c -qno-offload -I/usr/local/easybuild/software/MATLAB/2016a/extern/include -I/usr/local/matlab/R2014a/simulink/include -DMATLAB_MEX_FILE -ansi -D_GNU_SOURCE  -fexceptions -fPIC -fno-omit-frame-pointer -pthread -std=c99 -fopenmp  -DMX_COMPAT_32 -O3 -DNDEBUG  "doIRFconvolution.c"
-      icc -O3 -pthread -shared -static-intel -openmp-link=static -Wl,--version-script,/usr/local/easybuild/software/MATLAB/2016a/extern/lib/glnxa64/mexFunction.map -Wl,--no-undefined -fopenmp -o  "doIRFconvolution.mexa64"  doIRFconvolution.o  -Wl,-rpath-link,/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -L/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -lmx -lmex -lmat -lm -lstdc++ -Wl,-rpath,/usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/intel64 -lintlc -liomp5
-   
-
- * Additionally, to run Xeon Phi jobs on a cluster the following commands
- * will most likely be required prior to running the job:
-      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/mkl/bin/mklvars.sh intel64
-      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/bin/compilervars.sh intel64      
-             
-      source /usr/local/easybuild/software/icc/2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/mkl/bin/mklvars.sh intel64
-      source /usr/local/easybuild/software/icc/2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/compilervars.sh intel64    
- 
- * To monitor Xeon Phi usage, the following command is also useful: micsmc
-*/
-
-
-#include "math.h"
-#include "mex.h"
-#include "time.h"
-#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
-    #include "offload.h"
-    #define ALLOC alloc_if(1)
-    #define FREE free_if(1)
-    #define RETAIN free_if(0)
-    #define REUSE alloc_if(0)    
-#endif
-
-void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) 
-{    
-    /* Declare constants for matrix size and index counter */
-    const int nTheta  = (int)mxGetM(prhs[0] );
-    const int nIndex = (int)mxGetN(prhs[1]);    
-    int iIndex;
-    
-    /* Declare output data*/
-    double *result;
-    
-    /* Declare impulse response fuction vector and input forcing*/
-    double *theta  = mxGetPr( prhs[0] );
-    
-    /* Declare vector of starting rows for transforming theta to matrix for all start dates */
-    double *theta_indexes_start = mxGetPr( prhs[1] );
-            
-    /* Declare vector of ending rows for transforming theta to matrix for all start dates */
-    const int theta_indexes_end = (int)mxGetScalar(prhs[2]) + 1;        
-     
-    /* Declare input forcing*/    
-    double *forcing = mxGetPr( prhs[3] );
-    const int nForcing = (int)mxGetM(prhs[3] );
-    
-    /* Get the flag for the type of integration to undertake:
-     * 0: Trapazoidal integration for focing that is an integral of the daily flux (eg pecip).
-     * 1: Simpsons 3/8 composite integration for continuous forcing eg free-drainage from the soil model. */
-    const int isForcingAnIntegral = (int)mxGetScalar(prhs[4]);    
-   
-    /* Get the high precision estimate of the integral of the theta function 
-     * from 0 to 1. To derive this convolution with the forcing, the mean 
-     * forcing over the first day is adopted. */
-    const double inteTheta_0to1 = mxGetScalar(prhs[5]);    
-    
-#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
-   /* Delacre offloaded functions */
-   __declspec(target(mic:coprocessorNum))  double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);
-   __declspec(target(mic:coprocessorNum))  double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);          
-#else
-   /* Delacre on CPU functions */   
-    double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);
-    double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);      
-#endif
-    
-    /* Declare output vectors for results*/
-    plhs[0] = mxCreateDoubleMatrix(1,nIndex,mxREAL);
-    result = mxGetPr(plhs[0]);
-        
-    /*Cycle though all theta tiem points and create matrix of theta values.   */      
-#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
-
-   static int coprocessorNum=-999;
-   static int isCoprocessorMemAlloc = 0;
-   static int isCPUMemAlloc = 0;
-   int debugOffload=0;
-   
-   if (coprocessorNum==-999) {
-      /* Randomly select a coprocessor */
-      time_t t;    
-      srand((unsigned) time(&t));
-      int coprocessorCount = _Offload_number_of_devices();      
-      coprocessorNum = rand() % coprocessorCount;          
-   }
-     
-    /* Static variables for pre-allocating arrays on coprocessor*/
-    __declspec( target (mic:coprocessorNum)) static int nTheta_offload = 0, nIndex_offload = 0;
-    __declspec( target (mic:coprocessorNum)) static int nForcing_offload = 0, theta_indexes_end_offload  = 0;
-    __declspec( target (mic:coprocessorNum)) static double *result_offload, *theta_offload, *theta_indexes_start_offload, *forcing_offload, inteTheta_0to1_offload;
-    
-    /* Error handling */
-    _Offload_status x;
-
-    /* If the input vectors are of zero length then free the memory on 
-     * the coprocessor and static variables and then return*/
-   if (nTheta==0 && nIndex==0 && nForcing ==0) {    
-      if (debugOffload==1)
-         mexPrintf("Freeing memory on CPU and card %d. \n",coprocessorNum); 
-            
-      if (isCoprocessorMemAlloc==1) {
-         #pragma offload target(mic:coprocessorNum) \
-         nocopy(nIndex_offload : FREE REUSE) \
-         nocopy(theta_indexes_start_offload : FREE REUSE) \
-         nocopy(theta_indexes_end_offload : FREE REUSE) \
-         nocopy(theta_offload : FREE REUSE) \
-         nocopy(forcing_offload : FREE REUSE) \
-         nocopy(inteTheta_0to1_offload : FREE REUSE) \
-         nocopy(result_offload : FREE REUSE) status(x) optional
-         {}  
-         
-         if (x.result == OFFLOAD_SUCCESS)
-            isCoprocessorMemAlloc=0;   
-      }
-      
-      if (isCPUMemAlloc==1) {
-         mxFree(theta_offload);
-         mxFree(result_offload);
-         mxFree(theta_indexes_start_offload);
-         mxFree(forcing_offload);
-         isCPUMemAlloc=0;
-      }
-    
-      nTheta_offload = 0;
-      nIndex_offload = 0; 
-      nForcing_offload = 0;            
-      
-      coprocessorNum==-999;
-      
-      return;
-   }
-    
-   /* Allocate data on array if either the static variables have not yet 
-    * been set (ie this is the first call to the function) or the input 
-    * data appears to have changed since the prior call to the function */
-   if (nTheta_offload==0 || nIndex_offload==0 || nForcing_offload ==0 
-   || nTheta!=nTheta_offload || nIndex!=nIndex_offload || nForcing!=nForcing_offload) {
-      if (debugOffload==1)
-         mexPrintf("Offload Initialisation to card %d. \n",coprocessorNum); 
-      
-      /* Update static array size variables.*/
-      nTheta_offload  = nTheta;
-      nIndex_offload = nIndex;
-      nForcing_offload = nForcing;
-      theta_indexes_end_offload = theta_indexes_end;
-      inteTheta_0to1_offload = inteTheta_0to1;
-      
-      /* Allocate variables on host*/
-      if (debugOffload==1)
-         mexPrintf("Allocating CPU offload variables. \n");               
-         
-      result_offload = (double *)mxCalloc(nIndex_offload,sizeof(double));
-      theta_offload = (double *)mxCalloc(nTheta_offload,sizeof(double));
-      theta_indexes_start_offload = (double *)mxCalloc(nIndex_offload,sizeof(double));
-      forcing_offload = (double *)mxCalloc(nForcing_offload,sizeof(double));  
-      isCPUMemAlloc = 1;
-      
-      
-      mexMakeMemoryPersistent(result_offload);
-      mexMakeMemoryPersistent(theta_offload);
-      mexMakeMemoryPersistent(theta_indexes_start_offload);
-      mexMakeMemoryPersistent(forcing_offload);
-      
-      /* Allocate variables on the coprocessor.*/
-      if (debugOffload==1)
-         mexPrintf("Allocating coprocessor memory. \n");               
-      
-      #pragma offload target(mic:coprocessorNum) \
-      in(nIndex_offload : ALLOC RETAIN) \
-      in(theta_indexes_start_offload : length(nIndex_offload) ALLOC RETAIN) \
-      in(theta_indexes_end_offload : ALLOC RETAIN) \
-      in(theta_offload: length(nTheta) ALLOC RETAIN) \
-      in(forcing_offload : length(nForcing) ALLOC RETAIN) \
-      in(inteTheta_0to1_offload : ALLOC RETAIN) \
-      in(result_offload : length(nIndex) ALLOC RETAIN)  status(x) optional
-      {}            
-      
-      if (x.result == OFFLOAD_SUCCESS)
-         isCoprocessorMemAlloc=1;   
-   }
-
-   if (isForcingAnIntegral==0 ) {      
-        if (debugOffload==1)
-            mexPrintf("Offloading to card %d. \n",coprocessorNum); 
-      
-        #pragma offload target(mic:coprocessorNum) \
-        in(nIndex: into (nIndex_offload) ) \
-        in(theta_indexes_start:length(nIndex_offload) into (theta_indexes_start_offload)) \
-        in(theta_indexes_end: into (theta_indexes_end_offload)) \
-        in(theta: length(nTheta) into (theta_offload)) \
-        in(forcing: length(nForcing) into(forcing_offload)) \
-        in(inteTheta_0to1: into(inteTheta_0to1_offload)) \
-        out(result_offload: length(nIndex) into(result)) \
-        status(x) optional
-        {       
-            int iIndex;
-            #pragma omp parallel for
-            for(iIndex=0;iIndex<nIndex_offload; iIndex++) 
-                result_offload[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start_offload[iIndex], theta_indexes_end_offload, theta_offload + (int)theta_indexes_start_offload[iIndex]- 1, forcing_offload, &inteTheta_0to1_offload);
-        }        
-        if (x.result != OFFLOAD_SUCCESS) {  
-            if (debugOffload==1) 
-               mexPrintf("Offload unsuccessful. Error type: %d. Falling back to CPU \n",x.result);
-
-            if (isCoprocessorMemAlloc==1) {
-               if (debugOffload==1)
-                  mexPrintf("Freeing coprocessor memory. \n");               
-               
-               #pragma offload target(mic:coprocessorNum) \
-               nocopy(nIndex_offload : FREE REUSE) \
-               nocopy(theta_indexes_start_offload : FREE REUSE) \
-               nocopy(theta_indexes_end_offload : FREE REUSE) \
-               nocopy(theta_offload : FREE REUSE) \
-               nocopy(forcing_offload : FREE REUSE) \
-               nocopy(inteTheta_0to1_offload : FREE REUSE) \
-               nocopy(result_offload : FREE REUSE) status(x) optional
-               {}  
-               
-               if (x.result == OFFLOAD_SUCCESS)
-                  isCoprocessorMemAlloc=0;   
-            }
-                     
-            if (isCPUMemAlloc==1) {
-               if (debugOffload==1)
-                  mexPrintf("Freeing CPU memory. \n");
-                              
-               mxFree(theta_offload);
-               mxFree(result_offload);
-               mxFree(theta_indexes_start_offload);
-               mxFree(forcing_offload);
-               isCPUMemAlloc=0;
-            }    
-            
-            nTheta_offload = 0;
-            nIndex_offload = 0; 
-            nForcing_offload = 0;            
-            
-            if (debugOffload==1)
-               mexPrintf("Running CPU only calculation. \n");
-            
-            for(iIndex=0;iIndex<nIndex; iIndex++) 
-                result[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);
-        }
-        else if (debugOffload==1)
-            mexPrintf("Offload successful! \n");        
-   }
-   else {
-
-        if (debugOffload==1) 
-         mexPrintf("Offloading to card %d. \n",coprocessorNum); 
-        
-        #pragma offload target(mic:coprocessorNum) \
-        in(nIndex: into (nIndex_offload) ) \
-        in(theta_indexes_start:length(nIndex_offload) into (theta_indexes_start_offload)) \
-        in(theta_indexes_end: into (theta_indexes_end_offload)) \
-        in(theta: length(nTheta) into (theta_offload)) \
-        in(forcing: length(nForcing) into(forcing_offload)) \
-        in(inteTheta_0to1: into(inteTheta_0to1_offload)) \
-        out(result_offload: length(nIndex) into(result)) \
-        status(x) optional
-        {       
-            int iIndex;
-            #pragma omp parallel for
-            for(iIndex=0;iIndex<nIndex_offload; iIndex++) 
-                result_offload[iIndex] = trapazoidal((int)theta_indexes_start_offload[iIndex], theta_indexes_end_offload, theta_offload + (int)theta_indexes_start_offload[iIndex]- 1, forcing_offload, &inteTheta_0to1_offload);        
-
-        }
-        if (x.result != OFFLOAD_SUCCESS) {      
-            if (debugOffload==1) 
-               mexPrintf("Offload unsuccessful. Error type: %d. Falling back to CPU \n",x.result);
-
-            if (isCoprocessorMemAlloc==1) {
-               if (debugOffload==1)
-                  mexPrintf("Freeing coprocessor memory. \n");               
-               
-               #pragma offload target(mic:coprocessorNum) \
-               nocopy(nIndex_offload : FREE REUSE) \
-               nocopy(theta_indexes_start_offload : FREE REUSE) \
-               nocopy(theta_indexes_end_offload : FREE REUSE) \
-               nocopy(theta_offload : FREE REUSE) \
-               nocopy(forcing_offload : FREE REUSE) \
-               nocopy(inteTheta_0to1_offload : FREE REUSE) \
-               nocopy(result_offload : FREE REUSE) status(x) optional
-               {}  
-               
-               if (x.result == OFFLOAD_SUCCESS)
-                  isCoprocessorMemAlloc=0;   
-            }
-         
-            if (debugOffload==1)
-               mexPrintf("Freeing CPU memory. \n");
-            
-            if (isCPUMemAlloc==1) {
-               mxFree(theta_offload);
-               mxFree(result_offload);
-               mxFree(theta_indexes_start_offload);
-               mxFree(forcing_offload);
-               isCPUMemAlloc=0;
-            }
-               
-            
-            nTheta_offload = 0;
-            nIndex_offload = 0; 
-            nForcing_offload = 0;                       
-            
-            if (debugOffload==1)
-               mexPrintf("Running CPU only calculation. \n");          
-            
-            for(iIndex=0;iIndex<nIndex; iIndex++) 
-                result[iIndex] = trapazoidal((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);                 
-        }
-        else if (debugOffload==1)
-            mexPrintf("Offload successful! \n");
-        
-   }
-#else
-
-    if (nTheta==0 && nIndex==0 && nForcing ==0) {    
-      return;
-    }
-
-    if (isForcingAnIntegral==0 ) {      
-        /*for(int iIndex=0;iIndex<nIndex; iIndex++) */
-        for(iIndex=nIndex; iIndex--;) 
-            result[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);
-    }
-    else {
-        /*for(int iIndex=0;iIndex<nIndex; iIndex++) */
-        for(iIndex=nIndex; iIndex--;)
-            result[iIndex] = trapazoidal((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);        
-                
-    }
-#endif       
-
-
-}
-
-
-/* Integration using the Trapazoidal rule under the assumption that the
- * forcing is the integral over the day. 
- */
-double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta)
-{           
-    int i;
-    const int n = (int)(theta_index_end - theta_index_start);     /*number of elements using Matlab indexes - hence no + 1 required.*/
-    const int endIndex = n - 1;                                   /*index to the last element*/
-    /* Use high precision estimate over the first time step 
-     NOTE: the 2* term is bacause of the returned value being halved!.*/    
-    double ret_val=2 * *intTheta * dy[endIndex];
-
-    /* Integrate remaining points*/
-    for (i = 1; i <= n; i++)
-        ret_val += (dx[endIndex-i] + dx[endIndex-i-1]) * dy[endIndex-i];
-    
-    return 0.5*ret_val;
-} /* trapazoidal_ */
-
-/* Integreation using Simspsons composite rule (Eq 4.1.14, p160, sec 4.1.3 in Press et al (2007) Numerical Recipes.) */
-double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta)
-{
-    int i;
-    const int n =  (int)(theta_index_end - theta_index_start);    /*number of elements using Matlab indexes - hence no + 1 required.*/
-    const int endIndex = n - 1;                                   /*C index to the last element*/
-    double ret_val;   
-
-    /* Integrate first three term.
-    % NOTE: This is from t=1. The integration from 0 to 1 is
-    % undertaken after the Simpson's integration. */
-    ret_val = 3./8. * dx[endIndex-1] * dy[endIndex-1] + 
-              7./6. * dx[endIndex-2] * dy[endIndex-2] + 
-              23./24. * dx[endIndex-3] * dy[endIndex-3];
- 
-    /* Calculate internal points for Simpon's composite rule */
-    for (i = 3; i <= endIndex-4; i++) {
-        ret_val += dx[i] * dy[i];        
-    }
-    
-    /* Integrate last three term.*/
-    ret_val += 23./24. * dx[2] * dy[2] + 
-              7./6. * dx[1] * dy[1] + 
-              3./8. * dx[0] * dy[0];
-    
-    /* Add high precision estimate over the first time step */
-    ret_val +=  *intTheta * 0.5 * (dy[endIndex] + dy[endIndex-1]);     
-    
-    return ret_val;
-}
+/* doIRFconvolution - is a very computationally efficient numerical integration routine.
+ * It can be compiled to use Intel Xeon Phi Coprocessors with the commands below.
+ * Importantly, the paths have been defined. Edit for your versions of the intel compiler
+ * (which must be >=2013.1) and mtalab.
+ * 
+ *
+ * Load compiled (only required on a PBS cluster where code is to be compiled)
+ * Also, edit for the required version of intel icc compiler
+      module load icc      
+      module load MATLAB/2016a 
+      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/bin/compilervars.sh intel64      
+
+ * Intel compiler commands for compiling with offload
+      cp doIRFconvolution.c doIRFconvolutionPhi.c
+      icc -c -qoffload -restrict -I/usr/local/easybuild/software/MATLAB/2016a/extern/include -I/usr/local/easybuild/software/MATLAB/2016a/simulink/include -DMATLAB_MEX_FILE -ansi -D_GNU_SOURCE  -fexceptions -fPIC -fno-omit-frame-pointer -pthread -std=c99 -fopenmp  -DMX_COMPAT_32 -O3 -DNDEBUG  "doIRFconvolutionPhi.c"
+      icc -O3 -pthread -shared -Wl,--version-script,/usr/local/easybuild/software/MATLAB/2016a/extern/lib/glnxa64/mexFunction.map -Wl,--no-undefined -fopenmp -o  "doIRFconvolutionPhi.mexa64"  doIRFconvolutionPhi.o  -Wl,-rpath-link,/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -L/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -lmx -lmex -lmat -lm -lstdc++ -Wl,-rpath,/usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/intel64 -lintlc
+      rm doIRFconvolutionPhi.c
+
+ * Intel compiler commands for compiling with NO offload
+      icc -c -qno-offload -I/usr/local/easybuild/software/MATLAB/2016a/extern/include -I/usr/local/matlab/R2014a/simulink/include -DMATLAB_MEX_FILE -ansi -D_GNU_SOURCE  -fexceptions -fPIC -fno-omit-frame-pointer -pthread -std=c99 -fopenmp  -DMX_COMPAT_32 -O3 -DNDEBUG  "doIRFconvolution.c"
+      icc -O3 -pthread -shared -static-intel -openmp-link=static -Wl,--version-script,/usr/local/easybuild/software/MATLAB/2016a/extern/lib/glnxa64/mexFunction.map -Wl,--no-undefined -fopenmp -o  "doIRFconvolution.mexa64"  doIRFconvolution.o  -Wl,-rpath-link,/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -L/usr/local/easybuild/software/MATLAB/2016a/bin/glnxa64 -lmx -lmex -lmat -lm -lstdc++ -Wl,-rpath,/usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/intel64 -lintlc -liomp5
+   
+
+ * Additionally, to run Xeon Phi jobs on a cluster the following commands
+ * will most likely be required prior to running the job:
+      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/mkl/bin/mklvars.sh intel64
+      source /usr/local/easybuild/software/icc/icc-2016.u3-GCC-4.9.2/bin/compilervars.sh intel64      
+             
+      source /usr/local/easybuild/software/icc/2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/mkl/bin/mklvars.sh intel64
+      source /usr/local/easybuild/software/icc/2016.u3-GCC-4.9.2/compilers_and_libraries_2016.3.210/linux/bin/compilervars.sh intel64    
+ 
+ * To monitor Xeon Phi usage, the following command is also useful: micsmc
+*/
+
+
+#include "math.h"
+#include "mex.h"
+#include "time.h"
+#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
+    #include "offload.h"
+    #define ALLOC alloc_if(1)
+    #define FREE free_if(1)
+    #define RETAIN free_if(0)
+    #define REUSE alloc_if(0)    
+#endif
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) 
+{    
+    /* Declare constants for matrix size and index counter */
+    const int nTheta  = (int)mxGetM(prhs[0] );
+    const int nIndex = (int)mxGetN(prhs[1]);    
+    int iIndex;
+    
+    /* Declare output data*/
+    double *result;
+    
+    /* Declare impulse response fuction vector and input forcing*/
+    double *theta  = mxGetPr( prhs[0] );
+    
+    /* Declare vector of starting rows for transforming theta to matrix for all start dates */
+    double *theta_indexes_start = mxGetPr( prhs[1] );
+            
+    /* Declare vector of ending rows for transforming theta to matrix for all start dates */
+    const int theta_indexes_end = (int)mxGetScalar(prhs[2]) + 1;        
+     
+    /* Declare input forcing*/    
+    double *forcing = mxGetPr( prhs[3] );
+    const int nForcing = (int)mxGetM(prhs[3] );
+    
+    /* Get the flag for the type of integration to undertake:
+     * 0: Trapazoidal integration for focing that is an integral of the daily flux (eg pecip).
+     * 1: Simpsons 3/8 composite integration for continuous forcing eg free-drainage from the soil model. */
+    const int isForcingAnIntegral = (int)mxGetScalar(prhs[4]);    
+   
+    /* Get the high precision estimate of the integral of the theta function 
+     * from 0 to 1. To derive this convolution with the forcing, the mean 
+     * forcing over the first day is adopted. */
+    const double inteTheta_0to1 = mxGetScalar(prhs[5]);    
+    
+#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
+   /* Delacre offloaded functions */
+   __declspec(target(mic:coprocessorNum))  double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);
+   __declspec(target(mic:coprocessorNum))  double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);          
+#else
+   /* Delacre on CPU functions */   
+    double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);
+    double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta);      
+#endif
+    
+    /* Declare output vectors for results*/
+    plhs[0] = mxCreateDoubleMatrix(1,nIndex,mxREAL);
+    result = mxGetPr(plhs[0]);
+        
+    /*Cycle though all theta tiem points and create matrix of theta values.   */      
+#if defined(__INTEL_COMPILER) && defined(__INTEL_OFFLOAD)
+
+   static int coprocessorNum=-999;
+   static int isCoprocessorMemAlloc = 0;
+   static int isCPUMemAlloc = 0;
+   int debugOffload=0;
+   
+   if (coprocessorNum==-999) {
+      /* Randomly select a coprocessor */
+      time_t t;    
+      srand((unsigned) time(&t));
+      int coprocessorCount = _Offload_number_of_devices();      
+      coprocessorNum = rand() % coprocessorCount;          
+   }
+     
+    /* Static variables for pre-allocating arrays on coprocessor*/
+    __declspec( target (mic:coprocessorNum)) static int nTheta_offload = 0, nIndex_offload = 0;
+    __declspec( target (mic:coprocessorNum)) static int nForcing_offload = 0, theta_indexes_end_offload  = 0;
+    __declspec( target (mic:coprocessorNum)) static double *result_offload, *theta_offload, *theta_indexes_start_offload, *forcing_offload, inteTheta_0to1_offload;
+    
+    /* Error handling */
+    _Offload_status x;
+
+    /* If the input vectors are of zero length then free the memory on 
+     * the coprocessor and static variables and then return*/
+   if (nTheta==0 && nIndex==0 && nForcing ==0) {    
+      if (debugOffload==1)
+         mexPrintf("Freeing memory on CPU and card %d. \n",coprocessorNum); 
+            
+      if (isCoprocessorMemAlloc==1) {
+         #pragma offload target(mic:coprocessorNum) \
+         nocopy(nIndex_offload : FREE REUSE) \
+         nocopy(theta_indexes_start_offload : FREE REUSE) \
+         nocopy(theta_indexes_end_offload : FREE REUSE) \
+         nocopy(theta_offload : FREE REUSE) \
+         nocopy(forcing_offload : FREE REUSE) \
+         nocopy(inteTheta_0to1_offload : FREE REUSE) \
+         nocopy(result_offload : FREE REUSE) status(x) optional
+         {}  
+         
+         if (x.result == OFFLOAD_SUCCESS)
+            isCoprocessorMemAlloc=0;   
+      }
+      
+      if (isCPUMemAlloc==1) {
+         mxFree(theta_offload);
+         mxFree(result_offload);
+         mxFree(theta_indexes_start_offload);
+         mxFree(forcing_offload);
+         isCPUMemAlloc=0;
+      }
+    
+      nTheta_offload = 0;
+      nIndex_offload = 0; 
+      nForcing_offload = 0;            
+      
+      coprocessorNum==-999;
+      
+      return;
+   }
+    
+   /* Allocate data on array if either the static variables have not yet 
+    * been set (ie this is the first call to the function) or the input 
+    * data appears to have changed since the prior call to the function */
+   if (nTheta_offload==0 || nIndex_offload==0 || nForcing_offload ==0 
+   || nTheta!=nTheta_offload || nIndex!=nIndex_offload || nForcing!=nForcing_offload) {
+      if (debugOffload==1)
+         mexPrintf("Offload Initialisation to card %d. \n",coprocessorNum); 
+      
+      /* Update static array size variables.*/
+      nTheta_offload  = nTheta;
+      nIndex_offload = nIndex;
+      nForcing_offload = nForcing;
+      theta_indexes_end_offload = theta_indexes_end;
+      inteTheta_0to1_offload = inteTheta_0to1;
+      
+      /* Allocate variables on host*/
+      if (debugOffload==1)
+         mexPrintf("Allocating CPU offload variables. \n");               
+         
+      result_offload = (double *)mxCalloc(nIndex_offload,sizeof(double));
+      theta_offload = (double *)mxCalloc(nTheta_offload,sizeof(double));
+      theta_indexes_start_offload = (double *)mxCalloc(nIndex_offload,sizeof(double));
+      forcing_offload = (double *)mxCalloc(nForcing_offload,sizeof(double));  
+      isCPUMemAlloc = 1;
+      
+      
+      mexMakeMemoryPersistent(result_offload);
+      mexMakeMemoryPersistent(theta_offload);
+      mexMakeMemoryPersistent(theta_indexes_start_offload);
+      mexMakeMemoryPersistent(forcing_offload);
+      
+      /* Allocate variables on the coprocessor.*/
+      if (debugOffload==1)
+         mexPrintf("Allocating coprocessor memory. \n");               
+      
+      #pragma offload target(mic:coprocessorNum) \
+      in(nIndex_offload : ALLOC RETAIN) \
+      in(theta_indexes_start_offload : length(nIndex_offload) ALLOC RETAIN) \
+      in(theta_indexes_end_offload : ALLOC RETAIN) \
+      in(theta_offload: length(nTheta) ALLOC RETAIN) \
+      in(forcing_offload : length(nForcing) ALLOC RETAIN) \
+      in(inteTheta_0to1_offload : ALLOC RETAIN) \
+      in(result_offload : length(nIndex) ALLOC RETAIN)  status(x) optional
+      {}            
+      
+      if (x.result == OFFLOAD_SUCCESS)
+         isCoprocessorMemAlloc=1;   
+   }
+
+   if (isForcingAnIntegral==0 ) {      
+        if (debugOffload==1)
+            mexPrintf("Offloading to card %d. \n",coprocessorNum); 
+      
+        #pragma offload target(mic:coprocessorNum) \
+        in(nIndex: into (nIndex_offload) ) \
+        in(theta_indexes_start:length(nIndex_offload) into (theta_indexes_start_offload)) \
+        in(theta_indexes_end: into (theta_indexes_end_offload)) \
+        in(theta: length(nTheta) into (theta_offload)) \
+        in(forcing: length(nForcing) into(forcing_offload)) \
+        in(inteTheta_0to1: into(inteTheta_0to1_offload)) \
+        out(result_offload: length(nIndex) into(result)) \
+        status(x) optional
+        {       
+            int iIndex;
+            #pragma omp parallel for
+            for(iIndex=0;iIndex<nIndex_offload; iIndex++) 
+                result_offload[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start_offload[iIndex], theta_indexes_end_offload, theta_offload + (int)theta_indexes_start_offload[iIndex]- 1, forcing_offload, &inteTheta_0to1_offload);
+        }        
+        if (x.result != OFFLOAD_SUCCESS) {  
+            if (debugOffload==1) 
+               mexPrintf("Offload unsuccessful. Error type: %d. Falling back to CPU \n",x.result);
+
+            if (isCoprocessorMemAlloc==1) {
+               if (debugOffload==1)
+                  mexPrintf("Freeing coprocessor memory. \n");               
+               
+               #pragma offload target(mic:coprocessorNum) \
+               nocopy(nIndex_offload : FREE REUSE) \
+               nocopy(theta_indexes_start_offload : FREE REUSE) \
+               nocopy(theta_indexes_end_offload : FREE REUSE) \
+               nocopy(theta_offload : FREE REUSE) \
+               nocopy(forcing_offload : FREE REUSE) \
+               nocopy(inteTheta_0to1_offload : FREE REUSE) \
+               nocopy(result_offload : FREE REUSE) status(x) optional
+               {}  
+               
+               if (x.result == OFFLOAD_SUCCESS)
+                  isCoprocessorMemAlloc=0;   
+            }
+                     
+            if (isCPUMemAlloc==1) {
+               if (debugOffload==1)
+                  mexPrintf("Freeing CPU memory. \n");
+                              
+               mxFree(theta_offload);
+               mxFree(result_offload);
+               mxFree(theta_indexes_start_offload);
+               mxFree(forcing_offload);
+               isCPUMemAlloc=0;
+            }    
+            
+            nTheta_offload = 0;
+            nIndex_offload = 0; 
+            nForcing_offload = 0;            
+            
+            if (debugOffload==1)
+               mexPrintf("Running CPU only calculation. \n");
+            
+            for(iIndex=0;iIndex<nIndex; iIndex++) 
+                result[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);
+        }
+        else if (debugOffload==1)
+            mexPrintf("Offload successful! \n");        
+   }
+   else {
+
+        if (debugOffload==1) 
+         mexPrintf("Offloading to card %d. \n",coprocessorNum); 
+        
+        #pragma offload target(mic:coprocessorNum) \
+        in(nIndex: into (nIndex_offload) ) \
+        in(theta_indexes_start:length(nIndex_offload) into (theta_indexes_start_offload)) \
+        in(theta_indexes_end: into (theta_indexes_end_offload)) \
+        in(theta: length(nTheta) into (theta_offload)) \
+        in(forcing: length(nForcing) into(forcing_offload)) \
+        in(inteTheta_0to1: into(inteTheta_0to1_offload)) \
+        out(result_offload: length(nIndex) into(result)) \
+        status(x) optional
+        {       
+            int iIndex;
+            #pragma omp parallel for
+            for(iIndex=0;iIndex<nIndex_offload; iIndex++) 
+                result_offload[iIndex] = trapazoidal((int)theta_indexes_start_offload[iIndex], theta_indexes_end_offload, theta_offload + (int)theta_indexes_start_offload[iIndex]- 1, forcing_offload, &inteTheta_0to1_offload);        
+
+        }
+        if (x.result != OFFLOAD_SUCCESS) {      
+            if (debugOffload==1) 
+               mexPrintf("Offload unsuccessful. Error type: %d. Falling back to CPU \n",x.result);
+
+            if (isCoprocessorMemAlloc==1) {
+               if (debugOffload==1)
+                  mexPrintf("Freeing coprocessor memory. \n");               
+               
+               #pragma offload target(mic:coprocessorNum) \
+               nocopy(nIndex_offload : FREE REUSE) \
+               nocopy(theta_indexes_start_offload : FREE REUSE) \
+               nocopy(theta_indexes_end_offload : FREE REUSE) \
+               nocopy(theta_offload : FREE REUSE) \
+               nocopy(forcing_offload : FREE REUSE) \
+               nocopy(inteTheta_0to1_offload : FREE REUSE) \
+               nocopy(result_offload : FREE REUSE) status(x) optional
+               {}  
+               
+               if (x.result == OFFLOAD_SUCCESS)
+                  isCoprocessorMemAlloc=0;   
+            }
+         
+            if (debugOffload==1)
+               mexPrintf("Freeing CPU memory. \n");
+            
+            if (isCPUMemAlloc==1) {
+               mxFree(theta_offload);
+               mxFree(result_offload);
+               mxFree(theta_indexes_start_offload);
+               mxFree(forcing_offload);
+               isCPUMemAlloc=0;
+            }
+               
+            
+            nTheta_offload = 0;
+            nIndex_offload = 0; 
+            nForcing_offload = 0;                       
+            
+            if (debugOffload==1)
+               mexPrintf("Running CPU only calculation. \n");          
+            
+            for(iIndex=0;iIndex<nIndex; iIndex++) 
+                result[iIndex] = trapazoidal((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);                 
+        }
+        else if (debugOffload==1)
+            mexPrintf("Offload successful! \n");
+        
+   }
+#else
+
+    if (nTheta==0 && nIndex==0 && nForcing ==0) {    
+      return;
+    }
+
+    if (isForcingAnIntegral==0 ) {      
+        /*for(int iIndex=0;iIndex<nIndex; iIndex++) */
+        for(iIndex=nIndex; iIndex--;) 
+            result[iIndex] = Simpsons_ExtendedRule((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);
+    }
+    else {
+        /*for(int iIndex=0;iIndex<nIndex; iIndex++) */
+        for(iIndex=nIndex; iIndex--;)
+            result[iIndex] = trapazoidal((int)theta_indexes_start[iIndex], theta_indexes_end, theta + (int)theta_indexes_start[iIndex]- 1, forcing, &inteTheta_0to1);        
+                
+    }
+#endif       
+
+
+}
+
+
+/* Integration using the Trapazoidal rule under the assumption that the
+ * forcing is the integral over the day. 
+ */
+double trapazoidal(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta)
+{           
+    int i;
+    const int n = (int)(theta_index_end - theta_index_start);     /*number of elements using Matlab indexes - hence no + 1 required.*/
+    const int endIndex = n - 1;                                   /*index to the last element*/
+    /* Use high precision estimate over the first time step 
+     NOTE: the 2* term is bacause of the returned value being halved!.*/    
+    double ret_val=2 * *intTheta * dy[endIndex];
+
+    /* Integrate remaining points*/
+    for (i = 1; i <= n; i++)
+        ret_val += (dx[endIndex-i] + dx[endIndex-i-1]) * dy[endIndex-i];
+    
+    return 0.5*ret_val;
+} /* trapazoidal_ */
+
+/* Integreation using Simspsons composite rule (Eq 4.1.14, p160, sec 4.1.3 in Press et al (2007) Numerical Recipes.) */
+double Simpsons_ExtendedRule(const int theta_index_start, const int theta_index_end, const double *dx, const double *dy, const double *intTheta)
+{
+    int i;
+    const int n =  (int)(theta_index_end - theta_index_start);    /*number of elements using Matlab indexes - hence no + 1 required.*/
+    const int endIndex = n - 1;                                   /*C index to the last element*/
+    double ret_val;   
+
+    /* Integrate first three term.
+    % NOTE: This is from t=1. The integration from 0 to 1 is
+    % undertaken after the Simpson's integration. */
+    ret_val = 3./8. * dx[endIndex-1] * dy[endIndex-1] + 
+              7./6. * dx[endIndex-2] * dy[endIndex-2] + 
+              23./24. * dx[endIndex-3] * dy[endIndex-3];
+ 
+    /* Calculate internal points for Simpon's composite rule */
+    for (i = 3; i <= endIndex-4; i++) {
+        ret_val += dx[i] * dy[i];        
+    }
+    
+    /* Integrate last three term.*/
+    ret_val += 23./24. * dx[2] * dy[2] + 
+              7./6. * dx[1] * dy[1] + 
+              3./8. * dx[0] * dy[0];
+    
+    /* Add high precision estimate over the first time step */
+    ret_val +=  *intTheta * 0.5 * (dy[endIndex] + dy[endIndex-1]);     
+    
+    return ret_val;
+}
diff --git a/algorithms/models/TransferNoise/model_TFN.m b/algorithms/models/TransferNoise/model_TFN.m
index 02b8203..aefb593 100644
--- a/algorithms/models/TransferNoise/model_TFN.m
+++ b/algorithms/models/TransferNoise/model_TFN.m
@@ -1,2837 +1,2848 @@
-classdef model_TFN < model_abstract
-% Class definition for Transfer Function Noise (TFN) model for use with HydroSight
-%
-% Description: 
-%   The class defines a transfer function Noise (TFN) model for
-%   simulating time series of groundwater head. The model should be
-%   defined, calibrated and solved using HydroSight() or 
-%   the graphical user interface.
-%
-%   This class uses an object-oriented structure to provide a highly flexible 
-%   model structure and the efficient inclusion of new structural
-%   componants. Currently, linear and nonlinear TFN model can be built.
-%   The linear models are based upon von Asmuth et al. 2002 and von Asmuth et al. 
-%   2008 and the nonlinear models use a nonlinear transform of the input
-%   climate data to account for runoff and nonlinear free drainage (see
-%   Peterson & Western, 2014) 
-%
-%   Additional key features of the model include:
-%
-%       - long term historic daily climate forcing is used to estimate the
-%       groundwater head at each time point via use of continuous transfer
-%       function. 
-%
-%       - the non-linear response of groundwater head to climate forcing
-%       can be accounted for by transforming the input forcing data.
-%       Currently, a simple 1-D vertically integrated soil model is
-%       available (see climateTransform_soilMoistureModels), allowing the
-%       construction of a soil model with between 1 and 7 parameters.
-%
-%       - Pumping drawdown can be simulated using the Ferris-Knowles 
-%       response function for confined aquifers, the Ferris-
-%       Knowles-Jacobs function for an unconfined aquifer
-%       or the Hantush function for a leaky aquifer. Each of these
-%       functions allow multiple production bores to be accounted for and
-%       each can have recharge or no-flow boundary conditions.
-%
-%       - The influence of streamflow can be approximated using the Bruggeman 
-%       reponse function. Importantly, this function is a prototype and
-%       should be used with caution. It is likely that the covariance
-%       between rainfall and streamflow complicates the estimation of the
-%       impacts of streamflow.
-%
-%       - a model can be fit to irregularly sampled groundwater head
-%       observations via use of an exponential noise function.
-%
-%       - new data transfer functions can easily be defined simply
-%       by the creation of new response function class definitions.
-%
-%       - calibration of the model is not to the observed head, but to the
-%       innovations between time steps. That is, the residual between the
-%       observed and modelled head is derived and the innovation is
-%       calculated as the prior residual minus the later residual
-%       multiplied by the exponental noise function.
-%
-%       - the contribution of a given driver to the head is calculated as
-%       the integral of the historic forcing times the weighting function.
-%       This is undertaken by get_h_star() and the mex .c compiled function
-%       doIRFconvolution(). If the forcing is instantaneous (for example a
-%       flux a soil moisture model) then Simpon's integration is
-%       undertaken. However, if the forcing is a daily integral (such as
-%       precipitation or daily pumping volumes) then daily trapazoidal
-%       integration of the weighting function is undertaken and then 
-%       multiplied by the daily flux.
-%
-%   Below are links to the details of the public methods of this class. See
-%   the 'model_TFN' constructor for details of how to build a model.
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Dependencies:
-%   responseFunction_Pearsons.m
-%   responseFunction_Hantush.m
-%   responseFunction_Bruggeman.m
-%   responseFunction_FerrisKnowles.m
-%   responseFunction_Hantush.m
-%   responseFunction_FerrisKnowlesJacobs.m
-%   derivedweighting_PearsonsNegativeRescaled.m
-%   derivedweighting_PearsonsPositiveRescaled.m
-%   climateTransform_soilMoistureModels.m
-%
-% References:
-%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
-%   dunction-noise modeling in continuous time using predefined impulse
-%   response functions.
-%
-%   von Asmuth J. R., Mass K., Bakker M., Peterson J., 2008, Modeling time
-%   series of ground water head fluctuations subject to multiple stresses.
-%   Groundwater, 46(1), pp30-40.
-%
-%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
-%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-       
-    properties
-    end
-
-%%  STATIC METHODS        
-% Static methods used by the Graphical User Interface to inform the
-% user of the available model types. Any new models must be listed here
-% in order to be accessable within the GUI.
-    methods(Static)
-        % Provides a simple description of the function for the user
-        % interface.
-        function str = description()
-           
-            str = {['"model_TFN" is a highly flexible nonlinear transfer function noise time-series model. ', ...
-                   'It allows the statistical modelling of irregular groundwater head observations by the weighting ', ...
-                   'of observed forcing data (e.g. daily rainfall, pumping or landuse change).', ...
-                   'The forcing data can also be transformed to account for non-linear processes such as runoff or free drainage.'], ...
-                   '', ...
-                   'The model has the following additional features:', ...
-                   '   - time-series extrapolation and interpolation.', ...
-                   '   - decomposition of the hydrograph to individual drivers ', ...
-                   '     (e.g. seperation of climate and pumping impacts).', ...
-                   '   - decomposition of the hydrograph over time ', ...
-                   '     (i.e. decomposition to influence from 1, 2, 5 and 10 years ago).', ...
-                   '   - estimation of hydraulic properties (if pumping is simulated).', ...
-                   '', ...
-                   'For further details of the algorithms use the help menu or see:', ...
-                   '', ...
-                   '   - Peterson, T. J., and A. W. Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355, doi:10.1002/2013WR014800.', ...
-                   '', ...
-                   '   - Shapoori V., Peterson T. J., Western A. W. and Costelloe J. F., (2015), Top-down groundwater hydrograph time series modeling for climate-pumping decomposition. Hydrogeology Journal'};                     
-               
-        end        
-    end        
-    
-%%  PUBLIC METHODS              
-    methods
-        
-%% Model constructor
-        function obj = model_TFN(bore_ID, obsHead, forcingData_data,  forcingData_colnames, siteCoordinates, varargin)           
-% model_TFN constructs for linear and nonlinear Transfer Function Noise model
-%
-% Syntax:
-%   model = model_TFN(bore_ID, obsHead, forcingData_data, ...
-%           forcingData_colnames, siteCoordinates, modelOptions)
-%
-% Description:
-%   Builds the model_TFN object, declares initial parameters and sets the
-%   observation and forcing data within the object. This method should be 
-%   called from HydroSight.
-%
-%   A wide range of models can be built within this method. See the inputs
-%   section below for details of the model components that can be built and 
-%   Peterson and Western (2014).
-%
-% Inputs:
-%   obsHead_input - matrix of observed groundwater elevation data. The
-%   matrix must comprise of two columns: date (as a double data type) 
-%   and water level elevation. The data must be of a daily time-step or 
-%   larger and can be non-uniform.
-%
-%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
-%   must comprise of the following columns: year, month, day and
-%   observation data. The observation data must include precipitation but
-%   may include other data as defined within the input model options. 
-%   The record must be continuous (no skipped days) and cannot contain blank 
-%   observations.
-%
-%   forcingData_colnames - 1 x N cell array of the column names within the
-%   input numerical array "forcingData_data". The first three columns must
-%   be: year month, day. The latter columns must define the forcing data
-%   column names. Imprtantly, each forcing data column name must be listed
-%   within the input "siteCoordinates". 
-%
-%   siteCoordinates - Mx3 cell matrix containing the following columns :
-%   site ID, projected easting, projected northing. Importantly,
-%   the input 'bore_ID' must be listed and all columns within
-%   the forcing data (excluding the year, month,day). When each response 
-%   function is created, the coordinates for the observation bore and the 
-%   corresponding forcing are are passed to the response function. This is
-%   required for simulating the impacts of pumping. Additionally,
-%   coordinates not listed in the input 'forcingData' can be input.
-%   This provides a means for image wells to be input (via the input 
-%   modelOptions).
-%
-%   modelOptions - cell matrix defining the model components, ie how the
-%   model should be constructed. The cell matrix must be three columns,
-%   where the first, second and third columns define the model component
-%   type to be set, the component property to be set, and the value to be
-%   set to the component property. Each model componant can have the following
-%   property inputs ((i.e. 2nd column):
-%
-%       - 'weightingfunction': Property value is to define the component type.
-%       Available component types include:
-%           - 'responseFunction_Pearsons' (for climate forcing)
-%           - 'responseFunction_Hantush' (for groundwater pumping)
-%           - 'responseFunction_FerrisKnowles' (for groundwater pumping)
-%           - 'responseFunction_Hantush' (for groundwater pumping)
-%           - 'responseFunction_FerrisKnowlesJacobs' (for groundwater pumping)
-%           - 'responseFunction_Bruggeman' (for streamflow)
-%           - 'derivedweighting_PearsonsNegativeRescaled' (see below)
-%           - 'derivedweighting_PearsonsPositiveRescaled' (see below)
-%            
-%       - 'forcingdata': Property value is the column number, column name 
-%       within forcingData (as input to HydroSight) or a cell 
-%       array containing the options for a forcing transformation
-%       object. The forcing transformation input must be a Nx2 cell array  
-%       with the following property (left column) and value (right column) 
-%       settings:
-%           - 'transformfunction' property and the transformation class name 
-%           e.g. 'climateTransform_soilMoistureModels'
-%
-%           - 'forcingdata' property and a Nx2 cell array declaring the 
-%           input forcing variable required by the transformation function
-%           (left column) and the name of the input forcing data or column
-%           number. eg   {'precip',3;'et',4}.
-%
-%           -  'outputdata' property and the output flux to be output from
-%           the forcing transformation function. 
-%
-%           - 'options' propert and an input entirely dependent upon the
-%           forcing transformation function. For
-%           'climateTransform_soilMoistureModels' this is used to define
-%           the form of the soil moisture model i.e. which ODE parameters
-%           to fix and which to calibrate. See the documenttation for each
-%           transformation function for details.
-%
-%       - 'inputcomponent': Property value is the name of another model
-%       component (i.e.  the first column value). This option allows,
-%       for example, the parameterised Pearson's weighting function for 
-%       a precipitation componant to also be used for, say, an ET
-%       componant. When building such a model, the weighting function
-%       should be a derived function such as
-%       'derivedweighting_PearsonsNegativeRescaled'
-%
-%       - 'options': cell array options specific for a weighting function.
-%       The structure of the weighting function options are entirely
-%       dependent upon the weighting function. Currently, only the
-%       groundwater pumping weighting functions allow the input of options.
-%       These pumping options allow the simulation of multiple recharge or
-%       no-flow image wells per pumping bore. These options must be a Nx3 cell
-%       array. The first column must be a string for the site ID of a
-%       production bore. The second column must be the site of an image
-%       well. Coordinate for both the production bore ID and the image
-%       well must be listed within the input coordinated cell array.
-%       The third column gives the type of image well. The availabes
-%       type are: "recharge" for say a river; and "no flow" for say a
-%       aquifer no-flow boundary.
-%
-% Outputs:
-%   model - model_TFN class object 
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% References:
-%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
-%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-            
-            % Expand cell structure within varargin. It is required to
-            % contain the model options
-            varargin = varargin{1};
-            
-            % Check there are an even number model type options, ie for
-            % option option type there is an option value.
-            if length(varargin) <= 0 
-               display('Default model of only precipitation forcing has been adopted because no model components were input');
-            elseif size( varargin , 2) ~=3
-               error('Invalid model options. The inputs must of three columns in the following order: model_component, property, property_value');
-            end            
-            
-            % Check the forcing data does not contain nan or infs
-            if any(any( isnan(forcingData_data) | isinf(forcingData_data)))
-                ind = find(any(isnan(forcingData_data) | isinf(forcingData_data),2),1,'first');
-                error(['Forcing data contains empty, nan or inf values (line ', num2str(ind),')']);
-            end            
-            
-            % Check that forcing data exists before the first head
-            % observation.
-            if floor(min(forcingData_data(:,1))) >= floor(min(obsHead(:,1)))
-                error('Forcing data must be input prior to the first water level observation. Ideally, the forcing data should start some years prior to the head.'); 
-            end
-                            
-            % Set bore ID, observed head and forcing data and site coordinates
-            obj.inputData.bore_ID = bore_ID;            
-            obj.inputData.head = obsHead;            
-            obj.inputData.siteCoordinates = siteCoordinates;
-            setForcingData(obj, forcingData_data, forcingData_colnames);
-            
-            % Cycle though the model components and their option and
-            % declare instances of objects were appropriate.
-            valid_properties = {'weightingfunction','forcingdata','options', 'inputcomponent'};            
-            valid_transformProperties = {'transformfunction','forcingdata','outputdata','options', 'inputcomponent'};
-            
-            % Check the model options.
-            for ii=1: size(varargin,1)                
-            
-                modelComponent = varargin{ii,1};
-                propertyType = varargin{ii,2};                
-                propertyValue = varargin{ii,3};
-                
-
-                % Check the component properties is valid.
-                ind = find(strncmpi(propertyType, valid_properties, length(propertyType)));
-                if (length(ind)~=1)                    
-                    error(['Invalid component property type: ',char(propertyType) ]);
-                end
-
-                % Check the component properties are valid.
-                %----------------------------------------------------------
-                if strcmp(propertyType, valid_properties{1})        
-                    % If the propertyType is weightingfunction, check it is
-                    % a valid object.
-                    if isempty(meta.class.fromName(propertyValue)) 
-                        % Report error if the input is not a class definition.
-                        error(['The following weighting function name does not appear to be a class object:',propertyValue]);
-                    end 
-                elseif strcmp(propertyType, valid_properties{2})   
-                    % If the propertyType is forcingdata, check if the
-                    % input is cell, character (ie forcing site name) or an
-                    % integer(s) (ie forcing data column number). If the
-                    % input is a cell, then check if it is a list of
-                    % forcing site names or a function name and inputs to
-                    % transform forcing data (eg using a soil moisture
-                    % model).
-                    if iscell(propertyValue)
-                        % If 'propertyValue' is a vector then it should be
-                        % a list of forcing sites.
-                        if isvector(propertyValue)
-                            % Check each forcing site name is valid.
-                            for j=1:length(propertyValue)                            
-                                if isnumeric(propertyValue{j}) && ( propertyValue{j}<0 || propertyValue{j}+2>size(forcingData_data,2))
-                                    error(['Invalid forcing column number for component:', modelComponent,'. It must be an intereger >0 and <= the number of forcing data columns.' ]);
-                                elseif ischar(propertyValue{j})
-                                    % Check the forcing column name is valid.
-                                    filt = cellfun(@(x)(strcmp(x,propertyValue{j})),forcingData_colnames);                                
-                                    if isempty(find(filt))
-                                        error(['Invalid forcing column name for component:', modelComponent,'. Each forcing name must be listed within the input forcing data column names.' ]);
-                                    end
-                                end
-                            end
-                        else
-                            % Check that the input is of the expected
-                            % format for creating a class to derive the
-                            % forcing data. The Expected format is an Nx2
-                            % matrix (where N<=5) and the first colum
-                            % should have rows for 'tranformationfunction'
-                            % and 'outputvariable'.
-                            if size(propertyValue,2) ~= 2 || size(propertyValue,1) < 2 || size(propertyValue,1) > length(valid_transformProperties)
-                                error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. The input cell array must have >=2 and <=5 rows and 2 columns.' ]);
-                            end
-                            
-                            % Check it has the required minimum inputs to
-                            % the first columns.
-                            if sum( strcmp(propertyValue(:,1), valid_transformProperties{1}))~=1 || ...
-                            sum( strcmp(propertyValue(:,1), valid_transformProperties{3}))~=1    
-                                error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. The first column of the input cell array must have rows for "transformfunction" and "outputdata".' ]);
-                            end
-                            
-                            % Check that the first column only contains
-                            % inputs that are acceptable.
-                            for j=1:size(propertyValue,1)                                                                
-                                if sum( strcmp(valid_transformProperties, propertyValue{j,1}))~=1    
-                                    error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. Read the help for "model_TFN" to see the accepted values.' ]);
-                                end
-                            end
-                            
-                            % Check that the transformation function name
-                            % is a valid class definition name.
-                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{1});
-                            if isempty(meta.class.fromName(propertyValue{filt,2})) 
-                                error(['The following forcing transform function name is not accessible:',propertyValue{filt,2}]);
-                            end   
-                            
-                            % Check that the forcing transformation
-                            % function name is consistent with the abstract
-                            % 'forcingTransform_abstract'.
-                            try
-                                if ~any(strcmp(findAbstractName( propertyValue{filt,2}),'forcingTransform_abstract')) && ~any(strcmp(findAbstractName( propertyValue{filt,2}),'derivedForcingTransform_abstract'))
-                                    error(['The following forcing transform function class definition is not derived from the "forcingTransform_abstract.m" abstract:',propertyValue{filt,2}]);
-                                end
-                            catch ME
-                                display('... Warning: Checking that the required abstract for the forcing transform class definition was used failed. This is may be because the version of matlab is pre-2014a.');
-                            end
-                            
-                            % Get a list of required forcing inputs.
-                            [requiredFocingInputs, isOptionalInput] = feval(strcat(propertyValue{filt,2},'.inputForcingData_required'),bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-                                                        
-                            % Check that the input forcing cell array has
-                            % the correct dimensions.
-                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{2});                            
-                            if any(filt)
-                                if size(propertyValue{filt,2},2) ~=2 || ...
-                                (size(propertyValue{filt,2},1) ~= length(requiredFocingInputs(~isOptionalInput)) && size(propertyValue{filt,2},1) ~= length(requiredFocingInputs) ...
-                                && size(propertyValue{filt,2},1)>1)
-                                    error(['Invalid forcing data for the forcing transform function name for component:', modelComponent,'. It must be a cell array of two columns and ',num2str(length(requiredFocingInputs)), ' rows (one row for each required input forcing).']);
-                                end
-                            
-                                % Check that the required inputs are specified.
-                                for j=1:length(requiredFocingInputs)
-                                    if isOptionalInput(j)
-                                        continue
-                                    end
-                                    if ~any( strcmp(propertyValue{filt,2}, requiredFocingInputs{j}))
-                                        error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. A forcing data site name must be specified for the following transform function input:',requiredFocingInputs{j} ]);
-                                    end
-                                end
-                            end
-                            
-                            % Get a list of valid output forcing variable names.
-                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{1});
-                            [optionalFocingOutputs] = feval([propertyValue{filt,2},'.outputForcingdata_options'], bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
-                                                                                    
-                            % Check that the output variable a char or cell vector.
-                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{3});                              
-                            if isnumeric(propertyValue{filt,2}) || (iscell(propertyValue{filt,2}) && ~isvector(propertyValue{filt,2}))
-                                error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . It must be a string or a cell vector of strings of valid output variable names.']);
-                            end
-                            
-                            % Check each output variable name is valid.
-                            if ischar(propertyValue{filt,2})
-                                if ~any(strcmp(optionalFocingOutputs, propertyValue{filt,2}))
-                                    error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . The output variable name must be equal to one of the listed output variables for the transformaion function.']);
-                                end
-                            else                                
-                                for j=1:length(propertyValue{filt,2})
-                                    if ~any(strcmp(optionalFocingOutputs, propertyValue{filt,2}{j}))
-                                        error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . Each output variable name must be equal to one of the listed output variables for the transformaion function.']);
-                                    end
-                                end
-                            end
-                            
-                            % Get a list of unique model components that have transformed forcing data
-                            % and the transformation model does not require
-                            % the input of another component.
-                            k=0;
-                            for j=1: size(varargin,1)
-                                if  strcmp( varargin{j,2}, valid_properties{2}) ...
-                                && iscell(varargin{j,3}) && ~isvector(varargin{j,3})
-                            
-                                    % Check if the transformation model
-                                    % input as the required inputs and does
-                                    % not require the input of another
-                                    % component.                                    
-                                    if  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
-                                    &&  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
-                                    &&  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
-                                    && ~any(strcmp(varargin{j,3}(:,1), valid_transformProperties{5}))
-                                        k = k+1;
-                                        % Record the component name.
-                                        modelComponets_transformed{k,1} =  varargin{j,1};
-                                        
-                                        % Add the transformation model
-                                        % name.
-                                        filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{1});
-                                        modelComponets_transformed{k,2} =  varargin{j,3}{filt,2};
-                                    end
-                                end
-                            end
-
-                            % Check that each complete transforamtion model
-                            % is unique.
-                            if size( unique(modelComponets_transformed(:,2)),1) ...
-                            ~= size( modelComponets_transformed(:,2),1)
-                                error('Non-unique complete transformation models. Each complete transformation model must be input for only one model component. A complete model is that having at least the following inputs: transformfunction, forcingdata, outputdata');
-                            end
-                            
-                            % Get a unique list of model components with
-                            % complete transforamtion models and a list of
-                            % unieq tranformation functions
-                            modelComponets_transformed_uniqueComponents = unique(modelComponets_transformed(:,1));
-                            modelComponets_transformed_uniqueTransFunc = unique(modelComponets_transformed(:,2));
-                            
-                            % Check the inputcomponent is a valid
-                            % component.
-                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{5});
-                            if any(filt) && ischar(propertyValue{filt,2})
-                                if ~any(strcmp(modelComponets_transformed_uniqueTransFunc(:,1), propertyValue{filt,2}))
-                                    error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input function must be a tranformation function name that is listed within the model.']);
-                                end
-                            elseif any(filt) && iscell(propertyValue{filt,2})   
-                                for j=1:length(propertyValue{filt,2})
-                                    if ~any(strcmp(modelComponets_transformed_uniqueTransFunc(:,1), propertyValue{filt,2}{j}))
-                                        error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input function must be a tranformation function name that is listed within the model.']);                                        
-                                    end
-                                end
-                            elseif any(filt)
-                                error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input component must be a a tranformation function name (string or cell vector of strings) that is listed within the model input options and it must also have a function transformation.']);                                
-                            end
-                        end
-                        
-                    elseif ~isnumeric( propertyValue ) &&  (isnumeric(propertyValue) && any(propertyValue<0)) ...
-                    && ~ischar(propertyValue)                                
-                        error(['Invalid property value for property type ', valid_properties{2},'. It must be a forcing data site name or column number intereger >0 (scalar or vector)' ]);
-                    end
-                                            
-                elseif strcmp(propertyType, valid_properties{4})    % If the propertyType is inputcomponent, check the input is a string and a valid component name.
-                
-                    if ischar(propertyValue)
-                        % Check that it is valid component name.
-                        filt = cellfun(@(x)(strcmp(x,propertyValue)), varargin(:,1));
-                        if isempty(filt)
-                            error(['Invalid input component name for component:', modelComponent,'. The named input componant must be a component within the model.' ]);
-                        end
-                    else
-                        error(['Invalid input component name for component:', modelComponent,'. It must be a string defining a componant type.' ]);
-                    end
-                    
-                end
-                
-            end
-
-            % Record the source data column for each component or the
-            % transformation model and output variables and input transformation component.
-            filt = find(strcmpi(varargin(:,2),'forcingdata'));
-            transformFunctionIndexes_noInputComponents = [];
-            transformFunctionIndexes_inputComponents = [];
-            for ii=filt'
-               modelComponent = varargin{ii,1};
-               if isnumeric(varargin{ii,3})
-                  obj.inputData.componentData.(modelComponent).dataColumn = varargin{ii,3}-2;
-               elseif ischar(varargin{ii,3})
-                  filt_forcingCols = cellfun(@(x,y)(strcmp(x,varargin{ii,3})), forcingData_colnames);
-                  obj.inputData.componentData.(modelComponent).dataColumn  = find(filt_forcingCols);
-               elseif iscell(varargin{ii,3})
-                  % If it is a cell vector, then get the column number for
-                  % each element. Else, if it is a Nx2 cell array with the
-                  % required format, the componant uses transformed forcing
-                  % data so store the transformation object name and the
-                  % required output variable names.
-                  if isvector(varargin{ii,3}) && ~iscell(varargin{ii,3}{1})
-                    for j=1:size(varargin{ii,3},1)
-                        filt_forcingCols = cellfun(@(x,y)(strcmp(x,varargin{ii,3}{j})), forcingData_colnames);
-                        obj.inputData.componentData.(modelComponent).dataColumn(j,1) = find(filt_forcingCols);                                    
-                    end
-                  elseif (size(varargin{ii,3},2) == 2 ...
-                  && any(strcmp(varargin{ii,3}(:,1), valid_transformProperties{1})) ...
-                  && any(strcmp(varargin{ii,3}(:,1), valid_transformProperties{3}))) ...
-                  || (size(varargin{ii,3}{1},2) == 2 ...
-                  && any(strcmp(varargin{ii,3}{1}(:,1), valid_transformProperties{1})) ...
-                  && any(strcmp(varargin{ii,3}{1}(:,1), valid_transformProperties{3})))
-              
-                    
-                    % Record the transformation model name and required
-                    % output forcing data from the transformation model for
-                    % input to the model component.
-                    if size(varargin{ii,3},2) == 2
-                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
-                        obj.inputData.componentData.(modelComponent).forcing_object = varargin{ii,3}{filt,2};
-                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
-                        obj.inputData.componentData.(modelComponent).outputVariable = varargin{ii,3}{filt,2};
-                    else
-                        for j=1:size(varargin{ii,3},1)                           
-                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{1});
-                            obj.inputData.componentData.(modelComponent).forcing_object = varargin{ii,3}{j}{filt,2};
-                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
-                            obj.inputData.componentData.(modelComponent).outputVariable{j,1} = varargin{ii,3}{j}{filt,2};                        
-                        end
-                    end
-                    % Record the input forcing data columns for the 
-                    % transforamtion model if provided. Else, seach the other
-                    % transforamtion componants for the forcing data
-                    % columns.
-                    if size(varargin{ii,3},2) == 2
-                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});                    
-                        if any(filt)
-                            obj.inputData.componentData.(modelComponent).inputForcing = varargin{ii,3}{filt,2};
-                        else
-
-                            % Get the name of the source transforamtion
-                            % model.
-                            filt_transf = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
-                            sourceTransformModel = varargin{ii,3}(filt_transf,2);
-
-                            % Create a filter for the forcingData property.
-                            filt_transf = find(strcmp(varargin(:,2), valid_properties{2}));
-
-                            % Loop through all forcingData inputs to find the
-                            % transformationModel that is the source to the
-                            % current transformation model.
-                            for j=filt_transf'
-                               if size(varargin{j,3},2) == 2 ...
-                               && iscell(varargin{j,3}) ...        
-                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
-                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
-                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
-                               && any(strcmp(varargin{j,3}(:,2), sourceTransformModel))
-                                   % Get the row containing the input forcing data
-                                   filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{2});
-
-                                   % Assign source forcing names to the current
-                                   % model component.
-                                   obj.inputData.componentData.(modelComponent).inputForcing = varargin{j,3}{filt,2};                               
-                               end
-                            end
-
-                        end
-                    else
-                        for j=1:size(varargin{ii,3},1)  
-                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});                    
-                            if any(filt)
-                                inputForcing = varargin{ii,3}{j}{filt,2};
-                                filt = cellfun( @(x) ~isempty(x), inputForcing(:,2));
-                                inputForcing = inputForcing(filt,:);                                
-                                obj.inputData.componentData.(modelComponent).inputForcing = inputForcing ;
-%                             else
-%                                 error('Inheritated transformation models with multiple outputs is not operational.');
-%                                 % Get the name of the source transforamtion
-%                                 % model.
-%                                 filt_transf = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
-%                                 sourceTransformModel = varargin{ii,3}(filt_transf,2);
-% 
-%                                 % Create a filter for the forcingData property.
-%                                 filt_transf = find(strcmp(varargin(:,2), valid_properties{2}));
-% 
-%                                 % Loop through all forcingData inputs to find the
-%                                 % transformationModel that is the source to the
-%                                 % current transformation model.
-%                                 for j=filt_transf'
-%                                    if size(varargin{j,3},2) == 2 ...
-%                                    && iscell(varargin{j,3}) ...        
-%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
-%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
-%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
-%                                    && any(strcmp(varargin{j,3}(:,2), sourceTransformModel))
-%                                        % Get the row containing the input forcing data
-%                                        filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{2});
-% 
-%                                        % Assign source forcing names to the current
-%                                        % model component.
-%                                        obj.inputData.componentData.(modelComponent).inputForcing = varargin{j,3}{filt,2};                               
-%                                    end
-%                                 end
-
-                            end                                                        
-                        end
-                    end
-                    % Check that forcing data was identified for the
-                    % current component.
-                    if ~isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
-                        error(['Invalid forcing data for component:', modelComponent,'. No forcing data names or column numbers were input or found by searching for complete transformation models of the same name as input for this component.' ]);
-                    end
-                    
-
-                    % Subtract '2' from the column numbers (if
-                    % not a string). This is done because when
-                    % the user inputs the column number the
-                    % first three columns are year,month,day.
-                    % However, when passed to model_TFN(), the
-                    % first three columns are replaced by a
-                    % single column of the date number.
-                    for j=1:size(obj.inputData.componentData.(modelComponent).inputForcing,1)
-                        if isnumeric(obj.inputData.componentData.(modelComponent).inputForcing{j,2})
-                            obj.inputData.componentData.(modelComponent).inputForcing{j,2} = ...
-                            obj.inputData.componentData.(modelComponent).inputForcing{j,2} - 2;
-                        end
-                    end
-                    
-                    % Record indexes to each component with a
-                    % transformation model and the type of model.
-                    % Additionally, if the transformation model uses inputs
-                    % from a transformation model, then also record this data.
-                    if size(varargin{ii,3},2) == 2
-                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{5});
-                        if any(filt)
-
-                            % Only add an index for the creation the
-                            % transformation object if input and output forcing
-                            % is defined. If only output is defined, then the 
-                            % transforamtion is assumed to be simply calling
-                            % a transformation object created for another
-                            % component.
-                            filt_inputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
-                            filt_outputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
-                            if any(filt_inputForcing) && any(filt_outputForcing)
-                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
-                                obj.inputData.componentData.(modelComponent).inputForcingComponent = varargin{ii,3}{filt,2};
-                                transformFunctionIndexes_inputComponents = [transformFunctionIndexes_inputComponents; ii];
-                            else
-                                error(['Invalid forcing transformation for component:', modelComponent,'. When a transformation model uses another transformation model as an input then the forcing data and the output variable must be specified.' ]);
-                            end
-                        else
-
-                            % Only add an index for the creation of the
-                            % transformation object if input and output forcing
-                            % is defined. If only output is defined, then the 
-                            % transforamtion is assumed to be simply calling
-                            % a transformation object created for another
-                            % component.
-                            filt_inputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
-                            filt_outputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
-                            if any(filt_inputForcing) && any(filt_outputForcing)
-                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
-                                transformFunctionIndexes_noInputComponents = [transformFunctionIndexes_noInputComponents; ii];                            
-                            elseif ~any(filt_inputForcing) && any(filt_outputForcing)
-                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = false;
-                            end
-                        end
-                    else
-                        for j=1:size(varargin{ii,3},1)  
-                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{5});
-                            if any(filt)
-
-                                % Only add an index for the creation the
-                                % transformation object if input and output forcing
-                                % is defined. If only output is defined, then the 
-                                % transforamtion is assumed to be simply calling
-                                % a transformation object created for another
-                                % component.
-                                filt_inputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
-                                filt_outputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
-                                if any(filt_inputForcing) && any(filt_outputForcing)
-                                    obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
-                                    obj.inputData.componentData.(modelComponent).inputForcingComponent = varargin{ii,3}{j}{filt,2};
-                                    transformFunctionIndexes_inputComponents = [transformFunctionIndexes_inputComponents; ii];
-                                else
-                                    error(['Invalid forcing transformation for component:', modelComponent,'. When a transformation model uses another transformation model as an input then the forcing data and the output variable must be specified.' ]);
-                                end
-                            else
-
-                                % Only add an index for the creation of the
-                                % transformation object if input and output forcing
-                                % is defined. If only output is defined, then the 
-                                % transforamtion is assumed to be simply calling
-                                % a transformation object created for another
-                                % component.
-                                filt_inputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
-                                filt_outputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
-                                if any(filt_inputForcing) && any(filt_outputForcing)
-                                    obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
-                                    transformFunctionIndexes_noInputComponents = [transformFunctionIndexes_noInputComponents; ii];                            
-%                                 elseif ~any(filt_inputForcing) && any(filt_outputForcing)
-%                                     obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = false;
-                                end
-                            end                        
-                        end
-                    end
-                    
-                  else
-                    error(['An unexpected error occured for component :', modelComponent,'. The input for "forcingdata" should be either a forcing data site name(s) or number(s) or a cell array for an input transformation model.' ]);
-                  end
-               else
-                  error('The data colum for each model component must be either the column number within the forcing data matrix or a string of the forcing site name.'); 
-               end                   
-            end
-            
-            % Check that each component has a forcing column or a forcing 
-            % transformation defined.
-            modelComponent = unique(varargin(:,1));
-            for i=1:length(modelComponent)                
-                if ~isfield(obj.inputData.componentData,modelComponent{i})                    
-                    error(['The input model options must specify a forcing data column or forcing transformation model for each model components. The following component has no such input:', modelComponent{i}]);                    
-                end
-            end
-            
-            % Get a list of rows in varargin defining the weighting
-            % function names.
-            weightingFunctionIndexes = find(strcmpi(varargin(:,2), valid_properties{1}));
-                                   
-            % Build the objects for forcing transformations that DO NOT
-            % require an input weighting function object.            
-            for ii = transformFunctionIndexes_noInputComponents'
-                
-                modelComponent = varargin{ii,1};
-                propertyValue = varargin{ii,3};
-
-                % Get the following inputs to build the model object:
-                % transformation function name, input data column names,
-                % the variables to which the input data is to be assigned
-                % too, and additional transformation model options.
-                if size(varargin{ii,3},2) == 2
-                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
-                    transformObject_name = varargin{ii,3}{filt,2};
-                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
-                    transformObject_inputs = varargin{ii,3}{filt,2};
-                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{4});
-                    if any(filt)
-                        transformObject_options = varargin{ii,3}{filt,2};
-                    else
-                        transformObject_options ={};
-                    end
-                else
-                    for j=1:size(varargin{ii,3},1)  
-                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{1});
-                        transformObject_name = varargin{ii,3}{j}{filt,2};
-                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
-                        transformObject_inputs = varargin{ii,3}{j}{filt,2};
-                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{4});
-                        if any(filt)
-                            transformObject_options = varargin{ii,3}{j}{filt,2};
-                            break;
-                        end                    
-                    end
-                end
-                
-                % Get the names (or column numbers) of the input forcing data. 
-                forcingData_inputs = obj.inputData.componentData.(modelComponent).inputForcing(:,2);                            
-                
-                % Remove empty rows
-                filt = cellfun( @(x) ~isempty(x) && ~strcmp(x,'(none)'), forcingData_inputs);
-                forcingData_inputs = forcingData_inputs(filt);
-                filt = cellfun( @(x) ~isempty(x) && ~strcmp(x,'(none)'),transformObject_inputs(:,2));
-                transformObject_inputs = transformObject_inputs(filt,:);
-                
-                % Find the required columns in forcing data so that only the
-                % required data is input.
-                colNum=1;
-                rowFilt = true( size(transformObject_inputs,1),1);
-                for j=1:size(transformObject_inputs,1)
-                    % Skip if optional forcing input
-                    if strcmp(transformObject_inputs{j,2},'(none)')
-                        rowFilt(j) = false;
-                        continue                        
-                    end                    
-                    if isnumeric(transformObject_inputs{j,2})
-                        colNum = [colNum; transformObject_inputs{j,2}-2];                        
-                    elseif ischar(transformObject_inputs{j,2})
-                        filt = find(strcmpi(forcingData_colnames, transformObject_inputs{j,2}));
-                        if sum(filt)==0
-                            error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
-                        end
-                        colNum = [colNum; filt];          
-                        
-                    else
-                        error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
-                    end
-                    transformObject_inputs{j,2} = j+1;
-                end
-                
-                % Filter transformed object input rows to remove those that
-                % are input as options ie '(none)'.
-                transformObject_inputs = transformObject_inputs(rowFilt,:);
-                
-                try
-                    %colNum_extras = true(length(forcingData_colnames),1);
-                    %colNum_extras(colNum) = false;
-                    %colNum_extras = find(colNum_extras);
-                    %forcingData_colnamesTmp = forcingData_colnames([colNum;colNum_extras]);
-                    %forcingData_dataTmp = forcingData_data(:,[colNum;colNum_extras]);
-                    obj.parameters.(transformObject_name) = feval(transformObject_name, bore_ID, forcingData_data(:,colNum), forcingData_colnames(colNum), siteCoordinates, transformObject_inputs, transformObject_options);
-                     
-                    % Check each output variable is valid. If not valid, then remove it. 
-                    % If valid then add coordinates for the output
-                    % variable. If there is no valid output variable then
-                    % throw an error.                    
-                    variableName = obj.inputData.componentData.(modelComponent).outputVariable;
-                    if ischar(variableName)
-                        variableNameAsCell{1} = variableName;
-                        variableName = variableNameAsCell;
-                        clear variableNameAsCell
-                    end
-                    isValidVariableName = false(1,length(variableName ));
-                    t = obj.inputData.forcingData(:,1);
-                    setTransformedForcing(obj.parameters.(transformObject_name), t, true)
-                    for j=1:length(variableName)
-                    	try                                                         
-                            forcingData = getTransformedForcing(obj.parameters.(transformObject_name), variableName{j});
-                            isValidVariableName(j) = true;
-                        catch ME
-                            isValidVariableName(j) = false;
-                        end
-                    end
-                    if ~any(isValidVariableName)
-                        error(['No transformed forcing data for component "',modelComponent,'" could be derived']); 
-                    end
-                    variableName = variableName(isValidVariableName);
-                    obj.inputData.componentData.(modelComponent).outputVariable = variableName;
-                    coordinatesTmp = getCoordinates(obj.parameters.(transformObject_name), variableName);
-                    siteCoordinates(size(siteCoordinates,1)+1: size(siteCoordinates,1) + size(coordinatesTmp,1),1:3) = coordinatesTmp;
-                    
-                catch exception
-                    display(['ERROR: Invalid model component class object for forcing transform: ', transformObject_name ]);
-                    rethrow(exception);
-                end                     
-            end            
-                        
-            % Build the objects for forcing transformations that DO
-            % require an input weighting function object.
-            % NOTE: if the component is derived from another componant
-            % object, then like the construction of the weighting function
-            % object, the forcing for the source component is input in the
-            % construction of the forcing transformation. If the source
-            % component forcing is also a transformation, then the
-            % transformtion object is passed. If not, then the input
-            % forcing data is passed.            
-            for ii = transformFunctionIndexes_inputComponents'
-                
-                modelComponent = varargin{ii,1};
-                propertyValue = varargin{ii,3};
-
-                % Get the following inputs to build the model object:
-                % transformation function name, input data column names,
-                % the variables to which the input data is to be assigned
-                % too, and additional transformation model options.
-                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
-                transformObject_name = varargin{ii,3}{filt,2};
-                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
-                transformObject_inputs = varargin{ii,3}{filt,2};
-                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{4});
-                if any(filt)
-                    transformObject_options = varargin{ii,3}{filt,2};
-                else
-                    transformObject_options = {};
-                end
-                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{5});
-                transformObject_sourceName = varargin{ii,3}{filt,2};
-                
-%                 % Find the name of the input transformation object for the
-%                 % specified input component name.
-%                 transformObject_inputComponentName = obj.inputData.componentData.(transformObject_inputComponentName).forcing_object;
-                
-                % Find the required columns in forcing data so that only the
-                % required data is input.
-                colNum=1;
-                for j=1:size(forcingData_inputs,1)
-                    if isnumeric(forcingData_inputs{j,1})
-                        colNum = [colNum; forcingData_inputs{j,1}];             
-                    elseif ischar(forcingData_inputs{j,1})
-                        filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
-                        if sum(filt)==0
-                            error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
-                        end
-                        colNum = [colNum; filt];                                
-                    else
-                        error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
-                    end
-                end
-                
-                try                
-                    %colNum_extras = true(length(forcingData_colnames),1);
-                    %colNum_extras(colNum) = false;
-                    %colNum_extras = find(colNum_extras);
-                    %forcingData_colnamesTmp = forcingData_colnames([colNum;colNum_extras]);
-                    %forcingData_dataTmp = forcingData_data(:,[colNum;colNum_extras]);                                                                                    
-                    obj.parameters.(transformObject_name) = feval(transformObject_name, bore_ID, forcingData_data(:,colNum), forcingData_colnames(colNum), siteCoordinates, transformObject_inputs, obj.parameters.(transformObject_sourceName), transformObject_options);
-                    
-                    % Add coordinates for the output variable
-                    variableName = obj.inputData.componentData.(modelComponent).outputVariable;
-                    coordinatesTmp = getCoordinates(obj.parameters.(transformObject_name), variableName);
-                    siteCoordinates(size(siteCoordinates,1)+1: size(siteCoordinates,1) + size(coordinatesTmp,1),1:3) = coordinatesTmp;
-                                        
-                catch exception
-                    display(['ERROR: Invalid model component class object for forcing transform: ', transformObject_name ]);
-                    rethrow(exception);
-                end                     
-            end
-
-            % Build the component weighting object for each component that DO NOT require an input weighting function object
-            for ii = weightingFunctionIndexes'
-            
-                modelComponent = varargin{ii,1};
-                propertyValue = varargin{ii,3};
-
-                % Check if the current model object requires input of
-                % another weighting function object.
-                inputWeightingFunctionIndex = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{4}));
-                
-                % Build weighting function for those NOT requiring the
-                % input of other weighting function objects.
-                if isempty(inputWeightingFunctionIndex)
-                    try                       
-                        % Get the column number for forcing data
-                        if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
-                            colNum = obj.inputData.componentData.(modelComponent).dataColumn;
-                            forcingData_inputs = forcingData_colnames(colNum);
-                        elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
-
-                            % Get the names (or column numbers) of the input forcing data. 
-                            forcingData_inputs = obj.inputData.componentData.(modelComponent).outputVariable;
-                            
-                            % Remove empty rows
-                            if iscell(forcingData_inputs)
-                                filt = cellfun( @(x) ~isempty(x), forcingData_inputs);
-                                forcingData_inputs = forcingData_inputs(filt);
-                            end
-%                             for j=1:size(forcingData_inputs,1)
-%                                 % Skip if optional forcing input
-%                                 if strcmp(forcingData_inputs{j,1},'(none)')
-%                                     continue
-%                                 end
-%                                 if isnumeric(forcingData_inputs{j,1})
-%                                     colNum = [colNum; forcingData_inputs{j,1}];             
-%                                 elseif ischar(forcingData_inputs{j,1})
-%                                     filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
-%                                     if sum(filt)==0
-%                                         error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
-%                                     end
-%                                     colNum = [colNum; filt];                                
-%                                 else
-%                                     error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
-%                                 end
-%                             end
-                        end
-
-                        % Check that the response 
-                        % function name is consistent with the abstract
-                        % 'responseFunction_abstract'.
-                        try
-                            if ~strcmp(findAbstractName( propertyValue),'responseFunction_abstract')
-                                error(['The following response function function class definition is not derived from the "responseFunction_abstract.m" anstract:',propertyValue]);
-                            end
-                        catch
-                            display('... Warning: Checking that the required abstract for the response function transform class definition was used failed. This is may be because the version of matlab is pre-2014a.');
-                        end                        
-
-                        % Filter for options.
-                        filt = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{3}));                       
-                        
-                        % Convert input forcing data to cell if string
-                        if ischar(forcingData_inputs);
-                            forcingData_inputs_tmp{1}=forcingData_inputs;
-                            forcingData_inputs = forcingData_inputs_tmp;
-                            clear forcingData_inputs_tmp;
-                        end
-                        
-                        % Call the object 
-                        if any(filt)
-                            obj.parameters.(modelComponent) = feval(propertyValue, bore_ID, forcingData_inputs, siteCoordinates, varargin{filt,3} );                             
-                        else
-                            obj.parameters.(modelComponent) = feval(propertyValue, bore_ID, forcingData_inputs, siteCoordinates, {}); 
-                        end
-                    catch exception
-                         display(['ERROR: Invalid weighting function class object name: ',char(propertyValue),'. The weighting function object could not be created.']);
-                         rethrow(exception);
-                    end                            
-                end
-            end
-            
-            % Build the component weighting object for each component that DO require an input weighting function object
-            for ii = weightingFunctionIndexes'
-            
-                modelComponent = varargin{ii,1};
-                propertyValue = varargin{ii,3};
-
-                % Check if the current model object requires input of
-                % another weighting function object.
-                inputWeightingFunctionIndex = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{4}));
-                
-                % Build weighting function for those that DO require the
-                % input of other weighting function objects.
-                if ~isempty(inputWeightingFunctionIndex)
-                    
-                    % Get the name of the component to be input to the
-                    % weighting function.
-                    inputWeightingFunctionName = varargin{inputWeightingFunctionIndex,3};                    
-                    
-                    % Add input component name to input data fields
-                    obj.inputData.componentData.(modelComponent).inputWeightingComponent = inputWeightingFunctionName;
-                    
-                    % Check that the input weighting function has been
-                    % created.
-                    if ~isfield(obj.parameters,inputWeightingFunctionName)
-                        error(['The input component name for component "', modelComponent,'" must be a component that itself is not derived from another componant.' ]);
-                    end
-                    
-                    try
-
-                        % Get the column number for forcing data
-                        if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
-                            colNum = obj.inputData.componentData.(modelComponent).dataColumn;
-                            forcingData_inputs = forcingData_colnames(colNum);
-                        elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
-
-                            % Get the names (or column numbers) of the input forcing data. 
-                            forcingData_inputs = obj.inputData.componentData.(modelComponent).outputVariable;
-                            
-                            % Remove empty rows
-                            if iscell(forcingData_inputs)
-                                filt = cellfun( @(x) ~isempty(x), forcingData_inputs);
-                                forcingData_inputs = forcingData_inputs(filt);
-                            end
-                        end
-                        
-%                         % Get the column number for forcing data
-%                         colNum = 1;
-%                         if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
-%                             colNum = obj.inputData.componentData.(modelComponent).dataColumn;
-%                         elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
-% 
-%                             % Get the names (or column numbers) of the input forcing data. 
-%                             forcingData_inputs = obj.inputData.componentData.(modelComponent).inputForcing(:,2);
-%                             
-%                             for j=1:size(forcingData_inputs,1)
-%                                 if isnumeric(forcingData_inputs{j,1})
-%                                     colNum = [colNum; forcingData_inputs{j,1}];             
-%                                 elseif ischar(forcingData_inputs{j,1})
-%                                     filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
-%                                     if sum(filt)==0
-%                                         error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
-%                                     end
-%                                     colNum = [colNum; filt];                                
-%                                 else
-%                                     error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
-%                                 end
-%                             end
-%                         end                    
-
-                        % Convert input forcing data to cell if string
-                        if ischar(forcingData_inputs);
-                            forcingData_inputs_tmp{1}=forcingData_inputs;
-                            forcingData_inputs = forcingData_inputs_tmp;
-                            clear forcingData_inputs_tmp;
-                        end
-
-                        % Filter for options.
-                        filt = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{3}));
-                        
-                        % Call the object. 
-                        % NOTE: these objects have an additional input
-                        % (compared to those created above) for the input
-                        % of a previously build model weighting function
-                        % object.
-                        obj.parameters.(modelComponent) = feval(propertyValue, bore_ID,forcingData_inputs, siteCoordinates, obj.parameters.(inputWeightingFunctionName), varargin(filt,3)); 
-                    catch exception
-                         display(['ERROR: Invalid weighting function class object name: ',char(propertyValue),'. The weighting function object could not be created.']);
-                         rethrow(exception);
-                    end                            
-                end
-            end                        
-            
-            % Add noise component
-            obj.parameters.noise.type = 'transferfunction';
-            obj.parameters.noise.alpha = log10(0.1);    
-            
-            % Set the parameter names variable.   
-            [junk, obj.variables.param_names] = getParameters(obj);            
-
-            % Set variable declarign that calibration is not being
-            % undertaken.
-            obj.variables.doingCalibration = false;
-            
-            % Store only input forcing data required by the model. The
-            % forcing data columns are removed to reduce RAM requirements.
-            %-------------
-            
-            % Find required columns.
-            modelComponentAll = fieldnames(obj.inputData.componentData);
-            forcingData_requiredColNames = {};
-            for i=1:length(modelComponentAll)
-                if isfield(obj.inputData.componentData.(modelComponentAll{i}), 'dataColumn')
-                    forcingData_requiredColNames = {forcingData_requiredColNames{:}, forcingData_colnames{obj.inputData.componentData.(modelComponentAll{i}).dataColumn}};
-                else
-                    forcingData_requiredColNames = {forcingData_requiredColNames{:}, obj.inputData.componentData.(modelComponentAll{i}).inputForcing{:,2}};
-                end
-            end
-            
-            % Create filter for required colnames
-            filt = cellfun(@(x) any(strcmp(x,forcingData_requiredColNames)) , forcingData_colnames);
-            filt(1) = true;
-            
-            forcingData_data = forcingData_data(:,filt);
-            forcingData_colnames = forcingData_colnames(filt);
-            setForcingData(obj, forcingData_data, forcingData_colnames);
-
-            %-------------
-        end
-        
-        % Get the observed head
-        function head = getObservedHead(obj)
-            head = obj.inputData.head;
-        end
-        
-        %% Get the forcing data from the model
-        function [forcingData, forcingData_colnames] = getForcingData(obj)
-            forcingData = obj.inputData.forcingData;
-            forcingData_colnames = obj.inputData.forcingData_colnames;            
-        end
-        
-        function [forcingData, forcingData_colnames] = getDerivedForcingData(obj, t)
-           
-            % Initialise outputs
-            forcingData = [];
-            forcingData_colnames = {};                        
-            
-            % Get the derived forcing data in the sub-model objects
-            if ~isempty(obj.parameters)
-                modelnames = fieldnames(obj.parameters);
-                for i=1:length(modelnames)
-                    if isobject(obj.parameters.(modelnames{i}))
-                        forcingData_colnames_tmp = {};
-                        forcingData_tmp = [];
-                        if any(strcmp(methods(obj.parameters.(modelnames{i})),'setTransformedForcing')) && ...
-                        any(strcmp(methods(obj.parameters.(modelnames{i})),'getTransformedForcing'))
-                            % Get the list of all possible forcing data outputs.                                
-                            % In doing so, handle situations where older
-                            % HydroSight models did not have the bore ID or
-                            % site coordinates stored within the object.
-                            if ~isfield(obj.inputData,'bore_ID')
-                                bore_ID='';
-                            else
-                                bore_ID=obj.inputData.bore_ID;
-                            end
-                            if ~isfield(obj.inputData,'siteCoordinates')
-                                siteCoordinates=table();
-                            else
-                                siteCoordinates=obj.inputData.siteCoordinates;
-                            end                            
-                            variable_names = feval([modelnames{i},'.outputForcingdata_options'],bore_ID , obj.inputData.forcingData, obj.inputData.forcingData_colnames, siteCoordinates);
-
-                            % Set the forcing
-                            setTransformedForcing(obj.parameters.(modelnames{i}),t, false);
-                            
-                            % Get the forcing for each variable
-                            for j=1:length(variable_names)
-                                try
-                                    forcingData = [forcingData, getTransformedForcing(obj.parameters.(modelnames{i}),variable_names{j})];
-                                    forcingData_colnames = {forcingData_colnames{:}, variable_names{j}};
-                                catch ME
-                                    continue;
-                                end
-                            end                            
-                        end
-                    end
-                end
-            end                
-        end
-        
-        %% Set the forcing data from the model
-        function setForcingData(obj, forcingData, forcingData_colnames)
-            obj.inputData.forcingData = forcingData;
-            obj.inputData.forcingData_colnames = forcingData_colnames;
-            
-            % Update forcing data in the sub-model objects
-            if ~isempty(obj.parameters)
-                modelnames = fieldnames(obj.parameters);
-                for i=1:length(modelnames)
-                    if isobject(obj.parameters.(modelnames{i}))
-                        try
-                            setForcingData(obj.parameters.(modelnames{i}), forcingData, forcingData_colnames)
-                        catch
-                            % do nothing
-                        end
-                    end
-                end
-            end
-            
-        end        
-        
-        function stochForcingData = getStochForcingData(obj)
-            stochForcingData = [];
-            % Get derived forcing data in the sub-model objects
-            if ~isempty(obj.parameters)
-                modelnames = fieldnames(obj.parameters);
-                for i=1:length(modelnames)
-                    if isobject(obj.parameters.(modelnames{i}))
-                        if any(strcmp('getStochForcingData',methods(obj.parameters.(modelnames{i}))))
-                            forcingData_tmp =getStochForcingData(obj.parameters.(modelnames{i}));
-                            stochForcingData.(modelnames{i}) = forcingData_tmp;
-                        end
-                    end
-                end
-            end            
-        end
-           
-        function  finishedStochForcing = updateStochForcingData(obj, stochForcingData, refineStochForcingMethod)
-            % Set derived forcing data in the sub-model objects
-            %isValidDerivedForcing =false;
-            componantNames = fieldnames(obj.parameters);
-            finishedStochForcing = false;
-            for i=1:length(componantNames)
-                if isobject(obj.parameters.(componantNames{i}))
-                    if any(strcmp('updateStochForcingData',methods(obj.parameters.(componantNames{i}))))
-                        if nargin==1
-                            updateStochForcingData(obj.parameters.(componantNames{i}));                                
-                        else
-                            forcingDataComponant = fieldnames(stochForcingData);
-                            filt = strcmp(forcingDataComponant,componantNames{i});
-                            forcingDataComponant=forcingDataComponant{filt};
-                            
-                            if nargin == 2
-                                updateStochForcingData(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant));
-                            elseif nargin==3
-                                finishedStochForcing = updateStochForcingData(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant), refineStochForcingMethod);
-                            end                            
-                        end
-                    end
-                end
-            end
-        end        
-        
-        function params = updateStochForcingParameters(obj, stochForcingData, params)
-            % Set the input parameters
-            setParameters(obj, params, obj.variables.param_names);       
-            
-            % For those parameter compant objects with a method
-            % 'updateStochForcingParameters', update the componant
-            % paramerters.
-            componantNames = fieldnames(obj.parameters);
-            didUpdate = false;
-            for i=1:length(componantNames)
-                if isobject(obj.parameters.(componantNames{i}))
-                    if any(strcmp('updateStochForcingParameters',methods(obj.parameters.(componantNames{i}))))
-                        didUpdate = true;
-                        
-                        forcingDataComponant = fieldnames(stochForcingData);
-                        filt = strcmp(forcingDataComponant,componantNames{i});
-                        forcingDataComponant=forcingDataComponant{filt};
-                            
-                        updateStochForcingParameters(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant));
-                    end
-                end
-            end
-            
-            % If updated, then Get the new parameters.
-            if didUpdate
-                params = getParameters(obj);
-            end
-            
-        end 
-        
-        function setStochForcingState(obj, doingCalibration, t_start_calib, t_end_calib)
-            % Finalsie the calibration of the stochastic forcing data.
-            if ~isempty(obj.parameters)
-                modelnames = fieldnames(obj.parameters);
-                for i=1:length(modelnames)
-                    if isobject(obj.parameters.(modelnames{i}))
-                        if any(strcmp('setStochForcingState',methods(obj.parameters.(modelnames{i}))))
-                            setStochForcingState(obj.parameters.(modelnames{i}),doingCalibration,t_start_calib, t_end_calib);
-                        end
-                    end
-                end
-            end            
-        end        
-        
-%% Solve the model for the input time points
-        function [head, colnames, noise] = solve(obj, time_points)
-% solve solves the model for the input time points.
-%
-% Syntax:
-%   [head, colnames, noise] = solve(obj, time_points)
-%
-% Description:
-%   Solves the model using the model parameters and, depending upon
-%   the method's inputs, limits the groundwater head to the
-%   contribution from various periods of climate forcing and plots the
-%   results. The latter is achieved by inputting min and max values for
-%   tor but to date is not incorporated into the HydroSight()
-%   callign methods.
-%
-% Input:
-%   obj -  model object
-%
-%   time_points - column vector of the time points to be simulated.
-%
-% Outputs:
-%   head - MxN matrix of simulated head with the following columns: date/time,
-%   head, head due to model component i.
-%
-%   colnames - Nx1 column names for matrix 'head'.
-%
-%   noise - Mx3 matrix of the estimated upper and lower magnitude of the
-%   time-series noise componat at M time steps.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-
-            % Check that the model has first been calibrated.
-            if ~isfield(obj.variables, 'd')
-                error('The model does not appear to have first been calibrated. Please calibrate the model before running a simulation.');
-            end
-                        
-            % Clear obj.variables of temporary variables.
-            if isfield(obj.variables, 'theta_est_indexes_min'), obj.variables = rmfield(obj.variables, 'theta_est_indexes_min'); end
-            if isfield(obj.variables, 'theta_est_indexes_max'), obj.variables = rmfield(obj.variables, 'theta_est_indexes_max'); end
-            %if isfield(obj.variables, 'SMS_frac'), obj.variables = rmfield(obj.variables, 'SMS_frac'); end            
-            %if isfield(obj.variables, 'recharge'), obj.variables = rmfield(obj.variables, 'recharge'); end            
-            %if isfield(obj.variables, 'SMSC'), obj.variables = rmfield(obj.variables, 'SMSC'); end            
-            %if isfield(obj.variables, 'SMSC_1'), obj.variables = rmfield(obj.variables, 'SMSC_1'); end            
-            %if isfield(obj.variables, 'SMSC_2'), obj.variables = rmfield(obj.variables, 'SMSC_2'); end            
-            %if isfield(obj.variables, 'f'), obj.variables = rmfield(obj.variables, 'f'); end            
-            %if isfield(obj.variables, 'b'), obj.variables = rmfield(obj.variables, 'b'); end                                    
-            %if isfield(obj.variables, 'ksat'), obj.variables = rmfield(obj.variables, 'ksat'); end
-            %if isfield(obj.variables, 'ksat_1'), obj.variables = rmfield(obj.variables, 'ksat_1'); end
-            %if isfield(obj.variables, 'ksat_2'), obj.variables = rmfield(obj.variables, 'ksat_2'); end
-            
-            % Set a flag to indicate that calibration is NOT being undertaken.
-            % obj.variables.doingCalibration = getInnovations;
-            
-            % Setup matrix of indexes for tor at each time_points
-            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
-            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(filt,1)+1]');
-            ntor =  size( tor, 1);                                                     
-            clear tor;            
-            
-            obj.variables.theta_est_indexes_min = zeros(1,length(time_points) );
-            obj.variables.theta_est_indexes_max = zeros(1,length(time_points) );
-                        
-            for ii= 1:length(time_points)              
-                ntheta = sum( obj.inputData.forcingData ( : ,1) <= time_points(ii) );                                
-                obj.variables.theta_est_indexes_min(ii) = ntor-ntheta;
-                obj.variables.theta_est_indexes_max(ii) = max(1,ntor);
-            end  
-            
-            % Free memory within mex function (just in case there'se been a
-            % prior calibration that crashed prior to clearing the MEX
-            % sttaic variables)
-            % Free memory within mex function
-            try
-                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
-            catch ME
-                % continue               
-            end          
-            
-            % Get the parameter sets (for use in resetting if >sets)
-            [params, param_names] = getParameters(obj);
-            
-            % If the number of parameter sets is >1 then temporarily apply 
-            % only the first parameter set. This is done only to reduce the
-            % RAM requirements for the broadcasting of the obj variable in
-            % the following parfor.
-            if size(params,2)>1
-                setParameters(obj, params(:,1), param_names);
-            end
-            
-            % Set percentile for noise 
-            Pnoise = 0.95;
-            
-            % Solve the modle using each parameter set.
-            obj.variables.delta_time = diff(time_points);
-            headtmp=cell(1,size(params,2));
-            noisetmp=cell(1,size(params,2));
-            companants = fieldnames(obj.inputData.componentData);
-            nCompanants = size(companants,1);                             
-            for ii=1:size(params,2)
-                % Get the calibration estimate of the mean forcing for the
-                % current parameter set. This is a bit of a work around to
-                % handle the issue of each parameter set having a unique
-                % mean forcing (if a forcing transform is undertaken). The
-                % workaround was required when DREAM was addded.
-                for j=1:nCompanants                    
-                    calibData(ii,1).mean_forcing.(companants{j}) = obj.variables.(companants{j}).forcingMean(:,ii);
-                end                
-                              
-                % Add drainage elevation to the varargin variable sent to
-                % objectiveFunction.                
-                calibData(ii,1).drainage_elevation = obj.variables.d(ii);
-                
-                % Add noise std dev
-                if isfield(obj.variables,'sigma_n');
-                    calibData(ii,1).sigma_n = obj.variables.sigma_n(ii);
-                else
-                    calibData(ii,1).sigma_n = 0;
-                end
-                
-            end
-            
-            % Solve model and add drainage constants
-            [~, headtmp{1}, colnames] = objectiveFunction(params(:,1), time_points, obj, calibData(1));                        
-            if size(params,2)>1
-                parfor jj=2:size(params,2)
-                    [~, headtmp{jj}] = objectiveFunction(params(:,jj), time_points, obj, calibData(jj));                        
-                end              
-            end
-            
-            % Add drainage constants and calculate total error bounds.
-            for ii=1:size(params,2)
-                headtmp{ii}(:,2) = headtmp{ii}(:,2) + calibData(ii).drainage_elevation;
-            
-                noisetmp{ii} = [headtmp{ii}(:,1), ones(size(headtmp{ii},1),2) .* norminv(Pnoise,0,1) .* calibData(ii).sigma_n];
-            end                            
-            head = zeros(size(headtmp{1},1),size(headtmp{1},2), size(params,2));
-            noise = zeros(size(headtmp{1},1),3, size(params,2));            
-            for ii=1:size(params,2)
-                head(:,:,ii) = headtmp{ii};
-                noise(:,:,ii) = noisetmp{ii};
-            end
-            %colnames = colnames{1};
-            clear headtmp noisetmp
-                             
-            % Set the parameters if >1 parameter sets
-            if size(params,2)>1
-                setParameters(obj,params, param_names);
-            end
-            
-            % Clear matrix of indexes for tor at each time_points
-            obj.variables = rmfield(obj.variables, 'theta_est_indexes_min');
-            obj.variables = rmfield(obj.variables, 'theta_est_indexes_max');
-            
-        end
-        
-%% Initialise the model prior to calibration.
-        function [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
-% calibration_initialise initialises the model prior to calibration.
-%
-% Syntax:
-%   [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
-%
-% Description:
-%   Sets up model variables required for calibration. Most imporantly, it
-%   calculates the mean observed head, h_bar, and row indexes, 
-%   theta_est_indexes,  for efficient calculation of the response
-%   functions. The calibration also requires the method to return a column 
-%   vector of the initial parameters and a column vector of time points for
-%   which observation data exists.
-%
-% Input:
-%   obj -  model object
-%
-%   t_start - scaler start time, eg datenum(1995,1,1);
-%
-%   t_end - scaler end time, eg datenum(2005,1,1);
-%
-% Outputs:
-%   params_initial - column vector of the initial parameters.
-%
-%   time_points - column vector of time points forwhich observation data
-%   exists.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014
-
-            % clear old variables
-            obj.variables = [];
-
-            % Set a flag to indicate that calibration is being undertaken
-            % and the calibraion start and end dates            
-            obj.variables.doingCalibration = true;
-            obj.variables.t_start = t_start;
-            obj.variables.t_end = t_end;
-                        
-            % Free memory within mex function (just in case there'se been a
-            % prior calibration that crashed prior to clearing the MEX
-            % sttaic variables)
-            % Free memory within mex function
-            try
-                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
-            catch ME
-                % continue               
-            end            
-                        
-            % Get parameter names and initial values
-            [params_initial, obj.variables.param_names] = getParameters(obj);
-            
-            % Extract time steps
-            t_filt = find( obj.inputData.head(:,1) >=t_start  ...
-                & obj.inputData.head(:,1) <= t_end );   
-            time_points = obj.inputData.head(t_filt,1);
-            obj.variables.time_points = time_points;
-            
-            % Set the stochastic forcing to be in a 'calibration' state.
-            setStochForcingState(obj, obj.variables.doingCalibration, t_start, t_end);            
-            
-            % Set initial time for trend term
-            obj.variables.trend_startTime = obj.variables.time_points(1);
-            
-            % Calc time step sizes. 
-            obj.variables.delta_time = diff( obj.variables.time_points  );
-            
-            % Calcaulte mean head.
-            obj.variables.h_bar = mean(obj.inputData.head( t_filt,2 ));                            
-            
-            % Setup matrix of indexes for tor at each time_points
-            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
-            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(filt,1)+1]');
-            ntor =  size(tor, 1);                                                     
-            clear tor;
-            
-            obj.variables.theta_est_indexes_min = zeros(1,length(time_points) );
-            obj.variables.theta_est_indexes_max = zeros(1,length(time_points) );
-                        
-            for ii= 1:length(time_points)              
-                ntheta = sum( obj.inputData.forcingData ( : ,1) <= time_points(ii) );                                
-                obj.variables.theta_est_indexes_min(ii) = ntor-ntheta;
-                obj.variables.theta_est_indexes_max(ii) = max(1,ntor);
-            end  
-
-            obj.variables.nobjectiveFunction_calls=0;
-            
-        end        
-        
-%% Finalise the model following calibration.
-        function calibration_finalise(obj, params, useLikelihood)            
-% calibration_finalise finalises the model following calibration.
-%
-% Syntax:
-%   calibration_finalise(obj, params)   
-%
-% Description:
-%   Finalises the model following calibration and assigns the final 
-%   parameters and additional variables to the object for later simulation. 
-%   Of the variables calculated, the most essential for the model 
-%   is the scalar drainage, obj.variables.d. Other variables that are also 
-%   important include: 
-%       - a vector of innovations,  obj.variables.innov, for detection 
-%         of serial correlation in the model errors; 
-%       - the noise standard deviation, obj.variables.sigma_n.
-%
-% Input:
-%   obj -  model object
-%
-%   params - column vector of the optima parameters derived from
-%   calibration.
-%
-% Outputs:
-%   (none, the results are output to obj.variables)
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014            
-
-            % Set the stochastic forcing to NOT be in a 'calibration' state.
-            setStochForcingState(obj, false, obj.variables.t_start, obj.variables.t_end);
-
-            for j=1:size(params,2)
-                % Re-calc objective function and deterministic component of the head and innocations.
-                % Importantly, the drainage elevation (ie the constant term for
-                % the regression) is calculated within 'objectiveFunction' and
-                % assigned to the object. When the calibrated model is solved
-                % for a different time period (or climate data) then this
-                % drainage value will be used by 'objectiveFunction'.
-                try
-                    [obj.variables.objFn(:,j), h_star, ~ , obj.variables.d(j)] = objectiveFunction(params(:,j), obj.variables.time_points, obj,useLikelihood);                        
-                catch ME
-                    if size(params,2)>1
-                        continue
-                    else
-                        error(ME.message);
-                    end
-                end
-
-                % Calculate the mean forcing rates. These mean rates are used
-                % to calculate the contribution from the tail of the theta
-                % weighting beyond the last observation. That is, the theta
-                % function is integrated from the last time point of the
-                % forcing to negative infinity. This integral is then
-                % multiplied by the mean forcing rate. To ensure future
-                % simulations use an identical mean forcing rate, the means are
-                % calculated here and used in all future simulations.
-                companants = fieldnames(obj.inputData.componentData);
-                nCompanants = size(companants,1);
-                for i=1:nCompanants
-                    obj.variables.(companants{i}).forcingMean(:,j) = mean(obj.variables.(companants{i}).forcingData)';
-                end
-
-                t_filt = find( obj.inputData.head(:,1) >=obj.variables.time_points(1)  ...
-                    & obj.inputData.head(:,1) <= obj.variables.time_points(end) );               
-                obj.variables.resid(:,j) = obj.inputData.head(t_filt,2)  -  (h_star(:,2) + obj.variables.d(j));
-
-                % Calculate mean of noise. This should be zero +- eps()
-                % because the drainage value is approximated assuming n-bar = 0.
-                obj.variables.n_bar(j) = real(mean( obj.variables.resid(:,j) ));
-
-                % Calculate innovations
-                innov = obj.variables.resid(2:end,j) - obj.variables.resid(1:end-1,j).*exp( -10.^obj.parameters.noise.alpha .* obj.variables.delta_time );
-
-                % Calculate noise standard deviation.
-                obj.variables.sigma_n(j) = sqrt(mean( innov.^2 ./ (1 - exp( -2 .* 10.^obj.parameters.noise.alpha .* obj.variables.delta_time ))));
-            end
-            
-            % Set a flag to indicate that calibration is complete.
-            obj.variables.doingCalibration = false;
-            
-            % Set model parameters (if params are multiple sets)
-            if size(params,2)>1
-                setParameters(obj, params, obj.variables.param_names);            
-            end
-            
-            % Free memory within mex function
-            try
-                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
-            catch ME
-                % continue               
-            end
-        end        
-
-%% Calculate objective function vector. 
-        function [objFn, h_star, colnames, drainage_elevation] = objectiveFunction(params, time_points, obj, varargin)
-% objectiveFunction calculates the objective function vector. 
-%
-% Syntax:
-%   [objFn, h_star, colnames, drainage_elevation] = objectiveFunction(params,time_points, obj)
-%
-% Description:
-%   Solves the model for the input parameters and calculates the objective
-%   function vector. Importantly, the objective function vector is not
-%   simply the difference between the observed and modelled heads. Because
-%   the model uses a noise model, the residual between the observed 
-%   and modelled head is first derived and then the innovation
-%   is calculated as the prior residual minus the later residual multiplied
-%   by the exponental noise function. Finally, the objective function
-%   weights this vector according to the time-step between observations.
-%
-%   Imporantly, the numerator of the weighting equation from von Asmuth et al 2002
-%   rounds to zero when the number of samples is very large. This occurs
-%   because it is effecively a geometric mean and its product term for n
-%   (where n is the number of observation for calibration minus 1) rounds
-%   to zero as a result of machine precision. This was overcome by adoption
-%   of a restructuring of the geometric meazn in term of exp and log terms. 
-%
-% Inputs:
-%   params - column vector of the optima parameters derived from
-%   calibration.
-%
-%   time_points - column vector of the time points to be simulated.  
-%
-%   obj -  model object
-%
-% Outputs:
-%   objFn - scalar objective function value.
-%
-%   h_star - matrix of the contribution from various model components and
-%   their summed influence. The matrix columns are in the order of:
-%   date/time, summed contribution to the head, contribution from
-%   component i.
-%
-%   colnames - column names for matrix 'head'.
-%
-%   drainage_elevation - drainage elevation constant.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% References:
-%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
-%   dunction-noise modeling in continuous time using predefined impulse
-%   response functions.
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014    
-            
-            % Set model parameters
-            setParameters(obj, params, obj.variables.param_names);
-            
-            % If varargin is a structural variable then the model
-            % simulation is to use predefined values of the drainage
-            % elevation and the mean forcing. Note, these inputs are only
-            % to be provided if not doing simulation.
-            getLikelihood = false;
-            drainage_elevation=[];
-            mean_forcing=[];
-            if ~isempty(varargin)
-                if isstruct(varargin{1})
-                    drainage_elevation=varargin{1}.drainage_elevation;
-                    mean_forcing=varargin{1}.mean_forcing;
-                elseif islogical(varargin{1})
-                    getLikelihood=varargin{1};
-                else
-                    error('The input varargin must be either a logical or a structural variable.');
-                end
-            end
-            
-            % Calc deterministic component of the head.
-            if isempty(mean_forcing)
-                [h_star, colnames] = get_h_star(obj, time_points);                 
-            else
-                [h_star, colnames] = get_h_star(obj, time_points,mean_forcing);                 
-            end
-            
-            % Return of there are nan or inf value
-            if any(isnan(h_star(:,2)) | isinf(h_star(:,2)));
-                objFn = inf;
-                return;
-            end
-                            
-            % If the results from this method call are not to be used for
-            % summarising calibration results, then exit here. This is
-            % required because the innovations can only be calculated at
-            % time points for which there are observations. 
-            if ~obj.variables.doingCalibration
-                objFn = [];
-                return;
-            end
-            
-            % Calculate residual between observed and modelled.
-            % Importantly, an approximation of the drainage level,d, is
-            % required here to calculate the residuals. To achieve this
-            % requires an assumption that the mean noise, n_bar, equals
-            % zero. If this is not the case, then d_bar calculated below
-            % may differ from d calculated within 'calibration_finalise'.
-            t_filt = find( obj.inputData.head(:,1) >=time_points(1)  ...
-                & obj.inputData.head(:,1) <= time_points(end) );          
-            if isempty(drainage_elevation)
-                drainage_elevation = obj.variables.h_bar - mean(h_star(:,2));      
-            end
-            resid= obj.inputData.head(t_filt,2)  - (h_star(:,2) +  drainage_elevation);                 
-            
-            % Calculate innovations using residuals from the deterministic components.            
-            innov = resid(2:end) - resid(1:end-1).*exp( -10.^obj.parameters.noise.alpha .* obj.variables.delta_time );
-            
-            % Calculate objective function
-            objFn = sum( exp(mean(log( 1- exp( -2.*10.^obj.parameters.noise.alpha .* obj.variables.delta_time) ))) ...
-                    ./(1- exp( -2.*10.^obj.parameters.noise.alpha .* obj.variables.delta_time )) .* innov.^2);
-  
-
-            % Calculate log liklihood    
-            if getLikelihood
-                N = size(resid,1);
-                objFn = -0.5 * N * ( log(2*pi) + log(objFn./N)+1); 
-            end
-            % Increment count of function calls
-            obj.variables.nobjectiveFunction_calls = obj.variables.nobjectiveFunction_calls +  size(params,2);
-            
-        end
-           
-%% Set the model parameters to the model object from a vector.
-        function setParameters(obj, params, param_names)
-% setParameters set the model parameters to the model object from a vector.
-%
-% Syntax:
-%   setParameters(obj, params, param_names)
-%
-% Description:
-%   Assigns a vector of parameter values to the model object using the
-%   input component and parameter names. This method is predominately used
-%   by the model calibration.
-%
-% Input:
-%   obj -  model object
-%
-%   params - column vector of the parameter values.
-%
-%   param_names - two column n-row (for n parameters) cell matrix of
-%   compnant name (column 1) and parameter name (column 2).
-%
-% Outputs:
-%   params_initial - column vector of the initial parameters.
-%
-%   time_points - column vector of time points forwhich observation data
-%   exists.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014            
-            
-            % Get unique component names and count the number of parameters
-            % for this component.
-            nCompanants = 1;
-            companants{nCompanants ,1} =  param_names{1,1};
-            companants{nCompanants ,2} = 1;
-            if size(param_names,1) >1
-                for ii=2: size(param_names,1)
-                    if ~strcmp( param_names{ii,1} , param_names{ii-1,1} )
-                        nCompanants  = nCompanants +1;
-                        companants{nCompanants ,1} =  param_names{ii,1};
-                        companants{nCompanants ,2} =  1;
-                    elseif ~strcmp( param_names{ii,2} , 'type' )
-                        companants{nCompanants ,2} =  companants{nCompanants ,2} + 1;
-                    end
-                end
-            end
-                        
-            params_index=0;
-            for ii=1: size(companants,1)
-                currentField = char( companants{ii,1} ) ;
-                
-                % Get parameter names for this component
-                if isobject( obj.parameters.( currentField ) )
-                    [param_values, companant_params] = getParameters(obj.parameters.( currentField ));
-                    clear param_values;
-                else
-                     companant_params = fieldnames( obj.parameters.( currentField ) ) ;
-                end
-
-                % Scan though and remove those that are called 'type'
-                param_index = 1:size(companant_params,1) ;
-                for j = 1: size(companant_params,1)
-                    if strcmp( companant_params{j}, 'type') || strcmp( companant_params{j}, 'variables')
-                        param_index(j)=0;
-                    end
-                end
-                companant_params = companant_params(param_index>0);
-                
-                % Check all parameters for this component are to be set
-                % with new valeus.
-                if companants{ii ,2} ~= size(companant_params,1)
-                    error(['The number of parameters to be set for the following model', ...
-                    'component must equal the total number of parameters for the component: ,',currentField]);
-                end
-                
-                % Get input parameter values for this component.
-                component_param_vals = zeros(size(companant_params,1),size(params,2));
-                for j=1: size(companant_params,1)
-                    params_index = params_index + 1;
-                    component_param_vals(j,:) =  params(params_index,:);
-                end
-                
-                % Get model parameters for each componant.
-                % If the componant is an object, then call the objects
-                % getParameters method.
-                if isobject( obj.parameters.( currentField ) )                    
-                    % Call object method to set parameter values.
-                    setParameters( obj.parameters.( currentField ), component_param_vals );                   
-                else
-                    % Non-object componant.
-                    for j=1:size(companant_params,1)               
-                        obj.parameters.( currentField ).( char(companant_params{j}) ) = component_param_vals(j,:);
-                    end 
-                end
-            end
-            
-            
-        end
-        
-%% Returns the model parameters from the model object.
-        function [params, param_names] = getParameters(obj)
-% getParameters returns the model parameters from the model object.
-%
-% Syntax:
-%   [params, param_names] = getParameters(obj)
-%
-% Description:
-%   Cycles through all model components and parameters and returns a vector
-%   of parameter values and a cell matrix of their espective componant
-%   and parameter names.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params - column vector of the parameter values.
-%
-%   param_names - two column n-row (for n parameters) cell matrix of
-%   compnant name (column 1) and parameter name (column 2).
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014            
-            
-            param_names = {};
-            params = [];
-            nparams=0;
-            % Get model componants
-            companants = fieldnames(obj.parameters);
-            
-            for ii=1: size(companants,1)
-                currentField = char( companants(ii) ) ;
-                % Get model parameters for each componant.
-                % If the componant is an object, then call the objects
-                % getParameters method.
-                if isobject( obj.parameters.( currentField ) )
-                    % Call object method.
-                    [params_temp, param_names_temp] = getParameters( obj.parameters.( currentField ) );
-                    
-                    for j=1: size(param_names_temp,1)
-                        nparams = nparams + 1;
-                        param_names{ nparams , 1} = currentField;
-                        param_names{ nparams , 2} = param_names_temp{j};
-                        params( nparams,: ) = params_temp(j,:);                        
-                    end
-                    
-                else
-                    % Non-object componant.
-                    companant_params = fieldnames( obj.parameters.( currentField ) );
-                    for j=1: size(companant_params,1)
-                       if ~strcmp( companant_params(j), 'type')
-                          nparams = nparams + 1;
-                          param_names{nparams,1} =  currentField;
-                          param_names{nparams,2} =  char(companant_params(j));
-                          params(nparams,:) = obj.parameters.( currentField ).( char(companant_params(j)) );
-                       end
-                    end
-                end
-            end
-        end
-        
-        
-%% Returns the model parameters from the model object.
-        function [params, param_names] = getDerivedParameters(obj)
-% getDerivedParameters returns the derived parameters from the model object.
-%
-% Syntax:
-%   [params, param_names] = getDerivedParameters(obj)
-%
-% Description:
-%   Cycles through all model componants and parameters and returns a vector
-%   of derived parameter values and a cell matrix of their respective componant
-%   and parameter names. The derived parameters are calibrated parameters
-%   or constants in the componants but variables derived from the
-%   calibrated parameters. For example, the drawdown response function
-%   (e.g. responseFunction_FerrisKnowles) can calculate the T and S from
-%   the calibrated parameters.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params - column vector of the parameter values.
-%
-%   param_names - two column n-row (for n parameters) cell matrix of
-%   compnant name (column 1) and parameter name (column 2).
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014            
-            
-            param_names = {};
-            params = [];
-            nparams=0;
-            % Get model componants
-            companants = fieldnames(obj.parameters);
-            
-            for ii=1: size(companants,1)
-                currentField = char( companants(ii) ) ;
-                % Get model parameters for each componant.
-                % If the componant is an object, then call the objects
-                % getParameters method.
-                if isobject( obj.parameters.( currentField ) )
-                    % Call object method.
-                    [params_temp, param_names_temp] = getDerivedParameters( obj.parameters.( currentField ) );
-                    
-                    for j=1: size(param_names_temp,1)
-                        nparams = nparams + 1;
-                        param_names{ nparams , 1} = currentField;
-                        param_names{ nparams , 2} = param_names_temp{j};
-                        params( nparams,: ) = params_temp(j,:);                        
-                    end
-                end
-            end
-        end
-             
-        function derivedData_types = getDerivedDataTypes(obj)
-
-            % Initialise outputs
-            derivedData_types = cell(0,2);
-            
-            % Get model componants
-            companants = fieldnames(obj.parameters);
-            
-            derivedData_types_p1={};
-            derivedData_types_p2={};
-            for ii=1: size(companants,1)
-                modelComponant = char( companants(ii) ) ;
-                % Get derived data for each componant.
-                if isobject( obj.parameters.( modelComponant ))  && ...
-                any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedData')) && ...
-                any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedDataTypes'))                    
-            
-                    dataTypes_tmp = getDerivedDataTypes(obj.parameters.( modelComponant ));
-                    if ~iscell(dataTypes_tmp)
-                        dataTypes = cell(1,1);
-                        dataTypes{1}=dataTypes_tmp;
-                    else
-                        dataTypes = dataTypes_tmp;
-                    end
-                    k=size(derivedData_types,1);
-                    for j=1:length(dataTypes)
-                        derivedData_types{k+j,1} = modelComponant;
-                        derivedData_types{k+j,2} = dataTypes{j};
-                    end
-                end
-            end    
-        end
-        
-        function [derivedData, derivedData_names] = getDerivedData(obj, modelComponant, derivedData_variable, t, plot_axes)
-
-            % Initialise outputs
-            derivedData = [];
-            derivedData_names={};
-            
-            if isobject( obj.parameters.( modelComponant ))  && ...
-            any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedData'))
-                [derivedData, derivedData_names] = getDerivedData( obj.parameters.( modelComponant ),derivedData_variable,t,plot_axes );  
-            end
-        end        
-        
-        function [stochForcingData_new, accepted] = acceptStochForcingSolution(obj, objFuncVal, objFuncVal_prior, stochForcingData)
-
-            % Initialse some outputs
-            %stochForcingData_new = stochForcingData;
-            stochForcingData_new=[];
-            
-            % Get model componants
-            companants = fieldnames(obj.parameters);
-            accepted = true(size(companants,1),1);
-            for ii=1: size(companants,1)
-                currentField = char( companants(ii) ) ;
-                % Get model parameters for each componant.
-                % If the componant is an object, then call the objects
-                % getParameters method.
-                if isobject( obj.parameters.( currentField ) ) && ...
-                any(strcmp(methods(obj.parameters.( currentField )),'acceptStochForcingSolution'))
-
-                    % Check if the current componant is listed within the
-                    % input stochastic forcing data.
-                    forcingDataComponant = fieldnames(stochForcingData);
-                    filt = strcmp(forcingDataComponant,companants{ii});
-                    
-                    % Test if the solution should be accepted. Pass the
-                    % stochastic forcing data is required.
-                    if isempty(filt)
-                        accepted(ii) = acceptStochForcingSolution(obj.parameters.(currentField), objFuncVal, objFuncVal_prior);
-                    else
-                        forcingDataComponant=forcingDataComponant{filt};
-                        [stochForcingData_new.(forcingDataComponant), accepted(ii)] = acceptStochForcingSolution(obj.parameters.(currentField), objFuncVal, objFuncVal_prior, stochForcingData.(forcingDataComponant));
-                    end
-                end                
-            end
-            if any(~accepted)
-                accepted=false;
-            else
-                accepted=true;
-            end
-        end               
-        
-        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-% getParameters_physicalLimit returns the physical limits to each model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-%
-% Description:
-%   Cycles through all model componants and parameters and returns a vector
-%   of the physical upper and lower parameter bounds as defined by the
-%   weighting functions.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter bounds
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-
-            params_lowerLimit = [];
-            params_upperLimit = [];
-            companants = fieldnames(obj.parameters);            
-            for ii=1: size(companants,1)
-                currentField = char( companants(ii) ) ;
-                % Get model parameters for each componant.
-                % If the componant is an object, then call the objects
-                % getParameters_physicalLimit method for each parameter.
-                if isobject( obj.parameters.( currentField ) )
-                    % Call object method.
-                    [params_upperLimit_temp, params_lowerLimit_temp] = getParameters_physicalLimit( obj.parameters.( currentField ) );                
-                    
-                    params_upperLimit = [params_upperLimit; params_upperLimit_temp];
-                    params_lowerLimit = [params_lowerLimit; params_lowerLimit_temp];
-                                        
-                else
-
-                    [params_plausibleUpperLimit, params_plausibleLowerLimit] = getParameters_plausibleLimit(obj);
-                    
-                    % This parameter is assumed to be the noise parameter 'alpha'.  
-                    ind = length(params_upperLimit)+1;
-                    params_upperLimit = [params_upperLimit; params_plausibleUpperLimit(ind)];                                                        
-                    params_lowerLimit = [params_lowerLimit; params_plausibleLowerLimit(ind)];                                
-                end
-            end            
-        end        
-        
-    function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-% getParameters_plausibleLimit returns the plausible limits to each model parameter.
-%
-% Syntax:
-%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-%
-% Description:
-%   Cycles though all model componants and parameters and returns a vector
-%   of the plausible upper and lower parameter range as defined by the
-%   weighting functions.
-%
-% Input:
-%   obj -  model object.
-%
-% Outputs:
-%   params_upperLimit - column vector of the upper parameter plausible bounds.
-%
-%   params_lowerLimit - column vector of the lower parameter plausible bounds
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-                        
-        params_lowerLimit = [];
-        params_upperLimit = [];
-        companants = fieldnames(obj.parameters);            
-        for ii=1: size(companants,1)
-            currentField = char( companants(ii) ) ;
-            % Get model parameters for each componant.
-            % If the componant is an object, then call the objects
-            % getParameters_physicalLimit method for each parameter.
-            if isobject( obj.parameters.( currentField ) )
-                % Call object method.
-                [params_upperLimit_temp, params_lowerLimit_temp] = getParameters_plausibleLimit( obj.parameters.( currentField ) );
-
-                params_upperLimit = [params_upperLimit; params_upperLimit_temp];
-                params_lowerLimit = [params_lowerLimit; params_lowerLimit_temp];
-            else
-                companant_params = fieldnames( obj.parameters.( currentField ) );
-                for j=1: size(companant_params,1)
-                   if ~strcmp( companant_params(j), 'type')
-                       if strcmp(currentField,'et') && strcmp( companant_params(j), 'k')
-                            % This parameter is assumed to be the ET
-                            % parameter scaling when the ET
-                            % uses the precipitation transformation
-                            % function.
-                            params_upperLimit = [params_upperLimit; 1];
-                            params_lowerLimit = [params_lowerLimit; 0];
-                       elseif strcmp(currentField,'landchange') && (strcmp( companant_params(j), 'precip_scalar') ...
-                       || strcmp(currentField,'landchange') && strcmp( companant_params(j), 'et_scalar'))  
-                           % This parameter is the scaling parameter
-                           % for either the ET or precip transformation
-                           % functions.
-                           params_upperLimit = [params_upperLimit; 1.0];
-                           params_lowerLimit = [params_lowerLimit; -1.0];
-                       else
-                            % This parameter is assumed to be the noise parameter 'alpha'.  
-                            alpha_upperLimit = 100; 
-                            while abs(sum( exp( -2.*alpha_upperLimit .* obj.variables.delta_time ) )) < eps() ...
-                            || exp(mean(log( 1- exp( -2.*alpha_upperLimit .* obj.variables.delta_time) ))) < eps()
-                                alpha_upperLimit = alpha_upperLimit - 0.01;
-                                if alpha_upperLimit <= eps()                                   
-                                    break;
-                                end
-                            end
-                            if alpha_upperLimit <= eps()
-                                alpha_upperLimit = inf;
-                            else
-                                % Transform alpha log10 space.
-                                alpha_upperLimit = log10(alpha_upperLimit);
-                            end                           
-                            
-                            params_upperLimit = [params_upperLimit; alpha_upperLimit];
-                            params_lowerLimit = [params_lowerLimit; log10(sqrt(eps()))+4];
-
-                       end
-                   end
-                end
-            end
-        end
-    end 
-        
-    function isValidParameter = getParameterValidity(obj, params, time_points)
-% isValidParameter returns a logical vector for the validity or each parameter.
-%
-% Syntax:
-%   isValidParameter = getParameterValidity(obj, params, time_points)
-%
-% Description:
-%   Cycles though all model components and parameters and returns a logical 
-%   vector denoting if each parameter is valid as defined by each weighting
-%   function.
-%
-% Input:
-%   obj -  model object.
-%
-%   params - vector of model parameters
-%
-%   time_points - vector of simulation time points
-%
-% Outputs:
-%   isValidParameter - column vector of the parameter validity.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   get_h_star: main_method_for_calculating_the_head_contributions.
-%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014   
-          
-        
-        isValidParameter = true(size(params));
-        companants = fieldnames(obj.parameters);            
-        
-        [junk, param_names] = getParameters(obj);
-        
-        for ii=1: size(companants,1)
-            currentField = char( companants(ii) ) ;
-            
-            % Find parameters for the component
-            filt = strcmp(companants(ii), param_names(:,1));
-
-            % Get model parameters for each componant.
-            % If the componant is an object, then call the objects
-            % getParameterValidity method for each parameter.
-            if isobject( obj.parameters.( currentField ) )
-                
-                
-                % Call object method and pass it the parameter vector and
-                % parameter names.
-                % NOTE: the parameters are not set within the componant
-                % object. This was done to avoid a call to
-                % setParameters().
-                isValidParameter(filt,:) = getParameterValidity( obj.parameters.( currentField ), params(filt,:), param_names(filt,2) );
-             
-            % Check the alphanoise parameter is large enough not to cause numerical
-            % problems in the calcuation of the objective function.
-            elseif strcmp('noise',currentField);                
-                alpha = params(filt,:); 
-                filt_noiseErr = exp(mean(log( 1- exp( bsxfun(@times,-2.*10.^alpha , obj.variables.delta_time) )),1)) <= eps() ...
-                             | abs(sum( exp( bsxfun(@times,-2.*10.^alpha , obj.variables.delta_time) ),1)) < eps();                
-                isValidParameter(filt,filt_noiseErr)= false;                
-                
-            else
-                isValidParameter(filt,:) = true;
-            end
-            
-            % Break if any parameter sets are invalid!
-            if size(params,2)==1 && any(any(~isValidParameter));
-                return;
-            end
-        end
-    end
-    
-    function plot_transferfunctions(obj, t_max)
-% plot_transferfunctions plot the weighting functions
-%
-% Syntax:
-%   plot_transferfunctions(obj, t_max)
-%
-% Description:
-%   Creates a plot of each weighting function. 
-%
-% Input:
-%   obj -  model object
-%
-%   t_max - scaler number of the maximum duration to plot (in days)
-%
-% Output:  
-%   (none)
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%
-% Dependencies
-%   model_TFN.m
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   7 May 2012
-%%        
-        % Get componants of the model.
-        companants = fieldnames(obj.parameters);
-
-        % Derive filter for componants with linear contribution to
-        % head, eg remove index to soil moisture componant.
-        componant_indexes = true( size(companants) );
-        componant_indexes( strcmp(companants,'soilmoisture') ) = false;
-        componant_indexes( end ) = false;
-        componant_indexes = find(componant_indexes)'; 
-
-        % Calculate componants with transfer function.
-        nTranfterFunctions = 0;
-        tor = (1:t_max)';
-        for ii=componant_indexes
-
-            % Get model parameters for each componant.
-            % If the componant is an object, then call the objects
-            % getParameters method.
-            if isobject( obj.parameters.( char(companants(ii))) )
-                                
-                
-                % Calcule theta for each time point of forcing data.
-                try
-                    tmp = theta(obj.parameters.( char(companants(ii))), tor );
-                    nTranfterFunctions =  nTranfterFunctions + 1;
-                    if size(tmp,2)>1
-                        theta_est(:,nTranfterFunctions) = sum(tmp,2);
-                    else
-                        theta_est(:,nTranfterFunctions) = tmp;
-                    end
-                catch
-                    % do nothing
-                end
-            end
-        end
-        
-        figure();
-        for ii=1:nTranfterFunctions
-            subplot(nTranfterFunctions,1,ii);
-            plot( tor,  theta_est(:,ii));
-            xlabel('Duration into the past (days)');
-            ylabel('Tranfer function weight');
-            title(['Tranfer function for: ', char(companants(ii))]);
-            box on;
-        end
-    end
-    
-    function delete(obj)
-% delete class destructor
-%
-% Syntax:
-%   delete(obj)
-%
-% Description:
-%   Loops through parameters and, if not an object, empties them. Else, calls
-%   the sub-object's destructor.
-%
-% Input:
-%   obj -  model object
-%
-% Output:  
-%   (none)
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
-%   The University of Melbourne.
-%
-% Date:
-%   24 Aug 2016
-%%            
-        propNames = properties(obj);
-        for i=1:length(propNames)
-           if isobject(obj.(propNames{i}))
-            delete(obj.(propNames{i}));
-           else               
-            obj.(propNames{i}) = []; 
-           end
-        end
-    end
-    
-    end
-%%  END PUBLIC METHODS    
-
-%%  PRIVATE METHODS    
-    methods(Access=protected)
-       
-%% Main method calculating the contribution to the head from each model componant.
-        function [h_star, colnames] = get_h_star(obj, time_points, varargin)
-% get_h_star private method calculating the contribution to the head from each model componant.
-%
-% Syntax:
-%   [h_star, colnames] = get_h_star(obj, time_points)
-%
-% Description:
-%   This method performs the main calculates of the model. The method 
-%   is private and is called by either solve() or objectiveFunction() public
-%   methods. The method is highly flexible, allowing any number of model
-%   components and forcing transformations. The numerical integration of
-%   the convolution function is also using a highly efficient MEX .c
-%   'doIRFconvolution.c', which undertakes trapazoidal integration for
-%   integrated forcing (eg daily precipitation) or Simpson's 3/8 composite
-%   inegration for instantaneous fluxes.
-
-%   Depending upon the user set model componants, this method 
-%   undertakes the following seqential steps:
-%
-%   1) Transform the forcing data. This is undertaken using for the nonlinear
-%   TFN models of Peterson and Westenr (2014). 
-%
-%   2) If model componant 'i' is an impulse response function (IRF), the 
-%   componant method 'theta' is called with all daily step time points less
-%   than or equal to the latest date head for simulation. Theta and the
-%   forcing data are then passed to doIRFconvolution() for the integration at
-%   each water level observation time point.
-%   
-%   3) If the componant is not an IRF, then a user specified
-%   IRF matrix from step 2 (which is often precipitation or ET) is scaled 
-%   by an appropriate parameter and daily forcing data and then integrated
-%   using doIRFconvolution().
-%
-%   4) Finally, the contribution from all componants are summed to produce
-%   h* (as per von Asmuth et al 2002).
-%
-% Inputs:
-%   obj -  model object
-%
-%   time_points - column vector of the time points to be simulated.  
-%
-% Outputs:
-%
-%   h_star - matrix of the contribution from various model componants and
-%   their summed influence. The matrix columns are in the order of:
-%   date/time, summed contribution to the head, contribution frolm
-%   componant i.
-%
-%   colnames - column names for matrix 'head'.
-%
-% Example:
-%   see HydroSight: time_series_model_calibration_and_construction;
-%
-% See also:
-%   HydroSight: time_series_model_calibration_and_construction;
-%   model_TFN: model_construction;
-%   calibration_finalise: initialisation_of_model_prior_to_calibration;
-%   calibration_initialise: initialisation_of_model_prior_to_calibration;
-%   getParameters: returns_a_vector_of_parameter_values_and_names;
-%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
-%   solve: solve_the_model_at_user_input_sime_points;
-%
-% References:
-%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
-%   dunction-noise modeling in continuous time using predefined impulse
-%   response functions.
-%
-% Author: 
-%   Dr. Tim Peterson, The Department of Infrastructure
-%   Engineering, The University of Melbourne.
-%
-% Date:
-%   26 Sept 2014    
-
-            % Get componants of the model.
-            companants = fieldnames(obj.inputData.componentData);
-            nCompanants = size(companants,1);  
-
-            % Calc theta_t for max time to initial time (NOTE: min tor = 0).                        
-            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
-            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(1,1)+1]');
-            tor_end = tor( obj.variables.theta_est_indexes_min(1,:) )';            
-            t = obj.inputData.forcingData( filt ,1);
-
-            % Calculate the transformation models that DO NOT require the
-            % input of a forcing model and the forcing model is denoted as the
-            % complete model to be ran (ie not also used by another
-            % component).
-            for i=1:nCompanants
-                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
-                && ~isfield(obj.inputData.componentData.(companants{i}),'inputForcingComponent') ...
-                && isfield(obj.inputData.componentData.(companants{i}),'isForcingModel2BeRun') ...
-                && obj.inputData.componentData.(companants{i}).isForcingModel2BeRun
-                    setTransformedForcing(obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), t, true)                    
-                end
-            end
-            
-            % Calculate the transformation models that DO require the
-            % input of a forcing model  and the forcing model is denoted as the
-            % complete model to be ran (ie not also used by another
-            % component).
-            for i=1:nCompanants
-                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
-                && isfield(obj.inputData.componentData.(companants{i}),'inputForcingComponent') ...
-                && isfield(obj.inputData.componentData.(companants{i}),'isForcingModel2BeRun') ...
-                && obj.inputData.componentData.(companants{i}).isForcingModel2BeRun            
-                    setTransformedForcing(obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), t, true)
-                end
-            end            
-            
-            % Assign the forcing data for each model componant.
-            % Also, if the model is being calibrated, then also
-            % calculate the mean forcing. The later is essential to reduce
-            % the calibration error when using a forcing series
-            % significantly shorter than the point back in time (ie tor) at
-            % which the transfer function is approx. zero.
-            nOutputColumns=0;
-            isForcingADailyIntegral = false(nCompanants,1);
-            for i=1:nCompanants
-               
-                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
-                && isfield(obj.inputData.componentData.(companants{i}),'outputVariable')                    
-
-                    % Get te transformed forcing. 
-                    % NOTE: getTransformedForcing() must return a boolean
-                    % scaler deonting if the forcing is an instantaneous flux
-                    % or an integral over the day. For the former, Simpson's
-                    % 3/8 integration is used for the convolution while for
-                    % the latter trapzoidal integration of theta is undertaken and then 
-                    % the daily integration result multiplied by the integrated daily forcing.
-                    [obj.variables.(companants{i}).forcingData, isForcingADailyIntegral(i)] = ...
-                    getTransformedForcing( obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), ...
-                    obj.inputData.componentData.(companants{i}).outputVariable);
-                
-                    obj.variables.(companants{i}).forcingData_colnames = obj.inputData.componentData.(companants{i}).outputVariable;
-                                        
-                else
-                    
-                    % Non-transformed forcing is assumed to be a daily
-                    % integral. Hence, doIRFconvolution() undertakes daily
-                    % trapzoidal integration of theta and multiplies it by
-                    % the integrated daily forcing.
-                    isForcingADailyIntegral(i) = true;
-                    
-                    obj.variables.(companants{i}).forcingData = obj.inputData.forcingData(filt, ...
-                    obj.inputData.componentData.(companants{i}).dataColumn);
-
-                
-                    obj.variables.(companants{i}).forcingData_colnames =  ...
-                    obj.inputData.forcingData_colnames(obj.inputData.componentData.(companants{i}).dataColumn);
-                end
-                % Increase the number of output columns for method.
-                nOutputColumns = nOutputColumns + size(obj.variables.(companants{i}).forcingData,2);
-            end            
-            
-            % Initialise ouput vector.
-            h_star = zeros( size(time_points,1),  nOutputColumns);       
-            iOutputColumns = 0;
-            % Calculate each transfer function.
-            for i=1:nCompanants
-                                
-                % Calcule theta for each time point of forcing data.
-                theta_est_temp = theta(obj.parameters.( char(companants(i))), tor);                
-
-                % Get analytical esitmates of lower and upper theta tails
-                integralTheta_upperTail = intTheta_upperTail2Inf(obj.parameters.( char(companants(i))), tor_end);                           
-                integralTheta_lowerTail = intTheta_lowerTail(obj.parameters.( char(companants(i))), 1);
-
-                % Get the mean forcing.
-                if ~isempty(varargin) && isfield(varargin{1},companants{i})
-                    %forcingMean = obj.variables.(companants{i}).forcingMean                    
-                    forcingMean = varargin{1}.(companants{i});
-                else
-                    forcingMean = mean(obj.variables.(companants{i}).forcingData);
-                end                
-                
-                % Integrate transfer function over tor.
-                for j=1: size(theta_est_temp,2)
-                    % Increment the output volumn index.
-                    iOutputColumns = iOutputColumns + 1;
-
-                    % Try to call doIRFconvolution using Xeon Phi
-                    % Offload coprocessors. This will only work if the
-                    % computer has (1) the intel compiler >2013.1 and (2)
-                    % xeon phi cards. The code first tried to call the
-                    % mex function. 
-                    if ~isfield(obj.variables,'useXeonPhiCard')
-                        obj.variables.useXeonPhiCard = true;
-                    end
-                    
-                    try
-                        if obj.variables.useXeonPhiCard
-                            %display('Offloading convolution algorithm to Xeon Phi coprocessor!');
-                            h_star(:,iOutputColumns) = doIRFconvolutionPhi(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
-                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
-                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
-                        else                            
-                            h_star(:,iOutputColumns) = doIRFconvolution(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
-                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
-                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
-                        end
-                            
-                    catch
-                        %display('Offloading convolution algorithm to Xeon Phi coprocessor failed - falling back to CPU!');
-                        obj.variables.useXeonPhiCard = false;
-                        h_star(:,iOutputColumns) = doIRFconvolution(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
-                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
-                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
-                    end                    
-                    % Transform the h_star estimate for the current
-                    % componant. This feature was included so that h_star
-                    % estimate fro groundwater pumping could be corrected 
-                    % for an unconfined aquifer using Jacobs correction.
-                    % Peterson Feb 2013.
-                    h_star(:,iOutputColumns) = transform_h_star(obj.parameters.( char(companants(i))), [time_points, h_star(:,iOutputColumns)]);
-                        
-                    % Add output name to the cell array
-                    if ischar(obj.variables.(companants{i}).forcingData_colnames)
-                        colnames{iOutputColumns} = companants{i};
-                    else
-                        colnames{iOutputColumns} = [companants{i}, ' - ',obj.variables.(companants{i}).forcingData_colnames{j}];
-                    end
-                end
-            end
-            
-            % Sum all componants (excluding soil moisture) and add time
-            % vector.
-            if size(h_star,2)>1
-                h_star = [time_points , sum(h_star,2), h_star];
-                colnames = {'time','Head',colnames{:}};
-            else
-                h_star = [ time_points, h_star];
-                colnames = {'time','Head'};               
-            end
-                                
-        end
-    end
-    
-
+classdef model_TFN < model_abstract
+% Class definition for Transfer Function Noise (TFN) model for use with HydroSight
+%
+% Description: 
+%   The class defines a transfer function Noise (TFN) model for
+%   simulating time series of groundwater head. The model should be
+%   defined, calibrated and solved using HydroSight() or 
+%   the graphical user interface.
+%
+%   This class uses an object-oriented structure to provide a highly flexible 
+%   model structure and the efficient inclusion of new structural
+%   componants. Currently, linear and nonlinear TFN model can be built.
+%   The linear models are based upon von Asmuth et al. 2002 and von Asmuth et al. 
+%   2008 and the nonlinear models use a nonlinear transform of the input
+%   climate data to account for runoff and nonlinear free drainage (see
+%   Peterson & Western, 2014) 
+%
+%   Additional key features of the model include:
+%
+%       - long term historic daily climate forcing is used to estimate the
+%       groundwater head at each time point via use of continuous transfer
+%       function. 
+%
+%       - the non-linear response of groundwater head to climate forcing
+%       can be accounted for by transforming the input forcing data.
+%       Currently, a simple 1-D vertically integrated soil model is
+%       available (see climateTransform_soilMoistureModels), allowing the
+%       construction of a soil model with between 1 and 7 parameters.
+%
+%       - Pumping drawdown can be simulated using the Ferris-Knowles 
+%       response function for confined aquifers, the Ferris-
+%       Knowles-Jacobs function for an unconfined aquifer
+%       or the Hantush function for a leaky aquifer. Each of these
+%       functions allow multiple production bores to be accounted for and
+%       each can have recharge or no-flow boundary conditions.
+%
+%       - The influence of streamflow can be approximated using the Bruggeman 
+%       reponse function. Importantly, this function is a prototype and
+%       should be used with caution. It is likely that the covariance
+%       between rainfall and streamflow complicates the estimation of the
+%       impacts of streamflow.
+%
+%       - a model can be fit to irregularly sampled groundwater head
+%       observations via use of an exponential noise function.
+%
+%       - new data transfer functions can easily be defined simply
+%       by the creation of new response function class definitions.
+%
+%       - calibration of the model is not to the observed head, but to the
+%       innovations between time steps. That is, the residual between the
+%       observed and modelled head is derived and the innovation is
+%       calculated as the prior residual minus the later residual
+%       multiplied by the exponental noise function.
+%
+%       - the contribution of a given driver to the head is calculated as
+%       the integral of the historic forcing times the weighting function.
+%       This is undertaken by get_h_star() and the mex .c compiled function
+%       doIRFconvolution(). If the forcing is instantaneous (for example a
+%       flux a soil moisture model) then Simpon's integration is
+%       undertaken. However, if the forcing is a daily integral (such as
+%       precipitation or daily pumping volumes) then daily trapazoidal
+%       integration of the weighting function is undertaken and then 
+%       multiplied by the daily flux.
+%
+%   Below are links to the details of the public methods of this class. See
+%   the 'model_TFN' constructor for details of how to build a model.
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Dependencies:
+%   responseFunction_Pearsons.m
+%   responseFunction_Hantush.m
+%   responseFunction_Bruggeman.m
+%   responseFunction_FerrisKnowles.m
+%   responseFunction_Hantush.m
+%   responseFunction_FerrisKnowlesJacobs.m
+%   derivedweighting_PearsonsNegativeRescaled.m
+%   derivedweighting_PearsonsPositiveRescaled.m
+%   climateTransform_soilMoistureModels.m
+%
+% References:
+%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
+%   dunction-noise modeling in continuous time using predefined impulse
+%   response functions.
+%
+%   von Asmuth J. R., Mass K., Bakker M., Peterson J., 2008, Modeling time
+%   series of ground water head fluctuations subject to multiple stresses.
+%   Groundwater, 46(1), pp30-40.
+%
+%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
+%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+       
+    properties
+    end
+
+%%  STATIC METHODS        
+% Static methods used by the Graphical User Interface to inform the
+% user of the available model types. Any new models must be listed here
+% in order to be accessable within the GUI.
+    methods(Static)
+        % Provides a simple description of the function for the user
+        % interface.
+        function str = description()
+           
+            str = {['"model_TFN" is a highly flexible nonlinear transfer function noise time-series model. ', ...
+                   'It allows the statistical modelling of irregular groundwater head observations by the weighting ', ...
+                   'of observed forcing data (e.g. daily rainfall, pumping or landuse change).', ...
+                   'The forcing data can also be transformed to account for non-linear processes such as runoff or free drainage.'], ...
+                   '', ...
+                   'The model has the following additional features:', ...
+                   '   - time-series extrapolation and interpolation.', ...
+                   '   - decomposition of the hydrograph to individual drivers ', ...
+                   '     (e.g. seperation of climate and pumping impacts).', ...
+                   '   - decomposition of the hydrograph over time ', ...
+                   '     (i.e. decomposition to influence from 1, 2, 5 and 10 years ago).', ...
+                   '   - estimation of hydraulic properties (if pumping is simulated).', ...
+                   '', ...
+                   'For further details of the algorithms use the help menu or see:', ...
+                   '', ...
+                   '   - Peterson, T. J., and A. W. Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355, doi:10.1002/2013WR014800.', ...
+                   '', ...
+                   '   - Shapoori V., Peterson T. J., Western A. W. and Costelloe J. F., (2015), Top-down groundwater hydrograph time series modeling for climate-pumping decomposition. Hydrogeology Journal'};                     
+               
+        end        
+    end        
+    
+%%  PUBLIC METHODS              
+    methods
+        
+%% Model constructor
+        function obj = model_TFN(bore_ID, obsHead, forcingData_data,  forcingData_colnames, siteCoordinates, varargin)           
+% model_TFN constructs for linear and nonlinear Transfer Function Noise model
+%
+% Syntax:
+%   model = model_TFN(bore_ID, obsHead, forcingData_data, ...
+%           forcingData_colnames, siteCoordinates, modelOptions)
+%
+% Description:
+%   Builds the model_TFN object, declares initial parameters and sets the
+%   observation and forcing data within the object. This method should be 
+%   called from HydroSight.
+%
+%   A wide range of models can be built within this method. See the inputs
+%   section below for details of the model components that can be built and 
+%   Peterson and Western (2014).
+%
+% Inputs:
+%   obsHead_input - matrix of observed groundwater elevation data. The
+%   matrix must comprise of two columns: date (as a double data type) 
+%   and water level elevation. The data must be of a daily time-step or 
+%   larger and can be non-uniform.
+%
+%   forcingData_data - M x N numeric matrix of daily foring data. The matrix 
+%   must comprise of the following columns: year, month, day and
+%   observation data. The observation data must include precipitation but
+%   may include other data as defined within the input model options. 
+%   The record must be continuous (no skipped days) and cannot contain blank 
+%   observations.
+%
+%   forcingData_colnames - 1 x N cell array of the column names within the
+%   input numerical array "forcingData_data". The first three columns must
+%   be: year month, day. The latter columns must define the forcing data
+%   column names. Imprtantly, each forcing data column name must be listed
+%   within the input "siteCoordinates". 
+%
+%   siteCoordinates - Mx3 cell matrix containing the following columns :
+%   site ID, projected easting, projected northing. Importantly,
+%   the input 'bore_ID' must be listed and all columns within
+%   the forcing data (excluding the year, month,day). When each response 
+%   function is created, the coordinates for the observation bore and the 
+%   corresponding forcing are are passed to the response function. This is
+%   required for simulating the impacts of pumping. Additionally,
+%   coordinates not listed in the input 'forcingData' can be input.
+%   This provides a means for image wells to be input (via the input 
+%   modelOptions).
+%
+%   modelOptions - cell matrix defining the model components, ie how the
+%   model should be constructed. The cell matrix must be three columns,
+%   where the first, second and third columns define the model component
+%   type to be set, the component property to be set, and the value to be
+%   set to the component property. Each model componant can have the following
+%   property inputs ((i.e. 2nd column):
+%
+%       - 'weightingfunction': Property value is to define the component type.
+%       Available component types include:
+%           - 'responseFunction_Pearsons' (for climate forcing)
+%           - 'responseFunction_Hantush' (for groundwater pumping)
+%           - 'responseFunction_FerrisKnowles' (for groundwater pumping)
+%           - 'responseFunction_Hantush' (for groundwater pumping)
+%           - 'responseFunction_FerrisKnowlesJacobs' (for groundwater pumping)
+%           - 'responseFunction_Bruggeman' (for streamflow)
+%           - 'derivedweighting_PearsonsNegativeRescaled' (see below)
+%           - 'derivedweighting_PearsonsPositiveRescaled' (see below)
+%            
+%       - 'forcingdata': Property value is the column number, column name 
+%       within forcingData (as input to HydroSight) or a cell 
+%       array containing the options for a forcing transformation
+%       object. The forcing transformation input must be a Nx2 cell array  
+%       with the following property (left column) and value (right column) 
+%       settings:
+%           - 'transformfunction' property and the transformation class name 
+%           e.g. 'climateTransform_soilMoistureModels'
+%
+%           - 'forcingdata' property and a Nx2 cell array declaring the 
+%           input forcing variable required by the transformation function
+%           (left column) and the name of the input forcing data or column
+%           number. eg   {'precip',3;'et',4}.
+%
+%           -  'outputdata' property and the output flux to be output from
+%           the forcing transformation function. 
+%
+%           - 'options' propert and an input entirely dependent upon the
+%           forcing transformation function. For
+%           'climateTransform_soilMoistureModels' this is used to define
+%           the form of the soil moisture model i.e. which ODE parameters
+%           to fix and which to calibrate. See the documenttation for each
+%           transformation function for details.
+%
+%       - 'inputcomponent': Property value is the name of another model
+%       component (i.e.  the first column value). This option allows,
+%       for example, the parameterised Pearson's weighting function for 
+%       a precipitation componant to also be used for, say, an ET
+%       componant. When building such a model, the weighting function
+%       should be a derived function such as
+%       'derivedweighting_PearsonsNegativeRescaled'
+%
+%       - 'options': cell array options specific for a weighting function.
+%       The structure of the weighting function options are entirely
+%       dependent upon the weighting function. Currently, only the
+%       groundwater pumping weighting functions allow the input of options.
+%       These pumping options allow the simulation of multiple recharge or
+%       no-flow image wells per pumping bore. These options must be a Nx3 cell
+%       array. The first column must be a string for the site ID of a
+%       production bore. The second column must be the site of an image
+%       well. Coordinate for both the production bore ID and the image
+%       well must be listed within the input coordinated cell array.
+%       The third column gives the type of image well. The availabes
+%       type are: "recharge" for say a river; and "no flow" for say a
+%       aquifer no-flow boundary.
+%
+% Outputs:
+%   model - model_TFN class object 
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% References:
+%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
+%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+            
+            % Expand cell structure within varargin. It is required to
+            % contain the model options
+            varargin = varargin{1};
+            
+            % Check there are an even number model type options, ie for
+            % option option type there is an option value.
+            if length(varargin) <= 0 
+               display('Default model of only precipitation forcing has been adopted because no model components were input');
+            elseif size( varargin , 2) ~=3
+               error('Invalid model options. The inputs must of three columns in the following order: model_component, property, property_value');
+            end            
+            
+            % Check the forcing data does not contain nan or infs
+            if any(any( isnan(forcingData_data) | isinf(forcingData_data)))
+                ind = find(any(isnan(forcingData_data) | isinf(forcingData_data),2),1,'first');
+                error(['Forcing data contains empty, nan or inf values (line ', num2str(ind),')']);
+            end            
+            
+            % Check that forcing data exists before the first head
+            % observation.
+            if floor(min(forcingData_data(:,1))) >= floor(min(obsHead(:,1)))
+                error('Forcing data must be input prior to the first water level observation. Ideally, the forcing data should start some years prior to the head.'); 
+            end
+                            
+            % Set bore ID, observed head and forcing data and site coordinates
+            obj.inputData.bore_ID = bore_ID;            
+            obj.inputData.head = obsHead;            
+            obj.inputData.siteCoordinates = siteCoordinates;
+            setForcingData(obj, forcingData_data, forcingData_colnames);
+            
+            % Cycle though the model components and their option and
+            % declare instances of objects were appropriate.
+            valid_properties = {'weightingfunction','forcingdata','options', 'inputcomponent'};            
+            valid_transformProperties = {'transformfunction','forcingdata','outputdata','options', 'inputcomponent'};
+            
+            % Check the model options.
+            for ii=1: size(varargin,1)                
+            
+                modelComponent = varargin{ii,1};
+                propertyType = varargin{ii,2};                
+                propertyValue = varargin{ii,3};
+                
+
+                % Check the component properties is valid.
+                ind = find(strncmpi(propertyType, valid_properties, length(propertyType)));
+                if (length(ind)~=1)                    
+                    error(['Invalid component property type: ',char(propertyType) ]);
+                end
+
+                % Check the component properties are valid.
+                %----------------------------------------------------------
+                if strcmp(propertyType, valid_properties{1})        
+                    % If the propertyType is weightingfunction, check it is
+                    % a valid object.
+                    if isempty(meta.class.fromName(propertyValue)) 
+                        % Report error if the input is not a class definition.
+                        error(['The following weighting function name does not appear to be a class object:',propertyValue]);
+                    end 
+                elseif strcmp(propertyType, valid_properties{2})   
+                    % If the propertyType is forcingdata, check if the
+                    % input is cell, character (ie forcing site name) or an
+                    % integer(s) (ie forcing data column number). If the
+                    % input is a cell, then check if it is a list of
+                    % forcing site names or a function name and inputs to
+                    % transform forcing data (eg using a soil moisture
+                    % model).
+                    if iscell(propertyValue)
+                        % If 'propertyValue' is a vector then it should be
+                        % a list of forcing sites.
+                        if isvector(propertyValue)
+                            % Check each forcing site name is valid.
+                            for j=1:length(propertyValue)                            
+                                if isnumeric(propertyValue{j}) && ( propertyValue{j}<0 || propertyValue{j}+2>size(forcingData_data,2))
+                                    error(['Invalid forcing column number for component:', modelComponent,'. It must be an intereger >0 and <= the number of forcing data columns.' ]);
+                                elseif ischar(propertyValue{j})
+                                    % Check the forcing column name is valid.
+                                    filt = cellfun(@(x)(strcmp(x,propertyValue{j})),forcingData_colnames);                                
+                                    if isempty(find(filt))
+                                        error(['Invalid forcing column name for component:', modelComponent,'. Each forcing name must be listed within the input forcing data column names.' ]);
+                                    end
+                                end
+                            end
+                        else
+                            % Check that the input is of the expected
+                            % format for creating a class to derive the
+                            % forcing data. The Expected format is an Nx2
+                            % matrix (where N<=5) and the first colum
+                            % should have rows for 'tranformationfunction'
+                            % and 'outputvariable'.
+                            if size(propertyValue,2) ~= 2 || size(propertyValue,1) < 2 || size(propertyValue,1) > length(valid_transformProperties)
+                                error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. The input cell array must have >=2 and <=5 rows and 2 columns.' ]);
+                            end
+                            
+                            % Check it has the required minimum inputs to
+                            % the first columns.
+                            if sum( strcmp(propertyValue(:,1), valid_transformProperties{1}))~=1 || ...
+                            sum( strcmp(propertyValue(:,1), valid_transformProperties{3}))~=1    
+                                error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. The first column of the input cell array must have rows for "transformfunction" and "outputdata".' ]);
+                            end
+                            
+                            % Check that the first column only contains
+                            % inputs that are acceptable.
+                            for j=1:size(propertyValue,1)                                                                
+                                if sum( strcmp(valid_transformProperties, propertyValue{j,1}))~=1    
+                                    error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. Read the help for "model_TFN" to see the accepted values.' ]);
+                                end
+                            end
+                            
+                            % Check that the transformation function name
+                            % is a valid class definition name.
+                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{1});
+                            if isempty(meta.class.fromName(propertyValue{filt,2})) 
+                                error(['The following forcing transform function name is not accessible:',propertyValue{filt,2}]);
+                            end   
+                            
+                            % Check that the forcing transformation
+                            % function name is consistent with the abstract
+                            % 'forcingTransform_abstract'.
+                            try
+                                if ~any(strcmp(findAbstractName( propertyValue{filt,2}),'forcingTransform_abstract')) && ~any(strcmp(findAbstractName( propertyValue{filt,2}),'derivedForcingTransform_abstract'))
+                                    error(['The following forcing transform function class definition is not derived from the "forcingTransform_abstract.m" abstract:',propertyValue{filt,2}]);
+                                end
+                            catch ME
+                                display('... Warning: Checking that the required abstract for the forcing transform class definition was used failed. This is may be because the version of matlab is pre-2014a.');
+                            end
+                            
+                            % Get a list of required forcing inputs.
+                            [requiredFocingInputs, isOptionalInput] = feval(strcat(propertyValue{filt,2},'.inputForcingData_required'),bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+                                                        
+                            % Check that the input forcing cell array has
+                            % the correct dimensions.
+                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{2});                            
+                            if any(filt)
+                                if size(propertyValue{filt,2},2) ~=2 || ...
+                                (size(propertyValue{filt,2},1) ~= length(requiredFocingInputs(~isOptionalInput)) && size(propertyValue{filt,2},1) ~= length(requiredFocingInputs) ...
+                                && size(propertyValue{filt,2},1)>1)
+                                    error(['Invalid forcing data for the forcing transform function name for component:', modelComponent,'. It must be a cell array of two columns and ',num2str(length(requiredFocingInputs)), ' rows (one row for each required input forcing).']);
+                                end
+                            
+                                % Check that the required inputs are specified.
+                                for j=1:length(requiredFocingInputs)
+                                    if isOptionalInput(j)
+                                        continue
+                                    end
+                                    if ~any( strcmp(propertyValue{filt,2}, requiredFocingInputs{j}))
+                                        error(['Invalid inputs for transforming the forcing data for component:', modelComponent,'. A forcing data site name must be specified for the following transform function input:',requiredFocingInputs{j} ]);
+                                    end
+                                end
+                            end
+                            
+                            % Get a list of valid output forcing variable names.
+                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{1});
+                            [optionalFocingOutputs] = feval([propertyValue{filt,2},'.outputForcingdata_options'], bore_ID, forcingData_data,  forcingData_colnames, siteCoordinates);
+                                                                                    
+                            % Check that the output variable a char or cell vector.
+                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{3});                              
+                            if isnumeric(propertyValue{filt,2}) || (iscell(propertyValue{filt,2}) && ~isvector(propertyValue{filt,2}))
+                                error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . It must be a string or a cell vector of strings of valid output variable names.']);
+                            end
+                            
+                            % Check each output variable name is valid.
+                            if ischar(propertyValue{filt,2})
+                                if ~any(strcmp(optionalFocingOutputs, propertyValue{filt,2}))
+                                    error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . The output variable name must be equal to one of the listed output variables for the transformaion function.']);
+                                end
+                            else                                
+                                for j=1:length(propertyValue{filt,2})
+                                    if ~any(strcmp(optionalFocingOutputs, propertyValue{filt,2}{j}))
+                                        error(['Invalid output forcing variable for the forcing transform function name for component:', modelComponent,' . Each output variable name must be equal to one of the listed output variables for the transformaion function.']);
+                                    end
+                                end
+                            end
+                            
+                            % Get a list of unique model components that have transformed forcing data
+                            % and the transformation model does not require
+                            % the input of another component.
+                            k=0;
+                            for j=1: size(varargin,1)
+                                if  strcmp( varargin{j,2}, valid_properties{2}) ...
+                                && iscell(varargin{j,3}) && ~isvector(varargin{j,3})
+                            
+                                    % Check if the transformation model
+                                    % input as the required inputs and does
+                                    % not require the input of another
+                                    % component.                                    
+                                    if  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
+                                    &&  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
+                                    &&  any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
+                                    && ~any(strcmp(varargin{j,3}(:,1), valid_transformProperties{5}))
+                                        k = k+1;
+                                        % Record the component name.
+                                        modelComponets_transformed{k,1} =  varargin{j,1};
+                                        
+                                        % Add the transformation model
+                                        % name.
+                                        filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{1});
+                                        modelComponets_transformed{k,2} =  varargin{j,3}{filt,2};
+                                    end
+                                end
+                            end
+
+                            % Check that each complete transforamtion model
+                            % is unique.
+                            if size( unique(modelComponets_transformed(:,2)),1) ...
+                            ~= size( modelComponets_transformed(:,2),1)
+                                error('Non-unique complete transformation models. Each complete transformation model must be input for only one model component. A complete model is that having at least the following inputs: transformfunction, forcingdata, outputdata');
+                            end
+                            
+                            % Get a unique list of model components with
+                            % complete transforamtion models and a list of
+                            % unieq tranformation functions
+                            modelComponets_transformed_uniqueComponents = unique(modelComponets_transformed(:,1));
+                            modelComponets_transformed_uniqueTransFunc = unique(modelComponets_transformed(:,2));
+                            
+                            % Check the inputcomponent is a valid
+                            % component.
+                            filt  =  strcmp(propertyValue(:,1), valid_transformProperties{5});
+                            if any(filt) && ischar(propertyValue{filt,2})
+                                if ~any(strcmp(modelComponets_transformed_uniqueTransFunc(:,1), propertyValue{filt,2}))
+                                    error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input function must be a tranformation function name that is listed within the model.']);
+                                end
+                            elseif any(filt) && iscell(propertyValue{filt,2})   
+                                for j=1:length(propertyValue{filt,2})
+                                    if ~any(strcmp(modelComponets_transformed_uniqueTransFunc(:,1), propertyValue{filt,2}{j}))
+                                        error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input function must be a tranformation function name that is listed within the model.']);                                        
+                                    end
+                                end
+                            elseif any(filt)
+                                error(['Invalid source function for the forcing transform function name for component:', modelComponent,' . The input component must be a a tranformation function name (string or cell vector of strings) that is listed within the model input options and it must also have a function transformation.']);                                
+                            end
+                        end
+                        
+                    elseif ~isnumeric( propertyValue ) &&  (isnumeric(propertyValue) && any(propertyValue<0)) ...
+                    && ~ischar(propertyValue)                                
+                        error(['Invalid property value for property type ', valid_properties{2},'. It must be a forcing data site name or column number intereger >0 (scalar or vector)' ]);
+                    end
+                                            
+                elseif strcmp(propertyType, valid_properties{4})    % If the propertyType is inputcomponent, check the input is a string and a valid component name.
+                
+                    if ischar(propertyValue)
+                        % Check that it is valid component name.
+                        filt = cellfun(@(x)(strcmp(x,propertyValue)), varargin(:,1));
+                        if isempty(filt)
+                            error(['Invalid input component name for component:', modelComponent,'. The named input componant must be a component within the model.' ]);
+                        end
+                    else
+                        error(['Invalid input component name for component:', modelComponent,'. It must be a string defining a componant type.' ]);
+                    end
+                    
+                end
+                
+            end
+
+            % Record the source data column for each component or the
+            % transformation model and output variables and input transformation component.
+            filt = find(strcmpi(varargin(:,2),'forcingdata'));
+            transformFunctionIndexes_noInputComponents = [];
+            transformFunctionIndexes_inputComponents = [];
+            for ii=filt'
+               modelComponent = varargin{ii,1};
+               if isnumeric(varargin{ii,3})
+                  obj.inputData.componentData.(modelComponent).dataColumn = varargin{ii,3}-2;
+               elseif ischar(varargin{ii,3})
+                  filt_forcingCols = cellfun(@(x,y)(strcmp(x,varargin{ii,3})), forcingData_colnames);
+                  obj.inputData.componentData.(modelComponent).dataColumn  = find(filt_forcingCols);
+               elseif iscell(varargin{ii,3})
+                  % If it is a cell vector, then get the column number for
+                  % each element. Else, if it is a Nx2 cell array with the
+                  % required format, the componant uses transformed forcing
+                  % data so store the transformation object name and the
+                  % required output variable names.
+                  if isvector(varargin{ii,3}) && ~iscell(varargin{ii,3}{1})
+                    for j=1:size(varargin{ii,3},1)
+                        filt_forcingCols = cellfun(@(x,y)(strcmp(x,varargin{ii,3}{j})), forcingData_colnames);
+                        obj.inputData.componentData.(modelComponent).dataColumn(j,1) = find(filt_forcingCols);                                    
+                    end
+                  elseif (size(varargin{ii,3},2) == 2 ...
+                  && any(strcmp(varargin{ii,3}(:,1), valid_transformProperties{1})) ...
+                  && any(strcmp(varargin{ii,3}(:,1), valid_transformProperties{3}))) ...
+                  || (size(varargin{ii,3}{1},2) == 2 ...
+                  && any(strcmp(varargin{ii,3}{1}(:,1), valid_transformProperties{1})) ...
+                  && any(strcmp(varargin{ii,3}{1}(:,1), valid_transformProperties{3})))
+              
+                    
+                    % Record the transformation model name and required
+                    % output forcing data from the transformation model for
+                    % input to the model component.
+                    if size(varargin{ii,3},2) == 2
+                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
+                        obj.inputData.componentData.(modelComponent).forcing_object = varargin{ii,3}{filt,2};
+                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
+                        obj.inputData.componentData.(modelComponent).outputVariable = varargin{ii,3}{filt,2};
+                    else
+                        for j=1:size(varargin{ii,3},1)                           
+                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{1});
+                            obj.inputData.componentData.(modelComponent).forcing_object = varargin{ii,3}{j}{filt,2};
+                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
+                            obj.inputData.componentData.(modelComponent).outputVariable{j,1} = varargin{ii,3}{j}{filt,2};                        
+                        end
+                    end
+                    % Record the input forcing data columns for the 
+                    % transforamtion model if provided. Else, seach the other
+                    % transforamtion componants for the forcing data
+                    % columns.
+                    if size(varargin{ii,3},2) == 2
+                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});                    
+                        if any(filt)
+                            obj.inputData.componentData.(modelComponent).inputForcing = varargin{ii,3}{filt,2};
+                        else
+
+                            % Get the name of the source transforamtion
+                            % model.
+                            filt_transf = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
+                            sourceTransformModel = varargin{ii,3}(filt_transf,2);
+
+                            % Create a filter for the forcingData property.
+                            filt_transf = find(strcmp(varargin(:,2), valid_properties{2}));
+
+                            % Loop through all forcingData inputs to find the
+                            % transformationModel that is the source to the
+                            % current transformation model.
+                            for j=filt_transf'
+                               if size(varargin{j,3},2) == 2 ...
+                               && iscell(varargin{j,3}) ...        
+                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
+                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
+                               && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
+                               && any(strcmp(varargin{j,3}(:,2), sourceTransformModel))
+                                   % Get the row containing the input forcing data
+                                   filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{2});
+
+                                   % Assign source forcing names to the current
+                                   % model component.
+                                   obj.inputData.componentData.(modelComponent).inputForcing = varargin{j,3}{filt,2};                               
+                               end
+                            end
+
+                        end
+                    else
+                        for j=1:size(varargin{ii,3},1)  
+                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});                    
+                            if any(filt)
+                                inputForcing = varargin{ii,3}{j}{filt,2};
+                                filt = cellfun( @(x) ~isempty(x), inputForcing(:,2));
+                                inputForcing = inputForcing(filt,:);                                
+                                obj.inputData.componentData.(modelComponent).inputForcing = inputForcing ;
+%                             else
+%                                 error('Inheritated transformation models with multiple outputs is not operational.');
+%                                 % Get the name of the source transforamtion
+%                                 % model.
+%                                 filt_transf = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
+%                                 sourceTransformModel = varargin{ii,3}(filt_transf,2);
+% 
+%                                 % Create a filter for the forcingData property.
+%                                 filt_transf = find(strcmp(varargin(:,2), valid_properties{2}));
+% 
+%                                 % Loop through all forcingData inputs to find the
+%                                 % transformationModel that is the source to the
+%                                 % current transformation model.
+%                                 for j=filt_transf'
+%                                    if size(varargin{j,3},2) == 2 ...
+%                                    && iscell(varargin{j,3}) ...        
+%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{1})) ...
+%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{2})) ...
+%                                    && any(strcmp(varargin{j,3}(:,1), valid_transformProperties{3})) ...
+%                                    && any(strcmp(varargin{j,3}(:,2), sourceTransformModel))
+%                                        % Get the row containing the input forcing data
+%                                        filt = strcmp(varargin{j,3}(:,1), valid_transformProperties{2});
+% 
+%                                        % Assign source forcing names to the current
+%                                        % model component.
+%                                        obj.inputData.componentData.(modelComponent).inputForcing = varargin{j,3}{filt,2};                               
+%                                    end
+%                                 end
+
+                            end                                                        
+                        end
+                    end
+                    % Check that forcing data was identified for the
+                    % current component.
+                    if ~isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
+                        error(['Invalid forcing data for component:', modelComponent,'. No forcing data names or column numbers were input or found by searching for complete transformation models of the same name as input for this component.' ]);
+                    end
+                    
+
+                    % Subtract '2' from the column numbers (if
+                    % not a string). This is done because when
+                    % the user inputs the column number the
+                    % first three columns are year,month,day.
+                    % However, when passed to model_TFN(), the
+                    % first three columns are replaced by a
+                    % single column of the date number.
+                    for j=1:size(obj.inputData.componentData.(modelComponent).inputForcing,1)
+                        if isnumeric(obj.inputData.componentData.(modelComponent).inputForcing{j,2})
+                            obj.inputData.componentData.(modelComponent).inputForcing{j,2} = ...
+                            obj.inputData.componentData.(modelComponent).inputForcing{j,2} - 2;
+                        end
+                    end
+                    
+                    % Record indexes to each component with a
+                    % transformation model and the type of model.
+                    % Additionally, if the transformation model uses inputs
+                    % from a transformation model, then also record this data.
+                    if size(varargin{ii,3},2) == 2
+                        filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{5});
+                        if any(filt)
+
+                            % Only add an index for the creation the
+                            % transformation object if input and output forcing
+                            % is defined. If only output is defined, then the 
+                            % transforamtion is assumed to be simply calling
+                            % a transformation object created for another
+                            % component.
+                            filt_inputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
+                            filt_outputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
+                            if any(filt_inputForcing) && any(filt_outputForcing)
+                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
+                                obj.inputData.componentData.(modelComponent).inputForcingComponent = varargin{ii,3}{filt,2};
+                                transformFunctionIndexes_inputComponents = [transformFunctionIndexes_inputComponents; ii];
+                            else
+                                error(['Invalid forcing transformation for component:', modelComponent,'. When a transformation model uses another transformation model as an input then the forcing data and the output variable must be specified.' ]);
+                            end
+                        else
+
+                            % Only add an index for the creation of the
+                            % transformation object if input and output forcing
+                            % is defined. If only output is defined, then the 
+                            % transforamtion is assumed to be simply calling
+                            % a transformation object created for another
+                            % component.
+                            filt_inputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
+                            filt_outputForcing = strcmp(varargin{ii,3}(:,1), valid_transformProperties{3});
+                            if any(filt_inputForcing) && any(filt_outputForcing)
+                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
+                                transformFunctionIndexes_noInputComponents = [transformFunctionIndexes_noInputComponents; ii];                            
+                            elseif ~any(filt_inputForcing) && any(filt_outputForcing)
+                                obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = false;
+                            end
+                        end
+                    else
+                        for j=1:size(varargin{ii,3},1)  
+                            filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{5});
+                            if any(filt)
+
+                                % Only add an index for the creation the
+                                % transformation object if input and output forcing
+                                % is defined. If only output is defined, then the 
+                                % transforamtion is assumed to be simply calling
+                                % a transformation object created for another
+                                % component.
+                                filt_inputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
+                                filt_outputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
+                                if any(filt_inputForcing) && any(filt_outputForcing)
+                                    obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
+                                    obj.inputData.componentData.(modelComponent).inputForcingComponent = varargin{ii,3}{j}{filt,2};
+                                    transformFunctionIndexes_inputComponents = [transformFunctionIndexes_inputComponents; ii];
+                                else
+                                    error(['Invalid forcing transformation for component:', modelComponent,'. When a transformation model uses another transformation model as an input then the forcing data and the output variable must be specified.' ]);
+                                end
+                            else
+
+                                % Only add an index for the creation of the
+                                % transformation object if input and output forcing
+                                % is defined. If only output is defined, then the 
+                                % transforamtion is assumed to be simply calling
+                                % a transformation object created for another
+                                % component.
+                                filt_inputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
+                                filt_outputForcing = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{3});
+                                if any(filt_inputForcing) && any(filt_outputForcing)
+                                    obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = true;
+                                    transformFunctionIndexes_noInputComponents = [transformFunctionIndexes_noInputComponents; ii];                            
+%                                 elseif ~any(filt_inputForcing) && any(filt_outputForcing)
+%                                     obj.inputData.componentData.(modelComponent).isForcingModel2BeRun = false;
+                                end
+                            end                        
+                        end
+                    end
+                    
+                  else
+                    error(['An unexpected error occured for component :', modelComponent,'. The input for "forcingdata" should be either a forcing data site name(s) or number(s) or a cell array for an input transformation model.' ]);
+                  end
+               else
+                  error('The data colum for each model component must be either the column number within the forcing data matrix or a string of the forcing site name.'); 
+               end                   
+            end
+            
+            % Check that each component has a forcing column or a forcing 
+            % transformation defined.
+            modelComponent = unique(varargin(:,1));
+            for i=1:length(modelComponent)                
+                if ~isfield(obj.inputData.componentData,modelComponent{i})                    
+                    error(['The input model options must specify a forcing data column or forcing transformation model for each model components. The following component has no such input:', modelComponent{i}]);                    
+                end
+            end
+            
+            % Get a list of rows in varargin defining the weighting
+            % function names.
+            weightingFunctionIndexes = find(strcmpi(varargin(:,2), valid_properties{1}));
+                                   
+            % Build the objects for forcing transformations that DO NOT
+            % require an input weighting function object.            
+            for ii = transformFunctionIndexes_noInputComponents'
+                
+                modelComponent = varargin{ii,1};
+                propertyValue = varargin{ii,3};
+
+                % Get the following inputs to build the model object:
+                % transformation function name, input data column names,
+                % the variables to which the input data is to be assigned
+                % too, and additional transformation model options.
+                if size(varargin{ii,3},2) == 2
+                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
+                    transformObject_name = varargin{ii,3}{filt,2};
+                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
+                    transformObject_inputs = varargin{ii,3}{filt,2};
+                    filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{4});
+                    if any(filt)
+                        transformObject_options = varargin{ii,3}{filt,2};
+                    else
+                        transformObject_options ={};
+                    end
+                else
+                    for j=1:size(varargin{ii,3},1)  
+                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{1});
+                        transformObject_name = varargin{ii,3}{j}{filt,2};
+                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{2});
+                        transformObject_inputs = varargin{ii,3}{j}{filt,2};
+                        filt = strcmp(varargin{ii,3}{j}(:,1), valid_transformProperties{4});
+                        if any(filt)
+                            transformObject_options = varargin{ii,3}{j}{filt,2};
+                            break;
+                        end                    
+                    end
+                end
+                
+                % Get the names (or column numbers) of the input forcing data. 
+                forcingData_inputs = obj.inputData.componentData.(modelComponent).inputForcing(:,2);                            
+                
+                % Remove empty rows
+                filt = cellfun( @(x) ~isempty(x) && ~strcmp(x,'(none)'), forcingData_inputs);
+                forcingData_inputs = forcingData_inputs(filt);
+                filt = cellfun( @(x) ~isempty(x) && ~strcmp(x,'(none)'),transformObject_inputs(:,2));
+                transformObject_inputs = transformObject_inputs(filt,:);
+                
+                % Find the required columns in forcing data so that only the
+                % required data is input.
+                colNum=1;
+                rowFilt = true( size(transformObject_inputs,1),1);
+                for j=1:size(transformObject_inputs,1)
+                    % Skip if optional forcing input
+                    if strcmp(transformObject_inputs{j,2},'(none)')
+                        rowFilt(j) = false;
+                        continue                        
+                    end                    
+                    if isnumeric(transformObject_inputs{j,2})
+                        colNum = [colNum; transformObject_inputs{j,2}-2];                        
+                    elseif ischar(transformObject_inputs{j,2})
+                        filt = find(strcmpi(forcingData_colnames, transformObject_inputs{j,2}));
+                        if sum(filt)==0
+                            error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
+                        end
+                        colNum = [colNum; filt];          
+                        
+                    else
+                        error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
+                    end
+                    transformObject_inputs{j,2} = j+1;
+                end
+                
+                % Filter transformed object input rows to remove those that
+                % are input as options ie '(none)'.
+                transformObject_inputs = transformObject_inputs(rowFilt,:);
+                
+                try
+                    %colNum_extras = true(length(forcingData_colnames),1);
+                    %colNum_extras(colNum) = false;
+                    %colNum_extras = find(colNum_extras);
+                    %forcingData_colnamesTmp = forcingData_colnames([colNum;colNum_extras]);
+                    %forcingData_dataTmp = forcingData_data(:,[colNum;colNum_extras]);
+                    obj.parameters.(transformObject_name) = feval(transformObject_name, bore_ID, forcingData_data(:,colNum), forcingData_colnames(colNum), siteCoordinates, transformObject_inputs, transformObject_options);
+                     
+                    % Check each output variable is valid. If not valid, then remove it. 
+                    % If valid then add coordinates for the output
+                    % variable. If there is no valid output variable then
+                    % throw an error.                    
+                    variableName = obj.inputData.componentData.(modelComponent).outputVariable;
+                    if ischar(variableName)
+                        variableNameAsCell{1} = variableName;
+                        variableName = variableNameAsCell;
+                        clear variableNameAsCell
+                    end
+                    isValidVariableName = false(1,length(variableName ));
+                    t = obj.inputData.forcingData(:,1);
+                    setTransformedForcing(obj.parameters.(transformObject_name), t, true)
+                    for j=1:length(variableName)
+                    	try                                                         
+                            forcingData = getTransformedForcing(obj.parameters.(transformObject_name), variableName{j});
+                            isValidVariableName(j) = true;
+                        catch ME
+                            isValidVariableName(j) = false;
+                        end
+                    end
+                    if ~any(isValidVariableName)
+                        error(['No transformed forcing data for component "',modelComponent,'" could be derived']); 
+                    end
+                    variableName = variableName(isValidVariableName);
+                    obj.inputData.componentData.(modelComponent).outputVariable = variableName;
+                    coordinatesTmp = getCoordinates(obj.parameters.(transformObject_name), variableName);
+                    siteCoordinates(size(siteCoordinates,1)+1: size(siteCoordinates,1) + size(coordinatesTmp,1),1:3) = coordinatesTmp;
+                    
+                catch exception
+                    display(['ERROR: Invalid model component class object for forcing transform: ', transformObject_name ]);
+                    rethrow(exception);
+                end                     
+            end            
+                        
+            % Build the objects for forcing transformations that DO
+            % require an input weighting function object.
+            % NOTE: if the component is derived from another componant
+            % object, then like the construction of the weighting function
+            % object, the forcing for the source component is input in the
+            % construction of the forcing transformation. If the source
+            % component forcing is also a transformation, then the
+            % transformtion object is passed. If not, then the input
+            % forcing data is passed.            
+            for ii = transformFunctionIndexes_inputComponents'
+                
+                modelComponent = varargin{ii,1};
+                propertyValue = varargin{ii,3};
+
+                % Get the following inputs to build the model object:
+                % transformation function name, input data column names,
+                % the variables to which the input data is to be assigned
+                % too, and additional transformation model options.
+                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{1});
+                transformObject_name = varargin{ii,3}{filt,2};
+                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{2});
+                transformObject_inputs = varargin{ii,3}{filt,2};
+                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{4});
+                if any(filt)
+                    transformObject_options = varargin{ii,3}{filt,2};
+                else
+                    transformObject_options = {};
+                end
+                filt = strcmp(varargin{ii,3}(:,1), valid_transformProperties{5});
+                transformObject_sourceName = varargin{ii,3}{filt,2};
+                
+%                 % Find the name of the input transformation object for the
+%                 % specified input component name.
+%                 transformObject_inputComponentName = obj.inputData.componentData.(transformObject_inputComponentName).forcing_object;
+                
+                % Find the required columns in forcing data so that only the
+                % required data is input.
+                colNum=1;
+                for j=1:size(forcingData_inputs,1)
+                    if isnumeric(forcingData_inputs{j,1})
+                        colNum = [colNum; forcingData_inputs{j,1}];             
+                    elseif ischar(forcingData_inputs{j,1})
+                        filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
+                        if sum(filt)==0
+                            error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
+                        end
+                        colNum = [colNum; filt];                                
+                    else
+                        error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
+                    end
+                end
+                
+                try                
+                    %colNum_extras = true(length(forcingData_colnames),1);
+                    %colNum_extras(colNum) = false;
+                    %colNum_extras = find(colNum_extras);
+                    %forcingData_colnamesTmp = forcingData_colnames([colNum;colNum_extras]);
+                    %forcingData_dataTmp = forcingData_data(:,[colNum;colNum_extras]);                                                                                    
+                    obj.parameters.(transformObject_name) = feval(transformObject_name, bore_ID, forcingData_data(:,colNum), forcingData_colnames(colNum), siteCoordinates, transformObject_inputs, obj.parameters.(transformObject_sourceName), transformObject_options);
+                    
+                    % Add coordinates for the output variable
+                    variableName = obj.inputData.componentData.(modelComponent).outputVariable;
+                    coordinatesTmp = getCoordinates(obj.parameters.(transformObject_name), variableName);
+                    siteCoordinates(size(siteCoordinates,1)+1: size(siteCoordinates,1) + size(coordinatesTmp,1),1:3) = coordinatesTmp;
+                                        
+                catch exception
+                    display(['ERROR: Invalid model component class object for forcing transform: ', transformObject_name ]);
+                    rethrow(exception);
+                end                     
+            end
+
+            % Build the component weighting object for each component that DO NOT require an input weighting function object
+            for ii = weightingFunctionIndexes'
+            
+                modelComponent = varargin{ii,1};
+                propertyValue = varargin{ii,3};
+
+                % Check if the current model object requires input of
+                % another weighting function object.
+                inputWeightingFunctionIndex = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{4}));
+                
+                % Build weighting function for those NOT requiring the
+                % input of other weighting function objects.
+                if isempty(inputWeightingFunctionIndex)
+                    try                       
+                        % Get the column number for forcing data
+                        if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
+                            colNum = obj.inputData.componentData.(modelComponent).dataColumn;
+                            forcingData_inputs = forcingData_colnames(colNum);
+                        elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
+
+                            % Get the names (or column numbers) of the input forcing data. 
+                            forcingData_inputs = obj.inputData.componentData.(modelComponent).outputVariable;
+                            
+                            % Remove empty rows
+                            if iscell(forcingData_inputs)
+                                filt = cellfun( @(x) ~isempty(x), forcingData_inputs);
+                                forcingData_inputs = forcingData_inputs(filt);
+                            end
+%                             for j=1:size(forcingData_inputs,1)
+%                                 % Skip if optional forcing input
+%                                 if strcmp(forcingData_inputs{j,1},'(none)')
+%                                     continue
+%                                 end
+%                                 if isnumeric(forcingData_inputs{j,1})
+%                                     colNum = [colNum; forcingData_inputs{j,1}];             
+%                                 elseif ischar(forcingData_inputs{j,1})
+%                                     filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
+%                                     if sum(filt)==0
+%                                         error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
+%                                     end
+%                                     colNum = [colNum; filt];                                
+%                                 else
+%                                     error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
+%                                 end
+%                             end
+                        end
+
+                        % Check that the response 
+                        % function name is consistent with the abstract
+                        % 'responseFunction_abstract'.
+                        try
+                            if ~strcmp(findAbstractName( propertyValue),'responseFunction_abstract')
+                                error(['The following response function function class definition is not derived from the "responseFunction_abstract.m" anstract:',propertyValue]);
+                            end
+                        catch
+                            display('... Warning: Checking that the required abstract for the response function transform class definition was used failed. This is may be because the version of matlab is pre-2014a.');
+                        end                        
+
+                        % Filter for options.
+                        filt = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{3}));                       
+                        
+                        % Convert input forcing data to cell if string
+                        if ischar(forcingData_inputs);
+                            forcingData_inputs_tmp{1}=forcingData_inputs;
+                            forcingData_inputs = forcingData_inputs_tmp;
+                            clear forcingData_inputs_tmp;
+                        end
+                        
+                        % Call the object 
+                        if any(filt)
+                            obj.parameters.(modelComponent) = feval(propertyValue, bore_ID, forcingData_inputs, siteCoordinates, varargin{filt,3} );                             
+                        else
+                            obj.parameters.(modelComponent) = feval(propertyValue, bore_ID, forcingData_inputs, siteCoordinates, {}); 
+                        end
+                    catch exception
+                         display(['ERROR: Invalid weighting function class object name: ',char(propertyValue),'. The weighting function object could not be created.']);
+                         rethrow(exception);
+                    end                            
+                end
+            end
+            
+            % Build the component weighting object for each component that DO require an input weighting function object
+            for ii = weightingFunctionIndexes'
+            
+                modelComponent = varargin{ii,1};
+                propertyValue = varargin{ii,3};
+
+                % Check if the current model object requires input of
+                % another weighting function object.
+                inputWeightingFunctionIndex = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{4}));
+                
+                % Build weighting function for those that DO require the
+                % input of other weighting function objects.
+                if ~isempty(inputWeightingFunctionIndex)
+                    
+                    % Get the name of the component to be input to the
+                    % weighting function.
+                    inputWeightingFunctionName = varargin{inputWeightingFunctionIndex,3};                    
+                    
+                    % Add input component name to input data fields
+                    obj.inputData.componentData.(modelComponent).inputWeightingComponent = inputWeightingFunctionName;
+                    
+                    % Check that the input weighting function has been
+                    % created.
+                    if ~isfield(obj.parameters,inputWeightingFunctionName)
+                        error(['The input component name for component "', modelComponent,'" must be a component that itself is not derived from another componant.' ]);
+                    end
+                    
+                    try
+
+                        % Get the column number for forcing data
+                        if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
+                            colNum = obj.inputData.componentData.(modelComponent).dataColumn;
+                            forcingData_inputs = forcingData_colnames(colNum);
+                        elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
+
+                            % Get the names (or column numbers) of the input forcing data. 
+                            forcingData_inputs = obj.inputData.componentData.(modelComponent).outputVariable;
+                            
+                            % Remove empty rows
+                            if iscell(forcingData_inputs)
+                                filt = cellfun( @(x) ~isempty(x), forcingData_inputs);
+                                forcingData_inputs = forcingData_inputs(filt);
+                            end
+                        end
+                        
+%                         % Get the column number for forcing data
+%                         colNum = 1;
+%                         if isfield(obj.inputData.componentData.(modelComponent),'dataColumn')
+%                             colNum = obj.inputData.componentData.(modelComponent).dataColumn;
+%                         elseif isfield(obj.inputData.componentData.(modelComponent),'inputForcing')
+% 
+%                             % Get the names (or column numbers) of the input forcing data. 
+%                             forcingData_inputs = obj.inputData.componentData.(modelComponent).inputForcing(:,2);
+%                             
+%                             for j=1:size(forcingData_inputs,1)
+%                                 if isnumeric(forcingData_inputs{j,1})
+%                                     colNum = [colNum; forcingData_inputs{j,1}];             
+%                                 elseif ischar(forcingData_inputs{j,1})
+%                                     filt = find(strcmpi(forcingData_colnames, forcingData_inputs{j,1}));
+%                                     if sum(filt)==0
+%                                         error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing site name that is not listed within the input forcing data column names.' ]);
+%                                     end
+%                                     colNum = [colNum; filt];                                
+%                                 else
+%                                     error(['An unexpected error occured for component :', modelComponent,'. Within the input cell array for "forcingdata", the second column contains a forcing data column number or site name that is listed within the input forcing data column names.' ]);
+%                                 end
+%                             end
+%                         end                    
+
+                        % Convert input forcing data to cell if string
+                        if ischar(forcingData_inputs);
+                            forcingData_inputs_tmp{1}=forcingData_inputs;
+                            forcingData_inputs = forcingData_inputs_tmp;
+                            clear forcingData_inputs_tmp;
+                        end
+
+                        % Filter for options.
+                        filt = find( strcmpi(varargin(:,1),modelComponent) & strcmpi(varargin(:,2),valid_properties{3}));
+                        
+                        % Call the object. 
+                        % NOTE: these objects have an additional input
+                        % (compared to those created above) for the input
+                        % of a previously build model weighting function
+                        % object.
+                        obj.parameters.(modelComponent) = feval(propertyValue, bore_ID,forcingData_inputs, siteCoordinates, obj.parameters.(inputWeightingFunctionName), varargin(filt,3)); 
+                    catch exception
+                         display(['ERROR: Invalid weighting function class object name: ',char(propertyValue),'. The weighting function object could not be created.']);
+                         rethrow(exception);
+                    end                            
+                end
+            end                        
+            
+            % Add noise component
+            obj.parameters.noise.type = 'transferfunction';
+            obj.parameters.noise.alpha = log10(0.1);    
+            
+            % Set the parameter names variable.   
+            [junk, obj.variables.param_names] = getParameters(obj);            
+
+            % Set variable declarign that calibration is not being
+            % undertaken.
+            obj.variables.doingCalibration = false;
+            
+            % Store only input forcing data required by the model. The
+            % forcing data columns are removed to reduce RAM requirements.
+            %-------------
+            
+            % Find required columns.
+            modelComponentAll = fieldnames(obj.inputData.componentData);
+            forcingData_requiredColNames = {};
+            for i=1:length(modelComponentAll)
+                if isfield(obj.inputData.componentData.(modelComponentAll{i}), 'dataColumn')
+                    forcingData_requiredColNames = {forcingData_requiredColNames{:}, forcingData_colnames{obj.inputData.componentData.(modelComponentAll{i}).dataColumn}};
+                else
+                    forcingData_requiredColNames = {forcingData_requiredColNames{:}, obj.inputData.componentData.(modelComponentAll{i}).inputForcing{:,2}};
+                end
+            end
+            
+            % Create filter for required colnames
+            filt = cellfun(@(x) any(strcmp(x,forcingData_requiredColNames)) , forcingData_colnames);
+            filt(1) = true;
+            
+            forcingData_data = forcingData_data(:,filt);
+            forcingData_colnames = forcingData_colnames(filt);
+            setForcingData(obj, forcingData_data, forcingData_colnames);
+
+            %-------------
+        end
+        
+        % Get the observed head
+        function head = getObservedHead(obj)
+            head = obj.inputData.head;
+        end
+        
+        %% Get the forcing data from the model
+        function [forcingData, forcingData_colnames] = getForcingData(obj)
+            forcingData = obj.inputData.forcingData;
+            forcingData_colnames = obj.inputData.forcingData_colnames;            
+        end
+        
+        function [forcingData, forcingData_colnames] = getDerivedForcingData(obj, t)
+           
+            % Initialise outputs
+            forcingData = [];
+            forcingData_colnames = {};                        
+            
+            % Get the derived forcing data in the sub-model objects
+            if ~isempty(obj.parameters)
+                modelnames = fieldnames(obj.parameters);
+                for i=1:length(modelnames)
+                    if isobject(obj.parameters.(modelnames{i}))
+                        forcingData_colnames_tmp = {};
+                        forcingData_tmp = [];
+                        if any(strcmp(methods(obj.parameters.(modelnames{i})),'setTransformedForcing')) && ...
+                        any(strcmp(methods(obj.parameters.(modelnames{i})),'getTransformedForcing'))
+                            % Get the list of all possible forcing data outputs.                                
+                            % In doing so, handle situations where older
+                            % HydroSight models did not have the bore ID or
+                            % site coordinates stored within the object.
+                            if ~isfield(obj.inputData,'bore_ID')
+                                bore_ID='';
+                            else
+                                bore_ID=obj.inputData.bore_ID;
+                            end
+                            if ~isfield(obj.inputData,'siteCoordinates')
+                                siteCoordinates=table();
+                            else
+                                siteCoordinates=obj.inputData.siteCoordinates;
+                            end                            
+                            variable_names = feval([modelnames{i},'.outputForcingdata_options'],bore_ID , obj.inputData.forcingData, obj.inputData.forcingData_colnames, siteCoordinates);
+
+                            % Set the forcing
+                            setTransformedForcing(obj.parameters.(modelnames{i}),t, false);
+                            
+                            
+                            % Get the forcing for each variable
+                            for j=1:length(variable_names)
+                                try
+                                    forcingData = [forcingData, getTransformedForcing(obj.parameters.(modelnames{i}),variable_names{j})];
+                                    forcingData_colnames = {forcingData_colnames{:}, variable_names{j}}; % SOMETHING GOES WRONG HERE SO THAT RUNOFF IS NOT STORED OR CALCULATED PROPERLY. 
+                                catch ME
+                                    continue;
+                                end
+                            end                            
+                        end
+                    end
+                end
+            end                
+        end
+        
+        %% Set the forcing data from the model
+        function setForcingData(obj, forcingData, forcingData_colnames)
+            obj.inputData.forcingData = forcingData;
+            obj.inputData.forcingData_colnames = forcingData_colnames;
+            
+            % Update forcing data in the sub-model objects
+            if ~isempty(obj.parameters)
+                modelnames = fieldnames(obj.parameters);
+                for i=1:length(modelnames)
+                    if isobject(obj.parameters.(modelnames{i}))
+                        try
+                            setForcingData(obj.parameters.(modelnames{i}), forcingData, forcingData_colnames)
+                        catch
+                            % do nothing
+                        end
+                    end
+                end
+            end
+            
+        end        
+        
+        function stochForcingData = getStochForcingData(obj)
+            stochForcingData = [];
+            % Get derived forcing data in the sub-model objects
+            if ~isempty(obj.parameters)
+                modelnames = fieldnames(obj.parameters);
+                for i=1:length(modelnames)
+                    if isobject(obj.parameters.(modelnames{i}))
+                        if any(strcmp('getStochForcingData',methods(obj.parameters.(modelnames{i}))))
+                            forcingData_tmp =getStochForcingData(obj.parameters.(modelnames{i}));
+                            stochForcingData.(modelnames{i}) = forcingData_tmp;
+                        end
+                    end
+                end
+            end            
+        end
+           
+        function  finishedStochForcing = updateStochForcingData(obj, stochForcingData, refineStochForcingMethod)
+            % Set derived forcing data in the sub-model objects
+            %isValidDerivedForcing =false;
+            componantNames = fieldnames(obj.parameters);
+            finishedStochForcing = false;
+            for i=1:length(componantNames)
+                if isobject(obj.parameters.(componantNames{i}))
+                    if any(strcmp('updateStochForcingData',methods(obj.parameters.(componantNames{i}))))
+                        if nargin==1
+                            updateStochForcingData(obj.parameters.(componantNames{i}));                                
+                        else
+                            forcingDataComponant = fieldnames(stochForcingData);
+                            filt = strcmp(forcingDataComponant,componantNames{i});
+                            forcingDataComponant=forcingDataComponant{filt};
+                            
+                            if nargin == 2
+                                updateStochForcingData(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant));
+                            elseif nargin==3
+                                finishedStochForcing = updateStochForcingData(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant), refineStochForcingMethod);
+                            end                            
+                        end
+                    end
+                end
+            end
+        end        
+        
+        function params = updateStochForcingParameters(obj, stochForcingData, params)
+            % Set the input parameters
+            setParameters(obj, params, obj.variables.param_names);       
+            
+            % For those parameter compant objects with a method
+            % 'updateStochForcingParameters', update the componant
+            % paramerters.
+            componantNames = fieldnames(obj.parameters);
+            didUpdate = false;
+            for i=1:length(componantNames)
+                if isobject(obj.parameters.(componantNames{i}))
+                    if any(strcmp('updateStochForcingParameters',methods(obj.parameters.(componantNames{i}))))
+                        didUpdate = true;
+                        
+                        forcingDataComponant = fieldnames(stochForcingData);
+                        filt = strcmp(forcingDataComponant,componantNames{i});
+                        forcingDataComponant=forcingDataComponant{filt};
+                            
+                        updateStochForcingParameters(obj.parameters.(componantNames{i}),stochForcingData.(forcingDataComponant));
+                    end
+                end
+            end
+            
+            % If updated, then Get the new parameters.
+            if didUpdate
+                params = getParameters(obj);
+            end
+            
+        end 
+        
+        function setStochForcingState(obj, doingCalibration, t_start_calib, t_end_calib)
+            % Finalsie the calibration of the stochastic forcing data.
+            if ~isempty(obj.parameters)
+                modelnames = fieldnames(obj.parameters);
+                for i=1:length(modelnames)
+                    if isobject(obj.parameters.(modelnames{i}))
+                        if any(strcmp('setStochForcingState',methods(obj.parameters.(modelnames{i}))))
+                            setStochForcingState(obj.parameters.(modelnames{i}),doingCalibration,t_start_calib, t_end_calib);
+                        end
+                    end
+                end
+            end            
+        end        
+        
+%% Solve the model for the input time points
+        function [head, colnames, noise] = solve(obj, time_points)
+% solve solves the model for the input time points.
+%
+% Syntax:
+%   [head, colnames, noise] = solve(obj, time_points)
+%
+% Description:
+%   Solves the model using the model parameters and, depending upon
+%   the method's inputs, limits the groundwater head to the
+%   contribution from various periods of climate forcing and plots the
+%   results. The latter is achieved by inputting min and max values for
+%   tor but to date is not incorporated into the HydroSight()
+%   callign methods.
+%
+% Input:
+%   obj -  model object
+%
+%   time_points - column vector of the time points to be simulated.
+%
+% Outputs:
+%   head - MxN matrix of simulated head with the following columns: date/time,
+%   head, head due to model component i.
+%
+%   colnames - Nx1 column names for matrix 'head'.
+%
+%   noise - Mx3 matrix of the estimated upper and lower magnitude of the
+%   time-series noise componat at M time steps.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+
+            % Check that the model has first been calibrated.
+            if ~isfield(obj.variables, 'd')
+                error('The model does not appear to have first been calibrated. Please calibrate the model before running a simulation.');
+            end
+                        
+            % Clear obj.variables of temporary variables.
+            if isfield(obj.variables, 'theta_est_indexes_min'), obj.variables = rmfield(obj.variables, 'theta_est_indexes_min'); end
+            if isfield(obj.variables, 'theta_est_indexes_max'), obj.variables = rmfield(obj.variables, 'theta_est_indexes_max'); end
+            %if isfield(obj.variables, 'SMS_frac'), obj.variables = rmfield(obj.variables, 'SMS_frac'); end            
+            %if isfield(obj.variables, 'recharge'), obj.variables = rmfield(obj.variables, 'recharge'); end            
+            %if isfield(obj.variables, 'SMSC'), obj.variables = rmfield(obj.variables, 'SMSC'); end            
+            %if isfield(obj.variables, 'SMSC_1'), obj.variables = rmfield(obj.variables, 'SMSC_1'); end            
+            %if isfield(obj.variables, 'SMSC_2'), obj.variables = rmfield(obj.variables, 'SMSC_2'); end            
+            %if isfield(obj.variables, 'f'), obj.variables = rmfield(obj.variables, 'f'); end            
+            %if isfield(obj.variables, 'b'), obj.variables = rmfield(obj.variables, 'b'); end                                    
+            %if isfield(obj.variables, 'ksat'), obj.variables = rmfield(obj.variables, 'ksat'); end
+            %if isfield(obj.variables, 'ksat_1'), obj.variables = rmfield(obj.variables, 'ksat_1'); end
+            %if isfield(obj.variables, 'ksat_2'), obj.variables = rmfield(obj.variables, 'ksat_2'); end
+            
+            % Set a flag to indicate that calibration is NOT being undertaken.
+            % obj.variables.doingCalibration = getInnovations;
+            
+            % Setup matrix of indexes for tor at each time_points
+            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
+            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(filt,1)+1]');
+            ntor =  size( tor, 1);                                                     
+            clear tor;            
+            
+            obj.variables.theta_est_indexes_min = zeros(1,length(time_points) );
+            obj.variables.theta_est_indexes_max = zeros(1,length(time_points) );
+                        
+            for ii= 1:length(time_points)              
+                ntheta = sum( obj.inputData.forcingData ( : ,1) <= time_points(ii) );                                
+                obj.variables.theta_est_indexes_min(ii) = ntor-ntheta;
+                obj.variables.theta_est_indexes_max(ii) = max(1,ntor);
+            end  
+            
+            % Free memory within mex function (just in case there'se been a
+            % prior calibration that crashed prior to clearing the MEX
+            % sttaic variables)
+            % Free memory within mex function
+            try
+                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
+            catch ME
+                % continue               
+            end          
+            
+            % Get the parameter sets (for use in resetting if >sets)
+            [params, param_names] = getParameters(obj);
+            
+            % If the number of parameter sets is >1 then temporarily apply 
+            % only the first parameter set. This is done only to reduce the
+            % RAM requirements for the broadcasting of the obj variable in
+            % the following parfor.
+            if size(params,2)>1
+                setParameters(obj, params(:,1), param_names);
+            end
+            
+            % Set percentile for noise 
+            Pnoise = 0.95;
+            
+            % Solve the modle using each parameter set.
+            obj.variables.delta_time = diff(time_points);
+            headtmp=cell(1,size(params,2));
+            noisetmp=cell(1,size(params,2));
+            companants = fieldnames(obj.inputData.componentData);
+            nCompanants = size(companants,1);                             
+            for ii=1:size(params,2)
+                % Get the calibration estimate of the mean forcing for the
+                % current parameter set. This is a bit of a work around to
+                % handle the issue of each parameter set having a unique
+                % mean forcing (if a forcing transform is undertaken). The
+                % workaround was required when DREAM was addded.
+                for j=1:nCompanants                    
+                    calibData(ii,1).mean_forcing.(companants{j}) = obj.variables.(companants{j}).forcingMean(:,ii);
+                end                
+                    
+                
+                if ~isempty(obj.variables.d(ii))
+                    % Add drainage elevation to the varargin variable sent to
+                    % objectiveFunction.                
+                    calibData(ii,1).drainage_elevation = obj.variables.d(ii);
+                else
+                    fprintf('obj.variables.d(ii) must exist and have a value. How did it disappear?');
+                    break;
+                end
+
+                % % Add drainage elevation to the varargin variable sent to
+                % % objectiveFunction.                
+                % calibData(ii,1).drainage_elevation = obj.variables.d(ii);
+                
+                % Add noise std dev
+                if isfield(obj.variables,'sigma_n');
+                    calibData(ii,1).sigma_n = obj.variables.sigma_n(ii);
+                else
+                    calibData(ii,1).sigma_n = 0;
+                end
+                
+            end
+            
+            % Solve model and add drainage constants
+            [~, headtmp{1}, colnames] = objectiveFunction(params(:,1), time_points, obj, calibData(1));                        
+            if size(params,2)>1
+                parfor jj=2:size(params,2)
+                    [~, headtmp{jj}] = objectiveFunction(params(:,jj), time_points, obj, calibData(jj));                        
+                end              
+            end
+            
+            % Add drainage constants and calculate total error bounds.
+            for ii=1:size(params,2)
+                headtmp{ii}(:,2) = headtmp{ii}(:,2) + calibData(ii).drainage_elevation;
+            
+                noisetmp{ii} = [headtmp{ii}(:,1), ones(size(headtmp{ii},1),2) .* norminv(Pnoise,0,1) .* calibData(ii).sigma_n];
+            end                            
+            head = zeros(size(headtmp{1},1),size(headtmp{1},2), size(params,2));
+            noise = zeros(size(headtmp{1},1),3, size(params,2));            
+            for ii=1:size(params,2)
+                head(:,:,ii) = headtmp{ii};
+                noise(:,:,ii) = noisetmp{ii};
+            end
+            %colnames = colnames{1};
+            clear headtmp noisetmp
+                             
+            % Set the parameters if >1 parameter sets
+            if size(params,2)>1
+                setParameters(obj,params, param_names);
+            end
+            
+            % Clear matrix of indexes for tor at each time_points
+            obj.variables = rmfield(obj.variables, 'theta_est_indexes_min');
+            obj.variables = rmfield(obj.variables, 'theta_est_indexes_max');
+            
+        end
+        
+%% Initialise the model prior to calibration.
+        function [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
+% calibration_initialise initialises the model prior to calibration.
+%
+% Syntax:
+%   [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
+%
+% Description:
+%   Sets up model variables required for calibration. Most imporantly, it
+%   calculates the mean observed head, h_bar, and row indexes, 
+%   theta_est_indexes,  for efficient calculation of the response
+%   functions. The calibration also requires the method to return a column 
+%   vector of the initial parameters and a column vector of time points for
+%   which observation data exists.
+%
+% Input:
+%   obj -  model object
+%
+%   t_start - scaler start time, eg datenum(1995,1,1);
+%
+%   t_end - scaler end time, eg datenum(2005,1,1);
+%
+% Outputs:
+%   params_initial - column vector of the initial parameters.
+%
+%   time_points - column vector of time points forwhich observation data
+%   exists.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014
+
+            % clear old variables
+            obj.variables = [];
+
+            % Set a flag to indicate that calibration is being undertaken
+            % and the calibraion start and end dates            
+            obj.variables.doingCalibration = true;
+            obj.variables.t_start = t_start;
+            obj.variables.t_end = t_end;
+                        
+            % Free memory within mex function (just in case there'se been a
+            % prior calibration that crashed prior to clearing the MEX
+            % sttaic variables)
+            % Free memory within mex function
+            try
+                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
+            catch ME
+                % continue               
+            end            
+                        
+            % Get parameter names and initial values
+            [params_initial, obj.variables.param_names] = getParameters(obj);
+            
+            % Extract time steps
+            t_filt = find( obj.inputData.head(:,1) >=t_start  ...
+                & obj.inputData.head(:,1) <= t_end );   
+            time_points = obj.inputData.head(t_filt,1);
+            obj.variables.time_points = time_points;
+            
+            % Set the stochastic forcing to be in a 'calibration' state.
+            setStochForcingState(obj, obj.variables.doingCalibration, t_start, t_end);            
+            
+            % Set initial time for trend term
+            obj.variables.trend_startTime = obj.variables.time_points(1);
+            
+            % Calc time step sizes. 
+            obj.variables.delta_time = diff( obj.variables.time_points  );
+            
+            % Calcaulte mean head.
+            obj.variables.h_bar = mean(obj.inputData.head( t_filt,2 ));                            
+            
+            % Setup matrix of indexes for tor at each time_points
+            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
+            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(filt,1)+1]');
+            ntor =  size(tor, 1);                                                     
+            clear tor;
+            
+            obj.variables.theta_est_indexes_min = zeros(1,length(time_points) );
+            obj.variables.theta_est_indexes_max = zeros(1,length(time_points) );
+                        
+            for ii= 1:length(time_points)              
+                ntheta = sum( obj.inputData.forcingData ( : ,1) <= time_points(ii) );                                
+                obj.variables.theta_est_indexes_min(ii) = ntor-ntheta;
+                obj.variables.theta_est_indexes_max(ii) = max(1,ntor);
+            end  
+
+            obj.variables.nobjectiveFunction_calls=0;
+            
+        end        
+        
+%% Finalise the model following calibration.
+        function calibration_finalise(obj, params, useLikelihood)            
+% calibration_finalise finalises the model following calibration.
+%
+% Syntax:
+%   calibration_finalise(obj, params)   
+%
+% Description:
+%   Finalises the model following calibration and assigns the final 
+%   parameters and additional variables to the object for later simulation. 
+%   Of the variables calculated, the most essential for the model 
+%   is the scalar drainage, obj.variables.d. Other variables that are also 
+%   important include: 
+%       - a vector of innovations,  obj.variables.innov, for detection 
+%         of serial correlation in the model errors; 
+%       - the noise standard deviation, obj.variables.sigma_n.
+%
+% Input:
+%   obj -  model object
+%
+%   params - column vector of the optima parameters derived from
+%   calibration.
+%
+% Outputs:
+%   (none, the results are output to obj.variables)
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014            
+
+            % Set the stochastic forcing to NOT be in a 'calibration' state.
+            setStochForcingState(obj, false, obj.variables.t_start, obj.variables.t_end);
+
+            for j=1:size(params,2)
+                % Re-calc objective function and deterministic component of the head and innocations.
+                % Importantly, the drainage elevation (ie the constant term for
+                % the regression) is calculated within 'objectiveFunction' and
+                % assigned to the object. When the calibrated model is solved
+                % for a different time period (or climate data) then this
+                % drainage value will be used by 'objectiveFunction'.
+                try
+                    [obj.variables.objFn(:,j), h_star, ~ , obj.variables.d(j)] = objectiveFunction(params(:,j), obj.variables.time_points, obj,useLikelihood);                        
+                catch ME
+                    if size(params,2)>1
+                        continue
+                    else
+                        error(ME.message);
+                    end
+                end
+
+                % Calculate the mean forcing rates. These mean rates are used
+                % to calculate the contribution from the tail of the theta
+                % weighting beyond the last observation. That is, the theta
+                % function is integrated from the last time point of the
+                % forcing to negative infinity. This integral is then
+                % multiplied by the mean forcing rate. To ensure future
+                % simulations use an identical mean forcing rate, the means are
+                % calculated here and used in all future simulations.
+                companants = fieldnames(obj.inputData.componentData);
+                nCompanants = size(companants,1);
+                for i=1:nCompanants
+                    obj.variables.(companants{i}).forcingMean(:,j) = mean(obj.variables.(companants{i}).forcingData)';
+                end
+
+                t_filt = find( obj.inputData.head(:,1) >=obj.variables.time_points(1)  ...
+                    & obj.inputData.head(:,1) <= obj.variables.time_points(end) );               
+                obj.variables.resid(:,j) = obj.inputData.head(t_filt,2)  -  (h_star(:,2) + obj.variables.d(j));
+
+                % Calculate mean of noise. This should be zero +- eps()
+                % because the drainage value is approximated assuming n-bar = 0.
+                obj.variables.n_bar(j) = real(mean( obj.variables.resid(:,j) ));
+
+                % Calculate innovations
+                innov = obj.variables.resid(2:end,j) - obj.variables.resid(1:end-1,j).*exp( -10.^obj.parameters.noise.alpha .* obj.variables.delta_time );
+
+                % Calculate noise standard deviation.
+                obj.variables.sigma_n(j) = sqrt(mean( innov.^2 ./ (1 - exp( -2 .* 10.^obj.parameters.noise.alpha .* obj.variables.delta_time ))));
+            end
+            
+            % Set a flag to indicate that calibration is complete.
+            obj.variables.doingCalibration = false;
+            
+            % Set model parameters (if params are multiple sets)
+            if size(params,2)>1
+                setParameters(obj, params, obj.variables.param_names);            
+            end
+            
+            % Free memory within mex function
+            try
+                junk=doIRFconvolutionPhi([], [], [], [], false, 0);            
+            catch ME
+                % continue               
+            end
+        end        
+
+%% Calculate objective function vector. 
+        function [objFn, h_star, colnames, drainage_elevation] = objectiveFunction(params, time_points, obj, varargin)
+% objectiveFunction calculates the objective function vector. 
+%
+% Syntax:
+%   [objFn, h_star, colnames, drainage_elevation] = objectiveFunction(params,time_points, obj)
+%
+% Description:
+%   Solves the model for the input parameters and calculates the objective
+%   function vector. Importantly, the objective function vector is not
+%   simply the difference between the observed and modelled heads. Because
+%   the model uses a noise model, the residual between the observed 
+%   and modelled head is first derived and then the innovation
+%   is calculated as the prior residual minus the later residual multiplied
+%   by the exponental noise function. Finally, the objective function
+%   weights this vector according to the time-step between observations.
+%
+%   Imporantly, the numerator of the weighting equation from von Asmuth et al 2002
+%   rounds to zero when the number of samples is very large. This occurs
+%   because it is effecively a geometric mean and its product term for n
+%   (where n is the number of observation for calibration minus 1) rounds
+%   to zero as a result of machine precision. This was overcome by adoption
+%   of a restructuring of the geometric meazn in term of exp and log terms. 
+%
+% Inputs:
+%   params - column vector of the optima parameters derived from
+%   calibration.
+%
+%   time_points - column vector of the time points to be simulated.  
+%
+%   obj -  model object
+%
+% Outputs:
+%   objFn - scalar objective function value.
+%
+%   h_star - matrix of the contribution from various model components and
+%   their summed influence. The matrix columns are in the order of:
+%   date/time, summed contribution to the head, contribution from
+%   component i.
+%
+%   colnames - column names for matrix 'head'.
+%
+%   drainage_elevation - drainage elevation constant.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% References:
+%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
+%   dunction-noise modeling in continuous time using predefined impulse
+%   response functions.
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014    
+            
+            % Set model parameters
+            setParameters(obj, params, obj.variables.param_names);
+            
+            % If varargin is a structural variable then the model
+            % simulation is to use predefined values of the drainage
+            % elevation and the mean forcing. Note, these inputs are only
+            % to be provided if not doing simulation.
+            getLikelihood = false;
+            drainage_elevation=[];
+            mean_forcing=[];
+            if ~isempty(varargin)
+                if isstruct(varargin{1})
+                    drainage_elevation=varargin{1}.drainage_elevation;
+                    mean_forcing=varargin{1}.mean_forcing;
+                elseif islogical(varargin{1})
+                    getLikelihood=varargin{1};
+                else
+                    error('The input varargin must be either a logical or a structural variable.');
+                end
+            end
+            
+            % Calc deterministic component of the head.
+            if isempty(mean_forcing)
+                [h_star, colnames] = get_h_star(obj, time_points);                 
+            else
+                [h_star, colnames] = get_h_star(obj, time_points,mean_forcing);                 
+            end
+            
+            % Return of there are nan or inf value
+            if any(isnan(h_star(:,2)) | isinf(h_star(:,2)));
+                objFn = inf;
+                return;
+            end
+                            
+            % If the results from this method call are not to be used for
+            % summarising calibration results, then exit here. This is
+            % required because the innovations can only be calculated at
+            % time points for which there are observations. 
+            if ~obj.variables.doingCalibration
+                objFn = [];
+                return;
+            end
+            
+            % Calculate residual between observed and modelled.
+            % Importantly, an approximation of the drainage level,d, is
+            % required here to calculate the residuals. To achieve this
+            % requires an assumption that the mean noise, n_bar, equals
+            % zero. If this is not the case, then d_bar calculated below
+            % may differ from d calculated within 'calibration_finalise'.
+            t_filt = find( obj.inputData.head(:,1) >=time_points(1)  ...
+                & obj.inputData.head(:,1) <= time_points(end) );          
+            if isempty(drainage_elevation)
+                drainage_elevation = obj.variables.h_bar - mean(h_star(:,2));      
+            end
+            resid= obj.inputData.head(t_filt,2)  - (h_star(:,2) +  drainage_elevation);                 
+            
+            % Calculate innovations using residuals from the deterministic components.            
+            innov = resid(2:end) - resid(1:end-1).*exp( -10.^obj.parameters.noise.alpha .* obj.variables.delta_time );
+            
+            % Calculate objective function
+            objFn = sum( exp(mean(log( 1- exp( -2.*10.^obj.parameters.noise.alpha .* obj.variables.delta_time) ))) ...
+                    ./(1- exp( -2.*10.^obj.parameters.noise.alpha .* obj.variables.delta_time )) .* innov.^2);
+  
+
+            % Calculate log liklihood    
+            if getLikelihood
+                N = size(resid,1);
+                objFn = -0.5 * N * ( log(2*pi) + log(objFn./N)+1); 
+            end
+            % Increment count of function calls
+            obj.variables.nobjectiveFunction_calls = obj.variables.nobjectiveFunction_calls +  size(params,2);
+            
+        end
+           
+%% Set the model parameters to the model object from a vector.
+        function setParameters(obj, params, param_names)
+% setParameters set the model parameters to the model object from a vector.
+%
+% Syntax:
+%   setParameters(obj, params, param_names)
+%
+% Description:
+%   Assigns a vector of parameter values to the model object using the
+%   input component and parameter names. This method is predominately used
+%   by the model calibration.
+%
+% Input:
+%   obj -  model object
+%
+%   params - column vector of the parameter values.
+%
+%   param_names - two column n-row (for n parameters) cell matrix of
+%   compnant name (column 1) and parameter name (column 2).
+%
+% Outputs:
+%   params_initial - column vector of the initial parameters.
+%
+%   time_points - column vector of time points forwhich observation data
+%   exists.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014            
+            
+            % Get unique component names and count the number of parameters
+            % for this component.
+            nCompanants = 1;
+            companants{nCompanants ,1} =  param_names{1,1};
+            companants{nCompanants ,2} = 1;
+            if size(param_names,1) >1
+                for ii=2: size(param_names,1)
+                    if ~strcmp( param_names{ii,1} , param_names{ii-1,1} )
+                        nCompanants  = nCompanants +1;
+                        companants{nCompanants ,1} =  param_names{ii,1};
+                        companants{nCompanants ,2} =  1;
+                    elseif ~strcmp( param_names{ii,2} , 'type' )
+                        companants{nCompanants ,2} =  companants{nCompanants ,2} + 1;
+                    end
+                end
+            end
+                        
+            params_index=0;
+            for ii=1: size(companants,1)
+                currentField = char( companants{ii,1} ) ;
+                
+                % Get parameter names for this component
+                if isobject( obj.parameters.( currentField ) )
+                    [param_values, companant_params] = getParameters(obj.parameters.( currentField ));
+                    clear param_values;
+                else
+                     companant_params = fieldnames( obj.parameters.( currentField ) ) ;
+                end
+
+                % Scan though and remove those that are called 'type'
+                param_index = 1:size(companant_params,1) ;
+                for j = 1: size(companant_params,1)
+                    if strcmp( companant_params{j}, 'type') || strcmp( companant_params{j}, 'variables')
+                        param_index(j)=0;
+                    end
+                end
+                companant_params = companant_params(param_index>0);
+                
+                % Check all parameters for this component are to be set
+                % with new valeus.
+                if companants{ii ,2} ~= size(companant_params,1)
+                    error(['The number of parameters to be set for the following model', ...
+                    'component must equal the total number of parameters for the component: ,',currentField]);
+                end
+                
+                % Get input parameter values for this component.
+                component_param_vals = zeros(size(companant_params,1),size(params,2));
+                for j=1: size(companant_params,1)
+                    params_index = params_index + 1;
+                    component_param_vals(j,:) =  params(params_index,:);
+                end
+                
+                % Get model parameters for each componant.
+                % If the componant is an object, then call the objects
+                % getParameters method.
+                if isobject( obj.parameters.( currentField ) )                    
+                    % Call object method to set parameter values.
+                    setParameters( obj.parameters.( currentField ), component_param_vals );                   
+                else
+                    % Non-object componant.
+                    for j=1:size(companant_params,1)               
+                        obj.parameters.( currentField ).( char(companant_params{j}) ) = component_param_vals(j,:);
+                    end 
+                end
+            end
+            
+            
+        end
+        
+%% Returns the model parameters from the model object.
+        function [params, param_names] = getParameters(obj)
+% getParameters returns the model parameters from the model object.
+%
+% Syntax:
+%   [params, param_names] = getParameters(obj)
+%
+% Description:
+%   Cycles through all model components and parameters and returns a vector
+%   of parameter values and a cell matrix of their espective componant
+%   and parameter names.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params - column vector of the parameter values.
+%
+%   param_names - two column n-row (for n parameters) cell matrix of
+%   compnant name (column 1) and parameter name (column 2).
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014            
+            
+            param_names = {};
+            params = [];
+            nparams=0;
+            % Get model componants
+            companants = fieldnames(obj.parameters);
+            
+            for ii=1: size(companants,1)
+                currentField = char( companants(ii) ) ;
+                % Get model parameters for each componant.
+                % If the componant is an object, then call the objects
+                % getParameters method.
+                if isobject( obj.parameters.( currentField ) )
+                    % Call object method.
+                    [params_temp, param_names_temp] = getParameters( obj.parameters.( currentField ) );
+                    
+                    for j=1: size(param_names_temp,1)
+                        nparams = nparams + 1;
+                        param_names{ nparams , 1} = currentField;
+                        param_names{ nparams , 2} = param_names_temp{j};
+                        params( nparams,: ) = params_temp(j,:);                        
+                    end
+                    
+                else
+                    % Non-object componant.
+                    companant_params = fieldnames( obj.parameters.( currentField ) );
+                    for j=1: size(companant_params,1)
+                       if ~strcmp( companant_params(j), 'type')
+                          nparams = nparams + 1;
+                          param_names{nparams,1} =  currentField;
+                          param_names{nparams,2} =  char(companant_params(j));
+                          params(nparams,:) = obj.parameters.( currentField ).( char(companant_params(j)) );
+                       end
+                    end
+                end
+            end
+        end
+        
+        
+%% Returns the model parameters from the model object.
+        function [params, param_names] = getDerivedParameters(obj)
+% getDerivedParameters returns the derived parameters from the model object.
+%
+% Syntax:
+%   [params, param_names] = getDerivedParameters(obj)
+%
+% Description:
+%   Cycles through all model componants and parameters and returns a vector
+%   of derived parameter values and a cell matrix of their respective componant
+%   and parameter names. The derived parameters are calibrated parameters
+%   or constants in the componants but variables derived from the
+%   calibrated parameters. For example, the drawdown response function
+%   (e.g. responseFunction_FerrisKnowles) can calculate the T and S from
+%   the calibrated parameters.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params - column vector of the parameter values.
+%
+%   param_names - two column n-row (for n parameters) cell matrix of
+%   compnant name (column 1) and parameter name (column 2).
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014            
+            
+            param_names = {};
+            params = [];
+            nparams=0;
+            % Get model componants
+            companants = fieldnames(obj.parameters);
+            
+            for ii=1: size(companants,1)
+                currentField = char( companants(ii) ) ;
+                % Get model parameters for each componant.
+                % If the componant is an object, then call the objects
+                % getParameters method.
+                if isobject( obj.parameters.( currentField ) )
+                    % Call object method.
+                    [params_temp, param_names_temp] = getDerivedParameters( obj.parameters.( currentField ) );
+                    
+                    for j=1: size(param_names_temp,1)
+                        nparams = nparams + 1;
+                        param_names{ nparams , 1} = currentField;
+                        param_names{ nparams , 2} = param_names_temp{j};
+                        params( nparams,: ) = params_temp(j,:);                        
+                    end
+                end
+            end
+        end
+             
+        function derivedData_types = getDerivedDataTypes(obj)
+
+            % Initialise outputs
+            derivedData_types = cell(0,2);
+            
+            % Get model componants
+            companants = fieldnames(obj.parameters);
+            
+            derivedData_types_p1={};
+            derivedData_types_p2={};
+            for ii=1: size(companants,1)
+                modelComponant = char( companants(ii) ) ;
+                % Get derived data for each componant.
+                if isobject( obj.parameters.( modelComponant ))  && ...
+                any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedData')) && ...
+                any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedDataTypes'))                    
+            
+                    dataTypes_tmp = getDerivedDataTypes(obj.parameters.( modelComponant ));
+                    if ~iscell(dataTypes_tmp)
+                        dataTypes = cell(1,1);
+                        dataTypes{1}=dataTypes_tmp;
+                    else
+                        dataTypes = dataTypes_tmp;
+                    end
+                    k=size(derivedData_types,1);
+                    for j=1:length(dataTypes)
+                        derivedData_types{k+j,1} = modelComponant;
+                        derivedData_types{k+j,2} = dataTypes{j};
+                    end
+                end
+            end    
+        end
+        
+        function [derivedData, derivedData_names] = getDerivedData(obj, modelComponant, derivedData_variable, t, plot_axes)
+
+            % Initialise outputs
+            derivedData = [];
+            derivedData_names={};
+            
+            if isobject( obj.parameters.( modelComponant ))  && ...
+            any(strcmp(methods(obj.parameters.( modelComponant )),'getDerivedData'))
+                [derivedData, derivedData_names] = getDerivedData( obj.parameters.( modelComponant ),derivedData_variable,t,plot_axes );  
+            end
+        end        
+        
+        function [stochForcingData_new, accepted] = acceptStochForcingSolution(obj, objFuncVal, objFuncVal_prior, stochForcingData)
+
+            % Initialse some outputs
+            %stochForcingData_new = stochForcingData;
+            stochForcingData_new=[];
+            
+            % Get model componants
+            companants = fieldnames(obj.parameters);
+            accepted = true(size(companants,1),1);
+            for ii=1: size(companants,1)
+                currentField = char( companants(ii) ) ;
+                % Get model parameters for each componant.
+                % If the componant is an object, then call the objects
+                % getParameters method.
+                if isobject( obj.parameters.( currentField ) ) && ...
+                any(strcmp(methods(obj.parameters.( currentField )),'acceptStochForcingSolution'))
+
+                    % Check if the current componant is listed within the
+                    % input stochastic forcing data.
+                    forcingDataComponant = fieldnames(stochForcingData);
+                    filt = strcmp(forcingDataComponant,companants{ii});
+                    
+                    % Test if the solution should be accepted. Pass the
+                    % stochastic forcing data is required.
+                    if isempty(filt)
+                        accepted(ii) = acceptStochForcingSolution(obj.parameters.(currentField), objFuncVal, objFuncVal_prior);
+                    else
+                        forcingDataComponant=forcingDataComponant{filt};
+                        [stochForcingData_new.(forcingDataComponant), accepted(ii)] = acceptStochForcingSolution(obj.parameters.(currentField), objFuncVal, objFuncVal_prior, stochForcingData.(forcingDataComponant));
+                    end
+                end                
+            end
+            if any(~accepted)
+                accepted=false;
+            else
+                accepted=true;
+            end
+        end               
+        
+        function [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+% getParameters_physicalLimit returns the physical limits to each model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+%
+% Description:
+%   Cycles through all model componants and parameters and returns a vector
+%   of the physical upper and lower parameter bounds as defined by the
+%   weighting functions.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter bounds
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+
+            params_lowerLimit = [];
+            params_upperLimit = [];
+            companants = fieldnames(obj.parameters);            
+            for ii=1: size(companants,1)
+                currentField = char( companants(ii) ) ;
+                % Get model parameters for each componant.
+                % If the componant is an object, then call the objects
+                % getParameters_physicalLimit method for each parameter.
+                if isobject( obj.parameters.( currentField ) )
+                    % Call object method.
+                    [params_upperLimit_temp, params_lowerLimit_temp] = getParameters_physicalLimit( obj.parameters.( currentField ) );                
+                    
+                    params_upperLimit = [params_upperLimit; params_upperLimit_temp];
+                    params_lowerLimit = [params_lowerLimit; params_lowerLimit_temp];
+                                        
+                else
+
+                    [params_plausibleUpperLimit, params_plausibleLowerLimit] = getParameters_plausibleLimit(obj);
+                    
+                    % This parameter is assumed to be the noise parameter 'alpha'.  
+                    ind = length(params_upperLimit)+1;
+                    params_upperLimit = [params_upperLimit; params_plausibleUpperLimit(ind)];                                                        
+                    params_lowerLimit = [params_lowerLimit; params_plausibleLowerLimit(ind)];                                
+                end
+            end            
+        end        
+        
+    function [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+% getParameters_plausibleLimit returns the plausible limits to each model parameter.
+%
+% Syntax:
+%   [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+%
+% Description:
+%   Cycles though all model componants and parameters and returns a vector
+%   of the plausible upper and lower parameter range as defined by the
+%   weighting functions.
+%
+% Input:
+%   obj -  model object.
+%
+% Outputs:
+%   params_upperLimit - column vector of the upper parameter plausible bounds.
+%
+%   params_lowerLimit - column vector of the lower parameter plausible bounds
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+                        
+        params_lowerLimit = [];
+        params_upperLimit = [];
+        companants = fieldnames(obj.parameters);            
+        for ii=1: size(companants,1)
+            currentField = char( companants(ii) ) ;
+            % Get model parameters for each componant.
+            % If the componant is an object, then call the objects
+            % getParameters_physicalLimit method for each parameter.
+            if isobject( obj.parameters.( currentField ) )
+                % Call object method.
+                [params_upperLimit_temp, params_lowerLimit_temp] = getParameters_plausibleLimit( obj.parameters.( currentField ) );
+
+                params_upperLimit = [params_upperLimit; params_upperLimit_temp];
+                params_lowerLimit = [params_lowerLimit; params_lowerLimit_temp];
+            else
+                companant_params = fieldnames( obj.parameters.( currentField ) );
+                for j=1: size(companant_params,1)
+                   if ~strcmp( companant_params(j), 'type')
+                       if strcmp(currentField,'et') && strcmp( companant_params(j), 'k')
+                            % This parameter is assumed to be the ET
+                            % parameter scaling when the ET
+                            % uses the precipitation transformation
+                            % function.
+                            params_upperLimit = [params_upperLimit; 1];
+                            params_lowerLimit = [params_lowerLimit; 0];
+                       elseif strcmp(currentField,'landchange') && (strcmp( companant_params(j), 'precip_scalar') ...
+                       || strcmp(currentField,'landchange') && strcmp( companant_params(j), 'et_scalar'))  
+                           % This parameter is the scaling parameter
+                           % for either the ET or precip transformation
+                           % functions.
+                           params_upperLimit = [params_upperLimit; 1.0];
+                           params_lowerLimit = [params_lowerLimit; -1.0];
+                       else
+                            % This parameter is assumed to be the noise parameter 'alpha'.  
+                            alpha_upperLimit = 100; 
+                            while abs(sum( exp( -2.*alpha_upperLimit .* obj.variables.delta_time ) )) < eps() ...
+                            || exp(mean(log( 1- exp( -2.*alpha_upperLimit .* obj.variables.delta_time) ))) < eps()
+                                alpha_upperLimit = alpha_upperLimit - 0.01;
+                                if alpha_upperLimit <= eps()                                   
+                                    break;
+                                end
+                            end
+                            if alpha_upperLimit <= eps()
+                                alpha_upperLimit = inf;
+                            else
+                                % Transform alpha log10 space.
+                                alpha_upperLimit = log10(alpha_upperLimit);
+                            end                           
+                            
+                            params_upperLimit = [params_upperLimit; alpha_upperLimit];
+                            params_lowerLimit = [params_lowerLimit; log10(sqrt(eps()))+4];
+
+                       end
+                   end
+                end
+            end
+        end
+    end 
+        
+    function isValidParameter = getParameterValidity(obj, params, time_points)
+% isValidParameter returns a logical vector for the validity or each parameter.
+%
+% Syntax:
+%   isValidParameter = getParameterValidity(obj, params, time_points)
+%
+% Description:
+%   Cycles though all model components and parameters and returns a logical 
+%   vector denoting if each parameter is valid as defined by each weighting
+%   function.
+%
+% Input:
+%   obj -  model object.
+%
+%   params - vector of model parameters
+%
+%   time_points - vector of simulation time points
+%
+% Outputs:
+%   isValidParameter - column vector of the parameter validity.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   get_h_star: main_method_for_calculating_the_head_contributions.
+%   objectiveFunction: returns_a_vector_of_innovation_errors_for_calibration;  
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014   
+          
+        
+        isValidParameter = true(size(params));
+        companants = fieldnames(obj.parameters);            
+        
+        [junk, param_names] = getParameters(obj);
+        
+        for ii=1: size(companants,1)
+            currentField = char( companants(ii) ) ;
+            
+            % Find parameters for the component
+            filt = strcmp(companants(ii), param_names(:,1));
+
+            % Get model parameters for each componant.
+            % If the componant is an object, then call the objects
+            % getParameterValidity method for each parameter.
+            if isobject( obj.parameters.( currentField ) )
+                
+                
+                % Call object method and pass it the parameter vector and
+                % parameter names.
+                % NOTE: the parameters are not set within the componant
+                % object. This was done to avoid a call to
+                % setParameters().
+                isValidParameter(filt,:) = getParameterValidity( obj.parameters.( currentField ), params(filt,:), param_names(filt,2) );
+             
+            % Check the alphanoise parameter is large enough not to cause numerical
+            % problems in the calcuation of the objective function.
+            elseif strcmp('noise',currentField);                
+                alpha = params(filt,:); 
+                filt_noiseErr = exp(mean(log( 1- exp( bsxfun(@times,-2.*10.^alpha , obj.variables.delta_time) )),1)) <= eps() ...
+                             | abs(sum( exp( bsxfun(@times,-2.*10.^alpha , obj.variables.delta_time) ),1)) < eps();                
+                isValidParameter(filt,filt_noiseErr)= false;                
+                
+            else
+                isValidParameter(filt,:) = true;
+            end
+            
+            % Break if any parameter sets are invalid!
+            if size(params,2)==1 && any(any(~isValidParameter));
+                return;
+            end
+        end
+    end
+    
+    function plot_transferfunctions(obj, t_max)
+% plot_transferfunctions plot the weighting functions
+%
+% Syntax:
+%   plot_transferfunctions(obj, t_max)
+%
+% Description:
+%   Creates a plot of each weighting function. 
+%
+% Input:
+%   obj -  model object
+%
+%   t_max - scaler number of the maximum duration to plot (in days)
+%
+% Output:  
+%   (none)
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%
+% Dependencies
+%   model_TFN.m
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   7 May 2012
+%%        
+        % Get componants of the model.
+        companants = fieldnames(obj.parameters);
+
+        % Derive filter for componants with linear contribution to
+        % head, eg remove index to soil moisture componant.
+        componant_indexes = true( size(companants) );
+        componant_indexes( strcmp(companants,'soilmoisture') ) = false;
+        componant_indexes( end ) = false;
+        componant_indexes = find(componant_indexes)'; 
+
+        % Calculate componants with transfer function.
+        nTranfterFunctions = 0;
+        tor = (1:t_max)';
+        for ii=componant_indexes
+
+            % Get model parameters for each componant.
+            % If the componant is an object, then call the objects
+            % getParameters method.
+            if isobject( obj.parameters.( char(companants(ii))) )
+                                
+                
+                % Calcule theta for each time point of forcing data.
+                try
+                    tmp = theta(obj.parameters.( char(companants(ii))), tor );
+                    nTranfterFunctions =  nTranfterFunctions + 1;
+                    if size(tmp,2)>1
+                        theta_est(:,nTranfterFunctions) = sum(tmp,2);
+                    else
+                        theta_est(:,nTranfterFunctions) = tmp;
+                    end
+                catch
+                    % do nothing
+                end
+            end
+        end
+        
+        figure();
+        for ii=1:nTranfterFunctions
+            subplot(nTranfterFunctions,1,ii);
+            plot( tor,  theta_est(:,ii));
+            xlabel('Duration into the past (days)');
+            ylabel('Tranfer function weight');
+            title(['Tranfer function for: ', char(companants(ii))]);
+            box on;
+        end
+    end
+    
+    function delete(obj)
+% delete class destructor
+%
+% Syntax:
+%   delete(obj)
+%
+% Description:
+%   Loops through parameters and, if not an object, empties them. Else, calls
+%   the sub-object's destructor.
+%
+% Input:
+%   obj -  model object
+%
+% Output:  
+%   (none)
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
+%   The University of Melbourne.
+%
+% Date:
+%   24 Aug 2016
+%%            
+        propNames = properties(obj);
+        for i=1:length(propNames)
+           if isobject(obj.(propNames{i}))
+            delete(obj.(propNames{i}));
+           else               
+            obj.(propNames{i}) = []; 
+           end
+        end
+    end
+    
+    end
+%%  END PUBLIC METHODS    
+
+%%  PRIVATE METHODS    
+    methods(Access=protected)
+       
+%% Main method calculating the contribution to the head from each model componant.
+        function [h_star, colnames] = get_h_star(obj, time_points, varargin)
+% get_h_star private method calculating the contribution to the head from each model componant.
+%
+% Syntax:
+%   [h_star, colnames] = get_h_star(obj, time_points)
+%
+% Description:
+%   This method performs the main calculates of the model. The method 
+%   is private and is called by either solve() or objectiveFunction() public
+%   methods. The method is highly flexible, allowing any number of model
+%   components and forcing transformations. The numerical integration of
+%   the convolution function is also using a highly efficient MEX .c
+%   'doIRFconvolution.c', which undertakes trapazoidal integration for
+%   integrated forcing (eg daily precipitation) or Simpson's 3/8 composite
+%   inegration for instantaneous fluxes.
+
+%   Depending upon the user set model componants, this method 
+%   undertakes the following seqential steps:
+%
+%   1) Transform the forcing data. This is undertaken using for the nonlinear
+%   TFN models of Peterson and Westenr (2014). 
+%
+%   2) If model componant 'i' is an impulse response function (IRF), the 
+%   componant method 'theta' is called with all daily step time points less
+%   than or equal to the latest date head for simulation. Theta and the
+%   forcing data are then passed to doIRFconvolution() for the integration at
+%   each water level observation time point.
+%   
+%   3) If the componant is not an IRF, then a user specified
+%   IRF matrix from step 2 (which is often precipitation or ET) is scaled 
+%   by an appropriate parameter and daily forcing data and then integrated
+%   using doIRFconvolution().
+%
+%   4) Finally, the contribution from all componants are summed to produce
+%   h* (as per von Asmuth et al 2002).
+%
+% Inputs:
+%   obj -  model object
+%
+%   time_points - column vector of the time points to be simulated.  
+%
+% Outputs:
+%
+%   h_star - matrix of the contribution from various model componants and
+%   their summed influence. The matrix columns are in the order of:
+%   date/time, summed contribution to the head, contribution frolm
+%   componant i.
+%
+%   colnames - column names for matrix 'head'.
+%
+% Example:
+%   see HydroSight: time_series_model_calibration_and_construction;
+%
+% See also:
+%   HydroSight: time_series_model_calibration_and_construction;
+%   model_TFN: model_construction;
+%   calibration_finalise: initialisation_of_model_prior_to_calibration;
+%   calibration_initialise: initialisation_of_model_prior_to_calibration;
+%   getParameters: returns_a_vector_of_parameter_values_and_names;
+%   setParameters: sets_model_parameters_from_input_vector_of_parameter_values;
+%   solve: solve_the_model_at_user_input_sime_points;
+%
+% References:
+%   von Asmuth J. R., Bierkens M. F. P., Mass K., 2002, Transfer
+%   dunction-noise modeling in continuous time using predefined impulse
+%   response functions.
+%
+% Author: 
+%   Dr. Tim Peterson, The Department of Infrastructure
+%   Engineering, The University of Melbourne.
+%
+% Date:
+%   26 Sept 2014    
+
+            % Get componants of the model.
+            companants = fieldnames(obj.inputData.componentData);
+            nCompanants = size(companants,1);  
+
+            % Calc theta_t for max time to initial time (NOTE: min tor = 0).                        
+            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points(end));
+            tor = flipud([0:time_points(end)  - obj.inputData.forcingData(1,1)+1]');
+            tor_end = tor( obj.variables.theta_est_indexes_min(1,:) )';            
+            t = obj.inputData.forcingData( filt ,1);
+
+            % Calculate the transformation models that DO NOT require the
+            % input of a forcing model and the forcing model is denoted as the
+            % complete model to be ran (ie not also used by another
+            % component).
+            for i=1:nCompanants
+                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
+                && ~isfield(obj.inputData.componentData.(companants{i}),'inputForcingComponent') ...
+                && isfield(obj.inputData.componentData.(companants{i}),'isForcingModel2BeRun') ...
+                && obj.inputData.componentData.(companants{i}).isForcingModel2BeRun
+                    setTransformedForcing(obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), t, true)                    
+                end
+            end
+            
+            % Calculate the transformation models that DO require the
+            % input of a forcing model  and the forcing model is denoted as the
+            % complete model to be ran (ie not also used by another
+            % component).
+            for i=1:nCompanants
+                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
+                && isfield(obj.inputData.componentData.(companants{i}),'inputForcingComponent') ...
+                && isfield(obj.inputData.componentData.(companants{i}),'isForcingModel2BeRun') ...
+                && obj.inputData.componentData.(companants{i}).isForcingModel2BeRun            
+                    setTransformedForcing(obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), t, true)
+                end
+            end            
+            
+            % Assign the forcing data for each model componant.
+            % Also, if the model is being calibrated, then also
+            % calculate the mean forcing. The later is essential to reduce
+            % the calibration error when using a forcing series
+            % significantly shorter than the point back in time (ie tor) at
+            % which the transfer function is approx. zero.
+            nOutputColumns=0;
+            isForcingADailyIntegral = false(nCompanants,1);
+            for i=1:nCompanants
+               
+                if isfield(obj.inputData.componentData.(companants{i}),'forcing_object') ...
+                && isfield(obj.inputData.componentData.(companants{i}),'outputVariable')                    
+
+                    % Get te transformed forcing. 
+                    % NOTE: getTransformedForcing() must return a boolean
+                    % scaler deonting if the forcing is an instantaneous flux
+                    % or an integral over the day. For the former, Simpson's
+                    % 3/8 integration is used for the convolution while for
+                    % the latter trapzoidal integration of theta is undertaken and then 
+                    % the daily integration result multiplied by the integrated daily forcing.
+                    [obj.variables.(companants{i}).forcingData, isForcingADailyIntegral(i)] = ...
+                    getTransformedForcing( obj.parameters.(obj.inputData.componentData.(companants{i}).forcing_object), ...
+                    obj.inputData.componentData.(companants{i}).outputVariable);
+                
+                    obj.variables.(companants{i}).forcingData_colnames = obj.inputData.componentData.(companants{i}).outputVariable;
+                                        
+                else
+                    
+                    % Non-transformed forcing is assumed to be a daily
+                    % integral. Hence, doIRFconvolution() undertakes daily
+                    % trapzoidal integration of theta and multiplies it by
+                    % the integrated daily forcing.
+                    isForcingADailyIntegral(i) = true;
+                    
+                    obj.variables.(companants{i}).forcingData = obj.inputData.forcingData(filt, ...
+                    obj.inputData.componentData.(companants{i}).dataColumn);
+
+                
+                    obj.variables.(companants{i}).forcingData_colnames =  ...
+                    obj.inputData.forcingData_colnames(obj.inputData.componentData.(companants{i}).dataColumn);
+                end
+                % Increase the number of output columns for method.
+                nOutputColumns = nOutputColumns + size(obj.variables.(companants{i}).forcingData,2);
+            end            
+            
+            % Initialise ouput vector.
+            h_star = zeros( size(time_points,1),  nOutputColumns);       
+            iOutputColumns = 0;
+            % Calculate each transfer function.
+            for i=1:nCompanants
+                                
+                % Calcule theta for each time point of forcing data.
+                theta_est_temp = theta(obj.parameters.( char(companants(i))), tor);                
+
+                % Get analytical esitmates of lower and upper theta tails
+                integralTheta_upperTail = intTheta_upperTail2Inf(obj.parameters.( char(companants(i))), tor_end);                           
+                integralTheta_lowerTail = intTheta_lowerTail(obj.parameters.( char(companants(i))), 1);
+
+                % Get the mean forcing.
+                if ~isempty(varargin) && isfield(varargin{1},companants{i})
+                    %forcingMean = obj.variables.(companants{i}).forcingMean                    
+                    forcingMean = varargin{1}.(companants{i});
+                else
+                    forcingMean = mean(obj.variables.(companants{i}).forcingData);
+                end                
+                
+                % Integrate transfer function over tor.
+                for j=1: size(theta_est_temp,2)
+                    % Increment the output volumn index.
+                    iOutputColumns = iOutputColumns + 1;
+
+                    % Try to call doIRFconvolution using Xeon Phi
+                    % Offload coprocessors. This will only work if the
+                    % computer has (1) the intel compiler >2013.1 and (2)
+                    % xeon phi cards. The code first tried to call the
+                    % mex function. 
+                    if ~isfield(obj.variables,'useXeonPhiCard')
+                        obj.variables.useXeonPhiCard = true;
+                    end
+                    
+                    try
+                        if obj.variables.useXeonPhiCard
+                            %display('Offloading convolution algorithm to Xeon Phi coprocessor!');
+                            h_star(:,iOutputColumns) = doIRFconvolutionPhi(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
+                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
+                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
+                        else                            
+                            h_star(:,iOutputColumns) = doIRFconvolution(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
+                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
+                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
+                        end
+                            
+                    catch
+                        %display('Offloading convolution algorithm to Xeon Phi coprocessor failed - falling back to CPU!');
+                        obj.variables.useXeonPhiCard = false;
+                        h_star(:,iOutputColumns) = doIRFconvolution(theta_est_temp(:,j), obj.variables.theta_est_indexes_min, obj.variables.theta_est_indexes_max(1), ...
+                                obj.variables.(companants{i}).forcingData(:,j), isForcingADailyIntegral(i), integralTheta_lowerTail(j))' ...
+                                + integralTheta_upperTail(j,:)' .* forcingMean(j);
+                    end                    
+                    % Transform the h_star estimate for the current
+                    % componant. This feature was included so that h_star
+                    % estimate fro groundwater pumping could be corrected 
+                    % for an unconfined aquifer using Jacobs correction.
+                    % Peterson Feb 2013.
+                    h_star(:,iOutputColumns) = transform_h_star(obj.parameters.( char(companants(i))), [time_points, h_star(:,iOutputColumns)]);
+                        
+                    % Add output name to the cell array
+                    if ischar(obj.variables.(companants{i}).forcingData_colnames)
+                        colnames{iOutputColumns} = companants{i};
+                    else
+                        colnames{iOutputColumns} = [companants{i}, ' - ',obj.variables.(companants{i}).forcingData_colnames{j}];
+                    end
+                end
+            end
+            
+            % Sum all componants (excluding soil moisture) and add time
+            % vector.
+            if size(h_star,2)>1
+                h_star = [time_points , sum(h_star,2), h_star];
+                colnames = {'time','Head',colnames{:}};
+            else
+                h_star = [ time_points, h_star];
+                colnames = {'time','Head'};               
+            end
+                                
+        end
+    end
+    
+
 end
\ No newline at end of file
diff --git a/algorithms/models/TransferNoise/model_TFN_SW_GW.m b/algorithms/models/TransferNoise/model_TFN_SW_GW.m
new file mode 100644
index 0000000..4f53bc2
--- /dev/null
+++ b/algorithms/models/TransferNoise/model_TFN_SW_GW.m
@@ -0,0 +1,792 @@
+classdef model_TFN_SW_GW < model_TFN & model_abstract
+    % Class definition for Transfer Function Noise (TFN) model for use with
+    % HydroSight to jointly calibrate and simulate Stremflow and GW head. 
+
+    %   Model uses the weighting function of the GW head interpolation to
+    %   inform the behavior of baseflow, whether it is discharge to or recharging from the river.
+
+    properties
+
+
+    end
+
+
+    %%  STATIC METHODS
+    % Static methods used by the Graphical User Interface to inform the
+    % user of the available model types. Any new models must be listed here
+    % in order to be accessable within the GUI.
+    methods(Static)
+        % Provides a simple description of the function for the user
+        % interface.
+        function str = description()
+
+            str = {['"model_TFN_SW_GW" is a highly flexible nonlinear transfer function noise time-series model. '], ...
+                'The model has the following additional features:', ...
+                '   - joint simulation of groundwater head and streamflow time-series', ...
+                '', ...
+                'For further details of the algorithms use the help menu or see:', ...
+                '', ...                				
+				'   - Bonotto, Peterson, Fowler, & Western (2022), HydroSight SW-GW: lumped rainfall-runoff model for the joint simulation of streamflow and groundwater in a drying climate, Geoscientific Model Development , , –', ...
+			    '', ...
+				'   - Bonotto, Peterson, Fowler, & Western (2022), Can the joint simulation of daily streamflow and groundwater head help to explain the Millennium Drought hydrology?, Water Resour. Res., , –\', ...
+                '', ...
+                '   - Peterson, T. J., and A. W. Western (2014), Nonlinear time-series modeling of unconfined groundwater head, Water Resour. Res., 50, 8330–8355, doi:10.1002/2013WR014800.', ...
+                '', ...
+                '   - Shapoori V., Peterson T. J., Western A. W. and Costelloe J. F., (2015), Top-down groundwater hydrograph time series modeling for climate-pumping decomposition. Hydrogeology Journal'};
+
+        end
+    end
+
+
+
+    %%  PUBLIC METHODS
+
+    methods
+
+
+        %% Model constructor
+        function obj = model_TFN_SW_GW(model_label, bore_ID, obsData, obsDataFlow, forcingData_data,  forcingData_colnames, siteCoordinates, varargin)
+            % model_TFN_SW_GW constructs for linear and nonlinear Transfer Function Noise model for the joint calibration and simulation of GW head and streamflow
+            
+            
+            % Use sub-class constructor to inherit properties from model_TFN.
+            obj = obj@model_TFN(bore_ID, obsData, forcingData_data,  forcingData_colnames, siteCoordinates, varargin{1});
+
+            % including the model_name into the model object
+            obj.inputData.model_name = model_label;
+
+            % including the plot_hydrographs flag into the model object.
+            % This is used if user wants to see the simulated and observed
+            % GW and SW hydrographs during each model iteration. 
+			% By default, it is set as false.
+            obj.inputData.plot_hydrographs = false;
+
+            % including the save_output_data flag into the model object.
+            % This is used if user wants to save the simulated and observed
+            % GW and SW hydrographs.
+			% By default, it is set as false.
+            obj.inputData.save_output_data = false;
+            
+            % Derive columns of year, month, day etc to matlab date value
+            % for the observed streamflow time-series
+            switch size(obsDataFlow,2)-1
+                case 3
+                    obsDates = datenum(obsDataFlow(:,1), obsDataFlow(:,2), obsDataFlow(:,3));
+                case 4
+                    obsDates = datenum(obsDataFlow(:,1), obsDataFlow(:,2), obsDataFlow(:,3),obsDataFlow(:,4), zeros(size(obsDataFlow,1),1), zeros(size(obsDataFlow,1),1));
+                case 5
+                    obsDates = datenum(obsDataFlow(:,1), obsDataFlow(:,2), obsDataFlow(:,3),obsDataFlow(:,4),obsDataFlow(:,5), zeros(size(obsDataFlow,1),1));
+                case 6
+                    obsDates = datenum(obsDataFlow(:,1), obsDataFlow(:,2), obsDataFlow(:,3),obsDataFlow(:,4),obsDataFlow(:,5),obsDataFlow(:,6));
+                otherwise
+                    error('The input observed head must be 4 to 7 columns with right hand column being the head and the left columns: year; month; day; hour (optional), minute (optionl), second (optional).');
+            end
+
+
+            % Use all streamflow input data
+            obsDataFlow = [obsDates, obsDataFlow(:,end)];
+          
+
+            % Trim streamflow data to be <= GW head end date
+            endDate  = min(obsData(end,1), obsDataFlow(end,1));
+            filt = obsDataFlow(:,1)<=endDate;
+            obsDataFlow = obsDataFlow(filt,:);
+            
+            % Check streamflow for Nans
+            filt = isnan(obsDataFlow(:,2));
+            obsDataFlow = obsDataFlow(~filt,:);
+
+            % including the flow data into the input data object
+            obj.inputData.flow = obsDataFlow;
+
+
+            %% Set Obj parameters for the baseflow calculation
+            % Choose "baseflow" object from the following options:
+            %-----------------------------------------------------
+            % baseflow_v1, baseflow_v2, baseflow_m1, baseflow_m2, baseflow_m3,
+            % baseflow_m4, baseflow_m5, baseflow_m6, baseflow_m7,
+            % baseflow_m8, baseflow_m9, baseflow_bi_1, baseflow_bi_2, baseflow_bi_3, baseflow_bi_4
+            %----------------------------------------------------
+
+            if contains(model_label,'baseflow_v1')
+                obj.parameters.baseflow = baseflow_v1(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_v2')
+                obj.parameters.baseflow = baseflow_v2(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m1')
+                obj.parameters.baseflow = baseflow_m1(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m2')
+                obj.parameters.baseflow = baseflow_m2(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m3')
+                obj.parameters.baseflow = baseflow_m3(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m4')
+                obj.parameters.baseflow = baseflow_m4(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m5')
+                obj.parameters.baseflow = baseflow_m5(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m6')
+                obj.parameters.baseflow = baseflow_m6(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m7')
+                obj.parameters.baseflow = baseflow_m7(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m8')
+                obj.parameters.baseflow = baseflow_m8(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_m9')
+                obj.parameters.baseflow = baseflow_m9(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_bi_1')
+                obj.parameters.baseflow = baseflow_bi_1(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_bi_2')
+                obj.parameters.baseflow = baseflow_bi_2(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_bi_3')
+                obj.parameters.baseflow = baseflow_bi_3(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            elseif contains(model_label,'baseflow_bi_4')
+                obj.parameters.baseflow = baseflow_bi_4(bore_ID, forcingData_data, forcingData_colnames, siteCoordinates, [], []);
+            else
+                error('Please, include a valid baseflow object option in the model_label. Options are: baseflow_v1, baseflow_v2, baseflow_m1, baseflow_m2, baseflow_m3, baseflow_m4, baseflow_m5, baseflow_m6, baseflow_m7, baseflow_m8, baseflow_m9, baseflow_bi_1, baseflow_bi_2, baseflow_bi_3, baseflow_bi_4');
+            end
+
+            obj.parameters.baseflow % print the baseflow object to see if indeed it is using the one assigned
+        end
+
+
+        %         function  [params_initial, time_points_head, time_points_streamflow ] = calibration_initialise(obj, t_start, t_end);
+        function  [params_initial, time_points_head, time_points_streamflow ] = calibration_initialise_joint(obj, t_start, t_end);
+
+            % Set a flag to indicate that calibration is being undertaken.
+            obj.variables.doingCalibration = true;
+
+            % Get parameter names and initial values
+            [params_initial, obj.variables.param_names] = getParameters(obj);
+
+            % Using calibration_initialize function from model_TFN object.
+            % calibration_initialise@model_TFN(obj, t_start, t_end); This
+            % step includes the properties needed for the drainage convolution and
+            % and GW head Obj-Function and calibration
+            calibration_initialise(obj, t_start, t_end);
+
+            % Derive time points for groundwater head objective
+            % function calc.
+            obsHead = getObservedHead(obj);
+            t_filt = find( obsHead(:,1) >=t_start  ...
+                & obsHead(:,1) <= t_end );
+            time_points_head = obsHead(t_filt,1);
+            obj.variables.time_points_head = time_points_head;
+         
+
+            % Derive time steps for streamflow data matching the period
+            % with gw head obs. data
+            t_filt = find( obj.inputData.flow(:,1) >=time_points_head(1)  ...
+                & obj.inputData.flow(:,1) <= time_points_head(end) );
+            time_points_streamflow = obj.inputData.flow(t_filt,1);
+            obj.variables.time_points_streamflow = time_points_streamflow;
+
+            
+            % Setup matrix of indexes for tor at each runoff time_points
+            % that match GW head observation period
+            % Setup matrix of indexes for tor at each time_points
+            filt = obj.inputData.forcingData ( : ,1) <= ceil(time_points_streamflow(end));
+            tor = flipud([0:time_points_streamflow(end)  - obj.inputData.forcingData(filt,1)+1]');
+            ntor =  size(tor, 1);
+            clear tor;
+
+            obj.variables.theta_est_indexes_min_flow = zeros(1,length(time_points_streamflow) );
+            obj.variables.theta_est_indexes_max_flow = zeros(1,length(time_points_streamflow) );
+
+            for ii= 1:length(time_points_streamflow)
+                ntheta = sum( obj.inputData.forcingData ( : ,1) <= time_points_streamflow(ii) );
+                obj.variables.theta_est_indexes_min_flow(ii) = ntor-ntheta;
+                obj.variables.theta_est_indexes_max_flow(ii) = max(1,ntor);
+            end
+
+
+
+        end
+
+
+
+        function [objFn_joint, objFn_head, objFn_flow, objFn_flow_NSE, objFn_flow_NNSE, objFn_flow_RMSE, objFn_flow_SSE, objFn_flow_bias, objFn_flow_KGE, totalFlow_sim, colnames, drainage_elevation] = objectiveFunction_joint(params, time_points_head, time_points_streamflow, obj, varargin)
+         
+
+            % Call objectiveFunction in model_TFN to get GW head objFn
+            [objFn_head, h_star, colnames, drainage_elevation] = objectiveFunction(params, time_points_head, obj, false);  
+            
+            % Add the drainage elevation to the object. This
+            % is just done because the model needs to be solved for the
+            % streamflow time steps, and to do so model_TFN assumes the
+            % calibration has assigned obj.variables.d.            
+            obj.variables.d = drainage_elevation;
+            obj.variables.doingCalibration = false; 
+
+            % Store some variables that are cleared when the model_TFN
+            % solve() is called. These will be added back.
+            theta_est_indexes_min = obj.variables.theta_est_indexes_min;
+            theta_est_indexes_max = obj.variables.theta_est_indexes_max;
+            delta_time = obj.variables.delta_time;
+
+            % adding the forcingMean in obj.variables."precip or
+            % ET".forcingMean, which function "solves" requires
+            companants = fieldnames(obj.inputData.componentData);
+            nCompanants = size(companants,1);
+
+            for j=1:nCompanants
+                obj.variables.(companants{j}).forcingMean(:,1) = mean(obj.variables.(companants{j}).forcingData);
+            end
+
+			% Call method in model_TFN_SW_GW to return simulated flow
+            % (using the simulated head to calculate baseflow at the timepoints
+            % with stream obs.)
+            [totalFlow_sim, baseFlow, quickFlow] = getStreamFlow(time_points_streamflow, obj, varargin, theta_est_indexes_min, theta_est_indexes_max, delta_time, params);
+
+
+            % Call method in model_TFN_SW_GW to return obs flow
+            obsFlow = getObservedFlow(obj);
+
+            % trim obs. flow data to match the period used for calibration
+            t_filt = find( obsFlow(:,1) >=time_points_streamflow(1)  ...
+                & obsFlow(:,1) <= time_points_streamflow(end) );
+            obsFlow = obsFlow(t_filt,:);
+
+
+            % Calc. stremflow objFunctions:
+
+            % NSE
+            objFn_flow_NSE = 1 - ( sum((totalFlow_sim(:,2) - obsFlow(:,2)).^2)./ ...
+                sum((obsFlow(:,2) - mean(obsFlow(:,2))).^2));
+            % normalized NSE, where zero is equivalent to -Inf, 0.5 to 0, and 1 to 1 in NSE (Nossent and Bauwens, EGU 2012)
+            objFn_flow_NNSE = 1 / (2- objFn_flow_NSE);
+            % RMSE
+            objFn_flow_RMSE = sqrt(sum((totalFlow_sim(:,2) - obsFlow(:,2)).^2)/ size(obsFlow,1));
+            % SSE
+            objFn_flow_SSE = sum((totalFlow_sim(:,2) - obsFlow(:,2)).^2);
+            % Bias
+            objFn_flow_bias = sum(totalFlow_sim(:,2) - obsFlow(:,2))/length(obsFlow(:,2));
+            % KGE
+            w = [1,1,1];                                        % no weights specified, use defaults
+            c(1) = corr(obsFlow(:,2),totalFlow_sim(:,2));       % r: linear correlation
+            c(2) = std(totalFlow_sim(:,2))/std(obsFlow(:,2));   % alpha: ratio of standard deviations
+            c(3) = mean(totalFlow_sim(:,2))/mean(obsFlow(:,2)); % beta: bias
+            objFn_flow_KGE = 1-sqrt((w(1)*(c(1)-1))^2 + (w(2)*(c(2)-1))^2 + (w(3)*(c(3)-1))^2); % weighted KGE
+            if isnan(objFn_flow_KGE)
+                objFn_flow_KGE = -9998;
+            end
+
+            % Choosing the streamflow objective function for AMALGAM. 
+			% IMPORTANT: in AMALGAM we want to minize the obj-func!
+            %         objFn_flow = 1 - objFn_flow_NSE;       %1-NSE
+            %         objFn_flow = 1 - objFn_flow_NNSE;      %1-NNSE
+            %         objFn_flow = objFn_flow_RMSE;          %RMSE
+            %         objFn_flow = objFn_flow_SSE;           %SSE
+            %         objFn_flow = abs(objFn_flow_bias);     %BIAS
+            objFn_flow = 1 - objFn_flow_KGE;               %1-KGE
+
+
+            % Get the parameter names for the soil moisture component
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                [params, param_names] = getParameters(obj.parameters.climateTransform_soilMoistureModels_v2);
+
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                [params, param_names] = getParameters(obj.parameters.climateTransform_soilMoistureModels_interflow);
+            end
+
+            % check if parameters are valid according to physical bounds and Budyko criteria
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                validParams = getParameterValidity(obj.parameters.climateTransform_soilMoistureModels_v2, params, time_points_streamflow);
+
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                validParams = getParameterValidity(obj.parameters.climateTransform_soilMoistureModels_interflow, params, time_points_streamflow);
+            end
+
+            % Checking if parameter are valid according to the mass-balance criteria between total GW-recharge
+            % (free-drainage) and total baseflow. It has to be done here as it uses both soilMoisture and baseflow components
+            % Get the parameter names for all model components
+            validParams2 = getMassBalanceValidity(obj,time_points_streamflow);
+
+            %  if parameters are not valid, set ObjFun to large values so AMALGAM don't propagate them in the evolutionary optmization.
+            if any(~validParams) | any(~validParams2)
+                % Merging objFunctions for head and flow
+                objFn_joint = [99999, 99999]
+            else                
+                objFn_joint = [objFn_head, objFn_flow]
+            end
+
+
+            if obj.inputData.plot_hydrographs == false
+                % no hydrograph diagnosis plot is generated
+            else
+                
+				% Creating the Observed head/flow vs. Simulated head/flow plots
+
+                %  plotting scatter_obs_sim_daily_GWhead
+                figure(1)
+                scatter (obj.inputData.head(:,2), (h_star(:,2) +  drainage_elevation))
+                title(' Observed Vs. Simulated Head')
+                xlabel('Obs. Head (mAHD)')
+                ylabel('Sim. Head (mAHD)')
+                axis square
+                %                 daspect([1 1 1])
+                myRefLine = refline(1);
+                myRefLine.Color = 'r';
+                myRefLine.LineStyle = '--';
+
+                %  plotting obs_sim_daily_head
+                figure(2)
+                plot (obj.inputData.head(:,1), obj.inputData.head(:,2))
+                title(' Observed and Simulated Head')
+                xlabel('Date')
+                ylabel('Head (mAHD)')
+                hold on
+                plot (h_star(:,1), (h_star(:,2) +  drainage_elevation))
+                legend('Obs. Head','Sim. Head')
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                dynamicDateTicks()
+                hold off
+
+                %  plotting scatter_obs_sim_daily_runoff
+                figure(3)
+                scatter (obsFlow(:,2), totalFlow_sim(:,2))
+                title(' Observed Vs. Simulated Flow')
+                xlabel('Obs. Flow (mm/day)')
+                ylabel('Sim. Flow (mm/day)')
+                axis square
+                %         daspect([1 1 1])
+                myRefLine = refline(1);
+                myRefLine.Color = 'r';
+                myRefLine.LineStyle = '--';
+
+                %  plotting obs_sim_daily_runoff
+                figure(4) 
+                plot (obsFlow(:,1), obsFlow(:,2))
+                title(' Observed and Simulated Flow')
+                xlabel('Date')
+                ylabel('Flow (mm/day)')
+                hold on
+                plot (totalFlow_sim(:,1), totalFlow_sim(:,2))
+                legend('Obs. Flow','Sim. Flow')
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                dynamicDateTicks()
+                hold off
+
+
+                %  plotting obs_sim_daily_baseflow_runoff
+                figure(5)
+                plot(totalFlow_sim(:,1),totalFlow_sim(:,2))
+                title('observed and simulated streamflow')
+                xlabel('Date')
+                ylabel('mm/day')
+                ylim([0 (max(obsFlow(:,2))+10)])
+                grid on
+                ax = gca;
+                ax.FontSize = 11;
+                hold on
+                plot(totalFlow_sim(:,1),baseFlow)
+                hold on
+                plot(quickFlow(:,1),quickFlow(:,2))
+                hold on
+                plot (obsFlow(:,1), obsFlow(:,2))
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                dynamicDateTicks()
+                hold off
+                legend('totalFlow_sim','baseFlow','quickFlow','Observed_Flow')
+
+                % ploting obs_daily_runoff
+                figure(6)
+                plot( obsFlow(:,1), obsFlow(:,2))
+                title(' streamflow observations')
+                xlabel('Date ')
+                ylabel('mm/day')
+                grid on
+                ax = gca;
+                ax.FontSize = 13;
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                dynamicDateTicks()
+
+                % ploting daily_baseflow
+                figure(7)
+                plot(totalFlow_sim(:,1),baseFlow)
+                title(' baseflow simulated')
+                xlabel('Date ')
+                ylabel('mm/day')
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                dynamicDateTicks()
+                grid on
+                ax = gca;
+                ax.FontSize = 13;
+                legend('baseFlow')
+
+
+                figure(8); % subdaily_SMS_SMSC
+                if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                    plot(obj.parameters.climateTransform_soilMoistureModels_v2.variables.SMS, 'g')
+                    y = obj.parameters.climateTransform_soilMoistureModels_v2.SMSC;
+                elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                    plot(obj.parameters.climateTransform_soilMoistureModels_interflow.variables.SMS, 'g')
+                    y = obj.parameters.climateTransform_soilMoistureModels_interflow.SMSC;
+                end
+                line([0,68000],[10.^(y),10.^(y)])
+                xlabel('Sub-daily time-steps')
+                ylabel('Soil Moisture')
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                legend('SMS', 'SMSC')
+
+
+                figure(9); % subdaily_EPS                
+                if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                    xx = obj.parameters.climateTransform_soilMoistureModels_v2.variables.SMS ./ (10 .^ obj.parameters.climateTransform_soilMoistureModels_v2.SMSC);
+                elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                    xx = obj.parameters.climateTransform_soilMoistureModels_interflow.variables.SMS ./ (10 .^ obj.parameters.climateTransform_soilMoistureModels_interflow.SMSC);
+                end
+                plot(xx)
+                title('SMS / SMSC')
+                xlabel('Sub-daily time-steps')
+                ylabel('ratio')
+                ylim([0 1])
+                % datetick('x', 'dd/mm/yy', 'keepticks')
+                legend('(SMS / SMSC)')
+
+
+                %  plotting ZOOM_obs_sim_daily_baseflow_runoff 
+                %  choosing the ZOOM PERIOD
+                zoom_min = datenum(2007, 03, 01);
+                zoom_max = datenum(2008, 03, 01);
+                t_filt = find( totalFlow_sim(:,1) >=zoom_min  ...
+                    & totalFlow_sim(:,1) <= zoom_max );                
+                figure(10)
+                plot(totalFlow_sim(t_filt,1),totalFlow_sim(t_filt,2))
+                title('observed and simulated streamflow')
+                xlabel('Date')
+                ylabel('mm/day')
+                ylim([0 (max(obsFlow(t_filt,2))+2.5)])
+                grid on
+                ax = gca;
+                ax.FontSize = 11;
+                hold on
+                plot(totalFlow_sim(t_filt,1),baseFlow(t_filt,1))
+                hold on
+                plot(quickFlow(t_filt,1),quickFlow(t_filt,2))
+                hold on
+                plot (obsFlow(t_filt,1), obsFlow(t_filt,2))
+                datetick('x', 'dd/mm/yy', 'keepticks')
+%                 dynamicDateTicks()
+                hold off
+                legend('totalFlow_sim','baseFlow','quickFlow','Observed_Flow')
+
+            end
+
+
+            % Save diagnosis plot if not running in testing_only mode
+            if obj.inputData.save_output_data == true
+
+                model_label = obj.inputData.model_name;
+
+				% Path used to store the hydrographs
+				folder = 'C:\Users\bonottog\OneDrive - The University of Melbourne (1)\1 - UNIMELB\5 - HydroSight\10 - Run Results\Interflow_Scenarios\500k';
+
+                f = figure(1);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'scatter_obs_sim_daily_GWhead';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);                
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(2);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'obs_sim_daily_head';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);                
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(3);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'scatter_obs_sim_daily_runoff';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(4);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'obs_sim_daily_runoff';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(5);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'obs_sim_daily_baseflow_runoff';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(6);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'obs_daily_runoff';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(7);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'daily_baseflow';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(8);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'subdaily_SMS_SMSC';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(9);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'subdaily_EPS';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+                f = figure(10);
+                set(f, 'Color', 'w');
+                A1 = model_label;
+                A2 = 'ZOOM_obs_sim_daily_baseflow_runoff';
+                formatSpec = '%1$s %2$s';
+                Filename = sprintf(formatSpec,A1,A2);
+                saveas(f, fullfile(folder, Filename), 'png');
+
+
+            else
+                % continue without saving the hydrograph plots.
+            end
+
+            obj.variables.doingCalibration = true; % true to allow the parfor loop in AMALGAM - TURN THIS OFF when not using AMALGAM
+
+        end
+
+        % get quickFlow and baseFlow using simulated head and streamflow
+        function [totalFlow, baseFlow, quickFlow] = getStreamFlow(time_points_streamflow, obj, varargin, theta_est_indexes_min, theta_est_indexes_max, delta_time, params)
+
+
+            % get simulated head at the streamflow time points
+            [head, colnames, noise] = solve(obj, time_points_streamflow); 
+            obj.parameters.variables.doingCalibration = true;
+
+            % Add model_TFN variables back that are cleared when the model_TFN
+            % solve() is called. This is necessary for the next AMALGAM iteration
+            obj.variables.theta_est_indexes_min = theta_est_indexes_min;
+            obj.variables.theta_est_indexes_max = theta_est_indexes_max;
+            obj.variables.delta_time = delta_time;
+            clear theta_est_indexes_min theta_est_indexes_max delta_time
+
+            % set head in "baseflow" object (using setForcingData)
+            setForcingData(obj.parameters.baseflow, head, 'head');
+
+            % calc. baseflow using setTransformedForcing in "baseflow" object
+            setTransformedForcing(obj.parameters.baseflow, time_points_streamflow, true);
+
+            % get calculated baseflow in baseflow
+            baseFlow = getTransformedForcing(obj.parameters.baseflow, time_points_streamflow);
+
+            % Get the runoff from the forcing tranform function.
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                quickFlow_all = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_v2, 'runoff', 1, true); % set "true" to get daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                quickFlow_all = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_interflow, 'runoff_total', 1, true); 
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_2layer_v2"))>0
+                quickFlow_all = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_2layer_v2, 'runoff_total', 1, true); 
+            end
+
+            % Create filter for the days with streamflow obs.
+            [forcingData, forcingData_colnames] = getForcingData(obj);
+            time_points_forcing = forcingData(:,1);
+            filt =  time_points_forcing <= obj.variables.time_points_streamflow(end); % cause forcing data goes beyond more recent observed flow.
+            time_points_forcing = time_points_forcing(filt);
+
+            % Add the time steps for the runoff.
+            quickFlow = [time_points_forcing quickFlow_all];
+
+            % Filter the daily runoff data (without gaps) to the dates
+            % having observed flow.
+            filt =  quickFlow(:,1) >= time_points_streamflow(1) ...
+                & quickFlow(:,1) <= time_points_streamflow(end);
+            quickFlow = quickFlow(filt,:);
+            
+            
+            % getting the derived forcing data, which includes the quick flow
+            % (runoff and interflow)
+            [allDerivedForcingData, derivedForcingData_colnames] = getDerivedForcingData(obj, time_points_streamflow);  
+            
+
+            % get 'infiltration_fractional_capacity', both daily and sub-daily time-steps to quality check
+            % daily data
+            [tf, column_number] = ismember('infiltration_fractional_capacity', derivedForcingData_colnames);
+            if ~tf
+                error('infiltration_fractional_capacity is not one of the derived forcings variables. Check if using new soil models or model_TFN_SW_GW')
+            end
+            infiltration_fractional_capacity = allDerivedForcingData(:,column_number);
+            
+            % sub-daily data
+            SMSnumber=1;
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                [infiltration_fractional_capacity_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_v2, 'infiltration_fractional_capacity', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                [infiltration_fractional_capacity_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_interflow, 'infiltration_fractional_capacity', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_2layer_v2"))>0
+                [infiltration_fractional_capacity_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_2layer_v2, 'infiltration_fractional_capacity', SMSnumber, false); % set "true" if need daily data
+            end
+
+
+            % get 'mass_balance_error', both daily and sub-daily time-steps to quality check
+            % daily data
+            [tf, column_number] = ismember('mass_balance_error', derivedForcingData_colnames);
+            if ~tf
+                error('mass_balance_error is not one of the derived forcings variables. Check if using new soil models or model_TFN_SW_GW')
+            end
+            mass_balance_error_plot = allDerivedForcingData(:,column_number);
+
+            % sub-daily data
+            SMSnumber=1;
+
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                [mass_balance_error_plot_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_v2, 'mass_balance_error', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                [mass_balance_error_plot_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_interflow, 'mass_balance_error', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_2layer_v2"))>0
+                [mass_balance_error_plot_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_2layer_v2, 'mass_balance_error', SMSnumber, false); % set "true" if need daily data
+            end
+
+
+            % get 'infiltration', both daily and sub-daily time-steps to quality check
+            % daily data
+            [tf, column_number] = ismember('infiltration', derivedForcingData_colnames);
+            if ~tf
+                error('infiltration is not one of the derived forcings variables. Check if using 2-layer v2 soil model or model_TFN_SW_GW')
+            end
+            infiltration = allDerivedForcingData(:,column_number);
+            % sub-daily data
+            SMSnumber=1;
+            
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                [infiltration_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_v2, 'infiltration', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                [infiltration_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_interflow, 'infiltration', SMSnumber, false); % set "true" if need daily data
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_2layer_v2"))>0
+                [infiltration_2, isDailyIntegralFlux] = getTransformedForcing(obj.parameters.climateTransform_soilMoistureModels_2layer_v2, 'infiltration', SMSnumber, false); % set "true" if need daily data
+            end
+
+            % get 'SMS' (soil moisture), both daily and sub-daily time-steps to quality check
+            % daily data
+            [tf, column_number] = ismember('SMS', derivedForcingData_colnames);
+            if ~tf
+                error('SMS is not one of the derived forcings variables. Check if using 2-layer v2 soil model or model_TFN_SW_GW')
+            end
+            soil_moisture = allDerivedForcingData(:,column_number);
+            
+            % sub-daily data                       
+            if sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_v2"))>0
+                soil_moisture_2 = obj.parameters.climateTransform_soilMoistureModels_v2.variables.SMS ;
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_interflow"))>0
+                soil_moisture_2 = obj.parameters.climateTransform_soilMoistureModels_interflow.variables.SMS ;
+            elseif sum(strcmp(fieldnames(obj.parameters), "climateTransform_soilMoistureModels_2layer_v2"))>0
+                soil_moisture_2 = obj.parameters.climateTransform_soilMoistureModels_2layer_v2.variables.SMS ;
+            end
+
+
+            % Initialise total flow
+            obj.variables.totalFlow = [];
+
+            % Get the runoff from the derived forcing output            
+            [tf, column_number] = ismember('runoff', derivedForcingData_colnames); % shall we now use runoff_total??
+
+            if ~tf
+                error('runoff_total is not one of the derivedForcingData variables. Check if using 2-layer_v2 soil model')
+            end
+
+                       
+            % calculate total flow
+            totalFlow = max(0, quickFlow(:,2) + baseFlow); % limiting the total flow to be equal or above zero
+            totalFlow = [quickFlow(:,1) totalFlow];
+
+            % Storing total flow
+            obj.variables.totalFlow = totalFlow;
+            
+            
+            % checking parameters used in the model
+            [params, param_names] = getParameters(obj);
+            check_parameters = {};
+            check_parameters(:,1) = param_names(1:end,2);
+            check_parameters(:,2)= num2cell(params(1:end));
+            
+            if obj.inputData.plot_hydrographs == false
+                % dont print check_parameters if not plotting hydrographs.
+            else
+                check_parameters
+            end
+
+        end
+
+        % Get the observed streamflow
+        function obsFlow = getObservedFlow(obj)
+            obsFlow = obj.inputData.flow;
+        end
+
+        function isValidParameter = getMassBalanceValidity(obj,time_points_streamflow)
+            % Checking if parameters are valid according to the mass-balance criteria between total GW-recharge
+            % (free-drainage) and total baseflow. It has to be done here as it uses both soilMoisture and baseflow components
+            
+			% Get the parameter names for all model components
+            [params, param_names] = getParameters(obj);
+
+            % Initialise output
+            isValidParameter = true(size(params));
+
+            for i=1:size(params,1)
+                % Calculate total total GW-recharge (free-drainage)
+				
+				[allDerivedForcingData, derivedForcingData_colnames] = getDerivedForcingData(obj,time_points_streamflow);  
+
+                [tf, column_number] = ismember('drainage', derivedForcingData_colnames); 
+
+                if ~tf
+                    error('drainage is not one of the derivedForcingData variables...')
+                end
+
+                free_drainage = allDerivedForcingData(:,column_number);
+
+                % Create filter for the days with streamflow obs.
+                [forcingData, forcingData_colnames] = getForcingData(obj);
+                time_points_forcing = forcingData(:,1);
+                filt =  time_points_forcing <= obj.variables.time_points_streamflow(end); % cause forcing data goes beyond more recent observed flow.
+                time_points_forcing = time_points_forcing(filt);
+
+                % Add the time steps for the runoff.
+                free_drainage = [time_points_forcing free_drainage];
+
+                % Filter the daily runoff data (without gaps) to the dates
+                % having observed flow.
+                filt =  free_drainage(:,1) >= time_points_streamflow(1) ...
+                    & free_drainage(:,1) <= time_points_streamflow(end);
+                free_drainage = free_drainage(filt,:);
+
+                % integral of the free_drainage from soil moisture into groundwater
+                total_free_drainage =sum(free_drainage(:,2));
+
+                % Get baseflow
+                baseflow = obj.parameters.baseflow.variables.baseFlow;
+                
+				% integral of the baseflow from groundwater into streamflow
+                total_baseflow =sum(baseflow);
+
+                % Check if the baseflow is within the recharge bounds
+                if total_baseflow >= total_free_drainage
+                    isValidParameter(i,:) = false;
+                end
+            end
+        end
+
+    end
+end
diff --git a/algorithms/models/model_abstract.m b/algorithms/models/model_abstract.m
index 2047989..c657f10 100644
--- a/algorithms/models/model_abstract.m
+++ b/algorithms/models/model_abstract.m
@@ -1,42 +1,42 @@
-classdef model_abstract < handle
-% Abstract class for creating univariate time series models
-    
-    properties
-        
-        % Model parameters structure
-        parameters 
-        
-        % Observation data
-        inputData
-        
-        % Calculation variables
-        variables
-        
-        % Results
-        results
-    end    
-    
-    methods (Abstract)
-        
-        [head, colnames, noise] = solve(obj, time_points)
-            
-        [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
-            
-        calibration_finalise(obj, params, useLikelihood)
-            
-        [objFn, h_star] = objectiveFunction(params, time_points, obj)                
-        
-        setParameters(obj, params, param_names)
-        
-        [params, param_names] = getParameters(obj)
-        
-        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
-        
-        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
-        
-        isValidParameter = getParameterValidity(obj, params, time_points)
-        
-    end
-    
-end
-
+classdef model_abstract < handle
+% Abstract class for creating univariate time series models
+    
+    properties
+        
+        % Model parameters structure
+        parameters 
+        
+        % Observation data
+        inputData
+        
+        % Calculation variables
+        variables
+        
+        % Results
+        results
+    end    
+    
+    methods (Abstract)
+        
+        [head, colnames, noise] = solve(obj, time_points)
+            
+        [params_initial, time_points] = calibration_initialise(obj, t_start, t_end)
+            
+        calibration_finalise(obj, params, useLikelihood)
+            
+        [objFn, h_star] = objectiveFunction(params, time_points, obj)                
+        
+        setParameters(obj, params, param_names)
+        
+        [params, param_names] = getParameters(obj)
+        
+        [params_upperLimit, params_lowerLimit] = getParameters_physicalLimit(obj)
+        
+        [params_upperLimit, params_lowerLimit] = getParameters_plausibleLimit(obj)
+        
+        isValidParameter = getParameterValidity(obj, params, time_points)
+        
+    end
+    
+end
+
diff --git a/algorithms/utilities/addaxis6/Readme.txt b/algorithms/utilities/addaxis6/Readme.txt
new file mode 100644
index 0000000..1f3d648
--- /dev/null
+++ b/algorithms/utilities/addaxis6/Readme.txt
@@ -0,0 +1,85 @@
+%  Removed debugging line 11/15/2005
+
+%  Added zoom support for version R14SP2 11/24/2005
+
+%  Updated installation notes 11/24/2005
+
+%  In addaxisplot, only automatically change color to the axis color, if no
+%  other color specified  11/28/2005
+
+%  changed implementation to use appdata instead of userdata. (setaddaxisdata.m, getaddaxisdata.m)  
+%  Also, added addaxisreset to delete added axes and reset the parent axis
+%  and addaxisset which is used to set the ylimits of any axis after plotting  12/23/05
+
+%  changed splot.m to aa_splot.m to avoid the same name as splot.m that already 
+%  exists in another toolbox.  2/15/06
+
+%  changed addaxislabel so it doesn't matter if you use it addaxislabel(axis_number,label)
+%  or addaxislabel(label,axis_number) 2/15/06
+
+%  Zoom support for R2010a (maybe later versions as well)
+
+
+Files
+
+addaxis.m
+addaxisplot.m
+addaxislabel.m
+addaxisset.m
+addaxisreset.m
+getaddaxisdata.m
+setaddaxisdata.m
+aa_splot.m
+addaxis_zoom_post.m
+addaxis_zoom_pre.m
+
+
+
+zoom_R11.1_.m     
+
+zoom_R14SP2_.m
+buttonupfcn2D_R14SP2_.m
+
+
+Installation Notes:
+-------------------------------------------
+
+Put the addaxis files and aa_splot anywhere in your matlab path.
+
+Finally updated added axis zoom support for R2010a and maybe later versions using the 
+ActionPreCallback and ActionPostCallback
+
+
+-------------------------------------------
+Zoom modifications not necessary for R2010a and maybe later or earlier versions
+
+addaxis will work fine, and zoom properly except the axis limits of the added 
+axes will not update unless the following modifications to the zoom function(s) 
+are made.  These have been tested in R11.1 and R14SP2.  The R14 files should 
+work in later versions.  If not, please let me know.
+
+
+Zoom modifications
+------------------------------------------
+
+For version R11.1, inside <matlab_directory>/toolbox/matlab/graph2d/
+rename zoom.m to zoom_orig.m, copy zoom_R11.1_.m into the directory
+and rename it zoom.m.  
+
+For version R14SP2, inside <matlab_diretory>/toolbox/matlab/graph2d/
+rename zoom.m to zoom_orig.m, copy zoom_R14SP2_.m into the directory
+and rename it zoom.m.  ALSO, inside
+<matlab_directory>/toolbox/matlab/graphics/@graphics/@zoom/  
+rename buttonupfcn2D.m to buttonupfcn2D_orig.m, copy
+buttonupfcn2D_R14SP2.m into the directory and rename it
+buttonupfcn2D.m 
+
+
+The modifications to the zoom capability in matlab simply get the
+ylimits before and after any zoom operation is completed and then
+scales the added axes limits accordingly.  
+
+
+
+
+
diff --git a/algorithms/utilities/addaxis6/aa_splot.m b/algorithms/utilities/addaxis6/aa_splot.m
new file mode 100644
index 0000000..d8419ef
--- /dev/null
+++ b/algorithms/utilities/addaxis6/aa_splot.m
@@ -0,0 +1,34 @@
+function varargout = aa_splot(varargin)
+%AA_SPLOT replaces plot command and automatically changes color
+%
+%  This function is a replacement for the plot command.  
+%  It automatically changes the color with subsequent
+%  uses of aa_splot.  
+%  
+%  Usage is exactly the same as for plot.  
+
+np = get(gca,'nextplot');
+oldplots = get(gca,'children');
+
+cord = get(gca,'colorord');
+
+if ~isempty(oldplots)
+lastcolor = get(oldplots(1),'color');
+if lastcolor == cord(1,:),
+  set(gca,'colorord',cord(mod([0:6]+1,7)+1,:));
+end
+end
+
+hold on;
+
+h = plot(varargin{:});
+
+for IND = 1:nargout
+   varargout(IND) = {h};
+end
+
+%if nargout > 0, varargout{:} = h; end;
+
+set(gca,'colorord',cord(mod([0:6]+1,7)+1,:));
+set(gca,'nextplot',np);
+set(gca,'box','on');
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/addaxis.m b/algorithms/utilities/addaxis6/addaxis.m
new file mode 100644
index 0000000..e112e23
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxis.m
@@ -0,0 +1,181 @@
+function addaxis(varargin)
+%ADDAXIS  adds an axis to the current plot
+%  you can add as many axes as you want.
+%
+%  usage:  
+%  use it just like plot, except, you need to specify the abscissa
+%  and the third input argument should be the axis limits, if at all.  
+%
+%  example:  
+%
+%  x = 0:.1:4*pi;
+%  plot(x,sin(x));
+%  addaxis(x,sin(x-pi/3));
+%  addaxis(x,sin(x-pi/2),[-2 5],'linewidth',2);
+%  addaxis(x,sin(x-pi/1.5),[-2 2],'v-','linewidth',2);
+%  addaxis(x,5.3*sin(x-pi/1.3),':','linewidth',2);
+%
+%  addaxislabel(1,'one');
+%  addaxislabel(2,'two');
+%  addaxislabel(3,'three');
+%  addaxislabel(4,'four');
+%  addaxislabel(5,'five');
+%
+%  addaxisplot(x,sin(x-pi/2.3)+2,3,'--','linewidth',2);
+%  addaxisplot(x,sin(x-pi/1),5,'--','linewidth',2);
+%
+%  legend('one','two','three','four','five','three-2','five-2');
+%  
+%
+%  
+%  Also requires AA_SPLOT.m, a modified plot function that automatically
+%  changes colors everytime you plot.  
+%
+%  See also
+%  ADDAXISPLOT, ADDAXISLABEL, AA_SPLOT 
+
+%  NOTE:  the 'userdata' of the main axis holds a cell array for each axis
+%         each cell holds a vector.  The first element is the axis handle
+%         the rest are handles to lines that correspond to that axis lines.  
+  
+  
+  %  get current axis
+  cah = gca;
+  
+  %  new for R2010a
+  hzoom = zoom;
+  set(hzoom,'ActionPostCallback',@addaxis_zoom_post);
+  set(hzoom,'ActionPreCallback',@addaxis_zoom_pre);
+
+  if nargin>=3 & ~isstr(varargin{3}) 
+    yl2 = varargin{3};
+    indkeep = setdiff(1:nargin,3);
+    [varargintemp{1:nargin-1}] = deal(varargin{indkeep});
+    varargin = varargintemp;
+  end
+  
+  %  assume existing plot has axes scaled the way you want.
+  yl = get(cah,'ylim');
+  cpos = get(cah,'position');
+  set(cah,'box','off');
+  
+  %  get userdata of current axis.  this will hold handles to
+  %  additional axes and the handles to their corresponding plots
+  %  in the main axis
+%  axh = get(cah,'userdata');
+  axh = getaddaxisdata(cah,'axisdata');
+
+  ledge = cpos(1);
+  if length(axh)>=1
+    if length(axh)/2 == round(length(axh)/2)
+      rpos = get(axh{end-1}(1),'position');
+      redge = rpos(1);
+      lpos = get(axh{end}(1),'position');
+      ledge = lpos(1);
+    else
+      rpos = get(axh{end}(1),'position');
+      redge = rpos(1);
+      if length(axh)>1
+	lpos = get(axh{end-1}(1),'position');
+	ledge = lpos(1);
+      end    
+    end   
+  else
+    redge = cpos(3)+cpos(1);
+    ledge = cpos(1);
+  end
+  
+  totwid = redge-ledge;
+      
+%  assume axes are added on right, then left, then right, etc.
+  numax = length(axh)+1;
+
+  
+  %  parameters setting axis separation
+  axcompleft=0.12;
+  if numax == 1
+    axcompright = 0.0;
+  else 
+    axcompright = 0.12;
+  end
+  
+  if numax/2 == round(numax/2)
+    side = 'left';
+    xpos = ledge-axcompleft*totwid;
+  else
+    side = 'right';
+    xpos = redge+axcompright*totwid;
+  end
+
+  h_ax = axes('position',[xpos, cpos(2), cpos(3)*.015, cpos(4)]);
+%  plot in new axis to get the automatically generated ylimits
+  hplt = plot(varargin{:});  
+
+  if ~exist('yl2')
+    yl2 = get(h_ax,'ylim');
+  end
+
+
+  set(h_ax,'yaxislocation',side);
+  set(h_ax,'color',get(gcf,'color'));
+  set(h_ax,'box','off');
+  set(h_ax,'xtick',[]);
+  set(hplt,'visible','off');
+
+  set(h_ax,'ylim',yl2);
+
+
+%  rescale all y-values
+  y = varargin{2};
+  
+  y = (y-yl2(1))./(yl2(2)-yl2(1)).*(yl(2)-yl(1))+yl(1);
+  
+  varargin{2} = y;
+  axes(cah)
+  hplts = aa_splot(varargin{:});
+  set(gca,'ylim',yl);
+  
+  %  store the handles in the axis userdata
+  axh{length(axh)+1} = [h_ax;hplts];
+% set(cah,'userdata',axh);  
+  setaddaxisdata(cah,axh,'axisdata');
+  set(cah,'box','off');
+  
+  %  set the axis color if a single line was added to the plot
+  if length(hplts)==1
+    set(h_ax,'ycolor',get(hplts,'color'));
+  end
+ 
+  %  Now, compress main axis so the extra axes don't interfere
+  %  or dissappear
+  
+  %  get axis handles
+  axhand = cah;
+  postot(1,:) = get(cah,'position');
+  for I = 1:length(axh)
+    axhand(I+1) = axh{I}(1);
+    postot(I+1,:) = get(axhand(I+1),'position');
+  end
+  
+  if numax/2 == round(numax/2)
+%    side = 'left';
+
+    set(cah,'position',[postot(1,1)+axcompleft*totwid,postot(1,2), ...
+			postot(1,3)-axcompleft*totwid, postot(1,4)]);
+    indshift = [2:2:size(postot,1)-1];
+    for I = 1:length(indshift)
+      set(axhand(indshift(I)+1),'position',[postot(indshift(I)+1,1)+axcompleft*totwid, ...
+		                            postot(indshift(I)+1,2:end)]);
+    end
+  
+  else
+ %   side = 'right';
+
+    set(cah,'position',[postot(1,1),postot(1,2),postot(1,3)-axcompright*totwid,postot(1,4)]);
+    indshift = [1:2:size(postot,1)-1];
+    for I = 1:length(indshift)
+      set(axhand(indshift(I)+1),'position',[postot(indshift(I)+1,1)-axcompright*totwid, ...
+		    postot(indshift(I)+1,2:end)]);
+    end
+ 
+  end
diff --git a/algorithms/utilities/addaxis6/addaxis_zoom_post.m b/algorithms/utilities/addaxis6/addaxis_zoom_post.m
new file mode 100644
index 0000000..b47746f
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxis_zoom_post.m
@@ -0,0 +1,41 @@
+function addaxis_zoom_post(obj,evd)
+  %  update the added axes with new zoom
+  
+
+%  disp('zoom post');
+  
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+try
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  ystart = getaddaxisdata(gca,'ystart');
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+catch 
+end
+
+%  END of modification
+%================================================================
+
+  
+  end
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/addaxis_zoom_pre.m b/algorithms/utilities/addaxis6/addaxis_zoom_pre.m
new file mode 100644
index 0000000..0145bc0
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxis_zoom_pre.m
@@ -0,0 +1,14 @@
+function addaxis_zoom_pre(obj,evd)
+  %  update the added axes with new zoom
+  
+  
+ % disp('zoom pre');
+  
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================
+
+  setaddaxisdata(gca,ystart,'ystart');
+  end
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/addaxislabel.m b/algorithms/utilities/addaxis6/addaxislabel.m
new file mode 100644
index 0000000..ff0a142
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxislabel.m
@@ -0,0 +1,40 @@
+function varargout = addaxislabel(varargin)
+%ADDAXISLABEL adds axis labels to axes made with ADDAXIS.m
+%
+%  handle_to_text = addaxislabel(axis_number, label);
+%
+%  See also
+%  ADDAXISPLOT, ADDAXIS, SPLOT 
+  
+  if isstr(varargin{1}), 
+     axnum = varargin{2}; 
+     label = varargin{1};
+  else
+     label = varargin{2};
+     axnum = varargin{1};
+  end
+  
+%  get current axis
+  cah = gca;
+%  axh = get(cah,'userdata');
+  axh = getaddaxisdata(cah,'axisdata');
+  
+%  get axis handles
+  axhand = cah;
+  postot(1,:) = get(cah,'position');
+  for I = 1:length(axh)
+    axhand(I+1) = axh{I}(1);
+    postot(I+1,:) = get(axhand(I+1),'position');
+  end  
+
+%  set current axis to the axis to be labeled
+axes(axhand(axnum));
+htxt = ylabel(label);
+set(htxt,'color',get(axhand(axnum),'ycolor'));
+
+%  set current axis back to the main axis
+axes(cah);
+
+if nargout == 1
+  varargout{1} = htxt;
+end
diff --git a/algorithms/utilities/addaxis6/addaxisplot.m b/algorithms/utilities/addaxis6/addaxisplot.m
new file mode 100644
index 0000000..76886b6
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxisplot.m
@@ -0,0 +1,82 @@
+function varargout = addaxisplot(varargin)
+%ADDAXISPLOT adds a plot to an axis.
+%
+%  handle_to_plots = addaxisplot(x,y,axis_number,...);
+%
+%  See also
+%  ADDAXIS, ADDAXISLABEL, AA_SPLOT 
+  
+  
+%  get current axis
+  cah = gca;
+%  axh = get(cah,'userdata');
+  axh = getaddaxisdata(cah,'axisdata');  
+  
+  if nargin>=3 & ~isstr(varargin{3}) 
+    plotaxis = varargin{3};
+    indkeep = setdiff(1:nargin,3);
+    [varargintemp{1:nargin-1}] = deal(varargin{indkeep});
+    varargin = varargintemp;
+  else
+    plotaxis = 1;
+  end
+  
+  
+%  get axis handles
+  axhand = cah;
+  for I = 1:length(axh)
+    axhand(I+1) = axh{I}(1);
+  end  
+
+%  get the limits for the axis that the plot will go with
+  yl = get(axhand(1),'ylim');
+  yl2 = get(axhand(plotaxis),'ylim');
+
+  y = varargin{2};
+  y = (y-yl2(1))./(yl2(2)-yl2(1)).*(yl(2)-yl(1))+yl(1);
+  varargin{2} = y;
+  
+%  set current axis back to the main axis
+  axes(cah);
+  hplts = aa_splot(varargin{:});
+  
+%  Parse varargin to see if a color was chosen, otherwise set
+%  color equal to plotaxis
+  changecolor = 1;
+  coloroptions = ['b';'r';'m';'c';'g';'y';'w';'k'];
+%  look at third input to plot if it exists, should be color/style string  
+  if length(varargin)>2
+     if isstr(varargin{3})
+       for K = 1:length(coloroptions)
+	 if ~isempty(findstr(varargin{3},coloroptions(K))) & ...
+	       ~strcmp(varargin{3},'linewidth')
+	   changecolor = 0;
+	 end
+       end
+     end
+  end
+ 
+%  Now check to see if 'color' option is used  
+  for I = 1:length(varargin)
+    if isstr(varargin{I})
+      if strmatch(lower(varargin{I}),'color')
+	changecolor = 0;
+      end
+    end
+  end
+  
+
+  if length(hplts)==1 & changecolor
+    set(hplts,'color',get(axhand(plotaxis),'ycolor'));
+  end
+  set(gca,'ylim',yl);
+  
+  if plotaxis>1
+    axh{plotaxis-1} = [axh{plotaxis-1};hplts];
+    setaddaxisdata(cah,axh,'axisdata');
+  end
+
+  if nargout == 1
+    varargout{1} = hplts;
+  end
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/addaxisreset.m b/algorithms/utilities/addaxis6/addaxisreset.m
new file mode 100644
index 0000000..2431147
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxisreset.m
@@ -0,0 +1,44 @@
+function addaxisreset(axishandle)
+%  addaxisreset(axishandle)
+%  
+%  axishandle is the handle to the parent axis (default = current axis)
+%  removes all added axes and deletes the addaxis_data appdata
+%
+%  Example
+%  x = 0:10;
+%  plot(x,x)
+%  addaxis(x,x.^1.5,'-o');
+%  addaxis(x,x.^.5,'-s','linewidth',2);
+%  'press any key to continue'
+%  pause
+%  addaxisreset
+%  'press any key to continue'
+%  pause
+%  cla
+
+
+  
+  if nargin < 1, axishandle = gca;end;
+  
+  aad = getappdata(axishandle,'addaxis_data');
+
+  if length(aad)>0
+    figpos = aad.resetinfo;
+    axisdata = aad.axisdata;
+ 
+%  delete added axes
+    for I = 1:length(axisdata)
+      delete(axisdata{I}(1));
+    end
+
+%  restore original axis position    
+    set(axishandle,'position',figpos);
+    
+%  remove the appdata addaxis_data.    
+    rmappdata(axishandle,'addaxis_data');    
+  end
+  
+%    focus = gca;
+%    axes(axishandle);
+%    cla;
+%    axes(focus);
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/addaxisset.m b/algorithms/utilities/addaxis6/addaxisset.m
new file mode 100644
index 0000000..526a584
--- /dev/null
+++ b/algorithms/utilities/addaxis6/addaxisset.m
@@ -0,0 +1,60 @@
+function addaxisset(yl2,axisnumber)
+%  addaxisset(new_ylim, axisnumber)
+%
+%  new_ylim = two element vector with the new y-limits
+%  axisnumber = number of axis to be scaled (parent axis = 1)
+%  
+%  Example
+%  x = 0:10;
+%  plot(x,x)
+%  addaxis(x,x.^2,'-o');
+%  addaxisplot(x,x.^1.5,2,'-s','linewidth',2);
+%  'press any key to continue'
+%  pause
+%  addaxisset([0 200],2);
+%  'press any key to continue'
+%  pause
+%  addaxisset([0 15],1);
+%  'press any key to continue'
+%  pause
+%  addaxisset([0 100],2);
+  
+  cah = gca;
+  yp = get(cah,'ylim');
+  
+  axh = getaddaxisdata(cah,'axisdata');
+  
+  if axisnumber > 1
+    axhan = axh{axisnumber-1}(1);
+    plthan = axh{axisnumber-1}(2:end);
+    
+    yl = get(axhan,'ylim');  %  original ylimits
+    set(axhan,'ylim',yl2);
+    
+    for I = 1:length(plthan)
+      y = get(plthan(I),'ydata');
+%  rescale y-values
+      ynew = (((y-yp(1))./(yp(2)-yp(1)).*(yl(2)-yl(1))+yl(1))-yl2(1))./...
+	     (yl2(2)-yl2(1)).*(yp(2)-yp(1))+yp(1);
+      set(plthan(I),'ydata',ynew);
+    end
+    set(cah,'ylim',yp);
+  else
+%  rescaling parent axis.  All other axes need to be scaled
+    ystart = yp;
+    yend = yl2;
+    for I = 1:length(axh)
+      axhan = axh{I}(1);
+      axyl = get(axhan,'ylim');
+      axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	  (axyl(2)-axyl(1));
+      axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	  (axyl(2)-axyl(1));
+      set(axhan,'ylim',axylnew);
+    end
+    set(cah,'ylim',yend);
+  end
+  
+
+    
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/buttonupfcn2D_R14SP2_.m b/algorithms/utilities/addaxis6/buttonupfcn2D_R14SP2_.m
new file mode 100644
index 0000000..db584bc
--- /dev/null
+++ b/algorithms/utilities/addaxis6/buttonupfcn2D_R14SP2_.m
@@ -0,0 +1,286 @@
+function buttonupfcn2D(hZoom, cpMulAxes)
+%MOUSEBTNUPFCN Mouse button up function
+
+% Copyright 2003-2004 The MathWorks, Inc.
+
+% This code originates from simulink zoom. 
+
+% This constant specifies the number of pixels the mouse
+% must move in order to do a rbbox zoom.
+POINT_MODE_MAX_PIXELS = 5; % pixels
+
+% Get necessary handles
+hFig = get(hZoom,'FigureHandle');
+hLines     = get(hZoom,'LineHandles');
+currentAxes = get(hZoom,'CurrentAxes');
+
+% Get net mouse movement in pixels
+orig_units = get(hFig,'Units');
+set(hFig,'Units','Pixels');
+fp = get(hFig,'CurrentPoint');
+set(hFig,'Units',orig_units);
+orig_fp = get(hZoom,'MousePoint');
+dpixel = abs(orig_fp-fp);
+
+% The first point of line 1 is always the zoom origin.
+XDat   = get(hLines(1), 'XData');
+YDat   = get(hLines(1), 'YData');
+origin = [XDat(1), YDat(1)];
+
+% Loop through all the currentAxes and zoom-in each of them
+for k = 1:length(currentAxes),
+    cAx = currentAxes(k);
+
+    isPointMode = false;
+    
+    % cpMulAxes is the zoom origin for multiple axes if any.
+    if k > 1
+        origin = cpMulAxes(k-1,:);
+    end
+    
+    % Get the current limits.
+    currentXLim = get(cAx, 'XLim');
+    newXLim = currentXLim;
+    currentYLim = get(cAx, 'YLim');
+    newYLim = currentYLim;
+    
+    % Perform zoom operation based on zoom mode.
+    switch(get(hZoom,'Constraint')),
+        case 'none',
+            %
+            % Both x and y zoom.
+            % RBBOX - lines:
+            % 
+            %          2
+            %    o-------------
+            %    |            |
+            %  1 |            | 4
+            %    |            |
+            %    --------------
+            %          3
+            %
+            
+            % Determine the end point of zoom operation.
+            
+            % Get current point.
+            cp = get(cAx, 'CurrentPoint'); cp = cp(1,1:2);
+            xcp = cp(1);
+            ycp = cp(2);
+            
+            % Uncomment to clip rbbox zoom to current axes limits
+            %if xcp > currentXLim(2),
+            %    xcp = currentXLim(2);
+            %end
+            %if xcp < currentXLim(1),
+            %    xcp = currentXLim(1);
+            %end
+            %if ycp > currentYLim(2),
+            %    ycp = currentYLim(2);
+            %end
+            %if ycp < currentYLim(1),
+            %    ycp = currentYLim(1);
+            %end
+            
+            endPt = [xcp ycp];
+            
+            % Determine the mode: POINT or RBBOX.
+            if (dpixel(1) <= POINT_MODE_MAX_PIXELS) ...
+                 && (dpixel(2) <= POINT_MODE_MAX_PIXELS)
+                isPointMode = true;
+            end
+            newXLim(k,:) = localGetNewXLim(cAx,k,isPointMode,xcp,origin,endPt);
+            newYLim(k,:) = localGetNewYLim(cAx,k,isPointMode,ycp,origin,endPt);
+
+        case 'horizontal',
+            % x only zoom.
+            % RBBOX - lines (only 1-3 used):
+            %   
+            %    |     1      |
+            %  2 o------------| 3 
+            %    |            |
+            %             
+            %
+            
+            % Determine the end point of zoom operation.
+            cp = get(cAx, 'CurrentPoint'); cp = cp(1,1:2);
+            xcp = cp(1);
+            
+            % Uncomment to clip rbbox zoom to current axes limits
+            % if xcp > currentXLim(2),
+            %    xcp = currentXLim(2);
+            % end
+            % if xcp < currentXLim(1),
+            %    xcp = currentXLim(1);
+            % end
+            
+            endPt = [xcp origin(2)];
+            
+            % Determine mode: POINT or RBBOX.
+            if dpixel(1)  <= POINT_MODE_MAX_PIXELS
+                isPointMode = true;
+            end
+
+            newXLim(k,:) = localGetNewXLim(cAx,k,isPointMode,xcp,origin,endPt);
+            newYLim(k,:) = currentYLim;
+            
+        case 'vertical',
+            % y only zoom.
+            % RBBOX - lines (only 1-3 used):
+            %    2
+            %  --o--  
+            %    |
+            %  1 |
+            %    |
+            %  -----           
+            %    3
+            %
+            
+            % Determine the end point of zoom operation.
+            
+            % End pt is the 2nd point of line 1.
+            cp = get(cAx, 'CurrentPoint'); cp = cp(1,1:2);
+            ycp = cp(2);
+            
+            % Uncomment to clip rbbox zoom to current axes limits
+            % if ycp > currentYLim(2),
+            %    ycp = currentYLim(2);
+            % end
+            % if ycp < currentYLim(1),
+            %    ycp = currentYLim(1);
+            % end
+            
+            endPt = [origin(1) ycp];
+            
+            % Determine mode: POINT or RBBOX.
+            if dpixel(2) <= POINT_MODE_MAX_PIXELS
+                isPointMode = true;
+            end
+
+            newYLim(k,:) = localGetNewYLim(cAx,k,isPointMode,ycp,origin,endPt);
+            newXLim(k,:) = currentXLim; 
+          
+    end % switch
+    
+    % Actual zoom operation
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================    
+    axis(cAx,[newXLim(k,:),newYLim(k,:)]);   
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+%  END of modification
+%================================================================
+
+        
+end % for
+
+% Delete the RBBOX lines.
+if ishandle(hLines)
+    scribefiglisten(hFig,'off');
+    delete(hLines);
+    scribefiglisten(hFig,'on');
+end
+
+% Call drawnow to flush axes update since the next line, create2Dundo,
+% will take a long time when called for the first time (class loading).
+drawnow expose
+    
+if length(currentAxes)==1
+    % This runs slow the first time due to UDD class loading 
+    create2Dundo(hZoom,cAx,[currentXLim,currentYLim],[newXLim(k,:),newYLim(k,:)]);
+end
+
+%----------------------------------------------------%
+function [newXLim] = localGetNewXLim(hAxes,k,isPointMode,xcp,origin,endPt)
+% ToDo: clean up input arguments
+
+% Calculate the new X-Limits.
+if ~isPointMode % Bounding Box Mode.
+    x_lim = [origin(1) endPt(1)];
+    if x_lim(1) > x_lim(2),
+           x_lim = x_lim([2 1]);
+    end
+else % Point Mode.
+    % Divide the vertical into 5 divisions.
+    x_lim = get(hAxes, 'XLim'); 
+    if strcmp(get(hAxes, 'XScale'), 'log'),
+         diff_log = diff(log10(x_lim))/5;                    
+         xcp_log = log10(xcp);
+         xmin = 10.^(xcp_log-diff_log);
+         xmax = 10.^(xcp_log+diff_log);
+         x_lim = [xmin,xmax];                    
+     else
+         XDiff = (x_lim(2) - x_lim(1)) / 5;
+         x_lim = [xcp - XDiff, xcp + XDiff];
+     end
+end  
+            
+% Set new Xlimits.
+% NOTE: Check that the limits aren't equal.  This happens
+%   at very small limits.  In this case, we do nothing.
+%
+if abs(x_lim(1) - x_lim(2)) > 1e-10*(abs(x_lim(1)) + abs(x_lim(2)))
+     newXLim(k,:) = x_lim;                
+else
+     newXLim = xlim(hAxes);
+end
+
+
+%----------------------------------------------------%
+function [newYLim] = localGetNewYLim(hAxes,k,isPointMode,ycp,origin,endPt)
+% ToDo: clean up input arguments
+
+% Calculate the new Y-Limits.
+if ~isPointMode % Bounding Box Mode.
+    y_lim = [origin(2) endPt(2)];
+    if y_lim(1) > y_lim(2),
+           y_lim = y_lim([2 1]);
+    end
+else % Point Mode.
+    % Divide the vertical into 5 divisions.
+    y_lim = get(hAxes, 'YLim'); 
+    if strcmp(get(hAxes, 'YScale'), 'log'),
+        diff_log = diff(log10(y_lim))/5;                    
+        ycp_log = log10(ycp);
+        ymin = 10.^(ycp_log-diff_log);
+        ymax = 10.^(ycp_log+diff_log);
+        y_lim = [ymin,ymax];                    
+     else
+         YDiff = (y_lim(2) - y_lim(1)) / 5;
+         y_lim = [ycp - YDiff, ycp + YDiff];
+     end
+end  
+            
+% Set new Ylimits.
+% NOTE: Check that the limits aren't equal.  This happens
+%   at very small limits.  In this case, we do nothing.
+%
+if abs(y_lim(1) - y_lim(2)) > 1e-10*(abs(y_lim(1)) + abs(y_lim(2)))
+     newYLim(k,:) = y_lim;                
+else
+     newYLim = ylim(hAxes);
+end
+
diff --git a/algorithms/utilities/addaxis6/getaddaxisdata.m b/algorithms/utilities/addaxis6/getaddaxisdata.m
new file mode 100644
index 0000000..6e79e75
--- /dev/null
+++ b/algorithms/utilities/addaxis6/getaddaxisdata.m
@@ -0,0 +1,30 @@
+function retdata = getaddaxisdata(axishandle,flag)
+%  retdata = getaddaxisdata(axishandle,flag)
+%
+%  internal function for addaxis
+%  axishandle is handle to the parent axis onto which other axes were added
+%  flag is either 'axisdata', or 'resetinfo' to return the added axis
+%  handles and plot handles, or the position of the figure before axes
+%  were added. 
+  
+  if nargin<2, flag = 'axisdata'; end;
+  
+  aad = getappdata(axishandle,'addaxis_data');
+  if length(aad)>=1
+    switch flag
+     case 'axisdata'
+      retdata = aad.axisdata;
+     case 'resetinfo'
+      retdata = aad.resetinfo;
+     case 'ystart'
+      retdata = aad.ystart;
+     otherwise
+      retdata = 0;
+    end
+  else
+    retdata = aad;
+    aadnew.resetinfo = get(axishandle,'position');
+    aadnew.axisdata = [];
+    setappdata(axishandle,'addaxis_data',aadnew);
+  end
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/license.txt b/algorithms/utilities/addaxis6/license.txt
new file mode 100644
index 0000000..cafa8be
--- /dev/null
+++ b/algorithms/utilities/addaxis6/license.txt
@@ -0,0 +1,24 @@
+Copyright (c) 2016, Harry Lee
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/algorithms/utilities/addaxis6/setaddaxisdata.m b/algorithms/utilities/addaxis6/setaddaxisdata.m
new file mode 100644
index 0000000..153363c
--- /dev/null
+++ b/algorithms/utilities/addaxis6/setaddaxisdata.m
@@ -0,0 +1,21 @@
+function setaddaxisdata(axishandle,data,flag);
+%  setaddaxisdata(axishandle,data,flag)
+%
+%  internal function for addaxis to update the added axes handles and plot 
+%  handles, or to save the original figure position.
+  
+  if nargin<3, flag = 'axisdata'; end;
+  
+  aad = getappdata(axishandle,'addaxis_data');
+  switch flag
+   case 'axisdata'
+    aad.axisdata = data;
+   case 'resetinfo'
+    aad.resetinfo = data;
+   case 'ystart'
+    aad.ystart = data;
+   otherwise
+  end
+  
+  setappdata(axishandle,'addaxis_data',aad);
+  
\ No newline at end of file
diff --git a/algorithms/utilities/addaxis6/zoom_R11.1_.m b/algorithms/utilities/addaxis6/zoom_R11.1_.m
new file mode 100644
index 0000000..5c62834
--- /dev/null
+++ b/algorithms/utilities/addaxis6/zoom_R11.1_.m
@@ -0,0 +1,599 @@
+function out = zoom(varargin)
+%ZOOM   Zoom in and out on a 2-D plot.
+%   ZOOM with no arguments toggles the zoom state.
+%   ZOOM(FACTOR) zooms the current axis by FACTOR.
+%       Note that this does not affect the zoom state.
+%   ZOOM ON turns zoom on for the current figure.  
+%   ZOOM OFF turns zoom off in the current figure.
+%   ZOOM OUT returns the plot to its initial (full) zoom.
+%   ZOOM XON or ZOOM YON turns zoom on for the x or y axis only.
+%   ZOOM RESET clears the zoom out point.
+%
+%   When zoom is on, click the left mouse button to zoom in on the
+%   point under the mouse.  Click the right mouse button to zoom out
+%   (shift-click on the Macintosh).  Each time you click, the axes
+%   limits will be changed by a factor of 2 (in or out).  You can also
+%   click and drag to zoom into an area.  Double clicking zooms out to
+%   the point at which zoom was first turned on for this figure.  Note
+%   that turning zoom on, then off does not reset the zoom point.
+%   This may be done explicitly with ZOOM RESET.
+%   
+%   ZOOM(FIG,OPTION) applies the zoom command to the figure specified
+%   by FIG. OPTION can be any of the above arguments.
+
+%   ZOOM FILL scales a plot such that it is as big as possible
+%   within the axis position rectangle for any azimuth and elevation.
+
+%   Clay M. Thompson 1-25-93
+%   Revised 11 Jan 94 by Steven L. Eddins
+%   Copyright (c) 1984-98 by The MathWorks, Inc.
+%   $Revision: 5.42 $  $Date: 1998/09/30 13:43:56 $
+
+%   Note: zoom uses the figure buttondown and buttonmotion functions
+%
+%   ZOOM XON zooms x-axis only
+%   ZOOM YON zooms y-axis only
+
+switch nargin,
+   
+   %%%%%%%%%%%%%%%%%%%%%%%%%%
+   %%% No Input Arguments %%%
+   %%%%%%%%%%%%%%%%%%%%%%%%%%
+case 0,
+   fig=get(0,'currentfigure');
+   if isempty(fig), return, end
+   zoomCommand='toggle';
+   
+   %%%%%%%%%%%%%%%%%%%%%%%%%%
+   %%% One Input Argument %%%
+   %%%%%%%%%%%%%%%%%%%%%%%%%%
+case 1,
+   
+   % If the argument is a string, the argument is a zoom command
+   % (i.e. (on, off, down, xdown, etc.).  Otherwise, the argument is
+   % assumed to be a figure handle, in which case all we do is
+   % toggle the zoom status.
+   
+   if isstr(varargin{1}),
+      fig=get(0,'currentfigure');
+      if isempty(fig), return, end
+      
+      zoomCommand=varargin{1};
+   else
+      scale_factor=varargin{1};
+      zoomCommand='scale';
+      fig = gcf;
+   end % if
+   
+   %%%%%%%%%%%%%%%%%%%%%%%%%%%
+   %%% Two Input Arguments %%%
+   %%%%%%%%%%%%%%%%%%%%%%%%%%%
+case 2,
+   fig=varargin{1};
+   zoomCommand=varargin{2};
+   
+otherwise,
+   error(nargchk(0, 2, nargin));
+   
+end % switch nargin
+
+%
+% handle 'off' commands first
+%
+if strcmp(zoomCommand,'off'),
+   %
+   % turn off zoom, and take a hike
+   %
+   set(findall(fig,'Tag','figToolZoomIn'),'State','off');
+   set(findall(fig,'Tag','figToolZoomOut'),'State','off');   
+   if ~isempty(getappdata(fig,'ZoomFigureMode'))
+      rmappdata(fig,'ZOOMFigureMode');
+   end
+
+   state = getuprop(fig,'ZOOMFigureState');
+   if ~isempty(state),
+      uirestore(state);
+      clruprop(fig,'ZOOMFigureState');
+   end
+   return
+end % if
+
+ax=get(fig,'currentaxes');
+
+rbbox_mode = 0;
+zoomx = 1; zoomy = 1; % Assume no constraints
+
+zoomCommand=lower(zoomCommand);
+
+if ~isempty(ax) & any(get(ax,'view')~=[0 90]) ...
+           & ~(strcmp(zoomCommand,'scale') | ...
+           strcmp(zoomCommand,'fill')),
+   fZoom3d = 1;
+   % set(findall(fig,'Tag','figToolZoom'),'State','off');   
+   % warning('Zoom is only supported for 2D plots.');
+   % return % Do nothing
+else
+   fZoom3d = 0;
+end
+
+if strcmp(zoomCommand,'toggle'),
+   state = getuprop(fig,'ZOOMFigureState');
+   if isempty(state)
+      zoom(fig,'on');
+   else
+      zoom(fig,'off');
+   end
+   return
+end % if
+
+% Catch constrained zoom
+if strcmp(zoomCommand,'xdown'),
+   zoomy = 0; zoomCommand = 'down'; % Constrain y
+elseif strcmp(zoomCommand,'ydown')
+   zoomx = 0; zoomCommand = 'down'; % Constrain x
+end
+
+
+switch zoomCommand
+case 'down',
+   % Activate axis that is clicked in
+   allAxes = findobj(datachildren(fig),'flat','type','axes');
+   ZOOM_found = 0;
+   
+   % this test may be causing failures for 3d axes
+   for i=1:length(allAxes),
+      ax=allAxes(i);
+      ZOOM_Pt1 = get(ax,'CurrentPoint');
+      xlim = get(ax,'xlim');
+      ylim = get(ax,'ylim');
+      if (xlim(1) <= ZOOM_Pt1(1,1) & ZOOM_Pt1(1,1) <= xlim(2) & ...
+            ylim(1) <= ZOOM_Pt1(1,2) & ZOOM_Pt1(1,2) <= ylim(2))
+         ZOOM_found = 1;
+         set(fig,'currentaxes',ax);
+         break
+      end % if
+   end % for
+
+   if ZOOM_found==0, return, end
+   
+   % Check for selection type
+   selection_type = get(fig,'SelectionType');
+   zoomMode = getappdata(fig,'ZOOMFigureMode');
+
+   axz = get(ax,'ZLabel');
+   
+      if fZoom3d
+
+      viewData = getappdata(axz,'ZOOMAxesView');
+      if isempty(viewData)
+         viewProps = { 'CameraTarget'...
+                    'CameraTargetMode'...
+                    'CameraViewAngle'...
+                    'CameraViewAngleMode'};
+         setappdata(axz,'ZOOMAxesViewProps', viewProps);
+         setappdata(axz,'ZOOMAxesView', get(ax,viewProps));
+      end
+      
+      if isempty(zoomMode) | strcmp(zoomMode,'in');
+         zoomLeftFactor = 1.5;
+         zoomRightFactor = .75;         
+      elseif strcmp(zoomMode,'out');
+         zoomLeftFactor = .75;
+         zoomRightFactor = 1.5;
+      end
+      
+      switch selection_type
+         case 'open'
+            set(ax,getappdata(axz,'ZOOMAxesViewProps'),...
+                    getappdata(axz,'ZOOMAxesView'));
+         case 'normal'
+            newTarget = mean(get(ax,'CurrentPoint'),1);
+            set(ax,'CameraTarget',newTarget);
+            camzoom(ax,zoomLeftFactor);
+         otherwise
+            newTarget = mean(get(ax,'CurrentPoint'),1);
+            set(ax,'CameraTarget',newTarget);
+            camzoom(ax,zoomRightFactor);
+      end
+
+      return
+   end
+
+   if isempty(zoomMode) | strcmp(zoomMode,'in');
+      switch selection_type
+         case 'normal'
+            % Zoom in
+            m = 1;
+            scale_factor = 2; % the default zooming factor
+         case 'open'
+            % Zoom all the way out
+            zoom(fig,'out');
+            return;
+         otherwise
+            % Zoom partially out
+            m = -1;
+            scale_factor = 2;
+      end
+   elseif strcmp(zoomMode,'out')
+      switch selection_type
+         case 'normal'
+            % Zoom partially out
+            m = -1;
+            scale_factor = 2;
+         case 'open'
+            % Zoom all the way out
+            zoom(fig,'out');
+            return;
+         otherwise
+            % Zoom in
+            m = 1;
+            scale_factor = 2; % the default zooming factor
+      end
+   else % unrecognized zoomMode
+      return
+   end
+   
+   
+   ZOOM_Pt1 = get_currentpoint(ax);
+   ZOOM_Pt2 = ZOOM_Pt1;
+   center = ZOOM_Pt1;
+   
+   if (m == 1)
+      % Zoom in
+      units = get(fig,'units'); set(fig,'units','pixels')
+      rbbox([get(fig,'currentpoint') 0 0],get(fig,'currentpoint'));
+      ZOOM_Pt2 = get_currentpoint(ax);
+      set(fig,'units',units)
+      
+      % Note the currentpoint is set by having a non-trivial up function.
+      if min(abs(ZOOM_Pt1-ZOOM_Pt2)) >= ...
+            min(.01*[diff(get_xlim(ax)) diff(get_ylim(ax))]),
+         % determine axis from rbbox 
+         a = [ZOOM_Pt1;ZOOM_Pt2]; a = [min(a);max(a)];
+         
+         % Undo the effect of get_currentpoint for log axes
+         if strcmp(get(ax,'XScale'),'log'),
+            a(1:2) = 10.^a(1:2);
+         end
+         if strcmp(get(ax,'YScale'),'log'),
+            a(3:4) = 10.^a(3:4);
+         end
+         rbbox_mode = 1;
+      end
+   end
+   limits = zoom(fig,'getlimits');
+   
+case 'scale',
+   if all(get(ax,'view')==[0 90]), % 2D zooming with scale_factor
+      
+      % Activate axis that is clicked in
+      ZOOM_found = 0;
+      ax = gca;
+      xlim = get(ax,'xlim');
+      ylim = get(ax,'ylim');
+      ZOOM_Pt1 = [sum(xlim)/2 sum(ylim)/2];
+      ZOOM_Pt2 = ZOOM_Pt1;
+      center = ZOOM_Pt1;
+      
+      if (xlim(1) <= ZOOM_Pt1(1,1) & ZOOM_Pt1(1,1) <= xlim(2) & ...
+            ylim(1) <= ZOOM_Pt1(1,2) & ZOOM_Pt1(1,2) <= ylim(2))
+         ZOOM_found = 1;
+      end % if
+      
+      if ZOOM_found==0, return, end
+      
+      if (scale_factor >= 1)
+         m = 1;
+      else
+         m = -1;
+      end
+      
+   else % 3D
+      old_CameraViewAngle = get(ax,'CameraViewAngle')*pi/360;
+      ncva = atan(tan(old_CameraViewAngle)*(1/scale_factor))*360/pi;
+      set(ax,'CameraViewAngle',ncva);
+      return;
+   end
+   
+   limits = zoom(fig,'getlimits');
+
+case 'getmode'
+   state = getuprop(fig,'ZOOMFigureState');
+   if isempty(state)
+      out = 'off';
+   else
+      mode = getappdata(fig,'ZOOMFigureMode');
+      if isempty(mode)
+         out = 'on';
+      else
+         out = mode;
+      end
+   end
+   return
+
+   
+case 'on',
+   
+   set(findall(fig,'Tag','figToolZoomIn'),'State','on');
+   
+   state = getuprop(fig,'ZOOMFigureState');
+   if isempty(state),
+      state = uiclearmode(fig,'zoom',fig,'off');
+      setuprop(fig,'ZOOMFigureState',state);
+   end
+   set(fig,'windowbuttondownfcn','zoom down', ...
+      'windowbuttonupfcn','ones;', ...
+      'windowbuttonmotionfcn','','buttondownfcn','', ...
+      'interruptible','on');
+   set(ax,'interruptible','on')
+   return
+   
+case 'inmode'
+   zoom(fig,'on');
+   set(findall(fig,'Tag','figToolZoomIn'),'State','on');   
+   set(findall(fig,'Tag','figToolZoomOut'),'State','off');      
+   setappdata(fig,'ZOOMFigureMode','in');
+   return   
+   
+case 'outmode'
+   zoom(fig,'on');
+   set(findall(fig,'Tag','figToolZoomIn'),'State','off');   
+   set(findall(fig,'Tag','figToolZoomOut'),'State','on');      
+   setappdata(fig,'ZOOMFigureMode','out');
+   return
+   
+case 'reset',
+   axz = get(ax,'ZLabel');
+   if isappdata(axz,'ZoomAxesData')
+     rmappdata(axz,'ZOOMAxesData');
+   end
+   return
+   
+case 'xon',
+   zoom(fig,'on') % Set up userprop
+   set(fig,'windowbuttondownfcn','zoom xdown', ...
+      'windowbuttonupfcn','ones;', ...
+      'windowbuttonmotionfcn','','buttondownfcn','',...
+      'interruptible','on');
+   set(ax,'interruptible','on')
+   return
+   
+case 'yon',
+   zoom(fig,'on') % Set up userprop
+   set(fig,'windowbuttondownfcn','zoom ydown', ...
+      'windowbuttonupfcn','ones;', ...
+      'windowbuttonmotionfcn','','buttondownfcn','',...
+      'interruptible','on');
+   set(ax,'interruptible','on')
+   return
+   
+case 'out',
+   limits = zoom(fig,'getlimits');
+   center = [sum(get_xlim(ax))/2 sum(get_ylim(ax))/2];
+   m = -inf; % Zoom totally out
+   
+case 'getlimits', % Get axis limits
+   axz = get(ax,'ZLabel');
+   limits = getappdata(axz,'ZOOMAxesData');
+   % Do simple checking of userdata
+   if size(limits,2)==4 & size(limits,1)<=2, 
+      if all(limits(1,[1 3])<limits(1,[2 4])), 
+         getlimits = 0; out = limits(1,:); return   % Quick return
+      else
+         getlimits = -1; % Don't munge data
+      end
+   else
+      if isempty(limits), getlimits = 1; else getlimits = -1; end
+   end
+   
+   % If I've made it to here, we need to compute appropriate axis
+   % limits.
+   
+   if isempty(getappdata(axz,'ZOOMAxesData')),
+      % Use quick method if possible
+      xlim = get_xlim(ax); xmin = xlim(1); xmax = xlim(2); 
+      ylim = get_ylim(ax); ymin = ylim(1); ymax = ylim(2); 
+      
+   elseif strcmp(get(ax,'xLimMode'),'auto') & ...
+         strcmp(get(ax,'yLimMode'),'auto'),
+      % Use automatic limits if possible
+      xlim = get_xlim(ax); xmin = xlim(1); xmax = xlim(2); 
+      ylim = get_ylim(ax); ymin = ylim(1); ymax = ylim(2); 
+      
+   else
+      % Use slow method only if someone else is using the userdata
+      h = get(ax,'Children');
+      xmin = inf; xmax = -inf; ymin = inf; ymax = -inf;
+      for i=1:length(h),
+         t = get(h(i),'Type');
+         if ~strcmp(t,'text'),
+            if strcmp(t,'image'), % Determine axis limits for image
+               x = get(h(i),'Xdata'); y = get(h(i),'Ydata');
+               x = [min(min(x)) max(max(x))];
+               y = [min(min(y)) max(max(y))];
+               [ma,na] = size(get(h(i),'Cdata'));
+               if na>1, dx = diff(x)/(na-1); else dx = 1; end
+               if ma>1, dy = diff(y)/(ma-1); else dy = 1; end
+               x = x + [-dx dx]/2; y = y + [-dy dy]/2;
+            end
+            xmin = min(xmin,min(min(x)));
+            xmax = max(xmax,max(max(x)));
+            ymin = min(ymin,min(min(y)));
+            ymax = max(ymax,max(max(y)));
+         end
+      end
+      
+      % Use automatic limits if in use (override previous calculation)
+      if strcmp(get(ax,'xLimMode'),'auto'),
+         xlim = get_xlim(ax); xmin = xlim(1); xmax = xlim(2); 
+      end
+      if strcmp(get(ax,'yLimMode'),'auto'),
+         ylim = get_ylim(ax); ymin = ylim(1); ymax = ylim(2); 
+      end
+   end
+   
+   limits = [xmin xmax ymin ymax];
+   if getlimits~=-1, % Don't munge existing data.
+      % Store limits ZOOMAxesData
+      % store it with the ZLabel, so that it's cleared if the 
+      % user plots again into this axis.  If that happens, this
+      % state is cleared
+      axz = get(ax,'ZLabel');
+      setappdata(axz,'ZOOMAxesData',limits);
+   end
+   
+   out = limits;
+   return
+   
+case 'getconnect', % Get connected axes
+   axz = get(ax,'ZLabel');
+   limits = getappdata(axz,'ZOOMAxesData');
+   if all(size(limits)==[2 4]), % Do simple checking
+      out = limits(2,[1 2]);
+   else
+      out = [ax ax];
+   end
+   return
+   
+case 'fill',
+   old_view = get(ax,'view');
+   view(45,45);
+   set(ax,'CameraViewAngleMode','auto');
+   set(ax,'CameraViewAngle',get(ax,'CameraViewAngle'));
+   view(old_view);
+   return
+   
+otherwise
+   error(['Unknown option: ',zoomCommand,'.']);
+end
+
+%
+% Actual zoom operation
+%
+
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================
+
+if ~rbbox_mode,
+   xmin = limits(1); xmax = limits(2);   
+   ymin = limits(3); ymax = limits(4);   
+   
+   if m==(-inf),
+      dx = xmax-xmin;
+      dy = ymax-ymin;
+   else
+      dx = diff(get_xlim(ax))*(scale_factor.^(-m-1)); dx = min(dx,xmax-xmin);
+      dy = diff(get_ylim(ax))*(scale_factor.^(-m-1)); dy = min(dy,ymax-ymin);
+   end
+   
+   % Limit zoom.
+   center = max(center,[xmin ymin] + [dx dy]);
+   center = min(center,[xmax ymax] - [dx dy]);
+   a = [max(xmin,center(1)-dx) min(xmax,center(1)+dx) ...
+         max(ymin,center(2)-dy) min(ymax,center(2)+dy)];
+   
+   % Check for log axes and return to linear values.
+   if strcmp(get(ax,'XScale'),'log'),
+      a(1:2) = 10.^a(1:2);
+   end
+   if strcmp(get(ax,'YScale'),'log'),
+      a(3:4) = 10.^a(3:4);
+   end
+   
+end
+
+% Check for axis equal and update a as necessary
+if strcmp(get(ax,'plotboxaspectratiomode'),'manual') & ...
+   strcmp(get(ax,'dataaspectratiomode'),'manual')
+   ratio = get(ax,'plotboxaspectratio')./get(ax,'dataaspectratio');
+   dx = a(2)-a(1);
+   dy = a(4)-a(3);
+   [kmax,k] = max([dx dy]./ratio(1:2));
+   if k==1
+      dy = kmax*ratio(2);
+      a(3:4) = mean(a(3:4))+[-dy dy]/2;
+   else
+     dx = kmax*ratio(1);
+     a(1:2) = mean(a(1:2))+[-dx dx]/2;
+   end
+end
+
+% Update circular list of connected axes
+list = zoom(fig,'getconnect'); % Circular list of connected axes.
+if zoomx,
+   if a(1)==a(2), return, end % Short circuit if zoom is moot.
+   set(ax,'xlim',a(1:2))
+   h = list(1);
+   while h ~= ax,
+      set(h,'xlim',a(1:2))
+      % Get next axes in the list
+      hz = get(h,'ZLabel');
+      next = getappdata(hz,'ZOOMAxesData');
+      if all(size(next)==[2 4]), h = next(2,1); else h = ax; end
+   end
+end
+if zoomy,
+   if a(3)==a(4), return, end % Short circuit if zoom is moot.
+   set(ax,'ylim',a(3:4))
+   h = list(2);
+   while h ~= ax,
+      set(h,'ylim',a(3:4))
+      % Get next axes in the list
+      hz = get(h,'ZLabel');
+      next = getappdata(hz,'ZOOMAxesData');
+      if all(size(next)==[2 4]), h = next(2,2); else h = ax; end
+   end
+end
+
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+%  END of modification
+%================================================================
+
+function p = get_currentpoint(ax)
+%GET_CURRENTPOINT Return equivalent linear scale current point
+p = get(ax,'currentpoint'); p = p(1,1:2);
+if strcmp(get(ax,'XScale'),'log'),
+   p(1) = log10(p(1));
+end
+if strcmp(get(ax,'YScale'),'log'),
+   p(2) = log10(p(2));
+end
+
+function xlim = get_xlim(ax)
+%GET_XLIM Return equivalent linear scale xlim
+xlim = get(ax,'xlim');
+if strcmp(get(ax,'XScale'),'log'),
+   xlim = log10(xlim);
+end
+
+function ylim = get_ylim(ax)
+%GET_YLIM Return equivalent linear scale ylim
+ylim = get(ax,'ylim');
+if strcmp(get(ax,'YScale'),'log'),
+   ylim = log10(ylim);
+end
diff --git a/algorithms/utilities/addaxis6/zoom_R14SP2_.m b/algorithms/utilities/addaxis6/zoom_R14SP2_.m
new file mode 100644
index 0000000..859e7a9
--- /dev/null
+++ b/algorithms/utilities/addaxis6/zoom_R14SP2_.m
@@ -0,0 +1,751 @@
+function out = zoom(varargin)
+%ZOOM   Zoom in and out on a 2-D plot.
+%   ZOOM with no arguments toggles the zoom state.
+%   ZOOM(FACTOR) zooms the current axis by FACTOR.
+%       Note that this does not affect the zoom state.
+%   ZOOM ON turns zoom on for the current figure.
+%   ZOOM XON or ZOOM YON turns zoom on for the x or y axis only.
+%   ZOOM OFF turns zoom off in the current figure.
+%
+%   ZOOM RESET resets the zoom out point to the current zoom.
+%   ZOOM OUT returns the plot to its current zoom out point.
+%   If ZOOM RESET has not been called this is the original
+%   non-zoomed plot.  Otherwise it is the zoom out point
+%   set by ZOOM RESET.
+%
+%   When zoom is on, click the left mouse button to zoom in on the
+%   point under the mouse.  Click the right mouse button to zoom out.
+%   Each time you click, the axes limits will be changed by a factor 
+%   of 2 (in or out).  You can also click and drag to zoom into an area.
+%   It is not possible to zoom out beyond the plots' current zoom out
+%   point.  If ZOOM RESET has not been called the zoom out point is the
+%   original non-zoomed plot.  If ZOOM RESET has been called the zoom out
+%   point is the zoom point that existed when it was called.
+%   Double clicking zooms out to the current zoom out point - 
+%   the point at which zoom was first turned on for this figure 
+%   (or to the point to which the zoom out point was set by ZOOM RESET).
+%   Note that turning zoom on, then off does not reset the zoom out point.
+%   This may be done explicitly with ZOOM RESET.
+%   
+%   ZOOM(FIG,OPTION) applies the zoom command to the figure specified
+%   by FIG. OPTION can be any of the above arguments.
+%
+%   Use LINKAXES to link zooming across multiple axes.
+%
+%   See also PAN, LINKAXES.
+
+% Copyright 1993-2004 The MathWorks, Inc.
+
+% Internal use undocumented syntax (this may be removed in a future
+% release)
+% Additional syntax not already ded in zoom m-help
+%
+% ZOOM(FIG,'UIContextMenu',...)
+%    Specify UICONTEXTMENU for use in zoom mode 
+% ZOOM(FIG,'Constraint',...)
+%    Specify constrain option:
+%       'none'       - No constraint (default)
+%       'horizontal' - Horizontal zoom only for 2-D plots
+%       'vertical'   - Vertical zoom only for 2-D plots
+% ZOOM(FIG,'Direction',...) 
+%    Specify zoom direction 'in' or 'out'
+% OUT = ZOOM(FIG,'IsOn') 
+%    Returns true if zoom is on, otherwise returns false.
+% OUT = ZOOM(FIG,'Constraint') 
+%    Returns 'none','horizontal', or 'vertical'
+% OUT = ZOOM(FIG,'Direction')
+%    Returns 'in' or 'out'
+
+% Undocumented syntax that will never get documented
+% (but we have to keep it around for legacy reasons)
+% OUT = ZOOM(FIG,'getmode') 'in'|'out'|'off' 
+%
+% Undocumented zoom object registration methods
+% OUT = ZOOM(FIG,'getzoom')
+%    Get zoom object
+% ZOOM(FIG,'setzoom',...)
+%    Set zoom object
+
+%   Note: zoom uses the figure buttondown and buttonmotion functions
+%
+%   ZOOM XON zooms x-axis only
+%   ZOOM YON zooms y-axis only
+
+%   ZOOM v6 off Switches to new zoom implementation
+%   ZOOM v6 on Switches to old zoom implementation
+
+%   ZOOM FILL scales a plot such that it is as big as possible
+%   within the axis position rectangle for any azimuth and elevation.
+
+% Undocumented switch to v6 zoom implementation. This will be removed.
+if nargin==2 && ...
+   isstr(varargin{1}) && ...
+   strcmp(varargin{1},'v6') && ...
+   isstr(varargin{2})
+      if strcmp(varargin{2},'on')
+         localSetV6Zoom(true);   
+      else
+         localSetV6Zoom(false);
+      end
+      return;
+end
+
+% Bypass to v6 zoom
+if localIsV6Zoom
+    if nargout==0
+         v6_zoom(varargin{:});
+    else
+         out = v6_zoom(varargin{:});
+    end
+    return;
+end
+
+% Parse input arguments
+[target,action,action_data] = localParseArgs(varargin{:});
+
+% If setting zoom object, ZOOM(H,HZOOM), then return early
+if strcmp(action,'setzoom')
+  localRegisterZoomObject(action_data);
+  return;
+end
+
+% Return early if target is not an axes or figure
+if isempty(target) || ...
+   (~isa(target,'hg.axes') && ~isa(target,'hg.figure')) 
+   return;
+end
+hFigure = ancestor(target,'hg.figure');
+
+% Return early if setting zoom off and there's no app data
+% this avoids making any objects or setting app data when
+% it doesn't need to. For example, hgload calls zoom(fig,'off') 
+appdata = localGetData(hFigure);
+if strcmp(action,'off') && isempty(appdata.uistate)
+   return;
+end
+
+% Get zoom object 
+hZoom = localGetRegisteredZoomObject(hFigure);
+
+% Update zoom target in case it changed
+set(hZoom,'target',target);
+   
+% Get current axes
+hCurrentAxes = get(hFigure,'CurrentAxes');
+
+% Parse various zoom options
+change_ui = [];
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================
+switch lower(action)
+
+    case 'on'
+       set(hZoom,'Constraint','none');
+       change_ui = 'on';
+    
+    case 'xon'
+       set(hZoom,'Constraint','horizontal');
+       change_ui = 'on';
+    
+    case 'yon'
+       set(hZoom,'Constraint','vertical');
+       change_ui = 'on';
+    
+    case 'getzoom'
+       out = hZoom;   
+    case 'getmode'
+       if localIsZoomOn(hZoom)
+          out = get(hZoom,'Direction');
+       else
+          out = 'off';
+       end
+    case 'constraint'
+       out = get(hZoom,'Constraint');
+    case 'direction'
+       out = get(hZoom,'Direction');
+    case 'ison'
+       out = localIsZoomOn(hZoom);
+    case 'ison' %TBD: Remove
+     out = localIsZoomOn(hZoom);
+    case 'getstyle' %TBD: Remove
+       out = get(hZoom,'Constraint');       
+    case 'getdirection' %TBD: Remove
+       out = get(hZoom,'Direction');
+    case 'toggle'
+       if localIsZoomOn(hZoom)
+         change_ui = 'off';
+       else
+         change_ui = 'on';
+       end
+      
+    % Undocumented legacy API, used by 'ident', see g194435
+    % It would be nice to get rid to dependencies on this API, but
+    % many old toolboxes seem to be calling this API.
+    case 'down'
+       buttondownfcn(hZoom,'dorightclick',true);
+       hLine = get(hZoom,'LineHandles');
+       if ishandle(hLine)
+          % Mimic rbbox, don't return until line handles are
+          % removed
+          waitfor(hLine(1));
+       end
+
+    case 'off'
+       change_ui = 'off';
+    case 'inmode'
+       set(hZoom,'Direction','in');
+       change_ui = 'on';
+    case 'outmode'
+       set(hZoom,'Direction','out');      
+       change_ui = 'on';
+    case 'scale'
+       if ~isempty(hCurrentAxes)    
+          % Register current axes view for reset view support
+          resetplotview(hCurrentAxes,'InitializeCurrentView');
+          applyzoomfactor(hZoom,hCurrentAxes,action_data);
+       end
+    case 'fill'
+       if ~isempty(hCurrentAxes)
+          resetplot(hZoom,hCurrentAxes);
+       end
+    case 'reset'
+       resetplotview(hCurrentAxes,'SaveCurrentView');
+    case 'out'
+       if ~isempty(hCurrentAxes)
+          resetplot(hZoom,hCurrentAxes);
+       end
+    case 'setzoomproperties'
+       % undocumented 
+       set(hZoom,action_data{:});
+    otherwise
+       return
+end
+
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+%  END of modification
+%================================================================
+
+% Update the user interface 
+if ~isempty(change_ui)
+   localSetZoomState(hZoom,change_ui);
+end
+
+%-----------------------------------------------%
+function localSetZoomState(hZoom,state)
+
+hFigure = get(hZoom,'FigureHandle');
+appdata = localGetData(hFigure);
+uistate = appdata.uistate;
+
+if strcmp(state,'on')    
+      
+    % Turn off all other interactive modes
+    if isempty(uistate)
+        % Specify uninstaller callback: zoom(fig,'off') 
+        uistate = uiclearmode(double(hFigure),...
+                              'docontext',...
+                              'zoom',double(hFigure),'off');
+        % restore button down functions for uicontrol children of the figure
+        uirestore(uistate,'uicontrols');
+        appdata.uistate = uistate;
+    end
+
+    % Enable zoom mode  
+    appdata.ison = true;
+    set(hZoom,'IsOn',true);
+    localSetData(hFigure,appdata);
+    set(hFigure,'WindowButtonMotionFcn',{@localMotionFcn,hZoom});
+    set(hFigure,'WindowButtonUpFcn',[])
+    set(hFigure,'WindowButtonDownFcn',{@localWindowButtonDownFcn,hZoom});
+    set(hFigure,'KeyPressFcn',{@localKeyPressFcn,hZoom});     
+    set(hFigure,'PointerShapeHotSpot',[5 5]);
+    localMotionFcn(hFigure,[],hZoom);
+    
+    % Flush queue to update figure pointer display asap.
+    drawnow expose;
+        
+    % Turn on Zoom UI (i.e. toolbar buttons, menus)
+    % This must be called AFTER uiclear to avoid uiclear state munging
+    zoom_direction = get(hZoom,'Direction');
+    switch zoom_direction
+       case 'in'
+          localUISetZoomIn(hFigure);
+       case 'out'
+          localUISetZoomOut(hFigure);
+    end
+    local_scribefiglisten(hFigure,'on');
+
+    % Define appdata to avoid breaking code in 
+    % scribefiglisten, hgsave, and figtoolset
+    setappdata(hFigure,'ZoomOnState','on');
+   
+% zoom off    
+elseif strcmp(state,'off')
+    if ~isempty(uistate)
+        % restore figure and non-uicontrol children
+        % don't restore uicontrols because they were restored
+        % already when zoom was turned on
+        uirestore(uistate,'nouicontrols');
+        
+        % Turn off Zoom UI (i.e. toolbar buttons, menus)
+        localUISetZoomOff(hFigure);
+        local_scribefiglisten(hFigure,'off');
+        
+        % Remove uicontextmenu 
+        hui = get(hZoom,'UIContextMenu');
+        if ishandle(hui)
+            delete(hui);
+        end
+    end
+    
+    % Reset appdata
+    localRmData(hFigure);
+    
+    % Remove appdata to avoid breaking code in 
+    % scribefiglisten, hgsave, and figtoolset
+    if isappdata(hFigure,'ZoomOnState');
+       rmappdata(hFigure,'ZoomOnState');
+    end    
+end
+    
+%-----------------------------------------------%
+function [bool] = localIsZoomOn(hZoom)
+
+% TBD, when zoom enabled property is wired correctly
+% do the following:
+%bool = strcmp(get(hZoom,'Enabled'),'on'));
+
+% For now, use app data
+hFigure = get(hZoom,'FigureHandle');
+appdata = localGetData(hFigure);
+bool = appdata.ison;
+
+%-----------------------------------------------%
+function localWindowButtonDownFcn(hFigure,evd,hZoom)
+
+if ~ishandle(hZoom)
+    return;
+end
+
+fig_sel_type = get(hFigure,'SelectionType');
+fig_mod = get(hFigure,'CurrentModifier');
+
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================
+
+switch (lower(fig_sel_type))
+    case 'alt' % right click
+        % display context menu
+        hui = localGetContextMenu(hZoom);
+                
+        curr_point = get(hFigure,'CurrentPoint');
+        curr_pixel = hgconvertunits(hFigure,[curr_point 0 0],...
+                           get(hFigure,'Units'),'pixels',hFigure);
+                                              
+        set(hui,'Position',curr_pixel(1:2),'Visible','on')
+        
+    otherwise % left click, center click, double click
+        % Zoom out if user clicked on 'alt'
+        if strcmp(fig_mod,'alt')
+           switch get(hZoom,'Direction')
+              case 'in'
+                  applyzoomfactor(hZoom,findaxes(hZoom),.9);   
+              case 'out'
+                  applyzoomfactor(hZoom,findaxes(hZoom),2);
+           end
+       % Delegate to registered zoom object
+	else
+            buttondownfcn(hZoom);
+	end 
+end
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+%  END of modification
+%================================================================
+
+%-----------------------------------------------%
+function localMotionFcn(hFigure,evd,hZoom)
+
+if ishandle(hZoom)
+    hTarget = handle(hittest(hFigure));
+    if ~isempty(ancestor(hTarget,'axes'))
+        if strcmp(get(hZoom,'Direction'),'in')
+            setptr(hFigure,'glassplus');
+        else
+            setptr(hFigure,'glassminus');
+        end
+    else
+        setptr(hFigure,'arrow');
+    end
+end
+
+%-----------------------------------------------%
+function localKeyPressFcn(hFigure,evd,hZoom)
+
+% Delegate to registered zoom object
+if ishandle(hZoom)
+    keypressfcn(hZoom,evd);
+end
+
+%-----------------------------------------------%
+function [hZoom] = localGetRegisteredZoomObject(hFigure,dopeek)
+
+% TBD Get Zoom object from Figure Tool Manager
+hZoom = getappdata(hFigure,'ZoomObject');
+if isempty(hZoom) || ~isa(hZoom,'graphics.zoom')
+  hZoom = graphics.zoom(hFigure);
+  setappdata(hFigure,'ZoomObject',hZoom);
+end
+
+%-----------------------------------------------%
+function localRegisterZoomObject(hFigure,hZoom)
+hFigure = hZoom.FigureHandle;
+setappdata(hFigure,'ZoomObject',hZoom);
+
+%-----------------------------------------------%
+function [appdata] = localGetData(fig)
+appdata = getappdata(fig,'ZoomFigureState');
+if isempty(appdata) || ~isfield(appdata,'uistate')
+    appdata.uistate = [];
+    appdata.ison = false;
+end
+
+%-----------------------------------------------%
+function localSetData(fig,appdata)
+setappdata(fig,'ZoomFigureState',appdata);
+
+%-----------------------------------------------%
+function localRmData(fig)
+appdata = localGetData(fig);
+appdata.uistate = [];
+appdata.ison = false;
+localSetData(fig,appdata);
+
+%-----------------------------------------------% 
+function [hui] = localUICreateDefaultContextMenu(hZoom)
+% Create default context menu
+
+hFig = get(hZoom,'FigureHandle');
+props_context.Parent = hFig;
+props_context.Tag = 'ZoomContextMenu';
+props_context.Callback = {@localUIContextMenuCallback,hZoom};
+props_context.ButtonDown = {@localUIContextMenuCallback,hZoom};
+hui = uicontextmenu(props_context);
+
+% Generic attributes for all zoom context menus
+props.Callback = {@localUIContextMenuCallback,hZoom};
+props.Parent = hui;
+
+props.Label = 'Zoom Out       Alt-Click';
+props.Tag = 'ZoomInOut';
+props.Separator = 'off';
+uzoomout = uimenu(props);
+
+% Full View context menu
+props.Label = 'Reset to Original View';
+props.Tag = 'ResetView';
+props.Separator = 'off';
+ufullview = uimenu(props);
+
+% Zoom Constraint context menu
+props.Callback = '';
+props.Label = 'Zoom Options';
+props.Tag = 'Constraint';
+props.Separator = 'on';
+uConstraint = uimenu(props);
+
+props.Parent = uConstraint;
+
+props.Callback = {@localUIContextMenuCallback,hZoom};
+props.Label = 'Unconstrained Zoom';
+props.Tag = 'ZoomUnconstrained';
+props.Separator = 'off';
+uimenu(props);
+
+props.Label = 'Horizontal Zoom (2-D Plots Only)';
+props.Tag = 'ZoomHorizontal';
+uimenu(props);
+
+props.Label = 'Vertical Zoom (2-D Plots Only)';
+props.Tag = 'ZoomVertical';
+uimenu(props);
+
+localUIContextMenuUpdate(hZoom,get(hZoom,'Constraint'));
+
+%-----------------------------------------------% 
+function [hui] = localGetContextMenu(hZoom)
+% Create context menu
+
+hui = get(hZoom,'UIContextMenu');
+if isempty(hui) || ~ishandle(hui) 
+   local_scribefiglisten(get(hZoom,'Figure'),'off');
+   hui = localUICreateDefaultContextMenu(hZoom);
+   local_scribefiglisten(get(hZoom,'Figure'),'on');
+   set(hZoom,'UIContextMenu',hui);
+   localUIUpdateContextMenuLabel(hZoom);
+   drawnow expose;
+end
+
+%-------------------------------------------------%  
+function localUIContextMenuCallback(obj,evd,hZoom)
+
+tag = get(obj,'tag');
+
+%========================================================
+%  Part of modification for ADDAXIS commands
+ystart = get(gca,'ylim');
+%========================================================
+
+switch(tag)    
+    case 'ZoomInOut'
+        switch get(hZoom,'Direction')
+            case 'in'
+                applyzoomfactor(hZoom,findaxes(hZoom),.9);
+            case 'out'
+                applyzoomfactor(hZoom,findaxes(hZoom),2);
+        end
+    case 'ResetView'
+        hAxes = findaxes(hZoom);
+        resetplotview(hAxes,'ApplyStoredView');
+    case 'ZoomContextMenu'
+        localUIContextMenuUpdate(hZoom,get(hZoom,'Constraint'));
+    case 'ZoomUnconstrained'
+        localUIContextMenuUpdate(hZoom,'none');
+    case 'ZoomHorizontal'
+        localUIContextMenuUpdate(hZoom,'horizontal');
+    case 'ZoomVertical'
+        localUIContextMenuUpdate(hZoom,'vertical');
+end
+
+%====================================================================
+%  This is a modification to accomodate the ADDAXIS commands.  
+%  Check to see if add axis has been used and update the axes using 
+%  the same scale factors.  (also put ystart = get(gca,'ylim'); 
+%  before the zoom is started above)
+
+yend = get(gca,'ylim');
+
+%  ystart and yend are the starting and ending yaxis limits
+%  now go through all of the added axes and scale their limits
+%axh = get(gca,'userdata');
+axh = getaddaxisdata(gca,'axisdata');
+if ~isempty(axh)
+  for I = 1:length(axh)
+    axhan = axh{I}(1);
+    axyl = get(axhan,'ylim');
+    axylnew(1) = axyl(1)+(yend(1)-ystart(1))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    axylnew(2) = axyl(2)-(ystart(2)-yend(2))/(ystart(2)-ystart(1)).*...
+	                 (axyl(2)-axyl(1));
+    set(axhan,'ylim',axylnew);
+  end
+end
+
+%  END of modification
+%================================================================
+
+
+%-------------------------------------------------%  
+function localUIContextMenuUpdate(hZoom,zoom_Constraint)
+
+hFigure = get(hZoom,'FigureHandle');
+ux = findall(hFigure,'Tag','ZoomHorizontal','Type','UIMenu');
+uy = findall(hFigure,'Tag','ZoomVertical','Type','UIMenu');
+uxy = findall(hFigure,'Tag','ZoomUnconstrained','Type','UIMenu');
+
+switch(zoom_Constraint) 
+  
+  case 'none'
+      set(hZoom,'Constraint','none');
+      set(ux,'checked','off');
+      set(uy,'checked','off');
+      set(uxy,'checked','on');
+
+  case 'horizontal'
+      set(hZoom,'Constraint','horizontal');
+      set(ux,'checked','on');
+      set(uy,'checked','off');
+      set(uxy,'checked','off');
+
+  case 'vertical'
+      set(hZoom,'Constraint','vertical');
+      set(ux,'checked','off');
+      set(uy,'checked','on');
+      set(uxy,'checked','off');
+end
+  
+%-----------------------------------------------%
+function localUIMenuUpdate(fig)
+ 
+set(findall(fig,'Tag','figMenuZoomIn'),'Checked','on');
+
+%-----------------------------------------------%
+function localUISetZoomIn(fig)
+set(uigettoolbar(fig,'Exploration.ZoomIn'),'State','on');   
+set(uigettoolbar(fig,'Exploration.ZoomOut'),'State','off');    
+
+%-----------------------------------------------%
+function localUISetZoomOut(fig)
+h = findall(fig,'type','uitoolbar');
+set(uigettool(h,'Exploration.ZoomIn'),'State','off');   
+set(uigettool(h,'Exploration.ZoomOut'),'State','on');    
+
+%-----------------------------------------------%
+function localUISetZoomOff(fig)
+h = findall(fig,'type','uitoolbar');
+set(uigettool(h,'Exploration.ZoomIn'),'State','off');
+set(uigettool(h,'Exploration.ZoomOut'),'State','off');   
+
+% Remove the following lines after UITOOLBARFACTORY API is on by default
+set(findall(fig,'Tag','figToolZoomIn'),'State','off');   
+set(findall(fig,'Tag','figToolZoomOut'),'State','off'); 
+
+%-----------------------------------------------%
+function localUIUpdateContextMenuLabel(hZoom);
+
+h = findobj(get(hZoom,'UIContextMenu'),'Tag','ZoomInOut');
+zoom_direction = get(hZoom,'Direction');
+if strcmp(zoom_direction,'in')
+    set(h,'Label','Zoom Out       Alt-Click');
+else
+    set(h,'Label','Zoom In        Alt-Click');
+end
+
+
+%-----------------------------------------------%
+function localSetNewZoom(bool)
+
+setappdata(0,'NewZoomImplementation',bool);
+
+%-----------------------------------------------%
+function [target,action,action_data] = localParseArgs(varargin)
+
+target = [];
+action = [];
+action_data = [];
+errstr = {'Zoom:InvalidSyntax','Invalid Syntax'};
+target = get(0,'CurrentFigure');
+
+% zoom
+if nargin==0 
+    action = 'toggle';
+    
+elseif nargin==1
+    arg1 = varargin{1};
+    
+    % zoom(SCALE)
+    if all(size(arg1)==[1,1]) & isnumeric(arg1)
+        action = 'scale';
+        action_data = arg1;
+        
+    % zoom(OPTION)    
+    elseif isstr(arg1)
+        action = arg1;
+
+    % zoom(FIG)        
+    % zoom(HZOOM)
+    elseif ishandle(arg1)
+        if isa(handle(arg1),'graphics.zoom')
+           target = get(arg1,'target');
+           action = 'setzoom';
+        elseif isa(handle(arg1),'hg.figure')  
+           target = arg1;
+           action = 'toggle';
+        end     
+    else
+        error(errstr{:});
+    end
+    
+
+elseif nargin==2 
+    
+    % zoom('newzoom',0)
+    if isstr(varargin{1})
+       action = varargin{1};
+       action_data = varargin{2};
+    
+    % zoom(FIG,SCALE)
+    % zoom(FIG,OPTION) 
+    elseif ishandle(varargin{1}) 
+       target = varargin{1};
+       arg2 = varargin{2};
+       if isstr(arg2)
+           action = arg2;
+       elseif isnumeric(arg2)
+           action = 'scale';
+           action_data = arg2;
+       end
+    end 
+    
+% zoom(FIG,<paramater/value pairs>);
+elseif nargin>=3
+   target = varargin{1};
+   arg2 = varargin{2};
+   if ishandle(target) & isstr(arg2)
+        action = 'setzoomproperties';
+        action_data = {varargin{2:end}};
+   end
+end
+
+target = handle(target);
+
+%-----------------------------------------------%
+function localSetV6Zoom(bool)
+setappdata(0,'V6Zoom',bool);
+
+%-----------------------------------------------%
+function [bool] = localIsV6Zoom
+bool = getappdata(0,'V6Zoom');
+
+%-----------------------------------------------%
+function local_scribefiglisten(hFigure,val)
+
+scribefiglisten(hFigure,val);
diff --git a/algorithms/utilities/day.m b/algorithms/utilities/day.m
index 6f93425..c14acdb 100644
--- a/algorithms/utilities/day.m
+++ b/algorithms/utilities/day.m
@@ -1,15 +1,15 @@
-function d = day(date_asNum)
-%DAY Summary of this function goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get year and reformat to the same shape as input data.
-d = reshape(c(:,3),size(date_asNum)); 
-
-end
-
+function d = day(date_asNum)
+%DAY Summary of this function goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get year and reformat to the same shape as input data.
+d = reshape(c(:,3),size(date_asNum)); 
+
+end
+
diff --git a/algorithms/utilities/findAbstractName.m b/algorithms/utilities/findAbstractName.m
index 2ee25b7..2250d07 100644
--- a/algorithms/utilities/findAbstractName.m
+++ b/algorithms/utilities/findAbstractName.m
@@ -1,50 +1,50 @@
-function [ abstractNames ] = findAbstractName( className )
-
-    abstractNames = {};
-    if strcmp(className,'handle')
-        return;
-    end
-    
-    metaclass = meta.class.fromName(className);
-    if any(strcmp(properties(metaclass),'Abstract'))
-        abstractNames{1} = metaclass.Name;
-    else
-        % Loop though all methods. If all methods are abstract, then the
-        % class is deemed as an abstract classdef.
-        isAbstract=true;
-        for i=1:size( metaclass.Methods,1)
-            if ~metaclass.Methods{i,1}.Abstract ...
-            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, metaclass.Methods{i,1}.Name) ...
-            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, 'handle')
-                isAbstract=false;
-                break;
-            end
-        end
-        if isAbstract;
-            abstractNames{1} = metaclass.Name;
-        end
-    end
-    
-    % Get names of superclasses. Note: The field names of chnages between
-    % matlab 2010 and matlab 2014a. The following attempts to handle both
-    % formats.
-    if any(strcmp(properties(metaclass),'SuperclassList'))
-        SuperclassList = metaclass.SuperclassList;
-    elseif any(strcmp(properties(metaclass),'SuperClasses'))
-        SuperclassList = metaclass.SuperClasses;
-    end
-        
-    % Assign results to output cell array.
-    for i=1:length(SuperclassList)
-        if iscell(SuperclassList)
-            tmp = findAbstractName( SuperclassList{i}.Name );            
-        else
-            tmp = findAbstractName( SuperclassList(i).Name );
-        end
-        if ~isempty(tmp)
-            abstractNames(size(abstractNames,1)+1:size(abstractNames,1)+size(tmp,1),1) = tmp;
-        end
-    end
-
-end
-
+function [ abstractNames ] = findAbstractName( className )
+
+    abstractNames = {};
+    if strcmp(className,'handle')
+        return;
+    end
+    
+    metaclass = meta.class.fromName(className);
+    if any(strcmp(properties(metaclass),'Abstract'))
+        abstractNames{1} = metaclass.Name;
+    else
+        % Loop though all methods. If all methods are abstract, then the
+        % class is deemed as an abstract classdef.
+        isAbstract=true;
+        for i=1:size( metaclass.Methods,1)
+            if ~metaclass.Methods{i,1}.Abstract ...
+            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, metaclass.Methods{i,1}.Name) ...
+            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, 'handle')
+                isAbstract=false;
+                break;
+            end
+        end
+        if isAbstract;
+            abstractNames{1} = metaclass.Name;
+        end
+    end
+    
+    % Get names of superclasses. Note: The field names of chnages between
+    % matlab 2010 and matlab 2014a. The following attempts to handle both
+    % formats.
+    if any(strcmp(properties(metaclass),'SuperclassList'))
+        SuperclassList = metaclass.SuperclassList;
+    elseif any(strcmp(properties(metaclass),'SuperClasses'))
+        SuperclassList = metaclass.SuperClasses;
+    end
+        
+    % Assign results to output cell array.
+    for i=1:length(SuperclassList)
+        if iscell(SuperclassList)
+            tmp = findAbstractName( SuperclassList{i}.Name );            
+        else
+            tmp = findAbstractName( SuperclassList(i).Name );
+        end
+        if ~isempty(tmp)
+            abstractNames(size(abstractNames,1)+1:size(abstractNames,1)+size(tmp,1),1) = tmp;
+        end
+    end
+
+end
+
diff --git a/algorithms/utilities/findClassDefsUsingAbstractName.m b/algorithms/utilities/findClassDefsUsingAbstractName.m
index ec1388f..46662a8 100644
--- a/algorithms/utilities/findClassDefsUsingAbstractName.m
+++ b/algorithms/utilities/findClassDefsUsingAbstractName.m
@@ -1,104 +1,104 @@
-function [classNames] = findClassDefsUsingAbstractName( abstractName, model_file_name)
-
-    % Set some file names to ignore. This is undertaken because
-    % requiredFilesAndProducts() (using matlab 2014b) appears to give inconsistent
-    % results and for to reduce GUI start up time.
-    fnames_ignore = {'model_TFN' ...
-                     'example_TFN_model' ...
-                     'HydroSight' ...
-                     'ipdm' ...
-                     'cmaes' ...
-                     'fminsearchbnd' ...
-                     'variogram' ...
-                     'variogramfit' ...
-                     'forcingTransform_abstract' ...
-                     'derivedForcingTransform_abstract' ...
-                     'stochForcingTransform_abstract' ...
-                     'responseFunction_abstract' ...
-                     'derivedResponseFunction_abstract' ...
-                     'doIRFconvolution' ...
-                     'findAbstractName' ...
-                     'model_abstract'};
-    fnames_ignore = unique([abstractName fnames_ignore ]);
-
-
-    % Check 'abstractName' is an abstract class 
-    classNames = {};
-    if strcmp(abstractName,'handle')
-        return;
-    end
-    
-    metaclass = meta.class.fromName(abstractName);
-    if any(strcmp(properties(metaclass),'Abstract'))
-        try
-            abstractNames{1} = metaclass.Name;
-        catch ME
-            abstractNames{1} = '';
-        end
-            
-    else
-        % Loop though all methods. If all methods are abstract, then 
-        % 'abstractName' is deemed as an abstract classdef.
-        isAbstract=true;
-        for i=1:size( metaclass.Methods,1)
-            if ~metaclass.Methods{i,1}.Abstract ...
-            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, metaclass.Methods{i,1}.Name) ...
-            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, 'handle')
-                isAbstract=false;
-                break;
-            end
-        end
-        if ~isAbstract;
-            return;
-        end
-    end
-    
-    % Find path to the specified model.
-    if exist(model_file_name)
-        modelPath = fileparts(which(model_file_name));        
-    else
-        error(['The following model file does not exist:',model_file_name]);
-    end
-    
-    % Get list of all .m files listed within the model folder
-    allFoldersFiles = rdir(fullfile(modelPath, ['**',filesep,'*.m']));
-    
-    % Get just the file names
-    all_m_Files = cell( size(allFoldersFiles));
-    for i=1:size(allFoldersFiles,1)
-        [pathstr, name] = fileparts(allFoldersFiles(i).name);
-        all_m_Files{i} = name;
-    end
-    
-    isFileFromAbstract = false(size(allFoldersFiles));
-    
-    % Determine which version of depfun to use. Post Matlab 2014b, depfun
-    % was removed.
-    useDepFun = year(version('-date'))<=2014;
-    
-    % Loop through each file name and assess if the file is dependent on
-    % the specified abstract
-    for i=1:size(allFoldersFiles,1)
-
-        if ~any(cellfun(@(x) ~isempty(x), strfind( fnames_ignore , all_m_Files{i})))            
-
-           % Get list of dependent function                              
-           if useDepFun
-               depfunlist = depfun(all_m_Files(i),'-quiet');               
-           else
-               depfunlist = matlab.codetools.requiredFilesAndProducts(all_m_Files(i));
-               depfunlist = depfunlist';                
-           end
-
-           % Find if there is dependence upon the required abstract name
-           if any(cellfun(@(x) ~isempty(x), strfind( depfunlist, abstractName)))
-               isFileFromAbstract(i) = true;               
-           end
-               
-        end
-    end
-    
-    % File the list of class names to those dependent upon the abstract
-    classNames = all_m_Files(isFileFromAbstract)';
-end
-
+function [classNames] = findClassDefsUsingAbstractName( abstractName, model_file_name)
+
+    % Set some file names to ignore. This is undertaken because
+    % requiredFilesAndProducts() (using matlab 2014b) appears to give inconsistent
+    % results and for to reduce GUI start up time.
+    fnames_ignore = {'model_TFN' ...
+                     'example_TFN_model' ...
+                     'HydroSight' ...
+                     'ipdm' ...
+                     'cmaes' ...
+                     'fminsearchbnd' ...
+                     'variogram' ...
+                     'variogramfit' ...
+                     'forcingTransform_abstract' ...
+                     'derivedForcingTransform_abstract' ...
+                     'stochForcingTransform_abstract' ...
+                     'responseFunction_abstract' ...
+                     'derivedResponseFunction_abstract' ...
+                     'doIRFconvolution' ...
+                     'findAbstractName' ...
+                     'model_abstract'};
+    fnames_ignore = unique([abstractName fnames_ignore ]);
+
+
+    % Check 'abstractName' is an abstract class 
+    classNames = {};
+    if strcmp(abstractName,'handle')
+        return;
+    end
+    
+    metaclass = meta.class.fromName(abstractName);
+    if any(strcmp(properties(metaclass),'Abstract'))
+        try
+            abstractNames{1} = metaclass.Name;
+        catch ME
+            abstractNames{1} = '';
+        end
+            
+    else
+        % Loop though all methods. If all methods are abstract, then 
+        % 'abstractName' is deemed as an abstract classdef.
+        isAbstract=true;
+        for i=1:size( metaclass.Methods,1)
+            if ~metaclass.Methods{i,1}.Abstract ...
+            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, metaclass.Methods{i,1}.Name) ...
+            && ~strcmp(metaclass.Methods{i,1}.DefiningClass.Name, 'handle')
+                isAbstract=false;
+                break;
+            end
+        end
+        if ~isAbstract;
+            return;
+        end
+    end
+    
+    % Find path to the specified model.
+    if exist(model_file_name)
+        modelPath = fileparts(which(model_file_name));        
+    else
+        error(['The following model file does not exist:',model_file_name]);
+    end
+    
+    % Get list of all .m files listed within the model folder
+    allFoldersFiles = rdir(fullfile(modelPath, ['**',filesep,'*.m']));
+    
+    % Get just the file names
+    all_m_Files = cell( size(allFoldersFiles));
+    for i=1:size(allFoldersFiles,1)
+        [pathstr, name] = fileparts(allFoldersFiles(i).name);
+        all_m_Files{i} = name;
+    end
+    
+    isFileFromAbstract = false(size(allFoldersFiles));
+    
+    % Determine which version of depfun to use. Post Matlab 2014b, depfun
+    % was removed.
+    useDepFun = year(version('-date'))<=2014;
+    
+    % Loop through each file name and assess if the file is dependent on
+    % the specified abstract
+    for i=1:size(allFoldersFiles,1)
+
+        if ~any(cellfun(@(x) ~isempty(x), strfind( fnames_ignore , all_m_Files{i})))            
+
+           % Get list of dependent function                              
+           if useDepFun
+               depfunlist = depfun(all_m_Files(i),'-quiet');               
+           else
+               depfunlist = matlab.codetools.requiredFilesAndProducts(all_m_Files(i));
+               depfunlist = depfunlist';                
+           end
+
+           % Find if there is dependence upon the required abstract name
+           if any(cellfun(@(x) ~isempty(x), strfind( depfunlist, abstractName)))
+               isFileFromAbstract(i) = true;               
+           end
+               
+        end
+    end
+    
+    % File the list of class names to those dependent upon the abstract
+    classNames = all_m_Files(isFileFromAbstract)';
+end
+
diff --git a/algorithms/utilities/hour.m b/algorithms/utilities/hour.m
index 1a42a18..3e2b4ea 100644
--- a/algorithms/utilities/hour.m
+++ b/algorithms/utilities/hour.m
@@ -1,15 +1,15 @@
-function h = hour(date_asNum)
-%DAY Summary of this function goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get year and reformat to the same shape as input data.
-h = reshape(c(:,4),size(date_asNum)); 
-
-end
-
+function h = hour(date_asNum)
+%DAY Summary of this function goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get year and reformat to the same shape as input data.
+h = reshape(c(:,4),size(date_asNum)); 
+
+end
+
diff --git a/algorithms/utilities/minute.m b/algorithms/utilities/minute.m
index a991ff1..f7874b0 100644
--- a/algorithms/utilities/minute.m
+++ b/algorithms/utilities/minute.m
@@ -1,15 +1,15 @@
-function m = minute(date_asNum)
-%DAY Summary of this function goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get year and reformat to the same shape as input data.
-m = reshape(c(:,5),size(date_asNum)); 
-
-end
-
+function m = minute(date_asNum)
+%DAY Summary of this function goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get year and reformat to the same shape as input data.
+m = reshape(c(:,5),size(date_asNum)); 
+
+end
+
diff --git a/algorithms/utilities/month.m b/algorithms/utilities/month.m
index 7d4012d..cf8e702 100644
--- a/algorithms/utilities/month.m
+++ b/algorithms/utilities/month.m
@@ -1,15 +1,15 @@
-function m = month(date_asNum)
-%MONTH Summary of this function goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get month and reformat to the same shape as input data.
-m = reshape(c(:,2),size(date_asNum)); 
-
-end
-
+function m = month(date_asNum)
+%MONTH Summary of this function goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get month and reformat to the same shape as input data.
+m = reshape(c(:,2),size(date_asNum)); 
+
+end
+
diff --git a/algorithms/utilities/plotmatrix.m b/algorithms/utilities/plotmatrix.m
index 19093be..731f093 100644
--- a/algorithms/utilities/plotmatrix.m
+++ b/algorithms/utilities/plotmatrix.m
@@ -1,247 +1,247 @@
-function [h,ax,BigAx,patches,pax] = plotmatrix(varargin)
-%PLOTMATRIX Scatter plot matrix.
-%   PLOTMATRIX(X,Y) scatter plots the columns of X against the columns
-%   of Y.  If X is P-by-M and Y is P-by-N, PLOTMATRIX will produce a
-%   N-by-M matrix of axes. PLOTMATRIX(Y) is the same as PLOTMATRIX(Y,Y)
-%   except that the diagonal will be replaced by HIST(Y(:,i)). 
-%
-%   PLOTMATRIX(...,'LineSpec') uses the given line specification in the
-%   string 'LineSpec'; '.' is the default (see PLOT for possibilities).  
-%
-%   PLOTMATRIX(AX,...) uses AX as the BigAx instead of GCA.
-%
-%   [H,AX,BigAx,P,PAx] = PLOTMATRIX(...) returns a matrix of handles
-%   to the objects created in H, a matrix of handles to the individual
-%   subaxes in AX, a handle to big (invisible) axes that frame the
-%   subaxes in BigAx, a matrix of handles for the histogram plots in
-%   P, and a matrix of handles for invisible axes that control the
-%   histogram axes scales in PAx.  BigAx is left as the CurrentAxes so
-%   that a subsequent TITLE, XLABEL, or YLABEL will be centered with
-%   respect to the matrix of axes.
-%
-%   Example:
-%       x = randn(50,3); y = x*[-1 2 1;2 0 1;1 -2 3;]';
-%       plotmatrix(y)
-
-%   Clay M. Thompson 10-3-94
-%   Copyright 1984-2005 The MathWorks, Inc.
-%   $Revision: 1.1 $  $Date: 2008-11-29 12:04:58 $
-
-% Parse possible Axes input
-[cax,args,nargs] = axescheck(varargin{:});
-error(nargchk(1,4,nargs,'struct'));
-nin = nargs;
-
-sym = '.'; % Default scatter plot symbol.
-dohist = 0;
-
-xx = 1;
-linespec = 0;
-putlabels = 0;
-if nin == 1 % plotmatrix(x)
-	xx = 1;
-elseif nin == 2 
-	if ischar(args{2}) %plotmatrix(x,line)
-		xx=1;
-		linespec = 1;
-		sym = args{2};
-		[l,c,m,msg] = colstyle(sym); %#ok
-		if ~isempty(msg), error(msg); end %#ok
-
-	elseif iscellstr(args{2}) % plotmatrix(x,text)
-		xx=1;
-		putlabels = 1;
-		varnames = args{2};
-	else % plotmatrix(x,y)
-		xx = 0;
-	end
-elseif nin == 3
-	linespec = 1;
-	if iscellstr(args{3}) & ischar(args{2}) %plotmatrix(x,line,text)
-		xx = 1;
-		putlabels = 1;
-		varnames = args{3};
-		linespec = 1;
-		sym = args{2};
-		[l,c,m,msg] = colstyle(sym); %#ok
-		if ~isempty(msg), error(msg); end %#ok
-
-	elseif isfloat(args{2}) & iscellstr(args{3}) % plotmatrix(x,y,text)
-		xx = 0;
-		putlabels = 1;
-		varnames = args{3};
-
-	else  % plotmatrix(x,y,line)
-		xx = 0;
-		putlabels = 0;
-		sym = args{3};
-		[l,c,m,msg] = colstyle(sym); %#ok
-		if ~isempty(msg), error(msg); end %#ok
-	end
-else % plotmatrix(x,y,text,line)
-	lipespec = 1;
-	xx = 0;
-	putlabels = 1;
-	varnames = args{3};
-	sym = args{4};
-	[l,c,m,msg] = colstyle(sym); %#ok
-	if ~isempty(msg), error(msg); end %#ok
-end
-
-%if ischar(linespec),
-%  sym = args{nin};
-%  [l,c,m,msg] = colstyle(sym); %#ok
-%  if ~isempty(msg), error(msg); end %#ok
-%  nin = nin - 1;
-%end
-
-if xx, % plotmatrix(y)
-  rows = size(args{1},2); cols = rows;
-  x = args{1}; y = args{1};
-  nvar = size(x,2);
-  dohist = 1;
-elseif ~xx % plotmatrix(x,y)
-  rows = size(args{2},2); cols = size(args{1},2);
-  x = args{1}; y = args{2};
-  nvar = size(x,2) + size(y,2);
-else
-  error('MATLAB:plotmatrix:InvalidLineSpec',...
-        'Invalid marker specification. Type ''help plot''.');
-end
-if putlabels & length(varnames) ~= nvar; error('texto sem valores suficientes'); end
-
-% Don't plot anything if either x or y is empty
-patches = [];
-pax = [];
-if isempty(rows) || isempty(cols),
-   if nargout>0, h = []; ax = []; BigAx = []; end
-   return
-end
-
-if ndims(x)>2 || ndims(y)>2,
-  error(id('InvalidXYMatrices'),'X and Y must be 2-D.')
-end
-if size(x,1)~=size(y,1) || size(x,3)~=size(y,3),
-  error(id('XYSizeMismatch'),'X and Y must have the same number of rows and pages.');
-end
-
-% Create/find BigAx and make it invisible
-BigAx = newplot(cax);
-fig = ancestor(BigAx,'figure');
-hold_state = ishold(BigAx);
-set(BigAx,'Visible','off','color','none')
-
-if any(sym=='.'),
-  units = get(BigAx,'units');
-  set(BigAx,'units','pixels');
-  pos = get(BigAx,'Position');
-  set(BigAx,'units',units);
-  markersize = max(1,min(15,round(15*min(pos(3:4))/max(1,size(x,1))/max(rows,cols))));
-else
-  markersize = get(0,'defaultlinemarkersize');
-end
-
-% Create and plot into axes
-ax = zeros(rows,cols);
-pos = get(BigAx,'Position');
-width = pos(3)/cols;
-height = pos(4)/rows;
-space = .02; % 2 percent space between axes
-pos(1:2) = pos(1:2) + space*[width height];
-m = size(y,1);
-k = size(y,3);
-xlim = zeros([rows cols 2]);
-ylim = zeros([rows cols 2]);
-BigAxHV = get(BigAx,'HandleVisibility');
-BigAxParent = get(BigAx,'Parent');
-for i=rows:-1:1,
-  for j=cols:-1:1,
-    axPos = [pos(1)+(j-1)*width pos(2)+(rows-i)*height ...
-             width*(1-space) height*(1-space)];
-    findax = findobj(fig,'Type','axes','Position',axPos);
-    if isempty(findax),
-      ax(i,j) = axes('Position',axPos,'HandleVisibility',BigAxHV,'parent',BigAxParent);
-      set(ax(i,j),'visible','on');
-    else
-      ax(i,j) = findax(1);
-    end
-    hh(i,j,:) = plot(reshape(x(:,j,:),[m k]), ...
-                     reshape(y(:,i,:),[m k]),sym,'parent',ax(i,j))';
-    set(hh(i,j,:),'markersize',markersize);
-    set(ax(i,j),'xlimmode','auto','ylimmode','auto','xgrid','off','ygrid','off')
-    xlim(i,j,:) = get(ax(i,j),'xlim');
-    ylim(i,j,:) = get(ax(i,j),'ylim');
-  end
-end
-
-if putlabels & ~xx
-	count = 1;
-	% xlabel
-	for i = 1:size(ax,2); set(get(ax(end,i),'xlabel'),'String',varnames{count}); count = count + 1; end
-
-	% ylabel
-	for i = 1:size(ax,1); set(get(ax(i,1),'ylabel'),'String',varnames{count}); count = count + 1; end
-end
-
-%xlimmin = min(xlim(:,:,1),[],1); xlimmax = max(xlim(:,:,2),[],1);
-%ylimmin = min(ylim(:,:,1),[],2); ylimmax = max(ylim(:,:,2),[],2);
-xlimmin = min(x,[],1);  xlimmax = max(eps,max(x,[],1));
-ylimmin = min(y,[],1)'; ylimmax = max(eps,max(y,[],1))';
-
-% Try to be smart about axes limits and labels.  Set all the limits of a
-% row or column to be the same and inset the tick marks by 10 percent.
-inset = .15;
-for i=1:rows,
-  set(ax(i,1),'ylim',[ylimmin(i,1) ylimmax(i,1)]);
-  dy = diff(get(ax(i,1),'ylim'))*inset;
-  set(ax(i,:),'ylim',[ylimmin(i,1)-dy ylimmax(i,1)+dy]);
-end
-dx = zeros(1,cols);
-for j=1:cols,
-  set(ax(1,j),'xlim',[xlimmin(1,j) xlimmax(1,j)]);
-  dx(j) = diff(get(ax(1,j),'xlim'))*inset ;
-  set(ax(:,j),'xlim',[xlimmin(1,j)-dx(j) xlimmax(1,j)+dx(j)]);
-end
-
-set(ax(1:rows-1,:),'xticklabel','')
-set(ax(:,2:cols),'yticklabel','')
-set(BigAx,'XTick',get(ax(rows,1),'xtick'),'YTick',get(ax(rows,1),'ytick'), ...
-          'userdata',ax,'tag','PlotMatrixBigAx')
-
-if dohist, % Put a histogram on the diagonal for plotmatrix(y) case
-  for i=rows:-1:1,
-    histax = axes('Position',get(ax(i,i),'Position'),'HandleVisibility',BigAxHV,'parent',BigAxParent);
-    [nn,xx] = hist(reshape(y(:,i,:),[m k]));
-    patches(i,:) = bar(histax,xx,nn,'hist');
-    if putlabels; 
-	    xt = 0.5*(max(y(:,i,:)) + min(y(:,i,:)));
-	    yt = 0.9*max(nn);
-	    txt = varnames{i};
-  	    text(xt,yt,txt)
-    end
-    set(histax,'xtick',[],'ytick',[],'xgrid','off','ygrid','off');
-    set(histax,'xlim',[xlimmin(1,i)-dx(i) xlimmax(1,i)+dx(i)]);
-    pax(i) = histax;  % ax handles for histograms
-  end
-  patches = patches';
-end
-
-% A bug seems to occur when plotmatrix is ran to produce a plot inside a GUI 
-% whereby the default fig menu items and icons appear. Commenting out the code below fixed the issue.
-% Tim PEterson -  April 2016
-% Make BigAx the CurrentAxes
-% set(fig,'CurrentAx',BigAx)
-% if ~hold_state,
-%    set(fig,'NextPlot','replace')
-% end
-
-% Also set Title and X/YLabel visibility to on and strings to empty
-set([get(BigAx,'Title'); get(BigAx,'XLabel'); get(BigAx,'YLabel')], ...
- 'String','','Visible','on')
- 
- if nargout~=0,
-   h = hh;
- end
- 
-function str=id(str)
-str = ['MATLAB:plotmatrix:' str];
+function [h,ax,BigAx,patches,pax] = plotmatrix(varargin)
+%PLOTMATRIX Scatter plot matrix.
+%   PLOTMATRIX(X,Y) scatter plots the columns of X against the columns
+%   of Y.  If X is P-by-M and Y is P-by-N, PLOTMATRIX will produce a
+%   N-by-M matrix of axes. PLOTMATRIX(Y) is the same as PLOTMATRIX(Y,Y)
+%   except that the diagonal will be replaced by HIST(Y(:,i)). 
+%
+%   PLOTMATRIX(...,'LineSpec') uses the given line specification in the
+%   string 'LineSpec'; '.' is the default (see PLOT for possibilities).  
+%
+%   PLOTMATRIX(AX,...) uses AX as the BigAx instead of GCA.
+%
+%   [H,AX,BigAx,P,PAx] = PLOTMATRIX(...) returns a matrix of handles
+%   to the objects created in H, a matrix of handles to the individual
+%   subaxes in AX, a handle to big (invisible) axes that frame the
+%   subaxes in BigAx, a matrix of handles for the histogram plots in
+%   P, and a matrix of handles for invisible axes that control the
+%   histogram axes scales in PAx.  BigAx is left as the CurrentAxes so
+%   that a subsequent TITLE, XLABEL, or YLABEL will be centered with
+%   respect to the matrix of axes.
+%
+%   Example:
+%       x = randn(50,3); y = x*[-1 2 1;2 0 1;1 -2 3;]';
+%       plotmatrix(y)
+
+%   Clay M. Thompson 10-3-94
+%   Copyright 1984-2005 The MathWorks, Inc.
+%   $Revision: 1.1 $  $Date: 2008-11-29 12:04:58 $
+
+% Parse possible Axes input
+[cax,args,nargs] = axescheck(varargin{:});
+error(nargchk(1,4,nargs,'struct'));
+nin = nargs;
+
+sym = '.'; % Default scatter plot symbol.
+dohist = 0;
+
+xx = 1;
+linespec = 0;
+putlabels = 0;
+if nin == 1 % plotmatrix(x)
+	xx = 1;
+elseif nin == 2 
+	if ischar(args{2}) %plotmatrix(x,line)
+		xx=1;
+		linespec = 1;
+		sym = args{2};
+		[l,c,m,msg] = colstyle(sym); %#ok
+		if ~isempty(msg), error(msg); end %#ok
+
+	elseif iscellstr(args{2}) % plotmatrix(x,text)
+		xx=1;
+		putlabels = 1;
+		varnames = args{2};
+	else % plotmatrix(x,y)
+		xx = 0;
+	end
+elseif nin == 3
+	linespec = 1;
+	if iscellstr(args{3}) & ischar(args{2}) %plotmatrix(x,line,text)
+		xx = 1;
+		putlabels = 1;
+		varnames = args{3};
+		linespec = 1;
+		sym = args{2};
+		[l,c,m,msg] = colstyle(sym); %#ok
+		if ~isempty(msg), error(msg); end %#ok
+
+	elseif isfloat(args{2}) & iscellstr(args{3}) % plotmatrix(x,y,text)
+		xx = 0;
+		putlabels = 1;
+		varnames = args{3};
+
+	else  % plotmatrix(x,y,line)
+		xx = 0;
+		putlabels = 0;
+		sym = args{3};
+		[l,c,m,msg] = colstyle(sym); %#ok
+		if ~isempty(msg), error(msg); end %#ok
+	end
+else % plotmatrix(x,y,text,line)
+	lipespec = 1;
+	xx = 0;
+	putlabels = 1;
+	varnames = args{3};
+	sym = args{4};
+	[l,c,m,msg] = colstyle(sym); %#ok
+	if ~isempty(msg), error(msg); end %#ok
+end
+
+%if ischar(linespec),
+%  sym = args{nin};
+%  [l,c,m,msg] = colstyle(sym); %#ok
+%  if ~isempty(msg), error(msg); end %#ok
+%  nin = nin - 1;
+%end
+
+if xx, % plotmatrix(y)
+  rows = size(args{1},2); cols = rows;
+  x = args{1}; y = args{1};
+  nvar = size(x,2);
+  dohist = 1;
+elseif ~xx % plotmatrix(x,y)
+  rows = size(args{2},2); cols = size(args{1},2);
+  x = args{1}; y = args{2};
+  nvar = size(x,2) + size(y,2);
+else
+  error('MATLAB:plotmatrix:InvalidLineSpec',...
+        'Invalid marker specification. Type ''help plot''.');
+end
+if putlabels & length(varnames) ~= nvar; error('texto sem valores suficientes'); end
+
+% Don't plot anything if either x or y is empty
+patches = [];
+pax = [];
+if isempty(rows) || isempty(cols),
+   if nargout>0, h = []; ax = []; BigAx = []; end
+   return
+end
+
+if ndims(x)>2 || ndims(y)>2,
+  error(id('InvalidXYMatrices'),'X and Y must be 2-D.')
+end
+if size(x,1)~=size(y,1) || size(x,3)~=size(y,3),
+  error(id('XYSizeMismatch'),'X and Y must have the same number of rows and pages.');
+end
+
+% Create/find BigAx and make it invisible
+BigAx = newplot(cax);
+fig = ancestor(BigAx,'figure');
+hold_state = ishold(BigAx);
+set(BigAx,'Visible','off','color','none')
+
+if any(sym=='.'),
+  units = get(BigAx,'units');
+  set(BigAx,'units','pixels');
+  pos = get(BigAx,'Position');
+  set(BigAx,'units',units);
+  markersize = max(1,min(15,round(15*min(pos(3:4))/max(1,size(x,1))/max(rows,cols))));
+else
+  markersize = get(0,'defaultlinemarkersize');
+end
+
+% Create and plot into axes
+ax = zeros(rows,cols);
+pos = get(BigAx,'Position');
+width = pos(3)/cols;
+height = pos(4)/rows;
+space = .02; % 2 percent space between axes
+pos(1:2) = pos(1:2) + space*[width height];
+m = size(y,1);
+k = size(y,3);
+xlim = zeros([rows cols 2]);
+ylim = zeros([rows cols 2]);
+BigAxHV = get(BigAx,'HandleVisibility');
+BigAxParent = get(BigAx,'Parent');
+for i=rows:-1:1,
+  for j=cols:-1:1,
+    axPos = [pos(1)+(j-1)*width pos(2)+(rows-i)*height ...
+             width*(1-space) height*(1-space)];
+    findax = findobj(fig,'Type','axes','Position',axPos);
+    if isempty(findax),
+      ax(i,j) = axes('Position',axPos,'HandleVisibility',BigAxHV,'parent',BigAxParent);
+      set(ax(i,j),'visible','on');
+    else
+      ax(i,j) = findax(1);
+    end
+    hh(i,j,:) = plot(reshape(x(:,j,:),[m k]), ...
+                     reshape(y(:,i,:),[m k]),sym,'parent',ax(i,j))';
+    set(hh(i,j,:),'markersize',markersize);
+    set(ax(i,j),'xlimmode','auto','ylimmode','auto','xgrid','off','ygrid','off')
+    xlim(i,j,:) = get(ax(i,j),'xlim');
+    ylim(i,j,:) = get(ax(i,j),'ylim');
+  end
+end
+
+if putlabels & ~xx
+	count = 1;
+	% xlabel
+	for i = 1:size(ax,2); set(get(ax(end,i),'xlabel'),'String',varnames{count}); count = count + 1; end
+
+	% ylabel
+	for i = 1:size(ax,1); set(get(ax(i,1),'ylabel'),'String',varnames{count}); count = count + 1; end
+end
+
+%xlimmin = min(xlim(:,:,1),[],1); xlimmax = max(xlim(:,:,2),[],1);
+%ylimmin = min(ylim(:,:,1),[],2); ylimmax = max(ylim(:,:,2),[],2);
+xlimmin = min(x,[],1);  xlimmax = max(eps,max(x,[],1));
+ylimmin = min(y,[],1)'; ylimmax = max(eps,max(y,[],1))';
+
+% Try to be smart about axes limits and labels.  Set all the limits of a
+% row or column to be the same and inset the tick marks by 10 percent.
+inset = .15;
+for i=1:rows,
+  set(ax(i,1),'ylim',[ylimmin(i,1) ylimmax(i,1)]);
+  dy = diff(get(ax(i,1),'ylim'))*inset;
+  set(ax(i,:),'ylim',[ylimmin(i,1)-dy ylimmax(i,1)+dy]);
+end
+dx = zeros(1,cols);
+for j=1:cols,
+  set(ax(1,j),'xlim',[xlimmin(1,j) xlimmax(1,j)]);
+  dx(j) = diff(get(ax(1,j),'xlim'))*inset ;
+  set(ax(:,j),'xlim',[xlimmin(1,j)-dx(j) xlimmax(1,j)+dx(j)]);
+end
+
+set(ax(1:rows-1,:),'xticklabel','')
+set(ax(:,2:cols),'yticklabel','')
+set(BigAx,'XTick',get(ax(rows,1),'xtick'),'YTick',get(ax(rows,1),'ytick'), ...
+          'userdata',ax,'tag','PlotMatrixBigAx')
+
+if dohist, % Put a histogram on the diagonal for plotmatrix(y) case
+  for i=rows:-1:1,
+    histax = axes('Position',get(ax(i,i),'Position'),'HandleVisibility',BigAxHV,'parent',BigAxParent);
+    [nn,xx] = hist(reshape(y(:,i,:),[m k]));
+    patches(i,:) = bar(histax,xx,nn,'hist');
+    if putlabels; 
+	    xt = 0.5*(max(y(:,i,:)) + min(y(:,i,:)));
+	    yt = 0.9*max(nn);
+	    txt = varnames{i};
+  	    text(xt,yt,txt)
+    end
+    set(histax,'xtick',[],'ytick',[],'xgrid','off','ygrid','off');
+    set(histax,'xlim',[xlimmin(1,i)-dx(i) xlimmax(1,i)+dx(i)]);
+    pax(i) = histax;  % ax handles for histograms
+  end
+  patches = patches';
+end
+
+% A bug seems to occur when plotmatrix is ran to produce a plot inside a GUI 
+% whereby the default fig menu items and icons appear. Commenting out the code below fixed the issue.
+% Tim PEterson -  April 2016
+% Make BigAx the CurrentAxes
+% set(fig,'CurrentAx',BigAx)
+% if ~hold_state,
+%    set(fig,'NextPlot','replace')
+% end
+
+% Also set Title and X/YLabel visibility to on and strings to empty
+set([get(BigAx,'Title'); get(BigAx,'XLabel'); get(BigAx,'YLabel')], ...
+ 'String','','Visible','on')
+ 
+ if nargout~=0,
+   h = hh;
+ end
+ 
+function str=id(str)
+str = ['MATLAB:plotmatrix:' str];
diff --git a/algorithms/utilities/plotyyy/license.txt b/algorithms/utilities/plotyyy/license.txt
new file mode 100644
index 0000000..27f0944
--- /dev/null
+++ b/algorithms/utilities/plotyyy/license.txt
@@ -0,0 +1,24 @@
+Copyright (c) 2001, Denis Gilbert
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/algorithms/utilities/plotyyy/plotyy_scatter.m b/algorithms/utilities/plotyyy/plotyy_scatter.m
new file mode 100644
index 0000000..a6dd2ff
--- /dev/null
+++ b/algorithms/utilities/plotyyy/plotyy_scatter.m
@@ -0,0 +1,402 @@
+function [axout,h1,h2]=plotyy_scatter(varargin)
+%PLOTYY Graphs with y tick labels on the left and right
+%   PLOTYY(X1,Y1,X2,Y2) plots Y1 versus X1 with y-axis labeling
+%   on the left and plots Y2 versus X2 with y-axis labeling on
+%   the right.
+%
+%   PLOTYY(X1,Y1,X2,Y2,FUN) uses the plotting function FUN
+%   instead of PLOT to produce each graph. FUN can be a
+%   function handle or a string that is the name of a plotting
+%   function, e.g. plot, semilogx, semilogy, loglog, stem,
+%   etc. or any function that accepts the syntax H = FUN(X,Y).
+%   For example
+%      PLOTYY(X1,Y1,X2,Y2,@loglog)  % Function handle
+%      PLOTYY(X1,Y1,X2,Y2,'loglog') % String
+%
+%   PLOTYY(X1,Y1,X2,Y2,FUN1,FUN2) uses FUN1(X1,Y1) to plot the data for
+%   the left axes and FUN2(X2,Y2) to plot the data for the right axes.
+%
+%   PLOTYY(AX,...) plots into AX as the main axes, instead of GCA.  If AX
+%   is the vector of axes handles returned by a previous call to PLOTYY,
+%   then the secondary axes will be ignored.
+%
+%   [AX,H1,H2] = PLOTYY(...) returns the handles of the two axes created in
+%   AX and the handles of the graphics objects from each plot in H1
+%   and H2. AX(1) is the left axes and AX(2) is the right axes.
+%
+%   See also PLOT, function_handle
+
+%   Copyright 1984-2015 The MathWorks, Inc.
+%     
+
+% Parse possible Axes input
+%   First see if we have a vector of associated PLOTYY axes
+
+% Make sure we have at least four input arguments
+narginchk(4,inf);
+
+if length(varargin{1}(:))==2 && ...
+        all(ishghandle(varargin{1}(:))) && ...
+        isequal(lower(char(get(varargin{1},'type'))),['axes';'axes']) && ... 
+        ~isempty(findall(varargin{1}(1),'tag','PlotyyDeleteProxy')) && ...
+        ~isempty(findall(varargin{1}(2),'tag','PlotyyDeleteProxy'))
+    varargin{1} = varargin{1}(1);
+end
+[cax,args,nargs] = axescheck(varargin{:});
+caxspecified = ~isempty(cax);
+
+cax = newplot(cax);
+fig = ancestor(cax,'figure');
+
+[x1,y1,x2,y2] = deal(args{1:4});
+% if nargs<5, fun1 = @plot; else fun1 = args{5}; end
+if nargs<5, fun1 = @scatter; else fun1 = args{5}; end
+if nargs<6, fun2 = fun1;  else fun2 = args{6}; end
+
+hold_state = ishold(cax);
+
+% Plot first plot
+ax(1) = cax;
+set(fig,'NextPlot','add')
+[h1,ax(1)] = fevalfun(fun1,ax(1),x1,y1,caxspecified);
+slm = getappdata(fig,'SubplotListenersManager');
+if ~isempty(slm)
+    disable(slm);
+end
+set(ax(1),'Box','on','ActivePositionProperty','position')
+if ~isempty(slm)
+    enable(slm);
+end
+xlim1 = get(ax(1),'XLim');
+ylim1 = get(ax(1),'YLim');
+
+% Try to produce different colored lines on each plot by
+% shifting the colororder by the number of different colors
+% in the first plot.
+colors = get(findobj(h1,'-property','Color'),{'Color'});
+if ~isempty(colors),
+    colors = unique(cat(1,colors{:}),'rows');
+    n = size(colors,1);
+    % Set the axis color to match the single colored line
+    if n==1, set(ax(1),'YColor',colors), end
+    
+    % Create a cleanup object to restore defaults that we change
+    defaultResetter = localCreateDefaultsCleanup(fig); %#ok<NASGU>
+    
+    % Set the default ColorOrder so that the new axes pick the value up
+    % even if the plotting function clears the axes.  The default Mode
+    % value is explicitly altered to 'manual' so that code-generation
+    % notices the changed ColorOrder.
+    co = get(fig,'DefaultAxesColorOrder');
+    set(fig,'DefaultAxesColorOrder',co([n+1:end 1:n],:))
+    set(fig,'DefaultAxesColorOrderMode', 'manual');
+end
+
+% Plot second plot
+ax1hv = get(ax(1),'HandleVisibility');
+ax(2) = axes('HandleVisibility',ax1hv,'Units',get(ax(1),'Units'), ...
+    'Position',get(ax(1),'Position'),'Parent',get(ax(1),'Parent'));
+[h2,ax(2)] = fevalfun(fun2,ax(2),x2,y2,caxspecified);
+set(ax(2),'YAxisLocation','right','Color','none', ...
+    'XGrid','off','YGrid','off','Box','off', ...
+    'HitTest','off');
+xlim2 = get(ax(2),'XLim');
+ylim2 = get(ax(2),'YLim');
+ 
+% Check to see if the second plot also has one colored line
+colors = get(findobj(h2,'-property','Color'),{'Color'});
+if ~isempty(colors),
+    colors = unique(cat(1,colors{:}),'rows');
+    n = size(colors,1);
+    if n==1, set(ax(2),'YColor',colors), end
+end
+
+% turn off second axes' XRuler since we now have anti-aliased text 
+XRuler2 = get(ax(2),'XRuler');
+set(XRuler2,'Visible','off');
+
+islog1 = strcmp(get(ax(1),'YScale'),'log');
+islog2 = strcmp(get(ax(2),'YScale'),'log');
+
+% Since logs of negative reals produce complex numbers, we shall temporarily
+% switch all-negative YLims to all-positive for tick calculations, and then
+% switch back.
+isneg1 = all(ylim1 < 0);
+isneg2 = all(ylim2 < 0);
+
+if islog1
+    if isneg1
+        ylim1 = fliplr(-ylim1);
+    end
+    ylim1 = log10(ylim1);
+end
+if islog2
+    if isneg2
+        ylim2 = fliplr(-ylim2);
+    end
+    ylim2 = log10(ylim2);
+end
+
+% Find bestscale that produces the same number of y-ticks for both
+% the left and the right.
+[low,high,ticks] = bestscale(ylim1(1),ylim1(2),ylim2(1),ylim2(2),islog1,islog2);
+
+if ~isempty(low)
+    if islog1
+      yticks1 = logsp(low(1),high(1),ticks(1));
+        decade1 =  abs(floor(log10(yticks1)) - log10(yticks1));
+      low(1) = 10.^low(1);
+      high(1) = 10.^high(1);
+        if isneg1
+            tmp = low(1);
+            low(1) = -high(1);
+            high(1) = -tmp;
+            yticks1 = fliplr(-yticks1);
+        end
+    else
+        yticks1 = linspace(low(1),high(1),ticks(1));
+    end
+
+    if islog2
+      yticks2 = logsp(low(2),high(2),ticks(2));
+        decade2 =  abs(floor(log10(yticks2)) - log10(yticks2));
+      low(2) = 10.^low(2);
+      high(2) = 10.^high(2);
+        if isneg2
+            tmp = low(2);
+            low(2) = -high(2);
+            high(2) = -tmp;
+            yticks2 = fliplr(-yticks2);
+        end
+    else
+        yticks2 = linspace(low(2),high(2),ticks(2));
+    end
+
+    % Set ticks on both plots the same
+    set(ax(1),'YLim',[low(1) high(1)],'YTick',yticks1);
+    set(ax(2),'YLim',[low(2) high(2)],'YTick',yticks2);
+    set(ax,'XLim',[min(xlim1(1),xlim2(1)) max(xlim1(2),xlim2(2))])
+
+    % Set tick labels if axis ticks aren't at decade boundaries
+    % when in log mode
+    if islog1 && any(decade1 > 0.1)
+        ytickstr1 = cell(length(yticks1),1);
+        for i=length(yticks1):-1:1
+            ytickstr1{i} = sprintf('%3g',yticks1(i));
+        end
+        set(ax(1),'YTickLabel',ytickstr1)
+    end
+
+    if islog2 && any(decade2 > 0.1)
+      ytickstr2 = cell(length(yticks2),1);
+      for i=length(yticks2):-1:1
+            ytickstr2{i} = sprintf('%3g',yticks2(i));
+      end
+      set(ax(2),'YTickLabel',ytickstr2)
+    end
+
+else
+    % Use the default automatic scales and turn off the box so we
+    % don't get double tick marks on each side.  We'll still get
+    % the grid from the left axes though (if it is on).
+    set(ax,'Box','off')
+end
+
+% create DeleteProxy objects (an invisible text object in
+% the first axes) so that the other axes will be deleted
+% properly.
+DeleteProxy(1) = text('Parent',ax(1),'Visible','off',...
+    'Tag','PlotyyDeleteProxy',...
+    'HandleVisibility','off',...
+    'DeleteFcn','try;delete(get(gcbo,''userdata''));end');
+DeleteProxy(2) = text('Parent',ax(2),'Visible','off',...
+    'Tag','PlotyyDeleteProxy',...
+    'HandleVisibility','off',...
+    'DeleteFcn','try;delete(get(gcbo,''userdata''));end');
+set(DeleteProxy(1),'UserData',ax(2));
+set(DeleteProxy(2),'UserData',DeleteProxy(1));
+
+% Turn off rotate on plotyy charts
+b1 = hggetbehavior(ax(1), 'Rotate3d');
+b1.Enable = false;
+b2 = hggetbehavior(ax(2), 'Rotate3d');
+b2.Enable = false;
+
+% Each axes must be able to point to its peer. Rather than use
+% an instance property, we will use appdata for speed.
+setappdata(ax(1),'graphicsPlotyyPeer',ax(2));
+setappdata(ax(2),'graphicsPlotyyPeer',ax(1));
+
+matlab.graphics.internal.PlotYYListenerManager(ax(1),ax(2));
+
+if ~hold_state, hold(cax,'off'), end
+
+% Reset current axes
+set(fig, 'CurrentAxes',ax(1))
+
+if nargout>0, axout = ax; end
+
+
+
+%---------------------------------------------------
+function [low,high,ticks] = bestscale(umin,umax,vmin,vmax,isulog,isvlog)
+%BESTSCALE Returns parameters for "best" yy scale.
+
+penalty = 0.02;
+
+% Determine the good scales
+[ulow,uhigh,uticks] = goodscales(umin,umax);
+[vlow,vhigh,vticks] = goodscales(vmin,vmax);
+
+% Find good scales where the number of ticks match
+[u,v] = meshgrid(uticks,vticks);
+[j,i] = find(u==v);
+
+if isulog && isvlog
+    % When both Y-axes are logspace, we try to match tickmarks with powers of 10
+    for k=length(i):-1:1
+        utest = logsp(ulow(i(k)),uhigh(i(k)),uticks(i(k)));
+        vtest = logsp(vlow(j(k)),vhigh(j(k)),vticks(j(k)));
+        upot = abs(log10(utest)-round(log10(utest))) <= 10*eps*log10(utest);
+        vpot = abs(log10(vtest)-round(log10(vtest))) <= 10*eps*log10(vtest);
+        if ~isequal(upot,vpot),
+            i(k) = [];
+            j(k) = [];
+        end
+    end
+elseif isulog || isvlog
+    % When one Y-axis is linspace and the other is logspace, the only
+    % choices are the cases with just upper and lower tickmarks
+    for k=length(i):-1:1
+        if ~isequal(uticks(i(k)),2)
+            i(k) = [];
+            j(k) = [];
+        end
+    end
+end
+
+if ~isempty(i)
+    udelta = umax-umin;
+    vdelta = vmax-vmin;
+    ufit = ((uhigh(i)-ulow(i)) - udelta)./(uhigh(i)-ulow(i));
+    vfit = ((vhigh(j)-vlow(j)) - vdelta)./(vhigh(j)-vlow(j));
+
+    fit = ufit + vfit + penalty*(max(uticks(i)-6,1)).^2;
+
+    % Choose base fit
+    k = find(fit == min(fit)); k=k(1);
+    low = [ulow(i(k)) vlow(j(k))];
+    high = [uhigh(i(k)) vhigh(j(k))];
+    ticks = [uticks(i(k)) vticks(j(k))];
+else
+    % Return empty to signal calling routine that we weren't able to
+    % find matching scales.
+    low = [];
+    high = [];
+    ticks = [];
+end
+
+
+
+%------------------------------------------------------------
+function [low,high,ticks] = goodscales(xmin,xmax)
+%GOODSCALES Returns parameters for "good" scales.
+%
+% [LOW,HIGH,TICKS] = GOODSCALES(XMIN,XMAX) returns lower and upper
+% axis limits (LOW and HIGH) that span the interval (XMIN,XMAX)
+% with "nice" tick spacing.  The number of major axis ticks is
+% also returned in TICKS.
+
+BestDelta = [ .1 .2 .5 1 2 5 10 20 50 ];
+%penalty = 0.02;
+
+% Compute xmin, xmax if matrices passed.
+if length(xmin) > 1, xmin = min(xmin(:)); end
+if length(xmax) > 1, xmax = max(xmax(:)); end
+if xmin==xmax, low=xmin; high=xmax+1; ticks = 1; return, end
+
+% Compute fit error including penalty on too many ticks
+Xdelta = xmax-xmin;
+delta = 10.^(round(log10(Xdelta)-1))*BestDelta;
+high = delta.*ceil(xmax./delta);
+low = delta.*floor(xmin./delta);
+ticks = round((high-low)./delta)+1;
+
+
+
+%---------------------------------------------
+function  y = logsp(low,high,n)
+%LOGSP Generate nice ticks for log plots
+%   LOGSP produces linear ramps between 10^k values.
+
+y = linspace(low,high,n);
+
+k = find(abs(y-round(y))<=10*eps*max(y));
+dk = diff(k);
+p = find(dk > 1);
+
+y = 10.^y;
+
+for i=1:length(p)
+    r = linspace(0,1,dk(p(i))+1)*y(k(p(i)+1));
+    y(k(p(i))+1:k(p(i)+1)-1) = r(2:end-1);
+end
+
+
+
+%---------------------------------------------
+function [h,ax] = fevalfun(func,ax,x,y,caxspecified)
+%FEVALFUN Evaluate the function with or without a specified axes
+%    FEVALFUN returns an appropriate error if PLOTYY cannot determine
+%    the syntax for calling it with or without a specified axes.
+
+if ~caxspecified
+    % If no axes input was specified, then assume we are dealing with
+    %   a handlevisibility/on axes returned by NEWPLOT
+    h = feval(func,x,y);
+else
+    % If an axes input was specified, then first try to call func using
+    %   the axes input as the first input argument
+    try
+        h = feval(func,ax,x,y);
+    catch errFeval %#ok<NASGU>
+        % If func won't accept an axes a the first input argument, try
+        %   to call func with a parent/axes pair
+        try
+            h = feval(func,x,y,'Parent',ax);
+        catch errFevalParent
+            % Make a string out of the function name
+            if ~ischar(func)
+                funcstr = func2str(func);
+            else
+                funcstr = func;
+            end
+            % Finally throw the error
+            warning(message('MATLAB:plotyy:InvalidAxesHandleSyntax', funcstr))
+            rethrow(errFevalParent)
+        end
+    end
+end
+ax = get(h(1),'Parent');
+
+
+
+%---------------------------------------------
+function defaultRemover = localCreateDefaultsCleanup(fig)
+% Get the current defaults for ColorOrder and ColorOrderMode so that we can
+% reset them.  If there is no default set then we'll remove it again at the
+% end.
+defaults = get(fig,'Default');
+if isfield(defaults, 'defaultAxesColorOrder')
+    defCO = defaults.defaultAxesColorOrder;
+else
+    defCO = 'remove';
+end
+if isfield(defaults, 'defaultAxesColorOrderMode')
+    defCOMode = defaults.defaultAxesColorOrderMode;
+else
+    defCOMode = 'remove';
+end
+defaultRemover = onCleanup(...
+    @() set(fig, 'defaultAxesColorOrder', defCO, ...
+        'defaultAxesColorOrderMode', defCOMode));
diff --git a/algorithms/utilities/plotyyy/plotyyy.m b/algorithms/utilities/plotyyy/plotyyy.m
new file mode 100644
index 0000000..65ab8c5
--- /dev/null
+++ b/algorithms/utilities/plotyyy/plotyyy.m
@@ -0,0 +1,79 @@
+function [ax,hlines] = plotyyy(x1,y1,x2,y2,x3,y3,ylabels)
+%PLOTYYY - Extends plotyy to include a third y-axis
+%
+%Syntax:  [ax,hlines] = plotyyy(x1,y1,x2,y2,x3,y3,ylabels)
+%
+%Inputs: x1,y1 are the xdata and ydata for the first axes' line
+%        x2,y2 are the xdata and ydata for the second axes' line
+%        x3,y3 are the xdata and ydata for the third axes' line
+%        ylabels is a 3x1 cell array containing the ylabel strings
+%
+%Outputs: ax -     3x1 double array containing the axes' handles
+%         hlines - 3x1 double array containing the lines' handles
+%
+%Example:
+%x=0:10; 
+%y1=x;  y2=x.^2;   y3=x.^3;
+%ylabels{1}='First y-label';
+%ylabels{2}='Second y-label';
+%ylabels{3}='Third y-label';
+%[ax,hlines] = plotyyy(x,y1,x,y2,x,y3,ylabels);
+%legend(hlines, 'y = x','y = x^2','y = x^3',2)
+%
+%m-files required: none
+
+%Author: Denis Gilbert, Ph.D., physical oceanography
+%Maurice Lamontagne Institute
+%Dept. of Fisheries and Oceans Canada
+%email: gilbertd@dfo-mpo.gc.ca  
+%Web: http://www.qc.dfo-mpo.gc.ca/iml/
+%April 2000; Last revision: 14-Nov-2001
+
+if nargin==6
+   %Use empty strings for the ylabels
+   ylabels{1}=' '; ylabels{2}=' '; ylabels{3}=' ';
+elseif nargin > 7
+   error('Too many input arguments')
+elseif nargin < 6
+   error('Not enough input arguments')
+end
+
+figure('units','normalized',...
+       'DefaultAxesXMinorTick','on','DefaultAxesYminorTick','on'); 
+
+%Plot the first two lines with plotyy
+[ax,hlines(1),hlines(2)] = plotyy_scatter(x1,y1,x2,y2);
+cfig = get(gcf,'color');
+pos = [0.1  0.1  0.7  0.8];
+offset = pos(3)/5.5;
+
+%Reduce width of the two axes generated by plotyy 
+pos(3) = pos(3) - offset/2;
+set(ax,'position',pos);  
+
+%Determine the position of the third axes
+pos3=[pos(1) pos(2) pos(3)+offset pos(4)];
+
+%Determine the proper x-limits for the third axes
+limx1=get(ax(1),'xlim');
+limx3=[limx1(1)   limx1(1) + 1.2*(limx1(2)-limx1(1))];
+%Bug fix 14 Nov-2001: the 1.2 scale factor in the line above
+%was contributed by Mariano Garcia (BorgWarner Morse TEC Inc)
+
+ax(3)=axes('Position',pos3,'box','off',...
+   'Color','none','XColor','k','YColor','r',...   
+   'xtick',[],'xlim',limx3,'yaxislocation','right');
+
+hlines(3) = line(x3,y3,'Color','r','Parent',ax(3));
+limy3=get(ax(3),'YLim');
+
+%Hide unwanted portion of the x-axis line that lies
+%between the end of the second and third axes
+line([limx1(2) limx3(2)],[limy3(1) limy3(1)],...
+   'Color',cfig,'Parent',ax(3),'Clipping','off');
+axes(ax(2))
+
+%Label all three y-axes
+set(get(ax(1),'ylabel'),'string',ylabels{1})
+set(get(ax(2),'ylabel'),'string',ylabels{2})
+set(get(ax(3),'ylabel'),'string',ylabels{3})
diff --git a/algorithms/utilities/plotyyy/plotyyy_GB.m b/algorithms/utilities/plotyyy/plotyyy_GB.m
new file mode 100644
index 0000000..ab593b5
--- /dev/null
+++ b/algorithms/utilities/plotyyy/plotyyy_GB.m
@@ -0,0 +1,80 @@
+function [ax,hlines] = plotyyy_GB(x1,y1,x2,y2,x3,y3,ylabels,type)
+%PLOTYYY - Extends plotyy to include a third y-axis
+%
+%Syntax:  [ax,hlines] = plotyyy(x1,y1,x2,y2,x3,y3,ylabels)
+%           type = string of plot type, i.e. "scatter"
+%
+%Inputs: x1,y1 are the xdata and ydata for the first axes' line
+%        x2,y2 are the xdata and ydata for the second axes' line
+%        x3,y3 are the xdata and ydata for the third axes' line
+%        ylabels is a 3x1 cell array containing the ylabel strings
+%
+%Outputs: ax -     3x1 double array containing the axes' handles
+%         hlines - 3x1 double array containing the lines' handles
+%
+%Example:
+%x=0:10; 
+%y1=x;  y2=x.^2;   y3=x.^3;
+%ylabels{1}='First y-label';
+%ylabels{2}='Second y-label';
+%ylabels{3}='Third y-label';
+%[ax,hlines] = plotyyy(x,y1,x,y2,x,y3,ylabels);
+%legend(hlines, 'y = x','y = x^2','y = x^3',2)
+%
+%m-files required: none
+
+%Author: Denis Gilbert, Ph.D., physical oceanography
+%Maurice Lamontagne Institute
+%Dept. of Fisheries and Oceans Canada
+%email: gilbertd@dfo-mpo.gc.ca  
+%Web: http://www.qc.dfo-mpo.gc.ca/iml/
+%April 2000; Last revision: 14-Nov-2001
+
+if nargin==6
+   %Use empty strings for the ylabels
+   ylabels{1}=' '; ylabels{2}=' '; ylabels{3}=' ';
+elseif nargin > 8
+   error('Too many input arguments')
+elseif nargin < 6
+   error('Not enough input arguments')
+end
+
+figure('units','normalized',...
+       'DefaultAxesXMinorTick','on','DefaultAxesYminorTick','on'); 
+
+%Plot the first two lines with plotyy
+[ax,hlines(1),hlines(2)] = plotyy(x1,y1,x2,y2,type);
+cfig = get(gcf,'color');
+pos = [0.1  0.1  0.7  0.8];
+offset = pos(3)/5.5;
+
+%Reduce width of the two axes generated by plotyy 
+pos(3) = pos(3) - offset/2;
+set(ax,'position',pos);  
+
+%Determine the position of the third axes
+pos3=[pos(1) pos(2) pos(3)+offset pos(4)];
+
+%Determine the proper x-limits for the third axes
+limx1=get(ax(1),'xlim');
+limx3=[limx1(1)   limx1(1) + 1.2*(limx1(2)-limx1(1))];
+%Bug fix 14 Nov-2001: the 1.2 scale factor in the line above
+%was contributed by Mariano Garcia (BorgWarner Morse TEC Inc)
+
+ax(3)=axes('Position',pos3,'box','off',...
+   'Color','none','XColor','k','YColor','r',...   
+   'xtick',[],'xlim',limx3,'yaxislocation','right');
+
+hlines(3) = line(x3,y3,'Color','r','Parent',ax(3));
+limy3=get(ax(3),'YLim');
+
+%Hide unwanted portion of the x-axis line that lies
+%between the end of the second and third axes
+line([limx1(2) limx3(2)],[limy3(1) limy3(1)],...
+   'Color',cfig,'Parent',ax(3),'Clipping','off');
+axes(ax(2))
+
+%Label all three y-axes
+set(get(ax(1),'ylabel'),'string',ylabels{1})
+set(get(ax(2),'ylabel'),'string',ylabels{2})
+set(get(ax(3),'ylabel'),'string',ylabels{3})
diff --git a/algorithms/utilities/plotyyy/plotyyy_scatter.m b/algorithms/utilities/plotyyy/plotyyy_scatter.m
new file mode 100644
index 0000000..efb5330
--- /dev/null
+++ b/algorithms/utilities/plotyyy/plotyyy_scatter.m
@@ -0,0 +1,79 @@
+function [ax,hlines] = plotyyy_scatter(x1,y1,x2,y2,x3,y3,ylabels, type)
+%PLOTYYY - Extends plotyy to include a third y-axis
+%
+%Syntax:  [ax,hlines] = plotyyy(x1,y1,x2,y2,x3,y3,ylabels)
+%
+%Inputs: x1,y1 are the xdata and ydata for the first axes' line
+%        x2,y2 are the xdata and ydata for the second axes' line
+%        x3,y3 are the xdata and ydata for the third axes' line
+%        ylabels is a 3x1 cell array containing the ylabel strings
+%
+%Outputs: ax -     3x1 double array containing the axes' handles
+%         hlines - 3x1 double array containing the lines' handles
+%
+%Example:
+%x=0:10; 
+%y1=x;  y2=x.^2;   y3=x.^3;
+%ylabels{1}='First y-label';
+%ylabels{2}='Second y-label';
+%ylabels{3}='Third y-label';
+%[ax,hlines] = plotyyy(x,y1,x,y2,x,y3,ylabels);
+%legend(hlines, 'y = x','y = x^2','y = x^3',2)
+%
+%m-files required: none
+
+%Author: Denis Gilbert, Ph.D., physical oceanography
+%Maurice Lamontagne Institute
+%Dept. of Fisheries and Oceans Canada
+%email: gilbertd@dfo-mpo.gc.ca  
+%Web: http://www.qc.dfo-mpo.gc.ca/iml/
+%April 2000; Last revision: 14-Nov-2001
+
+if nargin==6
+   %Use empty strings for the ylabels
+   ylabels{1}=' '; ylabels{2}=' '; ylabels{3}=' ';
+elseif nargin > 7
+   error('Too many input arguments')
+elseif nargin < 6
+   error('Not enough input arguments')
+end
+
+figure('units','normalized',...
+       'DefaultAxesXMinorTick','on','DefaultAxesYminorTick','on'); 
+
+%Plot the first two lines with plotyy
+[ax,hlines(1),hlines(2)] = plotyy(x1,y1,x2,y2,type);
+cfig = get(gcf,'color');
+pos = [0.1  0.1  0.7  0.8];
+offset = pos(3)/5.5;
+
+%Reduce width of the two axes generated by plotyy 
+pos(3) = pos(3) - offset/2;
+set(ax,'position',pos);  
+
+%Determine the position of the third axes
+pos3=[pos(1) pos(2) pos(3)+offset pos(4)];
+
+%Determine the proper x-limits for the third axes
+limx1=get(ax(1),'xlim');
+limx3=[limx1(1)   limx1(1) + 1.2*(limx1(2)-limx1(1))];
+%Bug fix 14 Nov-2001: the 1.2 scale factor in the line above
+%was contributed by Mariano Garcia (BorgWarner Morse TEC Inc)
+
+ax(3)=axes('Position',pos3,'box','off',...
+   'Color','none','XColor','k','YColor','r',...   
+   'xtick',[],'xlim',limx3,'yaxislocation','right');
+
+hlines(3) = line(x3,y3,'Color','r','Parent',ax(3));
+limy3=get(ax(3),'YLim');
+
+%Hide unwanted portion of the x-axis line that lies
+%between the end of the second and third axes
+line([limx1(2) limx3(2)],[limy3(1) limy3(1)],...
+   'Color',cfig,'Parent',ax(3),'Clipping','off');
+axes(ax(2))
+
+%Label all three y-axes
+set(get(ax(1),'ylabel'),'string',ylabels{1})
+set(get(ax(2),'ylabel'),'string',ylabels{2})
+set(get(ax(3),'ylabel'),'string',ylabels{3})
diff --git a/algorithms/utilities/second.m b/algorithms/utilities/second.m
index ae7a303..bf52cd1 100644
--- a/algorithms/utilities/second.m
+++ b/algorithms/utilities/second.m
@@ -1,18 +1,18 @@
-function s = second(date_asNum)
-%DAY Summary of this function goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get year and reformat to the same shape as input data.
-s = reshape(c(:,6),size(date_asNum)); 
-
-% from finance toolbox second.m
-s = round(1000.*s)./1000;
-
-end
-
+function s = second(date_asNum)
+%DAY Summary of this function goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get year and reformat to the same shape as input data.
+s = reshape(c(:,6),size(date_asNum)); 
+
+% from finance toolbox second.m
+s = round(1000.*s)./1000;
+
+end
+
diff --git a/algorithms/utilities/year.m b/algorithms/utilities/year.m
index 81ee850..7151540 100644
--- a/algorithms/utilities/year.m
+++ b/algorithms/utilities/year.m
@@ -1,16 +1,16 @@
-function y = year(date_asNum)
-%YEAR Summary of this function goes here
-%   Detailed explanation goes here
-
-if ischar(date_asNum) 
-  error('date_asNum must be a date vector, not character.');
-end 
- 
-% Get date vectors
-c = datevec(date_asNum(:));
-    
-% Get year and reformat to the same shape as input data.
-y = reshape(c(:,1),size(date_asNum)); 
-
-end
-
+function y = year(date_asNum)
+%YEAR Summary of this function goes here
+%   Detailed explanation goes here
+
+if ischar(date_asNum) 
+  error('date_asNum must be a date vector, not character.');
+end 
+ 
+% Get date vectors
+c = datevec(date_asNum(:));
+    
+% Get year and reformat to the same shape as input data.
+y = reshape(c(:,1),size(date_asNum)); 
+
+end
+
diff --git a/dataPreparationAnalysis/doDataQualityAnalysis.m b/dataPreparationAnalysis/doDataQualityAnalysis.m
index 1c22680..d1ee6d7 100644
--- a/dataPreparationAnalysis/doDataQualityAnalysis.m
+++ b/dataPreparationAnalysis/doDataQualityAnalysis.m
@@ -1,190 +1,190 @@
-function [headData,noise_sigma, ARMA_params, exp_model] = doDataQualityAnalysis( headData, boreDepth, surface_elevation, casing_length, constuction_date, ...
-   checkMinSartDate, checkMaxEndDate, chechDuplicateDates, checkMinHead, checkMaxHead, RateofChangeThreshold, ConstHeadThreshold, outlierNumStDevs, outlierForwadBackward)
-%EXPORTDATATABLE Summary of this function goes here
-%   Detailed explanation goes here
-
-    % Handle situation where outlierForwadBackward is not set.
-    if nargin <14
-        outlierForwadBackward = true;
-    end
-
-    % Minimum number of non-errorous observations required for undertaking
-    % ARAM outlier detection;
-    minObsforOutlierDetection = 12;
-
-    % Duration for constant head error checkd min ob 
-    constHeadThreshold_minObs = 3;
-    
-    % Assign plausible dates for water level obs    
-    plausibleEndDate = now();    
-    
-    errCode = -9999.99;
-    
-    % Check there is enough data to run the analysis
-    if size(headData,1)<=1
-        return;
-    end
-    
-    
-    % Sort by date 
-    headData = sortrows(headData,1);
-
-    % Convert the table data to arrays
-    if istable(headData)
-        headData = headData{:,[1:2]};
-    end
-
-    % Filter for plausible dates
-    filt_date = false(size(headData,1),1);
-    if checkMinSartDate
-        filt_date = headData(:,1) < constuction_date;
-    end
-    if checkMaxEndDate
-        filt_date = headData(:,1)>now() | filt_date;
-    end    
-    isErrorObs = filt_date;
-
-    % Filter date duplicates
-    filt_duplicates = false(size(headData,1),1);
-    if chechDuplicateDates
-        timeStep = [diff( headData(:,1)); inf];
-        filt_duplicates = abs(timeStep) <sqrt(eps);    
-        filt_duplicates(isErrorObs) = false;        
-    end
-    isErrorObs = filt_date | filt_duplicates;
-
-    % Check head is above the bottom of the bore.
-    filt_minHead = false(size(headData,1),1);
-    if checkMinHead                    
-        filt_minHead_tmp = headData(~isErrorObs,2) < surface_elevation - boreDepth;        
-        filt_minHead(~isErrorObs ) = filt_minHead_tmp;
-        clear filt_minHead_tmp;    
-    end
-    isErrorObs = filt_date | filt_duplicates | filt_minHead;
-
-    
-    % Check the head is below the top of the casing (assumes the aquifer is
-    % unconfined)
-    filt_maxHead = false(size(headData,1),1);
-    if checkMaxHead
-        filt_maxHead_tmp = headData(~isErrorObs,2) > surface_elevation + casing_length;        
-        filt_maxHead(~isErrorObs ) = filt_maxHead_tmp;
-        clear filt_maxHead_tmp;
-    end
-    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead;
-
-    
-    % Filter for rapd change in headData
-    filt_rapid = false(size(headData,1),1);    
-    d_headData_dt = diff( headData(~isErrorObs,2))./ diff( headData(~isErrorObs,1));
-    filt_rapid(~isErrorObs) = [false; abs(d_headData_dt) >= RateofChangeThreshold];          
-    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead | filt_rapid;
-            
-    % Filter out bore with a constant head for > 'ConstHeadThreshold' days. First the
-    % duration of 'flat' periods is assessed.
-    filt_flatExtendedDuration = false(size(isErrorObs,1),1);
-    if RateofChangeThreshold>0
-        headData_tmp = headData(~isErrorObs,:);    
-        delta_headData_fwd = [false; headData_tmp(2:end,2) - headData_tmp(1:end-1,2)];
-        delta_headData_rvs = [headData_tmp(1:end-1,2) - headData_tmp(2:end,2); false];
-        filt_flat = delta_headData_fwd==0 | delta_headData_rvs==0;
-        if any(filt_flat)
-
-            filt_flatExtendedDuration_tmp = false(sum(~isErrorObs),1);
-
-            % Filt out prior identified errors
-            headData_filt = headData(~isErrorObs,:);
-
-            startDate = 0;
-            endDate = 0;
-            startheadData = nan;
-            for j=2:size(headData_filt,1)
-                try
-                    if (filt_flat(j) && ~filt_flat(j-1)) || (j==2 && filt_flat(j))
-                        if j==2 && filt_flat(j-1)
-                            startDate = headData_filt(j-1,1)-sqrt(eps());
-                            startheadData = headData_filt(j-1,2);
-                        else
-                            startDate = headData_filt(j,1);
-                            startheadData = headData_filt(j,2);
-                        end                            
-                        endDate = 0;                            
-                    elseif startDate>0 && (~filt_flat(j) || j==size(headData_filt,1) || headData_filt(j,2)~=startheadData) 
-                        endDate = headData_filt(j,1);
-                        if j==size(headData_filt,1) && headData_filt(j,2)==headData_filt(j-1,2)
-                            endDate = endDate+sqrt(eps());
-                        end
-
-                        % Assess if the zero period is >60 days long
-                        % and are > constHeadThreshold_minObs
-                        filt_tmp = headData_filt(:,1)>= startDate & headData_filt(:,1) < endDate;
-                        consHead_dates =  headData_filt(filt_tmp,1);
-                        if  consHead_dates(end) - consHead_dates(1) >= ConstHeadThreshold ...
-                        &&  sum(filt_tmp)>=constHeadThreshold_minObs       
-                            filt_flatExtendedDuration_tmp(filt_tmp) = true;
-                        end    
-
-                        % Reset markers
-                        startDate = 0;
-                        endDate = 0;   
-                        startheadData = nan;                            
-                    end
-                catch
-                   display('err'); 
-                end
-            end     
-            filt_flatExtendedDuration = false(size(isErrorObs,1),1);
-            filt_flatExtendedDuration(~isErrorObs) = filt_flatExtendedDuration_tmp;
-        end
-    end
-
-    % Aggregare Errors filters
-    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead | filt_rapid | filt_flatExtendedDuration;  
-
-
-    % Detect remaining outliers using a calibrated ARMA(1) model.            
-    if sum(~isErrorObs)>minObsforOutlierDetection && outlierNumStDevs>0
-        try
-            % Analyse outliers in forward time.
-            [ isOutlierObs_forward, noise_sigma, ARMA_params, exp_model ] = outlierDetection(  headData, isErrorObs, outlierNumStDevs);
-            isOutlierObs = isOutlierObs_forward; 
-            
-            if outlierForwadBackward
-                % Analyse outliers in reverse time.
-                headData_reverse = headData(size(headData,1):-1:1,:);
-                isErrorObs_reverse = isErrorObs(size(headData,1):-1:1,:);
-                headData_reverse(:,1) = headData(end,1) - headData_reverse(:,1);
-                isOutlierObs_reverse = outlierDetection(  headData_reverse, isErrorObs_reverse, outlierNumStDevs);
-                isOutlierObs_reverse = isOutlierObs_reverse(size(headData,1):-1:1,:);            
-
-                % Define as outlier if detected forward and reverse in time.
-                isOutlierObs = isOutlierObs_forward & isOutlierObs_reverse;
-            end
-        catch ME
-            display(['    WARNING: Outlier detection failed.']); 
-            isOutlierObs = false(size(isErrorObs_reverse));
-            noise_sigma = [];
-            ARMA_params = [];           
-            exp_model = [];
-        end
-    else
-        isOutlierObs = false(size(isErrorObs));
-        noise_sigma = [];
-        ARMA_params = [];                
-        exp_model = [];
-    end
-
-
-    % Delete calibration data files
-    delete('*.dat');
-    
-    % Aggregate the logical data from the analyis into a table and combine
-    % with the observated data.
-    headData = table(year(headData(:,1)), month(headData(:,1)), day(headData(:,1)), hour(headData(:,1)), minute(headData(:,1)), headData(:,2), filt_date, filt_duplicates, filt_minHead, filt_maxHead, filt_rapid, filt_flatExtendedDuration, isOutlierObs, ...
-        'VariableNames',{'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'});
-
-end
-
-
-                   
-                
+function [headData,noise_sigma, ARMA_params, exp_model] = doDataQualityAnalysis( headData, boreDepth, surface_elevation, casing_length, constuction_date, ...
+   checkMinSartDate, checkMaxEndDate, chechDuplicateDates, checkMinHead, checkMaxHead, RateofChangeThreshold, ConstHeadThreshold, outlierNumStDevs, outlierForwadBackward)
+%EXPORTDATATABLE Summary of this function goes here
+%   Detailed explanation goes here
+
+    % Handle situation where outlierForwadBackward is not set.
+    if nargin <14
+        outlierForwadBackward = true;
+    end
+
+    % Minimum number of non-errorous observations required for undertaking
+    % ARAM outlier detection;
+    minObsforOutlierDetection = 12;
+
+    % Duration for constant head error checkd min ob 
+    constHeadThreshold_minObs = 3;
+    
+    % Assign plausible dates for water level obs    
+    plausibleEndDate = now();    
+    
+    errCode = -9999.99;
+    
+    % Check there is enough data to run the analysis
+    if size(headData,1)<=1
+        return;
+    end
+    
+    
+    % Sort by date 
+    headData = sortrows(headData,1);
+
+    % Convert the table data to arrays
+    if istable(headData)
+        headData = headData{:,[1:2]};
+    end
+
+    % Filter for plausible dates
+    filt_date = false(size(headData,1),1);
+    if checkMinSartDate
+        filt_date = headData(:,1) < constuction_date;
+    end
+    if checkMaxEndDate
+        filt_date = headData(:,1)>now() | filt_date;
+    end    
+    isErrorObs = filt_date;
+
+    % Filter date duplicates
+    filt_duplicates = false(size(headData,1),1);
+    if chechDuplicateDates
+        timeStep = [diff( headData(:,1)); inf];
+        filt_duplicates = abs(timeStep) <sqrt(eps);    
+        filt_duplicates(isErrorObs) = false;        
+    end
+    isErrorObs = filt_date | filt_duplicates;
+
+    % Check head is above the bottom of the bore.
+    filt_minHead = false(size(headData,1),1);
+    if checkMinHead                    
+        filt_minHead_tmp = headData(~isErrorObs,2) < surface_elevation - boreDepth;        
+        filt_minHead(~isErrorObs ) = filt_minHead_tmp;
+        clear filt_minHead_tmp;    
+    end
+    isErrorObs = filt_date | filt_duplicates | filt_minHead;
+
+    
+    % Check the head is below the top of the casing (assumes the aquifer is
+    % unconfined)
+    filt_maxHead = false(size(headData,1),1);
+    if checkMaxHead
+        filt_maxHead_tmp = headData(~isErrorObs,2) > surface_elevation + casing_length;        
+        filt_maxHead(~isErrorObs ) = filt_maxHead_tmp;
+        clear filt_maxHead_tmp;
+    end
+    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead;
+
+    
+    % Filter for rapd change in headData
+    filt_rapid = false(size(headData,1),1);    
+    d_headData_dt = diff( headData(~isErrorObs,2))./ diff( headData(~isErrorObs,1));
+    filt_rapid(~isErrorObs) = [false; abs(d_headData_dt) >= RateofChangeThreshold];          
+    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead | filt_rapid;
+            
+    % Filter out bore with a constant head for > 'ConstHeadThreshold' days. First the
+    % duration of 'flat' periods is assessed.
+    filt_flatExtendedDuration = false(size(isErrorObs,1),1);
+    if RateofChangeThreshold>0
+        headData_tmp = headData(~isErrorObs,:);    
+        delta_headData_fwd = [false; headData_tmp(2:end,2) - headData_tmp(1:end-1,2)];
+        delta_headData_rvs = [headData_tmp(1:end-1,2) - headData_tmp(2:end,2); false];
+        filt_flat = delta_headData_fwd==0 | delta_headData_rvs==0;
+        if any(filt_flat)
+
+            filt_flatExtendedDuration_tmp = false(sum(~isErrorObs),1);
+
+            % Filt out prior identified errors
+            headData_filt = headData(~isErrorObs,:);
+
+            startDate = 0;
+            endDate = 0;
+            startheadData = nan;
+            for j=2:size(headData_filt,1)
+                try
+                    if (filt_flat(j) && ~filt_flat(j-1)) || (j==2 && filt_flat(j))
+                        if j==2 && filt_flat(j-1)
+                            startDate = headData_filt(j-1,1)-sqrt(eps());
+                            startheadData = headData_filt(j-1,2);
+                        else
+                            startDate = headData_filt(j,1);
+                            startheadData = headData_filt(j,2);
+                        end                            
+                        endDate = 0;                            
+                    elseif startDate>0 && (~filt_flat(j) || j==size(headData_filt,1) || headData_filt(j,2)~=startheadData) 
+                        endDate = headData_filt(j,1);
+                        if j==size(headData_filt,1) && headData_filt(j,2)==headData_filt(j-1,2)
+                            endDate = endDate+sqrt(eps());
+                        end
+
+                        % Assess if the zero period is >60 days long
+                        % and are > constHeadThreshold_minObs
+                        filt_tmp = headData_filt(:,1)>= startDate & headData_filt(:,1) < endDate;
+                        consHead_dates =  headData_filt(filt_tmp,1);
+                        if  consHead_dates(end) - consHead_dates(1) >= ConstHeadThreshold ...
+                        &&  sum(filt_tmp)>=constHeadThreshold_minObs       
+                            filt_flatExtendedDuration_tmp(filt_tmp) = true;
+                        end    
+
+                        % Reset markers
+                        startDate = 0;
+                        endDate = 0;   
+                        startheadData = nan;                            
+                    end
+                catch
+                   display('err'); 
+                end
+            end     
+            filt_flatExtendedDuration = false(size(isErrorObs,1),1);
+            filt_flatExtendedDuration(~isErrorObs) = filt_flatExtendedDuration_tmp;
+        end
+    end
+
+    % Aggregare Errors filters
+    isErrorObs = filt_date | filt_duplicates | filt_minHead | filt_maxHead | filt_rapid | filt_flatExtendedDuration;  
+
+
+    % Detect remaining outliers using a calibrated ARMA(1) model.            
+    if sum(~isErrorObs)>minObsforOutlierDetection && outlierNumStDevs>0
+        try
+            % Analyse outliers in forward time.
+            [ isOutlierObs_forward, noise_sigma, ARMA_params, exp_model ] = outlierDetection(  headData, isErrorObs, outlierNumStDevs);
+            isOutlierObs = isOutlierObs_forward; 
+            
+            if outlierForwadBackward
+                % Analyse outliers in reverse time.
+                headData_reverse = headData(size(headData,1):-1:1,:);
+                isErrorObs_reverse = isErrorObs(size(headData,1):-1:1,:);
+                headData_reverse(:,1) = headData(end,1) - headData_reverse(:,1);
+                isOutlierObs_reverse = outlierDetection(  headData_reverse, isErrorObs_reverse, outlierNumStDevs);
+                isOutlierObs_reverse = isOutlierObs_reverse(size(headData,1):-1:1,:);            
+
+                % Define as outlier if detected forward and reverse in time.
+                isOutlierObs = isOutlierObs_forward & isOutlierObs_reverse;
+            end
+        catch ME
+            display(['    WARNING: Outlier detection failed.']); 
+            isOutlierObs = false(size(isErrorObs_reverse));
+            noise_sigma = [];
+            ARMA_params = [];           
+            exp_model = [];
+        end
+    else
+        isOutlierObs = false(size(isErrorObs));
+        noise_sigma = [];
+        ARMA_params = [];                
+        exp_model = [];
+    end
+
+
+    % Delete calibration data files
+    delete('*.dat');
+    
+    % Aggregate the logical data from the analyis into a table and combine
+    % with the observated data.
+    headData = table(year(headData(:,1)), month(headData(:,1)), day(headData(:,1)), hour(headData(:,1)), minute(headData(:,1)), headData(:,2), filt_date, filt_duplicates, filt_minHead, filt_maxHead, filt_rapid, filt_flatExtendedDuration, isOutlierObs, ...
+        'VariableNames',{'Year', 'Month', 'Day', 'Hour', 'Minute', 'Head', 'Date_Error', 'Duplicate_Date_Error', 'Min_Head_Error','Max_Head_Error','Rate_of_Change_Error','Const_Hear_Error','Outlier_Obs'});
+
+end
+
+
+                   
+                
diff --git a/dataPreparationAnalysis/outlierDetection.m b/dataPreparationAnalysis/outlierDetection.m
index a824e1a..c0949da 100644
--- a/dataPreparationAnalysis/outlierDetection.m
+++ b/dataPreparationAnalysis/outlierDetection.m
@@ -1,160 +1,160 @@
-function [ isOutlier, noise_sigma, x_opt, model_calib ] = outlierDetection( headData, isOutlier, nSigma_threshold)
-
-    % Initialise outputs    
-    noise_sigma = inf;
-    x_opt = [];
-    model_calib = [];
-     
-    % Initialise 'isOutliers' if its not supplied by the user
-    if isempty(isOutlier)
-        isOutlier = false(size(headData,1));
-    end
-    isNewOutlier = false(size(headData,1));
-    isOutlier_input = isOutlier;
-    
-    % Build inputs for exponential smoothing model
-    t = headData(:,1);
-    h_obs = headData(:,2);
-    dummyBoreID = 'BoreID_123';
-    coordinates = {dummyBoreID, -999, -999; 'Precip', -999, -999};
-    forcingData = [t(1)-10: t(end)+10]';
-    forcingData = table(year(forcingData),month(forcingData),day(forcingData),zeros(size(forcingData,1),1),'VariableNames',{'Year';'Month';'Day';'Precip'});   
-    h_obs_model = [year(t), month(t), day(t), hour(t), minute(t), second(t), h_obs];
-    
-    % Calibrate exponential smoothing model
-    summaryStr={};
-    noise_sigma = 0;
-    i=1;
-    doFinalCalibration = false;
-    el=0;
-    while i==1 || sum(isNewOutlier)>0 || doFinalCalibration
-
-        % Build model        
-        model_calib = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_model(~isOutlier,:), -999, forcingData, coordinates, false);
-        
-        % Calibrate model
-        calibrateModel(model_calib, [],0, inf, 'SPUCI',2)
-        
-        % Get the standard deviation of the noise.
-        noise_sigma = model_calib.model.variables.sigma_n;
-        
-        % Exit if the this loop is being undertaken 
-        if doFinalCalibration
-            break
-        end
-        
-        % Store calibrated parameters
-        alpha = model_calib.model.parameters.alpha;
-        beta = model_calib.model.parameters.beta;
-        gamma = model_calib.model.parameters.gamma;
-        meanHead_calib = model_calib.model.variables.meanHead_calib;
-        initialHead = model_calib.model.variables.initialHead;
-        initialTrend = model_calib.model.variables.initialTrend; 
-         
-        % Loop through each non-outlier observation to omit is from the
-        % simulation. THis is doen to exclude a possible outlier point from
-        % the smoothened estimate and the resulting calculation of the
-        % noise. If the difference between the current obs point and the
-        % forcast is greater than this noise estimate, then it is denoted
-        % as an outlier. Importantly, in calculating the noise the min and
-        % max points are also excluded.
-        isNewOutlier = false(size(isOutlier));        
-        filt = isOutlier;
-        ind = find(~isOutlier)';   
-        k=1;
-        for j=ind(2:end)
-            % Get a vector of obs points excluding the current obs point, point ind(j).
-            k=k+1;
-            filt(j) = true;
-            time_points_trim =  headData(~filt,1);
-            delta_t = headData(j,1) - headData(j-1,1);
-            h_obs_trim = headData(~filt,2);
-            h_obs_trim = [year(time_points_trim), month(time_points_trim), day(time_points_trim), hour(time_points_trim), minute(time_points_trim), second(time_points_trim), h_obs_trim];
-
-            % Rebuild the model without the current time point, assign the
-            % calibrated parameters and solve the model.
-            model = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_trim, -999, forcingData, coordinates, false);
-            model.model.parameters.alpha = alpha;
-            model.model.parameters.beta = beta;
-            model.model.parameters.gamma = gamma;
-            model.model.variables.meanHead_calib = meanHead_calib;
-            model.model.variables.calibraion_time_points = time_points_trim;            
-            model.model.variables.initialHead = initialHead;
-            model.model.variables.initialTrend = initialTrend; 
-            
-            % Add current point back in the simulation. Note, when
-            % the simulation is undertaken for a point does not exist in
-            % model, then it is forecast.
-            filt(j) = false;
-            time_points_trimExtended =  headData(~filt,1);            
-            h_mod_trim = solveModel(model, time_points_trimExtended, [], 'NoLabel',false);    
-            h_forecast_trim = model.model.variables.h_forecast;            
-            
-            % Create a filter to remove the current point from the forecast
-            % and then calculate the residuals
-            obs_filt = [1:k-1,k+1:length(ind)];
-            resid_trim = h_obs_trim(:,end) - h_forecast_trim(obs_filt);
-                        
-            % To minimise the impacts of outliers as yet identified, create
-            % a filter to remove the most negative and posative values from
-            % the residuals
-            resid_filt = resid_trim> min(resid_trim) & resid_trim < max(resid_trim);
-            resid_trim = resid_trim(resid_filt);
-            time_points_trim = time_points_trim(resid_filt);
-            
-            % Calculate innovations
-            innov = resid_trim(2:end) - resid_trim(1:end-1).*exp( -10.^model.model.parameters.beta .*diff(time_points_trim) );      
-                        
-            % Calculate st. dev. of residuals noise
-            %sigma_n_trimmed = sqrt(mean(innov.^2 ./ (1 - exp( -2 .* 10.^model.model.parameters.beta .* diff(time_points_trim) ))));        
-            
-            % Calculate st. dev. of residual for the current forecast only
-            % Note: estimate of sigma_n at a spacific time step is derived
-            % from von Asmuth 2015  doi:10.1029/2004WR00372 eqn A7 but with
-            % the innovations at t, v_t, replaced with the mean. The was
-            % undertaken so that sigma_n,t is independent from the residual forecast.
-            sigma_n_trimmed = sqrt(mean(innov.^2) ./ (1 - exp( -2 .* 10.^model.model.parameters.beta .* delta_t )));        
-                        
-            % Calculate residual for omitted obs point.
-            resid_point = h_obs(j) - h_forecast_trim(k);
-            
-            % Break for-loop if an outlier is detected.
-            if abs(resid_point) >= nSigma_threshold* sigma_n_trimmed;
-                isNewOutlier(j) = true;
-                el=el+1;
-                 summaryStr{el} = ['Date : ',datestr(t(j)),', Head : ',num2str(h_obs(j)), ...
-                       ', Smoothed forecast head : ',num2str( h_forecast_trim(k)),', Residual head : ',num2str(resid_point), ...
-                       ', St. dev of noise : ',num2str(sigma_n_trimmed)];
-                break;
-            end
-        end
-        
-        % Aggregate new outliers with previously detected outliers
-        isOutlier = isOutlier | isNewOutlier;
-        
-        % If the while loop is to exit, then set flag to do one last
-        % calibration so that the noise is best estimated.
-        if sum(isNewOutlier)==0
-            doFinalCalibration = true;
-        end
-        
-        %update counter
-        i=i+1;        
-    end
-
-    % Assign the final parameters 
-    x_opt = getParameters(model_calib.model);
-    
-    % Exclude input outliers from those input. That is, only return the
-    % outliers identified from the exponential smoothing model
-    isOutlier(isOutlier_input) = false;
-    
-    % Print summary:
-    display('Summary of Outiers Detected');
-    display('---------------------------');
-    for i=1:el
-        display(summaryStr{i});
-    end
-    display('---------------------------');
-end
-
+function [ isOutlier, noise_sigma, x_opt, model_calib ] = outlierDetection( headData, isOutlier, nSigma_threshold)
+
+    % Initialise outputs    
+    noise_sigma = inf;
+    x_opt = [];
+    model_calib = [];
+     
+    % Initialise 'isOutliers' if its not supplied by the user
+    if isempty(isOutlier)
+        isOutlier = false(size(headData,1));
+    end
+    isNewOutlier = false(size(headData,1));
+    isOutlier_input = isOutlier;
+    
+    % Build inputs for exponential smoothing model
+    t = headData(:,1);
+    h_obs = headData(:,2);
+    dummyBoreID = 'BoreID_123';
+    coordinates = {dummyBoreID, -999, -999; 'Precip', -999, -999};
+    forcingData = [t(1)-10: t(end)+10]';
+    forcingData = table(year(forcingData),month(forcingData),day(forcingData),zeros(size(forcingData,1),1),'VariableNames',{'Year';'Month';'Day';'Precip'});   
+    h_obs_model = [year(t), month(t), day(t), hour(t), minute(t), second(t), h_obs];
+    
+    % Calibrate exponential smoothing model
+    summaryStr={};
+    noise_sigma = 0;
+    i=1;
+    doFinalCalibration = false;
+    el=0;
+    while i==1 || sum(isNewOutlier)>0 || doFinalCalibration
+
+        % Build model        
+        model_calib = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_model(~isOutlier,:), -999, forcingData, coordinates, false);
+        
+        % Calibrate model
+        calibrateModel(model_calib, [],0, inf, 'SPUCI',2)
+        
+        % Get the standard deviation of the noise.
+        noise_sigma = model_calib.model.variables.sigma_n;
+        
+        % Exit if the this loop is being undertaken 
+        if doFinalCalibration
+            break
+        end
+        
+        % Store calibrated parameters
+        alpha = model_calib.model.parameters.alpha;
+        beta = model_calib.model.parameters.beta;
+        gamma = model_calib.model.parameters.gamma;
+        meanHead_calib = model_calib.model.variables.meanHead_calib;
+        initialHead = model_calib.model.variables.initialHead;
+        initialTrend = model_calib.model.variables.initialTrend; 
+         
+        % Loop through each non-outlier observation to omit is from the
+        % simulation. THis is doen to exclude a possible outlier point from
+        % the smoothened estimate and the resulting calculation of the
+        % noise. If the difference between the current obs point and the
+        % forcast is greater than this noise estimate, then it is denoted
+        % as an outlier. Importantly, in calculating the noise the min and
+        % max points are also excluded.
+        isNewOutlier = false(size(isOutlier));        
+        filt = isOutlier;
+        ind = find(~isOutlier)';   
+        k=1;
+        for j=ind(2:end)
+            % Get a vector of obs points excluding the current obs point, point ind(j).
+            k=k+1;
+            filt(j) = true;
+            time_points_trim =  headData(~filt,1);
+            delta_t = headData(j,1) - headData(j-1,1);
+            h_obs_trim = headData(~filt,2);
+            h_obs_trim = [year(time_points_trim), month(time_points_trim), day(time_points_trim), hour(time_points_trim), minute(time_points_trim), second(time_points_trim), h_obs_trim];
+
+            % Rebuild the model without the current time point, assign the
+            % calibrated parameters and solve the model.
+            model = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_trim, -999, forcingData, coordinates, false);
+            model.model.parameters.alpha = alpha;
+            model.model.parameters.beta = beta;
+            model.model.parameters.gamma = gamma;
+            model.model.variables.meanHead_calib = meanHead_calib;
+            model.model.variables.calibraion_time_points = time_points_trim;            
+            model.model.variables.initialHead = initialHead;
+            model.model.variables.initialTrend = initialTrend; 
+            
+            % Add current point back in the simulation. Note, when
+            % the simulation is undertaken for a point does not exist in
+            % model, then it is forecast.
+            filt(j) = false;
+            time_points_trimExtended =  headData(~filt,1);            
+            h_mod_trim = solveModel(model, time_points_trimExtended, [], 'NoLabel',false);    
+            h_forecast_trim = model.model.variables.h_forecast;            
+            
+            % Create a filter to remove the current point from the forecast
+            % and then calculate the residuals
+            obs_filt = [1:k-1,k+1:length(ind)];
+            resid_trim = h_obs_trim(:,end) - h_forecast_trim(obs_filt);
+                        
+            % To minimise the impacts of outliers as yet identified, create
+            % a filter to remove the most negative and posative values from
+            % the residuals
+            resid_filt = resid_trim> min(resid_trim) & resid_trim < max(resid_trim);
+            resid_trim = resid_trim(resid_filt);
+            time_points_trim = time_points_trim(resid_filt);
+            
+            % Calculate innovations
+            innov = resid_trim(2:end) - resid_trim(1:end-1).*exp( -10.^model.model.parameters.beta .*diff(time_points_trim) );      
+                        
+            % Calculate st. dev. of residuals noise
+            %sigma_n_trimmed = sqrt(mean(innov.^2 ./ (1 - exp( -2 .* 10.^model.model.parameters.beta .* diff(time_points_trim) ))));        
+            
+            % Calculate st. dev. of residual for the current forecast only
+            % Note: estimate of sigma_n at a spacific time step is derived
+            % from von Asmuth 2015  doi:10.1029/2004WR00372 eqn A7 but with
+            % the innovations at t, v_t, replaced with the mean. The was
+            % undertaken so that sigma_n,t is independent from the residual forecast.
+            sigma_n_trimmed = sqrt(mean(innov.^2) ./ (1 - exp( -2 .* 10.^model.model.parameters.beta .* delta_t )));        
+                        
+            % Calculate residual for omitted obs point.
+            resid_point = h_obs(j) - h_forecast_trim(k);
+            
+            % Break for-loop if an outlier is detected.
+            if abs(resid_point) >= nSigma_threshold* sigma_n_trimmed;
+                isNewOutlier(j) = true;
+                el=el+1;
+                 summaryStr{el} = ['Date : ',datestr(t(j)),', Head : ',num2str(h_obs(j)), ...
+                       ', Smoothed forecast head : ',num2str( h_forecast_trim(k)),', Residual head : ',num2str(resid_point), ...
+                       ', St. dev of noise : ',num2str(sigma_n_trimmed)];
+                break;
+            end
+        end
+        
+        % Aggregate new outliers with previously detected outliers
+        isOutlier = isOutlier | isNewOutlier;
+        
+        % If the while loop is to exit, then set flag to do one last
+        % calibration so that the noise is best estimated.
+        if sum(isNewOutlier)==0
+            doFinalCalibration = true;
+        end
+        
+        %update counter
+        i=i+1;        
+    end
+
+    % Assign the final parameters 
+    x_opt = getParameters(model_calib.model);
+    
+    % Exclude input outliers from those input. That is, only return the
+    % outliers identified from the exponential smoothing model
+    isOutlier(isOutlier_input) = false;
+    
+    % Print summary:
+    display('Summary of Outiers Detected');
+    display('---------------------------');
+    for i=1:el
+        display(summaryStr{i});
+    end
+    display('---------------------------');
+end
+
diff --git a/documentation/figures/soilModel.svg b/documentation/figures/soilModel.svg
index f42240e..1012fd3 100644
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-  </style></head><body><div class="content"><h1>HydroSight - Calibration</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Global Calibration Concepts</a></li><li><a href="#3">HydroSight Calibration Schemes</a></li><li><a href="#4">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>Global calibration is a fundamental feature of the toolbox. To achieve reproducible and reliable calibration results some knowledge of calibration is required. This page provides a brief overview of global calibration and details the calibration methods available within the toolbox.</p><h2>Global Calibration Concepts<a name="2"></a></h2><p>Global calibration uses a numerical algorithm to minimise the difference between an observed data set, in this case the observed hydrograph, and a model of the observed hydrograph. To achieve this, the difference between the observed and modelled data is reduced to a single number called the <i>objective function</i>, which can be as simple as the difference between the square of the observed data and modelled data at each time point and then summed.</p><p>In calibrating the model, the aim is to find the very lowest objective function value and this is achieved by adjusting the model's parameter. Importantly, when the parameters are systematically changed (for example, across a 2-D grid of values for two parameters) and the objective function is calculated at each combination of the two parameters the result is often a smooth surface of model error, called a <i>response surface</i>, and similar to a topographic map.</p><p>To illustrate these concepts, the animation below shows a grid of two model parameters, <i>x1</i> and <i>x2</i>, and green contours of the model error, which are similar to elevation contours of a topographic map. In this example, the contours show many <i>basins</i> or <i>troughs</i> each represented by small circles, that is <i>local optima</i>. Additionally, the <i>deepest</i> optima are those in the bottom-left of the figure. The aim of a numerical global calibration scheme is to find the deepest optima, i.e. the optima with the very lowest possible model error.</p><p>Many numerical global calibration schemes find the parameter values giving the lowest error by using a population of parameter sets (i.e. many points within the response surface) that <i>evolve</i> toward the global optima using a processes of similar to natural selection and mutation. Hence, the methods are referred to as <i>evolutionary</i> methods. This is illustrated in the figure below where the initial population of points are randomly distributed over the entire parameter region and they evolve toward the global optima in the bottom left.</p><p>An important aspect of global calibration is that the solutions are very rarely 100% reproducible. That is, if the calibration is re-ran three times it is not guaranteed to converge to the same optima each time. This is illustrated in the figure below where one of the runs converges to an optima at <i>x1</i> ~= 1.5 and <i>x2</i> ~= 0.5 while the other two runs converge to the true global optima at <i>x1</i> ~= 0.5 and <i>x2</i> ~= 1.5. This occurs because global calibration methods rely on randomness in the initial parameter sampling and in the evolutionary process and so each time the scheme is ran it can converge to a different optima. Therefore for all but the most trivial of problems the calibration solution cannot be confirmed to be the global optima. The probability of finding the global optima can however be inferred by re-running the calibration scheme multiple times and reviewing the results. If after running the calibration, say, 10 times the objective function value achieved on the first run is not bettered then it is likely the global optima has been located.</p><p>For these reasons, applying a global calibration scheme is more complex than just turning it <i>on</i> and pressing <i>go</i>. All schemes have settings, such as the number of initial points, that need to be <i>tuned</i>. Often this is done to increase the probability that the global optima has been located but it comes at the expense of computational efficiency. To illustrate, a time-series model with, say, 5 parameters requires that a a five dimensional rectangle be searched for the global optima. Using the default settings for the calibration scheme may require time-series model to be run, say, 10,000 times. However, if it is very important that the global optima be located then more rigorous settings may require &gt;100,000 model runs to converge to an optima. Furthermore, different schemes may perform different and so multiple runs may be required using different schemes. In summary, global calibration must be undertaken in the context of the importance of the problem being modelled and the available computational resources.</p><p><img vspace="5" hspace="5" src="Estimation_of_Distribution_Algorithm_animation.gif" alt=""> </p><p>Figure Attribution: Created by Pasimi. Obtained from <a href="https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif">https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif</a>  via Wikimedia Commons</p><h2>HydroSight Calibration Schemes<a name="3"></a></h2><p>The toolkit provides the following global calibration methods to fit a model to an observed hydrograph. One calibration setting must also be input by the user and the setting is specific to the chosen method.</p><div><ul><li><i>Covariance Matrix Adaptation Evolution Strategy (CMA-ES)</i>: is an evolutionary global calibration scheme that uses a population of points to approximate the curvature of the response surface, which is then used to evolve the population of points toward a global optima. See Hanson (2006) and <a href="https://www.lri.fr/~hansen/cmaesintro.html">https://www.lri.fr/~hansen/cmaesintro.html</a>  for details. For the toolbox, the code from the above link was modified to account for complex parameter boundaries and efficient sampling of parameter sets within the boundaries. In applying CMA-ES within the toolbox, the setting controls the number of scheme re-runs with each re-run using double the number of random initial points of the previous run. Peterson and Western (2014) found that four re-runs achieved an acceptable reliability in calibrating nonlinear TFN models.</li><li><i>SP-UCI</i> : the shuffled complex evolution with principal components analysis - University of California at Irvine (SP-UCI) method is a global optimisation algorithm designed for high-dimensional and complex problems and it is the default scheme for the toolbox. It is based on the Shuffled Complex Evolution (SCE-UA) Method (Duan et al. 1992), but resolves a serious problem in searching over high-dimensional spaces, namely population degeneration. The population degeneration problem refers to the phenomenon that, when searching over the high-dimensional parameter spaces, the population of the searching points is very likely to collapse into a subspace of the parameter space, therefore losing the capability of exploring the entire parameter space. In addition, the SP-UCI method also combines the strength of shuffled complex, the Nelder-Mead simplex, and multi-normal resampling to achieve efficient and effective high-dimensional optimisation. The above description of SP-UCI and the code used in the toolbox was obtained from <a href="http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method">http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method</a>. The code was, however, edited by Tim Peterson to allow the inclusion of parameter constraints and parallel calculation of each complex. For details of the algorithm see Chu et al. (2010). Finally, in applying SP-UCI within the toolbox, the setting controls the number of complexes (i.e. the number of <i>communities</i> of parameters) per model parameter. Trial have indicated that a value of two gives acceptable results.</li><li><i>DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)</i>: is an evolutionary global calibration scheme that, instead of attempting to identify the very best single parameter set (i.e. lowest objective function value), it identifies 1,000s of parameter sets that are plausible given the observed input forcing and head data (Vrugt, 2016). These parameter seta are called the <i>posterior</i> parameters and are identified using a likelihood function (instead of an objective function) and a <a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo">Markov chain Monte Carlo</a> algorithm. By running simulations with each of these parameter sets such output as the median simulation with the 5th and 95th predictions uncertainty can be derived. In implementing DREAM into the toolkit, the setting controls the number of generations (i.e. evolutionary iterations). That is, a value of 2 runs 2*10,000 generations. When DREAM has finished, following Vrugt (2016, p293) the plausible parameter sets are identified as those where the R-statistic is &lt;1.2. If the number of parameter sets is greater than an linearly interpolated recommended minimum (Vrugt 2016, p293) then the calibration is deemed successful. If successful, then the maximum number of returned parameter sets is double the recommended minimum. If less than the recommended minimum, but greater than 10% of the recommended minimum, the calibration is deemed partially successful. If &lt;10% then the calibration is deemed to have failed. Finally, the initial Markov chains are obtained by randomly sampling a normal distribution, where the mean is the centre of the plausible parameter range and the standard deviation 1/3 of the plausible parameter range. During the calibration, the chains are limited to within the physical parameter range.</li></ul></div><h2>References<a name="4"></a></h2><div><ul><li>Chu W., Gao X. and Sorooshian S. (2011). A new evolutionary search strategy for global optimization of high-dimensional problems. <i>Information Sciences</i>, 181(22), 4909&#8211;4927. DOI: <a href="http://dx.doi.org/10.1016/j.ins.2011.06.024">10.1016/j.ins.2011.06.024</a></li><li>Duan Q. Y., Sorooshian S. and Gupta, V. (1992). Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. <i>Water Resources Research</i>, 28, 1015-1031, DOI: <a href="http://dx.doi.org/10.1029/91WR02985">10.1029/91WR02985</a></li><li>Hansen N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. Larra&ntilde;aga, I. Inza and E. Bengoetxea (Eds.). <i>Towards a new evolutionary computation. Advances in estimation of distribution algorithms.</i> Springer, pp. 75-102.</li><li>Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  <i>Water Resources Research</i>, 50, 8330&#8211;8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Vrugt J. (2016). Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation. Environmental Modelling &amp; Software 75 (2016) 273-316, DOI: <a href="http://dx.doi.org/10.1016/j.envsoft.2015.08.013">10.1016/j.envsoft.2015.08.013</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - Calibration
-
-%% Overview
-% 
-% Global calibration is a fundamental feature of the toolbox. To achieve 
-% reproducible and reliable calibration results some knowledge of
-% calibration is required. This page provides a brief overview of global calibration and
-% details the calibration methods available within the toolbox.
-
-%% Global Calibration Concepts
-%
-% Global calibration uses a numerical algorithm to minimise the difference
-% between an observed data set, in this case the observed hydrograph, and a
-% model of the observed hydrograph. To achieve this, the difference between the
-% observed and modelled data is reduced to a single number called the
-% _objective function_, which can be as simple as the difference between the
-% square of the observed data and modelled data at each time point and then summed.
-%
-% In calibrating the model, the aim is to find the very lowest objective function
-% value and this is achieved by adjusting the model's parameter.
-% Importantly, when the parameters are systematically changed (for example,
-% across a 2-D grid of values for two parameters) and the objective function is
-% calculated at each combination of the two parameters the result is often
-% a smooth surface of model error, called a _response surface_, and similar
-% to a topographic map. 
-%
-% To illustrate these concepts, the animation below shows a grid of two 
-% model parameters, _x1_ and _x2_, and green contours of the model error,
-% which are similar to elevation contours of a topographic map. In this example, 
-% the contours show many _basins_ or _troughs_ each represented by small circles, that is
-% _local optima_. Additionally, the _deepest_ optima are those in the
-% bottom-left of the figure. The aim of a numerical global
-% calibration scheme is to find the deepest optima, i.e. the optima with the very
-% lowest possible model error.
-%
-% Many numerical global calibration schemes find the parameter values
-% giving the lowest error by using a population of parameter sets (i.e.
-% many points within the response surface) that _evolve_ toward the global
-% optima using a processes of similar to natural selection and mutation. Hence, the
-% methods are referred to as _evolutionary_ methods. This is illustrated in
-% the figure below where the initial population of points are randomly distributed
-% over the entire parameter region and they evolve toward the global optima in the
-% bottom left.
-%
-% An important aspect of global calibration is that the solutions are very
-% rarely 100% reproducible. That is, if the calibration is re-ran three
-% times it is not guaranteed to converge to the same optima each time. This is
-% illustrated in the figure below where one of the runs converges to an
-% optima at _x1_ ~= 1.5 and _x2_ ~= 0.5 while the other two runs converge to
-% the true global optima at _x1_ ~= 0.5 and _x2_ ~= 1.5. This occurs because
-% global calibration methods rely on randomness in the initial parameter sampling 
-% and in the evolutionary process and so each time the scheme is ran it can 
-% converge to a different optima. Therefore for all but the most trivial
-% of problems the calibration solution cannot be confirmed to be the global
-% optima. The probability of finding the global optima can however be
-% inferred by re-running the calibration scheme multiple times and
-% reviewing the results. If after running the calibration, say, 10 times
-% the objective function value achieved on the first run is not bettered
-% then it is likely the global optima has been located. 
-%
-% For these reasons, applying a global calibration scheme is more complex
-% than just turning it _on_ and pressing _go_. All schemes have settings, such as the 
-% number of initial points, that need to be _tuned_. Often this is done to
-% increase the probability that the global optima has been located but it 
-% comes at the expense of computational efficiency. To illustrate, a time-series model with, 
-% say, 5 parameters requires that a a five dimensional rectangle be searched 
-% for the global optima. Using the default settings for the calibration scheme 
-% may require time-series model to be run, say, 10,000 times. However, if it is very important 
-% that the global optima be located then more rigorous settings may require  
-% >100,000 model runs to converge to an optima. Furthermore, different schemes may perform different
-% and so multiple runs may be required using different schemes. In summary, 
-% global calibration must be undertaken in the context of the importance of
-% the problem being modelled and the available computational resources.
-% 
-% <<Estimation_of_Distribution_Algorithm_animation.gif>>
-%
-% Figure Attribution: Created by Pasimi. Obtained from 
-% <https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif>  via Wikimedia Commons
-% 
-%% HydroSight Calibration Schemes
-%
-% The toolkit provides the following global calibration methods to fit
-% a model to an observed hydrograph. One calibration setting must also be input
-% by the user and the setting is specific to the chosen method.
-%
-% * _Covariance Matrix Adaptation Evolution Strategy (CMA-ES)_: is an evolutionary 
-% global calibration scheme that uses a population of points to approximate
-% the curvature of the response surface, which is then used to evolve the population 
-% of points toward a global optima. See Hanson (2006) and
-% <https://www.lri.fr/~hansen/cmaesintro.html>  for details. For the toolbox, the 
-% code from the above link was modified to account for complex parameter
-% boundaries and efficient sampling of parameter sets within the
-% boundaries. In applying CMA-ES within the toolbox, the setting controls
-% the number of scheme re-runs with each re-run using double the number of
-% random initial points of the previous run. Peterson and Western (2014)
-% found that four re-runs achieved an acceptable reliability in calibrating nonlinear
-% TFN models.
-% * _SP-UCI_ : the shuffled complex evolution with principal components analysis - University 
-% of California at Irvine (SP-UCI) method is a global optimisation algorithm 
-% designed for high-dimensional and complex problems and it is the default 
-% scheme for the toolbox. It is based on the 
-% Shuffled Complex Evolution (SCE-UA) Method (Duan et al. 1992), but resolves
-% a serious problem in searching over high-dimensional spaces, namely population
-% degeneration. The population degeneration problem refers to the phenomenon
-% that, when searching over the high-dimensional parameter spaces, the 
-% population of the searching points is very likely to collapse into a
-% subspace of the parameter space, therefore losing the capability of 
-% exploring the entire parameter space. In addition, the SP-UCI method also
-% combines the strength of shuffled complex, the Nelder-Mead simplex, and
-% multi-normal resampling to achieve efficient and effective high-dimensional 
-% optimisation. The above description of SP-UCI and the code used in the
-% toolbox was obtained from <http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pcaREPLACE_WITH_DASH_DASHsp-uciREPLACE_WITH_DASH_DASHmethod>.
-% The code was, however, edited by Tim Peterson to allow the inclusion of
-% parameter constraints and parallel calculation of each complex. For
-% details of the algorithm see Chu et al. (2010). Finally, in applying SP-UCI 
-% within the toolbox, the setting controls the number of complexes (i.e. 
-% the number of _communities_ of parameters) per
-% model parameter. Trial have indicated that a value of two gives
-% acceptable results.
-% * _DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)_: is an evolutionary 
-% global calibration scheme that, instead of attempting to identify the very best single
-% parameter set (i.e. lowest objective function value), it identifies 1,000s of parameter sets that
-% are plausible given the observed input forcing and head data (Vrugt, 2016). These parameter seta are called the 
-% _posterior_ parameters and are identified using a likelihood function (instead of an objective 
-% function) and a <https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo Markov chain Monte Carlo>
-% algorithm. By running simulations with each of these parameter sets such output as the median simulation
-% with the 5th and 95th predictions uncertainty can be derived. In implementing DREAM into the toolkit, 
-% the setting controls the number of generations (i.e. evolutionary iterations). That is, 
-% a value of 2 runs 2*10,000 generations. When DREAM has finished, following Vrugt (2016, p293) the 
-% plausible parameter sets are identified as those where the R-statistic is <1.2. If the number of parameter sets is 
-% greater than an linearly interpolated recommended minimum (Vrugt 2016, p293) then the calibration is deemed successful. If successful, then the
-% maximum number of returned parameter sets is double the recommended minimum. If less than the recommended minimum, but greater than 10% 
-% of the recommended minimum, the calibration is deemed partially
-% successful. If <10% then the calibration is deemed to have failed.
-% Finally, the initial Markov chains are obtained by randomly sampling a
-% normal distribution, where the mean is the centre of the plausible
-% parameter range and the standard deviation 1/3 of the plausible parameter
-% range. During the calibration, the chains are limited to within the
-% physical parameter range.
-
-%% References
-%
-% * Chu W., Gao X. and Sorooshian S. (2011). A new evolutionary search
-% strategy for global optimization of high-dimensional problems.
-% _Information Sciences_, 181(22), 4909–4927. DOI: <http://dx.doi.org/10.1016/j.ins.2011.06.024 10.1016/j.ins.2011.06.024>  
-% * Duan Q. Y., Sorooshian S. and Gupta, V. (1992). Effective and Efficient
-% Global Optimization for Conceptual Rainfall-Runoff Models. _Water
-% Resources Research_, 28, 1015-1031, DOI: <http://dx.doi.org/10.1029/91WR02985 10.1029/91WR02985>
-% * Hansen N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. 
-% Larrañaga, I. Inza and E. Bengoetxea (Eds.). _Towards a new evolutionary computation. 
-% Advances in estimation of distribution algorithms._ Springer, pp. 75-102.
-% * Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  _Water
-% Resources Research_, 50, 8330–8355, DOI:
-% <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>.
-% <papers/Peterson_Western_2014.pdf PDF Copy>
-% * Vrugt J. (2016). Markov chain Monte Carlo simulation using the DREAM software
-% package: Theory, concepts, and MATLAB implementation. Environmental Modelling & Software 75 (2016) 273-316, 
-% DOI: <http://dx.doi.org/10.1016/j.envsoft.2015.08.013 10.1016/j.envsoft.2015.08.013>
-
-##### SOURCE END #####
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+  </style></head><body><div class="content"><h1>HydroSight - Calibration</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Global Calibration Concepts</a></li><li><a href="#3">HydroSight Calibration Schemes</a></li><li><a href="#4">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>Global calibration is a fundamental feature of the toolbox. To achieve reproducible and reliable calibration results some knowledge of calibration is required. This page provides a brief overview of global calibration and details the calibration methods available within the toolbox.</p><h2>Global Calibration Concepts<a name="2"></a></h2><p>Global calibration uses a numerical algorithm to minimise the difference between an observed data set, in this case the observed hydrograph, and a model of the observed hydrograph. To achieve this, the difference between the observed and modelled data is reduced to a single number called the <i>objective function</i>, which can be as simple as the difference between the square of the observed data and modelled data at each time point and then summed.</p><p>In calibrating the model, the aim is to find the very lowest objective function value and this is achieved by adjusting the model's parameter. Importantly, when the parameters are systematically changed (for example, across a 2-D grid of values for two parameters) and the objective function is calculated at each combination of the two parameters the result is often a smooth surface of model error, called a <i>response surface</i>, and similar to a topographic map.</p><p>To illustrate these concepts, the animation below shows a grid of two model parameters, <i>x1</i> and <i>x2</i>, and green contours of the model error, which are similar to elevation contours of a topographic map. In this example, the contours show many <i>basins</i> or <i>troughs</i> each represented by small circles, that is <i>local optima</i>. Additionally, the <i>deepest</i> optima are those in the bottom-left of the figure. The aim of a numerical global calibration scheme is to find the deepest optima, i.e. the optima with the very lowest possible model error.</p><p>Many numerical global calibration schemes find the parameter values giving the lowest error by using a population of parameter sets (i.e. many points within the response surface) that <i>evolve</i> toward the global optima using a processes of similar to natural selection and mutation. Hence, the methods are referred to as <i>evolutionary</i> methods. This is illustrated in the figure below where the initial population of points are randomly distributed over the entire parameter region and they evolve toward the global optima in the bottom left.</p><p>An important aspect of global calibration is that the solutions are very rarely 100% reproducible. That is, if the calibration is re-ran three times it is not guaranteed to converge to the same optima each time. This is illustrated in the figure below where one of the runs converges to an optima at <i>x1</i> ~= 1.5 and <i>x2</i> ~= 0.5 while the other two runs converge to the true global optima at <i>x1</i> ~= 0.5 and <i>x2</i> ~= 1.5. This occurs because global calibration methods rely on randomness in the initial parameter sampling and in the evolutionary process and so each time the scheme is ran it can converge to a different optima. Therefore for all but the most trivial of problems the calibration solution cannot be confirmed to be the global optima. The probability of finding the global optima can however be inferred by re-running the calibration scheme multiple times and reviewing the results. If after running the calibration, say, 10 times the objective function value achieved on the first run is not bettered then it is likely the global optima has been located.</p><p>For these reasons, applying a global calibration scheme is more complex than just turning it <i>on</i> and pressing <i>go</i>. All schemes have settings, such as the number of initial points, that need to be <i>tuned</i>. Often this is done to increase the probability that the global optima has been located but it comes at the expense of computational efficiency. To illustrate, a time-series model with, say, 5 parameters requires that a a five dimensional rectangle be searched for the global optima. Using the default settings for the calibration scheme may require time-series model to be run, say, 10,000 times. However, if it is very important that the global optima be located then more rigorous settings may require &gt;100,000 model runs to converge to an optima. Furthermore, different schemes may perform different and so multiple runs may be required using different schemes. In summary, global calibration must be undertaken in the context of the importance of the problem being modelled and the available computational resources.</p><p><img vspace="5" hspace="5" src="Estimation_of_Distribution_Algorithm_animation.gif" alt=""> </p><p>Figure Attribution: Created by Pasimi. Obtained from <a href="https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif">https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif</a>  via Wikimedia Commons</p><h2>HydroSight Calibration Schemes<a name="3"></a></h2><p>The toolkit provides the following global calibration methods to fit a model to an observed hydrograph. One calibration setting must also be input by the user and the setting is specific to the chosen method.</p><div><ul><li><i>Covariance Matrix Adaptation Evolution Strategy (CMA-ES)</i>: is an evolutionary global calibration scheme that uses a population of points to approximate the curvature of the response surface, which is then used to evolve the population of points toward a global optima. See Hanson (2006) and <a href="https://www.lri.fr/~hansen/cmaesintro.html">https://www.lri.fr/~hansen/cmaesintro.html</a>  for details. For the toolbox, the code from the above link was modified to account for complex parameter boundaries and efficient sampling of parameter sets within the boundaries. In applying CMA-ES within the toolbox, the setting controls the number of scheme re-runs with each re-run using double the number of random initial points of the previous run. Peterson and Western (2014) found that four re-runs achieved an acceptable reliability in calibrating nonlinear TFN models.</li><li><i>SP-UCI</i> : the shuffled complex evolution with principal components analysis - University of California at Irvine (SP-UCI) method is a global optimisation algorithm designed for high-dimensional and complex problems and it is the default scheme for the toolbox. It is based on the Shuffled Complex Evolution (SCE-UA) Method (Duan et al. 1992), but resolves a serious problem in searching over high-dimensional spaces, namely population degeneration. The population degeneration problem refers to the phenomenon that, when searching over the high-dimensional parameter spaces, the population of the searching points is very likely to collapse into a subspace of the parameter space, therefore losing the capability of exploring the entire parameter space. In addition, the SP-UCI method also combines the strength of shuffled complex, the Nelder-Mead simplex, and multi-normal resampling to achieve efficient and effective high-dimensional optimisation. The above description of SP-UCI and the code used in the toolbox was obtained from <a href="http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method">http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pca--sp-uci--method</a>. The code was, however, edited by Tim Peterson to allow the inclusion of parameter constraints and parallel calculation of each complex. For details of the algorithm see Chu et al. (2010). Finally, in applying SP-UCI within the toolbox, the setting controls the number of complexes (i.e. the number of <i>communities</i> of parameters) per model parameter. Trial have indicated that a value of two gives acceptable results.</li><li><i>DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)</i>: is an evolutionary global calibration scheme that, instead of attempting to identify the very best single parameter set (i.e. lowest objective function value), it identifies 1,000s of parameter sets that are plausible given the observed input forcing and head data (Vrugt, 2016). These parameter seta are called the <i>posterior</i> parameters and are identified using a likelihood function (instead of an objective function) and a <a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo">Markov chain Monte Carlo</a> algorithm. By running simulations with each of these parameter sets such output as the median simulation with the 5th and 95th predictions uncertainty can be derived. In implementing DREAM into the toolkit, the setting controls the number of generations (i.e. evolutionary iterations). That is, a value of 2 runs 2*10,000 generations. When DREAM has finished, following Vrugt (2016, p293) the plausible parameter sets are identified as those where the R-statistic is &lt;1.2. If the number of parameter sets is greater than an linearly interpolated recommended minimum (Vrugt 2016, p293) then the calibration is deemed successful. If successful, then the maximum number of returned parameter sets is double the recommended minimum. If less than the recommended minimum, but greater than 10% of the recommended minimum, the calibration is deemed partially successful. If &lt;10% then the calibration is deemed to have failed. Finally, the initial Markov chains are obtained by randomly sampling a normal distribution, where the mean is the centre of the plausible parameter range and the standard deviation 1/3 of the plausible parameter range. During the calibration, the chains are limited to within the physical parameter range.</li></ul></div><h2>References<a name="4"></a></h2><div><ul><li>Chu W., Gao X. and Sorooshian S. (2011). A new evolutionary search strategy for global optimization of high-dimensional problems. <i>Information Sciences</i>, 181(22), 4909&#8211;4927. DOI: <a href="http://dx.doi.org/10.1016/j.ins.2011.06.024">10.1016/j.ins.2011.06.024</a></li><li>Duan Q. Y., Sorooshian S. and Gupta, V. (1992). Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. <i>Water Resources Research</i>, 28, 1015-1031, DOI: <a href="http://dx.doi.org/10.1029/91WR02985">10.1029/91WR02985</a></li><li>Hansen N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. Larra&ntilde;aga, I. Inza and E. Bengoetxea (Eds.). <i>Towards a new evolutionary computation. Advances in estimation of distribution algorithms.</i> Springer, pp. 75-102.</li><li>Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  <i>Water Resources Research</i>, 50, 8330&#8211;8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Vrugt J. (2016). Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation. Environmental Modelling &amp; Software 75 (2016) 273-316, DOI: <a href="http://dx.doi.org/10.1016/j.envsoft.2015.08.013">10.1016/j.envsoft.2015.08.013</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - Calibration
+
+%% Overview
+% 
+% Global calibration is a fundamental feature of the toolbox. To achieve 
+% reproducible and reliable calibration results some knowledge of
+% calibration is required. This page provides a brief overview of global calibration and
+% details the calibration methods available within the toolbox.
+
+%% Global Calibration Concepts
+%
+% Global calibration uses a numerical algorithm to minimise the difference
+% between an observed data set, in this case the observed hydrograph, and a
+% model of the observed hydrograph. To achieve this, the difference between the
+% observed and modelled data is reduced to a single number called the
+% _objective function_, which can be as simple as the difference between the
+% square of the observed data and modelled data at each time point and then summed.
+%
+% In calibrating the model, the aim is to find the very lowest objective function
+% value and this is achieved by adjusting the model's parameter.
+% Importantly, when the parameters are systematically changed (for example,
+% across a 2-D grid of values for two parameters) and the objective function is
+% calculated at each combination of the two parameters the result is often
+% a smooth surface of model error, called a _response surface_, and similar
+% to a topographic map. 
+%
+% To illustrate these concepts, the animation below shows a grid of two 
+% model parameters, _x1_ and _x2_, and green contours of the model error,
+% which are similar to elevation contours of a topographic map. In this example, 
+% the contours show many _basins_ or _troughs_ each represented by small circles, that is
+% _local optima_. Additionally, the _deepest_ optima are those in the
+% bottom-left of the figure. The aim of a numerical global
+% calibration scheme is to find the deepest optima, i.e. the optima with the very
+% lowest possible model error.
+%
+% Many numerical global calibration schemes find the parameter values
+% giving the lowest error by using a population of parameter sets (i.e.
+% many points within the response surface) that _evolve_ toward the global
+% optima using a processes of similar to natural selection and mutation. Hence, the
+% methods are referred to as _evolutionary_ methods. This is illustrated in
+% the figure below where the initial population of points are randomly distributed
+% over the entire parameter region and they evolve toward the global optima in the
+% bottom left.
+%
+% An important aspect of global calibration is that the solutions are very
+% rarely 100% reproducible. That is, if the calibration is re-ran three
+% times it is not guaranteed to converge to the same optima each time. This is
+% illustrated in the figure below where one of the runs converges to an
+% optima at _x1_ ~= 1.5 and _x2_ ~= 0.5 while the other two runs converge to
+% the true global optima at _x1_ ~= 0.5 and _x2_ ~= 1.5. This occurs because
+% global calibration methods rely on randomness in the initial parameter sampling 
+% and in the evolutionary process and so each time the scheme is ran it can 
+% converge to a different optima. Therefore for all but the most trivial
+% of problems the calibration solution cannot be confirmed to be the global
+% optima. The probability of finding the global optima can however be
+% inferred by re-running the calibration scheme multiple times and
+% reviewing the results. If after running the calibration, say, 10 times
+% the objective function value achieved on the first run is not bettered
+% then it is likely the global optima has been located. 
+%
+% For these reasons, applying a global calibration scheme is more complex
+% than just turning it _on_ and pressing _go_. All schemes have settings, such as the 
+% number of initial points, that need to be _tuned_. Often this is done to
+% increase the probability that the global optima has been located but it 
+% comes at the expense of computational efficiency. To illustrate, a time-series model with, 
+% say, 5 parameters requires that a a five dimensional rectangle be searched 
+% for the global optima. Using the default settings for the calibration scheme 
+% may require time-series model to be run, say, 10,000 times. However, if it is very important 
+% that the global optima be located then more rigorous settings may require  
+% >100,000 model runs to converge to an optima. Furthermore, different schemes may perform different
+% and so multiple runs may be required using different schemes. In summary, 
+% global calibration must be undertaken in the context of the importance of
+% the problem being modelled and the available computational resources.
+% 
+% <<Estimation_of_Distribution_Algorithm_animation.gif>>
+%
+% Figure Attribution: Created by Pasimi. Obtained from 
+% <https://commons.wikimedia.org/wiki/File%3AEstimation_of_Distribution_Algorithm_animation.gif>  via Wikimedia Commons
+% 
+%% HydroSight Calibration Schemes
+%
+% The toolkit provides the following global calibration methods to fit
+% a model to an observed hydrograph. One calibration setting must also be input
+% by the user and the setting is specific to the chosen method.
+%
+% * _Covariance Matrix Adaptation Evolution Strategy (CMA-ES)_: is an evolutionary 
+% global calibration scheme that uses a population of points to approximate
+% the curvature of the response surface, which is then used to evolve the population 
+% of points toward a global optima. See Hanson (2006) and
+% <https://www.lri.fr/~hansen/cmaesintro.html>  for details. For the toolbox, the 
+% code from the above link was modified to account for complex parameter
+% boundaries and efficient sampling of parameter sets within the
+% boundaries. In applying CMA-ES within the toolbox, the setting controls
+% the number of scheme re-runs with each re-run using double the number of
+% random initial points of the previous run. Peterson and Western (2014)
+% found that four re-runs achieved an acceptable reliability in calibrating nonlinear
+% TFN models.
+% * _SP-UCI_ : the shuffled complex evolution with principal components analysis - University 
+% of California at Irvine (SP-UCI) method is a global optimisation algorithm 
+% designed for high-dimensional and complex problems and it is the default 
+% scheme for the toolbox. It is based on the 
+% Shuffled Complex Evolution (SCE-UA) Method (Duan et al. 1992), but resolves
+% a serious problem in searching over high-dimensional spaces, namely population
+% degeneration. The population degeneration problem refers to the phenomenon
+% that, when searching over the high-dimensional parameter spaces, the 
+% population of the searching points is very likely to collapse into a
+% subspace of the parameter space, therefore losing the capability of 
+% exploring the entire parameter space. In addition, the SP-UCI method also
+% combines the strength of shuffled complex, the Nelder-Mead simplex, and
+% multi-normal resampling to achieve efficient and effective high-dimensional 
+% optimisation. The above description of SP-UCI and the code used in the
+% toolbox was obtained from <http://www.mathworks.com/matlabcentral/fileexchange/37949-shuffled-complex-evolution-with-pcaREPLACE_WITH_DASH_DASHsp-uciREPLACE_WITH_DASH_DASHmethod>.
+% The code was, however, edited by Tim Peterson to allow the inclusion of
+% parameter constraints and parallel calculation of each complex. For
+% details of the algorithm see Chu et al. (2010). Finally, in applying SP-UCI 
+% within the toolbox, the setting controls the number of complexes (i.e. 
+% the number of _communities_ of parameters) per
+% model parameter. Trial have indicated that a value of two gives
+% acceptable results.
+% * _DiffeRential Evolution Adaptive Metropolis algorithm (DREAM)_: is an evolutionary 
+% global calibration scheme that, instead of attempting to identify the very best single
+% parameter set (i.e. lowest objective function value), it identifies 1,000s of parameter sets that
+% are plausible given the observed input forcing and head data (Vrugt, 2016). These parameter seta are called the 
+% _posterior_ parameters and are identified using a likelihood function (instead of an objective 
+% function) and a <https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo Markov chain Monte Carlo>
+% algorithm. By running simulations with each of these parameter sets such output as the median simulation
+% with the 5th and 95th predictions uncertainty can be derived. In implementing DREAM into the toolkit, 
+% the setting controls the number of generations (i.e. evolutionary iterations). That is, 
+% a value of 2 runs 2*10,000 generations. When DREAM has finished, following Vrugt (2016, p293) the 
+% plausible parameter sets are identified as those where the R-statistic is <1.2. If the number of parameter sets is 
+% greater than an linearly interpolated recommended minimum (Vrugt 2016, p293) then the calibration is deemed successful. If successful, then the
+% maximum number of returned parameter sets is double the recommended minimum. If less than the recommended minimum, but greater than 10% 
+% of the recommended minimum, the calibration is deemed partially
+% successful. If <10% then the calibration is deemed to have failed.
+% Finally, the initial Markov chains are obtained by randomly sampling a
+% normal distribution, where the mean is the centre of the plausible
+% parameter range and the standard deviation 1/3 of the plausible parameter
+% range. During the calibration, the chains are limited to within the
+% physical parameter range.
+
+%% References
+%
+% * Chu W., Gao X. and Sorooshian S. (2011). A new evolutionary search
+% strategy for global optimization of high-dimensional problems.
+% _Information Sciences_, 181(22), 4909–4927. DOI: <http://dx.doi.org/10.1016/j.ins.2011.06.024 10.1016/j.ins.2011.06.024>  
+% * Duan Q. Y., Sorooshian S. and Gupta, V. (1992). Effective and Efficient
+% Global Optimization for Conceptual Rainfall-Runoff Models. _Water
+% Resources Research_, 28, 1015-1031, DOI: <http://dx.doi.org/10.1029/91WR02985 10.1029/91WR02985>
+% * Hansen N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. 
+% Larrañaga, I. Inza and E. Bengoetxea (Eds.). _Towards a new evolutionary computation. 
+% Advances in estimation of distribution algorithms._ Springer, pp. 75-102.
+% * Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  _Water
+% Resources Research_, 50, 8330–8355, DOI:
+% <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>.
+% <papers/Peterson_Western_2014.pdf PDF Copy>
+% * Vrugt J. (2016). Markov chain Monte Carlo simulation using the DREAM software
+% package: Theory, concepts, and MATLAB implementation. Environmental Modelling & Software 75 (2016) 273-316, 
+% DOI: <http://dx.doi.org/10.1016/j.envsoft.2015.08.013 10.1016/j.envsoft.2015.08.013>
+
+##### SOURCE END #####
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-  </style></head><body><div class="content"><h1>HydroSight - Exponential Smoothing Models</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>This model simulates the head using a double exponential smoothing time-series model for irregular spaced observations (Cipra et al. 2008). The double exponential smoothing undertakes a smoothing of the trend in the head and exponential smoothing of the component of the head not captured by the trend. Importantly, the trend is linear and is updated at each observation. Therefore, when the model is used for interpolation of the head the estimate has a discontinuity at each observation because of the updating of the trend.</p><p>In fitting the exponential model to an observed hydrograph, the exponential noise model from Peterson &amp; Western (2014) was added. This provides a weighted least squares objective function and hence a means for minimisation all three of the model parameters.</p><p>In using the model, it does not use any forcing data, such as rainfall or extraction rate. However, the toolkit user interface does require all models to have forcing data and coordinate data. Therefore, in using this model, forcing and coordinate data files must be input but the files can contain junk data, e.g. all rainfall days can be zero.</p><p>Lastly, there are no user options for constructing this model.</p><h2>References<a name="2"></a></h2><div><ul><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330&#8211;8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Cipra T. and Hanz&aacute;k T. (2008). Exponential smoothing for irregular time series. <i>Kybernetika</i>,  44(3), 385-399.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - Exponential Smoothing Models
-
-%% Overview
-% This model simulates the head using a double exponential smoothing time-series model for irregular spaced  
-% observations (Cipra et al. 2008). The double exponential smoothing undertakes a 
-% smoothing of the trend in the head and exponential smoothing of the component of the 
-% head not captured by the trend. Importantly, the trend is linear and is updated at each
-% observation. Therefore, when the model is used for interpolation of the head the estimate
-% has a discontinuity at each observation because of the updating of the trend.
-% 
-% In fitting the exponential model to an observed hydrograph, the exponential noise model
-% from Peterson & Western (2014) was added. This provides a weighted least squares 
-% objective function and hence a means for minimisation all three of the model parameters.
-% 
-% In using the model, it does not use any forcing data, such as rainfall or extraction rate.
-% However, the toolkit user interface does require all models to have forcing data and 
-% coordinate data. Therefore, in using this model, forcing and coordinate data files must be
-% input but the files can contain junk data, e.g. all rainfall days can be zero. 
-% 
-% Lastly, there are no user options for constructing this model.
-%
-
-%% References
-%
-% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330–8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
-% * Cipra T. and Hanzák T. (2008). Exponential smoothing for irregular time series. _Kybernetika_,  44(3), 385-399.
-
-##### SOURCE END #####
+
+<!DOCTYPE html
+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+   <!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - Exponential Smoothing Models</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_ExpSmooth.m"><style type="text/css">
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+.footer p { margin:0px; }
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+
+
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+
+
+  </style></head><body><div class="content"><h1>HydroSight - Exponential Smoothing Models</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>This model simulates the head using a double exponential smoothing time-series model for irregular spaced observations (Cipra et al. 2008). The double exponential smoothing undertakes a smoothing of the trend in the head and exponential smoothing of the component of the head not captured by the trend. Importantly, the trend is linear and is updated at each observation. Therefore, when the model is used for interpolation of the head the estimate has a discontinuity at each observation because of the updating of the trend.</p><p>In fitting the exponential model to an observed hydrograph, the exponential noise model from Peterson &amp; Western (2014) was added. This provides a weighted least squares objective function and hence a means for minimisation all three of the model parameters.</p><p>In using the model, it does not use any forcing data, such as rainfall or extraction rate. However, the toolkit user interface does require all models to have forcing data and coordinate data. Therefore, in using this model, forcing and coordinate data files must be input but the files can contain junk data, e.g. all rainfall days can be zero.</p><p>Lastly, there are no user options for constructing this model.</p><h2>References<a name="2"></a></h2><div><ul><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330&#8211;8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Cipra T. and Hanz&aacute;k T. (2008). Exponential smoothing for irregular time series. <i>Kybernetika</i>,  44(3), 385-399.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - Exponential Smoothing Models
+
+%% Overview
+% This model simulates the head using a double exponential smoothing time-series model for irregular spaced  
+% observations (Cipra et al. 2008). The double exponential smoothing undertakes a 
+% smoothing of the trend in the head and exponential smoothing of the component of the 
+% head not captured by the trend. Importantly, the trend is linear and is updated at each
+% observation. Therefore, when the model is used for interpolation of the head the estimate
+% has a discontinuity at each observation because of the updating of the trend.
+% 
+% In fitting the exponential model to an observed hydrograph, the exponential noise model
+% from Peterson & Western (2014) was added. This provides a weighted least squares 
+% objective function and hence a means for minimisation all three of the model parameters.
+% 
+% In using the model, it does not use any forcing data, such as rainfall or extraction rate.
+% However, the toolkit user interface does require all models to have forcing data and 
+% coordinate data. Therefore, in using this model, forcing and coordinate data files must be
+% input but the files can contain junk data, e.g. all rainfall days can be zero. 
+% 
+% Lastly, there are no user options for constructing this model.
+%
+
+%% References
+%
+% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330–8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
+% * Cipra T. and Hanzák T. (2008). Exponential smoothing for irregular time series. _Kybernetika_,  44(3), 385-399.
+
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
diff --git a/documentation/html/doc_GUI.html b/documentation/html/doc_GUI.html
index 53d699a..b3119a1 100644
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-
-
-
-
-
-  </style></head><body><div class="content"><h1>HydroSight - <i>User Interface</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">System Requirements</a></li><li><a href="#3">Installation</a></li><li><a href="#4">Data Requirements</a></li><li><a href="#5">Getting Started</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The graphical user interface (GUI) for HydroSight provides an efficient means to build a wide range of groundwater time-series models without knowledge of Matlab programming or a copy of Matlab. It comprises of two independent modules:</p><div><ul><li>Data Preparation: a data analysis and outlier detection module for detecting and removing erroneous water level observations prior to time-series modelling.</li><li>Time-series Modelling: a time-series modelling module for simulating hydrographs and, for example, decomposition of the hydrographs to drivers.</li></ul></div><p>To get started using the GUI, this page details the GUI system requirements, installation, data requirements and details of the key modelling steps.</p><p>The GUI also has built in help. Aspects of this help are summarised below and shown within the following figure.</p><div><ul><li>tool-tips are provided within most sections of the GUI.</li><li>transfer function noise models (TFN) sub-functions show a summary pane describing the function (see bottom-right of screenshot).</li><li>example TFN models can be loaded from the GUI menu.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI.PNG" alt=""> </p><h2>System Requirements<a name="2"></a></h2><p>The system requirements to run the GUI as a stand alone application are:</p><div><ul><li>Windows 64 bit operating system</li><li>At least 4GB RAM</li><li>Multiple CPU cores - this is required to reduce the model calibration time.</li></ul></div><h2>Installation<a name="3"></a></h2><p>To install the GUI as a stand alone application, run the installation program called <i>HydroSight_install.exe</i>. Once installed, the program can be started via your Windows 'Start' icon.</p><h2>Data Requirements<a name="4"></a></h2><p>To get started modelling a groundwater hydrograph, the following data is required in .csv files:</p><div><ul><li>observed groundwater head data with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>. Note, <i>hours</i> and <i>minute</i> can be included after <i>day</i>.</li><li>forcing data with columns <i>year</i>, <i>month</i>, <i>day</i> and the columns for each forcing time series. Importantly, the first row must contain the column names (without spaces in names), the data must have a daily time-step and have no gaps and should extend prior to the first head observation.</li><li>projected coordinates for the bore ID and each forcing data with columns <i>Site ID</i>, <i>Easting</i>, <i>Northing</i>.</li></ul></div><p>Examples of the required data formats can be obtained from the inbuilt TFN model examples. Simply select an example model and input where the data files are to be saved. The saved .csv files can then be inpsected.</p><h2>Getting Started<a name="5"></a></h2><p>The modelling steps are defined by the following five tabs within the GUI. For data analysis and preparation, only step two is required. For time-series modelling, steps three and four are requires and five is optional.</p><div><ol><li><a href="doc_GUI_projectDescription.html">Project Description</a> : optional step to record the project title and description.</li><li><a href="doc_GUI_dataPrep.html">Data Preparation</a>:  a data analysis and outlier detection module for detecting and removing erroneous water level observation.</li><li><a href="doc_GUI_ModelConstruct.html">Model Construction</a>: the time-series module for inputting data and defining the model structure.</li><li><a href="doc_GUI_ModelCalib.html">Model Calibration</a>: the time-series module for calibrating the constructed models.</li><li><a href="doc_GUI_ModelSimulate.html">Model Simulation</a>: the time-series module to use a calibrated model for scenario investigations or hydrograph interpolation or extrapolation.</li></ol></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _User Interface_
-
-%% Overview
-% The graphical user interface (GUI) for HydroSight 
-% provides an efficient means to build a wide range of groundwater
-% time-series models without knowledge of Matlab programming or a copy of
-% Matlab. It comprises of two independent modules:
-%
-% * Data Preparation: a data analysis and outlier detection
-% module for detecting and removing erroneous water level observations
-% prior to time-series modelling.
-% * Time-series Modelling: a time-series modelling module for simulating
-% hydrographs and, for example, decomposition of the hydrographs to drivers.
-%
-% To get started using the GUI, this page details the GUI system requirements,
-% installation, data requirements and details of the key modelling steps.
-%
-% The GUI also has built in help. Aspects of this help are summarised below and shown within the following figure. 
-% 
-% * tool-tips are provided within most sections of the GUI. 
-% * transfer function noise models (TFN) sub-functions show a summary pane describing the 
-% function (see bottom-right of screenshot).
-% * example TFN models can be loaded from the GUI menu.
-%
-% <<diagrams/doc_GUI.PNG>>
-% 
-
-%% System Requirements
-% The system requirements to run the GUI as a stand alone application are:
-%
-% * Windows 64 bit operating system
-% * At least 4GB RAM
-% * Multiple CPU cores - this is required to reduce the model calibration time.
-
-%% Installation
-% To install the GUI as a stand alone application, run the installation
-% program called _HydroSight_install.exe_. Once installed, the 
-% program can be started via your Windows 'Start' icon.
-
-%% Data Requirements
-% To get started modelling a groundwater hydrograph, the following data is
-% required in .csv files:
-%
-% * observed groundwater head data with columns _bore ID_, _year_, _month_, _day_, _head_. Note, _hours_ and _minute_ can be included after _day_.
-% * forcing data with columns _year_, _month_, _day_ and the columns for
-% each forcing time series. Importantly, the first row must contain the column names (without spaces in names), the data must have a daily time-step and have no gaps
-% and should extend prior to the first head observation.
-% * projected coordinates for the bore ID and each forcing data with
-% columns _Site ID_, _Easting_, _Northing_.
-%
-% Examples of the required data formats can be obtained from the inbuilt
-% TFN model examples. Simply select an example model and input where the data files
-% are to be saved. The saved .csv files can then be inpsected.
-
-%% Getting Started
-% 
-% The modelling steps are defined by the following five tabs within the
-% GUI. For data analysis and preparation, only step two is required.
-% For time-series modelling, steps three and four are requires and five
-% is optional.
-%
-% # <doc_GUI_projectDescription.html Project Description> : optional
-% step to record the project title and description.
-% # <doc_GUI_dataPrep.html Data Preparation>:  a data analysis and outlier detection
-% module for detecting and removing erroneous water level observation.
-% # <doc_GUI_ModelConstruct.html Model Construction>: the time-series module for inputting data and defining the model structure.
-% # <doc_GUI_ModelCalib.html Model Calibration>: the time-series
-% module for calibrating the constructed models.
-% # <doc_GUI_ModelSimulate.html Model Simulation>: the time-series
-% module to use a calibrated model for scenario investigations or hydrograph
-% interpolation or extrapolation.
-
-
-##### SOURCE END #####
+
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+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+   <!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - User Interface</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_GUI.m"><style type="text/css">
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+
+
+  </style></head><body><div class="content"><h1>HydroSight - <i>User Interface</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">System Requirements</a></li><li><a href="#3">Installation</a></li><li><a href="#4">Data Requirements</a></li><li><a href="#5">Getting Started</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The graphical user interface (GUI) for HydroSight provides an efficient means to build a wide range of groundwater time-series models without knowledge of Matlab programming or a copy of Matlab. It comprises of two independent modules:</p><div><ul><li>Data Preparation: a data analysis and outlier detection module for detecting and removing erroneous water level observations prior to time-series modelling.</li><li>Time-series Modelling: a time-series modelling module for simulating hydrographs and, for example, decomposition of the hydrographs to drivers.</li></ul></div><p>To get started using the GUI, this page details the GUI system requirements, installation, data requirements and details of the key modelling steps.</p><p>The GUI also has built in help. Aspects of this help are summarised below and shown within the following figure.</p><div><ul><li>tool-tips are provided within most sections of the GUI.</li><li>transfer function noise models (TFN) sub-functions show a summary pane describing the function (see bottom-right of screenshot).</li><li>example TFN models can be loaded from the GUI menu.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI.PNG" alt=""> </p><h2>System Requirements<a name="2"></a></h2><p>The system requirements to run the GUI as a stand alone application are:</p><div><ul><li>Windows 64 bit operating system</li><li>At least 4GB RAM</li><li>Multiple CPU cores - this is required to reduce the model calibration time.</li></ul></div><h2>Installation<a name="3"></a></h2><p>To install the GUI as a stand alone application, run the installation program called <i>HydroSight_install.exe</i>. Once installed, the program can be started via your Windows 'Start' icon.</p><h2>Data Requirements<a name="4"></a></h2><p>To get started modelling a groundwater hydrograph, the following data is required in .csv files:</p><div><ul><li>observed groundwater head data with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>. Note, <i>hours</i> and <i>minute</i> can be included after <i>day</i>.</li><li>forcing data with columns <i>year</i>, <i>month</i>, <i>day</i> and the columns for each forcing time series. Importantly, the first row must contain the column names (without spaces in names), the data must have a daily time-step and have no gaps and should extend prior to the first head observation.</li><li>projected coordinates for the bore ID and each forcing data with columns <i>Site ID</i>, <i>Easting</i>, <i>Northing</i>.</li></ul></div><p>Examples of the required data formats can be obtained from the inbuilt TFN model examples. Simply select an example model and input where the data files are to be saved. The saved .csv files can then be inpsected.</p><h2>Getting Started<a name="5"></a></h2><p>The modelling steps are defined by the following five tabs within the GUI. For data analysis and preparation, only step two is required. For time-series modelling, steps three and four are requires and five is optional.</p><div><ol><li><a href="doc_GUI_projectDescription.html">Project Description</a> : optional step to record the project title and description.</li><li><a href="doc_GUI_dataPrep.html">Data Preparation</a>:  a data analysis and outlier detection module for detecting and removing erroneous water level observation.</li><li><a href="doc_GUI_ModelConstruct.html">Model Construction</a>: the time-series module for inputting data and defining the model structure.</li><li><a href="doc_GUI_ModelCalib.html">Model Calibration</a>: the time-series module for calibrating the constructed models.</li><li><a href="doc_GUI_ModelSimulate.html">Model Simulation</a>: the time-series module to use a calibrated model for scenario investigations or hydrograph interpolation or extrapolation.</li></ol></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _User Interface_
+
+%% Overview
+% The graphical user interface (GUI) for HydroSight 
+% provides an efficient means to build a wide range of groundwater
+% time-series models without knowledge of Matlab programming or a copy of
+% Matlab. It comprises of two independent modules:
+%
+% * Data Preparation: a data analysis and outlier detection
+% module for detecting and removing erroneous water level observations
+% prior to time-series modelling.
+% * Time-series Modelling: a time-series modelling module for simulating
+% hydrographs and, for example, decomposition of the hydrographs to drivers.
+%
+% To get started using the GUI, this page details the GUI system requirements,
+% installation, data requirements and details of the key modelling steps.
+%
+% The GUI also has built in help. Aspects of this help are summarised below and shown within the following figure. 
+% 
+% * tool-tips are provided within most sections of the GUI. 
+% * transfer function noise models (TFN) sub-functions show a summary pane describing the 
+% function (see bottom-right of screenshot).
+% * example TFN models can be loaded from the GUI menu.
+%
+% <<diagrams/doc_GUI.PNG>>
+% 
+
+%% System Requirements
+% The system requirements to run the GUI as a stand alone application are:
+%
+% * Windows 64 bit operating system
+% * At least 4GB RAM
+% * Multiple CPU cores - this is required to reduce the model calibration time.
+
+%% Installation
+% To install the GUI as a stand alone application, run the installation
+% program called _HydroSight_install.exe_. Once installed, the 
+% program can be started via your Windows 'Start' icon.
+
+%% Data Requirements
+% To get started modelling a groundwater hydrograph, the following data is
+% required in .csv files:
+%
+% * observed groundwater head data with columns _bore ID_, _year_, _month_, _day_, _head_. Note, _hours_ and _minute_ can be included after _day_.
+% * forcing data with columns _year_, _month_, _day_ and the columns for
+% each forcing time series. Importantly, the first row must contain the column names (without spaces in names), the data must have a daily time-step and have no gaps
+% and should extend prior to the first head observation.
+% * projected coordinates for the bore ID and each forcing data with
+% columns _Site ID_, _Easting_, _Northing_.
+%
+% Examples of the required data formats can be obtained from the inbuilt
+% TFN model examples. Simply select an example model and input where the data files
+% are to be saved. The saved .csv files can then be inpsected.
+
+%% Getting Started
+% 
+% The modelling steps are defined by the following five tabs within the
+% GUI. For data analysis and preparation, only step two is required.
+% For time-series modelling, steps three and four are requires and five
+% is optional.
+%
+% # <doc_GUI_projectDescription.html Project Description> : optional
+% step to record the project title and description.
+% # <doc_GUI_dataPrep.html Data Preparation>:  a data analysis and outlier detection
+% module for detecting and removing erroneous water level observation.
+% # <doc_GUI_ModelConstruct.html Model Construction>: the time-series module for inputting data and defining the model structure.
+% # <doc_GUI_ModelCalib.html Model Calibration>: the time-series
+% module for calibrating the constructed models.
+% # <doc_GUI_ModelSimulate.html Model Simulation>: the time-series
+% module to use a calibrated model for scenario investigations or hydrograph
+% interpolation or extrapolation.
+
+
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
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-      --><title>HydroSight - Model Calibration GUI Tab</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_GUI_ModelCalib.m"><style type="text/css">
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-
-
-
-
-  </style></head><body><div class="content"><h1>HydroSight - <i>Model Calibration GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Calibration</i> tab allows the global calibration of constructed time-series models. The global optima can be identified as a single best parameter set or a population of parameter sets, which allows quantification of the parameter and simulation nonlinear uncertainty. For the former, use the calibration methods <i>CMA-ES</i> or <i>SP-UCI</i> (a variant on Shuffled Complex Evolution). For uncertainty estimation, use the metthod <i>DREAM</i>. Finally, if you are unfamiliar with global calibration, see <a href="doc_Calibration.html">here</a> for details.</p><p>The time-series models can be challenging to calibrate and it often requires 100,000+ model runs. To reduce the calibration time, use a PC with many cores and for the TFN models consider limiting the years of input forcing data prior to the first water level observation to ~20 years.</p><p>The calibration time can also be reduced by offloading the calibration to a high performance cluster. This allows numerous models to be calibrated simultaneously. Curently, this is a <i>beta</i> feature and is limited to Linux clusters using PBS queue system and <i>mpiexec.hydra</i> and with matlab &gt;=2014b installed. To offload the calibration, click on the button <i>HPC Offload</i> (only available when runnign from within Matlab). Once the calibration is complete, click on <i>HPC Retrieval</i> to inport the results back into your project. Additionally, the model TFN time-series model can also be calibrated using Xeon Phi co-processor cards. If a co-processor card is available on the cluster then the numerical intergration step of the TFN model will be undertaken using the co-processor card(s). To use this features, the cluster will also require the Intel compiler ICC &gt;=2013.</p><p>Once a model is calibrated the following tasks can be undertaken:</p><div><ul><li>simulation of the groundwater head.</li><li>split-sample evaluation of the calibration using observation data not included in the calibration.</li><li>calculation of various performance statistics and a variogram  of the residuals.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand table lists the bores that have been constructed and can be calibrated.</li><li>Unique calibration setting can be defined to each row of the table.</li><li>Detailed calibration results are shown in the right-hand pane. The drop-down menu (see screenshot) lists the available results to display.</li><li>Calibration performance metrics are listed in the right-hand columns of the table. These allow the efficient identification of the acceptable models or the identification of the best model structure for a bore.</li><li>Buttons above the table allow export of the table or import of a .csv file.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelCalib.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To calibrate a time-series model, complete the following steps:</p><div><ol><li>Locate the required model to calibrate within the <i>Model Label</i> column.</li><li>Input a start and end date for the calibration. Note, by default these dates are set to the start and end date of the observed hydrograph.</li><li>Select a calibration scheme and input the required one calibration setting (see the tool tips for details, or click <a href="doc_Calibration.html">here</a>.</li><li>Select the models to be calibrated using the left-hand tick boxes.</li><li>Click on the button <i>Calibrate Selected Models</i> to calibrate the models. The progress is displayed in the column <i>Calib. Status</i>.</li><li>Review the calibration results using the performance statistics within the table and the detailed results within the right-hand pane.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required:</p><div><ul><li><b><i>Calib. Start Date</i></b>: The start date for selecting water level observations to be used in the calibration. Observations prior to this date will be used in the evaluation calculations.</li><li><b><i>Calib. End Date</i></b>: The end date for selecting water level observations to be used in the calibration. Observations after this date will be used in the evaluation calculations.</li><li><b><i>Calib. Method</i></b>: The global calibration method. See <a href="doc_Calibration.html">here</a> for details of the available methods.</li><li><b><i>Calib. Setting</i></b>: A setting for the selected global calibration method that controls the rigor of the global calibration.</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Calib. Status</i> : the status of the model calibration is displayed within the table. Error messages for the calibration are also displayed.</li><li><i>Calib. Period CoE</i> : The coefficient of efficiency calculated from observations within the calibration period. Note, 1 denotes a perfect fit to the observation data, 0 denotes a fit equal to that from just using the mean observed water level, and &lt;0 denotes a fit worse than using the mean observed water level.</li><li><i>Eval Period Unbiased CoE</i> : The unbiased coefficient of efficiency calculated from observations outside of the calibration start and end dates. The unbiased term denotes that the calculation was undertaken after the simulated water level was adjusted to have an equal mean to the mean observed water level during the evaluation period.</li><li><i>Calib. Period AIC</i> : The Akaike information criterion during the calibration period. It is a measure of model performance that accounts for the number of model parameters. It is useful for comparing different models applied to the same data. A lower AIC denotes a more parsimonious model (see <a href="https://en.wikipedia.org/wiki/Akaike_information_criterion">here</a> for details. Note, the AIC is calculated using the least squares estimate of the liklihood function.</li><li><i>Eval. Period AIC</i> : The Akaike information criterion during the evaluation period.</li></ul></div><p>In addition to the above table of outputs, detailed outputs can be shown in the right-hand pane. The available outputs are detailed below. To access these outputs, select the required output from the right-hand drop-down menu and then place the cursor in a cell for the required model.</p><div><ul><li><i>Data &amp; residuals</i> : table of estimated water level and noise. The table can be selected and copied and pasted into, say, Excel.</li><li><i>Parameter values</i> : the optimal calibrated parameter values.</li><li><i>Derived Variables</i> : derived variables specific to the model type.</li><li><i>Simulation time series plot</i> : plot of the simulated and observed head.</li><li><i>Residuals time series plot</i> : scatter plot of the observed minus estimated water level. These should be randomly distributed, have a mean of zero and have no temporal trend.</li><li><i>Histogram of calib. residuals</i> : histogram of observed minus estimated water level during the calibration period. The distribution should have a mean of zero and have a narrow range.</li><li><i>Histogram of eval. residuals</i> : histogram of observed minus estimated water level during the evaluation period. The distribution should have a mean of zero and have a narrow range.</li><li><i>Scatter plot of obs. vs model</i> : plots the calibration period observed vs simulated water level. The points should be centred around the 1:1 line and have a similar spread along the 1:1 line.</li><li><i>Scatter plot of residuals vs obs</i> : plots the observed water level vs residuals. Ideally, the residuals should show no relationship with the observed water level.</li><li><i>Variogram of residuals</i> : plots the temporal correlation in the residuals and fits an exponential model to the experimental variogram. Ideally, the fitted variogram curve should approach its maximum value after a short temporal duration.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Model Calibration GUI Tab_
-
-%% Overview
-% The _Model Calibration_ tab allows the global calibration of constructed
-% time-series models. The global optima can be identified as a single best 
-% parameter set or a population of parameter sets, which allows quantification
-% of the parameter and simulation nonlinear uncertainty. For the former, use the calibration 
-% methods _CMA-ES_ or _SP-UCI_ (a variant on Shuffled Complex Evolution). For 
-% uncertainty estimation, use the metthod _DREAM_. Finally, if you are 
-% unfamiliar with global calibration, see <doc_Calibration.html here> for details. 
-%
-% The time-series models can be challenging to calibrate
-% and it often requires 100,000+ model runs. To reduce the calibration
-% time, use a PC with many cores and for the TFN models consider limiting 
-% the years of input forcing data prior to the first water level observation to ~20 years. 
-%
-% The calibration time can also be reduced by offloading the calibration to
-% a high performance cluster. This allows numerous models to be calibrated
-% simultaneously. Curently, this is a _beta_ feature and is limited to Linux
-% clusters using PBS queue system and _mpiexec.hydra_ and with matlab 
-% >=2014b installed. To offload the
-% calibration, click on the button _HPC Offload_ (only available when
-% runnign from within Matlab). Once the calibration is complete, click on
-% _HPC Retrieval_ to inport the results back into your project.
-% Additionally, the model TFN time-series model can also be calibrated
-% using Xeon Phi co-processor cards. If a co-processor card is available on the
-% cluster then the numerical intergration step of the TFN model will be
-% undertaken using the co-processor card(s). To use this features, the
-% cluster will also require the Intel compiler ICC >=2013.
-%
-% Once a model is calibrated the following tasks can be undertaken: 
-%
-% * simulation of the groundwater head.
-% * split-sample evaluation of the calibration using observation data not
-% included in the calibration.
-% * calculation of various performance statistics and a variogram
-%  of the residuals.
-%
-% The screenshot below illustrates the main features of the tab. Specifically:
-%
-% * The left-hand table lists the bores that have been constructed and can be calibrated.
-% * Unique calibration setting can be defined to each row of the table.
-% * Detailed calibration results are shown in the right-hand pane. The
-% drop-down menu (see screenshot) lists the available results to
-% display.
-% * Calibration performance metrics are listed in the right-hand columns of
-% the table. These allow the efficient identification of the acceptable
-% models or the identification of the best model structure for a bore.
-% * Buttons above the table allow export of the table or import of a .csv file.
-%
-% <<diagrams/doc_GUI_ModelCalib.png>>
-%
-
-%% Getting Started
-%
-% To calibrate a time-series model, complete the following steps:
-%
-% # Locate the required model to calibrate within the _Model Label_ column.
-% # Input a start and end date for the calibration. Note, by default these
-% dates are set to the start and end date of the observed hydrograph.
-% # Select a calibration scheme and input the required one calibration setting (see
-% the tool tips for details, or click <doc_Calibration.html here>.
-% # Select the models to be calibrated using the left-hand tick boxes.
-% # Click on the button _Calibrate Selected Models_ to calibrate the
-% models. The progress is displayed in the column _Calib. Status_.
-% # Review the calibration results using the performance statistics within 
-% the table and the detailed results within the right-hand pane.
-
-%% Inputs
-%
-% The following inputs are available for this tab. The *bold* inputs are required:
-%
-% * *_Calib. Start Date_*: The start date for selecting water level
-% observations to be used in the calibration. Observations prior to this
-% date will be used in the evaluation calculations.
-% * *_Calib. End Date_*: The end date for selecting water level
-% observations to be used in the calibration. Observations after this
-% date will be used in the evaluation calculations.
-% * *_Calib. Method_*: The global calibration method. See
-% <doc_Calibration.html here> for details of the available methods.
-% * *_Calib. Setting_*: A setting for the selected global calibration
-% method that controls the rigor of the global calibration.
-
-%% Outputs
-%
-% The following outputs are presented within the table:
-%
-% * _Calib. Status_ : the status of the model calibration is
-% displayed within the table. Error messages for the calibration are also
-% displayed.
-% * _Calib. Period CoE_ : The coefficient of efficiency calculated from
-% observations within the calibration period. Note, 1 denotes a perfect fit
-% to the observation data, 0 denotes a fit equal to that from just using the
-% mean observed water level, and <0 denotes a fit worse than using the mean
-% observed water level.
-% * _Eval Period Unbiased CoE_ : The unbiased coefficient of efficiency calculated from
-% observations outside of the calibration start and end dates. The unbiased
-% term denotes that the calculation was undertaken after the simulated water
-% level was adjusted to have an equal mean to the mean observed water level
-% during the evaluation period.
-% * _Calib. Period AIC_ : The Akaike information criterion during the calibration period. It is a measure of
-% model performance that accounts for the number of model parameters. It is
-% useful for comparing different models applied to the same data. A lower
-% AIC denotes a more parsimonious model (see
-% <https://en.wikipedia.org/wiki/Akaike_information_criterion here> for
-% details. Note, the AIC is calculated using the least squares estimate of
-% the liklihood function.
-% * _Eval. Period AIC_ : The Akaike information criterion during the evaluation period.
-%
-% In addition to the above table of outputs, detailed outputs can be shown
-% in the right-hand pane. The available outputs are detailed below. To access these outputs, 
-% select the required output from the right-hand drop-down menu and then place the cursor 
-% in a cell for the required model.
-%
-% * _Data & residuals_ : table of estimated water level and noise. The table
-% can be selected and copied and pasted into, say, Excel.
-% * _Parameter values_ : the optimal calibrated parameter values.
-% * _Derived Variables_ : derived variables specific to the model type.
-% * _Simulation time series plot_ : plot of the simulated and observed
-% head.
-% * _Residuals time series plot_ : scatter plot of the observed minus
-% estimated water level. These should be randomly distributed, have a mean
-% of zero and have no temporal trend.
-% * _Histogram of calib. residuals_ : histogram of observed minus
-% estimated water level during the calibration period. The distribution should have a mean of zero and
-% have a narrow range.
-% * _Histogram of eval. residuals_ : histogram of observed minus
-% estimated water level during the evaluation period. The distribution should have a mean of zero and
-% have a narrow range.
-% * _Scatter plot of obs. vs model_ : plots the calibration period observed
-% vs simulated water level. The points should be centred around the 1:1
-% line and have a similar spread along the 1:1 line.
-% * _Scatter plot of residuals vs obs_ : plots the observed water level 
-% vs residuals. Ideally, the residuals should show no relationship with
-% the observed water level.
-% * _Variogram of residuals_ : plots the temporal correlation in the
-% residuals and fits an exponential model to the experimental variogram.
-% Ideally, the fitted variogram curve should approach its maximum value
-% after a short temporal duration. 
-
-##### SOURCE END #####
+
+<!DOCTYPE html
+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
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+This HTML was auto-generated from MATLAB code.
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+  </style></head><body><div class="content"><h1>HydroSight - <i>Model Calibration GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Calibration</i> tab allows the global calibration of constructed time-series models. The global optima can be identified as a single best parameter set or a population of parameter sets, which allows quantification of the parameter and simulation nonlinear uncertainty. For the former, use the calibration methods <i>CMA-ES</i> or <i>SP-UCI</i> (a variant on Shuffled Complex Evolution). For uncertainty estimation, use the metthod <i>DREAM</i>. Finally, if you are unfamiliar with global calibration, see <a href="doc_Calibration.html">here</a> for details.</p><p>The time-series models can be challenging to calibrate and it often requires 100,000+ model runs. To reduce the calibration time, use a PC with many cores and for the TFN models consider limiting the years of input forcing data prior to the first water level observation to ~20 years.</p><p>The calibration time can also be reduced by offloading the calibration to a high performance cluster. This allows numerous models to be calibrated simultaneously. Curently, this is a <i>beta</i> feature and is limited to Linux clusters using PBS queue system and <i>mpiexec.hydra</i> and with matlab &gt;=2014b installed. To offload the calibration, click on the button <i>HPC Offload</i> (only available when runnign from within Matlab). Once the calibration is complete, click on <i>HPC Retrieval</i> to inport the results back into your project. Additionally, the model TFN time-series model can also be calibrated using Xeon Phi co-processor cards. If a co-processor card is available on the cluster then the numerical intergration step of the TFN model will be undertaken using the co-processor card(s). To use this features, the cluster will also require the Intel compiler ICC &gt;=2013.</p><p>Once a model is calibrated the following tasks can be undertaken:</p><div><ul><li>simulation of the groundwater head.</li><li>split-sample evaluation of the calibration using observation data not included in the calibration.</li><li>calculation of various performance statistics and a variogram  of the residuals.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand table lists the bores that have been constructed and can be calibrated.</li><li>Unique calibration setting can be defined to each row of the table.</li><li>Detailed calibration results are shown in the right-hand pane. The drop-down menu (see screenshot) lists the available results to display.</li><li>Calibration performance metrics are listed in the right-hand columns of the table. These allow the efficient identification of the acceptable models or the identification of the best model structure for a bore.</li><li>Buttons above the table allow export of the table or import of a .csv file.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelCalib.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To calibrate a time-series model, complete the following steps:</p><div><ol><li>Locate the required model to calibrate within the <i>Model Label</i> column.</li><li>Input a start and end date for the calibration. Note, by default these dates are set to the start and end date of the observed hydrograph.</li><li>Select a calibration scheme and input the required one calibration setting (see the tool tips for details, or click <a href="doc_Calibration.html">here</a>.</li><li>Select the models to be calibrated using the left-hand tick boxes.</li><li>Click on the button <i>Calibrate Selected Models</i> to calibrate the models. The progress is displayed in the column <i>Calib. Status</i>.</li><li>Review the calibration results using the performance statistics within the table and the detailed results within the right-hand pane.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required:</p><div><ul><li><b><i>Calib. Start Date</i></b>: The start date for selecting water level observations to be used in the calibration. Observations prior to this date will be used in the evaluation calculations.</li><li><b><i>Calib. End Date</i></b>: The end date for selecting water level observations to be used in the calibration. Observations after this date will be used in the evaluation calculations.</li><li><b><i>Calib. Method</i></b>: The global calibration method. See <a href="doc_Calibration.html">here</a> for details of the available methods.</li><li><b><i>Calib. Setting</i></b>: A setting for the selected global calibration method that controls the rigor of the global calibration.</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Calib. Status</i> : the status of the model calibration is displayed within the table. Error messages for the calibration are also displayed.</li><li><i>Calib. Period CoE</i> : The coefficient of efficiency calculated from observations within the calibration period. Note, 1 denotes a perfect fit to the observation data, 0 denotes a fit equal to that from just using the mean observed water level, and &lt;0 denotes a fit worse than using the mean observed water level.</li><li><i>Eval Period Unbiased CoE</i> : The unbiased coefficient of efficiency calculated from observations outside of the calibration start and end dates. The unbiased term denotes that the calculation was undertaken after the simulated water level was adjusted to have an equal mean to the mean observed water level during the evaluation period.</li><li><i>Calib. Period AIC</i> : The Akaike information criterion during the calibration period. It is a measure of model performance that accounts for the number of model parameters. It is useful for comparing different models applied to the same data. A lower AIC denotes a more parsimonious model (see <a href="https://en.wikipedia.org/wiki/Akaike_information_criterion">here</a> for details. Note, the AIC is calculated using the least squares estimate of the liklihood function.</li><li><i>Eval. Period AIC</i> : The Akaike information criterion during the evaluation period.</li></ul></div><p>In addition to the above table of outputs, detailed outputs can be shown in the right-hand pane. The available outputs are detailed below. To access these outputs, select the required output from the right-hand drop-down menu and then place the cursor in a cell for the required model.</p><div><ul><li><i>Data &amp; residuals</i> : table of estimated water level and noise. The table can be selected and copied and pasted into, say, Excel.</li><li><i>Parameter values</i> : the optimal calibrated parameter values.</li><li><i>Derived Variables</i> : derived variables specific to the model type.</li><li><i>Simulation time series plot</i> : plot of the simulated and observed head.</li><li><i>Residuals time series plot</i> : scatter plot of the observed minus estimated water level. These should be randomly distributed, have a mean of zero and have no temporal trend.</li><li><i>Histogram of calib. residuals</i> : histogram of observed minus estimated water level during the calibration period. The distribution should have a mean of zero and have a narrow range.</li><li><i>Histogram of eval. residuals</i> : histogram of observed minus estimated water level during the evaluation period. The distribution should have a mean of zero and have a narrow range.</li><li><i>Scatter plot of obs. vs model</i> : plots the calibration period observed vs simulated water level. The points should be centred around the 1:1 line and have a similar spread along the 1:1 line.</li><li><i>Scatter plot of residuals vs obs</i> : plots the observed water level vs residuals. Ideally, the residuals should show no relationship with the observed water level.</li><li><i>Variogram of residuals</i> : plots the temporal correlation in the residuals and fits an exponential model to the experimental variogram. Ideally, the fitted variogram curve should approach its maximum value after a short temporal duration.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Model Calibration GUI Tab_
+
+%% Overview
+% The _Model Calibration_ tab allows the global calibration of constructed
+% time-series models. The global optima can be identified as a single best 
+% parameter set or a population of parameter sets, which allows quantification
+% of the parameter and simulation nonlinear uncertainty. For the former, use the calibration 
+% methods _CMA-ES_ or _SP-UCI_ (a variant on Shuffled Complex Evolution). For 
+% uncertainty estimation, use the metthod _DREAM_. Finally, if you are 
+% unfamiliar with global calibration, see <doc_Calibration.html here> for details. 
+%
+% The time-series models can be challenging to calibrate
+% and it often requires 100,000+ model runs. To reduce the calibration
+% time, use a PC with many cores and for the TFN models consider limiting 
+% the years of input forcing data prior to the first water level observation to ~20 years. 
+%
+% The calibration time can also be reduced by offloading the calibration to
+% a high performance cluster. This allows numerous models to be calibrated
+% simultaneously. Curently, this is a _beta_ feature and is limited to Linux
+% clusters using PBS queue system and _mpiexec.hydra_ and with matlab 
+% >=2014b installed. To offload the
+% calibration, click on the button _HPC Offload_ (only available when
+% runnign from within Matlab). Once the calibration is complete, click on
+% _HPC Retrieval_ to inport the results back into your project.
+% Additionally, the model TFN time-series model can also be calibrated
+% using Xeon Phi co-processor cards. If a co-processor card is available on the
+% cluster then the numerical intergration step of the TFN model will be
+% undertaken using the co-processor card(s). To use this features, the
+% cluster will also require the Intel compiler ICC >=2013.
+%
+% Once a model is calibrated the following tasks can be undertaken: 
+%
+% * simulation of the groundwater head.
+% * split-sample evaluation of the calibration using observation data not
+% included in the calibration.
+% * calculation of various performance statistics and a variogram
+%  of the residuals.
+%
+% The screenshot below illustrates the main features of the tab. Specifically:
+%
+% * The left-hand table lists the bores that have been constructed and can be calibrated.
+% * Unique calibration setting can be defined to each row of the table.
+% * Detailed calibration results are shown in the right-hand pane. The
+% drop-down menu (see screenshot) lists the available results to
+% display.
+% * Calibration performance metrics are listed in the right-hand columns of
+% the table. These allow the efficient identification of the acceptable
+% models or the identification of the best model structure for a bore.
+% * Buttons above the table allow export of the table or import of a .csv file.
+%
+% <<diagrams/doc_GUI_ModelCalib.png>>
+%
+
+%% Getting Started
+%
+% To calibrate a time-series model, complete the following steps:
+%
+% # Locate the required model to calibrate within the _Model Label_ column.
+% # Input a start and end date for the calibration. Note, by default these
+% dates are set to the start and end date of the observed hydrograph.
+% # Select a calibration scheme and input the required one calibration setting (see
+% the tool tips for details, or click <doc_Calibration.html here>.
+% # Select the models to be calibrated using the left-hand tick boxes.
+% # Click on the button _Calibrate Selected Models_ to calibrate the
+% models. The progress is displayed in the column _Calib. Status_.
+% # Review the calibration results using the performance statistics within 
+% the table and the detailed results within the right-hand pane.
+
+%% Inputs
+%
+% The following inputs are available for this tab. The *bold* inputs are required:
+%
+% * *_Calib. Start Date_*: The start date for selecting water level
+% observations to be used in the calibration. Observations prior to this
+% date will be used in the evaluation calculations.
+% * *_Calib. End Date_*: The end date for selecting water level
+% observations to be used in the calibration. Observations after this
+% date will be used in the evaluation calculations.
+% * *_Calib. Method_*: The global calibration method. See
+% <doc_Calibration.html here> for details of the available methods.
+% * *_Calib. Setting_*: A setting for the selected global calibration
+% method that controls the rigor of the global calibration.
+
+%% Outputs
+%
+% The following outputs are presented within the table:
+%
+% * _Calib. Status_ : the status of the model calibration is
+% displayed within the table. Error messages for the calibration are also
+% displayed.
+% * _Calib. Period CoE_ : The coefficient of efficiency calculated from
+% observations within the calibration period. Note, 1 denotes a perfect fit
+% to the observation data, 0 denotes a fit equal to that from just using the
+% mean observed water level, and <0 denotes a fit worse than using the mean
+% observed water level.
+% * _Eval Period Unbiased CoE_ : The unbiased coefficient of efficiency calculated from
+% observations outside of the calibration start and end dates. The unbiased
+% term denotes that the calculation was undertaken after the simulated water
+% level was adjusted to have an equal mean to the mean observed water level
+% during the evaluation period.
+% * _Calib. Period AIC_ : The Akaike information criterion during the calibration period. It is a measure of
+% model performance that accounts for the number of model parameters. It is
+% useful for comparing different models applied to the same data. A lower
+% AIC denotes a more parsimonious model (see
+% <https://en.wikipedia.org/wiki/Akaike_information_criterion here> for
+% details. Note, the AIC is calculated using the least squares estimate of
+% the liklihood function.
+% * _Eval. Period AIC_ : The Akaike information criterion during the evaluation period.
+%
+% In addition to the above table of outputs, detailed outputs can be shown
+% in the right-hand pane. The available outputs are detailed below. To access these outputs, 
+% select the required output from the right-hand drop-down menu and then place the cursor 
+% in a cell for the required model.
+%
+% * _Data & residuals_ : table of estimated water level and noise. The table
+% can be selected and copied and pasted into, say, Excel.
+% * _Parameter values_ : the optimal calibrated parameter values.
+% * _Derived Variables_ : derived variables specific to the model type.
+% * _Simulation time series plot_ : plot of the simulated and observed
+% head.
+% * _Residuals time series plot_ : scatter plot of the observed minus
+% estimated water level. These should be randomly distributed, have a mean
+% of zero and have no temporal trend.
+% * _Histogram of calib. residuals_ : histogram of observed minus
+% estimated water level during the calibration period. The distribution should have a mean of zero and
+% have a narrow range.
+% * _Histogram of eval. residuals_ : histogram of observed minus
+% estimated water level during the evaluation period. The distribution should have a mean of zero and
+% have a narrow range.
+% * _Scatter plot of obs. vs model_ : plots the calibration period observed
+% vs simulated water level. The points should be centred around the 1:1
+% line and have a similar spread along the 1:1 line.
+% * _Scatter plot of residuals vs obs_ : plots the observed water level 
+% vs residuals. Ideally, the residuals should show no relationship with
+% the observed water level.
+% * _Variogram of residuals_ : plots the temporal correlation in the
+% residuals and fits an exponential model to the experimental variogram.
+% Ideally, the fitted variogram curve should approach its maximum value
+% after a short temporal duration. 
+
+##### SOURCE END #####
 --></body></html>
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-  </style></head><body><div class="content"><h1>HydroSight - <i>Model Construction GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Construction</i> tab allows the construction of any number of time-series models, each with different inputs and settings. Once models are constructed, the models can be calibrated using the next GUI tab. Additionally, a large number of models can be very efficiently constructed using the copy and paste functionality of the table (right click to access) and the import and append button.</p><p>The tab allows the following to be undertaken:</p><div><ul><li>Input water level data and forcing data.</li><li>Selection of the category of time-series model.</li><li>Detailed construction of the selected category of model.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand window tabulates the bores to be constructed.</li><li>Unique data and model structures can be defined for each row of the table.</li><li>The right-hand pane allows detailed construction of the selected type of model.</li><li>For some model types, the bottom of the right-hand pane shows sub-options for model construction. In the screenshot below, sub-options are shown for the soil moisture model to transform rainfall to recharge.</li><li>Buttons above the table allow export of the table or appending of a .csv file.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelConstruct.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To construct a time-series model for a bore, complete the following steps:</p><div><ol><li>Input a label for the model. This is used in the next tabs to define the model and must be unique.</li><li>Input the observed head, forcing data and coordinates files.</li><li>Input the bore ID to model. When you place the cursor in the <i>Bore ID</i> column a list of bore IDs should be displayed on the right, from which you can select a bore ID.</li><li>Select the type of model. The available models are <a href="doc_TFN.html">Transfer Function Noise (TFN) models</a> and <a href="doc_ExpSmooth.html">Exponential Smoothing models</a>. The TFN model is the recommended model for forcing decomposition and interpolation or extrapolation.</li><li>Define the model options. Selected models (e.g. TFN models) will display a pane on the right for you to define the model. See <a href="doc_TFN.html">here</a> for details on building a TFN model.</li><li>Select the models to be constructed using the left column of tick boxes.</li><li>Click on the button <i>Build Selected Models</i> to build the models.</li><li>If any models were successfully constructed then you can proceed to the calibration tab, where these models will be listed.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required.</p><div><ul><li><b>Model Label</b> : Short unique string labelling the model.</li><li><b>Obs. Head File</b> : full path to the .csv file of observed head.</li><li><b>Forcing Data File</b> : full path to the .csv file of forcing data.</li><li><b>Coordinates File</b> : full path to the .csv file of bore and forcing data coordinates.</li><li><b>Bore ID</b> : bore ID for modelling, input using the right-hand list of bore IDs.</li><li><b>Model Type</b> : the category of model. Based upon the selection, different model options are presented.</li><li>Model Options : selected model types may allow various options to be input. For the TFN model, the user must input a weighting function (e.g. Pearson's function for climate forcing) and select a forcing to be weighted. The forcing can also be transformed prior to the weighting using an optional first step (see the above screenshot).</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Build Status</i> : the status of the model construction is displayed within the table. Error messages for the construction are also displayed.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Model Construction GUI Tab_
-
-%% Overview
-%
-% The _Model Construction_ tab allows the construction of any number of
-% time-series models, each with different inputs and settings. Once models 
-% are constructed, the models can be calibrated using the next GUI tab.
-% Additionally, a large number of models can be very efficiently
-% constructed using the copy and paste functionality of the table (right
-% click to access) and the import and append button.
-%
-% The tab allows the following to be undertaken:
-%
-% * Input water level data and forcing data.
-% * Selection of the category of time-series model.
-% * Detailed construction of the selected category of model.
-%
-% The screenshot below illustrates the main features of the tab. Specifically:
-%
-% * The left-hand window tabulates the bores to be constructed.
-% * Unique data and model structures can be defined for each row of the table.
-% * The right-hand pane allows detailed construction of the selected type of model.
-% * For some model types, the bottom of the right-hand pane shows
-% sub-options for model construction. In the screenshot below, sub-options
-% are shown for the soil moisture model to transform rainfall to recharge.
-% * Buttons above the table allow export of the table or appending of a .csv file.
-%
-% <<diagrams/doc_GUI_ModelConstruct.png>>
-%
-
-%% Getting Started
-% To construct a time-series model for a bore, complete the following steps:
-%
-% # Input a label for the model. This is used in the next tabs to define
-% the model and must be unique.
-% # Input the observed head, forcing data and coordinates files.
-% # Input the bore ID to model. When you place the cursor in the _Bore ID_
-% column a list of bore IDs should be displayed on the right, from which you
-% can select a bore ID.
-% # Select the type of model. The available models are <doc_TFN.html
-% Transfer Function Noise (TFN) models> and <doc_ExpSmooth.html Exponential
-% Smoothing models>. The TFN model is the recommended model for forcing
-% decomposition and interpolation or extrapolation.
-% # Define the model options. Selected models (e.g. TFN models) will display
-% a pane on the right for you to define the model. See <doc_TFN.html
-% here> for details on building a TFN model.
-% # Select the models to be constructed using the left column of tick boxes.
-% # Click on the button _Build Selected Models_ to build the models.
-% # If any models were successfully constructed then you can proceed to the
-% calibration tab, where these models will be listed.
-
-%% Inputs
-%
-% The following inputs are available for this tab. The *bold* inputs are required. 
-%
-% *  *Model Label* : Short unique string labelling the model.
-% *  *Obs. Head File* : full path to the .csv file of observed head.
-% *  *Forcing Data File* : full path to the .csv file of forcing data.
-% *  *Coordinates File* : full path to the .csv file of bore and forcing data coordinates.
-% *  *Bore ID* : bore ID for modelling, input using the right-hand list of bore IDs.
-% *  *Model Type* : the category of model. Based upon the selection,
-% different model options are presented.
-% * Model Options : selected model types may allow various options to be
-% input. For the TFN model, the user must input a weighting function (e.g.
-% Pearson's function for climate forcing) and select a forcing to be
-% weighted. The forcing can also be transformed prior to the weighting
-% using an optional first step (see the above screenshot).
-
-%% Outputs
-%
-% The following outputs are presented within the table:
-%
-% * _Build Status_ : the status of the model construction is
-% displayed within the table. Error messages for the construction are also
-% displayed.
-##### SOURCE END #####
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+
+
+
+
+
+  </style></head><body><div class="content"><h1>HydroSight - <i>Model Construction GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Construction</i> tab allows the construction of any number of time-series models, each with different inputs and settings. Once models are constructed, the models can be calibrated using the next GUI tab. Additionally, a large number of models can be very efficiently constructed using the copy and paste functionality of the table (right click to access) and the import and append button.</p><p>The tab allows the following to be undertaken:</p><div><ul><li>Input water level data and forcing data.</li><li>Selection of the category of time-series model.</li><li>Detailed construction of the selected category of model.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand window tabulates the bores to be constructed.</li><li>Unique data and model structures can be defined for each row of the table.</li><li>The right-hand pane allows detailed construction of the selected type of model.</li><li>For some model types, the bottom of the right-hand pane shows sub-options for model construction. In the screenshot below, sub-options are shown for the soil moisture model to transform rainfall to recharge.</li><li>Buttons above the table allow export of the table or appending of a .csv file.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelConstruct.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To construct a time-series model for a bore, complete the following steps:</p><div><ol><li>Input a label for the model. This is used in the next tabs to define the model and must be unique.</li><li>Input the observed head, forcing data and coordinates files.</li><li>Input the bore ID to model. When you place the cursor in the <i>Bore ID</i> column a list of bore IDs should be displayed on the right, from which you can select a bore ID.</li><li>Select the type of model. The available models are <a href="doc_TFN.html">Transfer Function Noise (TFN) models</a> and <a href="doc_ExpSmooth.html">Exponential Smoothing models</a>. The TFN model is the recommended model for forcing decomposition and interpolation or extrapolation.</li><li>Define the model options. Selected models (e.g. TFN models) will display a pane on the right for you to define the model. See <a href="doc_TFN.html">here</a> for details on building a TFN model.</li><li>Select the models to be constructed using the left column of tick boxes.</li><li>Click on the button <i>Build Selected Models</i> to build the models.</li><li>If any models were successfully constructed then you can proceed to the calibration tab, where these models will be listed.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required.</p><div><ul><li><b>Model Label</b> : Short unique string labelling the model.</li><li><b>Obs. Head File</b> : full path to the .csv file of observed head.</li><li><b>Forcing Data File</b> : full path to the .csv file of forcing data.</li><li><b>Coordinates File</b> : full path to the .csv file of bore and forcing data coordinates.</li><li><b>Bore ID</b> : bore ID for modelling, input using the right-hand list of bore IDs.</li><li><b>Model Type</b> : the category of model. Based upon the selection, different model options are presented.</li><li>Model Options : selected model types may allow various options to be input. For the TFN model, the user must input a weighting function (e.g. Pearson's function for climate forcing) and select a forcing to be weighted. The forcing can also be transformed prior to the weighting using an optional first step (see the above screenshot).</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Build Status</i> : the status of the model construction is displayed within the table. Error messages for the construction are also displayed.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Model Construction GUI Tab_
+
+%% Overview
+%
+% The _Model Construction_ tab allows the construction of any number of
+% time-series models, each with different inputs and settings. Once models 
+% are constructed, the models can be calibrated using the next GUI tab.
+% Additionally, a large number of models can be very efficiently
+% constructed using the copy and paste functionality of the table (right
+% click to access) and the import and append button.
+%
+% The tab allows the following to be undertaken:
+%
+% * Input water level data and forcing data.
+% * Selection of the category of time-series model.
+% * Detailed construction of the selected category of model.
+%
+% The screenshot below illustrates the main features of the tab. Specifically:
+%
+% * The left-hand window tabulates the bores to be constructed.
+% * Unique data and model structures can be defined for each row of the table.
+% * The right-hand pane allows detailed construction of the selected type of model.
+% * For some model types, the bottom of the right-hand pane shows
+% sub-options for model construction. In the screenshot below, sub-options
+% are shown for the soil moisture model to transform rainfall to recharge.
+% * Buttons above the table allow export of the table or appending of a .csv file.
+%
+% <<diagrams/doc_GUI_ModelConstruct.png>>
+%
+
+%% Getting Started
+% To construct a time-series model for a bore, complete the following steps:
+%
+% # Input a label for the model. This is used in the next tabs to define
+% the model and must be unique.
+% # Input the observed head, forcing data and coordinates files.
+% # Input the bore ID to model. When you place the cursor in the _Bore ID_
+% column a list of bore IDs should be displayed on the right, from which you
+% can select a bore ID.
+% # Select the type of model. The available models are <doc_TFN.html
+% Transfer Function Noise (TFN) models> and <doc_ExpSmooth.html Exponential
+% Smoothing models>. The TFN model is the recommended model for forcing
+% decomposition and interpolation or extrapolation.
+% # Define the model options. Selected models (e.g. TFN models) will display
+% a pane on the right for you to define the model. See <doc_TFN.html
+% here> for details on building a TFN model.
+% # Select the models to be constructed using the left column of tick boxes.
+% # Click on the button _Build Selected Models_ to build the models.
+% # If any models were successfully constructed then you can proceed to the
+% calibration tab, where these models will be listed.
+
+%% Inputs
+%
+% The following inputs are available for this tab. The *bold* inputs are required. 
+%
+% *  *Model Label* : Short unique string labelling the model.
+% *  *Obs. Head File* : full path to the .csv file of observed head.
+% *  *Forcing Data File* : full path to the .csv file of forcing data.
+% *  *Coordinates File* : full path to the .csv file of bore and forcing data coordinates.
+% *  *Bore ID* : bore ID for modelling, input using the right-hand list of bore IDs.
+% *  *Model Type* : the category of model. Based upon the selection,
+% different model options are presented.
+% * Model Options : selected model types may allow various options to be
+% input. For the TFN model, the user must input a weighting function (e.g.
+% Pearson's function for climate forcing) and select a forcing to be
+% weighted. The forcing can also be transformed prior to the weighting
+% using an optional first step (see the above screenshot).
+
+%% Outputs
+%
+% The following outputs are presented within the table:
+%
+% * _Build Status_ : the status of the model construction is
+% displayed within the table. Error messages for the construction are also
+% displayed.
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
diff --git a/documentation/html/doc_GUI_ModelSimulate.html b/documentation/html/doc_GUI_ModelSimulate.html
index 8c716e3..4e189a3 100644
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-
-  </style></head><body><div class="content"><h1>HydroSight - <i>Model Simulation GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Simulation</i> tab allows calibrated time-series models to be used for the following simulations:</p><div><ul><li>interpolation or extrapolation of the observed hydrograph to, say, a daily time-step.</li><li>decomposition of the hydrograph to individual drivers.</li><li>scenario investigations using different forcing data.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand table shows the simulations that have been defined. An unlimited number of simulations can be undertaken for each model.</li><li>Unique simulation setting can be defined for each simulation.</li><li>The simulated water level and decomposition to drivers (if available from the chosen model type) can be plotted on the right-hand pane.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelSimulate.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To simulate the water level using a calibrated time-series model, complete the following steps:</p><div><ol><li>Select the required model label from the drop-down menu in the <i>Model Label</i> column (second column).</li><li>Input a label for the simulation in the column <i>Simulation Label</i>. This is a required input and must be unique for the model.</li><li>Consider providing optional inputs, such as new forcing data, a fixed time step or kriging the residuals (observed head minus simulated head) to ensure the simulated head honours the observed head.</li><li>Select the models to be simulated using the left tick boxes.</li><li>Click on the button <i>Simulate Selected Models</i> to simulate the models. The progress is displayed in the column <i>Simulation Status</i>.</li><li>Review the simulation results using the right-hand results pane.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required:</p><div><ul><li><b><i>Model Label</i></b> : the calibrated model for simulation. The list of calibrated models is provided in a drop-down menu.</li><li><b><i>Simulation Label</i></b> : a unique label for the selected model label.</li><li><i>Forcing Data File</i> : the full file name of a .csv data file containing different forcing data to that used in the calibration. The new forcing data must have the same column headings as that used in the calibration but can cover different time periods and have different forcing values.</li><li><i>Simulation Start Date</i> : the start date for the simulation. The date must be within the time period of forcing data but can be different to that used in the calibration. This allows extrapolation of the groundwater hydrograph back in time. The default is the start date of observed hydrograph.</li><li><i>Simulation End Date</i> : the end date for the simulation. The date must be within the time period of forcing data but can be different to that used in the calibration. This allows extrapolation of the groundwater hydrograph forward in time. The default is the end date of observed hydrograph.</li><li><i>Simulation Time Step</i> : The time step for the simulation. The options are: daily, weekly, monthly, yearly. This input can be used independently of the above dates. By default simulations are undertaken only at the date of water level observations.</li><li><i>Krig Simulation Residuals?</i> :  Kriging of the simulation residuals can be undertaken (if calibration forcing data is used) to ensure simulations honour the observations. This requires the time-step to be input. The analysis is undertaken by (i) calculating the experimental variogram of the residuals, (ii) fitting an exponential variogram to the experimental data, (iii) undertaking universal local kriging of the residuals and adding the result to the simulated water level, and (iv) scaling the time-series noise estimates by the normalised kriging variance.</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Simulation Status</i> : the status of the model simulation is displayed within the table. Error messages for the simulation are also displayed.</li></ul></div><p>In addition to the above table of outputs, detailed outputs can be shown in the right-hand pane. The available outputs are detailed below. To access these outputs, select the required output from the right-hand drop-down menu and then place the cursor in a cell for the required simulation.</p><div><ul><li><i>Simulation Data</i> : data table of simulated water level and the decomposition to each driver (if available for the model type). The table can be selected and copied and pasted into, say, Excel.</li><li><i>Simulation &amp; Decomposition plots</i> : plot of the simulated water level and plots for the contribution to the hydrograph from each driver (if available from the model type).</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Model Simulation GUI Tab_
-
-%% Overview
-% The _Model Simulation_ tab allows calibrated time-series models to be
-% used for the following simulations:
-% 
-% * interpolation or extrapolation of the observed hydrograph to, say, a daily time-step.
-% * decomposition of the hydrograph to individual drivers.
-% * scenario investigations using different forcing data.
-%
-% The screenshot below illustrates the main features of the tab. Specifically:
-%
-% * The left-hand table shows the simulations that have been defined.
-% An unlimited number of simulations can be undertaken for
-% each model. 
-% * Unique simulation setting can be defined for each simulation.
-% * The simulated water level and decomposition to drivers (if available
-% from the chosen model type) can be plotted on the right-hand pane.
-%
-% <<diagrams/doc_GUI_ModelSimulate.png>>
-
-%% Getting Started
-%
-% To simulate the water level using a calibrated time-series model, complete the following steps:
-%
-% # Select the required model label from the drop-down menu in the _Model Label_ column (second column).
-% # Input a label for the simulation in the column _Simulation Label_. This
-% is a required input and must be unique for the model.
-% # Consider providing optional inputs, such as new forcing data, a fixed
-% time step or kriging the residuals (observed head minus simulated head)
-% to ensure the simulated head honours the observed head.
-% # Select the models to be simulated using the left tick boxes.
-% # Click on the button _Simulate Selected Models_ to simulate the
-% models. The progress is displayed in the column _Simulation Status_.
-% # Review the simulation results using the right-hand results pane.
-%
-
-%% Inputs
-%
-% The following inputs are available for this tab. The *bold* inputs are required:
-%
-% * *_Model Label_* : the calibrated model for simulation. The list of
-% calibrated models is provided in a drop-down menu.
-% * *_Simulation Label_* : a unique label for the selected model label.
-% * _Forcing Data File_ : the full file name of a .csv data file
-% containing different forcing data to that used in the calibration. The
-% new forcing data must have the same column headings as that used in the
-% calibration but can cover different time periods and have different forcing values.
-% * _Simulation Start Date_ : the start date for the simulation. The date
-% must be within the time period of forcing data but can be different to
-% that used in the calibration. This allows extrapolation of the
-% groundwater hydrograph back in time. The default is the start date of
-% observed hydrograph.
-% * _Simulation End Date_ : the end date for the simulation. The date
-% must be within the time period of forcing data but can be different to
-% that used in the calibration. This allows extrapolation of the
-% groundwater hydrograph forward in time. The default is the end date of
-% observed hydrograph.
-% * _Simulation Time Step_ : The time step for the simulation. The options
-% are: daily, weekly, monthly, yearly. This input can be used independently
-% of the above dates. By default simulations are undertaken only at the
-% date of water level observations.
-% * _Krig Simulation Residuals?_ :  Kriging of the simulation residuals can 
-% be undertaken (if calibration forcing data is used) to ensure simulations
-% honour the observations. This requires the time-step to be input. The 
-% analysis is undertaken by (i) calculating the experimental
-% variogram of the residuals, (ii) fitting an exponential variogram to the
-% experimental data, (iii) undertaking universal local kriging of the residuals
-% and adding the result to the simulated water level, and (iv) scaling the
-% time-series noise estimates by the normalised kriging variance.
-%
-%% Outputs
-%
-% The following outputs are presented within the table:
-%
-% * _Simulation Status_ : the status of the model simulation is
-% displayed within the table. Error messages for the simulation are also
-% displayed.
-%
-% In addition to the above table of outputs, detailed outputs can be shown
-% in the right-hand pane. The available outputs are detailed below. To access these outputs, 
-% select the required output from the right-hand drop-down menu and then place the cursor 
-% in a cell for the required simulation.
-%
-% * _Simulation Data_ : data table of simulated water level and the decomposition to each driver 
-% (if available for the model type). The table can be selected and copied and pasted into, say, Excel.
-% * _Simulation & Decomposition plots_ : plot of the simulated water level and plots for the contribution 
-% to the hydrograph from each driver (if available from the model type).
-
-
-##### SOURCE END #####
+
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+
+
+
+
+
+  </style></head><body><div class="content"><h1>HydroSight - <i>Model Simulation GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Model Simulation</i> tab allows calibrated time-series models to be used for the following simulations:</p><div><ul><li>interpolation or extrapolation of the observed hydrograph to, say, a daily time-step.</li><li>decomposition of the hydrograph to individual drivers.</li><li>scenario investigations using different forcing data.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand table shows the simulations that have been defined. An unlimited number of simulations can be undertaken for each model.</li><li>Unique simulation setting can be defined for each simulation.</li><li>The simulated water level and decomposition to drivers (if available from the chosen model type) can be plotted on the right-hand pane.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_ModelSimulate.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To simulate the water level using a calibrated time-series model, complete the following steps:</p><div><ol><li>Select the required model label from the drop-down menu in the <i>Model Label</i> column (second column).</li><li>Input a label for the simulation in the column <i>Simulation Label</i>. This is a required input and must be unique for the model.</li><li>Consider providing optional inputs, such as new forcing data, a fixed time step or kriging the residuals (observed head minus simulated head) to ensure the simulated head honours the observed head.</li><li>Select the models to be simulated using the left tick boxes.</li><li>Click on the button <i>Simulate Selected Models</i> to simulate the models. The progress is displayed in the column <i>Simulation Status</i>.</li><li>Review the simulation results using the right-hand results pane.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required:</p><div><ul><li><b><i>Model Label</i></b> : the calibrated model for simulation. The list of calibrated models is provided in a drop-down menu.</li><li><b><i>Simulation Label</i></b> : a unique label for the selected model label.</li><li><i>Forcing Data File</i> : the full file name of a .csv data file containing different forcing data to that used in the calibration. The new forcing data must have the same column headings as that used in the calibration but can cover different time periods and have different forcing values.</li><li><i>Simulation Start Date</i> : the start date for the simulation. The date must be within the time period of forcing data but can be different to that used in the calibration. This allows extrapolation of the groundwater hydrograph back in time. The default is the start date of observed hydrograph.</li><li><i>Simulation End Date</i> : the end date for the simulation. The date must be within the time period of forcing data but can be different to that used in the calibration. This allows extrapolation of the groundwater hydrograph forward in time. The default is the end date of observed hydrograph.</li><li><i>Simulation Time Step</i> : The time step for the simulation. The options are: daily, weekly, monthly, yearly. This input can be used independently of the above dates. By default simulations are undertaken only at the date of water level observations.</li><li><i>Krig Simulation Residuals?</i> :  Kriging of the simulation residuals can be undertaken (if calibration forcing data is used) to ensure simulations honour the observations. This requires the time-step to be input. The analysis is undertaken by (i) calculating the experimental variogram of the residuals, (ii) fitting an exponential variogram to the experimental data, (iii) undertaking universal local kriging of the residuals and adding the result to the simulated water level, and (iv) scaling the time-series noise estimates by the normalised kriging variance.</li></ul></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Simulation Status</i> : the status of the model simulation is displayed within the table. Error messages for the simulation are also displayed.</li></ul></div><p>In addition to the above table of outputs, detailed outputs can be shown in the right-hand pane. The available outputs are detailed below. To access these outputs, select the required output from the right-hand drop-down menu and then place the cursor in a cell for the required simulation.</p><div><ul><li><i>Simulation Data</i> : data table of simulated water level and the decomposition to each driver (if available for the model type). The table can be selected and copied and pasted into, say, Excel.</li><li><i>Simulation &amp; Decomposition plots</i> : plot of the simulated water level and plots for the contribution to the hydrograph from each driver (if available from the model type).</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Model Simulation GUI Tab_
+
+%% Overview
+% The _Model Simulation_ tab allows calibrated time-series models to be
+% used for the following simulations:
+% 
+% * interpolation or extrapolation of the observed hydrograph to, say, a daily time-step.
+% * decomposition of the hydrograph to individual drivers.
+% * scenario investigations using different forcing data.
+%
+% The screenshot below illustrates the main features of the tab. Specifically:
+%
+% * The left-hand table shows the simulations that have been defined.
+% An unlimited number of simulations can be undertaken for
+% each model. 
+% * Unique simulation setting can be defined for each simulation.
+% * The simulated water level and decomposition to drivers (if available
+% from the chosen model type) can be plotted on the right-hand pane.
+%
+% <<diagrams/doc_GUI_ModelSimulate.png>>
+
+%% Getting Started
+%
+% To simulate the water level using a calibrated time-series model, complete the following steps:
+%
+% # Select the required model label from the drop-down menu in the _Model Label_ column (second column).
+% # Input a label for the simulation in the column _Simulation Label_. This
+% is a required input and must be unique for the model.
+% # Consider providing optional inputs, such as new forcing data, a fixed
+% time step or kriging the residuals (observed head minus simulated head)
+% to ensure the simulated head honours the observed head.
+% # Select the models to be simulated using the left tick boxes.
+% # Click on the button _Simulate Selected Models_ to simulate the
+% models. The progress is displayed in the column _Simulation Status_.
+% # Review the simulation results using the right-hand results pane.
+%
+
+%% Inputs
+%
+% The following inputs are available for this tab. The *bold* inputs are required:
+%
+% * *_Model Label_* : the calibrated model for simulation. The list of
+% calibrated models is provided in a drop-down menu.
+% * *_Simulation Label_* : a unique label for the selected model label.
+% * _Forcing Data File_ : the full file name of a .csv data file
+% containing different forcing data to that used in the calibration. The
+% new forcing data must have the same column headings as that used in the
+% calibration but can cover different time periods and have different forcing values.
+% * _Simulation Start Date_ : the start date for the simulation. The date
+% must be within the time period of forcing data but can be different to
+% that used in the calibration. This allows extrapolation of the
+% groundwater hydrograph back in time. The default is the start date of
+% observed hydrograph.
+% * _Simulation End Date_ : the end date for the simulation. The date
+% must be within the time period of forcing data but can be different to
+% that used in the calibration. This allows extrapolation of the
+% groundwater hydrograph forward in time. The default is the end date of
+% observed hydrograph.
+% * _Simulation Time Step_ : The time step for the simulation. The options
+% are: daily, weekly, monthly, yearly. This input can be used independently
+% of the above dates. By default simulations are undertaken only at the
+% date of water level observations.
+% * _Krig Simulation Residuals?_ :  Kriging of the simulation residuals can 
+% be undertaken (if calibration forcing data is used) to ensure simulations
+% honour the observations. This requires the time-step to be input. The 
+% analysis is undertaken by (i) calculating the experimental
+% variogram of the residuals, (ii) fitting an exponential variogram to the
+% experimental data, (iii) undertaking universal local kriging of the residuals
+% and adding the result to the simulated water level, and (iv) scaling the
+% time-series noise estimates by the normalised kriging variance.
+%
+%% Outputs
+%
+% The following outputs are presented within the table:
+%
+% * _Simulation Status_ : the status of the model simulation is
+% displayed within the table. Error messages for the simulation are also
+% displayed.
+%
+% In addition to the above table of outputs, detailed outputs can be shown
+% in the right-hand pane. The available outputs are detailed below. To access these outputs, 
+% select the required output from the right-hand drop-down menu and then place the cursor 
+% in a cell for the required simulation.
+%
+% * _Simulation Data_ : data table of simulated water level and the decomposition to each driver 
+% (if available for the model type). The table can be selected and copied and pasted into, say, Excel.
+% * _Simulation & Decomposition plots_ : plot of the simulated water level and plots for the contribution 
+% to the hydrograph from each driver (if available from the model type).
+
+
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
diff --git a/documentation/html/doc_GUI_dataPrep.html b/documentation/html/doc_GUI_dataPrep.html
index 734ff33..bf3c153 100644
--- a/documentation/html/doc_GUI_dataPrep.html
+++ b/documentation/html/doc_GUI_dataPrep.html
@@ -1,158 +1,158 @@
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-To make changes, update the MATLAB code and republish this document.
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-  </style></head><body><div class="content"><h1>HydroSight - <i>Data Preparation GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Data Preparation</i> tab is the second tab from the left within the GUI (see below). It allows an unlimited number of unique bore IDs to be analysed for erroneous observations and outliers and provides summary statistics for each bore. This allows observations resulting from known problems (e.g. pump tests, failed bores) to be removed from the monitoring data prior to time-series modelling. Importantly, no outlier detection algorithm works for all types of data or and can detect all types of outliers. It is essential that the outlier detection algorithm and results be assessed as appropriate for each application.</p><p>The tab allows the following analysis to be undertaken:</p><div><ul><li>The observation start and end date can be checked to be within the construction date and today's date.</li><li>The water level can be checked to ensure it is above the bottom of the bore.</li><li>The water level can be checked to ensure it is below the top of the bore casing.</li><li>The daily absolute rate of water level change can be checked to ensure it is below a user set threshold.</li><li>Periods of constant water level beyond a user set threshold duration can be identified.</li><li>Outlier observations can be identified using a double-exponential smoothing model. The model is used to estimate the standard deviation of the noise &amp; the first observation beyond a user set number of standard deviations is omitted and the model is then re-calibrated and the outlier analysis repeated. The processes is repeated until fewer outlier observations are identified than is expected for the probability at the input number of standard deviations.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand window tabulates the bores to be analysed.</li><li>Unique analysis settings can be applied to each bore.</li><li>Buttons above the table allow export of the table or appending of a .csv file.</li><li>The results of the analysis can exported or displayed and edited using the right-hand results pane.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_dataPrep.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To analyse a series of bores, complete the following steps:</p><div><ol><li>Input the required settings (see below) for each bore.</li><li>Select the bores to be analysed using the left tick box.</li><li>Click on the button <i>Analyse Selected Bores</i>.</li><li>Review the results for a bore by placing the cursor in one of its cells. This should display the hydrograph, with the outliers denoted, and a table of data. Importantly, the assessment of each water level observation can be manually edited using the table.</li><li>Export the analysed data using the <i>Export Results</i> button and select the desired format.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required.</p><div><ol><li><i><b>Obs. Head File</b></i>: a .csv file of the observed head with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>.</li><li><i><b>Bore ID</b></i>: a string for the bore ID to analyse. A pane on the right should be displayed when you place the cursor in this cell that lists the bore IDs within the above file.</li><li><i>Bore Depth (Below Surface)</i>: used for checking if the water level is implausibly deep. Units are assumed to be the same as the the water level observations.</li><li><i>Surface Elevation</i>: used for checking if the water level is implausibly high (i.e. &gt; land elevation + casing length).</li><li><i>Casing Length (above surface)</i>: as for surface elevation input. Units are assumed to be the same as the the water level observations.</li><li><i>Construction Date</i>: used for checking if the observations were recorded before the bore was constructed.</li><li><i>Check Start Date?</i>: option to check the date of observations against the construction date.</li><li><i>Check End Date?</i>: option to check the date of observations against the current date.</li><li><i>Check Head Above Bore Depth?</i>: option to check if the water level is higher than the bottom of the bore.</li><li><i>Check Head Below Casing?</i>: option to check if the water level is shallower than the top of the casing elevation.</li><li><i>Threshold for Max. Daily abs(Head) Change</i>: the maximum absolute water level change per day that is deemed acceptable. Default is 10 m/day.</li><li><i>Threshold Duration for Constant Head (days)</i>: the minimum duration of constant water level that is deemed acceptable. Default is 120 days.</li><li><i>Auto-Outlier Num. St. dev</i>: the number of standard deviations of the noise (estimated from the modelling) for estimation of outliers. The default is 3.</li></ol></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Analysis Status</i> : the status of the analysis for each bore is displayed within the table. Error messages for the analysis are also displayed in this column.</li><li><i>No. Erroneous Obs.</i> : after successful analysis, the number of erroneous observations are displayed for each bore within the table. This can be used to identify the bore analysis requiring visual review.</li><li><i>No. Outlier Obs.</i> : after successful analysis, the number of outlier observations is also displayed for each bore within the table. Again, this can be used to identify the bore analysis requiring visual review.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Data Preparation GUI Tab_
-
-%% Overview
-% The _Data Preparation_ tab is the second tab from the left within the GUI (see below). 
-% It allows an unlimited number of unique bore IDs to be analysed for 
-% erroneous observations and outliers and provides summary statistics for
-% each bore. This allows observations resulting from known problems 
-% (e.g. pump tests, failed bores) to be removed from the monitoring data 
-% prior to time-series modelling. Importantly, no outlier detection
-% algorithm works for all types of data or and can detect all types of
-% outliers. It is essential that the outlier detection algorithm and results
-% be assessed as appropriate for each application.
-%
-% The tab allows the following analysis to be undertaken:
-%
-% * The observation start and end date can be checked to be within the construction date and today's date.
-% * The water level can be checked to ensure it is above the bottom of the bore.
-% * The water level can be checked to ensure it is below the top of the bore casing.
-% * The daily absolute rate of water level change can be checked to ensure it is below a user set threshold.
-% * Periods of constant water level beyond a user set threshold duration can be identified.
-% * Outlier observations can be identified using a double-exponential
-% smoothing model. The model is used to estimate the standard deviation of the
-% noise & the first observation beyond a user set number of standard
-% deviations is omitted and the model is then re-calibrated and the outlier
-% analysis repeated. The processes is repeated until fewer outlier observations are
-% identified than is expected for the probability at the input number of standard deviations.
-%
-% The screenshot below illustrates the main features of the tab. Specifically:
-%
-% * The left-hand window tabulates the bores to be analysed.
-% * Unique analysis settings can be applied to each bore.
-% * Buttons above the table allow export of the table or appending of a .csv file.
-% * The results of the analysis can exported or displayed and edited using the right-hand results pane.
-%
-% <<diagrams/doc_GUI_dataPrep.png>>
-%
-%% Getting Started
-% To analyse a series of bores, complete the following steps:
-%
-% # Input the required settings (see below) for each bore.
-% # Select the bores to be analysed using the left tick box.
-% # Click on the button _Analyse Selected Bores_.
-% # Review the results for a bore by placing the cursor in one of its
-% cells. This should display the hydrograph, with the outliers denoted, and
-% a table of data. Importantly, the assessment of each water level observation can be manually edited
-% using the table.
-% # Export the analysed data using the _Export Results_ button and select
-% the desired format.
-%
-
-%% Inputs
-%
-% The following inputs are available for this tab. The *bold* inputs are required. 
-%
-% # _*Obs. Head File*_: a .csv file of the observed head with columns _bore ID_, _year_, _month_, _day_, _head_.
-% # _*Bore ID*_: a string for the bore ID to analyse. A pane on the right
-% should be displayed when you place the cursor in this cell that lists the bore IDs within the above file.
-% # _Bore Depth (Below Surface)_: used for checking if the water level is
-% implausibly deep. Units are assumed to be the same as the the water level observations. 
-% # _Surface Elevation_: used for checking if the water level is implausibly high (i.e. > land elevation + casing length).
-% # _Casing Length (above surface)_: as for surface elevation input. Units are assumed to be the same as the the water level observations. 
-% # _Construction Date_: used for checking if the observations were recorded before the bore was constructed.
-% # _Check Start Date?_: option to check the date of observations against the construction date.
-% # _Check End Date?_: option to check the date of observations against the current date.                       
-% # _Check Head Above Bore Depth?_: option to check if the water level is higher than the bottom of the bore.
-% # _Check Head Below Casing?_: option to check if the water level is shallower than the top of the casing elevation.
-% # _Threshold for Max. Daily abs(Head) Change_: the maximum absolute water level change per day that is deemed acceptable. Default is 10 m/day.
-% # _Threshold Duration for Constant Head (days)_: the minimum duration of
-% constant water level that is deemed acceptable. Default is 120 days.
-% # _Auto-Outlier Num. St. dev_: the number of standard deviations of the
-% noise (estimated from the modelling) for estimation of outliers. The default is 3. 
-
-%% Outputs
-%
-% The following outputs are presented within the table:
-%
-% * _Analysis Status_ : the status of the analysis for each bore is
-% displayed within the table. Error messages for the analysis are also
-% displayed in this column.
-% * _No. Erroneous Obs._ : after successful analysis, the number of
-% erroneous observations are displayed for each bore within the table.
-% This can be used to identify the bore analysis requiring visual review.
-% * _No. Outlier Obs._ : after successful analysis, the number of
-% outlier observations is also displayed for each bore within the table.
-% Again, this can be used to identify the bore analysis requiring visual review.
-
-##### SOURCE END #####
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+
+
+
+
+
+  </style></head><body><div class="content"><h1>HydroSight - <i>Data Preparation GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Getting Started</a></li><li><a href="#3">Inputs</a></li><li><a href="#4">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Data Preparation</i> tab is the second tab from the left within the GUI (see below). It allows an unlimited number of unique bore IDs to be analysed for erroneous observations and outliers and provides summary statistics for each bore. This allows observations resulting from known problems (e.g. pump tests, failed bores) to be removed from the monitoring data prior to time-series modelling. Importantly, no outlier detection algorithm works for all types of data or and can detect all types of outliers. It is essential that the outlier detection algorithm and results be assessed as appropriate for each application.</p><p>The tab allows the following analysis to be undertaken:</p><div><ul><li>The observation start and end date can be checked to be within the construction date and today's date.</li><li>The water level can be checked to ensure it is above the bottom of the bore.</li><li>The water level can be checked to ensure it is below the top of the bore casing.</li><li>The daily absolute rate of water level change can be checked to ensure it is below a user set threshold.</li><li>Periods of constant water level beyond a user set threshold duration can be identified.</li><li>Outlier observations can be identified using a double-exponential smoothing model. The model is used to estimate the standard deviation of the noise &amp; the first observation beyond a user set number of standard deviations is omitted and the model is then re-calibrated and the outlier analysis repeated. The processes is repeated until fewer outlier observations are identified than is expected for the probability at the input number of standard deviations.</li></ul></div><p>The screenshot below illustrates the main features of the tab. Specifically:</p><div><ul><li>The left-hand window tabulates the bores to be analysed.</li><li>Unique analysis settings can be applied to each bore.</li><li>Buttons above the table allow export of the table or appending of a .csv file.</li><li>The results of the analysis can exported or displayed and edited using the right-hand results pane.</li></ul></div><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_dataPrep.png" alt=""> </p><h2>Getting Started<a name="2"></a></h2><p>To analyse a series of bores, complete the following steps:</p><div><ol><li>Input the required settings (see below) for each bore.</li><li>Select the bores to be analysed using the left tick box.</li><li>Click on the button <i>Analyse Selected Bores</i>.</li><li>Review the results for a bore by placing the cursor in one of its cells. This should display the hydrograph, with the outliers denoted, and a table of data. Importantly, the assessment of each water level observation can be manually edited using the table.</li><li>Export the analysed data using the <i>Export Results</i> button and select the desired format.</li></ol></div><h2>Inputs<a name="3"></a></h2><p>The following inputs are available for this tab. The <b>bold</b> inputs are required.</p><div><ol><li><i><b>Obs. Head File</b></i>: a .csv file of the observed head with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>.</li><li><i><b>Bore ID</b></i>: a string for the bore ID to analyse. A pane on the right should be displayed when you place the cursor in this cell that lists the bore IDs within the above file.</li><li><i>Bore Depth (Below Surface)</i>: used for checking if the water level is implausibly deep. Units are assumed to be the same as the the water level observations.</li><li><i>Surface Elevation</i>: used for checking if the water level is implausibly high (i.e. &gt; land elevation + casing length).</li><li><i>Casing Length (above surface)</i>: as for surface elevation input. Units are assumed to be the same as the the water level observations.</li><li><i>Construction Date</i>: used for checking if the observations were recorded before the bore was constructed.</li><li><i>Check Start Date?</i>: option to check the date of observations against the construction date.</li><li><i>Check End Date?</i>: option to check the date of observations against the current date.</li><li><i>Check Head Above Bore Depth?</i>: option to check if the water level is higher than the bottom of the bore.</li><li><i>Check Head Below Casing?</i>: option to check if the water level is shallower than the top of the casing elevation.</li><li><i>Threshold for Max. Daily abs(Head) Change</i>: the maximum absolute water level change per day that is deemed acceptable. Default is 10 m/day.</li><li><i>Threshold Duration for Constant Head (days)</i>: the minimum duration of constant water level that is deemed acceptable. Default is 120 days.</li><li><i>Auto-Outlier Num. St. dev</i>: the number of standard deviations of the noise (estimated from the modelling) for estimation of outliers. The default is 3.</li></ol></div><h2>Outputs<a name="4"></a></h2><p>The following outputs are presented within the table:</p><div><ul><li><i>Analysis Status</i> : the status of the analysis for each bore is displayed within the table. Error messages for the analysis are also displayed in this column.</li><li><i>No. Erroneous Obs.</i> : after successful analysis, the number of erroneous observations are displayed for each bore within the table. This can be used to identify the bore analysis requiring visual review.</li><li><i>No. Outlier Obs.</i> : after successful analysis, the number of outlier observations is also displayed for each bore within the table. Again, this can be used to identify the bore analysis requiring visual review.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Data Preparation GUI Tab_
+
+%% Overview
+% The _Data Preparation_ tab is the second tab from the left within the GUI (see below). 
+% It allows an unlimited number of unique bore IDs to be analysed for 
+% erroneous observations and outliers and provides summary statistics for
+% each bore. This allows observations resulting from known problems 
+% (e.g. pump tests, failed bores) to be removed from the monitoring data 
+% prior to time-series modelling. Importantly, no outlier detection
+% algorithm works for all types of data or and can detect all types of
+% outliers. It is essential that the outlier detection algorithm and results
+% be assessed as appropriate for each application.
+%
+% The tab allows the following analysis to be undertaken:
+%
+% * The observation start and end date can be checked to be within the construction date and today's date.
+% * The water level can be checked to ensure it is above the bottom of the bore.
+% * The water level can be checked to ensure it is below the top of the bore casing.
+% * The daily absolute rate of water level change can be checked to ensure it is below a user set threshold.
+% * Periods of constant water level beyond a user set threshold duration can be identified.
+% * Outlier observations can be identified using a double-exponential
+% smoothing model. The model is used to estimate the standard deviation of the
+% noise & the first observation beyond a user set number of standard
+% deviations is omitted and the model is then re-calibrated and the outlier
+% analysis repeated. The processes is repeated until fewer outlier observations are
+% identified than is expected for the probability at the input number of standard deviations.
+%
+% The screenshot below illustrates the main features of the tab. Specifically:
+%
+% * The left-hand window tabulates the bores to be analysed.
+% * Unique analysis settings can be applied to each bore.
+% * Buttons above the table allow export of the table or appending of a .csv file.
+% * The results of the analysis can exported or displayed and edited using the right-hand results pane.
+%
+% <<diagrams/doc_GUI_dataPrep.png>>
+%
+%% Getting Started
+% To analyse a series of bores, complete the following steps:
+%
+% # Input the required settings (see below) for each bore.
+% # Select the bores to be analysed using the left tick box.
+% # Click on the button _Analyse Selected Bores_.
+% # Review the results for a bore by placing the cursor in one of its
+% cells. This should display the hydrograph, with the outliers denoted, and
+% a table of data. Importantly, the assessment of each water level observation can be manually edited
+% using the table.
+% # Export the analysed data using the _Export Results_ button and select
+% the desired format.
+%
+
+%% Inputs
+%
+% The following inputs are available for this tab. The *bold* inputs are required. 
+%
+% # _*Obs. Head File*_: a .csv file of the observed head with columns _bore ID_, _year_, _month_, _day_, _head_.
+% # _*Bore ID*_: a string for the bore ID to analyse. A pane on the right
+% should be displayed when you place the cursor in this cell that lists the bore IDs within the above file.
+% # _Bore Depth (Below Surface)_: used for checking if the water level is
+% implausibly deep. Units are assumed to be the same as the the water level observations. 
+% # _Surface Elevation_: used for checking if the water level is implausibly high (i.e. > land elevation + casing length).
+% # _Casing Length (above surface)_: as for surface elevation input. Units are assumed to be the same as the the water level observations. 
+% # _Construction Date_: used for checking if the observations were recorded before the bore was constructed.
+% # _Check Start Date?_: option to check the date of observations against the construction date.
+% # _Check End Date?_: option to check the date of observations against the current date.                       
+% # _Check Head Above Bore Depth?_: option to check if the water level is higher than the bottom of the bore.
+% # _Check Head Below Casing?_: option to check if the water level is shallower than the top of the casing elevation.
+% # _Threshold for Max. Daily abs(Head) Change_: the maximum absolute water level change per day that is deemed acceptable. Default is 10 m/day.
+% # _Threshold Duration for Constant Head (days)_: the minimum duration of
+% constant water level that is deemed acceptable. Default is 120 days.
+% # _Auto-Outlier Num. St. dev_: the number of standard deviations of the
+% noise (estimated from the modelling) for estimation of outliers. The default is 3. 
+
+%% Outputs
+%
+% The following outputs are presented within the table:
+%
+% * _Analysis Status_ : the status of the analysis for each bore is
+% displayed within the table. Error messages for the analysis are also
+% displayed in this column.
+% * _No. Erroneous Obs._ : after successful analysis, the number of
+% erroneous observations are displayed for each bore within the table.
+% This can be used to identify the bore analysis requiring visual review.
+% * _No. Outlier Obs._ : after successful analysis, the number of
+% outlier observations is also displayed for each bore within the table.
+% Again, this can be used to identify the bore analysis requiring visual review.
+
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
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index 4bf916d..dd88dd9 100644
--- a/documentation/html/doc_GUI_projectDescription.html
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-  </style></head><body><div class="content"><h1>HydroSight - <i>Project Description GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Inputs</a></li><li><a href="#3">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Project Description</i> tab is the left most tab of HydroSight GUI. It is an optional step and allows documentation of the modelling project. The user can input a title for the project and an extended description. There are no limitations on the format or length of these inputs. Below is a screen shot of the project description tab.</p><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_projectDescription.png" alt=""> </p><h2>Inputs<a name="2"></a></h2><p>The following inputs are available:</p><div><ul><li><i>Project Title</i> : A brief title for the project. There are no limitations on the length or format.</li></ul></div><div><ul><li><i>Project Description</i>: An extended description of the project. There are no limitations on the format or length. However, a bug has been observed whereby, when new paragraphs are created, the spacing between the paragraphs may increase each time the description is displayed.</li></ul></div><h2>Outputs<a name="3"></a></h2><p>This tab creates no outputs.</p><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Project Description GUI Tab_
-
-%% Overview
-% The _Project Description_ tab is the left most tab of HydroSight GUI. It is 
-% an optional step and allows documentation of the modelling project. The user
-% can input a title for the project and an extended description. There are
-% no limitations on the format or length of these inputs. Below is a screen
-% shot of the project description tab.
-%
-% <<diagrams/doc_GUI_projectDescription.png>>
-
-%% Inputs
- % The following inputs are available:
- %
- % * _Project Title_ : A brief title for the project. There are no
- % limitations on the length or format.
- %
- % * _Project Description_: An extended description of the project. There
- % are no limitations on the format or length. However, a bug has been observed whereby, when new paragraphs
- % are created, the spacing between the paragraphs may increase each time
- % the description is displayed.
-
-%% Outputs
-% This tab creates no outputs.
-
-##### SOURCE END #####
+
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+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+   <!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - Project Description GUI Tab</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_GUI_projectDescription.m"><style type="text/css">
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+
+
+
+
+
+  </style></head><body><div class="content"><h1>HydroSight - <i>Project Description GUI Tab</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Inputs</a></li><li><a href="#3">Outputs</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The <i>Project Description</i> tab is the left most tab of HydroSight GUI. It is an optional step and allows documentation of the modelling project. The user can input a title for the project and an extended description. There are no limitations on the format or length of these inputs. Below is a screen shot of the project description tab.</p><p><img vspace="5" hspace="5" src="diagrams/doc_GUI_projectDescription.png" alt=""> </p><h2>Inputs<a name="2"></a></h2><p>The following inputs are available:</p><div><ul><li><i>Project Title</i> : A brief title for the project. There are no limitations on the length or format.</li></ul></div><div><ul><li><i>Project Description</i>: An extended description of the project. There are no limitations on the format or length. However, a bug has been observed whereby, when new paragraphs are created, the spacing between the paragraphs may increase each time the description is displayed.</li></ul></div><h2>Outputs<a name="3"></a></h2><p>This tab creates no outputs.</p><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Project Description GUI Tab_
+
+%% Overview
+% The _Project Description_ tab is the left most tab of HydroSight GUI. It is 
+% an optional step and allows documentation of the modelling project. The user
+% can input a title for the project and an extended description. There are
+% no limitations on the format or length of these inputs. Below is a screen
+% shot of the project description tab.
+%
+% <<diagrams/doc_GUI_projectDescription.png>>
+
+%% Inputs
+ % The following inputs are available:
+ %
+ % * _Project Title_ : A brief title for the project. There are no
+ % limitations on the length or format.
+ %
+ % * _Project Description_: An extended description of the project. There
+ % are no limitations on the format or length. However, a bug has been observed whereby, when new paragraphs
+ % are created, the spacing between the paragraphs may increase each time
+ % the description is displayed.
+
+%% Outputs
+% This tab creates no outputs.
+
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
diff --git a/documentation/html/doc_License_Disclaimer.html b/documentation/html/doc_License_Disclaimer.html
index d0ea02b..5dba2a2 100644
--- a/documentation/html/doc_License_Disclaimer.html
+++ b/documentation/html/doc_License_Disclaimer.html
@@ -1,114 +1,114 @@
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-This HTML was auto-generated from MATLAB code.
-To make changes, update the MATLAB code and republish this document.
-      --><title>HydroSight - Disclaimer and Licenses</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_License_Disclaimer.m"><style type="text/css">
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-
-
-
-
-
-  </style></head><body><div class="content"><h1>HydroSight - <i>Disclaimer and Licenses</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Disclaimer</a></li><li><a href="#2">License</a></li><li><a href="#3">Externally Licensed Components</a></li></ul></div><h2>Disclaimer<a name="1"></a></h2><p>HydroSight is provided on an "as is" basis and without any representation as to functionality, performance, suitability or fitness for purpose. You acknowledge and agree that, to the extent permitted under law, and subject to Disclaimers, the University of Melbourne makes no representations, warranties or guarantees:</p><div><ol><li>in relation to the functionality, performance, availability, suitability, continuity, reliability, accuracy, currency or security of the Program; or</li><li>that the Program is free from computer viruses or any other defect or error which may affect Your program or systems or third party software or systems.</li></ol></div><p>You further acknowledge and agree that the University of Melbourne has no obligation to (and makes no representation that it will) maintain or update, or correct any errors or defects in, the Program</p><h2>License<a name="2"></a></h2><p>HydroSight toolkit is licensed under the open-source license GPL3.0 (or later). See the following for details: <a href="http://www.gnu.org/licenses/gpl-3.0.en.html">http://www.gnu.org/licenses/gpl-3.0.en.html</a></p><h2>Externally Licensed Components<a name="3"></a></h2><p>The following components of the HydroSight were developed by others and companent each has its own license. These components and their licence are within the source code folder: <i>algorithms</i> &gt; <i>calibration</i>.</p><div><ul><li><i>cmaes.m</i> calibration algorithm.</li><li><i>DREAM.m</i> as supporting files for probablistic calibration.</li><li><i>GUI Layout Toolbox 2.1</i> for constructing the panels of the GUI</li><li><i>ipdm.m</i> for calculating the inter-point distance used in HydroSight temporal kriging.</li><li><i>SPUCI.m</i> calibration algorithm and its associated functions.</li><li><i>uical.m</i> for displaying a graphical calendar within the GUI.</li><li><i>variogram.m</i>, <i>variogramfit.m</i> and <i>fminsearchbnd.m</i> for estimating and fitting variograms.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - _Disclaimer and Licenses_
-
-%% Disclaimer
-%
-% HydroSight is provided on an "as is" basis and without any representation 
-% as to functionality, performance, suitability or fitness for purpose.  
-% You acknowledge and agree that, to the extent permitted under law, and 
-% subject to Disclaimers, the University of Melbourne makes no representations, 
-% warranties or guarantees:
-%
-% # in relation to the functionality, performance, availability, suitability, 
-% continuity, reliability, accuracy, currency or security of the Program; or
-% # that the Program is free from computer viruses or any other defect or 
-% error which may affect Your program or systems or third party software or systems. 
-%
-% You further acknowledge and agree that the University of Melbourne has no obligation
-% to (and makes no representation that it will) maintain or update, or correct any errors 
-% or defects in, the Program
-%
-
-%% License
-% 
-% HydroSight toolkit is licensed under the
-% open-source license GPL3.0 (or later). See the following for details: <http://www.gnu.org/licenses/gpl-3.0.en.html>
-
-%% Externally Licensed Components
-%
-% The following components of the HydroSight were developed by
-% others and companent each has its own license. These components and their licence are within the
-% source code folder: _algorithms_ > _calibration_.
-%
-% * _cmaes.m_ calibration algorithm.
-% * _DREAM.m_ as supporting files for probablistic calibration.
-% * _GUI Layout Toolbox 2.1_ for constructing the panels of the GUI
-% * _ipdm.m_ for calculating the inter-point distance used in HydroSight temporal kriging.
-% * _SPUCI.m_ calibration algorithm and its associated functions.
-% * _uical.m_ for displaying a graphical calendar within the GUI.
-% * _variogram.m_, _variogramfit.m_ and _fminsearchbnd.m_ for estimating and fitting
-% variograms.
-
-
-
-##### SOURCE END #####
+
+<!DOCTYPE html
+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+   <!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - Disclaimer and Licenses</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_License_Disclaimer.m"><style type="text/css">
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+  </style></head><body><div class="content"><h1>HydroSight - <i>Disclaimer and Licenses</i></h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Disclaimer</a></li><li><a href="#2">License</a></li><li><a href="#3">Externally Licensed Components</a></li></ul></div><h2>Disclaimer<a name="1"></a></h2><p>HydroSight is provided on an "as is" basis and without any representation as to functionality, performance, suitability or fitness for purpose. You acknowledge and agree that, to the extent permitted under law, and subject to Disclaimers, the University of Melbourne makes no representations, warranties or guarantees:</p><div><ol><li>in relation to the functionality, performance, availability, suitability, continuity, reliability, accuracy, currency or security of the Program; or</li><li>that the Program is free from computer viruses or any other defect or error which may affect Your program or systems or third party software or systems.</li></ol></div><p>You further acknowledge and agree that the University of Melbourne has no obligation to (and makes no representation that it will) maintain or update, or correct any errors or defects in, the Program</p><h2>License<a name="2"></a></h2><p>HydroSight toolkit is licensed under the open-source license GPL3.0 (or later). See the following for details: <a href="http://www.gnu.org/licenses/gpl-3.0.en.html">http://www.gnu.org/licenses/gpl-3.0.en.html</a></p><h2>Externally Licensed Components<a name="3"></a></h2><p>The following components of the HydroSight were developed by others and companent each has its own license. These components and their licence are within the source code folder: <i>algorithms</i> &gt; <i>calibration</i>.</p><div><ul><li><i>cmaes.m</i> calibration algorithm.</li><li><i>DREAM.m</i> as supporting files for probablistic calibration.</li><li><i>GUI Layout Toolbox 2.1</i> for constructing the panels of the GUI</li><li><i>ipdm.m</i> for calculating the inter-point distance used in HydroSight temporal kriging.</li><li><i>SPUCI.m</i> calibration algorithm and its associated functions.</li><li><i>uical.m</i> for displaying a graphical calendar within the GUI.</li><li><i>variogram.m</i>, <i>variogramfit.m</i> and <i>fminsearchbnd.m</i> for estimating and fitting variograms.</li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - _Disclaimer and Licenses_
+
+%% Disclaimer
+%
+% HydroSight is provided on an "as is" basis and without any representation 
+% as to functionality, performance, suitability or fitness for purpose.  
+% You acknowledge and agree that, to the extent permitted under law, and 
+% subject to Disclaimers, the University of Melbourne makes no representations, 
+% warranties or guarantees:
+%
+% # in relation to the functionality, performance, availability, suitability, 
+% continuity, reliability, accuracy, currency or security of the Program; or
+% # that the Program is free from computer viruses or any other defect or 
+% error which may affect Your program or systems or third party software or systems. 
+%
+% You further acknowledge and agree that the University of Melbourne has no obligation
+% to (and makes no representation that it will) maintain or update, or correct any errors 
+% or defects in, the Program
+%
+
+%% License
+% 
+% HydroSight toolkit is licensed under the
+% open-source license GPL3.0 (or later). See the following for details: <http://www.gnu.org/licenses/gpl-3.0.en.html>
+
+%% Externally Licensed Components
+%
+% The following components of the HydroSight were developed by
+% others and companent each has its own license. These components and their licence are within the
+% source code folder: _algorithms_ > _calibration_.
+%
+% * _cmaes.m_ calibration algorithm.
+% * _DREAM.m_ as supporting files for probablistic calibration.
+% * _GUI Layout Toolbox 2.1_ for constructing the panels of the GUI
+% * _ipdm.m_ for calculating the inter-point distance used in HydroSight temporal kriging.
+% * _SPUCI.m_ calibration algorithm and its associated functions.
+% * _uical.m_ for displaying a graphical calendar within the GUI.
+% * _variogram.m_, _variogramfit.m_ and _fminsearchbnd.m_ for estimating and fitting
+% variograms.
+
+
+
+##### SOURCE END #####
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-
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-  </style></head><body><div class="content"><h1>HydroSight</h1><!--introduction--><p><i>A toolbox for data-driven hydrogeological insights.</i></p><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Modelling Fundamentals</a></li><li><a href="#3">Collaborations and Consulation Services</a></li><li><a href="#4">Reporting Bugs and Issues</a></li><li><a href="#5">Source Code</a></li><li><a href="#6">The Developers</a></li><li><a href="#7">Acknowledgements</a></li><li><a href="#8">License and Disclaimer</a></li><li><a href="#9">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>HydroSight is a highly flexible statistical toolbox and user interface for getting more quantitative value from groundwater level monitoring data.  Currently, the toolbox contains a highly flexible groundwater hydrograph time-series modeling framework (Peterson and Western, 2014) that facilitates the following:</p><div><ul><li>decomposition of a groundwater hydrograph into individual drivers, such as climate and pumping or climate (Shapoori et al. 2015a) and landuse change (Peterson and Western, 2014).</li><li>estimation of aquifer hydraulic properties from the hydrograph (Shapoori et al. 2015b)</li><li>statistical identification of the major groundwater processes (Shapoori et al. 2015a).</li><li>interpolation or extrapolation of the observed hydrograph.</li><li>simulation of groundwater head under different climate or, say, pumping scenarios.</li><li>numerical identification of hydrograph monitoring errors and outliers.</li></ul></div><p>HydroSight can be used in two ways:</p><div><ul><li><a href="doc_Programmatically.html">Programmatically</a> : using a collection Matlab of object-oriented classes that provides enormous flexibility to built time-series models and develop new models and components.</li><li><a href="doc_GUI.html">Graphical User Interface</a> : stand alone application that provides a highly flexible framework for the efficient construction, modelling and simulation of an unlimited number of models within a simple graphical environment.</li></ul></div><p>To get started with HydroSight, click on the link above for the way in which you'd like to use HydroSight. Each link provides details of how to install the software, system requirements and how to get started building models.</p><p>For further details on the the fundementals, the project, obtaining code or reporting bugs please see the sections below.</p><h2>Modelling Fundamentals<a name="2"></a></h2><p>HydroSight allows for different types of time-series models to be built. Below is a summary of the key types of models:</p><div><ul><li><a href="doc_TFN.html">Transfer Function Noise (TFN) models</a>: weight one or multiple time series of forcing data, such as rainfall or pumping, to simulate the observed hydrograph. This is the recommended model for most applications.</li><li><a href="doc_ExpSmooth.html">Exponential Smoothing models</a>: use a double exponential smoothing filter to simulate the smoothed trend of observed the hydrograph and does so without using observed forcing data.</li></ul></div><p>Data requirements for the models differ. While both can use irregularly spaced observed hydrograph head observations, the TFN model requires daily observed forcing data and as a minimum it should use daily rainfall and potential evapotranspiration.</p><p>Once the model has been constructed for a given bore, the parameters of the model must be calibrated; that is, adjusted to achieve a minimum error between the observed and modelled hydrograph. This is achieved by reducing the difference between the observed and modelled hydrograph to a single number, called an objective function value, which is this case is effectively the weighted least squares. To minimise this objective function value, HydroSight includes global calibration schemes (see <a href="doc_Calibration.html">here</a> for details). This can be very computationally demanding and it is always uncertain if the very best solution (i.e. the global optima) has been located. For tips on how to best undertake the calibration see the above link.</p><h2>Collaborations and Consulation Services<a name="3"></a></h2><p>Some groundwater statistical problems can be particularly challenging to investigate using HydroSight, or it may not contain the features you require. If so, consider contacting Dr. Tim Peterson (<a href="mailto:timjp@unimelb.edu.au">timjp@unimelb.edu.au</a>) to discuss collaboration or consultation options.</p><h2>Reporting Bugs and Issues<a name="4"></a></h2><p>Bugs and problems with HydroSight can be reported at <a href="https://github.com/peterson-tim-j/HydroSight/issues">https://github.com/peterson-tim-j/HydroSight/issues</a>. In reporting an issue please provide as much detail and data as possible to allow the bug or issue to be reproduced.</p><h2>Source Code<a name="5"></a></h2><p>The source code is available from GitHub at <a href="https://github.com/peterson-tim-j/HydroSight">https://github.com/peterson-tim-j/HydroSight</a>. If you would like to contribute to the project or add components, please join the project at GitHub.</p><h2>The Developers<a name="6"></a></h2><p>HydroSight has been developed by the following academics at the University of Melbourne:</p><div><ul><li>Tim Peterson</li><li>Andrew Western</li><li>Eleanor Gee</li><li>Vahid Shapoori</li></ul></div><p>To find out more about the research within the group, or relevant publications, see:</p><div><ul><li>Research group: <a href="http://www.ie.eng.unimelb.edu.au/research/water/index.html">http://www.ie.eng.unimelb.edu.au/research/water/index.html</a></li><li>Research Gate : <a href="https://www.researchgate.net/profile/Tim_Peterson7">https://www.researchgate.net/profile/Tim_Peterson7</a></li><li>Google Scholar: <a href="http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&amp;hl=en&amp;oi=ao">http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&amp;hl=en&amp;oi=ao</a></li></ul></div><p>Alternatively, to stay connected with developments and new versions join us at LinkedIn:</p><div><ul><li>LinkedIn: <a href="https://au.linkedin.com/pub/tim-peterson/81/40/739">https://au.linkedin.com/pub/tim-peterson/81/40/739</a></li></ul></div><h2>Acknowledgements<a name="7"></a></h2><p>HydroSight has been generously supported by the following organisations:</p><div><ul><li>The Australian Research Council grants LP0991280, LP130100958.</li><li>The Bureau of Meteorology (Aust.)</li><li>The Department of Environment, Land, Water and Planning (Vic., Aust.)</li><li>The Department of Economic Development, Jobs, Transport and Resources (Vic., Aus.)</li><li>Power and Water Corporation (N. T., Aus.)</li></ul></div><h2>License and Disclaimer<a name="8"></a></h2><p>HydroSight is licensed under the open-source license GPL3.0 (or later). See <a href="doc_License_Disclaimer.html">here</a> for details of the disclaimer and licenses.</p><h2>References<a name="9"></a></h2><div><ul><li>Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph modeling. <i>Hydrological Processes</i>, Accepted June 2015, DOI: <a href="http://dx.doi.org/10.1002/hyp.10583">10.1002/hyp.10583</a>. <a href="papers/Shapoori_2015C.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight
-% _A toolbox for data-driven hydrogeological insights._ 
-
-%% Overview
-% HydroSight is a highly flexible statistical toolbox and user 
-% interface for getting more quantitative value from groundwater
-% level monitoring data.  Currently, the toolbox contains a highly flexible groundwater
-% hydrograph time-series modeling framework (Peterson and Western, 2014) that facilitates the following:
-% 
-% * decomposition of a groundwater hydrograph into individual drivers, such as climate
-% and pumping or climate (Shapoori et al. 2015a) and landuse change (Peterson and Western, 2014).
-% * estimation of aquifer hydraulic properties from the hydrograph (Shapoori et al. 2015b)
-% * statistical identification of the major groundwater processes (Shapoori et al. 2015a).
-% * interpolation or extrapolation of the observed hydrograph.
-% * simulation of groundwater head under different climate or, say, pumping scenarios.
-% * numerical identification of hydrograph monitoring errors and outliers.
-%
-% HydroSight can be used in two ways:
-%
-% * <doc_Programmatically.html Programmatically> : using a 
-% collection Matlab of object-oriented classes that provides enormous flexibility
-% to built time-series models and develop new models and components.
-% * <doc_GUI.html Graphical User Interface> : stand alone
-% application that provides a highly flexible framework for the efficient construction, 
-% modelling and simulation of an unlimited number of models within a simple
-% graphical environment. 
-%
-% To get started with HydroSight, click on the link above for the way in which
-% you'd like to use HydroSight. Each link provides details of how to install the
-% software, system requirements and how to get started building models.
-%
-% For further details on the the fundementals, the project, obtaining code or reporting bugs
-% please see the sections below. 
-
-%% Modelling Fundamentals
-% HydroSight allows for different types of time-series models to be built.
-% Below is a summary of the key types of models:
-%
-% * <doc_TFN.html Transfer Function Noise (TFN) models>: weight one or multiple time series of
-% forcing data, such as rainfall or pumping, to simulate the observed
-% hydrograph. This is the recommended model for most applications. 
-% * <doc_ExpSmooth.html Exponential Smoothing models>: use a double
-% exponential smoothing filter to simulate the smoothed trend of observed the
-% hydrograph and does so without using observed forcing data.
-%
-% Data requirements for the models differ. While both can use irregularly
-% spaced observed hydrograph head observations, the TFN model requires
-% daily observed forcing data and as a minimum it should use daily rainfall
-% and potential evapotranspiration. 
-%
-% Once the model has been constructed for a given bore, the parameters of
-% the model must be calibrated; that is, adjusted to achieve a minimum
-% error between the observed and modelled hydrograph. This is achieved
-% by reducing the difference between the observed and modelled hydrograph
-% to a single number, called an objective function value, which is this
-% case is effectively the weighted least squares. To minimise this objective
-% function value, HydroSight includes global calibration schemes (see <doc_Calibration.html here> for details).
-% This can be very computationally demanding and it is always uncertain if
-% the very best solution (i.e. the global optima) has been located. For
-% tips on how to best undertake the calibration see the above link.
-
-%% Collaborations and Consulation Services
-% Some groundwater statistical problems can be particularly challenging to 
-% investigate using HydroSight, or it may not contain
-% the features you require. If so, consider contacting Dr. Tim Peterson
-% (timjp@unimelb.edu.au) to discuss collaboration or consultation options.
- 
-%% Reporting Bugs and Issues
-% Bugs and problems with HydroSight can be reported at <https://github.com/peterson-tim-j/HydroSight/issues>.
-% In reporting an issue please provide as much detail and data as possible to
-% allow the bug or issue to be reproduced. 
-
-%% Source Code
-% The source code is available from GitHub at
-% <https://github.com/peterson-tim-j/HydroSight>. If you would like to contribute to the project or add components, please join the project at GitHub.
-
-%% The Developers
-% HydroSight has been developed by the following academics at the University of Melbourne:
-%
-% * Tim Peterson
-% * Andrew Western
-% * Eleanor Gee
-% * Vahid Shapoori
-%
-% To find out more about the research within the group, or relevant publications, see:
-%
-% * Research group: <http://www.ie.eng.unimelb.edu.au/research/water/index.html>
-% * Research Gate : <https://www.researchgate.net/profile/Tim_Peterson7>
-% * Google Scholar: <http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&hl=en&oi=ao> 
-%
-% Alternatively, to stay connected with developments and new versions join us at LinkedIn:
-%
-% * LinkedIn: <https://au.linkedin.com/pub/tim-peterson/81/40/739>
-% 
-
-%% Acknowledgements
-% HydroSight has been generously supported by the following organisations:
-%
-% * The Australian Research Council grants LP0991280, LP130100958.
-% * The Bureau of Meteorology (Aust.)
-% * The Department of Environment, Land, Water and Planning (Vic., Aust.)
-% * The Department of Economic Development, Jobs, Transport and Resources (Vic., Aus.)
-% * Power and Water Corporation (N. T., Aus.)
-
-%% License and Disclaimer
-% HydroSight is licensed under the
-% open-source license GPL3.0 (or later). See <doc_License_Disclaimer.html here> for 
-% details of the disclaimer and licenses.
-%
-
-%% References
-%
-% * Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  _Water
-% Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015).
-% Top-down groundwater hydrograph time-series modeling for climate-pumping
-% decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F.
-% (accepted). Estimating aquifer properties using groundwater hydrograph
-% modeling. _Hydrological Processes_, Accepted June 2015, DOI:
-% <http://dx.doi.org/10.1002/hyp.10583 10.1002/hyp.10583>. <papers/Shapoori_2015C.pdf PDF Copy>
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+  </style></head><body><div class="content"><h1>HydroSight</h1><!--introduction--><p><i>A toolbox for data-driven hydrogeological insights.</i></p><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Modelling Fundamentals</a></li><li><a href="#3">Collaborations and Consulation Services</a></li><li><a href="#4">Reporting Bugs and Issues</a></li><li><a href="#5">Source Code</a></li><li><a href="#6">The Developers</a></li><li><a href="#7">Acknowledgements</a></li><li><a href="#8">License and Disclaimer</a></li><li><a href="#9">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>HydroSight is a highly flexible statistical toolbox and user interface for getting more quantitative value from groundwater level monitoring data.  Currently, the toolbox contains a highly flexible groundwater hydrograph time-series modeling framework (Peterson and Western, 2014) that facilitates the following:</p><div><ul><li>decomposition of a groundwater hydrograph into individual drivers, such as climate and pumping or climate (Shapoori et al. 2015a) and landuse change (Peterson and Western, 2014).</li><li>estimation of aquifer hydraulic properties from the hydrograph (Shapoori et al. 2015b)</li><li>statistical identification of the major groundwater processes (Shapoori et al. 2015a).</li><li>interpolation or extrapolation of the observed hydrograph.</li><li>simulation of groundwater head under different climate or, say, pumping scenarios.</li><li>numerical identification of hydrograph monitoring errors and outliers.</li></ul></div><p>HydroSight can be used in two ways:</p><div><ul><li><a href="doc_Programmatically.html">Programmatically</a> : using a collection Matlab of object-oriented classes that provides enormous flexibility to built time-series models and develop new models and components.</li><li><a href="doc_GUI.html">Graphical User Interface</a> : stand alone application that provides a highly flexible framework for the efficient construction, modelling and simulation of an unlimited number of models within a simple graphical environment.</li></ul></div><p>To get started with HydroSight, click on the link above for the way in which you'd like to use HydroSight. Each link provides details of how to install the software, system requirements and how to get started building models.</p><p>For further details on the the fundementals, the project, obtaining code or reporting bugs please see the sections below.</p><h2>Modelling Fundamentals<a name="2"></a></h2><p>HydroSight allows for different types of time-series models to be built. Below is a summary of the key types of models:</p><div><ul><li><a href="doc_TFN.html">Transfer Function Noise (TFN) models</a>: weight one or multiple time series of forcing data, such as rainfall or pumping, to simulate the observed hydrograph. This is the recommended model for most applications.</li><li><a href="doc_ExpSmooth.html">Exponential Smoothing models</a>: use a double exponential smoothing filter to simulate the smoothed trend of observed the hydrograph and does so without using observed forcing data.</li></ul></div><p>Data requirements for the models differ. While both can use irregularly spaced observed hydrograph head observations, the TFN model requires daily observed forcing data and as a minimum it should use daily rainfall and potential evapotranspiration.</p><p>Once the model has been constructed for a given bore, the parameters of the model must be calibrated; that is, adjusted to achieve a minimum error between the observed and modelled hydrograph. This is achieved by reducing the difference between the observed and modelled hydrograph to a single number, called an objective function value, which is this case is effectively the weighted least squares. To minimise this objective function value, HydroSight includes global calibration schemes (see <a href="doc_Calibration.html">here</a> for details). This can be very computationally demanding and it is always uncertain if the very best solution (i.e. the global optima) has been located. For tips on how to best undertake the calibration see the above link.</p><h2>Collaborations and Consulation Services<a name="3"></a></h2><p>Some groundwater statistical problems can be particularly challenging to investigate using HydroSight, or it may not contain the features you require. If so, consider contacting Dr. Tim Peterson (<a href="mailto:timjp@unimelb.edu.au">timjp@unimelb.edu.au</a>) to discuss collaboration or consultation options.</p><h2>Reporting Bugs and Issues<a name="4"></a></h2><p>Bugs and problems with HydroSight can be reported at <a href="https://github.com/peterson-tim-j/HydroSight/issues">https://github.com/peterson-tim-j/HydroSight/issues</a>. In reporting an issue please provide as much detail and data as possible to allow the bug or issue to be reproduced.</p><h2>Source Code<a name="5"></a></h2><p>The source code is available from GitHub at <a href="https://github.com/peterson-tim-j/HydroSight">https://github.com/peterson-tim-j/HydroSight</a>. If you would like to contribute to the project or add components, please join the project at GitHub.</p><h2>The Developers<a name="6"></a></h2><p>HydroSight has been developed by the following academics at the University of Melbourne:</p><div><ul><li>Tim Peterson</li><li>Andrew Western</li><li>Eleanor Gee</li><li>Vahid Shapoori</li></ul></div><p>To find out more about the research within the group, or relevant publications, see:</p><div><ul><li>Research group: <a href="http://www.ie.eng.unimelb.edu.au/research/water/index.html">http://www.ie.eng.unimelb.edu.au/research/water/index.html</a></li><li>Research Gate : <a href="https://www.researchgate.net/profile/Tim_Peterson7">https://www.researchgate.net/profile/Tim_Peterson7</a></li><li>Google Scholar: <a href="http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&amp;hl=en&amp;oi=ao">http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&amp;hl=en&amp;oi=ao</a></li></ul></div><p>Alternatively, to stay connected with developments and new versions join us at LinkedIn:</p><div><ul><li>LinkedIn: <a href="https://au.linkedin.com/pub/tim-peterson/81/40/739">https://au.linkedin.com/pub/tim-peterson/81/40/739</a></li></ul></div><h2>Acknowledgements<a name="7"></a></h2><p>HydroSight has been generously supported by the following organisations:</p><div><ul><li>The Australian Research Council grants LP0991280, LP130100958.</li><li>The Bureau of Meteorology (Aust.)</li><li>The Department of Environment, Land, Water and Planning (Vic., Aust.)</li><li>The Department of Economic Development, Jobs, Transport and Resources (Vic., Aus.)</li><li>Power and Water Corporation (N. T., Aus.)</li></ul></div><h2>License and Disclaimer<a name="8"></a></h2><p>HydroSight is licensed under the open-source license GPL3.0 (or later). See <a href="doc_License_Disclaimer.html">here</a> for details of the disclaimer and licenses.</p><h2>References<a name="9"></a></h2><div><ul><li>Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph modeling. <i>Hydrological Processes</i>, Accepted June 2015, DOI: <a href="http://dx.doi.org/10.1002/hyp.10583">10.1002/hyp.10583</a>. <a href="papers/Shapoori_2015C.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight
+% _A toolbox for data-driven hydrogeological insights._ 
+
+%% Overview
+% HydroSight is a highly flexible statistical toolbox and user 
+% interface for getting more quantitative value from groundwater
+% level monitoring data.  Currently, the toolbox contains a highly flexible groundwater
+% hydrograph time-series modeling framework (Peterson and Western, 2014) that facilitates the following:
+% 
+% * decomposition of a groundwater hydrograph into individual drivers, such as climate
+% and pumping or climate (Shapoori et al. 2015a) and landuse change (Peterson and Western, 2014).
+% * estimation of aquifer hydraulic properties from the hydrograph (Shapoori et al. 2015b)
+% * statistical identification of the major groundwater processes (Shapoori et al. 2015a).
+% * interpolation or extrapolation of the observed hydrograph.
+% * simulation of groundwater head under different climate or, say, pumping scenarios.
+% * numerical identification of hydrograph monitoring errors and outliers.
+%
+% HydroSight can be used in two ways:
+%
+% * <doc_Programmatically.html Programmatically> : using a 
+% collection Matlab of object-oriented classes that provides enormous flexibility
+% to built time-series models and develop new models and components.
+% * <doc_GUI.html Graphical User Interface> : stand alone
+% application that provides a highly flexible framework for the efficient construction, 
+% modelling and simulation of an unlimited number of models within a simple
+% graphical environment. 
+%
+% To get started with HydroSight, click on the link above for the way in which
+% you'd like to use HydroSight. Each link provides details of how to install the
+% software, system requirements and how to get started building models.
+%
+% For further details on the the fundementals, the project, obtaining code or reporting bugs
+% please see the sections below. 
+
+%% Modelling Fundamentals
+% HydroSight allows for different types of time-series models to be built.
+% Below is a summary of the key types of models:
+%
+% * <doc_TFN.html Transfer Function Noise (TFN) models>: weight one or multiple time series of
+% forcing data, such as rainfall or pumping, to simulate the observed
+% hydrograph. This is the recommended model for most applications. 
+% * <doc_ExpSmooth.html Exponential Smoothing models>: use a double
+% exponential smoothing filter to simulate the smoothed trend of observed the
+% hydrograph and does so without using observed forcing data.
+%
+% Data requirements for the models differ. While both can use irregularly
+% spaced observed hydrograph head observations, the TFN model requires
+% daily observed forcing data and as a minimum it should use daily rainfall
+% and potential evapotranspiration. 
+%
+% Once the model has been constructed for a given bore, the parameters of
+% the model must be calibrated; that is, adjusted to achieve a minimum
+% error between the observed and modelled hydrograph. This is achieved
+% by reducing the difference between the observed and modelled hydrograph
+% to a single number, called an objective function value, which is this
+% case is effectively the weighted least squares. To minimise this objective
+% function value, HydroSight includes global calibration schemes (see <doc_Calibration.html here> for details).
+% This can be very computationally demanding and it is always uncertain if
+% the very best solution (i.e. the global optima) has been located. For
+% tips on how to best undertake the calibration see the above link.
+
+%% Collaborations and Consulation Services
+% Some groundwater statistical problems can be particularly challenging to 
+% investigate using HydroSight, or it may not contain
+% the features you require. If so, consider contacting Dr. Tim Peterson
+% (timjp@unimelb.edu.au) to discuss collaboration or consultation options.
+ 
+%% Reporting Bugs and Issues
+% Bugs and problems with HydroSight can be reported at <https://github.com/peterson-tim-j/HydroSight/issues>.
+% In reporting an issue please provide as much detail and data as possible to
+% allow the bug or issue to be reproduced. 
+
+%% Source Code
+% The source code is available from GitHub at
+% <https://github.com/peterson-tim-j/HydroSight>. If you would like to contribute to the project or add components, please join the project at GitHub.
+
+%% The Developers
+% HydroSight has been developed by the following academics at the University of Melbourne:
+%
+% * Tim Peterson
+% * Andrew Western
+% * Eleanor Gee
+% * Vahid Shapoori
+%
+% To find out more about the research within the group, or relevant publications, see:
+%
+% * Research group: <http://www.ie.eng.unimelb.edu.au/research/water/index.html>
+% * Research Gate : <https://www.researchgate.net/profile/Tim_Peterson7>
+% * Google Scholar: <http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&hl=en&oi=ao> 
+%
+% Alternatively, to stay connected with developments and new versions join us at LinkedIn:
+%
+% * LinkedIn: <https://au.linkedin.com/pub/tim-peterson/81/40/739>
+% 
+
+%% Acknowledgements
+% HydroSight has been generously supported by the following organisations:
+%
+% * The Australian Research Council grants LP0991280, LP130100958.
+% * The Bureau of Meteorology (Aust.)
+% * The Department of Environment, Land, Water and Planning (Vic., Aust.)
+% * The Department of Economic Development, Jobs, Transport and Resources (Vic., Aus.)
+% * Power and Water Corporation (N. T., Aus.)
+
+%% License and Disclaimer
+% HydroSight is licensed under the
+% open-source license GPL3.0 (or later). See <doc_License_Disclaimer.html here> for 
+% details of the disclaimer and licenses.
+%
+
+%% References
+%
+% * Peterson, T. J., and A. W. Western (2014). Nonlinear time-series modeling of unconfined groundwater head.  _Water
+% Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015).
+% Top-down groundwater hydrograph time-series modeling for climate-pumping
+% decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F.
+% (accepted). Estimating aquifer properties using groundwater hydrograph
+% modeling. _Hydrological Processes_, Accepted June 2015, DOI:
+% <http://dx.doi.org/10.1002/hyp.10583 10.1002/hyp.10583>. <papers/Shapoori_2015C.pdf PDF Copy>
+
+##### SOURCE END #####
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-
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-  </style></head><body><div class="content"><h1>HydroSight - Programmatic Modelling</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#3">System Requirements</a></li><li><a href="#4">Installation</a></li><li><a href="#6">Data Requirements</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>HydroSight can be ran from the command line from within Matlab. This allows considerable flexibility in how models are developed and allows much of the analysis to be done using Matlab scripts. The toolkit is also written using object-oriented programming and this allows models to be easily built and incorporated into the toolkit. To view the code documentation, change the Matlab working directory to where the source code was downloaded and run the following Matlab commands:</p><pre class="codeinput">cd <span class="string">algorithms</span>
-addpath(genpath(pwd))
-doc <span class="string">HydroSightModel</span>
-</pre><h2>System Requirements<a name="3"></a></h2><p>The system requirements to run the Toolkit programmatically are:</p><div><ul><li>Matlab 2014B or later with the Statistics Toolbox</li><li>At least 4GB RAM</li><li>Multiple CPU cores - this is required to reduce the model calibration time.</li></ul></div><h2>Installation<a name="4"></a></h2><p>To install the programmatic version of the toolbox:</p><div><ol><li>download the source code (see <a href="doc_Overview.html#5">Overview</a>)</li><li>Open Matlab and change the Current Folder to the location where the source code was saved.</li><li>Add the current folder and all sub-folders to the Matlab search path.</li><li>The algorithms are within the folder <i>algorithms</i>. To run the toolbox GUI, enter the command:</li></ol></div><pre class="codeinput">HydroSight
-</pre><h2>Data Requirements<a name="6"></a></h2><p>To get started modelling a groundwater hydrograph, the following data is required:</p><div><ul><li>observed groundwater head data (as a numerical matrix) with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>. Note, <i>hours</i> and <i>minute</i> can be included after <i>day</i>.</li><li>forcing data (as a Matlab table variable) with columns <i>year</i>, <i>month</i>, <i>day</i> and the columns for each forcing time series. Importantly, the table must include the column names (without spaces in names), the data must have a daily time-step and have no gaps and should extend prior to the first head observation.</li><li>projected coordinates (as a cell array or table variable) for the bore ID and each forcing data with columns <i>Site ID</i>, <i>Easting</i>, <i>Northing</i>.</li></ul></div><p>Examples of the required data formats can be obtained from the example TFN model at <i>algorithms</i> &gt; <i>models</i> &gt; <i>TransferNoise</i> &gt; <i>Example model</i>.</p><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - Programmatic Modelling
-
-%% Overview
-% HydroSight can be ran from the command line from within
-% Matlab. This allows considerable flexibility in how models are developed
-% and allows much of the analysis to be done using Matlab scripts. The
-% toolkit is also written using object-oriented programming and this allows
-% models to be easily built and incorporated into the toolkit. To view the
-% code documentation, change the Matlab working directory to where the source code was downloaded and 
-% run the following Matlab commands:
-%
-%%
-cd algorithms
-addpath(genpath(pwd))
-doc HydroSightModel
-
-%% System Requirements
-% The system requirements to run the Toolkit programmatically are:
-%
-% * Matlab 2014B or later with the Statistics Toolbox
-% * At least 4GB RAM
-% * Multiple CPU cores - this is required to reduce the model calibration time.
-
-%% Installation
-% To install the programmatic version of the toolbox:
-%
-% # download the source code (see <doc_Overview.html#5 Overview>)
-% # Open Matlab and change the Current Folder to the location where the
-% source code was saved.
-% # Add the current folder and all sub-folders to the Matlab search path. 
-% # The algorithms are within the folder _algorithms_. To run the toolbox
-% GUI, enter the command:
-%%
-HydroSight
-
-%% Data Requirements
-% To get started modelling a groundwater hydrograph, the following data is
-% required:
-%
-% * observed groundwater head data (as a numerical matrix) with columns _bore ID_, _year_, _month_, _day_, _head_. Note, _hours_ and _minute_ can be included after _day_.
-% * forcing data (as a Matlab table variable) with columns _year_, _month_, _day_ and the columns for
-% each forcing time series. Importantly, the table must include the column names (without spaces in names), the data must have a daily time-step and have no gaps
-% and should extend prior to the first head observation.
-% * projected coordinates (as a cell array or table variable) for the bore ID and each forcing data with
-% columns _Site ID_, _Easting_, _Northing_.
-%
-% Examples of the required data formats can be obtained from the example TFN
-% model at _algorithms_ > _models_ > _TransferNoise_ >
-% _Example model_.
-##### SOURCE END #####
+
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+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
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+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - Programmatic Modelling</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_Programmatically.m"><style type="text/css">
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+
+
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+
+
+  </style></head><body><div class="content"><h1>HydroSight - Programmatic Modelling</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#3">System Requirements</a></li><li><a href="#4">Installation</a></li><li><a href="#6">Data Requirements</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>HydroSight can be ran from the command line from within Matlab. This allows considerable flexibility in how models are developed and allows much of the analysis to be done using Matlab scripts. The toolkit is also written using object-oriented programming and this allows models to be easily built and incorporated into the toolkit. To view the code documentation, change the Matlab working directory to where the source code was downloaded and run the following Matlab commands:</p><pre class="codeinput">cd <span class="string">algorithms</span>
+addpath(genpath(pwd))
+doc <span class="string">HydroSightModel</span>
+</pre><h2>System Requirements<a name="3"></a></h2><p>The system requirements to run the Toolkit programmatically are:</p><div><ul><li>Matlab 2014B or later with the Statistics Toolbox</li><li>At least 4GB RAM</li><li>Multiple CPU cores - this is required to reduce the model calibration time.</li></ul></div><h2>Installation<a name="4"></a></h2><p>To install the programmatic version of the toolbox:</p><div><ol><li>download the source code (see <a href="doc_Overview.html#5">Overview</a>)</li><li>Open Matlab and change the Current Folder to the location where the source code was saved.</li><li>Add the current folder and all sub-folders to the Matlab search path.</li><li>The algorithms are within the folder <i>algorithms</i>. To run the toolbox GUI, enter the command:</li></ol></div><pre class="codeinput">HydroSight
+</pre><h2>Data Requirements<a name="6"></a></h2><p>To get started modelling a groundwater hydrograph, the following data is required:</p><div><ul><li>observed groundwater head data (as a numerical matrix) with columns <i>bore ID</i>, <i>year</i>, <i>month</i>, <i>day</i>, <i>head</i>. Note, <i>hours</i> and <i>minute</i> can be included after <i>day</i>.</li><li>forcing data (as a Matlab table variable) with columns <i>year</i>, <i>month</i>, <i>day</i> and the columns for each forcing time series. Importantly, the table must include the column names (without spaces in names), the data must have a daily time-step and have no gaps and should extend prior to the first head observation.</li><li>projected coordinates (as a cell array or table variable) for the bore ID and each forcing data with columns <i>Site ID</i>, <i>Easting</i>, <i>Northing</i>.</li></ul></div><p>Examples of the required data formats can be obtained from the example TFN model at <i>algorithms</i> &gt; <i>models</i> &gt; <i>TransferNoise</i> &gt; <i>Example model</i>.</p><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - Programmatic Modelling
+
+%% Overview
+% HydroSight can be ran from the command line from within
+% Matlab. This allows considerable flexibility in how models are developed
+% and allows much of the analysis to be done using Matlab scripts. The
+% toolkit is also written using object-oriented programming and this allows
+% models to be easily built and incorporated into the toolkit. To view the
+% code documentation, change the Matlab working directory to where the source code was downloaded and 
+% run the following Matlab commands:
+%
+%%
+cd algorithms
+addpath(genpath(pwd))
+doc HydroSightModel
+
+%% System Requirements
+% The system requirements to run the Toolkit programmatically are:
+%
+% * Matlab 2014B or later with the Statistics Toolbox
+% * At least 4GB RAM
+% * Multiple CPU cores - this is required to reduce the model calibration time.
+
+%% Installation
+% To install the programmatic version of the toolbox:
+%
+% # download the source code (see <doc_Overview.html#5 Overview>)
+% # Open Matlab and change the Current Folder to the location where the
+% source code was saved.
+% # Add the current folder and all sub-folders to the Matlab search path. 
+% # The algorithms are within the folder _algorithms_. To run the toolbox
+% GUI, enter the command:
+%%
+HydroSight
+
+%% Data Requirements
+% To get started modelling a groundwater hydrograph, the following data is
+% required:
+%
+% * observed groundwater head data (as a numerical matrix) with columns _bore ID_, _year_, _month_, _day_, _head_. Note, _hours_ and _minute_ can be included after _day_.
+% * forcing data (as a Matlab table variable) with columns _year_, _month_, _day_ and the columns for
+% each forcing time series. Importantly, the table must include the column names (without spaces in names), the data must have a daily time-step and have no gaps
+% and should extend prior to the first head observation.
+% * projected coordinates (as a cell array or table variable) for the bore ID and each forcing data with
+% columns _Site ID_, _Easting_, _Northing_.
+%
+% Examples of the required data formats can be obtained from the example TFN
+% model at _algorithms_ > _models_ > _TransferNoise_ >
+% _Example model_.
+##### SOURCE END #####
 --></body></html>
\ No newline at end of file
diff --git a/documentation/html/doc_Publications.html b/documentation/html/doc_Publications.html
index 9085cec..d8e4d6b 100644
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-
-
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-
-  </style></head><body><div class="content"><h1>HydroSight - Publications</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The following papers on HydroSight are available within the GUI:</p><div><ul><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph modeling. <i>Hydrological Processes</i>, Accepted June 2015, DOI: <a href="http://dx.doi.org/10.1002/hyp.10583">10.1002/hyp.10583</a>. <a href="papers/Shapoori_2015C.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, <i>Hydrogeology Journal</i>, DOI: <a href="http://dx.doi.org/10.1007/s10040-015-1269-7">10.1007/s10040-015-1269-7</a>. <a href="papers/Shapoori_2015A.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - Publications
-
-%% Overview
-% The following papers on HydroSight are available within the GUI:
-%
-% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph
-% modeling. _Hydrological Processes_, Accepted June 2015, DOI: <http://dx.doi.org/10.1002/hyp.10583 10.1002/hyp.10583>. <papers/Shapoori_2015C.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, _Hydrogeology Journal_, DOI: <http://dx.doi.org/10.1007/s10040-015-1269-7 10.1007/s10040-015-1269-7>. <papers/Shapoori_2015A.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
-##### SOURCE END #####
+
+<!DOCTYPE html
+  PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head>
+      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+   <!--
+This HTML was auto-generated from MATLAB code.
+To make changes, update the MATLAB code and republish this document.
+      --><title>HydroSight - Publications</title><meta name="generator" content="MATLAB 9.0"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2016-10-19"><meta name="DC.source" content="doc_Publications.m"><style type="text/css">
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+  </style></head><body><div class="content"><h1>HydroSight - Publications</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The following papers on HydroSight are available within the GUI:</p><div><ul><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph modeling. <i>Hydrological Processes</i>, Accepted June 2015, DOI: <a href="http://dx.doi.org/10.1002/hyp.10583">10.1002/hyp.10583</a>. <a href="papers/Shapoori_2015C.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, <i>Hydrogeology Journal</i>, DOI: <a href="http://dx.doi.org/10.1007/s10040-015-1269-7">10.1007/s10040-015-1269-7</a>. <a href="papers/Shapoori_2015A.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - Publications
+
+%% Overview
+% The following papers on HydroSight are available within the GUI:
+%
+% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (accepted). Estimating aquifer properties using groundwater hydrograph
+% modeling. _Hydrological Processes_, Accepted June 2015, DOI: <http://dx.doi.org/10.1002/hyp.10583 10.1002/hyp.10583>. <papers/Shapoori_2015C.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, _Hydrogeology Journal_, DOI: <http://dx.doi.org/10.1007/s10040-015-1269-7 10.1007/s10040-015-1269-7>. <papers/Shapoori_2015A.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
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-  </style></head><body><div class="content"><h1>HydroSight - Transfer Function Noise Models</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Weighting Function Components</a></li><li><a href="#3">Forcing Transform Components</a></li><li><a href="#4">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The transfer function noise model (TFN) simulates an observed hydrograph by weighting the historic input forcing data, such as rainfall or groundwater pumping, and estimating the random noise as an exponential decay function, which allows irregular water level observations to be simulated (von Asmuth et al., 2005). That is, for each water level observation the entire historic daily forcing data is obtained and a weight is applied to each day's past forcing data. This weighted forcing is then integrated over time (i.e. summed) to give the contribution of the historic forcing to the water level at the time point of interest. This is then repeated for every water level observation. Importantly, the weights are not uniform back in time but are defined by a simple function and can have, say, a decayed exponential distribution, a Guassian-like skewed distribution or a distribution derived from the Theis drawdown equation. In applying the TFN model, the parameters for the weighting functions are adjusted using a <a href="doc_Calibration.html">global calibration scheme</a> (Peterson and Western, 2014).</p><p>A weakness of simply weighting past rainfall is, however, that the nonlinear partitioning of rainfall is not accounted for. This is addressed within the Toolbox by the ability to transform forcing data prior to the weighting (Peterson and Western 2015). For example, a simple 1-D vertically lumped soil model can be included in the time-series modelling and calibrated to account for the often nonlinear response of the water table to rainfall.</p><p>Lastly, the model TFN time-series model can also be calibrated using Xeon Phi co-processor cards. If a co-processor card is available then the numerical intergration step of the TFN model will be undertaken using the co-processor card(s). To use this features, the computer requires Intel compiler ICC &gt;=2013.</p><h2>Weighting Function Components<a name="2"></a></h2><p>The toolkit allows the user to select one of the following weighting functions for each forcing data time series:</p><div><ul><li><i>responseFunction_Bruggeman</i> : used for simulating the streamflow influence on head (von Asmuth et al. 2008). Note, this function is still to be tested and must be used with caution.</li><li><i>responseFunction_FerrisKnowles</i> : used for simulating the drawdown from pumping with an instantaneous version for the Theis drawdown equation (Shapoori et al. 2015a, 2015b).</li><li><i>responseFunction_FerrisKnowlesJacobs</i> : as for <i>responseFunction_FerrisKnowles</i> but with Jacob's correction added to approximate an unconfined aquifer (Shapoori et al. 2015b).</li><li><i>responseFunction_Hantush</i> : used for simulating drawdown from pumping assuming a leaky aquifer response (Shapoori et al. 2015a).</li><li><i>responseFunction_Pearsons</i> : used for simulating climatic influences that increase the head, for example recharge  (Peterson and Western 2014).</li><li><i>responseFunction_PearsonsNegative</i> : as for <i>responseFunction_Pearsons</i> but with a sign change to simulate climatic influences that lower the head, for example ET (Peterson and Western 2014).</li></ul></div><p>The above weighting functions can also be used as inputs to the following derived weighting functions. This allow, for example, the impact from ET to be simulated not as an additional weighting function but by simply re-scaling the Pearson's weighting function used for recharge, and hence eliminating two model parameters from the model.</p><div><ul><li><i>derivedweighting_UnconstrainedRescaled</i> : rescales an input weighting function whereby the recsaling can be positive or negative. This function can be used to simulate the impacts of, say, revegetation by rescaling <i>responseFunction_Pearsons</i> used to simulate recharge. For details, see the GUI example <i>TFN model - Landuse change</i>.</li><li><i>derivedweighting_PearsonsNegativeRescaled</i> : similar to <i>derivedweighting_UnconstrainedRescaled</i> but only to be used to rescale <i>responseFunction_Pearsons</i>. The function uses a normalised Pearson's function and then applies a scaling parameter. This may reduce the parameter covariance between the input weighting function and the rescaling and hence contribute to reliable calibration.</li><li><i>derivedweighting_PearsonsPositiveRescaled</i> : as for <i>responseFunction_Pearsons</i> but using a negative rescaling.</li></ul></div><h2>Forcing Transform Components<a name="3"></a></h2><p>The toolkit allows the user to select the following forcing transformation function:</p><div><ul><li><i>climateTransform_soilMoistureModels</i> : is a generalised vertically lumped 1-D   soil moisture model. It is used to transform daily climate data   for use in the transfer noise function groundwater time-series model.   The soil moisture model is defined by the following ordinary   differential equation (Kavetski et al. 2006) and the following soil   fluxes/data can be used in the time-series modelling. See Peterson and   Western (2014) for details and recommendations on the form of the soil   model to adopt.</li><li><i>climateTransform_soilMoistureModels_2layer</i> : is identifcal to the   above one layer model but has a second deeper soil store whereby the   free-drainage from the upper store drains into the deeper store; and ET   does not occur from the deeper store.</li></ul></div><p>
-<table border=1>
-<tr><td>Soil Model Flux/Data</td><td>Description</td></tr>
-<tr><td>Drainage</td><td>soil free drainage ranging (0 to k_sat) at the end of the day.</td></tr>
-<tr><td>Drainage bypass flow</td><td>Free drainage plus a parameter set fraction of runoff.</td></tr>
-<tr><td>Drainage normalised </td><td>Normalised free drainage (0 to 1) at the end of the day.</td></tr>
-<tr><td>Soil evaporation</td><td>Estimated soil ET at the end of the day.</td></tr>
-<tr><td>Infiltration</td><td>Daily total infiltration rate.</td></tr>
-<tr><td>Groundwater evaporative potential</td><td>Groundwater evaporative potential (PET - soil ET).</td></tr>
-<tr><td>Runoff</td><td>Total daily runoff.</td></tr>
-<tr><td>SMS</td><td>soil moisture storage at the end of each day.</td></tr>
-</table>
-</p><p><img src="doc_TFN_eq03172746311882613811.png" alt="$\frac{dS}{dt} = P_{inf} (1 - \frac{S}{SMSC})^\alpha - k_{sat} (\frac{S}{SMSC})^\beta - PET (\frac{S}{SMSC})^\gamma$"></p><p>The soil moisture model can also be used to simulate the impacts from different vegetation; for example, trees and pastures. This is achieved by simulating a soil store for upto two land types and then weighting required flux from each soil model by an input time series of the fraction of the second land type. A challange with the input time series of land cover is, however, that while the fraction of, say, land data clearing over time may be known the fraction of the catchment area cleared that influences a bore hydrograph is unknown. To address this, the modelling also include a parameter 'treeArea_frac' for the fraction of the second land cover (notially trees) that is influencing the bore.</p><p>An alternative approach for simulating the impacts of land cover change is to use the forcing transformation functions as inputs to the following derived transformation functions. This allow, for example, the impact of revegetation to be simulated by creating a new flux from the soil moisture model that is scaled by the fraction of revegetation. That is, the free drainage from the soil moisture model could be scaled by a time series of the fraction of revegetation to produce an estimate of the change in recharge from the revegetation. For details, see the GUI example <i>TFN model - Landuse change</i>.</p><div><ul><li><i>derivedForcing_linearUnconstrainedScaling</i> : rescales a transformation function flux whereby the rescaling can be positive or negative. Additionally, in the calibration the rescaling is initially assumed to be +- 0.2 of the transformation function flux.</li></ul></div><h2>References<a name="4"></a></h2><div><ul><li>von Asmuth, J. R. and Bierkens M. F. P.  (2005). Modeling irregularly spaced residual series as a continuous stochastic process, <i>Water Resources Research</i>, 41, W12404, DOI: <a href="http://dx.doi.org/10.1029/2004WR003726">10.1029/2004WR003726</a>.</li><li>von Asmuth J. R., Mass K., Bakker M., Peterson J., (2008). Modeling time series of ground water head fluctuations subject to multiple stresses. <i>Groundwater</i>, 46(1), 30-40. DOI: <a href="http://dx.doi.org/10.1111/j.1745-6584.2007.00382.x">10.1111/j.1745-6584.2007.00382.x</a></li><li>Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, <i>Water Resources Research</i>, 42, W03407, DOI: <a href="http://dx.doi.org/10.1029/2005WR004368">10.1029/2005WR004368</a>.</li><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, <i>Hydrogeology Journal</i>, DOI: <a href="http://dx.doi.org/10.1007/s10040-015-1269-7">10.1007/s10040-015-1269-7</a>. <a href="papers/Shapoori_2015A.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
-##### SOURCE BEGIN #####
-%% HydroSight - Transfer Function Noise Models
-
-%% Overview
-% The transfer function noise model (TFN) simulates an observed hydrograph
-% by weighting the historic input forcing data, such as rainfall or groundwater pumping, 
-% and estimating the random noise as an exponential decay
-% function, which allows irregular water level observations to be
-% simulated (von Asmuth et al., 2005). That is, for each water level
-% observation the entire historic daily forcing data is obtained and a
-% weight is applied to each day's past forcing data. This weighted forcing is
-% then integrated over time (i.e. summed) to give the contribution of the historic forcing to
-% the water level at the time point of interest. This is then repeated for every water level
-% observation. Importantly, the weights are not uniform back in time but
-% are defined by a simple function and can have, say, a decayed exponential 
-% distribution, a Guassian-like skewed distribution or a distribution derived from 
-% the Theis drawdown equation. In applying the TFN
-% model, the parameters for the weighting functions are adjusted using a <doc_Calibration.html global
-% calibration scheme> (Peterson and Western, 2014).
-% 
-% A weakness of simply weighting past rainfall is, however, that the
-% nonlinear partitioning of rainfall is not accounted for. This is
-% addressed within the Toolbox by the ability to transform forcing data
-% prior to the weighting (Peterson and Western 2015). For example, a simple 
-% 1-D vertically lumped soil model can be included in the time-series 
-% modelling and calibrated to account for the often nonlinear response of
-% the water table to rainfall.
-%
-% Lastly, the model TFN time-series model can also be calibrated
-% using Xeon Phi co-processor cards. If a co-processor card is available 
-% then the numerical intergration step of the TFN model will be
-% undertaken using the co-processor card(s). To use this features, the
-% computer requires Intel compiler ICC >=2013.
-
-%% Weighting Function Components
-%
-% The toolkit allows the user to select one of the following weighting
-% functions for each forcing data time series:
-%
-% * _responseFunction_Bruggeman_ : used for simulating the streamflow influence on head (von Asmuth et al. 2008). 
-% Note, this function is still to be tested and must be used with caution.
-% * _responseFunction_FerrisKnowles_ : used for simulating the drawdown
-% from pumping with an instantaneous version for the Theis drawdown equation (Shapoori et al.
-% 2015a, 2015b).
-% * _responseFunction_FerrisKnowlesJacobs_ : as for _responseFunction_FerrisKnowles_ but 
-% with Jacob's correction added to approximate an unconfined aquifer (Shapoori et al. 2015b).
-% * _responseFunction_Hantush_ : used for simulating drawdown from pumping
-% assuming a leaky aquifer response (Shapoori et al. 2015a).
-% * _responseFunction_Pearsons_ : used for simulating climatic influences
-% that increase the head, for example recharge  (Peterson and Western
-% 2014).
-% * _responseFunction_PearsonsNegative_ : as for _responseFunction_Pearsons_ but with a 
-% sign change to simulate climatic influences
-% that lower the head, for example ET (Peterson and Western 2014).
-%
-% The above weighting functions can also be used as inputs to the following
-% derived weighting functions. This allow, for example, the impact from ET to be
-% simulated not as an additional weighting function but by simply
-% re-scaling the Pearson's weighting function used for recharge, and hence
-% eliminating two model parameters from the model.
-%
-% * _derivedweighting_UnconstrainedRescaled_ : rescales an input weighting
-% function whereby the recsaling can be positive or negative. This
-% function can be used to simulate the impacts of, say, revegetation by
-% rescaling _responseFunction_Pearsons_ used to simulate recharge.
-% For details, see the GUI example _TFN model - Landuse change_.
-% * _derivedweighting_PearsonsNegativeRescaled_ : similar to
-% _derivedweighting_UnconstrainedRescaled_ but only to be used to rescale
-% _responseFunction_Pearsons_. The function uses a normalised Pearson's
-% function and then applies a scaling parameter. This may reduce the
-% parameter covariance between the input weighting function and the
-% rescaling and hence contribute to reliable calibration.
-% * _derivedweighting_PearsonsPositiveRescaled_ : as for
-% _responseFunction_Pearsons_ but using a negative rescaling.
-
-%% Forcing Transform Components
-%
-% The toolkit allows the user to select the following forcing transformation function:
-%
-% * _climateTransform_soilMoistureModels_ : is a generalised vertically lumped 1-D
-%   soil moisture model. It is used to transform daily climate data
-%   for use in the transfer noise function groundwater time-series model.  
-%   The soil moisture model is defined by the following ordinary
-%   differential equation (Kavetski et al. 2006) and the following soil
-%   fluxes/data can be used in the time-series modelling. See Peterson and
-%   Western (2014) for details and recommendations on the form of the soil
-%   model to adopt.
-% * _climateTransform_soilMoistureModels_2layer_ : is identifcal to the
-%   above one layer model but has a second deeper soil store whereby the
-%   free-drainage from the upper store drains into the deeper store; and ET
-%   does not occur from the deeper store.
-%
-% <html>
-% <table border=1>
-% <tr><td>Soil Model Flux/Data</td><td>Description</td></tr>
-% <tr><td>Drainage</td><td>soil free drainage ranging (0 to k_sat) at the end of the day.</td></tr>
-% <tr><td>Drainage bypass flow</td><td>Free drainage plus a parameter set fraction of runoff.</td></tr>
-% <tr><td>Drainage normalised </td><td>Normalised free drainage (0 to 1) at the end of the day.</td></tr>
-% <tr><td>Soil evaporation</td><td>Estimated soil ET at the end of the day.</td></tr>
-% <tr><td>Infiltration</td><td>Daily total infiltration rate.</td></tr>
-% <tr><td>Groundwater evaporative potential</td><td>Groundwater evaporative potential (PET - soil ET).</td></tr>
-% <tr><td>Runoff</td><td>Total daily runoff.</td></tr>
-% <tr><td>SMS</td><td>soil moisture storage at the end of each day.</td></tr>
-% </table>
-% </html>
-%
-% $\frac{dS}{dt} = P_{inf} (1 - \frac{S}{SMSC})^\alpha - k_{sat} (\frac{S}{SMSC})^\beta - PET (\frac{S}{SMSC})^\gamma$
-%
-%
-% The soil moisture model can also be used to simulate the impacts from
-% different vegetation; for example, trees and pastures. This is achieved
-% by simulating a soil store for upto two land types and then weighting
-% required flux from each soil model by an input time series of the
-% fraction of the second land type. A challange with the input time series 
-% of land cover is, however, that while the fraction of, say, land
-% data clearing over time may be known the fraction of the catchment area
-% cleared that influences a bore hydrograph is unknown. To address this,
-% the modelling also include a parameter 'treeArea_frac' for the fraction
-% of the second land cover (notially trees) that is influencing the bore.
-%   
-% An alternative approach for simulating the impacts of land cover change is 
-% to use the forcing transformation functions as inputs to the following
-% derived transformation functions. This allow, for example, the impact of
-% revegetation to be simulated by creating a new flux from the soil
-% moisture model that is scaled by the fraction of revegetation. That is,
-% the free drainage from the soil moisture model could be scaled by a time
-% series of the fraction of revegetation to produce an estimate of the
-% change in recharge from the revegetation. For details, see the GUI example _TFN model - Landuse change_.
-%
-% * _derivedForcing_linearUnconstrainedScaling_ : rescales a
-% transformation function flux whereby the rescaling can be positive or
-% negative. Additionally, in the calibration the rescaling is initially
-% assumed to be +- 0.2 of the transformation function flux. 
-
-%% References
-%
-% * von Asmuth, J. R. and Bierkens M. F. P.  (2005). Modeling irregularly spaced residual series as a continuous stochastic process, _Water Resources Research_, 41, W12404, DOI: <http://dx.doi.org/10.1029/2004WR003726 10.1029/2004WR003726>.
-% * von Asmuth J. R., Mass K., Bakker M., Peterson J., (2008). Modeling time series of ground water head fluctuations subject to multiple
-% stresses. _Groundwater_, 46(1), 30-40. DOI: <http://dx.doi.org/10.1111/j.1745-6584.2007.00382.x 10.1111/j.1745-6584.2007.00382.x>
-% * Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, _Water Resources Research_, 42, W03407, DOI: <http://dx.doi.org/10.1029/2005WR004368 10.1029/2005WR004368>. 
-% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, _Hydrogeology Journal_, DOI: <http://dx.doi.org/10.1007/s10040-015-1269-7 10.1007/s10040-015-1269-7>. <papers/Shapoori_2015A.pdf PDF Copy>
-% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
-##### SOURCE END #####
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+  </style></head><body><div class="content"><h1>HydroSight - Transfer Function Noise Models</h1><!--introduction--><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Overview</a></li><li><a href="#2">Weighting Function Components</a></li><li><a href="#3">Forcing Transform Components</a></li><li><a href="#4">References</a></li></ul></div><h2>Overview<a name="1"></a></h2><p>The transfer function noise model (TFN) simulates an observed hydrograph by weighting the historic input forcing data, such as rainfall or groundwater pumping, and estimating the random noise as an exponential decay function, which allows irregular water level observations to be simulated (von Asmuth et al., 2005). That is, for each water level observation the entire historic daily forcing data is obtained and a weight is applied to each day's past forcing data. This weighted forcing is then integrated over time (i.e. summed) to give the contribution of the historic forcing to the water level at the time point of interest. This is then repeated for every water level observation. Importantly, the weights are not uniform back in time but are defined by a simple function and can have, say, a decayed exponential distribution, a Guassian-like skewed distribution or a distribution derived from the Theis drawdown equation. In applying the TFN model, the parameters for the weighting functions are adjusted using a <a href="doc_Calibration.html">global calibration scheme</a> (Peterson and Western, 2014).</p><p>A weakness of simply weighting past rainfall is, however, that the nonlinear partitioning of rainfall is not accounted for. This is addressed within the Toolbox by the ability to transform forcing data prior to the weighting (Peterson and Western 2015). For example, a simple 1-D vertically lumped soil model can be included in the time-series modelling and calibrated to account for the often nonlinear response of the water table to rainfall.</p><p>Lastly, the model TFN time-series model can also be calibrated using Xeon Phi co-processor cards. If a co-processor card is available then the numerical intergration step of the TFN model will be undertaken using the co-processor card(s). To use this features, the computer requires Intel compiler ICC &gt;=2013.</p><h2>Weighting Function Components<a name="2"></a></h2><p>The toolkit allows the user to select one of the following weighting functions for each forcing data time series:</p><div><ul><li><i>responseFunction_Bruggeman</i> : used for simulating the streamflow influence on head (von Asmuth et al. 2008). Note, this function is still to be tested and must be used with caution.</li><li><i>responseFunction_FerrisKnowles</i> : used for simulating the drawdown from pumping with an instantaneous version for the Theis drawdown equation (Shapoori et al. 2015a, 2015b).</li><li><i>responseFunction_FerrisKnowlesJacobs</i> : as for <i>responseFunction_FerrisKnowles</i> but with Jacob's correction added to approximate an unconfined aquifer (Shapoori et al. 2015b).</li><li><i>responseFunction_Hantush</i> : used for simulating drawdown from pumping assuming a leaky aquifer response (Shapoori et al. 2015a).</li><li><i>responseFunction_Pearsons</i> : used for simulating climatic influences that increase the head, for example recharge  (Peterson and Western 2014).</li><li><i>responseFunction_PearsonsNegative</i> : as for <i>responseFunction_Pearsons</i> but with a sign change to simulate climatic influences that lower the head, for example ET (Peterson and Western 2014).</li></ul></div><p>The above weighting functions can also be used as inputs to the following derived weighting functions. This allow, for example, the impact from ET to be simulated not as an additional weighting function but by simply re-scaling the Pearson's weighting function used for recharge, and hence eliminating two model parameters from the model.</p><div><ul><li><i>derivedweighting_UnconstrainedRescaled</i> : rescales an input weighting function whereby the recsaling can be positive or negative. This function can be used to simulate the impacts of, say, revegetation by rescaling <i>responseFunction_Pearsons</i> used to simulate recharge. For details, see the GUI example <i>TFN model - Landuse change</i>.</li><li><i>derivedweighting_PearsonsNegativeRescaled</i> : similar to <i>derivedweighting_UnconstrainedRescaled</i> but only to be used to rescale <i>responseFunction_Pearsons</i>. The function uses a normalised Pearson's function and then applies a scaling parameter. This may reduce the parameter covariance between the input weighting function and the rescaling and hence contribute to reliable calibration.</li><li><i>derivedweighting_PearsonsPositiveRescaled</i> : as for <i>responseFunction_Pearsons</i> but using a negative rescaling.</li></ul></div><h2>Forcing Transform Components<a name="3"></a></h2><p>The toolkit allows the user to select the following forcing transformation function:</p><div><ul><li><i>climateTransform_soilMoistureModels</i> : is a generalised vertically lumped 1-D   soil moisture model. It is used to transform daily climate data   for use in the transfer noise function groundwater time-series model.   The soil moisture model is defined by the following ordinary   differential equation (Kavetski et al. 2006) and the following soil   fluxes/data can be used in the time-series modelling. See Peterson and   Western (2014) for details and recommendations on the form of the soil   model to adopt.</li><li><i>climateTransform_soilMoistureModels_2layer</i> : is identifcal to the   above one layer model but has a second deeper soil store whereby the   free-drainage from the upper store drains into the deeper store; and ET   does not occur from the deeper store.</li></ul></div><p>
+<table border=1>
+<tr><td>Soil Model Flux/Data</td><td>Description</td></tr>
+<tr><td>Drainage</td><td>soil free drainage ranging (0 to k_sat) at the end of the day.</td></tr>
+<tr><td>Drainage bypass flow</td><td>Free drainage plus a parameter set fraction of runoff.</td></tr>
+<tr><td>Drainage normalised </td><td>Normalised free drainage (0 to 1) at the end of the day.</td></tr>
+<tr><td>Soil evaporation</td><td>Estimated soil ET at the end of the day.</td></tr>
+<tr><td>Infiltration</td><td>Daily total infiltration rate.</td></tr>
+<tr><td>Groundwater evaporative potential</td><td>Groundwater evaporative potential (PET - soil ET).</td></tr>
+<tr><td>Runoff</td><td>Total daily runoff.</td></tr>
+<tr><td>SMS</td><td>soil moisture storage at the end of each day.</td></tr>
+</table>
+</p><p><img src="doc_TFN_eq03172746311882613811.png" alt="$\frac{dS}{dt} = P_{inf} (1 - \frac{S}{SMSC})^\alpha - k_{sat} (\frac{S}{SMSC})^\beta - PET (\frac{S}{SMSC})^\gamma$"></p><p>The soil moisture model can also be used to simulate the impacts from different vegetation; for example, trees and pastures. This is achieved by simulating a soil store for upto two land types and then weighting required flux from each soil model by an input time series of the fraction of the second land type. A challange with the input time series of land cover is, however, that while the fraction of, say, land data clearing over time may be known the fraction of the catchment area cleared that influences a bore hydrograph is unknown. To address this, the modelling also include a parameter 'treeArea_frac' for the fraction of the second land cover (notially trees) that is influencing the bore.</p><p>An alternative approach for simulating the impacts of land cover change is to use the forcing transformation functions as inputs to the following derived transformation functions. This allow, for example, the impact of revegetation to be simulated by creating a new flux from the soil moisture model that is scaled by the fraction of revegetation. That is, the free drainage from the soil moisture model could be scaled by a time series of the fraction of revegetation to produce an estimate of the change in recharge from the revegetation. For details, see the GUI example <i>TFN model - Landuse change</i>.</p><div><ul><li><i>derivedForcing_linearUnconstrainedScaling</i> : rescales a transformation function flux whereby the rescaling can be positive or negative. Additionally, in the calibration the rescaling is initially assumed to be +- 0.2 of the transformation function flux.</li></ul></div><h2>References<a name="4"></a></h2><div><ul><li>von Asmuth, J. R. and Bierkens M. F. P.  (2005). Modeling irregularly spaced residual series as a continuous stochastic process, <i>Water Resources Research</i>, 41, W12404, DOI: <a href="http://dx.doi.org/10.1029/2004WR003726">10.1029/2004WR003726</a>.</li><li>von Asmuth J. R., Mass K., Bakker M., Peterson J., (2008). Modeling time series of ground water head fluctuations subject to multiple stresses. <i>Groundwater</i>, 46(1), 30-40. DOI: <a href="http://dx.doi.org/10.1111/j.1745-6584.2007.00382.x">10.1111/j.1745-6584.2007.00382.x</a></li><li>Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, <i>Water Resources Research</i>, 42, W03407, DOI: <a href="http://dx.doi.org/10.1029/2005WR004368">10.1029/2005WR004368</a>.</li><li>Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. <i>Water Resources Research</i>, 50, 8330-8355, DOI: <a href="http://dx.doi.org/10.1002/2013WR014800">10.1002/2013WR014800</a>. <a href="papers/Peterson_Western_2014.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, <i>Hydrogeology Journal</i>, DOI: <a href="http://dx.doi.org/10.1007/s10040-015-1269-7">10.1007/s10040-015-1269-7</a>. <a href="papers/Shapoori_2015A.pdf">PDF Copy</a></li><li>Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, <i>Hydrogeology Journal</i>, 23(4), 819-83, DOI: <a href="http://dx.doi.org/10.1007/s10040-014-1223-0">10.1007/s10040-014-1223-0</a>. <a href="papers/Shapoori_2015B.pdf">PDF Copy</a></li></ul></div><p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2016a</a><br></p></div><!--
+##### SOURCE BEGIN #####
+%% HydroSight - Transfer Function Noise Models
+
+%% Overview
+% The transfer function noise model (TFN) simulates an observed hydrograph
+% by weighting the historic input forcing data, such as rainfall or groundwater pumping, 
+% and estimating the random noise as an exponential decay
+% function, which allows irregular water level observations to be
+% simulated (von Asmuth et al., 2005). That is, for each water level
+% observation the entire historic daily forcing data is obtained and a
+% weight is applied to each day's past forcing data. This weighted forcing is
+% then integrated over time (i.e. summed) to give the contribution of the historic forcing to
+% the water level at the time point of interest. This is then repeated for every water level
+% observation. Importantly, the weights are not uniform back in time but
+% are defined by a simple function and can have, say, a decayed exponential 
+% distribution, a Guassian-like skewed distribution or a distribution derived from 
+% the Theis drawdown equation. In applying the TFN
+% model, the parameters for the weighting functions are adjusted using a <doc_Calibration.html global
+% calibration scheme> (Peterson and Western, 2014).
+% 
+% A weakness of simply weighting past rainfall is, however, that the
+% nonlinear partitioning of rainfall is not accounted for. This is
+% addressed within the Toolbox by the ability to transform forcing data
+% prior to the weighting (Peterson and Western 2015). For example, a simple 
+% 1-D vertically lumped soil model can be included in the time-series 
+% modelling and calibrated to account for the often nonlinear response of
+% the water table to rainfall.
+%
+% Lastly, the model TFN time-series model can also be calibrated
+% using Xeon Phi co-processor cards. If a co-processor card is available 
+% then the numerical intergration step of the TFN model will be
+% undertaken using the co-processor card(s). To use this features, the
+% computer requires Intel compiler ICC >=2013.
+
+%% Weighting Function Components
+%
+% The toolkit allows the user to select one of the following weighting
+% functions for each forcing data time series:
+%
+% * _responseFunction_Bruggeman_ : used for simulating the streamflow influence on head (von Asmuth et al. 2008). 
+% Note, this function is still to be tested and must be used with caution.
+% * _responseFunction_FerrisKnowles_ : used for simulating the drawdown
+% from pumping with an instantaneous version for the Theis drawdown equation (Shapoori et al.
+% 2015a, 2015b).
+% * _responseFunction_FerrisKnowlesJacobs_ : as for _responseFunction_FerrisKnowles_ but 
+% with Jacob's correction added to approximate an unconfined aquifer (Shapoori et al. 2015b).
+% * _responseFunction_Hantush_ : used for simulating drawdown from pumping
+% assuming a leaky aquifer response (Shapoori et al. 2015a).
+% * _responseFunction_Pearsons_ : used for simulating climatic influences
+% that increase the head, for example recharge  (Peterson and Western
+% 2014).
+% * _responseFunction_PearsonsNegative_ : as for _responseFunction_Pearsons_ but with a 
+% sign change to simulate climatic influences
+% that lower the head, for example ET (Peterson and Western 2014).
+%
+% The above weighting functions can also be used as inputs to the following
+% derived weighting functions. This allow, for example, the impact from ET to be
+% simulated not as an additional weighting function but by simply
+% re-scaling the Pearson's weighting function used for recharge, and hence
+% eliminating two model parameters from the model.
+%
+% * _derivedweighting_UnconstrainedRescaled_ : rescales an input weighting
+% function whereby the recsaling can be positive or negative. This
+% function can be used to simulate the impacts of, say, revegetation by
+% rescaling _responseFunction_Pearsons_ used to simulate recharge.
+% For details, see the GUI example _TFN model - Landuse change_.
+% * _derivedweighting_PearsonsNegativeRescaled_ : similar to
+% _derivedweighting_UnconstrainedRescaled_ but only to be used to rescale
+% _responseFunction_Pearsons_. The function uses a normalised Pearson's
+% function and then applies a scaling parameter. This may reduce the
+% parameter covariance between the input weighting function and the
+% rescaling and hence contribute to reliable calibration.
+% * _derivedweighting_PearsonsPositiveRescaled_ : as for
+% _responseFunction_Pearsons_ but using a negative rescaling.
+
+%% Forcing Transform Components
+%
+% The toolkit allows the user to select the following forcing transformation function:
+%
+% * _climateTransform_soilMoistureModels_ : is a generalised vertically lumped 1-D
+%   soil moisture model. It is used to transform daily climate data
+%   for use in the transfer noise function groundwater time-series model.  
+%   The soil moisture model is defined by the following ordinary
+%   differential equation (Kavetski et al. 2006) and the following soil
+%   fluxes/data can be used in the time-series modelling. See Peterson and
+%   Western (2014) for details and recommendations on the form of the soil
+%   model to adopt.
+% * _climateTransform_soilMoistureModels_2layer_ : is identifcal to the
+%   above one layer model but has a second deeper soil store whereby the
+%   free-drainage from the upper store drains into the deeper store; and ET
+%   does not occur from the deeper store.
+%
+% <html>
+% <table border=1>
+% <tr><td>Soil Model Flux/Data</td><td>Description</td></tr>
+% <tr><td>Drainage</td><td>soil free drainage ranging (0 to k_sat) at the end of the day.</td></tr>
+% <tr><td>Drainage bypass flow</td><td>Free drainage plus a parameter set fraction of runoff.</td></tr>
+% <tr><td>Drainage normalised </td><td>Normalised free drainage (0 to 1) at the end of the day.</td></tr>
+% <tr><td>Soil evaporation</td><td>Estimated soil ET at the end of the day.</td></tr>
+% <tr><td>Infiltration</td><td>Daily total infiltration rate.</td></tr>
+% <tr><td>Groundwater evaporative potential</td><td>Groundwater evaporative potential (PET - soil ET).</td></tr>
+% <tr><td>Runoff</td><td>Total daily runoff.</td></tr>
+% <tr><td>SMS</td><td>soil moisture storage at the end of each day.</td></tr>
+% </table>
+% </html>
+%
+% $\frac{dS}{dt} = P_{inf} (1 - \frac{S}{SMSC})^\alpha - k_{sat} (\frac{S}{SMSC})^\beta - PET (\frac{S}{SMSC})^\gamma$
+%
+%
+% The soil moisture model can also be used to simulate the impacts from
+% different vegetation; for example, trees and pastures. This is achieved
+% by simulating a soil store for upto two land types and then weighting
+% required flux from each soil model by an input time series of the
+% fraction of the second land type. A challange with the input time series 
+% of land cover is, however, that while the fraction of, say, land
+% data clearing over time may be known the fraction of the catchment area
+% cleared that influences a bore hydrograph is unknown. To address this,
+% the modelling also include a parameter 'treeArea_frac' for the fraction
+% of the second land cover (notially trees) that is influencing the bore.
+%   
+% An alternative approach for simulating the impacts of land cover change is 
+% to use the forcing transformation functions as inputs to the following
+% derived transformation functions. This allow, for example, the impact of
+% revegetation to be simulated by creating a new flux from the soil
+% moisture model that is scaled by the fraction of revegetation. That is,
+% the free drainage from the soil moisture model could be scaled by a time
+% series of the fraction of revegetation to produce an estimate of the
+% change in recharge from the revegetation. For details, see the GUI example _TFN model - Landuse change_.
+%
+% * _derivedForcing_linearUnconstrainedScaling_ : rescales a
+% transformation function flux whereby the rescaling can be positive or
+% negative. Additionally, in the calibration the rescaling is initially
+% assumed to be +- 0.2 of the transformation function flux. 
+
+%% References
+%
+% * von Asmuth, J. R. and Bierkens M. F. P.  (2005). Modeling irregularly spaced residual series as a continuous stochastic process, _Water Resources Research_, 41, W12404, DOI: <http://dx.doi.org/10.1029/2004WR003726 10.1029/2004WR003726>.
+% * von Asmuth J. R., Mass K., Bakker M., Peterson J., (2008). Modeling time series of ground water head fluctuations subject to multiple
+% stresses. _Groundwater_, 46(1), 30-40. DOI: <http://dx.doi.org/10.1111/j.1745-6584.2007.00382.x 10.1111/j.1745-6584.2007.00382.x>
+% * Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, _Water Resources Research_, 42, W03407, DOI: <http://dx.doi.org/10.1029/2005WR004368 10.1029/2005WR004368>. 
+% * Peterson, T. J. and Western A. W. (2014). Nonlinear time-series modeling of unconfined groundwater head. _Water Resources Research_, 50, 8330-8355, DOI: <http://dx.doi.org/10.1002/2013WR014800 10.1002/2013WR014800>. <papers/Peterson_Western_2014.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015a). Decomposing groundwater head variations into meteorological and pumping components: a synthetic study, _Hydrogeology Journal_, DOI: <http://dx.doi.org/10.1007/s10040-015-1269-7 10.1007/s10040-015-1269-7>. <papers/Shapoori_2015A.pdf PDF Copy>
+% * Shapoori V., Peterson T.J. , Western A.W. and Costelloe J. F. (2015b). Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition, _Hydrogeology Journal_, 23(4), 819-83, DOI: <http://dx.doi.org/10.1007/s10040-014-1223-0 10.1007/s10040-014-1223-0>. <papers/Shapoori_2015B.pdf PDF Copy>
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