Workbench

README.md

@@ -6,7 +6,7 @@ Triangular plotting and diagnostics for FESOM2 + command line utility similar to
 fdiag using plotting suite of [email protected] (former fesom2/view_pscholz folder). 
 Data reading got updated to xarray, plotting got updated to cartopy, actually works well for 
 python=3.9, 3.10, 3.11 and 3.12 (python 3.8 made recently some problems to resolve all the cartopy dependencies). 
-It is tested upto dart mesh size (3M surface vertices) where it allows for a decend 
+It is tested up to dart mesh size (3M surface vertices) where it allows for a decent
 working speed (but only when used in parallel)
 <br />
 version: 0.3.0
@@ -32,17 +32,13 @@ make sure your conda environment uses python 3.9, 3.10, 3.11 or 3.12
 #  gets one thread
 # -install libstdcxx-ng with conda since there is no pip package for it is needed
 #  so that pyvista is able to plot interactively
+# -all other packages will be automatically installed by pip
 conda install -c conda-forge libstdcxx-ng
 cd tripyview
 pip install -e .
 
-# If you want to use the interactive features in tools/do_topo@elem_or_node.ipynb
-# due to python issues you will need jupyter_server=1.23.6, jupyter_client=7.3.2, 
-# tornad=6.1 --> its the only combination where this features works in moment!!!
-(pip install jupyter_server==1.23.6 jupyter_client==7.3.2 tornado==6.1)
-
 # On my laptop i had problems with the classic notebook support beyond jupyter_server==2.0.0
-# and had to downgrade it to jupyter_server<2.0.0. However on HPC albedo that didnt
+# and had to downgrade it to jupyter_server<2.0.0. However on HPC albedo that did not
 # seems to be the case.
 (pip install "jupyter_server<2.0.0")
 ```
@@ -105,9 +101,9 @@ vertice/element- resolution and area)
 
 
 ### General time plots: 
-* `hovm`             - plot hovmöller diagramms for 3d ocean variable over depth and time averaged over
+* `hovm`             - plot hovmöller diagrams for 3d ocean variable over depth and time averaged over
 specific region that is defined by shapefile (see. tripyview/shapefiles/...)
-* `hovm_clim`        - plot hovmöller diagramms for  temperature, salinity and density climatology 
+* `hovm_clim`        - plot hovmöller diagrams for  temperature, salinity and density climatology 
 over depth and time averaged over specific region that is defined by shapefile (see. tripyview/shapefiles/...)
 
 * `var_t`            - plots time-series of globally or shapefile (see. tripyview/shapefiles/...) averaged 2d and 3d variables 
@@ -123,11 +119,11 @@ for the: Atlantic(amoc), Atlantic+Arctic(aamoc), Pacific(pmoc), Pacific+Indian O
 </p>
 
 
-* `zmoc_t`           - plot time-series of atlantic meridional overturning circulation at specific latitudes 
+* `zmoc_t`           - plot time-series of Atlantic meridional overturning circulation at specific latitudes 
 (default: which_lats: [26, 40, 'max'])
 
 
-* `dmoc`             - plot meridional overturning circulation in density coordinates using FESOM2 density MOC diagnostics (graditues goes to **[email protected]**), Globally, 
+* `dmoc`             - plot meridional overturning circulation in density coordinates using FESOM2 density MOC diagnostics (gratitude goes to **[email protected]**), Globally, 
 for the: Atlantic(amoc), Atlantic+Arctic(aamoc), Pacific(pmoc), Pacific+Indian Ocean(ipmoc) and Indian Ocean(imoc)
 * `dmoc_srf`         - plot surface transformation of meridional overturning circulation in density coordinates
 * `dmoc_inner`       - plot inner transformation of meridional overturning circulation in density coordinates
@@ -151,7 +147,7 @@ for the: Atlantic(amoc), Atlantic+Arctic(aamoc), Pacific(pmoc), Pacific+Indian O
 * `dmoc_srfcbflx`    - plot surface buoyancy flux transformation for specific water-class
 
 
-* `dmoc_t`           - plot time-series of atlantic meridional overturning circulation in density coordinates at specific latitudes 
+* `dmoc_t`           - plot time-series of Atlantic meridional overturning circulation in density coordinates at specific latitudes 
 (default: which_lats: [26, 55, 'max'])
 
 
@@ -164,7 +160,7 @@ for the: Atlantic(amoc), Atlantic+Arctic(aamoc), Pacific(pmoc), Pacific+Indian O
 
 
 ### 2D automatic multipanel plotting: 
-* plot automatised figure multipanels depending on number of choosen datasets and number
+* plot automatised figure multi-panels depending on number of chosen datasets and number
 of panel columns ncol (the panel arrangement uses ideas from **[email protected]**)
 <p align="center" width="100%">
   <img width=75% src="tripyview/tripyview_multipanel.png">
@@ -185,15 +181,15 @@ of panel columns ncol (the panel arrangement uses ideas from **Nils.Brueggemann@
 
 ## Command line batch diagnostics:
 * `tripyrun  <workflowfile.yml>` - command line diagnostics similar to fdiag (fdiag, served as a 
-template, graditues go to **[email protected]**), creates html files with linked plots.
+template, gratitude go to **[email protected]**), creates html files with linked plots.
 
 * `tripyrun  <workflowfile.yml> -d <driver>` - re-run one specific driver from .yml file 
 
 * `tripyrun  <workflowfile.yml> -d <driver> -v <var1 var2 ...>` - re-run specific variable 
 subset of one specific driver driver from .yml file
 
 * `tripyrun  <workflowfile.yml> -r` - for the case the diagnostic did not fully finish you can 
-render the html file from hand based on hte saved json file. The json file is update after each finished
+render the html file from hand based on the saved json file. The json file is update after each finished
 driver section.
 
 <p align="center" width="100%">

tripyview/sub_dmoc.py

@@ -445,7 +445,7 @@ def calc_dmoc(mesh, data_dMOC, dlat=1.0, which_moc='gmoc', which_transf=None, do
     #___________________________________________________________________________
     # create meridional bins --> this trick is from Nils Brückemann (ICON)
     lat_bin = xr.DataArray(data=np.round(data_dMOC['lat'].data/dlat)*dlat, dims='elem', name='lat')
-    lat    = np.arange(lat_bin.min(), lat_bin.max()+dlat, dlat)
+    lat    = np.arange(lat_bin.data.min(), lat_bin.data.max()+dlat, dlat)
 
     #___________________________________________________________________________
     # define subroutine for binning over latitudes, allows for parallelisation

tripyview/sub_plot.py

@@ -746,7 +746,8 @@ def plot_hmesh( mesh                   ,
 
     #___________________________________________________________________________
     # --> check if input data is a list
-    if not isinstance(mesh, list): mesh = [mesh]
+    if not isinstance(mesh  , list): mesh   = [mesh]
+    if not isinstance(do_lsm, list): do_lsm = [do_lsm]*len(mesh)
     ndat = len(mesh)
 
     #___________________________________________________________________________
@@ -799,7 +800,6 @@ def plot_hmesh( mesh                   ,
             tri = do_triangulation(hax, mesh[ii], proj_to, box)
 
             #___________________________________________________________________
-            print(data)
             if data != None and data != 'None':
                 if  data in ['resolution', 'resol', 'n_resol', 'nresol']:
                     if len(mesh[ii].n_resol)==0: mesh[ii]=mesh[ii].compute_n_resol()
@@ -825,7 +825,6 @@ def plot_hmesh( mesh                   ,
                     data_plot = np.abs(mesh[ii].zlev[mesh[ii].e_iz])
                     cb_label, cb_lunit = 'element depth', 'm'
 
-                print(data_plot.shape)
                 #_______________________________________________________________
                 cinfo_plot = do_setupcinfo(cinfo, [data_plot], do_rescale, mesh=mesh[ii], tri=tri)
                 norm_plot  = do_data_norm(cinfo_plot, do_rescale)
@@ -852,7 +851,7 @@ def plot_hmesh( mesh                   ,
 
             #___________________________________________________________________
             # add mesh land-sea mask
-            h0 = do_plt_lsmask(hax_ii, do_lsm, mesh[ii], lsm_opt=lsm_opt, resolution=lsm_res)
+            h0 = do_plt_lsmask(hax_ii, do_lsm[ii], mesh[ii], lsm_opt=lsm_opt, resolution=lsm_res)
             hlsm.append(h0)  
 
             #___________________________________________________________________
@@ -879,7 +878,7 @@ def plot_hmesh( mesh                   ,
                 hcb_ii = do_cbar(hcb_ii, hax_ii, hp, data, cinfo_plot, norm_plot, 
                                 cb_label, cb_lunit, cb_ltime, cb_ldep, cb_opt=cb_opt, cbl_opt=cbl_opt, cbtl_opt=cbtl_opt)
         else:
-            hcb_ii.remove()
+            if hcb_ii != 0: hcb_ii.remove()
         #_______________________________________________________________________
         # hfig.canvas.draw()   
 
@@ -5351,7 +5350,8 @@ def inverse_ysig(y, *args):
             if   xlim is not None: 
                 hax_ii.set_xlim(xlim[0]  ,xlim[-1])
             elif xlim is     None and data_x is not None: 
-                hax_ii.set_xlim(data_x[0], data_x[-1])
+                if   data_x.ndim==1: hax_ii.set_xlim(data_x[0], data_x[-1])
+                elif data_x.ndim==2: hax_ii.set_xlim(data_x[0,0], data_x[0,-1])
 
             #___________________________________________________________________
             # invert y-axis

tripyview/sub_zmoc.py

@@ -203,7 +203,7 @@ def calc_zmoc(mesh, data, dlat=1.0, which_moc='gmoc', do_onelem=False,
         #_______________________________________________________________________
         # create meridional bins --> this trick is from Nils Brückemann (ICON)
         lat_bin = xr.DataArray(data=np.round(data.lat/dlat)*dlat, dims='nod2', name='lat')
-        lat     = np.arange(lat_bin.min(), lat_bin.max()+dlat, dlat)
+        lat     = np.arange(lat_bin.data.min(), lat_bin.data.max()+dlat, dlat)
         warnings.resetwarnings()
         #t4 = clock.time()
         #print('  --> comp. lat_bin', t4-t3)