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ojf_freeyaw.py
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ojf_freeyaw.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Feb 14 18:01:56 2013
Select all the OJF free yaw cases, manually or automatically only take those
time ranges so we can actually calculate how fast the yaw angle is recovering
@author: dave
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
# built in modules
import os
from itertools import chain
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib import pyplot as plt
import ojfdb
import ojfresult
import plotting
import filters
from staircase import StairCase, StairCaseNG
#import misc
plt.rc('font', family='serif')
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
plt.rc('axes', labelsize=12)
plt.rc('text', usetex=True)
plt.rc('legend', fontsize=11)
plt.rc('legend', numpoints=1)
plt.rc('legend', borderaxespad=0)
mpl.rcParams['text.latex.unicode'] = True
PATH_DB = 'database/'
PATH_DATA_CAL_HDF = 'data/calibrated/DataFrame/'
###############################################################################
### YAW ERRORS, OLD
###############################################################################
def results_filtering(cr, figpath, fig_descr, arg_stair):
"""
Like the ComboResults.dashboard_a3, but now include the filtered signal
and the subtracted values.
Saves the datastair data, it is formatted as: ndarray(stair_nr, data_chan)
[RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, windspeed, temperature]
Note that this is actually not necessary and rendundant, since we properly
added all the cases as subecases to the MeasureDb database
Parameters
----------
cr : ComboResults instance
A ojfresult.ComboResults instance
figpath : str
fig_descr : str
small adition to the file name
arg_stair : array(2,n)
Start/stop indices to n stairs
"""
try:
bmax = cr.blade.time.max()
# if there are no blade data measurements, there is no such thing
except AttributeError:
bmax = cr.dspace.time.max()
window_dspace, window_blade = [0, bmax], [0, bmax]
# save each stair and corresponding data points
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, windspeed, temp]
datastair = np.ndarray( (arg_stair.shape[1],10) )
datastair[:,:] = np.nan
# and fill in the same wind speed for all the stairs
ch_t = 1
ch_w = 4
try:
datastair[:,8] = cr.dspace.ojf.data[:,ch_w].mean()
datastair[:,9] = cr.dspace.ojf.data[:,ch_t].mean()
except AttributeError:
pass
if cr.dspace_is_cal:
# convert labels for calibrated signals
ylabels = {}
fa_key = 'Tower Strain For-Aft'
ss_key = 'Tower Strain Side-Side'
if cr.dspace_yawcal:
ylabels['Yaw Laser'] = '[deg]'
if cr.dspace_towercal:
ylabels[fa_key] = 'Tower base FA moment [Nm]'
ylabels[ss_key] = 'Tower base SS moment [Nm]'
if cr.dspace_towercal_psicor:
ylabels[fa_key] = 'Tower base $\psi$ moment [Nm]'
ylabels[ss_key] = 'Tower base $\psi_{90}$ moment [Nm]'
# -------------------------------------------------
# setup the figure
# -------------------------------------------------
nr_plots = 8
pa4 = plotting.A4Tuned()
# escape any underscores in the file name for latex printing
grandtitle = cr.resfile.replace('_', '\_')
pa4.setup(figpath + cr.resfile + fig_descr, nr_plots=nr_plots,
grandtitle=grandtitle, wsleft_cm=2., wsright_cm=1.0,
hspace_cm=2., wspace_cm=4.2)
# -------------------------------------------------
# data selection from dSPACE
# -------------------------------------------------
channels = []
# make sure the order here is the same as for datastair!
channels.append(cr.dspace.labels_ch['RPM'])
channels.append(cr.dspace.labels_ch['Yaw Laser'])
try:
channels.append(cr.dspace.labels_ch['Tower Strain For-Aft'])
channels.append(cr.dspace.labels_ch['Tower Strain Side-Side'])
except KeyError:
key = 'Tower Strain For-Aft filtered'
channels.append(cr.dspace.labels_ch[key])
key = 'Tower Strain Side-Side filtered'
channels.append(cr.dspace.labels_ch[key])
# -------------------------------------------------
# the arguments ready to go as slices and SAVE IN DB
# -------------------------------------------------
# and plot each stair! See if RPM and yaw are realy constant!
ppp = plotting.A4Tuned()
# escape any underscores in the file name for latex printing
grandtitle = cr.resfile.replace('_', '\_')
target = figpath + cr.resfile + fig_descr + '_stairs_rawdata'
ppp.setup(target, nr_plots=arg_stair.shape[1],
grandtitle=grandtitle, wsleft_cm=1.6, wsright_cm=1.6,
hspace_cm=2.5, wspace_cm=7.0)
isel = []
for k in range(arg_stair.shape[1]):
i1 = arg_stair[0,k]
i2 = arg_stair[1,k]
isel.append(np.r_[i1:i2])
# calculate all the means, std, min, max and range for each channel
cr.statistics(i1=i1, i2=i2)
# quickly check if the average RPM and actuall measurements are close
# also assess how messy the yaw angle is bumping around
axx = ppp.fig.add_subplot(ppp.nr_rows, ppp.nr_cols, k+1)
irpm = cr.dspace.labels_ch['RPM']
iyaw = cr.dspace.labels_ch['Yaw Laser']
# give same title as the key in the OJF db_stats database
title = '%s_STC_%i' % (cr.resfile, i1/100)
axx.set_title(title.replace('_','\_'))
axx.plot(cr.dspace.time[i1:i2], cr.dspace.data[i1:i2,irpm], 'b',
label='rpm')
axx2 = axx.twinx()
axx2.plot(cr.dspace.time[i1:i2], cr.dspace.data[i1:i2,iyaw], 'r',
label='yaw')
axx.axhline(y=cr.stats['dspace mean'][cr.dspace.labels_ch['RPM']])
axx.grid()
# lines = axx.lines + axx2.lines
# labels = [l.get_label() for l in lines]
# leg = axx.legend(lines, labels, loc='best')
axx.set_ylabel('RPM')
axx2.set_ylabel('Yaw angle')
ppp.save_fig()
# -------------------------------------------------
# plotting dSPACE selection
# -------------------------------------------------
filt = filters.Filters()
# sample rate
sps = int(cr.dspace.sample_rate)
plot_nr = 1
for ii, ch in enumerate(channels):
time, data = cr.dspace.time, cr.dspace.data[:,ch]
# filter the signal
dataf, N, delay = filt.fir(time, data, ripple_db=20, sample_rate=sps,
freq_trans_width=2.5, cutoff_hz=5)
ax1 = pa4.fig.add_subplot(pa4.nr_rows, pa4.nr_cols, plot_nr)
ax1.plot(time, data, 'b')
ax1.plot(time[N-1:]-delay, dataf[N-1:], 'r')
# and mark all the average data
for k in range(arg_stair.shape[1]):
ax1.plot(time[isel[k]].mean(), data[isel[k]].mean(), 'ko')
# save the mean data from the stair
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, V, temp]
datastair[k,ii] = data[isel[k]].mean()
ax1.set_title(cr.dspace.labels[ch])
ax1.grid(True)
ch_key = cr.dspace.labels[ch]
if ylabels.has_key(ch_key):# and caldict_dspace.has_key(ch_key):
ax1.set_ylabel(ylabels[cr.dspace.labels[ch]])
ax1.set_xlim(window_dspace)
plot_nr += 1
# -------------------------------------------------
# plotting Blade selection
# -------------------------------------------------
# Blade 1: channels 3 (M1 root) and 4 (M2 mid section)
# Blade 2: channels 1 (M1 root) and 2 (M2 mid section)
try:
time = cr.blade.time
data = cr.blade.data
# -----------------------
# Blade 1 root
# -----------------------
ax1 = pa4.fig.add_subplot(pa4.nr_rows, pa4.nr_cols, plot_nr)
ax1.plot(cr.blade.time, cr.blade.data[:,2], 'b', label='root')
# filtered channel
dataf, N, delay = filt.fir(time, data[:,2], ripple_db=20, cutoff_hz=5,
freq_trans_width=2.5, sample_rate=sps)
ax1.plot(time[N-1:]-delay, dataf[N-1:], 'r', label='filtered')
# and the staircase mean data points
for k in range(arg_stair.shape[1]):
ax1.plot(time[isel[k]].mean(), data[isel[k],2].mean(), 'ko')
# and save the data stair
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, V, temp]
datastair[k,4] = data[isel[k],1].mean()
ax1.set_title('blade 1 strain root')
ax1.grid(True)
ax1.set_xlim(window_blade)
if cr.blade_is_cal:
ax1.set_ylabel('flapwise bending moment [Nm]')
plot_nr += 1
# -----------------------
# blade 2 root
# -----------------------
ax2 = pa4.fig.add_subplot(pa4.nr_rows, pa4.nr_cols, plot_nr)
ax2.plot(cr.blade.time, cr.blade.data[:,0], 'b', label='30\%')
# filtered channel
dataf, N, delay = filt.fir(time, data[:,0], ripple_db=20, cutoff_hz=5,
freq_trans_width=2.5, sample_rate=sps)
ax2.plot(time[N-1:]-delay, dataf[N-1:], 'r', label='filtered')
# and the staircase mean data points
for k in range(arg_stair.shape[1]):
ax2.plot(time[isel[k]].mean(), data[isel[k],0].mean(), 'ko')
# and save the data stair
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, V, temp]
datastair[k,6] = data[isel[k],0].mean()
ax2.set_title('blade 2 strain root')
ax2.grid(True)
ax2.set_xlim(window_blade)
if cr.blade_is_cal:
ax2.set_ylabel('flapwise bending moment [Nm]')
plot_nr += 1
# -----------------------
# Blade 1 30%
# -----------------------
ax3 = pa4.fig.add_subplot(pa4.nr_rows, pa4.nr_cols, plot_nr)
ax3.plot(cr.blade.time, cr.blade.data[:,3], 'b', label='root')
# filtered channel
dataf, N, delay = filt.fir(time, data[:,3], ripple_db=20, cutoff_hz=5,
freq_trans_width=2.5, sample_rate=sps)
ax3.plot(time[N-1:]-delay, dataf[N-1:], 'r', label='filtered')
# and the staircase mean data points
for k in range(arg_stair.shape[1]):
ax3.plot(time[isel[k]].mean(), data[isel[k],3].mean(), 'ko')
# and save the data stair
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, V, temp]
datastair[k,5] = data[isel[k],3].mean()
ax3.set_title('blade 1 strain 30\%')
ax3.grid(True)
ax3.set_xlim(window_blade)
if cr.blade_is_cal:
ax3.set_ylabel('flapwise bending moment [Nm]')
plot_nr += 1
# -----------------------
# Blade 2 30%
# -----------------------
ax4 = pa4.fig.add_subplot(pa4.nr_rows, pa4.nr_cols, plot_nr)
ax4.plot(cr.blade.time, cr.blade.data[:,1], 'b', label='root')
# filtered channel
dataf, N, delay = filt.fir(time, data[:,1], ripple_db=20, cutoff_hz=5,
freq_trans_width=2.5, sample_rate=sps)
ax4.plot(time[N-1:]-delay, dataf[N-1:], 'r', label='filtered')
# and the staircase mean data points
for k in range(arg_stair.shape[1]):
ax4.plot(time[isel[k]].mean(), data[isel[k],1].mean(), 'ko')
# and save the data stair
# indices: [RPM, yaw, FA, SS, B1_r, B1_30, B2_r, B2_30, V, temp]
datastair[k,7] = data[isel[k],1].mean()
ax4.set_title('blade 2 strain 30\%')
ax4.grid(True)
ax4.set_xlim(window_blade)
if cr.blade_is_cal:
ax4.set_ylabel('flapwise bending moment [Nm]')
plot_nr += 1
# set the same y range on both axis
lims1 = ax1.get_ylim()
lims2 = ax2.get_ylim()
lims3 = ax3.get_ylim()
lims4 = ax4.get_ylim()
ymin = min([lims1[0], lims2[0], lims3[0], lims4[0]])
ymax = max([lims1[1], lims2[1], lims3[1], lims4[1]])
ax1.set_ylim([ymin, ymax])
ax2.set_ylim([ymin, ymax])
ax3.set_ylim([ymin, ymax])
ax4.set_ylim([ymin, ymax])
except AttributeError:
pass
# -------------------------------------------------
# save figure and data from the stairs
# -------------------------------------------------
pa4.save_fig()
np.savetxt(figpath + cr.resfile + fig_descr, datastair)
def freeyaw_steady_points(resfile, db, figpath):
"""
Select the steady points on the forced free yaw OJF cases.
We only consider data with sane tower strain, so only April!
"""
respath = os.path.join(PATH_DB, 'symlinks_all/')
res = ojfresult.ComboResults(respath, resfile, silent=False, sync=True,
cal=True)
# RPM from pulse only returns the pulses, nothing else is done
# # we use the standard calcict cases
# cd = ojfresult.CalibrationData
# res._calibrate_dspace(cd.caldict_dspace_04)
# res._calibrate_blade(cd.caldict_blade_04)
res._resample()
# res.dashboard_a3(figpath)
# and see if the staircase filter can get what we want
irpm = res.dspace.labels_ch['RPM']
iyaw = res.dspace.labels_ch['Yaw Laser']
ifa = res.dspace.labels_ch['Tower Strain For-Aft']
# because we don't get the indices in a reliable manner, do it 3 times and
# hope we have the same points?? Or use other filtering technique?
# # Select the stair ranges based on steady, constant RPM's
# ext = '_RPMargs'
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=resfile+ext, runid=resfile+ext)
# time_stair, data_stair = sc.setup_filter(res.dspace.time,
# res.dspace.data[:,irpm], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=5.0e-4,
# stair_step_tresh=5.0, smoothen='moving',
# points_per_stair=500)
# results_filtering(res, figpath, ext, sc.arg_stair)
# Select the stair ranges based on steady, constant yaw angles
# this is a more robust approach when combined with the moving the start
# index of the stair argument more close to the end index
sps = int(round(1/np.diff(res.dspace.time).mean(), 0))
ext = '_YAWargs'
sc = StairCase(plt_progress=False, pprpath=figpath, figpath=figpath,
figfile=resfile+'_yaw', runid=resfile+'_yaw')
time_stair, data_stair = sc.setup_filter(res.dspace.time,
res.dspace.data[:,iyaw], smooth_window=1.5,
cutoff_hz=False, dt=1, dt_treshold=2.0e-4,
stair_step_tresh=1.5, smoothen='moving',
points_per_stair=sps*3)
# # and for the thrust
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=resfile+'_fa', runid=resfile+'_fa')
# time_stair, data_stair = sc.setup_filter(res.dspace.time,
# res.dspace.data[:,ifa], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=2.0e-4,
# stair_step_tresh=1.0, smoothen='moving',
# points_per_stair=800)
# # the blade channel 1
# pltid = resfile + '_B2_R'
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=pltid, runid=pltid)
# time_stair, data_stair = sc.setup_filter(res.blade.time,
# res.blade.data[:,0], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=5.0e-6,
# stair_step_tresh=0.005, smoothen='moving',
# points_per_stair=800)
# # the blade channel 2
# pltid = resfile + '_B2_30'
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=pltid, runid=pltid)
# time_stair, data_stair = sc.setup_filter(res.blade.time,
# res.blade.data[:,1], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=5.0e-6,
# stair_step_tresh=0.015, smoothen='moving',
# points_per_stair=800)
# # the blade channel 3
# pltid = resfile + '_B1_R'
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=pltid, runid=pltid)
# time_stair, data_stair = sc.setup_filter(res.blade.time,
# res.blade.data[:,2], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=5.0e-6,
# stair_step_tresh=0.005, smoothen='moving',
# points_per_stair=800)
# # the blade channel 4
# pltid = resfile + '_B1_30'
# sc = ojfresult.StairCase(plt_progress=False, pprpath=figpath,
# figpath=figpath, figfile=pltid, runid=pltid)
# time_stair, data_stair = sc.setup_filter(res.blade.time,
# res.blade.data[:,3], smooth_window=1.5,
# cutoff_hz=False, dt=1, dt_treshold=5.0e-6,
# stair_step_tresh=0.015, smoothen='moving',
# points_per_stair=800)
# for the yaw angle selection: the yaw angle is steady for the
# whole duration of the stair, but the other measurements are not!
# therefore delay the starting stair point 50%
if ext.find('YAW') > -1:
delta = (sc.arg_stair[1,:] - sc.arg_stair[0,:]) / 1.25
sc.arg_stair[0,:] += delta.astype(np.int)
# make plots to inspect the extraction process, save stair step points in
# text files
results_filtering(res, figpath, ext, sc.arg_stair)
# save statistics of each stair step to database
df_res = res.to_df()
# db = ojfdb.MeasureDb(load_index=False)
db.add_staircase_stats(resfile, df_res, sc.arg_stair)
# db.save_stats(prefix='yawstairs')
return db
def add_yawcontrol_stair_steps():
"""
Use the new DataFrame index to select the cases.
yaw_mode = freeyawforced
yaw_mode = freeyaw and the_rest = forced
Extract the steps from the staircase measurements, and add to index
and statistics database.
"""
figpath = 'figures/forced_yaw_error/'
# fname = os.path.join(PATH_DB, 'db_index_symlinks_all.h5')
# df_db = pd.read_hdf(fname, 'table')
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
idf = db.index
df_sel = idf[(idf.yaw_mode == 'freeyawforced') |
((idf.yaw_mode == 'freeyaw') & (idf.the_rest == 'forced')) |
(idf.yaw_mode2 == 'yawcontrol')]
# df_sel = idf[(idf.yaw_mode == 'freeyawforced') |
# (idf.yaw_mode == 'freeyaw') |
# (idf.yaw_mode2 == 'yawcontrol')]
for case in df_sel.index:
# '0410_run_332'
if not int(case.split('_')[2]) == 413:
# plotting of all the cases
db = freeyaw_steady_points(case, db, figpath)
#
else:
continue
db.add_df_dict2stat(update=True)
db.save(complib=None)
return db
def remove_yawcontrol_db():
"""Remove the yawcontrol cases from the database, they where added using
the older stair extraction process.
"""
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
idf = db.index
idf = idf[(idf.yaw_mode == 'freeyawforced') |
(idf.yaw_mode2 == 'yawcontrol') |
((idf.yaw_mode == 'freeyaw') & (idf.the_rest == 'forced'))]
# extract the old steps to compare
idf_old = idf[idf.run_type == 'stair_step'].copy()
db.load_stats()
# mean_old = db.mean.loc[idf_old.index]
# plt.figure('yaw angle vs rpm')
# plt.plot(mean_old.yaw_angle, mean_old.rpm, 'rs')
db.remove_from_stats_index(idf_old.index)
db.save(complib=None)
###############################################################################
### STEADY YAW ERRORS, NG
###############################################################################
def remove_stair_step():
"""Remove all the stair step cases again from index and stats. This is
helpfull when we want to run the stair extraction process again
"""
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
db.load_stats()
id_rem = db.index[db.index.run_type=='stair_step'].index
db.remove_from_stats_index(id_rem)
db.save(complib='zlib')
def add_yawcontrol_steps_ng():
"""
"""
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
idf = db.index#[db.index.month == 2]
idf = idf[(idf.yaw_mode == 'freeyawforced') |
(idf.yaw_mode2 == 'yawcontrol') |
((idf.yaw_mode == 'freeyaw') & (idf.the_rest == 'forced'))]
# # remove previous extracted steps from the same cases
# idf = idf[idf.run_type=='']
# db.index = idf
figpath = 'figures/forced_yaw_error_ng/'
for case in idf.index:
try:
fname = os.path.join(PATH_DATA_CAL_HDF, case + '.h5')
res = pd.read_hdf(fname, 'table')
time = res.time.values
figname = os.path.join(figpath, case + '_filter_stairs.png')
sc = StairCaseNG(time, freq_ds=10)
sigs = [res.yaw_angle.values, res.rpm.values]
weights = [1.5, 1.0]
# low RPM: much bigger window compared to high RPM?
if res.rpm.mean() < 200:
w_lregr = 3.5
x_threshold = 0.3
min_step_window = 2.5
else:
w_lregr = 1.5
x_threshold = 0.6
min_step_window = 1.0
steps = sc.get_steps(sigs, weights, cutoff_hz=1.0, window=w_lregr,
x_threshold=x_threshold, figname=figname,
min_step_window=min_step_window, order=2)
db.add_staircase_stats(case, res, steps)
except Exception as e:
print(e)
print('*'*80)
# # evaluate the performance
# db._dict2df()
# # compare with old results
# plt.figure('yaw angle vs rpm')
# plt.plot(mean_old.yaw_angle, mean_old.rpm, 'rs')
# plt.plot(db.df_mean.yaw_angle, db.df_mean.rpm, 'b>', alpha=0.7)
db.add_df_dict2stat(update=True)
db.save(complib='zlib')
return db
def add_freeyaw_steady_steps():
"""
"""
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
idf = db.index
idf = idf[(idf.rpm_change=='') & (idf.sweepid=='') &
(idf.yaw_mode.str.startswith('free'))]
# remove cases that have been used for add_yawcontrol_stair_steps()
idf = idf[(idf.yaw_mode != 'freeyawforced') & (idf.the_rest != 'forced') &
(idf.yaw_mode2 != 'yawcontrol') ]
figpath = 'figures/steps_freeyaw_play/'
for case in idf.index:
try:
fname = os.path.join(PATH_DATA_CAL_HDF, case + '.h5')
res = pd.read_hdf(fname, 'table')
time = res.time.values
figname = os.path.join(figpath, case + '_filter_stairs.png')
sc = StairCaseNG(time, freq_ds=10)
sigs = [res.yaw_angle.values, res.rpm.values]
# low RPM: much bigger window compared to high RPM?
if res.rpm.mean() < 200:
w_lregr = 3.5
x_threshold = 0.25
min_step_window = 3.0
weights = [1.5, 1.0]
else:
w_lregr = 1.5
x_threshold = 0.6
min_step_window = 1.0
weights = [1.5, 1.0]
steps = sc.get_steps(sigs, weights, cutoff_hz=1.0, window=w_lregr,
x_threshold=x_threshold, figname=figname,
min_step_window=min_step_window, order=2)
db.add_staircase_stats(case, res, steps)
except Exception as e:
print(e)
print('*'*80)
db.add_df_dict2stat(update=True)
db.save(complib='zlib')
return db
def test_add():
db = ojfdb.MeasureDb(prefix='symlinks_all', path_db='database/')
idf = db.index
df_sel = idf[(idf.yaw_mode == 'freeyawforced') |
((idf.yaw_mode == 'freeyaw') & (idf.the_rest == 'forced')) |
(idf.yaw_mode2 == 'yawcontrol')]
case = df_sel.index.values[0]
fname = os.path.join('data', 'calibrated', 'DataFrame', case+'.h5')
df_res = pd.read_hdf(fname, 'table')
arg_stair = np.array([[1,200, 500], [100, 300,1000]])
i1 = arg_stair[0,1]
i2 = arg_stair[1,1]
resfile_step = '%s_i0_%i_i1_%i' % (case, i1, i2)
db.add_stats(resfile_step, df_res[i1:i2])
mean = db.df_dict_mean
db._init_df_dict_stats()
db._add_stats_df_dict(case, df_res[i1:i2].mean(), db.df_dict_mean)
mean = db.df_dict_mean
maxx = db.df_dict_max
###############################################################################
### DYNAMIC YAW ERR PERF
###############################################################################
# from the OJF data and HAWC2 results: subtract the yaw response parameters:
# how quickly is it falling back to steady yaw error
# Much of the work is done manually: many freeyaw cases have been performed
# in one case, so we have to manually specify the time ranges
class plot_freeyaw_response(object):
"""Plot response of single free yaw case in one plot
"""
def __init__(self, df, t0, duration, ncols=1):
if ncols == 1:
self.fig, self.axes = plotting.subplots(nrows=1, ncols=ncols,
figsize=(4,2), dpi=120)
self.axes = self.axes.flatten()
self.ax = self.axes[0]
self.first(df, t0, duration)
else:
self.fig, self.axes = plotting.subplots(nrows=1, ncols=2,
figsize=(7,2), dpi=120)
self.axes = self.axes.flatten()
self.ax = self.axes[0]
self.ax2 = self.axes[1]
self.first(df, t0, duration)
def first(self, df, t0, duration):
nre = len(df.time)
te = df.time.values[-1]
i0 = int(t0*nre/te)
i1 = i0 + int(duration*nre/te)
self.ax.plot(df.time[i0:i1]-t0, df.rpm[i0:i1], 'b-', label='RPM')
self.axr = self.ax.twinx()
self.axr.plot(df.time[i0:i1]-t0, df.yaw_angle[i0:i1], 'r-',
label='yaw [deg]', alpha=0.7)
self.ax.set_xlabel('time [s]')
self.ax.set_ylabel('rotor speed [RPM]')
self.axr.set_ylabel('yaw angle [deg]')
self.ax.set_xlim([0, duration])
self.ax.grid(True)
def second(self, df, t0, duration):
nre = len(df.time)
te = df.time.values[-1]
i0 = int(t0*nre/te)
i1 = i0 + int(duration*nre/te)
self.ax2.plot(df.time[i0:i1]-t0, df.rpm[i0:i1], 'b-', label='RPM')
self.ax2r = self.ax2.twinx()
self.ax2r.plot(df.time[i0:i1]-t0, df.yaw_angle[i0:i1], 'r-',
label='yaw [deg]', alpha=0.7)
self.ax2.set_xlabel('time [s]')
self.ax2r.set_ylabel('yaw angle [deg]')
self.ax2.set_xlim([0, duration])
self.ax2.grid(True)
def set_ylim_ticks(self, ylim, nrticks=9):
self.ax.set_ylim([ylim[0], ylim[1]])
self.ax.yaxis.set_ticks(np.linspace(ylim[0], ylim[1], nrticks).tolist())
def legend_axmatch(self):
self.ax, self.axr = plotting.match_yticks(self.ax, self.axr)
self.leg = plotting.one_legend(self.ax, self.axr, loc='best',
borderaxespad=0)
self.leg.get_frame().set_alpha(0.6)
def save(self, fname):
self.fig.tight_layout()
self.fig.subplots_adjust(top=0.92, bottom=0.25, right=0.85)
print('saving: %s' % fname)
self.fig.savefig(fname + '.png')
self.fig.savefig(fname + '.eps')
class FreeyawRespons(ojfdb.MeasureDb):
def __init__(self, interactive=True):
# initialize the MeasureDb first
super(FreeyawRespons, self).__init__()
self.load_stats()
self.save_stats_index = False
self.fpath = 'data/calibrated/DataFrame'
self.fdir = 'figures/freeyaw'
self.interactive = interactive
cols = ['runid', 'index', 't0', 'i0', 't1', 'i1', 'duration']
cols_df = ['time', 'rpm', 'yaw_angle', 'tower_strain_fa',
'tower_strain_ss', 'towertop_acc_fa', 'towertop_acc_ss',
'towertop_acc_z', 'voltage_filt', 'current_filt',
'rotor_azimuth', 'power', 'power2', 'hs_trigger',
'hs_trigger_start_end', 'rpm_pulse', 'temperature',
'wind_speed', 'static_p', 'blade2_root', 'blade2_30pc',
'blade1_root', 'blade1_30pc', 'blade_rpm_pulse']
self.cols_df = set(cols_df)
self.df_dict = {col:[] for col in cols}
for col in cols_df:
self.df_dict['%s_init' % col] = []
self.df_dict['%s_end' % col] = []
def add_run(self, df, t0, duration, resfile, t_s_init=1.9, t_s_end=1.9):
nre = len(df.time)
te = df.time.values[-1]
i0 = int(t0*nre/te)
i1 = i0 + int(duration*nre/te)
i_s_init = int(t_s_init*nre/te)
i_s_end = int(t_s_end*nre/te)
if df.yaw_angle.values[i0:i0+i_s_init].mean() >= 0.0:
app = 'fastside'
else:
app = 'slowside'
res_init = resfile + '_%s_t0_%05.02fs_init' % (app, t0)
index_init = self.index.loc[resfile].to_dict()
index_init['sweepid'] = '%i_%i' % (i0, i0+i_s_init)
index_init['run_type'] = 'fyr_%s_init' % app
self.add_stats(res_init, df[i0:i0+i_s_init], index_row=index_init)
res_end = resfile + '_%s_t0_%05.02fs_end' % (app, t0)
index_end = self.index.loc[resfile].to_dict()
index_end['sweepid'] = '%i_%i' % (i1-i_s_end, i1)
index_end['run_type'] = 'fyr_%s_end' % app
self.add_stats(res_end, df[i1-i_s_end:i1], index_row=index_end)
def do_all_runs(self, save_stats_index=True, complib='blosc'):
self.interactive = False
self.save_stats_index = save_stats_index
for item in dir(self):
if item[:4] == 'run_':
run = getattr(self, item)
run()
if save_stats_index:
self.save_all_runs(complib=complib)
def save_all_runs(self, complib='blosc'):
self.add_df_dict2stat(update=True)
self.save(complib=complib)
def run_263(self):
"""
"""
fpath = 'data/calibrated/DataFrame'
fdir = 'figures/freeyaw'
fname = '0405_run_263_7.0ms_dc0.4_flexies_freeyaw_highrpm'
df = pd.read_hdf(os.path.join(fpath, fname+'.h5'), 'table')
# overview plot
if self.interactive:
plt.figure('rpm, 0405_run_263_7.0ms')
plt.plot(df.time, df.rpm, 'b', label='RPM')
plt.grid()
plt.twinx()
plt.plot(df.time, df.yaw_angle, 'r', label='yaw')
plt.legend(loc='best')
# plt.figure('yaw, 0405_run_263_7.0ms')
# plt.plot(df.time, df.yaw_angle, label='flex')
# plt.legend(loc='best')
# plt.grid()
# ---------------------------------------------------------------------
# detailed report plots
t0 = 25.7
duration = 12.0
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([380, 540])
q.axr.set_ylim([-35, 5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
t0 = 52.6
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([380, 540])
q.axr.set_ylim([35, -5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
def run_264(self):
"""
"""
fpath = 'data/calibrated/DataFrame'
fdir = 'figures/freeyaw'
fname = '0405_run_264_7.0ms_dc0.6_flexies_freeyaw_highrpm'
df = pd.read_hdf(os.path.join(fpath, fname+'.h5'), 'table')
# overview plot
if self.interactive:
plt.figure('rpm, 0405_run_264_7.0ms')
plt.plot(df.time, df.rpm, 'b', label='RPM')
plt.grid()
plt.twinx()
plt.plot(df.time, df.yaw_angle, 'r', label='yaw')
plt.legend(loc='best')
# ---------------------------------------------------------------------
# detailed report plots
t0 = 15.82
duration = 14.0
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([340, 500])
q.axr.set_ylim([-35, 5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
# detailed report plots
t0 = 45.56
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([340, 500])
q.axr.set_ylim([35, -5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
def run_265(self):
"""
"""
fpath = 'data/calibrated/DataFrame'
fdir = 'figures/freeyaw'
fname = '0405_run_265_8.0ms_dc0.0_flexies_freeyaw_highrpm'
df = pd.read_hdf(os.path.join(fpath, fname+'.h5'), 'table')
# overview plot
if self.interactive:
plt.figure('rpm, 0405_run_265_8.0ms')
plt.plot(df.time, df.rpm, 'b', label='RPM')
plt.grid()
plt.twinx()
plt.plot(df.time, df.yaw_angle, 'r', label='yaw')
plt.legend(loc='best')
# ---------------------------------------------------------------------
# detailed report plots
t0 = 21.16 - 2
duration = 14.0
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.set_ylim_ticks([550, 750])
q.axr.set_ylim([-35, 5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
# detailed report plots
t0 = 52.34 - 2
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.set_ylim_ticks([550, 750])
q.axr.set_ylim([35, -5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
def run_266(self):
"""
"""
fpath = 'data/calibrated/DataFrame'
fdir = 'figures/freeyaw'
fname = '0405_run_266_8.0ms_dc0.4_flexies_freeyaw_highrpm'
df = pd.read_hdf(os.path.join(fpath, fname+'.h5'), 'table')
# overview plot
if self.interactive:
plt.figure('rpm, 0405_run_266_8.0ms')
plt.plot(df.time, df.rpm, 'b', label='RPM')
plt.grid()
plt.twinx()
plt.plot(df.time, df.yaw_angle, 'r', label='yaw')
plt.legend(loc='best')
# ---------------------------------------------------------------------
# detailed report plots
t0 = 18.12 - 2
duration = 14.0
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([500, 660])
q.axr.set_ylim([-35, 5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
# detailed report plots
t0 = 48.7 - 2
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([500, 660])
q.axr.set_ylim([35, -5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)
def run_267(self):
"""
"""
fpath = 'data/calibrated/DataFrame'
fdir = 'figures/freeyaw'
fname = '0405_run_267_8.0ms_dc0.6_flexies_freeyaw_highrpm'
df = pd.read_hdf(os.path.join(fpath, fname+'.h5'), 'table')
# overview plot
if self.interactive:
plt.figure('rpm, 0405_run_267_8.0ms')
plt.plot(df.time, df.rpm, 'b', label='RPM')
plt.grid()
plt.twinx()
plt.plot(df.time, df.yaw_angle, 'r', label='yaw')
plt.legend(loc='best')
# ---------------------------------------------------------------------
# detailed report plots
t0 = 14.48 - 2
duration = 13.0
figname = os.path.join(fdir, fname + '_t0_%03.02f' % t0)
q = plot_freeyaw_response(df, t0, duration)
q.ax.set_ylim([460, 620])
q.axr.set_ylim([-35, 5])
q.legend_axmatch()
q.save(figname)
if self.save_stats_index:
self.add_run(df, t0, duration, fname, t_s_init=1.9, t_s_end=1.9)