-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathdlcplots.py
1030 lines (875 loc) · 38.3 KB
/
dlcplots.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 16 10:21:11 2014
@author: dave
"""
import os
# import socket
import gc
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
#from matplotlib.figure import Figure
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigCanvas
#from scipy import interpolate as interp
#from scipy.optimize import fmin_slsqp
#from scipy.optimize import minimize
#from scipy.interpolate import interp1d
#import scipy.integrate as integrate
#http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
import pandas as pd
#import openpyxl as px
#import numpy as np
#import windIO
from wetb.prepost import mplutils
from wetb.prepost import Simulations as sim
from wetb.prepost import dlcdefs
plt.rc('font', family='serif')
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
plt.rc('axes', labelsize=12)
# do not use tex on Gorm and or Jess
#if not socket.gethostname()[:2] in ['g-', 'je', 'j-']:
# plt.rc('text', usetex=True)
plt.rc('legend', fontsize=11)
plt.rc('legend', numpoints=1)
plt.rc('legend', borderaxespad=0)
def merge_sim_ids(sim_ids, post_dirs, post_dir_save=False, columns=None):
"""
"""
cols_extra = ['[run_dir]', '[res_dir]', '[wdir]', '[DLC]', '[Case folder]']
min_itemsize={'channel':100, '[run_dir]':100, '[res_dir]':100, '[DLC]':10,
'[Case folder]':100}
# map the run_dir to the same order as the post_dirs, labels
run_dirs = []
# avoid saving merged cases if there is only one!
if type(sim_ids).__name__ == 'list' and len(sim_ids) == 1:
sim_ids = sim_ids[0]
# if sim_id is a list, combine the two dataframes into one
df_stats = pd.DataFrame()
if type(sim_ids).__name__ == 'list':
for ii, sim_id in enumerate(sim_ids):
if isinstance(post_dirs, list):
post_dir = post_dirs[ii]
else:
post_dir = post_dirs
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
df_stats, _, _ = cc.load_stats(leq=False)
if columns is not None:
df_stats = df_stats[columns]
# stats has only a few columns identifying the different cases
# add some more for selecting them
dfc = cc.cases2df()
if '[wsp]' in dfc.columns:
wsp = '[wsp]'
else:
wsp = '[Windspeed]'
# columns we want to add from cc.cases (cases dict) to stats
cols_cc = set(cols_extra + [wsp])
# do not add column twice, some might already be in df stats
add_cols = list(cols_cc - set(df_stats.columns))
add_cols.append('[case_id]')
dfc = dfc[add_cols]
df_stats = pd.merge(df_stats, dfc, on='[case_id]')
# FIXME: this is very messy, we can end up with both [wsp] and
# [Windspeed] columns
if '[Windspeed]' in df_stats.columns and '[wsp]' in df_stats.columns:
df_stats.drop('[wsp]', axis=1, inplace=True)
if wsp != '[Windspeed]':
df_stats.rename(columns={wsp:'[Windspeed]'}, inplace=True)
# map the run_dir to the same order as the post_dirs, labels
run_dirs.append(df_stats['[run_dir]'].unique()[0])
print('%s Cases loaded.' % sim_id)
# if specified, save the merged sims elsewhere
if post_dir_save:
fpath = os.path.join(post_dir_save, '-'.join(sim_ids) + '.h5')
try:
os.makedirs(post_dir_save)
except OSError:
pass
else:
fpath = os.path.join(post_dir, '-'.join(sim_ids) + '.h5')
fmerged = fpath.replace('.h5', '_statistics.h5')
if ii == 0:
# and save somewhere so we can add the second data frame on
# disc
store = pd.HDFStore(fmerged, mode='w', complevel=9, complib='zlib')
store.append('table', df_stats, min_itemsize=min_itemsize)
print(store.get_storer('table').table.description)
# df_stats.to_hdf(fmerged, 'table', mode='w', format='table',
# complevel=9, complib='blosc')
print('%s merged stats written to: %s' % (sim_id, fpath))
else:
# instead of doing a concat in memory, add to the hdf store
store.append('table', df_stats)
# will fail if there are longer string columns compared to ii=0
# df_stats.to_hdf(fmerged, 'table', mode='r+', format='table',
# complevel=9, complib='blosc', append=True)
print('%s merging stats into: %s' % (sim_id, fpath))
# df_stats = pd.concat([df_stats, df_stats2], ignore_index=True)
# df_stats2 = None
# we might run into memory issues
del df_stats, _, cc
gc.collect()
store.close()
# and load the reduced combined set
print('loading merged stats: %s' % fmerged)
df_stats = pd.read_hdf(fmerged, 'table')
else:
sim_id = sim_ids
sim_ids = [sim_id]
post_dir = post_dirs
if isinstance(post_dirs, list):
post_dir = post_dirs[0]
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
df_stats, _, _ = cc.load_stats(columns=columns, leq=False)
if columns is not None:
df_stats = df_stats[columns]
try:
run_dirs = [df_stats['[run_dir]'].unique()[0]]
except KeyError:
run_dirs = []
# stats has only a few columns identifying the different cases
# add some more for selecting them
dfc = cc.cases2df()
if '[wsp]' in dfc.columns:
wsp = '[wsp]'
else:
wsp = '[Windspeed]'
# columns we want to add from cc.cases (cases dict) to stats
cols_cc = set(cols_extra + [wsp])
# do not add column twice, some might already be in df stats
add_cols = list(cols_cc - set(df_stats.columns))
add_cols.append('[case_id]')
dfc = dfc[add_cols]
df_stats = pd.merge(df_stats, dfc, right_on='[case_id]', left_on='case_id')
if '[Windspeed]' in df_stats.columns and '[wsp]' in df_stats.columns:
df_stats.drop('[wsp]', axis=1, inplace=True)
if wsp != '[Windspeed]':
df_stats.rename(columns={wsp:'[Windspeed]'}, inplace=True)
return run_dirs, df_stats
# =============================================================================
### STAT PLOTS
# =============================================================================
def plot_stats2(sim_ids, post_dirs, plot_chans, fig_dir_base=None, labels=None,
post_dir_save=False, dlc_ignore=['00'], figsize=(8,6),
eps=False, ylabels=None, title=True, chans_ms_1hz={}):
"""
Map which channels have to be compared
"""
# reduce required memory, only use following columns
cols = ['[run_dir]', '[DLC]', 'channel', '[res_dir]', '[Windspeed]',
'mean', 'max', 'min', 'std', '[wdir]', '[Case folder]']
run_dirs, df_stats = merge_sim_ids(sim_ids, post_dirs,
post_dir_save=post_dir_save)
plot_dlc_stats(df_stats, plot_chans, fig_dir_base, labels=labels,
figsize=figsize, dlc_ignore=dlc_ignore, eps=eps,
ylabels=ylabels, title=title, chans_ms_1hz=chans_ms_1hz)
def plot_dlc_stats(df_stats, plot_chans, fig_dir_base, labels=None,
figsize=(8,6), dlc_ignore=['00'], run_dirs=None,
sim_ids=[], eps=False, ylabels=None, title=True,
chans_ms_1hz={}):
"""Create for each DLC an overview plot of the statistics.
df_stats required columns:
* [DLC]
* [run_dir]
* [wdir]
* [Windspeed]
* channel
* stat parameters
Parameters
----------
df_stats : pandas.DataFrame
plot_chans : dict
Dictionary of channels to be plotted. Key is used for the plot title,
value is a list of unique channel names that will be included for
the statistic values. For example,
plot_chans['$B123 M_x$'] = ['blade1-blade1-node-003-momentvec-x',
'blade2-blade2-node-003-momentvec-x']
fig_dir_base : str
Base directory of where to store all the figures. A new sub-directory
will be created for each DLC.
labels : list, default=None
Labels used in the legend when comparing various DLB's
figsize : tuple, default=(8,6)
dlc_ignore : list, default=['dlc00']
By default all but dlc00 (stair case, wind ramp) are plotted. Add
more dlc numbers here if necessary.
run_dirs : list, default=None
If run_dirs is not defined it will be taken from the unique values
in the DataFrame. The order of the elements in this list needs to be
consistent with labels, sim_ids (if defined).
sim_ids : list, default=[]
Only used when creating the file name of the figures: appended at
the end of the file name (which starts with the unique channel name).
chans_ms_1hz : dict, default={}
Key/value pairs of channel and list of to be plotten m values. Channel
refers to plot title/label as used as the key value in plot_chans.
"""
def fig_epilogue(fig, ax, fname_base):
ax.grid()
ax.set_xlim(xlims)
leg = ax.legend(bbox_to_anchor=(1, 1), loc='lower right', ncol=3)
leg.get_frame().set_alpha(0.7)
title_space = 0.0
if title:
fig_title = '%s %s' % (dlc_name, ch_dscr)
# FIXME: dlc_name is assumed to be not in math mode ($$), so
# escape underscores to avoid latex going bananas
if mpl.rcParams['text.usetex']:
fig_title = '%s %s' % (dlc_name.replace('_', '\\_'), ch_dscr)
fig.suptitle(fig_title)
title_space = 0.02
ax.set_xlabel(xlabel)
if ylabels is not None:
ax.set_ylabel(ylabels[ch_name])
fig.tight_layout()
spacing = 0.94 - title_space - (0.065 * (ii + 1))
fig.subplots_adjust(top=spacing)
fig_path = os.path.join(fig_dir, dlc_name)
if len(sim_ids)==1:
fname = fname_base + '.png'
else:
fname = '%s_%s.png' % (fname_base, '_'.join(sim_ids))
if not os.path.exists(fig_path):
os.makedirs(fig_path)
fig_path = os.path.join(fig_path, fname)
fig.savefig(fig_path)#.encode('latin-1')
if eps:
fig.savefig(fig_path.replace('.png', '.eps'))
fig.clear()
print('saved: %s' % fig_path)
mfcs1 = ['k', 'w']
mfcs2 = ['b', 'w']
mfcs3 = ['r', 'w']
mfcs4 = ['k', 'b']
mark4 = ['s', 'o', '<', '>']
mfls4 = ['-', '--']
required = ['[DLC]', '[run_dir]', '[wdir]', '[Windspeed]', '[res_dir]',
'[Case folder]']
cols = df_stats.columns
for col in required:
if col not in cols:
print('plot_dlc_stats requires DataFrame with following columns:')
print(required)
print('following column is missing in stats DataFrame:', col)
return
if run_dirs is None:
run_dirs = df_stats['[run_dir]'].unique()
if not sim_ids:
sim_ids = []
for run_dir in run_dirs:
# in case this is a windows path:
tmp = run_dir.replace('\\', '/').replace(':', '')
sim_ids.append(tmp.split('/')[-2])
# first, take each DLC appart
for gr_name, gr_dlc in df_stats.groupby(df_stats['[Case folder]']):
dlc_name = gr_name
if dlc_name[:3].lower() == 'dlc':
# FIXME: this is messy since this places a hard coded dependency
# between [Case folder] and [Case id.] when the tag [DLC] is
# defined in dlcdefs.py
dlc_name = gr_name.split('_')[0]
# do not plot the stats for dlc00
if dlc_name.lower() in dlc_ignore:
continue
# cycle through all the target plot channels
for ch_dscr, ch_names in plot_chans.items():
# second, group per channel. Note that when the channel names are not
# identical, we need to manually pick them.
# figure file name will be the first channel
if isinstance(ch_names, list):
ch_name = ch_names[0]
fname_base = ch_names[0].replace('/', '_')
df_chan = gr_dlc[gr_dlc.channel.isin(ch_names)]
else:
ch_name = ch_names
ch_names = [ch_names]
df_chan = gr_dlc[gr_dlc.channel == ch_names]
fname_base = ch_names.replace('/', '_')
# if not, than we are missing a channel description, or the channel
# is simply not available in the given result set
# if not len(df_chan.channel.unique()) == len(ch_names):
# continue
lens = []
# instead of groupby, select the run_dir in the same order as
# occuring in the labels and post_dirs lists
for run_dir in run_dirs:
lens.append(len(df_chan[df_chan['[run_dir]']==run_dir]))
# for key, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
# lens.append(len(gr_ch_dlc_sid))
# when the channel is simply not present
if len(lens) == 0:
continue
# when only one of the channels was present, but the set is still
# complete.
# FIXME: what if both channels are present?
if len(ch_names) > 1 and (lens[0] < 1):
continue
elif len(ch_names) > 1 and len(lens)==2 and lens[1] < 1:
continue
print('start plotting: %s %s' % (dlc_name.ljust(10), ch_dscr))
fig, axes = mplutils.make_fig(nrows=1, ncols=1,
figsize=figsize, dpi=120)
ax = axes
# seperate figure for the mean of the 1Hz equivalent loads
fig2, axes2 = mplutils.make_fig(nrows=1, ncols=1,
figsize=figsize, dpi=120)
ax2 = axes2
if fig_dir_base is None and len(sim_ids) < 2:
res_dir = df_chan['[res_dir]'][:1].values[0]
fig_dir = os.path.join(fig_dir_base, res_dir)
elif fig_dir_base is None and len(sim_ids) > 0:
fig_dir = os.path.join(fig_dir_base, '-'.join(sim_ids))
# elif fig_dir_base and len(sim_ids) < 2:
# res_dir = df_chan['[res_dir]'][:1].values[0]
# fig_dir = os.path.join(fig_dir_base, res_dir)
elif fig_dir_base is not None:
# create the compare directory if not defined
fig_dir = fig_dir_base
# if we have a list of different cases, we also need to group those
# because the sim_id wasn't saved before in the data frame,
# we need to derive that from the run dir
# if there is only one run dir nothing changes
# sid_names = []
# for clarity, set off-set on wind speed when comparing two DLB's
if len(lens)==2:
windoffset = [-0.2, 0.2]
dirroffset = [-5, 5]
else:
windoffset = [0]
dirroffset = [0]
# in case of a fully empty plot xlims will remain None and there
# is no need to save the plot
xlims = None
# instead of groupby, select the run_dir in the same order as
# occuring in the labels, post_dirs lists
for ii, run_dir in enumerate(run_dirs):
gr_ch_dlc_sid = df_chan[df_chan['[run_dir]']==run_dir]
if len(gr_ch_dlc_sid) < 1:
print('no data for run_dir:', run_dir)
continue
# for run_dir, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
if labels is None:
sid_name = sim_ids[ii]
else:
sid_name = labels[ii]
# sid_names.append(sid_name)
print(' sim_id/label:', sid_name)
# FIXME: will this go wrong in PY3?
if dlc_name.lower() in ['dlc61', 'dlc62']:
key = '[wdir]'
xdata = gr_ch_dlc_sid[key].values + dirroffset[ii]
xlabel = 'wind direction [deg]'
xlims = [0, 360]
else:
key = '[Windspeed]'
xdata = gr_ch_dlc_sid[key].values + windoffset[ii]
xlabel = 'Wind speed [m/s]'
xlims = [3, 27]
dmin = gr_ch_dlc_sid['min'].values
dmean = gr_ch_dlc_sid['mean'].values
dmax = gr_ch_dlc_sid['max'].values
dstd = gr_ch_dlc_sid['std'].values
if len(sim_ids)==1:
lab1 = 'mean'
lab2 = 'min'
lab3 = 'max'
lab4 = '1Hz EqL'
else:
lab1 = 'mean %s' % sid_name
lab2 = 'min %s' % sid_name
lab3 = 'max %s' % sid_name
lab4 = '1Hz EqL %s' % sid_name
mfc1 = mfcs1[ii]
mfc2 = mfcs2[ii]
mfc3 = mfcs3[ii]
ax.errorbar(xdata, dmean, mec='k', marker='o', mfc=mfc1, ls='',
label=lab1, alpha=0.7, yerr=dstd, ecolor='k')
ax.plot(xdata, dmin, mec='b', marker='^', mfc=mfc2, ls='',
label=lab2, alpha=0.7)
ax.plot(xdata, dmax, mec='r', marker='v', mfc=mfc3, ls='',
label=lab3, alpha=0.7)
# mean of 1Hz equivalent loads
ms = []
if ch_dscr in chans_ms_1hz:
ms = chans_ms_1hz[ch_dscr]
for im, m in enumerate(ms):
# average over seed and possibly yaw angles
# wind speed or yaw inflow according to dlc case
gr_key = gr_ch_dlc_sid[key]
d1hz = gr_ch_dlc_sid[m].groupby(gr_key).mean()
ax2.plot(d1hz.index, d1hz.values, mec=mfcs4[ii], alpha=0.7,
marker=mark4[im], ls=mfls4[ii], mfc=mfc1,
label=lab4, color=mfcs4[ii])
# for wind, gr_wind in gr_ch_dlc.groupby(df_stats['[Windspeed]']):
# wind = gr_wind['[Windspeed]'].values
# dmin = gr_wind['min'].values#.mean()
# dmean = gr_wind['mean'].values#.mean()
# dmax = gr_wind['max'].values#.mean()
## dstd = gr_wind['std'].mean()
# ax.plot(wind, dmean, 'ko', label='mean', alpha=0.7)
# ax.plot(wind, dmin, 'b^', label='min', alpha=0.7)
# ax.plot(wind, dmax, 'rv', label='max', alpha=0.7)
## ax.errorbar(wind, dmean, c='k', ls='', marker='s', mfc='w',
## label='mean and std', yerr=dstd)
# if str(dlc_name) not in ['61', '62']:
# ax.set_xticks(gr_ch_dlc_sid['[Windspeed]'].values)
# don't save empyt plots
if xlims is None:
continue
fig_epilogue(fig, ax, fname_base)
# don't save empty plots
if len(ms) < 1:
continue
fig_epilogue(fig2, ax2, fname_base + '_1hz_eql')
class PlotStats(object):
def __init__(self):
pass
def load_stats(self, sim_ids, post_dirs, post_dir_save=False):
self.sim_ids = sim_ids
self.post_dirs = post_dirs
# reduce required memory, only use following columns
cols = ['[run_dir]', '[DLC]', 'channel', '[res_dir]', '[Windspeed]',
'mean', 'max', 'min', 'std', '[wdir]']
# if sim_id is a list, combine the two dataframes into one
df_stats = pd.DataFrame()
if type(sim_ids).__name__ == 'list':
for ii, sim_id in enumerate(sim_ids):
if isinstance(post_dirs, list):
post_dir = post_dirs[ii]
else:
post_dir = post_dirs
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
df_stats, _, _ = cc.load_stats(columns=cols, leq=False)
print('%s Cases loaded.' % sim_id)
# if specified, save the merged sims elsewhere
if post_dir_save:
fpath = os.path.join(post_dir_save, '-'.join(sim_ids) + '.h5')
try:
os.makedirs(post_dir_save)
except OSError:
pass
else:
fpath = os.path.join(post_dir, '-'.join(sim_ids) + '.h5')
if ii == 0:
# and save somewhere so we can add the second data frame on
# disc
df_stats.to_hdf(fpath, 'table', mode='w', format='table',
complevel=9, complib='blosc')
print('%s merged stats written to: %s' % (sim_id, fpath))
else:
# instead of doing a concat in memory, add to the hdf store
df_stats.to_hdf(fpath, 'table', mode='r+', format='table',
complevel=9, complib='blosc', append=True)
print('%s merging stats into: %s' % (sim_id, fpath))
# we might run into memory issues
del df_stats, _, cc
gc.collect()
# and load the reduced combined set
print('loading merged stats: %s' % fpath)
df_stats = pd.read_hdf(fpath, 'table')
else:
sim_id = sim_ids
sim_ids = [sim_id]
post_dir = post_dirs
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
df_stats, _, _ = cc.load_stats(leq=False)
return df_stats
def select_extremes_blade_radial(self, df):
"""
For each radial position of the blade, find the extremes
"""
def selector(x):
"""
select following channels:
'local-blade%1i-node-%03i-momentvec-x'
'blade2-blade2-node-003-momentvec-y'
"""
if x[:11] == 'local-blade' and x[22:31] == 'momentvec':
return True
else:
return False
# find all blade local load channels
criteria = df.channel.map(lambda x: selector(x))
df = df[criteria]
# split channel columns so we can select channels properly
df = df.join(df.channel.apply(lambda x: pd.Series(x.split('-'))))
df_ext = {'dlc':[], 'case':[], 'node':[], 'max':[], 'min':[], 'comp':[]}
def fillvalues(x, ii, maxmin):
x['node'].append(m_group[3].ix[ii])
x['dlc'].append(m_group['[DLC]'].ix[ii])
x['case'].append(m_group['[case_id]'].ix[ii])
x['comp'].append(m_group[5].ix[ii])
if maxmin == 'max':
x['max'].append(m_group['max'].ix[ii])
x['min'].append(np.nan)
else:
x['max'].append(np.nan)
x['min'].append(m_group['min'].ix[ii])
return x
# we take blade1, blade2, and blade3
df_b2 = df[df[0]=='local']
# df_b2 = df_b2[df_b2[1]=='blade2']
df_b2 = df_b2[df_b2[4]=='momentvec']
# df_b2 = df_b2[df_b2[5]=='x']
# make sure we only have blade1, 2 and 3
assert set(df_b2[1].unique()) == set(['blade3', 'blade2', 'blade1'])
# # number of nodes
# nrnodes = len(df_b2[3].unique())
# group by node number, and take the max
for nodenr, group in df_b2.groupby(df_b2[3]):
print(nodenr, end=' ')
for comp, m_group in df_b2.groupby(group[5]):
print(comp)
imax = m_group['max'].argmax()
imin = m_group['min'].argmin()
df_ext = fillvalues(df_ext, imax, 'max')
df_ext = fillvalues(df_ext, imin, 'min')
df_ext = pd.DataFrame(df_ext)
df_ext.sort(columns='node', inplace=True)
return df_ext
def plot_extremes_blade_radial(self, df_ext, fpath):
nrows = 2
ncols = 2
figsize = (11,7.15)
fig, axes = mplutils.make_fig(nrows=nrows, ncols=ncols, figsize=figsize)
# self.col = ['r', 'k']
# self.alf = [1.0, 0.7]
# self.i = 0
mx_max = df_ext['max'][df_ext.comp == 'x'].dropna()
mx_min = df_ext['min'][df_ext.comp == 'x'].dropna()
my_max = df_ext['max'][df_ext.comp == 'y'].dropna()
my_min = df_ext['min'][df_ext.comp == 'y'].dropna()
# nodes = df_ext.node.ix[mx_max.index]
axes[0,0].plot(mx_max, 'r^', label='$M_{x_{max}}$')
axes[0,1].plot(mx_min, 'bv', label='$M_{x_{min}}$')
axes[1,0].plot(my_max, 'r^', label='$M_{y_{max}}$')
axes[1,1].plot(my_min, 'bv', label='$M_{y_{min}}$')
axs = axes.ravel()
for ax in axs:
ax.grid()
ax.legend(loc='best')
# axs[0].set_xticklabels([])
# axs[1].set_xticklabels([])
# axs[2].set_xticklabels([])
# axs[-1].set_xlabel('time [s]')
fig.tight_layout()
fig.subplots_adjust(hspace=0.06)
fig.subplots_adjust(top=0.98)
fdir = os.path.dirname(fpath)
# fname = os.path.basename(fpath)
if not os.path.exists(fdir):
os.makedirs(fdir)
print('saving: %s ...' % fpath, end='')
fig.savefig(fpath)#.encode('latin-1')
print('done')
fig.clear()
# save as tables
df_ext.ix[mx_max.index].to_excel(fpath.replace('.png', '_mx_max.xls'))
df_ext.ix[mx_min.index].to_excel(fpath.replace('.png', '_mx_min.xls'))
df_ext.ix[my_max.index].to_excel(fpath.replace('.png', '_my_max.xls'))
df_ext.ix[my_min.index].to_excel(fpath.replace('.png', '_my_min.xls'))
def extract_leq_blade_radial(self, df_leq, fpath):
def selector(x):
"""
select following channels:
'local-blade%1i-node-%03i-momentvec-x'
'blade2-blade2-node-003-momentvec-y'
"""
if x[:11] == 'local-blade' and x[22:31] == 'momentvec':
return True
else:
return False
# find all blade local load channels
criteria = df_leq.channel.map(lambda x: selector(x))
df = df_leq[criteria]
# split channel columns so we can select channels properly
df = df.join(df.channel.apply(lambda x: pd.Series(x.split('-'))))
df.sort(columns=3, inplace=True)
assert set(df[1].unique()) == set(['blade3', 'blade2', 'blade1'])
leqs = list(df.keys())[1:10]
df_ext = {leq:[] for leq in leqs}
df_ext['node'] = []
df_ext['comp'] = []
for nodenr, group in df.groupby(df[3]):
print(nodenr, end=' ')
for comp, m_group in df.groupby(group[5]):
print(comp)
for leq in leqs:
df_ext[leq].append(m_group[leq].max())
df_ext['node'].append(nodenr)
df_ext['comp'].append(comp)
df_ext = pd.DataFrame(df_ext)
df_ext.sort(columns='node', inplace=True)
df_ext[df_ext.comp=='x'].to_excel(fpath.replace('.xls', '_x.xls'))
df_ext[df_ext.comp=='y'].to_excel(fpath.replace('.xls', '_y.xls'))
df_ext[df_ext.comp=='z'].to_excel(fpath.replace('.xls', '_z.xls'))
return df_ext
class PlotPerf(object):
def __init__(self, nrows=4, ncols=1, figsize=(14,11)):
self.fig, self.axes = mplutils.make_fig(nrows=nrows, ncols=ncols,
figsize=figsize)
# self.axs = self.axes.ravel()
self.col = ['r', 'k']
self.alf = [1.0, 0.7]
self.i = 0
def plot(self, res, label_id):
"""
"""
i = self.i
sim_id = label_id
time = res.sig[:,0]
self.t0, self.t1 = time[0], time[-1]
# find the wind speed
for channame, chan in res.ch_dict.items():
if channame.startswith('windspeed-global-Vy-0.00-0.00'):
break
wind = res.sig[:,chan['chi']]
chi = res.ch_dict['bearing-shaft_rot-angle_speed-rpm']['chi']
rpm = res.sig[:,chi]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-x']['chi']
tx = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-y']['chi']
ty = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power = res.sig[:,chi]
try:
chi = res.ch_dict['Tors_e-1-100.11']['chi']
except KeyError:
chi = res.ch_dict['Tors_e-1-86.50']['chi']
tors_1 = res.sig[:,chi]
# try:
# chi = res.ch_dict['Tors_e-1-96.22']['chi']
# except:
# chi = res.ch_dict['Tors_e-1-83.13']['chi']
# tors_2 = res.sig[:,chi]
try:
chi = res.ch_dict['Tors_e-1-84.53']['chi']
except:
chi = res.ch_dict['Tors_e-1-73.21']['chi']
tors_3 = res.sig[:,chi]
ax = self.axes.ravel()
ax[0].plot(time, wind, self.col[i]+'--', label='%s wind speed' % sim_id,
alpha=self.alf[i])
ax[0].plot(time, pitch, self.col[i]+'-.', label='%s pitch' % sim_id,
alpha=self.alf[i])
ax[0].plot(time, rpm, self.col[i]+'-', label='%s RPM' % sim_id,
alpha=self.alf[i])
ax[1].plot(time, tx, self.col[i]+'--', label='%s Tower FA' % sim_id,
alpha=self.alf[i])
ax[1].plot(time, ty, self.col[i]+'-', label='%s Tower SS' % sim_id,
alpha=self.alf[i])
ax[2].plot(time, power/1e6, self.col[i]+'-', alpha=self.alf[i],
label='%s El Power' % sim_id)
ax[3].plot(time, tors_1, self.col[i]+'--', label='%s torsion tip' % sim_id,
alpha=self.alf[i])
# ax[3].plot(time, tors_2, self.col[i]+'-.', label='%s torsion 96 pc' % sim_id,
# alpha=self.alf[i])
# ax[3].plot(time, tors_3, self.col[i]+'-', label='%s torsion 84 pc' % sim_id,
# alpha=self.alf[i])
self.i += 1
def final(self, fig_path, fig_name):
axs = self.axes.ravel()
for ax in axs:
ax.set_xlim([self.t0, self.t1])
ax.grid()
ax.legend(loc='best')
axs[0].set_xticklabels([])
axs[1].set_xticklabels([])
axs[2].set_xticklabels([])
axs[-1].set_xlabel('time [s]')
self.fig.tight_layout()
self.fig.subplots_adjust(hspace=0.06)
self.fig.subplots_adjust(top=0.98)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
fname = os.path.join(fig_path, fig_name)
print('saving: %s ...' % fname, end='')
self.fig.savefig(fname)#.encode('latin-1')
print('done')
self.fig.clear()
def plot_dlc01_powercurve(sim_ids, post_dirs, run_dirs, fig_dir_base):
"""
Create power curve based on steady DLC01 results
Use the same format as for HS2 for easy comparison!
"""
def plot_dlc00(sim_ids, post_dirs, run_dirs, fig_dir_base=None, labels=None,
cnames=['dlc00_stair_wsp04_25_noturb.htc',
'dlc00_ramp_wsp04_25_04_noturb.htc'], figsize=(14,11)):
"""
This version is an update over plot_staircase.
"""
stairs = []
# if sim_id is a list, combine the two dataframes into one
if type(sim_ids).__name__ == 'list':
for ii, sim_id in enumerate(sim_ids):
if isinstance(post_dirs, list):
post_dir = post_dirs[ii]
else:
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
else:
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
for cname in cnames:
fp = PlotPerf(figsize=figsize)
for i, cc in enumerate(stairs):
if isinstance(cname, list):
_cname = cname[i]
else:
_cname = cname
if _cname in cc.cases_fail:
print('no result for %s' % cc.sim_id)
continue
cc.change_results_dir(run_dirs[i])
try:
res = cc.load_result_file(cc.cases[_cname])
except KeyError:
for k in sorted(cc.cases.keys()):
print(k)
print('-'*79)
print(cc.sim_id, _cname)
print('-'*79)
raise KeyError
if labels is not None:
label = labels[i]
else:
label = cc.sim_id
fp.plot(res, label)
dlcf = 'dlc' + cc.cases[_cname]['[DLC]']
fig_path = os.path.join(fig_dir_base, dlcf)
fp.final(fig_path, _cname.replace('.htc', '.png'))
def plot_staircase(sim_ids, post_dirs, run_dirs, fig_dir_base=None,
cname='dlc00_stair_wsp04_25_noturb.htc'):
"""
Default stair and ramp names:
dlc00_stair_wsp04_25_noturb
dlc00_ramp_wsp04_25_04_noturb
"""
stairs = []
col = ['r', 'k']
alf = [1.0, 0.7]
# if sim_id is a list, combine the two dataframes into one
if type(sim_ids).__name__ == 'list':
for ii, sim_id in enumerate(sim_ids):
if isinstance(post_dirs, list):
post_dir = post_dirs[ii]
else:
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
else:
sim_id = sim_ids
sim_ids = [sim_id]
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
fig, axes = mplutils.make_fig(nrows=3, ncols=1, figsize=(14,10))
ax = axes.ravel()
for i, cc in enumerate(stairs):
if cname in cc.cases_fail:
print('no result for %s' % cc.sim_id)
continue
cc.change_results_dir(run_dirs[i])
res = cc.load_result_file(cc.cases[cname])
respath = cc.cases[cname]['[run_dir]']
fname = os.path.join(respath, cname)
df_respost = pd.read_hdf(fname + '_postres.h5', 'table')
sim_id = cc.sim_id
time = res.sig[:,0]
t0, t1 = time[0], time[-1]
# find the wind speed
for channame, chan in res.ch_dict.items():
if channame.startswith('windspeed-global-Vy-0.00-0.00'):
break
wind = res.sig[:,chan['chi']]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['bearing-shaft_rot-angle_speed-rpm']['chi']
rpm = res.sig[:,chi]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-x']['chi']
tx = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-y']['chi']
ty = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power_mech = df_respost['stats-shaft-power']
ax[0].plot(time, wind, col[i]+'--', label='%s wind speed' % sim_id,
alpha=alf[i])
ax[0].plot(time, pitch, col[i]+'-.', label='%s pitch' % sim_id,
alpha=alf[i])
ax[0].plot(time, rpm, col[i]+'-', label='%s RPM' % sim_id,
alpha=alf[i])
ax[1].plot(time, tx, col[i]+'--', label='%s Tower FA' % sim_id,
alpha=alf[i])
ax[1].plot(time, ty, col[i]+'-', label='%s Tower SS' % sim_id,
alpha=alf[i])
ax[2].plot(time, power/1e6, col[i]+'-', label='%s El Power' % sim_id,
alpha=alf[i])
ax[2].plot(time, power_mech/1e3, col[i]+'-', alpha=alf[i],
label='%s Mech Power' % sim_id)
ax[0].set_xlim([t0, t1])
ax[0].grid()
ax[0].legend(loc='best')
ax[0].set_xticklabels([])
# ax[0].set_xlabel('time [s]')
ax[1].set_xlim([t0, t1])
ax[1].grid()
ax[1].legend(loc='best')
ax[1].set_xticklabels([])
# ax[1].set_xlabel('time [s]')
ax[2].set_xlim([t0, t1])
ax[2].grid()
ax[2].legend(loc='best')
ax[2].set_xlabel('time [s]')
fig.tight_layout()
fig.subplots_adjust(hspace=0.06)
fig.subplots_adjust(top=0.92)
if not os.path.exists(fig_dir_base):
os.makedirs(fig_dir_base)
fig_path = os.path.join(fig_dir_base, '-'.join(sim_ids) + '_stair.png')
print('saving: %s ...' % fig_path, end='')
fig.savefig(fig_path)#.encode('latin-1')
print('done')
fig.clear()
if __name__ == '__main__':
# auto configure directories: assume you are running in the root of the
# relevant HAWC2 model
# and assume we are in a simulation case of a certain turbine/project
P_RUN, P_SOURCE, PROJECT, sim_id, P_MASTERFILE, MASTERFILE, POST_DIR \
= dlcdefs.configure_dirs()
# -------------------------------------------------------------------------
# # manually configure all the dirs
# p_root_remote = '/mnt/hawc2sim'
# p_root_local = '/home/dave/DTU/Projects/AVATAR/'