-
Notifications
You must be signed in to change notification settings - Fork 2
/
RUINSapp.py
1031 lines (830 loc) · 42.7 KB
/
RUINSapp.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 -*-
import streamlit as st
import pandas as pd
import numpy as np
import xarray as xr
import seaborn as sns
sns.set_style('whitegrid', {'grid.linestyle': u'--'})
from sdm import SDM
#import statsmodels.api as sm
#import altair as alt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
from PIL import Image
from pathlib import Path
st.title('RUINS climate data app')
# helper functions
#@st.cache(persist=True)
def load_alldata():
weather = xr.load_dataset('data/weather.nc')
climate = xr.load_dataset('data/cordex_coast.nc')
# WARNING - bug fix for now:
# 'HadGEM2-ES' model runs are problematic and will be removed for now
# The issue is with the timestamp and requires revision of the ESGF reading routines
kys = [s for s in list(climate.keys()) if 'HadGEM2-ES' not in s] #remove all entries of HadGEM2-ES (6 entries)
climate = climate[kys]
return weather, climate
weather, climate = load_alldata()
def load_data(sel='Weather',regagg=None):
#Read data from netcdf files and return xarray
if sel == 'Weather':
data = xr.load_dataset('data/weather.nc')
elif sel == 'CORDEX':
data = xr.load_dataset('data/cordex_coast.nc')
else:
if regagg == 'North Sea Coast':
data = xr.load_dataset('data/cordex_coast.nc')
data.filter_by_attrs(RCP=sel)
return data
def read_markdown_file(markdown_file):
return Path(markdown_file).read_text()
def kde(data, cmdata='none', split_ts=1, cplot=True, eq_period=True):
# plot of kde with stripes
from sklearn.neighbors import KernelDensity
cxx = ['#E69F00', '#009E73', '#0072B2', '#D55E00', '#CC79A7']
cxx2 = ['#8c6bb1', '#810f7c']
data = data[~np.isnan(data)]
x_d = np.linspace(np.min(data) * 0.9, np.max(data) * 1.1, len(data))
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(data[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot & (split_ts == 1):
plt.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor='grey')
lp = np.exp(logprob)
xd = x_d
if type(cmdata) != str:
fig, (ax, cax, cax2) = plt.subplots(ncols=3, figsize=(10.5, 2.3), gridspec_kw={"width_ratios": [1, 0.02, 0.02]})
x_d2 = np.linspace(np.min(cmdata) * 0.9, np.max(cmdata) * 1.1, len(cmdata))
# instantiate and fit second KDE model
kde2 = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde2.fit(cmdata[:, None])
# score_samples returns the log of the probability density
logprob2 = kde2.score_samples(x_d2[:, None])
if cplot & (split_ts == 1):
ax.fill_between(x_d2, np.exp(logprob2), alpha=0.4, facecolor='grey')
lp2 = np.exp(logprob2)
xd2 = x_d2
else:
fig, (ax, cax) = plt.subplots(ncols=2, figsize=(10.5, 2.3), gridspec_kw={"width_ratios": [1, 0.06]})
if split_ts > 1:
if eq_period:
spliti = [0, len(data) - 40, len(data) - 20, len(data)]
else:
spliti = np.linspace(0, len(data), split_ts + 1).astype(int)
for i in np.arange(split_ts):
datax = data.iloc[spliti[i]:spliti[i + 1]]
x_d = np.linspace(np.min(datax) * 0.9, np.max(datax) * 1.1, len(datax))
try:
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(datax[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot:
ax.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor=cxx[i],
label='-'.join([str(datax.index.year.min()), str(datax.index.year.max())]))
except:
pass
if type(cmdata) != str:
# add climate model data
csplit_ts = [2040, 2080]
for i in np.arange(2):
datax = cmdata.loc[(cmdata.index.year >= csplit_ts[i]) & (cmdata.index.year < csplit_ts[i] + 20)]
x_d = np.linspace(np.min(datax) * 0.9, np.max(datax) * 1.1, len(datax))
try:
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(datax[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot:
ax.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor=cxx2[i],
label='-'.join([str(datax.index.year.min()), str(datax.index.year.max())]))
except:
pass
ax.legend(loc=1, ncol=5)
if cplot:
# cmap = plt.cm.get_cmap('cividis_r')
cmap = plt.cm.get_cmap('viridis_r')
colors = plt.cm.cividis_r(np.linspace(0, 1, len(data)))
colorsx = cmap(np.arange(cmap.N))
for i in np.arange(len(data)):
ax.plot([data.iloc[i], data.iloc[i]], [0, np.max(lp) * 0.9], c=colors[i])
labcb = 'Year'
if type(cmdata) != str:
cmap2 = plt.cm.get_cmap('plasma')
colors2 = plt.cm.plasma(np.linspace(0, 1, len(cmdata)))
colorsx2 = cmap2(np.arange(cmap2.N))
for i in np.arange(len(cmdata)):
ax.plot([cmdata.iloc[i], cmdata.iloc[i]], [0, np.max(lp) * 0.9], c=colors2[i])
cbar2 = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap2), cax=cax2, label=labcb,
ticks=[0, 0.2, 0.4, 0.6, 0.8, 1], fraction=0.0027, anchor=(1.0, 0.1))
cbar2.ax.set_yticklabels(
np.round(np.linspace(cmdata.index.year.min(), cmdata.index.year.max(), 6)).astype(int).astype(str))
labcb = ''
cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), cax=cax, label=labcb, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar.ax.set_yticklabels(
np.round(np.linspace(data.index.year.min(), data.index.year.max(), 6)).astype(int).astype(
str)) # vertically oriented colorbar
ax.set_ylabel('Occurrence (KDE)')
return ax
def yrplot_hm(sr, ref=[1980, 2000], ag='sum', qa=0.95, cbar_title='Temperature anomaly (K)', cmx='coolwarm', cmxeq=True, li=False):
# plot of heatmap with monthyl and annual stripes
yrs = sr.index.year.unique()
dummy = np.zeros((len(yrs), 14)) * np.nan
dummy = pd.DataFrame(dummy)
dummy.index = yrs
dummy.columns = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D', '', 'Year']
for i in sr.index:
dummy.iloc[i.year - yrs[0], i.month - 1] = sr.loc[i]
for i in yrs:
if ag == 'sum':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].sum()
cmx = 'coolwarm_r'
elif ag == 'min':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].min()
elif ag == 'max':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].max()
else: # ag == 'mean'
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].mean()
if ref == None:
pass
else:
refx = dummy.loc[ref[0]:ref[1]].mean(axis=0)
dummy = dummy - refx
if cmxeq:
vxU = dummy.abs().quantile(qa).quantile(qa)
vxL = -1. * vxU
else:
vxU = dummy.quantile(qa).quantile(qa)
vxL = dummy.quantile(1. - qa).quantile(1. - qa)
if ag == 'sum':
dummy.iloc[:, 13] = dummy.iloc[:, 13] / 12
plt.figure(figsize=(8,len(dummy)/15.))
ax = sns.heatmap(dummy, cmap=cmx, vmin=vxL, vmax=vxU, cbar_kws={'label': cbar_title})
if ref == None:
pass
else:
ax.add_patch(
plt.Rectangle((0, ref[0] - yrs[0]), 12, ref[1] - ref[0], fill=True, edgecolor='red', facecolor='gray', lw=2,
alpha=0.3, clip_on=False))
ax.add_patch(
plt.Rectangle((13, ref[0] - yrs[0]), 1, ref[1] - ref[0], fill=True, edgecolor='red', facecolor='gray', lw=2,
alpha=0.3, clip_on=False))
ax.annotate('Reference period', (0.5, ref[1] - yrs[0] - 2), color='white', weight='bold', ha='left',
va='bottom', alpha=0.6)
if type(li) == int:
ax.axhline(li - yrs[0], color='k', ls='--', lw=1, alpha=0.5)
ax.annotate(' >> observed', (12.5, li - yrs[0] - 0.5), color='k', ha='center', va='bottom', alpha=0.6,
rotation=90.)
ax.annotate('modelled << ', (12.5, li - yrs[0] + 0.5), color='k', ha='center', va='top', alpha=0.6,
rotation=90.)
# ax.add_patch(plt.Rectangle((0, li-yrs[0]), 12, 0, fill=False, edgecolor='k', ls='--', lw=1, alpha=0.5, clip_on=False))
ax.set_ylabel('Year')
ax.set_xlabel('Month ')
return
def monthlyx(dy, dyx=1, ylab='T (°C)', clab1='Monthly Mean in Year', clab2='Monthly Max in Year', pls='cividis_r'):
cmap = plt.cm.get_cmap(pls)
colors = cmap(np.linspace(0, 1, len(dy.index.year.unique())+1))
colorsx = cmap(np.arange(cmap.N))
idx1 = dy.index.year - dy.index.year.min()
idx1m = dy.index.month
if type(dyx) == int:
pass
else:
idx2 = (dyx.index.year - dyx.index.year.min()).astype(int)
idx2m = dyx.index.month
cmap1 = plt.cm.get_cmap('gist_heat_r')
colors1 = plt.cm.gist_heat_r(np.linspace(0, 1, len(dyx.index.year.unique()) + 1))
colorsx1 = cmap1(np.arange(cmap1.N))
for i in dy.columns:
plt.scatter(idx1m + (np.random.rand(len(dy)) - 1.5), dy[i].values.astype(np.float), c=colors[idx1], alpha=0.6, s=2)
if type(dyx) == int:
pass
else:
for i in dyx.columns:
plt.scatter(idx2m + (np.random.rand(len(dyx)) - 1.5), dyx[i].values.astype(np.float), c=colors1[idx2], alpha=0.6, s=2)
cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), label=clab1, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar.ax.set_yticklabels(np.round(np.linspace(dy.index.year.min(), dy.index.year.max(), 6)).astype(int).astype(str))
if type(dyx) == int:
pass
else:
cbar1 = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap1), label=clab2, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar1.ax.set_yticklabels(
np.round(np.linspace(dyx.index.year.min(), dyx.index.year.max(), 6)).astype(int).astype(str))
plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.ylabel(ylab)
return
def plt_map(sel='all',cm='none'):
import plotly.graph_objs as go
import plotly.express as px
dummy = xr.open_dataset('data/CORDEXgrid.nc')
dummy5 = xr.open_dataset('data/CMIP5grid.nc')
stats = pd.read_csv('data/stats.csv', index_col=0)
stats['ms'] = 15.
stats['color'] = 'gray'
mapbox_access_token = 'pk.eyJ1IjoiY29qYWNrIiwiYSI6IkRTNjV1T2MifQ.EWzL4Qk-VvQoaeJBfE6VSA'
px.set_mapbox_access_token(mapbox_access_token)
nodexy = pd.DataFrame([dummy.lon.values.ravel(), dummy.lat.values.ravel()]).T
nodexy.columns = ['lon', 'lat']
nodexy['hov'] = 'CORDEX grid'
def lin_grid(fig, dummy, chex='#d95f0e', opc=0.7):
x1, x2 = np.shape(dummy.lon)
lond = np.diff(dummy.lon.values)
latd = np.diff(dummy.lat.values)
lond1 = np.diff(dummy.lon.values, axis=0)
latd1 = np.diff(dummy.lat.values, axis=0)
for i in np.arange(x2)[1:-1]:
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[:, i] - 0.5 * lond[:, i - 1],
lat=dummy.lat.values[:, i] - 0.5 * latd[:, i - 1],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[:, i] + 0.5 * lond[:, i - 1],
lat=dummy.lat.values[:, i] + 0.5 * latd[:, i - 1],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
for i in np.arange(x1)[1:-1]:
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[i, :] - 0.5 * lond1[i - 1, :],
lat=dummy.lat.values[i, :] - 0.5 * latd1[i - 1, :],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[i, :] + 0.5 * lond1[i - 1, :],
lat=dummy.lat.values[i, :] + 0.5 * latd1[i - 1, :],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
return fig
def add_stats(sel='all'):
if sel == 'all':
ac = '#2c7fb8'
stats1 = stats
else:
ac = 'gray'
fig.add_trace(go.Scattermapbox(
lat=stats.lat,
lon=stats.lon,
mode='markers',
marker=go.scattermapbox.Marker(
size=10,
color=ac,
opacity=0.8
),
text=stats['Station name']
))
ac = '#df65b0'
if sel == 'krummhoern':
stats1 = stats.loc[stats.krummhoern == True]
elif sel == 'coast':
stats1 = stats.loc[stats.coast == True]
elif sel == 'niedersachsen':
stats1 = stats.loc[stats.niedersachsen == True]
elif sel == 'inland':
stats1 = stats.loc[stats.inland == True]
else:
try:
stats1 = pd.DataFrame(stats.loc[sel]).T
except:
ac = 'gray'
stats1 = pd.DataFrame(stats.loc['Norderney']).T
fig.add_trace(go.Scattermapbox(
lat=stats1.lat,
lon=stats1.lon,
mode='markers',
marker=go.scattermapbox.Marker(
size=10,
color=ac,
opacity=0.8
),
text=stats1['Station name']
))
return fig
def add_cmpx(cm):
maskcordex_coast = [[10, 4], [11, 4], [11, 5], [11, 6], [11, 7], [11, 8], [10, 8], [9, 8], [8, 8], [8, 9],
[8, 10], [9, 10], [10, 10], [10, 11], [11, 12], [12, 12], [12, 13], [12, 14], [13, 14],
[14, 14], [15, 14], [16, 14], [17, 14], [18, 14], [19, 15], [20, 15], [21, 14], [22, 14],
[22, 13],
[9, 4], [10, 5], [10, 6], [10, 7], [10, 8], [9, 7], [8, 7], [7, 7], [7, 8], [7, 9], [9, 9],
[10, 12], [11, 13], [11, 14], [14, 15], [15, 15], [16, 15], [17, 15], [18, 15], [19, 16],
[20, 16], [21, 15], [22, 15],
[11, 4], [12, 4], [12, 5], [12, 6], [12, 7], [12, 8], [11, 10], [11, 11], [13, 12],
[13, 13], [14, 13], [15, 13], [16, 13], [17, 13], [18, 13], [19, 14], [20, 14], [21, 13]]
for cc in maskcordex_coast:
fig.add_trace(go.Scattermapbox(
lat=[dummy.lat.values[tuple(cc)]],
lon=[dummy.lon.values[tuple(cc)]],
mode='markers',
marker=go.scattermapbox.Marker(
size=8,
color='#fec44f',
opacity=0.8
),
hoverinfo='skip'
))
return fig
# fig = go.Scattermapbox(lon = nodexy.lon, lat= nodexy.lat, mode='markers', marker_symbol='square', marker_size=15)
#fig = px.scatter_mapbox(nodexy, lat='lat', lon='lon', center={'lat': 53.0, 'lon': 8.3}, zoom=5, opacity=0.1, hover_data=['hov'])
fig = px.scatter_mapbox(stats, lat='lat', lon='lon', center={'lat': 53.0, 'lon': 8.6}, zoom=5, size='ms', opacity=0.8, color='color', hover_data=['Station name', 'lat', 'lon'], size_max=10)
if cm != 'none':
fig = lin_grid(fig, dummy)
fig = lin_grid(fig, dummy5, '#2c7fb8')
fig = add_cmpx(cm)
fig = add_stats(sel)
fig.update_layout(showlegend=False,width=300, height=350,margin=dict(l=10, r=10, b=10, t=10)) # ,center={'lat':54.0,'lon':8.3}, zoom=7)
st.sidebar.plotly_chart(fig)
return
def applySDM(wdata, data, meth='rel', cdf_threshold=0.9999999, lower_limit=0.1):
'''apply structured distribution mapping to climate data and return unbiased version of dataset'''
from sdm import SDM
data_ub = data.copy()
for k in data_ub.columns:
data_col = data_ub[k].dropna()
overlapx = pd.concat(
[wdata.loc[data_col.index[0]:wdata.index[-1]], data_col.loc[data_col.index[0]:wdata.index[-1]]], axis=1)
overlapx.columns = ['obs', 'cm']
overlapx = overlapx.dropna()
try:
data_ub[k] = SDM(overlapx.obs, overlapx.cm, data_col, meth, cdf_threshold, lower_limit)
except:
data_ub[k] = data_ub[k] * np.nan
data_ub[data_ub == 0.0000000] = np.nan
data_ub = data_ub.loc[data_ub.index[0]:pd.to_datetime('2099-12-31 23:59:59')]
return data_ub
def climate_indi(ts, indi='Summer days (Tmax ≥ 25°C)'):
'''
Calculate climate indicator days.
Input time series of meteorological data
'''
if pd.infer_freq(ts.index) != 'D':
print('Please provide daily data.')
return
if indi == 'Summer days (Tmax ≥ 25°C)': # summer days
return (ts.Tmax >= 25.).groupby(ts.index.year).sum()
elif indi == 'Ice days (Tmax < 0°C)': # ice days
return (ts.Tmax < 0.).groupby(ts.index.year).sum()
elif indi == 'Frost days (Tmin < 0°C)': # frost days
return (ts.Tmin < 0.).groupby(ts.index.year).sum()
elif indi == 'Hot days (Tmax ≥ 30°C)': # hot days
return (ts.Tmax >= 30.).groupby(ts.index.year).sum()
elif indi == 'Tropic nights (Tmin ≥ 20°C)': # tropic night
return (ts.Tmin >= 20.).groupby(ts.index.year).sum()
elif indi == 'Rainy days (Precip ≥ 1mm)': # rainy days
return (ts.Prec >= 1.).groupby(ts.index.year).sum()
else:
print('Nothing calculated.')
return
def climate_indices(stati='coast',cliproj=True):
cindi = ['Ice days (Tmax < 0°C)', 'Frost days (Tmin < 0°C)', 'Summer days (Tmax ≥ 25°C)', 'Hot days (Tmax ≥ 30°C)','Tropic nights (Tmin ≥ 20°C)', 'Rainy days (Precip ≥ 1mm)']
ci_topic = st.selectbox('Select Index:', cindi)
if ci_topic == 'Rainy days (Precip ≥ 1mm)':
vari = 'Prec'
meth = 'rel'
elif (ci_topic == 'Frost days (Tmin < 0°C)') | (ci_topic == 'Tropic nights (Tmin ≥ 20°C)'):
vari = 'Tmin'
meth = 'abs'
else:
vari = 'Tmax'
meth = 'abs'
w1 = weather[stati].sel(vars=vari).to_dataframe().dropna()
w1.columns = ['bla', vari]
plt.figure(figsize=(10,2.5))
wi = climate_indi(w1, ci_topic).astype(int)
wi.plot(style='.', color='steelblue', label='Coast weather')
wi.rolling(10, center=True).mean().plot(color='steelblue', label='Rolling mean\n(10 years)')
if cliproj:
c1 = climate.sel(vars=vari).to_dataframe()
c1 = c1[c1.columns[c1.columns != 'vars']]
c2 = applySDM(w1[vari], c1, meth=meth)
firstitem = True
for j in c2.columns:
dummyt = pd.DataFrame(c2[j])
dummyt.columns = [vari]
ci = pd.DataFrame(climate_indi(dummyt, ci_topic).astype(int))
ci.columns = [j]
if firstitem:
ci[j].plot(style='.', color='gray', alpha=0.1, label='all climate projections')
cid = ci
firstitem = False
else:
ci[j].plot(style='.', color='gray', alpha=0.1, label='_')
cid = pd.concat([cid, ci], axis=1)
rcpx = np.array([x[-5:] for x in cid.columns.values])
for n in np.unique(rcpx):
nx = n
if n == np.unique(rcpx)[-1]:
nx = n + '\n(Rolling of 5 years)'
cid[cid.columns[rcpx == n]].mean(axis=1).rolling(5, center=True).mean().plot(label='Mean of ' + nx)
plt.legend(ncol=2)
plt.ylabel('Number of days')
plt.title(ci_topic)
st.pyplot()
if ci_topic == 'Ice days (Tmax < 0°C)':
st.markdown('''Number of days in one year which persistently remain below 0°C air temperature.''')
elif ci_topic == 'Frost days (Tmin < 0°C)':
st.markdown('''Number of days in one year which reached below 0°C air temperature.''')
elif ci_topic == 'Summer days (Tmax ≥ 25°C)':
st.markdown('''Number of days in one year which reached or exceeded 25°C air temperature.''')
elif ci_topic == 'Hot days (Tmax ≥ 30°C)':
st.markdown('''Number of days in one year which reached or exceeded 30°C air temperature.''')
elif ci_topic == 'Tropic nights (Tmin ≥ 20°C)':
st.markdown('''Number of days in one year which persistently remained above 20°C air temperature.''')
elif ci_topic == 'Rainy days (Precip ≥ 1mm)':
st.markdown('''Number of days in one year which received at least 1 mm precipitation.''')
return
def weather_explorer():
#weather = load_data('Weather')
#aspects = ['Annual', 'Monthly', 'Season']
#w_aspect = st.sidebar.selectbox('Temporal aggegate:', aspects)
#cliproj = st.sidebar.checkbox('add climate projections',False)
statios = list(weather.keys())
stat1 = st.selectbox('Select station/group (see map in sidebar for location):', statios)
aspects = ['Annual', 'Monthly'] # , 'Season']
w_aspect = st.selectbox('Select temporal aggegate:', aspects)
cliproj = st.checkbox('add climate projections (for coastal region)',False)
if cliproj:
plt_map(stat1, 'CORDEX')
st.sidebar.markdown(
'''Map with available stations (<span style="color:blue">blue dots</span>) and selected reference station (<span style="color:magenta">magenta highlight</span>). The climate model grid is given in <span style="color:orange">orange</span> with the selected references as filled dots.''',
unsafe_allow_html=True)
else:
plt_map(stat1)
st.sidebar.markdown(
'''Map with available stations (<span style="color:blue">blue dots</span>) and selected reference station (<span style="color:magenta">magenta highlight</span>).''',
unsafe_allow_html=True)
if w_topic == 'Warming':
navi_vars = ['Maximum Air Temperature', 'Mean Air Temperature', 'Minimum Air Temperature']
navi_var = st.sidebar.radio("Select variable:", options=navi_vars)
if navi_var[:4] == 'Mini':
vari = 'Tmin'
afu = np.min
ag = 'min'
elif navi_var[:4] == 'Maxi':
vari = 'Tmax'
afu = np.max
ag = 'max'
else:
vari = 'T'
afu = np.mean
ag = 'mean'
if w_aspect == 'Annual':
wdata = weather[stat1].sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()[stat1]
wdata = wdata[~np.isnan(wdata)]
allw = weather.sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe().iloc[:, 1:]
dataLq = float(np.floor(allw.min().quantile(0.22)))
datamin = float(np.min([dataLq, np.round(allw.min().min(), 1)]))
if cliproj:
rcps = ['rcp26', 'rcp45', 'rcp85']
rcp = st.selectbox(
'RCP (Mean over all projections will be shown. For more details go to section "Climate Projections"):',
rcps)
data = climate.filter_by_attrs(RCP=rcp).sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
data_ub = applySDM(wdata, data, meth='abs')
dataUq = float(np.ceil(data_ub.max().quantile(0.76)))
datamax = float(np.max([dataUq, np.round(data_ub.max().max(), 1)]))
else:
dataUq = float(np.ceil(allw.max().quantile(0.76)))
datamax = float(np.max([dataUq,np.round(allw.max().max(), 1)]))
datarng = st.slider('Adjust data range on x-axis of plot:', min_value=datamin, max_value=datamax, value=(dataLq, dataUq), step=0.1, key='drangew')
if cliproj:
ax = kde(wdata, data_ub.mean(axis=1), split_ts=3)
else:
ax = kde(wdata, split_ts=3)
ax.set_title(stat1 + ' Annual ' + navi_var)
ax.set_xlabel('T (°C)')
ax.set_xlim(datarng[0],datarng[1])
st.pyplot()
sndstat = st.checkbox('Show second station for comparison')
if sndstat:
stat2 = st.selectbox('Select second station:', [x for x in statios if x != stat1])
wdata2 = weather[stat2].sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()[stat2]
ax2 = kde(wdata2, split_ts=3)
ax2.set_title(stat2 + ' Annual ' + navi_var)
ax2.set_xlabel('T (°C)')
ax2.set_xlim(datarng[0],datarng[1])
st.pyplot()
expl_md = read_markdown_file('explainer/stripes.md')
st.markdown(expl_md, unsafe_allow_html=True)
elif w_aspect == 'Monthly':
wdata = weather[stat1].sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()[stat1]
wdata = wdata[~np.isnan(wdata)]
ref_yr = st.slider('Reference period for anomaly calculation:', min_value=int(wdata.index.year.min()), max_value=2020,value=(max(1980, int(wdata.index.year.min())), 2000))
if cliproj:
rcps = ['rcp26', 'rcp45', 'rcp85']
rcp = st.selectbox('RCP (Mean over all projections will be shown. For more details go to section "Climate Projections"):', rcps)
data = climate.filter_by_attrs(RCP=rcp).sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
#ub = st.sidebar.checkbox('Apply SDM bias correction',True)
ub = True # simplify here and automatically apply bias correction
if ub:
data_ub = applySDM(wdata, data, meth='abs')
yrplot_hm(pd.concat([wdata.loc[wdata.index[0]:data.index[0] - pd.Timedelta('1M')], data_ub.mean(axis=1)]),ref_yr, ag, li=2006)
else:
yrplot_hm(pd.concat([wdata.loc[wdata.index[0]:data.index[0] - pd.Timedelta('1M')], data.mean(axis=1)]), ref_yr, ag, li=2006)
plt.title(stat1 + ' ' + navi_var + ' anomaly to ' + str(ref_yr[0]) + '-' + str(ref_yr[1]))
st.pyplot()
else:
yrplot_hm(wdata,ref_yr,ag)
plt.title(stat1 + ' ' + navi_var + ' anomaly to ' + str(ref_yr[0]) + '-' + str(ref_yr[1]))
st.pyplot()
sndstat = st.checkbox('Compare to a second station?')
if sndstat:
stat2 = st.selectbox('Select second station:', [x for x in statios if x != stat1])
data2 = weather[stat2].sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()[stat2]
data2 = data2[~np.isnan(data2)]
ref_yr2 = list(ref_yr)
if ref_yr2[1]<data2.index.year.min():
ref_yr2[0] = data2.index.year.min()
ref_yr2[1] = ref_yr2[0]+10
if ref_yr2[0]<data2.index.year.min():
ref_yr2[0] = data2.index.year.min()
if ref_yr2[1] - ref_yr2[0] < 10:
ref_yr2[1] = ref_yr2[0] + 10
yrplot_hm(data2, ref_yr2, ag)
plt.title(stat2 + ' ' + navi_var + ' anomaly to ' + str(ref_yr2[0]) + '-' + str(ref_yr2[1]))
st.pyplot()
expl_md = read_markdown_file('explainer/stripes_m.md')
st.markdown(expl_md, unsafe_allow_html=True)
elif w_topic == 'Weather Indices':
climate_indices(stat1,cliproj)
if cliproj:
st.markdown(
'''RCPs are scenarios about possible greenhouse gas concentrations by the year 2100. RCP2.6 is a world in which little further greenhouse gasses are emitted -- similar to the Paris climate agreement from 2015. RCP8.5 was intendent to explore a rather risky, more worst-case future with further increased emissions. RCP4.5 is one candidate of a more moderate greenhouse gas projection, which might be more likely to resemble a realistic situation. It is important to note that the very limited differentiation between RCP scenarios have been under debate for several years. One outcome is the definition of Shared Socioeconomic Pathways (SSPs) for which today, however, there are not very many model runs awailable. For more information, please check with the [Climatescenario Primer](https://climatescenarios.org/primer/), [CarbonBrief](https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change) and this [NatureComment](https://www.nature.com/articles/d41586-020-00177-3)''',
unsafe_allow_html=True)
def climate_explorer():
cliprojs = ["Global", "Regional"]
cliproj = st.sidebar.radio("Climate Model Scaling:", options=cliprojs)
expl_md = read_markdown_file('explainer/climatescale.md')
st.sidebar.markdown(expl_md, unsafe_allow_html=True)
if cliproj=='Regional':
regaggs = ['North Sea Coast', 'Krummhörn', 'Niedersachsen', 'Inland']
regagg = st.sidebar.selectbox('Spatial aggregation:', regaggs)
if regagg=='North Sea Coast':
climate = xr.load_dataset('data/cordex_coast.nc')
climate.filter_by_attrs(RCP='rcp45')
elif regagg=='Krummhörn':
climate = xr.load_dataset('data/cordex_krummh.nc')
st.subheader('Climate Model Comparison')
crefs = ['Weather Data', 'all RCPs', 'RCP2.6', 'RCP4.5','RCP8.5']
cref1 = st.sidebar.selectbox('Select first reference:', options=crefs)
if cref1=='Weather Data':
data1 = load_data('Weather')
data1 = data1.coast
drngx1 = (1980,2000)
elif cref1 == 'all RCPs':
data1 = load_data('CORDEX')
drngx1 = (2050, 2070)
elif cref1 == 'RCP2.6':
data1 = load_data('rcp26','North Sea Coast')
drngx1 = (2050, 2070)
elif cref1 == 'RCP4.5':
data1 = load_data('rcp45', 'North Sea Coast')
drngx1 = (2050, 2070)
elif cref1 == 'RCP8.5':
data1 = load_data('rcp85','North Sea Coast')
drngx1 = (2050, 2070)
drng1 = [pd.to_datetime(data1.isel(time=0, vars=1).time.values).year, pd.to_datetime(data1.isel(time=-1, vars=1).time.values).year]
datarng1 = st.sidebar.slider('Data range', drng1[0], drng1[1], drngx1, key='dr1')
cref2 = st.sidebar.selectbox('Select second reference:', options=crefs)
if cref2 == 'Weather Data':
data2 = load_data('Weather')
drngx2 = (1980, 2000)
elif cref2 == 'all RCPs':
data2 = load_data('CORDEX')
drngx2 = (2050, 2070)
elif cref2 == 'RCP2.6':
data2 = load_data('rcp26','North Sea Coast')
drngx2 = (2050, 2070)
elif cref2 == 'RCP4.5':
data2 = load_data('rcp45', 'North Sea Coast')
drngx2 = (2050, 2070)
elif cref2 == 'RCP8.5':
data2 = load_data('rcp85','North Sea Coast')
drngx2 = (2050, 2070)
drng2 = [pd.to_datetime(data2.isel(time=0, vars=1).time.values).year, pd.to_datetime(data2.isel(time=-1, vars=1).time.values).year]
datarng2 = st.sidebar.slider('Data range', drng2[0], drng2[1], drngx2, key='dr2')
if w_topic == 'Warming':
navi_vars = ['Maximum Air Temperature', 'Mean Air Temperature', 'Minimum Air Temperature']
navi_var = st.sidebar.radio("Select variable:", options=navi_vars)
if navi_var[:4] == 'Mini':
vari = 'Tmin'
afu = np.min
ag = 'min'
elif navi_var[:4] == 'Maxi':
vari = 'Tmax'
afu = np.max
ag = 'max'
else:
vari = 'T'
afu = np.mean
ag = 'mean'
dyp = data1.sel(vars=vari).to_dataframe().resample('1M').apply(afu)
dyp = dyp.loc[(dyp.index.year>=datarng1[0]) & (dyp.index.year<datarng1[1]),dyp.columns[dyp.columns!='vars']]
dyp2 = data2.sel(vars=vari).to_dataframe().resample('1M').apply(afu)
dyp2 = dyp2.loc[(dyp2.index.year >= datarng2[0]) & (dyp2.index.year < datarng2[1]),dyp2.columns[dyp2.columns!='vars']]
monthlyx(dyp, dyp2, 'Temperature (°C)', 'Monthly '+ag+' in Year ('+cref1+')', 'Monthly '+ag+' in Year ('+cref2+')')
st.pyplot()
def cropmodel(data,crop='wheat',rcp='rcp85',name='croprunname'):
import get_climate_data.cli_crop as clc
#read co2 concentrations
if rcp == 'rcp85':
CO2 = pd.read_csv('data/RCP85_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ
elif rcp == 'rcp45':
CO2 = pd.read_csv('data/RCP45_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ
else:
CO2 = pd.read_csv('data/RCP45_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ * 0. + 400.
if crop == 'maize':
x = np.array([9.375e+00, 3.198e+01, 1.973e+00, 8.700e+01, 1.144e+01, 3.630e+01, 7.260e-02, 1.237e+00, 2.180e+03, 1.501e-01, 5.230e-01, 5.678e+01, 6.970e+02])
elif crop == 'meadow':
x = np.array([6.543e+00, 1.238e+01, 1.029e+00, 8.706e+01, 1.510e+01, 3.253e+01, 1.199e+00, 1.535e+03, 7.784e+03, 6.530e+03, 8.030e+03, 8.092e+03, 5.884e+03])
elif crop == 'wheat':
x = np.array([1.257e+00, 1.276e+01, 1.101e+00, 1.010e+02, 2.578e+01, 2.769e+01, 3.416e-01, 4.940e-01, 1.906e+03, 1.921e-01, 4.595e-01, 6.066e+01, 5.360e+02])
else:
print('ERROR: Crop not specified with parameters.')
yields = clc.cli_SC(data, x, CO2, nme=name)
return yields
def ub_climate(cdata, wdata, ub=True):
varis = ['T', 'Tmax', 'Tmin', 'aP', 'Prec', 'RH', 'Rs', 'u2', 'EToHG']
firstitem = True
for vari in varis:
data = cdata.sel(vars=vari).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
if (vari == 'T') | (vari == 'Tmax') | (vari == 'Tmin'):
meth = 'abs'
else:
meth = 'rel'
if ub:
wdatax = wdata.sel(vars=vari).to_dataframe().iloc[:, -1].dropna()
data_ub = applySDM(wdatax, data, meth=meth)
else:
data_ub = data
data_ubx = data_ub.mean(axis=1)
data_ubx.columns = [vari]
if firstitem:
data_ubc = data_ubx
firstitem = False
else:
data_ubc = pd.concat([data_ubc, data_ubx], axis=1)
data_ubc.columns = varis
return data_ubc
def get_turbine(pfi, plotit=False):
dummy = pd.read_csv(pfi)
v_start = dummy.loc[3].astype(np.float).values[0]
v_max = dummy.loc[2].astype(np.float).values[0]
P_v = dummy.loc[4:28].astype(np.float)
P_v.index = np.arange(int(np.ceil(v_max)) + 1)[1:]
if plotit:
P_v.plot()
return [P_v, v_start, v_max]
def P_wind(wind, pfi):
[P_v, v_start, v_max] = get_turbine(pfi, False)
# stephours = (wind.index[2] - wind.index[1]).days * 24
# stephours = 1.
def interp(val):
if (val >= v_max) | (val < v_start):
return 0.
elif ~np.isnan(val):
if np.ceil(val) == np.floor(val):
return P_v.loc[int(np.floor(val))].values[0]
else:
x1 = P_v.loc[int(np.floor(val))].values[0]
x2 = P_v.loc[int(np.ceil(val))].values[0]
return ((val - np.floor(val)) * x2 + (1. - (val - np.floor(val))) * x1)
else:
return np.nan
ip_vec = np.vectorize(interp)
def get_windpower(u2):
val = np.fmax(-1. * np.cos(np.arange(0., 2 * np.pi, 0.27)) + u2, 0.)
return np.sum(ip_vec(val))
return wind.apply(get_windpower)
def water_proj():
#hsim = pd.read_csv('data/hsim.csv',index_col=0)
#hsim.index = pd.to_datetime(hsim.index)
hsim_collect = pd.read_csv('data/hsim_collect.csv', index_col=0)
hsim_collect.index = pd.to_datetime(hsim_collect.index)
all_vals = np.ravel(hsim_collect.values)[~np.isnan(np.ravel(hsim_collect.values))]
perci = st.slider('Adjust percentile of extreme events:', min_value=90., max_value=99.9999, value=99., key='perci')
firstitem = True
for i in hsim_collect.columns:
hc = hsim_collect.loc[hsim_collect[i] > np.percentile(all_vals, perci), i].resample('1Y').count()
hc.name = i
if firstitem:
h_count = hc
firstitem = False
else:
h_count = pd.concat([h_count, hc], axis=1)
fig = plt.figure(constrained_layout=True,figsize=(10,2.5))
gs = fig.add_gridspec(1, 5)
ax = fig.add_subplot(gs[0, :-1])
ax1 = ax.twinx()
ax2 = fig.add_subplot(gs[0, -1])
all_vals = np.ravel(hsim_collect.values)[~np.isnan(np.ravel(hsim_collect.values))]
sns.distplot(all_vals, ax=ax2)
ax2.vlines(np.percentile(all_vals, perci), 0., 0.06, colors='red')
ax2.text(np.percentile(all_vals, perci), 0.065, str(perci) + '%', c='red', ha='center')
ax2.set_xlim(-3, np.percentile(all_vals, 99.98))
for i in hsim_collect.columns:
hsim_collect[i].plot(style='.', label='_nolegend_', c='gray', alpha=0.2, ax=ax)
try:
hsim_collect.loc[hsim_collect[i] > np.percentile(all_vals, perci), i].plot(style='.', label='_nolegend_',
c='red', alpha=0.2, ax=ax)
except:
pass
(h_count.sum(axis=1) / len(h_count.columns)).rolling(7).mean().plot(ax=ax1, grid=False, ls='--', label='all')
cc = [x for x in h_count.columns if x.split('.')[-1] == 'rcp45']
(h_count[cc].sum(axis=1) / len(cc)).rolling(7).mean().plot(ax=ax1, grid=False, label='rcp45')
cc = [x for x in h_count.columns if x.split('.')[-1] == 'rcp85']
(h_count[cc].sum(axis=1) / len(cc)).rolling(7).mean().plot(ax=ax1, grid=False, label='rcp85')
ax1.legend()
ax.set_ylabel('Q (mm/day)')
ax1.set_ylabel('days w/ Q>' + str(np.round(perci / 100., 3)) + ' percentile\n7 year rolling mean')
st.pyplot()
def management_explorer():
rcps = ['rcp45', 'rcp85'] # 'rcp26',
rcp = st.sidebar.selectbox('RCP:', rcps)
data = climate.filter_by_attrs(RCP=rcp)
ub = st.sidebar.checkbox('Apply SDM bias correction', True)
cdata = ub_climate(data, weather['krummhoern'], ub)
if w_topic == 'Agriculture':
crops = ['wheat', 'maize', 'meadow']
crop = st.sidebar.selectbox('Crop:', crops)
yieldx = cropmodel(cdata,crop,rcp,name=crop+' @ '+rcp)
yieldx.rolling(7).mean().plot()
st.pyplot()
elif w_topic == 'Wind Energy':
storeddata = st.checkbox('Use pre-calculated data (calculation not yet optimized - very slow)', True)
if storeddata:
cwind_p = pd.read_csv('data/windpowerx.csv', index_col=0)
cwind_p.index = pd.to_datetime(cwind_p.index)
plt.figure(figsize=(10, 2.5))
cwind_p[[x for x in cwind_p.columns if rcp in x][::3]].mean(axis=1).plot(label='Enercon 3MW')
cwind_p[[x for x in cwind_p.columns if rcp in x][1::3]].mean(axis=1).plot(label='9x Enercon 0.33MW')
cwind_p[[x for x in cwind_p.columns if rcp in x][2::3]].mean(axis=1).plot(label='0.4x Enercon 7.5MW')
else:
wpengine = ['Enercon 3MW', 'Vestas 3MW', '9x Enercon 0.33MW', '13x Vestas 0.23MW', '0.4x Enercon 7.5MW']
#wpengine = ['Enercon 3MW', '9x Enercon 0.33MW', '0.4x Enercon 7.5MW']
turbine = st.sidebar.selectbox('Select turbine:', wpengine)
plt.figure(figsize=(10,2.5))
if turbine == 'Enercon 3MW':
pfi = 'data/pow/Enercon E-115 3000kW (MG).pow'
P_wind(cdata.u2, pfi).resample('1Y').sum().rolling(7).mean().plot(label=turbine)
elif turbine == 'Vestas 3MW':
pfi = 'data/pow/Vestas 112m 3MW (MT).pow'
P_wind(cdata.u2, pfi).resample('1Y').sum().rolling(7).mean().plot(label=turbine)
elif turbine == '9x Enercon 0.33MW':
pfi = 'data/pow/Enercon E33_33.4m _330kw(MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum()*9.).rolling(7).mean().plot(label=turbine)
elif turbine == '13x Vestas 0.23MW':
pfi = 'data/pow/Vestas V29-225kw(MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum() * 13.).rolling(7).mean().plot(label=turbine)
elif turbine == '0.4x Vestas 7.5MW':
pfi = 'data/pow/Enercon E-126_127m_7500kW (MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum() * 0.4).rolling(7).mean().plot(label=turbine)
plt.legend()
plt.ylabel('Wh/yr')
st.pyplot()
elif w_topic == 'Extreme Events':
water_proj()
# main app