forked from agmunozs/PyCPT
-
Notifications
You must be signed in to change notification settings - Fork 0
/
pycpt_functions.py
1055 lines (939 loc) · 60.8 KB
/
pycpt_functions.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
#This is PyCPT_functions.py (version1.2) -- 6 June 2019
#Authors: AG Muñoz (agmunoz@iri.columbia.edu) and AW Robertson (awr@iri.columbia.edu)
#Notes: be sure it matches version of PyCPT
#Log:
# 6 June 2019, AGM: fixed bug in PyIngrid related to the number of initializations used
# for the ECMWF model, and optimized reading multiple records in
# sequential Fortran binary (GrADS) files.
# 21 Apr 2019, AGM: added option to list average skill metrics for particular subdomains.
# 17 Apr 2019, AGM: fixed bug related to the inverse Gamma function.
#30 Mar 2019, AGM: added PCR option, CHIRPS as obs, flexible format plots,
# automatically uses retrospective for validation (due to
# the very high sample size). Solved problems related to
# masking missing values. ELR still has some problems
# (values are different from our R or Matlab codes -- working
# on it, so not included in this version).
#25 Aug 2018, AGM: plots are now raster maps, added CPC obs,
# fixed field shift due to sequential grads format in CPT,
# automatic colobar limits and field name for deterministic forecast
#24 Aug 2018, AWR: "obs_source" added for obs dataset selection (passed from main program)
#19 Aug 2018, AWR: Dictionary entry for GEFS added
#To Do: (as March 30th, 2019 -- AGM)
# + ELR proceedure is not reproducing results obtained in R or Matlab
# + Provide skill stats for user-defined regions
# + Simplify download functions: just one function, with the right arguments and dictionaries.
# + Check Hindcasts and Forecast_RFREQ
import os
import warnings
import struct
import xarray as xr
import numpy as np
import pandas as pd
from copy import copy
from scipy.stats import t
from scipy.stats import invgamma
import cartopy.crs as ccrs
from cartopy import feature
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.colors import LinearSegmentedColormap
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
warnings.filterwarnings("ignore")
def lines_that_equal(line_to_match, fp):
return [line for line in fp if line == line_to_match]
def lines_that_contain(string, fp):
return [line for line in fp if string in line]
def lines_that_start_with(string, fp):
return [line for line in fp if line.startswith(string)]
def lines_that_end_with(string, fp):
return [line for line in fp if line.endswith(string)]
def exceedprob(x,dof,lo,sc):
return t.sf(x, dof, loc=lo, scale=sc)*100
class MidpointNormalize(colors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
def download_data(url, authkey, outfile, force_download=False):
"""A smart function to download data from IRI Data Library
If the data can be read in and force_download is False, will read from file
Otherwise will download from IRIDL and then read from file
Written by J Doss-Gollin (2018)
PARAMETERS
----------
url: the url pointing to the data.nc file
authkey: the authentication key for IRI DL (see above)
outfile: the data filename
force_download: False if it's OK to read from file, True if data *must* be re-downloaded
"""
if not force_download:
try:
model = xr.open_dataset(outfile, decode_times=False)
except:
force_download = True
if force_download:
# calls curl to download data
command = "curl -C - -k -b '__dlauth_id={}' '{}' > {}".format(authkey, url, outfile)
get_ipython().system(command)
# open the data
model = xr.open_dataset(outfile, decode_times=False)
return model
def PrepFiles(rainfall_frequency, threshold_pctle, wlo1, wlo2,elo1, elo2,sla1, sla2,nla1, nla2, day1, day2, fday, nday, fyr, mon, os, authkey, wk, wetday_threshold, nlag, training_season, hstep, model, obs_source, hdate_last, force_download):
"""Function to download (or not) the needed files"""
if rainfall_frequency:
GetObs_RFREQ(day1, day2, mon, fyr, wlo2, elo2, sla2, nla2, nday, authkey, wk, wetday_threshold, threshold_pctle, nlag, training_season, hstep, model, obs_source, force_download)
print('Obs:rfreq file ready to go')
print('----------------------------------------------')
# nday added after nlag for GEFS & CFSv2
GetHindcasts(wlo1, elo1, sla1, nla1, day1, day2, fyr, mon, os, authkey, wk, nlag, nday, training_season, hstep, model, hdate_last, force_download)
#GetHindcasts_RFREQ(wlo1, elo1, sla1, nla1, day1, day2, nday, fyr, mon, os, authkey, wk, wetday_threshold, nlag, training_season, hstep, model, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
#GetForecast_RFREQ(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, authkey, wk, wetday_threshold, nlag, model, force_download)
GetForecast(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, authkey, wk, nlag, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
else:
#GetHindcasts(wlo1, elo1, sla1, nla1, day1, day2, fyr, mon, os, authkey, wk, nlag, training_season, hstep, model, force_download)
#nday added after nlag for GEFS & CFSv2
GetHindcasts(wlo1, elo1, sla1, nla1, day1, day2, fyr, mon, os, authkey, wk, nlag, nday, training_season, hstep, model, hdate_last, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
GetObs(day1, day2, mon, fyr, wlo2, elo2, sla2, nla2, nday, authkey, wk, nlag, training_season, hstep, model, obs_source, hdate_last, force_download)
print('Obs:precip file ready to go')
print('----------------------------------------------')
GetForecast(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, authkey, wk, nlag, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
def pltdomain(loni1,lone1,lati1,late1,loni2,lone2,lati2,late2):
"""A simple plot function for the geographical domain
PARAMETERS
----------
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
title: title
"""
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_shp',
scale='10m',
facecolor='none')
fig = plt.subplots(figsize=(15,15), subplot_kw=dict(projection=ccrs.PlateCarree()))
loni = [loni1,loni2]
lati = [lati1,lati2]
lone = [lone1,lone2]
late = [late1,late2]
title = ['Predictor', 'Predictand']
for i in range(2):
ax = plt.subplot(1, 2, i+1, projection=ccrs.PlateCarree())
ax.set_extent([loni[i],lone[i],lati[i],late[i]], ccrs.PlateCarree())
# Put a background image on for nice sea rendering.
ax.stock_img()
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
ax.set_title(title[i]+" domain")
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
plt.show()
def pltmap(score,loni,lone,lati,late,fprefix,mpref,training_season, mon, fday, nwk):
"""A simple function for ploting the statistical score
PARAMETERS
----------
score: the score
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
title: title
"""
plt.figure(figsize=(20,5))
for L in range(nwk):
wk=L+1
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
# ax = plt.subplot(nwk/2, 2, wk, projection=ccrs.PlateCarree())
ax = plt.subplot(1,nwk, wk, projection=ccrs.PlateCarree())
ax.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
ax.set_title(score+' for Week '+str(wk))
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0., linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
if score == 'CCAFCST_V' or score == 'PCRFCST_V':
f=open('../output/'+fprefix+'_'+score+'_'+training_season+'_'+mon+str(fday)+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
var[var==-999.]=np.nan #only sensible values
current_cmap = plt.cm.BrBG
current_cmap.set_bad('white',1.0)
current_cmap.set_under('white', 1.0)
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
#vmin=-max(np.max(var),np.abs(np.min(var))), #vmax=np.max(var),
norm=MidpointNormalize(midpoint=0.),
cmap=current_cmap,
transform=ccrs.PlateCarree())
ax.set_title("Deterministic forecast for Week "+str(wk))
if fprefix == 'RFREQ':
label ='Freq Rainy Days (days)'
elif fprefix == 'PRCP':
label = 'Rainfall anomaly (mm/week)'
f.close()
#current_cmap = plt.cm.get_cmap()
#current_cmap.set_bad(color='white')
#current_cmap.set_under('white', 1.0)
else:
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+fprefix+'_'+mpref+'_'+score+'_'+training_season+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
#define colorbars, depending on each score --This can be easily written as a function
if score == '2AFC':
var[var<0]=np.nan #only positive values
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = '2AFC (%)'
if score == 'RocAbove' or score=='RocBelow':
var[var<0]=np.nan #only positive values
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'ROC area'
if score == 'Spearman' or score=='Pearson':
var[var<-1.]=np.nan #only sensible values
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=-1,vmax=1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'Correlation'
plt.subplots_adjust(hspace=0)
#plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
#cbar_ax = plt.add_axes([0.85, 0.15, 0.05, 0.7])
#plt.tight_layout()
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label(label) #, rotation=270)
f.close()
def skilltab(score,wknam,lon1,lat1,lat2,lon2,loni,lone,lati,late,fprefix,mpref,training_season,mon,fday,nwk):
"""A simple function for ploting probabilities of exceedance and PDFs (for a given threshold)
PARAMETERS
----------
thrs: the threshold, in the units of the predictand
lon: longitude
lat: latitude
"""
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
#Find the gridbox:
lonrange = np.linspace(loni, loni+W*XD,num=W)
latrange = np.linspace(lati+H*YD, lati, num=H) #need to reverse the latitudes because of CPT (GrADS YREV option)
lon_grid, lat_grid = np.meshgrid(lonrange, latrange)
#first point
a = abs(lat_grid-lat1)+abs(lon_grid-lon1)
i1,j1 = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
#second point
a = abs(lat_grid-lat2)+abs(lon_grid-lon2)
i2,j2 = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
df = pd.DataFrame(index=wknam[0:nwk])
for L in range(nwk):
wk=L+1
for S in score:
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+fprefix+'_'+mpref+'_'+str(S)+'_'+training_season+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
var[var==-999.]=np.nan #only sensible values
df.at[wknam[L], str(S)] = round(np.nanmean(np.nanmean(var[i1:i2,j1:j2], axis=1), axis=0),2)
df.at[wknam[L], 'max('+str(S)+')'] = round(np.nanmax(var[i1:i2,j1:j2]),2)
df.at[wknam[L], 'min('+str(S)+')'] = round(np.nanmin(var[i1:i2,j1:j2]),2)
return df
f.close()
def pltmapProb(loni,lone,lati,late,fprefix,mpref,training_season, mon, fday, nwk):
"""A simple function for ploting probabilistic forecasts
PARAMETERS
----------
score: the score
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
title: title
"""
#Need this score to be defined by the calibration method!!!
score = 'CCAFCST_P'
plt.figure(figsize=(15,20))
for L in range(nwk):
wk=L+1
#Read grads binary file size H, W --it assumes that 2AFC file exists (template for final domain size)
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
#Prepare to read grads binary file [float32 for Fortran sequential binary files]
Record = np.dtype(('float32', H*W))
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
#B = np.fromfile('../output/'+fprefix+'_'+score+'_'+training_season+'_'+mon+str(fday)+'_wk'+str(wk)+'.dat',dtype=Record, count=-1).astype('float')
f=open('../output/'+fprefix+'_'+score+'_'+training_season+'_'+mon+str(fday)+'_wk'+str(wk)+'.dat','rb')
tit=['Below Normal','Normal','Above Normal']
for i in range(3):
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#We now read the field for that record (probabilistic files have 3 records: below, normal and above)
B=np.fromfile(f,dtype='float32',count=numval) #astype('float')
endrec=struct.unpack('i',f.read(4))[0]
var = np.flip(np.transpose(B.reshape((W, H), order='F')),0)
var[var<0]=np.nan #only positive values
ax2=plt.subplot(nwk, 3, (L*3)+(i+1),projection=ccrs.PlateCarree())
ax2.set_title("Week "+str(wk)+ ": "+tit[i])
ax2.add_feature(feature.LAND)
ax2.add_feature(feature.COASTLINE)
#ax2.set_ybound(lower=lati, upper=late)
pl2=ax2.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl2.xlabels_top = False
pl2.ylabels_left = True
pl2.ylabels_right = False
pl2.xformatter = LONGITUDE_FORMATTER
pl2.yformatter = LATITUDE_FORMATTER
ax2.add_feature(states_provinces, edgecolor='gray')
ax2.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
#ax2.set_ybound(lower=lati, upper=late)
#ax2.set_xbound(lower=loni, upper=lone)
#ax2.set_adjustable('box')
#ax2.set_aspect('auto',adjustable='datalim',anchor='C')
CS=ax2.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati,lati+H*YD, num=H), var,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
#plt.show(block=False)
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label('Probability (%)') #, rotation=270)
f.close()
def pltmapff(thrs,ntrain,loni,lone,lati,late,fprefix,mpref,training_season,mon,fday,nwk):
"""A simple function for ploting probabilistic forecasts in flexible format (for a given threshold)
PARAMETERS
----------
thrs: the threshold, in the units of the predictand
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
"""
#Implement: read degrees of freedom from CPT file
#Formally, for CCA, dof=ntrain - #CCAmodes -1 ; since ntrain is huge after concat, dof~=ntrain for now
dof=ntrain
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
plt.figure(figsize=(15,15))
for L in range(nwk):
wk=L+1
#Read mean
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+fprefix+'_'+mpref+'FCST_mu_'+training_season+'_'+str(mon)+str(fday)+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
muf = np.transpose(A.reshape((W, H), order='F'))
muf[muf==-999.]=np.nan #only sensible values
#Read variance
f=open('../output/'+fprefix+'_'+mpref+'FCST_var_'+training_season+'_'+str(mon)+str(fday)+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
vari = np.transpose(A.reshape((W, H), order='F'))
vari[vari<0.]=np.nan #only positive values
#Compute scale parameter for the t-Student distribution
scalef=np.sqrt((dof-2)/dof*vari)
fprob = exceedprob(thrs,dof,muf,scalef)
ax = plt.subplot(nwk/2, 2, wk, projection=ccrs.PlateCarree())
ax.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
ax.set_title('Probability (%) of Exceeding '+str(thrs)+" mm/week"+' for Week '+str(wk))
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), fprob,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'Probability (%) of Exceedance'
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label(label) #, rotation=270)
f.close()
def pltprobff(thrs,ntrain,lon,lat,loni,lone,lati,late,fprefix,mpref,training_season,mon,fday,nwk):
"""A simple function for ploting probabilities of exceedance and PDFs (for a given threshold)
PARAMETERS
----------
thrs: the threshold, in the units of the predictand
lon: longitude
lat: latitude
"""
#Implement: read degrees of freedom from CPT file
#Formally, for CCA, dof=ntrain - #CCAmodes -1 ; since ntrain is huge after concat, dof~=ntrain for now
dof=ntrain
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
with open('../output/'+fprefix+'_'+mpref+'FCST_Obs_'+training_season+'_'+str(mon)+str(fday)+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("TDEF", fp):
T = int(line.split()[1])
TD= 1 #not used
#Find the gridbox:
lonrange = np.linspace(loni, loni+W*XD,num=W)
latrange = np.linspace(lati+H*YD, lati, num=H) #need to reverse the latitudes because of CPT (GrADS YREV option)
lon_grid, lat_grid = np.meshgrid(lonrange, latrange)
a = abs(lat_grid-lat)+abs(lon_grid-lon)
i,j = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
#Now compute stuff and plot
plt.figure(figsize=(15,15))
for L in range(nwk):
wk=L+1
#Forecast files--------
#Read mean
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+fprefix+'_'+mpref+'FCST_mu_'+training_season+'_'+str(mon)+str(fday)+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
muf = np.transpose(A.reshape((W, H), order='F'))
muf[muf==-999.]=np.nan #identify NaNs
muf=muf[i,j]
#Read variance
f=open('../output/'+fprefix+'_'+mpref+'FCST_var_'+training_season+'_'+str(mon)+str(fday)+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
varf = np.transpose(A.reshape((W, H), order='F'))
varf[varf==-999.]=np.nan #identify NaNs
varf=varf[i,j]
#Obs file--------
#Compute obs mean and variance.
#
muc0=np.empty([T,H,W]) #define array for later use
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+fprefix+'_'+mpref+'FCST_Obs_'+training_season+'_'+str(mon)+str(fday)+'_wk'+str(wk)+'.dat','rb')
#cycle for all time steps (same approach to read GrADS files as before, but now read T times)
for it in range(T):
#Now we read the field
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize) #this if for each time stamp
A0=np.fromfile(f,dtype='float32',count=numval)
endrec=struct.unpack('i',f.read(4))[0] #needed as Fortran sequential repeats the header at the end of the record!!!
muc0[it,:,:]= np.transpose(A0.reshape((W, H), order='F'))
muc0[muc0==-999.]=np.nan #identify NaNs
#print(muc0)
muc=np.nanmean(muc0, axis=0) #axis 0 is T
#print(muc)
#Compute obs variance
varc=np.nanvar(muc0, axis=0) #axis 0 is T
#Select gridbox values
muc=muc[i,j]
print(muc)
varc=varc[i,j]
#Compute scale parameter for the t-Student distribution
scalef=np.sqrt(dof*varf) #due to transformation from Gamma
scalec=np.sqrt((dof-2)/dof*varc)
x = np.linspace(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)), 100)
style = dict(size=10, color='black')
#cprob = special.erfc((x-muc)/scalec)
cprob = exceedprob(thrs,dof,muc,scalec)
fprob = exceedprob(thrs,dof,muf,scalef)
cprobth = round(t.sf(thrs, dof, loc=muc, scale=scalec)*100,2)
fprobth = round(t.sf(thrs, dof, loc=muf, scale=scalef)*100,2)
cpdf=t.pdf(x, dof, loc=muc, scale=scalec)*100
fpdf=t.pdf(x, dof, loc=muf, scale=scalef)*100
oddsrc =(fprobth/cprobth)
fig, ax = plt.subplots(1, 2,figsize=(12,4))
#font = {'family' : 'Palatino',
# 'size' : 16}
#plt.rc('font', **font)
#plt.rc('text', usetex=True)
#plt.rc('font', family='serif')
plt.subplot(1, 2, 1)
plt.plot(x, t.sf(x, dof, loc=muc, scale=scalec)*100,'b-', lw=5, alpha=0.6, label='clim')
plt.plot(x, t.sf(x, dof, loc=muf, scale=scalef)*100,'r-', lw=5, alpha=0.6, label='fcst')
plt.axvline(x=thrs, color='k', linestyle='--')
plt.plot(thrs, fprobth,'ok')
plt.plot(thrs, cprobth,'ok')
plt.text(thrs+0.05, cprobth, str(cprobth)+'%', **style)
plt.text(thrs+0.05, fprobth, str(fprobth)+'%', **style)
#plt.text(0.1, 10, r'$\frac{P(fcst)}{P(clim)}=$'+str(round(oddsrc,1)), **style)
plt.text(min(t.ppf(0.0001, dof, loc=muf, scale=scalef),t.ppf(0.0001, dof, loc=muc, scale=scalec)), -20, 'P(fcst)/P(clim)='+str(round(oddsrc,1)), **style)
plt.legend(loc='best', frameon=False)
# Add title and axis names
plt.title('Probabilities of Exceedance for Week '+str(wk))
plt.xlabel('Rainfall')
plt.ylabel('Probability (%)')
# Limits for the Y axis
plt.xlim(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)))
plt.subplot(1, 2, 2)
plt.plot(x, cpdf,'b-', lw=5, alpha=0.6, label='clim')
plt.plot(x, fpdf,'r-', lw=5, alpha=0.6, label='fcst')
plt.axvline(x=thrs, color='k', linestyle='--')
#fill area under the curve --not done
#section = np.arange(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)), thrs, 1/20.)
#plt.fill_between(section,f(section))
plt.legend(loc='best', frameon=False)
# Add title and axis names
plt.title('Probability Density Functions for Week '+str(wk))
plt.xlabel('Rainfall')
plt.ylabel('')
# Limits for the Y axis
plt.xlim(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)))
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
#cax = plt.axes([0.2, 0.08, 0.6, 0.04])
#cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
#cbar.set_label(label) #, rotation=270)
f.close()
def GetHindcasts(wlo1, elo1, sla1, nla1, day1, day2, fyr, mon, os, key, week, nlag, nday, training_season, hstep, model, hdate_last, force_download):
if not force_download:
try:
ff=open("model_precip_"+mon+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Hindcasts file doesn't exist --downloading")
force_download = True
if force_download:
#dictionary:
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/3./mul/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/add/4./div/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/S/('+training_season+')/VALUES/S/'+str(hstep)+'/STEP/dup/S/npts//I/exch/NewIntegerGRID/replaceGRID/dup/I/5/splitstreamgrid/%5BI2%5Daverage/sub/I/3/-1/roll/.S/replaceGRID/L1/S/add/0/RECHUNK//name//T/def/2/%7Bexch%5BL1/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/c://name//water_density/def/998/%28kg/m3%29/:c/div//mm/unitconvert//name/(tp)/def/grid://name/%28T%29/def//units/%28months%20since%201960-01-01%29/def//standard_name/%28time%29/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/3001/ensotime/:grid/use_as_grid//name/(tp)/def//units/(mm)/def//long_name/(precipitation_amount)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%201%20'+mon+'%20'+str(fyr)+')%20(2300%2028%20'+mon+'%20'+str(fyr)+')/RANGE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/-999/setmissing_value/hdate/('+str(fyr-20)+')/('+str(hdate_last)+')/RANGE/dup/%5Bhdate%5Daverage/sub/%5BM%5Daverage/hdate//pointwidth/0/def/-6/shiftGRID/hdate/(days%20since%201960-01-01)/streamgridunitconvert/S/(days%20since%20'+str(fyr)+'-01-01)/streamgridunitconvert/S//units//days/def/L/hdate/add/add/0/RECHUNK/L/removeGRID//name//T/def/2/%7Bexch%5BS/hdate%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/T/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2060/ensotime/%3Agrid/replaceGRID//name/(tp)/def//units/(mm)/def//long_name/(precipitation_amount)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'GEFS':
'https://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%201999)/(0000%2028%20Dec%202015)/RANGEEDGES/S/(days%20since%201999-01-01)/streamgridunitconvert/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/%5BM%5Daverage/L/'+str(nday)+'/runningAverage/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.dc9915/.pr/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/L/'+str(nday)+'/runningAverage/S/(T)/renameGRID/pentadmean/T/(S)/renameGRID/%5BS%5DregridLinear/sub/S/('+training_season+')/VALUES/L/removeGRID/S/(T)/renameGRID/c%3A/0.001/(m3%20kg-1)/%3Ac/mul/c%3A/1000/(mm%20m-1)/%3Ac/mul/c%3A/86400/(s%20day-1)/%3Ac/mul/c%3A/7.0//units//days/def/%3Ac/mul/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2301/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
}
# calls curl to download data
url=dic[model]
print("\n Hindcasts URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > model_precip_"+mon+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f model_precip_"+mon+"_wk"+str(week)+".tsv.gz")
#! curl -g -k -b '__dlauth_id='$key'' ''$url'' > model_precip_${mo}.tsv
def GetHindcasts_RFREQ(wlo1, elo1, sla1, nla1, day1, day2, nday, fyr, mon, os, key, week, wetday_threshold, nlag, training_season, hstep,model, force_download):
if not force_download:
try:
ff=open("model_RFREQ_"+mon+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Hindcasts file doesn't exist --downloading")
force_download = True
if force_download:
#dictionary:
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/3./mul/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/add/4./div/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/S/('+training_season+')/VALUES/S/'+str(hstep)+'/STEP/dup/S/npts//I/exch/NewIntegerGRID/replaceGRID/dup/I/5/splitstreamgrid/%5BI2%5Daverage/sub/I/3/-1/roll/.S/replaceGRID/L1/S/add/0/RECHUNK//name//T/def/2/%7Bexch%5BL1/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/c://name//water_density/def/998/%28kg/m3%29/:c/div//mm/unitconvert//name/(tp)/def/grid://name/%28T%29/def//units/%28months%20since%201960-01-01%29/def//standard_name/%28time%29/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/3001/ensotime/:grid/use_as_grid//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/(0000%201%20'+mon+'%20'+str(fyr)+')%20(2300%2028%20'+mon+'%20'+str(fyr)+')/RANGE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/'+str(wetday_threshold)+'/flagge/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/-999/setmissing_value/hdate/('+str(fyr-20)+')/('+str(fyr-1)+')/RANGE/dup/%5Bhdate%5Daverage/sub/%5BM%5Daverage/hdate//pointwidth/0/def/-6/shiftGRID/hdate/(days%20since%201960-01-01)/streamgridunitconvert/S/(days%20since%20'+str(fyr)+'-01-01)/streamgridunitconvert/S//units//days/def/L/hdate/add/add/0/RECHUNK/L/removeGRID//name//T/def/2/%7Bexch%5BS/hdate%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/T/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2060/ensotime/%3Agrid/replaceGRID//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
# calls curl to download data
url=dic[model]
print("\n Hindcasts URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > model_RFREQ_"+mon+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f model_RFREQ_"+mon+"_wk"+str(week)+".tsv.gz")
#! curl -g -k -b '__dlauth_id='$key'' ''$url'' > model_precip_${mo}.tsv
def GetObs(day1, day2, mon, fyr, wlo2, elo2, sla2, nla2, nday, key, week, nlag, training_season, hstep, model, obs_source, hdate_last, force_download):
if not force_download:
try:
ff=open("obs_precip_"+mon+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Obs precip file doesn't exist --downloading")
force_download = True
if force_download:
#dictionary:
dic = {'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/S/(0000%201%20Jan%201999)/(0000%2031%20Dec%202010)/RANGEEDGES/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/S/('+training_season+')/VALUES/S/'+str(hstep)+'/STEP/L1/S/add/0/RECHUNK/name//T/def/2/%7Bexch%5BL1/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/'+obs_source+'/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/3/flagge/dup/pentadmean/%5BT%5D/regridLinear/sub/T/'+str(nday)+'/runningAverage/c%3A/7.0//units//days/def/%3Ac/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/I/3/-1/roll/.T/replaceGRID/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/3001/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%201%20'+mon+'%20'+str(fyr)+')%20(2300%2028%20'+mon+'%20'+str(fyr)+')/RANGE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/-999/setmissing_value/hdate/('+str(fyr-20)+')/('+str(hdate_last)+')/RANGE/dup/%5Bhdate%5Daverage/sub/%5BM%5Daverage/hdate//pointwidth/0/def/-6/shiftGRID/hdate/(days%20since%201960-01-01)/streamgridunitconvert/S/(days%20since%20'+str(fyr)+'-01-01)/streamgridunitconvert/S//units//days/def/L/hdate/add/add/0/RECHUNK/L/removeGRID//name//T/def/2/%7Bexch%5BS/hdate%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/'+obs_source+'/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/T/2/index/.T/SAMPLE/dup%5BT%5Daverage/sub/-999/setmissing_value/nip/T/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2060/ensotime/%3Agrid/replaceGRID//name/(tp)/def//units/(mm)/def//long_name/(precipitation_amount)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'GEFS': 'https://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%201999)/(0000%2028%20Dec%202015)/RANGEEDGES/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/L/'+str(nday)+'/runningAverage/S/('+training_season+')/VALUES/L/S/add/0/RECHUNK//name//T/def/2/%7Bexch%5BL/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/'+obs_source+'/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/dup/pentadmean/%5BT%5D/regridLinear/sub/T/'+str(nday)+'/runningAverage/c%3A/7.0//units//days/def/%3Ac/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/I/3/-1/roll/.T/replaceGRID/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2301/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
# calls curl to download data
url=dic[model]
print("\n Obs (Rainfall) data URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > obs_precip_"+mon+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f obs_precip_"+mon+"_wk"+str(week)+".tsv.gz")
#curl -g -k -b '__dlauth_id='$key'' ''$url'' > obs_precip_${mo}.tsv
def GetObs_RFREQ(day1, day2, mon, fyr, wlo2, elo2, sla2, nla2, nday, key, week, wetday_threshold, threshold_pctle, nlag, training_season, hstep, model, obs_source, force_download):
if not force_download:
try:
ff=open("obs_RFREQ_"+mon+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Obs freq-rainfall file doesn't exist --downloading")
force_download = True
if force_download:
#dictionaries:
if threshold_pctle:
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/S/(0000%201%20Jan%201999)/(0000%2031%20Dec%202010)/RANGEEDGES/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/S/('+training_season+')/VALUES/S/'+str(hstep)+'/STEP/L1/S/add/0/RECHUNK//name//T/def/2/%7Bexch%5BL1/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/SOURCES/.NASA/.GES-DAAC/.TRMM_L3/.TRMM_3B42/.v7/.daily/.precipitation/X/0./1.5/360./GRID/Y/-50/1.5/50/GRID/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/T/(1%20Jan%201999)/(31%20Dec%202011)/RANGEEDGES/%5BT%5Dpercentileover/'+str(wetday_threshold)+'/flagle/T/'+str(nday)+'/runningAverage/'+str(nday)+'/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/dup/I/5/splitstreamgrid/%5BI2%5Daverage/sub/I/3/-1/roll/.T/replaceGRID/-999/setmissing_value/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/3001/ensotime/%3Agrid/use_as_grid//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF':'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%201%20'+mon+'%20'+str(fyr)+')%20(2300%2028%20'+mon+'%20'+str(fyr)+')/RANGE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/-999/setmissing_value/hdate/('+str(fyr-20)+')/('+str(fyr-1)+')/RANGE/dup/%5Bhdate%5Daverage/sub/%5BM%5Daverage/hdate//pointwidth/0/def/-6/shiftGRID/hdate/(days%20since%201960-01-01)/streamgridunitconvert/S/(days%20since%20'+str(fyr)+'-01-01)/streamgridunitconvert/S//units//days/def/L/hdate/add/add/0/RECHUNK/L/removeGRID//name//T/def/2/%7Bexch%5BS/hdate%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/SOURCES/.NASA/.GES-DAAC/.TRMM_L3/.TRMM_3B42/.v7/.daily/.precipitation/X/0./1.5/360./GRID/Y/-50/1.5/50/GRID/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/%5BT%5Dpercentileover/'+str(wetday_threshold)+'/flagle/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/dup/I/5/splitstreamgrid/%5BI2%5Daverage/sub/I/3/-1/roll/.T/replaceGRID/-999/setmissing_value/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2060/ensotime/%3Agrid/use_as_grid//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
else:
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/S/(0000%201%20Jan%201999)/(0000%2031%20Dec%202010)/RANGEEDGES/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/S/('+training_season+')/VALUES/S/'+str(hstep)+'/STEP/L1/S/add/0/RECHUNK/name//T/def/2/%7Bexch%5BL1/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/SOURCES/.NASA/.GES-DAAC/.TRMM_L3/.TRMM_3B42/.v7/.daily/.precipitation/X/0./1.5/360./GRID/Y/-50/1.5/50/GRID/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/'+str(wetday_threshold)+'/flagge/dup/pentadmean/%5BT%5D/regridLinear/sub/T/'+str(nday)+'/runningAverage/c%3A/7.0//units//days/def/%3Ac/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/I/3/-1/roll/.T/replaceGRID/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/3001/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%201%20'+mon+'%20'+str(fyr)+')%20(2300%2028%20'+mon+'%20'+str(fyr)+')/RANGE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/-999/setmissing_value/hdate/('+str(fyr-20)+')/('+str(fyr-1)+')/RANGE/dup/%5Bhdate%5Daverage/sub/%5BM%5Daverage/hdate//pointwidth/0/def/-6/shiftGRID/hdate/(days%20since%201960-01-01)/streamgridunitconvert/S/(days%20since%20'+str(fyr)+'-01-01)/streamgridunitconvert/S//units//days/def/L/hdate/add/add/0/RECHUNK/L/removeGRID//name//T/def/2/%7Bexch%5BS/hdate%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/SOURCES/.NASA/.GES-DAAC/.TRMM_L3/.TRMM_3B42/.v7/.daily/.precipitation/X/0./1.5/360./GRID/Y/-50/1.5/50/GRID/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/'+str(wetday_threshold)+'/flagge/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/dup/I/5/splitstreamgrid/%5BI2%5Daverage/sub/I/3/-1/roll/.T/replaceGRID/-999/setmissing_value/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2060/ensotime/%3Agrid/use_as_grid//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'GEFS': 'https://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%201999)/(0000%2028%20Dec%202015)/RANGEEDGES/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/L/'+str(nday)+'/runningAverage/S/('+training_season+')/VALUES/L/S/add/0/RECHUNK//name//T/def/2/%7Bexch%5BL/S%5D//I/nchunk/NewIntegerGRID/replaceGRIDstream%7Drepeat/use_as_grid/SOURCES/.NASA/.GES-DAAC/.TRMM_L3/.TRMM_3B42/.v7/.daily/.precipitation/X/0./1.5/360./GRID/Y/-50/1.5/50/GRID/Y/'+str(sla2)+'/'+str(nla2)+'/RANGE/X/'+str(wlo2)+'/'+str(elo2)+'/RANGE/T/(days%20since%201960-01-01)/streamgridunitconvert/'+str(wetday_threshold)+'/flagge/dup/pentadmean/%5BT%5D/regridLinear/sub/T/'+str(nday)+'/runningAverage/c%3A/7.0//units//days/def/%3Ac/mul/T/2/index/.T/SAMPLE/nip/dup/T/npts//I/exch/NewIntegerGRID/replaceGRID/I/3/-1/roll/.T/replaceGRID/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/1901/ensotime/12./16/Jan/2301/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
# calls curl to download data
url=dic[model]
print("\n Obs (Freq) data URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > obs_RFREQ_"+mon+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f obs_RFREQ_"+mon+"_wk"+str(week)+".tsv.gz")
#curl -g -k -b '__dlauth_id='$key'' ''$url'' > obs_precip_${mo}.tsv
def GetForecast(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, key, week, nlag, model, force_download):
if not force_download:
try:
ff=open("modelfcst_precip_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Forecasts file doesn't exist --downloading")
force_download = True
if force_download:
#dictionary:
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/3./mul/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/add/4./div/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/L1/removeGRID/S/(0000%20'+str(fday)+'%20'+mon+')/VALUES/%5BS%5Daverage/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/SOURCES/.NOAA/.NCEP/.EMC/.CFSv2/.6_hourly_rotating/.FLXF/.surface/.PRATE/%5BL%5D1/0.0/boxAverage/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')VALUE/%5BX/Y%5DregridLinear/L/'+str(day1)+'/'+str(day2)+'/RANGEEDGES/%5BL%5Daverage/%5BS%5Daverage/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div/(mm/day)/unitconvert/'+str(nday)+'/mul//units/(mm)/def/exch/sub/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/1/Jan/3001/ensotime/12.0/1/Jan/3001/ensotime/%3Agrid/addGRID/T//pointwidth/0/def/pop//name/(tp)/def//units/(mm)/def//long_name/(precipitation_amount)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.forecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')/VALUE/%5BL%5Ddifferences/%5BM%5Daverage/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')/VALUE/%5BL%5Ddifferences/%5BM%5Daverage/%5Bhdate%5Daverage/sub/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/1/Jan/3001/ensotime/12.0/1/Jan/3001/ensotime/%3Agrid/addGRID/T//pointwidth/0/def/pop//name/(tp)/def//units/(mm)/def//long_name/(precipitation_amount)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'GEFS': 'https://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.forecast/.pr/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')/VALUES/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/%5BM%5Daverage/L/'+str(nday)+'/runningAverage/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.dc9915/.pr/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/RANGEEDGES/L/'+str(nday)+'/runningAverage/S/(T)/renameGRID/pentadmean/T/(S)/renameGRID/%5BS%5DregridLinear/S/1/setgridtype/pop/S/2/index/.S/SAMPLE/sub/c%3A/0.001/(m3%20kg-1)/%3Ac/mul/c%3A/1000/(mm%20m-1)/%3Ac/mul/c%3A/86400/(s%20day-1)/%3Ac/mul/c%3A/7.0//units//days/def/%3Ac/mul/S/(T)/renameGRID/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/16/Jan/3001/ensotime/12.0/16/Jan/3001/ensotime/%3Agrid/use_as_grid/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
# calls curl to download data
url=dic[model]
print("\n Forecast URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > modelfcst_precip_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f modelfcst_precip_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv.gz")
#curl -g -k -b '__dlauth_id='$key'' ''$url'' > modelfcst_precip_fday${fday}.tsv
def GetForecast_RFREQ(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, key, week, wetday_threshold, nlag, model, force_download):
if not force_download:
try:
ff=open("modelfcst_RFREQ_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv", 'r')
s = ff.readline()
except OSError as err:
print("OS error: {0}".format(err))
print("Forecasts file doesn't exist --downloading")
force_download = True
if force_download:
#dictionary: #CFSv2 needs to be transformed to RFREQ!
dic = { 'CFSv2': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/3./mul/SOURCES/.ECMWF/.S2S/.NCEP/.reforecast/.control/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag%5Daverage/add/4./div/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/L1/'+str(day1)+'/'+str(day2)+'/VALUES/%5BL1%5Ddifferences/L1/removeGRID/S/(0000%20'+str(fday)+'%20'+mon+')/VALUES/%5BS%5Daverage/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/SOURCES/.NOAA/.NCEP/.EMC/.CFSv2/.6_hourly_rotating/.FLXF/.surface/.PRATE/%5BL%5D1/0.0/boxAverage/S/-'+str(nlag-1)+'/1/0/shiftdatashort/%5BS_lag/M%5Daverage/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')VALUE/%5BX/Y%5DregridLinear/L/'+str(day1)+'/'+str(day2)+'/RANGEEDGES/%5BL%5Daverage/%5BS%5Daverage/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div/(mm/day)/unitconvert/'+str(nday)+'/mul//units/(mm)/def/exch/sub/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/1/Jan/3001/ensotime/12.0/1/Jan/3001/ensotime/%3Agrid/addGRID/T//pointwidth/0/def/pop//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz',
'ECMWF': 'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.forecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1)+')/('+str(day2)+')/VALUES/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')/VALUE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/'+str(wetday_threshold)+'/flagge/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/.sfc_precip/.tp/Y/'+str(sla1)+'/'+str(nla1)+'/RANGE/X/'+str(wlo1)+'/'+str(elo1)+'/RANGE/L/('+str(day1-1)+')/('+str(day2)+')/VALUES/S/(0000%20'+str(fday)+'%20'+mon+'%20'+str(fyr)+')/VALUE/%5BL%5Ddifferences/c%3A//name//water_density/def/998/(kg/m3)/%3Ac/div//mm/unitconvert/'+str(wetday_threshold)+'/flagge/T/'+str(nday)+'/runningAverage/'+str(nday)+'.0/mul/%5Bhdate%5Daverage/sub/grid%3A//name/(T)/def//units/(months%20since%201960-01-01)/def//standard_name/(time)/def//pointwidth/1/def/1/Jan/3001/ensotime/12.0/1/Jan/3001/ensotime/%3Agrid/addGRID/T//pointwidth/0/def/pop//name/(fp)/def//units/(unitless)/def//long_name/(rainfall_freq)/def/-999/setmissing_value/%5BX/Y%5D%5BT%5Dcptv10.tsv.gz'
}
# calls curl to download data
url=dic[model]
print("\n Forecast URL: \n\n "+url)
get_ipython().system("curl -g -k -b '__dlauth_id="+key+"' '"+url+"' > modelfcst_RFREQ_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv.gz")
get_ipython().system("gunzip -f modelfcst_RFREQ_"+mon+"_fday"+str(fday)+"_wk"+str(week)+".tsv.gz")
#curl -g -k -b '__dlauth_id='$key'' ''$url'' > modelfcst_precip_fday${fday}.tsv
def CPTscript(mon,fday,wk,nla1,sla1,wlo1,elo1,nla2,sla2,wlo2,elo2,fprefix,mpref,training_season,ntrain,rainfall_frequency,MOS):
"""Function to write CPT namelist file
"""
# Set up CPT parameter file
f=open("params","w")
if MOS=='CCA':
# Opens CCA
f.write("611\n")
elif MOS=='PCR':
# Opens PCR
f.write("612\n")
elif MOS=='PCR':
# Opens GCM; because the calibration takes place via sklearn.linear_model (in the Jupyter notebook)
f.write("614\n")
elif MOS=='None':
# Opens GCM (no calibration performed in CPT)
f.write("614\n")
else:
print ("MOS option is invalid")
# First, ask CPT to stop if error is encountered
f.write("571\n")
f.write("3\n")
# Opens X input file
f.write("1\n")
if rainfall_frequency:
file='../input/model_precip_'+mon+'_wk'+str(wk)+'.tsv\n'
else:
file='../input/model_precip_'+mon+'_wk'+str(wk)+'.tsv\n'
f.write(file)
# Nothernmost latitude
f.write(str(nla1)+'\n')
# Southernmost latitude
f.write(str(sla1)+'\n')
# Westernmost longitude
f.write(str(wlo1)+'\n')
# Easternmost longitude
f.write(str(elo1)+'\n')
if MOS=='CCA' or MOS=='PCR':
# Minimum number of X modes
f.write("1\n")
# Maximum number of X modes
f.write("10\n")
# Opens forecast (X) file
f.write("3\n")
if rainfall_frequency:
file='../input/modelfcst_precip_'+mon+'_fday'+str(fday)+'_wk'+str(wk)+'.tsv\n'
else:
file='../input/modelfcst_precip_'+mon+'_fday'+str(fday)+'_wk'+str(wk)+'.tsv\n'
f.write(file)
# Opens Y input file
f.write("2\n")
if rainfall_frequency:
file='../input/obs_RFREQ_'+mon+'_wk'+str(wk)+'.tsv\n'
else:
file='../input/obs_precip_'+mon+'_wk'+str(wk)+'.tsv\n'
f.write(file)
# Nothernmost latitude
f.write(str(nla2)+'\n')
# Southernmost latitude
f.write(str(sla2)+'\n')
# Westernmost longitude
f.write(str(wlo2)+'\n')
# Easternmost longitude
f.write(str(elo2)+'\n')
if MOS=='CCA':
# Minimum number of Y modes
f.write("1\n")
# Maximum number of Y modes
f.write("10\n")
# Minimum number of CCA modes
f.write("1\n")
# Maximum number of CCAmodes
f.write("5\n")
# X training period
f.write("4\n")
# First year of X training period
f.write("1901\n")
# Y training period
f.write("5\n")
# First year of Y training period
f.write("1901\n")
# Goodness index
f.write("531\n")
# Kendall's tau
f.write("3\n")
# Option: Length of training period
f.write("7\n")
# Length of training period
f.write(str(ntrain)+'\n')
# %store 55 >> params
# Option: Length of cross-validation window
f.write("8\n")
# Enter length
f.write("3\n")
# Turn ON Transform predictand data
f.write("541\n")
# Turn ON zero bound for Y data (automatically on by CPT if variable is precip)
#f.write("542\n")
# Turn ON synchronous predictors
f.write("545\n")
# Turn ON p-values for masking maps
#f.write("561\n")
### Missing value options
f.write("544\n")
# Missing value X flag:
blurb='-999\n'
f.write(blurb)
# Maximum % of missing values
f.write("10\n")
# Maximum % of missing gridpoints
f.write("10\n")
# Number of near-neighbors
f.write("1\n")
# Missing value replacement : best-near-neighbors
f.write("4\n")
# Y missing value flag
blurb='-999\n'
f.write(blurb)
# Maximum % of missing values
f.write("10\n")
# Maximum % of missing stations
f.write("10\n")
# Number of near-neighbors
f.write("1\n")
# Best near neighbor
f.write("4\n")
# Transformation settings
#f.write("554\n")
# Empirical distribution
#f.write("1\n")
#######BUILD MODEL AND VALIDATE IT !!!!!
# NB: Default output format is GrADS format
# select output format
f.write("131\n")
# GrADS format
f.write("3\n")
# save goodness index
f.write("112\n")
file='../output/'+fprefix+'_'+mpref+'_Kendallstau_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
# Cross-validation
f.write("311\n")
# cross-validated skill maps
f.write("413\n")
# save Pearson's Correlation
f.write("1\n")
file='../output/'+fprefix+'_'+mpref+'_Pearson_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
# cross-validated skill maps
f.write("413\n")
# save Spearmans Correlation
f.write("2\n")
file='../output/'+fprefix+'_'+mpref+'_Spearman_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
# cross-validated skill maps
f.write("413\n")
# save 2AFC score
f.write("3\n")
file='../output/'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
# cross-validated skill maps
f.write("413\n")
# save RocBelow score
f.write("10\n")
file='../output/'+fprefix+'_'+mpref+'_RocBelow_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
# cross-validated skill maps
f.write("413\n")
# save RocAbove score
f.write("11\n")
file='../output/'+fprefix+'_'+mpref+'_RocAbove_'+training_season+'_wk'+str(wk)+'\n'
f.write(file)
if MOS=='CCA' or MOS=='PCR': #DO NOT USE CPT to compute probabilities if MOS='None' --use IRIDL for direct counting