-
Notifications
You must be signed in to change notification settings - Fork 0
/
CSPP_VIIRS_LST_hdf5Plots.py
1002 lines (737 loc) · 40.9 KB
/
CSPP_VIIRS_LST_hdf5Plots.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
#!/usr/bin/env python
# encoding: utf-8
"""
CSPP_VIIRS_LST_hdf5Plots.py
Purpose: Create swath projection quicklook PNGs from the VIIRS LST EDR HDF5 files.
Images can be created for the EDR product or the associated quality flags.
Minimum commandline...
export CSPP_EDR_HOME=$(readlink -f /path/to/EDR)
source $CSPP_EDR_HOME/cspp_edr_env.sh
python CSPP_VIIRS_LST_hdf5Plots.py -i '/path/to/files/VLSTO*.h5'
or
python CSPP_VIIRS_LST_hdf5Plots.py --input_files=/path/to/files/VLSTO*.h5
Created by Geoff Cureton on 2013-06-04.
Copyright (c) 2013 University of Wisconsin SSEC. All rights reserved.
"""
file_Date = '$Date$'
file_Revision = '$Revision$'
file_Author = '$Author$'
file_HeadURL = '$HeadURL$'
file_Id = '$Id$'
__author__ = 'G.P. Cureton <geoff.cureton@ssec.wisc.edu>'
__version__ = '$Id$'
__docformat__ = 'Epytext'
#############
import os, sys
from os import path, uname, mkdir
from glob import glob
import string, logging, traceback
from time import time
import numpy as np
from numpy import ma as ma
import scipy as scipy
import matplotlib
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
from matplotlib.figure import Figure
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
# This must come *after* the backend is specified.
import matplotlib.pyplot as ppl
import optparse as optparse
from ViirsData import ViirsTrimTable
import viirs_edr_data
import tables as pytables
from tables import exceptions as pyEx
# every module should have a LOG object
# e.g. LOG.warning('my dog has fleas')
import logging
LOG = logging.getLogger(__file__)
dpi=200
### Moderate and Imager resolution trim table arrays. These are
### bool arrays, and the trim pixels are set to True.
trimObj = ViirsTrimTable()
modTrimMask = trimObj.createModTrimArray(nscans=48,trimType=bool)
def get_hdf5_dict(hdf5Path,filePrefix):
shortNameDict = {}
hdf5Path = path.abspath(path.expanduser(hdf5Path))
print "hdf5Path = %s" % (hdf5Path)
hdf5Dir = path.dirname(hdf5Path)
hdf5Glob = path.basename(hdf5Path)
print "hdf5Dir = %s" % (hdf5Dir)
print "hdf5Glob = %s" % (hdf5Glob)
if (hdf5Glob == '' or hdf5Glob == '*'):
print "prefix = %s" % (filePrefix)
hdf5Glob = path.join(hdf5Dir,'%s_*.h5'%(filePrefix))
else :
hdf5Glob = path.join(hdf5Dir,'%s'%(hdf5Glob))
print "Final hdf5Glob = %s" % (hdf5Glob)
hdf5Files = glob(hdf5Glob)
if hdf5Files != []:
hdf5Files.sort()
granIdDict = {}
for files in hdf5Files :
# Open the hdf5 file
fileObj = pytables.openFile(files)
# Get a "pointer" to the granules attribute group.
VIIRS_LST_EDR_Gran_0 = fileObj.getNode('/Data_Products/VIIRS-LST-EDR/VIIRS-LST-EDR_Gran_0')
# Retrieve a few attributes
granID = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Granule_ID')[0][0]
print 'N_Granule_ID = %s' % (granID)
dayNightFlag = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Day_Night_Flag')[0][0]
print 'N_Day_Night_Flag = %s' % (dayNightFlag)
shortName = fileObj.getNodeAttr('/Data_Products/VIIRS-LST-EDR','N_Collection_Short_Name')[0][0]
print 'N_Collection_Short_Name = %s' % (shortName)
# Strip the path from the filename
hdf5File = path.basename(files)
# Add the granule information to the dictionary, keyed with the granule ID...
granIdDict[granID] = [hdf5File,fileObj]
shortNameDict[shortName] = granIdDict
return shortNameDict
class LSTclass():
def __init__(self,hdf5Dir):
self.hdf5Dir = hdf5Dir
self.collShortNames = [
'VIIRS-LST-EDR',
]
self.plotDescr = {}
self.plotDescr['VIIRS-LST-EDR'] = ['Land Surface Temperature (K)']
self.plotLims = {}
self.plotLims['VIIRS-LST-EDR'] = [None,None]
self.dataName = {}
self.dataName['VIIRS-LST-EDR'] = ['/All_Data/VIIRS-LST-EDR_All/LandSurfaceTemperature']
self.dataFactors = {}
self.dataFactors['VIIRS-LST-EDR'] = ['/All_Data/VIIRS-LST-EDR_All/LSTFactors']
self.hdf5_dict = get_hdf5_dict(hdf5Dir,'VLSTO')
def plot_LST_granules(self,plotProd='EDR',vmin=None,vmax=None,pngDir=None,pngPrefix=None,annotation='',dpi=300):
if pngDir is None :
pngDir = path.abspath(path.curdir)
plotDescr = self.plotDescr
plotLims = self.plotLims
hdf5_dict = self.hdf5_dict
collShortNames = hdf5_dict.keys()
print 'collShortNames = %r' % (collShortNames)
for shortName in collShortNames :
print 'shortName = %s' % (shortName)
if (plotProd == 'EDR'):
dataNames = self.dataName[shortName]
factorsNames = self.dataFactors[shortName]
plotDescrs = plotDescr[shortName]
prodNames = ['LST']
granID_list = hdf5_dict[shortName].keys()
granID_list.sort()
for granID in granID_list :
print '%s --> %s ' % (shortName, granID)
hdf5Obj = hdf5_dict[shortName][granID][1]
VIIRS_LST_EDR_Gran_0 = hdf5Obj.getNode('/Data_Products/VIIRS-LST-EDR/VIIRS-LST-EDR_Gran_0')
dayNightFlag = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Day_Night_Flag')[0][0]
print 'N_Day_Night_Flag = %s' % (dayNightFlag)
orient = -1 if dayNightFlag == 'Day' else 1
for dataName,factorsName,plotDescr,prodName in zip(dataNames,factorsNames,plotDescrs,prodNames):
data = hdf5Obj.getNode(dataName)[:,:]
factors = hdf5Obj.getNode(factorsName)[:]
data = data*factors[0] + factors[1]
LSTqualFlag = hdf5Obj.getNode('/All_Data/VIIRS-LST-EDR_All/QF1_VIIRSLSTEDR')
LSTqualFlag = np.bitwise_and(LSTqualFlag,3) >> 0
LSTqualFlagMask = ma.masked_equal(LSTqualFlag,0).mask
pixelTrimValue = trimObj.sdrTypeFill['ONGROUND_PT_FILL'][data.dtype.name]
print "pixelTrimValue is %r" % (pixelTrimValue)
# Apply the moderate pixel trim, so that we can properly mask them out at plot time.
data = ma.array(data,mask=modTrimMask,fill_value=trimObj.sdrTypeFill['ONBOARD_PT_FILL'][data.dtype.name])
data = data.filled()
plotTitle = '%s : %s %s' % (shortName,granID,annotation)
cbTitle = plotDescr
# Create figure with default size, and create canvas to draw on
scale=1.5
fig = Figure(figsize=(scale*8,scale*3))
canvas = FigureCanvas(fig)
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax_rect = [0.05, 0.18, 0.9, 0.75 ] # [left,bottom,width,height]
ax = fig.add_axes(ax_rect)
# Granule axis title
ax_title = ppl.setp(ax,title=plotTitle)
ppl.setp(ax_title,fontsize=12)
ppl.setp(ax_title,family="sans-serif")
# Plot the data
print "%s is of kind %r" % (shortName,data.dtype.kind)
if (data.dtype.kind =='i' or data.dtype.kind =='u'):
fill_mask = ma.masked_greater(data,200).mask
else:
fill_mask = ma.masked_less(data,-800.).mask
# Construct the total mask
totalMask = LSTqualFlagMask + fill_mask
# Mask the aerosol so we only have the retrievals
data = ma.masked_array(data,mask=totalMask)
im = ax.imshow(data[::orient,::orient],interpolation='nearest',vmin=vmin,vmax=vmax)
ppl.setp(ax.get_xticklabels(), visible=False)
ppl.setp(ax.get_yticklabels(), visible=False)
ppl.setp(ax.get_xticklines(),visible=False)
ppl.setp(ax.get_yticklines(),visible=False)
# add a colorbar axis
cax_rect = [0.05 , 0.05, 0.9 , 0.10 ] # [left,bottom,width,height]
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
# Plot the colorbar.
cb = fig.colorbar(im, cax=cax, orientation='horizontal')
ppl.setp(cax.get_xticklabels(),fontsize=9)
ppl.setp(cax.get_xticklines(),visible=True)
# Colourbar title
cax_title = ppl.setp(cax,title=cbTitle)
ppl.setp(cax_title,fontsize=10)
# Turn off the tickmarks on the colourbar
#ppl.setp(cb.ax.get_xticklines(),visible=False)
#ppl.setp(cb.ax.get_xticklabels(),fontsize=9)
# Redraw the figure
canvas.draw()
# Save the figure to a png file...
pngFile = path.join(pngDir,'%s%s_%s_%s.png' % (pngPrefix,shortName,granID,prodName))
canvas.print_figure(pngFile,dpi=dpi)
print "Writing to %s..." % (pngFile)
ppl.close('all')
hdf5Obj.close()
def plot_LST_pass(self,plotProd='EDR',vmin=None,vmax=None,pngDir=None,
pngPrefix=None,annotation='',dpi=300):
if pngDir is None :
pngDir = path.abspath(path.curdir)
plotDescr = self.plotDescr
plotLims = self.plotLims
hdf5_dict = self.hdf5_dict
collShortNames = hdf5_dict.keys()
print 'collShortNames = %r' % (collShortNames)
for shortName in collShortNames :
print 'shortName = %s' % (shortName)
if (plotProd == 'EDR'):
dataNames = self.dataName[shortName]
factorsNames = self.dataFactors[shortName]
plotDescrs = plotDescr[shortName]
prodNames = ['LST']
granID_list = hdf5_dict[shortName].keys()
granID_list.sort()
for dataName,factorsName,plotDescr,prodName in zip(dataNames,factorsNames,plotDescrs,prodNames):
# Read in the data from the granules and concatenate
for granID in granID_list :
print '%s --> %s ' % (shortName, granID)
hdf5Obj = hdf5_dict[shortName][granID][1]
VIIRS_LST_EDR_Gran_0 = hdf5Obj.getNode('/Data_Products/VIIRS-LST-EDR/VIIRS-LST-EDR_Gran_0')
dayNightFlag = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Day_Night_Flag')[0][0]
orbitNumber = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Beginning_Orbit_Number')[0][0]
print 'N_Day_Night_Flag = %s' % (dayNightFlag)
print 'N_Beginning_Orbit_Number = %s' % (orbitNumber)
orient = -1 if dayNightFlag == 'Day' else 1
dataGranule = hdf5Obj.getNode(dataName)[:,:]
factors = hdf5Obj.getNode(factorsName)[:]
qf1ArrGranule = hdf5Obj.getNode('/All_Data/VIIRS-LST-EDR_All/QF1_VIIRSLSTEDR')
qf2ArrGranule = hdf5Obj.getNode('/All_Data/VIIRS-LST-EDR_All/QF2_VIIRSLSTEDR')
qf3ArrGranule = hdf5Obj.getNode('/All_Data/VIIRS-LST-EDR_All/QF3_VIIRSLSTEDR')
# Concatenate the granules.
try :
data = np.vstack((data,dataGranule))
qf1Arr = np.vstack((qf1Arr,qf1ArrGranule))
qf2Arr = np.vstack((qf2Arr,qf1ArrGranule))
qf3Arr = np.vstack((qf3Arr,qf1ArrGranule))
print "data shape = {}".format(data.shape)
print "qf1Arr shape = {}".format(qf1Arr.shape)
print "qf2Arr shape = {}".format(qf2Arr.shape)
print "qf3Arr shape = {}\n".format(qf3Arr.shape)
except NameError :
data = dataGranule[:,:]
qf1Arr = qf1ArrGranule[:,:]
qf2Arr = qf2ArrGranule[:,:]
qf3Arr = qf3ArrGranule[:,:]
print "data shape = {}".format(data.shape)
print "qf1Arr shape = {}".format(qf1Arr.shape)
print "qf2Arr shape = {}".format(qf2Arr.shape)
print "qf3Arr shape = {}\n".format(qf3Arr.shape)
print "Final data shape = {}".format(data.shape)
print "Final qf1Arr shape = {}".format(qf1Arr.shape)
print "Final qf2Arr shape = {}".format(qf2Arr.shape)
print "Final qf3Arr shape = {}\n".format(qf3Arr.shape)
try :
#Determine masks for each fill type, for the LST EDR
lstFillMasks = {}
for fillType in trimObj.sdrTypeFill.keys() :
fillValue = trimObj.sdrTypeFill[fillType][data.dtype.name]
if 'float' in fillValue.__class__.__name__ :
lstFillMasks[fillType] = ma.masked_inside(data,fillValue-eps,fillValue+eps).mask
if (lstFillMasks[fillType].__class__.__name__ != 'ndarray') :
lstFillMasks[fillType] = None
elif 'int' in fillValue.__class__.__name__ :
lstFillMasks[fillType] = ma.masked_equal(data,fillValue).mask
if (lstFillMasks[fillType].__class__.__name__ != 'ndarray') :
lstFillMasks[fillType] = None
else :
print "Dataset was neither int not float... a worry"
pass
#Construct the total mask from all of the various fill values
fillMask = ma.array(np.zeros(data.shape,dtype=np.bool))
for fillType in trimObj.sdrTypeFill.keys() :
if lstFillMasks[fillType] is not None :
fillMask = fillMask * ma.array(np.zeros(data.shape,dtype=np.bool),\
mask=lstFillMasks[fillType])
# Unscale the LST dataset
data = data * factors[0] + factors[1]
# LST quality mask
LSTqualFlag = np.bitwise_and(qf1Arr,3) >> 0
lstQualMask = ma.masked_equal(LSTqualFlag,3).mask
# LST valid range mask
LSTvalidRangeFlag = np.bitwise_and(qf2Arr,2) >> 1
lstValidRangeMask = ma.masked_equal(LSTvalidRangeFlag,1).mask
# LST water bodies LWM mask
#LSTwaterBackgroundFlag = np.bitwise_and(qf3Arr,7) >> 0
#lstWaterBackgroundMask = ma.masked_inside(LSTwaterBackgroundFlag,2,5).mask
# LST water bodies surface type mask
LSTwaterBodiesFlag = np.bitwise_and(qf3Arr,248) >> 3
lstWaterBodiesMask = ma.masked_equal(LSTwaterBodiesFlag,17).mask
# Combine the fill mask and quality masks...
totalMask = fillMask.mask + lstQualMask \
+ lstWaterBodiesMask
try :
data = ma.array(data,mask=totalMask)
except ma.core.MaskError :
print ">> error: Mask Error, probably mismatched geolocation and product array sizes, aborting..."
sys.exit(1)
except Exception, err :
print ">> error: %s..." % (str(err))
sys.exit(1)
plotTitle = '%s : orbit %s %s' % (shortName,orbitNumber,annotation)
cbTitle = plotDescr
# Create figure with default size, and create canvas to draw on
passRows = float(data.shape[0])
passCols = float(data.shape[1])
aspect = passRows/passCols
scale = 5.
fig = Figure(figsize=(scale*1.,scale*aspect))
canvas = FigureCanvas(fig)
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax_rect = [0.05, 0.18, 0.9, 0.75 ] # [left,bottom,width,height]
ax = fig.add_axes(ax_rect,axis_bgcolor='lightgray')
# Granule axis title
ax_title = ppl.setp(ax,title=plotTitle)
ppl.setp(ax_title,fontsize=12)
ppl.setp(ax_title,family="sans-serif")
# Remove the ticks and ticklabels on the main axis
ppl.setp(ax.get_xticklabels(), visible=False)
ppl.setp(ax.get_yticklabels(), visible=False)
ppl.setp(ax.get_xticklines(),visible=False)
ppl.setp(ax.get_yticklines(),visible=False)
# Plot the data
print "%s is of kind %r" % (shortName,data.dtype.kind)
if (data.dtype.kind =='i' or data.dtype.kind =='u'):
fill_mask = ma.masked_greater(data,200).mask
else:
fill_mask = ma.masked_less(data,-800.).mask
# Construct the total mask
totalMask = fill_mask# + LSTqualFlagMask
# Mask the aerosol so we only have the retrievals
data = ma.masked_array(data,mask=totalMask)
# Flip the pass depending on whether this is an ascending or decending pass
data = data[::orient,::orient]
im = ax.imshow(data,interpolation='nearest',vmin=vmin,vmax=vmax)
# add a colorbar axis
cax_rect = [0.05 , 0.05, 0.9 , 0.08 ] # [left,bottom,width,height]
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
# Plot the colorbar.
cb = fig.colorbar(im, cax=cax, orientation='horizontal')
ppl.setp(cax.get_xticklabels(),fontsize=9)
ppl.setp(cax.get_xticklines(),visible=True)
# Colourbar title
cax_title = ppl.setp(cax,title=cbTitle)
ppl.setp(cax_title,fontsize=10)
# Turn off the tickmarks on the colourbar
#ppl.setp(cb.ax.get_xticklines(),visible=False)
#ppl.setp(cb.ax.get_xticklabels(),fontsize=9)
# Redraw the figure
canvas.draw()
# Save the figure to a png file...
pngFile = path.join(pngDir,'%s%s_b%s_%s.png' % (pngPrefix,shortName,orbitNumber,prodName))
canvas.print_figure(pngFile,dpi=dpi)
print "Writing to %s..." % (pngFile)
ppl.close('all')
del(data)
del(qf1Arr)
del(qf2Arr)
del(qf3Arr)
def plot_LST_tests(self,plotProd='QF',pngDir=None,pngPrefix=None,annotation='',dpi=300):
if pngDir is None :
pngDir = path.abspath(path.curdir)
hdf5_dict = self.hdf5_dict
collShortNames = hdf5_dict.keys()
if (plotProd == 'QF'):
byteList = [0,1,2,3]
else :
byteList = [int(plotProd.strip('QF'))]
print 'collShortNames = %r' % (collShortNames)
CMD = viirs_edr_data.SeaSurfaceTempProdData
for shortName in collShortNames :
print 'shortName = %s' % (shortName)
granID_list = hdf5_dict[shortName].keys()
granID_list.sort()
for granID in granID_list :
print '%s --> %s ' % (shortName, granID)
hdf5Obj = hdf5_dict[shortName][granID][1]
VIIRS_LST_EDR_Gran_0 = hdf5Obj.getNode('/Data_Products/VIIRS-LST-EDR/VIIRS-LST-EDR_Gran_0')
dayNightFlag = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Day_Night_Flag')[0][0]
print 'N_Day_Night_Flag = %s' % (dayNightFlag)
orient = -1 if dayNightFlag == 'Day' else 1
for byte in byteList :
print ""
plots = list(CMD.ViirsLSTqualBitMaskNames[byte])
for item in plots:
if item == 'Spare':
plots.remove(item)
numPlots = len(plots)
numRows = np.ceil(float(numPlots)/2.)
figWidth = 15. # inches
figHeight = 3. * numPlots/2. # inches
fig = Figure(figsize=((figWidth,figHeight)))
canvas = FigureCanvas(fig)
Ax = []
Im = []
Txt = []
Cb = []
plotIdx = 1
for dSet in range(np.shape(CMD.ViirsLSTqualBitMasks[byte])[0]):
print '\ndSet = %d' %(dSet)
dSetName = '/All_Data/VIIRS-LST-EDR_All/QF%s_VIIRSLSTEDR'%(str(byte+1))
byteData = hdf5Obj.getNode(dSetName)[:,:]
plotTitle = '%s : %s %s (Byte %d)' % (shortName,granID,annotation,byte)
fig.text(0.5, 0.95, plotTitle, fontsize=16, color='black', ha='center', va='bottom', alpha=1.0)
if (CMD.ViirsLSTqualBitMaskNames[byte][dSet] == 'Spare') :
print "Skipping dataset with byte = %d, dSet = %d" % (byte, dSet)
else :
print "byte = %d, dSet = %d" % (byte, dSet)
byteMask = CMD.ViirsLSTqualBitMasks[byte][dSet]
byteShift = CMD.ViirsLSTqualBitShift[byte][dSet]
print "byteMask = %d, byteShift = %d, dSetName = %s" % (byteMask, byteShift, dSetName)
data = np.bitwise_and(byteData,byteMask) >> byteShift
vmin,vmax = CMD.ViirsLSTqualvalues[byte][dSet][0], CMD.ViirsLSTqualvalues[byte][dSet][-1]
print "vmin = %d, vmax = %d" % (vmin, vmax)
cmap = ListedColormap(CMD.ViirsLSTqualFillColours[byte][dSet])
numCats = np.array(CMD.ViirsLSTqualFillColours[byte][dSet]).size
numBounds = numCats + 1
tickPos = np.arange(float(numBounds))/float(numCats)
tickPos = tickPos[0 :-1] + tickPos[1]/2.
print "numCats = ",numCats
print "tickPos = ",tickPos
titleStr = CMD.ViirsLSTqualBitMaskNames[byte][dSet]
print "titleStr = %s" % (titleStr)
Ax.append(fig.add_subplot(numRows,2,plotIdx))
print "data.dtype.__str__() = %s" % (data.dtype.__str__())
Im.append(Ax[dSet].imshow(data.astype('int')[::orient,::orient], vmin=vmin, vmax=vmax, interpolation='nearest',cmap=cmap))
Txt.append(Ax[dSet].set_title(titleStr))
ppl.setp(Txt[dSet],fontsize=10)
ppl.setp(Ax[dSet].get_xticklines(), visible=False)
ppl.setp(Ax[dSet].get_yticklines(), visible=False)
ppl.setp(Ax[dSet].get_xticklabels(), visible=False)
ppl.setp(Ax[dSet].get_yticklabels(), visible=False)
Cb.append(fig.colorbar(Im[dSet], orientation='horizonal', pad=0.05))
print "Cb byte = %d, dSet = %d" % (byte, dSet)
print "CMD.ViirsLSTqualTickNames[%d][%d] = %s" % \
(byte,dSet,CMD.ViirsLSTqualTickNames[byte][dSet])
print "CMD.ViirsLSTqualFillColours[%d][%d] = %s" % \
(byte,dSet,CMD.ViirsLSTqualFillColours[byte][dSet])
Cb[dSet].set_ticks(vmax*tickPos)
ppl.setp(Cb[dSet].ax,xticklabels=CMD.ViirsLSTqualTickNames[byte][dSet])
ppl.setp(Cb[dSet].ax.get_xticklabels(),fontsize=6)
ppl.setp(Cb[dSet].ax.get_xticklines(),visible=False)
plotIdx += 1
pngFile = path.join(pngDir,'%s%s_%s_QF%s.png' % (pngPrefix,shortName,granID,str(byte+1)))
print "Writing to %s..." % (pngFile)
canvas.draw()
canvas.print_figure(pngFile,dpi=dpi)
ppl.close('all')
hdf5Obj.close()
def plot_LST_pass_tests(self,plotProd='QF',pngDir=None,pngPrefix=None,annotation='',dpi=300):
if pngDir is None :
pngDir = path.abspath(path.curdir)
hdf5_dict = self.hdf5_dict
collShortNames = hdf5_dict.keys()
if (plotProd == 'QF'):
byteList = [0,1,2,3]
else :
byteList = [int(plotProd.strip('QF'))-1]
print 'collShortNames = %r' % (collShortNames)
CMD = viirs_edr_data.SeaSurfaceTempProdData
for shortName in collShortNames :
print 'shortName = %s' % (shortName)
dataName = self.dataName[shortName]
granID_list = hdf5_dict[shortName].keys()
granID_list.sort()
for byte in byteList :
print ""
plots = list(CMD.ViirsLSTqualBitMaskNames[byte])
for item in plots:
if item == 'Spare':
plots.remove(item)
print "plots = ",plots
if (plots == []):
print "There are no valid datasets in this byte array, skipping..."
else :
for dSet in range(np.shape(CMD.ViirsLSTqualBitMasks[byte])[0]):
print '\ndSet = %d' %(dSet)
for granID in granID_list :
print '%s --> %s ' % (shortName, granID)
hdf5Obj = hdf5_dict[shortName][granID][1]
VIIRS_LST_EDR_Gran_0 = hdf5Obj.getNode('/Data_Products/VIIRS-LST-EDR/VIIRS-LST-EDR_Gran_0')
dayNightFlag = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Day_Night_Flag')[0][0]
orbitNumber = getattr(VIIRS_LST_EDR_Gran_0.attrs,'N_Beginning_Orbit_Number')[0][0]
print 'N_Day_Night_Flag = %s' % (dayNightFlag)
print 'N_Beginning_Orbit_Number = %s' % (orbitNumber)
orient = -1 if dayNightFlag == 'Day' else 1
dSetName = '/All_Data/VIIRS-LST-EDR_All/QF%s_VIIRSLSTEDR'%(str(byte+1))
byteDataGranule = hdf5Obj.getNode(dSetName)[:,:]
# Concatenate the granules.
try :
byteData = np.vstack((byteData,byteDataGranule))
print "byteData shape = %s\n" %(str(byteData.shape))
except :
byteData = byteDataGranule[:,:]
print "byteData shape = %s\n" %(str(byteData.shape))
# What value are the bowtie deletion pixels
ongroundPixelTrimValue = trimObj.sdrTypeFill['ONGROUND_PT_FILL'][byteData.dtype.name]
print "Onground Pixel Trim value is {}".format(ongroundPixelTrimValue)
onboardPixelTrimValue = trimObj.sdrTypeFill['ONBOARD_PT_FILL'][byteData.dtype.name]
print "Onboard Pixel Trim value is {}\n".format(onboardPixelTrimValue)
# Create onboard and onground pixel trim mask arrays, for the total number of
# scans in the pass...
numGranules = len(granID_list)
numScans = numGranules * 48
onboardTrimMask = trimObj.createOnboardModTrimArray(nscans=numScans,trimType=bool)
ongroundTrimMask = trimObj.createModTrimArray(nscans=numScans,trimType=bool)
# Apply the On-board pixel trim
byteData = ma.array(byteData,mask=onboardTrimMask,fill_value=ongroundPixelTrimValue)
#byteData = byteData.filled() # Substitute for the masked values with ongroundPixelTrimValue
# Apply the On-board pixel trim
byteData = ma.array(byteData,mask=ongroundTrimMask,fill_value=onboardPixelTrimValue)
#byteData = byteData.filled() # Substitute for the masked values with onboardPixelTrimValue
# Flip the pass depending on whether this is an ascending or decending pass
byteData = byteData[::orient,::orient]
if (CMD.ViirsLSTqualBitMaskNames[byte][dSet] == 'Spare') :
print "Skipping dataset with byte = %d, dSet = %d" % (byte, dSet)
else :
print "byte = %d, dSet = %d" % (byte, dSet)
byteMask = CMD.ViirsLSTqualBitMasks[byte][dSet]
byteShift = CMD.ViirsLSTqualBitShift[byte][dSet]
print "byteMask = %d, byteShift = %d, dSetName = %s" % (byteMask, byteShift, dSetName)
data = np.bitwise_and(byteData,byteMask) >> byteShift
vmin,vmax = CMD.ViirsLSTqualvalues[byte][dSet][0], CMD.ViirsLSTqualvalues[byte][dSet][-1]
print "vmin = %d, vmax = %d" % (vmin, vmax)
cmap = ListedColormap(CMD.ViirsLSTqualFillColours[byte][dSet])
numCats = np.array(CMD.ViirsLSTqualFillColours[byte][dSet]).size
numBounds = numCats + 1
tickPos = np.arange(float(numBounds))/float(numCats)
tickPos = tickPos[0 :-1] + tickPos[1]/2.
print "numCats = ",numCats
print "tickPos = ",tickPos
# Set the plot and colourbar titles...
plotTitle = '%s : orbit %s %s (Byte %d)' % (shortName,orbitNumber,annotation,byte)
cbTitle = CMD.ViirsLSTqualBitMaskNames[byte][dSet]
# Create figure with default size, and create canvas to draw on
passRows = float(data.shape[0])
passCols = float(data.shape[1])
aspect = passRows/passCols
scale = 5.
fig = Figure(figsize=(scale*1.,scale*aspect))
canvas = FigureCanvas(fig)
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax_rect = [0.05, 0.18, 0.9, 0.75 ] # [left,bottom,width,height]
ax = fig.add_axes(ax_rect,axis_bgcolor='lightgray')
# Granule axis title
ax_title = ppl.setp(ax,title=plotTitle)
ppl.setp(ax_title,fontsize=12)
ppl.setp(ax_title,family="sans-serif")
# Remove the ticks and ticklabels on the main axis
ppl.setp(ax.get_xticklabels(), visible=False)
ppl.setp(ax.get_yticklabels(), visible=False)
ppl.setp(ax.get_xticklines(),visible=False)
ppl.setp(ax.get_yticklines(),visible=False)
# Mask the data
if (data.dtype.kind =='i' or data.dtype.kind =='u'):
print "%s is of kind %r" % (shortName,data.dtype.kind)
data = ma.masked_greater(data,247)
else:
print "%s is of kind %r" % (shortName,data.dtype.kind)
data = ma.masked_less(data,-800.)
# Plot the dataset on the main plotting axis
im = ax.imshow(data,interpolation='nearest',vmin=vmin,vmax=vmax,cmap=cmap)
# add a colorbar axis
cax_rect = [0.05 , 0.05, 0.9 , 0.08 ] # [left,bottom,width,height]
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
# Plot the colorbar.
cb = fig.colorbar(im, cax=cax, orientation='horizontal')
ppl.setp(cax.get_xticklabels(),fontsize=9)
ppl.setp(cax.get_xticklines(),visible=False)
# Set the colourbar tick locations and ticklabels
#ppl.setp(cb.ax,xticks=CMD.ViirsCMTickPos) # In colorbar axis coords (0..1)
cb.set_ticks(vmax*tickPos) # In data coords (0..3)
ppl.setp(cb.ax,xticklabels=CMD.ViirsLSTqualTickNames[byte][dSet])
# Colourbar title
cax_title = ppl.setp(cax,title=cbTitle)
ppl.setp(cax_title,fontsize=10)
# Turn off the tickmarks on the colourbar
ppl.setp(cb.ax.get_xticklines(),visible=False)
ppl.setp(cb.ax.get_xticklabels(),fontsize=7)
# Redraw the figure
canvas.draw()
# Save the figure to a png file...
pngFile = path.join(pngDir,'%s%s_b%s_QF%s_%d.png' % (pngPrefix,shortName,orbitNumber,str(byte+1),dSet))
canvas.print_figure(pngFile,dpi=dpi)
print "Writing to %s..." % (pngFile)
ppl.close('all')
del(byteData)
###################################################
# Main Function #
###################################################
def main():
prodChoices=['EDR','QF','LST','QF1','QF2','QF3']
description = \
'''
This is a brief description of %prog
'''
usage = "usage: %prog [mandatory args] [options]"
version = version="%prog"
parser = optparse.OptionParser(description=description,usage=usage,version=version)
# Mandatory arguments
mandatoryGroup = optparse.OptionGroup(parser, "Mandatory Arguments",
"At a minimum these arguments must be specified")
mandatoryGroup.add_option('-i','--input_files',
action="store",
dest="hdf5Files" ,
type="string",
help="The fully qualified path to the input VLSTO HDF5 files. May be a directory or a file glob.")
parser.add_option_group(mandatoryGroup)
# Optional arguments
optionalGroup = optparse.OptionGroup(parser, "Extra Options",
"These options may be used to customize plot characteristics.")
optionalGroup.add_option('-r','--svn_revision',
action="store",
dest="svnRevision",
default=string.split(__version__," ")[2],
type="string",
help="The Subversion revision number/tag of this script")
optionalGroup.add_option('--pass',
action="store_true",
dest="plotPass",
help="Concatenate the granules")
optionalGroup.add_option('--plotMin',
action="store",
type="float",
dest="plotMin",
help="Minimum value to plot.")
optionalGroup.add_option('--plotMax',
action="store",
type="float",
dest="plotMax",
help="Maximum value to plot.")
optionalGroup.add_option('-d','--dpi',
action="store",
dest="dpi",
default='200.',
type="float",
help="The resolution in dots per inch of the output png file. [default: %default]")
optionalGroup.add_option('-a','--map_annotation',
action="store",
dest="mapAnn",
#default='',
type="string",
help="The map legend describing the dataset being shown. [default: IPPROD]")
optionalGroup.add_option('-p','--product',
action="store",
dest="plotProduct",
type="choice",
choices=prodChoices,
help='''The VIIRS LST EDR or QF datasets to plot.\n\n
Possible values are...
%s
''' % (prodChoices.__str__()[1:-1]))
optionalGroup.add_option('--png_dir',
action="store",
dest="pngDir" ,
type="string",
help="The directory where png files will be written.")
optionalGroup.add_option('-o','--output_file_prefix',
action="store",
dest="outputFilePrefix",
default="",
type="string",
help="""String to prefix to the automatically generated png names, which are of
the form <N_Collection_Short_Name>_<N_Granule_ID>_<dset>.png. [default: %default]""")
parser.add_option_group(optionalGroup)
# Parse the arguments from the command line
(options, args) = parser.parse_args()
# Check that all of the mandatory options are given. If one or more
# are missing, print error message and exit...
mandatories = ['hdf5Files']
mand_errors = ["Missing mandatory argument [-i HDF5FILES | --input_files=HDF5FILES]"
]
isMissingMand = False
for m,m_err in zip(mandatories,mand_errors):
if not options.__dict__[m]:
isMissingMand = True
print m_err
if isMissingMand :
parser.error("Incomplete mandatory arguments, aborting...")
vmin = options.plotMin
vmax = options.plotMax
hdf5Path = path.abspath(path.expanduser(options.hdf5Files))
print "hdf5Path = %s" % (hdf5Path)
pngDir = '.' if (options.pngDir is None) else options.pngDir
pngDir = path.abspath(path.expanduser(pngDir))
print "pngDir = %s" % (pngDir)
if not path.isdir(pngDir):
print "Output image directory %s does not exist, creating..." % (pngDir)
try:
mkdir(pngDir,0755)
except Exception, err :
print "%s" % (err)
print "Creating directory %s failed, aborting..." % (pngDir)
sys.exit(1)
pngPrefix = options.outputFilePrefix
dpi = options.dpi
plotProduct = options.plotProduct
plotPass = options.plotPass
plotEDR = False
plotQF = False
if (plotProduct is None):
plotEDR = True
plotQF = True
edrPlotProduct = 'EDR'
qfPlotProduct = 'QF'
else :
if ('EDR' in plotProduct) \
or ('Skin' in plotProduct) \
or ('Bulk' in plotProduct) :
plotEDR = True
edrPlotProduct = plotProduct
if ('QF' in plotProduct) :
plotQF = True
qfPlotProduct = plotProduct
if plotEDR :
try :
LSTobj = LSTclass(hdf5Path)
if plotPass :
LSTobj.plot_LST_pass(plotProd=edrPlotProduct,
vmin=vmin,vmax=vmax,pngDir=pngDir,pngPrefix=pngPrefix,dpi=dpi)
else:
LSTobj.plot_LST_granules(plotProd=edrPlotProduct,vmin=vmin,vmax=vmax,pngDir=pngDir,pngPrefix=pngPrefix,dpi=dpi)
pytables.file.close_open_files()
except Exception, err:
traceback.print_exc(file=sys.stdout)
pytables.file.close_open_files()
if plotQF :
try :
LSTobj = LSTclass(hdf5Path)
if plotPass :
LSTobj.plot_LST_pass_tests(plotProd=qfPlotProduct,pngDir=pngDir,pngPrefix=pngPrefix,dpi=dpi)
else:
LSTobj.plot_LST_tests(plotProd=qfPlotProduct,pngDir=pngDir,pngPrefix=pngPrefix,dpi=dpi)
pytables.file.close_open_files()
except Exception, err:
traceback.print_exc(file=sys.stdout)
pytables.file.close_open_files()
print "Exiting..."
sys.exit(0)