-
-
Notifications
You must be signed in to change notification settings - Fork 1.7k
/
test_image.py
1390 lines (1162 loc) · 58.1 KB
/
test_image.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
# Licensed under a 3-clause BSD style license - see PYFITS.rst
from __future__ import division, with_statement
import math
import os
import time
import warnings
import numpy as np
from ....io import fits
from ....utils.exceptions import (AstropyDeprecationWarning,
AstropyPendingDeprecationWarning)
from ....tests.helper import pytest, raises, catch_warnings
from ..hdu.compressed import SUBTRACTIVE_DITHER_1, DITHER_SEED_CHECKSUM
from .test_table import comparerecords
from . import FitsTestCase
from .util import ignore_warnings
class TestImageFunctions(FitsTestCase):
def test_constructor_name_arg(self):
"""Like the test of the same name in test_table.py"""
hdu = fits.ImageHDU()
assert hdu.name == ''
assert 'EXTNAME' not in hdu.header
hdu.name = 'FOO'
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
# Passing name to constructor
hdu = fits.ImageHDU(name='FOO')
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
# And overriding a header with a different extname
hdr = fits.Header()
hdr['EXTNAME'] = 'EVENTS'
hdu = fits.ImageHDU(header=hdr, name='FOO')
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
def test_constructor_copies_header(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/153
Ensure that a header from one HDU is copied when used to initialize new
HDU.
"""
ifd = fits.HDUList(fits.PrimaryHDU())
phdr = ifd[0].header
phdr['FILENAME'] = 'labq01i3q_rawtag.fits'
primary_hdu = fits.PrimaryHDU(header=phdr)
ofd = fits.HDUList(primary_hdu)
ofd[0].header['FILENAME'] = 'labq01i3q_flt.fits'
# Original header should be unchanged
assert phdr['FILENAME'] == 'labq01i3q_rawtag.fits'
@raises(ValueError)
def test_open(self):
# The function "open" reads a FITS file into an HDUList object. There
# are three modes to open: "readonly" (the default), "append", and
# "update".
# Open a file read-only (the default mode), the content of the FITS
# file are read into memory.
r = fits.open(self.data('test0.fits')) # readonly
# data parts are latent instantiation, so if we close the HDUList
# without touching data, data can not be accessed.
r.close()
r[1].data[:2, :2]
def test_open_2(self):
r = fits.open(self.data('test0.fits'))
info = ([(0, 'PRIMARY', 'PrimaryHDU', 138, (), '', '')] +
[(x, 'SCI', 'ImageHDU', 61, (40, 40), 'int16', '')
for x in range(1, 5)])
try:
assert r.info(output=False) == info
finally:
r.close()
def test_primary_with_extname(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/151
Tests that the EXTNAME keyword works with Primary HDUs as well, and
interacts properly with the .name attribute. For convenience
hdulist['PRIMARY'] will still refer to the first HDU even if it has an
EXTNAME not equal to 'PRIMARY'.
"""
prihdr = fits.Header([('EXTNAME', 'XPRIMARY'), ('EXTVER', 1)])
hdul = fits.HDUList([fits.PrimaryHDU(header=prihdr)])
assert 'EXTNAME' in hdul[0].header
assert hdul[0].name == 'XPRIMARY'
assert hdul[0].name == hdul[0].header['EXTNAME']
info = [(0, 'XPRIMARY', 'PrimaryHDU', 5, (), '', '')]
assert hdul.info(output=False) == info
assert hdul['PRIMARY'] is hdul['XPRIMARY']
assert hdul['PRIMARY'] is hdul[('XPRIMARY', 1)]
hdul[0].name = 'XPRIMARY2'
assert hdul[0].header['EXTNAME'] == 'XPRIMARY2'
hdul.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert hdul[0].name == 'XPRIMARY2'
@ignore_warnings(AstropyDeprecationWarning)
def test_io_manipulation(self):
# This legacy test also tests numerous deprecated interfaces for
# backwards compatibility
# Get a keyword value. An extension can be referred by name or by
# number. Both extension and keyword names are case insensitive.
with fits.open(self.data('test0.fits')) as r:
assert r['primary'].header['naxis'] == 0
assert r[0].header['naxis'] == 0
# If there are more than one extension with the same EXTNAME value,
# the EXTVER can be used (as the second argument) to distinguish
# the extension.
assert r['sci', 1].header['detector'] == 1
# append (using "update()") a new card
r[0].header['xxx'] = 1.234e56
assert (str(r[0].header.ascard[-3:]) ==
"EXPFLAG = 'NORMAL ' / Exposure interruption indicator \n"
"FILENAME= 'vtest3.fits' / File name \n"
"XXX = 1.234E+56 ")
# rename a keyword
r[0].header.rename_key('filename', 'fname')
pytest.raises(ValueError, r[0].header.rename_key, 'fname',
'history')
pytest.raises(ValueError, r[0].header.rename_key, 'fname',
'simple')
r[0].header.rename_key('fname', 'filename')
# get a subsection of data
assert (r[2].data[:3, :3] ==
np.array([[349, 349, 348],
[349, 349, 347],
[347, 350, 349]], dtype=np.int16)).all()
# We can create a new FITS file by opening a new file with "append"
# mode.
with fits.open(self.temp('test_new.fits'), mode='append') as n:
# Append the primary header and the 2nd extension to the new
# file.
n.append(r[0])
n.append(r[2])
# The flush method will write the current HDUList object back
# to the newly created file on disk. The HDUList is still open
# and can be further operated.
n.flush()
assert n[1].data[1, 1] == 349
# modify a data point
n[1].data[1, 1] = 99
# When the file is closed, the most recent additions of
# extension(s) since last flush() will be appended, but any HDU
# already existed at the last flush will not be modified
del n
# If an existing file is opened with "append" mode, like the
# readonly mode, the HDU's will be read into the HDUList which can
# be modified in memory but can not be written back to the original
# file. A file opened with append mode can only add new HDU's.
os.rename(self.temp('test_new.fits'),
self.temp('test_append.fits'))
with fits.open(self.temp('test_append.fits'), mode='append') as a:
# The above change did not take effect since this was made
# after the flush().
assert a[1].data[1, 1] == 349
a.append(r[1])
del a
# When changes are made to an HDUList which was opened with
# "update" mode, they will be written back to the original file
# when a flush/close is called.
os.rename(self.temp('test_append.fits'),
self.temp('test_update.fits'))
with fits.open(self.temp('test_update.fits'), mode='update') as u:
# When the changes do not alter the size structures of the
# original (or since last flush) HDUList, the changes are
# written back "in place".
assert u[0].header['rootname'] == 'U2EQ0201T'
u[0].header['rootname'] = 'abc'
assert u[1].data[1, 1] == 349
u[1].data[1, 1] = 99
u.flush()
# If the changes affect the size structure, e.g. adding or
# deleting HDU(s), header was expanded or reduced beyond
# existing number of blocks (2880 bytes in each block), or
# change the data size, the HDUList is written to a temporary
# file, the original file is deleted, and the temporary file is
# renamed to the original file name and reopened in the update
# mode. To a user, these two kinds of updating writeback seem
# to be the same, unless the optional argument in flush or
# close is set to 1.
del u[2]
u.flush()
# the write method in HDUList class writes the current HDUList,
# with all changes made up to now, to a new file. This method
# works the same disregard the mode the HDUList was opened
# with.
u.append(r[3])
u.writeto(self.temp('test_new.fits'))
del u
# Another useful new HDUList method is readall. It will "touch" the
# data parts in all HDUs, so even if the HDUList is closed, we can
# still operate on the data.
with fits.open(self.data('test0.fits')) as r:
r.readall()
assert r[1].data[1, 1] == 315
# create an HDU with data only
data = np.ones((3, 5), dtype=np.float32)
hdu = fits.ImageHDU(data=data, name='SCI')
assert (hdu.data ==
np.array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
dtype=np.float32)).all()
# create an HDU with header and data
# notice that the header has the right NAXIS's since it is constructed
# with ImageHDU
hdu2 = fits.ImageHDU(header=r[1].header, data=np.array([1, 2],
dtype='int32'))
assert (str(hdu2.header.ascard[1:5]) ==
"BITPIX = 32 / array data type \n"
"NAXIS = 1 / number of array dimensions \n"
"NAXIS1 = 2 \n"
"PCOUNT = 0 / number of parameters ")
def test_memory_mapping(self):
# memory mapping
f1 = fits.open(self.data('test0.fits'), memmap=1)
f1.close()
def test_verification_on_output(self):
# verification on output
# make a defect HDUList first
x = fits.ImageHDU()
hdu = fits.HDUList(x) # HDUList can take a list or one single HDU
with catch_warnings() as w:
hdu.verify()
text = "HDUList's 0th element is not a primary HDU."
assert len(w) == 3
assert text in str(w[1].message)
with catch_warnings() as w:
hdu.writeto(self.temp('test_new2.fits'), 'fix')
text = ("HDUList's 0th element is not a primary HDU. "
"Fixed by inserting one as 0th HDU.")
assert len(w) == 3
assert text in str(w[1].message)
def test_section(self):
# section testing
fs = fits.open(self.data('arange.fits'))
assert (fs[0].section[3, 2, 5] == np.array([357])).all()
assert (fs[0].section[3, 2, :] ==
np.array([352, 353, 354, 355, 356, 357, 358, 359, 360, 361,
362])).all()
assert (fs[0].section[3, 2, 4:] ==
np.array([356, 357, 358, 359, 360, 361, 362])).all()
assert (fs[0].section[3, 2, :8] ==
np.array([352, 353, 354, 355, 356, 357, 358, 359])).all()
assert (fs[0].section[3, 2, -8:8] ==
np.array([355, 356, 357, 358, 359])).all()
assert (fs[0].section[3, 2:5, :] ==
np.array([[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]])).all()
assert (fs[0].section[3, :, :][:3, :3] ==
np.array([[330, 331, 332],
[341, 342, 343],
[352, 353, 354]])).all()
dat = fs[0].data
assert (fs[0].section[3, 2:5, :8] == dat[3, 2:5, :8]).all()
assert (fs[0].section[3, 2:5, 3] == dat[3, 2:5, 3]).all()
assert (fs[0].section[3:6, :, :][:3, :3, :3] ==
np.array([[[330, 331, 332],
[341, 342, 343],
[352, 353, 354]],
[[440, 441, 442],
[451, 452, 453],
[462, 463, 464]],
[[550, 551, 552],
[561, 562, 563],
[572, 573, 574]]])).all()
assert (fs[0].section[:, :, :][:3, :2, :2] ==
np.array([[[0, 1],
[11, 12]],
[[110, 111],
[121, 122]],
[[220, 221],
[231, 232]]])).all()
assert (fs[0].section[:, 2, :] == dat[:, 2, :]).all()
assert (fs[0].section[:, 2:5, :] == dat[:, 2:5, :]).all()
assert (fs[0].section[3:6, 3, :] == dat[3:6, 3, :]).all()
assert (fs[0].section[3:6, 3:7, :] == dat[3:6, 3:7, :]).all()
def test_section_data_square(self):
a = np.arange(4).reshape((2, 2))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
def test_section_data_cube(self):
a = np.arange(18).reshape((2, 3, 3))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
# TODO: Generate these perumtions instead of having them all written
# out, yeesh!
assert (d.section[:, :, :] == dat[:, :, :]).all()
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[:] == dat[:]).all()
assert (d.section[0, :, :] == dat[0, :, :]).all()
assert (d.section[1, :, :] == dat[1, :, :]).all()
assert (d.section[0, 0, :] == dat[0, 0, :]).all()
assert (d.section[0, 1, :] == dat[0, 1, :]).all()
assert (d.section[0, 2, :] == dat[0, 2, :]).all()
assert (d.section[1, 0, :] == dat[1, 0, :]).all()
assert (d.section[1, 1, :] == dat[1, 1, :]).all()
assert (d.section[1, 2, :] == dat[1, 2, :]).all()
assert (d.section[0, 0, 0] == dat[0, 0, 0]).all()
assert (d.section[0, 0, 1] == dat[0, 0, 1]).all()
assert (d.section[0, 0, 2] == dat[0, 0, 2]).all()
assert (d.section[0, 1, 0] == dat[0, 1, 0]).all()
assert (d.section[0, 1, 1] == dat[0, 1, 1]).all()
assert (d.section[0, 1, 2] == dat[0, 1, 2]).all()
assert (d.section[0, 2, 0] == dat[0, 2, 0]).all()
assert (d.section[0, 2, 1] == dat[0, 2, 1]).all()
assert (d.section[0, 2, 2] == dat[0, 2, 2]).all()
assert (d.section[1, 0, 0] == dat[1, 0, 0]).all()
assert (d.section[1, 0, 1] == dat[1, 0, 1]).all()
assert (d.section[1, 0, 2] == dat[1, 0, 2]).all()
assert (d.section[1, 1, 0] == dat[1, 1, 0]).all()
assert (d.section[1, 1, 1] == dat[1, 1, 1]).all()
assert (d.section[1, 1, 2] == dat[1, 1, 2]).all()
assert (d.section[1, 2, 0] == dat[1, 2, 0]).all()
assert (d.section[1, 2, 1] == dat[1, 2, 1]).all()
assert (d.section[1, 2, 2] == dat[1, 2, 2]).all()
assert (d.section[:, 0, 0] == dat[:, 0, 0]).all()
assert (d.section[:, 0, 1] == dat[:, 0, 1]).all()
assert (d.section[:, 0, 2] == dat[:, 0, 2]).all()
assert (d.section[:, 1, 0] == dat[:, 1, 0]).all()
assert (d.section[:, 1, 1] == dat[:, 1, 1]).all()
assert (d.section[:, 1, 2] == dat[:, 1, 2]).all()
assert (d.section[:, 2, 0] == dat[:, 2, 0]).all()
assert (d.section[:, 2, 1] == dat[:, 2, 1]).all()
assert (d.section[:, 2, 2] == dat[:, 2, 2]).all()
assert (d.section[0, :, 0] == dat[0, :, 0]).all()
assert (d.section[0, :, 1] == dat[0, :, 1]).all()
assert (d.section[0, :, 2] == dat[0, :, 2]).all()
assert (d.section[1, :, 0] == dat[1, :, 0]).all()
assert (d.section[1, :, 1] == dat[1, :, 1]).all()
assert (d.section[1, :, 2] == dat[1, :, 2]).all()
assert (d.section[:, :, 0] == dat[:, :, 0]).all()
assert (d.section[:, :, 1] == dat[:, :, 1]).all()
assert (d.section[:, :, 2] == dat[:, :, 2]).all()
assert (d.section[:, 0, :] == dat[:, 0, :]).all()
assert (d.section[:, 1, :] == dat[:, 1, :]).all()
assert (d.section[:, 2, :] == dat[:, 2, :]).all()
assert (d.section[:, :, 0:1] == dat[:, :, 0:1]).all()
assert (d.section[:, :, 0:2] == dat[:, :, 0:2]).all()
assert (d.section[:, :, 0:3] == dat[:, :, 0:3]).all()
assert (d.section[:, :, 1:2] == dat[:, :, 1:2]).all()
assert (d.section[:, :, 1:3] == dat[:, :, 1:3]).all()
assert (d.section[:, :, 2:3] == dat[:, :, 2:3]).all()
assert (d.section[0:1, 0:1, 0:1] == dat[0:1, 0:1, 0:1]).all()
assert (d.section[0:1, 0:1, 0:2] == dat[0:1, 0:1, 0:2]).all()
assert (d.section[0:1, 0:1, 0:3] == dat[0:1, 0:1, 0:3]).all()
assert (d.section[0:1, 0:1, 1:2] == dat[0:1, 0:1, 1:2]).all()
assert (d.section[0:1, 0:1, 1:3] == dat[0:1, 0:1, 1:3]).all()
assert (d.section[0:1, 0:1, 2:3] == dat[0:1, 0:1, 2:3]).all()
assert (d.section[0:1, 0:2, 0:1] == dat[0:1, 0:2, 0:1]).all()
assert (d.section[0:1, 0:2, 0:2] == dat[0:1, 0:2, 0:2]).all()
assert (d.section[0:1, 0:2, 0:3] == dat[0:1, 0:2, 0:3]).all()
assert (d.section[0:1, 0:2, 1:2] == dat[0:1, 0:2, 1:2]).all()
assert (d.section[0:1, 0:2, 1:3] == dat[0:1, 0:2, 1:3]).all()
assert (d.section[0:1, 0:2, 2:3] == dat[0:1, 0:2, 2:3]).all()
assert (d.section[0:1, 0:3, 0:1] == dat[0:1, 0:3, 0:1]).all()
assert (d.section[0:1, 0:3, 0:2] == dat[0:1, 0:3, 0:2]).all()
assert (d.section[0:1, 0:3, 0:3] == dat[0:1, 0:3, 0:3]).all()
assert (d.section[0:1, 0:3, 1:2] == dat[0:1, 0:3, 1:2]).all()
assert (d.section[0:1, 0:3, 1:3] == dat[0:1, 0:3, 1:3]).all()
assert (d.section[0:1, 0:3, 2:3] == dat[0:1, 0:3, 2:3]).all()
assert (d.section[0:1, 1:2, 0:1] == dat[0:1, 1:2, 0:1]).all()
assert (d.section[0:1, 1:2, 0:2] == dat[0:1, 1:2, 0:2]).all()
assert (d.section[0:1, 1:2, 0:3] == dat[0:1, 1:2, 0:3]).all()
assert (d.section[0:1, 1:2, 1:2] == dat[0:1, 1:2, 1:2]).all()
assert (d.section[0:1, 1:2, 1:3] == dat[0:1, 1:2, 1:3]).all()
assert (d.section[0:1, 1:2, 2:3] == dat[0:1, 1:2, 2:3]).all()
assert (d.section[0:1, 1:3, 0:1] == dat[0:1, 1:3, 0:1]).all()
assert (d.section[0:1, 1:3, 0:2] == dat[0:1, 1:3, 0:2]).all()
assert (d.section[0:1, 1:3, 0:3] == dat[0:1, 1:3, 0:3]).all()
assert (d.section[0:1, 1:3, 1:2] == dat[0:1, 1:3, 1:2]).all()
assert (d.section[0:1, 1:3, 1:3] == dat[0:1, 1:3, 1:3]).all()
assert (d.section[0:1, 1:3, 2:3] == dat[0:1, 1:3, 2:3]).all()
assert (d.section[1:2, 0:1, 0:1] == dat[1:2, 0:1, 0:1]).all()
assert (d.section[1:2, 0:1, 0:2] == dat[1:2, 0:1, 0:2]).all()
assert (d.section[1:2, 0:1, 0:3] == dat[1:2, 0:1, 0:3]).all()
assert (d.section[1:2, 0:1, 1:2] == dat[1:2, 0:1, 1:2]).all()
assert (d.section[1:2, 0:1, 1:3] == dat[1:2, 0:1, 1:3]).all()
assert (d.section[1:2, 0:1, 2:3] == dat[1:2, 0:1, 2:3]).all()
assert (d.section[1:2, 0:2, 0:1] == dat[1:2, 0:2, 0:1]).all()
assert (d.section[1:2, 0:2, 0:2] == dat[1:2, 0:2, 0:2]).all()
assert (d.section[1:2, 0:2, 0:3] == dat[1:2, 0:2, 0:3]).all()
assert (d.section[1:2, 0:2, 1:2] == dat[1:2, 0:2, 1:2]).all()
assert (d.section[1:2, 0:2, 1:3] == dat[1:2, 0:2, 1:3]).all()
assert (d.section[1:2, 0:2, 2:3] == dat[1:2, 0:2, 2:3]).all()
assert (d.section[1:2, 0:3, 0:1] == dat[1:2, 0:3, 0:1]).all()
assert (d.section[1:2, 0:3, 0:2] == dat[1:2, 0:3, 0:2]).all()
assert (d.section[1:2, 0:3, 0:3] == dat[1:2, 0:3, 0:3]).all()
assert (d.section[1:2, 0:3, 1:2] == dat[1:2, 0:3, 1:2]).all()
assert (d.section[1:2, 0:3, 1:3] == dat[1:2, 0:3, 1:3]).all()
assert (d.section[1:2, 0:3, 2:3] == dat[1:2, 0:3, 2:3]).all()
assert (d.section[1:2, 1:2, 0:1] == dat[1:2, 1:2, 0:1]).all()
assert (d.section[1:2, 1:2, 0:2] == dat[1:2, 1:2, 0:2]).all()
assert (d.section[1:2, 1:2, 0:3] == dat[1:2, 1:2, 0:3]).all()
assert (d.section[1:2, 1:2, 1:2] == dat[1:2, 1:2, 1:2]).all()
assert (d.section[1:2, 1:2, 1:3] == dat[1:2, 1:2, 1:3]).all()
assert (d.section[1:2, 1:2, 2:3] == dat[1:2, 1:2, 2:3]).all()
assert (d.section[1:2, 1:3, 0:1] == dat[1:2, 1:3, 0:1]).all()
assert (d.section[1:2, 1:3, 0:2] == dat[1:2, 1:3, 0:2]).all()
assert (d.section[1:2, 1:3, 0:3] == dat[1:2, 1:3, 0:3]).all()
assert (d.section[1:2, 1:3, 1:2] == dat[1:2, 1:3, 1:2]).all()
assert (d.section[1:2, 1:3, 1:3] == dat[1:2, 1:3, 1:3]).all()
assert (d.section[1:2, 1:3, 2:3] == dat[1:2, 1:3, 2:3]).all()
def test_section_data_four(self):
a = np.arange(256).reshape((4, 4, 4, 4))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :, :, :] == dat[:, :, :, :]).all()
assert (d.section[:, :, :] == dat[:, :, :]).all()
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[:] == dat[:]).all()
assert (d.section[0, :, :, :] == dat[0, :, :, :]).all()
assert (d.section[0, :, 0, :] == dat[0, :, 0, :]).all()
assert (d.section[:, :, 0, :] == dat[:, :, 0, :]).all()
assert (d.section[:, 1, 0, :] == dat[:, 1, 0, :]).all()
assert (d.section[:, :, :, 1] == dat[:, :, :, 1]).all()
def test_section_data_scaled(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/143
This is like test_section_data_square but uses a file containing scaled
image data, to test that sections can work correctly with scaled data.
"""
hdul = fits.open(self.data('scale.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
# Test without having accessed the full data first
hdul = fits.open(self.data('scale.fits'))
d = hdul[0]
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
assert not d._data_loaded
def test_do_not_scale_image_data(self):
hdul = fits.open(self.data('scale.fits'), do_not_scale_image_data=True)
assert hdul[0].data.dtype == np.dtype('>i2')
hdul = fits.open(self.data('scale.fits'))
assert hdul[0].data.dtype == np.dtype('float32')
def test_append_uint_data(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/56
(BZERO and BSCALE added in the wrong location when appending scaled
data)
"""
fits.writeto(self.temp('test_new.fits'), data=np.array([],
dtype='uint8'))
d = np.zeros([100, 100]).astype('uint16')
fits.append(self.temp('test_new.fits'), data=d)
f = fits.open(self.temp('test_new.fits'), uint=True)
assert f[1].data.dtype == 'uint16'
def test_uint_header_consistency(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2305
This ensures that an HDU containing unsigned integer data always has
the apppriate BZERO value in its header.
"""
for int_size in (16, 32, 64):
# Just make an array of some unsigned ints that wouldn't fit in a
# signed int array of the same bit width
max_uint = (2 ** int_size) - 1
if int_size == 64:
# Otherwise may get an overflow error, at least on Python 2
max_uint = np.uint64(int_size)
dtype = 'uint%d' % int_size
arr = np.empty(100, dtype=dtype)
arr.fill(max_uint)
arr -= np.arange(100, dtype=dtype)
uint_hdu = fits.PrimaryHDU(data=arr)
assert np.all(uint_hdu.data == arr)
assert uint_hdu.data.dtype.name == 'uint%d' % int_size
assert 'BZERO' in uint_hdu.header
assert uint_hdu.header['BZERO'] == (2 ** (int_size - 1))
filename = 'uint%d.fits' % int_size
uint_hdu.writeto(self.temp(filename))
with fits.open(self.temp(filename), uint=True) as hdul:
new_uint_hdu = hdul[0]
assert np.all(new_uint_hdu.data == arr)
assert new_uint_hdu.data.dtype.name == 'uint%d' % int_size
assert 'BZERO' in new_uint_hdu.header
assert new_uint_hdu.header['BZERO'] == (2 ** (int_size - 1))
def test_blanks(self):
"""Test image data with blank spots in it (which should show up as
NaNs in the data array.
"""
arr = np.zeros((10, 10), dtype=np.int32)
# One row will be blanks
arr[1] = 999
hdu = fits.ImageHDU(data=arr)
hdu.header['BLANK'] = 999
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
assert np.isnan(hdul[1].data[1]).all()
def test_bzero_with_floats(self):
"""Test use of the BZERO keyword in an image HDU containing float
data.
"""
arr = np.zeros((10, 10)) - 1
hdu = fits.ImageHDU(data=arr)
hdu.header['BZERO'] = 1.0
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
arr += 1
assert (hdul[1].data == arr).all()
def test_rewriting_large_scaled_image(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/84 and
https://aeon.stsci.edu/ssb/trac/pyfits/ticket/101
"""
hdul = fits.open(self.data('fixed-1890.fits'))
orig_data = hdul[0].data
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul.close()
# Just as before, but this time don't touch hdul[0].data before writing
# back out--this is the case that failed in
# https://aeon.stsci.edu/ssb/trac/pyfits/ticket/84
hdul = fits.open(self.data('fixed-1890.fits'))
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul.close()
# Test opening/closing/reopening a scaled file in update mode
hdul = fits.open(self.data('fixed-1890.fits'),
do_not_scale_image_data=True)
hdul.writeto(self.temp('test_new.fits'), clobber=True,
output_verify='silentfix')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
orig_data = hdul[0].data
hdul.close()
hdul = fits.open(self.temp('test_new.fits'), mode='update')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul = fits.open(self.temp('test_new.fits'))
hdul.close()
def test_image_update_header(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/105
Replacing the original header to an image HDU and saving should update
the NAXISn keywords appropriately and save the image data correctly.
"""
# Copy the original file before saving to it
self.copy_file('test0.fits')
with fits.open(self.temp('test0.fits'), mode='update') as hdul:
orig_data = hdul[1].data.copy()
hdr_copy = hdul[1].header.copy()
del hdr_copy['NAXIS*']
hdul[1].header = hdr_copy
with fits.open(self.temp('test0.fits')) as hdul:
assert (orig_data == hdul[1].data).all()
def test_open_scaled_in_update_mode(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/119
(Don't update scaled image data if the data is not read)
This ensures that merely opening and closing a file containing scaled
image data does not cause any change to the data (or the header).
Changes should only occur if the data is accessed.
"""
# Copy the original file before making any possible changes to it
self.copy_file('scale.fits')
mtime = os.stat(self.temp('scale.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('scale.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('scale.fits')).st_mtime
# Insert a slight delay to ensure the mtime does change when the file
# is changed
time.sleep(1)
hdul = fits.open(self.temp('scale.fits'), 'update')
orig_data = hdul[0].data
hdul.close()
# Now the file should be updated with the rescaled data
assert mtime != os.stat(self.temp('scale.fits')).st_mtime
hdul = fits.open(self.temp('scale.fits'), mode='update')
assert hdul[0].data.dtype == np.dtype('>f4')
assert hdul[0].header['BITPIX'] == -32
assert 'BZERO' not in hdul[0].header
assert 'BSCALE' not in hdul[0].header
assert (orig_data == hdul[0].data).all()
# Try reshaping the data, then closing and reopening the file; let's
# see if all the changes are preseved properly
hdul[0].data.shape = (42, 10)
hdul.close()
hdul = fits.open(self.temp('scale.fits'))
assert hdul[0].shape == (42, 10)
assert hdul[0].data.dtype == np.dtype('>f4')
assert hdul[0].header['BITPIX'] == -32
assert 'BZERO' not in hdul[0].header
assert 'BSCALE' not in hdul[0].header
def test_scale_back(self):
"""A simple test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/120
The scale_back feature for image HDUs.
"""
self.copy_file('scale.fits')
with fits.open(self.temp('scale.fits'), mode='update',
scale_back=True) as hdul:
orig_bitpix = hdul[0].header['BITPIX']
orig_bzero = hdul[0].header['BZERO']
orig_bscale = hdul[0].header['BSCALE']
orig_data = hdul[0].data.copy()
hdul[0].data[0] = 0
with fits.open(self.temp('scale.fits'),
do_not_scale_image_data=True) as hdul:
assert hdul[0].header['BITPIX'] == orig_bitpix
assert hdul[0].header['BZERO'] == orig_bzero
assert hdul[0].header['BSCALE'] == orig_bscale
zero_point = int(math.floor(-orig_bzero / orig_bscale))
assert (hdul[0].data[0] == zero_point).all()
with fits.open(self.temp('scale.fits')) as hdul:
assert (hdul[0].data[1:] == orig_data[1:]).all()
def test_image_none(self):
"""
Regression test for https://github.com/spacetelescope/PyFITS/issues/27
"""
with fits.open(self.data('test0.fits')) as h:
h[1].data
h[1].data = None
h[1].writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as h:
assert h[1].data is None
assert h[1].header['NAXIS'] == 0
assert 'NAXIS1' not in h[1].header
assert 'NAXIS2' not in h[1].header
def test_invalid_blank(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2711
If the BLANK keyword contains an invalid value it should be ignored for
any calculations (though a warning should be issued).
"""
data = np.arange(100, dtype=np.float64)
hdu = fits.PrimaryHDU(data)
hdu.header['BLANK'] = 'nan'
hdu.writeto(self.temp('test.fits'))
with catch_warnings() as w:
with fits.open(self.temp('test.fits')) as hdul:
assert np.all(hdul[0].data == data)
assert len(w) == 2
msg = "Invalid value for 'BLANK' keyword in header"
assert msg in str(w[0].message)
msg = "Invalid 'BLANK' keyword"
assert msg in str(w[1].message)
def test_scaled_image_fromfile(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2710
"""
# Make some sample data
a = np.arange(100, dtype=np.float32)
hdu = fits.PrimaryHDU(data=a.copy())
hdu.scale(bscale=1.1)
hdu.writeto(self.temp('test.fits'))
with open(self.temp('test.fits'), 'rb') as f:
file_data = f.read()
hdul = fits.HDUList.fromstring(file_data)
assert np.allclose(hdul[0].data, a)
class TestCompressedImage(FitsTestCase):
def test_empty(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2595
"""
hdu = fits.CompImageHDU()
hdu.data is None
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits'), mode='update') as hdul:
assert len(hdul) == 2
assert isinstance(hdul[1], fits.CompImageHDU)
assert hdul[1].data is None
# Now test replacing the empty data with an array and see what
# happens
hdul[1].data = np.arange(100, dtype=np.int32)
with fits.open(self.temp('test.fits')) as hdul:
assert len(hdul) == 2
assert isinstance(hdul[1], fits.CompImageHDU)
assert np.all(hdul[1].data == np.arange(100, dtype=np.int32))
@pytest.mark.parametrize(
('data', 'compression_type', 'quantize_level', 'byte_order'),
sum([[(np.zeros((2, 10, 10), dtype=np.float32), 'RICE_1', 16, bo),
(np.zeros((2, 10, 10), dtype=np.float32), 'GZIP_1', -0.01, bo),
(np.zeros((100, 100)) + 1, 'HCOMPRESS_1', 16, bo)]
for bo in ('<', '>')], []))
def test_comp_image(self, data, compression_type, quantize_level,
byte_order):
data = data.newbyteorder(byte_order)
primary_hdu = fits.PrimaryHDU()
ofd = fits.HDUList(primary_hdu)
chdu = fits.CompImageHDU(data, name='SCI',
compressionType=compression_type,
quantizeLevel=quantize_level)
ofd.append(chdu)
ofd.writeto(self.temp('test_new.fits'), clobber=True)
ofd.close()
with fits.open(self.temp('test_new.fits')) as fd:
assert (fd[1].data == data).all()
assert fd[1].header['NAXIS'] == chdu.header['NAXIS']
assert fd[1].header['NAXIS1'] == chdu.header['NAXIS1']
assert fd[1].header['NAXIS2'] == chdu.header['NAXIS2']
assert fd[1].header['BITPIX'] == chdu.header['BITPIX']
@ignore_warnings(AstropyPendingDeprecationWarning)
def test_comp_image_hcompression_1_invalid_data(self):
"""
Tests compression with the HCOMPRESS_1 algorithm with data that is
not 2D and has a non-2D tile size.
"""
pytest.raises(ValueError, fits.CompImageHDU,
np.zeros((2, 10, 10), dtype=np.float32), name='SCI',
compressionType='HCOMPRESS_1', quantizeLevel=16,
tileSize=[2, 10, 10])
@ignore_warnings(AstropyPendingDeprecationWarning)
def test_comp_image_hcompress_image_stack(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/171
Tests that data containing more than two dimensions can be
compressed with HCOMPRESS_1 so long as the user-supplied tile size can
be flattened to two dimensions.
"""
cube = np.arange(300, dtype=np.float32).reshape((3, 10, 10))
hdu = fits.CompImageHDU(data=cube, name='SCI',
compressionType='HCOMPRESS_1',
quantizeLevel=16, tileSize=[5, 5, 1])
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert (hdul['SCI'].data == cube).all()
def test_subtractive_dither_seed(self):
"""
Regression test for https://github.com/spacetelescope/PyFITS/issues/32
Ensure that when floating point data is compressed with the
SUBTRACTIVE_DITHER_1 quantization method that the correct ZDITHER0 seed
is added to the header, and that the data can be correctly
decompressed.
"""
array = np.arange(100.0).reshape(10, 10)
csum = (array[0].view('uint8').sum() % 10000) + 1
hdu = fits.CompImageHDU(data=array,
quantize_method=SUBTRACTIVE_DITHER_1,
dither_seed=DITHER_SEED_CHECKSUM)
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert isinstance(hdul[1], fits.CompImageHDU)
assert 'ZQUANTIZ' in hdul[1]._header
assert hdul[1]._header['ZQUANTIZ'] == 'SUBTRACTIVE_DITHER_1'
assert 'ZDITHER0' in hdul[1]._header
assert hdul[1]._header['ZDITHER0'] == csum
assert np.all(hdul[1].data == array)
def test_disable_image_compression(self):
with catch_warnings():
# No warnings should be displayed in this case
warnings.simplefilter('error')
with fits.open(self.data('comp.fits'),
disable_image_compression=True) as hdul:
# The compressed image HDU should show up as a BinTableHDU, but
# *not* a CompImageHDU
assert isinstance(hdul[1], fits.BinTableHDU)
assert not isinstance(hdul[1], fits.CompImageHDU)
with fits.open(self.data('comp.fits')) as hdul:
assert isinstance(hdul[1], fits.CompImageHDU)
def test_open_comp_image_in_update_mode(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/167
Similar to test_open_scaled_in_update_mode(), but specifically for
compressed images.
"""
# Copy the original file before making any possible changes to it
self.copy_file('comp.fits')
mtime = os.stat(self.temp('comp.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('comp.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('comp.fits')).st_mtime
def test_open_scaled_in_update_mode_compressed(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/88 2
Identical to test_open_scaled_in_update_mode() but with a compressed
version of the scaled image.
"""
# Copy+compress the original file before making any possible changes to
# it
with fits.open(self.data('scale.fits'),
do_not_scale_image_data=True) as hdul:
chdu = fits.CompImageHDU(data=hdul[0].data,
header=hdul[0].header)
chdu.writeto(self.temp('scale.fits'))
mtime = os.stat(self.temp('scale.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('scale.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('scale.fits')).st_mtime
# Insert a slight delay to ensure the mtime does change when the file
# is changed
time.sleep(1)
hdul = fits.open(self.temp('scale.fits'), 'update')
hdul[1].data
hdul.close()