-
Notifications
You must be signed in to change notification settings - Fork 65
/
test_projection.py
951 lines (649 loc) · 27 KB
/
test_projection.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
import warnings
import pytest
import numpy as np
from astropy import units as u
from astropy.wcs import WCS
from astropy.io import fits
from radio_beam import Beam, Beams
from .helpers import assert_allclose
from .test_spectral_cube import cube_and_raw
from ..spectral_cube import SpectralCube
from ..masks import BooleanArrayMask
from ..lower_dimensional_structures import (Projection, Slice, OneDSpectrum,
VaryingResolutionOneDSpectrum)
from ..utils import SliceWarning, WCSCelestialError, BeamUnitsError
from . import path
# needed for regression in numpy
import sys
try:
from astropy.utils.compat import NUMPY_LT_1_22
except ImportError:
# if astropy is an old version, we'll just skip the test
# (this is only used in one place)
NUMPY_LT_1_22 = False
# set up for parametrization
LDOs = (Projection, Slice, OneDSpectrum)
LDOs_2d = (Projection, Slice,)
two_qty_2d = np.ones((2,2)) * u.Jy
twelve_qty_2d = np.ones((12,12)) * u.Jy
two_qty_1d = np.ones((2,)) * u.Jy
twelve_qty_1d = np.ones((12,)) * u.Jy
data_two = (two_qty_2d, two_qty_2d, two_qty_1d)
data_twelve = (twelve_qty_2d, twelve_qty_2d, twelve_qty_1d)
data_two_2d = (two_qty_2d, two_qty_2d,)
data_twelve_2d = (twelve_qty_2d, twelve_qty_2d,)
def load_projection(filename):
hdu = fits.open(filename)[0]
proj = Projection.from_hdu(hdu)
return proj, hdu
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs_2d, data_two_2d))
def test_slices_of_projections_not_projections(LDO, data):
# slices of projections that have <2 dimensions should not be projections
p = LDO(data, copy=False)
assert not isinstance(p[0,0], LDO)
assert not isinstance(p[0], LDO)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs_2d, data_twelve_2d))
def test_copy_false(LDO, data):
# copy the data so we can manipulate inplace without affecting other tests
image = data.copy()
p = LDO(image, copy=False)
image[3,4] = 2 * u.Jy
assert_allclose(p[3,4], 2 * u.Jy)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_write(LDO, data, tmpdir):
p = LDO(data)
p.write(tmpdir.join('test.fits').strpath)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs_2d, data_twelve_2d))
def test_preserve_wcs_to(LDO, data):
# regression for #256
image = data.copy()
p = LDO(image, copy=False)
image[3,4] = 2 * u.Jy
p2 = p.to(u.mJy)
assert_allclose(p[3,4], 2 * u.Jy)
assert_allclose(p[3,4], 2000 * u.mJy)
assert p2.wcs == p.wcs
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_multiplication(LDO, data):
# regression: 265
p = LDO(data, copy=False)
p2 = p * 5
assert p2.unit == u.Jy
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value == 5)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_unit_division(LDO, data):
# regression: 265
image = data
p = LDO(image, copy=False)
p2 = p / u.beam
assert p2.unit == u.Jy/u.beam
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs_2d, data_twelve_2d))
def test_isnan(LDO, data):
# Check that np.isnan strips units
image = data.copy()
image[5,6] = np.nan
p = LDO(image, copy=False)
mask = np.isnan(p)
assert mask.sum() == 1
assert not hasattr(mask, 'unit')
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_self_arith(LDO, data):
image = data
p = LDO(image, copy=False, wcs=WCS(naxis=image.ndim))
assert hasattr(p, '_wcs')
assert p.wcs is not None
p2 = p + p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==2)
p2 = p - p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==0)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_self_arith_with_beam(LDO, data):
exp_beam = Beam(1.0 * u.arcsec)
image = data
p = LDO(image, copy=False, wcs=WCS(naxis=image.ndim))
p = p.with_beam(exp_beam)
assert hasattr(p, 'beam')
assert hasattr(p, '_wcs')
assert p.wcs is not None
p2 = p + p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==2)
assert hasattr(p2, 'beam')
assert p2.beam == exp_beam
p2 = p - p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==0)
assert hasattr(p2, 'beam')
assert p2.beam == exp_beam
@pytest.mark.xfail(raises=ValueError, strict=True)
def test_VRODS_wrong_beams_shape():
'''
Check that passing Beams with a different shape than the data
is caught.
'''
exp_beams = Beams(np.arange(1, 4) * u.arcsec)
p = VaryingResolutionOneDSpectrum(twelve_qty_1d, copy=False,
beams=exp_beams)
def test_VRODS_with_beams():
exp_beams = Beams(np.arange(1, twelve_qty_1d.size + 1) * u.arcsec)
p = VaryingResolutionOneDSpectrum(twelve_qty_1d, copy=False, beams=exp_beams)
assert (p.beams == exp_beams).all()
new_beams = Beams(np.arange(2, twelve_qty_1d.size + 2) * u.arcsec)
p = p.with_beams(new_beams)
assert np.all(p.beams == new_beams)
def test_VRODS_slice_with_beams():
exp_beams = Beams(np.arange(1, twelve_qty_1d.size + 1) * u.arcsec)
p = VaryingResolutionOneDSpectrum(twelve_qty_1d, copy=False,
wcs=WCS(naxis=1),
beams=exp_beams)
assert np.all(p[:5].beams == exp_beams[:5])
def test_VRODS_arith_with_beams():
exp_beams = Beams(np.arange(1, twelve_qty_1d.size + 1) * u.arcsec)
p = VaryingResolutionOneDSpectrum(twelve_qty_1d, copy=False, beams=exp_beams)
p2 = p + p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==2)
assert np.all(p2.beams == exp_beams)
p2 = p - p
assert hasattr(p2, '_wcs')
assert p2.wcs == p.wcs
assert np.all(p2.value==0)
assert np.all(p2.beams == exp_beams)
def test_onedspectrum_specaxis_units():
test_wcs = WCS(naxis=1)
test_wcs.wcs.cunit = ["m/s"]
test_wcs.wcs.ctype = ["VELO-LSR"]
p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs)
assert p.spectral_axis.unit == u.Unit("m/s")
def test_onedspectrum_with_spectral_unit():
test_wcs = WCS(naxis=1)
test_wcs.wcs.cunit = ["m/s"]
test_wcs.wcs.ctype = ["VELO-LSR"]
p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs)
p_new = p.with_spectral_unit(u.km/u.s)
assert p_new.spectral_axis.unit == u.Unit("km/s")
np.testing.assert_equal(p_new.spectral_axis.value,
1e-3*p.spectral_axis.value)
def test_onedspectrum_input_mask_type():
test_wcs = WCS(naxis=1)
test_wcs.wcs.cunit = ["m/s"]
test_wcs.wcs.ctype = ["VELO-LSR"]
np_mask = np.ones(twelve_qty_1d.shape, dtype=bool)
np_mask[1] = False
bool_mask = BooleanArrayMask(np_mask, wcs=test_wcs,
shape=np_mask.shape)
# numpy array
p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs,
mask=np_mask)
assert (p.mask.include() == bool_mask.include()).all()
# MaskBase
p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs,
mask=bool_mask)
assert (p.mask.include() == bool_mask.include()).all()
# No mask
ones_mask = BooleanArrayMask(np.ones(twelve_qty_1d.shape, dtype=bool),
wcs=test_wcs, shape=np_mask.shape)
p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs,
mask=None)
assert (p.mask.include() == ones_mask.include()).all()
def test_slice_tricks():
test_wcs_1 = WCS(naxis=1)
test_wcs_2 = WCS(naxis=2)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
im = Slice(twelve_qty_2d, wcs=test_wcs_2)
with warnings.catch_warnings(record=True) as w:
new = spec[:,None,None] * im[None,:,:]
assert new.ndim == 3
# two warnings because we're doing BOTH slices!
assert len(w) == 2
assert w[0].category == SliceWarning
with warnings.catch_warnings(record=True) as w:
new = spec.array[:,None,None] * im.array[None,:,:]
assert new.ndim == 3
assert len(w) == 0
def test_array_property():
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
arr = spec.array
# these are supposed to be the same object, but the 'is' tests fails!
assert spec.array.data == spec.data
assert isinstance(arr, np.ndarray)
assert not isinstance(arr, u.Quantity)
def test_quantity_property():
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
arr = spec.quantity
# these are supposed to be the same object, but the 'is' tests fails!
assert spec.array.data == spec.data
assert isinstance(arr, u.Quantity)
assert not isinstance(arr, OneDSpectrum)
def test_projection_with_beam(data_55):
exp_beam = Beam(1.0 * u.arcsec)
proj, hdu = load_projection(data_55)
# uses from_hdu, which passes beam as kwarg
assert proj.beam == exp_beam
assert proj.meta['beam'] == exp_beam
# load beam from meta
exp_beam = Beam(1.5 * u.arcsec)
meta = {"beam": exp_beam}
new_proj = Projection(hdu.data, wcs=proj.wcs, meta=meta)
assert new_proj.beam == exp_beam
assert new_proj.meta['beam'] == exp_beam
# load beam from given header
exp_beam = Beam(2.0 * u.arcsec)
header = hdu.header.copy()
header = exp_beam.attach_to_header(header)
new_proj = Projection(hdu.data, wcs=proj.wcs, header=header,
read_beam=True)
assert new_proj.beam == exp_beam
assert new_proj.meta['beam'] == exp_beam
# load beam from beam object
exp_beam = Beam(3.0 * u.arcsec)
header = hdu.header.copy()
del header["BMAJ"], header["BMIN"], header["BPA"]
new_proj = Projection(hdu.data, wcs=proj.wcs, header=header,
beam=exp_beam)
assert new_proj.beam == exp_beam
assert new_proj.meta['beam'] == exp_beam
# Slice the projection with a beam and check it's still there
assert new_proj[:1, :1].beam == exp_beam
def test_ondespectrum_with_beam():
exp_beam = Beam(1.0 * u.arcsec)
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
# load beam from meta
meta = {"beam": exp_beam}
new_spec = OneDSpectrum(spec.data, wcs=spec.wcs, meta=meta)
assert new_spec.beam == exp_beam
assert new_spec.meta['beam'] == exp_beam
# load beam from given header
hdu = spec.hdu
exp_beam = Beam(2.0 * u.arcsec)
header = hdu.header.copy()
header = exp_beam.attach_to_header(header)
new_spec = OneDSpectrum(hdu.data, wcs=spec.wcs, header=header,
read_beam=True)
assert new_spec.beam == exp_beam
assert new_spec.meta['beam'] == exp_beam
# load beam from beam object
exp_beam = Beam(3.0 * u.arcsec)
header = hdu.header.copy()
new_spec = OneDSpectrum(hdu.data, wcs=spec.wcs, header=header,
beam=exp_beam)
assert new_spec.beam == exp_beam
assert new_spec.meta['beam'] == exp_beam
# Slice the spectrum with a beam and check it's still there
assert new_spec[:1].beam == exp_beam
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_ldo_attach_beam(LDO, data):
exp_beam = Beam(1.0 * u.arcsec)
newbeam = Beam(2.0 * u.arcsec)
p = LDO(data, copy=False, beam=exp_beam)
new_p = p.with_beam(newbeam)
assert p.beam == exp_beam
assert p.meta['beam'] == exp_beam
assert new_p.beam == newbeam
assert new_p.meta['beam'] == newbeam
@pytest.mark.xfail(raises=BeamUnitsError, strict=True)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_ldo_attach_beam_jybm_error(LDO, data):
exp_beam = Beam(1.0 * u.arcsec)
newbeam = Beam(2.0 * u.arcsec)
data = data.value * u.Jy / u.beam
p = LDO(data, copy=False, beam=exp_beam)
# Attaching with no beam should work.
new_p = p.with_beam(newbeam)
# Trying to change the beam should now raise a BeamUnitsError
new_p = new_p.with_beam(newbeam)
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs_2d, data_two_2d))
def test_projection_from_hdu(LDO, data):
p = LDO(data, copy=False)
hdu = p.hdu
p_new = LDO.from_hdu(hdu)
assert (p == p_new).all()
def test_projection_subimage(data_55):
proj, hdu = load_projection(data_55)
proj1 = proj.subimage(xlo=1, xhi=3)
proj2 = proj.subimage(xlo=24.06269 * u.deg,
xhi=24.06206 * u.deg)
proj3 = proj.subimage(xlo=24.06269*u.deg, xhi=3)
proj4 = proj.subimage(xlo=1, xhi=24.06206*u.deg)
assert proj1.shape == (5, 2)
assert proj2.shape == (5, 2)
assert proj3.shape == (5, 2)
assert proj4.shape == (5, 2)
assert proj1.wcs.wcs.compare(proj2.wcs.wcs)
assert proj1.wcs.wcs.compare(proj3.wcs.wcs)
assert proj1.wcs.wcs.compare(proj4.wcs.wcs)
assert proj.beam == proj1.beam
assert proj.beam == proj2.beam
proj4 = proj.subimage(ylo=1, yhi=3)
proj5 = proj.subimage(ylo=29.93464 * u.deg,
yhi=29.93522 * u.deg)
proj6 = proj.subimage(ylo=1, yhi=29.93522 * u.deg)
proj7 = proj.subimage(ylo=29.93464 * u.deg, yhi=3)
assert proj4.shape == (2, 5)
assert proj5.shape == (2, 5)
assert proj6.shape == (2, 5)
assert proj7.shape == (2, 5)
assert proj4.wcs.wcs.compare(proj5.wcs.wcs)
assert proj4.wcs.wcs.compare(proj6.wcs.wcs)
assert proj4.wcs.wcs.compare(proj7.wcs.wcs)
# Test mixed slicing in both spatial directions
proj1xy = proj.subimage(xlo=1, xhi=3, ylo=1, yhi=3)
proj2xy = proj.subimage(xlo=24.06269*u.deg, xhi=3,
ylo=1,yhi=29.93522 * u.deg)
proj3xy = proj.subimage(xlo=1, xhi=24.06206*u.deg,
ylo=29.93464 * u.deg, yhi=3)
assert proj1xy.shape == (2, 2)
assert proj2xy.shape == (2, 2)
assert proj3xy.shape == (2, 2)
assert proj1xy.wcs.wcs.compare(proj2xy.wcs.wcs)
assert proj1xy.wcs.wcs.compare(proj3xy.wcs.wcs)
proj5 = proj.subimage()
assert proj5.shape == proj.shape
assert proj5.wcs.wcs.compare(proj.wcs.wcs)
assert np.all(proj5.value == proj.value)
def test_projection_subimage_nocelestial_fail(data_255_delta, use_dask):
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
proj = cube.moment0(axis=1)
with pytest.raises(WCSCelestialError,
match="WCS does not contain two spatial axes."):
proj.subimage(xlo=1, xhi=3)
@pytest.mark.parametrize('LDO', LDOs_2d)
def test_twod_input_mask_type(LDO):
test_wcs = WCS(naxis=2)
test_wcs.wcs.cunit = ["deg", "deg"]
test_wcs.wcs.ctype = ["RA---SIN", 'DEC--SIN']
np_mask = np.ones(twelve_qty_2d.shape, dtype=bool)
np_mask[1] = False
bool_mask = BooleanArrayMask(np_mask, wcs=test_wcs,
shape=np_mask.shape)
# numpy array
p = LDO(twelve_qty_2d, wcs=test_wcs,
mask=np_mask)
assert (p.mask.include() == bool_mask.include()).all()
# MaskBase
p = LDO(twelve_qty_2d, wcs=test_wcs,
mask=bool_mask)
assert (p.mask.include() == bool_mask.include()).all()
# No mask
ones_mask = BooleanArrayMask(np.ones(twelve_qty_2d.shape, dtype=bool),
wcs=test_wcs, shape=np_mask.shape)
p = LDO(twelve_qty_2d, wcs=test_wcs,
mask=None)
assert (p.mask.include() == ones_mask.include()).all()
@pytest.mark.xfail
def test_mask_convolve():
# Numpy is fundamentally incompatible with the objects we have created.
# np.ma.is_masked(array) checks specifically for the array's _mask
# attribute. We would have to refactor deeply to correct this, and I
# really don't want to do that because 'None' is a much more reasonable
# and less dangerous default for a mask.
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
assert spec.mask is False
from astropy.convolution import convolve,Box1DKernel
convolve(spec, Box1DKernel(3))
def test_convolve():
test_wcs_1 = WCS(naxis=1)
spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1)
from astropy.convolution import Box1DKernel
specsmooth = spec.spectral_smooth(Box1DKernel(1))
np.testing.assert_allclose(spec, specsmooth)
def test_spectral_interpolate():
test_wcs_1 = WCS(naxis=1)
test_wcs_1.wcs.cunit[0] = 'GHz'
spec = OneDSpectrum(np.arange(12)*u.Jy, wcs=test_wcs_1)
new_xaxis = test_wcs_1.wcs_pix2world(np.linspace(0,11,23), 0)[0] * u.Unit(test_wcs_1.wcs.cunit[0])
new_spec = spec.spectral_interpolate(new_xaxis)
np.testing.assert_allclose(new_spec, np.linspace(0,11,23)*u.Jy)
def test_spectral_interpolate_with_mask(data_522_delta, use_dask):
hdu = fits.open(data_522_delta)[0]
# Swap the velocity axis so indiff < 0 in spectral_interpolate
hdu.header["CDELT3"] = - hdu.header["CDELT3"]
cube = SpectralCube.read(hdu, use_dask=use_dask)
mask = np.ones(cube.shape, dtype=bool)
mask[:2] = False
masked_cube = cube.with_mask(mask)
spec = masked_cube[:, 0, 0]
# midpoint between each position
sg = (spec.spectral_axis[1:] + spec.spectral_axis[:-1])/2.
result = spec.spectral_interpolate(spectral_grid=sg[::-1])
# The output makes CDELT3 > 0 (reversed spectral axis) so the masked
# portion are the final 2 channels.
np.testing.assert_almost_equal(result.filled_data[:].value,
[0.0, 0.5, np.nan, np.nan])
def test_spectral_interpolate_reversed(data_522_delta, use_dask):
cube, data = cube_and_raw(data_522_delta, use_dask=use_dask)
# Reverse spectral axis
sg = cube.spectral_axis[::-1]
spec = cube[:, 0, 0]
result = spec.spectral_interpolate(spectral_grid=sg)
np.testing.assert_almost_equal(sg.value, result.spectral_axis.value)
def test_spectral_interpolate_with_fillvalue(data_522_delta, use_dask):
cube, data = cube_and_raw(data_522_delta, use_dask=use_dask)
# Step one channel out of bounds.
sg = ((cube.spectral_axis[0]) -
(cube.spectral_axis[1] - cube.spectral_axis[0]) *
np.linspace(1,4,4))
spec = cube[:, 0, 0]
result = spec.spectral_interpolate(spectral_grid=sg,
fill_value=42)
np.testing.assert_almost_equal(result.value,
np.ones(4)*42)
def test_spectral_units(data_255_delta, use_dask):
# regression test for issue 391
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
sp = cube[:,0,0]
assert sp.spectral_axis.unit == u.km/u.s
assert sp.header['CUNIT1'] == 'km s-1'
sp = cube.with_spectral_unit(u.m/u.s)[:,0,0]
assert sp.spectral_axis.unit == u.m/u.s
assert sp.header['CUNIT1'] in ('m s-1', 'm/s')
def test_repr_1d(data_255_delta, use_dask):
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
sp = cube[:,0,0]
print(sp)
print(sp[1:-1])
assert 'OneDSpectrum' in sp.__repr__()
assert 'OneDSpectrum' in sp[1:-1].__repr__()
def test_1d_slices(data_255_delta, use_dask):
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
sp = cube[:,0,0]
assert sp.max() == cube.max(axis=0)[0,0]
assert not isinstance(sp.max(), OneDSpectrum)
sp = cube[:-1,0,0]
assert sp.max() == cube[:-1,:,:].max(axis=0)[0,0]
assert not isinstance(sp.max(), OneDSpectrum)
# TODO: Unpin when Numpy bug is resolved.
@pytest.mark.skipif(not NUMPY_LT_1_22 and sys.platform == 'win32',
reason='https://github.com/numpy/numpy/issues/20699')
@pytest.mark.parametrize('method',
('min', 'max', 'std', 'mean', 'sum', 'cumsum',
'var'),
)
def test_1d_slice_reductions(method, data_255_delta, use_dask):
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
sp = cube[:,0,0]
if hasattr(cube, method):
spmethod = getattr(sp, method)
cubemethod = getattr(cube, method)
assert spmethod() == cubemethod(axis=0)[0,0]
else:
method = getattr(sp, method)
result = method()
assert hasattr(sp, '_fill_value')
assert 'OneDSpectrum' in sp.__repr__()
assert 'OneDSpectrum' in sp[1:-1].__repr__()
def test_1d_slice_round(data_255_delta, use_dask):
cube, data = cube_and_raw(data_255_delta, use_dask=use_dask)
sp = cube[:,0,0]
assert all(sp.value.round() == sp.round().value)
assert hasattr(sp, '_fill_value')
assert hasattr(sp.round(), '_fill_value')
rnd = sp.round()
assert 'OneDSpectrum' in rnd.__repr__()
rndslc = sp[1:-1].round()
assert 'OneDSpectrum' in rndslc.__repr__()
def test_LDO_arithmetic(data_vda, use_dask):
cube, data = cube_and_raw(data_vda, use_dask=use_dask)
sp = cube[:,0,0]
spx2 = sp * 2
assert np.all(spx2.value == sp.value*2)
assert np.all(spx2.filled_data[:].value == sp.value*2)
def test_beam_jtok_2D(data_advs, use_dask):
cube, data = cube_and_raw(data_advs, use_dask=use_dask)
cube._meta['BUNIT'] = 'Jy / beam'
cube._unit = u.Jy / u.beam
plane = cube[0]
freq = cube.with_spectral_unit(u.GHz).spectral_axis[0]
equiv = plane.beam.jtok_equiv(freq)
jtok = plane.beam.jtok(freq)
Kplane = plane.to(u.K, equivalencies=equiv, freq=freq)
np.testing.assert_almost_equal(Kplane.value,
(plane.value * jtok).value)
# test that the beam equivalencies are correctly automatically defined
Kplane = plane.to(u.K, freq=freq)
np.testing.assert_almost_equal(Kplane.value,
(plane.value * jtok).value)
bunits_list = [u.Jy / u.beam, u.K, u.Jy / u.sr, u.Jy / u.pix, u.Jy / u.arcsec**2,
u.mJy / u.beam, u.mK]
@pytest.mark.parametrize(('init_unit'), bunits_list)
def test_unit_conversions_general_2D(data_advs, use_dask, init_unit):
cube, data = cube_and_raw(data_advs, use_dask=use_dask)
cube._meta['BUNIT'] = init_unit.to_string()
cube._unit = init_unit
plane = cube[0]
# Check all unit conversion combos:
for targ_unit in bunits_list:
newplane = plane.to(targ_unit)
if init_unit == targ_unit:
np.testing.assert_almost_equal(newplane.value,
plane.value)
else:
roundtrip_plane = newplane.to(init_unit)
np.testing.assert_almost_equal(roundtrip_plane.value,
plane.value)
# TODO: Our 1D object do NOT retain spatial info that is needed for other BUNIT conversion
# e.g., Jy/sr, Jy/pix. So we're limited to Jy/beam -> K conversion for now
# See: https://github.com/radio-astro-tools/spectral-cube/pull/395
bunits_list_1D = [u.Jy / u.beam, u.K,
u.mJy / u.beam, u.mK]
@pytest.mark.parametrize(('init_unit'), bunits_list_1D)
def test_unit_conversions_general_1D(data_advs, use_dask, init_unit):
cube, data = cube_and_raw(data_advs, use_dask=use_dask)
cube._meta['BUNIT'] = init_unit.to_string()
cube._unit = init_unit
spec = cube[:, 0, 0]
# Check all unit conversion combos:
for targ_unit in bunits_list_1D:
newspec = spec.to(targ_unit)
if init_unit == targ_unit:
np.testing.assert_almost_equal(newspec.value,
spec.value)
else:
roundtrip_spec = newspec.to(init_unit)
np.testing.assert_almost_equal(roundtrip_spec.value,
spec.value)
@pytest.mark.parametrize(('init_unit'), bunits_list_1D)
def test_multibeams_unit_conversions_general_1D(data_vda_beams, use_dask, init_unit):
cube, data = cube_and_raw(data_vda_beams, use_dask=use_dask)
cube._meta['BUNIT'] = init_unit.to_string()
cube._unit = init_unit
spec = cube[:, 0, 0]
# Check all unit conversion combos:
for targ_unit in bunits_list_1D:
newspec = spec.to(targ_unit)
if init_unit == targ_unit:
np.testing.assert_almost_equal(newspec.value,
spec.value)
else:
roundtrip_spec = newspec.to(init_unit)
np.testing.assert_almost_equal(roundtrip_spec.value,
spec.value)
def test_basic_arrayness(data_adv, use_dask):
cube, data = cube_and_raw(data_adv, use_dask=use_dask)
assert cube.shape == data.shape
spec = cube[:,0,0]
assert np.all(np.asanyarray(spec).value == data[:,0,0])
assert np.all(np.array(spec) == data[:,0,0])
assert np.all(np.asarray(spec) == data[:,0,0])
# These are commented out because it is presently not possible to convert
# projections to masked arrays
# assert np.all(np.ma.asanyarray(spec).value == data[:,0,0])
# assert np.all(np.ma.asarray(spec) == data[:,0,0])
# assert np.all(np.ma.array(spec) == data[:,0,0])
slc = cube[0,:,:]
assert np.all(np.asanyarray(slc).value == data[0,:,:])
assert np.all(np.array(slc) == data[0,:,:])
assert np.all(np.asarray(slc) == data[0,:,:])
# assert np.all(np.ma.asanyarray(slc).value == data[0,:,:])
# assert np.all(np.ma.asarray(slc) == data[0,:,:])
# assert np.all(np.ma.array(slc) == data[0,:,:])
def test_spatial_world_extrema_2D(data_522_delta, use_dask):
hdu = fits.open(data_522_delta)[0]
cube = SpectralCube.read(hdu, use_dask=use_dask)
plane = cube[0]
assert (cube.world_extrema == plane.world_extrema).all()
assert (cube.longitude_extrema == plane.longitude_extrema).all()
assert (cube.latitude_extrema == plane.latitude_extrema).all()
@pytest.mark.parametrize('view', (np.s_[:, :],
np.s_[::2, :],
np.s_[0]))
def test_spatial_world(view, data_adv, use_dask):
p = path(data_adv)
# d = fits.getdata(p)
# wcs = WCS(p)
# c = SpectralCube(d, wcs)
c = SpectralCube.read(p, use_dask=use_dask)
plane = c[0]
wcs = plane.wcs
shp = plane.shape
inds = np.indices(plane.shape)
pix = np.column_stack([i.ravel() for i in inds[::-1]])
world = wcs.all_pix2world(pix, 0).T
world = [w.reshape(shp) for w in world]
world = [w[view] * u.Unit(wcs.wcs.cunit[i])
for i, w in enumerate(world)][::-1]
w2 = plane.world[view]
for result, expected in zip(w2, world):
assert_allclose(result, expected)
# Test world_flattened here, too
# TODO: Enable once 2D masking is a thing
w2_flat = plane.flattened_world(view=view)
for result, expected in zip(w2_flat, world):
print(result.shape, expected.flatten().shape)
assert_allclose(result, expected.flatten())
@pytest.mark.parametrize(('LDO', 'data'),
zip(LDOs, data_twelve))
def test_unit_division(LDO, data):
# regression: 871
image = data
p = LDO(image, copy=False)
p._meta = None
# check that this does not raise an Exception
p.hdu