def test_ccdmask_not_2d():
# Fails when a CCDData has less than 2 dimensions
with pytest.raises(ValueError):
ccdmask(CCDData(np.ones(3), unit='adu'))
# Fails when scalar
with pytest.raises(ValueError):
ccdmask(CCDData(np.array(10), unit='adu'))
# Fails when more than 2d
with pytest.raises(ValueError):
ccdmask(CCDData(np.ones((3, 3, 3)), unit='adu'))
def test_ccdmask_pixels():
flat1 = CCDData(np.array([[
20044, 19829, 19936, 20162, 19948, 19965, 19919, 20004, 19951,
20002, 19926, 20151, 19886, 20014, 19928, 20025, 19921, 19996,
19912, 20017, 19969, 20103, 20161, 20110, 19977, 19922, 20004,
19802, 20079, 19981, 20083, 19871],
[20068, 20204, 20085, 20027, 20103, 19866, 20089, 19914, 20160,
19884, 19956, 20095, 20004, 20075, 19899, 20016, 19995, 20178,
19963, 20030, 20055, 20005, 20073, 19969, 19958, 20040, 19979,
19938, 19986, 19957, 20172, 20054],
[20099, 20180, 19912, 20050, 19930, 19930, 20036, 20006, 19833,
19984, 19879, 19815, 20105, 20011, 19949, 20062, 19837, 20070,
20047, 19855, 19956, 19928, 19878, 20102, 19940, 20001, 20082,
20080, 20019, 19991, 19919, 20121],
[20014, 20262, 19953, 20077, 19928, 20271, 19962, 20048, 20011,
20054, 20112, 19931, 20125, 19899, 19993, 19939, 19916, 19998,
19921, 19949, 20246, 20160, 19881, 19863, 19874, 19979, 19989,
19901, 19850, 19931, 20001, 20167],
[20131, 19991, 20073, 19945, 19980, 20021, 19938, 19964, 20002,
20177, 19888, 19901, 19919, 19977, 20280, 20035, 20045, 19849,
20169, 20074, 20113, 19993, 19965, 20026, 20018, 19966, 20023,
19965, 19962, 20082, 20027, 20145],
[20106, 20025, 19846, 19865, 19913, 20046, 19998, 20037, 19986,
20048, 20005, 19790, 20011, 19985, 19959, 19882, 20085, 19978,
19881, 19960, 20111, 19936, 19983, 19863, 19819, 19896, 19968,
20134, 19824, 19990, 20146, 19886],
[20162, 19997, 19966, 20110, 19822, 19923, 20029, 20129, 19936,
19882, 20077, 20112, 20040, 20051, 20177, 19763, 20097, 19898,
19832, 20061, 19919, 20056, 20010, 19929, 20010, 19995, 20124,
19965, 19922, 19860, 20021, 19989],
[20088, 20104, 19956, 19959, 20018, 19948, 19836, 20107, 19920,
20117, 19882, 20039, 20206, 20067, 19784, 20087, 20117, 19990,
20242, 19861, 19923, 19779, 20024, 20024, 19981, 19915, 20017,
20053, 19932, 20179, 20062, 19908],
[19993, 20047, 20008, 20172, 19977, 20054, 19980, 19952, 20138,
19940, 19995, 20029, 19888, 20191, 19958, 20007, 19938, 19959,
19933, 20139, 20069, 19905, 20101, 20086, 19904, 19807, 20131,
20048, 19927, 19905, 19939, 20030],
[20040, 20051, 19997, 20013, 19942, 20130, 19983, 19603, 19934,
19944, 19961, 19979, 20164, 19855, 20157, 20010, 20020, 19902,
20134, 19971, 20228, 19967, 19879, 20022, 19915, 20063, 19768,
19976, 19860, 20041, 19955, 19984],
[19807, 20066, 19986, 19999, 19975, 20115, 19998, 20056, 20059,
20016, 19970, 19964, 20053, 19975, 19985, 19973, 20041, 19918,
19875, 19997, 19954, 19777, 20117, 20248, 20034, 20019, 20018,
20058, 20027, 20121, 19909, 20094],
[19890, 20018, 20032, 20058, 19909, 19906, 19812, 20206, 19908,
19767, 20127, 20015, 19959, 20026, 20021, 19964, 19824, 19934,
20147, 19984, 20026, 20168, 19992, 20175, 20040, 20208, 20077,
19897, 20037, 19996, 19998, 20019],
[19966, 19897, 20062, 19914, 19780, 20004, 20029, 20140, 20057,
20134, 20125, 19973, 19894, 19929, 19876, 20135, 19981, 20057,
20015, 20113, 20107, 20115, 19924, 19987, 19926, 19885, 20013,
20058, 19950, 20155, 19825, 20092],
[19889, 20046, 20113, 19991, 19829, 20180, 19949, 20011, 20014,
20123, 19980, 19770, 20086, 20041, 19957, 19949, 20026, 19918,
19777, 20062, 19862, 20085, 20090, 20122, 19692, 19937, 19897,
20018, 19935, 20037, 19946, 19998],
[20001, 19940, 19994, 19835, 19959, 19895, 20017, 20002, 20007,
19851, 19900, 20044, 20354, 19814, 19869, 20148, 20001, 20143,
19778, 20146, 19975, 19859, 20008, 20041, 19937, 20072, 20203,
19778, 20027, 20075, 19877, 19999],
[19753, 19866, 20037, 20149, 20020, 20071, 19955, 20164, 19837,
19967, 19959, 20163, 20003, 20127, 20065, 20118, 20104, 19839,
20124, 20057, 19943, 20023, 20138, 19996, 19910, 20048, 20070,
19833, 19913, 20012, 19897, 19983]]), unit='adu')
flat2 = CCDData(np.array([[
20129, 20027, 19945, 20085, 19951, 20015, 20102, 19957, 20100,
19865, 19878, 20111, 20047, 19882, 19929, 20079, 19937, 19999,
20109, 19929, 19985, 19970, 19941, 19868, 20191, 20142, 19948,
20079, 19975, 19949, 19972, 20053],
[20075, 19980, 20035, 20014, 19865, 20058, 20091, 20030, 19931,
19806, 19990, 19902, 19895, 19789, 20079, 20048, 20040, 19968,
20049, 19946, 19982, 19865, 19766, 19903, 20025, 19916, 19904,
20128, 19865, 20103, 19864, 19832],
[20008, 19989, 20032, 19891, 20063, 20061, 20179, 19920, 19960,
19655, 19897, 19943, 20015, 20123, 20009, 19940, 19876, 19964,
20097, 19814, 20086, 20096, 20030, 20140, 19903, 19858, 19978,
19817, 20107, 19893, 19988, 19956],
[20105, 19873, 20003, 19671, 19993, 19981, 20234, 19976, 20079,
19882, 19982, 19959, 19882, 20103, 20008, 19960, 20084, 20025,
19864, 19969, 19945, 19979, 19937, 19965, 19981, 19957, 19906,
19959, 19839, 19679, 19988, 20154],
[20053, 20152, 19858, 20134, 19867, 20027, 20024, 19884, 20015,
19904, 19992, 20137, 19981, 20147, 19814, 20035, 19992, 19921,
20007, 20103, 19920, 19889, 20182, 19964, 19859, 20016, 20011,
20203, 19761, 19954, 20151, 19973],
[20029, 19863, 20217, 19819, 19984, 19950, 19914, 20028, 19980,
20033, 20016, 19796, 19901, 20027, 20078, 20136, 19995, 19915,
20014, 19920, 19996, 20216, 19939, 19967, 19949, 20023, 20024,
19949, 19949, 19902, 19980, 19895],
[19962, 19872, 19926, 20047, 20136, 19944, 20151, 19956, 19958,
20054, 19942, 20010, 19972, 19936, 20062, 20259, 20230, 19927,
20004, 19963, 20095, 19866, 19942, 19958, 20149, 19956, 20000,
19979, 19949, 19892, 20249, 20050],
[20019, 19999, 19954, 20095, 20045, 20002, 19761, 20187, 20113,
20048, 20117, 20002, 19938, 19968, 19993, 19995, 20094, 19913,
19963, 19813, 20040, 19950, 19992, 19958, 20043, 19925, 20036,
19930, 20057, 20055, 20040, 19937],
[19958, 19984, 19842, 19990, 19985, 19958, 20070, 19850, 20026,
20047, 20081, 20094, 20048, 20048, 19917, 19893, 19766, 19765,
20109, 20067, 19905, 19870, 19832, 20019, 19868, 20075, 20132,
19916, 19944, 19840, 20140, 20117],
[19995, 20122, 19998, 20039, 20125, 19879, 19911, 20010, 19944,
19994, 19903, 20057, 20021, 20139, 19972, 20026, 19922, 20132,
19976, 20025, 19948, 20038, 19807, 19809, 20145, 20003, 20090,
19848, 19884, 19936, 19997, 19944],
[19839, 19990, 20005, 19826, 20070, 19987, 20015, 19835, 20083,
19908, 19910, 20218, 19960, 19937, 19987, 19808, 19893, 19929,
20004, 20055, 19973, 19794, 20242, 20082, 20110, 20058, 19876,
20042, 20064, 19966, 20041, 20015],
[20048, 20203, 19855, 20011, 19888, 19926, 19973, 19893, 19986,
20152, 20030, 19880, 20012, 19848, 19959, 20002, 20027, 19935,
19975, 19905, 19932, 20190, 20188, 19903, 20012, 19943, 19954,
19891, 19947, 19939, 19974, 19808],
[20102, 20041, 20013, 20097, 20101, 19859, 20011, 20144, 19920,
19880, 20134, 19963, 19980, 20090, 20027, 19822, 20051, 19903,
19784, 19845, 20014, 19974, 20043, 20141, 19968, 20055, 20066,
20045, 20182, 20104, 20008, 19999],
[19932, 20023, 20042, 19894, 20070, 20015, 20172, 20024, 19988,
20181, 20180, 20023, 19978, 19989, 19976, 19870, 20152, 20003,
19984, 19903, 19904, 19940, 19990, 19922, 19911, 19976, 19841,
19946, 20273, 20085, 20142, 20122],
[19959, 20071, 20020, 20037, 20024, 19967, 20044, 20009, 19997,
20045, 19995, 19831, 20035, 19976, 20049, 19958, 20021, 19887,
19961, 19928, 19805, 20173, 19928, 19939, 19826, 20096, 20078,
20100, 19935, 19942, 19969, 19941],
[19876, 20056, 20071, 19886, 19979, 20174, 19978, 20037, 19933,
20184, 19948, 20034, 19896, 19905, 20138, 19870, 19936, 20085,
19971, 20063, 19936, 19941, 19928, 19937, 19970, 19931, 20036,
19965, 19855, 19949, 19965, 19821]]), unit='adu')
target_mask = np.zeros(flat1.shape, dtype=np.bool)
# No bad pixels in this scenario
ratio = flat1.divide(flat2)
mask = ccdmask(ratio, ncsig=9, nlsig=11)
assert mask.shape == ratio.shape
assert_array_equal(mask, target_mask)
# Check again with different ncsig and nlsig
ratio = flat1.divide(flat2)
mask = ccdmask(ratio, ncsig=11, nlsig=15)
assert mask.shape == ratio.shape
assert_array_equal(mask, target_mask)
# Add single bad pixel
flat1.data[14][3] = 65535
flat2.data[14][3] = 1
ratio = flat1.divide(flat2)
mask = ccdmask(ratio, ncsig=11, nlsig=15)
target_mask[14][3] = True
assert_array_equal(mask, target_mask)
# Add single bad column
flat1.data[:, 7] = 65535
flat2.data[:, 7] = 1
ratio = flat1.divide(flat2)
target_mask[:, 7] = True
mask = ccdmask(ratio, ncsig=11, nlsig=15)
assert_array_equal(mask, target_mask)
mask = ccdmask(ratio, ncsig=11, nlsig=15, byblocks=True)
assert_array_equal(mask, target_mask)
mask = ccdmask(ratio, ncsig=11, nlsig=15, findbadcolumns=True)
assert_array_equal(mask, target_mask)
mask = ccdmask(ratio, ncsig=11, nlsig=15, findbadcolumns=True,
byblocks=True)
assert_array_equal(mask, target_mask)
# Add bad column with gaps
flat1.data[0:8, 2] = 65535
flat1.data[11:, 2] = 65535
flat2.data[0:8, 2] = 1
flat2.data[11:, 2] = 1
ratio = flat1.divide(flat2)
mask = ccdmask(ratio, ncsig=11, nlsig=15, findbadcolumns=False)
target_mask[0:8, 2] = True
target_mask[11:, 2] = True
assert_array_equal(mask, target_mask)
mask = ccdmask(ratio, ncsig=11, nlsig=15, findbadcolumns=True)
target_mask[:, 2] = True
assert_array_equal(mask, target_mask)
def test_ccdmask_no_ccddata():
# Fails when a simple list is given.
with pytest.raises(ValueError):
ccdmask([[0, 0, 0], [0, 0, 0], [0, 0, 0]])