os.path.join(HERE, "test_data", "v_a_20180810_13_1_0_1.fits.gz[10]"))
# Create some bad data
some_bad_data = copy.copy(image_fits.data)
len_x, len_y = image_fits.data.shape
random_x = np.random.choice(np.arange(len_x), size=10, replace=False)
random_y = np.random.choice(np.arange(len_y), size=10, replace=False)
for i, j in zip(random_x, random_y):
some_bad_data[i, j] += 1e10
# Add a bad pixel on the spectrum
some_bad_data[130, 500] += 1e10
cmask, _ = util.create_cutoff_mask(image_fits.data, cutoff=1000)
bmask, _ = util.create_bad_pixel_mask(image_fits.data)
bad_mask = bmask & bmask
def test_add_bad_pixel_mask_numpy_array():
twodspec = spectral_reduction.TwoDSpec(log_file_name=None)
twodspec.add_bad_mask(bad_mask)
def test_add_bad_pixel_mask_hdu():
twodspec = spectral_reduction.TwoDSpec(log_file_name=None)
twodspec.add_bad_mask(fits.ImageHDU(bad_mask.astype("int")))
def test_add_bad_pixel_mask_hdu_list():
twodspec = spectral_reduction.TwoDSpec(log_file_name=None)
def test_bad_mask_does_nothing():
bad_mask, _ = util.create_bad_pixel_mask(dummy_image)
healed_image = util.bfixpix(dummy_image, bad_mask, retdat=True)
assert np.sum(healed_image) == dummy_sum
def test_bad_mask_list():
bad_mask, _ = util.create_bad_pixel_mask(dummy_image_none)
healed_image = util.bfixpix(dummy_image, bad_mask, retdat=True)
assert np.sum(healed_image) == np.size(dummy_image_none)
def test_bad_mask_does_nothing_grow_none():
bad_mask, _ = util.create_bad_pixel_mask(
dummy_image, grow=True, diagonal=True
)
healed_image = util.bfixpix(dummy_image, bad_mask, retdat=False)
assert np.sum(healed_image) is None
def test_bad_mask_grow():
bad_mask, _ = util.create_bad_pixel_mask(
dummy_image_nan, grow=True, diagonal=True
)
healed_image = util.bfixpix(dummy_image, bad_mask, retdat=True)
assert np.sum(healed_image) == np.size(dummy_image)
def test_bad_mask_1D_array():
util.create_bad_pixel_mask(np.ones(100))