def test_sky_crop_order():
# Test that order of cropping and subtraction doesn't affect result if inside cropped image
img_dim = 80
img = np.random.random((img_dim, img_dim))
xmax, ymax = np.random.randint(20, high=img_dim - 20, size=2)
img[ymax, xmax] = img.max() * 3 + 1 # Add max pixel at pre-determined location
isz = 2 * np.min([xmax, img.shape[1] - xmax - 1, ymax, img.shape[0] - ymax - 1]) + 1
dr = 2
r1 = isz // 2 - dr
# Correct then crop
correct = sky_correction(img, r1=r1, dr=dr, center=(xmax, ymax))[0]
correct_crop = crop_max(correct, isz, filtmed=False)[0]
# Crop then correct
cropped, center = crop_max(img, isz, filtmed=False)
crop_correct = sky_correction(cropped, r1=r1, dr=dr)[0]
# Make sure find expected center
assert center == (xmax, ymax)
# Use 10 * machine precision to make sure passes
assert np.all(np.abs(correct_crop - crop_correct) < 10 * np.finfo(float).eps)
def test_sky_dr_none():
# Make sure dr=None does not crash
img_dim = 80
img = np.ones((img_dim, img_dim))
# Outer radius beyond image corners
r1 = np.sqrt(2 * (img_dim / 2) ** 2) - 10
dr = None
sky_correction(img, r1=r1, dr=dr)
def test_sky_ring_mask():
img_dim = 80
img = np.random.random((img_dim, img_dim))
r1 = 20
dr = 2
mask = _get_ring_mask(r1, dr, img.shape[0])
img_mask = sky_correction(img, mask=mask)[0]
img_ring = sky_correction(img, r1=r1, dr=dr)[0]
assert np.all(img_mask == img_ring)
def test_sky_inner_only():
img_dim = 80
img = np.ones((img_dim, img_dim))
# Outer radius beyond image corners
r1 = np.sqrt(2 * (img_dim / 2) ** 2) - 10
dr = 100
with pytest.warns(
RuntimeWarning,
match="The outer radius is out of the image, using everything beyond r1 as background",
):
sky_correction(img, r1=r1, dr=dr)
def test_clean_crop_order():
# Test that clean_data performs both subtraction and cropping as expected
img_dim = 80
n_im = 5
data = np.random.random((n_im, img_dim, img_dim))
xmax, ymax = np.random.randint(20, high=img_dim - 20, size=2)
data[:, ymax, xmax] = data.max() * 3 + 1 # Add max pixel at pre-determined location
isz = (
2 * np.min([xmax, data.shape[2] - xmax - 1, ymax, data.shape[1] - ymax - 1]) + 1
)
dr = 2
r1 = isz // 2 - dr
# Clean cube all at once
cube = data.copy()
cube_clean = clean_data(cube, isz=isz, r1=r1, dr=dr, apod=False, f_kernel=None)
img_cube_clean = cube_clean[0]
# Correct then crop
img = data.copy()[0]
correct = sky_correction(img, r1=r1, dr=dr, center=(xmax, ymax))[0]
correct[correct < 0] = 0
img_correct_crop = crop_max(correct, isz, filtmed=False)[0]
assert np.all(np.abs(img_correct_crop - img_cube_clean) < 10 * np.finfo(float).eps)
def test_sky_correction_deprecation():
# Check deprecation warning is raised for r1 and dr
img_dim = 400 # Big image because default r1=100
img = np.random.random((img_dim, img_dim))
# FUTURE: Future AMICAL release should raise error, see sky_correction()
msg = (
"The default value of r1 and dr is now None. Either mask or r1 must be set"
" explicitely. In the future, this will result in an error."
" Setting r1=100 and dr=20"
)
with pytest.warns(
PendingDeprecationWarning,
match=msg,
):
img_default = sky_correction(img)[0]
img_corr = sky_correction(img, r1=100, dr=20)[0]
assert np.all(img_corr == img_default)
def test_sky_correction_mask_all():
# Check that full correction mask gives expected result
img_dim = 80
img = np.random.random((img_dim, img_dim))
mask = img.astype(bool)
img_sky = sky_correction(img, mask=mask)[0]
img_corr = img + 1.01 * np.abs(img.min())
img_corr = img_corr - np.mean(img_corr[mask])
assert np.all(img_sky == img_corr)
def test_sky_correction_mask_zeros():
# Check that empty correction mask gives warning and does nothing
img_dim = 80
img = np.random.random((img_dim, img_dim))
mask = np.zeros_like(img, dtype=bool)
with pytest.warns(
RuntimeWarning,
match="Background not computed because mask has no True values",
):
img_corr = sky_correction(img, mask=mask)[0]
assert np.all(img_corr == img)
def test_sky_out_image():
img_dim = 80
img = np.ones((img_dim, img_dim))
# Inner radius beyond image corners
r1 = np.sqrt(2 * (img_dim / 2) ** 2) + 2
with pytest.warns(
RuntimeWarning,
match="Background not computed, likely because specified radius is out of bounds",
):
img_bg, bg = sky_correction(img, r1=r1)
assert np.all(img_bg == img)
assert np.all(bg == 0)
def test_sky_correction_errors():
img_dim = 80
img = np.random.random((img_dim, img_dim))
# Test both r1 and mask given
with pytest.raises(TypeError, match="Only one of mask and r1 can be specified"):
sky_correction(img, r1=20, mask=np.ones_like(img, dtype=bool))
# Test dr but no r1
with pytest.raises(TypeError, match="dr cannot be set when r1 is None"):
sky_correction(img, dr=5, mask=np.ones_like(img, dtype=bool))
# Test bad mask shape
with pytest.raises(ValueError, match="mask should have the same shape as image"):
sky_correction(img, mask=np.ones(5))