def test_ids(self, get_image):
hdr, im = get_image
foreground_mask = np.full(shape=(41, 41), fill_value=True)
background_mask = foreground_mask.copy()
foreground_mask[25:28, 11:17] = False
background_mask[26:30, 9:14] = False
ap1 = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask,
source_id='comp star 1',
obs_id='345')
ap2 = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask,
source_id='MP_3456',
obs_id='haha')
ap3 = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask)
assert (ap1.source_id, ap1.obs_id) == ('comp star 1', '345')
assert (ap2.source_id, ap2.obs_id) == ('MP_3456', 'haha')
assert (ap3.source_id, ap3.obs_id) == ('', '')
def make_test_ap_object(xy_center):
""" So that we don't have to keep making this over and over; so that we have a standard object
against which to test.
:param xy_center: target (x,y) center of new ap object, in parent pixels. [2-tuple of floats]
:return: standard test object with concentric circular masks. [Old_Ap class object]
"""
im, hdr = get_test_image()
cutout_radius = 22
foreground_radius = 10
gap = 6
background_width = 5
cutout_size = 2 * cutout_radius + 1
xy_cutout_center = cutout_radius, cutout_radius
radius_inside = foreground_radius + gap
radius_outside = radius_inside + background_width
fg_mask = image.make_circular_mask(cutout_size,
xy_cutout_center,
radius=foreground_radius)
inner_bg_mask = np.logical_not(
image.make_circular_mask(cutout_size, xy_cutout_center, radius_inside))
outer_bg_mask = image.make_circular_mask(cutout_size,
xy_cutout_center,
radius=radius_outside)
bg_mask = np.logical_or(inner_bg_mask, outer_bg_mask)
ap_object = image.Ap(im,
xy_center=xy_center,
cutout_radius=cutout_radius,
foreground_mask=fg_mask,
background_mask=bg_mask)
return ap_object
def test_constructor_exceptions(self, get_image):
hdr, im = get_image
foreground_mask = np.full(shape=(41, 41), fill_value=True)
background_mask = np.full(shape=(40, 41), fill_value=True)
with pytest.raises(image.MaskError) as e:
ap = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask)
assert 'masks differ in shape' in str(e)
with pytest.raises(image.MaskError) as e:
ap = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=True)
assert 'mask type' in str(e) and 'bool' in str(
e) and 'not valid' in str(e)
def test_make_new_object(self, get_image):
hdr, im = get_image
foreground_mask = np.full(shape=(41, 41), fill_value=True)
background_mask = foreground_mask.copy()
foreground_mask[25:28, 11:17] = False
background_mask[26:30, 9:14] = False
ap = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask)
with pytest.raises(NotImplementedError):
_ = ap.make_new_object(new_xy_center=(445, 667))
def test_constructor_mask_overlap(self, get_image):
hdr, im = get_image
foreground_mask = np.full(shape=(41, 41), fill_value=True)
background_mask = foreground_mask.copy()
foreground_mask[25:28, 11:17] = False
background_mask[26:30, 9:14] = False
ap = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=background_mask)
# Quality criteria:
assert ap.is_valid is True
assert ap.all_inside_image is True
assert ap.all_outside_image is False
assert ap.any_foreground_outside_image is False
assert ap.mask_overlap_pixel_count == 6
def test_constructor_background_mask_zero(self, get_image):
hdr, im = get_image
foreground_mask = np.full(shape=(41, 41), fill_value=True)
foreground_mask[25:28, 11:17] = False
ap = image.Ap(im,
xy_center=(1000, 1100),
xy_offset=(980, 1080),
foreground_mask=foreground_mask,
background_mask=0)
# Quality criteria:
assert ap.is_valid is True
assert ap.all_inside_image is True
assert ap.all_outside_image is False
assert ap.any_foreground_outside_image is False
assert ap.mask_overlap_pixel_count == 0
# Verify background mask:
expected_background_mask = np.full(shape=(41, 41), fill_value=True)
assert np.array_equal(ap.background_mask, expected_background_mask)