def make_simple_convolver(simple_mask_5x5):
return aa.convolver(
mask=simple_mask_5x5,
kernel=aa.kernel.manual_2d([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]]),
)
def test__cross_mask_with_blurring_entries__returns_scaled_array(self):
mask = np.full((5, 5), True)
mask[2, 2] = False
mask[1, 2] = False
mask[3, 2] = False
mask[2, 1] = False
mask[2, 3] = False
cross_mask = aa.mask.manual(mask_2d=mask,
pixel_scales=(1.0, 1.0),
sub_size=1)
kernel = aa.kernel.manual_2d(
array=[[0, 0.2, 0], [0.2, 0.4, 0.2], [0, 0.2, 0]])
convolver = aa.convolver(mask=cross_mask, kernel=kernel)
image_array = aa.array.manual_1d(array=[1, 0, 0, 0, 0],
shape_2d=(5, 5))
blurring_array = aa.array.manual_1d(
array=[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
shape_2d=(5, 5))
result = convolver.convolved_image_from_image_and_blurring_image(
image=image_array, blurring_image=blurring_array)
assert (np.round(result, 1) == np.array([0.6, 0.2, 0.2, 0.0,
0.0])).all()
def test__compare_convolver_to_2d_convolution(self):
# Setup a blurred datas_, using the PSF to perform the convolution in 2D, then masks it to make a 1d array.
import scipy.signal
kernel = aa.kernel.manual_2d(array=np.arange(49).reshape(7, 7))
image = aa.array.manual_2d(array=np.arange(900).reshape(30, 30))
blurred_image = scipy.signal.convolve2d(image.in_2d,
kernel.in_2d,
mode="same")
blurred_image = aa.array.manual_2d(array=blurred_image)
mask = aa.mask.circular(shape_2d=(30, 30),
pixel_scales=(1.0, 1.0),
sub_size=1,
radius=4.0)
masked_image = mask.mapping.array_stored_1d_from_array_2d(
array_2d=image.in_2d)
blurred_masked_image = mask.mapping.array_stored_1d_from_array_2d(
array_2d=blurred_image.in_2d)
# Now reproduce this datas_ using the frame convolver_image
blurring_mask = mask.regions.blurring_mask_from_kernel_shape(
kernel_shape_2d=kernel.shape_2d)
convolver = aa.convolver(mask=mask, kernel=kernel)
blurring_image = blurring_mask.mapping.array_stored_1d_from_array_2d(
array_2d=image.in_2d)
blurred_masked_im_1 = convolver.convolved_image_from_image_and_blurring_image(
image=masked_image, blurring_image=blurring_image)
assert blurred_masked_image == pytest.approx(blurred_masked_im_1, 1e-4)
def test__asymetric_convolver__matrix_blurred_correctly(self):
mask = np.array([
[True, True, True, True, True, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, True, True, True, True, True],
])
asymmetric_kernel = aa.kernel.manual_2d(
array=[[0, 0.0, 0], [0.4, 0.2, 0.3], [0, 0.1, 0]])
convolver = aa.convolver(mask=mask, kernel=asymmetric_kernel)
mapping = np.array([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[
0,
1,
0,
], # The 0.3 should be 'chopped' from this pixel as it is on the right-most edge
[0, 0, 0],
[1, 0, 0],
[0, 0, 1],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
])
blurred_mapping = convolver.convolve_mapping_matrix(mapping)
assert (blurred_mapping == np.array([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0.4, 0],
[0, 0.2, 0],
[0.4, 0, 0],
[0.2, 0, 0.4],
[0.3, 0, 0.2],
[0, 0.1, 0.3],
[0, 0, 0],
[0.1, 0, 0],
[0, 0, 0.1],
[0, 0, 0],
])).all()
def test_masked_square__5x3_kernel__loses_edge_of_left_and_right_columns(
self, simple_mask_7x7):
convolver = aa.convolver(
mask=simple_mask_7x7,
kernel=aa.kernel.manual_2d([
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0],
[10.0, 11.0, 12.0],
[13.0, 14.0, 15.0],
]),
)
assert (convolver.image_frame_1d_kernels[0] == np.array([
8.0, 9.0, 11.0, 12.0, 14.0, 15.0, -1, -1, -1, -1, -1, -1, -1, -1,
-1
])).all()
assert (convolver.image_frame_1d_kernels[1] == np.array([
7.0,
8.0,
9.0,
10.0,
11.0,
12.0,
13.0,
14.0,
15.0,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_kernels[2] == np.array([
7.0, 8.0, 10.0, 11.0, 13.0, 14.0, -1, -1, -1, -1, -1, -1, -1, -1,
-1
])).all()
assert (convolver.image_frame_1d_kernels[3] == np.array([
5.0, 6.0, 8.0, 9.0, 11.0, 12.0, -1, -1, -1, -1, -1, -1, -1, -1, -1
])).all()
assert (convolver.image_frame_1d_kernels[4] == np.array([
4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, -1, -1, -1, -1, -1,
-1
])).all()
assert (convolver.image_frame_1d_kernels[5] == np.array([
4.0, 5.0, 7.0, 8.0, 10.0, 11.0, -1, -1, -1, -1, -1, -1, -1, -1, -1
])).all()
assert (convolver.image_frame_1d_kernels[6] == np.array(
[2.0, 3.0, 5.0, 6.0, 8.0, 9.0, -1, -1, -1, -1, -1, -1, -1, -1,
-1])).all()
assert (convolver.image_frame_1d_kernels[7] == np.array([
1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, -1, -1, -1, -1, -1, -1
])).all()
assert (convolver.image_frame_1d_kernels[8] == np.array(
[1.0, 2.0, 4.0, 5.0, 7.0, 8.0, -1, -1, -1, -1, -1, -1, -1, -1,
-1])).all()
def test__cross_mask(self, cross_mask):
convolver = aa.convolver(mask=cross_mask,
kernel=aa.kernel.ones(shape_2d=(1, 1)))
assert (convolver.mask_index_array == np.array([
[-1, -1, -1, -1, -1],
[-1, -1, 0, -1, -1],
[-1, 1, 2, 3, -1],
[-1, -1, 4, -1, -1],
[-1, -1, -1, -1, -1],
])).all()
def test__blurring_region_3x3_kernel(self, cross_mask):
convolver = aa.convolver(mask=cross_mask,
kernel=aa.kernel.ones(shape_2d=(3, 3)))
assert (convolver.blurring_frame_1d_indexes[4] == np.array(
[0, 1, 2, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_indexes[5] == np.array(
[0, 2, 3, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_indexes[10] == np.array(
[1, 2, 4, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_indexes[11] == np.array(
[2, 3, 4, -1, -1, -1, -1, -1, -1])).all()
def test__blurring_region_3x3_kernel(self, cross_mask):
convolver = aa.convolver(
mask=cross_mask,
kernel=aa.kernel.manual_2d([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]]),
)
assert (convolver.blurring_frame_1d_kernels[4] == np.array(
[6.0, 8.0, 9.0, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_kernels[5] == np.array(
[4.0, 7.0, 8.0, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_kernels[10] == np.array(
[2.0, 3.0, 6.0, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.blurring_frame_1d_kernels[11] == np.array(
[1.0, 2.0, 4.0, -1, -1, -1, -1, -1, -1])).all()
def test_simple_numbering(self, simple_mask_5x5, simple_mask_index_array):
convolver = aa.convolver(mask=simple_mask_5x5,
kernel=aa.kernel.ones(shape_2d=(1, 1)))
mask_index_array = convolver.mask_index_array
assert mask_index_array.shape == (5, 5)
# noinspection PyUnresolvedReferences
assert (mask_index_array == np.array([
[-1, -1, -1, -1, -1],
[-1, 0, 1, 2, -1],
[-1, 3, 4, 5, -1],
[-1, 6, 7, 8, -1],
[-1, -1, -1, -1, -1],
])).all()
def test__frames_are_from_examples_above__lengths_are_right(
self, simple_mask_7x7):
convolver = aa.convolver(mask=simple_mask_7x7,
kernel=aa.kernel.ones(shape_2d=(3, 5)))
# convolver_image.image_frame_indexes[0] == np.array([0, 1, 2, 3, 4, 5])
# convolver_image.image_frame_indexes[1] == np.array([0, 1, 2, 3, 4, 5])
# convolver_image.image_frame_indexes[2] == np.array([0, 1, 2, 3, 4, 5])
# convolver_image.image_frame_indexes[3] == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
# (convolver_image.image_frame_indexes[4] == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
# convolver_image.image_frame_indexes[5] == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
# convolver_image.image_frame_indexes[6] == np.array([3, 4, 5, 6, 7, 8])
# convolver_image.image_frame_indexes[7] == np.array([3, 4, 5, 6, 7, 8])
# convolver_image.image_frame_indexes[8] == np.array([3, 4, 5, 6, 7, 8])
assert (convolver.image_frame_1d_lengths == np.array(
[6, 6, 6, 9, 9, 9, 6, 6, 6])).all()
def test__masked_square__3x5_kernel__loses_edge_of_top_and_bottom_rows(
self, simple_mask_7x7):
convolver = aa.convolver(mask=simple_mask_7x7,
kernel=aa.kernel.ones(shape_2d=(3, 5)))
assert (convolver.image_frame_1d_indexes[0] == np.array(
[0, 1, 2, 3, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[1] == np.array(
[0, 1, 2, 3, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[2] == np.array(
[0, 1, 2, 3, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[3] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[4] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[5] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[6] == np.array(
[3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[7] == np.array(
[3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[8] == np.array(
[3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
def test__masked_square__5x3_kernel__loses_edge_of_left_and_right_columns(
self, simple_mask_7x7):
convolver = aa.convolver(mask=simple_mask_7x7,
kernel=aa.kernel.ones(shape_2d=(5, 3)))
assert (convolver.image_frame_1d_indexes[0] == np.array(
[0, 1, 3, 4, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[1] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[2] == np.array(
[1, 2, 4, 5, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[3] == np.array(
[0, 1, 3, 4, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[4] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[5] == np.array(
[1, 2, 4, 5, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[6] == np.array(
[0, 1, 3, 4, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[7] == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1])).all()
assert (convolver.image_frame_1d_indexes[8] == np.array(
[1, 2, 4, 5, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1])).all()
def make_convolver_7x7(mask_7x7, blurring_mask_7x7, psf_3x3):
return aa.convolver(mask=mask_7x7, kernel=psf_3x3)
def test__even_kernel_failure(self):
with pytest.raises(exc.ConvolverException):
aa.convolver(mask=np.full((3, 3), False),
kernel=aa.kernel.ones(shape_2d=(2, 2)))
def make_cross_convolver(cross_mask):
return aa.convolver(
mask=cross_mask,
kernel=aa.kernel.manual_2d([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]]),
)
def test__asymetric_convolver__multiple_overlapping_blurred_entires_in_matrix(
self):
mask = np.array([
[True, True, True, True, True, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, True, True, True, True, True],
])
asymmetric_kernel = aa.kernel.manual_2d(
array=[[0, 0.0, 0], [0.4, 0.2, 0.3], [0, 0.1, 0]])
convolver = aa.convolver(mask=mask, kernel=asymmetric_kernel)
mapping = np.array([
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[
0,
1,
0,
], # The 0.3 should be 'chopped' from this pixel as it is on the right-most edge
[1, 0, 0],
[1, 0, 0],
[0, 0, 1],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
])
blurred_mapping = convolver.convolve_mapping_matrix(mapping)
assert blurred_mapping == pytest.approx(
np.array([
[0, 0.6, 0],
[0, 0.9, 0],
[0, 0.5, 0],
[0, 0.3, 0],
[0, 0.1, 0],
[0, 0.1, 0],
[0, 0.5, 0],
[0, 0.2, 0],
[0.6, 0, 0],
[0.5, 0, 0.4],
[0.3, 0, 0.2],
[0, 0.1, 0.3],
[0.1, 0, 0],
[0.1, 0, 0],
[0, 0, 0.1],
[0, 0, 0],
]),
1e-4,
)
def test__frame_5x5_kernel__at_coords(self, simple_mask_7x7):
convolver = aa.convolver(
mask=simple_mask_7x7,
kernel=aa.kernel.manual_2d([
[1.0, 2.0, 3.0, 4.0, 5.0],
[6.0, 7.0, 8.0, 9.0, 10.0],
[11.0, 12.0, 13.0, 14.0, 15.0],
[16.0, 17.0, 18.0, 19.0, 20.0],
[21.0, 22.0, 23.0, 24.0, 25.0],
]),
)
frame, kernel_frame = convolver.frame_at_coordinates_jit(
coordinates=(2, 2),
mask=simple_mask_7x7,
mask_index_array=convolver.mask_index_array,
kernel_2d=convolver.kernel.in_2d,
)
assert (kernel_frame == np.array([
13.0,
14.0,
15.0,
18.0,
19.0,
20.0,
23.0,
24.0,
25.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
])).all()
frame, kernel_frame = convolver.frame_at_coordinates_jit(
coordinates=(3, 2),
mask=simple_mask_7x7,
mask_index_array=convolver.mask_index_array,
kernel_2d=convolver.kernel.in_2d,
)
assert (kernel_frame == np.array([
8.0,
9.0,
10.0,
13.0,
14.0,
15.0,
18.0,
19.0,
20.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1,
])).all()
frame, kernel_frame = convolver.frame_at_coordinates_jit(
coordinates=(3, 3),
mask=simple_mask_7x7,
mask_index_array=convolver.mask_index_array,
kernel_2d=convolver.kernel.in_2d,
)
assert (kernel_frame == np.array([
7.0,
8.0,
9.0,
12.0,
13.0,
14.0,
17.0,
18.0,
19.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1.0,
-1,
])).all()
def test__masked_square__7x7_kernel(self, simple_mask_7x7):
convolver = aa.convolver(mask=simple_mask_7x7,
kernel=aa.kernel.ones(shape_2d=(5, 5)))
assert (convolver.image_frame_1d_indexes[0] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[1] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[2] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[3] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[4] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[5] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[6] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[7] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()
assert (convolver.image_frame_1d_indexes[8] == np.array([
0,
1,
2,
3,
4,
5,
6,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
])).all()