def test__sub_array_to_1d_and_2d_and_back__returns_original_array(self):
mask = np.array([[False, False, True], [False, True, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = np.array([
1.0,
10.0,
2.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
4.0,
0.0,
0.0,
4.0,
])
sub_array_2d = mapping.sub_array_2d_from_sub_array_1d(
sub_array_1d=sub_array_1d)
sub_array_1d_new = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d(
sub_array_2d=sub_array_2d)
assert (sub_array_1d == sub_array_1d_new).all()
def test__same_as_above__but_remove_some_centre_pixels_and_change_order__order_does_not_change_mapping(
self):
ma = al.Mask(
array=np.array([[False, False, False], [False, False, False],
[False, False, False]]),
pixel_scale=1.0,
sub_size=1,
)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [2, 2],
[1, 1], [0, 2], [2, 0],
[0, 2]])
total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres(
total_sparse_pixels=total_masked_pixels,
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
assert (sparse_to_unmasked_sparse == np.array([0, 1, 2, 3, 4, 5,
6])).all()
def test__sub_array_2d_from_sub_array_1d__use_2x3_mask(self):
mask = np.array([[False, False, True], [False, True, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = np.array([
1.0,
1.0,
1.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
4.0,
4.0,
4.0,
4.0,
])
sub_array_2d = mapping.sub_array_2d_from_sub_array_1d(
sub_array_1d=sub_array_1d)
assert (sub_array_2d == np.array([
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
])).all()
def test__3x3_grid__extracts_max_min_coordinates__creates_grid_including_half_pixel_offset_from_edge(
self):
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=1.0))
grid = np.array([[-1.5, -1.5], [1.5, 1.5]])
plane_image = al.lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0)
mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False),
pixel_scale=1.0,
sub_size=1)
grid = al.Grid(
arr=np.array([
[-1.0, -1.0],
[-1.0, 0.0],
[-1.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
]),
mask=mask,
)
plane_image_galaxy = galaxy.profile_image_from_grid(grid)
assert (plane_image == plane_image_galaxy).all()
def test__same_as_above__different_mask_and_centres(self):
ma = al.Mask(
array=np.array([[False, False, True], [False, False, False],
[True, False, False]]),
pixel_scale=1.0,
sub_size=1,
)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[0, 2], [0, 2], [1, 1]])
total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres(
total_sparse_pixels=total_masked_pixels,
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
assert (sparse_to_unmasked_sparse == np.array([0, 1, 5])).all()
def test__3x3_grid__buffer_aligns_two_grids(self):
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=1.0))
grid_without_buffer = np.array([[-1.48, -1.48], [1.48, 1.48]])
plane_image = al.lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3),
grid=grid_without_buffer,
galaxies=[galaxy],
buffer=0.02)
mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False),
pixel_scale=1.0,
sub_size=1)
grid = al.Grid(
arr=np.array([
[-1.0, -1.0],
[-1.0, 0.0],
[-1.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
]),
mask=mask,
)
plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid)
assert (plane_image == plane_image_galaxy).all()
def test__same_as_above__but_4x3_mask(self):
ma = al.Mask(
array=np.array([
[True, False, True],
[True, False, True],
[False, False, False],
[True, False, True],
]),
pixel_scale=1.0,
sub_size=1,
)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[0, 2], [0, 2], [1, 1],
[2, 2], [3, 1]])
unmasked_sparse_to_sparse = al.sparse_mapping_util.unmasked_sparse_to_sparse_from_mask_and_pixel_centres(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
total_sparse_pixels=6,
)
assert (unmasked_sparse_to_sparse == np.array([0, 0, 1, 1, 1, 1, 2,
3])).all()
def test__mask_full_false__image_mask_and_pixel_centres_fully_overlap__each_sparse_maps_to_unmaked_sparse(
self):
ma = al.Mask(
array=np.array([[False, False, False], [False, False, False],
[False, False, False]]),
pixel_scale=1.0,
sub_size=1,
)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[1, 0], [1, 1], [1, 2],
[2, 0], [2, 1], [2, 2]])
total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres(
total_sparse_pixels=total_masked_pixels,
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
assert (sparse_to_unmasked_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
def test__mask_is_cross__some_pix_pixels_are_masked__omitted_from_mapping(
self):
ma = al.Mask(
array=np.array([[True, False, True], [False, False, False],
[True, False, True]]),
pixel_scale=1.0,
sub_size=1,
)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[1, 0], [1, 1], [1, 2],
[2, 0], [2, 1], [2, 2]])
total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres(
total_sparse_pixels=total_masked_pixels,
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres,
)
assert (sparse_to_unmasked_sparse == np.array([1, 3, 4, 5, 7])).all()
def test__scaled_array_2d_from_array_1d__compare_to_util(self):
mask = np.array([
[True, True, False, False],
[True, False, True, True],
[True, True, False, False],
])
array_1d = np.array([1.0, 6.0, 4.0, 5.0, 2.0])
array_2d_util = al.array_mapping_util.sub_array_2d_from_sub_array_1d_mask_and_sub_size(
sub_array_1d=array_1d, mask=mask, sub_size=1)
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=1)
mapping = al.Mapping(mask=mask)
array_2d = mapping.array_2d_from_array_1d(array_1d=array_1d)
assert (array_2d == array_2d_util).all()
scaled_array_2d = mapping.scaled_array_2d_from_array_1d(
array_1d=array_1d)
assert (scaled_array_2d == array_2d_util).all()
assert (scaled_array_2d.xticks == np.array([-6.0, -2.0, 2.0,
6.0])).all()
assert (scaled_array_2d.yticks == np.array([-4.5, -1.5, 1.5,
4.5])).all()
assert scaled_array_2d.shape_arcsec == (9.0, 12.0)
assert scaled_array_2d.pixel_scale == 3.0
assert scaled_array_2d.origin == (0.0, 0.0)
def test__sub_array_1d_from_sub_array_2d__numerical_values(self):
sub_array_2d = np.array([
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
])
mask = np.array([[False, False, True], [False, True, False]])
mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d(
sub_array_2d=sub_array_2d)
assert (sub_array_1d == np.array([
1.0,
1.0,
1.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
4.0,
4.0,
4.0,
4.0,
])).all()
def test__sub_grid_2d_with_sub_dimensions_from_sub_grid_1d__use_real_mask_and_grid(
self):
mask = np.array([[False, True], [False, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_grid_1d = np.array([
[1.0, 1.0],
[2.0, 2.0],
[3.0, 3.0],
[4.0, 4.0],
[9.0, 9.0],
[10.0, 10.0],
[11.0, 11.0],
[12.0, 12.0],
[13.0, 13.0],
[14.0, 14.0],
[15.0, 15.0],
[16.0, 16.0],
])
sub_grid_2d = mapping.sub_grid_2d_with_sub_dimensions_from_sub_grid_1d(
sub_grid_1d=sub_grid_1d)
assert (sub_grid_2d == np.array([
[[1.0, 1.0], [2.0, 2.0], [0.0, 0.0], [0.0, 0.0]],
[[3.0, 3.0], [4.0, 4.0], [0.0, 0.0], [0.0, 0.0]],
[[9.0, 9.0], [10.0, 10.0], [13.0, 13.0], [14.0, 14.0]],
[[11.0, 11.0], [12.0, 12.0], [15.0, 15.0], [16.0, 16.0]],
])).all()
def test__grid_2d_binned_from_sub_grid_1d__use_real_mask_and_grid(self):
mask = np.array([[False, True], [False, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_grid_1d = np.array([
[1.0, 1.0],
[2.0, 2.0],
[3.0, 3.0],
[4.0, 6.0],
[9.0, 9.0],
[10.0, 10.0],
[11.0, 11.0],
[12.0, 12.0],
[13.0, 13.0],
[14.0, 14.0],
[15.0, 15.0],
[16.0, 16.0],
])
grid_2d_binned = mapping.grid_2d_binned_from_sub_grid_1d(
sub_grid_1d=sub_grid_1d)
assert (grid_2d_binned == np.array([[[2.5, 3.0], [0.0, 0.0]],
[[10.5, 10.5], [14.5,
14.5]]])).all()
def test__scaled_array_from_sub_array_1d_by_binning_up(self):
mask = np.array([[False, False, True], [False, True, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = np.array([
1.0,
10.0,
2.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
4.0,
0.0,
0.0,
4.0,
])
scaled_array_2d = mapping.scaled_array_2d_binned_from_sub_array_1d(
sub_array_1d=sub_array_1d)
assert (scaled_array_2d == np.array([[3.5, 2.0, 0.0], [3.0, 0.0,
2.0]])).all()
assert scaled_array_2d.pixel_scales == (3.0, 3.0)
assert scaled_array_2d.origin == (0.0, 0.0)
def test__2x3_grid__shape_change_correct_and_coordinates_shift(self):
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=1.0))
grid = np.array([[-1.5, -1.5], [1.5, 1.5]])
plane_image = al.lens_util.plane_image_of_galaxies_from_grid(
shape=(2, 3), grid=grid, galaxies=[galaxy], buffer=0.0)
mask = al.Mask(array=np.full(shape=(2, 3), fill_value=False),
pixel_scale=1.0,
sub_size=1)
grid = al.Grid(
arr=np.array([
[-0.75, -1.0],
[-0.75, 0.0],
[-0.75, 1.0],
[0.75, -1.0],
[0.75, 0.0],
[0.75, 1.0],
]),
mask=mask,
)
plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid)
assert (plane_image == plane_image_galaxy).all()
def test__sub_mask_1d_index_to_mask_1d_index__compare_to_util(self):
mask = np.array([[True, False, True], [False, False, False],
[True, False, False]])
sub_mask_1d_index_to_mask_1d_index_util = al.mask_mapping_util.sub_mask_1d_index_to_mask_1d_index_from_mask(
mask=mask, sub_size=2)
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
assert (mapping.sub_mask_1d_index_to_mask_1d_index ==
sub_mask_1d_index_to_mask_1d_index_util).all()
def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self):
image = al.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
noise_map = al.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
galaxy_data = al.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=3.0)
mask = al.Mask(
array=np.array([[True, True, True], [True, False, True],
[True, True, True]]),
pixel_scale=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
def test__sub_mask_1d_index_to_submask_index__compare_to_array_util(self):
mask = np.array([[True, True, True], [True, False, False],
[True, True, False]])
mask = al.Mask(array=mask, pixel_scale=7.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_mask_1d_index_to_submask_index = al.mask_mapping_util.sub_mask_1d_index_to_submask_index_from_mask_and_sub_size(
mask=mask, sub_size=2)
assert mapping.sub_mask_1d_index_to_submask_index == pytest.approx(
sub_mask_1d_index_to_submask_index, 1e-4)
def test__sub_array_1d_from_sub_array_2d__compare_to_util(self):
sub_array_2d = np.array([
[1.0, 1.0, 2.0, 2.0, 3.0, 10.0],
[1.0, 1.0, 2.0, 2.0, 3.0, 10.0],
[3.0, 3.0, 8.0, 1.0, 4.0, 4.0],
[3.0, 3.0, 7.0, 2.0, 4.0, 4.0],
])
mask = np.array([[False, False, False], [True, True, False]])
mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d(
sub_array_2d=sub_array_2d)
sub_array_1d_util = al.array_mapping_util.sub_array_1d_from_sub_array_2d_mask_and_sub_size(
sub_array_2d=sub_array_2d, mask=mask, sub_size=2)
assert (sub_array_1d == sub_array_1d_util).all()
def test__solution_different_values__simple_blurred_mapping_matrix__correct_reconstructed_image(
self):
matrix_shape = (3, 3)
mask = al.Mask(
array=np.array([[True, True, True], [False, False, False],
[True, True, True]]),
pixel_scale=1.0,
sub_size=1,
)
grid = al.Grid.from_mask(mask=mask)
inv = al.Inversion.from_data_1d_mapper_and_regularization(
image_1d=np.ones(9),
noise_map_1d=np.ones(9),
convolver=mock_inversion.MockConvolver(matrix_shape),
mapper=mock_inversion.MockMapper(matrix_shape=matrix_shape,
grid=grid),
regularization=mock_inversion.MockRegularization(matrix_shape),
)
inv.pixelization_values = np.array([1.0, 2.0, 3.0, 4.0])
inv.blurred_mapping_matrix = np.array([[1.0, 1.0, 1.0, 1.0],
[1.0, 0.0, 1.0, 1.0],
[1.0, 0.0, 0.0, 0.0]])
# # Imaging pixel 0 maps to 4 pixs pixxels -> value is 1.0 + 2.0 + 3.0 + 4.0 = 10.0
# # Imaging pixel 1 maps to 3 pixs pixxels -> value is 1.0 + 3.0 + 4.0
# # Imaging pixel 2 maps to 1 pixs pixxels -> value is 1.0
assert (inv.reconstructed_data_1d == np.array([10.0, 8.0, 1.0])).all()
assert inv.reconstructed_data_2d == np.array([[0.0, 0.0, 0.0],
[10.0, 8.0, 1.0],
[0.0, 0.0, 0.0]])
assert inv.pixelization_errors_with_covariance == pytest.approx(
np.array([[0.7, -0.3, -0.3], [-0.3, 0.7, -0.3], [-0.3, -0.3,
0.7]]), 1.0e-4)
assert inv.pixelization_errors == pytest.approx(
np.array([0.7, 0.7, 0.7]), 1.0e-4)
def test__grid_2d_from_grid_1d__compare_to_util(self):
mask = np.array([
[True, True, False, False],
[True, False, True, True],
[True, True, False, False],
])
grid_1d = np.array([[1.0, 1.0], [6.0, 6.0], [4.0, 4.0], [5.0, 5.0],
[2.0, 2.0]])
grid_2d_util = al.grid_mapping_util.sub_grid_2d_from_sub_grid_1d_mask_and_sub_size(
sub_grid_1d=grid_1d, mask=mask, sub_size=1)
mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=1)
mapping = al.Mapping(mask=mask)
grid_2d = mapping.grid_2d_from_grid_1d(grid_1d=grid_1d)
assert (grid_2d_util == grid_2d).all()
def test__3x3_grid__extracts_max_min_coordinates__ignores_other_coordinates_more_central(
self):
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.EllipticalSersic(intensity=1.0))
grid = np.array([
[-1.5, -1.5],
[1.5, 1.5],
[0.1, -0.1],
[-1.0, 0.6],
[1.4, -1.3],
[1.5, 1.5],
])
plane_image = al.lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0)
mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False),
pixel_scale=1.0,
sub_size=1)
grid = al.Grid(
arr=np.array([
[-1.0, -1.0],
[-1.0, 0.0],
[-1.0, 1.0],
[0.0, -1.0],
[0.0, 0.0],
[0.0, 1.0],
[1.0, -1.0],
[1.0, 0.0],
[1.0, 1.0],
]),
mask=mask,
)
plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid,
return_binned=True,
return_in_2d=True)
assert (plane_image == plane_image_galaxy).all()
def test__array_1d_from_array_2d__compare_to_array_util(self):
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
mask = np.array([
[True, False, True],
[False, False, False],
[True, False, True],
[True, True, True],
])
array_1d_util = al.array_mapping_util.sub_array_1d_from_sub_array_2d_mask_and_sub_size(
mask=mask, sub_array_2d=array_2d, sub_size=1)
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=1)
mapping = al.Mapping(mask=mask)
array_1d = mapping.array_1d_from_array_2d(array_2d=array_2d)
assert (array_1d == array_1d_util).all()
def test__grid_1d_from_grid_2d__compare_to_util(self):
grid_2d = np.array([
[[1, 1], [2, 2], [3, 3], [4, 4]],
[[5, 5], [6, 6], [7, 7], [8, 8]],
[[9, 9], [10, 10], [11, 11], [12, 12]],
])
mask = np.array([
[True, False, True, True],
[False, False, False, True],
[True, False, True, False],
])
grid_1d_util = al.grid_mapping_util.sub_grid_1d_from_sub_grid_2d_mask_and_sub_size(
sub_grid_2d=grid_2d, mask=mask, sub_size=1)
mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=1)
mapping = al.Mapping(mask=mask)
grid_1d = mapping.grid_1d_from_grid_2d(grid_2d=grid_2d)
assert (grid_1d_util == grid_1d).all()
def test__scaled_sub_array_2d_from_sub_array_1d(self):
mask = np.array([[False, False, True], [False, True, False]])
mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2)
mapping = al.Mapping(mask=mask)
sub_array_1d = np.array([
1.0,
1.0,
1.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
4.0,
4.0,
4.0,
4.0,
])
scaled_sub_array_2d = mapping.scaled_array_2d_with_sub_dimensions_from_sub_array_1d(
sub_array_1d=sub_array_1d)
assert (scaled_sub_array_2d == np.array([
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[1.0, 1.0, 2.0, 2.0, 0.0, 0.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
[3.0, 3.0, 0.0, 0.0, 4.0, 4.0],
])).all()
assert scaled_sub_array_2d.pixel_scales == (1.5, 1.5)
assert scaled_sub_array_2d.origin == (0.0, 0.0)
import numpy as np
import pytest
import autofit as af
import autolens as al
directory = path.dirname(path.realpath(__file__))
@pytest.fixture(scope="session", autouse=True)
def do_something():
af.conf.instance = af.conf.Config(
config_path="{}/../../test_files/config/radial_min".format(directory))
mask_x1 = al.Mask(array=np.array([[False]]), pixel_scale=1.0, sub_size=1)
grid_10 = al.Grid(arr=np.array([[1.0, 0.0]]), mask=mask_x1)
grid_01 = al.Grid(arr=np.array([[0.0, 1.0]]), mask=mask_x1)
grid_11 = al.Grid(arr=np.array([[1.0, 1.0]]), mask=mask_x1)
grid_00 = al.Grid(arr=np.array([[0.0, 0.0]]), mask=mask_x1)
grid_minus_11 = al.Grid(arr=np.array([[-1.0, -1.0]]), mask=mask_x1)
grid_03 = al.Grid(arr=np.array([[0.0, 3.0]]), mask=mask_x1)
grid_34 = al.Grid(arr=np.array([[3.0, 4.0]]), mask=mask_x1)
class TestMemoize(object):
def test_add_to_cache(self):
class MyProfile(object):
# noinspection PyMethodMayBeStatic
@al.geometry_profiles.cache
def my_method(
def test__1x2_image__noise_not_1__alls_correct(self):
image = al.ScaledSquarePixelArray(array=5.0 * np.ones((3, 4)),
pixel_scale=1.0)
image[1, 2] = 4.0
noise_map = al.ScaledSquarePixelArray(array=2.0 * np.ones((3, 4)),
pixel_scale=1.0)
galaxy_data = al.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=3.0)
mask = al.Mask(
array=np.array([
[True, True, True, True],
[True, False, False, True],
[True, True, True, True],
]),
pixel_scale=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0, shape=2)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
def test__15_grid__no_sub_grid(self):
mask = np.array(
[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, False, False, False, False, False, True],
[True, False, False, False, False, False, True],
[True, False, False, False, False, False, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
]
)
mask = al.Mask(array=mask, pixel_scale=1.0, sub_size=1)
# There is no sub-grid, so our grid are just the masked_image grid (note the NumPy weighted_data structure
# ensures this has no sub-gridding)
grid = al.Grid(
arr=np.array(
[
[0.9, -0.9],
[1.0, -1.0],
[1.1, -1.1],
[0.9, 0.9],
[1.0, 1.0],
[1.1, 1.1],
[-0.01, 0.01],
[0.0, 0.0],
[0.01, 0.01],
[-0.9, -0.9],
[-1.0, -1.0],
[-1.1, -1.1],
[-0.9, 0.9],
[-1.0, 1.0],
[-1.1, 1.1],
]
),
mask=mask,
sub_size=1,
)
pixelization_grid = MockPixelizationGrid(arr=grid)
pix = al.pixelizations.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_pixelization_grid(
grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=False
)
assert mapper.is_image_plane_pixelization == False
assert mapper.geometry.shape_arcsec == pytest.approx((2.2, 2.2), 1.0e-4)
assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4)
assert (
mapper.mapping_matrix
== np.array(
[
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
]
)
).all()
assert mapper.shape == (3, 3)
reg = al.regularization.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_pixel_neighbors(
mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size
)
assert (
regularization_matrix
== np.array(
[
[2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
[-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0],
[0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0],
[0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0],
[0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001],
]
)
).all()
def test__3x3_simple_grid__include_mask_with_offset_centre(self):
mask = al.Mask(
array=np.array(
[
[True, True, True, False, True],
[True, True, False, False, False],
[True, True, True, False, True],
[True, True, True, True, True],
[True, True, True, True, True],
]
),
pixel_scale=1.0,
sub_size=1,
)
grid = np.array([[2.0, 1.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [0.0, 1.0]])
grid = al.Grid(arr=grid, mask=mask, sub_size=1)
pix = al.pixelizations.VoronoiMagnification(shape=(3, 3))
sparse_to_grid = al.SparseToGrid.from_grid_and_unmasked_2d_grid_shape(
unmasked_sparse_shape=pix.shape, grid=grid.unlensed_unsubbed_1d
)
pixelization_grid = MockPixelizationGrid(
arr=sparse_to_grid.sparse,
mask_1d_index_to_nearest_pixelization_1d_index=sparse_to_grid.mask_1d_index_to_sparse_1d_index,
)
mapper = pix.mapper_from_grid_and_pixelization_grid(
grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=False
)
assert mapper.is_image_plane_pixelization == True
assert mapper.geometry.shape_arcsec == pytest.approx((2.0, 2.0), 1.0e-4)
assert (mapper.geometry.pixel_centres == sparse_to_grid.sparse).all()
assert mapper.geometry.origin == pytest.approx((1.0, 1.0), 1.0e-4)
assert isinstance(mapper, al.VoronoiMapper)
assert (
mapper.mapping_matrix
== np.array(
[
[1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0],
]
)
).all()
reg = al.regularization.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_pixel_neighbors(
mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size
)
assert (
regularization_matrix
== np.array(
[
[3.00000001, -1.0, -1.0, -1.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0],
[-1.0, -1.0, 4.00000001, -1.0, -1.0],
[-1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, -1.0, -1.0, -1.0, 3.00000001],
]
)
).all()
def test__grid__requires_border_relocation(self):
mask = np.array(
[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, False, True, True, True],
[True, True, False, False, False, True, True],
[True, True, True, False, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
]
)
mask = al.Mask(array=mask, pixel_scale=1.0, sub_size=1)
grid = al.Grid(
arr=np.array(
[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [-1.0, -1.0]]
),
mask=mask,
sub_size=1,
)
pixelization_grid = MockPixelizationGrid(grid)
pix = al.pixelizations.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_pixelization_grid(
grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=True
)
assert mapper.is_image_plane_pixelization == False
assert mapper.geometry.shape_arcsec == pytest.approx((2.0, 2.0), 1.0e-4)
assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4)
assert (
mapper.mapping_matrix
== np.array(
[
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
]
)
).all()
assert mapper.shape == (3, 3)
reg = al.regularization.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_pixel_neighbors(
mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size
)
assert (
regularization_matrix
== np.array(
[
[2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
[-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0],
[0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0],
[0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0],
[0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001],
]
)
).all()
