def test__regularization_term__solution_all_1s__regularization_matrix_simple(self):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([1.0, 1.0, 1.0])
inversion.regularization_matrix = np.array(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]
)
# G_l term, Warren & Dye 2003 / Nightingale /2015 2018
# G_l = s_T * H * s
# Matrix multiplication:
# s_T * H = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [(1.0*1.0) + (1.0*0.0) + (1.0*0.0)] = [1.0, 1.0, 1.0]
# [1.0, 1.0, 1.0] [(1.0*0.0) + (1.0*1.0) + (1.0*0.0)]
# [1.0, 1.0, 1.0] [(1.0*0.0) + (1.0*0.0) + (1.0*1.0)]
# (s_T * H) * s = [1.0, 1.0, 1.0] * [1.0] = 3.0
# [1.0]
# [1.0]
assert inversion.regularization_term == 3.0
def test__func_testing_range_of_reconstruction(self):
mask = aa.Mask2D.manual(
mask=np.array(
[[True, True, True], [False, False, False], [True, True, True]]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
matrix_shape = (9, 3)
with pytest.raises(exc.InversionException):
inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape, source_grid_slim=grid
),
regularization=mock.MockRegularization(matrix_shape),
)
def test__interpolated_errors__also_on_image_grid__interpolates_values(self):
conf.instance = conf.Config(
path.join(directory, path.join("files", "inversion_image_grid")),
path.join(directory, "output"),
)
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
inversion.curvature_reg_matrix = np.eye(N=9)
interpolated_errors = inversion.interpolated_errors_from_shape_native()
assert interpolated_errors.shape_native == (5, 5)
assert interpolated_errors.slim == pytest.approx(np.ones(shape=(25,)), 1.0e-4)
assert interpolated_errors.native == pytest.approx(
np.ones(shape=(5, 5)), 1.0e-4
)
assert interpolated_errors.pixel_scales == pytest.approx((1.0, 1.0), 1.0e-4)
def test__reconstruction_different_values__simple_blurred_mapping_matrix__correct_reconstructed_image(
self,
):
matrix_shape = (9, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[[True, True, True], [False, False, False], [True, True, True]]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape, source_grid_slim=grid),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([1.0, 2.0, 3.0, 4.0])
inversion.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 (
inversion.mapped_reconstructed_image == np.array([10.0, 8.0, 1.0])
).all()
assert (
inversion.mapped_reconstructed_image.native
== np.array([[0.0, 0.0, 0.0], [10.0, 8.0, 1.0], [0.0, 0.0, 0.0]])
).all()
assert inversion.errors_with_covariance == pytest.approx(
np.array(
[
[0.6785, -0.3214, -0.3214],
[-0.3214, 0.6785, -0.3214],
[-0.3214, -0.3214, 0.6785],
]
),
1.0e-2,
)
assert inversion.errors == pytest.approx(
np.array([0.6785, 0.6785, 0.6785]), 1.0e-3
)
def test__brightest_reconstruction_pixel_and_centre(self):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape,
source_pixelization_grid=aa.Grid2DVoronoi.manual_slim(
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [5.0, 0.0]]
),
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([2.0, 3.0, 5.0, 0.0])
assert inversion.brightest_reconstruction_pixel == 2
assert inversion.brightest_reconstruction_pixel_centre.in_list == [(5.0, 6.0)]
def test__preloads(self):
matrix_shape = (9, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[[True, True, True], [False, False, False], [True, True, True]]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
blurred_mapping_matrix = 2.0 * np.ones(matrix_shape)
curvature_matrix = 18.0 * np.ones((matrix_shape[1], matrix_shape[1]))
curvature_matrix_sparse_preload, curvature_matrix_preload_counts = aa.util.inversion.curvature_matrix_sparse_preload_via_mapping_matrix_from(
mapping_matrix=blurred_mapping_matrix
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape, source_grid_slim=grid),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
preloads=aa.Preloads(
blurred_mapping_matrix=blurred_mapping_matrix,
curvature_matrix_sparse_preload=curvature_matrix_sparse_preload,
curvature_matrix_preload_counts=curvature_matrix_preload_counts,
),
)
assert (inversion.blurred_mapping_matrix == blurred_mapping_matrix).all()
def test__regularization_term__solution_and_regularization_matrix_range_of_values(
self,
):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
# G_l term, Warren & Dye 2003 / Nightingale /2015 2018
# G_l = s_T * H * s
# Matrix multiplication:
# s_T * H = [2.0, 3.0, 5.0] * [2.0, -1.0, 0.0] = [(2.0* 2.0) + (3.0*-1.0) + (5.0 *0.0)] = [1.0, -1.0, 7.0]
# [-1.0, 2.0, -1.0] [(2.0*-1.0) + (3.0* 2.0) + (5.0*-1.0)]
# [ 0.0, -1.0, 2.0] [(2.0* 0.0) + (3.0*-1.0) + (5.0 *2.0)]
# (s_T * H) * s = [1.0, -1.0, 7.0] * [2.0] = 34.0
# [3.0]
# [5.0]
inversion.reconstruction = np.array([2.0, 3.0, 5.0])
inversion.regularization_matrix = np.array(
[[2.0, -1.0, 0.0], [-1.0, 2.0, -1.0], [0.0, -1.0, 2.0]]
)
assert inversion.regularization_term == 34.0
def test__interp__manual_shape_native__uses_input_shape_native(self):
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[1.0, 1.0], [1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(1.0, 1.0), 1.0e-4
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([3.0, 3.0, 3.0, 3.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[3.0, 3.0], [3.0, 3.0]])
).all()
assert interpolated_reconstruction.pixel_scales == (1.0, 1.0)
def test__interpolated_reconstruction__config_is_source_grid__grid_is_zoomed_as_uses_mapper_grid(
self,
):
conf.instance = conf.Config(
path.join(directory, path.join("files", "inversion_source_grid")),
path.join(directory, "output"),
)
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
interpolated_reconstruction = (
inversion.interpolated_reconstructed_data_from_shape_native()
)
assert (
interpolated_reconstruction.slim
== np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native
== np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(0.66666, 0.66666), 1.0e-4
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[1.0, 1.0], [1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(1.0, 1.0), 1.0e-4
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([3.0, 3.0, 3.0, 3.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[3.0, 3.0], [3.0, 3.0]])
).all()
assert interpolated_reconstruction.pixel_scales == (1.0, 1.0)
