def make_voronoi_inversion_9_3x3(masked_imaging_7x7, voronoi_mapper_9_3x3):
regularization = aa.reg.Constant(coefficient=1.0)
return aa.inversion(
masked_dataset=masked_imaging_7x7,
mapper=voronoi_mapper_9_3x3,
regularization=regularization,
)
def make_rectangular_inversion_7x7_3x3(masked_imaging_7x7,
rectangular_mapper_7x7_3x3):
regularization = aa.reg.Constant(coefficient=1.0)
return aa.inversion(
masked_dataset=masked_imaging_7x7,
mapper=rectangular_mapper_7x7_3x3,
regularization=regularization,
)
[-0.6, -0.5],
[-0.4, -1.1],
[-1.2, 0.8],
[-1.5, 0.9],
],
shape_2d=(3, 3),
pixel_scales=1.0,
)
voronoi_grid = aa.grid_voronoi(
grid_1d=grid_9,
nearest_pixelization_1d_index_for_mask_1d_index=np.zeros(
shape=grid_7x7.shape_1d, dtype="int"
),
)
voronoi_mapper = aa.mapper(grid=grid_7x7, pixelization_grid=voronoi_grid)
regularization = aa.reg.Constant(coefficient=1.0)
inversion = aa.inversion(
masked_dataset=masked_imaging, mapper=voronoi_mapper, regularization=regularization
)
aplt.inversion.subplot_inversion(
inversion=inversion,
image_positions=[(0.05, 0.05)],
lines=[(0.0, 0.0), (0.1, 0.1)],
image_pixel_indexes=[0],
source_pixel_indexes=[5],
)
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 = aa.mask.manual(mask_2d=mask, pixel_scales=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 = aa.masked.grid.manual_1d(
grid=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,
)
pix = aa.pix.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid, sparse_grid=None, inversion_uses_border=False)
assert mapper.is_image_plane_pixelization == False
assert mapper.pixelization_grid.shape_2d_scaled == pytest.approx(
(2.2, 2.2), 1.0e-4)
assert mapper.pixelization_grid.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_2d == (3, 3)
reg = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
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()
image = aa.array.ones(shape_2d=(7, 7))
noise_map = aa.array.ones(shape_2d=(7, 7))
psf = aa.kernel.no_blur()
imaging = aa.imaging(image=image, noise_map=noise_map, psf=psf)
masked_data = aa.masked.imaging(imaging=imaging, mask=mask)
inversion = aa.inversion(masked_dataset=masked_data,
mapper=mapper,
regularization=reg)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(15), 1.0e-4)
def test__interferometer(self):
visibilities_mask = np.full(fill_value=False, shape=(7, 2))
real_space_mask = np.array([
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
])
real_space_mask = aa.mask.manual(mask_2d=real_space_mask,
pixel_scales=0.1,
sub_size=1)
grid = aa.masked.grid.from_mask(mask=real_space_mask)
pix = aa.pix.VoronoiMagnification(shape=(7, 7))
sparse_grid = pix.sparse_grid_from_grid(grid=grid)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid, sparse_grid=sparse_grid, inversion_uses_border=False)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.visibilities.manual_1d(visibilities=[
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
[1.0, 0.0],
])
noise_map = aa.visibilities.ones(shape_1d=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
)
masked_data = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
inversion = aa.inversion(masked_dataset=masked_data,
mapper=mapper,
regularization=reg)
assert inversion.mapped_reconstructed_visibilities[:,
0] == pytest.approx(
np.ones(shape=(
7, )),
1.0e-4)
assert inversion.mapped_reconstructed_visibilities[:,
1] == pytest.approx(
np.zeros(shape=(
7, )),
1.0e-4)
def test__3x3_simple_grid__include_mask_with_offset_centre(self):
mask = aa.mask.manual(
mask_2d=np.array([
[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, True, True, True, True, True, True],
]),
pixel_scales=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 = aa.masked.grid.manual_1d(grid=grid, mask=mask)
pix = aa.pix.VoronoiMagnification(shape=(3, 3))
sparse_grid = grids.SparseGrid.from_grid_and_unmasked_2d_grid_shape(
grid=grid, unmasked_sparse_shape=pix.shape)
pixelization_grid = aa.grid_voronoi(
grid_1d=sparse_grid.sparse,
nearest_pixelization_1d_index_for_mask_1d_index=sparse_grid.
sparse_1d_index_for_mask_1d_index,
)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=pixelization_grid,
inversion_uses_border=False)
assert mapper.is_image_plane_pixelization == True
assert mapper.pixelization_grid.shape_2d_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert (mapper.pixelization_grid == sparse_grid.sparse).all()
# assert mapper.pixelization_grid.origin == pytest.approx((1.0, 1.0), 1.0e-4)
assert isinstance(mapper, mappers.MapperVoronoi)
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 = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
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()
image = aa.array.ones(shape_2d=(7, 7))
noise_map = aa.array.ones(shape_2d=(7, 7))
psf = aa.kernel.no_blur()
imaging = aa.imaging(image=image, noise_map=noise_map, psf=psf)
masked_data = aa.masked.imaging(imaging=imaging, mask=mask)
inversion = aa.inversion(masked_dataset=masked_data,
mapper=mapper,
regularization=reg)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(5), 1.0e-4)
def test__5_simple_grid__include_sub_grid(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 = aa.mask.manual(mask_2d=mask, pixel_scales=2.0, sub_size=2)
# Assume a 2x2 sub-grid, so each of our 5 masked_image-pixels are split into 4.
# The grid below is unphysical in that the (0.0, 0.0) terms on the end of each sub-grid probably couldn't
# happen for a real lens calculation. This is to make a mapping_matrix matrix which explicitly tests the
# sub-grid.
grid = aa.masked.grid.manual_1d(
grid=np.array([
[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],
[-1.0, -1.0],
[-1.0, 1.0],
[-1.0, 1.0],
[-1.0, 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],
[0.0, 0.0],
[0.0, 0.0],
]),
mask=mask,
)
pix = aa.pix.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid, sparse_grid=None, inversion_uses_border=False)
assert mapper.is_image_plane_pixelization == False
assert mapper.pixelization_grid.shape_2d_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert mapper.pixelization_grid.origin == pytest.approx((0.0, 0.0),
1.0e-4)
assert (mapper.mapping_matrix == np.array([
[0.75, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.75],
[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],
])).all()
assert mapper.shape_2d == (3, 3)
reg = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
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()
image = aa.array.ones(shape_2d=(7, 7))
noise_map = aa.array.ones(shape_2d=(7, 7))
psf = aa.kernel.no_blur()
imaging = aa.imaging(image=image, noise_map=noise_map, psf=psf)
masked_data = aa.masked.imaging(imaging=imaging, mask=mask)
inversion = aa.inversion(masked_dataset=masked_data,
mapper=mapper,
regularization=reg)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(5), 1.0e-4)
imaging = aa.imaging(
image=aa.array.ones(shape_2d=(7, 7), pixel_scales=0.3),
noise_map=aa.array.ones(shape_2d=(7, 7), pixel_scales=0.3),
psf=aa.kernel.ones(shape_2d=(3, 3), pixel_scales=0.3),
)
masked_imaging = aa.masked.imaging(imaging=imaging, mask=mask)
grid_7x7 = aa.grid.from_mask(mask=mask)
rectangular_grid = aa.grid_rectangular.overlay_grid(grid=grid_7x7,
shape_2d=(3, 3))
rectangular_mapper = aa.mapper(grid=grid_7x7,
pixelization_grid=rectangular_grid)
regularization = aa.reg.Constant(coefficient=1.0)
inversion = aa.inversion(
masked_dataset=masked_imaging,
mapper=rectangular_mapper,
regularization=regularization,
)
aplt.inversion.subplot_inversion(
inversion=inversion,
image_positions=[(0.05, 0.05)],
lines=[(0.0, 0.0), (0.1, 0.1)],
image_pixel_indexes=[0],
source_pixel_indexes=[5],
)
