def test__interferometer(self):
visibilities_mask = np.full(fill_value=False, shape=(7, ))
real_space_mask = aa.Mask2D.unmasked(shape_native=(7, 7),
pixel_scales=0.1,
sub_size=1)
grid = aa.Grid2D.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,
settings=aa.SettingsPixelization(use_border=False),
)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.Visibilities.manual_slim(visibilities=[
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
])
noise_map = aa.VisibilitiesNoiseMap.ones(shape_slim=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.Interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
)
inversion = aa.Inversion(
dataset=interferometer,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
assert inversion.mapped_reconstructed_visibilities == pytest.approx(
1.0 + 0.0j * np.ones(shape=(7, )), 1.0e-4)
assert (np.imag(inversion.mapped_reconstructed_visibilities) <
0.0001).all()
assert (np.imag(inversion.mapped_reconstructed_visibilities) >
0.0).all()
def test__interferometer_linear_operator(self):
real_space_mask = aa.Mask2D.unmasked(shape_native=(7, 7),
pixel_scales=0.1,
sub_size=1)
grid = aa.Grid2D.from_mask(mask=real_space_mask)
pix = aa.pix.Rectangular(shape=(7, 7))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=None,
settings=aa.SettingsPixelization(use_border=False),
)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.Visibilities.manual_slim(visibilities=[
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
])
noise_map = aa.VisibilitiesNoiseMap.ones(shape_slim=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.Interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
settings=aa.SettingsInterferometer(
transformer_class=aa.TransformerNUFFT),
)
inversion = aa.Inversion(
dataset=interferometer,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(use_linear_operators=True,
check_solution=False),
)
assert inversion.mapped_reconstructed_visibilities == pytest.approx(
1.0 + 0.0j * np.ones(shape=(7, )), 1.0e-4)
assert (np.imag(inversion.mapped_reconstructed_visibilities) <
0.0001).all()
assert (np.imag(inversion.mapped_reconstructed_visibilities) >
0.0).all()
def test__15_grid__no_sub_grid(self):
mask = aa.Mask2D.manual(
mask=[
[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],
],
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.Grid2D.manual_mask(
grid=[
[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,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.data_pixelization_grid == None
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.2, 2.2), 1.0e-4)
assert mapper.source_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_native == (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.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
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__3x3_simple_grid__include_mask_with_offset_centre(self):
mask = aa.Mask2D.manual(
mask=[
[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.Grid2D.manual_mask(grid=grid, mask=mask)
pix = aa.pix.VoronoiMagnification(shape=(3, 3))
sparse_grid = aa.Grid2DSparse.from_grid_and_unmasked_2d_grid_shape(
grid=grid, unmasked_sparse_shape=pix.shape)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=sparse_grid,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert (mapper.source_pixelization_grid == sparse_grid).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.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
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 = aa.Mask2D.manual(
mask=[
[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],
],
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.Grid2D.manual_mask(
grid=[
[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,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.data_pixelization_grid == None
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert mapper.source_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_native == (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.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
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)