def test__pixel_scales_work_for_voronoi_mapper(self, grid_7x7, image_7x7):
pixelization_grid = np.array(
[[0.1, 0.1], [1.1, 0.1], [2.1, 0.1], [0.1, 1.1], [1.1, 1.1], [2.1, 1.1]]
)
nearest_pixelization_1d_index_for_mask_1d_index = np.array(
[0, 0, 1, 0, 0, 1, 2, 2, 3]
)
pixelization_grid = aa.grid_voronoi(
grid_1d=pixelization_grid,
nearest_pixelization_1d_index_for_mask_1d_index=nearest_pixelization_1d_index_for_mask_1d_index,
)
mapper = aa.mapper(
grid=grid_7x7,
pixelization_grid=pixelization_grid,
hyper_image=image_7x7,
)
pixel_signals = mapper.pixel_signals_from_signal_scale(signal_scale=2.0)
pixel_signals_util = aa.util.mapper.adaptive_pixel_signals_from_images(
pixels=6,
signal_scale=2.0,
pixelization_1d_index_for_sub_mask_1d_index=mapper.pixelization_1d_index_for_sub_mask_1d_index,
mask_1d_index_for_sub_mask_1d_index=grid_7x7.regions._mask_1d_index_for_sub_mask_1d_index,
hyper_image=image_7x7,
)
assert (pixel_signals == pixel_signals_util).all()
def test__sub_to_pix_of_mapper_matches_nearest_neighbor_calculation(
self, grid_7x7
):
pixelization_grid = np.array(
[[0.1, 0.1], [1.1, 0.1], [2.1, 0.1], [0.1, 1.1], [1.1, 1.1], [2.1, 1.1]]
)
sub_to_pix_nearest_neighbour = grid_to_pixel_pixels_via_nearest_neighbour(
grid_7x7, pixelization_grid
)
nearest_pixelization_1d_index_for_mask_1d_index = np.array(
[0, 0, 1, 0, 0, 1, 2, 2, 3]
)
pixelization_grid = aa.grid_voronoi(
grid_1d=pixelization_grid,
nearest_pixelization_1d_index_for_mask_1d_index=nearest_pixelization_1d_index_for_mask_1d_index,
)
mapper = aa.mapper(grid=grid_7x7, pixelization_grid=pixelization_grid)
assert (
mapper.pixelization_1d_index_for_sub_mask_1d_index
== sub_to_pix_nearest_neighbour
).all()
def make_voronoi_pixelization_grid_9(grid_7x7):
grid_9 = aa.grid.manual_1d(
grid=[
[0.6, -0.3],
[0.5, -0.8],
[0.2, 0.1],
[0.0, 0.5],
[-0.3, -0.8],
[-0.6, -0.5],
[-0.4, -1.1],
[-1.2, 0.8],
[-1.5, 0.9],
],
shape_2d=(3, 3),
pixel_scales=1.0,
)
return aa.grid_voronoi(
grid_1d=grid_9,
nearest_pixelization_1d_index_for_mask_1d_index=np.zeros(
shape=grid_7x7.shape_1d, dtype="int"),
)
[0.6, -0.3],
[0.5, -0.8],
[0.2, 0.1],
[0.0, 0.5],
[-0.3, -0.8],
[-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)],
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)
