def test__different_imaging_without_mock_objects__customize_constructor_inputs(
self
):
psf = aa.kernel.ones(shape_2d=(7, 7), pixel_scales=3.0)
imaging = aa.imaging(
image=aa.array.ones(shape_2d=(19, 19), pixel_scales=3.0),
psf=psf,
noise_map=aa.array.full(
fill_value=2.0, shape_2d=(19, 19), pixel_scales=3.0
),
)
mask = aa.mask.unmasked(
shape_2d=(19, 19), pixel_scales=1.0, invert=True, sub_size=8
)
mask[9, 9] = False
masked_imaging = aa.masked.imaging.manual(
imaging=imaging, mask=mask, psf_shape_2d=(7, 7)
)
assert (masked_imaging.imaging.image.in_2d == np.ones((19, 19))).all()
assert (masked_imaging.imaging.noise_map.in_2d == 2.0 * np.ones((19, 19))).all()
assert (masked_imaging.psf.in_2d == np.ones((7, 7))).all()
assert masked_imaging.convolver.kernel.shape_2d == (7, 7)
assert (masked_imaging.image == np.array([1.0])).all()
assert (masked_imaging.noise_map == np.array([2.0])).all()
def test__image_and_model_are_identical__inversion_included__changes_certain_properties(
self):
mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.masked.array.manual_1d(array=np.array([1.0, 2.0, 3.0, 4.0]),
mask=mask)
noise_map = aa.masked.array.manual_1d(array=np.array(
[2.0, 2.0, 2.0, 2.0]),
mask=mask)
imaging = aa.imaging(image=data, noise_map=noise_map)
masked_imaging = aa.masked.imaging(imaging=imaging, mask=mask)
model_data = aa.masked.array.manual_1d(array=np.array(
[1.0, 2.0, 3.0, 4.0]),
mask=mask)
inversion = mock_inversion.MockFitInversion(
regularization_term=2.0,
log_det_curvature_reg_matrix_term=3.0,
log_det_regularization_matrix_term=4.0,
)
fit = aa.fit(masked_dataset=masked_imaging,
model_data=model_data,
inversion=inversion)
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
assert fit.likelihood_with_regularization == -0.5 * (
fit.chi_squared + 2.0 + fit.noise_normalization)
assert fit.evidence == -0.5 * (fit.chi_squared + 2.0 + 3.0 - 4.0 +
fit.noise_normalization)
assert fit.figure_of_merit == fit.evidence
def make_imaging_7x7(
image_7x7,
psf_3x3,
noise_map_7x7,
background_noise_map_7x7,
poisson_noise_map_7x7,
exposure_time_map_7x7,
background_sky_map_7x7,
):
return aa.imaging(
image=image_7x7,
pixel_scales=image_7x7.pixel_scales,
psf=psf_3x3,
noise_map=noise_map_7x7,
background_noise_map=background_noise_map_7x7,
poisson_noise_map=poisson_noise_map_7x7,
exposure_time_map=exposure_time_map_7x7,
background_sky_map=background_sky_map_7x7,
name="mock_imaging_7x7",
)
def make_imaging_6x6():
image = aa.array.full(shape_2d=(6, 6), fill_value=1.0)
psf = aa.kernel.full(shape_2d=(3, 3), fill_value=1.0)
noise_map = aa.array.full(shape_2d=(6, 6), fill_value=2.0)
background_noise_map = aa.array.full(shape_2d=(6, 6), fill_value=3.0)
poisson_noise_map = aa.array.full(shape_2d=(6, 6), fill_value=4.0)
exposure_time_map = aa.array.full(shape_2d=(6, 6), fill_value=5.0)
background_sky_map = aa.array.full(shape_2d=(6, 6), fill_value=6.0)
return aa.imaging(
image=image,
pixel_scales=1.0,
psf=psf,
noise_map=noise_map,
background_noise_map=background_noise_map,
poisson_noise_map=poisson_noise_map,
exposure_time_map=exposure_time_map,
background_sky_map=background_sky_map,
name="mock_imaging_6x6",
)
import autoarray as aa
import autoarray.plot as aplt
import numpy as np
mask = aa.mask.circular(shape_2d=(7, 7), pixel_scales=0.3, radius=0.6)
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)
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,
)
import autoarray as aa import autoarray.plot as aplt array = aa.array.ones(shape_2d=(31, 31), pixel_scales=(1.0, 1.0), sub_size=1) array[0] = 3.0 kernel = aa.kernel.ones(shape_2d=(3, 3), pixel_scales=(1.0, 1.0)) kernel[0] = 3.0 imaging = aa.imaging(image=array, noise_map=array, psf=kernel) # aplt.imaging.image(imaging=imaging) aplt.imaging.subplot_imaging(imaging=imaging)
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__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)
def test__image_and_model_are_identical__no_masking__check_values_are_correct(
self):
mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.masked.array.manual_1d(array=np.array([1.0, 2.0, 3.0, 4.0]),
mask=mask)
noise_map = aa.masked.array.manual_1d(array=np.array(
[2.0, 2.0, 2.0, 2.0]),
mask=mask)
imaging = aa.imaging(image=data, noise_map=noise_map)
masked_imaging = aa.masked.imaging(imaging=imaging, mask=mask)
model_data = aa.masked.array.manual_1d(array=np.array(
[1.0, 2.0, 3.0, 4.0]),
mask=mask)
fit = aa.fit(masked_dataset=masked_imaging, model_data=model_data)
assert (fit.mask == np.array([[False, False], [False, False]])).all()
assert (fit.image.in_1d == np.array([1.0, 2.0, 3.0, 4.0])).all()
assert (fit.image.in_2d == np.array([[1.0, 2.0], [3.0, 4.0]])).all()
assert (fit.noise_map.in_1d == np.array([2.0, 2.0, 2.0, 2.0])).all()
assert (fit.noise_map.in_2d == np.array([[2.0, 2.0], [2.0,
2.0]])).all()
assert (fit.signal_to_noise_map.in_1d == np.array([0.5, 1.0, 1.5,
2.0])).all()
assert (fit.signal_to_noise_map.in_2d == np.array([[0.5, 1.0],
[1.5, 2.0]])).all()
assert (fit.model_image.in_1d == np.array([1.0, 2.0, 3.0, 4.0])).all()
assert (fit.model_image.in_2d == np.array([[1.0, 2.0], [3.0,
4.0]])).all()
assert (fit.residual_map.in_1d == np.array([0.0, 0.0, 0.0, 0.0])).all()
assert (fit.residual_map.in_2d == np.array([[0.0, 0.0], [0.0,
0.0]])).all()
assert (fit.normalized_residual_map.in_1d == np.array(
[0.0, 0.0, 0.0, 0.0])).all()
assert (fit.normalized_residual_map.in_2d == np.array([[0.0, 0.0],
[0.0,
0.0]])).all()
assert (fit.chi_squared_map.in_1d == np.array([0.0, 0.0, 0.0,
0.0])).all()
assert (fit.chi_squared_map.in_2d == np.array([[0.0, 0.0],
[0.0, 0.0]])).all()
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
import autoarray as aa
import autoarray.plot as aplt
grid_7x7 = aa.grid.uniform(shape_2d=(7, 7), pixel_scales=1.0)
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)
image = aa.array.ones(shape_2d=(7, 7), pixel_scales=1.0)
image[0:4] = 5.0
noise_map = aa.array.ones(shape_2d=(7, 7), pixel_scales=1.0)
imaging = aa.imaging(image=image, noise_map=noise_map)
aplt.mapper.subplot_image_and_mapper(
image=imaging,
mapper=rectangular_mapper,
include=aplt.Include(
inversion_grid=False, inversion_border=True, inversion_pixelization_grid=False
),
image_pixel_indexes=[0, 1, 2, 3],
source_pixel_indexes=[[3, 4], [5]],
)