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test: move sparse-operator parity check to autolens_workspace_test #489

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Merged
Jammy2211 merged 1 commit into from
May 1, 2026
Merged

test: move sparse-operator parity check to autolens_workspace_test #489

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102 changes: 0 additions & 102 deletions test_autolens/imaging/test_simulate_and_fit_imaging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,108 +616,6 @@ def test__simulate_imaging_data_and_fit__linear_light_profiles_and_pixelization_
assert fit_linear.figure_of_merit == pytest.approx(-190.6935526756, 1.0e-4)


def test__simulate_imaging_data_and_fit__complex_fit_compare_mapping_matrix_sparse_operator():

grid = al.Grid2D.uniform(shape_native=(15, 15), pixel_scales=0.1)

psf = al.Convolver.from_gaussian(
shape_native=(3, 3), pixel_scales=0.2, sigma=0.75, normalize=True
)

lens_0 = al.Galaxy(
redshift=0.1,
light=al.lp.Sersic(centre=(0.1, 0.1)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

lens_1 = al.Galaxy(
redshift=0.2,
light=al.lp.Sersic(centre=(0.2, 0.2)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

lens_2 = al.Galaxy(
redshift=0.3,
light=al.lp.Sersic(centre=(0.3, 0.3)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

source_0 = al.Galaxy(redshift=0.4, bulge=al.lp.Sersic(centre=(0.3, 0.3)))
source_1 = al.Galaxy(redshift=0.5, bulge=al.lp.Sersic(centre=(0.3, 0.3)))
tracer = al.Tracer(galaxies=[lens_0, lens_1, lens_2, source_0, source_1])

dataset = al.SimulatorImaging(exposure_time=300.0, psf=psf, add_poisson_noise_to_data=False)

dataset = dataset.via_tracer_from(tracer=tracer, grid=grid)
dataset.sub_size = 2
dataset.noise_map = al.Array2D.ones(
shape_native=dataset.data.shape_native, pixel_scales=0.2
)
mask = al.Mask2D.circular(
shape_native=dataset.data.shape_native, pixel_scales=0.2, radius=0.8
)

masked_dataset = dataset.apply_mask(mask=mask)

lens_0 = al.Galaxy(
redshift=0.1,
light=al.lp_linear.Sersic(centre=(0.1, 0.1)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

lens_1 = al.Galaxy(
redshift=0.2,
light=al.lp_linear.Sersic(centre=(0.2, 0.2)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

lens_2 = al.Galaxy(
redshift=0.3,
light=al.lp_linear.Sersic(centre=(0.3, 0.3)),
mass=al.mp.Isothermal(centre=(0.1, 0.1), einstein_radius=0.2),
)

pixelization = al.Pixelization(
mesh=al.mesh.RectangularUniform(shape=(3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
)

source_0 = al.Galaxy(redshift=0.4, pixelization=pixelization)

pixelization = al.Pixelization(
mesh=al.mesh.RectangularUniform(shape=(3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
)

source_1 = al.Galaxy(redshift=0.5, pixelization=pixelization)

tracer = al.Tracer(
galaxies=[lens_0, lens_1, lens_2, source_0, source_1]
)

fit_mapping = al.FitImaging(
dataset=masked_dataset,
tracer=tracer,
)

masked_dataset_sparse_operator = masked_dataset.apply_sparse_operator()

fit_sparse_operator = al.FitImaging(
dataset=masked_dataset_sparse_operator,
tracer=tracer,
)

assert fit_mapping.inversion.curvature_matrix == pytest.approx(
fit_sparse_operator.inversion.curvature_matrix,
1.0e-4,
)

assert fit_mapping.inversion.regularization_matrix == pytest.approx(
fit_sparse_operator.inversion.regularization_matrix,
1.0e-4,
)


def test__fit_figure_of_merit__mge_mass_model(masked_imaging_7x7, masked_imaging_covariance_7x7):

grid = al.Grid2D.uniform(shape_native=(11, 11), pixel_scales=0.2,
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