def test___fit_galaxy_model_visibilities_dict__has_profile_visibilitiess_and_inversion_mapped_reconstructed_visibilities(
self, interferometer_7
):
g0 = ag.Galaxy(redshift=0.5, light_profile=ag.lp.EllSersic(intensity=1.0))
g1 = ag.Galaxy(redshift=0.5, light_profile=ag.lp.EllSersic(intensity=2.0))
g2 = ag.Galaxy(redshift=0.5)
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
galaxy_pix = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1, g2, galaxy_pix])
fit = ag.FitInterferometer(interferometer=interferometer_7, plane=plane)
g0_visibilities = g0.profile_visibilities_from_grid_and_transformer(
grid=interferometer_7.grid, transformer=interferometer_7.transformer
)
g1_visibilities = g1.profile_visibilities_from_grid_and_transformer(
grid=interferometer_7.grid, transformer=interferometer_7.transformer
)
profile_visibilities = g0_visibilities + g1_visibilities
profile_subtracted_visibilities = (
interferometer_7.visibilities - profile_visibilities
)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=interferometer_7.grid,
settings=ag.SettingsPixelization(use_border=False),
)
inversion = inversions.InversionInterferometerMatrix.from_data_mapper_and_regularization(
visibilities=profile_subtracted_visibilities,
noise_map=interferometer_7.noise_map,
transformer=interferometer_7.transformer,
mapper=mapper,
regularization=reg,
)
assert (
fit.galaxy_model_visibilities_dict[g2] == 0.0 + 0.0j * np.zeros((7,))
).all()
assert fit.galaxy_model_visibilities_dict[g0].slim == pytest.approx(
g0_visibilities.slim, 1.0e-4
)
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
g1_visibilities.slim, 1.0e-4
)
assert fit.galaxy_model_visibilities_dict[galaxy_pix].slim == pytest.approx(
inversion.mapped_reconstructed_visibilities.slim, 1.0e-4
)
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g0].slim
+ fit.galaxy_model_visibilities_dict[g1].slim
+ inversion.mapped_reconstructed_visibilities.slim,
1.0e-4,
)
def test___all_fit_quantities__no_hyper_methods(self, interferometer_7):
galaxy_light = ag.Galaxy(
redshift=0.5, light_profile=ag.lp.EllSersic(intensity=1.0)
)
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
galaxy_pix = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[galaxy_light, galaxy_pix])
fit = ag.FitInterferometer(interferometer=interferometer_7, plane=plane)
profile_visibilities = plane.profile_visibilities_from_grid_and_transformer(
grid=interferometer_7.grid, transformer=interferometer_7.transformer
)
assert profile_visibilities.slim == pytest.approx(fit.profile_visibilities.slim)
profile_subtracted_visibilities = (
interferometer_7.visibilities - profile_visibilities
)
assert profile_subtracted_visibilities.slim == pytest.approx(
fit.profile_subtracted_visibilities.slim
)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=interferometer_7.grid,
settings=ag.SettingsPixelization(use_border=False),
)
inversion = inversions.InversionInterferometerMatrix.from_data_mapper_and_regularization(
visibilities=profile_subtracted_visibilities,
noise_map=interferometer_7.noise_map,
transformer=interferometer_7.transformer,
mapper=mapper,
regularization=reg,
)
model_visibilities = (
profile_visibilities + inversion.mapped_reconstructed_visibilities
)
assert model_visibilities.slim == pytest.approx(fit.model_visibilities.slim)
residual_map = ag.util.fit.residual_map_from(
data=interferometer_7.visibilities, model_data=model_visibilities
)
assert residual_map.slim == pytest.approx(fit.residual_map.slim)
normalized_residual_map = ag.util.fit.normalized_residual_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7.noise_map
)
assert normalized_residual_map.slim == pytest.approx(
fit.normalized_residual_map.slim
)
chi_squared_map = ag.util.fit.chi_squared_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7.noise_map
)
assert chi_squared_map.slim == pytest.approx(fit.chi_squared_map.slim)
chi_squared = ag.util.fit.chi_squared_complex_from(
chi_squared_map=chi_squared_map
)
noise_normalization = ag.util.fit.noise_normalization_complex_from(
noise_map=interferometer_7.noise_map
)
log_likelihood = ag.util.fit.log_likelihood_from(
chi_squared=chi_squared, noise_normalization=noise_normalization
)
assert log_likelihood == pytest.approx(fit.log_likelihood, 1e-4)
log_likelihood_with_regularization = ag.util.fit.log_likelihood_with_regularization_from(
chi_squared=chi_squared,
regularization_term=inversion.regularization_term,
noise_normalization=noise_normalization,
)
assert log_likelihood_with_regularization == pytest.approx(
fit.log_likelihood_with_regularization, 1e-4
)
log_evidence = ag.util.fit.log_evidence_from(
chi_squared=chi_squared,
regularization_term=inversion.regularization_term,
log_curvature_regularization_term=inversion.log_det_curvature_reg_matrix_term,
log_regularization_term=inversion.log_det_regularization_matrix_term,
noise_normalization=noise_normalization,
)
assert log_evidence == fit.log_evidence
assert log_evidence == fit.figure_of_merit
mapped_reconstructed_image = ag.util.inversion.mapped_reconstructed_data_from(
mapping_matrix=fit.inversion.mapper.mapping_matrix,
reconstruction=fit.inversion.reconstruction,
)
assert (
fit.inversion.mapped_reconstructed_image.slim == mapped_reconstructed_image
).all()
def test___all_fit_quantities__uses_linear_operator_inversion(
self, interferometer_7_lop
):
# Ensures the inversion grid is used, as this would cause the test to fail.
interferometer_7_lop.grid[0, 0] = -100.0
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=0.01)
g0 = ag.Galaxy(redshift=0.5, pixelization=pix, regularization=reg)
plane = ag.Plane(galaxies=[ag.Galaxy(redshift=0.5), g0])
fit = ag.FitInterferometer(
interferometer=interferometer_7_lop,
plane=plane,
settings_inversion=ag.SettingsInversion(use_linear_operators=True),
)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=interferometer_7_lop.grid_inversion, sparse_grid=None
)
inversion = inversions.InversionInterferometerLinearOperator.from_data_mapper_and_regularization(
mapper=mapper,
regularization=reg,
visibilities=interferometer_7_lop.visibilities,
noise_map=interferometer_7_lop.noise_map,
transformer=interferometer_7_lop.transformer,
settings=ag.SettingsInversion(use_linear_operators=True),
)
assert inversion.mapped_reconstructed_visibilities == pytest.approx(
fit.model_visibilities, 1.0e-4
)
residual_map = ag.util.fit.residual_map_from(
data=interferometer_7_lop.visibilities,
model_data=inversion.mapped_reconstructed_visibilities,
)
assert residual_map.slim == pytest.approx(fit.residual_map.slim, 1.0e-4)
normalized_residual_map = ag.util.fit.normalized_residual_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7_lop.noise_map
)
assert normalized_residual_map.slim == pytest.approx(
fit.normalized_residual_map.slim, 1.0e-4
)
chi_squared_map = ag.util.fit.chi_squared_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7_lop.noise_map
)
assert chi_squared_map.slim == pytest.approx(fit.chi_squared_map.slim, 1.0e-4)
chi_squared = ag.util.fit.chi_squared_complex_from(
chi_squared_map=chi_squared_map
)
noise_normalization = ag.util.fit.noise_normalization_complex_from(
noise_map=interferometer_7_lop.noise_map
)
log_likelihood = ag.util.fit.log_likelihood_from(
chi_squared=chi_squared, noise_normalization=noise_normalization
)
assert log_likelihood == pytest.approx(fit.log_likelihood, 1e-4)
log_likelihood_with_regularization = ag.util.fit.log_likelihood_with_regularization_from(
chi_squared=chi_squared,
regularization_term=inversion.regularization_term,
noise_normalization=noise_normalization,
)
assert log_likelihood_with_regularization == pytest.approx(
fit.log_likelihood_with_regularization, 1e-4
)
log_evidence = ag.util.fit.log_evidence_from(
chi_squared=chi_squared,
regularization_term=inversion.regularization_term,
log_curvature_regularization_term=inversion.log_det_curvature_reg_matrix_term,
log_regularization_term=inversion.log_det_regularization_matrix_term,
noise_normalization=noise_normalization,
)
assert log_evidence == fit.log_evidence
assert log_evidence == fit.figure_of_merit
mapped_reconstructed_image = ag.util.inversion.mapped_reconstructed_data_from(
mapping_matrix=fit.inversion.mapper.mapping_matrix,
reconstruction=fit.inversion.reconstruction,
)
assert (
fit.inversion.mapped_reconstructed_image.slim == mapped_reconstructed_image
).all()
def make_gal_x1_lp_x1_mp():
return ag.Galaxy(
redshift=0.5, light_profile_0=make_lp_0(), mass_profile_0=make_mp_0()
)
def make_gal_x2_mp():
return ag.Galaxy(
redshift=0.5, mass_profile_0=make_mp_0(), mass_profile_1=make_mp_1()
)
def make_gal_x2_lp():
return ag.Galaxy(
redshift=0.5, light_profile_0=make_lp_0(), light_profile_1=make_lp_1()
)