def test__furthest_separation_of_source_plane_positions(self):
positions = al.Grid2DIrregular(grid=[(0.0, 0.0), (0.0, 1.0)])
noise_map = al.ValuesIrregular([[1.0, 1.0]])
tracer = mock.MockTracer(traced_grid=positions)
fit = al.FitPositionsSourceMaxSeparation(positions=positions,
noise_map=noise_map,
tracer=tracer)
assert fit.furthest_separations_of_source_plane_positions.in_list == [
1.0, 1.0
]
assert fit.max_separation_of_source_plane_positions == 1.0
assert fit.max_separation_within_threshold(threshold=2.0) == True
assert fit.max_separation_within_threshold(threshold=0.5) == False
positions = al.Grid2DIrregular(grid=[(0.0, 0.0), (0.0, 1.0), (0.0,
3.0)])
noise_map = al.ValuesIrregular([1.0, 1.0, 1.0])
tracer = mock.MockTracer(traced_grid=positions)
fit = al.FitPositionsSourceMaxSeparation(positions=positions,
noise_map=noise_map,
tracer=tracer)
assert fit.furthest_separations_of_source_plane_positions.in_list == [
3.0,
2.0,
3.0,
]
assert fit.max_separation_of_source_plane_positions == 3.0
assert fit.max_separation_within_threshold(threshold=3.5) == True
assert fit.max_separation_within_threshold(threshold=2.0) == False
assert fit.max_separation_within_threshold(threshold=0.5) == False
def test__update_stellar_mass_priors_using_einstein_radius(self):
grid = al.Grid2D.uniform(shape_native=(200, 200), pixel_scales=0.05)
tracer = mock.MockTracer(einstein_radius=1.0, einstein_mass=4.0)
fit = mock.MockFit(grid=grid)
result = mock.MockResult(max_log_likelihood_tracer=tracer,
max_log_likelihood_fit=fit)
bulge_prior_model = af.PriorModel(al.lmp.SphericalSersic)
bulge_prior_model.intensity = af.UniformPrior(lower_limit=0.99,
upper_limit=1.01)
bulge_prior_model.effective_radius = af.UniformPrior(lower_limit=0.99,
upper_limit=1.01)
bulge_prior_model.sersic_index = af.UniformPrior(lower_limit=2.99,
upper_limit=3.01)
setup = al.SetupMassLightDark(bulge_prior_model=bulge_prior_model)
setup.update_stellar_mass_priors_from_result(
prior_model=bulge_prior_model,
result=result,
einstein_mass_range=[0.001, 10.0],
bins=10,
)
assert setup.bulge_prior_model.mass_to_light_ratio.lower_limit == pytest.approx(
0.00040519, 1.0e-1)
assert setup.bulge_prior_model.mass_to_light_ratio.upper_limit == pytest.approx(
4.051935, 1.0e-1)
def test__one_set_of_fluxes__residuals_likelihood_correct(self):
tracer = mock.MockTracer(
magnification=al.ValuesIrregular([2.0, 2.0]),
profile=al.ps.PointSourceFlux(flux=2.0),
)
fluxes = al.ValuesIrregular([1.0, 2.0])
noise_map = al.ValuesIrregular([3.0, 1.0])
positions = al.Grid2DIrregular([(0.0, 0.0), (3.0, 4.0)])
fit = al.FitFluxes(
name="point_0",
fluxes=fluxes,
noise_map=noise_map,
positions=positions,
tracer=tracer,
)
assert fit.fluxes.in_list == [1.0, 2.0]
assert fit.noise_map.in_list == [3.0, 1.0]
assert fit.model_fluxes.in_list == [4.0, 4.0]
assert fit.residual_map.in_list == [-3.0, -2.0]
assert fit.normalized_residual_map.in_list == [-1.0, -2.0]
assert fit.chi_squared_map.in_list == [1.0, 4.0]
assert fit.chi_squared == pytest.approx(5.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(5.87297, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-5.43648, 1.0e-4)
def test__auto_einstein_radius_is_used__einstein_radius_used_in_analysis(
self, imaging_7x7, mask_7x7):
# If position threshold is input (not None) and positions are input, make the positions part of the lens dataset.
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(settings_lens=al.SettingsLens(
auto_einstein_radius_factor=None)),
)
tracer = mock.MockTracer(einstein_radius=2.0)
phase_imaging_7x7.modify_settings(
dataset=imaging_7x7,
results=mock.MockResults(max_log_likelihood_tracer=tracer),
)
analysis = phase_imaging_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7,
results=mock.MockResults())
assert analysis.settings.settings_lens.einstein_radius_estimate == None
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(settings_lens=al.SettingsLens(
auto_einstein_radius_factor=1.0)),
)
tracer = mock.MockTracer(einstein_radius=2.0)
phase_imaging_7x7.modify_settings(
dataset=imaging_7x7,
results=mock.MockResults(max_log_likelihood_tracer=tracer),
)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7,
mask=mask_7x7,
results=mock.MockResults(max_log_likelihood_tracer=tracer),
)
assert analysis.settings.settings_lens.einstein_radius_estimate == 2.0