def test__multi_plane_calculation(self, gal_x1_mp):
g0 = al.Galaxy(redshift=0.5,
mass=al.mp.SphIsothermal(einstein_radius=1.0))
g1 = al.Galaxy(redshift=1.0, point_0=al.ps.PointSourceFlux(flux=1.0))
g2 = al.Galaxy(redshift=2.0, point_1=al.ps.PointSourceFlux(flux=2.0))
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fluxes = al.ValuesIrregular([1.0])
noise_map = al.ValuesIrregular([3.0])
positions = al.Grid2DIrregular([(2.0, 0.0)])
fit_0 = al.FitFluxes(
name="point_0",
fluxes=fluxes,
noise_map=noise_map,
positions=positions,
tracer=tracer,
)
deflections_func = partial(tracer.deflections_between_planes_from_grid,
plane_i=0,
plane_j=1)
magnification_0 = tracer.magnification_via_hessian_from_grid(
grid=positions, deflections_func=deflections_func)
assert fit_0.magnifications[0] == magnification_0
fit_1 = al.FitFluxes(
name="point_1",
fluxes=fluxes,
noise_map=noise_map,
positions=positions,
tracer=tracer,
)
deflections_func = partial(tracer.deflections_between_planes_from_grid,
plane_i=0,
plane_j=2)
magnification_1 = tracer.magnification_via_hessian_from_grid(
grid=positions, deflections_func=deflections_func)
assert fit_1.magnifications[0] == magnification_1
assert fit_0.magnifications[0] != pytest.approx(
fit_1.magnifications[0], 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__use_real_tracer(self, gal_x1_mp):
point_source = al.ps.PointSourceFlux(centre=(0.1, 0.1), flux=2.0)
galaxy_point_source = al.Galaxy(redshift=1.0, point_0=point_source)
tracer = al.Tracer.from_galaxies(galaxies=[gal_x1_mp, galaxy_point_source])
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(
fluxes=fluxes, noise_map=noise_map, positions=positions, tracer=tracer
)
assert fit.model_fluxes.in_list[1] == pytest.approx(2.5, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-3.11702, 1.0e-4)
def test__figure_of_merit__includes_fit_fluxes(self, positions_x2,
positions_x2_noise_map,
fluxes_x2,
fluxes_x2_noise_map):
lens_galaxy = al.Galaxy(
redshift=0.5,
sis=al.mp.SphericalIsothermal(einstein_radius=1.0),
light=al.ps.PointSourceFlux(flux=1.0),
)
phase_positions_x2 = al.PhasePointSource(
galaxies=dict(lens=lens_galaxy),
settings=al.SettingsPhasePositions(),
search=mock.MockSearch(),
positions_solver=mock.MockPositionsSolver(
model_positions=positions_x2),
)
analysis = phase_positions_x2.make_analysis(
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
fluxes=fluxes_x2,
fluxes_noise_map=fluxes_x2_noise_map,
results=mock.MockResults(),
)
instance = phase_positions_x2.model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_for_instance(instance=instance)
positions_solver = mock.MockPositionsSolver(
model_positions=positions_x2)
fit_positions = al.FitPositionsImage(
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
fit_fluxes = al.FitFluxes(
fluxes=fluxes_x2,
noise_map=fluxes_x2_noise_map,
positions=positions_x2,
tracer=tracer,
)
assert (fit_positions.log_likelihood +
fit_fluxes.log_likelihood == fit_figure_of_merit)
model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)])
positions_solver = mock.MockPositionsSolver(
model_positions=model_positions)
phase_positions_x2 = al.PhasePointSource(
galaxies=dict(lens=lens_galaxy),
settings=al.SettingsPhasePositions(),
search=mock.MockSearch(),
positions_solver=positions_solver,
)
analysis = phase_positions_x2.make_analysis(
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
results=mock.MockResults(),
)
instance = phase_positions_x2.model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(
instance=instance)
fit_positions = al.FitPositionsImage(
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit_positions.residual_map.in_list == [1.0, 1.0]
assert fit_positions.chi_squared == 2.0
assert fit_positions.log_likelihood == fit_figure_of_merit
def test__figure_of_merit__includes_fit_fluxes(self, positions_x2,
positions_x2_noise_map,
fluxes_x2,
fluxes_x2_noise_map):
point_source_dataset = al.PointSourceDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
fluxes=fluxes_x2,
fluxes_noise_map=fluxes_x2_noise_map,
)
point_source_dict = al.PointSourceDict(
point_source_dataset_list=[point_source_dataset])
model = af.Collection(galaxies=af.Collection(lens=al.Galaxy(
redshift=0.5,
sis=al.mp.SphIsothermal(einstein_radius=1.0),
point_0=al.ps.PointSourceFlux(flux=1.0),
)))
solver = mock.MockPositionsSolver(model_positions=positions_x2)
analysis = al.AnalysisPointSource(point_source_dict=point_source_dict,
solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_for_instance(instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
positions_solver=solver,
)
fit_fluxes = al.FitFluxes(
name="point_0",
fluxes=fluxes_x2,
noise_map=fluxes_x2_noise_map,
positions=positions_x2,
tracer=tracer,
)
assert (fit_positions.log_likelihood +
fit_fluxes.log_likelihood == analysis_log_likelihood)
model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)])
solver = mock.MockPositionsSolver(model_positions=model_positions)
analysis = al.AnalysisPointSource(point_source_dict=point_source_dict,
solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
positions_solver=solver,
)
fit_fluxes = al.FitFluxes(
name="point_0",
fluxes=fluxes_x2,
noise_map=fluxes_x2_noise_map,
positions=positions_x2,
tracer=tracer,
)
assert fit_positions.residual_map.in_list == [1.0, 1.0]
assert fit_positions.chi_squared == 2.0
assert (fit_positions.log_likelihood +
fit_fluxes.log_likelihood == analysis_log_likelihood)