def test__positions__settings_inputs_are_used_in_positions(
self, positions_x2, positions_x2_noise_map):
phase_positions_x2 = al.PhasePointSource(
settings=al.SettingsPhasePositions(),
search=mock.MockSearch(),
positions_solver=mock.MockPositionsSolver(
model_positions=positions_x2),
)
assert isinstance(phase_positions_x2.settings,
al.SettingsPhasePositions)
analysis = phase_positions_x2.make_analysis(
positions=positions_x2, positions_noise_map=positions_x2_noise_map)
assert analysis.positions.in_list == positions_x2.in_list
assert analysis.noise_map.in_list == positions_x2_noise_map.in_list
def test__fit_using_positions(self, positions_x2, positions_x2_noise_map,
samples_with_result):
phase_positions_x2 = al.PhasePointSource(
galaxies=dict(
lens=al.GalaxyModel(redshift=0.5,
light=al.lp.EllipticalSersic),
source=al.GalaxyModel(redshift=1.0, point=al.ps.PointSource),
),
search=mock.MockSearch(samples=samples_with_result),
positions_solver=mock.MockPositionsSolver(
model_positions=positions_x2),
)
result = phase_positions_x2.run(
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
results=mock.MockResults(),
)
assert isinstance(result.instance.galaxies[0], al.Galaxy)
assert isinstance(result.instance.galaxies[0], al.Galaxy)
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, positions_x2, positions_x2_noise_map):
lens_galaxy = al.Galaxy(redshift=0.5,
light=al.ps.PointSource(centre=(0.0, 0.0)))
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,
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,
)
assert fit_positions.chi_squared == 0.0
assert fit_positions.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):
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 make_phase_positions_x2():
return al.PhasePointSource(
positions_solver=MockPositionsSolver(
model_positions=make_positions_x2()),
search=MockSearch(name="test_phase"),
)