def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, positions_x2, positions_x2_noise_map):
point_source_dataset = al.PointSourceDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_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, point_0=al.ps.PointSource(centre=(0.0, 0.0)))))
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,
)
assert fit_positions.chi_squared == 0.0
assert fit_positions.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)
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,
)
assert fit_positions.residual_map.in_list == [1.0, 1.0]
assert fit_positions.chi_squared == 2.0
assert fit_positions.log_likelihood == analysis_log_likelihood
def test__more_model_positions_than_data_positions__pairs_closest_positions(
self):
g0 = al.Galaxy(redshift=1.0,
point_0=al.ps.PointSource(centre=(0.1, 0.1)))
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5), g0])
positions = al.Grid2DIrregular([(0.0, 0.0), (3.0, 4.0)])
noise_map = al.ValuesIrregular([0.5, 1.0])
model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0),
(1.0, 0.0), (0.0, 1.0)])
positions_solver = mock.MockPositionsSolver(
model_positions=model_positions)
fit = al.FitPositionsImage(
name="point_0",
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions.in_list == [(1.0, 0.0), (2.0, 3.0)]
assert fit.noise_map.in_list == [0.5, 1.0]
assert fit.residual_map.in_list == [1.0, np.sqrt(2.0)]
assert fit.normalized_residual_map.in_list == [2.0, np.sqrt(2.0) / 1.0]
assert fit.chi_squared_map.in_list == [4.0, np.sqrt(2.0)**2.0]
assert fit.chi_squared == pytest.approx(6.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(2.289459, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-4.144729, 1.0e-4)
def test__make_result__result_imaging_is_returned(self, point_dict):
model = af.Collection(galaxies=af.Collection(lens=al.Galaxy(
redshift=0.5, point_0=al.ps.Point(centre=(0.0, 0.0)))))
search = mock.MockSearch(name="test_search")
solver = mock.MockPositionsSolver(
model_positions=point_dict["point_0"].positions)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, res.ResultPoint)
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__two_sets_of_positions__residuals_likelihood_correct(self):
tracer = MockTracerPositions(positions=None)
positions = al.GridIrregularGrouped([[(0.0, 0.0), (3.0, 4.0)], [(3.0, 3.0)]])
noise_map = al.ValuesIrregularGrouped([[0.5, 1.0], [1.0]])
model_positions = al.GridIrregularGrouped(
[[(3.0, 1.0), (2.0, 3.0)], [(3.0, 3.0)]]
)
positions_solver = mock.MockPositionsSolver(model_positions=model_positions)
fit = al.FitPositionsImage(
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions_all.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.model_positions.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.noise_map.in_grouped_list == [[0.5, 1.0], [1.0]]
assert fit.residual_map.in_grouped_list == [
[np.sqrt(10.0), np.sqrt(2.0)],
[0.0],
]
assert fit.normalized_residual_map.in_grouped_list == [
[np.sqrt(10.0) / 0.5, np.sqrt(2.0) / 1.0],
[0.0],
]
assert fit.chi_squared_map.in_grouped_list == [
[(np.sqrt(10.0) / 0.5) ** 2, np.sqrt(2.0) ** 2.0],
[0.0],
]
assert fit.chi_squared == pytest.approx(42.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(4.12733, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-23.06366, 1.0e-4)
def test__more_model_positions_than_data_positions__pairs_closest_positions(self):
tracer = MockTracerPositions(positions=None)
positions = al.GridIrregularGrouped([[(0.0, 0.0), (3.0, 4.0)], [(3.0, 3.0)]])
noise_map = al.ValuesIrregularGrouped([[0.5, 1.0], [1.0]])
model_positions = al.GridIrregularGrouped(
[[(3.0, 1.0), (2.0, 3.0), (1.0, 0.0), (0.0, 1.0)], [(3.0, 3.0), (4.0, 4.0)]]
)
positions_solver = mock.MockPositionsSolver(model_positions=model_positions)
fit = al.FitPositionsImage(
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions_all.in_grouped_list == [
[(3.0, 1.0), (2.0, 3.0), (1.0, 0.0), (0.0, 1.0)],
[(3.0, 3.0), (4.0, 4.0)],
]
assert fit.model_positions.in_grouped_list == [
[(1.0, 0.0), (2.0, 3.0)],
[(3.0, 3.0)],
]
assert fit.noise_map.in_grouped_list == [[0.5, 1.0], [1.0]]
assert fit.residual_map.in_grouped_list == [[1.0, np.sqrt(2.0)], [0.0]]
assert fit.normalized_residual_map.in_grouped_list == [
[2.0, np.sqrt(2.0) / 1.0],
[0.0],
]
assert fit.chi_squared_map.in_grouped_list == [
[4.0, np.sqrt(2.0) ** 2.0],
[0.0],
]
assert fit.chi_squared == pytest.approx(6.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(4.12733, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-5.06366, 1.0e-4)
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__two_sets_of_positions__residuals_likelihood_correct(self):
point_source = al.ps.PointSource(centre=(0.1, 0.1))
galaxy_point_source = al.Galaxy(redshift=1.0, point_0=point_source)
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5), galaxy_point_source])
positions = al.Grid2DIrregular([(0.0, 0.0), (3.0, 4.0)])
noise_map = al.ValuesIrregular([0.5, 1.0])
model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)])
positions_solver = mock.MockPositionsSolver(
model_positions=model_positions)
fit = al.FitPositionsImage(
name="point_0",
positions=positions,
noise_map=noise_map,
tracer=tracer,
positions_solver=positions_solver,
)
assert fit.model_positions.in_list == [(3.0, 1.0), (2.0, 3.0)]
assert fit.model_positions.in_list == [(3.0, 1.0), (2.0, 3.0)]
assert fit.noise_map.in_list == [0.5, 1.0]
assert fit.residual_map.in_list == [np.sqrt(10.0), np.sqrt(2.0)]
assert fit.normalized_residual_map.in_list == [
np.sqrt(10.0) / 0.5,
np.sqrt(2.0) / 1.0,
]
assert fit.chi_squared_map.in_list == [
(np.sqrt(10.0) / 0.5)**2,
np.sqrt(2.0)**2.0,
]
assert fit.chi_squared == pytest.approx(42.0, 1.0e-4)
assert fit.noise_normalization == pytest.approx(2.28945, 1.0e-4)
assert fit.log_likelihood == pytest.approx(-22.14472, 1.0e-4)
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 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)
