def test__max_log_likelihood_plane_available_as_result(
self, analysis_imaging_7x7):
galaxy_0 = ag.Galaxy(redshift=0.5,
light=ag.lp.EllSersic(intensity=1.0))
galaxy_1 = ag.Galaxy(redshift=0.5,
light=ag.lp.EllSersic(intensity=2.0))
model = af.Collection(
galaxies=af.Collection(galaxy_0=galaxy_0, galaxy_1=galaxy_1))
max_log_likelihood_plane = ag.Plane(galaxies=[galaxy_0, galaxy_1])
search = MockSearch(
name="test_search",
samples=MockSamples(
max_log_likelihood_instance=max_log_likelihood_plane),
)
result = search.fit(model=model, analysis=analysis_imaging_7x7)
assert isinstance(result.max_log_likelihood_plane, ag.Plane)
assert result.max_log_likelihood_plane.galaxies[
0].light.intensity == 1.0
assert result.max_log_likelihood_plane.galaxies[
1].light.intensity == 2.0
def make_factor_graph_model():
model_factor_1 = g.AnalysisFactor(af.Collection(one=af.UniformPrior()),
af.m.MockAnalysis())
model_factor_2 = g.AnalysisFactor(af.Collection(one=af.UniformPrior()),
af.m.MockAnalysis())
return g.FactorGraphModel(model_factor_1, model_factor_2)
def test__figure_of_merit__includes_hyper_image_and_noise__matches_fit(
interferometer_7):
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
lens_galaxy = al.Galaxy(redshift=0.5, light=al.lp.EllSersic(intensity=0.1))
model = af.Collection(
galaxies=af.Collection(lens=lens_galaxy),
hyper_background_noise=hyper_background_noise,
)
analysis = al.AnalysisInterferometer(dataset=interferometer_7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == analysis_log_likelihood
def test__tracer_for_instance(analysis_imaging_7x7):
model = af.Collection(
galaxies=af.Collection(
lens=al.Galaxy(
redshift=0.5,
light=al.lp.SphSersic(intensity=2.0),
mass=al.mp.SphIsothermal(centre=(0.0, 0.0),
einstein_radius=1.0),
),
source=al.Galaxy(redshift=1.0),
),
clumps=af.Collection(clump=al.Galaxy(
redshift=0.5,
light=al.lp.SphSersic(intensity=0.1),
mass=al.mp.SphIsothermal(einstein_radius=0.2),
)),
)
instance = model.instance_from_unit_vector([])
tracer = analysis_imaging_7x7.tracer_via_instance_from(instance=instance)
assert tracer.galaxies[0].redshift == 0.5
assert tracer.galaxies[0].light.intensity == 2.0
assert tracer.galaxies[0].mass.centre == pytest.approx((0.0, 0.0), 1.0e-4)
assert tracer.galaxies[0].mass.einstein_radius == 1.0
assert tracer.galaxies[1].redshift == 0.5
assert tracer.galaxies[1].light.intensity == 0.1
assert tracer.galaxies[1].mass.einstein_radius == 0.2
def test__tracer_for_instance__subhalo_redshift_rescale_used(
analysis_imaging_7x7):
model = af.Collection(galaxies=af.Collection(
lens=al.Galaxy(
redshift=0.5,
mass=al.mp.SphIsothermal(centre=(0.0, 0.0), einstein_radius=1.0),
),
subhalo=al.Galaxy(redshift=0.25, mass=al.mp.SphNFW(centre=(0.1, 0.2))),
source=al.Galaxy(redshift=1.0),
))
instance = model.instance_from_unit_vector([])
tracer = analysis_imaging_7x7.tracer_via_instance_from(instance=instance)
assert tracer.galaxies[0].mass.centre == pytest.approx((0.1, 0.2), 1.0e-4)
model = af.Collection(galaxies=af.Collection(
lens=al.Galaxy(
redshift=0.5,
mass=al.mp.SphIsothermal(centre=(0.0, 0.0), einstein_radius=1.0),
),
subhalo=al.Galaxy(redshift=0.75, mass=al.mp.SphNFW(centre=(0.1, 0.2))),
source=al.Galaxy(redshift=1.0),
))
instance = model.instance_from_unit_vector([])
tracer = analysis_imaging_7x7.tracer_via_instance_from(instance=instance)
assert tracer.galaxies[1].mass.centre == pytest.approx(
(-0.19959, -0.39919), 1.0e-4)
def test__fit_figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, interferometer_7
):
hyper_background_noise = ag.hyper_data.HyperBackgroundNoise(noise_scale=1.0)
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=0.1))
model = af.Collection(
hyper_background_noise=hyper_background_noise,
galaxies=af.Collection(galaxy=galaxy),
)
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
instance = model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
plane = analysis.plane_for_instance(instance=instance)
fit = ag.FitInterferometer(
interferometer=interferometer_7,
plane=plane,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == fit_figure_of_merit
def make_model():
return af.Collection(
galaxies=af.Collection(
galaxy=af.Model(ag.Galaxy, redshift=0.5, light=ag.lp.EllSersic),
source=af.Model(ag.Galaxy, redshift=1.0, light=ag.lp.EllSersic),
)
)
def test__figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, masked_imaging_7x7):
hyper_image_sky = al.hyper_data.HyperImageSky(sky_scale=1.0)
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
lens_galaxy = al.Galaxy(redshift=0.5,
light=al.lp.EllSersic(intensity=0.1))
model = af.Collection(
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
galaxies=af.Collection(lens=lens_galaxy),
)
analysis = al.AnalysisImaging(dataset=masked_imaging_7x7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.FitImaging(
dataset=masked_imaging_7x7,
tracer=tracer,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == analysis_log_likelihood
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, dataset_quantity_7x7_array_2d):
galaxy = al.Galaxy(redshift=0.5,
light=al.mp.EllIsothermal(einstein_radius=1.0))
model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
analysis = al.AnalysisQuantity(dataset=dataset_quantity_7x7_array_2d,
func_str="convergence_2d_from")
instance = model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
tracer=tracer,
func_str="convergence_2d_from",
)
assert fit.log_likelihood == fit_figure_of_merit
fit = al.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
tracer=tracer,
func_str="potential_2d_from",
)
assert fit.log_likelihood != fit_figure_of_merit
def test__stochastic_model_from():
model = af.Collection(galaxies=af.Collection(
lens=af.Model(
ag.Galaxy,
redshift=0.5,
light=ag.lp.SphSersic(),
mass=ag.mp.SphIsothermal(),
),
source=af.Model(
ag.Galaxy,
redshift=1.0,
pixelization=ag.pix.VoronoiBrightnessImage(),
regularization=ag.reg.AdaptiveBrightness(),
),
))
instance = model.instance_from_prior_medians()
model = af.Collection(galaxies=af.Collection(
lens=af.Model(ag.Galaxy, redshift=0.5)))
result = mock.MockResult(instance=instance, model=model)
model = ag.util.model.stochastic_model_from(result=result)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
float)
model = ag.util.model.stochastic_model_from(result=result,
include_lens_light=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, af.LogUniformPrior)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
float)
model = ag.util.model.stochastic_model_from(result=result,
include_pixelization=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels,
af.UniformPrior)
assert not isinstance(
model.galaxies.source.regularization.inner_coefficient,
af.UniformPrior)
model = ag.util.model.stochastic_model_from(result=result,
include_regularization=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
af.UniformPrior)
def test_assertions_collection(source_gaussian, target_gaussian):
target_gaussian.add_assertion(
target_gaussian.centre <= target_gaussian.normalization)
target_collection = af.Collection(gaussian=target_gaussian)
source_collection = af.Collection(gaussian=source_gaussian)
with pytest.raises(AssertionError):
target_collection.take_attributes(source_collection)
def test__use_border__determines_if_border_pixel_relocation_is_used(
self, masked_imaging_7x7
):
model = af.Collection(
galaxies=af.Collection(
lens=al.Galaxy(
redshift=0.5, mass=al.mp.SphIsothermal(einstein_radius=100.0)
),
source=al.Galaxy(
redshift=1.0,
pixelization=al.pix.Rectangular(shape=(3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
),
)
)
masked_imaging_7x7 = masked_imaging_7x7.apply_settings(
settings=al.SettingsImaging(sub_size_inversion=2)
)
analysis = al.AnalysisImaging(
dataset=masked_imaging_7x7,
settings_pixelization=al.SettingsPixelization(use_border=True),
)
analysis.dataset.grid_inversion[4] = np.array([[500.0, 0.0]])
instance = model.instance_from_unit_vector([])
tracer = analysis.tracer_for_instance(instance=instance)
fit = analysis.fit_imaging_for_tracer(
tracer=tracer, hyper_image_sky=None, hyper_background_noise=None
)
assert fit.inversion.linear_obj_list[0].source_grid_slim[4][0] == pytest.approx(
97.19584, 1.0e-2
)
assert fit.inversion.linear_obj_list[0].source_grid_slim[4][1] == pytest.approx(
-3.699999, 1.0e-2
)
analysis = al.AnalysisImaging(
dataset=masked_imaging_7x7,
settings_pixelization=al.SettingsPixelization(use_border=False),
)
analysis.dataset.grid_inversion[4] = np.array([300.0, 0.0])
instance = model.instance_from_unit_vector([])
tracer = analysis.tracer_for_instance(instance=instance)
fit = analysis.fit_imaging_for_tracer(
tracer=tracer, hyper_image_sky=None, hyper_background_noise=None
)
assert fit.inversion.linear_obj_list[0].source_grid_slim[4][0] == pytest.approx(
200.0, 1.0e-4
)
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_instance():
model = af.Collection(collection=af.Collection(gaussian=af.Gaussian()))
parameterization = model.parameterization
assert parameterization == (
"""model CollectionPriorModel (N=0)
collection CollectionPriorModel (N=0)
gaussian Gaussian (N=0)"""
)
def test_parameterization():
model = af.Collection(collection=af.Collection(
gaussian=af.Model(af.Gaussian)))
parameterization = model.parameterization
assert parameterization == (
"""model CollectionPriorModel (N=3)
collection CollectionPriorModel (N=3)
gaussian Gaussian (N=3)"""
)
def test__make_result__result_interferometer_is_returned(self, interferometer_7):
model = af.Collection(galaxies=af.Collection(galaxy_0=ag.Galaxy(redshift=0.5)))
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, res.ResultInterferometer)
def test__make_result__result_imaging_is_returned(self, masked_imaging_7x7):
model = af.Collection(galaxies=af.Collection(galaxy_0=ag.Galaxy(redshift=0.5)))
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7)
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, res.ResultImaging)
def test__hyper_model_from__adds_hyper_galaxies():
model = af.Collection(
galaxies=af.Collection(
galaxy_0=af.Model(ag.Galaxy, redshift=0.5),
galaxy_1=af.Model(ag.Galaxy, redshift=1.0, bulge=ag.lp.EllSersic),
)
)
instance = model.instance_from_prior_medians()
path_galaxy_tuples = [
(("galaxies", "galaxy_0"), ag.Galaxy(redshift=0.5)),
(("galaxies", "galaxy_1"), ag.Galaxy(redshift=1.0)),
]
hyper_galaxy_image_path_dict = {
("galaxies", "galaxy_0"): ag.Array2D.ones(
shape_native=(3, 3), pixel_scales=1.0
),
("galaxies", "galaxy_1"): ag.Array2D.full(
fill_value=2.0, shape_native=(3, 3), pixel_scales=1.0
),
}
result = mock.MockResult(
instance=instance,
path_galaxy_tuples=path_galaxy_tuples,
hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict,
)
setup_hyper = ag.SetupHyper()
setup_hyper.hyper_galaxy_names = ["galaxy_0"]
model = ag.util.model.hyper_model_from(result=result, setup_hyper=setup_hyper)
assert isinstance(model.galaxies.galaxy_0, af.Model)
assert model.galaxies.galaxy_0.redshift == 0.5
assert model.galaxies.galaxy_0.hyper_galaxy.cls is ag.HyperGalaxy
assert model.galaxies.galaxy_1.hyper_galaxy is None
setup_hyper = ag.SetupHyper()
setup_hyper.hyper_galaxy_names = ["galaxy_0", "galaxy_1"]
model = ag.util.model.hyper_model_from(result=result, setup_hyper=setup_hyper)
assert isinstance(model.galaxies.galaxy_0, af.Model)
assert model.galaxies.galaxy_0.redshift == 0.5
assert model.galaxies.galaxy_0.hyper_galaxy.cls is ag.HyperGalaxy
assert isinstance(model.galaxies.galaxy_1, af.Model)
assert model.galaxies.galaxy_1.redshift == 1.0
assert model.galaxies.galaxy_1.hyper_galaxy.cls is ag.HyperGalaxy
def test__max_log_likelihood_tracer_available_as_result(
analysis_imaging_7x7, samples_with_result):
model = af.Collection(galaxies=af.Collection(
lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)))
search = al.m.MockSearch(name="test_search_2", samples=samples_with_result)
result = search.fit(model=model, analysis=analysis_imaging_7x7)
assert isinstance(result.max_log_likelihood_tracer, al.Tracer)
assert result.max_log_likelihood_tracer.galaxies[0].light.intensity == 1.0
assert result.max_log_likelihood_tracer.galaxies[1].light.intensity == 2.0
def test__make_result__result_quantity_is_returned(
self, dataset_quantity_7x7_array_2d):
model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(
redshift=0.5)))
analysis = al.AnalysisQuantity(dataset=dataset_quantity_7x7_array_2d,
func_str="convergence_2d_from")
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, ResultQuantity)
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 = al.m.MockSearch(name="test_search")
solver = al.m.MockPointSolver(
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, ResultPoint)
def test_unique_tag():
search = af.MockSearch()
search.fit(model=af.Collection(), analysis=af.mock.mock.MockAnalysis())
identifier = search.paths.identifier
search = af.MockSearch(unique_tag="dataset")
search.fit(
model=af.Collection(),
analysis=af.mock.mock.MockAnalysis(),
)
assert search.paths.identifier != identifier
def test__table__write_table():
model = af.Collection(mock_class_1=af.m.MockClassx4)
parameters = [
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[21.0, 22.0, 23.0, 24.0],
[0.0, 1.0, 2.0, 3.0],
]
samples_x5 = af.Samples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[1.0, 2.0, 3.0, 10.0, 5.0],
log_prior_list=[0.0, 0.0, 0.0, 0.0, 0.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
),
)
filename = "samples.csv"
samples_x5.write_table(filename=filename)
assert os.path.exists(filename)
os.remove(filename)
def test__gaussian_priors():
parameters = [
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.1],
[1.0, 2.0, 3.0, 4.1],
[0.88, 1.88, 2.88, 3.88],
[1.12, 2.12, 3.12, 4.32],
]
model = af.Collection(mock_class=af.m.MockClassx4)
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[10.0, 0.0, 0.0, 0.0, 0.0],
log_prior_list=[0.0, 0.0, 0.0, 0.0, 0.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
),
)
gaussian_priors = samples_x5.gaussian_priors_at_sigma(sigma=1.0)
assert gaussian_priors[0][0] == 1.0
assert gaussian_priors[1][0] == 2.0
assert gaussian_priors[2][0] == 3.0
assert gaussian_priors[3][0] == 4.0
assert gaussian_priors[0][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[1][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[2][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[3][1] == pytest.approx(0.32, 1.0e-4)
def test_tuples(samples):
model = af.Collection(
lens=af.Model(
af.m.MockWithTuple
)
)
samples = af.Samples(
model=model,
sample_list=[
af.Sample(
log_likelihood=0,
log_prior=0,
weight=0,
kwargs={
path: i
for i, path
in enumerate(
model.paths
)
}
)
]
)
samples = samples.without_paths(
[
("lens", "tup",),
]
)
assert len(samples.parameter_lists) == 1
def test__instance_from_sample_index():
model = af.Collection(mock_class=af.m.MockClassx4)
parameters = [
[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.1, 2.1, 3.1, 4.1],
]
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[0.0, 0.0, 0.0, 0.0, 0.0],
log_prior_list=[0.0, 0.0, 0.0, 0.0, 0.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
),
)
instance = samples_x5.instance_from_sample_index(sample_index=0)
assert instance.mock_class.one == 1.0
assert instance.mock_class.two == 2.0
assert instance.mock_class.three == 3.0
assert instance.mock_class.four == 4.0
instance = samples_x5.instance_from_sample_index(sample_index=1)
assert instance.mock_class.one == 5.0
assert instance.mock_class.two == 6.0
assert instance.mock_class.three == 7.0
assert instance.mock_class.four == 8.0
def test__addition_of_samples__raises_error_if_model_mismatch(samples_x5):
model = af.Collection(mock_class_1=af.m.MockClassx2)
parameters = [
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[21.0, 22.0],
[0.0, 1.0],
]
samples_different_model = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[1.0, 2.0],
log_prior_list=[0.0, 0.0],
weight_list=[1.0, 1.0],
),
)
with pytest.raises(af.exc.SamplesException):
samples_x5 + samples_different_model
def test__log_prior_list_and_max_log_posterior_vector_and_instance():
model = af.Collection(mock_class_1=af.m.MockClassx4)
parameters = [
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[21.0, 22.0, 23.0, 24.0],
]
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[1.0, 2.0, 3.0, 0.0, 5.0],
log_prior_list=[1.0, 2.0, 3.0, 10.0, 6.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
),
)
assert samples_x5.log_posterior_list == [2.0, 4.0, 6.0, 10.0, 11.0]
assert samples_x5.max_log_posterior_vector == [21.0, 22.0, 23.0, 24.0]
instance = samples_x5.max_log_posterior_instance
assert instance.mock_class_1.one == 21.0
assert instance.mock_class_1.two == 22.0
assert instance.mock_class_1.three == 23.0
assert instance.mock_class_1.four == 24.0
def test_mapper_from_prior_arguments_simple_collection():
old = af.UniformPrior()
new = af.UniformPrior()
collection = af.Collection(value=old)
collection = collection.mapper_from_prior_arguments({old: new})
assert collection.value == new
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
masked_imaging_7x7):
lens = al.Galaxy(redshift=0.5, light=al.lp.EllSersic(intensity=0.1))
model = af.Collection(galaxies=af.Collection(lens=lens))
analysis = al.AnalysisImaging(dataset=masked_imaging_7x7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer)
assert fit.log_likelihood == analysis_log_likelihood
