def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, positions_x2, positions_x2_noise_map):
point_dataset = al.PointDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
)
point_dict = al.PointDict(point_dataset_list=[point_dataset])
model = af.Collection(galaxies=af.Collection(lens=al.Galaxy(
redshift=0.5, point_0=al.ps.Point(centre=(0.0, 0.0)))))
solver = al.m.MockPointSolver(model_positions=positions_x2)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_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 = al.m.MockPointSolver(model_positions=model_positions)
analysis = al.AnalysisPoint(point_dict=point_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,
point_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__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)
all three lens galaxies where the priors on the centres have been paired to thei brightest pixels in the observed image,
alongside a source galaxy which is modeled as a point source.
"""
model_path = path.join("scripts", "clusters", "modeling", "models")
model_file = path.join(model_path, "lens_x3__source_x1.json")
model = af.Collection.from_json(file=model_file)
"""
__Search + Analysis + Model-Fit (Search 1)__
We are now able to model this dataset as a point source, using the exact same tools we used in the point source
overview.
"""
search_1 = af.DynestyStatic(name="overview_clusters_group")
analysis = al.AnalysisPoint(point_dict=point_dict, solver=positions_solver)
result_1 = search_1.fit(model=model, analysis=analysis)
"""
__Full Image Fitting__
For group-scale lenses like this one, with a modest number of lens and source galaxies, **PyAutoLens** has all the
tools you need to perform extended surface-brightness fitting to the source's extended emission, including the use
of a pixelized source reconstruction.
This will extract a lot more information from the data than the point-source model and the source reconstruction means
that you can study the properties of the highly magnified source galaxy.
This type of modeling uses a lot of **PyAutoLens**'s advanced model-fitting features which are described in chapters 3
and 4 of the **HowToLens** tutorials. An example performing this analysis to the lens above can be found in the
notebook `clusters/modeling/chaining/lens_x3__source_x1.ipynb`.
def test__figure_of_merit__includes_fit_fluxes(self, positions_x2,
positions_x2_noise_map,
fluxes_x2,
fluxes_x2_noise_map):
point_dataset = al.PointDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
fluxes=fluxes_x2,
fluxes_noise_map=fluxes_x2_noise_map,
)
point_dict = al.PointDict(point_dataset_list=[point_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.PointFlux(flux=1.0),
)))
solver = al.m.MockPointSolver(model_positions=positions_x2)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_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 = al.m.MockPointSolver(model_positions=model_positions)
analysis = al.AnalysisPoint(point_dict=point_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,
point_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)
""" __Non-linear Search__ We again choose the non-linear search `dynesty` (https://github.com/joshspeagle/dynesty). """ search = af.DynestyStatic(name="overview_point_source") """ __Analysis__ Whereas we previously used an `AnalysisImaging` object, we instead use an `AnalysisPoint` object which fits the lens model in the correct way for a point source dataset. This includes mapping the `name`'s of each dataset in the `PointDict` to the names of the point sources in the lens model. """ analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) """ __Model-Fit__ We can now begin the model-fit by passing the model and analysis object to the search, which performs a non-linear search to find which models fit the data with the highest likelihood. The results can be found in the `output/overview_point_source` folder in the `autolens_workspace`. """ result = search.fit(model=model, analysis=analysis) """ __Result__ The **PyAutoLens** visualization library and `FitPoint` object includes specific methods for plotting the results. """ """
def make_analysis_point_x2():
return al.AnalysisPoint(
point_dict=make_point_dict(),
solver=MockPointSolver(model_positions=make_positions_x2()),
)
