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()), )