def make_dataset():
return al.PointSourceDataset(
"name",
positions=al.Grid2DIrregular([(1, 2)]),
positions_noise_map=al.ValuesIrregular([1]),
fluxes=al.ValuesIrregular([2]),
fluxes_noise_map=al.ValuesIrregular([3]),
)
def make_point_source_dataset():
return al.PointSourceDataset(
name="point_0",
positions=make_positions_x2(),
positions_noise_map=make_positions_noise_map_x2(),
fluxes=make_fluxes_x2(),
fluxes_noise_map=make_fluxes_noise_map_x2(),
)
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__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)
mat_plot_2d = aplt.MatPlot2D(
output=aplt.Output(path=dataset_path, format="png"))
tracer_plotter = aplt.TracerPlotter(tracer=tracer,
grid=grid,
mat_plot_2d=mat_plot_2d)
tracer_plotter.subplot_tracer()
"""
Create a point-source dictionary data object and output this to a `.json` file, which is the format used to load and
analyse the dataset.
"""
point_source_dataset = al.PointSourceDataset(
name="point_0",
positions=positions,
positions_noise_map=positions.values_from_value(value=grid.pixel_scale),
fluxes=fluxes,
fluxes_noise_map=al.ValuesIrregular(values=[1.0, 1.0, 1.0, 1.0]),
)
point_source_dict = al.PointSourceDict(
point_source_dataset_list=[point_source_dataset])
point_source_dict.output_to_json(file_path=path.join(dataset_path,
"point_source_dict.json"),
overwrite=True)
"""
Pickle the `Tracer` in the dataset folder, ensuring the true `Tracer` is safely stored and available if we need to
check how the dataset was simulated in the future.
This will also be accessible via the `Aggregator` if a model-fit is performed using the dataset.