def test__fits_dataset__positions_only():
point_source = al.ps.Point(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)])
point_solver = al.m.MockPointSolver(model_positions=model_positions)
point_dataset_0 = al.PointDataset(name="point_0",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert fit["point_0"].positions.log_likelihood == pytest.approx(
-22.14472, 1.0e-4)
assert fit["point_0"].flux == None
assert fit.log_likelihood == fit["point_0"].positions.log_likelihood
point_dataset_1 = al.PointDataset(name="point_1",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(
point_dataset_list=[point_dataset_0, point_dataset_1])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert fit["point_0"].positions.log_likelihood == pytest.approx(
-22.14472, 1.0e-4)
assert fit["point_0"].flux == None
assert fit["point_1"].positions == None
assert fit["point_1"].flux == None
assert fit.log_likelihood == fit["point_0"].positions.log_likelihood
def test__model_has_image_and_source_chi_squared__fits_both_correctly():
galaxy_point_image = al.Galaxy(redshift=1.0,
point_0=al.ps.Point(centre=(0.1, 0.1)))
galaxy_point_source = al.Galaxy(redshift=1.0,
point_1=al.ps.PointSourceChi(centre=(0.1,
0.1)))
tracer = al.Tracer.from_galaxies(galaxies=[
al.Galaxy(redshift=0.5), galaxy_point_image, 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)])
point_solver = al.m.MockPointSolver(model_positions=model_positions)
point_dataset_0 = al.PointDataset(name="point_0",
positions=positions,
positions_noise_map=noise_map)
point_dataset_1 = al.PointDataset(name="point_1",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(
point_dataset_list=[point_dataset_0, point_dataset_1])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert isinstance(fit["point_0"].positions, al.FitPositionsImage)
assert isinstance(fit["point_1"].positions, al.FitPositionsSource)
assert fit[
"point_0"].positions.model_positions.in_list == model_positions.in_list
assert fit[
"point_1"].positions.model_positions.in_list == positions.in_list
In this example, where there is just one source, name pairing appears unecessary. However, point-source datasets may
have many source galaxies in them, and name pairing is necessary to ensure every point source in the lens model is
fitted to its particular lensed images in the `PointDict`!
__Fitting__
Just like we used a `Tracer` to fit imaging and interferometer data, we can use it to fit point-source data via the
`FitPoint` object.
This uses the names of each point-source in the dataset and model to create individual fits to the `positions`,
`fluxes` and other attributes that could be fitted. This allows us to inspect the residual-map,
chi-squared, likelihood, etc of every individual fit to part of our point source dataset.
"""
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=solver)
print(fit["point_0"].positions.residual_map)
print(fit["point_0"].positions.chi_squared_map)
print(fit["point_0"].positions.log_likelihood)
"""
__Model__
We first compose the model, in the same way described in the `modeling.py` overview script:
"""
lens_galaxy_model = af.Model(al.Galaxy,
redshift=0.5,
bulge=al.lp.EllSersic,
mass=al.mp.EllIsothermal)
def make_fit_point_dict_x2_plane():
return al.FitPointDict(
point_dict=make_point_dict(),
tracer=make_tracer_x2_plane_point(),
point_solver=make_point_solver(),
)