def test__max_log_likelihood_tracer_source_inversion_centres_correct(
analysis_imaging_7x7):
lens = al.Galaxy(redshift=0.5, light=al.lp.SphSersic(intensity=1.0))
source = al.Galaxy(
redshift=1.0,
pixelization=al.pix.Rectangular((3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
)
tracer = al.Tracer.from_galaxies(galaxies=[lens, source])
samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
result = ResultImaging(samples=samples,
analysis=analysis_imaging_7x7,
model=None)
assert (result.source_plane_inversion_centre.in_list[0] ==
result.max_log_likelihood_fit.inversion.
brightest_reconstruction_pixel_centre_list[0].in_list[0])
lens = al.Galaxy(redshift=0.5, light=al.lp.SphSersic(intensity=1.0))
source = al.Galaxy(redshift=1.0)
tracer = al.Tracer.from_galaxies(galaxies=[lens, source])
samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
result = ResultImaging(samples=samples,
analysis=analysis_imaging_7x7,
model=None)
assert result.source_plane_inversion_centre == None
def test___image_dict(self, analysis_imaging_7x7):
galaxies = af.ModelInstance()
galaxies.lens = al.Galaxy(redshift=0.5)
galaxies.source = al.Galaxy(redshift=1.0)
instance = af.ModelInstance()
instance.galaxies = galaxies
result = ResultImaging(
samples=mock.MockSamples(max_log_likelihood_instance=instance),
model=af.ModelMapper(),
analysis=analysis_imaging_7x7,
search=None,
)
image_dict = result.image_galaxy_dict
assert isinstance(image_dict[("galaxies", "lens")], np.ndarray)
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
result.instance.galaxies.lens = al.Galaxy(redshift=0.5)
image_dict = result.image_galaxy_dict
assert (image_dict[("galaxies", "lens")].native == np.zeros(
(7, 7))).all()
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
def test__max_log_likelihood_tracer__multiple_image_positions_of_source_plane_centres_and_separations(
analysis_imaging_7x7):
lens = al.Galaxy(
redshift=0.5,
mass=al.mp.EllIsothermal(centre=(0.001, 0.001),
einstein_radius=1.0,
elliptical_comps=(0.0, 0.111111)),
)
source = al.Galaxy(
redshift=1.0,
light=al.lp.SphSersic(centre=(0.0, 0.0), intensity=2.0),
light1=al.lp.SphSersic(centre=(0.0, 0.1), intensity=2.0),
pixelization=al.pix.Rectangular((3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
)
tracer = al.Tracer.from_galaxies(galaxies=[lens, source])
samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
result = ResultImaging(samples=samples,
analysis=analysis_imaging_7x7,
model=None)
multiple_images = result.image_plane_multiple_image_positions
assert multiple_images.in_list[0][0] == pytest.approx(1.20556641, 1.0e-4)
assert multiple_images.in_list[0][1] == pytest.approx(-1.10205078, 1.0e-4)
assert multiple_images.in_list[1][0] == pytest.approx(-0.19287109, 1.0e-4)
assert multiple_images.in_list[1][1] == pytest.approx(0.27978516, 1.0e-4)
def test__max_log_likelihood_tracer_source_centres_correct(
analysis_imaging_7x7):
lens = al.Galaxy(redshift=0.5, light=al.lp.SphSersic(intensity=1.0))
source = al.Galaxy(
redshift=1.0,
light=al.lp.SphSersic(centre=(9.0, 8.0), intensity=2.0),
pixelization=al.pix.Rectangular((3, 3)),
regularization=al.reg.Constant(coefficient=1.0),
)
tracer = al.Tracer.from_galaxies(galaxies=[lens, source])
samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
result = ResultImaging(samples=samples,
analysis=analysis_imaging_7x7,
model=None)
assert result.source_plane_centre.in_list[0] == pytest.approx(
(-0.916666, -0.916666), 1.0e-4)
def make_result(
self,
samples: af.PDFSamples,
model: af.Collection,
sigma=1.0,
use_errors=True,
use_widths=False,
) -> ResultImaging:
"""
After the non-linear search is complete create its `Result`, which includes:
- The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute
the maximum likelihood model, posteriors and other properties.
- The model used to fit the data, which uses the samples to create specific instances of the model (e.g.
an instance of the maximum log likelihood model).
- The non-linear search used to perform the model fit.
The `ResultImaging` object contains a number of methods which use the above objects to create the max
log likelihood `Tracer`, `FitImaging`, hyper-galaxy images,etc.
Parameters
----------
samples
A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC
run of samples of the nested sampler.
model
The PyAutoFit model object, which includes model components representing the galaxies that are fitted to
the imaging data.
Returns
-------
ResultImaging
The result of fitting the model to the imaging dataset, via a non-linear search.
"""
return ResultImaging(samples=samples, model=model, analysis=self)