def test__stochastic_model_from():
model = af.Collection(galaxies=af.Collection(
lens=af.Model(
ag.Galaxy,
redshift=0.5,
light=ag.lp.SphSersic(),
mass=ag.mp.SphIsothermal(),
),
source=af.Model(
ag.Galaxy,
redshift=1.0,
pixelization=ag.pix.VoronoiBrightnessImage(),
regularization=ag.reg.AdaptiveBrightness(),
),
))
instance = model.instance_from_prior_medians()
model = af.Collection(galaxies=af.Collection(
lens=af.Model(ag.Galaxy, redshift=0.5)))
result = mock.MockResult(instance=instance, model=model)
model = ag.util.model.stochastic_model_from(result=result)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
float)
model = ag.util.model.stochastic_model_from(result=result,
include_lens_light=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, af.LogUniformPrior)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
float)
model = ag.util.model.stochastic_model_from(result=result,
include_pixelization=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels,
af.UniformPrior)
assert not isinstance(
model.galaxies.source.regularization.inner_coefficient,
af.UniformPrior)
model = ag.util.model.stochastic_model_from(result=result,
include_regularization=True)
assert isinstance(model.galaxies.lens.mass.centre, af.TuplePrior)
assert isinstance(model.galaxies.lens.light.intensity, float)
assert isinstance(model.galaxies.source.pixelization.pixels, int)
assert isinstance(model.galaxies.source.regularization.inner_coefficient,
af.UniformPrior)
def test__uses_hyper_fit_correctly(self, masked_imaging_7x7):
galaxies = af.ModelInstance()
galaxies.galaxy = ag.Galaxy(redshift=0.5,
light=ag.lp.EllSersic(intensity=1.0),
mass=ag.mp.SphIsothermal)
galaxies.source = ag.Galaxy(redshift=1.0, light=ag.lp.EllSersic())
instance = af.ModelInstance()
instance.galaxies = galaxies
galaxy_hyper_image = ag.Array2D.ones(shape_native=(3, 3),
pixel_scales=0.1)
galaxy_hyper_image[4] = 10.0
hyper_model_image = ag.Array2D.full(fill_value=0.5,
shape_native=(3, 3),
pixel_scales=0.1)
hyper_galaxy_image_path_dict = {
("galaxies", "galaxy"): galaxy_hyper_image
}
result = mock.MockResult(
hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict,
hyper_model_image=hyper_model_image,
)
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7,
hyper_dataset_result=result)
hyper_galaxy = ag.HyperGalaxy(contribution_factor=1.0,
noise_factor=1.0,
noise_power=1.0)
instance.galaxies.galaxy.hyper_galaxy = hyper_galaxy
fit_likelihood = analysis.log_likelihood_function(instance=instance)
g0 = ag.Galaxy(
redshift=0.5,
light_profile=instance.galaxies.galaxy.light,
mass_profile=instance.galaxies.galaxy.mass,
hyper_galaxy=hyper_galaxy,
hyper_model_image=hyper_model_image,
hyper_galaxy_image=galaxy_hyper_image,
hyper_minimum_value=0.0,
)
g1 = ag.Galaxy(redshift=1.0,
light_profile=instance.galaxies.source.light)
plane = ag.Plane(galaxies=[g0, g1])
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
assert (fit.plane.galaxies[0].hyper_galaxy_image == galaxy_hyper_image
).all()
assert fit_likelihood == fit.log_likelihood
def test__hyper_model_from__adds_hyper_galaxies():
model = af.Collection(
galaxies=af.Collection(
galaxy_0=af.Model(ag.Galaxy, redshift=0.5),
galaxy_1=af.Model(ag.Galaxy, redshift=1.0, bulge=ag.lp.EllSersic),
)
)
instance = model.instance_from_prior_medians()
path_galaxy_tuples = [
(("galaxies", "galaxy_0"), ag.Galaxy(redshift=0.5)),
(("galaxies", "galaxy_1"), ag.Galaxy(redshift=1.0)),
]
hyper_galaxy_image_path_dict = {
("galaxies", "galaxy_0"): ag.Array2D.ones(
shape_native=(3, 3), pixel_scales=1.0
),
("galaxies", "galaxy_1"): ag.Array2D.full(
fill_value=2.0, shape_native=(3, 3), pixel_scales=1.0
),
}
result = mock.MockResult(
instance=instance,
path_galaxy_tuples=path_galaxy_tuples,
hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict,
)
setup_hyper = ag.SetupHyper()
setup_hyper.hyper_galaxy_names = ["galaxy_0"]
model = ag.util.model.hyper_model_from(result=result, setup_hyper=setup_hyper)
assert isinstance(model.galaxies.galaxy_0, af.Model)
assert model.galaxies.galaxy_0.redshift == 0.5
assert model.galaxies.galaxy_0.hyper_galaxy.cls is ag.HyperGalaxy
assert model.galaxies.galaxy_1.hyper_galaxy is None
setup_hyper = ag.SetupHyper()
setup_hyper.hyper_galaxy_names = ["galaxy_0", "galaxy_1"]
model = ag.util.model.hyper_model_from(result=result, setup_hyper=setup_hyper)
assert isinstance(model.galaxies.galaxy_0, af.Model)
assert model.galaxies.galaxy_0.redshift == 0.5
assert model.galaxies.galaxy_0.hyper_galaxy.cls is ag.HyperGalaxy
assert isinstance(model.galaxies.galaxy_1, af.Model)
assert model.galaxies.galaxy_1.redshift == 1.0
assert model.galaxies.galaxy_1.hyper_galaxy.cls is ag.HyperGalaxy
def test__associate_hyper_images(self, masked_imaging_7x7):
galaxies = af.ModelInstance()
galaxies.galaxy = ag.Galaxy(redshift=0.5)
galaxies.source = ag.Galaxy(redshift=1.0)
instance = af.ModelInstance()
instance.galaxies = galaxies
hyper_galaxy_image_path_dict = {
("galaxies", "galaxy"): ag.Array2D.ones(
shape_native=(3, 3), pixel_scales=1.0
),
("galaxies", "source"): ag.Array2D.full(
fill_value=2.0, shape_native=(3, 3), pixel_scales=1.0
),
}
result = mock.MockResult(
instance=instance,
hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict,
hyper_model_image=ag.Array2D.full(
fill_value=3.0, shape_native=(3, 3), pixel_scales=1.0
),
)
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7, hyper_result=result)
instance = analysis.associate_hyper_images(instance=instance)
assert instance.galaxies.galaxy.hyper_galaxy_image.native == pytest.approx(
np.ones((3, 3)), 1.0e-4
)
assert instance.galaxies.source.hyper_galaxy_image.native == pytest.approx(
2.0 * np.ones((3, 3)), 1.0e-4
)
assert instance.galaxies.galaxy.hyper_model_image.native == pytest.approx(
3.0 * np.ones((3, 3)), 1.0e-4
)
assert instance.galaxies.source.hyper_model_image.native == pytest.approx(
3.0 * np.ones((3, 3)), 1.0e-4
)
def test__hyper_model_from():
model = af.Collection(
galaxies=af.Collection(
galaxy=af.Model(
ag.Galaxy,
redshift=0.5,
pixelization=ag.pix.Rectangular,
regularization=ag.reg.Constant,
),
galaxy_1=af.Model(ag.Galaxy, redshift=1.0, bulge=ag.lp.EllSersic),
)
)
instance = model.instance_from_prior_medians()
result = mock.MockResult(instance=instance)
model = ag.util.model.hyper_model_from(setup_hyper=ag.SetupHyper(), result=result)
assert isinstance(model.galaxies.galaxy.pixelization, af.Model)
assert isinstance(model.galaxies.galaxy.regularization, af.Model)
assert model.galaxies.galaxy.pixelization.cls is ag.pix.Rectangular
assert model.galaxies.galaxy.regularization.cls is ag.reg.Constant
assert model.galaxies.galaxy_1.bulge.intensity == 1.0
assert model.hyper_image_sky is None
assert model.hyper_background_noise is None
model = ag.util.model.hyper_model_from(result=result, setup_hyper=None)
assert model == None
model = ag.util.model.hyper_model_from(
setup_hyper=ag.SetupHyper(
hyper_image_sky=ag.hyper_data.HyperImageSky,
hyper_background_noise=ag.hyper_data.HyperBackgroundNoise,
),
result=result,
include_hyper_image_sky=True,
)
assert isinstance(model.galaxies.galaxy.pixelization, af.Model)
assert isinstance(model.galaxies.galaxy.regularization, af.Model)
assert isinstance(model.hyper_image_sky, af.Model)
assert isinstance(model.hyper_background_noise, af.Model)
assert model.hyper_image_sky.cls == ag.hyper_data.HyperImageSky
assert model.hyper_background_noise.cls == ag.hyper_data.HyperBackgroundNoise
model = af.Collection(
galaxies=af.Collection(
galaxy=af.Model(ag.Galaxy, redshift=0.5),
galaxy_1=af.Model(ag.Galaxy, redshift=1.0, bulge=ag.lp.EllSersic),
)
)
instance = model.instance_from_prior_medians()
result = mock.MockResult(instance=instance)
model = ag.util.model.hyper_model_from(result=result, setup_hyper=ag.SetupHyper())
assert model == None
