def test__noise_map__with_and_without_hyper_galaxy(masked_imaging_7x7_no_blur):
g0 = ag.Galaxy(redshift=0.5,
light_profile=MockLightProfile(image_2d_value=1.0))
plane = ag.Plane(galaxies=[g0])
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur, plane=plane)
assert fit.noise_map.slim == pytest.approx(
np.full(fill_value=2.0, shape=(9, )), 1.0e-1)
hyper_image = ag.Array2D.ones(shape_native=(3, 3), pixel_scales=1.0)
g0 = ag.Galaxy(
redshift=0.5,
light_profile=MockLightProfile(image_2d_value=1.0),
hyper_galaxy=ag.HyperGalaxy(contribution_factor=1.0,
noise_factor=1.0,
noise_power=1.0),
hyper_model_image=hyper_image,
hyper_galaxy_image=hyper_image,
hyper_minimum_value=0.0,
)
plane = ag.Plane(galaxies=[g0])
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur, plane=plane)
assert fit.noise_map.slim == pytest.approx(
np.full(fill_value=4.0, shape=(9, )), 1.0e-1)
assert fit.log_likelihood == pytest.approx(-20.7470, 1.0e-4)
def test__all_individual_plotter__output_file_with_default_name(
plane_7x7,
sub_grid_2d_7x7,
mask_2d_7x7,
grid_2d_irregular_7x7_list,
include_2d_all,
plot_path,
plot_patch,
):
plane_plotter = aplt.PlanePlotter(
plane=plane_7x7,
grid=sub_grid_2d_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(plot_path, format="png")),
)
plane_plotter.figures_2d(image=True, plane_image=True, plane_grid=True)
assert path.join(plot_path, "image_2d.png") in plot_patch.paths
assert path.join(plot_path, "plane_image.png") in plot_patch.paths
assert path.join(plot_path, "plane_grid.png") in plot_patch.paths
plane_7x7.galaxies[0].hyper_galaxy = ag.HyperGalaxy()
plane_7x7.galaxies[0].hyper_model_image = ag.Array2D.ones(shape_native=(7,
7),
pixel_scales=0.1)
plane_7x7.galaxies[0].hyper_galaxy_image = ag.Array2D.ones(
shape_native=(7, 7), pixel_scales=0.1)
plane_plotter.figures_2d(contribution_map=True)
assert path.join(plot_path, "contribution_map_2d.png") in plot_patch.paths
def test__figures_2d__all_are_output(
gal_x1_lp_x1_mp,
sub_grid_2d_7x7,
mask_2d_7x7,
grid_2d_irregular_7x7_list,
include_2d_all,
plot_path,
plot_patch,
):
galaxy_plotter = aplt.GalaxyPlotter(
galaxy=gal_x1_lp_x1_mp,
grid=sub_grid_2d_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(plot_path, format="png")),
)
galaxy_plotter.figures_2d(image=True, convergence=True)
assert path.join(plot_path, "image_2d.png") in plot_patch.paths
assert path.join(plot_path, "convergence_2d.png") in plot_patch.paths
gal_x1_lp_x1_mp.hyper_galaxy = ag.HyperGalaxy()
gal_x1_lp_x1_mp.hyper_model_image = ag.Array2D.ones(shape_native=(7, 7),
pixel_scales=0.1)
gal_x1_lp_x1_mp.hyper_galaxy_image = ag.Array2D.ones(shape_native=(7, 7),
pixel_scales=0.1)
galaxy_plotter.figures_2d(contribution_map=True)
assert path.join(plot_path, "contribution_map_2d.png") in plot_patch.paths
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_noise_map_from(self):
noise_map = np.array([1.0, 2.0, 3.0])
contribution_map = np.ones((3, 1))
hyper_galaxy = ag.HyperGalaxy(
contribution_factor=0.0, noise_factor=2.0, noise_power=1.0
)
hyper_noise_map = hyper_galaxy.hyper_noise_map_from(
noise_map=noise_map, contribution_map=contribution_map
)
assert (hyper_noise_map == np.array([2.0, 4.0, 6.0])).all()
noise_map = np.array([1.0, 2.0, 3.0])
contribution_map = np.array([[0.0, 0.5, 1.0]])
hyper_galaxy = ag.HyperGalaxy(
contribution_factor=0.0, noise_factor=2.0, noise_power=1.0
)
hyper_noise_map = hyper_galaxy.hyper_noise_map_from(
noise_map=noise_map, contribution_map=contribution_map
)
assert (hyper_noise_map == np.array([0.0, 2.0, 6.0])).all()
noise_map = np.array([1.0, 2.0, 3.0])
contribution_map = np.array([[0.0, 0.5, 1.0]])
hyper_galaxy = ag.HyperGalaxy(
contribution_factor=0.0, noise_factor=2.0, noise_power=2.0
)
hyper_noise_map = hyper_galaxy.hyper_noise_map_from(
noise_map=noise_map, contribution_map=contribution_map
)
assert (hyper_noise_map == np.array([0.0, 2.0, 18.0])).all()
def test__contribution_map_from(self):
hyper_image = np.ones((3,))
hyp = ag.HyperGalaxy(contribution_factor=0.0)
contribution_map = hyp.contribution_map_from(
hyper_model_image=hyper_image, hyper_galaxy_image=hyper_image
)
assert (contribution_map == np.ones((3,))).all()
hyper_image = np.array([0.5, 1.0, 1.5])
hyp = ag.HyperGalaxy(contribution_factor=1.0)
contribution_map = hyp.contribution_map_from(
hyper_model_image=hyper_image, hyper_galaxy_image=hyper_image
)
assert (
contribution_map
== np.array([(0.5 / 1.5) / (1.5 / 2.5), (1.0 / 2.0) / (1.5 / 2.5), 1.0])
).all()
hyper_image = np.ones((3,))
hyp = ag.HyperGalaxy(contribution_factor=0.0)
galaxy = ag.Galaxy(
redshift=0.5,
hyper_galaxy=hyp,
hyper_galaxy_image=hyper_image,
hyper_model_image=hyper_image,
)
contribution_map = hyp.contribution_map_from(
hyper_model_image=hyper_image, hyper_galaxy_image=hyper_image
)
assert (contribution_map == galaxy.contribution_map).all()
def test__fit_figure_of_merit__include_hyper_methods(masked_imaging_7x7):
hyper_image_sky = ag.hyper_data.HyperImageSky(sky_scale=1.0)
hyper_background_noise = ag.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
hyper_galaxy = ag.HyperGalaxy(contribution_factor=1.0,
noise_factor=1.0,
noise_power=1.0)
g0 = ag.Galaxy(
redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0),
mass_profile=ag.mp.SphIsothermal(einstein_radius=1.0),
hyper_galaxy=hyper_galaxy,
hyper_model_image=np.ones(9),
hyper_galaxy_image=np.ones(9),
hyper_minimum_value=0.0,
)
g1 = ag.Galaxy(redshift=1.0, light_profile=ag.lp.EllSersic(intensity=1.0))
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1])
fit = ag.FitImaging(
dataset=masked_imaging_7x7,
plane=plane,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
)
assert (fit.image == np.full(fill_value=2.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=5.0, shape=(9, ))).all()
assert fit.log_likelihood == pytest.approx(-12104.68, 1e-4)
assert fit.figure_of_merit == pytest.approx(-12104.68, 1.0e-4)
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
g0 = ag.Galaxy(
redshift=0.5,
pixelization=pix,
regularization=reg,
hyper_galaxy=hyper_galaxy,
hyper_model_image=np.ones(9),
hyper_galaxy_image=np.ones(9),
hyper_minimum_value=0.0,
)
plane = ag.Plane(galaxies=[ag.Galaxy(redshift=0.5), g0])
fit = ag.FitImaging(
dataset=masked_imaging_7x7,
plane=plane,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
settings_inversion=ag.SettingsInversion(use_w_tilde=False),
)
assert (fit.image == np.full(fill_value=2.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=5.0, shape=(9, ))).all()
assert fit.log_evidence == pytest.approx(-30.1448, 1e-4)
assert fit.figure_of_merit == pytest.approx(-30.1448, 1.0e-4)
galaxy_light = ag.Galaxy(
redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0),
hyper_galaxy=hyper_galaxy,
hyper_model_image=np.ones(9),
hyper_galaxy_image=np.ones(9),
hyper_minimum_value=0.0,
)
galaxy_pix = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[galaxy_light, galaxy_pix])
fit = ag.FitImaging(
dataset=masked_imaging_7x7,
plane=plane,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
settings_inversion=ag.SettingsInversion(use_w_tilde=False),
)
assert (fit.image == np.full(fill_value=2.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=5.0, shape=(9, ))).all()
assert fit.log_evidence == pytest.approx(-1132.9297, 1e-4)
assert fit.figure_of_merit == pytest.approx(-1132.9297, 1.0e-4)
def test__hyper_model_inversion_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,
pixelization=ag.pix.Rectangular,
regularization=ag.reg.Constant,
),
))
instance = model.instance_from_prior_medians()
path_galaxy_tuples = [
(
("galaxies", "galaxy_0"),
ag.Galaxy(redshift=0.5,
hyper_galaxy=ag.HyperGalaxy(contribution_factor=1)),
),
(
("galaxies", "galaxy_1"),
ag.Galaxy(redshift=1.0,
hyper_galaxy=ag.HyperGalaxy(contribution_factor=2)),
),
]
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_inversion_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.contribution_factor == 1
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_inversion_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.contribution_factor == 1
assert isinstance(model.galaxies.galaxy_1, af.Model)
assert model.galaxies.galaxy_1.redshift == 1.0
assert model.galaxies.galaxy_1.hyper_galaxy.contribution_factor == 2
def make_hyper_galaxy():
return ag.HyperGalaxy(noise_factor=1.0,
noise_power=1.0,
contribution_factor=1.0)