def test__subtracted_images_of_galaxies(masked_imaging_7x7_no_blur):
g0 = ag.Galaxy(redshift=0.5,
light_profile=MockLightProfile(image_2d=np.ones(1)))
g1 = ag.Galaxy(redshift=1.0,
light_profile=MockLightProfile(image_2d=2.0 * np.ones(1)))
g2 = ag.Galaxy(redshift=1.0,
light_profile=MockLightProfile(image_2d=3.0 * np.ones(1)))
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1, g2])
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur, plane=plane)
assert fit.subtracted_images_of_galaxies[0].slim[0] == -4.0
assert fit.subtracted_images_of_galaxies[1].slim[0] == -3.0
assert fit.subtracted_images_of_galaxies[2].slim[0] == -2.0
g0 = ag.Galaxy(redshift=0.5,
light_profile=MockLightProfile(image_2d=np.ones(1)))
g1 = ag.Galaxy(redshift=0.5)
g2 = ag.Galaxy(redshift=1.0,
light_profile=MockLightProfile(image_2d=3.0 * np.ones(1)))
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1, g2])
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur, plane=plane)
assert fit.subtracted_images_of_galaxies[0].slim[0] == -2.0
assert fit.subtracted_images_of_galaxies[1].slim[0] == -3.0
assert fit.subtracted_images_of_galaxies[2].slim[0] == 0.0
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__fit_figure_of_merit(masked_imaging_7x7):
g0 = ag.Galaxy(
redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0),
mass_profile=ag.mp.SphIsothermal(einstein_radius=1.0),
)
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)
assert (fit.image == np.full(fill_value=1.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=2.0, shape=(9, ))).all()
assert fit.log_likelihood == pytest.approx(-75938.05, 1e-4)
assert fit.figure_of_merit == pytest.approx(-75938.05, 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)
plane = ag.Plane(galaxies=[ag.Galaxy(redshift=0.5), g0])
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
assert (fit.image == np.full(fill_value=1.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=2.0, shape=(9, ))).all()
assert fit.log_evidence == pytest.approx(-22.9005, 1e-4)
assert fit.figure_of_merit == pytest.approx(-22.9005, 1.0e-4)
galaxy_light = ag.Galaxy(redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0))
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
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)
assert (fit.image == np.full(fill_value=1.0, shape=(9, ))).all()
assert (fit.noise_map == np.full(fill_value=2.0, shape=(9, ))).all()
assert fit.log_evidence == pytest.approx(-6840.5851, 1e-4)
assert fit.figure_of_merit == pytest.approx(-6840.5851, 1.0e-4)
def fit_imaging_via_database_from(
fit: Fit,
settings_imaging: Optional[ag.SettingsImaging] = None,
settings_pixelization: Optional[ag.SettingsPixelization] = None,
settings_inversion: Optional[ag.SettingsInversion] = None,
):
"""
Returns a `FitImaging` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to describe
that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's generator
outputs such that the function can use the `Aggregator`'s map function to to create a `FitImaging` generator.
The `FitImaging` is created following the same method as the PyAutoGalaxy `Search` classes.
Parameters
----------
fit : af.SearchOutput
A PyAutoFit aggregator's SearchOutput object containing the generators of the results of PyAutoGalaxy model-fits.
"""
imaging = imaging_via_database_from(fit=fit, settings_imaging=settings_imaging)
plane = plane_via_database_from(fit=fit)
settings_pixelization = settings_pixelization or fit.value(
name="settings_pixelization"
)
settings_inversion = settings_inversion or fit.value(name="settings_inversion")
return ag.FitImaging(
imaging=imaging,
plane=plane,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
def test__figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, 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)
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=0.1))
model = af.Collection(
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
galaxies=af.Collection(galaxy=galaxy),
)
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7)
instance = model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
plane = analysis.plane_for_instance(instance=instance)
fit = ag.FitImaging(
imaging=masked_imaging_7x7,
plane=plane,
hyper_image_sky=hyper_image_sky,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == fit_figure_of_merit
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__model_image__with_and_without_psf_blurring(
masked_imaging_7x7_no_blur, masked_imaging_7x7):
g0 = ag.Galaxy(
redshift=0.5,
light_profile=MockLightProfile(image_2d_value=1.0,
image_2d_first_value=2.0),
)
plane = ag.Plane(galaxies=[g0])
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur, plane=plane)
assert fit.model_image.slim == pytest.approx(
np.array([2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]), 1.0e-4)
assert fit.log_likelihood == pytest.approx(-14.63377, 1.0e-4)
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
assert fit.model_image.slim == pytest.approx(
np.array([1.33, 1.16, 1.0, 1.16, 1.0, 1.0, 1.0, 1.0, 1.0]), 1.0e-1)
assert fit.log_likelihood == pytest.approx(-14.52960, 1.0e-4)
def test__image__with_and_without_hyper_background_sky(
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.image.slim == pytest.approx(
np.full(fill_value=1.0, shape=(9, )), 1.0e-1)
hyper_image_sky = ag.hyper_data.HyperImageSky(sky_scale=1.0)
fit = ag.FitImaging(dataset=masked_imaging_7x7_no_blur,
plane=plane,
hyper_image_sky=hyper_image_sky)
assert fit.image.slim == pytest.approx(
np.full(fill_value=2.0, shape=(9, )), 1.0e-1)
assert fit.log_likelihood == pytest.approx(-15.6337, 1.0e-4)
def test__noise_map__with_and_without_hyper_background(
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_background_noise = ag.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
fit = ag.FitImaging(
dataset=masked_imaging_7x7_no_blur,
plane=plane,
hyper_background_noise=hyper_background_noise,
)
assert fit.noise_map.slim == pytest.approx(
np.full(fill_value=3.0, shape=(9, )), 1.0e-1)
assert fit.log_likelihood == pytest.approx(-18.1579, 1.0e-4)
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, masked_imaging_7x7
):
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=0.1))
model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7)
instance = model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
plane = analysis.plane_for_instance(instance=instance)
fit = ag.FitImaging(imaging=masked_imaging_7x7, plane=plane)
assert fit.log_likelihood == fit_figure_of_merit
def fit_imaging_from_agg_obj(agg_obj):
"""
Returns a `FitImaging` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to describe
that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's generator
outputs such that the function can use the `Aggregator`'s map function to to create a `FitImaging` generator.
The `FitImaging` is created following the same method as the PyAutoGalaxy `Search` classes.
Parameters
----------
agg_obj : af.SearchOutput
A PyAutoFit aggregator's SearchOutput object containing the generators of the results of PyAutoGalaxy model-fits.
"""
imaging = imaging_from_agg_obj(agg_obj=agg_obj)
plane = plane_from_agg_obj(agg_obj=agg_obj)
return ag.FitImaging(
imaging=imaging,
plane=plane,
settings_pixelization=agg_obj.settings_pixelization,
settings_inversion=agg_obj.settings_inversion,
)
def make_fit_imaging_x2_galaxy_7x7():
plane = ag.Plane(galaxies=[make_gal_x1_lp(), make_gal_x1_lp()])
return ag.FitImaging(imaging=make_masked_imaging_7x7(), plane=plane)
def test__simulate_imaging_data_and_fit__include_psf_blurring__chi_squared_is_0__noise_normalization_correct(
):
grid = ag.Grid2D.uniform(shape_native=(11, 11),
pixel_scales=0.2,
sub_size=1)
psf = ag.Kernel2D.from_gaussian(shape_native=(3, 3),
pixel_scales=0.2,
sigma=0.75,
normalize=True)
lens_galaxy = ag.Galaxy(
redshift=0.5,
light=ag.lp.EllSersic(centre=(0.1, 0.1), intensity=0.1),
mass=ag.mp.EllIsothermal(centre=(0.1, 0.1), einstein_radius=1.8),
)
source_galaxy = ag.Galaxy(redshift=1.0,
light=ag.lp.EllExponential(centre=(0.1, 0.1),
intensity=0.5))
plane = ag.Plane(galaxies=[lens_galaxy, source_galaxy])
simulator = ag.SimulatorImaging(exposure_time=300.0,
psf=psf,
add_poisson_noise=False)
imaging = simulator.from_plane_and_grid(plane=plane, grid=grid)
imaging.noise_map = ag.Array2D.ones(
shape_native=imaging.image.shape_native, pixel_scales=0.2)
file_path = path.join(
"{}".format(path.dirname(path.realpath(__file__))),
"data_temp",
"simulate_and_fit",
)
try:
shutil.rmtree(file_path)
except FileNotFoundError:
pass
if path.exists(file_path) is False:
os.makedirs(file_path)
imaging.output_to_fits(
image_path=path.join(file_path, "image.fits"),
noise_map_path=path.join(file_path, "noise_map.fits"),
psf_path=path.join(file_path, "psf.fits"),
)
imaging = ag.Imaging.from_fits(
image_path=path.join(file_path, "image.fits"),
noise_map_path=path.join(file_path, "noise_map.fits"),
psf_path=path.join(file_path, "psf.fits"),
pixel_scales=0.2,
)
mask = ag.Mask2D.circular(
shape_native=imaging.image.shape_native,
pixel_scales=0.2,
sub_size=1,
radius=0.8,
)
masked_imaging = imaging.apply_mask(mask=mask)
masked_imaging = masked_imaging.apply_settings(
settings=ag.SettingsImaging(grid_class=ag.Grid2D, sub_size=1))
plane = ag.Plane(galaxies=[lens_galaxy, source_galaxy])
fit = ag.FitImaging(imaging=masked_imaging, plane=plane)
assert fit.chi_squared == pytest.approx(0.0, 1e-4)
file_path = path.join("{}".format(path.dirname(path.realpath(__file__))),
"data_temp")
if path.exists(file_path) is True:
shutil.rmtree(file_path)
def make_fit_imaging_x2_galaxy_inversion_7x7():
return ag.FitImaging(imaging=make_masked_imaging_7x7(),
plane=make_plane_x2_galaxy_inversion_7x7())
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__galaxy_model_image_dict(masked_imaging_7x7):
g0 = ag.Galaxy(
redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0),
mass_profile=ag.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = ag.Galaxy(redshift=1.0, light_profile=ag.lp.EllSersic(intensity=1.0))
g2 = ag.Galaxy(redshift=1.0)
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1, g2])
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
g0_blurred_image_2d = g0.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
blurring_grid=masked_imaging_7x7.blurring_grid,
convolver=masked_imaging_7x7.convolver,
)
g1_blurred_image_2d = g1.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
blurring_grid=masked_imaging_7x7.blurring_grid,
convolver=masked_imaging_7x7.convolver,
)
assert fit.galaxy_model_image_dict[g0] == pytest.approx(
g0_blurred_image_2d, 1.0e-4)
assert fit.galaxy_model_image_dict[g1] == pytest.approx(
g1_blurred_image_2d, 1.0e-4)
assert (fit.galaxy_model_image_dict[g2].slim == np.zeros(9)).all()
assert fit.model_image.native == pytest.approx(
fit.galaxy_model_image_dict[g0].native +
fit.galaxy_model_image_dict[g1].native,
1.0e-4,
)
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
g0 = ag.Galaxy(redshift=0.5)
g1 = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1])
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
mapper = pix.mapper_from(source_grid_slim=masked_imaging_7x7.grid,
source_pixelization_grid=None)
inversion = ag.Inversion(dataset=masked_imaging_7x7,
linear_obj_list=[mapper],
regularization_list=[reg])
assert (fit.galaxy_model_image_dict[g0] == np.zeros(9)).all()
assert fit.galaxy_model_image_dict[g1].native == pytest.approx(
inversion.mapped_reconstructed_image.native, 1.0e-4)
assert fit.model_image.native == pytest.approx(
fit.galaxy_model_image_dict[g1].native, 1.0e-4)
g0 = ag.Galaxy(redshift=0.5, light_profile=ag.lp.EllSersic(intensity=1.0))
g1 = ag.Galaxy(redshift=0.5, light_profile=ag.lp.EllSersic(intensity=2.0))
g2 = ag.Galaxy(redshift=0.5)
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
galaxy_pix = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[g0, g1, g2, galaxy_pix])
masked_imaging_7x7.image[0] = 3.0
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
g0_blurred_image = g0.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
convolver=masked_imaging_7x7.convolver,
blurring_grid=masked_imaging_7x7.blurring_grid,
)
g1_blurred_image = g1.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
convolver=masked_imaging_7x7.convolver,
blurring_grid=masked_imaging_7x7.blurring_grid,
)
blurred_image = g0_blurred_image + g1_blurred_image
profile_subtracted_image = masked_imaging_7x7.image - blurred_image
mapper = pix.mapper_from(
source_grid_slim=masked_imaging_7x7.grid,
settings=ag.SettingsPixelization(use_border=False),
)
inversion = ag.InversionImaging(
image=profile_subtracted_image,
noise_map=masked_imaging_7x7.noise_map,
convolver=masked_imaging_7x7.convolver,
w_tilde=masked_imaging_7x7.w_tilde,
linear_obj_list=[mapper],
regularization_list=[reg],
)
assert (fit.galaxy_model_image_dict[g2] == np.zeros(9)).all()
assert fit.galaxy_model_image_dict[g0].native == pytest.approx(
g0_blurred_image.native, 1.0e-4)
assert fit.galaxy_model_image_dict[g1].native == pytest.approx(
g1_blurred_image.native, 1.0e-4)
assert fit.galaxy_model_image_dict[galaxy_pix].native == pytest.approx(
inversion.mapped_reconstructed_image.native, 1.0e-4)
assert fit.model_image.native == pytest.approx(
fit.galaxy_model_image_dict[g0].native +
fit.galaxy_model_image_dict[g1].native +
inversion.mapped_reconstructed_image.native,
1.0e-4,
)
def make_fit_imaging_7x7():
return ag.FitImaging(imaging=make_masked_imaging_7x7(),
plane=make_plane_7x7())
def test___blurred_and_model_image_properties(masked_imaging_7x7):
g0 = ag.Galaxy(
redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0),
mass_profile=ag.mp.SphIsothermal(einstein_radius=1.0),
)
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)
g0_blurred_image = g0.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
convolver=masked_imaging_7x7.convolver,
blurring_grid=masked_imaging_7x7.blurring_grid,
)
g1_blurred_image = g1.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
convolver=masked_imaging_7x7.convolver,
blurring_grid=masked_imaging_7x7.blurring_grid,
)
assert g0_blurred_image.native == pytest.approx(
fit.model_images_of_galaxies[0].native, 1.0e-4)
assert g1_blurred_image.native == pytest.approx(
fit.model_images_of_galaxies[1].native, 1.0e-4)
unmasked_blurred_image = plane.unmasked_blurred_image_2d_via_psf_from(
grid=masked_imaging_7x7.grid, psf=masked_imaging_7x7.psf)
assert (unmasked_blurred_image == fit.unmasked_blurred_image).all()
unmasked_blurred_image_of_galaxies = plane.unmasked_blurred_image_2d_list_via_psf_from(
grid=masked_imaging_7x7.grid, psf=masked_imaging_7x7.psf)
assert (unmasked_blurred_image_of_galaxies[0] ==
fit.unmasked_blurred_image_of_galaxies[0]).all()
assert (unmasked_blurred_image_of_galaxies[1] ==
fit.unmasked_blurred_image_of_galaxies[1]).all()
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
g0 = ag.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
plane = ag.Plane(redshift=0.75, galaxies=[ag.Galaxy(redshift=0.5), g0])
fit = ag.FitImaging(dataset=masked_imaging_7x7, plane=plane)
mapper = pix.mapper_from(
source_grid_slim=masked_imaging_7x7.grid,
settings=ag.SettingsPixelization(use_border=False),
)
inversion = ag.Inversion(dataset=masked_imaging_7x7,
linear_obj_list=[mapper],
regularization_list=[reg])
assert (fit.model_images_of_galaxies[0].native == np.zeros((7, 7))).all()
assert inversion.mapped_reconstructed_image.native == pytest.approx(
fit.model_images_of_galaxies[1].native, 1.0e-4)
galaxy_light = ag.Galaxy(redshift=0.5,
light_profile=ag.lp.EllSersic(intensity=1.0))
pix = ag.pix.Rectangular(shape=(3, 3))
reg = ag.reg.Constant(coefficient=1.0)
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)
blurred_image = plane.blurred_image_2d_via_convolver_from(
grid=masked_imaging_7x7.grid,
convolver=masked_imaging_7x7.convolver,
blurring_grid=masked_imaging_7x7.blurring_grid,
)
profile_subtracted_image = masked_imaging_7x7.image - blurred_image
mapper = pix.mapper_from(
source_grid_slim=masked_imaging_7x7.grid,
settings=ag.SettingsPixelization(use_border=False),
)
inversion = ag.InversionImaging(
image=profile_subtracted_image,
noise_map=masked_imaging_7x7.noise_map,
convolver=masked_imaging_7x7.convolver,
w_tilde=masked_imaging_7x7.w_tilde,
linear_obj_list=[mapper],
regularization_list=[reg],
)
assert blurred_image.native == pytest.approx(
fit.model_images_of_galaxies[0].native, 1.0e-4)
assert inversion.mapped_reconstructed_image.native == pytest.approx(
fit.model_images_of_galaxies[1].native, 1.0e-4)
