def test__image(self, gal_data_7x7, sub_mask_2d_7x7):
masked_galaxy_dataset = ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7, mask=sub_mask_2d_7x7, use_image=True)
galaxy = ag.Galaxy(redshift=0.5,
light=ag.lp.SphSersic(centre=(1.0, 2.0),
intensity=1.0))
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[galaxy])
assert fit.model_galaxies == [galaxy]
model_data = galaxy.image_2d_from_grid(grid=masked_galaxy_dataset.grid)
residual_map = ag.util.fit.residual_map_from(
data=masked_galaxy_dataset.image, model_data=model_data.binned)
assert residual_map == pytest.approx(fit.residual_map, 1e-4)
chi_squared_map = ag.util.fit.chi_squared_map_from(
residual_map=residual_map,
noise_map=masked_galaxy_dataset.noise_map)
assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4)
chi_squared = ag.util.fit.chi_squared_from(
chi_squared_map=chi_squared_map)
noise_normalization = ag.util.fit.noise_normalization_from(
noise_map=masked_galaxy_dataset.noise_map)
log_likelihood = ag.util.fit.log_likelihood_from(
chi_squared=chi_squared, noise_normalization=noise_normalization)
assert log_likelihood == pytest.approx(fit.log_likelihood, 1e-4)
def test__deflections_y(self, gal_data_7x7, sub_mask_2d_7x7):
masked_galaxy_dataset = ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_deflections_y=True)
galaxy = ag.Galaxy(
redshift=0.5,
mass=ag.mp.SphIsothermal(centre=(1.0, 2.0), einstein_radius=1.0),
)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[galaxy])
assert fit.model_galaxies == [galaxy]
model_data = galaxy.deflections_2d_from_grid(
grid=masked_galaxy_dataset.grid).binned[:, 0]
residual_map = ag.util.fit.residual_map_from(
data=masked_galaxy_dataset.image, model_data=model_data)
assert residual_map == pytest.approx(fit.residual_map, 1e-4)
chi_squared_map = ag.util.fit.chi_squared_map_from(
residual_map=residual_map,
noise_map=masked_galaxy_dataset.noise_map)
assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4)
chi_squared = ag.util.fit.chi_squared_from(
chi_squared_map=chi_squared_map)
noise_normalization = ag.util.fit.noise_normalization_from(
noise_map=masked_galaxy_dataset.noise_map)
log_likelihood = ag.util.fit.log_likelihood_from(
chi_squared=chi_squared, noise_normalization=noise_normalization)
assert log_likelihood == pytest.approx(fit.log_likelihood, 1e-4)
def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self):
image = ag.Array2D.ones(shape_native=(3, 3), pixel_scales=1.0)
noise_map = ag.Array2D.ones(shape_native=(3, 3), pixel_scales=1.0)
galaxy_data = ag.GalaxyData(image=image,
noise_map=noise_map,
pixel_scales=3.0)
mask = ag.Mask2D.manual(
mask=np.array([[True, True, True], [True, False, True],
[True, True, True]]),
pixel_scales=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.log_likelihood == pytest.approx(
-0.5 * np.log(2 * np.pi * 1.0), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.log_likelihood == pytest.approx(
-0.5 * np.log(2 * np.pi * 1.0), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.log_likelihood == pytest.approx(
-0.5 * np.log(2 * np.pi * 1.0), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.log_likelihood == pytest.approx(
-0.5 * np.log(2 * np.pi * 1.0), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.log_likelihood == pytest.approx(
-0.5 * np.log(2 * np.pi * 1.0), 1.0e-4)
def test__1x2_image__noise_not_1__alls_correct(self):
image = ag.Array2D.full(fill_value=5.0,
shape_native=(3, 4),
pixel_scales=1.0)
image[6] = 4.0
noise_map = ag.Array2D.full(fill_value=2.0,
shape_native=(3, 4),
pixel_scales=1.0)
galaxy_data = ag.GalaxyData(image=image,
noise_map=noise_map,
pixel_scales=3.0)
mask = ag.Mask2D.manual(
mask=np.array([
[True, True, True, True],
[True, False, False, True],
[True, True, True, True],
]),
pixel_scales=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0, shape=2)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.log_likelihood == pytest.approx(
-0.5 * ((25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.log_likelihood == pytest.approx(
-0.5 * ((25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.log_likelihood == pytest.approx(
-0.5 * ((25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.log_likelihood == pytest.approx(
-0.5 * ((25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)), 1.0e-4)
masked_galaxy_dataset = ag.MaskedGalaxyDataset(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = ag.FitGalaxy(masked_galaxy_dataset=masked_galaxy_dataset,
model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.log_likelihood == pytest.approx(
-0.5 * ((25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)), 1.0e-4)
def make_gal_fit_7x7_deflections_x():
return ag.FitGalaxy(
masked_galaxy_dataset=make_gal_fit_data_7x7_deflections_x(),
model_galaxies=[make_gal_x1_mp()],
)
def make_gal_fit_7x7_potential():
return ag.FitGalaxy(
masked_galaxy_dataset=make_gal_fit_data_7x7_potential(),
model_galaxies=[make_gal_x1_mp()],
)
def make_gal_fit_7x7_convergence():
return ag.FitGalaxy(
masked_galaxy_dataset=make_gal_fit_data_7x7_convergence(),
model_galaxies=[make_gal_x1_mp()],
)
def make_gal_fit_7x7_image():
return ag.FitGalaxy(
masked_galaxy_dataset=make_gal_fit_data_7x7_image(),
model_galaxies=[make_gal_x1_lp()],
)