def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self):
image = aa.array.ones(shape_2d=(3, 3), pixel_scales=1.0)
noise_map = aa.array.ones(shape_2d=(3, 3), pixel_scales=1.0)
galaxy_data = aast.galaxy_data(image=image,
noise_map=noise_map,
pixel_scales=3.0)
mask = aa.mask.manual(
mask_2d=np.array([[True, True, True], [True, False, True],
[True, True, True]]),
pixel_scales=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.model_galaxies == [g0]
assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
def test__convergence(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_convergence=True)
galaxy = aast.Galaxy(
redshift=0.5,
mass=aast.mp.SphericalIsothermal(centre=(1.0, 2.0),
einstein_radius=1.0),
)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data,
model_galaxies=[galaxy])
assert fit.model_galaxies == [galaxy]
model_data = galaxy.convergence_from_grid(grid=galaxy_fit_data.grid)
residual_map = aa.util.fit.residual_map_from_data_and_model_data(
data=galaxy_fit_data.image, model_data=model_data.in_1d_binned)
assert residual_map == pytest.approx(fit.residual_map, 1e-4)
chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map(
residual_map=residual_map, noise_map=galaxy_fit_data.noise_map)
assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4)
chi_squared = aa.util.fit.chi_squared_from_chi_squared_map(
chi_squared_map=chi_squared_map)
noise_normalization = aa.util.fit.noise_normalization_from_noise_map(
noise_map=galaxy_fit_data.noise_map)
likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization(
chi_squared=chi_squared, noise_normalization=noise_normalization)
assert likelihood == pytest.approx(fit.likelihood, 1e-4)
def test__image(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = aast.masked_galaxy_data(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True)
galaxy = aast.Galaxy(
redshift=0.5,
light=aast.lp.SphericalSersic(centre=(1.0, 2.0), intensity=1.0),
)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data,
model_galaxies=[galaxy])
assert fit.model_galaxies == [galaxy]
model_data = galaxy.profile_image_from_grid(grid=galaxy_fit_data.grid)
residual_map = aa.util.fit.residual_map_from_data_and_model_data(
data=galaxy_fit_data.image, model_data=model_data.in_1d_binned)
assert residual_map == pytest.approx(fit.residual_map, 1e-4)
chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map(
residual_map=residual_map, noise_map=galaxy_fit_data.noise_map)
assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4)
chi_squared = aa.util.fit.chi_squared_from_chi_squared_map(
chi_squared_map=chi_squared_map)
noise_normalization = aa.util.fit.noise_normalization_from_noise_map(
noise_map=galaxy_fit_data.noise_map)
likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization(
chi_squared=chi_squared, noise_normalization=noise_normalization)
assert likelihood == pytest.approx(fit.likelihood, 1e-4)
def test__1x2_image__noise_not_1__alls_correct(self):
image = aa.array.full(fill_value=5.0,
shape_2d=(3, 4),
pixel_scales=1.0)
image[6] = 4.0
noise_map = aa.array.full(fill_value=2.0,
shape_2d=(3, 4),
pixel_scales=1.0)
galaxy_data = aast.galaxy_data(image=image,
noise_map=noise_map,
pixel_scales=3.0)
mask = aa.mask.manual(
mask_2d=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)
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, 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.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, 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.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, 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.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
galaxy_fit_data = aast.masked.galaxy_data(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0])
assert fit.chi_squared == (25.0 / 4.0)
assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
def make_gal_fit_7x7_deflections_x(gal_fit_data_7x7_deflections_x, gal_x1_mp):
return aast.fit_galaxy(galaxy_data=gal_fit_data_7x7_deflections_x,
model_galaxies=[gal_x1_mp])
def make_gal_fit_7x7_potential(gal_fit_data_7x7_potential, gal_x1_mp):
return aast.fit_galaxy(galaxy_data=gal_fit_data_7x7_potential,
model_galaxies=[gal_x1_mp])
def make_gal_fit_7x7_convergence(gal_fit_data_7x7_convergence, gal_x1_mp):
return aast.fit_galaxy(galaxy_data=gal_fit_data_7x7_convergence,
model_galaxies=[gal_x1_mp])
def make_gal_fit_7x7_image(gal_fit_data_7x7_image, gal_x1_lp):
return aast.fit_galaxy(galaxy_data=gal_fit_data_7x7_image,
model_galaxies=[gal_x1_lp])