def make_galaxy():
return aast.Galaxy(
redshift=3,
sersic=aast.lp.EllipticalSersic(),
exponential=aast.lp.EllipticalExponential(),
spherical=aast.mp.SphericalIsothermal(),
)
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__convergence_bounding_box__mass_profiles_are_only_point_masses__uses_their_einstein_radii(
self):
point_mass_0 = aast.mp.PointMass(einstein_radius=0.1)
convergence_bounding_box = point_mass_0.convergence_bounding_box()
assert convergence_bounding_box == pytest.approx(
[-0.3, 0.3, -0.3, 0.3], 1.0e-4)
galaxy = MockGalaxy(mass_profiles=[point_mass_0])
convergence_bounding_box = galaxy.convergence_bounding_box()
assert convergence_bounding_box == pytest.approx(
[-0.3, 0.3, -0.3, 0.3], 1.0e-4)
point_mass_1 = aast.mp.PointMass(einstein_radius=0.3)
galaxy = MockGalaxy(mass_profiles=[point_mass_0, point_mass_1])
convergence_bounding_box = galaxy.convergence_bounding_box()
assert convergence_bounding_box == pytest.approx(
[-1.2, 1.2, -1.2, 1.2], 1.0e-4)
sis_0 = aast.mp.SphericalIsothermal(einstein_radius=2.0)
galaxy = aast.Galaxy(redshift=0.5,
point0=point_mass_0,
point1=point_mass_1,
sis0=sis_0)
convergence_bounding_box = galaxy.convergence_bounding_box()
assert convergence_bounding_box != pytest.approx(
[-1.2, 1.2, -1.2, 1.2], 1.0e-4)
def make_gal_x2_mp(mp_0, mp_1):
return aast.Galaxy(redshift=0.5, mass_profile_0=mp_0, mass_profile_1=mp_1)
def make_gal_x1_mp(mp_0):
return aast.Galaxy(redshift=0.5, mass_profile_0=mp_0)
def make_gal_x2_lp(lp_0, lp_1):
return aast.Galaxy(redshift=0.5,
light_profile_0=lp_0,
light_profile_1=lp_1)
def make_gal_x1_lp(lp_0):
return aast.Galaxy(redshift=0.5, light_profile_0=lp_0)
def test__gal_data_7x7_deflections_x(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = aast.masked_galaxy_data(
galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_deflections_x=True
)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.in_2d == np.ones((7, 7))).all()
assert (
galaxy_fit_data.galaxy_data.noise_map.in_2d == 2.0 * np.ones((7, 7))
).all()
assert (galaxy_fit_data.image.in_1d == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.in_1d == 2.0 * np.ones(9)).all()
assert (
galaxy_fit_data.mask
== np.array(
[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
]
)
).all()
assert (
galaxy_fit_data.image.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
assert (
galaxy_fit_data.noise_map.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
galaxy = mock_galaxy.MockGalaxy(value=1, shape=36)
deflections_x = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy]
)
assert (deflections_x.in_1d_binned == np.ones(9)).all()
galaxy = aast.Galaxy(
redshift=0.5, mass=aast.mp.SphericalIsothermal(einstein_radius=1.0)
)
deflections_gal = galaxy.deflections_from_grid(grid=galaxy_fit_data.grid)
deflections_gal = np.asarray(
[
galaxy_fit_data.grid.mapping.array_stored_1d_binned_from_sub_array_1d(
deflections_gal[:, 0]
),
galaxy_fit_data.grid.mapping.array_stored_1d_binned_from_sub_array_1d(
deflections_gal[:, 1]
),
]
).T
deflections_gd = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy]
)
assert (deflections_gal[:, 1] == deflections_gd.in_1d_binned).all()
def test__gal_data_7x7_potential(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = aast.masked_galaxy_data(
galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_potential=True
)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.in_2d == np.ones((7, 7))).all()
assert (
galaxy_fit_data.galaxy_data.noise_map.in_2d == 2.0 * np.ones((7, 7))
).all()
assert (galaxy_fit_data.image.in_1d == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.in_1d == 2.0 * np.ones(9)).all()
assert (
galaxy_fit_data.mask
== np.array(
[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
]
)
).all()
assert (
galaxy_fit_data.image.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
assert (
galaxy_fit_data.noise_map.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
galaxy = mock_galaxy.MockGalaxy(value=1, shape=36)
potential = galaxy_fit_data.profile_quantity_from_galaxies(galaxies=[galaxy])
assert (potential.in_1d_binned == np.ones(9)).all()
galaxy = aast.Galaxy(
redshift=0.5, mass=aast.mp.SphericalIsothermal(einstein_radius=1.0)
)
potential_gal = galaxy.potential_from_grid(grid=galaxy_fit_data.grid)
potential_gd = galaxy_fit_data.profile_quantity_from_galaxies(galaxies=[galaxy])
assert (potential_gal == potential_gd).all()
def test__gal_data_7x7_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
)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.in_2d == np.ones((7, 7))).all()
assert (
galaxy_fit_data.galaxy_data.noise_map.in_2d == 2.0 * np.ones((7, 7))
).all()
assert (galaxy_fit_data.image.in_1d == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.in_1d == 2.0 * np.ones(9)).all()
assert (
galaxy_fit_data.mask
== np.array(
[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, False, False, False, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
]
)
).all()
assert (
galaxy_fit_data.image.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
assert (
galaxy_fit_data.noise_map.in_2d
== np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
).all()
galaxy = mock_galaxy.MockGalaxy(value=1, shape=36)
image = galaxy_fit_data.profile_quantity_from_galaxies(galaxies=[galaxy])
assert (image.in_1d_binned == np.ones(9)).all()
galaxy = aast.Galaxy(redshift=0.5, light=aast.lp.SphericalSersic(intensity=1.0))
image_gal = galaxy.profile_image_from_grid(grid=galaxy_fit_data.grid)
image_gd = galaxy_fit_data.profile_quantity_from_galaxies(galaxies=[galaxy])
assert (image_gal == image_gd).all()
