def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self):
image = al.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
noise_map = al.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
galaxy_data = al.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=3.0)
mask = al.Mask(
array=np.array([[True, True, True], [True, False, True],
[True, True, True]]),
pixel_scale=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = al.GalaxyFit(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__multiple_use_methods__raises_exception(self, image_7x7,
sub_mask_7x7):
gal_data_7x7 = al.GalaxyData(image=image_7x7,
noise_map=2.0 * np.ones((7, 7)),
pixel_scale=3.0)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True,
use_convergence=True,
)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True,
use_potential=True,
)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True,
use_deflections_y=True,
)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True,
use_convergence=True,
use_potential=True,
)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True,
use_convergence=True,
use_potential=True,
use_deflections_x=True,
)
def test__grid(self, gal_data_7x7, sub_mask_7x7, sub_grid_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True)
assert (galaxy_fit_data.grid == sub_grid_7x7).all()
def test__no_use_method__raises_exception(self, image_7x7, sub_mask_7x7):
gal_data_7x7 = al.GalaxyData(image=image_7x7,
noise_map=2.0 * np.ones((7, 7)),
pixel_scale=3.0)
with pytest.raises(exc.GalaxyException):
al.GalaxyFitData(galaxy_data=gal_data_7x7, mask=sub_mask_7x7)
def test__deflections_y(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_deflections_y=True)
galaxy = al.Galaxy(
redshift=0.5,
mass=al.mass_profiles.SphericalIsothermal(centre=(1.0, 2.0),
einstein_radius=1.0),
)
fit = al.GalaxyFit(galaxy_data=galaxy_fit_data,
model_galaxies=[galaxy])
assert fit.model_galaxies == [galaxy]
model_data_2d = galaxy.deflections_from_grid(
grid=galaxy_fit_data.grid,
return_in_2d=True,
return_binned=True,
bypass_decorator=False,
)[:, :, 0]
residual_map_2d = af.fit_util.residual_map_from_data_mask_and_model_data(
data=galaxy_fit_data.image(return_in_2d=True),
mask=galaxy_fit_data.mask,
model_data=model_data_2d,
)
assert residual_map_2d == pytest.approx(
fit.residual_map(return_in_2d=True), 1e-4)
chi_squared_map_2d = af.fit_util.chi_squared_map_from_residual_map_noise_map_and_mask(
residual_map=residual_map_2d,
mask=galaxy_fit_data.mask,
noise_map=galaxy_fit_data.noise_map(return_in_2d=True),
)
assert chi_squared_map_2d == pytest.approx(
fit.chi_squared_map(return_in_2d=True), 1e-4)
chi_squared = af.fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=chi_squared_map_2d, mask=sub_mask_7x7)
noise_normalization = af.fit_util.noise_normalization_from_noise_map_and_mask(
mask=galaxy_fit_data.mask,
noise_map=galaxy_fit_data.noise_map(return_in_2d=True),
)
likelihood = af.fit_util.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_noise_map_and_mask(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True)
assert galaxy_fit_data.pixel_scale == 1.0
assert (galaxy_fit_data.image(return_in_2d=True,
return_masked=False) == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.noise_map(return_in_2d=True,
return_masked=False) == 2.0 *
np.ones((7, 7))).all()
assert (galaxy_fit_data._image_1d == np.ones(9)).all()
assert (galaxy_fit_data._noise_map_1d == 2.0 * np.ones(9)).all()
assert (galaxy_fit_data._mask_1d == np.full(fill_value=False,
shape=(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(return_in_2d=True) == 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(return_in_2d=True) == 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()
def test__pixel_scale_interpolation_grid(self, image_7x7, sub_mask_7x7):
noise_map = al.ScaledSquarePixelArray(array=2.0 * np.ones((7, 7)),
pixel_scale=3.0)
gal_data_7x7 = al.GalaxyData(image=image_7x7,
noise_map=noise_map,
pixel_scale=3.0)
gal_data_7x7 = al.GalaxyFitData(
galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
pixel_scale_interpolation_grid=1.0,
use_image=True,
)
grid = al.Grid.from_mask(mask=sub_mask_7x7)
new_grid = grid.new_grid_with_interpolator(
pixel_scale_interpolation_grid=1.0)
assert (gal_data_7x7.grid == new_grid).all()
assert (gal_data_7x7.grid.interpolator.vtx == new_grid.interpolator.vtx
).all()
assert (gal_data_7x7.grid.interpolator.wts == new_grid.interpolator.wts
).all()
def test__1x2_image__noise_not_1__alls_correct(self):
image = al.ScaledSquarePixelArray(array=5.0 * np.ones((3, 4)),
pixel_scale=1.0)
image[1, 2] = 4.0
noise_map = al.ScaledSquarePixelArray(array=2.0 * np.ones((3, 4)),
pixel_scale=1.0)
galaxy_data = al.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=3.0)
mask = al.Mask(
array=np.array([
[True, True, True, True],
[True, False, False, True],
[True, True, True, True],
]),
pixel_scale=1.0,
sub_size=1,
)
g0 = MockGalaxy(value=1.0, shape=2)
galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_image=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_convergence=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_potential=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_y=True)
fit = al.GalaxyFit(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 = al.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_deflections_x=True)
fit = al.GalaxyFit(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 test__gal_data_7x7_image(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True)
assert galaxy_fit_data.pixel_scale == 1.0
assert (galaxy_fit_data.image(return_in_2d=True,
return_masked=False) == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.noise_map(return_in_2d=True,
return_masked=False) == 2.0 *
np.ones((7, 7))).all()
assert (galaxy_fit_data._image_1d == np.ones(9)).all()
assert (galaxy_fit_data._noise_map_1d == 2.0 * np.ones(9)).all()
assert (galaxy_fit_data._mask_1d == np.full(fill_value=False,
shape=(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(return_in_2d=True) == 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(return_in_2d=True) == 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 == np.ones(9)).all()
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.SphericalSersic(intensity=1.0))
image_gal = galaxy.profile_image_from_grid(grid=galaxy_fit_data.grid,
return_in_2d=False,
return_binned=True)
image_gd = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (image_gal == image_gd).all()
def test__gal_data_7x7_deflections_x(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_deflections_x=True)
assert galaxy_fit_data.pixel_scale == 1.0
assert (galaxy_fit_data.image(return_in_2d=True,
return_masked=False) == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.noise_map(return_in_2d=True,
return_masked=False) == 2.0 *
np.ones((7, 7))).all()
assert (galaxy_fit_data._image_1d == np.ones(9)).all()
assert (galaxy_fit_data._noise_map_1d == 2.0 * np.ones(9)).all()
assert (galaxy_fit_data._mask_1d == np.full(fill_value=False,
shape=(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(return_in_2d=True) == 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(return_in_2d=True) == 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 == np.ones(9)).all()
galaxy = al.Galaxy(
redshift=0.5,
mass=al.mass_profiles.SphericalIsothermal(einstein_radius=1.0))
deflections_gal = galaxy.deflections_from_grid(
grid=galaxy_fit_data.grid, return_in_2d=False, return_binned=False)
deflections_gal = np.asarray([
galaxy_fit_data.grid.mapping.array_1d_binned_from_sub_array_1d(
deflections_gal[:, 0]),
galaxy_fit_data.grid.mapping.array_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).all()
def test__gal_data_7x7_convergence(self, gal_data_7x7, sub_mask_7x7):
galaxy_fit_data = al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_convergence=True)
assert galaxy_fit_data.pixel_scale == 1.0
assert (galaxy_fit_data.image(return_in_2d=True,
return_masked=False) == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.noise_map(return_in_2d=True,
return_masked=False) == 2.0 *
np.ones((7, 7))).all()
assert (galaxy_fit_data._image_1d == np.ones(9)).all()
assert (galaxy_fit_data._noise_map_1d == 2.0 * np.ones(9)).all()
assert (galaxy_fit_data._mask_1d == np.full(fill_value=False,
shape=(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(return_in_2d=True) == 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(return_in_2d=True) == 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)
convergence = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (convergence == np.ones(9)).all()
galaxy = al.Galaxy(
redshift=0.5,
mass=al.mass_profiles.SphericalIsothermal(einstein_radius=1.0))
convergence_gal = galaxy.convergence_from_grid(
grid=galaxy_fit_data.grid, return_in_2d=False, return_binned=True)
convergence_gd = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (convergence_gal == convergence_gd).all()
def make_gal_fit_data_7x7_deflections_x(gal_data_7x7, sub_mask_7x7):
return al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_deflections_x=True)
def make_gal_fit_data_7x7_potential(gal_data_7x7, sub_mask_7x7):
return al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_potential=True)
def make_gal_fit_data_7x7_convergence(gal_data_7x7, sub_mask_7x7):
return al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_convergence=True)
def make_gal_fit_data_7x7_image(gal_data_7x7, sub_mask_7x7):
return al.GalaxyFitData(galaxy_data=gal_data_7x7,
mask=sub_mask_7x7,
use_image=True)
