def test__multiple_use_methods__raises_exception(self, image_7x7,
noise_map_7x7,
sub_mask_2d_7x7):
gal_data_7x7 = ag.GalaxyData(image=image_7x7,
noise_map=noise_map_7x7,
pixel_scales=3.0)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
use_convergence=True,
)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
use_potential=True,
)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
use_deflections_y=True,
)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
use_convergence=True,
use_potential=True,
)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
use_convergence=True,
use_potential=True,
use_deflections_x=True,
)
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__gal_data_7x7_image(self, gal_data_7x7, sub_mask_2d_7x7):
galaxy_fit_data = ag.MaskedGalaxyDataset(galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.native == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.galaxy_data.noise_map.native == 2.0 * np.ones(
(7, 7))).all()
assert (galaxy_fit_data.image.slim == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.slim == 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.native == 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.native == 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.MockGalaxy(value=1, shape=36)
image = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (image.binned == np.ones(9)).all()
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.SphSersic(intensity=1.0))
image_gal = galaxy.image_2d_from_grid(grid=galaxy_fit_data.grid)
image_gd = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (image_gal == image_gd).all()
def test__grid(
self,
gal_data_7x7,
sub_mask_2d_7x7,
grid_2d_7x7,
sub_grid_2d_7x7,
blurring_grid_2d_7x7,
grid_2d_iterate_7x7,
):
galaxy_fit_data = ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
grid_class=ag.Grid2D,
)
assert isinstance(galaxy_fit_data.grid, ag.Grid2D)
assert (galaxy_fit_data.grid == sub_grid_2d_7x7).all()
galaxy_fit_data = ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
grid_class=ag.Grid2DIterate,
)
assert isinstance(galaxy_fit_data.grid, ag.Grid2DIterate)
assert (galaxy_fit_data.grid.binned == grid_2d_iterate_7x7).all()
galaxy_fit_data = ag.MaskedGalaxyDataset(
galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True,
grid_class=ag.Grid2DInterpolate,
pixel_scales_interp=1.0,
)
grid = ag.Grid2DInterpolate.from_mask(mask=sub_mask_2d_7x7,
pixel_scales_interp=1.0)
assert isinstance(galaxy_fit_data.grid, ag.Grid2DInterpolate)
assert (galaxy_fit_data.grid == grid).all()
assert (galaxy_fit_data.grid.grid_interp == grid.grid_interp).all()
assert (galaxy_fit_data.grid.vtx == grid.vtx).all()
assert (galaxy_fit_data.grid.wts == grid.wts).all()
def test__no_use_method__raises_exception(self, image_7x7, noise_map_7x7,
sub_mask_2d_7x7):
gal_data_7x7 = ag.GalaxyData(image=image_7x7,
noise_map=noise_map_7x7,
pixel_scales=3.0)
with pytest.raises(exc.GalaxyException):
ag.MaskedGalaxyDataset(galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7)
def test__image_noise_map_and_mask(self, gal_data_7x7, sub_mask_2d_7x7):
galaxy_fit_data = ag.MaskedGalaxyDataset(galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_image=True)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.native == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.galaxy_data.noise_map.native == 2.0 * np.ones(
(7, 7))).all()
assert (galaxy_fit_data.image.slim == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.slim == 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.native == 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.native == 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__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 test__gal_data_7x7_deflections_x(self, gal_data_7x7, sub_mask_2d_7x7):
galaxy_fit_data = ag.MaskedGalaxyDataset(galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_deflections_x=True)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.native == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.galaxy_data.noise_map.native == 2.0 * np.ones(
(7, 7))).all()
assert (galaxy_fit_data.image.slim == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.slim == 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.native == 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.native == 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.MockGalaxy(value=1, shape=36)
deflections_x = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (deflections_x.binned == np.ones(9)).all()
galaxy = ag.Galaxy(redshift=0.5,
mass=ag.mp.SphIsothermal(einstein_radius=1.0))
deflections_gal = galaxy.deflections_2d_from_grid(
grid=galaxy_fit_data.grid)
deflections_gal = np.asarray([
np.multiply(
deflections_gal.mask.sub_fraction,
deflections_gal[:, 0].reshape(
-1, deflections_gal.mask.sub_length).sum(axis=1),
),
np.multiply(
deflections_gal.mask.sub_fraction,
deflections_gal[:, 1].reshape(
-1, deflections_gal.mask.sub_length).sum(axis=1),
),
]).T
deflections_gd = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (deflections_gal[:, 1] == deflections_gd.binned).all()
def test__gal_data_7x7_potential(self, gal_data_7x7, sub_mask_2d_7x7):
galaxy_fit_data = ag.MaskedGalaxyDataset(galaxy_data=gal_data_7x7,
mask=sub_mask_2d_7x7,
use_potential=True)
assert galaxy_fit_data.pixel_scales == (1.0, 1.0)
assert (galaxy_fit_data.galaxy_data.image.native == np.ones(
(7, 7))).all()
assert (galaxy_fit_data.galaxy_data.noise_map.native == 2.0 * np.ones(
(7, 7))).all()
assert (galaxy_fit_data.image.slim == np.ones(9)).all()
assert (galaxy_fit_data.noise_map.slim == 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.native == 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.native == 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.MockGalaxy(value=1, shape=36)
potential = galaxy_fit_data.profile_quantity_from_galaxies(
galaxies=[galaxy])
assert (potential.binned == np.ones(9)).all()
galaxy = ag.Galaxy(redshift=0.5,
mass=ag.mp.SphIsothermal(einstein_radius=1.0))
potential_gal = galaxy.potential_2d_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 make_gal_fit_data_7x7_deflections_x():
return ag.MaskedGalaxyDataset(
galaxy_data=make_gal_data_7x7(),
mask=make_sub_mask_2d_7x7(),
use_deflections_x=True,
)
def make_gal_fit_data_7x7_potential():
return ag.MaskedGalaxyDataset(galaxy_data=make_gal_data_7x7(),
mask=make_sub_mask_2d_7x7(),
use_potential=True)
def make_gal_fit_data_7x7_convergence():
return ag.MaskedGalaxyDataset(
galaxy_data=make_gal_data_7x7(),
mask=make_sub_mask_2d_7x7(),
use_convergence=True,
)
def make_gal_fit_data_7x7_image():
return ag.MaskedGalaxyDataset(galaxy_data=make_gal_data_7x7(),
mask=make_sub_mask_2d_7x7(),
use_image=True)