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 make_image():
image = al.ScaledSquarePixelArray(array=np.ones((3, 3)), pixel_scale=1.0)
noise_map = al.NoiseMap(array=2.0 * np.ones((3, 3)), pixel_scale=1.0)
psf = al.PSF(array=3.0 * np.ones((3, 3)), pixel_scale=1.0)
return al.ImagingData(image=image,
pixel_scale=1.0,
noise_map=noise_map,
psf=psf)
def galaxy_fit_phase():
pixel_scale = 0.1
image_shape = (150, 150)
integration_util.reset_paths(test_name=test_name, output_path=output_path)
grid = al.Grid.from_shape_pixel_scale_and_sub_size(shape=image_shape,
pixel_scale=pixel_scale,
sub_size=4)
galaxy = al.Galaxy(
redshift=0.5,
mass=al.mass_profiles.SphericalIsothermal(centre=(0.0, 0.0),
einstein_radius=1.0),
)
deflections = galaxy.deflections_from_grid(galaxies=[galaxy],
grid=grid,
return_in_2d=True,
bypass_decorator=False)
noise_map = al.ScaledSquarePixelArray(array=np.ones(deflections[:,
0].shape),
pixel_scale=pixel_scale)
data_y = al.GalaxyData(image=deflections[:, 0],
noise_map=noise_map,
pixel_scale=pixel_scale)
data_x = al.GalaxyData(image=deflections[:, 1],
noise_map=noise_map,
pixel_scale=pixel_scale)
phase1 = al.PhaseGalaxy(
phase_name=test_name + "/",
galaxies=dict(gal=al.GalaxyModel(
redshift=0.5, light=al.mass_profiles.SphericalIsothermal)),
use_deflections=True,
sub_size=4,
optimizer_class=af.MultiNest,
)
phase1.run(galaxy_data=[data_y, data_x])
def galaxy_fit_phase():
pixel_scale = 0.1
image_shape = (150, 150)
integration_util.reset_paths(test_name=test_name, output_path=output_path)
grid = al.Grid.from_shape_pixel_scale_and_sub_size(shape=image_shape,
pixel_scale=pixel_scale,
sub_size=4)
galaxy = al.Galaxy(
redshift=0.5,
light=al.light_profiles.SphericalExponential(centre=(0.0, 0.0),
intensity=1.0,
effective_radius=0.5),
)
image = galaxy.profile_image_from_grid(galaxies=[galaxy],
grid=grid,
return_in_2d=True,
bypass_decorator=False)
noise_map = al.ScaledSquarePixelArray(array=np.ones(image.shape),
pixel_scale=pixel_scale)
data = al.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=pixel_scale)
phase1 = al.PhaseGalaxy(
phase_name=test_name + "/",
galaxies=dict(gal=al.GalaxyModel(
redshift=0.5, light=al.light_profiles.SphericalExponential)),
use_image=True,
sub_size=4,
optimizer_class=af.MultiNest,
)
phase1.run(galaxy_data=[data])
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 __new__(cls, shape, value, pixel_scale=1.0):
return al.ScaledSquarePixelArray(array=value * np.ones(shape=shape),
pixel_scale=pixel_scale)