def phase():
pixel_scale = 0.1
image_shape = (150, 150)
tools.reset_paths(test_name=test_name, output_path=output_path)
grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(shape=image_shape, pixel_scale=pixel_scale,
sub_grid_size=4)
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0))
deflections = galaxy_util.deflections_of_galaxies_from_grid(galaxies=[galaxy], grid=grid_stack.sub)
deflections_y = grid_stack.regular.scaled_array_from_array_1d(array_1d=deflections[:,0])
deflections_x = grid_stack.regular.scaled_array_from_array_1d(array_1d=deflections[:,1])
noise_map = scaled_array.ScaledSquarePixelArray(array=np.ones(deflections_y.shape), pixel_scale=pixel_scale)
data_y = gd.GalaxyData(image=deflections_y, noise_map=noise_map, pixel_scale=pixel_scale)
data_x = gd.GalaxyData(image=deflections_x, noise_map=noise_map, pixel_scale=pixel_scale)
phase = ph.GalaxyFitPhase(galaxies=dict(gal=gm.GalaxyModel(light=mp.SphericalIsothermal)), use_deflections=True,
sub_grid_size=4,
optimizer_class=nl.MultiNest, phase_name=test_name+'/')
phase.run(galaxy_data=[data_y, data_x])
def phase():
pixel_scale = 0.1
image_shape = (150, 150)
tools.reset_paths(test_name=test_name, output_path=output_path)
grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(
shape=image_shape, pixel_scale=pixel_scale, sub_grid_size=4)
galaxy = g.Galaxy(mass=lp.SphericalExponential(
centre=(0.0, 0.0), intensity=1.0, effective_radius=0.5))
intensities = galaxy_util.intensities_of_galaxies_from_grid(
galaxies=[galaxy], grid=grid_stack.sub)
intensities = grid_stack.regular.scaled_array_from_array_1d(
array_1d=intensities)
noise_map = scaled_array.ScaledSquarePixelArray(array=np.ones(
intensities.shape),
pixel_scale=pixel_scale)
data = gd.GalaxyData(image=intensities,
noise_map=noise_map,
pixel_scale=pixel_scale)
phase = ph.GalaxyFitPhase(
galaxies=dict(gal=gm.GalaxyModel(light=lp.SphericalExponential)),
use_intensities=True,
sub_grid_size=4,
optimizer_class=nl.MultiNest,
phase_name=test_name + '/')
phase.run(galaxy_data=[data])
def make_galaxy_data(image, mask):
noise_map = sca.ScaledSquarePixelArray(array=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
galaxy_data = gd.GalaxyData(image=image,
noise_map=noise_map,
pixel_scale=3.0)
return gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
use_intensities=True)
def test__no_use_method__raises_exception(self, image, mask):
galaxy_data = gd.GalaxyData(image=image,
noise_map=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2)
def test__galaxy_data_deflections_x(self, image, mask):
galaxy_data = gd.GalaxyData(image=image,
noise_map=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
galaxy_fit_data = gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_deflections_x=True)
assert galaxy_fit_data.pixel_scale == 3.0
assert (galaxy_fit_data.image == np.ones((4, 4))).all()
assert (galaxy_fit_data.noise_map == 2.0 * np.ones((4, 4))).all()
assert (galaxy_fit_data.mask == np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, True,
True]])).all()
assert (galaxy_fit_data.image_1d == np.ones(4)).all()
assert (galaxy_fit_data.noise_map_1d == 2.0 * np.ones(4)).all()
assert (galaxy_fit_data.mask_1d == np.array(
[False, False, False, False])).all()
galaxy = MockGalaxy(value=1, shape=4)
deflections_x = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (deflections_x == np.array([1.0, 1.0, 1.0, 1.0])).all()
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(einstein_radius=1.0))
deflections_gal = galaxy.deflections_from_grid(
grid=galaxy_fit_data.grid_stack.sub)
deflections_gal = np.asarray([
galaxy_fit_data.grid_stack.sub.sub_data_to_regular_data(
deflections_gal[:, 0]),
galaxy_fit_data.grid_stack.sub.sub_data_to_regular_data(
deflections_gal[:, 1])
]).T
deflections_gd = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (deflections_gal[:, 1] == deflections_gd).all()
def test__multiple_use_methods__raises_exception(self, image, mask):
galaxy_data = gd.GalaxyData(image=image,
noise_map=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True,
use_surface_density=True)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True,
use_potential=True)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True,
use_deflections_y=True)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True,
use_surface_density=True,
use_potential=True)
with pytest.raises(exc.GalaxyException):
gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True,
use_surface_density=True,
use_potential=True,
use_deflections_x=True)
def test__galaxy_data_intensities(self, image, mask):
galaxy_data = gd.GalaxyData(image=image,
noise_map=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
galaxy_fit_data = gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_intensities=True)
assert galaxy_fit_data.pixel_scale == 3.0
assert (galaxy_fit_data.image == np.ones((4, 4))).all()
assert (galaxy_fit_data.noise_map == 2.0 * np.ones((4, 4))).all()
assert (galaxy_fit_data.mask == np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, True,
True]])).all()
assert (galaxy_fit_data.image_1d == np.ones(4)).all()
assert (galaxy_fit_data.noise_map_1d == 2.0 * np.ones(4)).all()
assert (galaxy_fit_data.mask_1d == np.array(
[False, False, False, False])).all()
galaxy = MockGalaxy(value=1, shape=4)
intensities = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (intensities == np.array([1.0, 1.0, 1.0, 1.0])).all()
galaxy = g.Galaxy(light=lp.SphericalSersic(intensity=1.0))
intensities_gal = galaxy.intensities_from_grid(
grid=galaxy_fit_data.grid_stack.sub)
intensities_gal = galaxy_fit_data.grid_stack.sub.sub_data_to_regular_data(
sub_array=intensities_gal)
intensities_gd = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (intensities_gal == intensities_gd).all()
# Now lets create a galaxy, using a simple singular isothermal sphere.
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0))
# Next, we'll generate its surface density profile. Note that, because we're using the galaxy_util surface density
# function, the sub-grid of the grid-stack that is passed to this function is used to over-sample the surface density.
# The surface density averages over this sub-grid, such that it is the shape of the image (250, 250).
surface_density = galaxy_util.surface_density_of_galaxies_from_grid(galaxies=[galaxy], grid=grid_stack.sub)
surface_density = grid_stack.regular.scaled_array_from_array_1d(array_1d=surface_density)
# Now, we'll set this surface density up as our 'galaxy-data', meaning that it is what we'll fit via a non-linear
# search phase. To perform a fit we need a noise-map to help define our chi-squared. Given we are fitting a direct
# lensing quantity the actual values of this noise-map arn't particularly important, so we'll just use a noise-map of
# all 0.1's
noise_map = scaled_array.ScaledSquarePixelArray(array=0.1*np.ones(surface_density.shape), pixel_scale=pixel_scale)
data = gd.GalaxyData(image=surface_density, noise_map=noise_map, pixel_scale=pixel_scale)
# The fit will use a mask, which we setup like any other fit. Lets use a circular mask of 2.0"
def mask_function_circular(image):
return msk.Mask.circular(shape=image.shape, pixel_scale=image.pixel_scale, radius_arcsec=2.0)
# We can now use a special phase, called a 'GalaxyFitPhase', to fit this surface density with a model galaxy the
# mass-profiles of which we get to choose. We'll fit it with a singular isothermal sphere and should see we retrieve
# the input model above.
phase = ph.GalaxyFitPhase(galaxies=dict(lens=gm.GalaxyModel(mass=mp.SphericalIsothermal)), use_surface_density=True,
sub_grid_size=4, mask_function=mask_function_circular,
optimizer_class=nl.MultiNest, phase_name='/galaxy_surface_density_fit')
phase.run(galaxy_data=[data])
# If you check your output folder, you should see that this fit has been performed and visualization specific to a
def make_galaxy_data(image, noise_map):
return gd.GalaxyData(image=image, noise_map=noise_map, pixel_scale=3.0)
tracer = ray_tracing.TracerMultiPlanes(
galaxies=[lens_galaxy, perturber, source_galaxy],
image_plane_grid_stack=grid_stack)
# We'll now extract the deflection angles from the tracer - we will extract the two deflection angle maps (y and x)
# separately.
deflections_y = tracer.deflections_y
deflections_x = tracer.deflections_x
# Next, we create each deflection angle map as its own GalaxyData object. Again, this needs a somewhat arbritary
# noise-map to be used in a fit.
noise_map = scaled_array.ScaledSquarePixelArray(array=0.1 *
np.ones(deflections_y.shape),
pixel_scale=pixel_scale)
data_y = gd.GalaxyData(image=deflections_y,
noise_map=noise_map,
pixel_scale=pixel_scale)
data_x = gd.GalaxyData(image=deflections_x,
noise_map=noise_map,
pixel_scale=pixel_scale)
# The fit will use a mask, which we setup like any other fit. Lets use a circular mask of 2.0"
def mask_function_circular(image):
return msk.Mask.circular(shape=image.shape,
pixel_scale=image.pixel_scale,
radius_arcsec=2.5)
# Again, we'll use a special phase, called a 'GalaxyFitPhase', to fit the deflections with our model galaxies. We'll
# fit it with two singular isothermal spheres at the same lens-plane, thus we should see how the absence of multi-plane