def test(dataset,
xy_mask,
tracer,
self_calibration,
transformer_class=al.TransformerFINUFFT):
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
grid = masked_dataset.grid
transformer = transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths, grid=grid.in_radians)
model_data = tracer.profile_visibilities_from_grid_and_transformer(
grid=grid, transformer=transformer)
if self_calibration:
phase_errors = calibration_utils.phase_errors_from_A_and_B_matrices(
phases=masked_dataset.phases,
model_phases=model_data.phases,
A=masked_dataset.A,
B=masked_dataset.B)
model_phases_corrected = np.add(
model_data.phases, np.matmul(masked_dataset.f.T, phase_errors))
model_data = aa.structures.visibilities.Visibilities(
visibilities_1d=np.stack(arrays=(
model_data.amplitudes * np.cos(model_phases_corrected),
model_data.amplitudes * np.sin(model_phases_corrected)),
axis=-1))
fit_temp = fit.DatasetFit(masked_dataset=masked_dataset,
model_data=model_data)
# print("likelihood = ", fit_temp.likelihood)
#
# fit_plots.residual_map(
# fit=fit_temp,
# transformer=transformer,
# output_filename=None,
# output_path=None,
# output_format="show",
# )
return fit_temp.likelihood
def test(dataset,
xy_mask,
tracer,
self_calibration,
transformer_class=al.TransformerFINUFFT):
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
grid = masked_dataset.grid
transformer = transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths, grid=grid.in_radians)
model_data = tracer.profile_visibilities_from_grid_and_transformer(
grid=grid, transformer=transformer)
fit_plots.residual_map(fit=fit.DatasetFit(
masked_dataset=masked_dataset, model_data=model_data),
transformer=transformer,
output_format="show")
def fit_from_model_data(self, model_data):
return f.DatasetFit(masked_dataset=self.masked_dataset,
model_data=model_data)
# NOTE: Plotting visibilities
# for i in range(dataset.visibilities.shape[0]):
# plt.figure()
# plt.plot(
# dataset.visibilities[i, :, 0],
# dataset.visibilities[i, :, 1],
# linestyle="None",
# marker="o",
# color="black"
# )
# plt.show()
# exit()
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
fit_temp = fit.DatasetFit(masked_dataset=masked_dataset,
model_data=visibilities)
# plot_utils.plot_cube(
# cube=dirty_cube_from_visibilities(
# visibilities=fit_temp.residual_map,
# transformers=transformers,
# shape=cube.shape
# ),
# ncols=8
# )
# print("likelihood = ", fit_temp.likelihood)
# exit()
model_1 = af.PriorModel(profiles.EllipticalSersic)
model_1.centre_0 = af.GaussianPrior(mean=0.0, sigma=0.25)
model_1.centre_1 = af.GaussianPrior(mean=0.0, sigma=0.25)
model_1.intensity = af.LogUniformPrior(lower_limit=5.0 * 10**-6.0,
def fit_from_masked_dataset_model_data_and_inversion(
self, masked_dataset, model_data, inversion):
return f.DatasetFit(masked_dataset=masked_dataset,
model_data=model_data,
inversion=inversion)
def test(dataset,
xy_mask,
profiles,
lens_redshift,
source_redshift,
transformer_class=al.TransformerFINUFFT):
def src_model_from_profiles(profiles, masked_dataset):
return sum([
profile.profile_cube_from_grid(
grid=masked_dataset.grid_3d.grid_2d,
shape_3d=masked_dataset.grid_3d.shape_3d,
z_step_kms=masked_dataset.z_step_kms)
for profile in profiles
])
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
transformers = []
for i in range(masked_dataset.uv_wavelengths.shape[0]):
transformers.append(
transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths[i],
grid=masked_dataset.grid_3d.grid_2d.in_radians))
len_profiles = []
src_profiles = []
for profile in profiles:
if isinstance(profile, al.mp.MassProfile):
len_profiles.append(profile)
else:
src_profiles.append(profile)
galaxies = []
for profile in len_profiles:
galaxies.append(al.Galaxy(
redshift=lens_redshift,
mass=profile,
))
galaxies.append(
al.Galaxy(redshift=source_redshift, light=al.lp.LightProfile()))
tracer = al.Tracer.from_galaxies(galaxies=galaxies)
cube = src_model_from_profiles(profiles=src_profiles,
masked_dataset=masked_dataset)
lensed_cube = autolens_tracer_utils.lensed_cube_from_tracer(
tracer=tracer, grid=masked_dataset.grid_3d.grid_2d, cube=cube)
model_data = np.zeros(shape=masked_dataset.data.shape)
for i in range(model_data.shape[0]):
model_data[i] = transformers[i].visibilities_from_image(
image=Image(array_2d=lensed_cube[i]))
# dirty_cube = autolens_plot_utils.dirty_cube_from_visibilities(
# visibilities=masked_dataset.data,
# transformers=transformers,
# shape=masked_dataset.grid_shape_3d
# )
#
# dirty_model_cube = autolens_plot_utils.dirty_cube_from_visibilities(
# visibilities=model_data,
# transformers=transformers,
# shape=masked_dataset.grid_shape_3d
# )
#
#
# velocities = np.linspace(
# -n_channels * z_step_kms / 2.0,
# +n_channels * z_step_kms / 2.0,
# n_channels
# )
# dirty_moment_0 = spectral_utils.moment_0(
# cube=dirty_cube,
# velocities=velocities
# )
# dirty_model_moment_0 = spectral_utils.moment_0(
# cube=dirty_model_cube,
# velocities=velocities
# )
# plt.figure()
# plt.imshow(dirty_moment_0 - dirty_model_moment_0, cmap="jet")
# plt.show()
# exit()
fit_plots.residual_map(fit=fit.DatasetFit(
masked_dataset=masked_dataset, model_data=model_data),
transformers=transformers)
