autolens.potential_correction.FitDpsiSrcImaging#

class FitDpsiSrcImaging[source]#

Bases: object

A joint linear inversion of an image for the pixelized source and the pixelized corrections dpsi to the lensing potential, fully accounting for their covariance: the mapping matrix is the horizontal block [F_src | -B D_s D_psi] and the regularization the block diagonal of the source and dpsi regularization matrices, with the Bayesian evidence used to set both regularization strengths.

Parameters:

masked_imaging – The masked al.Imaging dataset.
lens_start (Galaxy) – The lens galaxy of the smooth-model fit the corrections perturb.
source_start (SrcFactory) – The source factory evaluated for the source gradients (from the smooth-model fit’s source).
dpsi_pixelization (DpsiPixelization) – The dpsi mesh + regularization model.
src_pixelization (Pixelization) – The source pixelization of the joint inversion.
anchor_points (Optional[ndarray]) – The [3, 2] (y, x) anchor positions of the dpsi rescaling scheme.
adapt_image – The adapt image of the source pixelization’s image mesh.
src_image_mesh – An image mesh whose image-plane mesh grid is preloaded into the source inversion.
settings_inversion (Optional[Settings]) – The inversion settings; defaults to the positive-only solver with the border relocator.
preloads (Optional[dict]) – Precomputed attributes set directly onto the fit.

Methods

`do_source_inversion`	Runs the standard autolens source inversion at the starting lens model, caching its mapper, operated mapping matrix and regularization matrix as the source blocks of the joint inversion.
`draw_random_solutions`	Draws source/dpsi solutions consistent with the data at the level permitted by the noise and regularizations; when `return_dkappa` the dpsi block is transformed to convergence corrections via the dpsi mesh's Laplacian operator.
`solve_src_dpsi`

Attributes

`best_fit_dpsi`
`best_fit_source`
`curvature_matrix`
`curvature_regularization_matrix`
`data_vector`
`dpsi_gradient_matrix`
`dpsi_linear_obj`
`dpsi_mapping_matrix`
`dpsi_points`
`dpsi_regularization_matrix`
`inverse_noise_covariance_matrix`
`log_evidence`	noise normalization, the log-determinants of the curvature+regularization matrix and of the source and dpsi regularization matrices, the joint regularization penalty of the solution and the chi-squared of the image fit.
`mapping_matrix`
`pair_dpsi_data_obj`
`psf_matrix`
`regularization_matrix`
`rescaled_dpsi`	The dpsi solution rescaled by the plane zeroing it at the three anchor points (Suyu et al.'s scheme), plus the (a_y, a_x, c) coefficients.
`source_gradient_matrix`
`source_plane_data_grid`
`source_plane_source_gradient`
`src_mapping_matrix`
`src_regularization_matrix`

do_source_inversion()[source]#: Runs the standard autolens source inversion at the starting lens model, caching its mapper, operated mapping matrix and regularization matrix as the source blocks of the joint inversion.

property inverse_noise_covariance_matrix#

property psf_matrix#

property source_plane_data_grid#

property source_plane_source_gradient#

property source_gradient_matrix#

property pair_dpsi_data_obj#

property dpsi_gradient_matrix#

property dpsi_points#

property dpsi_linear_obj#

property dpsi_regularization_matrix#

property dpsi_mapping_matrix#

property src_regularization_matrix#

property src_mapping_matrix#

property mapping_matrix#

property regularization_matrix#

property data_vector#

property curvature_matrix#

property curvature_regularization_matrix#

solve_src_dpsi(return_error=False)[source]#

property log_evidence#

noise normalization, the log-determinants of the curvature+regularization matrix and of the source and dpsi regularization matrices, the joint regularization penalty of the solution and the chi-squared of the image fit.

Type:: The Bayesian evidence of the joint source+dpsi inversion

draw_random_solutions(n_solutions=300, return_dkappa=True)[source]#: Draws source/dpsi solutions consistent with the data at the level permitted by the noise and regularizations; when return_dkappa the dpsi block is transformed to convergence corrections via the dpsi mesh’s Laplacian operator.

property best_fit_source#

property best_fit_dpsi#

property rescaled_dpsi#: The dpsi solution rescaled by the plane zeroing it at the three anchor points (Suyu et al.’s scheme), plus the (a_y, a_x, c) coefficients. Falls back to the unrescaled solution when no valid anchor points are set.