autolens.potential_correction.FitDpsiImaging#

class FitDpsiImaging[source]#

Bases: object

A linear inversion of an image residual for pixelized corrections dpsi to the lensing potential, at fixed source: the residual is modelled as -B D_s D_psi dpsi (PSF blur matrix B, source-gradient matrix D_s, dpsi-gradient operator D_psi), regularized on the dpsi mesh and solved for the maximum-evidence correction.

Parameters:

masked_imaging – The masked al.Imaging dataset.
image_residual (ndarray) – The 1D (slim) image residual of the smooth-model fit, of shape [n_unmasked_data_pixels].
source_gradient (ndarray) – The [n_unmasked_data_pixels, 2] (dS/dy, dS/dx) source gradients at the ray-traced positions of the image pixels.
dpsi_pixelization (DpsiPixelization) – The dpsi mesh + regularization model.
anchor_points (Optional[ndarray]) – The [3, 2] (y, x) anchor positions of the dpsi rescaling scheme.
preloads (Optional[dict]) – Precomputed attributes set directly onto the fit (e.g. the psf_mat shared across evaluations).

Methods

`construct_useful_matrices`
`solve_dpsi`

Attributes

`curvature_regularization_matrix`
`data_vector`
`dpsi_gradient_matrix`
`dpsi_linear_obj`
`dpsi_points`
`dpsi_regularization_matrix`
`inverse_noise_covariance_matrix`
`log_evidence`	noise normalization, the log-determinants of the curvature+regularization and regularization matrices, the regularization penalty of the solution and the chi-squared of the residual fit.
`mapping_matrix`
`pair_dpsi_data_obj`
`psf_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`

property inverse_noise_covariance_matrix#

property psf_matrix#

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 mapping_matrix#

property data_vector#

property curvature_regularization_matrix#

construct_useful_matrices()[source]#

solve_dpsi(return_error=False)[source]#

property log_evidence#

noise normalization, the log-determinants of the curvature+regularization and regularization matrices, the regularization penalty of the solution and the chi-squared of the residual fit.

Type:: The Bayesian evidence of the dpsi inversion

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.