autolens.potential_correction.DpsiSrcInvAnalysis#

class DpsiSrcInvAnalysis[source]#

Bases: Analysis

Samples the joint source+dpsi pixelization (source pixelization and dpsi mesh/regularization hyper-parameters) of a joint inversion, with the inversion’s Bayesian evidence as the likelihood.

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.

  • 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 fit attributes shared across evaluations.

Methods

compute_latent_samples

Compute latent-variable samples for every posterior sample.

compute_latent_variables

Override to compute latent variables from the instance.

fit_for_visualization

Build the fit used by the visualizer.

log_likelihood_function

make_result

Returns the Result of the non-linear search after it is completed.

modify_after_fit

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

modify_before_fit

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

modify_model

perform_quick_update

print_vram_use

Print JAX VRAM use for a given batch size.

save_attributes

save_results

save_results_combined

shared_state_from

Optionally compute a per-evaluation object that is shared across the factors of a FactorGraphModel.

visualize

with_model

Associate an explicit model with this analysis.

Attributes

LATENT_BATCH_MODE

LATENT_KEYS

supports_background_update

Whether this analysis supports background quick updates.

supports_jax_visualization

Whether the visualizer can work directly with JAX arrays.
log_likelihood_function(instance)[source]#
visualize(paths, instance, during_analysis=True)[source]#