autolens.potential_correction.DpsiSrcInvInterferometerAnalysis#

class DpsiSrcInvInterferometerAnalysis[source]#

Bases: Analysis

Samples the joint source+dpsi pixelization of a visibility-space joint inversion (FitDpsiSrcInterferometer), with the inversion’s Bayesian evidence as the likelihood.

Parameters:

dataset – The al.Interferometer dataset; for the sparse-operator route (default) call dataset.apply_sparse_operator() first.
lens_start (Galaxy) – The smooth lens galaxy of the starting model.
source_start (SrcFactory) – The source factory evaluated for the source gradients.
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.
use_sparse_operator (bool) – Whether fits run through the sparse w-tilde operator (default) or the dense transformed mapping matrix.
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.
`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]#