autolens.potential_correction.DpsiInvAnalysis

class DpsiInvAnalysis

Bases: Analysis

Samples the dpsi pixelization (mesh factor + regularization hyper-parameters) of a dpsi-only inversion of an image residual, with the inversion’s Bayesian evidence as the likelihood.

Parameters:

• masked_imaging – The masked al.Imaging dataset.

• image_residual (ndarray) – The 1D (slim) image residual of the smooth-model fit.

• source_gradient (ndarray) – The [n_unmasked, 2] source gradients at the ray-traced image pixels.

• anchor_points (Optional[ndarray]) – The [3, 2] (y, x) anchor positions of the dpsi rescaling scheme.

• 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)