autolens.plot.subplot_sensitivity#

subplot_sensitivity(result, data_subtracted, output_path=None, output_format=None, colormap=None, use_log10=False)[source]#

Produce an 8-panel sensitivity-mapping summary subplot.

Displays the key figures of merit and raw statistics from a sensitivity mapping analysis in a 2 × 4 grid. Panels that cannot be computed (e.g. because log-evidence values are unavailable) are silently skipped.

The standard panels are:

  1. Lens-light-subtracted data image.

  2. Increase in log-evidence map.

  3. Increase in log-likelihood map.

  4. Binary detection map (log-likelihood increase > 5.0).

  5. Base (no-subhalo) log-evidence map (if available).

  6. Perturbed (with-subhalo) log-evidence map (if available).

  7. Base log-likelihood map (if available).

  8. Perturbed log-likelihood map (if available).

Panels 5–8 share a common colour scale so that absolute evidence / likelihood values can be compared across the two models.

Parameters:

  • result (SensitivityResult) – A sensitivity-mapping result object exposing figure_of_merit_array, log_evidences_base, log_evidences_perturbed, log_likelihoods_base, and log_likelihoods_perturbed.

  • data_subtracted (Array2D) – The lens-light-subtracted imaging data shown in the first panel.

  • output_path (Optional[str]) – Directory in which to save the figure. If None the figure is not saved to disk.

  • output_format (str) – Image format passed to save_figure.

  • colormap (Optional[str]) – Matplotlib colormap name.

  • use_log10 (bool) – If True a log10 stretch is applied to the data_subtracted panel.