petitRADTRANS.sbi.plotting
==========================

.. py:module:: petitRADTRANS.sbi.plotting

.. autoapi-nested-parse::

   Lightweight plotting helpers for SBI outputs.



Functions
---------

.. autoapisummary::

   petitRADTRANS.sbi.plotting.plot_posterior_marginals
   petitRADTRANS.sbi.plotting.plot_posterior_corner
   petitRADTRANS.sbi.plotting.plot_local_sensitivity_jacobians
   petitRADTRANS.sbi.plotting.plot_local_sensitivity_fisher_correlations
   petitRADTRANS.sbi.plotting.plot_local_sensitivity_singular_values
   petitRADTRANS.sbi.plotting.plot_posterior_predictive_report
   petitRADTRANS.sbi.plotting.plot_sbc_rank_histograms


Module Contents
---------------

.. py:function:: plot_posterior_marginals(samples: Any, parameter_names: Sequence[str] | None = None, bins: int = 40, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot one histogram per posterior dimension.

   Parameters
   ----------
   samples:
       Posterior samples with shape ``(n_samples, n_dim)`` or a scalar-vector
       equivalent.
   parameter_names:
       Optional display names for each posterior dimension.
   bins:
       Number of histogram bins per dimension.
   figsize:
       Optional Matplotlib figure size.

   Returns
   -------
   tuple[Any, np.ndarray]
       Matplotlib figure and axes array for further customization or saving.


.. py:function:: plot_posterior_corner(samples: Any, parameter_names: Sequence[str] | None = None, bins: int = 40, figsize: tuple[float, float] | None = None, max_points: int = 8192) -> tuple[Any, numpy.ndarray]

   Plot a lower-triangular corner view of posterior structure.

   Parameters
   ----------
   samples:
       Posterior samples with shape ``(n_samples, n_dim)`` or a scalar-vector
       equivalent.
   parameter_names:
       Optional display names for each posterior dimension.
   bins:
       Number of bins used for one- and two-dimensional histograms.
   figsize:
       Optional Matplotlib figure size.
   max_points:
       Maximum number of posterior draws plotted. Larger sample sets are
       evenly subsampled to keep rendering costs bounded.

   Returns
   -------
   tuple[Any, np.ndarray]
       Matplotlib figure and axes array for further customization or saving.


.. py:function:: plot_local_sensitivity_jacobians(report: petitRADTRANS.sbi.calibration.LocalSensitivityReport, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot whitened Jacobian heatmaps for each representative point.


.. py:function:: plot_local_sensitivity_fisher_correlations(report: petitRADTRANS.sbi.calibration.LocalSensitivityReport, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot Fisher-correlation heatmaps for each representative point.


.. py:function:: plot_local_sensitivity_singular_values(report: petitRADTRANS.sbi.calibration.LocalSensitivityReport, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot singular spectra of the whitened Jacobian for each point.


.. py:function:: plot_posterior_predictive_report(report: petitRADTRANS.sbi.calibration.PosteriorPredictiveReport, dataset_names: Sequence[str] | None = None, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot observed values against posterior-predictive means and intervals.

   Parameters
   ----------
   report:
       Posterior-predictive report to visualize.
   dataset_names:
       Optional subset of dataset names to plot. Defaults to all datasets in
       the report.
   figsize:
       Optional Matplotlib figure size.

   Returns
   -------
   tuple[Any, np.ndarray]
       Matplotlib figure and axes array.

   Notes
   -----
   For batched reports the helper currently visualizes only the first case for
   each dataset, which keeps the plot compact for quick inspection.


.. py:function:: plot_sbc_rank_histograms(report: petitRADTRANS.sbi.calibration.SimulationBasedCalibrationReport, parameter_names: Sequence[str] | None = None, figsize: tuple[float, float] | None = None) -> tuple[Any, numpy.ndarray]

   Plot one SBC rank histogram per inferred parameter.

   Parameters
   ----------
   report:
       SBC report containing per-parameter rank histogram counts.
   parameter_names:
       Optional display names for each inferred parameter.
   figsize:
       Optional Matplotlib figure size.

   Returns
   -------
   tuple[Any, np.ndarray]
       Matplotlib figure and axes array.