petitRADTRANS.sbi.plotting#

Lightweight plotting helpers for SBI outputs.

Functions#

`plot_posterior_marginals`(→ tuple[Any, numpy.ndarray])	Plot one histogram per posterior dimension.
`plot_posterior_corner`(→ tuple[Any, numpy.ndarray])	Plot a lower-triangular corner view of posterior structure.
`plot_local_sensitivity_jacobians`(→ tuple[Any, ...)	Plot whitened Jacobian heatmaps for each representative point.
`plot_local_sensitivity_fisher_correlations`(...)	Plot Fisher-correlation heatmaps for each representative point.
`plot_local_sensitivity_singular_values`(→ tuple[Any, ...)	Plot singular spectra of the whitened Jacobian for each point.
`plot_posterior_predictive_report`(→ tuple[Any, ...)	Plot observed values against posterior-predictive means and intervals.
`plot_sbc_rank_histograms`(→ tuple[Any, numpy.ndarray])	Plot one SBC rank histogram per inferred parameter.

Module Contents#

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.

petitRADTRANS.sbi.plotting.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.