supaernova.analysis.distribution

source module supaernova.analysis.distribution

Classes

• DistributionPlot — Create a new model by parsing and validating input data from keyword arguments.

• DistributionPlotter

source class DistributionPlot(**data: Any)

Bases : AbstractPlot

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Attributes

• model_extra : dict[str, Any] | None — Get extra fields set during validation.

• model_fields_set : set[str] — Returns the set of fields that have been explicitly set on this model instance.

source class DistributionPlotter()

Bases : Plotter

Methods

• prep_from_result

• prep_from_array

• plot_corner

source staticmethod DistributionPlotter.prep_from_result(data: AbstractStepResult, config: DistributionPlot) → pd.DataFrame

source staticmethod DistributionPlotter.prep_from_array(data: np.ndarray, config: DistributionPlot) → pd.DataFrame

source staticmethod DistributionPlotter.plot_corner(data: AbstractStepResult | np.ndarray | list[AbstractStepResult] | list[np.ndarray] | dict[str, Any], config: DistributionPlot, *, fig: Figure | None = None, ax: Axis | None = None, force: bool = False, save: bool = True, **chain_kwargs: Any) → tuple['Figure', 'Axis'] | None