pdstools.decision_analyzer.plots._components
============================================

.. py:module:: pdstools.decision_analyzer.plots._components

.. autoapi-nested-parse::

   Component / filter analysis plots.



Attributes
----------

.. autoapisummary::

   pdstools.decision_analyzer.plots._components._ECDF_MAX_ROWS


Functions
---------

.. autoapisummary::

   pdstools.decision_analyzer.plots._components.filtering_components
   pdstools.decision_analyzer.plots._components.component_action_impact
   pdstools.decision_analyzer.plots._components.component_drilldown
   pdstools.decision_analyzer.plots._components.plot_priority_component_distribution
   pdstools.decision_analyzer.plots._components.plot_component_overview


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

.. py:data:: _ECDF_MAX_ROWS
   :value: 50000


.. py:function:: filtering_components(self, stages: list[str], top_n, AvailableNBADStages, additional_filters: polars.Expr | list[polars.Expr] | None = None, return_df=False)

.. py:function:: component_action_impact(self, top_n: int = 10, scope: str = 'Action', additional_filters: polars.Expr | list[polars.Expr] | None = None, return_df=False)

   Horizontal bar chart showing which items each component filters most.

   One facet per component (top components by total filtering), bars show
   items sorted by filtered decision count. The scope controls whether the
   breakdown is at Issue, Group, or Action level.

   :param top_n: Maximum number of items per component.
   :type top_n: int, default 10
   :param scope: Granularity: ``"Issue"``, ``"Group"``, or ``"Action"``.
   :type scope: str, default "Action"
   :param additional_filters: Extra filters applied before aggregation.
   :type additional_filters: pl.Expr or list of pl.Expr, optional
   :param return_df: If True, return the DataFrame instead of a figure.
   :type return_df: bool, default False

   :rtype: go.Figure or pl.DataFrame


.. py:function:: component_drilldown(self, component_name: str, scope: str = 'Action', additional_filters: polars.Expr | list[polars.Expr] | None = None, sort_by: str = 'Filtered Decisions', return_df=False)

   Bar chart drilling into a single component's filtered actions with
   value context.

   Shows filtered actions sorted by the chosen metric, with secondary
   axis for average scoring values when available.

   :param component_name: The pxComponentName to drill into.
   :type component_name: str
   :param scope: The granularity level to display (Issue, Group, or Action).
   :type scope: str, default "Action"
   :param additional_filters: Extra filters applied before aggregation.
   :type additional_filters: pl.Expr or list of pl.Expr, optional
   :param sort_by: Column to sort by. Also accepts "avg_Value", "avg_Priority".
   :type sort_by: str, default "Filtered Decisions"
   :param return_df: If True, return the DataFrame instead of a figure.
   :type return_df: bool, default False

   :rtype: go.Figure or pl.DataFrame


.. py:function:: plot_priority_component_distribution(value_data: polars.LazyFrame, component: str, granularity: str, color_discrete_map: dict[str, str] | None = None)

   Violin + ECDF + summary statistics for a single prioritization component.

   :returns: violin_fig, ecdf_fig, stats_df
   :rtype: tuple of (go.Figure, go.Figure, pl.DataFrame)


.. py:function:: plot_component_overview(value_data: polars.LazyFrame, components: list[str], granularity: str) -> plotly.graph_objects.Figure

   Small-multiples violin panel showing all components side by side.

   Each component gets its own subplot with a fully independent x-axis
   so their different scales are always visible.

   :rtype: go.Figure