larvaworld.lib.plot.metric
==========================

.. py:module:: larvaworld.lib.plot.metric

.. autoapi-nested-parse::

   Calibration-related plotting



Functions
---------

.. autoapisummary::

   larvaworld.lib.plot.metric.plot_segmentation_definition
   larvaworld.lib.plot.metric.plot_stride_variability
   larvaworld.lib.plot.metric.plot_correlated_pars


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

.. py:function:: plot_segmentation_definition(subfolder: str = 'metric_definition', **kwargs: Any) -> Any

   Plot body segmentation definition analysis.

   Creates dual-panel plots showing regression scores and correlation analysis
   for different angular velocity combinations used in body segmentation.

   Args:
       subfolder: Subfolder for saving. Defaults to 'metric_definition'
       **kwargs: Additional arguments passed to AutoPlot

   Returns:
       Plot output (figure object or None based on return_fig setting)

   Example:
       >>> fig = plot_segmentation_definition(datasets=[d1, d2])


.. py:function:: plot_stride_variability(component_vels: bool = True, subfolder: str = 'metric_definition', **kwargs: Any) -> Any

   Plot stride spatiotemporal variability analysis.

   Creates scatter plots showing coefficient of variation for spatial vs
   temporal stride components across different velocity definitions.

   Args:
       component_vels: Include component velocities. Defaults to True
       subfolder: Subfolder for saving. Defaults to 'metric_definition'
       **kwargs: Additional arguments passed to AutoPlot

   Returns:
       Plot output (figure object or None based on return_fig setting)

   Example:
       >>> fig = plot_stride_variability(datasets=[d1, d2], component_vels=True)


.. py:function:: plot_correlated_pars(pars: Sequence[str], labels: Sequence[str], refID: Optional[str] = None, dataset: Any = None, save_to: Optional[str] = None, save_as: str = 'correlated_pars.pdf', return_fig: bool = False, show: bool = False) -> Any

   Create pairwise correlation plots for endpoint parameters.

   Generates seaborn PairGrid with scatter plots, KDE plots, and confidence
   ellipses showing correlations between three endpoint parameters.

   Args:
       pars: List of 3 parameter keys to analyze (currently only 3 supported)
       labels: List of 3 labels for the parameters
       refID: Reference dataset ID. Required if dataset is None
       dataset: Pre-loaded dataset. Loads from refID if None
       save_to: Directory to save plot. Uses dataset plot dir if None
       save_as: Filename for saved plot. Defaults to 'correlated_pars.pdf'
       return_fig: Whether to return figure object. Defaults to False
       show: Whether to display plot. Defaults to False

   Returns:
       Plot output (figure object or None based on return_fig setting)

   Example:
       >>> fig = plot_correlated_pars(pars=['cum_sd', 'run_tr', 'pau_tr'], labels=['Distance', 'Run', 'Pause'], refID='ref_01')