larvaworld.lib.process.calibration
==================================

.. py:module:: larvaworld.lib.process.calibration

.. autoapi-nested-parse::

   Methods for model calibration



Functions
---------

.. autoapisummary::

   larvaworld.lib.process.calibration.vel_definition
   larvaworld.lib.process.calibration.comp_stride_variation
   larvaworld.lib.process.calibration.fit_metric_definition
   larvaworld.lib.process.calibration.comp_segmentation


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

.. py:function:: vel_definition(d: larvaworld.lib.process.dataset.LarvaDataset) -> Dict[str, Any]

   Compute velocity-related metrics for model calibration.

   Combines stride variability analysis with bend-orientation correlation
   to determine optimal velocity calculation methods for larva movement.

   Args:
       d: LarvaDataset with computed spatial and angular data.
          Must have midline positions and angular velocities computed.

   Returns:
       Dict containing calibration metrics with keys:
       - 'stride_data': DataFrame with stride analysis
       - 'stride_variability': Variability coefficients
       - 'bend2or_regression': Regression parameters
       - 'bend2or_correlation': Correlation coefficients

   Side Effects:
       Updates d.vel_definition attribute and saves results to disk.

   Example:
       >>> d = LarvaDataset(dir='path/to/data')
       >>> d.comp_spatial()
       >>> results = vel_definition(d)


.. py:function:: comp_stride_variation(d: larvaworld.lib.process.dataset.LarvaDataset) -> Dict[str, Any]

   Compute stride variability metrics for movement analysis.

   Analyzes stride length and frequency variations across different
   movement conditions to characterize locomotor patterns.

   Args:
       d: LarvaDataset with spatial data and computed velocities.

   Returns:
       Dict with keys:
       - 'stride_data': DataFrame with stride analysis
       - 'stride_variability': Variability metrics (mean, std, CV)

   Example:
       >>> d = LarvaDataset(dir='path/to/data')
       >>> d.comp_spatial()
       >>> stride_vars = comp_stride_variation(d)


.. py:function:: fit_metric_definition(str_var: pandas.DataFrame, df_corr: pandas.DataFrame, c: larvaworld.lib.process.dataset.DatasetConfig) -> None

   Fit metric definitions using stride variability and correlation data.

   Determines optimal threshold parameters for movement classification
   based on stride variability patterns and behavioral correlations.

   Args:
       str_var: DataFrame containing stride variability metrics.
       df_corr: DataFrame with correlation coefficients between
               different movement parameters.
       c: Dataset configuration with angular parameters.

   Side Effects:
       Updates c.angular.best_combo and related configuration attributes.

   Example:
       >>> metrics = fit_metric_definition(stride_data, corr_data, config)


.. py:function:: comp_segmentation(s: pandas.DataFrame, e: pandas.DataFrame, c: larvaworld.lib.process.dataset.DatasetConfig) -> Dict[str, Any]

   Compute segmentation metrics for behavioral analysis.

   Analyzes movement segments to identify distinct behavioral
   patterns and transitions in larva locomotion using bend-orientation
   correlation analysis.

   Args:
       s: DataFrame with step segment data.
       e: DataFrame with epoch segment data.
       c: Dataset configuration with midline point information.

   Returns:
       Dict with keys:
       - 'bend2or_regression': Regression parameters
       - 'bend2or_correlation': Correlation coefficients

   Example:
       >>> segments = comp_segmentation(step_data, epoch_data, config)