larvaworld.lib.process.calibration

Methods for model calibration

Functions

`vel_definition`(→ Dict[str, Any])	Compute velocity-related metrics for model calibration.
`comp_stride_variation`(→ Dict[str, Any])	Compute stride variability metrics for movement analysis.
`fit_metric_definition`(→ None)	Fit metric definitions using stride variability and correlation data.
`comp_segmentation`(→ Dict[str, Any])	Compute segmentation metrics for behavioral analysis.

Module Contents

larvaworld.lib.process.calibration.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)

larvaworld.lib.process.calibration.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)

larvaworld.lib.process.calibration.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)

larvaworld.lib.process.calibration.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)