larvaworld.lib.plot.metric

Calibration-related plotting

Functions

plot_segmentation_definition(→ Any)	Plot body segmentation definition analysis.
plot_stride_variability(→ Any)	Plot stride spatiotemporal variability analysis.
plot_correlated_pars(→ Any)	Create pairwise correlation plots for endpoint parameters.

Module Contents

larvaworld.lib.plot.metric.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])

larvaworld.lib.plot.metric.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)

larvaworld.lib.plot.metric.plot_correlated_pars(pars: Sequence[str], labels: Sequence[str], refID: str | None = None, dataset: Any = None, save_to: str | None = 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')