larvaworld.lib.plot.grid ======================== .. py:module:: larvaworld.lib.plot.grid .. autoapi-nested-parse:: Composite grid-structured figure Functions --------- .. autoapisummary:: larvaworld.lib.plot.grid.calibration_plot larvaworld.lib.plot.grid.model_summary larvaworld.lib.plot.grid.velocity_definition larvaworld.lib.plot.grid.exploration_summary larvaworld.lib.plot.grid.dsp_summary larvaworld.lib.plot.grid.RvsS_summary larvaworld.lib.plot.grid.DoublePatch_summary larvaworld.lib.plot.grid.chemo_summary larvaworld.lib.plot.grid.result_summary larvaworld.lib.plot.grid.model_sample_track larvaworld.lib.plot.grid.eval_summary Module Contents --------------- .. py:function:: calibration_plot(save_to: Optional[str] = None, files: Optional[Sequence[str]] = None) -> Any Create calibration summary figure from existing plots. Combines multiple calibration plots (interference, bouts, orientation, angular parameters, bend) into a single composite figure. Args: save_to: Directory to save plot. Defaults to current directory files: List of image file paths to combine. Auto-generated if None Returns: Matplotlib figure object Example: >>> fig = calibration_plot(save_to='./calibration', files=['plot1.png', 'plot2.png']) .. py:function:: model_summary(mID: str, refID: Optional[str] = None, refDataset: Any = None, Nids: int = 1, model_table: bool = False, **kwargs: Any) -> Any Create comprehensive model summary grid figure. Generates multi-panel figure showing model configuration, parameter histograms, stride cycles, behavioral epochs, and sample tracks compared to reference data. Args: mID: Model ID to summarize refID: Reference dataset ID. Defaults to None refDataset: Pre-loaded reference dataset. Loads from refID if None Nids: Number of sample individuals to plot. Defaults to 1 model_table: Whether to include parameter table. Defaults to False **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = model_summary(mID='model_01', refID='reference', Nids=3) .. py:function:: velocity_definition(dataset: Any, save_to: Optional[str] = None, save_as: str = 'vel_definition.pdf', component_vels: bool = True, **kwargs: Any) -> None Create velocity definition comparison figure. Generates panel showing different velocity calculation methods with visual comparison of component velocities and their definitions. Args: dataset: Dataset containing velocity data save_to: Directory to save plot. Uses dataset plot dir if None save_as: Filename for saved plot. Defaults to 'vel_definition.pdf' component_vels: Whether to show component velocities. Defaults to True **kwargs: Additional plotting arguments Example: >>> velocity_definition(dataset, save_to='./figures', component_vels=True) .. py:function:: exploration_summary(datasets: Sequence[Any], target: Any = None, range: Tuple[int, int] = (0, 40), **kwargs: Any) -> Any Create exploration behavior summary grid figure. Generates comprehensive grid showing exploration metrics, spatial distributions, and trajectories over specified time range. Args: datasets: List of datasets to compare target: Target dataset for comparison. Defaults to None range: Time range (start, end) in minutes. Defaults to (0, 40) **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = exploration_summary(datasets=[d1, d2], range=(0, 60)) .. py:function:: dsp_summary(datasets: Sequence[Any], target: Any = None, range: Tuple[int, int] = (0, 40), **kwargs: Any) -> Any Create dispersal behavior summary grid figure. Generates comprehensive grid showing dispersal metrics, spatial spread, and movement patterns over specified time range. Args: datasets: List of datasets to compare target: Target dataset for comparison. Defaults to None range: Time range (start, end) in minutes. Defaults to (0, 40) **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = dsp_summary(datasets=[d1, d2], range=(0, 60)) .. py:function:: RvsS_summary(entrylist: Sequence[Dict[str, Any]], title: str, mdiff_df: Any, **kwargs: Any) -> Any Create Rover vs Sitter comparison summary grid figure. Generates multi-panel figure comparing rover and sitter behavioral phenotypes with statistical comparison table and multiple plot types. Args: entrylist: List of plot entry dictionaries with plotID, args, and name keys title: Main figure title mdiff_df: DataFrame with statistical comparison metrics **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = RvsS_summary(entrylist=[...], title='Phenotype Comparison', mdiff_df=df) .. py:function:: DoublePatch_summary(datasets: Dict[str, Sequence[Any]], title: str, mdiff_df: Any, ks: Optional[Sequence[str]] = None, name: Optional[str] = None, **kwargs: Any) -> Any Create double-patch assay summary grid figure. Generates comprehensive figure showing trajectories and behavioral metrics for double-patch food choice experiments across conditions. Args: datasets: Dictionary mapping condition names to dataset lists title: Main figure title mdiff_df: DataFrame with statistical comparison metrics ks: Parameter keys to plot. Uses default set if None name: Plot name for saving. Auto-generated if None **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = DoublePatch_summary(datasets={'control': [d1], 'test': [d2]}, title='Food Choice', mdiff_df=df) .. py:function:: chemo_summary(datasets: Dict[str, Sequence[Any]], mdiff_df: Any, title: str, **kwargs: Any) -> Any Create chemotaxis assay summary grid figure. Generates comprehensive figure showing chemotaxis behavior including trajectories, orientation metrics, and statistical comparisons. Args: datasets: Dictionary mapping condition names to dataset lists mdiff_df: DataFrame with statistical comparison metrics title: Main figure title **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = chemo_summary(datasets={'control': [d1], 'odor': [d2]}, mdiff_df=df, title='Chemotaxis') .. py:function:: result_summary(datasets: Sequence[Any], target: Any, **kwargs: Any) -> Any Create experiment result summary grid figure. Generates comprehensive grid showing experiment results with multiple panels for trajectories, metrics, and comparisons to target data. Args: datasets: List of datasets to summarize target: Target dataset for comparison **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = result_summary(datasets=[d1, d2], target=ref_dataset) .. py:function:: model_sample_track(mID: Optional[str] = None, m: Any = None, dur: float = 2 / 3, dt: float = 1 / 16, Nids: int = 1, min_turn_amp: float = 20, d: Any = None, **kwargs: Any) -> Any Create sample trajectory tracks from model simulation. Simulates and plots sample individual tracks showing trajectory, turns, and behavioral details for model validation. Args: mID: Model ID to simulate. Required if m is None m: Pre-loaded model object. Loads from mID if None dur: Simulation duration in minutes. Defaults to 2/3 (40 seconds) dt: Time step in seconds. Defaults to 1/16 Nids: Number of individuals to simulate. Defaults to 1 min_turn_amp: Minimum turn amplitude to highlight. Defaults to 20 degrees d: Pre-loaded dataset for comparison. Defaults to None **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = model_sample_track(mID='model_01', dur=1.0, Nids=3) .. py:function:: eval_summary(error_dict: Dict[str, Any], evaluation: Any, norm_mode: str = 'raw', eval_mode: str = 'pooled', **kwargs: Any) -> Any Create model evaluation summary grid figure. Generates comprehensive evaluation figure with error bar plots, statistical tables, and comparison metrics for model-data fit assessment. Args: error_dict: Dictionary containing error metrics by type evaluation: Evaluation metadata with model labels and colors norm_mode: Normalization mode ('raw', 'normalized'). Defaults to 'raw' eval_mode: Evaluation mode ('pooled', '1:1'). Defaults to 'pooled' **kwargs: Additional arguments passed to GridPlot Returns: Plot output (figure object or None based on return_fig setting) Example: >>> fig = eval_summary(error_dict=errors, evaluation=eval_meta, eval_mode='pooled')