bencher.results.holoview_results.distribution_result.violin_result

Classes

ViolinResult

A class for creating violin plots from benchmark results.

Module Contents

class bencher.results.holoview_results.distribution_result.violin_result.ViolinResult(bench_cfg: bencher.bench_cfg.BenchCfg)

Bases: bencher.results.holoview_results.distribution_result.distribution_result.DistributionResult

A class for creating violin plots from benchmark results.

Violin plots combine aspects of box plots with kernel density plots, showing the distribution shape of the data. This class provides methods to generate these plots from benchmark data, which is particularly useful for visualizing the distribution of metrics across different configurations or repetitions.

Violin plots display: - The full probability density of the data (the width of the “violin” at each point) - Summary statistics like median and interquartile ranges - The overall distribution shape, revealing features like multi-modality that

box plots might miss

to_plot(result_var: param.Parameter | None = None, override: bool = True, **kwargs: Any) → panel.panel | None

Generates a violin plot from benchmark data.

This method applies filters to ensure the data is appropriate for a violin plot and then passes the filtered data to to_violin_ds for rendering.

Parameters:

  • result_var (Parameter | None) – The result variable to plot. If None, uses the default.

  • override (bool) – Whether to override filter restrictions. Defaults to True.

  • **kwargs (Any) – Additional keyword arguments passed to the plot rendering.

Returns:

A panel containing the violin plot if data is appropriate, otherwise returns filter match results.

Return type:

pn.panel | None

to_violin_ds(dataset: xarray.Dataset, result_var: param.Parameter, **kwargs: Any) → holoviews.Violin

Creates a violin plot from the provided dataset.

Given a filtered dataset, this method generates a violin plot visualization showing the distribution of values for a result variable, potentially grouped by a categorical variable.

Parameters:

  • dataset (xr.Dataset) – The dataset containing benchmark results.

  • result_var (Parameter) – The result variable to plot.

  • **kwargs (Any) – Additional keyword arguments for plot customization, such as: - violin_color: Color for the violin body - inner_color: Color for inner statistics markers - line_width: Width of outline lines - bandwidth: Controls the smoothness of the density estimate

Returns:

A HoloViews Violin plot of the benchmark data.

Return type:

hv.Violin