bencher.results.bench_result
============================

.. py:module:: bencher.results.bench_result


Classes
-------

.. autoapisummary::

   bencher.results.bench_result.RerunResult
   bencher.results.bench_result.BenchResult


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

.. py:class:: RerunResult

.. py:class:: BenchResult(bench_cfg)

   Bases: :py:obj:`bencher.results.rerun_result.RerunResult`, :py:obj:`bencher.results.volume_result.VolumeResult`, :py:obj:`bencher.results.holoview_results.distribution_result.box_whisker_result.BoxWhiskerResult`, :py:obj:`bencher.results.holoview_results.distribution_result.violin_result.ViolinResult`, :py:obj:`bencher.results.holoview_results.distribution_result.scatter_jitter_result.ScatterJitterResult`, :py:obj:`bencher.results.holoview_results.scatter_result.ScatterResult`, :py:obj:`bencher.results.holoview_results.line_result.LineResult`, :py:obj:`bencher.results.holoview_results.bar_result.BarResult`, :py:obj:`bencher.results.holoview_results.heatmap_result.HeatmapResult`, :py:obj:`bencher.results.holoview_results.curve_result.CurveResult`, :py:obj:`bencher.results.holoview_results.band_result.BandResult`, :py:obj:`bencher.results.holoview_results.surface_result.SurfaceResult`, :py:obj:`bencher.results.histogram_result.HistogramResult`, :py:obj:`bencher.results.holoview_results.holoview_result.HoloviewResult`, :py:obj:`bencher.results.video_summary.VideoSummaryResult`, :py:obj:`bencher.results.dataset_result.DataSetResult`, :py:obj:`bencher.results.optuna_result.OptunaResult`


   Contains the results of the benchmark and has methods to cast the results to various datatypes and graphical representations


   .. py:attribute:: timings
      :value: None



   .. py:method:: from_existing(original: BenchResult) -> BenchResult
      :classmethod:



   .. py:method:: to(result_type: BenchResult, result_var: param.Parameter | None = None, override: bool = True, reduce: bencher.results.bench_result_base.ReduceType | None = None, aggregate: bool | int | list[str] | None = None, agg_fn: Literal['mean', 'sum', 'max', 'min', 'median'] = 'mean', **kwargs: Any) -> BenchResult

      Return the current instance of BenchResult.

      :returns: The current instance of the benchmark result
      :rtype: BenchResult



   .. py:method:: default_plot_callbacks() -> list[callable]
      :staticmethod:


      Get the default list of plot callback functions.

      These callbacks are used by default in the to_auto method if no specific
      plot list is provided.

      :returns: A list of plotting callback functions
      :rtype: list[callable]



   .. py:method:: plotly_callbacks() -> list[callable]
      :staticmethod:


      Get the list of Plotly-specific callback functions.

      :returns: A list of Plotly-based visualization callback functions
      :rtype: list[callable]



   .. py:method:: plot() -> panel.panel

      Plots the benchresult using the plot callbacks defined by the bench run.

      This method uses the plot_callbacks defined in the bench_cfg to generate
      plots for the benchmark results.

      :returns: A panel representation of the results, or None if no plot_callbacks defined
      :rtype: pn.panel



   .. py:method:: to_auto(plot_list: list[callable] | None = None, remove_plots: list[callable] | None = None, default_container=pn.Column, override: bool = False, numeric_only: bool = False, **kwargs) -> list[panel.panel]

      Automatically generate plots based on the provided plot callbacks.

      :param plot_list: List of plot callback functions to use. Defaults to None.
      :type plot_list: list[callable], optional
      :param remove_plots: List of plot callback functions to exclude. Defaults to None.
      :type remove_plots: list[callable], optional
      :param default_container: Default container type for the plots. Defaults to pn.Column.
      :type default_container: type, optional
      :param override: Whether to override unsupported plots. Defaults to False.
      :type override: bool, optional
      :param numeric_only: When True, skip pane-type result callbacks
                           (images, videos, rerun, etc.) that cannot be numerically aggregated.
                           Defaults to False.
      :type numeric_only: bool, optional
      :param \*\*kwargs: Additional keyword arguments for plot configuration.

      :returns: A list of panel objects containing the generated plots.
      :rtype: list[pn.panel]



   .. py:method:: to_auto_plots(extra_panels: collections.abc.Sequence[collections.abc.Callable[[BenchResult], panel.viewable.Viewable] | panel.viewable.Viewable] | None = None, **kwargs) -> panel.panel

      Given the dataset result of a benchmark run, automatically deduce how to plot the data based on the types of variables that were sampled.

      :param extra_panels: Extra panel callables or static panels to inject after the sweep
                           summary and before aggregate/auto plots. Each item is either a
                           callable(BenchResult) -> panel, or a static panel object.
      :param \*\*kwargs: Additional keyword arguments for plot configuration.

      :returns: A panel containing plot results.
      :rtype: pn.panel



   .. py:method:: _scalar_aggregate_summary() -> panel.pane.Markdown

      Render a Markdown table for a fully-aggregated (scalar) result.