csep.plots.plot_calibration_test

csep.plots.plot_calibration_test(evaluation_result, percentile=95, label=None, ax=None, show=False, **kwargs)

Plots a calibration test (Quantile-Quantile plot) with confidence intervals.

Parameters:

• evaluation_result (EvaluationResult) – The evaluation result object containing the test distribution.

• percentile (float, optional) – Percentile to build confidence interval. Defaults to 95.

• ax (matplotlib.axes.Axes, optional) – Axes object to plot on. If None, creates a new figure. Defaults to None.

• label (str, optional) – Label for the plotted data. If None, uses evaluation_result.sim_name. Defaults to None.

• show (bool, optional) – If True, displays the plot. Defaults to False.

• **kwargs (optional) –

  Additional keyword arguments for customizing the plot:

  • color (str): Color of the plot line and markers.

  • marker (str): Marker style for the data points.

  • markersize (float): Size of the markers.

  • grid (bool): Whether to display grid lines. Defaults to True.

  • title (str): Title of the plot.

  • title_fontsize (int): Font size for the plot title.

  • xlabel (str): Label for the X-axis.

  • xlabel_fontsize (int): Font size for the X-axis label.

  • ylabel (str): Label for the Y-axis.

  • ylabel_fontsize (int): Font size for the Y-axis label.

  • legend_loc (str): Location of the legend. Defaults to ‘best’.

  • legend_fontsize (int): Font size of the legend text.

  • tight_layout (bool): Whether to use tight layout for the figure. Defaults to True.

Returns:

The Matplotlib axes object containing the plot.

Return type:

matplotlib.axes.Axes

Changed in version 0.8.0: plot_args dictionary is only partially supported and will be removed in v1.0.0.

Added in version 0.8.0: Added percentile option.