csep.plots.plot_ROC_diagram

csep.plots.plot_ROC_diagram(forecast, catalog, linear=True, plot_uniform=True, ax=None, show=True, **kwargs)[source]

Plots the ROC (Receiver Operating Characteristic) curve for a given forecast and observed catalog.

Usage Tutorials

Parameters:

forecast (GriddedForecast) – Forecast object containing spatial forecast data.
catalog (CSEPCatalog) – Catalog object containing observed data.
linear (bool, optional) – If True, uses a linear scale for the X-axis, otherwise logarithmic. Defaults to True.
plot_uniform (bool, optional) – If True, plots the uniform (random) model as a reference. Defaults to True.
show (bool, optional) – If True, displays the plot. Defaults to True.
ax (matplotlib.axes.Axes, optional) – Axes object to plot on. If None, creates a new figure. Defaults to None.
**kwargs (optional) –
Additional keyword arguments for customization:
- figsize (tuple): The size of the figure.
- forecast_label (str): Label for the forecast data in the plot.
- color (str): Color for the ROC curve line.
- linestyle (str): Line style for the ROC curve.
- 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.
- xticks_fontsize (int): Font size for the X-axis ticks.
- yticks_fontsize (int): Font size for the Y-axis ticks.
- legend (bool): Whether to display a legend. Defaults to True.
- 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 Axes object with the plot.

Return type:

matplotlib.pyplot.Axes

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