cbr_fox.utils.plot_utils

Functions

visualize_combined_pyplot(cbr_fox_instance, ...)	Visualize the combined data and best cases' components using Matplotlib.
visualize_correlation_per_window(...)	Plot correlation values per window using Matplotlib.
visualize_pyplot(cbr_fox_instance, **kwargs)	Visualize the best cases' components using Matplotlib.
visualize_smoothed_correlation(...)	Visualize a smoothed correlation series with annotated peak and valley points. :param cbr_fox_instance: An object that must provide the following attributes: - smoothed_correlation : 1D array-like (numpy array, list, pandas Series) Smoothed correlation values to plot. - peak_index : int or array-like of int Index or indices of peak point(s) in smoothed_correlation to mark. - valley_index : int or array-like of int Index or indices of valley point(s) in smoothed_correlation to mark. :type cbr_fox_instance: object :param kwargs: Optional plotting parameters (all keys are optional): - fig_size : tuple(float, float) Figure size in inches. Default: (20, 12). - smoothed_label : str Label used for the primary line plot of smoothed_correlation. Default: "Smoothed Correlation per Window". - fmt : str Matplotlib format string for the second line plot (e.g. 'r--'). - plot_params : dict Additional keyword arguments forwarded to ax.plot for the second plot. - label : str Label for the second plot (default falls back to smoothed_label). - peak_params : dict Keyword arguments forwarded to ax.scatter when marking peak(s). - valley_params : dict Keyword arguments forwarded to ax.scatter when marking valley(s). :type kwargs: dict, optional.