Plotting Return Values for comparison between GPD and GEVD Models

[FranciscoSimoes22]

Dear George

I need to plot the Return_Values from GPD model and GEVD Model on the same graph for a visual comparison of both methods.

For instance:

Plotting Return_values

model_BM.plot_return_values(
return_period=np.logspace(0.1, 2, 100),
return_period_size="365.2425D",
alpha=0.95,
)
plt.show()

and

Plotting_Return_values

model_POT.plot_return_values(
return_period = np.logspace(0.1, 2, 100),
return_period_size="365.2425D",
alpha=0.95,
)
plt.show()

My question is: I don t need to plot the confidence bounds, i just need both the return_periods "line" on the same graph. Is this possible?

Thank you

[georgebv]

You can plot them onto the same axes, you should use the ax parameter of the method. Same for confidence - it's controlled by alpha.

pyextremes/src/pyextremes/eva.py

Lines 1332 to 1398 in ce530c4

	def plot_return_values(
	self,
	return_period=None,
	return_period_size: typing.Union[str, pd.Timedelta] = "365.2425D",
	alpha: typing.Optional[float] = None,
	plotting_position: typing.Literal[
	"ecdf",
	"hazen",
	"weibull",
	"tukey",
	"blom",
	"median",
	"cunnane",
	"gringorten",
	"beard",
	] = "weibull",
	ax: typing.Optional[plt.Axes] = None,
	figsize: typing.Tuple[float, float] = (8, 5),
	**kwargs,
	) -> tuple: # pragma: no cover
	"""
	Plot return values and confidence intervals for given return periods.
	Parameters
	----------
	return_period : array-like, optional
	Return period or 1D array of return periods.
	Given as a multiple of `return_period_size`.
	If None (default), calculates as 100 values uniformly spaced
	within the range of return periods of the extracted extreme values.
	return_period_size : str or pandas.Timedelta, optional
	Size of return periods (default='365.2425D').
	If set to '30D', then a return period of 12
	would be roughly equivalent to a 1 year return period (360 days).
	alpha : float, optional
	Width of confidence interval (0, 1).
	If None (default), confidence interval bounds are not plotted.
	plotting_position : str, optional
	Plotting position name (default='weibull'), not case-sensitive.
	Supported plotting positions:
	ecdf, hazen, weibull, tukey, blom, median, cunnane, gringorten, beard
	ax : matplotlib.axes._axes.Axes, optional
	Axes onto which the return value plot is drawn.
	If None (default), a new figure and axes objects are created.
	figsize : tuple, optional
	Figure size in inches in format (width, height).
	By default it is (8, 5).
	kwargs
	Model-specific keyword arguments.
	If alpha is None, keyword arguments are ignored
	(error still raised for unrecognized arguments).
	MLE model:
	n_samples : int, optional
	Number of bootstrap samples used to estimate
	confidence interval bounds (default=100).
	Emcee model:
	burn_in : int
	Burn-in value (number of first steps to discard for each walker).
	Returns
	-------
	figure : matplotlib.figure.Figure
	Figure object.
	axes : matplotlib.axes._axes.Axes
	Axes object.
	"""