arviz.plot_energy(data, kind=None, bfmi=True, figsize=None, legend=True, fill_alpha=(1, 0.75), fill_color=('C0', 'C5'), bw='experimental', textsize=None, fill_kwargs=None, plot_kwargs=None, ax=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot energy transition distribution and marginal energy distribution in HMC algorithms.

This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.


xarray.Dataset, or any object that can be converted (must represent sample_stats and have an energy variable).


Type of plot to display (“kde”, “hist”).


If True add to the plot the value of the estimated Bayesian fraction of missing information


Figure size. If None it will be defined automatically.


Flag for plotting legend. Defaults to True.

fill_alphatuple of floats

Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 (opaque). Defaults to (1, .75).

fill_colortuple of valid matplotlib color

Color for Marginal energy distribution and Energy transition distribution. Defaults to (‘C0’, ‘C5’).

bw: float or str, optional

If numeric, indicates the bandwidth and must be positive. If str, indicates the method to estimate the bandwidth and must be one of “scott”, “silverman”, “isj” or “experimental”. Defaults to “experimental”. Only works if kind='kde'.

textsize: float

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.

fill_kwargsdicts, optional

Additional keywords passed to arviz.plot_kde() (to control the shade).

plot_kwargsdicts, optional

Additional keywords passed to arviz.plot_kde() or matplotlib.pyplot.hist() (if type='hist').

ax: axes, optional

matplotlib.axes.Axes or bokeh.plotting.Figure.

backend: str, optional

Select plotting backend {“matplotlib”, “bokeh”}. Defaults to “matplotlib”.

backend_kwargs: bool, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure().

showbool, optional

Call backend show function.

axesmatplotlib axes or bokeh figures

See also


Calculate the estimated Bayesian fraction of missing information (BFMI).


Plot a default energy plot

>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_energy(data)

Represent energy plot via histograms

>>> az.plot_energy(data, kind='hist')