arviz.plot_kde

arviz.plot_kde(values, values2=None, cumulative=False, rug=False, label=None, bw='default', adaptive=False, quantiles=None, rotated=False, contour=True, hdi_probs=None, fill_last=False, figsize=None, textsize=None, plot_kwargs=None, fill_kwargs=None, rug_kwargs=None, contour_kwargs=None, contourf_kwargs=None, pcolormesh_kwargs=None, is_circular=False, ax=None, legend=True, backend=None, backend_kwargs=None, show=None, return_glyph=False, **kwargs)[source]

1D or 2D KDE plot taking into account boundary conditions.

Parameters
valuesarray-like

Values to plot

values2array-like, optional

Values to plot. If present, a 2D KDE will be estimated

cumulativebool

If true plot the estimated cumulative distribution function. Defaults to False. Ignored for 2D KDE

rugbool

If True adds a rugplot. Defaults to False. Ignored for 2D KDE

labelstring

Text to include as part of the legend

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” when is_circular is False and “taylor” (for now) when is_circular is True. Defaults to “default” which means “experimental” when variable is not circular and “taylor” when it is.

adaptive: bool, optional.

If True, an adaptative bandwidth is used. Only valid for 1D KDE. Defaults to False.

quantileslist

Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. Defaults to None.

rotatedbool

Whether to rotate the 1D KDE plot 90 degrees.

contourbool

If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE. Defaults to True.

hdi_probslist

Plots highest density credibility regions for the provided probabilities for a 2D KDE. Defaults to matplotlib chosen levels with no fixed probability associated.

fill_lastbool

If True fill the last contour of the 2D KDE plot. Defaults to False.

figsizetuple

Figure size. If None it will be defined automatically.

textsize: float

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. Not implemented for bokeh backend.

plot_kwargsdict

Keywords passed to the pdf line of a 1D KDE. See matplotlib.axes.Axes.plot() or bokeh.plotting.figure.Figure.line for a description of accepted values.

fill_kwargsdict

Keywords passed to the fill under the line (use fill_kwargs={‘alpha’: 0} to disable fill). Ignored for 2D KDE

rug_kwargsdict

Keywords passed to the rug plot. Ignored if rug=False or for 2D KDE Use space keyword (float) to control the position of the rugplot. The larger this number the lower the rugplot.

contour_kwargsdict

Keywords passed to ax.contour to draw contour lines. Ignored for 1D KDE.

contourf_kwargsdict

Keywords passed to ax.contourf to draw filled contours. Ignored for 1D KDE.

pcolormesh_kwargsdict

Keywords passed to ax.pcolormesh. Ignored for 1D KDE.

is_circular{False, True, “radians”, “degrees”}. Default False.

Select input type {“radians”, “degrees”} for circular histogram or KDE plot. If True, default input type is “radians”. When this argument is present, it interprets values is a circular variable measured in radians and a circular KDE is used. Inputs in “degrees” will undergo an internal conversion to radians.

ax: axes, optional

Matplotlib axes or bokeh figures.

legendbool

Add legend to the figure. By default True.

backend: str, optional

Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.

backend_kwargs: bool, optional

These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend.

showbool, optional

Call backend show function.

return_glyphbool, optional

Internal argument to return glyphs for bokeh

Returns
axesmatplotlib.Axes or bokeh.plotting.Figure

Object containing the kde plot

glyphslist, optional

Bokeh glyphs present in plot. Only provided if return_glyph is True.

See also

plot_kde

Compute and plot a kernel density estimate.

Examples

Plot default KDE

>>> import arviz as az
>>> non_centered = az.load_arviz_data('non_centered_eight')
>>> mu_posterior = np.concatenate(non_centered.posterior["mu"].values)
>>> tau_posterior = np.concatenate(non_centered.posterior["tau"].values)
>>> az.plot_kde(mu_posterior)
../../_images/arviz-plot_kde-1.png

Plot KDE with rugplot

>>> az.plot_kde(mu_posterior, rug=True)
../../_images/arviz-plot_kde-2.png

Plot KDE with adaptive bandwidth

>>> az.plot_kde(mu_posterior, adaptive=True)
../../_images/arviz-plot_kde-3.png

Plot KDE with a different bandwidth estimator

>>> az.plot_kde(mu_posterior, bw="scott")
../../_images/arviz-plot_kde-4.png

Plot KDE with a bandwidth specified manually

>>> az.plot_kde(mu_posterior, bw=0.4)
../../_images/arviz-plot_kde-5.png

Plot KDE for a circular variable

>>> rvs = np.random.vonmises(mu=np.pi, kappa=2, size=500)
>>> az.plot_kde(rvs, is_circular=True)
../../_images/arviz-plot_kde-6.png

Plot a cumulative distribution

>>> az.plot_kde(mu_posterior, cumulative=True)
../../_images/arviz-plot_kde-7.png

Rotate plot 90 degrees

>>> az.plot_kde(mu_posterior, rotated=True)
../../_images/arviz-plot_kde-8.png

Plot 2d contour KDE

>>> az.plot_kde(mu_posterior, values2=tau_posterior)
../../_images/arviz-plot_kde-9.png

Plot 2d contour KDE, without filling and contour lines using viridis cmap

>>> az.plot_kde(mu_posterior, values2=tau_posterior,
...             contour_kwargs={"colors":None, "cmap":plt.cm.viridis},
...             contourf_kwargs={"alpha":0});
../../_images/arviz-plot_kde-10.png

Plot 2d contour KDE, set the number of levels to 3.

>>> az.plot_kde(
...     mu_posterior, values2=tau_posterior,
...     contour_kwargs={"levels":3}, contourf_kwargs={"levels":3}
... );
../../_images/arviz-plot_kde-11.png

Plot 2d contour KDE with 30%, 60% and 90% HDI contours.

>>> az.plot_kde(mu_posterior, values2=tau_posterior, hdi_probs=[0.3, 0.6, 0.9])
../../_images/arviz-plot_kde-12.png

Plot 2d smooth KDE

>>> az.plot_kde(mu_posterior, values2=tau_posterior, contour=False)
../../_images/arviz-plot_kde-13.png