arviz.plot_violin#

arviz.plot_violin(data, var_names=None, combine_dims=None, filter_vars=None, transform=None, quartiles=True, rug=False, side='both', hdi_prob=None, shade=0.35, bw='default', circular=False, sharex=True, sharey=True, grid=None, figsize=None, textsize=None, labeller=None, ax=None, shade_kwargs=None, rug_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot posterior of traces as violin plot.

Parameters:

data: obj: Any object that can be converted to an arviz.InferenceData object Refer to documentation of arviz.convert_to_dataset() for details
var_names: list of variable names, optional: Variables to be plotted, if None all variable are plotted. Prefix the variables by ~ when you want to exclude them from the plot.
combine_dimsset_like of str, optional: List of dimensions to reduce. Defaults to reducing only the “chain” and “draw” dimensions. See the this section for usage examples.
filter_vars: {None, “like”, “regex”}, optional, default=None: If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A la pandas.filter.
transform: callable: Function to transform data (defaults to None i.e. the identity function).
quartiles: bool, optional: Flag for plotting the interquartile range, in addition to the hdi_prob * 100% intervals. Defaults to True.
rug: bool: If True adds a jittered rugplot. Defaults to False.
side{“both”, “left”, “right”}, default “both”: If both, both sides of the violin plot are rendered. If left or right, only the respective side is rendered. By separately plotting left and right halfs with different data, split violin plots can be achieved.
hdi_prob: float, optional: Plots highest posterior density interval for chosen percentage of density. Defaults to 0.94.
shade: float: Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 (opaque). Defaults to 0.
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 circular is False and “taylor” (for now) when circular is True. Defaults to “default” which means “experimental” when variable is not circular and “taylor” when it is.
circular: bool, optional.: If True, it interprets values is a circular variable measured in radians and a circular KDE is used. Defaults to False.
gridtuple: Number of rows and columns. Defaults to None, the rows and columns are automatically inferred.
figsize: tuple: Figure size. If None it will be defined automatically.
textsize: int: Text size of the point_estimates, axis ticks, and highest density interval. If None it will be autoscaled based on figsize.
labellerlabeller instance, optional: Class providing the method make_label_vert to generate the labels in the plot titles. Read the Label guide for more details and usage examples.
sharex: bool: Defaults to True, violinplots share a common x-axis scale.
sharey: bool: Defaults to True, violinplots share a common y-axis scale.
ax: numpy array-like of matplotlib axes or bokeh figures, optional: A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it).
shade_kwargs: dicts, optional: Additional keywords passed to matplotlib.axes.Axes.fill_between(), or matplotlib.axes.Axes.barh() to control the shade.
rug_kwargs: dict: Keywords passed to the rug plot. If true only the right half side of the violin will be plotted.
backend: str, optional: Select plotting backend {“matplotlib”,”bokeh”}. Default to “matplotlib”.
backend_kwargs: bool, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure(). For additional documentation check the plotting method of the backend.
show: bool, optional: Call backend show function.

Returns:

axes: matplotlib axes or bokeh figures