arviz.plot_parallel(data, var_names=None, filter_vars=None, coords=None, figsize=None, textsize=None, legend=True, colornd='k', colord='C1', shadend=0.025, labeller=None, ax=None, norm_method=None, backend=None, backend_config=None, backend_kwargs=None, show=None)[source]#

Plot parallel coordinates plot showing posterior points with and without divergences.

Described by

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

Variables to be plotted, if None all variables are plotted. Can be used to change the order of the plotted variables. Prefix the variables by ~ when you want to exclude them from the plot.

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.

coords: mapping, optional

Coordinates of var_names to be plotted. Passed to xarray.Dataset.sel().

figsize: tuple

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.

legend: bool

Flag for plotting legend (defaults to True)

colornd: valid matplotlib color

color for non-divergent points. Defaults to ‘k’

colord: valid matplotlib color

color for divergent points. Defaults to ‘C1’

shadend: float

Alpha blending value for non-divergent points, between 0 (invisible) and 1 (opaque). Defaults to .025

labellerlabeller instance, optional

Class providing the method make_label_vert to generate the labels in the plot. Read the Label guide for more details and usage examples.

ax: axes, optional

Matplotlib axes or bokeh figures.

norm_method: str

Method for normalizing the data. Methods include normal, minmax and rank. Defaults to none.

backend: str, optional

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

backend_config: dict, optional

Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams.

backend_kwargs: bool, optional

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

show: bool, optional

Call backend show function.

axes: matplotlib axes or bokeh figures

See also


Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal.


Plot distribution (histogram or kernel density estimates) and sampled values or rank plot


Plot default parallel plot

>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_parallel(data, var_names=["mu", "tau"])

Plot parallel plot with normalization

>>> az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="normal")

Plot parallel plot with minmax

>>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="minmax")
>>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45)

Plot parallel plot with rank

>>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="rank")
>>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45)