"""Plot posterior traces as violin plot."""
from ..data import convert_to_dataset
from ..labels import BaseLabeller
from ..sel_utils import xarray_var_iter
from ..utils import _var_names
from ..rcparams import rcParams
from .plot_utils import default_grid, filter_plotters_list, get_plotting_function
[docs]
def 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,
):
"""Plot posterior of traces as violin plot.
Notes
-----
If multiple chains are provided for a variable they will be combined
Parameters
----------
data: obj
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`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_dims : set_like of str, optional
List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions.
See the :ref:`this section <common_combine_dims>` 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``.
grid : tuple
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``.
labeller : labeller instance, optional
Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
Read the :ref:`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 :meth:`matplotlib.axes.Axes.fill_between`, or
:meth:`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
:func:`matplotlib.pyplot.subplots` or :func:`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
See Also
--------
plot_forest: Forest plot to compare HDI intervals from a number of distributions.
Examples
--------
Show a default violin plot
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_violin(data)
"""
if labeller is None:
labeller = BaseLabeller()
data = convert_to_dataset(data, group="posterior")
if transform is not None:
data = transform(data)
var_names = _var_names(var_names, data, filter_vars)
plotters = filter_plotters_list(
list(xarray_var_iter(data, var_names=var_names, combined=True, skip_dims=combine_dims)),
"plot_violin",
)
rows, cols = default_grid(len(plotters), grid=grid)
if hdi_prob is None:
hdi_prob = rcParams["stats.hdi_prob"]
elif not 1 >= hdi_prob > 0:
raise ValueError("The value of hdi_prob should be in the interval (0, 1]")
violinplot_kwargs = dict(
ax=ax,
plotters=plotters,
figsize=figsize,
rows=rows,
cols=cols,
sharex=sharex,
sharey=sharey,
shade_kwargs=shade_kwargs,
shade=shade,
rug=rug,
rug_kwargs=rug_kwargs,
side=side,
bw=bw,
textsize=textsize,
labeller=labeller,
circular=circular,
hdi_prob=hdi_prob,
quartiles=quartiles,
backend_kwargs=backend_kwargs,
show=show,
)
if backend is None:
backend = rcParams["plot.backend"]
backend = backend.lower()
if side not in ("both", "left", "right"):
raise ValueError(f"'side' can only be 'both', 'left', or 'right', got: '{side}'")
# TODO: Add backend kwargs
plot = get_plotting_function("plot_violin", "violinplot", backend)
ax = plot(**violinplot_kwargs)
return ax
