Source code for arviz.plots.violinplot

"""Plot posterior traces as violin plot."""
from 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, 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``. 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"] else: if 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, 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() # TODO: Add backend kwargs plot = get_plotting_function("plot_violin", "violinplot", backend) ax = plot(**violinplot_kwargs) return ax