Source code for arviz.plots.traceplot

"""Plot kde or histograms and values from MCMC samples."""
import warnings
from typing import Any, Callable, List, Mapping, Optional, Tuple, Union, Sequence

from import CoordSpec, InferenceData, convert_to_dataset
from ..labels import BaseLabeller
from ..rcparams import rcParams
from ..sel_utils import xarray_var_iter
from ..utils import _var_names, get_coords
from .plot_utils import KwargSpec, get_plotting_function

[docs] def plot_trace( data: InferenceData, var_names: Optional[Sequence[str]] = None, filter_vars: Optional[str] = None, transform: Optional[Callable] = None, coords: Optional[CoordSpec] = None, divergences: Optional[str] = "auto", kind: Optional[str] = "trace", figsize: Optional[Tuple[float, float]] = None, rug: bool = False, lines: Optional[List[Tuple[str, CoordSpec, Any]]] = None, circ_var_names: Optional[List[str]] = None, circ_var_units: str = "radians", compact: bool = True, compact_prop: Optional[Union[str, Mapping[str, Any]]] = None, combined: bool = False, chain_prop: Optional[Union[str, Mapping[str, Any]]] = None, legend: bool = False, plot_kwargs: Optional[KwargSpec] = None, fill_kwargs: Optional[KwargSpec] = None, rug_kwargs: Optional[KwargSpec] = None, hist_kwargs: Optional[KwargSpec] = None, trace_kwargs: Optional[KwargSpec] = None, rank_kwargs: Optional[KwargSpec] = None, labeller=None, axes=None, backend: Optional[str] = None, backend_config: Optional[KwargSpec] = None, backend_kwargs: Optional[KwargSpec] = None, show: Optional[bool] = None, ): """Plot distribution (histogram or kernel density estimates) and sampled values or rank plot. If `divergences` data is available in `sample_stats`, will plot the location of divergences as dashed vertical lines. 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: str or list of str, optional One or more variables to be plotted. 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: dict of {str: slice or array_like}, optional Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` divergences: {"bottom", "top", None}, optional Plot location of divergences on the traceplots. kind: {"trace", "rank_bars", "rank_vlines"}, optional Choose between plotting sampled values per iteration and rank plots. transform: callable, optional Function to transform data (defaults to None i.e.the identity function) figsize: tuple of (float, float), optional If None, size is (12, variables * 2) rug: bool, optional If True adds a rugplot of samples. Defaults to False. Ignored for 2D KDE. Only affects continuous variables. lines: list of tuple of (str, dict, array_like), optional List of (var_name, {'coord': selection}, [line, positions]) to be overplotted as vertical lines on the density and horizontal lines on the trace. circ_var_names : str or list of str, optional List of circular variables to account for when plotting KDE. circ_var_units : str Whether the variables in ``circ_var_names`` are in "degrees" or "radians". compact: bool, optional Plot multidimensional variables in a single plot. compact_prop: str or dict {str: array_like}, optional Defines the property name and the property values to distinguish different dimensions with compact=True. When compact=True it defaults to color, it is ignored otherwise. combined: bool, optional Flag for combining multiple chains into a single line. If False (default), chains will be plotted separately. chain_prop: str or dict {str: array_like}, optional Defines the property name and the property values to distinguish different chains. If compact=True it defaults to linestyle, otherwise it uses the color to distinguish different chains. legend: bool, optional Add a legend to the figure with the chain color code. plot_kwargs, fill_kwargs, rug_kwargs, hist_kwargs: dict, optional Extra keyword arguments passed to :func:`arviz.plot_dist`. Only affects continuous variables. trace_kwargs: dict, optional Extra keyword arguments passed to :meth:`matplotlib.axes.Axes.plot` 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. rank_kwargs : dict, optional Extra keyword arguments passed to :func:`arviz.plot_rank` axes: axes, optional Matplotlib axes or bokeh figures. backend: {"matplotlib", "bokeh"}, optional Select plotting backend. backend_config: dict, optional Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. backend_kwargs: dict, optional These are kwargs specific to the backend being used, passed to :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. show: bool, optional Call backend show function. Returns ------- axes: matplotlib axes or bokeh figures See Also -------- plot_rank : Plot rank order statistics of chains. Examples -------- Plot a subset variables and select them with partial naming .. plot:: :context: close-figs >>> import arviz as az >>> data = az.load_arviz_data('non_centered_eight') >>> coords = {'school': ['Choate', 'Lawrenceville']} >>> az.plot_trace(data, var_names=('theta'), filter_vars="like", coords=coords) Show all dimensions of multidimensional variables in the same plot .. plot:: :context: close-figs >>> az.plot_trace(data, compact=True) Display a rank plot instead of trace .. plot:: :context: close-figs >>> az.plot_trace(data, var_names=["mu", "tau"], kind="rank_bars") Combine all chains into one distribution and select variables with regular expressions .. plot:: :context: close-figs >>> az.plot_trace( >>> data, var_names=('^theta'), filter_vars="regex", coords=coords, combined=True >>> ) Plot reference lines against distribution and trace .. plot:: :context: close-figs >>> lines = (('theta_t',{'school': "Choate"}, [-1]),) >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, lines=lines) """ if kind not in {"trace", "rank_vlines", "rank_bars"}: raise ValueError("The value of kind must be either trace, rank_vlines or rank_bars.") if divergences == "auto": divergences = "top" if rug else "bottom" if divergences: try: divergence_data = convert_to_dataset(data, group="sample_stats").diverging.transpose( "chain", "draw" ) except (ValueError, AttributeError): # No sample_stats, or no `.diverging` divergences = None if coords is None: coords = {} if labeller is None: labeller = BaseLabeller() if divergences: divergence_data = get_coords( divergence_data, {k: v for k, v in coords.items() if k in ("chain", "draw")} ) else: divergence_data = False coords_data = get_coords(convert_to_dataset(data, group="posterior"), coords) if transform is not None: coords_data = transform(coords_data) var_names = _var_names(var_names, coords_data, filter_vars) skip_dims = set(coords_data.dims) - {"chain", "draw"} if compact else set() plotters = list( xarray_var_iter( coords_data, var_names=var_names, combined=True, skip_dims=skip_dims, dim_order=["chain", "draw"], ) ) max_plots = rcParams["plot.max_subplots"] max_plots = len(plotters) if max_plots is None else max(max_plots // 2, 1) if len(plotters) > max_plots: warnings.warn( "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " "of variables to plot ({len_plotters}), generating only {max_plots} " "plots".format(max_plots=max_plots, len_plotters=len(plotters)), UserWarning, ) plotters = plotters[:max_plots] # TODO: Check if this can be further simplified trace_plot_args = dict( # User Kwargs data=coords_data, var_names=var_names, # coords = coords, divergences=divergences, kind=kind, figsize=figsize, rug=rug, lines=lines, circ_var_names=circ_var_names, circ_var_units=circ_var_units, plot_kwargs=plot_kwargs, fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, hist_kwargs=hist_kwargs, trace_kwargs=trace_kwargs, rank_kwargs=rank_kwargs, compact=compact, compact_prop=compact_prop, combined=combined, chain_prop=chain_prop, legend=legend, labeller=labeller, # Generated kwargs divergence_data=divergence_data, # skip_dims=skip_dims, plotters=plotters, axes=axes, backend_config=backend_config, backend_kwargs=backend_kwargs, show=show, ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() plot = get_plotting_function("plot_trace", "traceplot", backend) axes = plot(**trace_plot_args) return axes