Source code for arviz.plots.mcseplot

"""Plot quantile MC standard error."""
import numpy as np
import xarray as xr

from import convert_to_dataset
from ..labels import BaseLabeller
from ..sel_utils import xarray_var_iter
from ..stats import mcse
from ..rcparams import rcParams
from ..utils import _var_names, get_coords
from .plot_utils import default_grid, filter_plotters_list, get_plotting_function

[docs] def plot_mcse( idata, var_names=None, filter_vars=None, coords=None, errorbar=False, grid=None, figsize=None, textsize=None, extra_methods=False, rug=False, rug_kind="diverging", n_points=20, labeller=None, ax=None, rug_kwargs=None, extra_kwargs=None, text_kwargs=None, backend=None, backend_kwargs=None, show=None, **kwargs ): """Plot quantile or local Monte Carlo Standard Error. Parameters ---------- idata : 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. 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, optional Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` errorbar : bool, optional Plot quantile value +/- mcse instead of plotting mcse. grid : tuple Number of rows and columns. Defaults to None, the rows and columns are automatically inferred. figsize : (float, float), optional Figure size. If None it will be defined automatically. textsize : float, optional Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. extra_methods : bool, optional Plot mean and sd MCSE as horizontal lines. Only taken into account when ``errorbar=False``. rug : bool Plot rug plot of values diverging or that reached the max tree depth. rug_kind : bool Variable in sample stats to use as rug mask. Must be a boolean variable. n_points : int Number of points for which to plot their quantile/local ess or number of subsets in the evolution plot. labeller : Labeller, 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. ax : 2D 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). rug_kwargs : dict kwargs passed to rug plot in :meth:`mpl:matplotlib.axes.Axes.plot` or :class:`bokeh:bokeh.models.glyphs.Scatter`. extra_kwargs : dict, optional kwargs passed as extra method lines in :meth:`mpl:matplotlib.axes.Axes.axhline` or :class:`bokeh:bokeh.models.Span` text_kwargs : dict, optional kwargs passed to :meth:`mpl:matplotlib.axes.Axes.annotate` for extra methods lines labels. It accepts the additional key ``x`` to set ``xy=(text_kwargs["x"], mcse)``. text_kwargs are ignored for the bokeh plotting backend. backend : str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". backend_kwargs : bool, optional These are kwargs specific to the backend being passed to :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. show: bool, optional Call backend show function. **kwargs Passed as-is to :meth:`mpl:matplotlib.axes.Axes.hist` or :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib depending on the value of `kind`. Returns ------- axes : matplotlib axes or bokeh figures See Also -------- :func:`arviz.mcse`: Calculate Markov Chain Standard Error statistic. References ---------- * Vehtari et al. (2019) see Examples -------- Plot quantile Monte Carlo Standard Error. .. plot:: :context: close-figs >>> import arviz as az >>> idata = az.load_arviz_data("centered_eight") >>> coords = {"school": ["Deerfield", "Lawrenceville"]} >>> az.plot_mcse( ... idata, var_names=["mu", "theta"], coords=coords ... ) """ mean_mcse = None sd_mcse = None if coords is None: coords = {} if "chain" in coords or "draw" in coords: raise ValueError("chain and draw are invalid coordinates for this kind of plot") if labeller is None: labeller = BaseLabeller() data = get_coords(convert_to_dataset(idata, group="posterior"), coords) var_names = _var_names(var_names, data, filter_vars) probs = np.linspace(1 / n_points, 1 - 1 / n_points, n_points) mcse_dataset = xr.concat( [mcse(data, var_names=var_names, method="quantile", prob=p) for p in probs], dim="mcse_dim" ) plotters = filter_plotters_list( list(xarray_var_iter(mcse_dataset, var_names=var_names, skip_dims={"mcse_dim"})), "plot_mcse", ) length_plotters = len(plotters) rows, cols = default_grid(length_plotters, grid=grid) if extra_methods: mean_mcse = mcse(data, var_names=var_names, method="mean") sd_mcse = mcse(data, var_names=var_names, method="sd") mcse_kwargs = dict( ax=ax, plotters=plotters, length_plotters=length_plotters, rows=rows, cols=cols, figsize=figsize, errorbar=errorbar, rug=rug, data=data, probs=probs, kwargs=kwargs, extra_methods=extra_methods, mean_mcse=mean_mcse, sd_mcse=sd_mcse, textsize=textsize, labeller=labeller, text_kwargs=text_kwargs, rug_kwargs=rug_kwargs, extra_kwargs=extra_kwargs, idata=idata, rug_kind=rug_kind, 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_mcse", "mcseplot", backend) ax = plot(**mcse_kwargs) return ax