Source code for arviz.plots.autocorrplot

"""Autocorrelation plot of data."""

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

[docs] def plot_autocorr( data, var_names=None, filter_vars=None, max_lag=None, combined=False, grid=None, figsize=None, textsize=None, labeller=None, ax=None, backend=None, backend_config=None, backend_kwargs=None, show=None, ): r"""Bar plot of the autocorrelation function (ACF) for a sequence of data. The ACF plots are helpful as a convergence diagnostic for posteriors from MCMC samples which display autocorrelation. Parameters ---------- data : InferenceData 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 str, optional Variables to be plotted. Prefix the variables by ``~`` when you want to exclude them from the plot. See :ref:`this section <common_var_names>` for usage examples. filter_vars : {None, "like", "regex"}, 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. See :ref:`this section <common_filter_vars>` for usage examples. max_lag : int, optional Maximum lag to calculate autocorrelation. By Default, the plot displays the first 100 lag or the total number of draws, whichever is smaller. combined : bool, default False Flag for combining multiple chains into a single chain. If False, chains will be plotted separately. grid : tuple, optional Number of rows and columns. Defaults to None, the rows and columns are automatically inferred. See :ref:`this section <common_grid>` for usage examples. figsize : (float, float), optional Figure size. If None it will be defined automatically. Note this is not used if `ax` is supplied. textsize : float, optional Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on `figsize`. 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_figure, 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). backend : {"matplotlib", "bokeh"}, default "matplotlib" 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 :class:`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 -------- autocov : Compute autocovariance estimates for every lag for the input array. autocorr : Compute autocorrelation using FFT for every lag for the input array. Examples -------- Plot default autocorrelation .. plot:: :context: close-figs >>> import arviz as az >>> data = az.load_arviz_data('centered_eight') >>> az.plot_autocorr(data) Plot subset variables by specifying variable name exactly .. plot:: :context: close-figs >>> az.plot_autocorr(data, var_names=['mu', 'tau'] ) Combine chains by variable and select variables by excluding some with partial naming .. plot:: :context: close-figs >>> az.plot_autocorr(data, var_names=['~thet'], filter_vars="like", combined=True) Specify maximum lag (x axis bound) .. plot:: :context: close-figs >>> az.plot_autocorr(data, var_names=['mu', 'tau'], max_lag=200, combined=True) """ data = convert_to_dataset(data, group="posterior") var_names = _var_names(var_names, data, filter_vars) # Default max lag to 100 or max length of chain if max_lag is None: max_lag = min(100, data["draw"].shape[0]) if labeller is None: labeller = BaseLabeller() plotters = filter_plotters_list( list(xarray_var_iter(data, var_names, combined, dim_order=["chain", "draw"])), "plot_autocorr", ) rows, cols = default_grid(len(plotters), grid=grid) autocorr_plot_args = dict( axes=ax, plotters=plotters, max_lag=max_lag, figsize=figsize, rows=rows, cols=cols, combined=combined, textsize=textsize, labeller=labeller, backend_kwargs=backend_kwargs, show=show, ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() if backend == "bokeh": autocorr_plot_args.update(backend_config=backend_config) # TODO: Add backend kwargs plot = get_plotting_function("plot_autocorr", "autocorrplot", backend) axes = plot(**autocorr_plot_args) return axes