Source code for arviz.plots.backends

# pylint: disable=no-member,invalid-name,redefined-outer-name
"""ArviZ plotting backends."""
import re

import numpy as np
from pandas import DataFrame

from ...rcparams import rcParams

__all__ = [

[docs]def to_cds( data, var_names=None, groups=None, dimensions=None, group_info=True, var_name_format=None, index_origin=None, ): """Transform data to ColumnDataSource (CDS) compatible with Bokeh. Uses `_ARVIZ_GROUP_` and `_ARVIZ_CDS_SELECTION_` to separate var_name from group and dimensions in CDS columns. Parameters ---------- data : obj Any object that can be converted to an az.InferenceData object Refer to documentation of az.convert_to_inference_data for details var_names : str or list of str, optional Variables to be processed, if None all variables are processed. groups : str or list of str, optional Select groups for CDS. Default groups are {"posterior_groups", "prior_groups", "posterior_groups_warmup"} - posterior_groups: posterior, posterior_predictive, sample_stats - prior_groups: prior, prior_predictive, sample_stats_prior - posterior_groups_warmup: warmup_posterior, warmup_posterior_predictive, warmup_sample_stats ignore_groups : str or list of str, optional Ignore specific groups from CDS. dimension : str, or list of str, optional Select dimensions along to slice the data. By default uses ("chain", "draw"). group_info : bool Add group info for `var_name_format` var_name_format : str or tuple of tuple of string, optional Select column name format for non-scalar input. Predefined options are {"brackets", "underscore", "cds"} "brackets": - add_group_info == False: ``theta[0,0]`` - add_group_info == True: ``theta_posterior[0,0]`` "underscore": - add_group_info == False: ``theta_0_0`` - add_group_info == True: ``theta_posterior_0_0_`` "cds": - add_group_info == False: ``theta_ARVIZ_CDS_SELECTION_0_0`` - add_group_info == True: ``theta_ARVIZ_GROUP_posterior__ARVIZ_CDS_SELECTION_0_0`` tuple: Structure: - tuple: (dim_info, group_info) - dim_info: (str: `.join` separator, str: dim_separator_start, str: dim_separator_end) - group_info: (str: group separator start, str: group separator end) Example: ((",", "[", "]"), ("_", "")) - add_group_info == False: ``theta[0,0]`` - add_group_info == True: ``theta_posterior[0,0]`` index_origin : int, optional Start parameter indices from `index_origin`. Either 0 or 1. Returns ------- bokeh.models.ColumnDataSource object """ from ...utils import flatten_inference_data_to_dict if var_name_format is None: var_name_format = "cds" cds_dict = flatten_inference_data_to_dict( data=data, var_names=var_names, groups=groups, dimensions=dimensions, group_info=group_info, index_origin=index_origin, var_name_format=var_name_format, ) cds_data = ColumnDataSource(DataFrame.from_dict(cds_dict, orient="columns")) return cds_data
[docs]def output_notebook(*args, **kwargs): """Wrap func:`bokeh.plotting.output_notebook`.""" import bokeh.plotting as bkp return bkp.output_notebook(*args, **kwargs)
[docs]def output_file(*args, **kwargs): """Wrap :func:`bokeh.plotting.output_file`.""" import bokeh.plotting as bkp return bkp.output_file(*args, **kwargs)
[docs]def ColumnDataSource(*args, **kwargs): """Wrap bokeh.models.ColumnDataSource.""" from bokeh.models import ColumnDataSource return ColumnDataSource(*args, **kwargs)
[docs]def create_layout(ax, force_layout=False): """Transform bokeh array of figures to layout.""" ax = np.atleast_2d(ax) subplot_order = rcParams["plot.bokeh.layout.order"] if force_layout: from bokeh.layouts import gridplot as layout ax = ax.tolist() layout_args = { "sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"], "toolbar_location": rcParams["plot.bokeh.layout.toolbar_location"], } elif any(item in subplot_order for item in ("row", "column")): # check number of rows match = re.match(r"(\d*)(row|column)", subplot_order) n = int( if is not None else 1 subplot_order = # set up 1D list of axes ax = [item for item in ax.ravel().tolist() if item is not None] layout_args = {"sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"]} if subplot_order == "row" and n == 1: from bokeh.layouts import row as layout elif subplot_order == "column" and n == 1: from bokeh.layouts import column as layout else: from bokeh.layouts import layout if n != 1: ax = np.array(ax + [None for _ in range(int(np.ceil(len(ax) / n)) - len(ax))]) if subplot_order == "row": ax = ax.reshape(n, -1) else: ax = ax.reshape(-1, n) ax = ax.tolist() else: if subplot_order in ("square", "square_trimmed"): ax = [item for item in ax.ravel().tolist() if item is not None] n = int(np.ceil(len(ax) ** 0.5)) ax = ax + [None for _ in range(n ** 2 - len(ax))] ax = np.array(ax).reshape(n, n) ax = ax.tolist() if (subplot_order == "square_trimmed") and any( all(item is None for item in row) for row in ax ): from bokeh.layouts import layout ax = [row for row in ax if not all(item is None for item in row)] layout_args = {"sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"]} else: from bokeh.layouts import gridplot as layout layout_args = { "sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"], "toolbar_location": rcParams["plot.bokeh.layout.toolbar_location"], } # ignore "fixed" sizing_mode without explicit width and height if layout_args.get("sizing_mode", "") == "fixed": layout_args.pop("sizing_mode") return layout(ax, **layout_args)
[docs]def show_layout(ax, show=True, force_layout=False): """Create a layout and call bokeh show.""" if show is None: show = rcParams[""] if show: import bokeh.plotting as bkp layout = create_layout(ax, force_layout=force_layout)
def _copy_docstring(lib, function): """Extract docstring from function.""" import importlib try: module = importlib.import_module(lib) func = getattr(module, function) doc = func.__doc__ except ImportError: doc = f"Failed to import function {function} from {lib}" if not isinstance(doc, str): doc = "" return doc # TODO: try copying substitutions too, or autoreplace them ourselves output_notebook.__doc__ += "\n\n" + _copy_docstring("bokeh.plotting", "output_notebook") output_file.__doc__ += "\n\n" + _copy_docstring("bokeh.plotting", "output_file") ColumnDataSource.__doc__ += "\n\n" + _copy_docstring("bokeh.models", "ColumnDataSource")