code: def _plot_posterior_op(
      idx,
      values,
      var_name,
      selection,
      ax,
      bw,
      circular,
      linewidth,
      bins,
      kind,
      point_estimate,
      hdi_prob,
      multimodal,
      skipna,
      ref_val,
      rope,
      ref_val_color,
      rope_color,
      ax_labelsize,
      round_to: Optional[int] = None,
      **kwargs,
  ):  # noqa: D202
      """Artist to draw posterior."""
  
      def format_as_percent(x, round_to=0):
          return "{0:.{1:d}f}%".format(100 * x, round_to)
  
      def display_ref_val(max_data):
          if ref_val is None:
              return
          elif isinstance(ref_val, dict):
              val = None
              for sel in ref_val.get(var_name, []):
                  if all(
                      k in selection and selection[k] == v
                      for k, v in sel.items()
                      if k != "ref_val"
                  ):
                      val = sel["ref_val"]
                      break
              if val is None:
                  return
          elif isinstance(ref_val, list):
              val = ref_val[idx]
          elif isinstance(ref_val, Number):
              val = ref_val
          else:
              raise ValueError(
                  "Argument `ref_val` must be None, a constant, a list or a "
                  'dictionary like {"var_name": [{"ref_val": ref_val}]}'
              )
          less_than_ref_probability = (values < val).mean()
          greater_than_ref_probability = (values >= val).mean()
          ref_in_posterior = "{} <{:g}< {}".format(
              format_as_percent(less_than_ref_probability, 1),
              val,
              format_as_percent(greater_than_ref_probability, 1),
          )
          ax.line(
              [val, val],
              [0, 0.8 * max_data],
              line_color=vectorized_to_hex(ref_val_color),
              line_alpha=0.65,
          )
  
          ax.text(
              x=[values.mean()],
              y=[max_data * 0.6],
              text=[ref_in_posterior],
              text_color=vectorized_to_hex(ref_val_color),
              text_align="center",
          )
  
      def display_rope(max_data):
          if rope is None:
              return
          elif isinstance(rope, dict):
              vals = None
              for sel in rope.get(var_name, []):
                  # pylint: disable=line-too-long
                  if all(
                      k in selection and selection[k] == v
                      for k, v in sel.items()
                      if k != "rope"
                  ):
                      vals = sel["rope"]
                      break
              if vals is None:
                  return
          elif len(rope) == 2:
              vals = rope
          else:
              raise ValueError(
                  "Argument `rope` must be None, a dictionary like"
                  '{"var_name": {"rope": (lo, hi)}}, or an'
                  "iterable of length 2"
              )
          rope_text = [
              f"{val:.{format_sig_figs(val, round_to)}g}"
              for val in vals
          ]
  
          ax.line(
              vals,
              (max_data * 0.02, max_data * 0.02),
              line_width=linewidth * 5,
              line_color=vectorized_to_hex(rope_color),
              line_alpha=0.7,
          )
          probability_within_rope = (
              (values > vals[0]) & (values <= vals[1])
          ).mean()
          text_props = dict(
              text_color=vectorized_to_hex(rope_color),
              text_align="center",
          )
          ax.text(
              x=values.mean(),
              y=[max_data * 0.45],
              text=[
                  f"{format_as_percent(probability_within_rope, 1)} in ROPE"
              ],
              **text_props,
          )
  
          ax.text(
              x=vals,
              y=[max_data * 0.2, max_data * 0.2],
              text_font_size=f"{ax_labelsize}pt",
              text=rope_text,
              **text_props,
          )
  
      def display_point_estimate(max_data):
          if not point_estimate:
              return
          point_value = calculate_point_estimate(
              point_estimate, values, bw, circular
          )
          sig_figs = format_sig_figs(point_value, round_to)
          point_text = (
              "{point_estimate}={point_value:.{sig_figs}g}".format(
                  point_estimate=point_estimate,
                  point_value=point_value,
                  sig_figs=sig_figs,
              )
          )
  
          ax.text(
              x=[point_value],
              y=[max_data * 0.8],
              text=[point_text],
              text_align="center",
          )
  
      def display_hdi(max_data):
          # np.ndarray with 2 entries, min and max
          # pylint: disable=line-too-long
          hdi_probs = hdi(
              values,
              hdi_prob=hdi_prob,
              circular=circular,
              multimodal=multimodal,
              skipna=skipna,
          )  # type: np.ndarray
  
          for hdi_i in np.atleast_2d(hdi_probs):
              ax.line(
                  hdi_i,
                  (max_data * 0.02, max_data * 0.02),
                  line_width=linewidth * 2,
                  line_color="black",
              )
  
              ax.text(
                  x=list(hdi_i) + [(hdi_i[0] + hdi_i[1]) / 2],
                  y=[max_data * 0.07, max_data * 0.07, max_data * 0.3],
                  text=(
                      list(
                          map(
                              str,
                              map(
                                  lambda x: round_num(x, round_to),
                                  hdi_i,
                              ),
                          )
                      )
                      + [f"{format_as_percent(hdi_prob)} HDI"]
                  ),
                  text_align="center",
              )
  
      def format_axes():
          ax.yaxis.visible = False
          ax.yaxis.major_tick_line_color = None
          ax.yaxis.minor_tick_line_color = None
          ax.yaxis.major_label_text_font_size = "0pt"
          ax.xgrid.grid_line_color = None
          ax.ygrid.grid_line_color = None
  
      if skipna:
          values = values[~np.isnan(values)]
  
      if kind == "kde" and values.dtype.kind == "f":
          kwargs.setdefault("line_width", linewidth)
          plot_kde(
              values,
              bw=bw,
              is_circular=circular,
              fill_kwargs={"fill_alpha": kwargs.pop("fill_alpha", 0)},
              plot_kwargs=kwargs,
              ax=ax,
              rug=False,
              backend="bokeh",
              backend_kwargs={},
              show=False,
          )
          max_data = values.max()
      elif values.dtype.kind == "i" or (
          values.dtype.kind == "f" and kind == "hist"
      ):
          if bins is None:
              bins = get_bins(values)
          kwargs.setdefault("align", "left")
          kwargs.setdefault("color", "blue")
          _, hist, edges = histogram(values, bins=bins)
          max_data = hist.max()
          ax.quad(
              top=hist,
              bottom=0,
              left=edges[:-1],
              right=edges[1:],
              fill_alpha=0.35,
              line_alpha=0.35,
          )
      elif values.dtype.kind == "b":
          if bins is None:
              bins = "auto"
          kwargs.setdefault("color", "blue")
  
          hist = np.array([(~values).sum(), values.sum()])
          max_data = hist.max()
          edges = np.array([-0.5, 0.5, 1.5])
          ax.quad(
              top=hist,
              bottom=0,
              left=edges[:-1],
              right=edges[1:],
              fill_alpha=0.35,
              line_alpha=0.35,
          )
          hdi_prob = "hide"
          ax.xaxis.ticker = [0, 1]
          ax.xaxis.major_label_overrides = {0: "False", 1: "True"}
      else:
          raise TypeError("Values must be float, integer or boolean")
  
      format_axes()
      if hdi_prob != "hide":
          display_hdi(max_data)
      display_point_estimate(max_data)
      display_ref_val(max_data)
      display_rope(max_data)
  
file_path: arviz/arviz/plots/backends/bokeh/posteriorplot.py
imports: from numbers import Number
  from typing import Optional
  import numpy
  from bokeh.models.annotations import Title
  from ....stats import hdi
  from ....stats.density_utils import get_bins, histogram
  from ...kdeplot import plot_kde
  from ...plot_utils import _scale_fig_size, calculate_point_estimate, format_sig_figs, round_num, vectorized_to_hex
  from ..None import show_layout
  from .None import backend_kwarg_defaults, create_axes_grid
label_desc: Use image node_id 2 for calling a global function with example usage: _plot_posterior_op(idx, values, var_name, selection, ax, bw, circular, linewidth, bins, kind, point_estimate, hdi_prob, multimodal, skipna, ref_val, rope, ref_val_color, rope_color, ax_labelsize, round_to) and returns: str