class_docstr: SmartPlotter is a Bridge pattern decoupling plotting functions from SmartExplainer.
  The smartplotter class includes all the methods used to display graphics
  Each SmartPlotter method is easy to use from a Smart explainer object,
  just use the following syntax
  Attributes :
  explainer: object
      SmartExplainer instance to point to.
  Example
  --------
  >>> xpl.plot.my_plot_method(param=value)
class_docstr_tok: ["SmartPlotter","is","a","Bridge","pattern","decoupling","plotting","functions","from","SmartExplainer",".","The","smartplotter","class","includes","all","the","methods","used","to","display","graphics","Each","SmartPlotter","method","is","easy","to","use","from","a","Smart","explainer","object",",","just","use","the","following","syntax","Attributes",":","explainer",":","object","SmartExplainer","instance","to","point","to",".","Example","--","--","--","--",">",">",">","xpl.plot.my_plot_method","(","param=value",")"]
docstr: adapts the color scale to the distribution of points
  Parameters
  ----------
  values: 1 column pd.DataFrame
      values ​​whose quantiles must be calculated
code: def tuning_colorscale(self, values):
      """
      adapts the color scale to the distribution of points
      Parameters
      ----------
      values: 1 column pd.DataFrame
          values ​​whose quantiles must be calculated
      """
      desc_df = values.describe(
          percentiles=np.arange(0.1, 1, 0.1).tolist()
      )
      min_pred, max_init = list(desc_df.loc[["min", "max"]].values)
      desc_pct_df = (
          desc_df.loc[~desc_df.index.isin(["count", "mean", "std"])]
          - min_pred
      ) / (max_init - min_pred)
      color_scale = list(
          map(
              list,
              (
                  zip(
                      desc_pct_df.values.flatten(),
                      self._style_dict["init_contrib_colorscale"],
                  )
              ),
          )
      )
      return color_scale
  
imports: import warnings
  from numbers import Number
  import random
  import copy
  import math
  import numpy
  from scipy.optimize import fsolve
  import scipy.cluster.hierarchy
  import pandas
  from plotly import graph_objs
  import plotly.express
  from plotly.subplots import make_subplots
  from plotly.offline import plot
  from shapash.manipulation.select_lines import select_lines
  from shapash.manipulation.summarize import compute_features_import, project_feature_values_1d, compute_corr
  from shapash.utils.utils import add_line_break, truncate_str, compute_digit_number, add_text, maximum_difference_sort_value, compute_sorted_variables_interactions_list_indices, compute_top_correlations_features
  from shapash.webapp.utils.utils import round_to_k
  from shapash.style.style_utils import colors_loading, select_palette, define_style
label_desc: Use image node_id 3 for calling the SmartPlotter obj's underlying member method code with example usage: obj.tuning_colorscale(values) and returns: color_scale