code: def calc_percentile_interval(self, conf_percentage):
          
          # Get the alpha % that corresponds to the given confidence percentage.
          alpha = bc.get_alpha_from_conf_percentage(conf_percentage)
          # Create the column names for the dataframe of confidence intervals
          single_column_names =\
              ['{:.3g}%'.format(alpha / 2.0),
               '{:.3g}%'.format(100 - alpha / 2.0)]
          # Calculate the desired confidence intervals.
          conf_intervals =\
              bc.calc_percentile_interval(self.bootstrap_replicates.values,
                                          conf_percentage)
          # Store the desired confidence intervals
          self.percentile_interval =\
              pd.DataFrame(conf_intervals.T,
                           index=self.mle_params.index,
                           columns=single_column_names)
          return None
docs: Calculates percentile bootstrap confidence intervals for one's model.
  
          Parameters
          ----------
          conf_percentage : scalar in the interval (0.0, 100.0).
              Denotes the confidence-level for the returned endpoints. For
              instance, to calculate a 95% confidence interval, pass `95`.
  
          Returns
          -------
          None. Will store the percentile intervals as `self.percentile_interval`
  
          Notes
          -----
          Must have all ready called `self.generate_bootstrap_replicates`.