code: def _summarize_simulations(self, mean_values, sim_vector, date_index, h, past_values):
                   
  
          error_bars = []
          for pre in range(5,100,5):
              error_bars.append(np.insert([np.percentile(i,pre) for i in sim_vector], 0, mean_values[-h-1]))
          if self.latent_variables.estimation_method in ['M-H']:
              forecasted_values = np.insert([np.mean(i) for i in sim_vector], 0, mean_values[-h-1])
          else:
              forecasted_values = mean_values[-h-1:]
          plot_values = mean_values[-h-past_values:]
          plot_index = date_index[-h-past_values:]
          return error_bars, forecasted_values, plot_values, plot_index
docs: Produces forecasted values to plot, along with prediction intervals
  
          This is a utility function that constructs the prediction intervals and other quantities 
          used for plot_predict() in particular.
  
          Parameters
          ----------
          mean_values : np.ndarray
              Mean predictions for h-step ahead forecasts
  
          sim_vector : np.ndarray
              N simulation predictions for h-step ahead forecasts
  
          date_index : pd.DateIndex or np.ndarray
              Dates for the simulations
  
          h : int
              How many steps ahead are forecast
  
          past_values : int
              How many past observations to include in the forecast plot