code: def point_consensus(self, consensus_type):
          
          if "mean" in consensus_type:
              consensus_data = np.mean(self.data, axis=0)
          elif "std" in consensus_type:
              consensus_data = np.std(self.data, axis=0)
          elif "median" in consensus_type:
              consensus_data = np.median(self.data, axis=0)
          elif "max" in consensus_type:
              consensus_data = np.max(self.data, axis=0)
          elif "percentile" in consensus_type:
              percentile = int(consensus_type.split("_")[1])
              consensus_data = np.percentile(self.data, percentile, axis=0)
          else:
              consensus_data = np.zeros(self.data.shape[1:])
          consensus = EnsembleConsensus(consensus_data, consensus_type, self.ensemble_name,
                                        self.run_date, self.variable, self.start_date, self.end_date, self.units)
          return consensus
docs: Calculate grid-point statistics across ensemble members.
  
          Args:
              consensus_type: mean, std, median, max, or percentile_nn
  
          Returns:
              EnsembleConsensus containing point statistic