code: def _analyze(self):
      
      output = defaultdict(list)
      output_by_name = defaultdict(list)
      scores = self.anomaly_detector.get_all_scores()
  
      if self.anomalies:
        for anomaly in self.anomalies:
          metrix_scores = scores
          start_t, end_t = anomaly.get_time_window()
          t = anomaly.exact_timestamp
  
          # Compute extended start timestamp and extended end timestamp.
          room = (end_t - start_t) / 2
          if not room:
            room = 30
          extended_start_t = start_t - room
          extended_end_t = end_t + room
          metrix_scores_cropped = metrix_scores.crop(extended_start_t, extended_end_t)
  
          # Adjust the two timestamps if not enough data points are included.
          while len(metrix_scores_cropped) < 2:
            extended_start_t = extended_start_t - room
            extended_end_t = extended_end_t + room
            metrix_scores_cropped = metrix_scores.crop(extended_start_t, extended_end_t)
  
          # Correlate with other metrics
          for entry in self.related_metrices:
            try:
              entry_correlation_result = Correlator(self.metrix, entry, time_period=(extended_start_t, extended_end_t),
                                                    use_anomaly_score=True).get_correlation_result()
              record = extended_start_t, extended_end_t, entry_correlation_result.__dict__, entry
              record_by_name = extended_start_t, extended_end_t, entry_correlation_result.__dict__
              output[t].append(record)
              output_by_name[entry].append(record_by_name)
            except exceptions.NotEnoughDataPoints:
              pass
  
      self.output = output
      self.output_by_name = output_by_name
docs: Analyzes if a matrix has anomalies.
      If any anomaly is found, determine if the matrix correlates with any other matrixes.
      To be implemented.