docstr: Analyzes if a matrix has anomalies.
  If any anomaly is found, determine if the matrix correlates with any other matrixes.
  To be implemented.
docstr_tok: ["Analyzes","if","a","matrix","has","anomalies",".","If","any","anomaly","is","found",",","determine","if","the","matrix","correlates","with","any","other","matrixes",".","To","be","implemented","."]
code: def _analyze(self):
      """
      Analyzes if a matrix has anomalies.
      If any anomaly is found, determine if the matrix correlates with any other matrixes.
      To be implemented.
      """
      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
  
file_path: luminol/demo/src/rca.py
imports: from collections import defaultdict
  import os
  import sys
  from luminol import utils, exceptions
  from luminol.anomaly_detector import AnomalyDetector
  from luminol.correlator import Correlator
  from luminol.modules.correlation_result import CorrelationResult
  from luminol.modules.time_series import TimeSeries
label_desc: Use image node_id 3 for calling the RCA obj's underlying member method code with example usage: obj._analyze() without return types