docstr: Initializer
  :param time_series_a: a TimeSeries, a dictionary or a path to a csv file(str).
  :param time_series_b: a TimeSeries, a dictionary or a path to a csv file(str).
  :param time_period: a tuple (start, end) representing a data period for considering correlation.
  :param str algorithm_name: name of the algorithm to use.
  :param dict algorithm_params: additional params for the specific algorithm.
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code: def __init__(
      self,
      time_series_a,
      time_series_b,
      time_period=None,
      use_anomaly_score=False,
      algorithm_name=None,
      algorithm_params=None,
  ):
      """
      Initializer
      :param time_series_a: a TimeSeries, a dictionary or a path to a csv file(str).
      :param time_series_b: a TimeSeries, a dictionary or a path to a csv file(str).
      :param time_period: a tuple (start, end) representing a data period for considering correlation.
      :param str algorithm_name: name of the algorithm to use.
      :param dict algorithm_params: additional params for the specific algorithm.
      """
      self.time_series_a = self._load(time_series_a)
      self.time_series_b = self._load(time_series_b)
      if use_anomaly_score:
          self.time_series_a = self._get_anomaly_scores(
              self.time_series_a
          )
          self.time_series_b = self._get_anomaly_scores(
              self.time_series_b
          )
      if time_period:
          start_p, end_p = time_period
          try:
              self.time_series_a = self.time_series_a.crop(
                  start_p, end_p
              )
              self.time_series_b = self.time_series_b.crop(
                  start_p, end_p
              )
          # No data points fall into the specific time range.
          except ValueError:
              raise exceptions.NotEnoughDataPoints
      self._sanity_check()
      self.algorithm_params = {
          "time_series_a": self.time_series_a,
          "time_series_b": self.time_series_b,
      }
      self._get_algorithm_and_params(algorithm_name, algorithm_params)
      self._correlate()
  
file_path: luminol/src/luminol/correlator.py
imports: from luminol import exceptions, utils
  from luminol.algorithms.correlator_algorithms.all import correlator_algorithms
  from luminol.anomaly_detector import AnomalyDetector
  from luminol.constants import CORRELATOR_ALGORITHM
  from luminol.modules.time_series import TimeSeries
label_desc: Use image node_id 1 to create a new Correlator object from inherited base classes: object with example: obj = Correlator(time_series_a, time_series_b, time_period, use_anomaly_score, algorithm_name, algorithm_params)