class_docstr: FrequencyPerturber performs FFT on the noise structure of the time series
  data, and removes high frequencies to from the spectra, and reconstruct the
  residual (noise) and added it back to the signal (mean) for generating the
  perturbed instance. Number of frequencies to be removed is specified by the
  truncate_frequencies argument.
class_docstr_tok: ["FrequencyPerturber","performs","FFT","on","the","noise","structure","of","the","time","series","data",",","and","removes","high","frequencies","to","from","the","spectra",",","and","reconstruct","the","residual","(","noise",")","and","added","it","back","to","the","signal","(","mean",")","for","generating","the","perturbed","instance",".","Number","of","frequencies","to","be","removed","is","specified","by","the","truncate_frequencies","argument","."]
code: def _fit(
      self,
      x: Union["tsFrame", np.ndarray],
  ):
      window_length = self._parameters.get("window_length")
      self._mean, self._residual = ts_split_mean_residual(
          x, window_size=window_length
      )
      self._data_length = x.shape[0]
      return self
  
file_path: AIX360/aix360/algorithms/tsutils/tsperturbers/frequency_perturber.py
imports: import numpy
  import pandas
  from typing import Union
  from aix360.algorithms.tsutils.tsframe import tsFrame
  from aix360.algorithms.tsutils.tsperturbers.tsperturber import BlockSelector, TSPerturber
  from aix360.algorithms.tsutils.tsperturbers.perturber_utils import ts_split_mean_residual
label_desc: Use image node_id 4 for calling the FrequencyPerturber obj's underlying member method code with example usage: obj._fit(x) and returns: self