class_docstr: Discrete Wavelet Transform Transformer.
  
  Performs the Haar wavelet transformation on a time series.
  
  Parameters
  ----------
  num_levels : int, number of levels to perform the Haar wavelet
               transformation.
class_docstr_tok: ["Discrete","Wavelet","Transform","Transformer",".","Performs","the","Haar","wavelet","transformation","on","a","time","series",".","Parameters","--","--","--","--","--","num_levels",":","int",",","number","of","levels","to","perform","the","Haar","wavelet","transformation","."]
docstr: Extract wavelet coefficients of a 2d array of time series.
  
  The coefficients correspond to the wavelet coefficients from levels 1 to
  num_levels followed by the approximation coefficients of the highest level.
docstr_tok: ["Extract","wavelet","coefficients","of","a","2d","array","of","time","series",".","The","coefficients","correspond","to","the","wavelet","coefficients","from","levels","1","to","num_levels","followed","by","the","approximation","coefficients","of","the","highest","level","."]
code: def _extract_wavelet_coefficients(self, data):
      """Extract wavelet coefficients of a 2d array of time series.
  
      The coefficients correspond to the wavelet coefficients from levels 1 to
      num_levels followed by the approximation coefficients of the highest level.
      """
      num_levels = self.num_levels
      res = []
  
      for x in data:
          if num_levels == 0:
              res.append(x)
          else:
              coeffs = []
              current = x
              approx = None
              for _ in range(num_levels):
                  approx = self._get_approx_coefficients(current)
                  wav_coeffs = self._get_wavelet_coefficients(current)
                  current = approx
                  wav_coeffs.reverse()
                  coeffs.extend(wav_coeffs)
              approx.reverse()
              coeffs.extend(approx)
              coeffs.reverse()
              res.append(coeffs)
  
      return res
  
file_path: sktime/sktime/transformations/panel/dwt.py
imports: import math
  import numpy
  import pandas
  from sktime.datatypes import convert
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the DWTTransformer obj's underlying member method code with example usage: obj._extract_wavelet_coefficients(data) and returns: res