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: Get the approximate coefficients at a given level.
docstr_tok: ["Get","the","approximate","coefficients","at","a","given","level","."]
code: def _get_approx_coefficients(self, arr):
      """Get the approximate coefficients at a given level."""
      new = []
      if len(arr) == 1:
          return [arr[0]]
      for x in range(math.floor(len(arr) / 2)):
          new.append((arr[2 * x] + arr[2 * x + 1]) / math.sqrt(2))
      return new
  
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 5 for calling the DWTTransformer obj's underlying member method code with example usage: obj._get_approx_coefficients(arr) and returns: new, list