docs: def energy_ratio_by_chunks(x, param):
      """
      Calculates the sum of squares of chunk i out of N chunks expressed as a ratio with the sum of squares over the whole
      series.
  
      Takes as input parameters the number num_segments of segments to divide the series into and segment_focus
      which is the segment number (starting at zero) to return a feature on.
  
      If the length of the time series is not a multiple of the number of segments, the remaining data points are
      distributed on the bins starting from the first. For example, if your time series consists of 8 entries, the
      first two bins will contain 3 and the last two values, e.g. `[ 0.,  1.,  2.], [ 3.,  4.,  5.]` and `[ 6.,  7.]`.
  
      Note that the answer for `num_segments = 1` is a trivial "1" but we handle this scenario
      in case somebody calls it. Sum of the ratios should be 1.0.
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param param: contains dictionaries {"num_segments": N, "segment_focus": i} with N, i both ints
      :return: the feature values
      :return type: list of tuples (index, data)
      """
      res_data = []
      res_index = []
      full_series_energy = np.sum(x ** 2)
  
      for parameter_combination in param:
          num_segments = parameter_combination["num_segments"]
          segment_focus = parameter_combination["segment_focus"]
          assert segment_focus < num_segments
          assert num_segments > 0
  
          res_data.append(np.sum(np.array_split(x, num_segments)[segment_focus] ** 2.0)/full_series_energy)
          res_index.append("num_segments_{}__segment_focus_{}".format(num_segments, segment_focus))
  
      return list(zip(res_index, res_data))