docs: def large_standard_deviation(x, r):
      """
      Boolean variable denoting if the standard dev of x is higher
      than 'r' times the range = difference between max and min of x.
      Hence it checks if
  
      .. math::
  
          std(x) > r * (max(X)-min(X))
  
      According to a rule of the thumb, the standard deviation should be a forth of the range of the values.
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param r: the percentage of the range to compare with
      :type r: float
      :return: the value of this feature
      :return type: bool
      """
      if not isinstance(x, (np.ndarray, pd.Series)):
          x = np.asarray(x)
      return np.std(x) > (r * (np.max(x) - np.min(x)))