code: def number_crossing_m(x, m):
      
      if not isinstance(x, (np.ndarray, pd.Series)):
          x = np.asarray(x)
      # From https://stackoverflow.com/questions/3843017/efficiently-detect-sign-changes-in-python
      positive = x > m
      return np.where(np.bitwise_xor(positive[1:], positive[:-1]))[0].size
docs: Calculates the number of crossings of x on m. A crossing is defined as two sequential values where the first value
      is lower than m and the next is greater, or vice-versa. If you set m to zero, you will get the number of zero
      crossings.
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param m: the threshold for the crossing
      :type m: float
      :return: the value of this feature
      :return type: int