code: def MAE(x1, x2=-1):
      
      e = get_valid_error(x1, x2)
      return np.sum(np.abs(e)) / float(len(e))
docs: Mean absolute error - this function accepts two series of data or directly
      one series with error.
  
      **Args:**
  
      * `x1` - first data series or error (1d array)
  
      **Kwargs:**
  
      * `x2` - second series (1d array) if first series was not error directly,\\
          then this should be the second series
  
      **Returns:**
  
      * `e` - MAE of error (float) obtained directly from `x1`, \\
          or as a difference of `x1` and `x2`