code: def rsr(obsvalues,  # type: Union[numpy.ndarray, List[Union[float, int]]]
              simvalues  # type: Union[numpy.ndarray, List[Union[float, int]]]
              ):
          # type: (...) -> Union[float, numpy.ScalarType]
          
          if len(obsvalues) != len(simvalues):
              raise ValueError("The size of observed and simulated values must be"
                               " the same for RSR calculation!")
          mean_obs = sum(obsvalues) / len(obsvalues)
          return sqrt(sum(map(lambda x, y: (x - y) ** 2, obsvalues, simvalues))) / \
                 sqrt(sum(map(lambda x, y: (x - y) ** 2, obsvalues, [mean_obs] * len(obsvalues))))
docs: Calculate RSR (RMSE-to-SD Ratio).
  
          Programmed according to equation (3) in
          Moriasi et al. 2007.  Model evalutaion guidelines for systematic quantification of accuracy 
          in watershed simulations. Transactions of the ASABE 50(3): 885-900.
  
          Args:
              obsvalues: observe values array
              simvalues: simulate values array
  
          Examples:
              >>> obs = [2.92, 2.75, 2.01, 1.09, 2.87, 1.43, 1.96,\
                         4.00, 2.24, 29.28, 5.88, 0.86, 13.21]
              >>> sim = [2.90, 2.87, 2.85, 2.83, 3.04, 2.81, 2.85,\
                         2.78, 2.76, 13.40, 2.70, 2.09, 1.62]
              >>> MathClass.rsr(obs, sim)  # doctest: +ELLIPSIS
              0.7404026155824978...
  
          Returns:
              RSR value, or raise exception