Calculate PBIAS, or percent model bias.

        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.pbias(obs, sim)  # doctest: +ELLIPSIS
            35.46099290780142...

        Returns:
            PBIAS value (percentage), or raise exception

def pbias(obsvalues,  # type: Union[numpy.ndarray, List[Union[float, int]]]
              simvalues  # type: Union[numpy.ndarray, List[Union[float, int]]]
              ):
        # type: (...) -> Union[float, numpy.ScalarType]
        """Calculate PBIAS, or percent model bias.

        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.pbias(obs, sim)  # doctest: +ELLIPSIS
            35.46099290780142...

        Returns:
            PBIAS value (percentage), or raise exception
        """
        if len(obsvalues) != len(simvalues):
            raise ValueError("The size of observed and simulated values must be"
                             " the same for PBIAS calculation!")
        return sum(map(lambda x, y: (x - y) * 100, obsvalues, simvalues)) / sum(obsvalues)