pymodeler/parameter.py
def symmetric_error(self):
        """Return the symmertic error

        Similar to above, but zero implies no error estimate,
        and otherwise this will either be the symmetric error,
        or the average of the low,high asymmetric errors.
        """
        # ADW: Should this be `np.nan`?
        if self.__errors__ is None:
            return 0.
        if np.isscalar(self.__errors__):
            return self.__errors__
        return 0.5 * (self.__errors__[0] + self.__errors__[1])
def symmetric_error(self):
        """Return the symmertic error

        Similar to above, but zero implies no error estimate,
        and otherwise this will either be the symmetric error,
        or the average of the low,high asymmetric errors.
        """
        # ADW: Should this be `np.nan`?
        if self.__errors__ is None:
            return 0.
        if np.isscalar(self.__errors__):
            return self.__errors__
        return 0.5 * (self.__errors__[0] + self.__errors__[1])
Return the symmertic error

        Similar to above, but zero implies no error estimate,
        and otherwise this will either be the symmetric error,
        or the average of the low,high asymmetric errors.