(self, f_values, idx)
return f_values
obsolete and subject to removal (TODO),
        return modified f-values such that for each mirror one becomes worst.

        This function is useless when selective mirroring is applied with no
        more than (lambda-mu)/2 solutions.

        Mirrors are leading and trailing values in ``f_values``.
obsolete and subject to removal (TODO),
        return modified f-values such that for each mirror one becomes worst.
obsolete and subject to removal ( TODO ) return modified f - values such that for each mirror one becomes worst .
def mirror_penalized(self, f_values, idx):
        """obsolete and subject to removal (TODO),
        return modified f-values such that for each mirror one becomes worst.

        This function is useless when selective mirroring is applied with no
        more than (lambda-mu)/2 solutions.

        Mirrors are leading and trailing values in ``f_values``.

        """
        assert len(f_values) >= 2 * len(idx)
        m = np.max(np.abs(f_values))
        for i in len(idx):
            if f_values[idx[i]] > f_values[-1-i]:
                f_values[idx[i]] += m
            else:
                f_values[-1-i] += m
        return f_values
def mirror_penalized ( self , f_values , idx ) : assert len ( f_values ) >= 2 * len ( idx ) m = np . max ( np . abs ( f_values ) ) for i in len ( idx ) : if f_values [ idx [ i ] ] > f_values [ - 1 - i ] : f_values [ idx [ i ] ] += m else : f_values [ - 1 - i ] += m return f_values