def selectivity(self, d):
    '\n        Return a measure of the peakedness of the distribution.  The\n        calculation differs depending on whether this is a cyclic\n        variable.  For a cyclic variable, returns the magnitude of the\n        vector_sum() divided by the sum_value() (see\n        _vector_selectivity for more details).  For a non-cyclic\n        variable, returns the max_value_bin()) as a proportion of the\n        sum_value() (see _relative_selectivity for more details).\n        '
    if (d.cyclic == True):
        return self._vector_selectivity(d)
    else:
        return self._relative_selectivity(d)
Return a measure of the peakedness of the distribution.  The
calculation differs depending on whether this is a cyclic
variable.  For a cyclic variable, returns the magnitude of the
vector_sum() divided by the sum_value() (see
_vector_selectivity for more details).  For a non-cyclic
variable, returns the max_value_bin()) as a proportion of the
sum_value() (see _relative_selectivity for more details).
def selectivity(self, d):
    '\n        Return a measure of the peakedness of the distribution.  The\n        calculation differs depending on whether this is a cyclic\n        variable.  For a cyclic variable, returns the magnitude of the\n        vector_sum() divided by the sum_value() (see\n        _vector_selectivity for more details).  For a non-cyclic\n        variable, returns the max_value_bin()) as a proportion of the\n        sum_value() (see _relative_selectivity for more details).\n        '
    if (d.cyclic == True):
        return self._vector_selectivity(d)
    else:
        return self._relative_selectivity(d)