def METHOD_NAME(self):
    if self.mask > self.num_half:
        return
    index = numpy.arange(self.num_half)
    index = (index * self.gap + self.offset) % self.num
    samples = self.a[index]
    ret = (samples & self.mask > 0)
    count = numpy.count_nonzero(ret)  # expectation: self.num_half / 2
    if count > self.num_half - count:
        count = self.num_half - count
    prob_le_count = self._calc_probability(count)
    if prob_le_count < 0.001:
        raise