def METHOD_NAME(self):
    tb = self.tb
    N = 10000  # number of samples
    history = 100  # num of samples to average
    data = make_random_complex_tuple(N, 1)  # generate random data
    #  pythonic MA filter
    data_padded = (history - 1) * \
        [complex(0.0, 0.0)] + list(data)  # history
    expected_result = []
    moving_sum = sum(data_padded[:history - 1])
    for i in range(N):
        moving_sum += data_padded[i + history - 1]
        expected_result.append(moving_sum)
        moving_sum -= data_padded[i]
    src = blocks.vector_source_c(data, False)
    op = blocks.moving_average_cc(history, 1)
    dst = blocks.vector_sink_c()
    tb.connect(src, op)
    tb.connect(op, dst)
    tb.run()
    dst_data = dst.data()
    # make sure result is close to zero
    self.assertListAlmostEqual(expected_result, dst_data, 4)