def METHOD_NAME():
    # The query can be found as a perfect match two times in the time series
    T = np.array([0.0, 1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, -0.5])
    Q = np.array([0.0, 1.0, 0.0])
    m = Q.shape[0]
    excl_zone = int(np.ceil(m / 4))
    for p in [1.0, 2.0, 3.0]:
        left = [[0, 0], [0, 5]]
        right = list(
            naive_aamp_match(
                Q,
                T,
                p=p,
                excl_zone=excl_zone,
                max_distance=0.001,  # Small max_distance as matches are identical
            )
        )
        # To avoid sorting errors we first sort based on distance and then based on
        # indices
        right.sort(key=lambda x: (x[1], x[0]))
        npt.assert_almost_equal(left, right)