def METHOD_NAME():
    # The query appears as a perfect match at location 1 and as very close matches
    # (z-normalized distance of 0.05) at location 0 and 7 (at index 11, the query is
    # not matched in the aamp case).
    # However, since we apply an exclusion zone, the match at index 0 is ignored
    T = np.array(
        [0.1, 1.0, 2.0, 0.0, 1.0, 2.0, -1.0, 0.1, 1.0, 2.0, -0.5, 0.2, 2.0, 4.0]
    )
    Q = np.array([0.0, 1.0, 2.0])
    m = Q.shape[0]
    # Extra large exclusion zone to exclude the first almost perfect match
    excl_zone = m
    for p in [1.0, 2.0, 3.0]:
        left = [
            [0, 3],
            [naive.distance(Q, T[7 : 7 + m], p=p), 7],
        ]
        right = list(
            naive_aamp_match(
                Q,
                T,
                p=p,
                excl_zone=excl_zone,
                max_distance=0.2,
            )
        )
        # To avoid sorting errors we first sort based on distance and then based on
        # indices
        right.sort(key=lambda x: (x[0], x[1]))
        npt.assert_almost_equal(left, right)