def METHOD_NAME(T):
    threshold = 0.2
    percentage = 1.0
    min_m = 3
    n = T.shape[0] - min_m + 1
    pan = aamp_stimp(
        T,
        min_m=min_m,
        max_m=None,
        step=1,
        percentage=percentage,
        pre_scraamp=True,
    )
    for i in range(n):
        pan.update()
    ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf)
    for idx, m in enumerate(pan.M_[:n]):
        zone = int(np.ceil(m / 4))
        ref_mp = naive.aamp(T, m, T_B=None, exclusion_zone=zone)
        ref_PAN[pan._bfs_indices[idx], : ref_mp.shape[0]] = ref_mp[:, 0]
    # Compare raw pan
    cmp_PAN = pan._PAN
    naive.replace_inf(ref_PAN)
    naive.replace_inf(cmp_PAN)
    npt.assert_almost_equal(ref_PAN, cmp_PAN)
    # Compare transformed pan
    cmp_pan = pan.PAN_
    ref_pan = naive.transform_pan(
        pan._PAN,
        pan._M,
        threshold,
        pan._bfs_indices,
        pan._n_processed,
        np.min(T),
        np.max(T),
    )
    naive.replace_inf(ref_pan)
    naive.replace_inf(cmp_pan)
    npt.assert_almost_equal(ref_pan, cmp_pan)