def METHOD_NAME():
    """
    check if cross correlogram is the same as autocorrelogram
    by removing n spike in bin zeros
    numpy method:
      * have problem for the left bin
      * have problem on center
    """
    methods = ["numpy"]
    if HAVE_NUMBA:
        methods.append("numba")
    num_spike = 2000
    spike_times = np.sort(np.unique(np.random.randint(0, 100000, num_spike)))
    num_spike = spike_times.size
    units_dict = {"1": spike_times, "2": spike_times}
    sorting = NumpySorting.from_unit_dict([units_dict], sampling_frequency=10000.0)
    for method in methods:
        correlograms, bins = compute_correlograms(sorting, window_ms=10.0, bin_ms=0.1, method=method)
        num_half_bins = correlograms.shape[2] // 2
        cc = correlograms[0, 1, :]
        ac = correlograms[0, 0, :]
        cc_corrected = cc.copy()
        cc_corrected[num_half_bins] -= num_spike
        if method == "numpy":
            # numpy method have some border effect on left
            assert np.array_equal(cc_corrected[1:num_half_bins], ac[1:num_half_bins])
            # numpy method have some problem on center
            assert np.array_equal(cc_corrected[num_half_bins + 1 :], ac[num_half_bins + 1 :])
        else:
            assert np.array_equal(cc_corrected, ac)
        # ~ import matplotlib.pyplot as plt
        # ~ fig, ax = plt.subplots()
        # ~ ax.plot(bins[:-1], cc, marker='*',  color='red', label='cross-corr')
        # ~ ax.plot(bins[:-1], cc_corrected, marker='*', color='orange', label='cross-corr corrected')
        # ~ ax.plot(bins[:-1], ac, marker='*', color='green', label='auto-corr')
        # ~ ax.set_title(method)
        # ~ ax.legend()
        # ~ ax.set_ylim(0, np.max(correlograms) * 1.1)
        # ~ plt.show()