How to use the yasa.others._zerocrossings function in yasa

# Now we compute the PTP amplitude and keep only the good peaks
    sw_ptp = np.abs(data_filt[idx_neg_peaks]) + data_filt[idx_pos_peaks]
    good_ptp = np.logical_and(sw_ptp > amp_ptp[0], sw_ptp < amp_ptp[1])

    # If good_ptp is all False
    if all(~good_ptp):
        logger.warning('No slow-wave with good amplitude. Returning None.')
        return None

    sw_ptp = sw_ptp[good_ptp]
    idx_neg_peaks = idx_neg_peaks[good_ptp]
    idx_pos_peaks = idx_pos_peaks[good_ptp]

    # Now we need to check the negative and positive phase duration
    # For that we need to compute the zero crossings of the filtered signal
    zero_crossings = _zerocrossings(data_filt)
    # Make sure that there is a zero-crossing after the last detected peak
    if zero_crossings[-1] < max(idx_pos_peaks[-1], idx_neg_peaks[-1]):
        # If not, append the index of the last peak
        zero_crossings = np.append(zero_crossings,
                                   max(idx_pos_peaks[-1], idx_neg_peaks[-1]))

    # Find distance to previous and following zc
    neg_sorted = np.searchsorted(zero_crossings, idx_neg_peaks)
    previous_neg_zc = zero_crossings[neg_sorted - 1] - idx_neg_peaks
    following_neg_zc = zero_crossings[neg_sorted] - idx_neg_peaks
    neg_phase_dur = (np.abs(previous_neg_zc) + following_neg_zc) / sf

    # Distance (in samples) between the positive peaks and the previous and
    # following zero-crossings
    pos_sorted = np.searchsorted(zero_crossings, idx_pos_peaks)
    previous_pos_zc = zero_crossings[pos_sorted - 1] - idx_pos_peaks