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

f, t, Sxx = stft_power(data, sf, window=2, step=.2, band=freq_broad,
                           interp=False, norm=True)
    idx_sigma = np.logical_and(f >= freq_sp[0], f <= freq_sp[1])
    rel_pow = Sxx[idx_sigma].sum(0)

    # Let's interpolate `rel_pow` to get one value per sample
    # Note that we could also have use the `interp=True` in the `stft_power`
    # function, however 2D interpolation is much slower than
    # 1D interpolation.
    func = interp1d(t, rel_pow, kind='cubic', bounds_error=False,
                    fill_value=0)
    t = np.arange(data.size) / sf
    rel_pow = func(t)

    # Now we apply moving RMS and correlation on the sigma-filtered signal
    _, mcorr = moving_transform(data_sigma, data, sf, window=.3, step=.1,
                                method='corr', interp=True)
    _, mrms = moving_transform(data_sigma, data, sf, window=.3, step=.1,
                               method='rms', interp=True)

    # Hilbert power (to define the instantaneous frequency / power)
    n = data_sigma.size
    nfast = next_fast_len(n)
    analytic = signal.hilbert(data_sigma, N=nfast)[:n]
    inst_phase = np.angle(analytic)
    inst_pow = np.square(np.abs(analytic))
    # inst_freq = sf / 2pi * 1st-derivative of the phase of the analytic signal
    inst_freq = (sf / (2 * np.pi) * np.ediff1d(inst_phase))

    # Let's define the thresholds
    if hypno is None:
        thresh_rms = mrms.mean() + thresh['rms'] * trimbothstd(mrms, cut=0.10)

idx_sigma = np.logical_and(f >= freq_sp[0], f <= freq_sp[1])
    rel_pow = Sxx[idx_sigma].sum(0)

    # Let's interpolate `rel_pow` to get one value per sample
    # Note that we could also have use the `interp=True` in the `stft_power`
    # function, however 2D interpolation is much slower than
    # 1D interpolation.
    func = interp1d(t, rel_pow, kind='cubic', bounds_error=False,
                    fill_value=0)
    t = np.arange(data.size) / sf
    rel_pow = func(t)

    # Now we apply moving RMS and correlation on the sigma-filtered signal
    _, mcorr = moving_transform(data_sigma, data, sf, window=.3, step=.1,
                                method='corr', interp=True)
    _, mrms = moving_transform(data_sigma, data, sf, window=.3, step=.1,
                               method='rms', interp=True)

    # Hilbert power (to define the instantaneous frequency / power)
    n = data_sigma.size
    nfast = next_fast_len(n)
    analytic = signal.hilbert(data_sigma, N=nfast)[:n]
    inst_phase = np.angle(analytic)
    inst_pow = np.square(np.abs(analytic))
    # inst_freq = sf / 2pi * 1st-derivative of the phase of the analytic signal
    inst_freq = (sf / (2 * np.pi) * np.ediff1d(inst_phase))

    # Let's define the thresholds
    if hypno is None:
        thresh_rms = mrms.mean() + thresh['rms'] * trimbothstd(mrms, cut=0.10)
    else:
        thresh_rms = mrms[mask].mean() + thresh['rms'] * \