How to use the biosppy.signals.tools function in biosppy

sampling_rate : int, float, optional
        Sampling frequency (Hz).
    nfreqs : int, optional
        Number of frequency points to compute.
    ax : axis, optional
        Plot Axis to use.

    Returns
    -------
    fig : Figure
        Figure object.

    """

    # compute frequency response
    freqs, resp = st._filter_resp(b, a,
                                  sampling_rate=sampling_rate,
                                  nfreqs=nfreqs)

    # plot
    if ax is None:
        fig = plt.figure()
        ax = fig.add_subplot(111)
    else:
        fig = ax.figure

    # amplitude
    pwr = 20. * np.log10(np.abs(resp))
    ax.semilogx(freqs, pwr, 'b', linewidth=MAJOR_LW)
    ax.set_ylabel('Amplitude (dB)', color='b')
    ax.set_xlabel('Frequency (Hz)')

v200ms = int(0.2 * sampling_rate)
    v1200ms = int(1.2 * sampling_rate)
    M_th = 0.4  # paper is 0.6

    # Pre-processing
    # 1. Moving averaging filter for power-line interference suppression:
    # averages samples in one period of the powerline
    # interference frequency with a first zero at this frequency.
    b = np.ones(int(0.02 * sampling_rate)) / 50.
    a = [1]
    X = ss.filtfilt(b, a, signal)
    # 2. Moving averaging of samples in 28 ms interval for electromyogram
    # noise suppression a filter with first zero at about 35 Hz.
    b = np.ones(int(sampling_rate / 35.)) / 35.
    X = ss.filtfilt(b, a, X)
    X, _, _ = st.filter_signal(signal=X,
                               ftype='butter',
                               band='lowpass',
                               order=7,
                               frequency=40.,
                               sampling_rate=sampling_rate)
    X, _, _ = st.filter_signal(signal=X,
                               ftype='butter',
                               band='highpass',
                               order=7,
                               frequency=9.,
                               sampling_rate=sampling_rate)

    k, Y, L = 1, [], len(X)
    for n in range(k + 1, L - k):
        Y.append(X[n] ** 2 - X[n - k] * X[n + k])
    Y = np.array(Y)