def IIR_filter_design(CentralFreq, bandwidth, transitionWidth, SampleFreq, GainStop=40, GainPass=0.01):
    """
    Function to calculate the coefficients of an IIR filter, 
    IMPORTANT NOTE: make_butterworth_bandpass_b_a and make_butterworth_b_a
    can produce IIR filters with higher sample rates and are prefereable
    due to this.

    Parameters
    ----------
    CentralFreq : float
        Central frequency of the IIR filter to be designed
    bandwidth : float
        The width of the passband to be created about the central frequency
    transitionWidth : float
        The width of the transition band between the pass-band and stop-band
    SampleFreq : float
        The sample frequency (rate) of the data to be filtered
    GainStop : float, optional
        The dB of attenuation within the stopband (i.e. outside the passband)
    GainPass : float, optional
        The dB attenuation inside the passband (ideally close to 0 for a bandpass filter)

    Returns
    -------
    b : ndarray
        coefficients multiplying the current and past inputs (feedforward coefficients)
    a : ndarray
        coefficients multiplying the past outputs (feedback coefficients)
    """
    NyquistFreq = SampleFreq / 2
    if (CentralFreq + bandwidth / 2 + transitionWidth > NyquistFreq):
        raise ValueError(
            "Need a higher Sample Frequency for this Central Freq, Bandwidth and transition Width")

    CentralFreqNormed = CentralFreq / NyquistFreq
    bandwidthNormed = bandwidth / NyquistFreq
    transitionWidthNormed = transitionWidth / NyquistFreq
    bandpass = [CentralFreqNormed - bandwidthNormed /
                2, CentralFreqNormed + bandwidthNormed / 2]
    bandstop = [CentralFreqNormed - bandwidthNormed / 2 - transitionWidthNormed,
                CentralFreqNormed + bandwidthNormed / 2 + transitionWidthNormed]
    print(bandpass, bandstop)
    b, a = scipy.signal.iirdesign(bandpass, bandstop, GainPass, GainStop)
    return b, a