def IFFT_filter(Signal, SampleFreq, lowerFreq, upperFreq, PyCUDA = False):
    """
    Filters data using fft -> zeroing out fft bins -> ifft

    Parameters
    ----------
    Signal : ndarray
        Signal to be filtered
    SampleFreq : float
        Sample frequency of signal
    lowerFreq : float
        Lower frequency of bandpass to allow through filter
    upperFreq : float
       Upper frequency of bandpass to allow through filter
    PyCUDA : bool, optional
       If True, uses PyCUDA to accelerate the FFT and IFFT
       via using your NVIDIA-GPU
       If False, performs FFT and IFFT with conventional
       scipy.fftpack

    Returns
    -------
    FilteredData : ndarray
        Array containing the filtered data
    """
    if PyCUDA==True:
        Signalfft=calc_fft_with_PyCUDA(Signal)
    else:
        print("starting fft")
        Signalfft = scipy.fftpack.fft(Signal)
    print("starting freq calc")
    freqs = _np.fft.fftfreq(len(Signal)) * SampleFreq
    print("starting bin zeroing")
    Signalfft[_np.where(freqs < lowerFreq)] = 0
    Signalfft[_np.where(freqs > upperFreq)] = 0
    if PyCUDA==True:
        FilteredSignal = 2 * calc_ifft_with_PyCUDA(Signalfft)
    else:
        print("starting ifft")
        FilteredSignal = 2 * scipy.fftpack.ifft(Signalfft)
    print("done")
    return _np.real(FilteredSignal)
Filters data using fft -> zeroing out fft bins -> ifft

    Parameters
    ----------
    Signal : ndarray
        Signal to be filtered
    SampleFreq : float
        Sample frequency of signal
    lowerFreq : float
        Lower frequency of bandpass to allow through filter
    upperFreq : float
       Upper frequency of bandpass to allow through filter
    PyCUDA : bool, optional
       If True, uses PyCUDA to accelerate the FFT and IFFT
       via using your NVIDIA-GPU
       If False, performs FFT and IFFT with conventional
       scipy.fftpack

    Returns
    -------
    FilteredData : ndarray
        Array containing the filtered data