process.py 文件源码

def iFFT(Y, output_length=None, window=False):
    """ Inverse real-valued Fourier Transform

    Parameters
    ----------
    Y : array_like
       Frequency domain data [Nsignals x Nbins]
    output_length : int, optional
       Lenght of returned time-domain signal (Default: 2 x len(Y) + 1)
    win : boolean, optional
       Weights the resulting time-domain signal with a Hann

    Returns
    -------
    y : array_like
       Reconstructed time-domain signal
    """
    Y = _np.atleast_2d(Y)
    y = _np.fft.irfft(Y, n=output_length)

    if window:
        if window not in {'hann', 'hamming', 'blackman', 'kaiser'}:
            raise ValueError('Selected window must be one of hann, hamming, blackman or kaiser')
        no_of_signals, no_of_samples = y.shape

        if window == 'hann':
            window_array = _np.hanning(no_of_samples)
        elif window == 'hamming':
            window_array = _np.hamming(no_of_samples)
        elif window == 'blackman':
            window_array = _np.blackman(no_of_samples)
        elif window == 'kaiser':
            window_array = _np.kaiser(no_of_samples, 3)
        y = window_array * y
    return y