units.py 文件源码

def get_ft_windows():
    """ Retrieve the available windows to be applied on signal data before FT.

    @return: dict with keys being the window name and items being again a dict
             containing the actual function and the normalization factor to
             calculate correctly the amplitude spectrum in the Fourier Transform

    To find out the amplitude normalization factor check either the scipy
    implementation on
        https://github.com/scipy/scipy/blob/v0.15.1/scipy/signal/windows.py#L336
    or just perform a sum of the window (oscillating parts of the window should
    be averaged out and constant offset factor will remain):
        MM=1000000  # choose a big number
        print(sum(signal.hanning(MM))/MM)
    """

    win = {'none': {'func': np.ones, 'ampl_norm': 1.0},
           'hamming': {'func': signal.hamming, 'ampl_norm': 1.0/0.54},
           'hann': {'func': signal.hann, 'ampl_norm': 1.0/0.5},
           'blackman': {'func': signal.blackman, 'ampl_norm': 1.0/0.42},
           'triang': {'func': signal.triang, 'ampl_norm': 1.0/0.5},
           'flattop': {'func': signal.flattop, 'ampl_norm': 1.0/0.2156},
           'bartlett': {'func': signal.bartlett, 'ampl_norm': 1.0/0.5},
           'parzen': {'func': signal.parzen, 'ampl_norm': 1.0/0.375},
           'bohman': {'func': signal.bohman, 'ampl_norm': 1.0/0.4052847},
           'blackmanharris': {'func': signal.blackmanharris, 'ampl_norm': 1.0/0.35875},
           'nuttall': {'func': signal.nuttall, 'ampl_norm': 1.0/0.3635819},
           'barthann': {'func': signal.barthann, 'ampl_norm': 1.0/0.5}}
    return win