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src/o/b/obspy-0.9.0/obspy/signal/freqattributes.py   obspy(Download)
    :return: **cfreq** - Central frequency in Hz
    """
    nfft = util.nextpow2(len(data))
    freq = np.linspace(0, fs, nfft + 1)
    freqaxis = freq[0:nfft / 2]
        period (windowed only).
    """
    nfft = util.nextpow2(data.shape[1])
    freqaxis = np.linspace(0, fs, nfft + 1)
    bwith = np.zeros(data.shape[0])
    """
    dataT = np.transpose(data)
    nfft = util.nextpow2(dataT.shape[0])
    fc = fftpack.fft(dataT, nfft, 0)
    f = fc[1:len(fc) / 2 + 1, :]

src/o/b/obspy-0.9.0/obspy/signal/cpxtrace.py   obspy(Download)
        input data.
    """
    nfft = util.nextpow2(data.shape[size(data.shape) - 1])
    A_cpx = np.zeros((data.shape), dtype='complex64')
    A_abs = np.zeros((data.shape), dtype='float64')

src/o/b/obspy-0.9.0/obspy/signal/invsim.py   obspy(Download)
    # Numerical Recipes p. 429 calculate next power of 2.
    if nfft_pow2:
        nfft = util.nextpow2(2 * ndat)
    # evalresp scales directly with nfft, therefor taking the next power of
    # two has a greater negative performance impact than the slow down of a

src/o/b/obspy-0.9.0/obspy/signal/hoctavbands.py   obspy(Download)
        fmin[i] = fc[i] / np.sqrt(float(5. / 3.))
        fmax[i] = fc[i] * np.sqrt(float(5. / 3.))
    nfft = util.nextpow2(data.shape[np.size(data.shape) - 1])
    #c = np.zeros((data.shape), dtype='complex64')
    c = fftpack.fft(data, nfft)