functions.py 文件源码

def GetSn(y, range_ff=[0.25, 0.5], method='mean'):
    """
    Estimate noise power through the power spectral density over the range of large frequencies

    Parameters
    ----------
    y : array, shape (T,)
        One dimensional array containing the fluorescence intensities with
        one entry per time-bin.
    range_ff : (1,2) array, nonnegative, max value <= 0.5
        range of frequency (x Nyquist rate) over which the spectrum is averaged
    method : string, optional, default 'mean'
        method of averaging: Mean, median, exponentiated mean of logvalues

    Returns
    -------
    sn : noise standard deviation
    """

    ff, Pxx = scipy.signal.welch(y)
    ind1 = ff > range_ff[0]
    ind2 = ff < range_ff[1]
    ind = np.logical_and(ind1, ind2)
    Pxx_ind = Pxx[ind]
    sn = {
        'mean': lambda Pxx_ind: np.sqrt(np.mean(Pxx_ind / 2)),
        'median': lambda Pxx_ind: np.sqrt(np.median(Pxx_ind / 2)),
        'logmexp': lambda Pxx_ind: np.sqrt(np.exp(np.mean(np.log(Pxx_ind / 2))))
    }[method](Pxx_ind)

    return sn