bpm.py 文件源码

def bpm_fit(a, p):
    if a is None:
        return None
    x = a['channel']['data'][p][:, 1]
    y = a['channel']['data'][p][:, 2]
    ar = np.trapz(y / np.sum(y) * len(y), x)
    mean = np.mean(x * y / np.sum(y) * len(y))
    rms = np.std(x * y / np.sum(y) * len(y))

    popt, pcov = curve_fit(gaussian, x, y, p0=[ar, mean, rms])

    return [popt, np.sqrt(pcov.diagonal())]