我如何做一个信号处理的最小平方？

Question

我正试着去理解scipy.optimize.minimize的语法。我已经读了一遍又一遍的文件和谷歌搜索，但我只是不明白如何做到这一点。希望有人能给我指出正确的方向：

我有一个信号（numpy array），我希望以这种方式与模板信号（相同长度的numpy数组）匹配，以确定哪个信号基线调整和振幅倍增因子将导致最小的卡方误差。我想我需要做些事情，尽量减少

（baselineadjust+amplitudefactor*模板阵列-信号阵列）**2

我想使用baselineadjust和amplitudefactor来修改信号，减去修改后的模板，即

新信号=baselineadjust+amplitudefactor*templatearray-signalarray

此操作的目的是有效地从实际信号中去除模板信号，留下剩余的信号/噪声。你知道吗

不确定这是否有意义，并乐于详细说明。你知道吗

#data is a (very) long list of floats, that contains a noisy signal and a repetitive signal of a specific length but with slight variations in amplitude, partially due to the noisy signal and partially because it inherently varies a bit. I wish to remove the repetitive signal from the data.

#Prior to this, I detect signals in data:
sigwindows = []

#Gather signals
for sig in signals:
    sigwindow = list(data[sig-200:sig+200])
    sigwindows += [sigwindow]

#Construct an averaged signal
avgsig = np.mean(sigwindows, axis=0)

for sig in signals:
    sigwindow = list(data[sig-200:sig+200])

    #First, I subtract the average signal
    data[sig-200:sig+200] = list(np.array(sigavg) - np.array(sigwindow))

    #This removes some of the signal, but due to the inherent variation in amplitude, some of it remains.
    #Instead would like to do this:

    data[sig-200:sig+200] = list(np.array(sigavg) - baselineadjust - amplitudefactor * np.array(sigwindow))

    #... having obtained 'baselineadjust' and 'amplitudefactor' as described above, by minimizing chi-square.