0

我正试图围绕scipy.optimize.minimize. 我一遍又一遍地阅读文档并用谷歌搜索，但我根本不明白如何做到这一点。希望有人能指出我正确的方向：

我有一个信号numpy array（我想我需要做一些最小化的事情

（基线调整 + 幅度因子 * 模板阵列 - 信号阵列）**2

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

新信号 = 基线调整 + 幅度因子 * 模板阵列 - 信号阵列

这种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。

不确定这是否有意义并且很乐意详细说明。


#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.
4

0 回答 0