计算互相关函数? [英] Computing cross-correlation function?

问题描述

在R中，我正在使用ccf或acf计算成对互相关函数，以便我可以找出哪个偏移使我获得了最大值.从它的外观来看，R给我一个规范化的值序列. Python的scipy中是否有类似的东西，还是应该使用fft模块来做到这一点?目前，我正在执行以下操作:

In R, I am using ccf or acf to compute the pair-wise cross-correlation function so that I can find out which shift gives me the maximum value. From the looks of it, R gives me a normalized sequence of values. Is there something similar in Python's scipy or am I supposed to do it using the fft module? Currently, I am doing it as follows:

xcorr = lambda x,y : irfft(rfft(x)*rfft(y[::-1]))
x = numpy.array([0,0,1,1])
y = numpy.array([1,1,0,0])
print xcorr(x,y)

推荐答案

要交叉关联1d数组，请使用

To cross-correlate 1d arrays use numpy.correlate.

对于2d数组，请使用 scipy.signal.correlate2d .

For 2d arrays, use scipy.signal.correlate2d.

还有 scipy .stsci.convolve.correlate2d .

还有 matplotlib.pyplot.xcorr 这是基于numpy.correlate.

There is also matplotlib.pyplot.xcorr which is based on numpy.correlate.

请参见SciPy邮件列表上的此信息一些指向不同实现的链接.

See this post on the SciPy mailing list for some links to different implementations.

@ user333700添加了指向此问题的科学票证.

@user333700 added a link to the SciPy ticket for this issue in a comment.

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