Python互相关 [英] Python cross correlation

问题描述

我有一对(长度不同)的一维数组，如下所示:

I have a pair of 1D arrays (of different lengths) like the following:

data1 = [0,0,0,1,1,1,0,1,0,0,1]
data2 = [0,1,1,0,1,0,0,1]

我想在python中获得2系列的最大互相关.在matlab中，xcorr()函数将其返回OK

I would like to get the max cross correlation of the 2 series in python. In matlab, the xcorr() function will return it OK

我尝试了以下2种方法:

I have tried the following 2 methods:

1. numpy.correlate(data1, data2)
2. signal.fftconvolve(data2, data1[::-1], mode='full')

1. numpy.correlate(data1, data2)
2. signal.fftconvolve(data2, data1[::-1], mode='full')

两个方法都给我相同的值，但是我从python中获得的值与从matlab中获得的值不同. Python给我的整数值> 1，而matlab给我的实际相关值在0和1之间.

Both methods give me the same values, but the values I get from python are different from what comes out of matlab. Python gives me integers values > 1, whereas matlab gives actual correlation values between 0 and 1.

我尝试过先对2个数组进行归一化处理(值均值/SD)，但是得到的互相关值却成千上万，这似乎并不正确.

I have tried normalizing the 2 arrays first (value-mean/SD), but the cross correlation values I get are in the thousands which doesnt seem correct.

Matlab也将为您提供一个互相关最大的滞后值.我认为使用索引很容易做到这一点，但是如果我的数组包含10的数千个值，最合适的方法是什么?

Matlab will also give you a lag value at which the cross correlation is the greatest. I assume it is easy to do this using indices but whats the most appropriate way of doing this if my arrays contain 10's of thousands of values?

我想模仿matlab的xcorr()函数，关于我将如何在python中做到这一点的任何想法?

I would like to mimic the xcorr() function that matlab has, any thoughts on how I would do that in python?

推荐答案

numpy.correlate(arr1,arr2,"full")

为我提供与

xcorr(arr1,arr2)

使用matlab

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