Python cross correlation

Question

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]

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

I have tried the following 2 methods:

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

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.

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 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?

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

Please post actual and expected values from matlab and the 2 python libs — Peter Gibson, Sep 14 '14 at 09:14
Refer this http://stackoverflow.com/questions/6991471/computing-cross-correlation-function — Raghav RV, Sep 14 '14 at 18:11
I saw that thread a while ago and have tried all of those methods. numpy.correlate gives me values anywhere from 1-500. and matplotlib's xcorr() requires the 2 1D arrays to be of the same length (mine aren't) — Simon, Sep 14 '14 at 23:44
"If x and y are not the same length, the shorter vector is zero-padded to the length of the longer vector." From Matlab's xcorr documentation. Perhaps try that along with the matplotlib's xcorr ? — Raghav RV, Sep 15 '14 at 01:08
numpy's correlate is not well documented it is truly frustrating. at the very least an example or two should be added with figures. — eric, Sep 29 '22 at 15:26

score 20 · Accepted Answer · answered Oct 05 '14 at 02:18

20

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

gave me same output as

xcorr(arr1,arr2)

gives in matlab

answered Oct 05 '14 at 02:18

Cheeku

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2

How do you get the time lag out of this? – bischoffingston Mar 30 '15 at 15:19
3

adding "full" to numpy.correlate() does the trick. To get the max time lag I just call argmax() on the return from correlate() – Simon Aug 04 '15 at 22:06
Just to clarify, lag of 0 corresponds to the center of the output array. So if c = xcorr(arr1,arr2) , then lag = argmax(c)-c.size/2 – argentum2f Jan 30 '18 at 20:09

score 5 · Answer 2 · edited May 16 '21 at 09:18

Implementation of MATLAB xcorr(x,y) and comparision of result with example.

import scipy.signal as signal
def xcorr(x,y):
    """
    Perform Cross-Correlation on x and y
    x    : 1st signal
    y    : 2nd signal

    returns
    lags : lags of correlation
    corr : coefficients of correlation
    """
    corr = signal.correlate(x, y, mode="full")
    lags = signal.correlation_lags(len(x), len(y), mode="full")
    return lags, corr

n = np.array([i for i in range(0,15)])
x = 0.84**n
y = np.roll(x,5);
lags,c = xcorr(x,y);
plt.figure()
plt.stem(lags,c)
plt.show()

Chitra Barvekar · Answer 3 · 2019-03-19T07:28:24.530

1

This code will help in finding the delay between two channels in audio file

xin, fs = sf.read('recording1.wav')
frame_len = int(fs*5*1e-3)
dim_x =xin.shape
M = dim_x[0] # No. of rows
N= dim_x[1] # No. of col
sample_lim = frame_len*100
tau = [0]
M_lim = 20000 # for testing as processing takes time
for i in range(1,N):
    c = np.correlate(xin[0:M_lim,0],xin[0:M_lim,i],"full")
    maxlags = M_lim-1
    c = c[M_lim -1 -maxlags: M_lim + maxlags]
    Rmax_pos = np.argmax(c)
    pos = Rmax_pos-M_lim+1
    tau.append(pos)
print(tau)

edited Mar 19 '19 at 07:28

answered Mar 15 '19 at 12:20

Chitra Barvekar

21
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While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Link-only answers can become invalid if the linked page changes. - [From Review](/review/low-quality-posts/22475624) – ascripter Mar 15 '19 at 16:46
@ascripter Actually there is an answer here that doesn't require the link. – Ajean Mar 15 '19 at 21:21
@Ajean You're right. It's actually a valuable hint. – ascripter Mar 15 '19 at 21:25

Python cross correlation

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