Question

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)

Answer 1

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

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

There is also scipy.stsci.convolve.correlate2d.

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

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

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