1
$\begingroup$

I have 2 different signals and I'm trying to cross-correlate then using Python 2.7 and scipy.signal.correlate. How do I normalize my results (such that the max amplitude is 1.0? I tried the following:

import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(0,2*np.pi,num=1000)
x1 = 10*np.sin(2*np.pi*t)
x2 = np.sin(2*np.pi*t+np.pi/2)
x12 = scipy.signal.correlate(x1,x2,'full')
plt.plot(x12)

results in the following plot enter image description here

Improve this question
$\endgroup$
2
2
$\begingroup$

When you say normalized cross-correlation I guess you mean the Pearson correlation. Anyways you just divide the cross correlation by the multiplication of the std(standard deviation) of both signal, or more conveniently: $ \rho_{xy} =\frac{<x,y>}{\sigma_x\sigma_y}$

and in code:

x1 = x1/x1.std()
x2 = x2/x2.std() and then as you did it
Improve this answer
$\endgroup$
2
  • 1
    $\begingroup$ i still have to divide x12 by the length of x1 (len(x1)=len(x2)) to get a normalized amplitude (rho(tau)). What is the relevance of that? $\endgroup$ – Devin Liner May 25 '17 at 18:14
  • $\begingroup$ Yea you'r right, you should divide with the length as well $\endgroup$ – Cherny May 26 '17 at 15:56

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.