Normalized Cross-Correlation in Python

I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is:

degrees of freedom according to Chelton(1983)

and I can't find a proper way to calculate the normalized cross correlation function using np.correlate, I always get an output that it isn't in between -1, 1.

Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?

Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:

Here i define the correlation as generally defined in signal processing textbooks.

c'_{ab}[k] = sum_n a[n] conj(b[n+k])

CODE: If a and b are the vectors:

a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')

References:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html

https://en.wikipedia.org/wiki/Cross-correlation