Problem calculating eigenvalues and eigenvectors

[Frank Einstein]

Hello everyone. I am trying to construct a functioning version of randomfields (specifically 2D_karhunen_loeve_identification_example.py) in Matlab. For that, I have to calculate the Karhunen-Loève expansion of 2D data, since this is what it says in the documentation. I also have some sample data to test my results.
I have a matrix of size 144*7, being 144 the number of points, 5 the number of random variables and the first two, the X and Y points of each value. If I ignore the first two rows and calculate

[evec, eval]=eig(cov(realizations));

the resulting eigenvectors and eigenvalues don't look at all like the ones of the sample data. I was wondering if this might happen because I am ignoring the grid positions at the time of calculating the covariance matrix.

Best regards.
Frank.

E. G. I will attach the stochastic realizations I have received and what I am expected to get

 

[RPinPA]

I'm struggling to understand your problem. What are the "grid positions" you're referring to?

I believe EIG does not return the eigenvalues / eigenvectors in any particular order. You may be comparing to a plot where they are sorted in order of descending eigenvalue magnitude.

Perform that sort and see if it improves your results.