Calculate entropy of a thresholded correlation matrix

Greetings All!

I am reasonably new to coding and would very much like your opinion and advice.

I have several time series for nodes of different networks. I want to create correlation matrices from selections of these time series and compare the entropy of different correlation matrices. I have the option of thresholding these matrices to get a top percentage (it is noisy data) and possibly even binarising these according to the threshold.

Can I simply use matlab's "entropy" function for a grey scaled image validly? https://uk.mathworks.com/help/images/ref/entropy.html.

Or could I use wentropy(x , 'shannon') https://uk.mathworks.com/help/wavelet/ref/wentropy.html which i belive is meant for wave signals

I understand that the entropy will vary according to the size of the matrix (my matrices vary from 61x61 to 213x213). Thus should I just code the normalised shannon entropy?

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X_uni = unique(X);

X_uni_size = numel(X_uni);

n = numel(X)

P = zeros(X_uni_size,1);

for i = 1:X_uni_size

P(i) = sum(X == X_uni(i));

end

P = P ./ numel(X);

% Compute the Normalised Shannon's Entropy

H = -sum(P .* log2(P))/log2(P).*n

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As an alternative I was thinking of just colapsing these matrices into a vector and using something like sample entropy.

Ideally I would want to use several types of complexity measure on these matrices so any suggestion is very welcome !

Best

Peter