Le 08/02/2018 à 20:16, Heinz a écrit :
Sorry, I am lost.

I have 10,000 xyz data and want to know, if there is some regularity in them
or if they are more or less random.

So you need and are speaking about the 3D autocorrelation of data(x,y,z).
Usually we compute it through a 3D convolution, that is easy to compute
through the FFT, noting that for an autocorrelation we just flip one of both
data array along x,y, and z before computing its FFT and going on with the algorithm.

Finally we use the inverse FFT to come back to the direct x,y,z space.
You may find good references on the web about the keyword 3D autocorrelation.

In scilab, you will mainly need

fft(A,sign,dims,incr[,option] )

since xcorr(), conv() conv2() convol2d() .. work only in 1D or 2D
(with too often duplicates to do the same thing, without "simple" extension to do more...)

Best regards

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