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<div class="moz-cite-prefix">Le 08/02/2018 à 20:16, Heinz a écrit :<br>
</div>
<blockquote cite="mid:004701d3a111$547c8900$fd759b00$@me.com"
type="cite">
<pre wrap="">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. </pre>
</blockquote>
<br>
So you need and are speaking about the 3D autocorrelation of
data(x,y,z).<br>
Usually we compute it through a 3D convolution, that is easy to
compute<br>
through the FFT, noting that for an autocorrelation we just flip one
of both<br>
data array along x,y, and z before computing its FFT and going on
with the algorithm.<br>
<br>
Finally we use the inverse FFT to come back to the direct x,y,z
space.<br>
You may find good references on the web about the keyword 3D
autocorrelation.<br>
<br>
In scilab, you will mainly need
<pre><span class="default"></span><span class="default"></span><span class="default">fft</span><span class="default">(</span><span class="default">A</span><span class="default">,</span><span class="default">sign</span><span class="default">,</span><span class="default">dims</span><span class="default">,</span><span class="default">incr</span><span class="default"> [,</span><span class="default">option</span><span class="default">] )</span></pre>
since xcorr(), conv() conv2() convol2d() .. work only in 1D or 2D<br>
(with too often duplicates to do the same thing, without "simple"
extension to do more...)<br>
<br>
Best regards<br>
Samuel<br>
<br>
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