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<p>If I understand your query correctly, I think numpy could do it,
first by flattening the matrices to simple vectors, and then
calculating the correlation between those, e.g.</p>
<p>np.corrcoef(np.array(matrix1).flatten(),
np.array(matrix2).flatten())</p>
<p>... assuming you're using the matrix datatype. If they are
vanilla 2D arrays, then you shouldn't need to recast:<br>
</p>
<p>np.corrcoef(matrix1.flatten(), matrix2.flatten())</p>
<p>best,</p>
<p>Brian</p>
<p><br>
</p>
<div class="moz-cite-prefix">On 28/10/16 20:44, David Soto wrote:<br>
</div>
<blockquote
cite="mid:CAPeRSSW8gGwdwkyZfbq2S2MCzfN2+=V5aWBSVZc7yR012FVVEQ@mail.gmail.com"
type="cite">
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<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000">hi, </div>
<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000"><br>
</div>
<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000">I
wonder whether PyMVPA can compute a similarity index
(correlation) between two 2D matrices, each comprised of 12
seconds & 300 voxels?</div>
<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000"><br>
</div>
<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000">thanks!</div>
<div class="gmail_default"
style="font-family:garamond,serif;font-size:large;color:#000000">david</div>
</div>
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