# [MATRIX-SIG] Question about Numeric.convolve

**Konrad Hinsen
**
hinsen@ibs.ibs.fr

*Wed, 11 Feb 1998 19:42:16 +0100*

>* I'm (trying) to use convolve to convolute a calculation with
*>* Gaussian resolution and am having some trouble sorting things
*>* out. I have two arrays, x and f = f(x) and I'd like to convolute
*>* f(x) with a Gaussian. So I calculate an array g like this:
*>* g = Numeric.zeros( x.shape,'Float').real
*>* for i in range(len(g)):
*>* g[i] = math.exp(-(math.pow((x[i]-mu)/sigma,2)))
*
First of all, that's complicated and inefficient. NumPy can
do much better:
g = Numeric.exp(((x-mu)/sigma)**2)
>* So, the question is - is there a way to convolute two arrays with
*>* x, rather than the array index, as the index. Or am I just
*>* totally clueless? Or both?
*
Numeric.convolve is not what you are looking for, as indeed it
convolutes according to the array index (which is equivalent for
equally-spaced data and much more efficient). A convolution for
unevenly spaced data amounts to a explicit numerical integration. If
that's what you want, well, Simpson's rule is easy enough to implement
in Python.
Konrad.
--
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Konrad Hinsen | E-Mail: hinsen@ibs.ibs.fr
Laboratoire de Dynamique Moleculaire | Tel.: +33-4.76.88.99.28
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