[MATRIX-SIG] Question about Numeric.convolve

[Konrad Hinsen]

> 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
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