[Numpy-discussion] Convolve returning zero array

[<R....@ma...>]

Hi list,

I already posted this on the numarray forum on freshmeat, but Jay T Miller
advised me to post my problem to this list. OK, now for the problem: I try
to convolve a Gaussian distribution with a binary pattern. For small values
of the sigma of the Gaussian distribution the convolution returns an array
of zeros. For a large value the results are OK.
I did some more research and found out that the zero array is returned if
the length of the Gaussian is smaller than the length of the binary
pattern. In the function call the Gaussian is the kernel and the binary
pattern is the data. The convolution mode is 'SAME'. I have swapped the
data and kernel in the convolve function call, but this has no influence on
the result, as this is swapped again in convolve.py. A quick and dirty
workaround is to always make the Gaussian distribution longer than the
binary pattern, but for very large binary patterns this increases the
calculation time significantly. Does anyone have an idea how to solve this
properly?

Met vriendelijke groeten,

Remco Jager

MAPPER Lithography
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