On Sat, Mar 20, 2010 at 7:51 AM, Nico Schlömer <nico.schloemer@gmail.com> wrote:
Hi,
I'm trying to compute the the convolution if s 2D array, and I see that there are several ways in SciPy to do that. As the original data C and the kernel R are about the same size in my case, I'd profit from an FFT-based implementation, which I see right now is given by
scipy.signal.fftconvolve( C, R, mode='same' )
and also
scipy.stsci.convolve.convolve2d( data=C, kernel=R, mode='constant', cval=0.0, fft=1 )
The second method is much faster than the first, and as far as I can see it would spit out the the same results.
Now, when the kernel is actually larger than the data, the resulting array would have the shape of the kernel. Is there any way to restrict the computations to the size of the data? At first, I thought that was what "mode='same'"" is for. I tried cutting the extra data off of the resulting array, but I'm not quite sure which is the part that I would like to get rid of.
Any hints here?
It's always good to take a look at the source fftconvolve elif mode == "same": if product(s1,axis=0) > product(s2,axis=0): osize = s1 else: osize = s2 return _centered(ret,osize) elif mode == "valid": return _centered(ret,abs(s2-s1)+1) from scipy.signal.signaltools import _centered and it should be possible to set osize to s1 = array(in1.shape) (C in your notation) scipy.stsci has been removed recently from scipy and will not be in the scipy 0.8 version (I didn't try myself whether it works) Josef
Cheers, Nico _______________________________________________ SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user