David Cournapeau wrote:
> Hi,
>
> After some delay, here are the win32 binaries for numpy 1.1.1rc2:
>
> http://www.ar.media.kyoto-u.ac.jp/members/david/numpy-1.1.1.dev5559-win32-superpack-python2.5.exe
>
I managed to screw up the link:
http://www.ar.media.kyoto-u.ac.jp/members/david/archiv
Hi,
After some delay, here are the win32 binaries for numpy 1.1.1rc2:
http://www.ar.media.kyoto-u.ac.jp/members/david/numpy-1.1.1.dev5559-win32-superpack-python2.5.exe
Notes on those binaries:
- Based on Atlas 3.8.2 (the 1.1.0 was built against 3.8.0, which had
a serious bug wrt dgemm,
Henrik Ronellenfitsch wrote:
> Thanks very much for your solution, this is exactly what I needed!
> If I'm not mistaken, though, you can achieve the same result with
>
> h = hamming(n)
> ham2d = sqrt(outer(h,h))
>
> which is a bit more compact.
>
> Regards,
> Henrik
Yes, that's nicer.
regards
Luis Pedro Coelho wrote:
> Hello all,
>
> Numpy arrays come with several reduce operations: sum(), std(), argmin(),
> min(),
>
> The traditional implementation of these suffers from two big problems: It is
> slow and it often allocates intermediate memory. I have code that is failing
> with
Hi,
Gary Ruben wrote:
> import numpy as np
> import scipy.signal as ss
>
> # read heightmap here - in my case it's a square numpy float array
>
> # build 2d window
> hm_len = heightmap.shape[0]
> bw2d = np.outer(ss.hamming(hm_len), np.ones(hm_len))
> bw2d = np.sqrt(bw2d * bw2d.T) # I don't know w
