2010/8/18, Zbyszek Szmek :
> thank you for your detailed answer. It seems that memcpy which should always
> be faster then memmove is sometimes slower! What happens is that
> using the slice assignment calls memmove() which calls
> _wordcopy_fwd_aligned() [1]
> which is apparently faster than memcp
On Wed, Aug 18, 2010 at 02:06:51AM +0100, Francesc Alted wrote:
> Hey Zbyszek,
>
> 2010/8/17, Zbyszek Szmek :
> > Hi,
> > this is a problem which came up when trying to replace a hand-written
> > array concatenation with a call to numpy.vstack:
> > for some array sizes,
> >
> >numpy.vstack(dat
Hey Zbyszek,
2010/8/17, Zbyszek Szmek :
> Hi,
> this is a problem which came up when trying to replace a hand-written
> array concatenation with a call to numpy.vstack:
> for some array sizes,
>
>numpy.vstack(data)
>
> runs > 20% longer than a loop like
>
>alldata = numpy.empty((tlen, dim)
Yes, concatenate is doing other work under the covers. In short, in supports
concatenating a list of arbitrary python sequences into an array and does
checking on each element of the tuple to ensure it is valid to concatenate.
On Tue, Aug 17, 2010 at 9:03 AM, Zbyszek Szmek wrote:
> Hi,
> this is
Hi,
this is a problem which came up when trying to replace a hand-written
array concatenation with a call to numpy.vstack:
for some array sizes,
numpy.vstack(data)
runs > 20% longer than a loop like
alldata = numpy.empty((tlen, dim))
for x in data:
step = x.shape[0]
all
