numpy 1.6.1, OSX, Core 2 Duo:
In [7]: timeit a.cumsum(0)
100 loops, best of 3: 6.67 ms per loop
In [8]: timeit a.T.cumsum(-1).T
100 loops, best of 3: 6.75 ms per loop
-E
On Thu, Feb 9, 2012 at 9:51 PM, Dave Cook wrote:
> On Thu, Feb 9, 2012 at 9:41 PM, Dave Cook wrote:
>
>>
>> Interesting.
On Thu, Feb 9, 2012 at 9:41 PM, Dave Cook wrote:
>
> Interesting. I should have mentioned that I'm using numpy 1.5.1 on 64-bit
> Ubuntu 10.10. This transpose/compute/transpose trick did not work for me.
>
>
Nor does it work under numpy 1.6.1 built with MKL under Windows 7 on a core
i7.
Dave Co
On Thu, Feb 9, 2012 at 9:21 PM, wrote:
> strange (if I didn't make a mistake)
>
> In [12]: timeit a.cumsum(0)
> 100 loops, best of 3: 7.17 ms per loop
>
> In [13]: timeit a.T.cumsum(-1).T
> 1000 loops, best of 3: 1.78 ms per loop
>
> In [14]: (a.T.cumsum(-1).T == a.cumsum(0)).all()
> Out[14]: Tru
On Thu, Feb 9, 2012 at 11:39 PM, Dave Cook wrote:
> Why is numpy.cumsum (along axis=0) so much slower than a simple loop? The
> same goes for numpy.add.accumulate
>
> # cumsumtest.py
> import numpy as np
>
> def loopcumsum(a):
> csum = np.empty_like(a)
> s = 0.0
> for i in range(len(a
Why is numpy.cumsum (along axis=0) so much slower than a simple loop? The
same goes for numpy.add.accumulate
# cumsumtest.py
import numpy as np
def loopcumsum(a):
csum = np.empty_like(a)
s = 0.0
for i in range(len(a)):
csum[i] = s = s + a[i]
return csum
npcumsum = lambda
On Thu, Feb 9, 2012 at 3:40 PM, Benjamin Root wrote:
>
>
> On Thursday, February 9, 2012, Sturla Molden wrote:
> >
> >
> > Den 9. feb. 2012 kl. 22:44 skrev eat :
> >
> >>
> > Maybe this issue is raised also earlier, but wouldn't it be more
> consistent to let arange operate only with integers (l
On Thursday, February 9, 2012, Sturla Molden wrote:
>
>
> Den 9. feb. 2012 kl. 22:44 skrev eat :
>
>>
> Maybe this issue is raised also earlier, but wouldn't it be more
consistent to let arange operate only with integers (like Python's range)
and let linspace handle the floats as well?
>
>
> Perha
Den 9. feb. 2012 kl. 22:44 skrev eat :
>
> Maybe this issue is raised also earlier, but wouldn't it be more consistent
> to let arange operate only with integers (like Python's range) and let
> linspace handle the floats as well?
>
>
Perhaps. Another possibility would be to let arange take
Hi,
On Thu, Feb 9, 2012 at 9:47 PM, Eric Firing wrote:
> On 02/09/2012 09:20 AM, Drew Frank wrote:
> > Eric Firing hawaii.edu> writes:
> >
> >>
> >> On 02/08/2012 09:31 PM, teomat wrote:
> >>>
> >>> Hi,
> >>>
> >>> Am I wrong or the numpy.arange() function is not correct 100%?
> >>>
> >>> Try
On Thu, Feb 9, 2012 at 2:47 PM, Eric Firing wrote:
> On 02/09/2012 09:20 AM, Drew Frank wrote:
> > Eric Firing hawaii.edu> writes:
> >
> >>
> >> On 02/08/2012 09:31 PM, teomat wrote:
> >>>
> >>> Hi,
> >>>
> >>> Am I wrong or the numpy.arange() function is not correct 100%?
> >>>
> >>> Try to do
On 02/09/2012 09:20 AM, Drew Frank wrote:
> Eric Firing hawaii.edu> writes:
>
>>
>> On 02/08/2012 09:31 PM, teomat wrote:
>>>
>>> Hi,
>>>
>>> Am I wrong or the numpy.arange() function is not correct 100%?
>>>
>>> Try to do this:
>>>
>>> In [7]: len(np.arange(3.1, 4.9, 0.1))
>>> Out[7]: 18
>>>
>>>
On Thu, Feb 9, 2012 at 12:20 PM, Drew Frank wrote:
> Eric Firing hawaii.edu> writes:
>
> >
> > On 02/08/2012 09:31 PM, teomat wrote:
> > >
> > > Hi,
> > >
> > > Am I wrong or the numpy.arange() function is not correct 100%?
> > >
> > > Try to do this:
> > >
> > > In [7]: len(np.arange(3.1, 4.9,
Eric Firing hawaii.edu> writes:
>
> On 02/08/2012 09:31 PM, teomat wrote:
> >
> > Hi,
> >
> > Am I wrong or the numpy.arange() function is not correct 100%?
> >
> > Try to do this:
> >
> > In [7]: len(np.arange(3.1, 4.9, 0.1))
> > Out[7]: 18
> >
> > In [8]: len(np.arange(8.1, 9.9, 0.1))
> > Out[
On 07.02.2012 18:38, Sturla Molden wrote:
> One potential problem I just discovered is dependency on a DLL called
> libpthreadGC2.dll.
This is not correct!!! :-D
Two threading APIs can be used for OpenBLAS/GotoBLAS2, Win32 threads or
OpenMP.
driver/others/blas_server_omp.c
driver/other
Thanks Mark!
John
On Wed, Feb 8, 2012 at 6:48 PM, Mark Wiebe wrote:
> Converting between date and datetime requires caution, because it depends
> on your time zone. Because all datetime64's are internally stored in UTC,
> simply casting as in your example treats it in UTC. The 'astype' function
On Thu, Feb 9, 2012 at 8:35 AM, mark florisson wrote:
> On 9 February 2012 15:29, Charles R Harris
> wrote:
> > Hi All,
> >
> > Does anyone know how to make Cython emit a C macro? I would like to be
> able
> > to
> >
> > #define NO_DEPRECATED_API
> >
> > and can do so by including a header file o
On 9 February 2012 15:29, Charles R Harris wrote:
> Hi All,
>
> Does anyone know how to make Cython emit a C macro? I would like to be able
> to
>
> #define NO_DEPRECATED_API
>
> and can do so by including a header file or futzing with the generator
> script, but I was wondering if there was an ea
Hi All,
Does anyone know how to make Cython emit a C macro? I would like to be able
to
#define NO_DEPRECATED_API
and can do so by including a header file or futzing with the generator
script, but I was wondering if there was an easy way to do it in Cython.
Chuck
Ok, that was an enlightening discussion, I guess I signed up for this
list a couple of days too late!
Thanks,
Eirik
On 09. feb. 2012 12:55, Olivier Delalleau wrote:
This was actually discussed very recently (for more details:
http://mail.scipy.org/pipermail/numpy-discussion/2012-February/06023
This was actually discussed very recently (for more details:
http://mail.scipy.org/pipermail/numpy-discussion/2012-February/060232.html).
It's caused by mixing slicing with advanced indexing. The resulting shape
is the concatenation of a first part obtained by broadcasting of the
non-slice items (
Hello,
(also sent to Scipy-User, sorry for duplicates).
This is (I think) a rather basic question about numpy slicing. I have
the following code:
In [29]: a.shape
Out[29]: (3, 4, 12288, 2)
In [30]: mask.shape
Out[30]: (3, 12288)
In [31]: mask.dtype
Out[31]: dtype('bool')
In [32]: sum(mask[0]
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