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]:
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:
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
Thanks Mark!
John
On Wed, Feb 8, 2012 at 6:48 PM, Mark Wiebe mwwi...@gmail.com 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
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
On Thu, Feb 9, 2012 at 12:20 PM, Drew Frank drewfr...@gmail.com wrote:
Eric Firing efiring at 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,
On 02/09/2012 09:20 AM, Drew Frank wrote:
Eric Firingefiringat 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,
Hi,
On Thu, Feb 9, 2012 at 9:47 PM, Eric Firing efir...@hawaii.edu wrote:
On 02/09/2012 09:20 AM, Drew Frank wrote:
Eric Firingefiringat 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 Thursday, February 9, 2012, Sturla Molden stu...@molden.no wrote:
Den 9. feb. 2012 kl. 22:44 skrev eat e.antero.ta...@gmail.com:
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
On Thu, Feb 9, 2012 at 3:40 PM, Benjamin Root ben.r...@ou.edu wrote:
On Thursday, February 9, 2012, Sturla Molden stu...@molden.no wrote:
Den 9. feb. 2012 kl. 22:44 skrev eat e.antero.ta...@gmail.com:
Maybe this issue is raised also earlier, but wouldn't it be more
consistent to
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 =
On Thu, Feb 9, 2012 at 11:39 PM, Dave Cook dav...@gmail.com 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
On Thu, Feb 9, 2012 at 9:21 PM, josef.p...@gmail.com 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()
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 dav...@gmail.com wrote:
On Thu, Feb 9, 2012 at 9:41 PM, Dave Cook
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