Hi, if there's anyone wants to have a look at the above issue this
week,
that would be great.
If there's a patch by this weekend I can create a second RC, so we can
still have the final release before the end of this month (needed for
Debian freeze). Otherwise a second RC won't be needed.
On 05/16/2012 09:01 PM, Ralf Gommers wrote:
On Tue, May 15, 2012 at 10:35 PM, Julian Taylor
jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com
wrote:
Hi, if there's anyone wants to have a look at the above issue this
week,
that would be great
could still add median of medians for
better worst case performance.
If no blockers appear I want to fix this up and file a pull request to
have this in numpy 1.8.
Guidance on details of implementation in numpys C api is highly
appreciated, its the first time I'm dealing with it.
Cheers,
Julian
On 05/29/2013 06:12 AM, josef.p...@gmail.com wrote:
On Tue, May 28, 2013 at 6:31 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
There is a PR adding quickselect to numpy as a function `partition`.
Comments on name and exposure in the numpy API are welcome.
I think the name
On 05.06.2013 16:33, Nathaniel Smith wrote:
On Wed, Jun 5, 2013 at 3:16 PM, Slavin, Jonathan
jsla...@cfa.harvard.edu wrote:
The simplest monotonicity test that I've seen is:
dx = np.diff(x)
monotonic = np.all(dx 0.) or np.all(dx 0.)
I expect that this is pretty fast, though I haven't
On 09.06.2013 12:10, josef.p...@gmail.com wrote:
On Wed, May 29, 2013 at 3:19 PM, josef.p...@gmail.com wrote:
On Wed, May 29, 2013 at 12:25 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
On 05/29/2013 06:12 AM, josef.p...@gmail.com wrote:
On Tue, May 28, 2013 at 6:31 PM, Charles R
On 11.06.2013 14:37, Jonathan J. Helmus wrote:
Julian,
Since I am the author of the current percentile PR
(https://github.com/numpy/numpy/pull/2970), I'm willing to try
reimplementing percentile with the new partition functionality. I
don't expect to have time to do this until the
On 12.06.2013 18:07, Uwe Schmitt wrote:
Dear all,
the following code hangs on my Ubuntu machine.
I use self compiled numpy 1.7.1 and Python
2.7.3
-
import numpy
import numpy.linalg
import multiprocessing
def classify():
X = numpy.random.random((10,3))
print before
hi,
I posted a pull with a minor change instructing the GCC compiler to
unroll the strided copy loops (gcc will almost never do that on its own,
not even on O3).
https://github.com/numpy/numpy/pull/3429
It improves performance of these copies by 20%-50% depending on the size
of the data copied
On 15.06.2013 21:12, Charles R Harris wrote:
On Sat, Jun 15, 2013 at 9:50 AM, Warren Weckesser
warren.weckes...@gmail.com mailto:warren.weckes...@gmail.com wrote:
On Sat, Jun 15, 2013 at 11:43 AM, Warren Weckesser
warren.weckes...@gmail.com mailto:warren.weckes...@gmail.com
On 15.06.2013 21:57, Warren Weckesser wrote:
On Sat, Jun 15, 2013 at 3:15 PM, Julian Taylor
@warren, can you please bisect the commit causing this?
Here's the culprit:
aef286debfd11a62f1c337dea55624cee7fd4d9e is the first bad commit
commit
wrote:
On Sat, Jun 15, 2013 at 4:03 PM, Julian Taylor
jtaylor.deb...@googlemail.com
mailto:jtaylor.deb...@googlemail.com wrote:
On 15.06.2013 21:57, Warren Weckesser wrote:
On Sat, Jun 15, 2013 at 3:15 PM, Julian Taylor
On 15.06.2013 22:26, Charles R Harris wrote:
On Sat, Jun 15, 2013 at 2:23 PM, Warren Weckesser
warren.weckes...@gmail.com mailto:warren.weckes...@gmail.com wrote:
Also on 2.7. The -O3 flag seems to cause the problem.
Chuck
I was wrong when I changed this flag.
The compiler is
On 17.06.2013 17:11, Frédéric Bastien wrote:
Hi,
I saw that recently Julian Taylor is doing many low level optimization
like using SSE instruction. I think it is great.
Last year, Mark Florisson released the minivect[1] project that he
worked on during is master thesis. minivect
Hi,
a selection algorithm [0] has now landed in the numpy development branch
[1].
The function exposing it is:
numpy.partition(data, kth=int/array, axis=-1, kind=introselect,
order=None)
Please see the docstrings on what it actually does (and report if they
are confusing).
Thanks to the numpy
On 04.09.2013 12:05, Graeme B. Bell wrote:
In my current GIS raster work I often have a situation where I generate code
something like this:
np.any([A4, A==2, B==5, ...])
However, np.any() is quite slow.
It's possible to use np.logical_or to solve the problem, but then you
-any-all.py
Graeme
On Sep 4, 2013, at 7:38 PM, Julian Taylor jtaylor.deb...@googlemail.com
wrote:
The result is 14 to 17x faster than np.any() for this use case.*
any/all and boolean operations have been significantly speed up by
vectorization in numpy 1.8 [0].
They are now around
try rebuilding everything from scratch.
setup.py dependency handling is a bit dodgy with the generated files.
On 09.09.2013 19:09, Frédéric Bastien wrote:
I don't have CFLAGS defined. But I have iothers env variable that point
to other python stuff like CPATH.
But even in that case, I don't
On 11.09.2013 12:33, antlarac wrote:
Hi, I have a numpy array, and I want to create another variable equal to it,
to back it up for later calculations, because the first array will change.
But after the first array changes, the second automatically changes to the
same value. An example of what
On 30.09.2013 17:17, Charles R Harris wrote:
Hi All,
NumPy 1.8.0rc1 is up now on sourceforge
http://sourceforge.net/projects/numpy/files/NumPy/1.8.0rc1/ .The
binary builds are included except for Python 3.3 on windows, which will
arrive later. Many thanks to Ralf for the binaries, and to
On 01.10.2013 01:30, Charles R Harris wrote:
On Mon, Sep 30, 2013 at 5:12 PM, Christoph Gohlke cgoh...@uci.edu
mailto:cgoh...@uci.edu wrote:
On 9/30/2013 3:45 PM, Charles R Harris wrote:
On Mon, Sep 30, 2013 at 3:51 PM, Christoph Gohlke cgoh...@uci.edu
your computation is symmetric so you only need to compute the upper or
lower triangle which will save both memory and time.
On Tue, Oct 8, 2013 at 10:06 AM, Ke Sun sunk...@gmail.com wrote:
Dear all,
I have written the following function to compute the square distances of a
large
matrix
Out of interest, how did you do this with matrix multiplication?
http://stackoverflow.com/a/4856692
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Tue, Oct 8, 2013 at 1:38 PM, Ke Sun sunk...@gmail.com wrote:
On a machine I had access to it took about 20 minutes.
How? I am using matrix multiplication (the same code as
http://stackoverflow.com/a/4856692) and it runs for around 18 hours.
make sure you are using an optimized BLAS
yes thats probably openblas fault.
Openblas crashes all the time as soon as your matrices get bigger than a
couple of megabytes.
I'll investigate and report it upstream (as I have already far too often
for the exact same reason ...)
On Wed, Oct 9, 2013 at 5:05 PM, Charanpal Dhanjal
https://github.com/xianyi/OpenBLAS/issues/304
On 09.10.2013 17:24, Julian Taylor wrote:
yes thats probably openblas fault.
Openblas crashes all the time as soon as your matrices get bigger than a
couple of megabytes.
I'll investigate and report it upstream (as I have already far too often
you have to use the offset keyword argument of np.memmap, else it will
always start from the beginning of the file
np.memmap(fd, dtype=float32, mode=r, offset=offset)
On Thu, Oct 10, 2013 at 2:43 PM, Andreas Hilboll li...@hilboll.de wrote:
Hi,
I have a problem using memmap correctly. I
On 10.10.2013 21:31, Bernhard Spinnler wrote:
On 10.10.2013, at 19:27, David Goldsmith d.l.goldsm...@gmail.com
mailto:d.l.goldsm...@gmail.com wrote:
On Wed, Oct 9, 2013 at 7:48 PM, Bernhard Spinnler
bernhard.spinn...@gmx.net mailto:bernhard.spinn...@gmx.net wrote:
Hi Richard,
On 29.10.2013 21:00, Charles R Harris wrote:
On Tue, Oct 29, 2013 at 1:57 PM, Charles R Harris
charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote:
Hi All,
I'm going to tag 1.7.2 soon. That is, unless someone else would like
the experience of making a
will
follow soon.
Concerning OS X currently only a single person can create the binary
installers which is not a good situation. If you have a suitable machine
[0] and want to help out please contact us.
Cheers,
Julian Taylor
[0] we currently base the releases on macos 10.6 using python.org python
On 12.11.2013 03:17, David Cournapeau wrote:
Hi there,
I have noticed more and more subtle and hard to track serious bugs in
numpy and scipy, due to the use of advanced optimization features
(flags, or gcc intrinsics).
I am wondering whether those are worth it: they compile wrongly under
On 13.11.2013 18:26, David Cournapeau wrote:
Can you narrow it down to a specific intrinsic? they can be enabled and
disabled in set ./numpy/core/setup_common.py
valgrind shows quite a few invalid read in BOOL_ functions when running
the scipy or sklearn test suite.
Will do, but the errors I am seeing only appear in the
simc.inc.src-based implementation of BOOL_logical_or (they disappear if
I disable the simd intrinsics manually in the numpy headers).
that is because the simd code always looks at the stride (as it only can
run with unit strides) while
On 25.11.2013 02:32, Yaroslav Halchenko wrote:
On Tue, 15 Oct 2013, Nathaniel Smith wrote:
What do you have to lose?
btw -- fresh results are here http://yarikoptic.github.io/numpy-vbench/ .
I have tuned benchmarking so it now reflects the best performance across
multiple executions of
there isn't that much code in numpy that profits from modern x86
instruction sets, even the simple arithmetic loops are strided and thus
unvectorizable by the compiler. They have been vectorized manually in
1.8 using sse2 and it is on my todo list to add runtime detected avx
support.
On
On 27.11.2013 21:51, Charles G. Waldman wrote:
If you convert an array of strings to datetime64s and 'NaT' (or one of
its variants) appears in the string, all subsequent values are
rendered as NaT:
thanks, a little embarrassing I didn't spot that when I fixed a
different bug in the function
On 29.11.2013 21:15, Dan Goodman wrote:
Hi,
Is it possible to get access to versions of ufuncs like sin and cos but
compiled with the -ffast-math compiler switch?
I recently noticed that my weave.inline code was much faster for some fairly
simple operations than my pure numpy code, and
On 01.12.2013 21:53, Dan Goodman wrote:
Julian Taylor jtaylor.debian at googlemail.com writes:
can you show the code that is slow in numpy?
which version of gcc and libc are you using?
with gcc 4.8 it uses the glibc 2.17 sin/cos with fast-math, so there
should be no difference.
In trying
On 01.12.2013 22:59, Dan Goodman wrote:
Julian Taylor jtaylor.debian at googlemail.com writes:
your sin and exp calls are loop invariants, they do not depend on the
loop iterable.
This allows to move the expensive functions out of the loop and only
leave some simple arithmetic in the body
related this PR attempts to improve the accuracy of summation:
https://github.com/numpy/numpy/pull/3685
but math.fsum gives the exact result so it would a valuable ufunc even
when that PR is merged.
python3.4 will have yet another accurate summation in the statistics module:
I opened a ticket for it, though thinking about it, its probably
intentional be intentional to find code that assumes it can use the
strides to get the itemsize.
https://github.com/numpy/numpy/issues/4091
On 02.12.2013 20:35, Neal Becker wrote:
I don't think that behavior is acceptable.
On 06.12.2013 19:06, Ralf Gommers wrote:
Hi all,
There are a few discussions on packaging for the scientific Python stack
ongoing, on the NumFOCUS and distutils lists:
https://groups.google.com/forum/#!topic/numfocus/mVNakFqfpZg
https://groups.google.com/forum/#!topic/numfocus/HUcwXTM_jNY
On 12.12.2013 19:58, David Jones wrote:
I'm trying to compile 32-bit numpy on a 64 bit Centos 6 system, but fails
with the message:
Broken toolchain: cannot link a simple C program
It get's the compile flags right, but not the linker:
C compiler: gcc -pthread -fno-strict-aliasing -g
On 12.12.2013 20:40, David Jones wrote:
On 12/12/13 15:54, Julian Taylor wrote:
On 12.12.2013 19:58, David Jones wrote:
I'm trying to compile 32-bit numpy on a 64 bit Centos 6 system, but fails
with the message:
Broken toolchain: cannot link a simple C program
...
this might work:
CC
On 13.12.2013 18:46, David Jones wrote:
...
Correction. Of course LD_LIBRARY_PATH isn't seen by the compiler. It
only applies at run time. How embarrasing:) This isn't the first time
I've been bitten by that.
I don't mind doing that with manual builds, but what about with pip? Is
On 27.12.2013 10:54, alex wrote:
median is faster in version 1.8
___
unfortunately that won't help here because masked median uses
apply_along_axis again which is very slow, especially if one wants to
calculate thousands of medians of 7 elements as in
On 31.12.2013 14:13, Amira Chekir wrote:
Hello together,
I try to load a (large) NIfTI file (DMRI from Human Connectome Project,
about 1 GB) with NiBabel.
import nibabel as nib
img = nib.load(dmri.nii.gz)
data = img.get_data()
The program crashes during img.get_data() with an
to the last release candidate four additional minor issues have
been fixed and compatibility with python 3.4b1 improved.
Source tarballs, installers and release notes can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.7.2
Cheers,
Julian Taylor
On 01.01.2014 16:50, Amira Chekir wrote:
On 31.12.2013 14:13, Amira Chekir wrote:
Hello together,
I try to load a (large) NIfTI file (DMRI from Human Connectome Project,
about 1 GB) with NiBabel.
import nibabel as nib
img = nib.load(dmri.nii.gz)
data = img.get_data()
The
On 18.07.2013 15:36, Nathaniel Smith wrote:
On Wed, Jul 17, 2013 at 5:57 PM, Frédéric Bastien no...@nouiz.org wrote:
On Wed, Jul 17, 2013 at 10:39 AM, Nathaniel Smith n...@pobox.com wrote:
On Tue, Jul 16, 2013 at 11:55 AM, Nathaniel Smith n...@pobox.com wrote:
It's entirely possible I
On Thu, Jan 9, 2014 at 3:54 PM, Daπid davidmen...@gmail.com wrote:
On 8 January 2014 22:39, Julian Taylor jtaylor.deb...@googlemail.comwrote:
As you can see even without real hardware support it is about 30% faster
than inplace unblocked numpy due better use of memory bandwidth. Its
even
) or
at least kahan summation, full overflow/invalid checks etc
On Thu, Jan 9, 2014 at 9:43 AM, Freddie Witherden fred...@witherden.org
wrote:
On 08/01/14 21:39, Julian Taylor wrote:
An issue is software emulation of real fma. This can be enabled in the
test ufunc with npfma.set_type(libc
On 10.01.2014 01:49, Frédéric Bastien wrote:
Do you know if those instruction are automatically used by gcc if we
use the good architecture parameter?
they are used if you enable -ffp-contract=fast. Do not set it to `on`
this is an alias to `off` due to the semantics of C.
-ffast-math
On Fri, Jan 10, 2014 at 3:48 AM, Nathaniel Smith n...@pobox.com wrote:
On Thu, Jan 9, 2014 at 11:21 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
[...]
After a bit more research, some further points to keep in mind:
Currently, PyDimMem_* and PyArray_* are just aliases for
On 10.01.2014 17:03, Nathaniel Smith wrote:
On Fri, Jan 10, 2014 at 9:18 AM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
On Fri, Jan 10, 2014 at 3:48 AM, Nathaniel Smith n...@pobox.com wrote:
[...]
For this reason and missing calloc I don't think we should use the Python
API for data
On 01/15/2014 11:25 AM, Daπid wrote:
On 15 January 2014 11:12, Hedieh Ebrahimi hedieh.ebrah...@amphos21.com
mailto:hedieh.ebrah...@amphos21.com wrote:
I try to print my fileContent array after I read it and it looks
like this :
On 01/15/2014 01:38 PM, Julian Taylor wrote:
On 01/15/2014 11:25 AM, Daπid wrote:
On 15 January 2014 11:12, Hedieh Ebrahimi hedieh.ebrah...@amphos21.com
...
for utf 8 data:
d = np.loadtxt(file, dtype='utf8')
ups this is a very bad example as we can't have utf8 as its variable
length
On 15.01.2014 18:57, Charles R Harris wrote:
...
There was a discussion of this long ago and UCS-4 was chosen as the
numpy standard. There are just too many complications that arise in
supporting both.
my guess is that that discussion was before python3 and you could still
simply treat
On 16.01.2014 00:42, Chris Barker wrote:
bump back to the OP:
On Wed, Jan 15, 2014 at 2:12 AM, Hedieh Ebrahimi
hedieh.ebrah...@amphos21.com mailto:hedieh.ebrah...@amphos21.com wrote:
fileContent=loadtxt(filePath,dtype=str)
do either of these work for you?
This thread is getting a little out of hand which is my fault for initially
mixing different topics in one mail, so let me try to summarize:
We have three issues here:
- a loadtxt bug when loading strings in python3
this has nothing to do with encodings or dtypes it is a bug that should be
fixed.
On Fri, Jan 17, 2014 at 1:44 PM, Oscar Benjamin
oscar.j.benja...@gmail.comwrote:
On Fri, Jan 17, 2014 at 10:59:27AM +, Pauli Virtanen wrote:
Julian Taylor jtaylor.debian at googlemail.com writes:
[clip]
For backward compatibility we *cannot* change S.
Do you mean to say
On Fri, Jan 17, 2014 at 2:10 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
On Fri, Jan 17, 2014 at 1:44 PM, Oscar Benjamin
oscar.j.benja...@gmail.com wrote:...
...
No latin1 de/encoding is required for anything, I don't know why you would
want do to that in this context.
Does
On Fri, Jan 17, 2014 at 2:40 PM, Oscar Benjamin
oscar.j.benja...@gmail.comwrote:
On Fri, Jan 17, 2014 at 02:10:19PM +0100, Julian Taylor wrote:
On Fri, Jan 17, 2014 at 1:44 PM, Oscar Benjamin
oscar.j.benja...@gmail.comwrote:
On Fri, Jan 17, 2014 at 10:59:27AM +, Pauli Virtanen wrote
On 17.01.2014 15:12, Julian Taylor wrote:
On Fri, Jan 17, 2014 at 2:40 PM, Oscar Benjamin
oscar.j.benja...@gmail.com mailto:oscar.j.benja...@gmail.com wrote:
On Fri, Jan 17, 2014 at 02:10:19PM +0100, Julian Taylor wrote:
On Fri, Jan 17, 2014 at 1:44 PM, Oscar Benjamin
On 22.01.2014 18:23, Ralf Juengling wrote:
Executing the following code,
import numpy as np
a = np.zeros((3,))
w = np.array([0, 1, 0, 1, 2])
v = np.array([10.0, 1, 10.0, 2, 9])
a[w] += v
I was expecting ‘a’ to be array([20., 3., 9.]. Instead I get
a
array([
On 26.01.2014 18:06, Stéfan van der Walt wrote:
On Sun, 26 Jan 2014 16:40:44 +0200, Pauli Virtanen wrote:
The Numpy Windows binaries distributed in the numpy project at
sourceforge.net are compiled with ATLAS, which should count as an
optimized BLAS. I don't recall what's the situation with
On 26.01.2014 22:33, Sturla Molden wrote:
Julian Taylor jtaylor.deb...@googlemail.com wrote:
if this issue disqualifies accelerate, it also disqualifies openblas as
a default. openblas has the same issue, we stuck a big fat warning into
the docs (site.cfg) for this now as people keep running
hi,
numpys no-C99 fallback keeps turning up issues in corner cases, e.g.
hypot https://github.com/numpy/numpy/issues/2385
log1p https://github.com/numpy/numpy/issues/4225
these only seem to happen on windows, on linux and mac it seems to use
the C99 math library just fine.
Are our binary builds
On 28.01.2014 19:44, Joseph McGlinchy wrote:
Hi numpy list!
I am trying to do some image processing on a number of images, 72 to be
specific. I am seeing the python memory usage continually increase.
which version of scipy are you using?
there is a memory leak in ndimage.label in
On 29.01.2014 20:44, Nathaniel Smith wrote:
On Wed, Jan 29, 2014 at 7:39 PM, Joseph McGlinchy jmcglin...@esri.com wrote:
Upon further investigation, I do believe it is within the scipy code where
there is a leak. I commented out my call to processBinaryImage(), which is
all scipy code calls,
which version of numpy are you using?
there seems to be a leak in the scalar return due to the PyObject_Malloc
usage in git master, but it doesn't affect 1.8.0
On Fri, Jan 31, 2014 at 7:20 AM, Chris Laumann chris.laum...@gmail.comwrote:
Hi all-
The following snippet appears to leak memory
On 31.01.2014 18:12, Nathaniel Smith wrote:
On Fri, Jan 31, 2014 at 4:29 PM, Benjamin Root ben.r...@ou.edu wrote:
Just to chime in here about the SciPy Superpack... this distribution tracks
the master branch of many projects, and then puts out releases, on the
assumption that master contains
On Tue, Feb 4, 2014 at 4:27 PM, RayS r...@blue-cove.com wrote:
At 07:09 AM 2/4/2014, you wrote:
On 04/02/2014 16:01, RayS wrote:
I was struggling with methods of reading large disk files into numpy
efficiently (not FITS or .npy, just raw files of IEEE floats from
numpy.tostring()).
Sent with Airmail
On January 31, 2014 at 9:31:40 AM, Julian Taylor
(jtaylor.deb...@googlemail.com mailto://jtaylor.deb...@googlemail.com)
wrote:
On 31.01.2014 18:12, Nathaniel Smith wrote:
On Fri, Jan 31, 2014 at 4:29 PM, Benjamin Root ben.r...@ou.edu wrote:
Just to chime in here about
On Thu, Feb 6, 2014 at 1:11 PM, Thomas Unterthiner
thomas_unterthi...@web.de wrote:
On 2014-02-06 11:10, Sturla Molden wrote:
BTW: The performance of OpenBLAS is far behind Eigen, MKL and ACML, but
better than ATLAS and Accelerate.
Hi there!
Sorry for going a bit off-topic, but: do you
meshgrid also has the sparse keyword argument which archives the same.
On 12.02.2014 20:04, Chris Barker wrote:
An extra note here:
One of the great things about numpy (as opposed, to say, MATLAB), is
array broadcasting Thus you generally don't need meshgrid -- why carry
all that extra
On 17.02.2014 15:18, Francesc Alted wrote:
On 2/17/14, 1:08 AM, josef.p...@gmail.com wrote:
On Sun, Feb 16, 2014 at 6:12 PM, Daπid davidmen...@gmail.com wrote:
On 16 February 2014 23:43, josef.p...@gmail.com wrote:
What's the fastest argsort for a 1d array with around 28 Million
elements,
hi,
I noticed that during some simplistic benchmarks (e.g.
https://github.com/numpy/numpy/issues/4310) a lot of time is spent in
the kernel zeroing pages.
This is because under linux glibc will always allocate large memory
blocks with mmap. As these pages can come from other processes the
kernel
On 17.02.2014 21:16, Sturla Molden wrote:
Julian Taylor jtaylor.deb...@googlemail.com wrote:
When an array is created it tries to get its memory from the cache and
when its deallocated it returns it to the cache.
Good idea, however there is already a C function that does this. It uses
On 17.02.2014 22:27, Sturla Molden wrote:
Nathaniel Smith n...@pobox.com wrote:
Also, I'd be pretty wary of caching large chunks of unused memory. People
already have a lot of trouble understanding their program's memory usage,
and getting rid of 'greedy free' will make this even worse.
A
On Tue, Feb 18, 2014 at 1:47 AM, David Cournapeau courn...@gmail.com wrote:
On Mon, Feb 17, 2014 at 7:31 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
hi,
I noticed that during some simplistic benchmarks (e.g.
https://github.com/numpy/numpy/issues/4310) a lot of time is spent
On Mon, Feb 17, 2014 at 9:42 PM, Nathaniel Smith n...@pobox.com wrote:
On 17 Feb 2014 15:17, Sturla Molden sturla.mol...@gmail.com wrote:
Julian Taylor jtaylor.deb...@googlemail.com wrote:
When an array is created it tries to get its memory from the cache and
when its deallocated
On 18.02.2014 16:21, Julian Taylor wrote:
On Mon, Feb 17, 2014 at 9:42 PM, Nathaniel Smith n...@pobox.com wrote:
On 17 Feb 2014 15:17, Sturla Molden sturla.mol...@gmail.com wrote:
Julian Taylor jtaylor.deb...@googlemail.com wrote:
When an array is created it tries to get its memory from
On Thu, Feb 20, 2014 at 1:25 AM, Nathaniel Smith n...@pobox.com wrote:
Hey all,
Just a heads up: thanks to the tireless work of Olivier Grisel, the OpenBLAS
development branch is now fork-safe when built with its default threading
support. (It is still not thread-safe when built using OMP for
On Thu, Feb 20, 2014 at 3:50 PM, Olivier Grisel
olivier.gri...@ensta.org wrote:
Thanks for sharing, this is all very interesting.
Have you tried to have a look at the memory usage and import time of
numpy when linked against libopenblas.dll?
--
this is probably caused by the memory warmup
On Tue, Feb 25, 2014 at 5:41 PM, Alexander Belopolsky ndar...@mac.com wrote:
On Tue, Feb 25, 2014 at 11:29 AM, Benjamin Root ben.r...@ou.edu wrote:
I seem to recall reading somewhere that pickles are not intended to be
long-term archives as there is no guarantee that a pickle made in one
On 26.02.2014 00:04, JB wrote:
At the risk of igniting a flame war...can someone please help me understand
the indexing behavior of NumPy? I will readily I admit I come from a Matlab
background, but I appreciate the power of Python and am trying to learn more.
From a Matlab user's
hi,
We want to start preparing the release candidate for the bugfix release
1.8.1rc1 this weekend, I'll start preparing the changelog tomorrow.
So if you want a certain issue fixed please scream now or better create
a pull request/patch on the maintenance/1.8.x branch.
Please only consider
while.
This issue limits quite a bit the number of operands in numexpr
expressions, and hence, to other projects that depends on it, like
PyTables or pandas. See for example this bug report:
https://github.com/PyTables/PyTables/issues/286
Thanks,
Francesc
On 2/27/14, 9:05 PM, Julian
, increasing the temporary arrays in nditer from 32kb
to 128kb is a bit worrying, but probably we should do some benchmarks
and see how much performance would be compromised (if any).
Francesc
On 2/28/14, 1:09 PM, Julian Taylor wrote:
hm increasing it for PyArrayMapIterObject would break the public
On 01.03.2014 00:32, Gökhan Sever wrote:
Hello,
Given this simple 2D array:
In [1]: np.arange(9).reshape((3,3))
Out[1]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [2]: a = np.arange(9).reshape((3,3))
In [3]: a[:1:]
Out[3]: array([[0, 1, 2]])
In [4]: a[:1,:]
hi,
as the numpy gsoc topic page is a little short on options I was thinking
about adding two topics for interested students. But as I have no
experience with gsoc or mentoring and the ideas are not very fleshed out
yet I'd like to ask if it might make sense at all:
1. configurable algorithm
On 04.03.2014 18:08, Christoph Gohlke wrote:
On 3/4/2014 4:49 AM, Thomas Unterthiner wrote:
Hi there!
I just tried setting up a new installation using numpy 1.8.1rc1 (+scipy
0.13.3 and matplotlib 1.3.1). I ran into problems when installing
matplotlib 1.3.1. The attached logfile shows the
On 06.03.2014 19:46, Skipper Seabold wrote:
Hi,
Should [1] be considered a release blocker for 1.8.1?
Skipper
[1] https://github.com/numpy/numpy/issues/4442
as far as I can tell its a regression of the 1.8.0 release but not the
1.8.1 release so I wouldn't consider it a blocker.
But its
this was caused by
something in the rc.
Cheers
Thomas
On 2014-03-03 17:23, Charles R Harris wrote:
Hi All,
Julian Taylor has put windows binaries and sources for the 1.8.1
release candidate up on sourceforge
http://sourceforge.net/projects/numpy/files/NumPy/1.8.1rc1/. If
things go well
libraries provide can be very
useful for this.
Regards,
Julian Taylor
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On 25.03.2014 00:28, Charles R Harris wrote:
Hi All,
The suggestion has been made the we drop Python 3.2 support in numpy 1.9
and scipy 0.15. The advantage, from my point of view, to supporting
Python = 3.3 is that the u'unicode' syntax is supported in 3.3 and this
makes it easier to
party applications. Please check the release notes for details.
Source tarballs, windows installers and release notes can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.8.1
Cheers,
Julian Taylor
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On 26.03.2014 16:27, Olivier Grisel wrote:
Hi Carl,
I installed Python 2.7.6 64 bits on a windows server instance from
rackspace cloud and then ran get-pip.py and then could successfully
install the numpy and scipy wheel packages from your google drive
folder. I tested dot products and
On 26.03.2014 21:41, Nathaniel Smith wrote:
On Wed, Mar 26, 2014 at 7:34 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
as for using openblas by default in binary builds, no.
pthread openblas build is now fork safe which is great but it is still
not reliable enough for a default.
E.g
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