[Numpy-discussion] NumPy 1.12.1 released

Hi All, I'm pleased to announce the release of NumPy 1.12.1. NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant parsing is fixed. Wheels for Linux, Windows, and OSX can be found on pypi. Archives can

[Numpy-discussion] NumPy pre-release 1.12.1rc1

Hi All, I'm pleased to announce the release of NumPy 1.12.1rc1. NumPy 1.12.1rc1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant parsing is fixed. Wheels for Linux, Windows, and OSX can be found on pypi. Archive

Re: [Numpy-discussion] Numpy Overhead

On Feb 28, 2017 2:57 PM, "Sebastian K" wrote: Yes it is true the execution time is much faster with the numpy function. The Code for numpy version: def createMatrix(n): Matrix = np.empty(shape=(n,n), dtype='float64') for x in range(n): for y in range(n): Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.

Re: [Numpy-discussion] Numpy Overhead

gnitude(s) slowdown, then you might need to use the naive method, > maybe implemented in Cython / C. > > Cheers, > > Matthew > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listi

Re: [Numpy-discussion] Numpy Overhead

Hi, On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K wrote: > Yes you are right. There is no need to add that line. I deleted it. But the > measured heap peak is still the same. You're applying the naive matrix multiplication algorithm, which is ideal for minimizing memory use during the computation

Re: [Numpy-discussion] Numpy Overhead

>>> > of nmemb*size of calloc(3), all size arguments of >>> > realloc(3), length arguments of mmap(2), and new_size >>> > arguments of mremap(2). >>> >>> Could you post the exact code you're comparing?

Re: [Numpy-discussion] Numpy Overhead

realloc(3), length arguments of mmap(2), and new_size >> > arguments of mremap(2). >> >> Could you post the exact code you're comparing? >> >> I think you'll find that a naive Python 3 matri

Re: [Numpy-discussion] Numpy Overhead

the exact code you're comparing? > > I think you'll find that a naive Python 3 matrix multiplication method > is much, much slower than the same thing with Numpy, with arrays of > any reasonable size. > > Cheers, > > Matthew > ___

Re: [Numpy-discussion] Numpy Overhead

Hi, On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K wrote: > Thank you for your answer. > For example a very simple algorithm is a matrix multiplication. I can see > that the heap peak is much higher for the numpy version in comparison to a > pure python 3 implementation. > The heap is measured with

Re: [Numpy-discussion] Numpy Overhead

;> >>> Hello everyone, >>> >>> I'm interested in the numpy project and tried a lot with the numpy >>> array. I'm wondering what is actually done that there is so much overhead >>> when I call a function in Numpy. Wh

Re: [Numpy-discussion] Numpy Overhead

; Sebastian Kaster >> >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > ______

Re: [Numpy-discussion] Numpy Overhead

erhead when I > call a function in Numpy. What is the reason? > Thanks in advance. > > Regards > > Sebastian Kaster > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > h

[Numpy-discussion] Numpy Overhead

Hello everyone, I'm interested in the numpy project and tried a lot with the numpy array. I'm wondering what is actually done that there is so much overhead when I call a function in Numpy. What is the reason? Thanks in advance. Regards Sebastian Kaster __

Re: [Numpy-discussion] Numpy Development Queries

> > Cheers, > Ralf > > > > _______ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Numpy Development Queries

On Tue, Feb 21, 2017 at 7:32 AM, ashwin.pathak < ashwin.pat...@students.iiit.ac.in> wrote: > Hello all, > I am new to this organization and wanted to start with some easy-fix > issues to get some knowledge about the soruce code. However, the issues > under easy-fix labels have already been solved

[Numpy-discussion] Numpy Development Queries

Hello all, I am new to this organization and wanted to start with some easy-fix issues to get some knowledge about the soruce code. However, the issues under easy-fix labels have already been solved or someone is at it. Can someone help me find such issues? ___

Re: [Numpy-discussion] Numpy development version wheels for testing

On Fri, Jan 27, 2017 at 9:24 PM, Matthew Brett wrote: > Hi, > > I've taken advantage of the new travis-ci cron job feature [1] to set > up daily builds of numpy manylinux and OSX wheels for the current > trunk, uploading to: > > https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2

[Numpy-discussion] Numpy development version wheels for testing

Hi, I've taken advantage of the new travis-ci cron job feature [1] to set up daily builds of numpy manylinux and OSX wheels for the current trunk, uploading to: https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com The numpy build process already builds Ubuntu Precise

Re: [Numpy-discussion] Numpy 1.11.3, scipy 0.18.1, MSVC 2015 and crashes in complex functions

gt; >> I am a bit suspicious about the whole thing as neither conda's or >> gholke's wheel crashed. Has anybody else encountered this ? >> >> David >> >> _______ >> NumPy-Discussion mailing list >> NumPy

Re: [Numpy-discussion] Numpy 1.11.3, scipy 0.18.1, MSVC 2015 and crashes in complex functions

> wheel crashed. Has anybody else encountered this ? > > David > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumP

[Numpy-discussion] Numpy 1.11.3, scipy 0.18.1, MSVC 2015 and crashes in complex functions

Hi there, While building the latest scipy on top of numpy 1.11.3, I have noticed crashes while running the scipy test suite, in scipy.special (e.g. in scipy.special hyp0f1 test).. This only happens on windows for python 3.5 (where we use MSVC 2015 compiler). Applying some violence to distutils, I

Re: [Numpy-discussion] NumPy 1.12.0 release

On Wed, Jan 18, 2017 at 3:43 AM, Julian Taylor wrote: > The version of gcc used will make a large difference in some places. > E.g. the AVX2 integer ufuncs require something around 4.5 to work and in > general the optimization level of gcc has improved greatly since the > clang competition showed

Re: [Numpy-discussion] NumPy 1.12.0 release

gt; suspect) or from the notebook infrastructure. > > > > > > > > HTH, > > > > -- > > > > Jérôme Kieffer > > > > > > > > ___ > > > > NumPy-Disc

Re: [Numpy-discussion] NumPy 1.12.0 release

Matthew Brett wrote: > On Tue, Jan 17, 2017 at 3:47 PM, Neal Becker wrote: >> Matthew Brett wrote: >> >>> Hi, >>> >>> On Tue, Jan 17, 2017 at 5:56 AM, Neal Becker >>> wrote: Charles R Harris wrote: > Hi All, > > I'm pleased to announce the NumPy 1.12.0 release. This release

Re: [Numpy-discussion] NumPy 1.12.0 release

_____ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > ___

Re: [Numpy-discussion] NumPy 1.12.0 release

nstallation was ~20% faster. > > > > I did not investigate further if those 20% came from the manylinux (I > > suspect) or from the notebook infrastructure. > > > > HTH, > > -- > > Jérôme Kieffer > > > > _____

Re: [Numpy-discussion] NumPy 1.12.0 release

On Tue, 17 Jan 2017 08:56:42 -0500 Neal Becker wrote: > I've installed via pip3 on linux x86_64, which gives me a wheel. My > question is, am I loosing significant performance choosing this pre-built > binary vs. compiling myself? For example, my processor might have some more > features tha

Re: [Numpy-discussion] NumPy 1.12.0 release

Hi, On Tue, Jan 17, 2017 at 5:56 AM, Neal Becker wrote: > Charles R Harris wrote: > >> Hi All, >> >> I'm pleased to announce the NumPy 1.12.0 release. This release supports >> Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be >> downloaded from PiPY >>

Re: [Numpy-discussion] NumPy 1.12.0 release

Charles R Harris wrote: > Hi All, > > I'm pleased to announce the NumPy 1.12.0 release. This release supports > Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be > downloaded from PiPY > , the tarball > and zip file

Re: [Numpy-discussion] NumPy 1.12.0 release

On Mon, Jan 16, 2017 at 12:43 PM, Charles R Harris < charlesr.har...@gmail.com> wrote: > Hi All, > > I'm pleased to announce the NumPy 1.12.0 release. This release supports > Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be > downloaded from PiPY >

[Numpy-discussion] NumPy 1.12.0 release

Hi All, I'm pleased to announce the NumPy 1.12.0 release. This release supports Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be downloaded from PiPY , the tarball and zip files may be downloaded from Github

Re: [Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

On Tue, 03 Jan 2017, Stephan Hoyer wrote: > >> testing on stable debian box with elderly numpy, where it does behave > >> sensibly: > >> $> python -c "import numpy; print('numpy version: ', numpy.__version__); > >> a=2; b=-2; print(pow(a,b)); print(pow(numpy.array(a), b))" > >> ('numpy version:

Re: [Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

On Tue, Jan 3, 2017 at 3:05 PM, Nathaniel Smith wrote: > It's possible we should back off to just issuing a deprecation warning in > 1.12? > > On Jan 3, 2017 1:47 PM, "Yaroslav Halchenko" wrote: > >> hm... testing on current master (first result is from python's pow) >> >> $> python -c "import n

Re: [Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

n op(a, b) > ValueError: Integers to negative integer powers are not allowed. > > ------ > Ran 1 test in 0.015s > > FAILED (errors=1) > > $> git describe --tags > v0.19.0-303-gb957f6f > > $> PYTHONPATH=/home/yoh/pr

Re: [Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

On Tue, 03 Jan 2017, Stephan Hoyer wrote: >On Tue, Jan 3, 2017 at 9:00 AM, Yaroslav Halchenko >wrote: > Sorry for coming too late to the discussion and after PR "addressing" > the issue by issuing an error was merged [1].A I got burnt by new > behavior while trying to bu

Re: [Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

On Tue, Jan 3, 2017 at 9:00 AM, Yaroslav Halchenko wrote: > Sorry for coming too late to the discussion and after PR "addressing" > the issue by issuing an error was merged [1]. I got burnt by new > behavior while trying to build fresh pandas release on Debian (we are > freezing for release way

[Numpy-discussion] numpy vs algebra Was: Integers to negative integer powers...

On Tue, 11 Oct 2016, Peter Creasey wrote: > >> I agree with Sebastian and Nathaniel. I don't think we can deviating from > >> the existing behavior (int ** int -> int) without breaking lots of existing > >> code, and if we did, yes, we would need a new integer power function. > >> I think it's be

[Numpy-discussion] NumPy 1.12.0rc2 release.

Hi All, I'm pleased to announce the NumPy 1.12.0rc2 New Year's release. This release supports Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be downloaded from PiPY , the tarball and zip files may be downloaded from

[Numpy-discussion] NumPy 1.12.0rc1

Hi All, I am pleased to announce the release of NumPy 1.12.0rc1. This release supports Python 2.7 and 3.4 - 3.6 and is the result of 406 pull requests submitted by 139 contributors and comprises a large number of fixes and improvements. Among the many improvements it is difficult to pick out ju

[Numpy-discussion] NumPy 1.11.3 release.

Hi All, I'm please to annouce the release of NumPy 1.11.3. This is a one bug fix release to take care of a bug that could corrupt large files opened in append mode and then used as an argument to ndarray.tofile. Thanks to Pavel Potocek for the fix. Cheers, Chuck -BEGIN PGP SIGNED MESSAGE---

Re: [Numpy-discussion] NumPy 1.12.0b1 released

On Nov 18, 2016 01:14, "Ralf Gommers" wrote: > > > > On Fri, Nov 18, 2016 at 9:08 PM, Matthew Brett wrote: >> >> Hi, >> >> On Thu, Nov 17, 2016 at 3:24 PM, Matti Picus wrote: >> > Congrats to all on the release.Two questions: >> > >> > Is there a guide to building standard wheels for NumPy? >> >

Re: [Numpy-discussion] NumPy 1.12.0b1 released

Thanks Nathan, That makes sense (compile using the oldest version of NumPy we wish to support). The information on https://github.com/MacPython/numpy-wheels will probably be very useful too (I've been meaning to try out appveyor at some point for Windows builds/testing). Regards, Peter On Fri,

Re: [Numpy-discussion] NumPy 1.12.0b1 released

s R Harris > >> To: numpy-discussion, SciPy Users List > >> , SciPy Developers > >> List, > >> python-announce-l...@python.org > >> Subject: [Numpy-discussion] NumPy 1.12.0b1 released. > >> > >> Hi All, > >>

Re: [Numpy-discussion] NumPy 1.12.0b1 released

opers >> List, >> python-announce-l...@python.org >> Subject: [Numpy-discussion] NumPy 1.12.0b1 released. >> >> Hi All, >> >> I'm pleased to annouce the release of NumPy 1.12.0b1. This release >> supports Python 2.7 and 3.4 - 3.6 and is the

Re: [Numpy-discussion] NumPy 1.12.0b1 released

On Fri, Nov 18, 2016 at 9:08 PM, Matthew Brett wrote: > Hi, > > On Thu, Nov 17, 2016 at 3:24 PM, Matti Picus > wrote: > > Congrats to all on the release.Two questions: > > > > Is there a guide to building standard wheels for NumPy? > > I don't think so - there is a repository that we use to buil

Re: [Numpy-discussion] NumPy 1.12.0b1 released

Hi, On Thu, Nov 17, 2016 at 3:24 PM, Matti Picus wrote: > Congrats to all on the release.Two questions: > > Is there a guide to building standard wheels for NumPy? I don't think so - there is a repository that we use to build the wheels, that has the Windows, OSX and manyllinux recipes for the s

Re: [Numpy-discussion] NumPy 1.12.0b1 released

wrote: Date: Wed, 16 Nov 2016 22:47:39 -0700 From: Charles R Harris To: numpy-discussion, SciPy Users List , SciPy Developers List, python-announce-l...@python.org Subject: [Numpy-discussion] NumPy 1.12.0b1 released. Hi All, I'm pleased to annouce the release of NumPy 1.1

[Numpy-discussion] NumPy 1.12.0b1 released.

Hi All, I'm pleased to annouce the release of NumPy 1.12.0b1. This release supports Python 2.7 and 3.4 - 3.6 and is the result of 388 pull requests submitted by 133 contributors. It is quite sizeable and rather than put the release notes inline I've attached them as a file and they may also be vi

Re: [Numpy-discussion] Numpy 1.12.x branched

On Thu, Nov 10, 2016 at 9:06 AM, Frédéric Bastien < frederic.bast...@gmail.com> wrote: > My change about numpy.mean in float16 aren't in the doc. > > Should I make a PR again numpy master or maintenance/1.12.x? > Make it against master. I may cut and paste the content into a bigger PR I will merg

Re: [Numpy-discussion] Numpy 1.12.x branched

:19, numpy-discussion-requ...@scipy.org wrote: >> >>> Date: Sun, 06 Nov 2016 17:56:12 +0100 >>> From: Sebastian Berg >>> To:numpy-discussion@scipy.org >>> Subject: Re: [Numpy-discussion] Numpy 1.12.x branched >>> Message-ID:<1478451372.3875.5.ca...@sip

Re: [Numpy-discussion] Numpy 1.12.x branched

On Mon, Nov 7, 2016 at 11:32 AM, Matti Picus wrote: > On 07/11/16 10:19, numpy-discussion-requ...@scipy.org wrote: > >> Date: Sun, 06 Nov 2016 17:56:12 +0100 >> From: Sebastian Berg >> To:numpy-discussion@scipy.org >> Subject: Re: [Numpy-discussion] Numpy

Re: [Numpy-discussion] Numpy 1.12.x branched

On 07/11/16 10:19, numpy-discussion-requ...@scipy.org wrote: Date: Sun, 06 Nov 2016 17:56:12 +0100 From: Sebastian Berg To:numpy-discussion@scipy.org Subject: Re: [Numpy-discussion] Numpy 1.12.x branched Message-ID:<1478451372.3875.5.ca...@sipsolutions.net> Content-Type: text/plain; charse

Re: [Numpy-discussion] Numpy 1.12.x branched

On Sa, 2016-11-05 at 17:04 -0600, Charles R Harris wrote: > Hi All, > > Numpy 1.12.x has been branched and the 1.13 development branch is > open. It would be helpful if folks could review the release notes as > it is likely I've missed something.  I'd like to make the first beta > release in a cou

[Numpy-discussion] Numpy 1.12.x branched

Hi All, Numpy 1.12.x has been branched and the 1.13 development branch is open. It would be helpful if folks could review the release notes as it is likely I've missed something. I'd like to make the first beta release in a couple of days. Chuck ___ Nu

[Numpy-discussion] Numpy scalar integers to negative scalar integer powers.

Hi All, I've put up a PR to deal with the numpy scalar integer powers at https://github.com/numpy/numpy/pull/8221. Note that for now everything goes through the np.power function. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https:

Re: [Numpy-discussion] Numpy integers to integer powers again again

ptions that aren't covered by uint64 + signed (which won't change) seem >>> to occur when the exponent can be safely cast to the base type. I suspect >>> that people have already come to depend on that, especially as python >>> integers on 64 bit linux convert to int64. So in those cases we should >>> perhaps raise a FutureWarning instead of an error. >>> >> >> >> >>> np.int64(2)**np.array(-1, np.int64) >> 0.5 >> >>> np.__version__ >> '1.10.4' >> >>> np.int64(2)**np.array([-1, 2], np.int64) >> array([0, 4], dtype=int64) >> >>> np.array(2, np.uint64)**np.array([-1, 2], np.int64) >> array([0, 4], dtype=int64) >> >>> np.array([2], np.uint64)**np.array([-1, 2], np.int64) >> array([ 0.5, 4. ]) >> >>> np.array([2], np.uint64).squeeze()**np.array([-1, 2], np.int64) >> array([0, 4], dtype=int64) >> >> >> (IMO: If you have to break backwards compatibility, break forwards not >> backwards.) >> > > Current master is different. I'm not too worried in the array cases as the > results for negative exponents were zero except then raising -1 to a power. > Since that result is incorrect raising an error falls on the fine line > between bug fix and compatibility break. If the pre-releases cause too much > trouble. > naive question: if cleaning up the inconsistencies already (kind of) breaks backwards compatibility and didn't result in a big outcry, why can we not go with a Future warning all the way to float. (i.e. use the power function with specified dtype instead of ** if you insist on int return) Josef > > Chuck > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Numpy integers to integer powers again again

') > > In [9]: (np.int8(2) ** np.int8(2)).dtype > Out[9]: dtype('int8') > >>> (np.array([2], dtype=np.int8) ** np.array(-1, dtype=np.int8).squeeze()).dtype dtype('int8') >>> (np.array([2], dtype=np.int8

Re: [Numpy-discussion] Numpy integers to integer powers again again

On Wed, Oct 26, 2016 at 1:39 PM, wrote: > > > On Wed, Oct 26, 2016 at 3:23 PM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Tue, Oct 25, 2016 at 10:14 AM, Stephan Hoyer wrote: >> >>> I am also concerned about adding more special cases for NumPy scalars vs >>> arrays. Thes

Re: [Numpy-discussion] Numpy integers to integer powers again again

On Wed, Oct 26, 2016 at 1:39 PM, Nathaniel Smith wrote: > On Wed, Oct 26, 2016 at 12:23 PM, Charles R Harris > wrote: > [...] > > What I have been concerned about are the follow combinations that > currently > > return floats > > > > num: , exp: , res: > 'numpy.float32'> > > num: , exp: , res:

Re: [Numpy-discussion] Numpy integers to integer powers again again

On Tue, Oct 25, 2016 at 10:14 AM, Stephan Hoyer wrote: > I am also concerned about adding more special cases for NumPy scalars vs > arrays. These cases are already confusing (e.g., making no distinction > between 0d arrays and scalars) and poorly documented. > > On Mon, Oct 24, 2016 at 4:30 PM, N

Re: [Numpy-discussion] Numpy integers to integer powers again again

On Wed, Oct 26, 2016 at 12:23 PM, Charles R Harris wrote: [...] > What I have been concerned about are the follow combinations that currently > return floats > > num: , exp: , res: 'numpy.float32'> > num: , exp: , res: 'numpy.float32'> > num: , exp: , res: 'numpy.float32'> > num: , exp: , res:

Re: [Numpy-discussion] Numpy integers to integer powers again again

that people have already come to depend on that, especially as python > integers on 64 bit linux convert to int64. So in those cases we should > perhaps raise a FutureWarning instead of an error. > >>> np.int64(2)**np.array(-1, np.int64) 0.5 >>> np.__version__ '1.10.4' >>> np.int64(2)**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) >>> np.array(2, np.uint64)**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) >>> np.array([2], np.uint64)**np.array([-1, 2], np.int64) array([ 0.5, 4. ]) >>> np.array([2], np.uint64).squeeze()**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) (IMO: If you have to break backwards compatibility, break forwards not backwards.) Josef http://www.stanlaurelandoliverhardy.com/nicemess.htm > > Chuck > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Numpy integers to integer powers again again

eyond that I'm not sure it really matters > *that* much what we do, and "special cases aren't special enough to > break the rules" and all that. > > -n > > -- > Nathaniel J. Smith -- https://vorpus.org > ___ > NumPy-D

Re: [Numpy-discussion] Numpy integers to integer powers again again

On Mon, Oct 24, 2016 at 3:41 PM, Charles R Harris wrote: > Hi All, > > I've been thinking about this some (a lot) more and have an alternate > proposal for the behavior of the `**` operator > > if both base and power are numpy/python scalar integers, convert to python > integers and call the `**`

[Numpy-discussion] Numpy integers to integer powers again again

Hi All, I've been thinking about this some (a lot) more and have an alternate proposal for the behavior of the `**` operator - if both base and power are numpy/python scalar integers, convert to python integers and call the `**` operator. That would solve both the precision and compatibi

Re: [Numpy-discussion] NumPy 1.11.2 released

I'm pleased to announce the release of Numpy 1.11.2. This release supports > Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in > Numpy 1.11.1. Wheels for Linux, Windows, and OSX can be found on PyPI. > Sources are available on both PyPI and Sourceforge >

Re: [Numpy-discussion] NumPy 1.11.2 released

port 8016, >BUG: Fix numpy.ma.median. >- #8031 <https://github.com/numpy/numpy/pull/8031>: Backport 8030, >BUG: fix np.ma.median with only one non-masked... >- #8032 <https://github.com/numpy/numpy/pull/8032>: Backport 8028, >DOC: Update 1.11.2 release not

Re: [Numpy-discussion] NumPy 1.11.2 released

On Mon, Oct 3, 2016 at 7:15 PM, Charles R Harris wrote: > Hi All, > > I'm pleased to announce the release of Numpy 1.11.2. This release supports > Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in > Numpy 1.11.1. Wheels for Linux, Windows, and OSX can be found on PyPI. > Sou

[Numpy-discussion] NumPy 1.11.2 released

*Hi All,* I'm pleased to announce the release of Numpy 1.11.2. This release supports Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in Numpy 1.11.1. Wheels for Linux, Windows, and OSX can be found on PyPI. Sources are available on both PyPI and Sourceforge

[Numpy-discussion] NumPy 1.11.2rc1

Hi All, I'm pleased to announce the release of Numpy 1.11.2rc1. This release supports Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in Numpy 1.11.1. Wheels for Linux, Windows, and OSX can be found on PyPI. Sources are available on both PyPI and Sourceforge

Re: [Numpy-discussion] Numpy 1.11.2

On Fri, Sep 9, 2016 at 1:20 AM, Sandro Tosi wrote: > what is the status for this? i checked on GH and > https://github.com/numpy/numpy/milestone/43 seems to report no issue > pending. the reason i'm asking is that i still have to package 1.11.1 > for debian, but i dont want to do all the work and

Re: [Numpy-discussion] Numpy 1.11.2

ro "morph" Tosi >> My website: http://sandrotosi.me/ >> Me at Debian: http://wiki.debian.org/SandroTosi >> G+: https://plus.google.com/u/0/+SandroTosi >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@s

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 119, Issue 11

On Aug 21, 2016 10:46 AM, "Dipankar “Dipu” Ganguly" wrote: > > Is there a way to use Wolframs’ Mathematica 11 within IPython on Jupyter running on Anaconda’s Navigator on a Mac with OS 10.11.6? Failing that, what Python package would give me those capabilities? This has nothing to do with numpy,

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 119, Issue 11

Is there a way to use Wolframs’ Mathematica 11 within IPython on Jupyter running on Anaconda’s Navigator on a Mac with OS 10.11.6? Failing that, what Python package would give me those capabilities? Thanks. Dipu Dipankar Ganguly Consultant: Strategy/Technology/Commercialization Bothell, WA Ce

Re: [Numpy-discussion] Numpy 1.11.2

info/numpy-discussion > > > > > > -- > Sandro "morph" Tosi > My website: http://sandrotosi.me/ > Me at Debian: http://wiki.debian.org/SandroTosi > G+: https://plus.google.com/u/0/+SandroTosi > ___

Re: [Numpy-discussion] Numpy 1.11.2

hey there, what happened here? do you still plan to release a 1.11.2rc1 soon? On Wed, Aug 3, 2016 at 9:09 PM, Charles R Harris wrote: > Hi All, > > I would like to release Numpy 1.11.2rc1 this weekend. It will contain a few > small fixes and enhancements for windows and the last Scipy release. If

Re: [Numpy-discussion] NumPy in PyPy

On Sat, Aug 6, 2016 at 9:20 AM, Benjamin Root wrote: > Don't know if it is what you are looking for, but NumPy has a built-in suite > of benchmarks: > http://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.Tester.bench.html > That's the very old (now unused) benchmark runner. Numpy

Re: [Numpy-discussion] NumPy in PyPy

> We have a numpy -- heavy app. bu tit, like many others, I'm sure, also > relies heavily on Cython-wrapped C++ code, as well as pure Cython extensions. > > As well as many other packages that are also wrappers around C libs, Cython > -optimized, etc. > > I've never tried to run it under PyPy

Re: [Numpy-discussion] NumPy in PyPy

> > On Fri, Aug 5, 2016 at 3:42 AM, Papa, Florin >> wrote: >> >>> Does anyone have knowledge of real life workloads that use NumPy and >>> cannot be run using PyPy? >>> >>> >>> >>> We are also interested in creating a repository with relevant benchmarks >>> for real world usage of NumPy, >>> >> W

Re: [Numpy-discussion] NumPy in PyPy

On Sat, Aug 6, 2016 at 9:20 AM, Benjamin Root wrote: > Don't know if it is what you are looking for, but NumPy has a built-in > suite of benchmarks: http://docs.scipy.org/doc/numpy/reference/generated/ > numpy.testing.Tester.bench.html > That's the very old (now unused) benchmark runner. Numpy h

Re: [Numpy-discussion] NumPy in PyPy

___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] NumPy in PyPy

Hi, This is Florin Papa from the Dynamic Scripting Languages Optimizations team in Intel Corporation. Our team is working on optimizing the PyPy interpreter and part of this work is to find and fix incompatibilities between NumPy and PyPy. Does anyone have knowledge of real life workloads that

[Numpy-discussion] Numpy 1.11.2

Hi All, I would like to release Numpy 1.11.2rc1 this weekend. It will contain a few small fixes and enhancements for windows and the last Scipy release. If there are any pending PRs that you think should go in or be backported for this release, please speak up. Chuck _

Re: [Numpy-discussion] Numpy set_printoptions, silent failure, bug?

ate: 20 July 2016 at 7:49:10 AM > To: Discussion of Numerical Python > > Subject: Re: [Numpy-discussion] Numpy set_printoptions, silent failure, > bug? > > Hi Robert, >> >> Thanks for your reply. If no one disagrees with you or with me that this >> is a Numpy b

Re: [Numpy-discussion] Numpy set_printoptions, silent failure, bug?

https://github.com/numpy/numpy/issues From: John Ladasky Reply: Discussion of Numerical Python Date: 20 July 2016 at 7:49:10 AM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] Numpy set_printoptions, silent failure, bug? Hi Robert, > > Thanks for your reply. If

Re: [Numpy-discussion] Numpy set_printoptions, silent failure, bug?

Hi Robert, Thanks for your reply. If no one disagrees with you or with me that this is a Numpy bug, I would appreciate being directed to the appropriate page to submit a bug-fix request. On Tue, Jul 19, 2016 at 2:43 PM, Robert Kern wrote: > On Tue, Jul 19, 2016 at 10:41 PM, John Ladasky wrot

Re: [Numpy-discussion] Numpy set_printoptions, silent failure, bug?

On Tue, Jul 19, 2016 at 10:41 PM, John Ladasky wrote: > Should this be considered a Numpy bug, or is there some reason that set_printoptions would legitimately need to accept a dictionary as a single argument? There is no such reason. One could certainly add more validation to the arguments to n

[Numpy-discussion] Numpy set_printoptions, silent failure, bug?

Hi there, I've been using Numpy for several years and appreciate it very much. The following minimal code has been tried on Python 3.4 and 3.5, with Numpy 1.8 and Numpy 1.11, respectively. I want to temporarily change the way that a Numpy array is printed, then change it back. import numpy as n

[Numpy-discussion] Numpy 1.11.1 release

Hi All, I'm pleased to announce the release of Numpy 1.11.1. This release supports Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in Numpy 1.11.0 as well as making several build related improvements. Wheels for Linux, Windows, and OSX can be found on PyPI. Sources are availa

Re: [Numpy-discussion] numpy threads crash when allocating arrays

On 06/14/2016 01:05 PM, Nathaniel Smith wrote: On Jun 14, 2016 12:38 PM, "Burlen Loring" > wrote: > > On 06/14/2016 12:28 PM, Julian Taylor wrote: >> >> On 14.06.2016 19:34, Burlen Loring wrote: >> >>> >>> here's my question: given Py_BEGIN_ALLOW_THREADS is used by numpy

Re: [Numpy-discussion] numpy threads crash when allocating arrays

On Jun 14, 2016 12:38 PM, "Burlen Loring" wrote: > > On 06/14/2016 12:28 PM, Julian Taylor wrote: >> >> On 14.06.2016 19:34, Burlen Loring wrote: >> >>> >>> here's my question: given Py_BEGIN_ALLOW_THREADS is used by numpy how >>> can numpy be thread safe? and how can someone using the C-API know

Re: [Numpy-discussion] numpy threads crash when allocating arrays

On 06/14/2016 12:28 PM, Julian Taylor wrote: On 14.06.2016 19:34, Burlen Loring wrote: here's my question: given Py_BEGIN_ALLOW_THREADS is used by numpy how can numpy be thread safe? and how can someone using the C-API know where it's necessary to acquire the GIL? Maybe someone can explain thi

Re: [Numpy-discussion] numpy threads crash when allocating arrays

On 14.06.2016 19:34, Burlen Loring wrote: here's my question: given Py_BEGIN_ALLOW_THREADS is used by numpy how can numpy be thread safe? and how can someone using the C-API know where it's necessary to acquire the GIL? Maybe someone can explain this? numpy only releases the GIL when it is n

Re: [Numpy-discussion] numpy threads crash when allocating arrays

ode and most people aren't seeing random segfaults, so the problem is most likely in your code. Sorry I can't help much more than that... I guess I'd start by triple-checking that the code really truly does hold the GIL every time that it calls into numpy/python APIs. I'd also try running it under valgrind in case it's some other random memory corruption that's just showing up in a weird way. -n ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] numpy threads crash when allocating arrays

Hi Burlen, On Jun 13, 2016 5:24 PM, "Burlen Loring" wrote: > > Hi All, > > I'm working on a threaded pipeline where we want the end user to be able to > code up Python functions to do numerical work. Threading is all done in C++11 > and in each thread we've acquired gill before we invoke the us

[Numpy-discussion] numpy threads crash when allocating arrays

Hi All, I'm working on a threaded pipeline where we want the end user to be able to code up Python functions to do numerical work. Threading is all done in C++11 and in each thread we've acquired gill before we invoke the user provided Python callback and release it only when the callback ret

Re: [Numpy-discussion] NumPy lesson at EuroScipy2016?

Dear Bartocz, thank you very much for proposing a tutorial on advanced NumPy for Euroscipy 2016! I think it's an awesome idea! Before the call for proposals, I did a survey about the subjects that people were interested in for the advanced tutorials, and advanced NumPy scored very high (see the po

Re: [Numpy-discussion] NumPy lesson at EuroScipy2016?

On Thu, Jun 9, 2016 at 11:25 PM, wrote: > Hi all, > > Recently I taught "Advanced NumPy" lesson at a Software Carpentry workshop > [1]. It covered a review of basic operations on numpy arrays and also more > advanced topics: indexing, broadcasting, dtypes and memory layout. I would > greatly appr

[Numpy-discussion] NumPy lesson at EuroScipy2016?

Hi all, Recently I taught "Advanced NumPy" lesson at a Software Carpentry workshop [1]. It covered a review of basic operations on numpy arrays and also more advanced topics: indexing, broadcasting, dtypes and memory layout. I would greatly appreciate your feedback on the lesson materials, whic

Re: [Numpy-discussion] NumPy 1.11 docs

_______ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

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