Re: [Numpy-discussion] Setting up a newcomers label on the issue tracker ?

On 11/26/2014 09:44 AM, David Cournapeau wrote: Hi, Would anybody mind if I create a label newcomers on GH, and start labelling simple issues ? This is in anticipation to the bloomberg lab event in London this WE. I will try to give a hand to people interested in numpy/scipy, David

Re: [Numpy-discussion] Scipy 0.15.0 beta 1 release

On 11/26/2014 02:19 PM, Charles R Harris wrote: On Wed, Nov 26, 2014 at 2:06 AM, Julian Taylor jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com wrote: On 11/26/2014 12:50 AM, Andrea Gavana wrote: On 25 November 2014 at 19:33, David Cournapeau courn

Re: [Numpy-discussion] Finding values in an array

On 28.11.2014 04:15, Alexander Belopolsky wrote: I probably miss something very basic, but how given two arrays a and b, can I find positions in a where elements of b are located? If a were sorted, I could use searchsorted, but I don't want to get valid positions for elements that are not in

Re: [Numpy-discussion] Finding values in an array

On 28.11.2014 09:37, Robert Kern wrote: On Fri, Nov 28, 2014 at 8:22 AM, Julian Taylor jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com wrote: On 28.11.2014 04:15, Alexander Belopolsky wrote: I probably miss something very basic, but how given two arrays a and b, can I

Re: [Numpy-discussion] GDB macros for numpy

On Sun, Nov 30, 2014 at 10:41 PM, David Cournapeau courn...@gmail.com wrote: On Sun, Nov 30, 2014 at 5:45 PM, Charles R Harris charlesr.har...@gmail.com wrote: On Sun, Nov 30, 2014 at 4:54 AM, David Cournapeau courn...@gmail.com wrote: Hi there, I remember having seen some

Re: [Numpy-discussion] GDB macros for numpy

On Mon, Dec 1, 2014 at 1:44 AM, Julian Taylor jtaylor.deb...@googlemail.com wrote: On Sun, Nov 30, 2014 at 10:41 PM, David Cournapeau courn...@gmail.com wrote: On Sun, Nov 30, 2014 at 5:45 PM, Charles R Harris charlesr.har...@gmail.com wrote: On Sun, Nov 30, 2014 at 4:54 AM, David

Re: [Numpy-discussion] Should ndarray be a context manager?

I don't think that makes much sense, context managers are useful for managing the lifetime of objects owning resources not already managed by the garbage collector. E.g. file descriptors, a gc has no clue that a piece of memory contains a descriptor and thus never has a reason to release it in

Re: [Numpy-discussion] help using np.correlate to produce correlograms.

I think it is a good time to discuss/implement further correlate improvements. I kind of favor the mode=(tuple of integers) api for your proposed change. Concerning the C-API we probably need to add a new wrapper function but thats ok, the C-API does not need to be as nice as the python API as it

Re: [Numpy-discussion] help using np.correlate to produce correlograms.

On 12/11/2014 03:24 PM, Pierre Haessig wrote: Le 11/12/2014 11:19, Julian Taylor a écrit : Also on a side note, in 1.10 np.convolve/correlate has been significantly speed up if one of the sequences is less than 12 elements Interesting! What is the origin of this speed up, and why a magic

Re: [Numpy-discussion] help using np.correlate to produce correlograms.

On 11.12.2014 19:01, Jose Guzman wrote: On 11/12/14 09:54, Pierre Haessig wrote: The basic idea is to enable the user to select the exact range of lags he wants. Unfortunately I didn't take the time to go further than the specification above... I would be particularly interested in

Re: [Numpy-discussion] Context manager for seterr

On 15.12.2014 01:12, Stefan van der Walt wrote: Hi all, Since the topic of context managers recently came up, what do you think of adding a context manager for seterr? with np.seterr(divide='ignore'): frac = num / denom already exists as np.errstate: with

Re: [Numpy-discussion] Context manager for seterr

On 15.12.2014 01:40, Stefan van der Walt wrote: On 2014-12-15 02:23:18, Julian Taylor jtaylor.deb...@googlemail.com wrote: already exists as np.errstate: with np.errstate(divide='ignore'): With 'ignore' a warning is still raised--is this by choice? import numpy as np x = np.array([0, 1

Re: [Numpy-discussion] Fast sizes for FFT

I still have the plan to add this function as public api to numpy's fft helper functions, though I didn't get to it yet. Its a relative simple task if someone wants to contribute. On 24.12.2014 04:33, Robert McGibbon wrote: Alex Griffing pointed out on github that this feature was recently added

Re: [Numpy-discussion] Fast sizes for FFT

On 24.12.2014 13:07, Sturla Molden wrote: On 24/12/14 04:33, Robert McGibbon wrote: Alex Griffing pointed out on github that this feature was recently added to scipy in https://github.com/scipy/scipy/pull/3144. Sweet! I use different padsize search than the one in SciPy. It would be

Re: [Numpy-discussion] simple reduction question

On 24.12.2014 16:25, Neal Becker wrote: What would be the most efficient way to compute: c[j] = \sum_i (a[i] * b[i,j]) where a[i] is a 1-d vector, b[i,j] is a 2-d array? This seems to be one way: import numpy as np a = np.arange (3) b = np.arange (12).reshape (3,4) c = np.dot (a,

Re: [Numpy-discussion] Sorting refactor

On 16.01.2015 12:33, Lars Buitinck wrote: 2015-01-16 11:55 GMT+01:00 numpy-discussion-requ...@scipy.org: Message: 2 Date: Thu, 15 Jan 2015 21:24:00 -0800 From: Jaime Fern?ndez del R?o jaime.f...@gmail.com Subject: [Numpy-discussion] Sorting refactor To: Discussion of Numerical Python

Re: [Numpy-discussion] Vectorizing computation

On 02/13/2015 11:51 AM, Francesc Alted wrote: Hi, I would like to vectorize the next computation: nx, ny, nz = 720, 180, 3 outheight = np.arange(nz) * 3 oro = np.arange(nx * ny).reshape((nx, ny)) def compute1(outheight, oro): result = np.zeros((nx, ny, nz)) for ix in

[Numpy-discussion] ANN: NumPy 1.9.2 release candidate

Hi, We have finished the first release candidate of NumPy 1.9.2. The 1.9.2 release will as usual be a bugfix only release to the 1.9.x series. The tarballs and win32 binaries are available on sourceforge: https://sourceforge.net/projects/numpy/files/NumPy/1.9.2rc1/ If no regressions show up the

Re: [Numpy-discussion] Aligned / configurable memory allocation

On 10.02.2015 22:33, Nathaniel Smith wrote: On 10 Feb 2015 13:10, Antoine Pitrou solip...@pitrou.net mailto:solip...@pitrou.net wrote: On Tue, 10 Feb 2015 11:26:22 -0800 Nathaniel Smith n...@pobox.com mailto:n...@pobox.com wrote: On 10 Feb 2015 09:11, Antoine Pitrou solip...@pitrou.net

Re: [Numpy-discussion] Vectorizing computation

On 02/13/2015 01:03 PM, Francesc Alted wrote: 2015-02-13 12:51 GMT+01:00 Julian Taylor jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com: On 02/13/2015 11:51 AM, Francesc Alted wrote: Hi, I would like to vectorize the next computation: nx, ny

Re: [Numpy-discussion] Build doesn't pass tests

On 01/08/2015 07:31 PM, Nikolay Mayorov wrote: Hi all! I'm trying to build numpy on Windows 64 bit, Python 3.4.2 64 bit. I do environment setup by the following command: CMD /K SET MSSdk=1 SET DISTUTILS_USE_SDK=1 C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\vcvarsall.bat

Re: [Numpy-discussion] Mathematical functions in Numpy

currently the math functions are wrapped via the generic PyUfunc_* functions in numpy/core/src/umath/loops.c.src which just apply some arbitrary function to a scalar from arbitrarily strided inputs. When adding variants one likely needs to add some special purpose loops to deal with the various

Re: [Numpy-discussion] numpy pickling problem - python 2 vs. python 3

On 07.03.2015 00:20, Pauli Virtanen wrote: 06.03.2015, 22:43, Eric Firing kirjoitti: On 2015/03/06 10:23 AM, Pauli Virtanen wrote: 06.03.2015, 20:00, Benjamin Root kirjoitti: A slightly different way to look at this is one of sharing data. If I am working on a system with 3.4 and I want to

Re: [Numpy-discussion] Introductory mail and GSoc Project Vector math library integration

On 03/12/2015 10:15 AM, Gregor Thalhammer wrote: Another note, numpy makes it easy to provide new ufuncs, see http://docs.scipy.org/doc/numpy-dev/user/c-info.ufunc-tutorial.html from a C function that operates on 1D arrays, but this function needs to support arbitrary spacing (stride)

Re: [Numpy-discussion] numpy array casting ruled not safe

On 08.03.2015 11:49, Sebastian Berg wrote: On Sa, 2015-03-07 at 18:21 -0800, Jaime Fernández del Río wrote: snip I note that on SO Jaime made the suggestion that take use unsafe casting and throw an error on out of bounds

Re: [Numpy-discussion] np.nonzero behavior with multidimensional arrays

On 23.02.2015 08:52, Jaime Fernández del Río wrote: This was raised in SO today: http://stackoverflow.com/questions/28663142/why-is-np-wheres-result-read-only-for-multi-dimensional-arrays/28664009 np.nonzero (and np.where for boolean arrays) behave differently for 1-D and higher

Re: [Numpy-discussion] PR, extended site.cfg capabilities

On 02/24/2015 02:16 PM, Nick Papior Andersen wrote: Dear all, I have initiated a PR-5597 https://github.com/numpy/numpy/pull/5597, which enables the reading of new flags from the site.cfg file. @rgommers requested that I posted some information on this site, possibly somebody could test it

Re: [Numpy-discussion] PR, extended site.cfg capabilities

On 02/24/2015 02:31 PM, Julian Taylor wrote: On 02/24/2015 02:16 PM, Nick Papior Andersen wrote: Dear all, I have initiated a PR-5597 https://github.com/numpy/numpy/pull/5597, which enables the reading of new flags from the site.cfg file. @rgommers requested that I posted some information

Re: [Numpy-discussion] GSoC'15 - mentors ideas

On 02/24/2015 05:41 PM, Charles R Harris wrote: On Mon, Feb 23, 2015 at 11:49 PM, Ralf Gommers ralf.gomm...@gmail.com mailto:ralf.gomm...@gmail.com wrote: Hi all, On Fri, Feb 20, 2015 at 10:05 AM, Ralf Gommers ralf.gomm...@gmail.com mailto:ralf.gomm...@gmail.com wrote:

Re: [Numpy-discussion] Asking proposal review/feedback for GSOC 15

On 03/26/2015 12:58 PM, Oğuzhan Ünlü wrote: Hi, Sorry for a bit late reply. I will express my thoughts for Ralf's suggestions, respectively. Regarding your schedule: - I would remove the parts related to benchmarks. There's no nice benchmark infrastructure in numpy

Re: [Numpy-discussion] element-wise array segmental function operation?

On 23.03.2015 07:46, oyster wrote: Hi, all I want to know wether there is a terse way to apply a function to every array element, where the function behaves according to the element value. for example [code] def fun(v): if 0=v60: return f1(v)#where f1 is a function

Re: [Numpy-discussion] performance of numpy.array()

numpy 1.9 makes array(list) performance similar in performance to vstack in 1.8 its very slow. On 29.04.2015 17:40, simona bellavista wrote: on cluster A 1.9.0 and on cluster B 1.8.2 2015-04-29 17:18 GMT+02:00 Nick Papior Andersen nickpap...@gmail.com mailto:nickpap...@gmail.com:

Re: [Numpy-discussion] performance of numpy.array()

On 29.04.2015 17:50, Robert Kern wrote: On Wed, Apr 29, 2015 at 4:05 PM, simona bellavista afy...@gmail.com mailto:afy...@gmail.com wrote: I work on two distinct scientific clusters. I have run the same python code on the two clusters and I have noticed that one is faster by an order of

Re: [Numpy-discussion] Using gentxt to import a csv with a string class label and hundreds of integer features

On Thu, May 7, 2015 at 2:26 AM, Dammy damilarefagb...@gmail.com wrote: Hi, I am trying to use numpy.gentxt to import a csv for classification using scikit-learn. The first column in the csv is a string type class label while 200+ extra columns are integer features. Please I wish to find out

Re: [Numpy-discussion] Open CV 3.0 + NPY_RELAXED_STRIDES

On Thu, Jun 11, 2015 at 11:39 AM, Sebastian Berg sebast...@sipsolutions.net wrote: On Mi, 2015-06-10 at 21:03 -0600, Charles R Harris wrote: snip * Relaxed stride checking will be the default in 1.10.0 Is this still the plan? Yes, but it won't be quite the same as

Re: [Numpy-discussion] Verify your sourceforge windows installer downloads

On 28.05.2015 19:46, Pauli Virtanen wrote: 28.05.2015, 20:35, Sturla Molden kirjoitti: Pauli Virtanen p...@iki.fi wrote: Is it possible to host them on github? I think there's an option to add release notes and (apparently) to upload binaries if you go to the Releases section --- there's one

[Numpy-discussion] Verify your sourceforge windows installer downloads

. Maybe someone familar with available tools can chime in. I have checked the numpy downloads and they still match what I uploaded, but as sourceforge does redirect based on OS and geolocation this may not mean much. Cheers, Julian Taylor ___ NumPy

Re: [Numpy-discussion] Question about unaligned access

On 06.07.2015 18:21, Francesc Alted wrote: 2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río jaime.f...@gmail.com mailto:jaime.f...@gmail.com: On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted fal...@gmail.com mailto:fal...@gmail.com wrote: Hi, I have stumbled

Re: [Numpy-discussion] Question about unaligned access

On 06.07.2015 18:21, Francesc Alted wrote: 2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río jaime.f...@gmail.com mailto:jaime.f...@gmail.com: On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted fal...@gmail.com mailto:fal...@gmail.com wrote: Hi, I have stumbled

Re: [Numpy-discussion] Question about unaligned access

On 06.07.2015 18:21, Francesc Alted wrote: 2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río jaime.f...@gmail.com mailto:jaime.f...@gmail.com: On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted fal...@gmail.com mailto:fal...@gmail.com wrote: Hi, I have stumbled

Re: [Numpy-discussion] Question about unaligned access

sorry for the 3 empty mails, my client bugged out... as a workaround you can align structured dtypes to avoid this issue: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=np.dtype([('f0', np.int64), ('f1', np.int32)], align=True)) On 06.07.2015 18:21, Francesc Alted wrote: 2015-07-06

Re: [Numpy-discussion] Strategy for OpenBLAS

On 05/26/2015 04:56 PM, Matthew Brett wrote: Hi, This morning I was wondering whether we ought to plan to devote some resources to collaborating with the OpenBLAS team. It is relatively easy to add tests using Python / numpy. We like tests. Why don't we propose a collaboration with

Re: [Numpy-discussion] Proposal: Deprecate np.int, np.float, etc.?

On 31.07.2015 08:24, Jason Newton wrote: Been using numpy in it's various forms since like 2005. burned on int, int_ just today with boost.python / ndarray conversions and a number of times before that. intc being C's int!? Didn't even know it existed till today. This isn't the first time,

Re: [Numpy-discussion] Proposal: Deprecate np.int, np.float, etc.?

On 07/23/2015 04:29 AM, Nathaniel Smith wrote: Hi all, So one of the things exposed in the numpy namespace are objects called np.int np.float np.bool etc. These are commonly used -- in fact, just yesterday on another project I saw a senior person reviewing a pull request

Re: [Numpy-discussion] reorganizing numpy internal extensions (was: Re: Should we drop support for "one file" compilation mode?)

On 10/08/2015 03:30 PM, David Cournapeau wrote: On Tue, Oct 6, 2015 at 8:04 PM, Nathaniel Smith > wrote: On Tue, Oct 6, 2015 at 11:52 AM, David Cournapeau > wrote: > > > On Tue, Oct 6,

Re: [Numpy-discussion] Numpy 1.10.0 release

On 10/06/2015 01:45 PM, Neal Becker wrote: Are extra_compile_args actually used in all compile steps? extra_compile_args is not used by numpy, its to support some third party use case I never understood. As the typical site.cfg used by numpy only contains binaries that are never compiled by

Re: [Numpy-discussion] [SciPy-Dev] Numpy 1.10.0 release

On 10/06/2015 02:08 PM, Daπid wrote: I don't get any failures on Fedora 22. I have installed it with pip, setting my CFLAGS to "-march=core-avx-i -O2 -pipe -mtune=native" and linking against openblas. With the new Numpy, Scipy full suite shows two errors, I am sorry I didn't think of running

Re: [Numpy-discussion] msvc9 comipiler problems

On 10/09/2015 06:50 PM, Charles R Harris wrote: Hi All, There is a compilation problem with 1.10.0 on 32 bit windows using the msvc9 compiler. One possible solution to this is to drop support for python 2.6. The last, and final, release of of that

Re: [Numpy-discussion] SHA256 mismatch on SourceForge downloads

The file is also not signed so the checksums are not trustworthy anyway. Please sign the releases as we did in the past. On 08/26/2015 10:28 AM, Antoine Pitrou wrote: Hello, The SourceForge download page for 1.10.0b1 mentions:

Re: [Numpy-discussion] OK to upload patched 1.9.2 for Python 3.5?

as due to the many incompatiblities in 1.10 many will likely not be able to update anytime soon, so I think putting out another 1.9.3 bugfix release would be a good idea. I can probably do the release management for it, though I haven't been keeping up with bugfixes recently so, please comment

Re: [Numpy-discussion] Problem while writing array with np.savetxt

numpy text io is fundamentally broken in python3, there are sometimes workarounds, but currently its probably best to stick to python2 or not use it. The workarounds usually just make actually fixing the functions harder. On 09/24/2015 09:24 AM, Eric Firing wrote: > On 2015/09/23 9:17 PM, Andrew

Re: [Numpy-discussion] When to stop supporting Python 2.6?

dropping 3.2: +-0 as it would remove some extra code in our broken py3 string handling but not much dropping 3.3: -1 doesn't gain us anything so far I know dropping 2.6: -1, I don't see not enough advantage the only issue I know of is an occasional set literal which gets caught by our test-suite

Re: [Numpy-discussion] When to stop supporting Python 2.6?

On 12/09/2015 12:10 AM, Ralf Gommers wrote: On Wed, Dec 9, 2015 at 12:01 AM, Chris Barker > wrote: drop 2.6 I still don't understand why folks insist that they need to run a (very)) old python on an old OS, but need the latest

Re: [Numpy-discussion] Should I use pip install numpy in linux?

On 09.01.2016 04:38, Nathaniel Smith wrote: > On Fri, Jan 8, 2016 at 7:17 PM, Nathan Goldbaum wrote: >> Doesn't building on CentOS 5 also mean using a quite old version of gcc? > > Yes. IIRC CentOS 5 ships with gcc 4.4, and you can bump that up to gcc > 4.8 by using the

Re: [Numpy-discussion] Defining a base linux-64 environment [was: Should I use pip install numpy in linux?]

On 09.01.2016 12:52, Robert McGibbon wrote: > Hi all, > > I went ahead and tried to collect a list of all of the libraries that > could be considered to constitute the "base" system for linux-64. The > strategy I used was to leverage off the work done by the folks at > Continuum by searching

Re: [Numpy-discussion] Problem writing array with savetxt (python3.5)

hi unfortunately numpy text io in python3 is very broken and best avoided. Technically you might be able to work around it by opening the file in binary mode but that is the wrong way of doing it and might break when we finally get around to really fixing it, also won't work for unicode and string

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

Re: [Numpy-discussion] Multi-distribution Linux wheels - please test

On 02/08/2016 05:23 PM, Daπid wrote: > > On 8 February 2016 at 16:19, Olivier Grisel > wrote: > > > > OPENBLAS_CORETYPE=Nehalem python3 -c "import numpy as np; from scipy > import linalg; linalg.eigh(np.random.randn(200,

Re: [Numpy-discussion] Fwd: Multi-distribution Linux wheels - please test

On 09.02.2016 04:59, Nathaniel Smith wrote: > On Mon, Feb 8, 2016 at 6:07 PM, Nathaniel Smith wrote: >> On Mon, Feb 8, 2016 at 6:04 PM, Matthew Brett >> wrote: >>> On Mon, Feb 8, 2016 at 5:26 PM, Nathaniel Smith wrote: On Mon, Feb

Re: [Numpy-discussion] Fwd: Multi-distribution Linux wheels - please test

On 09.02.2016 21:01, Nathaniel Smith wrote: > On Tue, Feb 9, 2016 at 11:37 AM, Julian Taylor > <jtaylor.deb...@googlemail.com> wrote: >> On 09.02.2016 04:59, Nathaniel Smith wrote: >>> On Mon, Feb 8, 2016 at 6:07 PM, Nathaniel Smith <n...@pobox.com> wrote: &g

Re: [Numpy-discussion] Fwd: Multi-distribution Linux wheels - please test

On 09.02.2016 20:52, Matthew Brett wrote: > On Mon, Feb 8, 2016 at 7:59 PM, Nathaniel Smith wrote: >> On Mon, Feb 8, 2016 at 6:07 PM, Nathaniel Smith wrote: >>> On Mon, Feb 8, 2016 at 6:04 PM, Matthew Brett >>> wrote: On Mon, Feb 8,

Re: [Numpy-discussion] Linking other libm-Implementation

On 02/08/2016 06:36 PM, Nathaniel Smith wrote: > On Feb 8, 2016 3:04 AM, "Nils Becker" > wrote: >> > [...] >> Very superficial benchmarks (see below) seem devastating for gnu libm. > It seems that openlibm (compiled with gcc -mtune=native

Re: [Numpy-discussion] Numpy 1.11.0b2 released

You most likely don't need a scipy build against it. You should be able to use the oldest scipy our project supports. Numpy does try to not break its reverse dependencies, if stuff breaks it should only occur in edge cases not affecting functionality of real applications (like warnings or

Re: [Numpy-discussion] Numpy pull requests getting out of hand.

I don't like that approach, closing PRs with valuable code causes them to get lost in the much larger set of closed ones. Instead we could tag them appropriately so they can be found by parties interested in using or finishing them. These could also be used for new contributors to work on. You

Re: [Numpy-discussion] Numpy 1.11.0b2 released

hi, even if it are good changes, I find it reasonable to ask for a delay in numpy release if you need more time to adapt. Of course this has to be within reason and can be rejected, but its very valuable to know changes break existing old workarounds. If pyfits broke there is probably a lot more

Re: [Numpy-discussion] Numpy 1.11.0b2 released

On 01/30/2016 06:27 PM, Ralf Gommers wrote: > > > On Fri, Jan 29, 2016 at 11:39 PM, Nathaniel Smith > wrote: > > It occurs to me that the best solution might be to put together a > .travis.yml for the release branches that does: "for pkg in >

Re: [Numpy-discussion] Numpy 1.11.0rc1 released.

that test needs about 500Mb of memory on windows as it doesn't have sparse allocations like most *nixes. It used to fail for me during release testing when I only gave the windows VM 1GB of ram. If its a problem for CI we could disable it on windows, or at least skip the complex double case. On

Re: [Numpy-discussion] Calling C code that assumes SIMD aligned data.

note that anything larger than 16 bytes alignment is unnecessary for simd purposes on current hardware (>= haswell). 16 byte is default malloc alignment on amd64. And even on older ones (sandy bridge) the penalty is pretty minor. On 05.05.2016 22:32, Charles R Harris wrote: On Thu, May 5,

Re: [Numpy-discussion] offset in fill diagonal

On 21.01.2017 16:10, josef.p...@gmail.com wrote: > Is there a simple way to fill in diagonal elements in an array for other > than main diagonal? > > As far as I can see, the diagxxx functions that have offset can only > read and not inplace modify, and the functions for modifying don't have >

Re: [Numpy-discussion] Could we simplify backporting?

On 24.02.2017 16:00, Evgeni Burovski wrote: >> I really don't like the double work and the large amount of noise coming >> from backporting every other PR to NumPy very quickly. For SciPy the policy >> is: >> - anyone can set the "backport-candidate" label >> - the release manager backports,

Re: [Numpy-discussion] NumPy 1.12.0 release

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 up around that time. centos 5 has 4.1 which is really ancient.

Re: [Numpy-discussion] Using library-specific headers

On 09/27/2016 11:09 PM, Pavlyk, Oleksandr wrote: Suppose I would like to take advantage of some functions from MKL in numpy C source code, which would require to use #include “mkl.h” Ideally this include line must not break the build of numpy when MKL is not present, so my initial approach

Re: [Numpy-discussion] automatically avoiding temporary arrays

On 03.10.2016 20:23, Chris Barker wrote: > > > On Mon, Oct 3, 2016 at 3:16 AM, Julian Taylor > <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> > wrote: > > the problem with this approach is that we don't really want numpy >

Re: [Numpy-discussion] automatically avoiding temporary arrays

On 30.09.2016 23:09, josef.p...@gmail.com wrote: > On Fri, Sep 30, 2016 at 9:38 AM, Julian Taylor > <jtaylor.deb...@googlemail.com> wrote: >> hi, >> Temporary arrays generated in expressions are expensive as the imply >> extra memory bandwidth which is the bottl

Re: [Numpy-discussion] Intel random number package

On 10/26/2016 06:00 PM, Julian Taylor wrote: On 10/26/2016 10:59 AM, Ralf Gommers wrote: On Wed, Oct 26, 2016 at 8:33 PM, Julian Taylor <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> wrote: On 26.10.2016 06:34, Charles R Harris wrote:

Re: [Numpy-discussion] Intel random number package

On 10/26/2016 10:59 AM, Ralf Gommers wrote: On Wed, Oct 26, 2016 at 8:33 PM, Julian Taylor <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> wrote: On 26.10.2016 06:34, Charles R Harris wrote: > Hi All, > > There is a proposed random

Re: [Numpy-discussion] Intel random number package

https://mail.scipy.org/pipermail/scipy-dev/2013-August/019149.html Robert On Thu, Oct 27, 2016 at 5:14 PM, Julian Taylor <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> wrote: On 10/27/2016 04:52 PM, Todd wrote: On Thu, Oct 27, 2016 at 10:43 AM, J

Re: [Numpy-discussion] ufunc for sum of squared difference

here is an old unfinished PR adding max_abs which has a similar purpose as yours: https://github.com/numpy/numpy/pull/5509 So far I know data is set during runtime construction and is part of the ufunc object, so it is not really very suitable to pass in constants on call. Its intended purpose is

Re: [Numpy-discussion] Intel random number package

On 10/27/2016 04:30 PM, Todd wrote: On Thu, Oct 27, 2016 at 4:25 AM, Ralf Gommers > wrote: On Thu, Oct 27, 2016 at 10:25 AM, Pavlyk, Oleksandr > wrote: Please

Re: [Numpy-discussion] Intel random number package

On 10/27/2016 04:52 PM, Todd wrote: On Thu, Oct 27, 2016 at 10:43 AM, Julian Taylor <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> wrote: On 10/27/2016 04:30 PM, Todd wrote: On Thu, Oct 27, 2016 at 4:25 AM, Ralf Gommers <ralf.go

Re: [Numpy-discussion] Intel random number package

On 26.10.2016 06:34, Charles R Harris wrote: > Hi All, > > There is a proposed random number package PR now up on github: > https://github.com/numpy/numpy/pull/8209. It is from > oleksandr-pavlyk and implements > the number random number package using MKL for

Re: [Numpy-discussion] missing from contributor list?

you can add multiple email addresses to your github settings so commits are properly attributed to your account. On 11/03/2016 11:10 AM, m...@telenczuk.pl wrote: Hi, I had the same problem when I changed my email address. It seems that github matches user profiles to commits using the email

Re: [Numpy-discussion] missing from contributor list?

On 02.11.2016 23:38, Sturla Molden wrote: > Why am I missing from the contributor hist here? > > https://github.com/numpy/numpy/blob/master/numpy/_build_utils/src/apple_sgemv_fix.c > > Probably because the file was moved and github does not track this. This is something you should ask gihub

Re: [Numpy-discussion] Does numpy.bincount support numpy.float128 type weights?

Also note that float128 is rarely what you want. It is not a quad precision value, it maps to C long double which is 80 bit on x86 and less on stuff like arm. On 30.11.2016 22:59, Nathan Goldbaum wrote: > I think this is a deficiency in the current implementation of bincount, > which always casts

Re: [Numpy-discussion] Release tags

probably the git quirk of not pulling tags by default, you need to run: git pull --tags On 23.03.2017 19:10, Matti Picus wrote: > I am searching for the exact git hash corresponding to numpy 1.12.1 > > Looking at the result of `git tag -l` yielded tags for v1.11.0, but > nothing higher. > >

[Numpy-discussion] caching large allocations on gnu/linux

Hi, As numpy often allocates large arrays and one factor in its performance is faulting memory from the kernel to the process. This has some cost that is relatively significant. For example in this operation on large arrays it accounts for 10-15% of the runtime: import numpy as np a =

Re: [Numpy-discussion] caching large allocations on gnu/linux

On 13.03.2017 16:21, Anne Archibald wrote: > > > On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor > <jtaylor.deb...@googlemail.com <mailto:jtaylor.deb...@googlemail.com>> > wrote: > > Should it be agreed that caching is worthwhile I would propose a very >

Re: [Numpy-discussion] caching large allocations on gnu/linux

On 13.03.2017 19:54, Francesc Alted wrote: > 2017-03-13 18:11 GMT+01:00 Julian Taylor <jtaylor.deb...@googlemail.com > <mailto:jtaylor.deb...@googlemail.com>>: > > On 13.03.2017 16:21, Anne Archibald wrote: > > > > > > On M

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