Mailman 3 December 2013 - NumPy-Discussion

wheels for OS X: request for testing
by Ralf Gommers 08 Dec '13

08 Dec '13

Hi all, At http://sourceforge.net/projects/numpy/files/wheels_to_test/ you can find a set of wheels, for numpy, scipy and ipython plus its dependencies. These are for the following configuration only: - OSX >= 10.6 - Python 2.7 32/64-bit from python.org It would be great if OS X users could test these. We're interested in whether these wheels work, and also if you run into any issues installing wheels. Since (a) I'm not really sure myself what the recommended and most robust way is to install wheels, and (b) it's interesting to see if the docs and usability of pip/wheel are ready for prime time, I'm not going to give the commands to execute but instead link to the relevant docs: http://www.pip-installer.org/en/latest/index.html http://python-packaging-user-guide.readthedocs.org/en/latest/ http://wheel.readthedocs.org/en/latest/index.html Please try this out and share your experience. Thanks, Ralf P.S.if you're wondering what on earth wheels are, they're the new binary install format for the standard python packaging and distribution tools (pip, setuptools).

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distributing wheels & SSE/superpack options
by Ralf Gommers 08 Dec '13

08 Dec '13

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 http://thread.gmane.org/gmane.comp.python.distutils.devel/20202 http://thread.gmane.org/gmane.comp.python.distutils.devel/20296 One of the things that we should start doing for numpy is distribute releases as wheels. On OS X at least this is quite simple, so I propose to just experiment with it. I can create some to try out and put them on a separate folder on SourceForge. If that works they can be put on PyPi. For Windows things are less simple, because the wheel format doesn't handle the multiple builds (no SSE, SSE2, SSE3) that are in the superpack installers. A problem is that we don't really know how many users still have old CPUs that don't support SSE3. The impact for those users is high, numpy will install but crash (see https://github.com/scipy/scipy/issues/1697). Questions: 1. does anyone have a good idea to obtain statistics? 2. in the absence of statistics, can we do an experiment by putting one wheel up on PyPi which contains SSE3 instructions, for python 3.3 I propose, and seeing for how many (if any) users this goes wrong? Ralf P.S. related question: did anyone check whether the recently merged NPY_HAVE_SSE2_INTRINSIC puts SSE2 instructions into the no-SSE binary?

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Deprecate boolean math operators?
by Sebastian Berg 08 Dec '13

08 Dec '13

Hey, there was a discussion that for numpy booleans math operators +,-,* (and the unary -), while defined, are not very helpful. I have set up a quick PR with start (needs some fixes inside numpy still): https://github.com/numpy/numpy/pull/4105 The idea is to deprecate these, since the binary operators |,^,| (and the unary ~ even if it is weird) behave identical. This would not affect sums of boolean arrays. For the moment I saw one "annoying" change in numpy, and that is `abs(x - y)` being used for allclose and working nicely currently. - Sebastian

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surprising behavior of np.asarray on masked arrays
by Faraz Mirzaei 06 Dec '13

06 Dec '13

Hi, If I pass a masked array through np.asarray, I get original unmasked array. Example: test = np.array([[1, 0], [-1, 3]]) testMasked = ma.masked_less_equal(test, 0) print testMasked [[1 --] [-- 3]] print testMasked.fill_value 999999 print np.asarray(testMasked) [[ 1 0] [-1 3]] Is this behavior intentional? How does the np.asarray access the original masked values? Shouldn't the masked values be at least filled with fill_value? Thanks, Faraz

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Re: [Numpy-discussion] no more search capability?
by Slavin, Jonathan 05 Dec '13

05 Dec '13

Answering part of my own question, I see that there is still a search capability on one of the numpy web pages, but it's not where it used to be and, in my opinion, is not easy to find. There used to be a search box on each web page of the numpy docs. Jon On Thu, Dec 5, 2013 at 1:00 PM, <numpy-discussion-request(a)scipy.org> wrote: > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)scipy.org > > You can reach the person managing the list at > numpy-discussion-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > Today's Topics: > > 1. no more search capability? (Slavin, Jonathan) > > > ---------- Forwarded message ---------- > From: "Slavin, Jonathan" <jslavin(a)cfa.harvard.edu> > To: numpy-discussion(a)scipy.org > Cc: > Date: Thu, 5 Dec 2013 11:04:19 -0500 > Subject: [Numpy-discussion] no more search capability? > Hi all, > > Although I like the look of the newly designed numpy/scipy web pages, I > have to say that I really miss the search capability. Is there any motion > toward restoring that? > > Jon > -- > ________________________________________________________ > Jonathan D. Slavin Harvard-Smithsonian CfA > jslavin(a)cfa.harvard.edu 60 Garden Street, MS 83 > phone: (617) 496-7981 Cambridge, MA 02138-1516 > fax: (617) 496-7577 USA > ________________________________________________________ > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- ________________________________________________________ Jonathan D. Slavin Harvard-Smithsonian CfA jslavin(a)cfa.harvard.edu 60 Garden Street, MS 83 phone: (617) 496-7981 Cambridge, MA 02138-1516 fax: (617) 496-7577 USA ________________________________________________________

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no more search capability?
by Slavin, Jonathan 05 Dec '13

05 Dec '13

Hi all, Although I like the look of the newly designed numpy/scipy web pages, I have to say that I really miss the search capability. Is there any motion toward restoring that? Jon -- ________________________________________________________ Jonathan D. Slavin Harvard-Smithsonian CfA jslavin(a)cfa.harvard.edu 60 Garden Street, MS 83 phone: (617) 496-7981 Cambridge, MA 02138-1516 fax: (617) 496-7577 USA ________________________________________________________

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Theano 0.6 released
by Frédéric Bastien 03 Dec '13

03 Dec '13

What's New ---------- We recommend that everybody update to this version. Highlights (since 0.6rc5): * Last release with support for Python 2.4 and 2.5. * We will try to release more frequently. * Fix crash/installation problems. * Use less memory for conv3d2d. 0.6rc4 skipped for a technical reason. Highlights (since 0.6rc3): * Python 3.3 compatibility with buildbot test for it. * Full advanced indexing support. * Better Windows 64 bit support. * New profiler. * Better error messages that help debugging. * Better support for newer NumPy versions (remove useless warning/crash). * Faster optimization/compilation for big graph. * Move in Theano the Conv3d2d implementation. * Better SymPy/Theano bridge: Make an Theano op from SymPy expression and use SymPy c code generator. * Bug fixes. Change from 0.6rc5: * Fix crash when specifing march in cxxflags Theano flag. (Frederic B., reported by FiReTiTi) * code cleanup (Jorg Bornschein) * Fix Canopy installation on windows when it was installed for all users: Raingo * Fix Theano tests due to a scipy change. (Frederic B.) * Work around bug introduced in scipy dev 0.14. (Frederic B.) * Fix Theano tests following bugfix in SciPy. (Frederic B., reported by Ziyuan Lin) * Add Theano flag cublas.lib (Misha Denil) * Make conv3d2d work more inplace (so less memory usage) (Frederic B., repoted by Jean-Philippe Ouellet) See https://pypi.python.org/pypi/Theano for more details. Download and Install -------------------- You can download Theano from http://pypi.python.org/pypi/Theano Installation instructions are available at http://deeplearning.net/software/theano/install.html Description ----------- Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features: * tight integration with NumPy: a similar interface to NumPy's. numpy.ndarrays are also used internally in Theano-compiled functions. * transparent use of a GPU: perform data-intensive computations up to 140x faster than on a CPU (support for float32 only). * efficient symbolic differentiation: Theano can compute derivatives for functions of one or many inputs. * speed and stability optimizations: avoid nasty bugs when computing expressions such as log(1+ exp(x)) for large values of x. * dynamic C code generation: evaluate expressions faster. * extensive unit-testing and self-verification: includes tools for detecting and diagnosing bugs and/or potential problems. Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal). Resources --------- About Theano: http://deeplearning.net/software/theano/ Theano-related projects: http://github.com/Theano/Theano/wiki/Related-projects About NumPy: http://numpy.scipy.org/ About SciPy: http://www.scipy.org/ Machine Learning Tutorial with Theano on Deep Architectures: http://deeplearning.net/tutorial/ Acknowledgments --------------- I would like to thank all contributors of Theano. For this particular release (since 0.5), many people have helped, notably: Frederic Bastien Pascal Lamblin Ian Goodfellow Olivier Delalleau Razvan Pascanu abalkin Arnaud Bergeron Nicolas Bouchard + Jeremiah Lowin + Matthew Rocklin Eric Larsen + James Bergstra David Warde-Farley John Salvatier + Vivek Kulkarni + Yann N. Dauphin Ludwig Schmidt-Hackenberg + Gabe Schwartz + Rami Al-Rfou' + Guillaume Desjardins Caglar + Sigurd Spieckermann + Steven Pigeon + Bogdan Budescu + Jey Kottalam + Mehdi Mirza + Alexander Belopolsky + Ethan Buchman + Jason Yosinski Nicolas Pinto + Sina Honari + Ben McCann + Graham Taylor Hani Almousli Ilya Dyachenko + Jan Schlüter + Jorg Bornschein + Micky Latowicki + Yaroslav Halchenko + Eric Hunsberger + Amir Elaguizy + Hannes Schulz + Huy Nguyen + Ilan Schnell + Li Yao Misha Denil + Robert Kern + Sebastian Berg + Vincent Dumoulin + Wei Li + XterNalz + A total of 51 people contributed to this release. People with a "+" by their names contributed a patch for the first time. Also, thank you to all NumPy and Scipy developers as Theano builds on their strengths. All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community )

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how to use install_clib with setup.py?
by Carl Kleffner 03 Dec '13

03 Dec '13

I have a question concerning install_clib on windows. What I want to do is to copy a dll (libopenblas.dll) to the numpy/core folder with setup.py install. The path to the dll is given in site.cfg. The dll itself is a external dependency. Somewhere i found a reference to install_clib to copy external dll's, but howto to apply this within setup.py? Regards Carl

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-ffast-math
by Dan Goodman 03 Dec '13

03 Dec '13

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 realised after some fiddling around that it was due to using this switch. The speed difference was enormous in my test example. I checked the difference in accuracy and over the range of values I'm interested in the errors are not really significant. If there's currently no way of using these faster versions of these functions in numpy, maybe it would be worth adding as a feature for future versions? Thanks, Dan Goodman

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nasty bug in 1.8.0??
by Neal Becker 03 Dec '13

03 Dec '13

This is np 1.8.0 on fedora x86_64: In [5]: x =np.array ((1,)) In [6]: x.shape Out[6]: (1,) In [7]: x.strides Out[7]: (9223372036854775807,)

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