Mailman 3 April 2013 - NumPy-Discussion

GSoC application time!
by Ralf Gommers 24 Apr '13

24 Apr '13

Hi, This is for all students planning to put in a GSoC'13 application for Numpy/Scipy. As you probably noticed, you are now able to submit your application in Melange. We're participating under the PSF org. Terri Oda, who is the PSF organizer, sent out the below announcement with tips and requirements as well as a helpful application template - please read those. When you have a draft proposal ready, I suggest you a) submit it already, since you can edit it later b) discuss it on the numpy-discussion or scipy-dev list If you haven't submitted a pull request yet, it's a good idea to do so asap - getting your first patch merged may be nontrivial and require some rework, so don't wait till the last day! Of all the requirements, I want to stress that discussing your proposal and interacting with the community is especially important. Not only will it help improve your proposal, it is also something we will pay attention to when ranking the proposals. Reason: it's a good predictor of the success of your project, both in terms of code/features contributed and of whether you're interested and likely to stay involved after GSoC ends. The latter is at least as important to us as the former. Cheers, Ralf P.S. I will be offline from April 25th to 29th. ---------- Forwarded message ---------- From: Terri Oda <terri(a)zone12.com> Date: Mon, Apr 22, 2013 at 12:22 AM Subject: [Soc2013-general] Student Application Template (Applications start April 22!) To: soc2013-general(a)python.org As hopefully all of you are aware, student applications to GSoC will be opening April 22 19:00 UTC (tomorrow to me) and closing May 3rd. I highly recommend that you all submit applications early -- you can modify them up until the final deadline. Google will not extend the deadline for any reason, including technical problems with the melange system (which have been known to happen at the last minute in the past), so the sooner you can get an application in the better! We have a template to help you prepare your application with the PSF: http://wiki.python.org/moin/**SummerOfCode/**ApplicationTemplate2013<http://wiki.python.org/moin/SummerOfCode/ApplicationTemplate2013> Your sub-organizations may have additional requirements; ask them if there's any extra information they need from you. Please note a few things we ask for that are not always required by other orgs: * We do require students to blog about their projects, so you will need to set up a GSoC blog for weekly status updates and any other thoughts you wish to record about your project. * We do require students to submit a link to some sort of code sample, preferably a patch to the sub-org to which you are applying. Talk to your mentors if you're uncertain what would be appropriate. * Don't forget to put the name of your sub-organization (e.g. OpenHatch, MNE-Python) into the title of your application. If you're not sure about how to write a good proposal, ask your prospective mentors: they're the ones who will be deciding if they hire you or not, so they get the final word as to what a good proposal looks like for them. Terri ______________________________**_________________ Soc2013-general mailing list Soc2013-general(a)python.org http://mail.python.org/**mailman/listinfo/soc2013-**general<http://mail.python.org/mailman/listinfo/soc2013-general>

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Vectorized percentile function in Numpy (PR #2970)
by Jonathan Helmus 24 Apr '13

24 Apr '13

Back in December it was pointed out on the scipy-user list[1] that numpy has a percentile function which has similar functionality to scipy's stats.scoreatpercentile. I've been trying to harmonize these two functions into a single version which has the features of both. Scipy PR 374[2] introduced a version which look the parameters from both the scipy and numpy percentile function and was accepted into Scipy with the plan that it would be depreciated when a similar function was introduced into Numpy. Then I moved to enhancing the Numpy version with Pull Request 2970 [3]. With some input from Sebastian Berg the percentile function was rewritten with further vectorization, but neither of us felt fully comfortable with the final product. Can someone look at implementation in the PR and suggest what should be done from here? Cheers, - Jonathan Helmus [1] http://thread.gmane.org/gmane.comp.python.scientific.user/33331 [2] https://github.com/scipy/scipy/pull/374 [3] https://github.com/numpy/numpy/pull/2970

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MapIter api
by Sebastian Berg 23 Apr '13

23 Apr '13

Hey, the MapIter API has only been made public in master right? So it is no problem at all to change at least the mapiter struct, right? I got annoyed at all those special cases that make things difficult to get an idea where to put i.e. to fix the boolean array-like stuff. So actually started rewriting it (and I already got one big function that does all index preparation -- ok it is untested but its basically there). I would guess it is not really a big problem even if it was public for longer, since you shouldn't do those direct struct access probably? But just checking. Since I got the test which mimics complex indexes in the tests, I thinks it should actually be feasible to do bigger refactoring there without having to worry too much about breaking things. Regards, Sebastian

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ANN: pandas 0.11.0 released!
by Wes McKinney 23 Apr '13

23 Apr '13

hi all, We've released pandas 0.11.0, a big release that span 3 months of continuous development, led primarily by the intrepid Jeff Reback and y-p. The release brings many new features, performance and API improvements, bug fixes, and other goodies. Some highlights: - New precision indexing fields loc, iloc, at, and iat, to reduce occasional ambiguity in the catch-all hitherto ix method. - Expanded support for NumPy data types in DataFrame - NumExpr integration to accelerate various operator evaluation - New Cookbook and 10 minutes to pandas pages in the documentation by Jeff Reback - Improved DataFrame to CSV exporting performance - Experimental "rplot" branch with faceted plots with matplotlib merged and open for community hacking Source archives and Windows installers are on PyPI. Thanks to all who contributed to this release, especially Jeff and y-p. What's new: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html Installers: http://pypi.python.org/pypi/pandas $ git log v0.10.1..v0.11.0 --pretty=format:%aN | sort | uniq -c | sort -rn 308 y-p 279 jreback 85 Vytautas Jancauskas 74 Wes McKinney 25 Stephen Lin 22 Andy Hayden 19 Chang She 13 Wouter Overmeire 8 Spencer Lyon 6 Phillip Cloud 6 Nicholaus E. Halecky 5 Thierry Moisan 5 Skipper Seabold 4 waitingkuo 4 Loïc Estève 4 Jeff Reback 4 Garrett Drapala 4 Alvaro Tejero-Cantero 3 lexual 3 Dražen Lučanin 3 dieterv77 3 dengemann 3 Dan Birken 3 Adam Greenhall 2 Will Furnass 2 Vytautas Jančauskas 2 Robert Gieseke 2 Peter Prettenhofer 2 Jonathan Chambers 2 Dieter Vandenbussche 2 Damien Garaud 2 Christopher Whelan 2 Chapman Siu 2 Brad Buran 1 vytas 1 Tim Akinbo 1 Thomas Kluyver 1 thauck 1 stephenwlin 1 K.-Michael Aye 1 Karmel Allison 1 Jeremy Wagner 1 James Casbon 1 Illia Polosukhin 1 Dražen Lučanin 1 davidjameshumphreys 1 Dan Davison 1 Chris Withers 1 Christian Geier 1 anomrake Happy data hacking! - Wes What is it ========== pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with relational, time series, or any other kind of labeled data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Links ===== Release Notes: http://github.com/pydata/pandas/blob/master/RELEASE.rst Documentation: http://pandas.pydata.org Installers: http://pypi.python.org/pypi/pandas Code Repository: http://github.com/pydata/pandas Mailing List: http://groups.google.com/group/pydata

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KeepDims flag?
by Sebastian Berg 22 Apr '13

22 Apr '13

Hi, just something that has been spooking around in my mind. Considering that matrix indexing does not really support fancy indexing, I was wondering about introducing a KeepDims flag. Maybe it is not worth it, at least not unless other subclasses could make use of it, too. And a big reason for it being not worth the trouble is probably that you could not use it inside the current matrix class because it would i.e. break workarounds for ufunc reductions (like np.multiply.reduce(a, axis=1).T, as the .T would be unnecessary). Such a flag could only be set (not unset) and never be set on base class arrays (basically it should be set by array_finalize). If set it would toggle ufunc reductions to always use keepdims (unless the reduction is to a scalar, maybe). And the same thing for indexing (meaning that some fancy indices and np.newaxis would just error out), though axes added by broadcasting should be caught by the subclass itself. That way, a matrix-like class would normally have a 1:1 mapping of old to new axes (even if they might be transposed or elements arbitrarily shuffled), and does not have to do magic to guess where to add the missing one (instead the magic is done in the core, where it is actually easier to implement). Anyway, as I never use matrix I personally have no real use for it, but I thought I would throw the thought out there. For starters one might rather think about something specific to indexing. Regards, Sebastian

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 79, Issue 67
by Christopher Ruesch 21 Apr '13

21 Apr '13

help On Sun, Apr 21, 2013 at 2:44 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. Can Numpy use static libraries from LAPACK? (James Jong) > 2. Re: Can Numpy use static libraries from LAPACK? (Ilan Schnell) > 3. Re: Can Numpy use static libraries from LAPACK? (James Jong) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sun, 21 Apr 2013 13:35:12 -0400 > From: James Jong <ribonucleico(a)gmail.com> > Subject: [Numpy-discussion] Can Numpy use static libraries from > LAPACK? > To: numpy-discussion(a)scipy.org > Message-ID: > < > CAD4ivxVdTNgbkMD0rJOGrUGpMUtieN10Gh8WzOz59Xr0196VgA(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > Note: I started a thread in StackOverflow a few days ago with this > question, but I have not received any response yet (the link is: > > http://stackoverflow.com/questions/16093910/numpy-and-scipy-static-vs-dynam… > ) > > The question is the following: > > Say that I build ATLAS with LAPACK as follows: > > wget > http://sourceforge.net/projects/math-atlas/files/Stable/3.10.1/atlas3.10.1.… > wget http://www.netlib.org/lapack/lapack-3.4.2.tgz > tar -jxvf atlas3.10.1.tar.bz2 > mkdir BUILD > cd BUILD > ../ATLAS/configure -b 64 -Fa alg -fPIC \ > --with-netlib-lapack-tarfile=../lapack-3.4.2.tgz \ > --prefix=<ATLAS_INSTALL_PATH> > make > cd lib > make shared > make ptshared > cd .. > make install > > Note that I did *not *pass the flag --shared in .my call to configure. > > I end up with the following files under BUILD/lib: > > Make.inc@ > Makefile > > the following .a files: > > libatlas.a > libcblas.a > libf77blas.a > libptf77blas.a > libtstatlas.a > liblapack.a > libf77refblas.a > libptlapack.a > libptcblas.a > > and the following .so files: > > libsatlas.so* > libtatlas.so* > > Finally, if I define: > > BLAS=/path_to_BUILD/lib/libcblas.a > LAPACK=/path_to_BUILD/lib/liblapack.a > ATLAS=/path_to_BUILD/lib/libatlas.a > > and add /path_to_BUILD/lib to LD_LIBRARY_PATH and to the library_dirs > variable > within thesite.cfg file in NumPy. > > Would NumPy and SciPy use my libraries? (even though they all seem to be > static?). > > Thanks, > > Jason >

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Can Numpy use static libraries from LAPACK?
by James Jong 21 Apr '13

21 Apr '13

Note: I started a thread in StackOverflow a few days ago with this question, but I have not received any response yet (the link is: http://stackoverflow.com/questions/16093910/numpy-and-scipy-static-vs-dynam… ) The question is the following: Say that I build ATLAS with LAPACK as follows: wget http://sourceforge.net/projects/math-atlas/files/Stable/3.10.1/atlas3.10.1.… wget http://www.netlib.org/lapack/lapack-3.4.2.tgz tar -jxvf atlas3.10.1.tar.bz2 mkdir BUILD cd BUILD ../ATLAS/configure -b 64 -Fa alg -fPIC \ --with-netlib-lapack-tarfile=../lapack-3.4.2.tgz \ --prefix=<ATLAS_INSTALL_PATH> make cd lib make shared make ptshared cd .. make install Note that I did *not *pass the flag --shared in .my call to configure. I end up with the following files under BUILD/lib: Make.inc@ Makefile the following .a files: libatlas.a libcblas.a libf77blas.a libptf77blas.a libtstatlas.a liblapack.a libf77refblas.a libptlapack.a libptcblas.a and the following .so files: libsatlas.so* libtatlas.so* Finally, if I define: BLAS=/path_to_BUILD/lib/libcblas.a LAPACK=/path_to_BUILD/lib/liblapack.a ATLAS=/path_to_BUILD/lib/libatlas.a and add /path_to_BUILD/lib to LD_LIBRARY_PATH and to the library_dirs variable within thesite.cfg file in NumPy. Would NumPy and SciPy use my libraries? (even though they all seem to be static?). Thanks, Jason

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Reading String type to find long "magic word"
by Bakhtiyor Zokhidov 20 Apr '13

20 Apr '13

Hello everybody, I just have one long string type:ThesampletextthatcouldbereadedthesameinbothordersArozaupalanalapuazorA The result I want to take is ArozaupalanalapuazorA - which means reading directly each letter should be the same as reading reversely ... Is there any function which can deals with this problem in Python??? Thanks for answer -- Bakhti

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what do I get if I build with MKL?
by Neal Becker 19 Apr '13

19 Apr '13

What sorts of functions take advantage of MKL? Linear Algebra (equation solving)? Something like dot product? exp, log, trig of matrix? basic numpy arithmetic? (add matrixes)

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numpy scalars and savez -- bug?
by Chris Barker - NOAA Federal 19 Apr '13

19 Apr '13

Folks, I've discovered somethign intertesting (bug?) with numpy scalars ans savz. If I save a numpy scalar, then reload it, ot comes back as rank-0 array -- similar, but not the same thing: In [144]: single_value, type(single_value) Out[144]: (2.0, numpy.float32) In [145]: np.savez('test.npz', single_value=single_value) In [146]: single_value2 = np.load('test.npz')['single_value'] In [147]: single_value, type(single_value) Out[147]: (2.0, numpy.float32) In [148]: single_value2, type(single_value2) Out[148]: (array(2.0, dtype=float32), numpy.ndarray) straight np.save has the same issue (which makes sense, I'm sure savez uses the save code under the hood): In [149]: single_value, type(single_value) Out[149]: (2.0, numpy.float32) In [150]: np.save('test.npy', single_value) In [151]: single_value2 = np.load('test.npy') In [152]: single_value2, type(single_value2) Out[152]: (array(2.0, dtype=float32), numpy.ndarray) This has been annoying, particular as rank-zero scalars are kind of a pain. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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