Mailman 3 August 2012 - NumPy-Discussion

distutils: compiler used by add_library
by Matyáš Novák 30 Aug '12

30 Aug '12

Hi, I wrote extension some numerical extension for python, that requires compiling and linking additional fortran sources. I find out, that these libraries can be easily added using config.add_library() function, but there is a problem. The --fcompiler command doesn't propagate to the stage where the libraries are compiled, so the default (and in my case wrong) compiler is used. (If I try compile the files using add_extension method, they are compiled by desired compiler, but I need combine more sources in one extension so I think that I can't use add_extension). Is there any way how to force python to use the right compiler, or at least hardcode the compiler in the setup.py? Thanks a lot for suggestions, Matyas

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Re: [Numpy-discussion] blaze lib announcement
by Michael Lehn 29 Aug '12

29 Aug '12

> This looks interesting: > > http://code.google.com/p/blaze-lib/ So maybe you also want to have a look at http://flens.sf.net Just to promote my own baby in this context too ;-) Cheers, Michael

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config.add_library()
by Chris Barker 29 Aug '12

29 Aug '12

Hi folks, I'm working on a package that will contain a bunch of cython extensions, all of which need to link against a pile of C++ code. What I think I need to do is build that C++ as a dynamic library, so I can link everything against it. It would be nice if I could leverage distutils to build that library for me. Particularly since Windows and OS-X and Linux all seem to need things a little (or q lot) different. It looks like numpy.distuitls' config.add_library might be what I need, but the docs are pretty sprarse: This page: http://docs.scipy.org/doc/numpy-1.5.x/reference/generated/numpy.distutils.m… confused me a bit: """ Parameters : name : str Name of the extension. """ Is this really building an extension, or is that some docs string brought over from the distutils Extension class? So -- is config.add_lirary() what I'm looking for? If so are there better docs and/or examples you canpoint me to? Thanks, -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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blaze lib announcement
by Neal Becker 29 Aug '12

29 Aug '12

This looks interesting: http://code.google.com/p/blaze-lib/

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Mark Florisson's GSoC, minivect, and NumPy
by Dag Sverre Seljebotn 27 Aug '12

27 Aug '12

I just wanted to draw the attention of NumPy devs to Mark Florisson's GSoC work. It is 'minivect', a tool to use for compiling array expressions (think (as a concept) a shared backend between Cython, Theano, numba, though it's only used in Cython currently). His M. Sc. thesis, "Techniques for Static and Dynamic Compilation of Array Expressions", is up here: https://github.com/markflorisson88/minivect/tree/master/thesis As you can see he even beats Intel Fortran for some array layouts, and in general have comparable performance with it. The benchmarks are mostly for two-operand operations, i.e. operations where NumPy semantics would be OK. IMO, if anybody ever wants to revamp NumPy's computation abilities and get that 2-3x speedup (e.g., make it multi-threaded), this is a very good place to start. Dag

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vectorized multi-matrix multiplication
by Alex Flint 26 Aug '12

26 Aug '12

I have two lists of 3x3 arrays and I would like to compute the matrix product of the i-th element in the first list with the i-th element in the second list. Of course, I could just loop over the lists: for i in range(n): out[i] = dot( matrices1[i], matrices2[i] ) However, the list is quite long, and each matrix is very small (3x3) so this turns out to be quite slow. Is there a way to do this with a single numpy call? I have looked at tensordot but it outputs an N x N x 3 x 3 array, whereas I want an N x 3 x 3 output. I've also looked at various broadcasting tricks but I haven't found anything that works yet. Alex

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[ANN] Call for abstracts: BigData minisymposium at CSE'13, February 2013, Boston
by Fernando Perez 23 Aug '12

23 Aug '12

Dear colleagues, next year's SIAM conference on Computational Science and Engineering, CSE'13, will take place in Boston, February 25-March 1 (http://www.siam.org/meetings/cse13), and for this version there will be a track focused on the topic of Big Data. This term has rapidly risen in recent discussions of science and even of mainstream business computing, and for good reasons. Today virtually all disciplines are facing a flood of quantitative information whose volumes have often grown faster than the quality of our tools for extracting insight from these data. SIAM hopes that CSE'13 will provide an excellent venue for discussing these problems, from the vantage point offered by a community whose expertise combines analytical insights, algorithmic development, software engineering and domain-specific applications. As part of this event, Titus Brown (http://ged.msu.edu) and I are organizing a minisymposium where we would like to have a group of presentations that address both novel algorithmic ideas and computational approaches as well as domain-specific problems. Data doesn't appear in a vacuum, and data from different domains presents a mix of common problems along with questions that may be specific to each; we hope that by engaging a dialog between those working on algorithmic and implementation questions and those with specific problems from the field, valuable insights can be obtained. If you would like to contribute to this minisymposium, please contact us directly at: "C. Titus Brown" <ctb(a)msu.edu>, "Fernando Perez" <Fernando.Perez(a)berkeley.edu> with your name and affiliation, the title of your proposed talk and a brief description (actual abstracts are due later so an informal description will suffice for now), by Wednesday August 29. For more details on the submission process, see: http://www.siam.org/meetings/cse13/submissions.php Please forward this to any interested colleagues. Regards, Titus and Fernando.

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by Amit Nagal 23 Aug '12

23 Aug '12

Please unsubscribe me Visit us at Booth No. 5 at 2012 ChemOutsourcing Conference, 10-13 Sept 2012, Ocean Place Resort - Long Branch, NJ, United States Visit us at Booth No. 4 at World Conference on Pharmacometrics, 5-7 Sept 2012, Grand Hilton Hotel, Seoul, Korea ________________________________ Notice: The information contained in this electronic mail message is intended only for the use of the designated recipient. This message is privileged and confidential. and the property of GVK BIO or its affiliates and subsidiaries. If the reader of this message is not the intended recipient or an agent responsible for delivering it to the intended recipient, you are hereby notified that you have received this message in error and that any review, dissemination, distribution, or copying of this message is strictly prohibited. If you have received this communication in error, please notify us immediately by telephone +91-40-66929999<tel:%2B91-40-66929999> and destroy any and all copies of this message in your possession (whether hard copies or electronically stored copies).

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Re: [Numpy-discussion] 64bit infrastructure
by Sayth Renshaw 22 Aug '12

22 Aug '12

> Message: 4 > Date: Fri, 17 Aug 2012 15:25:21 -0700 > From: Matthew Brett <matthew.brett(a)gmail.com> > Subject: Re: [Numpy-discussion] 64bit infrastructure > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Message-ID: > <CAH6Pt5qa27Zb+UkOZB5EzsLK5UuoB-f8UcwFjdL2YcEBRZ=Wdw(a)mail.gmail.com> > Content-Type: text/plain; charset=ISO-8859-1 > > Hi, > > On Fri, Aug 17, 2012 at 2:25 PM, Sayth Renshaw <flebber.crue(a)gmail.com> wrote: >>> >>> Hi Sayth, >>> >>> On Thu, Aug 16, 2012 at 8:51 PM, Sayth Renshaw <flebber.crue(a)gmail.com> wrote: >>>> Hi >>>> >>>> I was just wondering if the current absence of 64bit builds was as a result >>>> of an infrastructure or funding concern. >>>> >>>> I like many now have only 64 bit systems and am using the unofficial builds. >>>> >>>> If it was something we could raise funds to resolve, how much? Or is it more >>>> complex than that? >>> >>> Which platform, windows? >>> >>> Ondrej >>> >>> >> >> Yes I have a Windows 7 64Bit install and a ubuntu 64 Bit install. > > Isn't the numpy ubuntu install 64 bit by default? > > You may have seen that there are numpy and scipy 64-bit windows installers here: > > http://www.lfd.uci.edu/~gohlke/pythonlibs/ > > Do they work for you? > > Best, > > Matthew > > > ------------------------------ > Thanks Matthew yeah that's the one I am currently using and it is working ok but just thought if there was a way to help 64bit builds become official then I would ask. Sayth

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__array_wrap__ and dot
by Aronne Merrelli 22 Aug '12

22 Aug '12

Hello list, I posted an issue many months ago [1] about confusing __array_wrap__ behavior in a subclass of np.matrix. Since that time I wasn't really using the code that used the matrix subclass, but lately I have starting using the code so I've run into the issue again. Looking into a little more detail, I think the root of the problem is how __array_wrap__ interacts with the dot functions. Operations like M*2 will be processed differently depending on whether M is a ndarray or matrix subclass; it appears that a matrix will always call np.dot for that operation, while an ndarray will go through np.multiply. Since dot acts differently than multiply here, you get different results between the two subclasses. I updated the gist to show the problem [2]. The output I get on my system is pasted at the bottom. It looks like __array_wrap__ gets called sometimes, depending on the type of one of the arguments. I'm not seeing any logical pattern to it, which makes it confusing. My expectation (perhaps incorrect) is that __array_wrap__ should be invoked any time dot is called. I guess that depends on whether dot should be considered a ufunc - I don't know the answer to that question. If someone with more knowledge of the internals could take a look at this, that would be great - I think this is a bug, but I'm not really sure what is the expected behavior here. Thanks, Aronne [1] http://mail.scipy.org/pipermail/numpy-discussion/2011-December/059665.html [2] https://gist.github.com/1511354 Output from run_test (see the gist code) using ipython: In [1]: import matrix_array_wrap_test as mtest; import numpy as np In [2]: np.__version__ Out[2]: '1.6.1' In [3]: np.dot Out[3]: <function numpy.core._dotblas.dot> In [4]: mtest.run_test() aw called? | creation | use Yes | o=ArrSubClass(2) | o2 = o + 2 Yes | o=ArrSubClass(2) | o2 = o + 2.0 Yes | o=ArrSubClass(2) | o2 = o * 2 Yes | o=ArrSubClass(2) | o2 = o * 2.0 Yes | o=ArrSubClass(2) | o2 = o * o.T No | o=ArrSubClass(2) | o2 = np.dot(o,2) No | o=ArrSubClass(2) | o2 = np.dot(o,2.0) No | o=ArrSubClass(2) | o2 = np.dot(o,o.T) Yes | o=ArrSubClass(2) | o2 = np.core.multiarray.dot(o,2) Yes | o=ArrSubClass(2) | o2 = np.core.multiarray.dot(o,2.0) No | o=ArrSubClass(2) | o2 = np.core.multiarray.dot(o,o.T) Yes | o=MatSubClass([1, 1]) | o2 = o + 2 Yes | o=MatSubClass([1, 1]) | o2 = o + 2.0 Yes | o=MatSubClass([1, 1]) | o2 = o * 2 No | o=MatSubClass([1, 1]) | o2 = o * 2.0 No | o=MatSubClass([1, 1]) | o2 = o * o.T Yes | o=MatSubClass([1, 1]) | o2 = np.dot(o,2) No | o=MatSubClass([1, 1]) | o2 = np.dot(o,2.0) No | o=MatSubClass([1, 1]) | o2 = np.dot(o,o.T) Yes | o=MatSubClass([1, 1]) | o2 = np.core.multiarray.dot(o,2) Yes | o=MatSubClass([1, 1]) | o2 = np.core.multiarray.dot(o,2.0) No | o=MatSubClass([1, 1]) | o2 = np.core.multiarray.dot(o,o.T) Yes | o=MatSubClass([1.0, 1.0]) | o2 = o + 2 Yes | o=MatSubClass([1.0, 1.0]) | o2 = o + 2.0 No | o=MatSubClass([1.0, 1.0]) | o2 = o * 2 No | o=MatSubClass([1.0, 1.0]) | o2 = o * 2.0 No | o=MatSubClass([1.0, 1.0]) | o2 = o * o.T No | o=MatSubClass([1.0, 1.0]) | o2 = np.dot(o,2) No | o=MatSubClass([1.0, 1.0]) | o2 = np.dot(o,2.0) No | o=MatSubClass([1.0, 1.0]) | o2 = np.dot(o,o.T) Yes | o=MatSubClass([1.0, 1.0]) | o2 = np.core.multiarray.dot(o,2) Yes | o=MatSubClass([1.0, 1.0]) | o2 = np.core.multiarray.dot(o,2.0) No | o=MatSubClass([1.0, 1.0]) | o2 = np.core.multiarray.dot(o,o.T)

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NumPy-Discussion August 2012