Mailman 3 January 2012 - NumPy-Discussion

Numpy 1.5.1/1.6.1 doesn't build doc with sphinx 1.1.2
by Sandro Tosi Feb. 22, 2012

Feb. 22, 2012

Hello, with the new sphinx, v1.1.2, numpy documentation of 1.5.1 or 1.6.1 doesn't build. The full log of the debian package build is at: http://people.debian.org/~jwilk/tmp/python-numpy_1.6.1-3_i386.build attached is the file left in /tmp by sphinx for the error. Could you please give it a look? Cheers, -- Sandro Tosi (aka morph, morpheus, matrixhasu) My website: http://matrixhasu.altervista.org/ Me at Debian: http://wiki.debian.org/SandroTosi

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Download page still points to SVN
by Fernando Perez Feb. 15, 2012

Feb. 15, 2012

Hi folks, I was just pointing a colleague to the 'official download page' for numpy so he could find how to grab current sources: http://new.scipy.org/download.html but I was quite surprised to find that it still points to SVN for both numpy and scipy. It would probably not be a bad idea to update those and point them to github... Cheers, f

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dtype related deprecations
by Ralf Gommers Feb. 5, 2012

Feb. 5, 2012

Hi, I'm having some trouble cleaning up tests to deal with these two deprecations: DeprecationWarning: Setting NumPy dtype names is deprecated, the dtype will become immutable in a future version DeprecationWarning: DType strings 'O4' and 'O8' are deprecated because they are platform specific. Use 'O' instead They seem fairly invasive, judged by the test noise in both numpy and scipy. Record arrays rely on setting dtype names. There are tests for picking and the buffer protocol that generate warnings for O4/O8. Can anyone comment on the necessity of these deprecations and how to deal with them? Ralf

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GSOC
by Charles R Harris Feb. 5, 2012

Feb. 5, 2012

Hi All, I thought I'd raise this topic just to get some ideas out there. At the moment I see two areas that I'd like to see addressed. 1. Documentation editor. This would involve looking at the generated documentation and it's organization/coverage as well such things as style and maybe reviewing stuff on the documentation site. This would be more technical writing than coding. 2. Test coverage. There are a lot of areas of numpy that are not well tested as well as some tests that are still doc tests and should probably be updated. This is a substantial amount of work and would require some familiarity with numpy as well as a willingness to ping developers for clarification of some topics. Thoughts? Chuck

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histogram help
by Ruby Stevenson Feb. 2, 2012

Feb. 2, 2012

hi, all I am trying to figure out how to do histogram with numpy I have a three-dimension array A[x,y,z], another array (bins) has been allocated along Z dimension, z' how can I get the histogram of H[ x, y, z' ]? thanks for your help. Ruby

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Unexpected reorganization of internal data
by Mads Ipsen Feb. 2, 2012

Feb. 2, 2012

Hi, I am confused. Here's the reason: The following structure is a representation of N points in 3D space: U = numpy.array([[x1,y1,z1], [x1,y1,z1],...,[xn,yn,zn]]) So the array U has shape (N,3). This order makes sense to me since U[i] will give you the i'th point in the set. Now, I want to pass this array to a C++ function that does some stuff with the points. Here's how I do that void Foo::doStuff(int n, PyObject * numpy_data) { // Get pointer to data double * const positions = (double *) PyArray_DATA(numpy_data); // Print positions for (int i=0; i<n; ++i) { float x = static_cast<float>(positions[3*i+0]) float y = static_cast<float>(positions[3*i+1]) float z = static_cast<float>(positions[3*i+2]) printf("Pos[%d] = %f %f %f\n", x, y, z); } } When I call this routine, using a swig wrapped Python interface to the C++ class, everything prints out nice. Now, I want to apply a rotation to all the positions. So I set up some rotation matrix R like this: R = numpy.array([[r11,r12,r13], [r21,r22,r23], [r31,r32,r33]]) To apply the matrix to the data in one crunch, I do V = numpy.dot(R, U.transpose()).transpose() Now when I call my C++ function from the Python side, all the data in V is printed, but it has been transposed. So apparently the internal data structure handled by numpy has been reorganized, even though I called transpose() twice, which I would expect to cancel out each other. However, if I do: V = numpy.array(U.transpose()).transpose() and call the C++ routine, everything is perfectly fine, ie. the data structure is as expected. What went wrong? Best regards, Mads -- +-----------------------------------------------------+ | Mads Ipsen | +----------------------+------------------------------+ | Gåsebæksvej 7, 4. tv | | | DK-2500 Valby | phone: +45-29716388 | | Denmark | email: mads.ipsen(a)gmail.com | +----------------------+------------------------------+

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ufunc delegation to object method
by Warren Weckesser Feb. 1, 2012

Feb. 1, 2012

In the following code, numpy.sin() calls the object's sin() function: In [2]: class Foo(object): ...: def sin(self): ...: return "spam" ...: In [3]: f = Foo() In [4]: np.sin(f) Out[4]: 'spam' Is this, in fact, guaranteed behavior for a ufunc? It does not appear to be documented. This question came up in the discussion of SciPy pull request 138 ( https://github.com/scipy/scipy/pull/138), where the idea is to add numpy unary ufunc support to SciPy's sparse arrays. (Sorry if this email shows up twice. I sent it the first time while the Enthought servers were down, and eventually got an email back saying it had not been sent.) Warren

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Re: [Numpy-discussion] Cross-covariance function
by Elliot Saba Feb. 1, 2012

Feb. 1, 2012

Thank you Sturla, that's exactly what I want. I'm sorry that I was not able to reply for so long, but Pierre's code is similar to what I have already implemented, and I am in support of changing the functionality of cov(). I am unaware of any arguments for a covariance function that works in this way, except for the fact that the MATLAB cov() function behaves in the same way. [1] MATLAB, however, has an xcov() function, which is similar to what we have been discussing. [2] Unless you all wish to retain compatibility with MATLAB, I feel that the behaviour of cov() suggested by Pierre is the most straightforward method, and that if users wish to calculate the covariance of X concatenated with Y, then they may simply concatenate the matrices explicitly before passing into cov(), as this way the default method does not use 75% more CPU time. Again, if there is an argument for this functionality, I would love to learn of it! -E [1] http://www.mathworks.com/help//techdoc/ref/cov.html [2] http://www.mathworks.com/help/toolbox/signal/ref/xcov.html

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numpy all unexpected result (generator)
by Neal Becker Jan. 31, 2012

Jan. 31, 2012

I was just bitten by this unexpected behavior: In [24]: all ([i> 0 for i in xrange (10)]) Out[24]: False In [25]: all (i> 0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True So numpy.all doesn't seem to do what I would expect when given a generator. Bug?

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[ANN] Summer School "Advanced Scientific Programming in Python" in Kiel, Germany
by Tiziano Zito Jan. 31, 2012

Jan. 31, 2012

Advanced Scientific Programming in Python ========================================= a Summer School by the G-Node and the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location ================= September 2—7, 2012. Kiel, Germany. Preliminary Program =================== Day 0 (Sun Sept 2) — Best Programming Practices - Best Practices, Development Methodologies and the Zen of Python - Version control with git - Object-oriented programming & design patterns Day 1 (Mon Sept 3) — Software Carpentry - Test-driven development, unit testing & quality assurance - Debugging, profiling and benchmarking techniques - Best practices in data visualization - Programming in teams Day 2 (Tue Sept 4) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Advanced Python I: idioms, useful built-in data structures, generators Day 3 (Wed Sept 5) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 6) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Advanced Python II: decorators and context managers - Programming project Day 5 (Fri Sept 7) — Practical Software Development - Programming project - The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications ============ You can apply on-line at http://python.g-node.org Applications must be submitted before 23:59 UTC, May 1, 2012. Notifications of acceptance will be sent by June 1, 2012. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate last time was around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. You are encouraged to go through the introductory material available on the website. Faculty ======= - Francesc Alted, Continuum Analytics Inc., USA - Pietro Berkes, Enthought Inc., UK - Valentin Haenel, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Technologit GbR, Germany - Bartosz Teleńczuk, Unité de Neurosciences Information et Complexité, Centre National de la Recherche Scientifique, France - Stéfan van der Walt, Helen Wills Neuroscience Institute, University of California Berkeley, USA - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus Neurotechnology, Germany - Niko Wilbert, TNG Technology Consulting GmbH, Germany - Tiziano Zito, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Organized by Christian T. Steigies and Christian Drews of the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel , and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-info(a)g-node.org

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