Mailman 3 April 2016 - SciPy-Dev

dense output option in integrate.ode
by Evgeni Burovski 20 Feb '17

20 Feb '17

Hi David, Hi Jim, > I am new to SciPy-dev. Will the dense output option in scipy.integrate.ode > become available in 0.17.0? This is a feature already available in the > original FORTAN, but wasn't implemented in the wrapper. If the feature is sent as a pull request against the scipy master branch, the PR is reviewed by the maintainers of the integrate package and merged into master before the release split, then yes, it would be available in 0.17.0. So far I do not see any progress towards it. > I wrote these dense output extensions that you listed: > > https://github.com/jddmartin/scipy/tree/dense_output_from_dopri5_and_dop853 > https://github.com/jddmartin/dense_output_example_usage > > but I didn't issue any pull request to scipy. I sent this message to the > scipy developers list: > http://article.gmane.org/gmane.comp.python.scientific.devel/19635/ > explaining the changes. I was hoping for some feedback before issuing a > pull request. Ah, I see that the email likely fell through the cracks back in April. Sorry about that. You might want to ping that email thread once more or send a pull request on github (or both). <snip> Cheers, Evgeni

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scipy 0.17.1 release
by Evgeni Burovski 05 May '16

05 May '16

Hi, I'm planning to cut the point release of scipy 0.17.1 by the end of this week. Thoughts? Regressions in 0.17.0 so far seem reasonably mild: https://github.com/scipy/scipy/milestones/0.17.1 If there's anything else you think should be included in 0.17.1, please mention it in this email thread or on Github. Cheers, Evgeni

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PR for signal.planck (Planck-taper and Planck-Bessel window functions)
by Yuri D'Elia 29 Apr '16

29 Apr '16

Hi everyone, I'd like to bring to your attention this PR: https://github.com/scipy/scipy/pull/6012 which implements the Plack-taper window (http://arxiv.org/pdf/1003.2939) along with tests and documentation. It's a rarely-used window, with current citations only in the gravitational-wave field of research. I can definitely see why there might be reluctance in merging such a rarely used window. Nevertheless, I'd like to know if there is actual interesting or not, as I could furthermore provide the implementation of the Planck-Bessel and Cosine-Hyperbolic windows as well. Thanks!

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PR adding boundary value problem solver in scipy
by Pauli Virtanen 24 Apr '16

24 Apr '16

Hi, I'd like to draw attention to a PR from Nikolay adding a BVP problem solver to Scipy. If this is interesting to you, maybe try it out: https://github.com/scipy/scipy/pull/6025 Pauli

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coding project
by WESLEY WANG 18 Apr '16

18 Apr '16

Hi All! My name is Wesley Wang and I am currently working on a project for our class which involves helping contribute to a project. While I was looking through a list of projects, I became very interested in SciPy, which combines coding with math and science. Is there anything which I can possibly contribute to in terms of code? (As a chemistry major, I'm looking to maybe add or change some things in the constants.py file involving constants commonly used in chemistry) Any feedback would be nice! Thanks and have a great day! -Wesley

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On an example problem from optimize/nonlin.py
by Boris Faleichik 17 Apr '16

17 Apr '16

Dear all, my question is addressed to the authors of https://github.com/scipy/scipy/blob/master/scipy/optimize/nonlin.py. I am looking for more detailed description of the nonlinear integro-differential equation which is a test problem for Newton-Krylov iteration in the documentation: \nabla^2 P = 10 \left(\int_0^1\int_0^1\cosh(P)\,dx\,dy\right)^2 As a computational scientist I would like to know the origin of this problem and possibly to get some references on related scientific papers. Thank you very much! Best, Boris

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Adding surface area calculation for spherical polygons
by Tyler Reddy 15 Apr '16

15 Apr '16

Hi, I've opened an issue (https://github.com/scipy/scipy/issues/6069) that describes and demonstrates code for calculating the surface areas of spherical polygons. It would be helpful to have some input--I've put sample results there and linked to a github with Dockerfile, etc. for reproducing the results / modifying code if needed. Thanks, Tyler

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RFC: sparse DOK array
by Evgeni Burovski 15 Apr '16

15 Apr '16

Hi, TL;DR: Here's a draft implementation of a 'sparse array', suitable for incremental assembly. Comments welcome! https://github.com/ev-br/sparr Scipy sparse matrices are generally great. One area where they are not very great is the formats suitable for incremental assembly (esp LIL and DOK matrices). This has been discussed for a while, see, for instance, https://github.com/scipy/scipy/pull/2908#discussion_r16955546 I spent some time to see how hard is it to build a dictionary-of-keys data structure without Python overhead. Turns out, not very hard. Also, when constructing a sparse data structure now, it seems to make sense to make it a sparse *array* rather than *matrix*, so that it supports both elementwise and matrix multiplication. A draft implementation is available at https://github.com/ev-br/sparr and I'd be grateful for any comments, large or small. Some usage examples are in the README file which Github shows at the front page above. First and foremost, I'd like to gauge interest in the community ;-). Does it actually make sense? Would you use such a data structure? What is missing in the current version? Short to medium term, some issues I see are: * 32-bit vs 64-bit indices. Scipy sparse matrices switch between index types. I wonder if this is purely backwards compatibility. Naively, it seems to me that a new class could just always use 64-bit indices, but this might be too naive? * Data types and casting rules. For now, I basically piggy-back on numpy's rules. There are several slightly different ones (numba has one?), and there might be an opportunity to simplify the rules. OTOH, inventing one more subtly different set of rules might be a bad idea. * "Object" dtype. So far, there isn't one. I wonder if it's needed or having only numeric types would be enough. * Interoperation with numpy arrays and other sparse matrices. I guess __numpy_ufunc__ would *the* solution here, when available. For now, I do something simple based on special-casing and __array_priority__. Sparse matrices almost work, but there are glitches. This is just a non-exhaustive list of things I'm thinking about. If there's something else (and I'm sure there is plenty!) I'm all ears --- either in this email thread or in the issue tracker. If someone feels like commenting on implementation details, sure, none of those is cast in stone and plumbing is definitely in a bit of a flux. Cheers, Evgeni

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Increasing Minimum Required LAPACK Version
by Ian Henriksen 13 Apr '16

13 Apr '16

Hi everyone, I've opened a pull request (https://github.com/scipy/scipy/pull/6051) to increase the minimum required LAPACK version to 3.3.1 rather than 3.1.0. Here's some context. 3.1.0 was released on November 12, 2006. 3.3.1 was released on April 18 2011. 3.3.1 is also the LAPACK version available by default on the Travis CI build workers. There are several advantages to doing this. It lets us avoid running into bugs in older LAPACK versions (one example is https://github.com/scipy/scipy/issues/5039). It also allows us to add a significant number of LAPACK routines to the Cython LAPACK API. The Cython LAPACK API consists of the routines common to all supported LAPACK versions. Increasing the minimum requirement all the way up to 3.3.1 gives us access to most of the new routines added between 3.1.0 and 3.3.1. A full list of the routines this adds is available in the pull request. The main change I expect this to make for users is that people using systems sufficiently old to not have LAPACK 3.3.1 available through the package manager will now have to build LAPACK from source rather than linking against the LAPACK provided by their operating system. Given that systems that old are becoming less common and that many people use more recent optimized implementations of BLAS and LAPACK, I expect that this won't impact a huge number of people. Is this a change that makes sense to everyone? Best, Ian Henriksen

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scikit-kinematics released
by Thomas Haslwanter 11 Apr '16

11 Apr '16

I have gone ahead, and released my 3D kinematics routines (working with rotation matrices and quaternions, analyzing data from IMUs and from marker-based tracker systems, etc) as an open-source project. - The code can be found under https://github.com/thomas-haslwanter/scikit-kinematics - The documentation under http://work.thaslwanter.at/skinematics/html/ I still have one problem, where I would really appreciate assistance: the documentation builds cleanly on my system, under Windows as well as under Linux. However, when I build the documentation on https://readthedocs.org/, the descriptions of the functions in the modules are not built. It looks as if *readthedocs* ignores the sphinx-command "func". Check for example the link for *quat.quatmult* on http://work.thaslwanter.at/skinematics/html/quat.html (correctly working) and on http://scikit-kinematics.readthedocs.org/en/latest/quat.html (link not working) If you have had any experience with *readthedocs* and have an idea what the problem could be, please let me know. Thanks, thomas

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