Mailman 3 May 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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(no subject)
by Nathan Woods 25 Jul '16

25 Jul '16

There have been a number of problems (e.g. GitHub issues #5002, #4831) with the C interface of scipy.integrate.quad since work was done a couple of years ago to allow performance optimizations ( https://github.com/scipy/scipy/commit/f5183a34a959d625c6b534e99df5db7ca49bf…). Specifically, the QuadPack Fortran library does not really accommodate multivariate integrand functions, which leads to all sorts of monkey business in the API to hide that multivariate-ness. This is further complicated by the fact that the QuadPack interface is one of the oldest parts of SciPy and uses the Python C API directly, which many (most?) of us are unfamiliar with. I'd really like to figure out some kind of "good" solution to this. I think that the requirements of the problem are fairly simple: connect the existing scipy.integrate.quad routine to the underlying Fortran code, while allowing users to use compiled function pointers as integrand functions, if they want to (and are willing to put in some effort). I guess that the question here is, what is the best way to go about this? Should we simply find a way to fix the existing interface code? Should we roll back the changes and disallow the use of function pointers in quad? Should we replace the existing API with something else, using Cython or f2py? Should we alter the QuadPack code itself (perhaps by adding a void* argument to the function) to make it more friendly to multivariate functions in general? The current state of affairs bothers me, especially since I was involved in the PR that caused the new problems, but I haven't been able to come up with a good way to fix things yet. I'd love to get some ideas and feedback on how this could be resolved. Nathan Woods

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Numpy 1.11.1rc1 release
by Charles R Harris 27 May '16

27 May '16

Hi All, I am pleased to announce the release of Numpy 1.11.1rc1. The sources may be found on sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.1rc1/> and wheels for OS X, Windows, and Linux will be available on pypi sometime in the next few days. The pypi release is delayed due to the decision that the wheels should go up before the sources in order that people not get a source install when what they want are wheels. The Python versions supported are 2.6-2.7 and 3.2-3.5. This release has mostly small fixes and build enhancements and should be good out of the starting gate, but prudence requires a release candidate as there are a few bits not tested in master. The following fixes have been applied: - #7506 BUG: Make sure numpy imports on python 2.6 when nose is unavailable. - #7530 BUG: Floating exception with invalid axis in np.lexsort. - #7535 BUG: Extend glibc complex trig functions blacklist to glibc < 2.18. - #7551 BUG: Allow graceful recovery for no compiler. - #7558 BUG: Constant padding expected wrong type in constant_values. - #7578 BUG: Fix OverflowError in Python 3.x. in swig interface. - #7590 BLD: Fix configparser.InterpolationSyntaxError. - #7597 BUG: Make np.ma.take work on scalars. - #7608 BUG: linalg.norm(): Don't convert object arrays to float. - #7638 BLD: Correct C compiler customization in system_info.py. - #7654 BUG: ma.median of 1d array should return a scalar. - #7656 BLD: Remove hardcoded Intel compiler flag -xSSE4.2. - #7660 BUG: Temporary fix for str(mvoid) for object field types. - #7665 BUG: Fix incorrect printing of 1D masked arrays. - #7670 BUG: Correct initial index estimate in histogram. - #7671 BUG: Boolean assignment no GIL release when transfer needs API. - #7676 BUG: Fix handling of right edge of final histogram bin. - #7680 BUG: Fix np.clip bug NaN handling for Visual Studio 2015. The following people have contributed to this release - Allan Haldane - Amit Aronovitch - Charles Harris - Eric Wieser - Evgeni Burovski - Loïc Estève - Mathieu Lamarre - Matthew Brett - Matthias Geier - Nathaniel J. Smith - Nikola Forró - Ralf Gommers - Robert Kern - Sebastian Berg - Simon Conseil - Simon Gibbons - Sorin Sbarnea - chiffa Chuck

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Boundary conditions for CubicSpline
by Nikolay Mayorov 22 May '16

22 May '16

Hi! There is some discussion going on about how to implement periodic boundary conditions for CubicSpline class, which to appear in 0.18.0 scipy release: https://github.com/scipy/scipy/pull/6181 If it's of interest to you, please take a look and comment. Any other feedback is also welcome. Nikolay. Sent using Zoho Mail

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SciPy 2016 Conference (Scientific Computing with Python): Tutorials and Talks Announced
by Courtenay Godshall (Enthought) 19 May '16

19 May '16

**ANN: SciPy 2016 Conference (Scientific Computing with Python): Tutorials and Talks Announced** We're excited to announce this year's accepted Talks <http://scipy2016.scipy.org/ehome/146062/332963/> & Posters and Tutorial Schedule <http://scipy2016.scipy.org/ehome/146062/332960/> ! This year's 3 major talk tracks include Python in Data Science, High Performance Computing, and general Scientific Computing. Our six mini-symposia include: Earth and Space Science, Engineering, Medicine and Biology, Case Studies in Industry, Education, and Reproducibility. For tutorials, you can choose from <http://scipy2016.scipy.org/ehome/146062/332960/> 18 different SciPy tutorials, including a <https://katyhuff.github.io/2016-07-11-scipy> 1 day Software Carpentry Scientific Python course that assumes some programming experience but no Python knowledge, or a 2-day Software Carpentry Instructor Training. We hope you'll join us - early bird registration ENDS May 22, 2016. Register at: http://scipy2016.scipy.org/ehome/146062/332936/ About SciPy 2016 SciPy 2016 <http://www.scipy2016.scipy.org> , the 15th annual Scientific Computing with Python conference, will be held July 11-17, 2016 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 650 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of <http://scipy2016.scipy.org/ehome/146062/332960/> 2 days of tutorials (July 11-12), <http://scipy2016.scipy.org/ehome/146062/332963/> 3 days of talks (July 13-15), and 2days of <http://scipy2016.scipy.org/ehome/146062/332969/?&&> developer sprints (July 16-17). More info is available on the conference website at <http://www.scipy2016.scipy.org> http://scipy2016.scipy.org (where you can sign up for the mailing list); or follow <https://twitter.com/scipyconf> @scipyconf on Twitter.

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Re: [SciPy-Dev] [Numpy-discussion] Scipy 2016 attending
by Nathaniel Smith 19 May '16

19 May '16

Me too. On Wed, May 18, 2016 at 3:02 PM, Chris Barker <chris.barker(a)noaa.gov> wrote: > I'll be there. > > -CHB > > > On Wed, May 18, 2016 at 2:09 PM, Charles R Harris > <charlesr.harris(a)gmail.com> wrote: >> >> Hi All, >> >> Out of curiosity, who all here intends to be at Scipy 2016? >> >> Chuck >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion(a)scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > > -- > > 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 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Nathaniel J. Smith -- https://vorpus.org

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Scipy 2016 attending
by Charles R Harris 18 May '16

18 May '16

Hi All, Out of curiosity, who all here intends to be at Scipy 2016? Chuck

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scipy 0.17.1 release
by Evgeni Burovski 14 May '16

14 May '16

Hi, On behalf of the scipy development team, I'm pleased to announce the availability of scipy 0.17.1. This is a bugfix release with no new features compared to 0.17.0. Source tarballs and OS X wheels are available from PyPI or from GitHub releases at https://github.com/scipy/scipy/releases/tag/v0.17.1 We recommend that all users upgrade from scipy 0.17.0. Cheers, Evgeni ========================== SciPy 0.17.1 Release Notes ========================== SciPy 0.17.1 is a bug-fix release with no new features compared to 0.17.0. Issues closed for 0.17.1 ------------------------ - `#5817 <https://github.com/scipy/scipy/issues/5817>`__: BUG: skew, kurtosis return np.nan instead of "propagate" - `#5850 <https://github.com/scipy/scipy/issues/5850>`__: Test failed with sgelsy - `#5898 <https://github.com/scipy/scipy/issues/5898>`__: interpolate.interp1d crashes using float128 - `#5953 <https://github.com/scipy/scipy/issues/5953>`__: Massive performance regression in cKDTree.query with L_inf distance... - `#6062 <https://github.com/scipy/scipy/issues/6062>`__: mannwhitneyu breaks backward compatibility in 0.17.0 - `#6134 <https://github.com/scipy/scipy/issues/6134>`__: T test does not handle nans Pull requests for 0.17.1 ------------------------ - `#5902 <https://github.com/scipy/scipy/pull/5902>`__: BUG: interpolate: make interp1d handle np.float128 again - `#5957 <https://github.com/scipy/scipy/pull/5957>`__: BUG: slow down with p=np.inf in 0.17 cKDTree.query - `#5970 <https://github.com/scipy/scipy/pull/5970>`__: Actually propagate nans through stats functions with nan_policy="propagate" - `#5971 <https://github.com/scipy/scipy/pull/5971>`__: BUG: linalg: fix lwork check in *gelsy - `#6074 <https://github.com/scipy/scipy/pull/6074>`__: BUG: special: fixed violation of strict aliasing rules. - `#6083 <https://github.com/scipy/scipy/pull/6083>`__: BUG: Fix dtype for sum of linear operators - `#6100 <https://github.com/scipy/scipy/pull/6100>`__: BUG: Fix mannwhitneyu to be backward compatible - `#6135 <https://github.com/scipy/scipy/pull/6135>`__: Don't pass null pointers to LAPACK, even during workspace queries. - `#6148 <https://github.com/scipy/scipy/pull/6148>`__: stats: fix handling of nan values in T tests and kendalltau

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ANN: SfePy 2016.2
by Robert Cimrman 12 May '16

12 May '16

I am pleased to announce release 2016.2 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (preliminary support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this release -------------------------- - partial shell10x element implementation - parallel computation of homogenized coefficients - clean up of elastic terms - read support for msh file mesh format of gmsh For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman on behalf of the SfePy development team --- Contributors to this release in alphabetical order: Robert Cimrman Vladimir Lukes

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Looking for an example of using PchipInterpolator
by David Shi 11 May '16

11 May '16

Hello, Friends, I am looking for an example of using Scipy PchipInterpolator. However, when I used matplotlib to plot the data, the last section of the line is missing. Only data points are present. Looking forward to hearing from you. Regards. David

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