Mailman 3 October 2015 - SciPy-Dev

Limit number of iterations in sigma clipping
by Sri Krishna Nov. 29, 2015

Nov. 29, 2015

Hi, The current sigma clipping function has no iters keyword. I feel it would be useful to include it in the function, with a default like `iters=None` so that if people do want to use the keyword they will be able to do so. With the defaults of `iters = None` it shouldn't break anyone's workflow and will correspond to the current default behaviour. If there is any interest in this, I can file a pull request soon. Thanks, Srikrishna Sekhar

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plans for 0.17.0 release
by Ralf Gommers Nov. 22, 2015

Nov. 22, 2015

Hi all, We branched 0.16.x in early May, so it's time to start thinking about branching 0.17.x sometime soon. If anyone would be interested in being the release manager for 0.17.x, or co-managing it with me, that would be very welcome. In order to make releasing look less like black magic I've worked on automating stuff a bit more (has landed in master) and on writing fairly detailed documentation: https://github.com/scipy/scipy/pull/5424. Please have a look if you're interested. Cheers, Ralf

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Random matrices
by Philip DeBoer Nov. 11, 2015

Nov. 11, 2015

I would like to expose an interface for random matrix generation on scipy, in particular, SO(N) and O(N) functionality. I wrote some code to generate random correlation matrices that could also be exposed. These seem like basic multi-dimensional results that would be useful in a wide variety of situations. There is a "random_rot" function generating SO(N) matrices in scipy/linalg/tests/test_decomp. It has a bug that was since fixed in the MDP package from whence it came, but was never fixed in scipy. A variation of this will generate O(N) matrices, and a little more work yields random correlation matrices. In Oct 2008 when random_rot was added, it was suggested that linalg was a good place for these functions. Is that still the case? Or should they be somewhere in scipy.random? Do we need a new place for random matrices? I have not contributed to scipy before. Let me know what the steps are and I will do my best to take care of this. This could benefit a wide variety of users, and it would be good to have widely used code. Thanks, Philip

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deleting scipy/speed repo
by Ralf Gommers Nov. 7, 2015

Nov. 7, 2015

Hi, Does anyone object to deleting https://github.com/scipy/speed? There's not more than 200 lines of code in there and no one uses that repo anymore. The code that's in there is Python/Cython/Fortran code to solve the Laplace problem. For people interested in that, this blog post from Travis is much nicer than the repo: http://technicaldiscovery.blogspot.nl/2011/06/speeding-up-python-numpy-cyth… The only thing the blog post doesn't have is the Fortran code, but it's linked in the comments and originally came from https://github.com/certik/laplace_test Conclusion: nothing to move, just delete. Cheers, Ralf

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Retiring wiki.scipy.org?
by Pauli Virtanen Nov. 3, 2015

Nov. 3, 2015

Hi, I've been looking through the stuff that was on wiki.scipy.org, and it appears it would be the time to retire it. cf. http://pav.iki.fi/tmp/to-be-removed/localhost_8080/AllPages.html for the actual material that is there. I have the impression that: (1) Most of the material that is useful has been moved elsewhere. The download instructions, topical software, etc. are moved to scipy.org and/or numpy/scipy documentation. The Numpy/Scipy tutorials that were there have mostly been subsumed in the Numpy/Scipy documentation, or superceded by better resources elsewhere eg. scipy-lectures.org. "Numpy for Matlab users" was just added there too. The Cookbook section was converted to IPython notebooks some years ago: https://github.com/pv/SciPy-CookBook However, several of these are not so relevant today. (2) The relevance of what's left is not so clear. The rest of the content seems fairly miscellaneous. Recipes from converting code from Numeric/numarray are probably not highly useful any more. If you know something valuable there not listed above, it would be useful to point it out. Excluding edits to front/download/etc pages that are nowadays on github/scipy/scipy.org, the wiki was edited 95 times in the last 5 years; and 2 times within the last 2 years. (3) Would having a wiki be useful? Based on the content there it's not clear if there is a need for a wiki nowadays --- people can just put stuff up on github/bitbucket/pypi and blog about it. It might be useful to have a "cookbook" collection, but a github repository with ipython notebooks, or Scipy Central, sound like better solutions for today than a wiki. Thoughts? In view of the above, I would remove links to wiki.scipy.org regardless, https://github.com/scipy/scipy.org/pull/121 -- Pauli Virtanen

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Scipy wiki is down again
by Robert Pollak Oct. 30, 2015

Oct. 30, 2015

Dear server administrator, since several days, the website http://wiki.scipy.org/ only shows: > Internal Server Error > > The server encountered an internal error or misconfiguration and was unable to complete your request. > > Please contact the server administrator at root(a)enthought.com to inform them of the time this error occurred, and the actions you performed just before this error. > > More information about this error may be available in the server error log. Please make the wiki available again. I need its page "NumPy_for_Matlab_Users" for a workshop with my colleagues. Best regards, Robert Pollak

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ANN: Scipy 0.16.1 release
by Ralf Gommers Oct. 25, 2015

Oct. 25, 2015

Hi all, I'm happy to announce the availability of the Scipy 0.16.1 release. This is a bugfix only release; it contains no new features compared to 0.16.0. The sources and binary installers can be found at: - Source tarballs: at https://github.com/scipy/scipy/releases and on PyPi. - OS X: there are wheels on PyPi, so simply install with pip. - Windows: .exe installers can be found on https://github.com/scipy/scipy/releases Cheers, Ralf ========================== SciPy 0.16.1 Release Notes ========================== SciPy 0.16.1 is a bug-fix release with no new features compared to 0.16.0. Issues closed for 0.16.1 ------------------------ - `#5077 <https://github.com/scipy/scipy/issues/5077>`__: cKDTree not indexing properly for arrays with too many elements - `#5127 <https://github.com/scipy/scipy/issues/5127>`__: Regression in 0.16.0: solve_banded errors out in patsy test suite - `#5149 <https://github.com/scipy/scipy/issues/5149>`__: linalg tests apparently cause python to crash with numpy 1.10.0b1 - `#5154 <https://github.com/scipy/scipy/issues/5154>`__: 0.16.0 fails to build on OS X; can't find Python.h - `#5173 <https://github.com/scipy/scipy/issues/5173>`__: failing stats.histogram test with numpy 1.10 - `#5191 <https://github.com/scipy/scipy/issues/5191>`__: Scipy 0.16.x - TypeError: _asarray_validated() got an unexpected... - `#5195 <https://github.com/scipy/scipy/issues/5195>`__: tarballs missing documentation source - `#5363 <https://github.com/scipy/scipy/issues/5363>`__: FAIL: test_orthogonal.test_j_roots, test_orthogonal.test_js_roots Pull requests for 0.16.1 ------------------------ - `#5088 <https://github.com/scipy/scipy/pull/5088>`__: BUG: fix logic error in cKDTree.sparse_distance_matrix - `#5089 <https://github.com/scipy/scipy/pull/5089>`__: BUG: Don't overwrite b in lfilter's FIR path - `#5128 <https://github.com/scipy/scipy/pull/5128>`__: BUG: solve_banded failed when solving 1x1 systems - `#5155 <https://github.com/scipy/scipy/pull/5155>`__: BLD: fix missing Python include for Homebrew builds. - `#5192 <https://github.com/scipy/scipy/pull/5192>`__: BUG: backport as_inexact kwarg to _asarray_validated - `#5203 <https://github.com/scipy/scipy/pull/5203>`__: BUG: fix uninitialized use in lartg 0.16 backport - `#5204 <https://github.com/scipy/scipy/pull/5204>`__: BUG: properly return error to fortran from ode_jacobian_function... - `#5207 <https://github.com/scipy/scipy/pull/5207>`__: TST: Fix TestCtypesQuad failure on Python 3.5 for Windows - `#5352 <https://github.com/scipy/scipy/pull/5352>`__: TST: sparse: silence warnings about boolean indexing - `#5355 <https://github.com/scipy/scipy/pull/5355>`__: MAINT: backports for 0.16.1 release - `#5356 <https://github.com/scipy/scipy/pull/5356>`__: REL: update Paver file to ensure sdist contents are OK for releases. - `#5382 <https://github.com/scipy/scipy/pull/5382>`__: 0.16.x backport: MAINT: work around a possible numpy ufunc loop... - `#5393 <https://github.com/scipy/scipy/pull/5393>`__: TST:special: bump tolerance levels for test_j_roots and test_js_roots - `#5417 <https://github.co

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Add cut-plane optimization?
by Sturla Molden Oct. 24, 2015

Oct. 24, 2015

I am hearing good things about a new local optimization method called "cut-plane optimization". I think we should consider to add this method to scipy.optimize. http://phys.org/news/2015-10-general-purpose-optimization-algorithm-order-o… Sturla

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optimize - add algorithm for global optimization: GenSA
by Gubian, Sylvain Oct. 24, 2015

Oct. 24, 2015

Hi everyone, We would like to propose a new method, GenSA, for global optimization to be included in the optimize module. GenSA is an implementation of the General Simulated Annealing algorithm (GSA, http://www.sciencedirect.com/science/article/pii/S0378437196002713). This approach generalizes CSA (Classical Simulated Annealing) and FSA (Fast Simulated Annealing) to search for the global minimum more efficiently. The algorithm is explained in more detail in this reference: http://journal.r-project.org/archive/2013-1/xiang-gubian-suomela-etal.pdf. SciPy has already in the past included a method based on simulated annealing, called anneal, which has been deprecated in 0.14 (with an advice to use basinhopping) and eventually removed in 0.16. A previously published comparison of 18 optimization methods in the R language (http://www.jstatsoft.org/v60/i06/paper) shows that GenSA is, among the methods tested, one of the “most capable of consistently returning a solution near the global minimum of each test function”. This paper however did not consider basinhopping, so we have performed some tests which tend to show that GenSA is more efficient than basinhopping for high dimension problems. The results have been presented in a poster in PyCon UK 2015 (Coventry). The code is ready and passes unit tests and PEP8. We hope it would be a useful addition to SciPy and would be happy to have your opinion. Thanks, Sylvain.

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deprecating stats.histogram?
by Ralf Gommers Oct. 18, 2015

Oct. 18, 2015

Hi all, In 0.16.0 we deprecate stats.histogram2 (in PR 4655) but somehow forgot to think about deprecating the 1-D version. At least I can't find a record on this list or on Github of such a discussion. The difference between numpy.histogram and stats.histogram is very minor; basically stats.histogram uses the numpy version under the hood with only a different range. And it misses some features now added to numpy. Any objections to deprecating this function? Ralf

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