Mailman 3 November 2015 - 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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Imsave and imwrite
by Alan Wu 14 Dec '15

14 Dec '15

I moved imsave into imwrite and wrapped imsave to call imwrite, in order to be consistent in naming read/write and with other packages like matlab and opencv. I would like to submit it as a pull request . Thanks, Alan

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Python development on fedora 23.
by Charles R Harris 29 Nov '15

29 Nov '15

Hi Fedora users, Python distutils on fedora 23 is configured for hardening, hence `redhat-rpm-config` is a dependency if you want to build numpy, scipy, etc. The symptom is a "broken toolchain" error. A bug is open for this and it might get fixed on the Python end, but I don't expect anything soon. Chuck

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Limit number of iterations in sigma clipping
by Sri Krishna 29 Nov '15

29 Nov '15

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 22 Nov '15

22 Nov '15

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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[JOB] Project Jupyter is hiring two postdoctoral fellows @ UC Berkeley.
by Fernando Perez 19 Nov '15

19 Nov '15

Hi all, We are delighted to announce today that Project Jupyter/IPython has two postdoctoral fellowships open at UC Berkeley, open immediately. Interested candidates can apply here: https://aprecruit.berkeley.edu/apply/JPF00899 We hope to find candidates who will work on a number of challenging questions over the next few years, as described in our grant proposal here: http://blog.jupyter.org/2015/07/07/project-jupyter-computational-narratives… Interested candidates should carefully read that proposal before applying to familiarize themselves with the full scope of the questions we intend to tackle. We'd like to thank the support of the Helmsley Trust, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation. Cheers, Brian Granger and Fernando Perez. -- Fernando Perez (@fperez_org; http://fperez.org) fperez.net-at-gmail: mailing lists only (I ignore this when swamped!) fernando.perez-at-berkeley: contact me here for any direct mail

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Re: [SciPy-Dev] optimize - add algorithm for global optimization: GenSA
by Gubian, Sylvain 19 Nov '15

19 Nov '15

Dear All, Thanks a lot for Jake, Andrew, Chuck and Sturla comments. I would like to address some comments so that I would know how to go forward, like doing a pull request whenever you think it makes sense. @Jake: As you mentioned, there are situations where basinhopping may not work as well as PyGenSA, especially in cases of high number of parameters. This implementation has actually been focused on solving high dimension problems. However, it also works for very simple problems, but in those cases it might show non-optimal performance (in terms of avg number of function calls). The battery test shows that in general, the number of successes is almost always better than basinhopping, PyGenSA is a robust implementation. As attached, a PDF version of the poster I presented to PyCon UK Coventry in September 2015 showing a performance benchmark. @Chuck: About license, I am the author and maintainer of the R version. The python version is a pure python implementation and there is no code taken from R. Therefore, I don't think there is any license issue. Best regards, Sylvain. ------------------------------------------------------------------------------------------------------------------------------------ Date: Sat, 24 Oct 2015 08:39:44 -0600 From: Charles R Harris <charlesr.harris(a)gmail.com<mailto:charlesr.harris@gmail.com>> To: SciPy Developers List <scipy-dev(a)scipy.org<mailto:scipy-dev@scipy.org>> Subject: Re: [SciPy-Dev] optimize - add algorithm for global optimization: GenSA Message-ID: <CAB6mnxJy8n+yfJ9TMJAZWNGLJ4J4_hs58T-W3snrgdsmRs0b0w(a)mail.gmail.com<mailto:CAB6mnxJy8n+yfJ9TMJAZWNGLJ4J4_hs58T-W3snrgdsmRs0b0w@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" ... I'm a bit concerned about license issues If any of the code comes from R. R has an incompatible license. Chuck ------------------------------------------------------------------------------------------------------------------------------------ Date: Sat, 24 Oct 2015 10:00:22 +1100 From: Andrew Nelson <andyfaff(a)gmail.com<mailto:andyfaff@gmail.com>> To: SciPy Developers List <scipy-dev(a)scipy.org<mailto:scipy-dev@scipy.org>> Subject: Re: [SciPy-Dev] optimize - add algorithm for global optimization: GenSA Message-ID: <CAAbtOZctBQeqjc5+QucA4XADC=F1MqKjPTHJDMb6XQjB-dkM7w(a)mail.gmail.com<mailto:CAAbtOZctBQeqjc5+QucA4XADC=F1MqKjPTHJDMb6XQjB-dkM7w@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" ... I have a scipy PR for testing global optimisers. It has approx 120 functions, which is 2.5 times more than in the linked paper. I think for any global optimisers to be added it should give a good performance against those benchmarks. The main criterion is the number of successes and the avg number of function evaluations. Time is of secondary importance. Any optimisers added to scipy.optimize should conform to the generally standardised syntax (naming conventions, etc) used by the module. ------------------------------------------------------------------------------------------------------------------------------------ Date: Fri, 23 Oct 2015 14:06:17 +0000 From: Jacob Stevenson <jstevenson131(a)gmail.com<mailto:jstevenson131@gmail.com>> To: SciPy Developers List <scipy-dev(a)scipy.org<mailto:scipy-dev@scipy.org>> Subject: Re: [SciPy-Dev] optimize - add algorithm for global optimization: GenSA Message-ID: <CAEEL3ux+0Wxa+v6Azv6kdRZKBN8qts_RdU1pEG3S+hjeYNry-w(a)mail.gmail.com<mailto:CAEEL3ux+0Wxa+v6Azv6kdRZKBN8qts_RdU1pEG3S+hjeYNry-w@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" ... In my opinion a robust implementation of a simulated annealing based optimizer would be welcome. There are cases when this would be preferable to basinhopping, e.g. when non-smooth or non-continuous functions make the local optimization step in basinhopping less effective. I think the first step is to make a pull request (or send a link if you already did) where we can review the code and have discussions. Best, Jake ------------------------------------------------------------------------------------------------------------------------------------ Date: Fri, 23 Oct 2015 13:10:38 +0000 From: "Gubian, Sylvain" <Sylvain.Gubian(a)pmi.com<mailto:Sylvain.Gubian@pmi.com>> To: "scipy-dev(a)scipy.org<mailto:scipy-dev@scipy.org>" <scipy-dev(a)scipy.org<mailto:scipy-dev@scipy.org>> Subject: [SciPy-Dev] optimize - add algorithm for global optimization: GenSA Message-ID: <A3786E9825AE3D4F8CC0EC80FBFAD32E02349A0696(a)PMICHLAUEXM23.PMINTL.NET<mailto:A3786E9825AE3D4F8CC0EC80FBFAD32E02349A0696@PMICHLAUEXM23.PMINTL.NET>> Content-Type: text/plain; charset="Windows-1252" 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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Extend odeint to handle matrices and complex numbers
by Warren Weckesser 17 Nov '15

17 Nov '15

I wrote a wrapper of odeint called odeintw that is currently available on github: https://github.com/WarrenWeckesser/odeintw To quote from README.md in the repository: odeintw provides a wrapper of scipy.integrate.odeint that allows it to handle complex and matrix differential equations. That is, it can solve equations of the form dZ/dt = F(Z, t, param1, param2, ...) where t is real and Z is a real or complex array. What do folks think of adding this functionality directly to odeint in scipy? It would be completely backwards compatible, and would not introduce any overhead in the callbacks if the inputs are 1-d real arrays. Warren

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scipy Fedora test failure
by Orion Poplawski 16 Nov '15

16 Nov '15

I'm trying to get scipy updating in Fedora. Running into test failures. Full log is here: https://kojipkgs.fedoraproject.org//work/tasks/8123/11808123/build.log NumPy version 1.10.1 NumPy relaxed strides checking option: True NumPy is installed in /usr/lib64/python2.7/site-packages/numpy SciPy version 0.16.1 SciPy is installed in /builddir/build/BUILDROOT/scipy-0.16.1-3.fc24.x86_64/usr/lib64/python2.7/site-packages/scipy Python version 2.7.10 (default, Oct 15 2015, 20:40:13) [GCC 5.1.1 20150618 (Red Hat 5.1.1-4)] nose version 1.3.7 Traceback (most recent call last): File "<string>", line 1, in <module> File "/usr/lib64/python2.7/site-packages/numpy/testing/nosetester.py", line 438, in test t = NumpyTestProgram(argv=argv, exit=False, plugins=plugins) File "/usr/lib/python2.7/site-packages/nose/core.py", line 121, in __init__ **extra_args) File "/usr/lib64/python2.7/unittest/main.py", line 95, in __init__ self.runTests() File "/usr/lib64/python2.7/site-packages/numpy/testing/noseclasses.py", line 351, in runTests self.result = self.testRunner.run(self.test) File "/usr/lib/python2.7/site-packages/nose/core.py", line 66, in run result.printErrors() File "/usr/lib/python2.7/site-packages/nose/result.py", line 103, in printErrors _TextTestResult.printErrors(self) File "/usr/lib64/python2.7/unittest/runner.py", line 108, in printErrors self.printErrorList('ERROR', self.errors) File "/usr/lib64/python2.7/unittest/runner.py", line 114, in printErrorList self.stream.writeln("%s: %s" % (flavour,self.getDescription(test))) File "/usr/lib/python2.7/site-packages/nose/result.py", line 82, in getDescription return test.shortDescription() or str(test) File "/usr/lib/python2.7/site-packages/nose/case.py", line 51, in __str__ return str(self.test) File "/usr/lib/python2.7/site-packages/nose/case.py", line 286, in __str__ name = "%s%s" % (name, arg) File "/usr/lib64/python2.7/site-packages/numpy/core/numeric.py", line 1709, in array_repr ', ', "array(") File "/usr/lib64/python2.7/site-packages/numpy/core/arrayprint.py", line 447, in array2string separator, prefix, formatter=formatter) File "/usr/lib64/python2.7/site-packages/numpy/core/arrayprint.py", line 260, in _array2string 'int': IntegerFormat(data), File "/usr/lib64/python2.7/site-packages/numpy/core/arrayprint.py", line 637, in __init__ max_str_len = max(len(str(maximum.reduce(data))), DeprecationWarning: unorderable dtypes; returning scalar but in the future this will be an error RPM build errors: error: Bad exit status from /var/tmp/rpm-tmp.6SOdNx (%check) This *appears* to be what triggers the failure - but why does a DeprecationWarning do this? Or is there something further up in the test? -- Orion Poplawski Technical Manager 303-415-9701 x222 NWRA, Boulder/CoRA Office FAX: 303-415-9702 3380 Mitchell Lane orion(a)nwra.com Boulder, CO 80301 http://www.nwra.com

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SLSQP question
by Giselle Sosa Jones 15 Nov '15

15 Nov '15

Hello. I would like to know if anyone knows the difference between SQP and SLSQP? Where do the Least Squares come to use in the method? Thank you.

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