Mailman 3 October 2016 - SciPy-Dev

FFTLog
by Dieter Werthmüller 17 Apr '17

17 Apr '17

Dear SciPy-devs, I recently had the need for a logarithmic FFT routine, and did a quick f2py around Andrew Hamilton's FFTLog: http://casa.colorado.edu/~ajsh/FFTLog I put my f2py-setup and my pyf-file up on GitHub: https://github.com/prisae/fftlog I thought it might be a useful addition to the SciPy FFTPack. Specifically as three out of the five fortran-files, of which FFTLog consists, are already in scipy/fftpack. I have never contributed to SciPy and therefore do not know how much work it would involve to get it into SciPy. However, I thought I ask. If there is interest I expect that it would not take long for an experienced person, as it is a fairly small addition. Or someone could point me to the right direction on what to do to get it in. I append some comments on the involved files of FFTLog and some comments regarding their licenses. I believe it is compatible with SciPy. Thanks for all your good work! Dieter Files and Licenses/Permissions ------------------------------ Files of FFTLog [1] cdgamma.f [2] drfftb.f [3] drfftf.f [4] drffti.f [5] fftlog.f (plus a test routine, fftlogtest.f) -- [1] -- The original FFTLog states about this file: FFTLog uses [...] and a modified version of the complex Gamma function from the gamerf package at momonga.t.u-tokyo.ac.jp/~ooura/gamerf.html. The original gamerf copyright statement states: Copyright(C) 1996 Takuya OOURA (email: ooura(a)mmm.t.u-tokyo.ac.jp). You may use, copy, modify this code for any purpose and without fee. You may distribute this ORIGINAL package. Permission to distribute the modified gamma function code with the FFTLog package has been granted (email from Takuya Ooura to Andrew Hamilton dated 16 March 1999). Hence I think it is compatible with SciPy, or am I wrong? Alternatively it could be replaced with scipy.special.loggamma, I think, but I do not know if it is possible to mix Fortran and Python code with f2py. -- [2], [3], [4] -- They are from the NCAR suite of FFT routines (Swarztrauber 1979). As far as I can see they are already in SciPy, in scipy/fftpack/src/dfftpack/ (dfftb.f, dfftf.f, and dffti.f). So there is definitely not a licensing problem here. -- [5] -- This is the actual FFTLog routine from Hamilton. I asked Hamilton for permission before publishing his source code at github.com/prisae/fftlog. His email response was: -- start email from 28/09/2016 -- Dieter, You are welcome to use fftlog in any way you choose. Please note the credits commented in the code: c FFTLog uses the NCAR suite of FFT routines, c and a modified version of the complex Gamma function c from the gamerf package at c http://momonga.t.u-tokyo.ac.jp/~ooura/gamerf.html . c The original gamerf copyright statement states: c Copyright(C) 1996 Takuya OOURA (email: ooura(a)mmm.t.u-tokyo.ac.jp). c You may use, copy, modify this code for any purpose and c without fee. You may distribute this ORIGINAL package. c c Permission to distribute the modified gamma function code c with the FFTLog package has been granted c (email from Takuya Ooura to Andrew Hamilton dated 16 March 1999). Andrew -- end email from 28/09/2016 --

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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 governance model
by Ralf Gommers 18 Jan '17

18 Jan '17

Hi all, Starting with the summary of my email of earlier today: I'd like to push on with agreeing on a governance model and document. We had some discussions and a hangout on that last year [1]. In the hangout we decided to give people some time to read up on provided info on how this worked in other projects, the Karl Fogel book, etc. I was supposed to organize a follow-up, but failed to do so until now. I will send a separate email about this shortly. We're now at a point where most other major projects in the scientific Python ecosystem have a formal governance model. Many are modeled after the Jupyter one [2], which defines a BDFL, a steering council and contributors, and a voting system to make decisions (simplified, there's much more - the whole document is worth reading). NumPy chose another model, with a steering council and consensus-based decision making [3]. >From the outside, the Jupyter model seems to be working for them. The NumPy model hasn't been exercised too much yet, but should work well too. There are variations on those two models in use as well (like the Jupyter model minus the BDFL). An email discussion on this topic without a concrete proposal or summary document is likely to go on for a long time and may not converge easily. A video conference is also tricky, with dates/timezones and discussion often going off on tangents. So I have the following proposal: - We start by drafting an extended summary of the various models and getting a sense of which model the core team prefers. - We then work out that model, and bring it back to this list for discussion/finetuning/acceptance. - "We" here is the group of people who indicate, on this list or to me off-list, that they want to participate by the end of this week. Thoughts? Volunteers? Ralf [1] https://mail.scipy.org/pipermail/scipy-dev/2015-April/020636.html [2] Jupyter governance doc: https://github.com/jupyter/governance [3] NumPy governance doc: http://docs.scipy.org/doc/numpy-dev/dev/governance/index.html [4] Producing Open Source software (Karl Fogel): http://producingoss.com/

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inversefunc: a method to obtain the numerical inverse of a callable
by Alvaro Sanchez 27 Oct '16

27 Oct '16

Hi everyone! I have been lately working on a user-friendly tool to calculate the numerical inverse of a function, in the shape of a callable that is returned by the function and that lets the user evaluate the inverse function for any given value/array of values. There is an open PR (https://github.com/scipy/scipy/pull/6725), where there is a full description and some examples of usage. At this point I am quite happy with how the function is performing, however there is an ongoing discussion on whether it actually should be part of scipy or not. I would appreciate if you could take a look at the description (including a summary of the ongoing discussion at the end of the first comment), and give some feedback on this line as I would like to know whether I should invest more time on this or not. Thank you in advance :) A

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ENH: Add structural rank to sparse.csgraph
by Paul Nation 23 Oct '16

23 Oct '16

Greetings, I was recently looking for a quick way to find out if a given sparse matrix is singular. As it turns out the solution is to calculate the structural rank (an upper bound on the numerical rank) of the sparsity pattern associated with the matrix: http://www.mathworks.com/help/matlab/ref/sprank.html <http://www.mathworks.com/help/matlab/ref/sprank.html> http://www.cise.ufl.edu/research/sparse/matrices/legend.html <http://www.cise.ufl.edu/research/sparse/matrices/legend.html> Since this calculation is done via the maximum bipartite matching algorithm that I added to sparse.csgraph some time ago, I thought it would be a nice addition to include this straightforward structural rank calculation. The Pull for this feature is here: https://github.com/scipy/scipy/pull/6680 <https://github.com/scipy/scipy/pull/6680> Best regards, Paul

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pypi support
by Jonathan Stickel 17 Oct '16

17 Oct '16

Probably a bit off topic, but I am at a loss. How does one go about getting help with a PyPI problem you can't fix by yourself? I submitted a ticket on the sourceforge site, but it looks like there isn't much activity there. Thanks, Jonathan

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[ANN] SciPy India 2016: call for papers
by Prabhu Ramachandran 17 Oct '16

17 Oct '16

Hello, We are pleased to announce the SciPy India conference 2016. SciPy India is an annual conference on using Python for research and education. The conference is currently in its eighth year and will be held at IIT Bombay on 10th and 11th December, 2016. The registration and call for papers are open. Please visit http://scipy.in to register and submit your proposals. Please spread the word! Call for Papers ============= We look forward to your submissions on the use of Python for scientific computing and education. This includes pedagogy, exploration, modeling, and analysis from both applied and developmental perspectives. We welcome contributions from academia as well as industry. For details on the paper submission please see here: http://scipy.in/2016/cfp/ Important Dates ================ - Call for proposals end: 20th November 2016 - List of accepted proposals will be published: 1st December 2016. We look forward to seeing you at SciPy India. Regards, Prabhu Ramachandran (For the SciPy organizing team)

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Moving SciPy project organization forward
by Ralf Gommers 14 Oct '16

14 Oct '16

Hi all, The next weeks/months I should have quite a bit of bandwidth, which I'd like to use to push forward a few things related to project organization that we've mostly discussed before but never really finalized. The below is a summary of my view on where we are and where we need to go. To discuss individual topics I'll send separate follow-up emails with separate subjects. What we have: - Reasonably good docs for new contributors and core devs. - Established community process for communication and decision making. - A healthy group of active contributors and core devs. - A project that's overall in good shape. What we need: - Set up a mailing list for the core team. - Draft and accept a governance model & docs. - Draft and accept a Code of Conduct (CoC) or "community guidelines" [1] or similar. - Agree on an FSA with NumFOCUS. - Merge the roadmap PR. - Agree on when to do a 1.0 release, and what still has to happen for that. Mailing list ------------ This is not so we do more in private, but just to be more efficient in the small number of conversations that already happens in private (like giving out commit rights). The core team has grown large enough that we tend to forget people or use an old email address. I will set this one up within a week, probably using Google Groups (if anyone has a better idea, please speak up). My plan is to add all active core devs, and send other people with commit rights a personal email about this to ask if they want to be on it (because not everyone follows this list). Governance model & docs ----------------------- We had some discussions and a hangout on that last year [2]. In the hangout we decided to give people some time to read up on provided info on how this worked in other projects, the Karl Fogel book [4], etc. I was supposed to organize a follow-up, but failed to do so until now. I will send a separate email about this shortly. CoC or similar -------------- This is pretty standard to have nowadays, can be welcoming to new people, and is required now by all projects that join NumFOCUS. I'll send a separate email about this later on - probably right after we've merged a governance document. Fiscal Sponsorship Agreement (FSA) ---------------------------------- We need this so we have all our ducks in a row to start dealing with funding/donations in a better way. We did get donations in the past, and even used that money to support the MingwPy project to help improve our Fortran-on-Windows situation. This was OK as a one-off for a very small amount of money, but this needs to be organized better. We also do get people who come up with ideas that would improve SciPy a lot and that we could have supported with some money, had we had the infrastructure for that in place. Let's come back to this as well once we have governance and CoC documents (those are prerequisites). Roadmap PR (gh-2908) -------------------- Let's just pick a date at which the thing [3] has to be merged, in the state it is in then (updated for comments received). And then start improving it with new PRs if needed. Leaving the PR open until everything is perfect hasn't quite worked so far. I'll propose a date 2 weeks from now on the PR itself. SciPy 1.0 --------- We've discussed this several times before. We're now at the point where most of the major gaps have been filled though, so it looks to me like it's time to just pick a date for it (either after or instead of 0.19.0) and then fix up the last things that we think we really need for a 1.0 release. I'll send a separate email about this shortly. Ralf [1] https://github.com/scikit-image/scikit-image-web/pull/36 [2] https://mail.scipy.org/pipermail/scipy-dev/2015-April/020636.html [3] https://github.com/scipy/scipy/pull/2908 [4] http://producingoss.com/

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Re: [SciPy-Dev] Negative Data Points in Center of Mass
by G Young 11 Oct '16

11 Oct '16

Hello! I have a PR open (#6668 <https://github.com/scipy/scipy/pull/6668>) that issues a UserWarning if negative values for data inputs are passed in for "center_of_mass". While I cannot think of any applications of negative values, initial discussion suggested it would be prudent to double check with other developers in the community to see if there were any applications. Thanks!

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Why does forcing Numpy to compile with "sizeof(long double) == 8" fails tests?
by Gustavo Serra Scalet 10 Oct '16

10 Oct '16

Hi, I'm checking how Numpy performs on a POWER8 machine and I noticed that some tests related to machar.py are failing: ====================================================================== ERROR: test_singleton (test_getlimits.TestLongdouble) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/gut/python/venv3.5-b/lib/python3.4/site-packages/numpy-1.12.0.dev0+33d850d-py3.4-linux-ppc64le.egg/numpy/core/tests/test_getlimits.py", line 41, in test_singleton ftype = finfo(longdouble) File "/home/gut/python/venv3.5-b/lib/python3.4/site-packages/numpy-1.12.0.dev0+33d850d-py3.4-linux-ppc64le.egg/numpy/core/getlimits.py", line 124, in __new__ obj = object.__new__(cls)._init(dtype) File "/home/gut/python/venv3.5-b/lib/python3.4/site-packages/numpy-1.12.0.dev0+33d850d-py3.4-linux-ppc64le.egg/numpy/core/getlimits.py", line 154, in _init 'numpy %s precision floating point number' % precname) File "/home/gut/python/venv3.5-b/lib/python3.4/site-packages/numpy-1.12.0.dev0+33d850d-py3.4-linux-ppc64le.egg/numpy/core/machar.py", line 114, in __init__ self._do_init(float_conv, int_conv, float_to_float, float_to_str, title) File "/home/gut/python/venv3.5-b/lib/python3.4/site-packages/numpy-1.12.0.dev0+33d850d-py3.4-linux-ppc64le.egg/numpy/core/machar.py", line 127, in _do_init a = a + a RuntimeWarning: overflow encountered in add FAILED (KNOWNFAIL=4, SKIP=10, errors=4) That happened because native long double actually uses 2 doubles[1] (that method is called IBM Extended double[2]). To avoid that, I changed the compile flags to use -mlong-double-64 [3] and despite solving those errors, now I have some of these failures: ====================================================================== FAIL: test_in_from_2casttype (test_array_from_pyobj.test_LONGDOUBLE_gen) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/gut/python/venv3.5/lib/python3.4/site-packages/numpy-1.12.0.dev0+7a962d1-py3.4-linux-ppc64le.egg/numpy/f2py/tests/test_array_from_pyobj.py", line 319, in test_in_from_2casttype a = self.array([len(self.num2seq)], intent.in_, obj) File "<string>", line 6, in <lambda> File "/home/gut/python/venv3.5/lib/python3.4/site-packages/numpy-1.12.0.dev0+7a962d1-py3.4-linux-ppc64le.egg/numpy/f2py/tests/test_array_from_pyobj.py", line 275, in __init__ assert_(self.has_shared_memory()) File "/home/gut/python/venv3.5/lib/python3.4/site-packages/numpy-1.12.0.dev0+7a962d1-py3.4-linux-ppc64le.egg/numpy/testing/utils.py", line 90, in assert_ raise AssertionError(smsg) AssertionError FAILED (KNOWNFAIL=6, SKIP=16, failures=3) I wanted to know if there is a better approach to solve this issue. I guess it supports 'long double == double', right? Thanks in advance! Environment: Repository head at 33d850d522b3a1940c3ad06b63c79a466def11e9 (from git://github.com/numpy/numpy.git) + patch on [3] Python-3.5.2 [1] https://github.com/gcc-mirror/gcc/blob/3ff1ad36ed4ee3cc490e6178db87b1b8f232… [2] https://gcc.gnu.org/wiki/Ieee128PowerPC [3] https://github.com/PPC64/numpy/commit/be15f1073802ab127162c5dcdd995de7f6452…

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