Mailman 3 June 2014 - SciPy-Dev

Re: [SciPy-Dev] removing umfpack wrapper
by Robert Cimrman Nov. 23, 2014

Nov. 23, 2014

On 11/12/2013 11:22 PM, Ralf Gommers wrote: > On Tue, Nov 12, 2013 at 10:20 AM, Robert Cimrman <cimrman3(a)ntc.zcu.cz>wrote: > >> Hi Ralf, >> >> On 11/10/2013 04:36 PM, Ralf Gommers wrote: >>> Hi all, >>> >>> This is long overdue, but I think it's time to remove the UMFPACK wrapper >>> before the next release. I'd like some feedback on how to do that and to >>> what package (if any) to point existing users. >>> >>> As for why to remove the wrapper, see: >>> https://github.com/scipy/scipy/issues/3002 >>> http://permalink.gmane.org/gmane.comp.python.scientific.user/20451 >>> Short summary: UMFPACK changed from a BSD-compatible license to GPL at >> some >>> point. >>> >>> The deprecation warning in sparse.linalg has been referring people to >>> scikits.umfpack until now, however that package has disappeared >> completely >>> as far as I can tell. I suspect it was in the old scikits svn repo and >> was >>> never moved before that was killed. The alternatives seems to be >> Pysparse ( >> >> I missed that the scikit was lost in transition... >> >> How much work would be to move it to the new site - move from SVN to git, >> and? >> I would be willing to do it, if there is interest. >> > > Hi Robert, I think the moving wouldn't be a lot of work (assuming svn > access can still be arranged). If you'd revive the scikit it's not a > one-time effort though - it's only useful if the code is being maintained > imho. That can still be low effort perhaps, but with reviewing some PRs, > maintenance releases, putting it on pypi, etc. I'd expect at least an hour > a week or so. > > Are there scikits.umfpack users now? Would it make sense to salvage > whatever is useful from the scikit and contribute it to pysparse instead? We are using it in sfepy as the default direct solver. It seems to me that the pysparse interface requires the input matrix to be in the LL (linked list) format, which is unfortunate for us, as we use CSR. The scipy (and former scikit) umfpack wrappers have used CSR, so no copies were necessary. (After all, I have created the original scipy wrappers to be used from sfepy in the first place...) So in case others are not interested in having the scikit, I can see two possible solutions from my perspective: either enhance pysparse interface to allow CSR as well, or move the wrappers to sfepy (which I maintain anyway, but swig -> cython conversion would be needed). Not sure yet which solution I prefer. Cheers, r. > Cheers, > Ralf >> >> Cheers, >> r. >> >>> http://pysparse.sourceforge.net) and PyUBlasExt ( >>> https://pypi.python.org/pypi/PyUblasExt). Scikits.sparse is dead(-ish) >> and >>> doesn't wrap umfpack it looks like. PyUBlasExt has something that looks >>> like a wrapper but for C++ (?). So can we refer people to pysparse? >>> >>> Other question: do we call Pysparse instead of scikits.umfpack when >>> available? And if so, by default or not? Or do we rip out everything that >>> sits under useUmfpack=True? >>> >>> Cheers, >>> Ralf

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Linalg: function for diagonalize hermitian commuting matrices simultaneously
by Lars-Hendrik Frahm Oct. 7, 2014

Oct. 7, 2014

Dear SciPy Developers, I like to contribute a function for simultaneously diagonalize a set of hermitian commuting matrices. Especially for matrices with degenerate eigenvalues. Is there need for something like that in SciPy ? I think it could be helpful for someone else and I didn't find it in recent SciPy versions or on the mailing list. Hope to hear from you.

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Bootstrap confidence limits code
by Constantine Evans Oct. 4, 2014

Oct. 4, 2014

Hello everyone, A few years ago I implemented a scikit for bootstrap confidence limits (https://github.com/cgevans/scikits-bootstrap). I didn’t think much about it after that until recently, when I realized that some people are actually using it, and that there’s apparently been some talk about implementing this functionality in either scipy.stats or statsmodels (I should thank Randal Olson for discussing this and bringing it to my attention). As such I’ve rewritten most of the code, and written up some docstrings. The current code can do confidence intervals with basic percentile interval, bias-corrected accelerated, and approximate bootstrap confidence methods, and can also provide bootstrap and jackknife indexes. Most of it is implemented from the descriptions in Efron and Tibshirani’s Introduction to the Bootstrap, but the ABC code at the moment is a port from the modified-BSD-licensed bootstrap package for R (not the boot package) as I’m not entirely confident in my understanding of the method. And so, I have a few questions for everyone: * Is there any interest in including this sort of code in either scipy.stats or statsmodels? If so, where do people think would be the better place? The code is relatively small; at the moment it is less than 200 lines, with docstrings probably making up 100 of those lines. * Also, if so, what would need to be changed, added, and improved beyond what is mentioned in the Contributing to Scipy part of the reference guide? I’m never a fan of my own code, and imagine quite a bit would need to be fixed; I know tests will need to be added too. In addition, I have a few questions about what would be better practice for the API, and I haven’t really found a guide on best practices for Scipy: * When I started writing the code, I wrote a single function ci for confidence intervals, with a method argument to choose the method. This is easy for users, especially so that they don’t have to look through documentation to realize that BCA is the most generally useful method (at least from everything I’ve read) and that there really isn’t any reason to use many of the simpler methods. However, ABC takes different paramenters, and needs a statistic function that takes weights, which makes this single-function organization trickier. At the moment, I have a separate function for ABC. Would it be better to split up all the methods to their own functions? * ABC requires a statistic function that takes weights. I’ve noticed that things like np.average takes a weights= argument. Would it be better to require input of a stat(data,weights) function, or input of a stat(data,weights=) with weights as a named argument? The latter would be nice in terms of allowing the same function to be used for all methods, but would make it impossible to use a lambda for the function. Is there some other method of doing this entirely? * Are there any missing features that anyone thinks should be added? I apologize if much of this is answered elsewhere, I just haven’t found any of it; I also apologize if this is far too long-winded and confusing! Regards, Constantine Evans

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Re: [SciPy-Dev] SciPy-Dev Digest, Vol 128, Issue 5
by Matt Newville July 1, 2014

July 1, 2014

Hi William, Sturla, Josef, All Message: 2 > Date: Tue, 17 Jun 2014 09:32:44 -0400 > From: william ratcliff <william.ratcliff(a)gmail.com> > Subject: Re: [SciPy-Dev] Levenberg-Marquardt Implementation > To: SciPy Developers List <scipy-dev(a)scipy.org> > Message-ID: > < > CAFt3ydvxgC3KVofi0v4U8-pCmfgdo3bBhJfhrkne7ZLkWcTVfQ(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > This issue does seem to come up regularly. There is a BSD licensed > version: > https://github.com/newville/lmfit-py > > For mpfit.py, at one point, I got license permissions for scipy from the > original authors, but no one wanted to include it. Has that changed? I > haven't followed the issue since 2012. > > Best, > William > > > On Tue, Apr 1, 2014 at 7:57 AM, Johann cohen-tanugi < > johann.cohentanugi(a)gmail.com> wrote: > > > FWIW I came across a python LM implementation of the minpack LM routine > in > > https://code.google.com/p/astrolibpy/source/browse/mpfit/mpfit.py > > The license is GPLv3 though. > > Johann > > > > > > On Tue, Feb 25, 2014 at 7:25 PM, Pauli Virtanen <pav(a)iki.fi> wrote: > > > >> 25.02.2014 17:45, Benny Malengier kirjoitti: > >> > This is present already, See > >> > > >> > http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.… > >> > > >> > which wraps: http://www.math.utah.edu/software/minpack/minpack > >> > /lmder.html > >> > >> The current leastsq implementation does not support sparse Jacobians > >> or constraints. > >> > >> MINPACK does dense QR factorizations, and this approach doesn't work > >> well for problems where the number of variables is too big. This was one > >> of our the GSoC topic ideas [1] --- if you have suggestions on how to > >> improve these, please speak up. > >> > >> [1] https://github.com/scipy/scipy/wiki/GSoC-project-ideas > >> > >> -- > >> Pauli Virtanen > >> > >> > >> _______________________________________________ > >> SciPy-Dev mailing list > >> SciPy-Dev(a)scipy.org > >> http://mail.scipy.org/mailman/listinfo/scipy-dev > >> > > > > > > _______________________________________________ > > SciPy-Dev mailing list > > SciPy-Dev(a)scipy.org > > http://mail.scipy.org/mailman/listinfo/scipy-dev > > > > >

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SciPy 2014 BoF Panel
by Kyle Mandli June 29, 2014

June 29, 2014

Hello everyone, As one of the co-chairs in charge of organizing the birds-of-a-feather sesssions at the SciPy conference this year, I wanted to solicit through the SciPy list to start a discussion on what kind of BoF or set of BoF sessions might be worthwhile at SciPy this year. Since SciPy is particularly broad (especially if you include the sci-kits) I was thinking it might be worthwhile to cast a wide net and see what interest there was. I can help facilitate organization and ideas on what kind of format for each particular BoF that is proposed might have. Thanks! Kyle Manldi (and via proxy Matt McCormick)

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BoF at Scipy'14 about Numpy's future.
by Fernando Perez June 29, 2014

June 29, 2014

Hi folks, [ Please excuse the repost, this is just to try to get as many good ideas as possible in the agenda] I've just created a page on the numpy wiki: https://github.com/numpy/numpy/wiki/Numpy-BoF-at-Scipy-2014 I'd appreciate it if people could put down on that page the title of topics, plus a brief summary (with links to this or other relevant threads), they'd like to see discussed. BoFs can be very useful but they can also easily devolve into random digressions. I'll do my best to fulfill my role as discussion facilitator by pushing for an agenda for the discussion that can fit realistically in the time we have, and trying to keep us on track. It would help a lot if people put their ideas in advance, so we can sort/refine/prioritize a little bit. Hopefully this will lead to a more productive conversation. Cheers f -- 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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Use *trsm for scipy.linalg.solve_triangular?
by Sturla Molden June 28, 2014

June 28, 2014

scipy.linalg.solve_triangular uses *trtrs from LAPACK. I must take a great deal of the responsibility for this, since I might have suggested it... In retrospect I think this was a mistake. *trtrs can solve op(A) X = B where op(A) is A or A.T *trsm from BLAS is more general. It can solve op(A) X = alpha * B or X op(A) = alpha * B. The latter might e.g. be useful for constructing an efficient scipy.linalg.solve for C ordered arrays. *trtrs is just a shallow wrapper over *trsm. It optionally checks A for singularity and then calls *trsm. The optional singularity check just amounts to checking that none of the diagonal elements are zero. I think we should make scipy.linalg.blas expose *trsm and then use that method for solve_triangular. Later we can use *trsm to optimize scipy.linalg.solve for C ordered arrays (i.e. avoid generating transposed copies). Regards, Sturla

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Reordering methods for Sparse matrices
by simon tournier June 21, 2014

June 21, 2014

Hi, >> I have written two sparse matrix reordering functions in Cython and >> I >> am wondering if they would be worthy additions to the scipy.sparse >> module? >> These are currently being used in our QuTiP library (qutip.org). >> >> The first function finds the Reverse Cuthill-Mckee (RCM) ordering >> [1] >> of a symmetric sparse matrix, similar to the Matlab ‘symrcm' >> function. >> This is useful for reducing the bandwidth of the underlying matrix >> to >> help eliminate fill-in when using direct or iterative solvers. >> If the matrix is not symmetric, it looks at A+trans(A). Since the >> matrix is symmetric, this works for both CSR and CSC matrices. >> >> The second, is a maximal traversal algorithm based on a >> breadth-first-search >> method [2]. This returns a set of row permutations that makes >> the diagonal zero-free. Since the permutation is not symmetric, >> this works only on CSC matrices. Like RCM this is useful >> for preordering in both direct and iterative solvers. > Sparse matrix permutation algorithms would certainly be a welcome > addition. > > As the code is already written, integrating it in scipy.sparse is > probably fairly simple. If you have questions, Are these functions merged and now included in scipy.sparse or scipy.sparse.csgraph ? If yes, I have not seen the documentation and I have missed some points... so I'm sorry for the question. If no, is it planned ? Theses orderings would be a nice feature for the sparse linear algebra, in particular for both direct and iterative solvers. And a common version of RCM should be useful as explained in this thread: http://mail.scipy.org/pipermail/scipy-dev/2011-December/016786.html Moreover, to the question: "how to draw a good line between what belongs in scipy.sparse and what doesn't" [jakevdp] asked here: https://github.com/scipy/scipy/pull/119 and answered [rgommers] in the same thread by: -- should have potential uses on more than one project that depends on scipy -- should appear in an introductory algorithm book such as "Introduction to Algorithms", Cormen et al. from my experience of playing with sparse linear solvers, the useful algorithms are: Random, column, minimum degree, Dulmage-Mendelsohn and reverse Cuthill-McKee permutations. In other words, those included in matlab (http://www.mathworks.com/help/matlab/reordering-algorithms.html). I have maybe wrong and I am just not able to find them in scipy (or numpy). If not, is it planned to integrate them in scipy (or numpy) ? Are they already implemented in scikit-learn or sfepy or qutip or ... ? cheers, simon

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Use of `input` in ndimage
by Jaime Fernández del Río June 20, 2014

June 20, 2014

I submitted a PR ( https://github.com/scipy/scipy/pull/3745#issuecomment-46590839) to remove use of the built-in Python function name `input` from the ndimage module, where it is used as the argument name for the input data in almost every single function. A full list of functions involved has been pulled by Warren on the discussion of that PR. Warren wisely pointed out that changing the name of an argument changes the API, and should therefore not be done lightly, as it could break existing user code. One could argue. or at least wish, that, since `input` is typically the first argument of the functions involved, and is non-optional, calls using it as a keyword argument should be rare. But the potential harm is undeniable. It is ugly and a clear violation of PEP8, but although this kind of things are usually an accident waiting to happen, it has also proven harmless so far. So for now I have simply closed the PR, letting practicality beat purity. And so it will remain, unless the community thinks this special case is not special enough to break the rules. Inputs on how to handle the API change to minimize impact to users are also welcome. Thanks, Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Levenberg-Marquardt Implementation
by Nico Del Piano June 18, 2014

June 18, 2014

Hi all, I am Nicolas Del Piano, and currently studying the last year of computer science career. I was searching for a GSoC project that involves mathematical concepts, and I found one very interesting. I have experience on python programming and I studied calculus, so I have a background of the problem context. I am interested on the implementation and testing of Levenberg-Marquardt / trust region nonlinear least squares algorithm. Here are some useful links, that I have searched to having a reference about the problem: http://ananth.in/docs/lmtut.pdf (Introduction) www.cs.nyu.edu/~roweis/notes/lm.pdf (Optimization) http://scribblethink.org/Computer/Javanumeric/index.html (java implementation) I would be glad if there is an interested mentor to discuss the issues, talk about the problem and its possible implementations, and provide me some support and guide. Thanks! Regards.

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