Mailman 3 July 2013 - SciPy-Dev

Linalg: function for diagonalize hermitian commuting matrices simultaneously
by Lars-Hendrik Frahm 07 Oct '14

07 Oct '14

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 05 Oct '14

05 Oct '14

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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SLSQP Constrained Optimizer Status
by Rob Falck 25 Nov '13

25 Nov '13

I'm currently implementing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer, by Dieter Kraft, for use in Scipy. The Fortran code being wrapped with F2PY is here: http://www.netlib.org/toms/733 (its use within Scipy has been cleared) SLSQP provides for bounds on the independent variables, as well as equality and inequality constraint functions, which is a capability that doesn't exist in scipy.optimize. Currently the code works, although the constraint normals are being generated by approximation. I'm working on a way to pass these in. I think the most elegant way will be a single function that returns the matrix of constraint normals. For a demonstration of what the code can do, here is an optimization of f(x,y) = 2xy + 2x - x**2 - 2y**2 Example 14.2 in Chapra & Canale gives the maximum as x=2.0, y=1.0. The unbounded optimization tests find this solution. As expected, its faster when derivatives are provided rather than approximated. Unbounded optimization. Derivatives approximated. Elapsed time: 1.45792961121 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] Unbounded optimization. Derivatives provided. Elapsed time: 1.03211402893 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] The following example uses an equality constraint to find the optimal when x=y. Bound optimization. Derivatives approximated. Elapsed time: 1.384973526 ms Results [[0.99999996845920858, 0.99999996845920858], -0.99999999999999889, 4, 0, 'Optimization terminated successfully.'] I've tried to conform to the syntax used by the other optimizers in scipy.optimize. The function definition and doc string are below. If anyone is interested in testing it out, let me know. def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_cons=None,args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): """ Minimize a function using Sequential Least SQuares Programming Description: Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft. Inputs: func - Objective function (in the form func(x, *args)) x0 - Initial guess for the independent variable(s). eqcons - A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem f_eqcons - A function of the form f_eqcons(x, *args) that returns an array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored. ieqcons - A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully optimized problem f_ieqcons - A function of the form f_ieqcons(x0, *args) that returns an array in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored. bounds - A list of tuples specifying the lower and upper bound for each independent variable [ (xl0, xu0), (xl1, xu1), ...] fprime - A function that evaluates the partial derivatives of func fprime_cons - A function of the form f(x, *args) that returns the m by n array of constraint normals. If not provided, the normals will be approximated. Equality constraint normals precede inequality constraint normals. args - A sequence of additional arguments passed to func and fprime iter - The maximum number of iterations (int) acc - Requested accuracy (float) iprint - The verbosity of fmin_slsqp. iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary full_output - If 0, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon - The step size for finite-difference derivative estimates. Outputs: ( x, { fx, gnorm, its, imode, smode }) x - The final minimizer of func. fx - The final value of the objective function. its - The number of iterations. imode - The exit mode from the optimizer, as an integer. smode - A string describing the exit mode from the optimizer. Exit modes are defined as follows: -1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : Number of equality constraints larger than number of independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded -- - Rob Falck

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Vector Strength function
by Todd 22 Nov '13

22 Nov '13

I am interested in implementing a function for scipy. The function is called "vector strength". It is basically a measure of how reliably a set of events occur at a particular phase. It was originally developed for neuroscience research, to determine how well a set of neural events sync up with a periodic stimulus like a sound waveform. However, it is useful for determining how periodic a supposedly periodic set of events really are, for example: 1. Determining whether crime is really more common during a full moon and by how much 2. Determining how concentrated visitors to a coffee shop are during rush hour 3. Determining exactly how concentrated hurricanes are during hurricane season My thinking is that this could be implemented in stages: First would be a Numpy function that would add a set of vectors in polar coordinates. Given a number of magnitude/angle pairs it would provide a summed magnitude/angle pair. This would probably be combined with a cartesian<->polar conversion functions. Making use of this function would be a scipy function that would actually implement the vector strength calculation. This is done by treating each event as a unit vector with a phase, then taking the average of the vectors. If all events have the same phase, the result will have an amplitude of 1. If they all have a different phases, the result will have an amplitude of 0. It may even be worth having a dedicated polar dtype, although that may be too much. What does everyone think of this proposal?

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labeling issues as easy-fix
by Ralf Gommers 04 Aug '13

04 Aug '13

Hi, At https://github.com/scipy/scipy/issues I've just added an easy-fix label, to guide newcomers to issues that don't require too much scipy background or hard debugging. I had planned to do that for a while, and the upcoming EuroSciPy sprint is a good motivation to get it done. Any help would be much appreciated. Maybe module maintainers could go through their module? Adding labels requires commit rights, but anyone without those who wants to help out can either just leave a comment on the ticket or send me the issue ID. Ralf

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ipython notebook integration
by Surya Kasturi 30 Jul '13

30 Jul '13

Hi All, Regarding ipython notebook integration in scipy central 1. should we upload ipynb files or provide an editor (or Text Area)? which one would be nice? Thanks Surya

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Version 0.12.1? And some newcomer questions
by Juan Luis Cano 24 Jul '13

24 Jul '13

I would like to know if there are any plans to release a bugfix version of SciPy 0.12. I've been working with scipy.signal lately and I've stumbled upon some of the bugs that have been fixed on master in April and May since 0.12.0. But in a more general sense, I don't know if there is a roadmap to backport some bugfixes to maintenance/0.12.x (or if there is help needed?) because there is no hint in the issues milestones. I've just submitted my first code contribution to SciPy and as a newcomer (also willing to make some more) I was looking for this kind of information: workflow, guidelines, roadmap, future plans... And I have to say that it is scarce. For example, I was looking for the best way to work with SciPy source code and I ended up doing this: 1. Created a virtualenv 2. Cloned scipy git repo 3. Added `import setuptools` in setup.py 4. Sourced the virtualenv and run `python setup.py develop` this was the easiest way I found to run the tests while coding and not having to reinstall the whole thing every time I changed a file or try some tricky imports from the source directory. But I am sure there is a much better way (and I wonder what other SciPy devs do?). Thanks in advance, best regards, Juan Luis Cano

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Proposal: calling signal.lti object returns transfer function value
by Juan Luis Cano 24 Jul '13

24 Jul '13

Hello all, I've been trying the scipy.signal module lately for some easy things and I had an idea, but I thought it would be better to bring it up in the mailing list before filing a PR on GitHub. My proposal is to implement the __call__ method on signal.lti objects so it returns the value on the transfer function. I took the idea from the `bode` function. This line: y = numpy.polyval(sys.num, jw) / numpy.polyval(sys.den, jw) (https://github.com/scipy/scipy/blob/v0.12.0/scipy/signal/ltisys.py#L940) could be moved to signal.lti.__call__, and then in `bode` write y = sys(jw) The detail is trivial (so is the implementation) but I think it would be a useful thing. What do others think? Shall I open a pull request?

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Mixed Python + C debugging support in Visual Studio
by Shahrokh Mortazavi 19 Jul '13

19 Jul '13

Hi folks, 1st time poster - apologies if I'm breaking any protocols... We were told that this would be a good alias to announce this on: a few Python & OSS enthusiasts and Microsoft have created a plug-in for Visual Studio that enables Python <-> C/C++ debugging. You may find this useful for debugging your extension modules. A quick video overview of the mixed mode debugging feature: http://www.youtube.com/watch?v=wvJaKQ94lBY&hd=1 (HD) Documentation: https://pytools.codeplex.com/wikipage?title=Mixed-mode%20debugging Python Tools for Visual Studio is free (and OSS): http://pytools.codeplex.com Cheers, s

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using sqlite for storing submissions
by Surya Kasturi 16 Jul '13

16 Jul '13

Hi all, [idea] Right now we are using Hg for storing submissions inside static root..Is not totally good on a cloud platform with limited resources..its not fast too (takes lot of server load) I rather would like to use SQLite separately for storing submissions (I agree that using Postgre SQL for it is expensive on cloud).. What do you guys say? we dont have to provide all revisions as a Hg repo when downloading.. people can employ some tools to compare if they want... looking for your comments cheers surya

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