Mailman 3 June 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 04 Oct '14

04 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 24 Nov '13

24 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 21 Nov '13

21 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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Compiling scipy on AIX 6.1
by Bruno Luciano Amadio Caires 05 Jul '13

05 Jul '13

Hi, I am trying install scipy on AIX 6.1 power7, but I'm getting errors. I am using XL compiler, and scipy-0.12.0/scipy-0.8.0 I have numpy-1.7.0 working that was compiling here with XL. Has someone a HOWTO? C compiler: xlc_r -ma -I/opt/freeware/include -DAIX_GENUINE_CPLUSCPLUS -Wl,-brtl -qmaxmem=16384 -DSYSV -D_AIX -D_AIX32 -D_AIX41 -D_AIX43 -D_AIX51 -D_ALL_SOURCE -DFUNCPROTO=15 -O -I/opt/freeware/include -DNDEBUG -O compile options: '-DNO_ATLAS_INFO=1 -I/opt/freeware/lib/python2.6/site-packages/numpy/core/include -I/opt/freeware/include/python2.6 -c' xlc_r: scipy/integrate/_odepackmodule.c /opt/freeware/lib/python2.6/config/ld_so_aix /usr/bin/xlf95 -bI:/opt/freeware/lib/python2.6/config/python.exp -bshared -F/tmp/tmpcqh5Ki/dcxixc_xlf.cfg build/temp.aix-6.1-2.6/scipy/integrate/_odepackmodule.o -L/home/bruno/local/lib -Lbuild/temp.aix-6.1-2.6 -lodepack -llinpack_lite -lmach -lblas -o build/lib.aix-6.1-2.6/scipy/integrate/_odepack.so ld: 0711-224 WARNING: Duplicate symbol: guesses ld: 0711-345 Use the -bloadmap or -bnoquiet option to obtain more information. ld: 0711-317 ERROR: Undefined symbol: .Py_InitModule4_64 ld: 0711-317 ERROR: Undefined symbol: .daxpy_ ld: 0711-317 ERROR: Undefined symbol: .ddot_ ld: 0711-317 ERROR: Undefined symbol: .idamax_ ld: 0711-317 ERROR: Undefined symbol: .dscal_ ld: 0711-224 WARNING: Duplicate symbol: guesses ld: 0711-345 Use the -bloadmap or -bnoquiet option to obtain more information. ld: 0711-317 ERROR: Undefined symbol: .Py_InitModule4_64 ld: 0711-317 ERROR: Undefined symbol: .daxpy_ ld: 0711-317 ERROR: Undefined symbol: .ddot_ ld: 0711-317 ERROR: Undefined symbol: .idamax_ ld: 0711-317 ERROR: Undefined symbol: .dscal_ error: Command "/opt/freeware/lib/python2.6/config/ld_so_aix /usr/bin/xlf95 -bI:/opt/freeware/lib/python2.6/config/python.exp -bshared -F/tmp/tmpcqh5Ki/dcxixc_xlf.cfg build/temp.aix-6.1-2.6/scipy/integrate/_odepackmodule.o -L/home/bruno/local/lib -Lbuild/temp.aix-6.1-2.6 -lodepack -llinpack_lite -lmach -lblas -o build/lib.aix-6.1-2.6/scipy/integrate/_odepack.so" failed with exit status 8 att -- Bruno L. Amadio Caires Informática - FT Universidade Estadual de Campinas Fone:(19)2113-3470

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Matrices format
by Grégoire Pichon 30 Jun '13

30 Jun '13

Hello, I am working on a software using different matrices formats and i use scipy in order to compare my results. I succeed in using the matrix market format (.mtx) but not the matrix market format "bis" (.ijv). The first one is for complex matrices although the second one is for real matrices. I used scipy.io.mmread() in order to read the matrix but this function cannot be used to read real matrices. For that, i had to modify the initial function (in fact, adding 1 to some values). Do you know any way to read those matrices in Python? Should i share my extensions of scipy.io? Yours sincelery Grégoire Pichon, french student in computer science

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bool wrapper in sparse
by Blake Griffith 30 Jun '13

30 Jun '13

Hello scipy, I'm working on the bool_ops.h file in sparse/sparsetools which wraps numpy bool type for the c++ routines. Currently it just does typedef npy_int8 npy_bool_wrapper; So the underlying int can roll over to zero, giving a False when we should have a True. I'm trying to make a class which inherits from npy_int8, then override the addition operators to behave like normal boolean arithmetic (1 + 1 = 1). But I'm having a few problems. When I try to do something simple like: class npy_bool_wrapper : public npy_int8 {}; I get an error for that line: error: expected class-name before '{' token When I try to implement it like a class template, like in complex_ops.h template <class c_type, class npy_type> class bool_wrapper : public npy_type {}; typedef bool_wrapper<int, npy_int8> npy_bool_wrapper; I get: error: base type 'signed char' fails to be a struct or class type

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Is scipy central working?
by Surya Kasturi 29 Jun '13

29 Jun '13

Hi all, I am observing from very long time a 500 server error on scipy central.. Is it working? Thanks Surya

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regarding reputation system and commenting system of spc
by Surya Kasturi 27 Jun '13

27 Jun '13

Hi all, its been quite some time I have appeared on the list.. Regarding the development progress of scipy central, the follow are the list 1. Rss Feed syndication 2. Design (cleaning up) 3. Commenting system [2] PR isn't merged yet [3] under construction --some minor changes still needed. Currently it supports Restructured Text, Gravatar The other one I will start this week is "Reputation system". For this I request you to provide some input regarding the below points or any if you would like to add further 1. Should we have down-vote? (we are not Q&A site) 2. How to rate Users -- Any scale for providing points 3. Should we have points for comments? and should we have downvote ? Please do add any other important points I missed There has been some discussion earlier on scipy-central logo! Currently our side doesn't take too much space to fit in the scipy-globe (logo) --tried it but the image was not quite looking attractive at small size!! on the header http://surya-gsoc2013.blogspot.de/2013/06/cleaning-new-design.html you can find the preview pages of the design here. probably you can suggest some place to put it in.. or we don't have to put it.. Also, please let me know how the new design is looking --hope you like it. https://github.com/ksurya/scipycentral/tree/design is where you can find the code. The PR isn't merged and there is some slight chance that preview pages don't look the same at the end (but its very slight chance.. any changes might not effect the way it looks) Thanks Surya

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Scipy ecosystem flyer
by Nicolas Rougier 25 Jun '13

25 Jun '13

Hi all, It might a bit off-topic but I made a kind of poster/flyer for the scipy ecosystem. I do not know how useful it might be, but just in case, here is the result: https://github.com/rougier/scipy-ecosystem/blob/master/scipy-ecosystem.pdf Sources are at the same place ( you'll need the (free) Source Sans/Code Pro font). Feel free to use and modify it. Nicolas

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