Mailman 3 August 2013 - SciPy-Dev

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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SLSQP Constrained Optimizer Status
by Rob Falck Nov. 24, 2013

Nov. 24, 2013

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 Nov. 21, 2013

Nov. 21, 2013

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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Akima Interpolation
by Andreas Hilboll Sept. 10, 2013

Sept. 10, 2013

I'd like to have Akima interpolation in scipy.interpolate, as I couldn't find any. 1.) Did I miss something and there actually *is* an Akima interpolation in scipy? 2.) Is there interest in having Akima interpolation in Scipy? 3.) Does anyone have a good recommendation which implementation I should consider for pulling into Scipy? -- Andreas.

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ANN: Scipy 0.13.0 beta 1 release
by Ralf Gommers Sept. 2, 2013

Sept. 2, 2013

Hi all, I'm happy to announce the availability of the first beta release of Scipy 0.13.0. Please try this beta and report any issues on the scipy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/projects/scipy/files/scipy/0.13.0b1/. Windows and OS X installers will follow later (we have a minor infrastructure issue to solve, and I'm at EuroScipy now). Cheers, Ralf

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Purpose of commit 359cf0 ?
by David Cournapeau Aug. 31, 2013

Aug. 31, 2013

Hi there, I see that this fix broke visual studio build in a non trivial manner (a few cephes functions are declared in VS math.h, with incompatible linkage). I don't understand the fix: which platform did it fix, and why not having isfinite matter since we're using npy_ macro in struve.c ? thanks, David

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Integration module for N-D array-valued functions
by Saullo Castro Aug. 29, 2013

Aug. 29, 2013

I would like to know if there is an interest to add into SciPy some integrators for vector valued functions that I've been working on: - trapzv - trapz2dv - simpsv - simps2dv - polyv (1-D vector-valued functions integration using many nth order polynoms) Only polyv is for 1-D vector-valued functions, while the others are for any N-D array-valued function. If there is an interest, I have a suggestion to include all these function into a integratev.pyx file (since they are programmed in Cython), inside scipy/integrate. What do you think? Greetings, Saullo

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Many Problems Building w/ MKL 11, ifort, icc; scipy.test() Fails w/ Undefined Symbols
by Will Buckner Aug. 29, 2013

Aug. 29, 2013

I've tried this about 6 different ways now, and all successful builds result in the same error during scipy.test() (note that NumPy builds fine with same site.cfg): ====================================================================== ERROR: Failure: ImportError (/opt/intel/composer_xe_2013.5.192/mkl/lib/intel64/libmkl_intel_thread.so: undefined symbol: mkl_blas_dgemm_blk_info_bdz) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.7/dist-packages/nose/loader.py", line 413, in loadTestsFromName addr.filename, addr.module) File "/usr/local/lib/python2.7/dist-packages/nose/importer.py", line 47, in importFromPath return self.importFromDir(dir_path, fqname) File "/usr/local/lib/python2.7/dist-packages/nose/importer.py", line 94, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File "/usr/local/lib/python2.7/dist-packages/scipy/stats/__init__.py", line 320, in <module> from .stats import * File "/usr/local/lib/python2.7/dist-packages/scipy/stats/stats.py", line 242, in <module> import scipy.linalg as linalg File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/__init__.py", line 147, in <module> from .misc import * File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/misc.py", line 5, in <module> from . import blas File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/blas.py", line 113, in <module> from scipy.linalg import _fblas ImportError: /opt/intel/composer_xe_2013.5.192/mkl/lib/intel64/libmkl_intel_thread.so: undefined symbol: mkl_blas_dgemm_blk_info_bdz Here's some info on my environment: Intel Composer XE 2013.5.192, ifort+icc icc flags: -fPIC -DMKL_ILP64 -m64 -fp-model', 'strict', '-fomit-frame-pointer', '-openmp', '-xhost', '-parallel' ifort flags: -fPIC -i8 -xhost -openmp -fp-model strict OS: Ubuntu 12.04 24 Cores Intel(R) Xeon(R) CPU E5-2650 0 @ 2.00GHz Intel LibMKL 11.0.5 site.cfg: [mkl] library_dirs = /opt/intel/mkl/lib/intel64:/opt/intel/lib/intel64 include_dirs = /opt/intel/mkl/include:/opt/intel/include lapack_libs = mkl_lapack95_lp64 mkl_libs = mkl_def, mkl_intel_lp64, mkl_intel_thread, mkl_core, mkl_mc [fftw] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include libraries = fftw3 [amd] amd_libs = amd library_dirs = /usr/lib include_dirs = /usr/include/suitesparse/ [umfpack] umfpack_libs = umfpack library_dirs = /usr/lib include_dirs = /usr/include/suitesparse/ Built with: python setup.py clean python setup.py -v config --compiler=intelem --fcompiler=intelem build_clib --compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install FOUND: libraries = ['mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'pthread'] library_dirs = ['/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/mkl/include', '/opt/intel/include'] FOUND: libraries = ['mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'pthread'] library_dirs = ['/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/mkl/include', '/opt/intel/include'] FOUND: libraries = ['mkl_lapack95_lp64', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'pthread'] library_dirs = ['/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/mkl/include', '/opt/intel/include'] ( library_dirs = /usr/local/lib:/usr/lib:/usr/lib/x86_64-linux-gnu ) FOUND: libraries = ['mkl_lapack95_lp64', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'pthread'] library_dirs = ['/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/mkl/include', '/opt/intel/include'] **The problem is here:** ====================================================================== ERROR: Failure: ImportError (/opt/intel/composer_xe_2013.5.192/mkl/lib/intel64/libmkl_intel_thread.so: undefined symbol: mkl_blas_dgemm_blk_info_bdz) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.7/dist-packages/nose/loader.py", line 413, in loadTestsFromName addr.filename, addr.module) File "/usr/local/lib/python2.7/dist-packages/nose/importer.py", line 47, in importFromPath return self.importFromDir(dir_path, fqname) File "/usr/local/lib/python2.7/dist-packages/nose/importer.py", line 94, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File "/usr/local/lib/python2.7/dist-packages/scipy/stats/__init__.py", line 320, in <module> from .stats import * File "/usr/local/lib/python2.7/dist-packages/scipy/stats/stats.py", line 242, in <module> import scipy.linalg as linalg File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/__init__.py", line 147, in <module> from .misc import * File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/misc.py", line 5, in <module> from . import blas File "/usr/local/lib/python2.7/dist-packages/scipy/linalg/blas.py", line 113, in <module> from scipy.linalg import _fblas ImportError: /opt/intel/composer_xe_2013.5.192/mkl/lib/intel64/libmkl_intel_thread.so: undefined symbol: mkl_blas_dgemm_blk_info_bdz Now, where is mkl_blas_dgemm_blk_info_bdz defined? daisy@speedballcompute01:/opt/intel/mkl/lib/intel64$ nm libmkl_core.so | grep mkl_blas_dgemm_blk_info_bdz 00000000000c59f0 T mkl_blas_dgemm_blk_info_bdz daisy@speedballcompute01:/opt/intel/mkl/lib/intel64$ Right there ^. I included mkl_core in site.cfg and it seems to be linking it, so I'm not sure why this symbol is still undefined. Note that it *is* undefined in libmkl_intel_thread.so: daisy@speedballcompute01:/opt/intel/mkl/lib/intel64$ nm libmkl_intel_thread.so | grep mkl_blas_dgemm_blk_info_bdz U mkl_blas_dgemm_blk_info_bdz daisy@speedballcompute01:/opt/intel/mkl/lib/intel64$ I also tried the instructions provided by intel at http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl, using just mkl_rt. This had the same result, and seems incorrect, as it's not providing any lapack libraries (I'm assuming it would just fall back to "no lapack" and it seems to). This is highly confusing, as all of the MKL numpy/scipy instructions seem to be for older MKLs, before mkl_rt. Could anyone help me debug this? I'm hopelessly frustrated with it at this point and willing to do whatever to help you guys debug. Thanks! What additional info is needed?

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Triangulation on sphere surface with qhull(-wrapper)
by Andreas Hilboll Aug. 27, 2013

Aug. 27, 2013

Hi, I was wondering if qhull supports Delaunay triangulation of data on the unit sphere? If so, I'd like to implement an appropriate wrapper to have an alternative to the FITPACK-based interpolation on spheres. Otherwise, there's STRIPACK as an alternative, and it would probably be easy to wrap it (if license is compatible; so far I couldn't find any license info on STRIPACK). But I have a feeling qhull should be able to do this as well; I just couldn't figure out how ... -- Andreas.

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