Mailman 3 March 2008 - SciPy-Dev

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

4 16

Question about Kaiser Implementation in firwin
by Buehler, Eric (AGRE) 04 Oct '08

04 Oct '08

Hello, I have been using the signal.firwin FIR design tool and I have a question about the implementation. Scipy-0.4.8 Numpy-0.9.6 I am generating a kaiser window with the following parameters: N = 128 Cutoff = ~4e7 width = .3 All of the special kaiser generation parameters in the function follow Oppenheim and Schafer well. Even the sinc function for linear phase is follows exactly. However, the last line of the firwin function is causing me some heartburn. filter_design.py 1538 win = get_window(window,N,fftbins=1) 1539 alpha = N//2 1540 m = numpy.arange(0,N) 1541 h = win*special.sinc(cutoff*(m-alpha)) 1542 return h / sum(h) Line 1542 of filter_design.py, "return h / sum(h)", normalizes the function where it doesn't seem necessary, at least in the kaiser window case. Without the normalization, the kaiser window already returns a value of 1 at the zero frequency point. This normalization scales all of the data, making the window difficult to use in the frequency domain. Can someone point me to the rationale for this line? Looking at the code, this seems to be a pretty recent change (within the last year/year and a half). Thanks, Eric Buehler eric <dot> buehler <at> smiths-aerospace <dot> com ****************************************** The information contained in, or attached to, this e-mail, may contain confidential information and is intended solely for the use of the individual or entity to whom they are addressed and may be subject to legal privilege. If you have received this e-mail in error you should notify the sender immediately by reply e-mail, delete the message from your system and notify your system manager. Please do not copy it for any purpose, or disclose its contents to any other person. The views or opinions presented in this e-mail are solely those of the author and do not necessarily represent those of the company. The recipient should check this e-mail and any attachments for the presence of viruses. The company accepts no liability for any damage caused, directly or indirectly, by any virus transmitted in this email. ******************************************

3 2

Moving numexpr to its own project
by David M. Cooke 15 Apr '08

15 Apr '08

Hi, With the pending closing of the scipy.sandbox (anybody want to cough up more details on that, btw? When? How?), I want to move numexpr to its own project, likely hosted on Google Code. It's not something I think is "sciencey" enough for a scikit (plus, I don't like sharing a bug database with other, separate, projects), and it's more like weave (but simpler). Tim H., Francesc, Ivan? You're the main contributors; does this work for you? -- |>|\/|< /------------------------------------------------------------------\ |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ |cookedm(a)physics.mcmaster.ca

6 12

Google Summer of Code and scipy.learn (another trying)
by Anton Slesarev 07 Apr '08

07 Apr '08

Hi. I'm going to describe what problems I see in current version of scikits.learn. After that I'll write what I want to improve during Google Summer of Code. In my last letter I tried to numerate some limitations in other open-source frameworks such as PyML and Orange. Let's start about Scikits.learn. First of all is a lack of documentation. I can find nothing beside David Cournapeau proposal on google Summer of Code. Nothing in wiki and nothing in maillist. There are few examples in svm, of course. But it is very hard use only examples. I can't find parser of different data formats. Only for datasets. As I understand datasets don't support sparse data format. There is no common structure in ML package. It has scattered modules such as svm, em, ann, but no main idea. If I mistake in understanding current state of affair you can correct me. Well, now about what I want to change. I am going to make learn package appropriate for text classification. Also I want to copy most of PyML (pyml.sourceforge.net/) functionality. First of all we need sparse data format. I want to write parsers for a number of common data formats. We need some preprocessing utilities, such as normalization, feature selection algorithms. This part should be common for all of machine learning package. Also package is need a number of classifiers. There are at least 2 state-of-art approaches in text classification and categorization:svm and Bayesian logistic regression. Svm has already been implemented in Scikits. There are a lot of implementations of logistic regression. I am going to integrate one of them (http://www.stat.rutgers.edu/~madigan/BBR/) into scikits. It is need interpretation module, which consists of processing results(different metrics of quality), visualization, feature comparison. There are common text collection (for instance http://trec.nist.gov/data/reuters/reuters.html). I'll try to make work with them absolutely simple. After all, it is very important to write(or generate) reference documentation and tutorial. OK, that's all. I expect to hear your opinions. Particularly I want to see answer of David Cournapeau, who is ,as I understand, maintainer of the learn package. -- Anton Slesarev

9 23

Status of sandbox.spline and my other code.
by John Travers 07 Apr '08

07 Apr '08

Hi all, This email is intended to clarify the status of various bits of code I've committed/not committed so I don't leave too much of a mess after me. I'm about to start writing my PhD thesis, so I'll be too busy to work on scipy until September. 1. sandbox.spline stuff The module here was supposed to be a tidy up of scipy.interpolate. All of the dierkx functionality has been moved to f2py wrappers, which I think makes maintenance easier. However, it does not add any new functionality, and in retrospect appears to have been a waste of my time. So I'll leave to you to decide if you want to integrate it into scipy.interpolate or not; though it was originaly planned to be a seperate module to clear up the ambiguity between interpolation and smoothing spline functionality. However I have added 20 unit tests to the code (most of which simply check the wrapper against the pure fortran output, but still useful I think) which could easily be moved over to the current scipy.interpolate module. 2. Radial basis function module in sandbox (rbf) This code has recently had attention from Robert Hetland and is quite improved. I'm not sure if I will ever go into scipy though?? If not, I will put it into a scikit at some point. Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted. 3. Boundary value ODE solver (BVP_SOLVER) I have fairly functional code interfacing to some very good BVP solver code (http://cs.smu.ca/~muir/BVP_SOLVER_Webpage.shtml). The license is good for scipy (as confirmed by the authors), but the code if fortran 95 - therefore cannot apparently go into scipy. I'll make a scikit when I get time (probably September). 4. An interface to the pikaia genetic algorithm routine I have a python interface to pikaia fortran code for genetic algorithm optimisation of real valued functions (http://www.hao.ucar.edu/Public/models/pikaia/pikaia.html). The fortran code itself is robust and highly optimised for numerical work, though it is nowhere near as general as the sandbox.ga module. I think it is useful to go in scipy.optimization alongside the annealing module. If people disagree I'll make it into a scikit. I hope this clears a few things up. Best regards, John Travers

4 4

Import problem in scipy.interpolate.lagrange
by Rhys Ulerich 26 Mar '08

26 Mar '08

Hi all, scipy/interpolate/interpolate.py is missing an import for numpy.poly1d. Python 2.5 (r25:51908, Nov 6 2007, 15:55:44) [GCC 4.1.2 20070925 (Red Hat 4.1.2-27)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import scipy >>> import scipy.interpolate >>> scipy.interpolate.lagrange([0.0,1.0],[1.0,0.0]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64/python2.5/site-packages/scipy/interpolate/interpolate.py", line 30, in lagrange p = poly1d(0.0) NameError: global name 'poly1d' is not defined The problem still appears in svn trunk: http://svn.scipy.org/svn/scipy/trunk/scipy/interpolate/interpolate.py It can (I think) be fixed by adding 'poly1d' into the 'from numpy import...' list. It looks like the problem has existed since changeset 3068. How can I get a SciPy Trac login so I can file this ticket? - Rhys

2 1

ANN: SfePy 00.41.03
by Robert Cimrman 26 Mar '08

26 Mar '08

Greetings, I'm pleased to announce the release 00.41.03 of SfePy (formerly SFE) SfePy is a finite element analysis software in Python, based primarily on Numpy and SciPy. Mailing lists, issue tracking, mercurial repository: http://code.google.com/p/sfepy/ Home page: http://sfepy.kme.zcu.cz Major improvements: - works on 64 bits - support for various mesh formats - Schroedinger equation solver - see http://code.google.com/p/sfepy/wiki/Examples - new solvers: - generic time-dependent problem solver - pysparse, symeig, scipy-based eigenproblem solvers - scipy-based iterative solvers - many new terms For information on this release, see http://sfepy.googlecode.com/svn/web/releases/004103_RELEASE_NOTES.txt Best regards, r.

1 0

building SciPy for CentOS 5
by Joseph VanAndel 24 Mar '08

24 Mar '08

I'm trying to build SciPy for Python 2.5.1 on CentOS 5, and am having quite a few problems. First, I uninstalled any existing blas and lapack RPMs, since the existing RPMs are pretty old. I've built and installed refblas3, lapack3, and atlas, using the source RPMS from http://download.opensuse.org/repositories/home:/ashigabou/Fedora_7/src/ When I install the RPMS, I use the '--relocate' option to install the files in /usr/local, rather than /usr. (Our system admins prefer we not install 'unoffical' RPMS into /usr) I've installed numpy, using easy_install. I've edited site-packages/numpy/distutils/site.cfg to match where the blas and lapack libraries got installed. When I try to build scipy, it complains about various missing packages, but still builds, and installs files into <PYTHON>/site-packages. (I've attached the log file). However, scipy doesn't work: import scipy Traceback (most recent call last): File "<stdin>", line 1, in <module> File "scipy/__init__.py", line 54, in <module> from __config__ import show as show_config ImportError: No module named __config__ I'm an experienced programmer, and I've already invested 10+ hours in trying to install scipy. I'm a bit frustrated with the documentation and the build/install procedures - it just shouldn't be this hard to install scipy! I'd be happy to contribute detailed documentation (and RPMS) for build with CentOS 5, once I get a working system. mkl_info: libraries mkl,vml,guide not found in /usr/local/lib64/atlas/sse2 libraries mkl,vml,guide not found in /usr/local/lib64 libraries mkl,vml,guide not found in /usr/lib64 libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE fftw3_info: libraries fftw3 not found in /usr/local/lib64/atlas/sse2 libraries fftw3 not found in /usr/local/lib64 FOUND: libraries = ['fftw3'] library_dirs = ['/usr/lib64'] define_macros = [('SCIPY_FFTW3_H', None)] include_dirs = ['/usr/include'] djbfft_info: NOT AVAILABLE blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /usr/local/lib64/atlas/sse2 libraries mkl,vml,guide not found in /usr/local/lib64 libraries mkl,vml,guide not found in /usr/lib64 libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64/atlas/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64/atlas libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64 libraries ptf77blas,ptcblas,atlas not found in /usr/lib64/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/lib64 libraries ptf77blas,ptcblas,atlas not found in /usr/lib/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/lib NOT AVAILABLE atlas_blas_info: libraries f77blas,cblas,atlas not found in /usr/local/lib64/atlas/sse2 libraries f77blas,cblas,atlas not found in /usr/local/lib64/atlas libraries f77blas,cblas,atlas not found in /usr/local/lib64 libraries f77blas,cblas,atlas not found in /usr/lib64/sse2 libraries f77blas,cblas,atlas not found in /usr/lib64 libraries f77blas,cblas,atlas not found in /usr/lib/sse2 libraries f77blas,cblas,atlas not found in /usr/lib NOT AVAILABLE /net/opt_lnx/local_el5_64/lib/python2.5/site-packages/numpy/distutils/system_info.py:1314: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) blas_info: libraries blas not found in /usr/local/lib64/atlas/sse2 FOUND: libraries = ['blas'] library_dirs = ['/usr/local/lib64'] language = f77 FOUND: libraries = ['blas'] library_dirs = ['/usr/local/lib64'] define_macros = [('NO_ATLAS_INFO', 1)] language = f77 lapack_opt_info: lapack_mkl_info: NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64/atlas/sse2 libraries lapack_atlas not found in /usr/local/lib64/atlas/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64/atlas libraries lapack_atlas not found in /usr/local/lib64/atlas libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64 libraries lapack_atlas not found in /usr/local/lib64 libraries ptf77blas,ptcblas,atlas not found in /usr/lib64/sse2 libraries lapack_atlas not found in /usr/lib64/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/lib64 libraries lapack_atlas not found in /usr/lib64 libraries ptf77blas,ptcblas,atlas not found in /usr/lib/sse2 libraries lapack_atlas not found in /usr/lib/sse2 libraries ptf77blas,ptcblas,atlas not found in /usr/lib libraries lapack_atlas not found in /usr/lib numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: libraries f77blas,cblas,atlas not found in /usr/local/lib64/atlas/sse2 libraries lapack_atlas not found in /usr/local/lib64/atlas/sse2 libraries f77blas,cblas,atlas not found in /usr/local/lib64/atlas libraries lapack_atlas not found in /usr/local/lib64/atlas libraries f77blas,cblas,atlas not found in /usr/local/lib64 libraries lapack_atlas not found in /usr/local/lib64 libraries f77blas,cblas,atlas not found in /usr/lib64/sse2 libraries lapack_atlas not found in /usr/lib64/sse2 libraries f77blas,cblas,atlas not found in /usr/lib64 libraries lapack_atlas not found in /usr/lib64 libraries f77blas,cblas,atlas not found in /usr/lib/sse2 libraries lapack_atlas not found in /usr/lib/sse2 libraries f77blas,cblas,atlas not found in /usr/lib libraries lapack_atlas not found in /usr/lib numpy.distutils.system_info.atlas_info NOT AVAILABLE /net/opt_lnx/local_el5_64/lib/python2.5/site-packages/numpy/distutils/system_info.py:1221: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) lapack_info: libraries lapack not found in /usr/local/lib64/atlas/sse2 FOUND: libraries = ['lapack'] library_dirs = ['/usr/local/lib64'] language = f77 FOUND: libraries = ['lapack', 'blas'] library_dirs = ['/usr/local/lib64'] define_macros = [('NO_ATLAS_INFO', 1)] language = f77 non-existing path in 'scipy/linsolve': 'tests' umfpack_info: libraries umfpack not found in /usr/local/lib64/atlas/sse2 libraries umfpack not found in /usr/local/lib64 libraries umfpack not found in /usr/lib64 libraries umfpack not found in /usr/lib /net/opt_lnx/local_el5_64/lib/python2.5/site-packages/numpy/distutils/system_info.py:403: UserWarning: UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/) not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [umfpack]) or by setting the UMFPACK environment variable. warnings.warn(self.notfounderror.__doc__) NOT AVAILABLE running install running build running config_cc unifing config_cc, config, build_clib, build_ext, build commands --compiler options running config_fc unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options running build_src building py_modules sources building library "dfftpack" sources building library "linpack_lite" sources building library "mach" sources building library "quadpack" sources building library "odepack" sources building library "fitpack" sources building library "superlu_src" sources building library "odrpack" sources building library "minpack" sources building library "rootfind" sources building library "c_misc" sources building library "cephes" sources building library "mach" sources building library "toms" sources building library "amos" sources building library "cdf" sources building library "specfun" sources building library "statlib" sources building extension "scipy.cluster._vq" sources building extension "scipy.fftpack._fftpack" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.fftpack.convolve" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.integrate._quadpack" sources building extension "scipy.integrate._odepack" sources building extension "scipy.integrate.vode" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.interpolate._fitpack" sources building extension "scipy.interpolate.dfitpack" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. adding 'build/src.linux-x86_64-2.5/scipy/interpolate/dfitpack-f2pywrappers.f' to sources. building extension "scipy.io.numpyio" sources building extension "scipy.lib.blas.fblas" sources f2py options: ['skip:', ':'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. adding 'build/src.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scipy/lib/blas/fblas-f2pywrappers.f' to sources. building extension "scipy.lib.blas.cblas" sources adding 'build/src.linux-x86_64-2.5/scipy/lib/blas/cblas.pyf' to sources. f2py options: ['skip:', ':'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.lib.lapack.flapack" sources f2py options: ['skip:', ':'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.lib.lapack.clapack" sources adding 'build/src.linux-x86_64-2.5/scipy/lib/lapack/clapack.pyf' to sources. f2py options: ['skip:', ':'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.lib.lapack.calc_lwork" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.lib.lapack.atlas_version" sources building extension "scipy.linalg.fblas" sources adding 'build/src.linux-x86_64-2.5/scipy/linalg/fblas.pyf' to sources. f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. adding 'build/src.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scipy/linalg/fblas-f2pywrappers.f' to sources. building extension "scipy.linalg.cblas" sources adding 'build/src.linux-x86_64-2.5/scipy/linalg/cblas.pyf' to sources. f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.linalg.flapack" sources adding 'build/src.linux-x86_64-2.5/scipy/linalg/flapack.pyf' to sources. f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. adding 'build/src.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scipy/linalg/flapack-f2pywrappers.f' to sources. building extension "scipy.linalg.clapack" sources adding 'build/src.linux-x86_64-2.5/scipy/linalg/clapack.pyf' to sources. f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.linalg._flinalg" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.linalg.calc_lwork" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.linalg.atlas_version" sources building extension "scipy.linalg._iterative" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.linsolve._zsuperlu" sources building extension "scipy.linsolve._dsuperlu" sources building extension "scipy.linsolve._csuperlu" sources building extension "scipy.linsolve._ssuperlu" sources building extension "scipy.linsolve.umfpack.__umfpack" sources building extension "scipy.odr.__odrpack" sources building extension "scipy.optimize._minpack" sources building extension "scipy.optimize._zeros" sources building extension "scipy.optimize._lbfgsb" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.optimize.moduleTNC" sources building extension "scipy.optimize._cobyla" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.optimize.minpack2" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.signal.sigtools" sources building extension "scipy.signal.spline" sources building extension "scipy.sparse._sparsetools" sources building extension "scipy.special._cephes" sources building extension "scipy.special.specfun" sources f2py options: ['--no-wrap-functions'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.stats.statlib" sources f2py options: ['--no-wrap-functions'] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.stats.futil" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. building extension "scipy.stats.mvn" sources f2py options: [] adding 'build/src.linux-x86_64-2.5/fortranobject.c' to sources. adding 'build/src.linux-x86_64-2.5' to include_dirs. adding 'build/src.linux-x86_64-2.5/scipy/stats/mvn-f2pywrappers.f' to sources. building extension "scipy.ndimage._nd_image" sources building data_files sources running build_py copying build/src.linux-x86_64-2.5/scipy/__config__.py -> build/lib.linux-x86_64-2.5/scipy running build_clib customize UnixCCompiler customize UnixCCompiler using build_clib customize GnuFCompiler customize GnuFCompiler customize GnuFCompiler using build_clib running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext library 'mach' defined more than once, overwriting build_info {'sources': ['scipy/integrate/mach/r1mach.f', 'scipy/integrate/mach/i1mach.f', 'scipy/integrate/mach/xerror.f', 'scipy/integrate/mach/d1mach.f'], 'config_fc': {'noopt': ('scipy/integrate/setup.pyc', 1)}, 'source_languages': ['f77']} with {'sources': ['scipy/special/mach/r1mach.f', 'scipy/special/mach/i1mach.f', 'scipy/special/mach/xerror.f', 'scipy/special/mach/d1mach.f'], 'config_fc': {'noopt': ('scipy/special/setup.pyc', 1)}, 'source_languages': ['f77']}. extending extension 'scipy.linsolve._zsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)] extending extension 'scipy.linsolve._dsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)] extending extension 'scipy.linsolve._csuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)] extending extension 'scipy.linsolve._ssuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)] customize UnixCCompiler customize UnixCCompiler using build_ext customize GnuFCompiler customize GnuFCompiler customize GnuFCompiler using build_ext running install_lib copying build/lib.linux-x86_64-2.5/scipy/__config__.py -> /opt/local/lib/python2.5/site-packages/scipy byte-compiling /opt/local/lib/python2.5/site-packages/scipy/__config__.py to __config__.pyc running install_data running install_egg_info Removing /opt/local/lib/python2.5/site-packages/scipy-0.6.0-py2.5.egg-info Writing /opt/local/lib/python2.5/site-packages/scipy-0.6.0-py2.5.egg-info

7 15

Numpy/Cython Google Summer of Code project idea
by AJ Raczkowski 24 Mar '08

24 Mar '08

Hi, My name is Adam Raczkowski, and I'm interested in working on the proposed GSoC project for this summer, 2008. I am currently in my junior year as an undergrad at Tufts University. My majors are computer science and mathematics. I discovered SciPy/NumPy during a class I took this past fall in numerical linear algebra, and I remain very much interested in that field. I would like to discuss this project (conversion of code to cython) with a possible mentor so that I can ensure this would be a good fit for me. Regards, Adam

2 1

Splitting sparsetools: tahnk you
by David Cournapeau 21 Mar '08

21 Mar '08

Hi Nathan, I saw yesterday that you split the sparsetools module: the max memory usage during build is now half of what used to be necessary, thank you very much for your effort, cheers, David

2 1