Mailman 3 January 2011 - SciPy-Dev

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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request of a FGMRES krylov solver
by Thélesphonse Bigorneault Feb. 18, 2011

Feb. 18, 2011

I would like to have a FGMRES Krylov solver in scipy. FGMRES is a variant of the GMRES method with right preconditioning that enables the use of a different preconditioner at each step of the Arnoldi process. A possibility could be to "borrow" the fgmres function from pyamg (which as a compatible license) http://code.google.com/p/pyamg/source/browse/trunk/pyamg/krylov/_fgmres.py Including this code into scipy should be straightforward. Télesphore Bigorneault

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Starting a scikit for NUFFT
by David Kremer Feb. 5, 2011

Feb. 5, 2011

Hello, I would like to start a scikit on nufft. I already gather some materials to start my work. Source code here : http://www.cims.nyu.edu/cmcl/nufft/nufft.html References here : [DR] Fast Fourier transforms for nonequispaced data, A. Dutt and V. Rokhlin, SIAM J. Sci. Comput. 14, 1368-1383, 1993. [GL] Accelerating the Nonuniform Fast Fourier Transform, L. Greengard and J.-Y. Lee, SIAM Review 46, 443-454 (2004). I'm not an expert on it. Actually, I do need to perform a nufft, but I would appreciate any comment on the way it should be implemented. Please, provide me with good information on the better way to build a scikit. Thank you very much. David Kremer

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ANN: SciPy 0.9.0 release candidate 2
by Ralf Gommers Feb. 2, 2011

Feb. 2, 2011

Hi, I am pleased to announce the availability of the second release candidate of SciPy 0.9.0. This will be the first SciPy release to include support for Python 3 (all modules except scipy.weave), as well as for Python 2.7. Due to the Sourceforge outage I am not able to put binaries on the normal download site right now, that will probably only happen in a week or so. If you want to try the RC now please build from svn, and report any issues. Changes since release candidate 1: - fixes for build problems with MSVC + MKL (#1210, #1376) - fix pilutil test to work with numpy master branch - fix constants.codata to be backwards-compatible Enjoy, Ralf

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legendre regression
by Yaroslav Halchenko Jan. 31, 2011

Jan. 31, 2011

Switching to scipy (0.8.0) + numpy (1.5.1) from scipy (0.7.2) + numpy (1.4.1) reveals regression: *(Pdb) print legendre(1)(N.array([-0.999999999999 , -0.6, -0.2, 0.2, 0.6, 1. ])) [-1. -0.6 -0.2 0.2 0.6 1. ] *(Pdb) print legendre(1)(N.array([-1 , -0.6, -0.2, 0.2, 0.6, 1. ])) [ inf -0.6 -0.2 0.2 0.6 1. ] while before it was giving correct boundary value (-1). Could anyone please check if it is still present in current HEAD? imho worth a bugreport (haven't ran into similar one upon query but decided to check -- may be it was resolved already). -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic

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git-friendly cythonize script + Tempita templating
by Dag Sverre Seljebotn Jan. 31, 2011

Jan. 31, 2011

It's getting close to the finish of my Fwrap work on SciPy. I'll get something to the list on that soon. Meanwhile here's a small start re: Cython vs. SciPy: cythonize.py ------------- I've been working with a lot of Cython files. Since Cython isn't (currently at least) invoked by the build system and is not a build-time dependency, I created a development-time utility script to scan for Cython files and invoke Cython in a way that is fast and hopefully safe with git (I had a Makefile, but that didn't work well with switching git branch). https://github.com/dagss/scipy-refactor/blob/cythonize/cythonize.py (Ignore the "-refactor", it is based off scipy/master). It: - First, checks if there's been any changes since the last time it was regenerated by you (checked by sha1 hash against a local "cythonize.dat") - Then, checks with git to see if the .c has been committed in a child commit to the .pyx (it is assumed you use git) - Finally, resort to invoking Cython. I'm sure it needs some polish/Python 3 support etc. (but keep in mind that it is a development time script only). Also, it won't currently strip comments like, e.g., generate_qhull.py did. Feedback welcome. Tempita --------- I needed a template language, and since Robert Kern recommended Tempita, I went with that for wrappers I made with Fwrap: http://pythonpaste.org/tempita/ cythonize.py above will interpret ".pyx.in" as being Tempita-templated Cython source code. Later (too late) I discovered that Mako was already in use in SciPy in interpolate/interpnd.pyx. In the "cythonize" branch linked to above I've converted it to use Tempita instead, in case you agree with that. The advantage with Tempita is that it fits in a single ~1000-line Python file, so that it should be convenient to bundle it with the build system on a later date. Dag Sverre

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ANN: SciPy 0.9.0 release candidate 1
by Ralf Gommers Jan. 28, 2011

Jan. 28, 2011

Hi, I am pleased to announce the availability of the first release candidate of SciPy 0.9.0. This will be the first SciPy release to include support for Python 3 (all modules except scipy.weave), as well as for Python 2.7. Please try this release candidate and report any problems on the scipy-dev mailing list. Binaries, sources and release notes can be found at http://sourceforge.net/projects/scipy/files/scipy/0.9.0rc1/. Note that some OS X binaries are not uploaded yet, they should follow in the next few days. Enjoy, Ralf

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Lomb-Scargle periodogram
by Pim Schellart Jan. 27, 2011

Jan. 27, 2011

Dear SciPy developers, I have recently submitted code to calculate the Lomb-Scargle periodogram (see ticket http://projects.scipy.org/scipy/ticket/1352 for more information). After some iterations of review, recoding, adding documentation and adding unit tests the code (according to Ralf Gommers) is ready to go in. Ralf has nicely integrated the code into SciPy trunk in his git branch (https://github.com/rgommers/scipy/tree/lomb-scargle) and you may pull from there. Now the time has come to discuss where to put it and Ralf has suggested to have this discussion here: "I've played with your code a bit, changed a few things, and added it to scipy.signal in a github branch: https://github.com/rgommers/scipy/tree/lomb-scargle. I think it looks good, whether it really should go into signal should be discussed on the mailing list (but seemed like the logical place for it). ... The best name for the module I could think of was spectral_analysis, maybe there's a better one? It allows to add similar methods later. " I would like to suggest the module name "lssa" for "Least-squares spectral analysis". So the full path would be scipy.signal.lssa. This has several advantages. 1. It is a short name without underscores (personal preference) 2. It is a category name that can comprise several similar algorithms as desired. 3. It is immediately obvious from this name that this is different than FFT and therefore people will not be confused looking for FFT functions in this sub-module. 4. The name corresponds to the Wikipedia entry for this category of algorithms :) (but on a serious note this may help for people that want to find more information, or for people browsing wikipedia that are looking for an implementation). Please let me know what you think. Kind regards, Pim Schellart

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Re: [SciPy-Dev] Another Bug in KDTree
by Charles R. Schmidt Jan. 27, 2011

Jan. 27, 2011

Thanks for confirming. I created a ticket, http://projects.scipy.org/scipy/ticket/1373 - Charlie. > From: Ralf Gommers <ralf.gommers(a)googlemail.com> > Subject: Re: [SciPy-Dev] Another Bug in KDTree > To: SciPy Developers List <scipy-dev(a)scipy.org> > Message-ID: > <AANLkTin+VsAH0u8eGdOStZavM+=cqP5BJkP9kz9Yy+vQ(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > On Wed, Jan 26, 2011 at 12:32 AM, Charles R. Schmidt <schmidtc(a)gmail.com>wrote: > >> Has anyone been able to verify this bug? or know if it's already >> documented somewhere? >> > > Same output on my system, looks like a bug. I don't think it's documented > anywhere, could you open a ticket for it? > > Thanks, > Ralf > >

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Another Bug in KDTree
by Charles R. Schmidt Jan. 27, 2011

Jan. 27, 2011

There appears to be a bug in KDTree.query_ball_point, when using integer points neighbor points that lie on the radius are not always returned. Eg, with a 4x4 grid >>> import scipy.spatial >>> points = [(0, 0), (0, 1), (0, 2), (0, 3), \ ... (1, 0), (1, 1), (1, 2), (1, 3), \ ... (2, 0), (2, 1), (2, 2), (2, 3), \ ... (3, 0), (3, 1), (3, 2), (3, 3)] >>> kd=scipy.spatial.KDTree(points) >>> kd.query_ball_point((2,0),1) [8, 9, 12] >>> points = [(0.0, 0.0), (0.0, 1.0), (0.0, 2.0), (0.0, 3.0), \ ... (1.0, 0.0), (1.0, 1.0), (1.0, 2.0), (1.0, 3.0), \ ... (2.0, 0.0), (2.0, 1.0), (2.0, 2.0), (2.0, 3.0), \ ... (3.0, 0.0), (3.0, 1.0), (3.0, 2.0), (3.0, 3.0)] >>> kd=scipy.spatial.KDTree(points) >>> kd.query_ball_point((2,0),1) [4, 8, 9, 12]

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future of maxentropy module (was: sparse rmatvec and maxentropy)
by Ralf Gommers Jan. 25, 2011

Jan. 25, 2011

(excuse the cross-post, but this may be of interest to scipy-user and the scikits.learn crowd) On Sat, Jan 22, 2011 at 10:44 PM, <josef.pktd(a)gmail.com> wrote: > On Sat, Jan 22, 2011 at 8:50 AM, Ralf Gommers > <ralf.gommers(a)googlemail.com> wrote: > > On Thu, Jan 20, 2011 at 10:13 PM, Skipper Seabold <jsseabold(a)gmail.com> > > wrote: > >> > >> I picked up the montecarlo code when I was playing around with these. > >> > >> > http://bazaar.launchpad.net/~jsseabold/statsmodels/statsmodels-skipper-maxe…<http://bazaar.launchpad.net/%7Ejsseabold/statsmodels/statsmodels-skipper-ma…> > >> > >> I'm curious if the maxentropy stuff as it is in scipy wouldn't find > >> more use and maintenance in scikits.learn. The implementation is > >> somewhat use specific (natural language processing), though this is > >> not by any means set in stone. > >> > > Probably, but wouldn't it need a lot of work before it could be moved? It > > has a grand total of one test, mostly non-working examples, and is > obviously > > hardly used at all (see r6919 and r6920 for more examples of broken > code). > > > > Perhaps it's worth asking the scikits.learn guys, and otherwise consider > > deprecating it if they're not interested? > > I haven't seen or heard anyone using it besides Skipper. There are > also still some features that where designed for pysparse and never > fully updated to scipy.sparse. > http://projects.scipy.org/scipy/ticket/856 > > I also thought deprecating and removing maxentropy will be the best > idea, if nobody volunteers to give it a workout. > So I guess we just have to ask this out loud: is anyone using the scipy.maxentropy module or interested in doing so? If you are, would you be interested in putting some work into it, like making the examples work and adding some tests? The current status is that 3 out of 4 examples are broken, the module has only a single test, and from broken code that went unnoticed for a long time it is clear that there are very few users. If no one steps up, I propose to deprecate the module for the 0.10 release. If there are any users out there that missed this email and step up then, we can always un-deprecate again. To the scikits.learn developers: would this code fit better and see more use in scikits.learn than in scipy? Would you be interested to pick it up? Ralf

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[feature request] Smoothing data library
by David Kremer Jan. 25, 2011

Jan. 25, 2011

Hello, I'm a scipy user that use it for academic research. I would like to ask you if you could design a scipy.smooth module, because currently the smoothing abilitise of scipy are rather poor. Moreover, academic paper on smoothing 1D and 2D data set does exist, so it would be cool if you would like to implement some of them. Thank you very much

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Re: [SciPy-Dev] [feature request] Smoothing data library
by Jonathan Stickel Jan. 24, 2011

Jan. 24, 2011

On 1/24/11 11:00 , scipy-dev-request(a)scipy.org wrote: > Date: Mon, 24 Jan 2011 16:15:43 +0100 > From: David Kremer<david.kremer.dk(a)gmail.com> > Subject: [SciPy-Dev] [feature request] Smoothing data library > > Hello, I'm a scipy user that use it for academic research. > > I would like to ask you if you could design a scipy.smooth module, > because currently the smoothing abilitise of scipy are rather poor. > > Moreover, academic paper on smoothing 1D and 2D data set does exist, > so it would be cool if you would like to implement some of them. > > Thank you very much There are a few things scattered about. Smoothing splines are available in scipy.interpolate: http://docs.scipy.org/doc/scipy/reference/interpolate.html I submitted a scikit for smoothing by regularization of 1D arrays: http://pypi.python.org/pypi/scikits.datasmooth/0.5 I am open to contributions for improved packaging, documentation, and examples. I, too, think it would be nice if things were collected in a scipy module. Regards, Jonathan

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You know she wants it big
by Adam Machnik Jan. 23, 2011

Jan. 23, 2011

Huge discounts on huge growth http://argilaceous.pillclaytabletsdrugstore.net/

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Edit rights for doc.scipy.org
by Loïc Berthe Jan. 23, 2011

Jan. 23, 2011

Hi ! Could you give me edit rights for doc.scipy.org ? My username is loic. I would be happy to review some documentation and correct some mistakes. -- Loic

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sparse rmatvec and maxentropy
by josef.pktd＠gmail.com Jan. 22, 2011

Jan. 22, 2011

I don't know much about these two parts of scipy. Running an example for scipy.maxentropy raises an exception after upgrading to scipy trunk I didn't manage to find out with a Trac search whether rmatvec still exists. scipy.maxentropy maxentutils is still using it. Traceback (most recent call last): File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\examples\example_maxent.py", line 29, in <module> model.fit(K[i]) File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentropy.py", line 226, in fit File "\Programs\Python25\Lib\site-packages\scipy\optimize\optimize.py", line 636, in fmin_cg File "\Programs\Python25\Lib\site-packages\scipy\optimize\optimize.py", line 176, in function_wrapper File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentropy.py", line 420, in grad File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentropy.py", line 702, in expectations File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentropy.py", line 737, in pmf File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentropy.py", line 719, in logpmf File "\Programs\Python25\Lib\site-packages\scipy\maxentropy\maxentutils.py", line 381, in innerprodtranspose File "C:\Josef\_progs\Subversion\scipy_trunk_g09\dist\scipy-0.10.0.dev7027.win32\Programs\Python25\Lib\site-packages\scipy\sparse\base.py", line 384, in __getattr__ raise AttributeError(attr + " not found") AttributeError: rmatvec not found Josef

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postponing 0.9.0rc1 by one week
by Ralf Gommers Jan. 20, 2011

Jan. 20, 2011

Hi all, There is still one release blocker for 0.9.0, the change in behavior of convolve/correlate. See the old_behavior keyword in correlate/convolve/correlate2d/convolve2d, and also http://projects.scipy.org/scipy/ticket/1301. Therefore the release of RC1 is postponed by one week. If anyone has time to look at this, that would be great. Three other things that could be fixed as well are the following: 1. Medfilt test failure on linux 64-bit, see http://news.gmane.org/gmane.comp.python.scientific.devel. This will be marked as knownfail for this release if no one has time to investigate. 2. CorrelateComplex192 failures on Windows, see the other email I just sent. 3. Test warnings from interpolate and linalg, see below. Will just be filtered out for 0.9.0 unless they are fixed in time. /Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:673: UserWarning: The coefficients of the spline returned have been computed as the minimal norm least-squares solution of a (numerically) rank deficient system (deficiency=7). If deficiency is large, the results may be inaccurate. Deficiency may strongly depend on the value of eps. warnings.warn(message) ....../Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:604: UserWarning: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots. warnings.warn(message) /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:91: ComplexWarning: Casting complex values to real discards the imaginary part qr, tau, work, info = geqrf(a1, lwork=lwork, overwrite_a=overwrite_a) /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:126: ComplexWarning: Casting complex values to real discards the imaginary part Q, work, info = gor_un_gqr(qqr, tau, lwork=lwork, overwrite_a=1) ......................../Users/rgommers/Code/scipy/scipy/linalg/decomp_schur.py:72: ComplexWarning: Casting complex values to real discards the imaginary part result = gees(lambda x: None, a, lwork=result[-2][0], overwrite_a=overwrite_a) Cheers, Ralf

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correlate complex192 failures on win32
by Ralf Gommers Jan. 20, 2011

Jan. 20, 2011

Hi Warren, In r6982 you added correlate/convolve tests that are now failing on Windows for longdouble. Can you please have a look at that? If you don't have time soon I think it can be marked knownfail for 0.9.0 on Windows. Offending code: + +# Create three classes, one for each complex data type: TestCorrelateComplex64, +# TestCorrelateComplex128 and TestCorrelateComplex256. +# The second number in the pairs is used in the 'decimal' keyword argument of +# the array comparisons in the tests. +for i, decimal in [(np.csingle, 5), (np.cdouble, 10), (np.clongdouble, 15)]: Resulting in 16 failures like: ====================================================================== FAIL: test_rank1_full (test_signaltools.TestCorrelateComplex192) ---------------------------------------------------------------------- Traceback (most recent call last): File "Z:\Users\rgommers\.wine\drive_c\Python26\lib\site-packages\scipy\signal\tests\test_signaltools.py", line 630, in test_rank1_full assert_array_almost_equal(y, y_r, decimal=self.decimal) File "Z:\Users\rgommers\.wine\drive_c\Python26\lib\site-packages\numpy\testing\utils.py", line 774, in assert_array_almost_equal header='Arrays are not almost equal') File "Z:\Users\rgommers\.wine\drive_c\Python26\lib\site-packages\numpy\testing\utils.py", line 618, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 11.7647058824%) x: array([0.17161931+0.20853512j, 0.22923514+0.78246389j, -1.3237712+0.47060531j, -3.2990924+-0.50721318j, -2.9089895+0.084727311j, 1.5646499+-0.56960514j,... y: array([0.17161931+0.20853512j, 0.22923514+0.78246389j, -1.3237712+0.47060531j, -3.2990924+-0.50721318j, -2.9089895+0.084727311j, 1.5646499+-0.56960514j,... Ralf

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[Ann] EuroScipy 2011 - Call for papers
by Tiziano Zito Jan. 20, 2011

Jan. 20, 2011

========================= Announcing EuroScipy 2011 ========================= --------------------------------------------- The 4th European meeting on Python in Science --------------------------------------------- **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics =========== - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials ========= There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez =============================== We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates =============== Talk submission deadline: Sunday May 8 Program announced: Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers =============== We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines ===================== - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract ========================= To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers ========== Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website ======= http://www.euroscipy.org/conference/euroscipy_2011

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Re: [SciPy-Dev] postponing 0.9.0rc1 by one week
by Fabian Pedregosa Jan. 18, 2011

Jan. 18, 2011

On Mon, Jan 17, 2011 at 9:52 AM, Fabian Pedregosa <fabian.pedregosa(a)inria.fr> wrote: > On Sun, Jan 16, 2011 at 3:59 PM, Ralf Gommers > <ralf.gommers(a)googlemail.com> wrote: >> Hi all, >> >> There is still one release blocker for 0.9.0, the change in behavior of >> convolve/correlate. See the old_behavior keyword in >> correlate/convolve/correlate2d/convolve2d, and also >> http://projects.scipy.org/scipy/ticket/1301. Therefore the release of RC1 is >> postponed by one week. If anyone has time to look at this, that would be >> great. >> >> Three other things that could be fixed as well are the following: >> 1. Medfilt test failure on linux 64-bit, see >> http://news.gmane.org/gmane.comp.python.scientific.devel. This will be >> marked as knownfail for this release if no one has time to investigate. >> 2. CorrelateComplex192 failures on Windows, see the other email I just sent. >> 3. Test warnings from interpolate and linalg, see below. Will just be >> filtered out for 0.9.0 unless they are fixed in time. >> >> /Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:673: UserWarning: >> The coefficients of the spline returned have been computed as the >> minimal norm least-squares solution of a (numerically) rank deficient >> system (deficiency=7). If deficiency is large, the results may be >> inaccurate. Deficiency may strongly depend on the value of eps. >> warnings.warn(message) >> ....../Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:604: >> UserWarning: >> The required storage space exceeds the available storage space: nxest >> or nyest too small, or s too small. >> The weighted least-squares spline corresponds to the current set of >> knots. >> warnings.warn(message) >> >> /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:91: ComplexWarning: >> Casting complex values to real discards the imaginary part >> qr, tau, work, info = geqrf(a1, lwork=lwork, overwrite_a=overwrite_a) >> /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:126: ComplexWarning: >> Casting complex values to real discards the imaginary part >> Q, work, info = gor_un_gqr(qqr, tau, lwork=lwork, overwrite_a=1) > > These warnings come from the fact that lwork returned from a workspace > query has the same dtype as the principal matrix, but when we call the > computational routine it expects an integer value. A simple workaround > is here: I've updated my patch to also fix the warning in schur(): https://github.com/scipy/scipy-svn/pull/2 Fabian.

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Re: [SciPy-Dev] postponing 0.9.0rc1 by one week
by Fabian Pedregosa Jan. 17, 2011

Jan. 17, 2011

On Sun, Jan 16, 2011 at 3:59 PM, Ralf Gommers <ralf.gommers(a)googlemail.com> wrote: > Hi all, > > There is still one release blocker for 0.9.0, the change in behavior of > convolve/correlate. See the old_behavior keyword in > correlate/convolve/correlate2d/convolve2d, and also > http://projects.scipy.org/scipy/ticket/1301. Therefore the release of RC1 is > postponed by one week. If anyone has time to look at this, that would be > great. > > Three other things that could be fixed as well are the following: > 1. Medfilt test failure on linux 64-bit, see > http://news.gmane.org/gmane.comp.python.scientific.devel. This will be > marked as knownfail for this release if no one has time to investigate. > 2. CorrelateComplex192 failures on Windows, see the other email I just sent. > 3. Test warnings from interpolate and linalg, see below. Will just be > filtered out for 0.9.0 unless they are fixed in time. > > /Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:673: UserWarning: > The coefficients of the spline returned have been computed as the > minimal norm least-squares solution of a (numerically) rank deficient > system (deficiency=7). If deficiency is large, the results may be > inaccurate. Deficiency may strongly depend on the value of eps. > warnings.warn(message) > ....../Users/rgommers/Code/scipy/scipy/interpolate/fitpack2.py:604: > UserWarning: > The required storage space exceeds the available storage space: nxest > or nyest too small, or s too small. > The weighted least-squares spline corresponds to the current set of > knots. > warnings.warn(message) > > /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:91: ComplexWarning: > Casting complex values to real discards the imaginary part > qr, tau, work, info = geqrf(a1, lwork=lwork, overwrite_a=overwrite_a) > /Users/rgommers/Code/scipy/scipy/linalg/decomp_qr.py:126: ComplexWarning: > Casting complex values to real discards the imaginary part > Q, work, info = gor_un_gqr(qqr, tau, lwork=lwork, overwrite_a=1) These warnings come from the fact that lwork returned from a workspace query has the same dtype as the principal matrix, but when we call the computational routine it expects an integer value. A simple workaround is here: https://github.com/fabianp/scipy-svn/commit/5942b71bd321b0960fd179c9785273c… Although it would be more correct to convert it also to integer, i.e., lwork = int(work[0].real) Fabian.

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[PATCH] improvements for rq factorization
by Fabian Pedregosa Jan. 17, 2011

Jan. 17, 2011

Hi all. In this patch I implement several improvements for the RQ matrix factorization in function linalg.rq: - API: now uses the same keywords as qr function: implemented mode='economic' - type support: now supports non-square as well as complex arrays. This way I could uncomment a lot of tests. - performance: now uses LAPACK orgrq for computing the Q matrix instead of plain matrix multiplications. On a 500 x 500 array in my Core Duo, this makes it go from 17.6s s to 0.16s, i.e. gain of a 100x factor. - Also, some docstring fixes in linalg.qr function. Best, Fabian.

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unicode in THANKS.txt
by josef.pktd＠gmail.com Jan. 15, 2011

Jan. 15, 2011

What's the policy for unicode in text files? I added some contributers to THANKS.txt, but have problems with an accented name, Ernest Adrogué Is it possible to "internationalize" THANKS.txt ? Josef

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backporting scipy.stats fixes
by josef.pktd＠gmail.com Jan. 15, 2011

Jan. 15, 2011

I added 3 changesets in trunk, that should be backported to 0.9 7030 is a trivial fix to avoid an exception 7028 and 7029 correct the integration bounds in the expect methods and have, I think, sufficient test cases. Thanks, Josef

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updating numpy scipy crash - numpy on pypi
by josef.pktd＠gmail.com Jan. 15, 2011

Jan. 15, 2011

I *finally* updated numpy and scipy. For numpy 1.5.1, I used the files on pypi. When I installed scipy 0.9b1 it crashed (segfaulted) in the testsuite. With the superpack installer for numpy from the sourceforge download, scipy 0.9b1 passes all tests Ran 4806 tests in 287.843s OK (KNOWNFAIL=12, SKIP=35) <nose.result.TextTestResult run=4806 errors=0 failures=0> It looks like the non-superpack numpy files on pypi are not compatible with scipy in my setup (win32, sse2, python 2.5.2) Another question, are the clapack tests skipped on purpose, for example test_clapack_dsygv_1 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ dsygv_1 Clapack empty, skip flapack test test_clapack_dsygv_2 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ dsygv_2 Clapack empty, skip flapack test test_clapack_dsygv_3 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ dsygv_3 Clapack empty, skip flapack test test_clapack_ssygv_1 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ ssygv_1 Clapack empty, skip flapack test test_clapack_ssygv_2 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ ssygv_2 Clapack empty, skip flapack test test_clapack_ssygv_3 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ ssygv_3 Clapack empty, skip flapack test Josef

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