Mailman 3 September 2012 - SciPy-Dev

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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Scipy.org is down + question about "topical software"
by Michael Clerx Jan. 2, 2013

Jan. 2, 2013

Dear all, First of all, scipy.org appears to be down and has been for some time today. Yesterday, when it was still up, I registered an account and tried adding a link to the topical software section but got the error "too many links". Does anybody know how/if I can fix this? kind regards, Michael

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Scipy docstrings
by Cera, Tim Oct. 9, 2012

Oct. 9, 2012

A whole bunch of conflicts and merges showed up on the docstring editor at http://docs.scipy.org/scipy/merge/ Can't 'Accept Merges' because all of the merges that I looked at did not follow the docstring standard. There isn't a way to 'Reject Merges', so what I have done in the past is accept, then revert. Could this be handled in some other way? Kindest regards, Tim

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stats.distributions.py documentation
by nicky van foreest Oct. 7, 2012

Oct. 7, 2012

Hi, Below are two proposals to handle the documentation of the scipy distributions. The first is to add a set of examples to each distribution, see the list at the end of the mail as an example. However, I actually wonder whether it wouldn't be better to put this stuff in the stats tutorial. (I recently updated this, but given the list below, it is still not complete.) The list below is a bit long... too long perhaps. I actualy get the feeling that, given the enormous diversity of the distributions, it may not be possible to automatically generate a set of simple examples that work for each and every distributions. Such examples then would involve the usage of x.dist.b, and so on, and this is not particularly revealing to first (and second) time users. A possible resolution is to include just one or two generic examples in the example doc string (e.g., dist.rvs(size = (2,3)) ), and refer to the tutorial for the rest. The tutorial then should show extensive examples for each method of the norm distribution. I assume that then any user of other distributions can figure out how to proceed for his/her own distribution. The second possibility would be to follow Josef's suggestion: --snip snip Splitting up the distributions pdf docs in tutorial into separate pages for individual distributions, make them nicer with code and graphs and link them from the docstring of the distribution. This would keep the docstring itself from blowing up, but we could get the full html reference if we need to. --snip snip This idea offers a lot of opportunities. In a previous mail I mentioned that I don't quite like that the documentation is spread over multiple documents. There are doc strings in distributions.py (leading to a bloated file), and there is continuous.rst. Part of the implementation can be understood from the doc-string, typically, the density function, but not the rest; this requires continuous.rst. Besides this, in case some specific distribution requires extra explanation/examples, this will have to put in the doc-string, making distributions.py longer still. Thus, to take up Josef's suggestion, what about a documentation file organised like this: # some tag to tell that these are the docs for the norm distribution # eg. # norm_gen Normal Distribution ---------------------------- Notes ^^^^^^^ # should be used by the interpreter The probability density function for `norm` is:: norm.pdf(x) = exp(-x**2/2)/sqrt(2*pi) Simple Examples ^^^^^^^^^^^^^^^^^^^^ # used for by interpreter >>> norm.rvs( size = (2,3) ) Extensive Examples ^^^^^^^^^^^^^^^^^^^^^^^^ # Not used by the interpreter, but certainly by a html viewer, containing graphs, hard/specific examples. Mathematical Details ^^^^^^^^^^^^^^^^^^^^^^ Stuff from continuous.rst # dist2_gen Distribution number 2 ----------------------------------------- etc It shouldn't be too hard to parse such a document, and couple each piece of documentation to a distribution in distributions.py (or am I mistaken?) as we use the class name as the tag in the documentation file. The doc-string for a distribution in distributions.py can then be removed, Nicky Example for the examples section of the docstring of norm. Notes ----- The probability density function for `norm` is:: norm.pdf(x) = exp(-x**2/2)/sqrt(2*pi) #%(example)s Examples -------- Setting the mean and standard deviation: >>> from scipy.stats import norm >>> norm.cdf(0.0) >>> norm.cdf(0., 1) # set mu = loc = 1 >>> norm.cdf(0., 1, 2) # mu = loc = 1, scale = sigma = 2 >>> norm.cdf(0., loc = 1, scale = 2) # mu = loc = 1, scale = sigma = 2 Frozen rvs >>> norm(1., 2.).cdf(0) >>> x = norm(scale = 2.) >>> x.cdf(0.0) Moments >>> norm(loc = 2).stats() >>> norm.mean() >>> norm.moment(2, scale = 3.) >>> x.std() >>> x.var() Random number generation >>> norm.rvs(3, 1, size = (2,3)) # loc = 3, scale =1, array of shape (2,3) >>> norm.rvs(3, 1, size = [2,3]) >>> x.rvs(3) # array with 3 random deviates >>> x.rvs([3,4]) # array of shape (3,4) with deviates Expectations >>> norm.expect(lambda x: x, loc = 1) # 1.00000 >>> norm.expect(lambda x: x**2, loc = 1., scale = 2.) # second moment Support of the distribution >>> norm.a # left limit, -np.inf here >>> norm.b # right limit, np.inf here Plot of the cdf >>> import numpy as np >>> x = np.linspace(0, 3) >>> P = norm.cdf(x) >>> plt.plot(x,P) >>> plt.show()

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Make orthogonal eval_* ufuncs?
by Eric Moore Oct. 7, 2012

Oct. 7, 2012

Hi list, There are some issues with the eval_* routines for orthogonal polynomials. 1. They appear to support out parameters, but they don't. 2. They choke when handed lists rather than arrays. (This is ticket #1435) 3. special.binom is exported but doesn't appear in the docs on scipy.org. I started to fix these issues this morning by turning them into ufuncs and calling hyp2f1, Gamma and lgam from cephes.h and hyp1f1_wrap from specfun_wrappers.h directly. However the various error handling routines defined in _cephesmodule.c (line 1185+) are actually called in cephes/mtherr.c. This means that I can't actually do it that way, unless I move some things around. I'd propose moving all of the eval_* functions defined in orthogonal_eval.pyx to _cephesmodule.c and turning them all into ufuncs. Thoughts? Eric #1435: Problems when calling special.legendre function over a list for high order http://projects.scipy.org/scipy/ticket/1435

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Docstrings for the optimize functions
by The Helmbolds Oct. 1, 2012

Oct. 1, 2012

Problem: As we update the scipy.optimize docstrings, how should we deal with the difference between the "new style" or 'minimize' calling sequence and its return dictionary, and the "old style" or 'pre-minimize' calling sequence and return values? Background and Discussion: The docstring for the over-arching 'minimize' function is OK as far as it goes. However, while it mentions that the different methods differ in the number and/or kind of parameters in both their calling sequences and in their 'Results' dictionaries, it (quite properly) does not go into the nitty-gritty details of these items for each 'method'. Yet, those details should be described someplace, especially for new users. Suggested Approach: I propose the following approach to documenting the individual 'methods' while we are in this "transition" period: 1. In the Summary (or Extended Summary) of the method, include words to the effect that: Although this documentation for the most part describes the "old style" calling sequence and return values, it is strongly recommended that new code invoking this method use the "new style" or 'minimize' calling sequence and return values, and that serious consideration be given to updating existing code to the "new style". To ease the transition, one or more examples illustrating both the "old style" and the "new style" calling sequences and return values are provided in the Examples section. 2. Leave the docstring's sections on Parameters, Returns, Other Parameters, Raises, Notes, and References, discuss as though the "old style" calling sequence is being used. Add to these sections remarks on the "new style" only when clarity requires it. For example, it may be helpful to add a sentence to the Parameters and Returns sections stting that: The Examples section shows how to adapt this to the "new style" calling sequence and return values. 3. In the examples, first illustrate how to use the "old style" version. Then add an example illustrating how exactly the same problem would be handled when the "new style's" 'minimize' calling sequence is used. In that example discuss just so much of the details of the method's "options dictionary", "constraints dictionary sequence", and "Results dictionary" as seems necessary to guide a new user. In other words, adhere to the priniciple that "All documentation should be as brief as possible -- but no briefer !!!" Bob H

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Scipy.spatial.Delaunay fail to produce the tetrahedron for all particles
by Ilaudy Sept. 30, 2012

Sept. 30, 2012

Dear developer, I am using Scipy.spatial.Delaunay to do some analysis. I found that the Delaunay model can not produce the tetrahedron for my simulated 3D particles which is at the center of a halo. Lots of it are not included in the tri.vertices. For example, I select about ~2300 particles in the center, and do the triangulation. But the tri.vertices only have a shape of (484, 4)! I think this should be a problem of the Hull's accuracy setting. Can I know where I can tune this? Or any suggestions are welcome. I have report this in here, http://projects.scipy.org/scipy/ticket/1728. You can find the 3D particles' position in the attached .txt file Thanks. ilaudy

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ANN: SciPy 0.11.0 release
by Ralf Gommers Sept. 26, 2012

Sept. 26, 2012

Hi, I am pleased to announce the availability of SciPy 0.11.0. For this release many new features have been added, and over 120 tickets and pull requests have been closed. Also noteworthy is that the number of contributors for this release has risen to over 50. We hope to see this number continuing to increase! The highlights of this release are: - A new module, sparse.csgraph, has been added which provides a number of common sparse graph algorithms. - New unified interfaces to the existing optimization and root finding functions have been added. Sources and binaries can be found at http://sourceforge.net/projects/scipy/files/scipy/0.11.0/, release notes are copied below. Thanks to everyone who contributed to this release, Ralf ========================== SciPy 0.11.0 Release Notes ========================== .. contents:: SciPy 0.11.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. Highlights of this release are: - A new module has been added which provides a number of common sparse graph algorithms. - New unified interfaces to the existing optimization and root finding functions have been added. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Our development attention will now shift to bug-fix releases on the 0.11.x branch, and on adding new features on the master branch. This release requires Python 2.4-2.7 or 3.1-3.2 and NumPy 1.5.1 or greater. New features ============ Sparse Graph Submodule ---------------------- The new submodule :mod:`scipy.sparse.csgraph` implements a number of efficient graph algorithms for graphs stored as sparse adjacency matrices. Available routines are: - :func:`connected_components` - determine connected components of a graph - :func:`laplacian` - compute the laplacian of a graph - :func:`shortest_path` - compute the shortest path between points on a positive graph - :func:`dijkstra` - use Dijkstra's algorithm for shortest path - :func:`floyd_warshall` - use the Floyd-Warshall algorithm for shortest path - :func:`breadth_first_order` - compute a breadth-first order of nodes - :func:`depth_first_order` - compute a depth-first order of nodes - :func:`breadth_first_tree` - construct the breadth-first tree from a given node - :func:`depth_first_tree` - construct a depth-first tree from a given node - :func:`minimum_spanning_tree` - construct the minimum spanning tree of a graph ``scipy.optimize`` improvements ------------------------------- The optimize module has received a lot of attention this release. In addition to added tests, documentation improvements, bug fixes and code clean-up, the following improvements were made: - A unified interface to minimizers of univariate and multivariate functions has been added. - A unified interface to root finding algorithms for multivariate functions has been added. - The L-BFGS-B algorithm has been updated to version 3.0. Unified interfaces to minimizers ```````````````````````````````` Two new functions ``scipy.optimize.minimize`` and ``scipy.optimize.minimize_scalar`` were added to provide a common interface to minimizers of multivariate and univariate functions respectively. For multivariate functions, ``scipy.optimize.minimize`` provides an interface to methods for unconstrained optimization (`fmin`, `fmin_powell`, `fmin_cg`, `fmin_ncg`, `fmin_bfgs` and `anneal`) or constrained optimization (`fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla` and `fmin_slsqp`). For univariate functions, ``scipy.optimize.minimize_scalar`` provides an interface to methods for unconstrained and bounded optimization (`brent`, `golden`, `fminbound`). This allows for easier comparing and switching between solvers. Unified interface to root finding algorithms ```````````````````````````````````````````` The new function ``scipy.optimize.root`` provides a common interface to root finding algorithms for multivariate functions, embeding `fsolve`, `leastsq` and `nonlin` solvers. ``scipy.linalg`` improvements ----------------------------- New matrix equation solvers ``````````````````````````` Solvers for the Sylvester equation (``scipy.linalg.solve_sylvester``, discrete and continuous Lyapunov equations (``scipy.linalg.solve_lyapunov``, ``scipy.linalg.solve_discrete_lyapunov``) and discrete and continuous algebraic Riccati equations (``scipy.linalg.solve_continuous_are``, ``scipy.linalg.solve_discrete_are``) have been added to ``scipy.linalg``. These solvers are often used in the field of linear control theory. QZ and QR Decomposition ```````````````````````` It is now possible to calculate the QZ, or Generalized Schur, decomposition using ``scipy.linalg.qz``. This function wraps the LAPACK routines sgges, dgges, cgges, and zgges. The function ``scipy.linalg.qr_multiply``, which allows efficient computation of the matrix product of Q (from a QR decompostion) and a vector, has been added. Pascal matrices ``````````````` A function for creating Pascal matrices, ``scipy.linalg.pascal``, was added. Sparse matrix construction and operations ----------------------------------------- Two new functions, ``scipy.sparse.diags`` and ``scipy.sparse.block_diag``, were added to easily construct diagonal and block-diagonal sparse matrices respectively. ``scipy.sparse.csc_matrix`` and ``csr_matrix`` now support the operations ``sin``, ``tan``, ``arcsin``, ``arctan``, ``sinh``, ``tanh``, ``arcsinh``, ``arctanh``, ``rint``, ``sign``, ``expm1``, ``log1p``, ``deg2rad``, ``rad2deg``, ``floor``, ``ceil`` and ``trunc``. Previously, these operations had to be performed by operating on the matrices' ``data`` attribute. LSMR iterative solver --------------------- LSMR, an iterative method for solving (sparse) linear and linear least-squares systems, was added as ``scipy.sparse.linalg.lsmr``. Discrete Sine Transform ----------------------- Bindings for the discrete sine transform functions have been added to ``scipy.fftpack``. ``scipy.interpolate`` improvements ---------------------------------- For interpolation in spherical coordinates, the three classes ``scipy.interpolate.SmoothSphereBivariateSpline``, ``scipy.interpolate.LSQSphereBivariateSpline``, and ``scipy.interpolate.RectSphereBivariateSpline`` have been added. Binned statistics (``scipy.stats``) ----------------------------------- The stats module has gained functions to do binned statistics, which are a generalization of histograms, in 1-D, 2-D and multiple dimensions: ``scipy.stats.binned_statistic``, ``scipy.stats.binned_statistic_2d`` and ``scipy.stats.binned_statistic_dd``. Deprecated features =================== ``scipy.sparse.cs_graph_components`` has been made a part of the sparse graph submodule, and renamed to ``scipy.sparse.csgraph.connected_components``. Calling the former routine will result in a deprecation warning. ``scipy.misc.radon`` has been deprecated. A more full-featured radon transform can be found in scikits-image. ``scipy.io.save_as_module`` has been deprecated. A better way to save multiple Numpy arrays is the ``numpy.savez`` function. The `xa` and `xb` parameters for all distributions in ``scipy.stats.distributions`` already weren't used; they have now been deprecated. Backwards incompatible changes ============================== Removal of ``scipy.maxentropy`` ------------------------------- The ``scipy.maxentropy`` module, which was deprecated in the 0.10.0 release, has been removed. Logistic regression in scikits.learn is a good and modern alternative for this functionality. Minor change in behavior of ``splev`` ------------------------------------- The spline evaluation function now behaves similarly to ``interp1d`` for size-1 arrays. Previous behavior:: >>> from scipy.interpolate import splev, splrep, interp1d >>> x = [1,2,3,4,5] >>> y = [4,5,6,7,8] >>> tck = splrep(x, y) >>> splev([1], tck) 4. >>> splev(1, tck) 4. Corrected behavior:: >>> splev([1], tck) array([ 4.]) >>> splev(1, tck) array(4.) This affects also the ``UnivariateSpline`` classes. Behavior of ``scipy.integrate.complex_ode`` ------------------------------------------- The behavior of the ``y`` attribute of ``complex_ode`` is changed. Previously, it expressed the complex-valued solution in the form:: z = ode.y[::2] + 1j * ode.y[1::2] Now, it is directly the complex-valued solution:: z = ode.y Minor change in behavior of T-tests ----------------------------------- The T-tests ``scipy.stats.ttest_ind``, ``scipy.stats.ttest_rel`` and ``scipy.stats.ttest_1samp`` have been changed so that 0 / 0 now returns NaN instead of 1. Other changes ============= The SuperLU sources in ``scipy.sparse.linalg`` have been updated to version 4.3 from upstream. The function ``scipy.signal.bode``, which calculates magnitude and phase data for a continuous-time system, has been added. The two-sample T-test ``scipy.stats.ttest_ind`` gained an option to compare samples with unequal variances, i.e. Welch's T-test. ``scipy.misc.logsumexp`` now takes an optional ``axis`` keyword argument. Authors ======= This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order): * Jeff Armstrong * Chad Baker * Brandon Beacher + * behrisch + * borishim + * Matthew Brett * Lars Buitinck * Luis Pedro Coelho + * Johann Cohen-Tanugi * David Cournapeau * dougal + * Ali Ebrahim + * endolith + * Bjørn Forsman + * Robert Gantner + * Sebastian Gassner + * Christoph Gohlke * Ralf Gommers * Yaroslav Halchenko * Charles Harris * Jonathan Helmus + * Andreas Hilboll + * Marc Honnorat + * Jonathan Hunt + * Maxim Ivanov + * Thouis (Ray) Jones * Christopher Kuster + * Josh Lawrence + * Denis Laxalde + * Travis Oliphant * Joonas Paalasmaa + * Fabian Pedregosa * Josef Perktold * Gavin Price + * Jim Radford + * Andrew Schein + * Skipper Seabold * Jacob Silterra + * Scott Sinclair * Alexis Tabary + * Martin Teichmann * Matt Terry + * Nicky van Foreest + * Jacob Vanderplas * Patrick Varilly + * Pauli Virtanen * Nils Wagner + * Darryl Wally + * Stefan van der Walt * Liming Wang + * David Warde-Farley + * Warren Weckesser * Sebastian Werk + * Mike Wimmer + * Tony S Yu + A total of 55 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

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Fwd: Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion
by Orion Poplawski Sept. 24, 2012

Sept. 24, 2012

This is a plea for some help. We've been having trouble getting scipy to pass all of the tests in the Fedora 18 build with python 3.3 (although it seems to build okay in Fedora 19). Below are the logs of the build. There appears to be some kind of memory corruption that manifests itself a little differently on 32-bit vs. 64-bit. I really have no idea myself how to pursue debugging this, though I'm happy to provide any more needed information. Thanks, Orion -------- Original Message -------- Subject: Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion Date: Thu, 20 Sep 2012 21:01:54 +0000 (UTC) From: Fedora Koji Build System <buildsys(a)fedoraproject.org> To: ausil(a)fedoraproject.org, jspaleta(a)fedoraproject.org, voronov(a)fedoraproject.org, torwangjl(a)fedoraproject.org, alagunambi(a)fedoraproject.org, urkle(a)fedoraproject.org, orion(a)fedoraproject.org Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion ID: 350761 Started: Thu, 20 Sep 2012 20:39:45 UTC Finished: Thu, 20 Sep 2012 21:01:32 UTC scipy-0.11.0-0.1.rc2.fc18 (350761) failed on buildvm-33.phx2.fedoraproject.org (x86_64), buildvm-35.phx2.fedoraproject.org (i386), buildvm-34.phx2.fedoraproject.org (noarch): BuildError: error building package (arch x86_64), mock exited with status 1; see build.log for more information SRPMS: scipy-0.11.0-0.1.rc2.fc18.src.rpm Failed tasks: ------------- Task 4509076 on buildvm-33.phx2.fedoraproject.org Task Type: buildArch (scipy-0.11.0-0.1.rc2.fc18.src.rpm, x86_64) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=state.log Task 4509077 on buildvm-35.phx2.fedoraproject.org Task Type: buildArch (scipy-0.11.0-0.1.rc2.fc18.src.rpm, i686) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=state.log Task 4509063 on buildvm-34.phx2.fedoraproject.org Task Type: build (f18-candidate, /scipy:cb69bd06f0d930fbe8840d89b918b617e28af63f) Closed tasks: ------------- Task 4509064 on buildvm-30.phx2.fedoraproject.org Task Type: buildSRPMFromSCM (/scipy:cb69bd06f0d930fbe8840d89b918b617e28af63f) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=checkout.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=state.log Task Info: http://koji.fedoraproject.org/koji/taskinfo?taskID=4509063 Build Info: http://koji.fedoraproject.org/koji/buildinfo?buildID=350761

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