Mailman 3 August 2012 - SciPy-Dev

Bootstrap confidence limits code
by Constantine Evans 04 Oct '14

04 Oct '14

Hello everyone, A few years ago I implemented a scikit for bootstrap confidence limits (https://github.com/cgevans/scikits-bootstrap). I didn’t think much about it after that until recently, when I realized that some people are actually using it, and that there’s apparently been some talk about implementing this functionality in either scipy.stats or statsmodels (I should thank Randal Olson for discussing this and bringing it to my attention). As such I’ve rewritten most of the code, and written up some docstrings. The current code can do confidence intervals with basic percentile interval, bias-corrected accelerated, and approximate bootstrap confidence methods, and can also provide bootstrap and jackknife indexes. Most of it is implemented from the descriptions in Efron and Tibshirani’s Introduction to the Bootstrap, but the ABC code at the moment is a port from the modified-BSD-licensed bootstrap package for R (not the boot package) as I’m not entirely confident in my understanding of the method. And so, I have a few questions for everyone: * Is there any interest in including this sort of code in either scipy.stats or statsmodels? If so, where do people think would be the better place? The code is relatively small; at the moment it is less than 200 lines, with docstrings probably making up 100 of those lines. * Also, if so, what would need to be changed, added, and improved beyond what is mentioned in the Contributing to Scipy part of the reference guide? I’m never a fan of my own code, and imagine quite a bit would need to be fixed; I know tests will need to be added too. In addition, I have a few questions about what would be better practice for the API, and I haven’t really found a guide on best practices for Scipy: * When I started writing the code, I wrote a single function ci for confidence intervals, with a method argument to choose the method. This is easy for users, especially so that they don’t have to look through documentation to realize that BCA is the most generally useful method (at least from everything I’ve read) and that there really isn’t any reason to use many of the simpler methods. However, ABC takes different paramenters, and needs a statistic function that takes weights, which makes this single-function organization trickier. At the moment, I have a separate function for ABC. Would it be better to split up all the methods to their own functions? * ABC requires a statistic function that takes weights. I’ve noticed that things like np.average takes a weights= argument. Would it be better to require input of a stat(data,weights) function, or input of a stat(data,weights=) with weights as a named argument? The latter would be nice in terms of allowing the same function to be used for all methods, but would make it impossible to use a lambda for the function. Is there some other method of doing this entirely? * Are there any missing features that anyone thinks should be added? I apologize if much of this is answered elsewhere, I just haven’t found any of it; I also apologize if this is far too long-winded and confusing! Regards, Constantine Evans

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SLSQP Constrained Optimizer Status
by Rob Falck 24 Nov '13

24 Nov '13

I'm currently implementing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer, by Dieter Kraft, for use in Scipy. The Fortran code being wrapped with F2PY is here: http://www.netlib.org/toms/733 (its use within Scipy has been cleared) SLSQP provides for bounds on the independent variables, as well as equality and inequality constraint functions, which is a capability that doesn't exist in scipy.optimize. Currently the code works, although the constraint normals are being generated by approximation. I'm working on a way to pass these in. I think the most elegant way will be a single function that returns the matrix of constraint normals. For a demonstration of what the code can do, here is an optimization of f(x,y) = 2xy + 2x - x**2 - 2y**2 Example 14.2 in Chapra & Canale gives the maximum as x=2.0, y=1.0. The unbounded optimization tests find this solution. As expected, its faster when derivatives are provided rather than approximated. Unbounded optimization. Derivatives approximated. Elapsed time: 1.45792961121 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] Unbounded optimization. Derivatives provided. Elapsed time: 1.03211402893 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] The following example uses an equality constraint to find the optimal when x=y. Bound optimization. Derivatives approximated. Elapsed time: 1.384973526 ms Results [[0.99999996845920858, 0.99999996845920858], -0.99999999999999889, 4, 0, 'Optimization terminated successfully.'] I've tried to conform to the syntax used by the other optimizers in scipy.optimize. The function definition and doc string are below. If anyone is interested in testing it out, let me know. def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_cons=None,args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): """ Minimize a function using Sequential Least SQuares Programming Description: Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft. Inputs: func - Objective function (in the form func(x, *args)) x0 - Initial guess for the independent variable(s). eqcons - A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem f_eqcons - A function of the form f_eqcons(x, *args) that returns an array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored. ieqcons - A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully optimized problem f_ieqcons - A function of the form f_ieqcons(x0, *args) that returns an array in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored. bounds - A list of tuples specifying the lower and upper bound for each independent variable [ (xl0, xu0), (xl1, xu1), ...] fprime - A function that evaluates the partial derivatives of func fprime_cons - A function of the form f(x, *args) that returns the m by n array of constraint normals. If not provided, the normals will be approximated. Equality constraint normals precede inequality constraint normals. args - A sequence of additional arguments passed to func and fprime iter - The maximum number of iterations (int) acc - Requested accuracy (float) iprint - The verbosity of fmin_slsqp. iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary full_output - If 0, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon - The step size for finite-difference derivative estimates. Outputs: ( x, { fx, gnorm, its, imode, smode }) x - The final minimizer of func. fx - The final value of the objective function. its - The number of iterations. imode - The exit mode from the optimizer, as an integer. smode - A string describing the exit mode from the optimizer. Exit modes are defined as follows: -1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : Number of equality constraints larger than number of independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded -- - Rob Falck

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Scipy.org is down + question about "topical software"
by Michael Clerx 03 Jan '13

03 Jan '13

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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ANN: SciPy 0.11.0 release candidate 2
by Ralf Gommers 22 Sep '12

22 Sep '12

Hi, I am pleased to announce the availability of the second release candidate 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. Some of the highlights 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/0.11.0rc2/, release notes are copied below. For this release candidate all known issues (with the exception of one Qhull issue on Debian, s390x platform) have been solved. In the meantime also OS X 10.8 was released, this RC contains a few build fixes for that platform. If no more serious issues are reported, the final release will be in one week. Cheers, Ralf ========================== SciPy 0.11.0 Release Notes ========================== .. note:: Scipy 0.11.0 is not released yet! .. 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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Re: [SciPy-Dev] SciPy.org Website Move and Redesign
by Kyle Mandli 10 Sep '12

10 Sep '12

I kind of dropped the ball on this as I have been traveling for the past month. I think we are ready to move forward on this and at least get the pieces in place. I am still not clear exactly what the current status of the scipy.org-new and scipy.github.com repos on github are. Can anyone speak to this? On a related topic, I have been searching out someone who may be able to do some redesign work on the site but was not successful (at least a free option). Any suggestions for someone with some design experience that could update the look of the site a bit please let us know. Kyle On Friday, August 24, 2012 at 10:00 AM, scipy-dev-request(a)scipy.org wrote: > On Fri, Jul 27, 2012 at 11:35 AM, Fernando Perez <fperez.net(a)gmail.com (mailto:fperez.net@gmail.com)> wrote: > > And precisely having the site moved over to a system where: > > > > - it's trivial to delegate website work to a separate team who can > > collaborate with the regular github flow (branches, PRs, etc) to > > manage the web content. > > > > - form and content are separated, with templates and CSS for all formal display > > Is this transition still on track? We should push it through, even if > it means living with a slightly outdated website for a week or two. > (I'm sure we'll all feel much more comfortable updating the web site > using PRs than whatever the current mechanism might be.) > > St?fan

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extended functionality of max/min ufuncs
by Tom Grydeland 08 Sep '12

08 Sep '12

Hi all, I'm working on a system to translate (EXELIS/ITTVIS/RSI) IDL to Python/Numpy. The results are close to usable for many purposes, but some simple constructs are causing snags I was hoping you could help me with. IDL's MAX function supports a second (output) argument which will contain the index of the maximum value. One can write PRINT, MAX(x, id), id to get the value and index of the maximum element. In Numpy, I could do id = np.argmax(x); print x[id], id to get the same results in two statements. However, I can give MAX an extra keyword /NAN to tell it to ignore NaN and infinities. In Numpy, I have np.nanmax, but no np.nanargmax. In addition, MAX has the output keywords MIN=value and SUBSCRIPT_MIN=index to return the minimum value and its index from the same function call. Similarly, MIN has output keywords MAX=value and SUBSCRIPT_MAX=index, so I can get max/min values and their indices (ignoring NaNs) in a single call -- in Numpy I would need two calls, two indexing operations, one temporary array of values and one of indices. Would it be hard to add similar functionality to Numpy's min() and max() functions? Is it desirable? Regards, -- Tom Grydeland <Tom.Grydeland(a)(gmail.com)>

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Re: [SciPy-Dev] [SciPy-User] ANN: SciPy 0.11.0 release candidate 2
by Ralf Gommers 08 Sep '12

08 Sep '12

On Tue, Aug 14, 2012 at 12:09 AM, Christoph Gohlke <cgohlke(a)uci.edu> wrote: > On 8/13/2012 11:30 AM, Ralf Gommers wrote: > > Hi, > > > > I am pleased to announce the availability of the second release > > candidate 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. Some of the highlights 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/0.11.0rc2/, release > > notes are copied below. > > > > For this release candidate all known issues (with the exception of one > > Qhull issue on Debian, s390x platform) have been solved. In the meantime > > also OS X 10.8 was released, this RC contains a few build fixes for that > > platform. > > > > If no more serious issues are reported, the final release will be in one > > week. > > > > Cheers, > > Ralf > > > Hi Ralf, > > test_qz_double_sort is now failing in all msvc9/MKL builds (Python 2.6 > to 3.2, 32 and 64 bit): > Hmm, I think that that excludes anything compiler or ATLAS specific. This is the only test which really checks the results are correct for float input, almost all the other test only check that the output of qz is self-consistent (no hard-coded expected output). It seems that a couple more tests would be helpful. This issue has held up the release for too long already, so unless someone has time to get it resolved this week, I propose the following: 1. Figure out if the problem is the sort function or something else. 2. If it's sort, disable it. Otherwise remove the qz function from the 0.11.x branch. Ralf > > FAIL: test_qz_double_sort (test_decomp.TestQZ) > ---------------------------------------------------------------------- > Traceback (most recent call last): > File > "X:\Python32\lib\site-packages\scipy\linalg\tests\test_decomp.py", line > 1728, in test_qz_double_sort > [ 0. , 0. , 0. , -12.8217]]), 4) > File "X:\Python32\lib\site-packages\numpy\testing\utils.py", line > 800, in assert_array_almost_equal > header=('Arrays are not almost equal to %d decimals' % decimal)) > File "X:\Python32\lib\site-packages\numpy\testing\utils.py", line > 636, in assert_array_compare > raise AssertionError(msg) > AssertionError: > Arrays are not almost equal to 4 decimals > > (mismatch 62.5%) > x: array([[-14.66007968, -27.25220511, -31.55732717, -29.0823765 ], > [ 0. , 30.23027809, 42.47668118, 52.55438253], > [ 0. , 0. , 0.71600413, -2.77147791], > [ 0. , 0. , 0. , 2.50096525]]) > y: array([[ 3.57864000e+01, -8.09061000e+01, -1.20629000e+01, > -9.49800000e+00], > [ 0.00000000e+00, 2.76380000e+00, -2.35050000e+00,... > > > Christoph > > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user >

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A sad day for our community. John Hunter: 1968-2012.
by Fernando Perez 30 Aug '12

30 Aug '12

Dear friends and colleagues, [please excuse a possible double-post of this message, in-flight internet glitches] I am terribly saddened to report that yesterday, August 28 2012 at 10am, John D. Hunter died from complications arising from cancer treatment at the University of Chicago hospital, after a brief but intense battle with this terrible illness. John is survived by his wife Miriam, his three daughters Rahel, Ava and Clara, his sisters Layne and Mary, and his mother Sarah. Note: If you decide not to read any further (I know this is a long message), please go to this page for some important information about how you can thank John for everything he gave in a decade of generous contributions to the Python and scientific communities: http://numfocus.org/johnhunter. Just a few weeks ago, John delivered his keynote address at the SciPy 2012 conference in Austin centered around the evolution of matplotlib: http://www.youtube.com/watch?v=e3lTby5RI54 but tragically, shortly after his return home he was diagnosed with advanced colon cancer. This diagnosis was a terrible discovery to us all, but John took it with his usual combination of calm and resolve, and initiated treatment procedures. Unfortunately, the first round of chemotherapy treatments led to severe complications that sent him to the intensive care unit, and despite the best efforts of the University of Chicago medical center staff, he never fully recovered from these. Yesterday morning, he died peacefully at the hospital with his loved ones at his bedside. John fought with grace and courage, enduring every necessary procedure with a smile on his face and a kind word for all of his caretakers and becoming a loved patient of the many teams that ended up involved with his case. This was no surprise for those of us who knew him, but he clearly left a deep and lasting mark even amongst staff hardened by the rigors of oncology floors and intensive care units. I don't need to explain to this community the impact of John's work, but allow me to briefly recap, in case this is read by some who don't know the whole story. In 2002, John was a postdoc at the University of Chicago hospital working on the analysis of epilepsy seizure data in children. Frustrated with the state of the existing proprietary solutions for this class of problems, he started using Python for his work, back when the scientific Python ecosystem was much, much smaller than it is today and this could have been seen as a crazy risk. Furthermore, he found that there were many half-baked solutions for data visualization in Python at the time, but none that truly met his needs. Undeterred, he went on to create matplotlib (http://matplotlib.org) and thus overcome one of the key obstacles for Python to become the best solution for open source scientific and technical computing. Matplotlib is both an amazing technical achievement and a shining example of open source community building, as John not only created its backbone but also fostered the development of a very strong development team, ensuring that the talent of many others could also contribute to this project. The value and importance of this are now painfully clear: despite having lost John, matplotlib continues to thrive thanks to the leadership of Michael Droetboom, the support of Perry Greenfield at the Hubble Telescope Science Institute, and the daily work of the rest of the team. I want to thank Perry and Michael for putting their resources and talent once more behind matplotlib, securing the future of the project. It is difficult to overstate the value and importance of matplotlib, and therefore of John's contributions (which do not end in matplotlib, by the way; but a biography will have to wait for another day...). Python has become a major force in the technical and scientific computing world, leading the open source offers and challenging expensive proprietary platforms with large teams and millions of dollars of resources behind them. But this would be impossible without a solid data visualization tool that would allow both ad-hoc data exploration and the production of complex, fine-tuned figures for papers, reports or websites. John had the vision to make matplotlib easy to use, but powerful and flexible enough to work in graphical user interfaces and as a server-side library, enabling a myriad use cases beyond his personal needs. This means that now, matplotlib powers everything from plots in dissertations and journal articles to custom data analysis projects and websites. And despite having left his academic career a few years ago for a job in industry, he remained engaged enough that as of today, he is still the top committer to matplotlib; this is the git shortlog of those with more than 1000 commits to the project: 2145 John Hunter <jdh2358(a)gmail.com> 2130 Michael Droettboom <mdboom(a)gmail.com> 1060 Eric Firing <efiring(a)hawaii.edu> All of this was done by a man who had three children to raise and who still always found the time to help those on the mailing lists, solve difficult technical problems in matplotlib, teach courses and seminars about scientific Python, and more recently help create the NumFOCUS foundation project. Despite the challenges that raising three children in an expensive city like Chicago presented, he never once wavered from his commitment to open source. But unfortunately now he is not here anymore to continue providing for their well-being, and I hope that all those who have so far benefited from his generosity, will thank this wonderful man who always gave far more than he received. Thanks to the rapid action of Travis Oliphant, the NumFOCUS foundation is now acting as an escrow agent to accept donations that will go into a fund to support the education and care of his wonderful girls Rahel, Ava and Clara. If you have benefited from John's many contributions, please say thanks in the way that would matter most to him, by helping Miriam continue the task of caring for and educating Rahel, Ava and Clara. You will find all the information necessary to make a donation here: http://numfocus.org/johnhunter Remember that even a small donation helps! If all those who ever use matplotlib give just a little bit, in the long run I am sure that we can make a difference. If you are a company that benefits in a serious way from matplotlib, remember that John was a staunch advocate of keeping all scientific Python projects under the BSD license so that commercial users could benefit from them without worry. Please say thanks to John in a way commensurate with your resources (and check how much a yearly matlab license would cost you in case you have any doubts about the value you are getting...). John's family is planning a private burial in Tennessee, but (most likely in September) there will also be a memorial service in Chicago that friends and members of the community can attend. We don't have the final scheduling details at this point, but I will post them once we know. I would like to again express my gratitude to Travis Oliphant for moving quickly with the setup of the donation support, and to Eric Jones (the founder of Enthought and another one of the central figures in our community) who immediately upon learning of John's plight contributed resources to support the family with everyday logistics while John was facing treatment as well as my travel to Chicago to assist. This kind of immediate urge to come to the help of others that Eric and Travis displayed is a hallmark of our community. Before closing, I want to take a moment to publicly thank the incredible staff of the University of Chicago medical center. The last two weeks were an intense and brutal ordeal for John and his loved ones, but the hospital staff offered a sometimes hard to believe, unending supply of generosity, care and humanity in addition to their technical competence. The latter is something we expect from a first-rate hospital at a top university, where the attending physicians can be world-renowned specialists in their field. But the former is often forgotten in a world often ruled by a combination of science and concerns about regulations and liability. Instead, we found generous and tireless staff who did everything in their power to ease the pain, always putting our well being ahead of any mindless adherence to protocol, patiently tending to every need we had and working far beyond their stated responsibilities to support us. To name only one person (and many others are equally deserving), I want to thank Dr. Carla Moreira, chief surgical resident, who spent the last few hours of John's life with us despite having just completed a solid night shift of surgical work. Instead of resting she came to the ICU and worked to ensure that those last hours were as comfortable as possible for John; her generous actions helped us through a very difficult moment. It is now time to close this already too long message... John, thanks for everything you gave all of us, and for the privilege of knowing you. Fernando. ps - I have sent this with my 'mailing lists' email. If you need to contact me directly for anything regarding the above, please write to my regular address at Fernando.Perez(a)berkeley.edu, where I do my best to reply more promptly.

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A sad day for our community. John Hunter: 1968-2012.
by Fernando Perez 30 Aug '12

30 Aug '12

Dear friends and colleagues, I am terribly saddened to report that yesterday, August 28 2012 at 10am, John D. Hunter died from complications arising from cancer treatment at the University of Chicago hospital, after a brief but intense battle with this terrible illness. John is survived by his wife Miriam, his three daughters Rahel, Ava and Clara, his sisters Layne and Mary, and his mother Sarah. Note: If you decide not to read any further (I know this is a long message), please go to this page for some important information about how you can thank John for everything he gave in a decade of generous contributions to the Python and scientific communities: http://numfocus.org/johnhunter. Just a few weeks ago, John delivered his keynote address at the SciPy 2012 conference in Austin centered around the evolution of matplotlib: http://www.youtube.com/watch?v=e3lTby5RI54 but tragically, shortly after his return home he was diagnosed with advanced colon cancer. This diagnosis was a terrible discovery to us all, but John took it with his usual combination of calm and resolve, and initiated treatment procedures. Unfortunately, the first round of chemotherapy treatments led to severe complications that sent him to the intensive care unit, and despite the best efforts of the University of Chicago medical center staff, he never fully recovered from these. Yesterday morning, he died peacefully at the hospital with his loved ones at his bedside. John fought with grace and courage, enduring every necessary procedure with a smile on his face and a kind word for all of his caretakers and becoming a loved patient of the many teams that ended up involved with his case. This was no surprise for those of us who knew him, but he clearly left a deep and lasting mark even amongst staff hardened by the rigors of oncology floors and intensive care units. I don't need to explain to this community the impact of John's work, but allow me to briefly recap, in case this is read by some who don't know the whole story. In 2002, John was a postdoc at the University of Chicago hospital working on the analysis of epilepsy seizure data in children. Frustrated with the state of the existing proprietary solutions for this class of problems, he started using Python for his work, back when the scientific Python ecosystem was much, much smaller than it is today and this could have been seen as a crazy risk. Furthermore, he found that there were many half-baked solutions for data visualization in Python at the time, but none that truly met his needs. Undeterred, he went on to create matplotlib (http://matplotlib.org) and thus overcome one of the key obstacles for Python to become the best solution for open source scientific and technical computing. Matplotlib is both an amazing technical achievement and a shining example of open source community building, as John not only created its backbone but also fostered the development of a very strong development team, ensuring that the talent of many others could also contribute to this project. The value and importance of this are now painfully clear: despite having lost John, matplotlib continues to thrive thanks to the leadership of Michael Droetboom, the support of Perry Greenfield at the Hubble Telescope Science Institute, and the daily work of the rest of the team. I want to thank Perry and Michael for putting their resources and talent once more behind matplotlib, securing the future of the project. It is difficult to overstate the value and importance of matplotlib, and therefore of John's contributions (which do not end in matplotlib, by the way; but a biography will have to wait for another day...). Python has become a major force in the technical and scientific computing world, leading the open source offers and challenging expensive proprietary platforms with large teams and millions of dollars of resources behind them. But this would be impossible without a solid data visualization tool that would allow both ad-hoc data exploration and the production of complex, fine-tuned figures for papers, reports or websites. John had the vision to make matplotlib easy to use, but powerful and flexible enough to work in graphical user interfaces and as a server-side library, enabling a myriad use cases beyond his personal needs. This means that now, matplotlib powers everything from plots in dissertations and journal articles to custom data analysis projects and websites. And despite having left his academic career a few years ago for a job in industry, he remained engaged enough that as of today, he is still the top committer to matplotlib; this is the git shortlog of those with more than 1000 commits to the project: 2145 John Hunter <jdh2358(a)gmail.com> 2130 Michael Droettboom <mdboom(a)gmail.com> 1060 Eric Firing <efiring(a)hawaii.edu> All of this was done by a man who had three children to raise and who still always found the time to help those on the mailing lists, solve difficult technical problems in matplotlib, teach courses and seminars about scientific Python, and more recently help create the NumFOCUS foundation project. Despite the challenges that raising three children in an expensive city like Chicago presented, he never once wavered from his commitment to open source. But unfortunately now he is not here anymore to continue providing for their well-being, and I hope that all those who have so far benefited from his generosity, will thank this wonderful man who always gave far more than he received. Thanks to the rapid action of Travis Oliphant, the NumFOCUS foundation is now acting as an escrow agent to accept donations that will go into a fund to support the education and care of his wonderful girls Rahel, Ava and Clara. If you have benefited from John's many contributions, please say thanks in the way that would matter most to him, by helping Miriam continue the task of caring for and educating Rahel, Ava and Clara. You will find all the information necessary to make a donation here: http://numfocus.org/johnhunter Remember that even a small donation helps! If all those who ever use matplotlib give just a little bit, in the long run I am sure that we can make a difference. If you are a company that benefits in a serious way from matplotlib, remember that John was a staunch advocate of keeping all scientific Python projects under the BSD license so that commercial users could benefit from them without worry. Please say thanks to John in a way commensurate with your resources (and check how much a yearly matlab license would cost you in case you have any doubts about the value you are getting...). John's family is planning a private burial in Tennessee, but (most likely in September) there will also be a memorial service in Chicago that friends and members of the community can attend. We don't have the final scheduling details at this point, but I will post them once we know. I would like to again express my gratitude to Travis Oliphant for moving quickly with the setup of the donation support, and to Eric Jones (the founder of Enthought and another one of the central figures in our community) who immediately upon learning of John's plight contributed resources to support the family with everyday logistics while John was facing treatment as well as my travel to Chicago to assist. This kind of immediate urge to come to the help of others that Eric and Travis displayed is a hallmark of our community. Before closing, I want to take a moment to publicly thank the incredible staff of the University of Chicago medical center. The last two weeks were an intense and brutal ordeal for John and his loved ones, but the hospital staff offered a sometimes hard to believe, unending supply of generosity, care and humanity in addition to their technical competence. The latter is something we expect from a first-rate hospital at a top university, where the attending physicians can be world-renowned specialists in their field. But the former is often forgotten in a world often ruled by a combination of science and concerns about regulations and liability. Instead, we found generous and tireless staff who did everything in their power to ease the pain, always putting our well being ahead of any mindless adherence to protocol, patiently tending to every need we had and working far beyond their stated responsibilities to support us. To name only one person (and many others are equally deserving), I want to thank Dr. Carla Moreira, chief surgical resident, who spent the last few hours of John's life with us despite having just completed a solid night shift of surgical work. Instead of resting she came to the ICU and worked to ensure that those last hours were as comfortable as possible for John; her generous actions helped us through a very difficult moment. It is now time to close this already too long message... John, thanks for everything you gave all of us, and for the privilege of knowing you. Fernando. ps - I have sent this with my 'mailing lists' email. If you need to contact me directly for anything regarding the above, please write to my regular address at Fernando.Perez(a)berkeley.edu, where I do my best to reply more promptly.

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pull chatter are not updating
by josef.pktd＠gmail.com 30 Aug '12

30 Aug '12

Is there a new alternative to the feeds http://new.scipy.org/scipy-pull.xml http://new.scipy.org/scipy-chatter.xml They didn't update for a month. Thanks, Josef

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