Mailman 3 August 2012 - SciPy-User

odeint
by nicky van foreest Aug. 16, 2012

Aug. 16, 2012

Hi, I am using odeint to integrate the following delay differential equation df/dx = c + f(x) - f(x-1) To solve it (numerically) I use the following iterative approach. The values of f(x) are given on the interval [0,1]. Then with odeint I can obtain f on [1,2]. Now that f in the interval [1,2] is known, I can use this to compute f on [2,3], etc. All in all this is workable, but somewhat cumbersome. Is it possible in odeint to somehow get intermediate values of the function values? Thanks Nicky

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ANN: SciPy 0.11.0 release candidate 2
by Ralf Gommers Aug. 14, 2012

Aug. 14, 2012

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-User] Question about errors (uncertainties) in non-linear least squares fitting (Jonathan Helmus)
by Matt Newville Aug. 14, 2012

Aug. 14, 2012

Hi, On Mon, 13 Aug 2012 15:01:05 -0400 Jonathan Helmus <jjhelmus(a)gmail.com> wrote: > Pawel, > > leastsqbound cannot fix a parameter. If you want a fixed parameter > in the fit you can either rewrite your fitting/error function to take > the fixed parameters as an extra argument, or use a more "full featured" > constrained least squares fitting package like lmfit > (http://newville.github.com/lmfit-py/) whose class based Parameters > allow for additional control. > > For any aspiring developers out there, fixed parameter could be > added to leastsqbound (although it wouldn't be easy) but I'm not > planning on add it in the near term. I would accept a patch which added > this. > > - Jonathan Helmus Reading this conversation with Pawel over the past week or so, I was reminded and re-inspired to fix the setting of bounds in lmfit-py which had been pretty fragile to use the much more robust MINUT-style transformations for min/max bounds as Jonathan did in leastsqbound. Lmfit-py version 0.6.0 borrows heavily from leastsqbound (thanks Jonathan!) for it's implementation of setting bounds, and also includes the ability for users to change whether parameters in the fitting model are fixed or varied, and to set up simple mathematical constraints between parameters. It also provides support tools for more thoroughly investigating confidence intervals beyond the simple use of the covariance matrix (thanks to Till Stensitzki!). This latest version is now available from http://pypi.python.org/pypi/lmfit/0.6 with the development version at http://newville.github.com/lmfit-py/ Feedback, suggestions, and bug reports are most welcome. --Matt Newville <newville at cars.uchicago.edu>

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optimization with multiple processors.
by J. David Lee Aug. 13, 2012

Aug. 13, 2012

Hello everyone. For my work I will need to perform an optimization using an objective function that is slow to evaluate (a few to tens of minutes). I have access to a virtually unlimited number of processors for my work to speed this up, but the optimization routines in scipy are all single threaded. I see two simple ways to utilize multiple processors for newton-like or gradient-descent optimization methods: 1) numerical gradient or Jacobian calculation 2) line search To some extent, parallel processing could be applied to a simplex-like method as well (the simplest example would be doing parallel computation of reflection, extension and contraction points). I think this functionality would fit perfectly in scipy, and I will be working on it in any event, so if anyone would be interested in collaborating or providing guidance, please let me know. Thanks, David

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Question about errors (uncertainties) in non-linear least squares fitting
by Paweł Kwaśniewski Aug. 13, 2012

Aug. 13, 2012

Hi, I'm fitting some data using a wrapper around the scipy.optimize.leastsq method which can be found under http://code.google.com/p/nmrglue/source/browse/trunk/nmrglue/analysis/least… it allows for putting bounds on the fitted parameters, which is very important for me. I'm using the covariance matrix, returned by leastsq() function to estimate the errors of the fitted parameters. The fitting is done using real measurement uncertainties (which are ridiculously small, by the way), so I would expect the resulting parameter error to be reasonable. What don't understand, is that I'm getting extremely small errors on the fitted parameters (I calculate the errors as perr = sqrt(diag(fitres[1])), where fitres[1] is the covariance matrix returned by leastsq() function). For example, a parameter which has a fitted value of ~100 gets an error of ~1e-6. At the same time, when I calculate the reduced chi squared of the fit I'm getting an extremely large number (of the order of 1e8). I can understand the large chi^2 value - the data variance is extremely small and the model curve is not perfect, so even slight deviations of the fitted model from the data will blow up chi^2 into space. But how can the fitted parameter variance be so small, while at the same time the fit is garbage according to chi^2? I guess this requires a better understanding of how the covariance matrix is calculated. Some suggestions anyone? Cheers, Paweł

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Disable singular matrix warning
by Carlos Baptista Aug. 13, 2012

Aug. 13, 2012

Hello there, I am fairly new to Python. As a self-test I re-programmed an old MATLAB assignment in Python using NumPy, SciPy and Matplotlib. At a certain point I am getting the following warning: warning: (almost) singular matrix! (estimated cond. number: 1.87e+14) This result is however expected and therefore I would like to disable this warning. I am getting this warning from: scipy.sparse.linalg.spsolve(A,b) where A has a large condition number. I have already tried the following to no avail: ======================== import warnings with warnings.catch_warnings(): warnings.filterwarnings("ignore",category=Warning) from poisson import solvePoisson ======================== where solvePoisson is the function (located in poisson.py) in which the above solve statement is executed. How can I disable this singular matrix warning? With kind regards, Carlos

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Re: [SciPy-User] kmeans2 question/issue
by James Abel Aug. 11, 2012

Aug. 11, 2012

Thanks - sklearn works great! I got exactly what I expected each time I ran it! James import sys import numpy from sklearn.cluster import * print sys.version vals = numpy.array([[0.0],[0.1],[0.5],[0.6],[1.0],[1.1]]) print vals k_means_ex = KMeans(k=3) x = k_means_ex.fit_predict(vals) print x print k_means_ex.cluster_centers_ print k_means_ex.score(vals) 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)] [[ 0. ] [ 0.1] [ 0.5] [ 0.6] [ 1. ] [ 1.1]] [1 1 0 0 2 2] [[ 0.55] [ 0.05] [ 1.05]] -0.015

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Re: [SciPy-User] watershed
by dlibyp＠gmail.com Aug. 11, 2012

Aug. 11, 2012

Thanks Pawel, This is what I was looking for. Rig Message: 2 Date: Thu, 9 Aug 2012 11:02:03 +0200 From: Pawe? Kwa?niewski<pawel.kw(a)gmail.com> Subject: Re: [SciPy-User] watershed To: SciPy Users List<scipy-user(a)scipy.org> Message-ID: <CAAih6Wny6=wojRoJubKdCQPvGd1u5smoMNGO9Wp2PARpUcZxNw(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" Hi, I looked into the functions you're interested, although I'm not an expert but as far as I understand it - the watershed_ift() function is implemented in C, since the _nd_image module is a binary package. If you download the SciPy source code (http://sourceforge.net/projects/scipy/files/scipy/) you can find the C source of the function you're interested in the scipy/ndimage/src/ni_measure.c file. The function is called NI_WatershedIFT. I hope this helps, Pawe? 2012/8/8dlibyp(a)gmail.com <dlibyp(a)gmail.com> > Hi all, > My question is a digest code question, so that I post on scipy-user, > please tell me if I should no more do that. > I would like to see source code for watershed algorithm. Code in > scipy.ndimage.measurements for watershed is a wrapper for method > watershed_ift in _nd_image. Module is imported in measurements.py, but I > cannot find it in parent repository. > Sorry for naive question: does _nd_image refers to C implementation of > watershed_ift() method? > Thanks. > Rig > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user

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loadmat and figure
by x.piter＠gmail.com Aug. 10, 2012

Aug. 10, 2012

Hi all. Is it possible to load a malab figure using io.loadmat. It works well with a matrix saved as mat file, but if I try to load a fig file I get the following output: scipy.io.loadmat('test2.mat') {'a': 'Read error: Cannot read matlab functions', '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, Platform: GLNX86, Created on: Sat Aug 4 16:56:09 2012', '__globals__': []} >>> I use scipy shipped with the Debian stable repositories. Thanks. Petro.

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(no subject)
by Alia Aug. 9, 2012

Aug. 9, 2012

http://interracialmobilesex.com/index10_files/goog.php?neighbor233.html

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