Mailman 3 August 2012 - SciPy-User

Scipy.test() crashes Idle
by Chris Stehlik Aug. 25, 2012

Aug. 25, 2012

Hi, I am new to scipy and python itself. I was told to run scipy.test() to check it. I have a pc running Windows XP with the Enthought Free Python distribution 2.7.3 with Numpy 1.6.1 and Scipy 0.10.1. When I run scipy.test() in Idle, it crashes part way through. Numpy.test() goes through with no problems, but scipy.test() crashes each time. How does one go about reporting a bug one this ? Chris

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cholesky of sparse symmetric banded matrix
by josef.pktd＠gmail.com Aug. 23, 2012

Aug. 23, 2012

I would like to get a cholesky decomposition of a symmetric banded matrix and multiply it with a dense array I found http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.cholesky_b… and this http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dia_matrix… either 1) is there a way to do linear algebra (dot multiplication) directly in the "upper diagonal ordered form"? or 2) is there an efficient way to go from the "upper diagonal ordered form" to a sparse diagonal matrix (or both ways)? Is there code that uses this and that I can look at for the pattern? ------- my problem is standard linear least squares, where I have an explicit banded form for the (nobs, nobs) weighting matrix X'WX and X'Wy and I need a transformation X2 = W^(0.5) X and y2 = W^(0.5) y so I get X2'X2 and X2'y2 (nobs: number of observations, prime is transpose) My first example only has one upper and one lower off-diagonal, so working with dense is wasteful. Josef

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generic expectation operator
by nicky van foreest Aug. 23, 2012

Aug. 23, 2012

Hi, For numerous purposes I need to compute the expectation of some function with respect to some probability distribution. i.e. E f(X) = \int f(x) dG(x), where f is the function and G the distribution. When G is continuous, this is simple, just use scipy.integrate.quad. However, if G is discrete, things become more complicated since quad often misses the spikes in the density. To deal with this problem, in part, I came up with the following code, but it is not completely ok yet, see below. #!/usr/bin/env python import scipy.stats as stats import scipy.integrate from math import sqrt def E(f, X): if hasattr(X,'dist'): if isinstance(X.dist, stats.rv_discrete): # hint from Ralph g = X.pmf else: g = X.pdf return scipy.integrate.quad(lambda x: x*g(x), X.dist.a, X.dist.b) else: return sum(f(x)*p for x,p in zip(X.xk, X.pk)) # now the following works: X = stats.norm(3,sqrt(3)) print E(sqrt, X) # this is ok too grid = [50, 100, 150, 200, 250, 300] probs=[2,3,2,1.,0.5,0.5] tot = sum(probs) probs = [p/tot for p in probs] X = stats.rv_discrete(values=(grid,probs), name='sizeDist') print E(sqrt, X) # but this is not ok X = stats.poisson(3) print E(sqrt, X) ------------------------------------------ This is the output: 11.0383463182 (7.771634331185016e-19, 1.916327339868129e-17) (2.9999999999999996, 1.1770368678494054e-08) So indeed, quad misses the spikes in the poisson distribution. Is there any nice way to resolve this? Moreover, is there a better way to build the expectation operator? Perhaps, if all works, it would be a useful add-on to distributions.py, or else I can include it in the distribution tutorial, if other people also think this is useful. thanks Nicky

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[ANN] Call for abstracts: BigData minisymposium at CSE'13, February 2013, Boston
by Fernando Perez Aug. 23, 2012

Aug. 23, 2012

Dear colleagues, next year's SIAM conference on Computational Science and Engineering, CSE'13, will take place in Boston, February 25-March 1 (http://www.siam.org/meetings/cse13), and for this version there will be a track focused on the topic of Big Data. This term has rapidly risen in recent discussions of science and even of mainstream business computing, and for good reasons. Today virtually all disciplines are facing a flood of quantitative information whose volumes have often grown faster than the quality of our tools for extracting insight from these data. SIAM hopes that CSE'13 will provide an excellent venue for discussing these problems, from the vantage point offered by a community whose expertise combines analytical insights, algorithmic development, software engineering and domain-specific applications. As part of this event, Titus Brown (http://ged.msu.edu) and I are organizing a minisymposium where we would like to have a group of presentations that address both novel algorithmic ideas and computational approaches as well as domain-specific problems. Data doesn't appear in a vacuum, and data from different domains presents a mix of common problems along with questions that may be specific to each; we hope that by engaging a dialog between those working on algorithmic and implementation questions and those with specific problems from the field, valuable insights can be obtained. If you would like to contribute to this minisymposium, please contact us directly at: "C. Titus Brown" <ctb(a)msu.edu>, "Fernando Perez" <Fernando.Perez(a)berkeley.edu> with your name and affiliation, the title of your proposed talk and a brief description (actual abstracts are due later so an informal description will suffice for now), by Wednesday August 29. For more details on the submission process, see: http://www.siam.org/meetings/cse13/submissions.php Please forward this to any interested colleagues. Regards, Titus and Fernando.

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Constrained optimization
by Ted To Aug. 23, 2012

Aug. 23, 2012

Hi, The documentation for the constrained optimization routines seems pretty sparse and the googling I did revealed nothing in the way of examples of usage. I defined a function W(q) where q is a vector of length 2. Using fmin obviously fails (too many iterations) but works in the sense that it doesn't outright fail with an error. Using one of the constrained optimizers, fmin_l_bfgs_b for example, completely chokes with the Traceback output at http://pastebin.com/t8tU8YBm. The commands leading to this are: x0=[.1,.1] fmin_l_bfgs_b(W,x0,bounds=[(0,1),(0,1)]) so that the intention is to bound q[0] and q[1] between 0 and 1. Any help with this would be much appreciated. Cheers, Ted

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ANN: visvis 1.7 - The object oriented approach to visualization - now also in Py3k
by Almar Klein Aug. 22, 2012

Aug. 22, 2012

Dear all, On behalf of the Visvis development team, I'm pleased to announce version 1.7 of Visvis - the object oriented approach to visualization. Visvis is a pure Python library for visualization of 1D to 4D data in an object oriented way. Essentially, visvis is an object oriented layer of Python on top of OpenGl, thereby combining the power of OpenGl with the usability of Python. A Matlab-like interface in the form of a set of functions allows easy creation of objects (e.g. plot(), imshow(), volshow(), surf()). The most notable changes are: - support for Py3k - a PySide backend - full Unicode support and prettier rendering of text (by using FreeType) Website: https://code.google.com/p/visvis/ Discussion group: http://groups.google.com/group/visvis Release notes: https://code.google.com/p/visvis/wiki/releaseNotes Regards, Almar -- Almar Klein, PhD Science Applied phone: +31 6 19268652 e-mail: a.klein(a)science-applied.nl

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Hi I have a quastion to weave
by SungHwan Choi Aug. 22, 2012

Aug. 22, 2012

Hi I am Sunghwan Choi Now, I am interested on weave. First of all, I have c code to be wrapped by python. tmp.c file #include<xc.h> #include<stdio.h> int main(){ int np =3; int return_val; xc_func_type func; xc_func_init(&func, XC_GGA_X_B88, XC_UNPOLARIZED); xc_func_end(&func); return_val=np+131; printf("%d", return_val); } gcc -o exe tmp.c -L/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs/ -lxc -lm ./exe above code is my target code and now, from scipy.weave import inline np=3 code=\ """ xc_func_type func; xc_func_init(&func, XC_GGA_X_B88, XC_UNPOLARIZED); xc_func_end(&func); return_val=np+131; """ print inline(code,['np'],include_dirs = ["/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/"],library_dirs=["/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs"],libraries=["m","xc"],headers=["<xc.h>","<stdio.h>"],compiler='gcc') print np This is my solution but it give me a error In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1975:0, from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17, from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15, from /home/shchoi/.python27_compiled/sc_a383cf366f9347f9b6ff017e9aab78882.cpp:22: /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" /usr/bin/ld: /home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs/libxc.a(libxc_la-functionals.o): relocation R_X86_64_32 against `xc_functional_keys' can not be used when making a shared object; recompile with -fPIC /home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs/libxc.a: could not read symbols: Bad value collect2: ld returned 1 exit status /usr/bin/ld: /home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs/libxc.a(libxc_la-functionals.o): relocation R_X86_64_32 against `xc_functional_keys' can not be used when making a shared object; recompile with -fPIC /home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs/libxc.a: could not read symbols: Bad value collect2: ld returned 1 exit status Traceback (most recent call last): File "tmp.py", line 12, in <module> print inline(code,['np'],include_dirs = ["/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/"],library_dirs=["/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs"],libraries=["m","xc"],headers=["<xc.h>","<stdio.h>"],compiler='gcc') File "/usr/lib64/python2.7/site-packages/scipy/weave/inline_tools.py", line 355, in inline **kw) File "/usr/lib64/python2.7/site-packages/scipy/weave/inline_tools.py", line 482, in compile_function verbose=verbose, **kw) File "/usr/lib64/python2.7/site-packages/scipy/weave/ext_tools.py", line 367, in compile verbose = verbose, **kw) File "/usr/lib64/python2.7/site-packages/scipy/weave/build_tools.py", line 272, in build_extension setup(name = module_name, ext_modules = [ext],verbose=verb) File "/usr/lib64/python2.7/site-packages/numpy/distutils/core.py", line 186, in setup return old_setup(**new_attr) File "/usr/lib64/python2.7/distutils/core.py", line 169, in setup raise SystemExit, "error: " + str(msg) scipy.weave.build_tools.CompileError: error: Command "g++ -pthread -shared /tmp/shchoi/python27_intermediate/compiler_81874d258832299909898bc232bd652b/home/shchoi/.python27_compiled/sc_a383cf366f9347f9b6ff017e9aab78882.o /tmp/shchoi/python27_intermediate/compiler_81874d258832299909898bc232bd652b/usr/lib64/python2.7/site-packages/scipy/weave/scxx/weave_imp.o -L/home/shchoi/git/LagChem/LagChem/Basic/XCs/libxc-1.2.0/src/.libs -L/usr/lib64 -lm -lxc -lpython2.7 -o /home/shchoi/.python27_compiled/sc_a383cf366f9347f9b6ff017e9aab78882.so" failed with exit status 1 I have tried to solve for a while but now I give up. please help me anyone~

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rv_discrete
by nicky van foreest Aug. 22, 2012

Aug. 22, 2012

Hi, How can I find out whether a frozen rv is a subclass of rv_discrete or rv_continuous. This doesn't work: X = stats.norm(10, np.sqrt(10)) print issubclass(X.__class__, stats.rv_continuous) thanks Nicky

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(no subject)
by alind sharma Aug. 21, 2012

Aug. 21, 2012

http://www.transporthirereviews.co.uk/wp-content/themes/twentyten/makoler.h…

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Vector Field Generation
by Prashant Aug. 21, 2012

Aug. 21, 2012

Hi, I was googling for this problem where I myself don't know that exact term to use for. It could be vector field, follow behaviour, flow field, streamlines or anything else. Please have a look at attached image "vector_field.jpg". 1. Three black rectangles represent polygons in 2d space. 2. Red color points are Master Points/Vectors in 2d space 3. Green color points are child points (no vector, only position) Based on Master points I would like to generate vectors (vector field) on child points. I have also attached an image (image1.jpg) that shows desired result. In this image resulting child vectors has smooth transition which is what I am looking for. The four white arrows can be considered as Master vectors and rest of the points are childrens. I am not sure if nearest neighbour search algorithm would be used but if it's required then we have to consider a situation. I have pointed out a child point using darker black arrow in vector_field.jpg. If we calculate nearest master point for this point, it would give point "C", where correct point would be "A" as point "c" is far away based on polygon distribution. I would really appreciate any pointers or suggestions for the problem. Best Regards Prashant

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