Mailman 3 November 2011 - SciPy-User

test issues with 0.10
by Paul Anton Letnes Nov. 15, 2011

Nov. 15, 2011

Hi, when trying to build scipy 0.10 with python 2.7 (from homebrew/python.org), numpy 1.6.1 (built manually) on Mac OS X 10.7.2 and with gcc 4.6.0 (built from source), scipy.test() breaks. Any ideas? Cheers Paul Build and install with: i-courant ~/src/scipy-0.10.0 % ATLAS= BLAS=/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK=/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib CC=/usr/bin/gcc FC=/usr/local/bin/gfortran python setup.py build i-courant ~/src/scipy-0.10.0 % python setup.py install Test: i-courant ~/src % python -c 'import scipy; scipy.test(verbose=2)' Running unit tests for scipy NumPy version 1.6.1 NumPy is installed in /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/numpy SciPy version 0.10.0 SciPy is installed in /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy Python version 2.7.2 (default, Oct 9 2011, 18:03:13) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] nose version 1.1.2 Tests cophenet(Z) on tdist data set. ... ok Tests cophenet(Z, Y) on tdist data set. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) with empty linkage and condensed distance matrix. ... ok Tests num_obs_linkage with observation matrices of multiple sizes. ... ok Tests fcluster(Z, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests from_mlab_linkage on empty linkage array. ... ok Tests from_mlab_linkage on linkage array with multiple rows. ... ok Tests from_mlab_linkage on linkage array with single row. ... ok Tests inconsistency matrix calculation (depth=1) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=1, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=2, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=3, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=4, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=1) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a single linkage. ... ok Tests is_isomorphic on test case #1 (one flat cluster, different labellings) ... ok Tests is_isomorphic on test case #2 (two flat clusters, different labelings) ... ok Tests is_isomorphic on test case #3 (no flat clusters) ... ok Tests is_isomorphic on test case #4A (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #4B (3 flat clusters, different labelings, nonisomorphic) ... ok Tests is_isomorphic on test case #4C (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling, slightly non-isomorphic.) Run 3 times. ... ok Tests is_monotonic(Z) on 1x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting False. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False ... ok Tests is_monotonic(Z) on 3x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on an empty linkage. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on Iris data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Perturbing. Expecting False. ... ok Tests is_valid_im(R) on im over 2 observations. ... ok Tests is_valid_im(R) on im over 3 observations. ... ok Tests is_valid_im(R) with 3 columns. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link counts. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height means. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height standard deviations. ... ok Tests is_valid_im(R) with 5 columns. ... ok Tests is_valid_im(R) with empty inconsistency matrix. ... ok Tests is_valid_im(R) with integer type. ... ok Tests is_valid_linkage(Z) on linkage over 2 observations. ... ok Tests is_valid_linkage(Z) on linkage over 3 observations. ... ok Tests is_valid_linkage(Z) with 3 columns. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative counts. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative distances. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (left). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (right). ... ok Tests is_valid_linkage(Z) with 5 columns. ... ok Tests is_valid_linkage(Z) with empty linkage. ... ok Tests is_valid_linkage(Z) with integer type. ... ok Tests leaders using a flat clustering generated by single linkage. ... ok Tests leaves_list(Z) on a 1x4 linkage. ... ok Tests leaves_list(Z) on a 2x4 linkage. ... ok Tests leaves_list(Z) on the Iris data set using average linkage. ... ok Tests leaves_list(Z) on the Iris data set using centroid linkage. ... ok Tests leaves_list(Z) on the Iris data set using complete linkage. ... ok Tests leaves_list(Z) on the Iris data set using median linkage. ... ok Tests leaves_list(Z) on the Iris data set using single linkage. ... ok Tests leaves_list(Z) on the Iris data set using ward linkage. ... ok Tests linkage(Y, 'average') on the tdist data set. ... ok Tests linkage(Y, 'centroid') on the Q data set. ... ok Tests linkage(Y, 'complete') on the Q data set. ... ok Tests linkage(Y, 'complete') on the tdist data set. ... ok Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected. ... ok Tests linkage(Y, 'single') on the Q data set. ... ok Tests linkage(Y, 'single') on the tdist data set. ... ok Tests linkage(Y, 'weighted') on the Q data set. ... ok Tests linkage(Y, 'weighted') on the tdist data set. ... ok Tests maxdists(Z) on the Q data set using centroid linkage. ... ok Tests maxdists(Z) on the Q data set using complete linkage. ... ok Tests maxdists(Z) on the Q data set using median linkage. ... ok Tests maxdists(Z) on the Q data set using single linkage. ... ok Tests maxdists(Z) on the Q data set using Ward linkage. ... ok Tests maxdists(Z) on empty linkage. Expecting exception. ... ok Tests maxdists(Z) on linkage with one cluster. ... ok Tests maxinconsts(Z, R) on the Q data set using centroid linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using complete linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using median linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using single linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using Ward linkage. ... ok Tests maxinconsts(Z, R) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxinconsts(Z, R) on empty linkage. Expecting exception. ... ok Tests maxinconsts(Z, R) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 0) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 0) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 1) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 1) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 2) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 2) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 3) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3.3). Expecting exception. ... ok Tests maxRstat(Z, R, -1). Expecting exception. ... ok Tests maxRstat(Z, R, 4). Expecting exception. ... ok Tests num_obs_linkage(Z) on linkage over 2 observations. ... ok Tests num_obs_linkage(Z) on linkage over 3 observations. ... ok Tests num_obs_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests num_obs_linkage(Z) with empty linkage. ... ok Tests to_mlab_linkage on linkage array with multiple rows. ... ok Tests to_mlab_linkage on empty linkage array. ... ok Tests to_mlab_linkage on linkage array with single row. ... ok test_hierarchy.load_testing_files ... ok Ticket #505. ... ok Testing that kmeans2 init methods work. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 and its results. ... ok Regression test for #546: fail when k arg is 0. ... ok This will cause kmean to have a cluster with no points. ... ok test_kmeans_simple (test_vq.TestKMean) ... ok test_large_features (test_vq.TestKMean) ... ok test_py_vq (test_vq.TestVq) ... ok test_py_vq2 (test_vq.TestVq) ... ok test_vq (test_vq.TestVq) ... ok Test special rank 1 vq algo, python implementation. ... ok test_codata.test_find ... ok test_codata.test_basic_table_parse ... ok test_codata.test_basic_lookup ... ok test_codata.test_find_all ... ok test_codata.test_find_single ... ok test_codata.test_2002_vs_2006 ... ok Check that updating stored values with exact ones worked. ... ok test_constants.test_fahrenheit_to_celcius ... ok test_constants.test_celcius_to_kelvin ... ok test_constants.test_kelvin_to_celcius ... ok test_constants.test_fahrenheit_to_kelvin ... ok test_constants.test_kelvin_to_fahrenheit ... ok test_constants.test_celcius_to_fahrenheit ... ok test_constants.test_lambda_to_nu ... ok test_constants.test_nu_to_lambda ... ok test_definition (test_basic.TestDoubleFFT) ... ok test_djbfft (test_basic.TestDoubleFFT) ... ok test_n_argument_real (test_basic.TestDoubleFFT) ... ok test_definition (test_basic.TestDoubleIFFT) ... FAIL test_definition_real (test_basic.TestDoubleIFFT) ... ok test_djbfft (test_basic.TestDoubleIFFT) ... FAIL test_random_complex (test_basic.TestDoubleIFFT) ... zsh: segmentation fault python -c 'import scipy; scipy.test(verbose=2)'

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Install Scipy in a TG environment
by RAJESH J Nov. 15, 2011

Nov. 15, 2011

Hello All, I have installed tg2.1 on a virtual environment successfully. I would like to install scipy,numpy and matplotlib in the same environment. I have activated the environment and when trying to install following error message appears. func(*targs, **kargs) File "/tmp/easy_install-BYfIWo/numpy-1.6.1/numpy/distutils/misc_util.py", line 252, in clean_up_temporary_directory ImportError: No module named numpy.distutils Error in sys.exitfunc: Traceback (most recent call last): File "/usr/lib/python2.6/atexit.py", line 24, in _run_exitfuncs func(*targs, **kargs) File "/tmp/easy_install-BYfIWo/numpy-1.6.1/numpy/distutils/misc_util.py", line 252, in clean_up_temporary_directory ImportError: No module named numpy.distutils Please help me in this regard. Regards, Rajesh J

2 1

convolution of 2d arrays of exotic complex numbers (dual and split)
by Denis Yershov Nov. 14, 2011

Nov. 14, 2011

i used scipy.signal.convolve to work with complex-number arrays. now i need to convolve arrays of dual numbers (i*i=0) and split-complex numbers (i*i=1). so i have a class ( http://dl.dropbox.com/u/4988243/iComplex.py) which implements this numbers: class PlainComplex(Complex): def __mul__(self, other): other = ToComplex(other) return PlainComplex(self.re*other.re - self.im*other.im, self.im*other.re + self.re*other.im) class DualComplex(Complex): def __mul__(self, other): other = ToComplex(other) return DualComplex(self.re*other.re, self.im*other.re + self.re*other.im) class SplitComplex(Complex): def __mul__(self, other): other = ToComplex(other) return SplitComplex(self.re*other.re + self.im*other.im, self.im*other.re + self.re*other.im) as you can see only multiplication function is being changed. the question: is it possible to feed arrays of such objects to scipy's convolution function? when i try to do it for PlainComplex class it seems to work wrong (different from using python's complex numbers): http://dl.dropbox.com/u/4988243/sandbox.py - my complex http://dl.dropbox.com/u/4988243/sandbox2.py - python complex or maybe there is some alternative solution? thanx!

2 1

ANN: SciPy 0.10.0 released
by Ralf Gommers Nov. 13, 2011

Nov. 13, 2011

Hi all, I am pleased to announce the availability of SciPy 0.10.0. For this release over a 100 tickets and pull requests have been closed, and many new features have been added. Some of the highlights are: - support for Bento as a build system for scipy - generalized and shift-invert eigenvalue problems in sparse.linalg - addition of discrete-time linear systems in the signal module Sources and binaries can be found at , release notes are copied below. Enjoy, The SciPy developers ========================== SciPy 0.10.0 Release Notes ========================== .. contents:: SciPy 0.10.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a limited number of deprecations and backwards-incompatible changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.10.x branch, and on adding new features on the development master branch. Release highlights: - Support for Bento as optional build system. - Support for generalized eigenvalue problems, and all shift-invert modes available in ARPACK. This release requires Python 2.4-2.7 or 3.1- and NumPy 1.5 or greater. New features ============ Bento: new optional build system -------------------------------- Scipy can now be built with `Bento <http://cournape.github.com/Bento/>`_. Bento has some nice features like parallel builds and partial rebuilds, that are not possible with the default build system (distutils). For usage instructions see BENTO_BUILD.txt in the scipy top-level directory. Currently Scipy has three build systems, distutils, numscons and bento. Numscons is deprecated and is planned and will likely be removed in the next release. Generalized and shift-invert eigenvalue problems in ``scipy.sparse.linalg`` --------------------------------------------------------------------------- The sparse eigenvalue problem solver functions ``scipy.sparse.eigs/eigh`` now support generalized eigenvalue problems, and all shift-invert modes available in ARPACK. Discrete-Time Linear Systems (``scipy.signal``) ----------------------------------------------- Support for simulating discrete-time linear systems, including ``scipy.signal.dlsim``, ``scipy.signal.dimpulse``, and ``scipy.signal.dstep``, has been added to SciPy. Conversion of linear systems from continuous-time to discrete-time representations is also present via the ``scipy.signal.cont2discrete`` function. Enhancements to ``scipy.signal`` -------------------------------- A Lomb-Scargle periodogram can now be computed with the new function ``scipy.signal.lombscargle``. The forward-backward filter function ``scipy.signal.filtfilt`` can now filter the data in a given axis of an n-dimensional numpy array. (Previously it only handled a 1-dimensional array.) Options have been added to allow more control over how the data is extended before filtering. FIR filter design with ``scipy.signal.firwin2`` now has options to create filters of type III (zero at zero and Nyquist frequencies) and IV (zero at zero frequency). Additional decomposition options (``scipy.linalg``) --------------------------------------------------- A sort keyword has been added to the Schur decomposition routine (``scipy.linalg.schur``) to allow the sorting of eigenvalues in the resultant Schur form. Additional special matrices (``scipy.linalg``) ---------------------------------------------- The functions ``hilbert`` and ``invhilbert`` were added to ``scipy.linalg``. Enhancements to ``scipy.stats`` ------------------------------- * The *one-sided form* of Fisher's exact test is now also implemented in ``stats.fisher_exact``. * The function ``stats.chi2_contingency`` for computing the chi-square test of independence of factors in a contingency table has been added, along with the related utility functions ``stats.contingency.margins`` and ``stats.contingency.expected_freq``. Basic support for Harwell-Boeing file format for sparse matrices ---------------------------------------------------------------- Both read and write are support through a simple function-based API, as well as a more complete API to control number format. The functions may be found in scipy.sparse.io. The following features are supported: * Read and write sparse matrices in the CSC format * Only real, symmetric, assembled matrix are supported (RUA format) Deprecated features =================== ``scipy.maxentropy`` -------------------- The maxentropy module is unmaintained, rarely used and has not been functioning well for several releases. Therefore it has been deprecated for this release, and will be removed for scipy 0.11. Logistic regression in scikits.learn is a good alternative for this functionality. The ``scipy.maxentropy.logsumexp`` function has been moved to ``scipy.misc``. ``scipy.lib.blas`` ------------------ There are similar BLAS wrappers in ``scipy.linalg`` and ``scipy.lib``. These have now been consolidated as ``scipy.linalg.blas``, and ``scipy.lib.blas`` is deprecated. Numscons build system --------------------- The numscons build system is being replaced by Bento, and will be removed in one of the next scipy releases. Backwards-incompatible changes ============================== The deprecated name `invnorm` was removed from ``scipy.stats.distributions``, this distribution is available as `invgauss`. The following deprecated nonlinear solvers from ``scipy.optimize`` have been removed:: - ``broyden_modified`` (bad performance) - ``broyden1_modified`` (bad performance) - ``broyden_generalized`` (equivalent to ``anderson``) - ``anderson2`` (equivalent to ``anderson``) - ``broyden3`` (obsoleted by new limited-memory broyden methods) - ``vackar`` (renamed to ``diagbroyden``) Other changes ============= ``scipy.constants`` has been updated with the CODATA 2010 constants. ``__all__`` dicts have been added to all modules, which has cleaned up the namespaces (particularly useful for interactive work). An API section has been added to the documentation, giving recommended import guidelines and specifying which submodules are public and which aren't. Authors ======= This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order): * Jeff Armstrong + * Matthew Brett * Lars Buitinck + * David Cournapeau * FI$H 2000 + * Michael McNeil Forbes + * Matty G + * Christoph Gohlke * Ralf Gommers * Yaroslav Halchenko * Charles Harris * Thouis (Ray) Jones + * Chris Jordan-Squire + * Robert Kern * Chris Lasher + * Wes McKinney + * Travis Oliphant * Fabian Pedregosa * Josef Perktold * Thomas Robitaille + * Pim Schellart + * Anthony Scopatz + * Skipper Seabold + * Fazlul Shahriar + * David Simcha + * Scott Sinclair + * Andrey Smirnov + * Collin RM Stocks + * Martin Teichmann + * Jake Vanderplas + * Gaël Varoquaux + * Pauli Virtanen * Stefan van der Walt * Warren Weckesser * Mark Wiebe + A total of 35 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

1 0

Re: [SciPy-User] ImportError: *.so: cannot open shared object file: No such file or directory
by Pundurs, Mark Nov. 12, 2011

Nov. 12, 2011

Thanks, David! How do I (a Linux newbie) add paths to environment variable LD_LIBRARY_PATH? ------------------------------ Date: Wed, 2 Nov 2011 15:51:56 +0000 From: David Cournapeau <cournape(a)gmail.com> Hi Mark, On Wed, Nov 2, 2011 at 3:38 PM, Pundurs, Mark <mark.pundurs(a)navteq.com> wrote: > I want to use the function stats.norm.isf, but no matter how I try to import it I end up with the error "ImportError: <libname>.so: cannot open shared object file: No such file or directory". The .so files cited do exist in /usr/lib (as symbolic links to other .so files that also exist in that directory). From what I've read, that's where they're supposed to be - but I think the Python installation is in a nonstandard location. Is that the problem? How can I work around it? I believe RHEL 4 uses g77 as its default fortran compiler, so you have a custom gfortran build somewhere, am I right ? If so, you need to add the paths where libgfortran.so and liblapack.so are to the environment variable LD_LIBRARY_PATH. Given that scipy has been built (by someone else for you ?), you may want to ask them about it for the exact locations of those libraries. cheers, David The information contained in this communication may be CONFIDENTIAL and is intended only for the use of the recipient(s) named above. If you are not the intended recipient, you are hereby notified that any dissemination, distribution, or copying of this communication, or any of its contents, is strictly prohibited. If you have received this communication in error, please notify the sender and delete/destroy the original message and any copy of it from your computer or paper files.

2 1

np_inline 0.3 released
by J. David Lee Nov. 11, 2011

Nov. 11, 2011

Hello. This morning I released version 0.3 of np_inline, a module for in-lining C code in python. np_inline is a simple alternative to weave.inline for embedding C code in python. Its main selling points are: 1) simplicity - Implemented in a single file with less than 500 lines including the C-file template, comments, docstrings, and whitespace. 2) simplicity - Generated C files are human-readable and reasonably formatted. 3) multiprocessing support - Works properly with python threads or processes. I've been using this module daily for over a year without issue, and would be happy to hear any comments or criticisms. A source distribution and documentation is available on the web at: http://pages.cs.wisc.edu/~johnl/np_inline/ or on github: https://github.com/johnnylee/np_inline or using easy_install or pip. Thanks, David

2 1

Ticket #1187: ode crashes if rhs returns a tuple instead of a list
by Tony Yu Nov. 11, 2011

Nov. 11, 2011

I just want to draw attention to the bug report in http://projects.scipy.org/scipy/ticket/1187. Basically, scipy.integrate.ode takes a function as input, and the error occurs if that function returns a tuple (instead of, e.g., a list). If there isn't a simple fix (I can't tell b/c the error occurs within C-code, which I'm not at all proficient in), then I think this should print a more informative error message. Best, -Tony

3 6

Error using idlsave in IPython
by Gökhan Sever Nov. 10, 2011

Nov. 10, 2011

Hello, Could you help me to clarify the following idlsave data accessing issue? Quick update: This seems to be an issue with IPython. s1['dn'] call works fine within regular python shell. I1 from scipy import io I2 s1 = io.idl.readsav('test.sav') I3 s1? Type: AttrDict Base Class: <class 'scipy.io.idl.AttrDict'> String Form: {'dn': array([ 1.02282184e+07, 1.05383408e+07, 1.08758739e+07, 1.12449965e+07, 1. <...> (('r', 'R'), '>f8'), (('v', 'V'), '>f8')]), 'tfit': array([ 4.82394886e+02, 4.18176107e-01])} Namespace: Interactive Length: 11 File: /usr/lib64/python2.7/site-packages/scipy/io/idl.py Definition: s1(self, name) ### Why is the following call failing? ### I4 s1['dn'] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /home/gsever/Desktop/python-repo/ipython/IPython/core/prefilter.pyc in prefilter_lines(self, lines, continue_prompt) 358 for lnum, line in enumerate(llines) ]) 359 else: --> 360 out = self.prefilter_line(llines[0], continue_prompt) 361 362 return out /home/gsever/Desktop/python-repo/ipython/IPython/core/prefilter.pyc in prefilter_line(self, line, continue_prompt) 333 return normal_handler.handle(line_info) 334 --> 335 prefiltered = self.prefilter_line_info(line_info) 336 # print "prefiltered line: %r" % prefiltered 337 return prefiltered /home/gsever/Desktop/python-repo/ipython/IPython/core/prefilter.pyc in prefilter_line_info(self, line_info) 273 # print "prefilter_line_info: ", line_info 274 handler = self.find_handler(line_info) --> 275 return handler.handle(line_info) 276 277 def find_handler(self, line_info): /home/gsever/Desktop/python-repo/ipython/IPython/core/prefilter.pyc in handle(self, line_info) 813 814 force_auto = isinstance(obj, IPyAutocall) --> 815 auto_rewrite = getattr(obj, 'rewrite', True) 816 817 if esc == ESC_QUOTE: /usr/lib64/python2.7/site-packages/scipy/io/idl.pyc in __getitem__(self, name) 657 658 def __getitem__(self, name): --> 659 return super(AttrDict, self).__getitem__(name.lower()) 660 661 def __setitem__(self, key, value): KeyError: 'rewrite' Back then I was accessing this key via (when idlsave was a separate module): s1.variables['dn'] but it is long gone in the scipy. The following works. Is this the correct way to access variables from this dictionary? I14 s1.get('dn') O14 array([ 1.02282184e+07, 1.05383408e+07, 1.08758739e+07, 1.12449965e+07, 1.16508267e+07, 1.20997100e+07, Thanks. -- Gökhan

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Dotting two sparse rows
by Matt Henderson Nov. 10, 2011

Nov. 10, 2011

Hi there, I need to dot two csr matrices with shapes (1, 25675), (1, 25675) together, giving a single number. These rows are extremely sparse- with around 20 non-zero entries each. What's the fastest way to do this? I know the following works: np.dot(a, b.T).todense()[0,0] Cheers, Matt

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multidimensional least squares fitting
by Daniel Mader Nov. 9, 2011

Nov. 9, 2011

Hi everyone, I'd like to do some rather simple multidimensional curve fitting. Simple, because usually it's only a plane, or a weak 2nd order surface. Here's the same question which I tried to follow, but I have no clue how to feed the 2D arrays into leastsq(): http://stackoverflow.com/questions/529184/simple-multidimensional-curve-fit… Likely I am just missing a small piece of information, and I'd be happy to get a clue :) Thanks in advance, and here's some code to demonstrate what I want and to get started, Daniel import pylab import scipy import scipy.optimize from mpl_toolkits.mplot3d import Axes3D #import sys,os,platform #if platform.system() == 'Windows': # home = os.environ['HOMESHARE'] #elif platform.system() == 'Linux': # home = os.environ['HOME'] #sys.path.append(home + '/python') #sys.path.append(home + '/11_PythonWork') #import pylabSettings ##****************************************************************************** ##****************************************************************************** ''' f = p0 + p1*x + p2*y ''' ##------------------------------------------------------------------------------ def __residual(params, f, x, y): ''' Define fit function; Return residual error. ''' p0, p1, p2 = params return p0 + p1*x + p2*y - f ## load raw data (=create some dummy data): dataX = scipy.arange(0,11,1) dataY = dataX/10. dataZ = 0.5 + 1.1*dataX + 1.5*dataY dataXX, dataYY = scipy.meshgrid(dataX,dataY) dataZZ = 0.5 + 1.1*dataXX + 1.5*dataYY ## plot data pylab.close('all') fig = pylab.figure() ax = Axes3D(fig) ax.plot_wireframe(dataXX, dataYY, dataZZ) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') pylab.show() ## guess initial values for parameters p0 = [0., 1., 1.] print __residual(p0, dataZZ, dataXX, dataYY) ## works but is not 2D! p1, p_cov = scipy.optimize.leastsq(__residual, x0=p0, args=(dataZ, dataX, dataY)) print p1 ## doesn't work :() p1, p_cov = scipy.optimize.leastsq(__residual, x0=p0, args=(dataZZ, dataXX, dataYY)) print p1

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