Mailman 3 October 2013 - SciPy-User

ANN: Scipy 0.12.1 release
by Ralf Gommers 10 Oct '13

10 Oct '13

Hi, I'm happy to announce the availability of the scipy 0.12.1 release. This is a bugfix only release; it contains a fix for a security issue in the weave module. Source tarballs, binaries and release notes can be found at http://sourceforge.net/projects/scipy/files/scipy/0.12.1/. Cheers, Ralf ========================== SciPy 0.12.1 Release Notes ========================== SciPy 0.12.1 is a bug-fix release with no new features compared to 0.12.0. The single issue fixed by this release is a security issue in ``scipy.weave``, which was previously using temporary directories in an insecure manner under certain circumstances. Checksums ========= 464a7da15804638b4c272e7305200c0b release/installers/scipy-0.12.1-py2.7-python.org-macosx10.6.dmg 64951c17cfd79f86eb22d1e0a7c7f8fd release/installers/scipy-0.12.1-win32-superpack-python2.6.exe 81f80077871d7355ac0f659b5b617a34 release/installers/scipy-0.12.1-win32-superpack-python2.7.exe b45a398cc8702389caaf2feabeef5875 release/installers/scipy-0.12.1-win32-superpack-python3.2.exe 906278290152fedfe79029371ca584a5 release/installers/scipy-0.12.1.tar.gz 3f23065fc45152c92c3588dad2f20c62 release/installers/scipy-0.12.1.zip

1 0

Re: [SciPy-User] optimize.fmin_slsqp bounds problem
by Jean-Francois.Moulin＠hzg.de 10 Oct '13

10 Oct '13

>http://sscce.org/ > >Please provide a self-contained reproducible example. What is the exact objfn? > >I don't get any errors with > >------------------------------ >import numpy as np >from scipy.optimize import fmin_slsqp> > >np.random.seed(1234) >y = np.random.randn(9) * 100 >def objfn(x): > return ((x - y)**2).sum() > >bounds = [(-1e-09, 1e-08), (1e-07, 1e-05), (1e-07, 1e-05), (1e-10, >1e-08), (1e-10, 1e-08), (1e-10, 1e-08), (80.0, 200.0), (1.0, 5.0), (1.0, >5.0)] >x0 = np.array([ 0.00000000e+00, 1.01000000e-06, 3.03000000e-06, > 1.00000000e-09, 1.00000000e-09, 1.00000000e-09, > 1.50000000e+02, 2.00000000e+00, 2.00000000e+00]) >res = fmin_slsqp(objfn, >np.array(x0,dtype=float),bounds=bounds,full_output=True) >------------------------------ >The algorithm and code used (with permission from ACM) is this: >http://dl.acm.org/citation.cfm?id=192124 > >The only way to understand what goes wrong is probably to look in the >Fortran code and see what it does. > >-- >Pauli Virtanen Well my objective function is a quite complex recursive function of the 9 input parameters which in the end returns the sum of the squared deviation as expected by fmin_slsqp (it involves several of my own modules and a large dataset, so too long to be posted). I have checked that the return value is valid (not NAN eg) and as you see there is a single iteration done. I also have checked the minimizer with examples similar to yours, and it worked. I'll have to get my hand Fortran dirty it seems, I just hoped someone had alreay had this kind of symptom and found a "trivial" error. Thanks anyway! JF Helmholtz-Zentrum Geesthacht Zentrum für Material- und Küstenforschung GmbH Max-Planck-Straße 1 I 21502 Geesthacht I Deutschland/Germany Geschäftsführer/Board of Management: Prof. Dr. Wolfgang Kaysser, Dipl.-Ing. Michael Ganß Vorsitzender des Aufsichtsrates/Chairman of the Supervisory Board: MinDirig Wilfried Kraus Amtsgericht Lübeck HRB 285 GE (Register Court) Internet: http://www.hzg.de

2 3

optimize.fmin_slsqp bounds problem
by Jean-Francois.Moulin＠hzg.de 09 Oct '13

09 Oct '13

Hi Everyone, I am having trouble with scipy.optimize.fmin_slsqp, I get a bounds problem: my objective function objfn returns the sum of the squared deviations bounds = [(-1e-09, 1e-08), (1e-07, 1e-05), (1e-07, 1e-05), (1e-10, 1e-08), (1e-10, 1e-08), (1e-10, 1e-08), (80.0, 200.0), (1.0, 5.0), (1.0, 5.0)] x0 = array([ 0.00000000e+00, 1.01000000e-06, 3.03000000e-06, 1.00000000e-09, 1.00000000e-09, 1.00000000e-09, 1.50000000e+02, 2.00000000e+00, 2.00000000e+00]) res = fmin_slsqp(objfn,p.array(x0,dtype=float),bounds=bounds,full_output=True) ... Inequality constraints incompatible (Exit mode 4) Current function value: 450.575453753 Iterations: 1 Function evaluations: 11 Gradient evaluations: 1 Looking at what happens inside slsqp.py, in _minimize_slsqp I checked that the bounds and the trial value of the variable make sense: (xl<xu).all() True (x>xl).all() True (x<xu).all() True I have not used anything else than the bounds... Any clue? Thanks in advance! Helmholtz-Zentrum Geesthacht Zentrum für Material- und Küstenforschung GmbH Max-Planck-Straße 1 I 21502 Geesthacht I Deutschland/Germany Geschäftsführer/Board of Management: Prof. Dr. Wolfgang Kaysser, Dipl.-Ing. Michael Ganß Vorsitzender des Aufsichtsrates/Chairman of the Supervisory Board: MinDirig Wilfried Kraus Amtsgericht Lübeck HRB 285 GE (Register Court) Internet: http://www.hzg.de

2 1

ANN: OpenElectrophy 0.3.3
by Samuel Garcia 09 Oct '13

09 Oct '13

Hi all, pleased to announce the release of OpenElectrophy 0.3.3 OpenElectrophy is a python module for electrophysioly data analysis (intra- and extra-cellular). http://pythonhosted.org/OpenElectrophy/ But OpenElectrophy provide: * A complete offline spikesorting tool chain = GUI and/or command line. * A timefrequency toolbox = fast wavelet scalogram plotting + transient oscillation in LFP detection. * Viewers for neo objects. * A database for storage. Best, Samuel -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Samuel Garcia Lyon Neuroscience CNRS - UMR5292 - INSERM U1028 - Universite Claude Bernard LYON 1 Equipe R et D 50, avenue Tony Garnier 69366 LYON Cedex 07 FRANCE Tél : 04 37 28 74 24 Fax : 04 37 28 76 01 http://olfac.univ-lyon1.fr/unite/equipe-07/ http://neuralensemble.org/trac/OpenElectrophy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1 0

Fwd: Can scipy.sparse add support to other numeric type?
by cat fa 09 Oct '13

09 Oct '13

Hi everyone, In the source code of ..\scipy\sparse\sputils.py, the upcast function only support these types int8, uint8, short, ushort, intc, uintc, longlong, ulonglong, single, double, longdouble, csingle, cdouble, clongdouble. Now I want to use sparse matrix to store ad(an automatic differentiation object) type. When I do some calculation operation, I got TypeError exception: TypeError: no supported conversion for types: object Is there a easy way to get sparse matrix to support other numeric type?

1 0

Euo Scipy 2013 Proceedings
by Timmie 08 Oct '13

08 Oct '13

Hello, is there a page where I can download the processings from the conferenece? I cannot find them at: https://www.euroscipy.org/schedule/general-sessions/ Thanks in advance. Kind regards, Timmie

2 2

[ANN] Release python-neo 0.3.1
by Samuel Garcia 08 Oct '13

08 Oct '13

Hi all, Neo 0.3.1 have been released!! Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. http://pythonhosted.org/neo/ New: * lazy/cascading improvement * load_lazy_olbject() in neo.io added * added NeuroscopeIO Have a good day. The neo team. -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Samuel Garcia Lyon Neuroscience CNRS - UMR5292 - INSERM U1028 - Universite Claude Bernard LYON 1 Equipe R et D 50, avenue Tony Garnier 69366 LYON Cedex 07 FRANCE Tél : 04 37 28 74 24 Fax : 04 37 28 76 01 http://olfac.univ-lyon1.fr/unite/equipe-07/ http://neuralensemble.org/trac/OpenElectrophy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1 0

1.8.0rc1
by Charles R Harris 07 Oct '13

07 Oct '13

Hi All, NumPy 1.8.0rc1 is up now on sourceforge<http://sourceforge.net/projects/numpy/files/NumPy/1.8.0rc1/>.The binary builds are included except for Python 3.3 on windows, which will arrive later. Many thanks to Ralf for the binaries, and to those who found and fixed the bugs in the last beta. Any remaining bugs are all my fault ;) I hope this will be the last release before final, so please test it thoroughly. Chuck

7 17

Installing SciPy version 0.13.0b1: 1 test fail
by Sergio Rojas 07 Oct '13

07 Oct '13

Installing SciPy version 0.13.0b1 ended with a fail test message [1] which does not look too critical as the numerical values match close to single precision. I am curious, however, about a deprecated message given at the beginning of the test: " DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.linalg.lapack `" and a few others. How we are suppose to fix that in using scipy? Sergio [1] ======================================================================^M FAIL: test_qz_complex64 (test_decomp.TestQZ)^M ----------------------------------------------------------------------^M Traceback (most recent call last):^M File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/scipy/li nalg/tests/test_decomp.py", line 1781, in test_qz_complex64^M assert_array_almost_equal(dot(dot(Q,AA),Z.conjugate().T), A)^M File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/te sting/utils.py", line 812, in assert_array_almost_equal^M header=('Arrays are not almost equal to %d decimals' % decimal))^M File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/te sting/utils.py", line 645, in assert_array_compare^M raise AssertionError(msg)^M AssertionError: ^M Arrays are not almost equal to 6 decimals^M ^M (mismatch 4.0%)^M x: array([[ 0.92961568+0.729689j , 0.31637624+0.99401343j,^M 0.18391913+0.67687303j, 0.20456031+0.79082209j,^M 0.56772494+0.17091393j],...^M y: array([[ 0.92961609+0.72968906j, 0.31637555+0.99401456j,^M 0.18391882+0.67687368j, 0.20456028+0.79082251j,^M 0.56772500+0.17091426j],...^M ^M ----------------------------------------------------------------------^M Ran 9902 tests in 895.284s^M ^M FAILED (KNOWNFAIL=131, SKIP=324, failures=1)^M <nose.result.TextTestResult run=9902 errors=0 failures=1>^M Python 2.7.3 (default, Sep 30 2013, 13:55:58) ^M [GCC 4.6.3] on linux2^M Type "help", "copyright", "credits" or "license" for more information.^M Type "help", "copyright", "credits" or "license" for more information.^M >>> import scipy^M >>> scipy.show_config()^M amd_info:^M libraries = ['amd']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('SCIPY_AMD_H', None)]^M swig_opts = ['-I/home/myProg/NumLibs64b/include']^M include_dirs = ['/home/myProg/NumLibs64b/include']^M umfpack_info:^M libraries = ['umfpack', 'amd']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]^M swig_opts = ['-I/home/myProg/NumLibs64b/include', '-I/home/myProg/NumLibs64 b/include']^M include_dirs = ['/home/myProg/NumLibs64b/include']^M atlas_threads_info:^M libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('NO_ATLAS_INFO', -1)]^M language = f77^M include_dirs = ['/home/myProg/NumLibs64b/include']^M blas_opt_info:^M libraries = ['ptf77blas', 'ptcblas', 'atlas']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('NO_ATLAS_INFO', -1)]^M language = c^M include_dirs = ['/home/myProg/NumLibs64b/include']^M atlas_blas_threads_info:^M libraries = ['ptf77blas', 'ptcblas', 'atlas']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('NO_ATLAS_INFO', -1)]^M language = c^M include_dirs = ['/home/myProg/NumLibs64b/include']^M lapack_opt_info:^M libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']^M library_dirs = ['/home/myProg/NumLibs64b/lib']^M define_macros = [('NO_ATLAS_INFO', -1)]^M language = f77^M include_dirs = ['/home/myProg/NumLibs64b/include']^M lapack_mkl_info:^M NOT AVAILABLE^M blas_mkl_info:^M NOT AVAILABLE^M mkl_info:^M NOT AVAILABLE^M >>> scipy.test('full', verbose=2)^M Running unit tests for scipy^M NumPy version 1.7.0^M NumPy is installed in /home/myProg/Python273GNU/Linux64b/lib/python2.7/site-pac kages/numpy^M SciPy version 0.13.0b1^M SciPy is installed in /home/myProg/Python273GNU/Linux64b/lib/python2.7/site-pac kages/scipy^M Python version 2.7.3 (default, Sep 30 2013, 13:55:58) [GCC 4.6.3]^M nose version 1.3.0^M /home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/lib/utils. py:139: DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy.linalg.b las` instead!^M warnings.warn(depdoc, DeprecationWarning)^M /home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/lib/utils. py:139: DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.linalg .lapack` instead!^M ... ... ====================================================================== FAIL: test_qz_complex64 (test_decomp.TestQZ) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/scipy/li nalg/tests/test_decomp.py", line 1781, in test_qz_complex64 assert_array_almost_equal(dot(dot(Q,AA),Z.conjugate().T), A) File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/te sting/utils.py", line 812, in assert_array_almost_equal header=('Arrays are not almost equal to %d decimals' % decimal)) File "/home/myProg/Python273GNU/Linux64b/lib/python2.7/site-packages/numpy/te sting/utils.py", line 645, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal to 6 decimals (mismatch 4.0%) x: array([[ 0.92961568+0.729689j , 0.31637624+0.99401343j, 0.18391913+0.67687303j, 0.20456031+0.79082209j, 0.56772494+0.17091393j],... y: array([[ 0.92961609+0.72968906j, 0.31637555+0.99401456j, 0.18391882+0.67687368j, 0.20456028+0.79082251j, 0.56772500+0.17091426j],... ---------------------------------------------------------------------- Ran 9902 tests in 895.284s FAILED (KNOWNFAIL=131, SKIP=324, failures=1) <nose.result.TextTestResult run=9902 errors=0 failures=1>

2 1

scipy IFFT question ( time N+1 wraps back to time 0 )
by Matt Newcomb 07 Oct '13

07 Oct '13

Hi, I've got the transfer function of an LTI system (H(s)). The input to the system is a 1 ns pulse delayed by X ns ( x(t) ) If I compute the response function by taking the fft of the input to the system multiplying and then taking the ifft I see the impulse response to the system just shifted by X ns. That's expected but the problem is that the tail of the system response wraps around and appears at time 0 in the ifft. NOT what I wanted. How do I get rid of this? Thank you, Matt

1 0