Mailman 3 May 2013 - SciPy-User

ANN: New WinPython with Python 2.7.5 and 3.3.2 (32/64bit)
by Pierre Raybaut May 25, 2013

May 25, 2013

Hi all, I am pleased to announce that four new versions of WinPython have been released yesterday with Python 2.7.5 and 3.3.2, 32 and 64 bits. Many packages have been added or upgraded. Special thanks to Christoph Gohlke for building most of the binary packages bundled in WinPython. WinPython is a free open-source portable distribution of Python for Windows, designed for scientists. It is a full-featured (see http://code.google.com/p/winpython/wiki/PackageIndex) Python-based scientific environment: * Designed for scientists (thanks to the integrated libraries NumPy, SciPy, Matplotlib, guiqwt, etc.: * Regular *scientific users*: interactive data processing and visualization using Python with Spyder * *Advanced scientific users and software developers*: Python applications development with Spyder, version control with Mercurial and other development tools (like gettext) * *Portable*: preconfigured, it should run out of the box on any machine under Windows (without any installation requirements) and the folder containing WinPython can be moved to any location (local, network or removable drive) * *Flexible*: one can install (or should I write "use" as it's portable) as many WinPython versions as necessary (like isolated and self-consistent environments), even if those versions are running different versions of Python (2.7, 3.x in the near future) or different architectures (32bit or 64bit) on the same machine * *Customizable*: using the integrated package manager (wppm, as WinPython Package Manager), it's possible to install, uninstall or upgrade Python packages (see http://code.google.com/p/winpython/wiki/WPPM for more details on supported package formats). *WinPython is not an attempt to replace Python(x,y)*, this is just something different (see http://code.google.com/p/winpython/wiki/Roadmap): more flexible, easier to maintain, movable and less invasive for the OS, but certainly less user-friendly, with less packages/contents and without any integration to Windows explorer [*]. [*] Actually there is an optional integration into Windows explorer, providing the same features as the official Python installer regarding file associations and context menu entry (this option may be activated through the WinPython Control Panel), and adding shortcuts to Windows Start menu. Enjoy! -Pierre

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How to cite the scipy.optimize module in a paper?
by servant mathieu May 24, 2013

May 24, 2013

Dear community, I've recently fitted a model to empirical data using the Simplex routine implemented in the scipy.optimize module. How could I cite this work in a paper using the APA style? Cheers, Mathieu

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How to read CSV with missing data in numpy array
by zetah May 24, 2013

May 24, 2013

`genfromtxt()` works beautifully. Thanks Gary

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How to read CSV with missing data in numpy array?
by zetah May 24, 2013

May 24, 2013

Trying to read this csv: 6;6;7;8;3; 8;10;8;;5; 3;5;6;7;; with this code: loadtxt('test.csv', delimiter=';', usecols=range(5)) yields "ValueError: could not convert string to float: " Is there any easy way numpy to read this CSV? Thanks

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Dynamic library loading problem
by John Rowe May 23, 2013

May 23, 2013

I have installed python3.3.2 and the latest numpy and scipy on linux x86_64 using the Intel compilers and MKL following a few variants of the advice on http://www.scipy.org/Installing_SciPy/Linux and http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl. However, although the installation goes fine the moment I try to use it it fails, for example: > python3 -c 'import scipy; scipy.test()' Running unit tests for scipy NumPy version 1.7.0 NumPy is installed in /usr/local64/python3.3.2/lib/python3.3/site-packages/numpy SciPy version 0.12.0 SciPy is installed in /usr/local64/python3.3.2/lib/python3.3/site-packages/scipy Python version 3.3.2 (default, May 22 2013, 15:10:00) [GCC Intel(R) C++ gcc 4.3 mode] nose version 1.3.0 ............................................................................ *** libmkl_mc3.so *** failed with error : /opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t/libmkl_mc3.so: undefined symbol: mkl_dft_commit_descriptor_s_c2c_md_omp *** libmkl_def.so *** failed with error : /opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t/libmkl_def.so: undefined symbol: mkl_dft_commit_descriptor_s_c2c_md_omp MKL FATAL ERROR: Cannot load neither libmkl_mc3.so nor libmkl_def.so I get pretty much the same message with numpy.test() and if I try to run the actual program, but with different libraries and missing symbols. The missing symbols always appear to be ones that are found in the specified mkl shared libraries, eg in this case: > nm libmkl_intel_thread.so | grep \ mkl_dft_commit_descriptor_s_c2c_md_omp 00000000006181a0 T mkl_dft_commit_descriptor_s_c2c_md_omp This makes me think it's a systemic issue with shared libraries rather than me having just missed one out. Any help would be hugely appreciated (scipy config follows). Thanks John > python3 -c 'import scipy; scipy.show_config()' blas_mkl_info: libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'iomp5', 'pthread'] define_macros = [('SCIPY_MKL_H', None)] library_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/lib/intel64'] include_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/include/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/include/intel64'] lapack_mkl_info: libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'iomp5', 'pthread'] define_macros = [('SCIPY_MKL_H', None)] library_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/lib/intel64'] include_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/include/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/include/intel64'] lapack_opt_info: libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'iomp5', 'pthread'] define_macros = [('SCIPY_MKL_H', None)] library_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/lib/intel64'] include_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/include/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/include/intel64'] umfpack_info: NOT AVAILABLE mkl_info: libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'iomp5', 'pthread'] define_macros = [('SCIPY_MKL_H', None)] library_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/lib/intel64'] include_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/include/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/include/intel64'] blas_opt_info: libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'iomp5', 'pthread'] define_macros = [('SCIPY_MKL_H', None)] library_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/lib/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/lib/intel64'] include_dirs = ['/opt/intel/intel-fc/x86_64/11.1.064/mkl/include/em64t', '/opt/intel/intel-fc/x86_64/11.1.064/include/intel64']

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Can SciPy deal with a set of non-linear equations?
by LU, JIANMIN May 22, 2013

May 22, 2013

Hi guys, I know that non-linear equations can be solved by fsolve() in SciPy. Another question from me would be: Can SciPy handle the multiple precision number problem? Namely, can SciPy define a variable with arbitray precision and can fsolve() take that correctly? Thanks. Regards, Jianmin, from U. of South Carolina.

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Can SciPy deal with a set of non-linear equations?
by LU, JIANMIN May 22, 2013

May 22, 2013

Hi SciPy guys, I get a set of non-linear equations to be solved with Python and the goal is to find the roots of this set of equations. Can SciPy handle this? Regards, Jianmin, from U. of South Carolina.

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ANN: SfePy 2013.2
by Robert Cimrman May 22, 2013

May 22, 2013

I am pleased to announce release 2013.2 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method. The code is based on NumPy and SciPy packages. It is distributed under the new BSD license. Home page: http://sfepy.org Downloads, mailing list, wiki: http://code.google.com/p/sfepy/ Git (source) repository, issue tracker: http://github.com/sfepy Highlights of this release -------------------------- - automatic testing of term calls (many terms fixed w.r.t. corner cases) - new elastic contact plane term + example - translated low level base functions from Cython to C for reusability - improved gallery http://docs.sfepy.org/gallery/gallery For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman and Contributors (*) (*) Contributors to this release (alphabetical order): Vladimír Lukeš, Ankit Mahato, Matyáš Novák

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Is Macports version of Spyder 2.2.0 same as "official" release?
by Jerry May 21, 2013

May 21, 2013

Hi, I see that Spyder 2.2.0 (Scientific PYthon Development EnviRonment) is out. There is an official .dmg distribution available from the project's home page. According to the list of changes for 2.2.0 http://code.google.com/p/spyderlib/wiki/ChangeLog is this: "The App comes with its own interpreter, which has the main Python scientific libraries preinstalled: Numpy, SciPy, Matplotlib, IPython, Pandas, Sympy, Scikit-learn and Scikit-image." Does the MacPorts version of Spyder 2.2.0 include all of these add-ons? I hate duplicating things such as Python interpreters, Numpy, etc. all over the place if I can do it once with MacPorts so I would much prefer to use the MacPorts version if possible. Also, the MacPorts page lists both py-spyder 2.2.0 and py27-spyder 2.2.0. Are these the same? Thanks, Jerry

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Matlab imfilter/fspecial equivalent
by Brickle Macho May 17, 2013

May 17, 2013

I am porting some Matlab code to python. When I run the ported version the output differs. It appears the difference may be due to how I have translated Matlab's imfilter() and/or fspecial(). Below are my translation, are there any Scipy/Matlab gurus that can let me know if there are correct or if I have made some errors, or could suggest better translations. Matlab: imfilter(saliencyMap, fspecial('gaussian', [10, 10], 2.5)) Scipy: ndimage.gaussian_filter(saliencyMap, sigma=2.5) Also the following. At the rsik of highlighting my lack of understanding of the temrinology, Is uniform_filter the same as 'average'? Matlab: imfilter(myLogAmplitude, fspecial('average', 3), 'replicate'); Scipy: scipy.ndimage.uniform_filter(mylogAmplitude, size=3, mode="nearest") Thanks, Michael. -- If curious here are the 8 lines of matlab code I am trying to port: %% Read image from file inImg = im2double(rgb2gray(imread('yourImage.jpg'))); inImg = imresize(inImg, 64/size(inImg, 2)); %% Spectral Residual myFFT = fft2(inImg); myLogAmplitude = log(abs(myFFT)); myPhase = angle(myFFT); mySpectralResidual = myLogAmplitude - imfilter(myLogAmplitude, fspecial('average', 3), 'replicate'); saliencyMap = abs(ifft2(exp(mySpectralResidual + i*myPhase))).^2; %% After Effect saliencyMap = mat2gray(imfilter(saliencyMap, fspecial('gaussian', [10, 10], 2.5))); imshow(saliencyMap);

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