Mailman 3 September 2011 - NumPy-Discussion

Numpy - MKL - build error
by Igor Ying 13 Sep '11

13 Sep '11

Hi, I am very new to Numpy and trying to build Numpy 1.6.1 with Intel MKL 10.3.6 and getting the following errors. $python setup.py config --compiler=intel build_clib --compiler=intel build_ext --compiler=intel install Running from numpy source directory.F2PY Version 2 blas_opt_info: blas_mkl_info: libraries mkl_def,mkl_intel_lp64,mkl_intel_thread,mkl_core,iomp5md not found in /opt/intel/composer_xe_2011_sp1/mkl/lib/intel64 NOT AVAILABLE I have modified the site.cfg as below. [mkl] library_dirs = /opt/intel/composer_xe_2011_sp1/mkl/lib/intel64 include_dirs = /opt/intel/composer_xe_2011_sp1/mkl/include mkl_libs = mkl_def,mkl_intel_lp64,mkl_intel_thread,mkl_core,iomp5md lapack_libs = mkl_lapack_lp64 Environment variables are also set to point to mkl libraries. source /opt/intel/composer_xe_2011_sp1/bin/compilervars.sh intel64 lp64 export OMP_NUM_THREADS=4 LD_LIBRARY_PATH="/opt/intel/composer_xe_2011_sp1/mkl/lib/intel64:${LD_LIBRARY_PATH}"; export LD_LIBRARY_PATH export LD_RUN_PATH=/opt/lib:/opt1/composer_xe_2011_sp1/lib/intel64:/opt/composer_xe_2011_sp1/mkl/lib/intel64 Can anyone help me?

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using Matlab compiler to build Python-callable library?
by Reckoner 12 Sep '11

12 Sep '11

Hi, Has anybody ever tried using the Matlab compiler to build a standalone library that would be callable using Python? We have a lot of leftover Matlab code that we are trying to migrate. Thanks!

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f2py : NotImplementedError: Only MS compiler supported with gfortran on win64
by Jim Vickroy 12 Sep '11

12 Sep '11

Hello All, I'm attempting to create a python wrapper, for a Fortran subroutine, using f2py. My system details are: >>> sys.version '2.6.4 (r264:75708, Oct 26 2009, 08:23:19) [MSC v.1500 32 bit (Intel)]' >>> sys.getwindowsversion() (5, 1, 2600, 2, 'Service Pack 3') >>> scipy.__version__ '0.7.1' >>> numpy.__version__ '1.4.0' C:\>gfortran -dumpversion 4.7.0 C:\Python26\Lib\site-packages\numpy\f2py>f2py.py -c --help-fcompiler Traceback (most recent call last): File "C:\Python26\Scripts\f2py.py", line 24, in <module> main() File "C:\Python26\lib\site-packages\numpy\f2py\f2py2e.py", line 557, in main run_compile() File "C:\Python26\lib\site-packages\numpy\f2py\f2py2e.py", line 543, in run_compile setup(ext_modules = [ext]) File "C:\Python26\lib\site-packages\numpy\distutils\core.py", line 186, in setup return old_setup(**new_attr) File "C:\Python26\lib\distutils\core.py", line 138, in setup ok = dist.parse_command_line() File "C:\Python26\lib\distutils\dist.py", line 460, in parse_command_line args = self._parse_command_opts(parser, args) File "C:\Python26\lib\distutils\dist.py", line 574, in _parse_command_opts func() File "C:\Python26\lib\site-packages\numpy\distutils\command\config_compiler.py", line 13, in show_fortran_compilers show_fcompilers(dist) File "C:\Python26\lib\site-packages\numpy\distutils\fcompiler\__init__.py", line 855, in show_fcompilers c.customize(dist) File "C:\Python26\lib\site-packages\numpy\distutils\fcompiler\__init__.py", line 525, in customize self.set_libraries(self.get_libraries()) File "C:\Python26\lib\site-packages\numpy\distutils\fcompiler\gnu.py", line 306, in get_libraries raise NotImplementedError("Only MS compiler supported with gfortran on win64") NotImplementedError: Only MS compiler supported with gfortran on win64 Could someone help me to resolve this? Thanks, -- jv

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Matrix addition, +=
by Jonas Wallin 12 Sep '11

12 Sep '11

Hello, I am sure this question has been answered before but I can't find the right search word to find it. Why does MuY += MuY.transpose() and MuY = MuY + MuY.transpose() give different answers? thanks /Jonas Wallin

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recarrays and lists of lists
by Travis Vaught 11 Sep '11

11 Sep '11

Greetings, Is there a particular reason why a list of lists can't be passed in to create a recarray given a particular dtype? A list of tuples works fine. I keep getting bitten by this and was thinking it should be an easy check/convert for an allowance for a row to be a list _or_ a tuple? Here's a session: ~~~~~~~~~~ In [2]: import numpy as np In [3]: dt = np.dtype({'names':['a', 'b', 'c'], 'formats':[float]*3}) In [4]: dt Out[4]: dtype([('a', '<f8'), ('b', '<f8'), ('c', '<f8')]) In [5]: rows = [[1,2,3],[2,3,4],[3,4,5]] In [6]: ary = np.array(rows, dtype=dt) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/travis/git/trading/<ipython-input-6-b69b5c361254> in <module>() ----> 1 ary = np.array(rows, dtype=dt) TypeError: expected a readable buffer object In [7]: rows2 = [tuple(row) for row in rows] In [8]: ary = np.array(rows2, dtype=dt) In [9]: ary Out[9]: array([(1.0, 2.0, 3.0), (2.0, 3.0, 4.0), (3.0, 4.0, 5.0)], dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8')]) ~~~~~~~~~~ Thoughts? Best, Travis

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How to apply Numpy ufunc to Scipy sparse matrices?
by Pengkui Luo 11 Sep '11

11 Sep '11

It seems that numpy universal functions only apply to ndarray (or dense matrix). Is there a way to apply them to scipy sparse matrices also? For example, suppose S is an large sparse matrix (lil_matrix format, dtype=np.float). I want to get another sparse matrix B (of the same shape) that represents the signs of corresponding entries of S. I wrote down "B=np.sign(S)" first, but it did not give me the desired output. I realized that np.sign() might not work for a sparse matrix, so I have to convert S to a dense matrix, i.e. B = np.sign( S.todense() ) However, converting a large sparse matrix to dense would easily eat up the memory. Is there a way for np.sign (as well as other ufunc) to take a sparse matrix as parameter, and return a sparse matrix? (If I recall correctly, in Matlab all functions work on both dense matrices and sparse matrices.) -- Pengkui

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[ANN] ESCO2012 and PyHPC2011: Python in science in major computational science conference
by Gael Varoquaux 11 Sep '11

11 Sep '11

ESCO 2012 - European Seminar on Coupled Problems ================================================= ESCO2012 http://esco2012.femhub.com/ is the 3rd event in a series of interdisciplineary meetings dedicated to computational science challenges in multi-physics and PDEs. I was invited as ESCO last year. It was an aboslute pleasure, because it is a small conference that is very focused on discussions. I learned a lot and could sit down with people who code top notch PDE libraries such as FEniCS and have technical discussions. Besides, it is hosted in the historical brewery where the Pilsner was invented. Plenty of great beer. Application areas ------------------ Theoretical results as well as applications are welcome. Application areas include, but are not limited to: Computational electromagnetics, Civil engineering, Nuclear engineering, Mechanical engineering, Computational fluid dynamics, Computational geophysics, Geomechanics and rock mechanics, Computational hydrology, Subsurface modeling, Biomechanics, Computational chemistry, Climate and weather modeling, Wave propagation, Acoustics, Stochastic differential equations, and Uncertainty quantification. Minisymposia * Multiphysics and Multiscale Problems in Civil Engineering * Modern Numerical Methods for ODE * Porous Media Hydrodynamics * Nuclear Fuel Recycling Simulations * Adaptive Methods for Eigenproblems * Discontinuous Galerkin Methods for Electromagnetics * Undergraduate Projects in Technical Computing Software afternoon ------------------- Important part of each ESCO conference is a software afternoon featuring software projects by participants. Presented can be any computational software that has reached certain level of maturity, i.e., it is used outside of the author's institution, and it has a web page and a user documentation. Proceedings ----------- For each ESCO we strive to reserve a special issue of an international journal with impact factor. Proceedings of ESCO 2008 appeared in Math. Comput. Simul., proceedings of ESCO 2010 in CiCP and Appl. Math. Comput. Proceedings of ESCO 2012 will appear in Computing. Important Dates * December 15, 2011: Abstract submission deadline. * December 15, 2011: Minisymposia proposals. * January 15, 2012: Notification of acceptance. PyHPC: Python for High performance computing -------------------------------------------- If you are doing super computing, SC11, ( http://sc11.supercomputing.org/) the Super Computing conference is the reference conference. This year there will a workshop on high performance computing with Python: PyHPC (http://www.dlr.de/sc/desktopdefault.aspx/tabid-1183/1638_read-31733/). At the scipy conference, I was having a discussion with some of the attendees on how people often still do process management and I/O with Fortran in the big computing environment. This is counter productive. However, has success stories of supercomputing folks using high-level languages are not advertized, this is bound to stay. Come and tell us how you use Python for high performance computing! Topics * Python-based scientific applications and libraries * High performance computing * Parallel Python-based programming languages * Scientific visualization * Scientific computing education * Python performance and language issues * Problem solving environments with Python * Performance analysis tools for Python application Papers We invite you to submit a paper of up to 10 pages via the submission site. Authors are encouraged to use IEEE two column format. Important Dates * Full paper submission: September 19, 2011 * Notification of acceptance: October 7, 2011 * Camera-ready papers: October 31, 2011

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strange test failure in Cython code with numpy master
by Ralf Gommers 08 Sep '11

08 Sep '11

Hi, There's a test failure in scipy/io/matlab/mio_utils that shows up with numpy master but not 1.5.1, see http://projects.scipy.org/scipy/ticket/1512 I have a fix here: https://github.com/rgommers/scipy/commit/4ade7829649b9e2c251f5f7f370781b16f…, but I don't really understand why the code is failing in the first place. This is the relevant part: - arr = np.squeeze(arr) - if not arr.shape and arr.dtype.isbuiltin: # 0d coverted to scalar - return arr.item() - return arr + arr2 = np.squeeze(arr) + if (not arr2.shape) and arr2.dtype.isbuiltin: # 0d coverted to scalar + return arr2.item() + return arr2 All it does is rename arr to arr2. It fails with both Cython 0.13 and 0.15. Any idea? Ralf

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what to use in buildbot config for numpy testing
by Chris Kees 08 Sep '11

08 Sep '11

Hi, Is there a recommended way to run the numpy test suite as a buildbot test? Just run ad python -c "import numpy; numpy.test" as ShellCommand object? Thanks, Chris

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Numeric 24.2 for Python 2.4 on Windows 7?
by Dietmar Schwertberger 07 Sep '11

07 Sep '11

Hi! Does anybody have Numeric 24.2 binaries for Python 2.4 which are compatible with Windows 7 SP1? Regards, Dietmar

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