Mailman 3 September 2005 - SciPy-User

[ANN] PyTables 1.1.1
by Francesc Altet Sept. 14, 2005

Sept. 14, 2005

========================== Announcing PyTables 1.1.1 ========================== This is a maintenance release of PyTables. In it, several optimizations and bug fixes have been made. As some of the fixed bugs were quite important, it's strongly recommended for users to upgrade. Go to the PyTables web site for downloading the beast: http://pytables.sourceforge.net/ or keep reading for more info about the improvements and bugs fixed. Changes more in depth ===================== Improvements: - Optimized the opening of files with a large number of objects. Now, files with table objects open a 50% faster, and files with arrays open more than twice as fast (up to 2000 objects/s on a Pentium 4@2GHz). Hence, a file with a combination of both kinds of objects opens between a 50% and 100% faster than in 1.1. - Optimized the creation of ``NestedRecArray`` objects using ``NumArray`` objects as columns, so that filling a table with the ``Table.append()`` method achieves a performance similar to PyTables pre-1.1 releases. Bug fixes: - ``Table.readCoordinates()`` now converts the coords parameter into ``Int64`` indices automatically. - Fixed a bug that prevented appending to tables (though ``Table.append()``) using a list of ``NumArray`` objects. - ``Int32`` attributes are handled correctly in 64-bit platforms now. - Correction for accepting lists of numarrays as input for ``NestedRecArrays``. - Fixed a problem when creating rank 1 multi-dimensional string columns in ``Table`` objects. Closes SF bug #1269023. - Avoid errors when unpickling objects stored in attributes. See the section ``AttributeSet`` in the reference chapter of the User's Manual for more information. Closes SF bug #1254636. - Assignment for ``*Array`` slices has been improved in order to solve some issues with shapes. Closes SF bug #1288792. - The indexation properties were lost in case the table was closed before an index was created. Now, these properties are saved even in this case. Known bugs: - Classes inheriting from ``IsDescription`` subclasses do not inherit columns defined in the super-class. See SF bug #1207732 for more info. - Time datatypes are non-portable between big-endian and little-endian architectures. This is ultimately a consequence of a HDF5 limitation. See SF bug #1234709 for more info. Backward-incompatible changes: - None (that we are aware of). Important note for MacOSX users =============================== UCL compressor works badly on MacOSX platforms. Recent investigation seems to point to a bug in the development tools in MacOSX. Until the problem is isolated and eventually solved, UCL support will not be compiled by default on MacOSX platforms, even if the installer finds it in the system. However, if you still want to get UCL support on MacOSX, you can use the ``--force-ucl`` flag in ``setup.py``. Important note for Python 2.4 and Windows users =============================================== If you are willing to use PyTables with Python 2.4 in Windows platforms, you will need to get the HDF5 library compiled for MSVC 7.1, aka .NET 2003. It can be found at: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win-net.ZIP Users of Python 2.3 on Windows will have to download the version of HDF5 compiled with MSVC 6.0 available in: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win.ZIP What it is ========== **PyTables** is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data (with support for full 64-bit file addressing). It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code, makes it a very easy-to-use tool for high performance data storage and retrieval. PyTables runs on top of the HDF5 library and numarray (Numeric is also supported) package for achieving maximum throughput and convenient use. Besides, PyTables I/O for table objects is buffered, implemented in C and carefully tuned so that you can reach much better performance with PyTables than with your own home-grown wrappings to the HDF5 library. PyTables sports indexing capabilities as well, allowing doing selections in tables exceeding one billion of rows in just seconds. Platforms ========= This version has been extensively checked on quite a few platforms, like Linux on Intel32 (Pentium), Win on Intel32 (Pentium), Linux on Intel64 (Itanium2), FreeBSD on AMD64 (Opteron), Linux on PowerPC and MacOSX on PowerPC. For other platforms, chances are that the code can be easily compiled and run without further problems. Please, contact us in case you are experiencing problems. Resources ========= Go to the PyTables web site for more details: http://pytables.sourceforge.net/ About the HDF5 library: http://hdf.ncsa.uiuc.edu/HDF5/ About numarray: http://www.stsci.edu/resources/software_hardware/numarray To know more about the company behind the PyTables development, see: http://www.carabos.com/ Acknowledgments =============== Thanks to various the users who provided feature improvements, patches, bug reports, support and suggestions. See THANKS file in distribution package for a (incomplete) list of contributors. Many thanks also to SourceForge who have helped to make and distribute this package! And last but not least, a big thank you to THG (http://www.hdfgroup.org/) for sponsoring many of the new features recently introduced in PyTables. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team

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Scipy Platform
by Greg Novak Sept. 14, 2005

Sept. 14, 2005

Ah, yes, I nearly forgot that I promised to get back to the list about what platforms people were using for scientific computing. Mandrake, Gentoo, Fedora, Windows, Quantian, and OS X were mentioned. Debian was the big winner, getting twice as many votes as the other distributions (ie, two votes instead of one). Cheers, Greg

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scipy on SUSE with Python 2.4?
by Christian Meesters Sept. 13, 2005

Sept. 13, 2005

Hi Anybody scipy running on SUSE (or a similar distribution) with Python 2.4? How was this accomplished? Any hints on how to compile scipy (including ATLAS)? (I have no idea how to install it on SUSE - it's my first time - and I'm anticipating pitfalls ...) TIA Christian

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ANN: MayaVi-1.5 released
by prabhu_r＠users.sf.net Sept. 13, 2005

Sept. 13, 2005

Hi, This is to announce the availability of the MayaVi Data Visualizer version 1.5. MayaVi is a free, easy to use, scientific data visualizer. It is written in Python, uses the Visualization Toolkit (VTK) for the graphics and provides a GUI written using Tkinter. MayaVi is distributed under a BSD license. It is also cross platform and should run on any platform where both Python and VTK are available (this includes Linux, Windows and Mac OSX). Also bundled with MayaVi is a VTK pipeline browser written in Python and a utility module that makes using VTK easier from the Python interpreter. For more information, sources, binaries, screenshots, installation instructions, documentation etc. visit the MayaVi home page at: Home page: http://mayavi.sourceforge.net SourceForge: http://sourceforge.net/projects/mayavi Download: http://sourceforge.net/project/showfiles.php?group_id=27020&release_id=3561… New in this release ------------------- * Added new LUT editor contributed by Gerald Knizia and Arnd Baecker. The LUT editor allows one to edit the colors in RGB or HSV mode. The editor can also be used standalone. * Added support to easily add user defined sources (works just like the user defined modules and filters). Contributed by Ross Harder. * Support for VolumeProMapper contributed by Daniel James White and Heikki Uuksulainen. * Added a Vorticity filter (contributed by Gareth Clay) that computes the vorticity of input vector data. * Updated MayaVi and vtkPipeline to work with VTK-CVS and VTK-5.x. Support for VTK versions < 4.0 has been removed. MayaVi and the pipeline browser will continue to work with VTK-4.x. * Added a utility vtk_doc.py script that allows one to search the VTK class docs using a GUI. * Fixed severe bug in opening data files belonging to a time-series under Windows. * Fixed various other bugs (details in full changelog). Acknowledgements ---------------- Many thanks to SourceForge for their continued support in hosting MayaVi. Thanks also to various the users who provided feature improvements, patches, bug reports, support and suggestions. Have fun! prabhu

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Correct syntax for optimize.fmin_powell
by J. Stark Sept. 13, 2005

Sept. 13, 2005

Can someone point me to the correct syntax for optimize.fmin_powell when the objective function takes side arguments. I have tried every possibility I can think of, most of which work fine for optimize.fmin, but keep getting error messages, mainly due to an incorrect number of arguments. I have looked at the source, but that hasn't helped. I am typically trying to something like def test_arg((x,y),(a,b)): return (x-a)**2 + (y-b)**2 min = optimize.fmin_powell(test_arg,(1.0,2.0), ((5.0,6.0),)) This works with optimize.fmin, but fails with optimize.fmin_powell. Thanks Jaroslav

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More installation troubles
by Alexander Borghgraef Sept. 13, 2005

Sept. 13, 2005

Some more problems installing SciPy: firstly the installer doesn't seem to find the fftw library. I did set an environment variable FFTW pointing to the local lib and include directories where libfftw3.a, libfftw3.la <http://libfftw3.la>, fftw3.f and fftw3.h are to be found. Still I get: fftw_info: NOT AVAILABLE dfftw_info: NOT AVAILABLE FFTW (http://www.fftw.org/) libraries not found. Directories to search for the libraries can be specified in the scipy_distutils/site.cfg file (section [fftw]) or by setting the FFTW environment variable. djbfft_info: NOT AVAILABLE DJBFFT (http://cr.yp.to/djbfft.html) libraries not found. Directories to search for the libraries can be specified in the scipy_distutils/site.cfg file (section [djbfft]) or by setting the DJBFFT environment variable. Any idea why this is? Then it goes on to find blas, lapack and atlas, gives a warning that lapack is probably incomplete (which is strange, since I compiled it from source following the instructions on the 'building scipy' page), finds x11, and crashes with the following error: ATLAS version 3.6.0 Traceback (most recent call last): File "setup.py", line 111, in ? setup_package(ignore_packages) File "setup.py", line 85, in setup_package ignore_packages = ignore_packages) File "scipy_core/scipy_distutils/misc_util.py", line 475, in get_subpackages config = setup_module.configuration(*args) File "/home/morlet/aborghgr/incoming/SciPy_complete-0.3.2/scipy_core/scipy_base/setup_scipy_base.py", line 18, in configuration numpy_info = get_info('numpy') File "scipy_core/scipy_distutils/system_info.py", line 192, in get_info return cl().get_info(notfound_action) File "scipy_core/scipy_distutils/system_info.py", line 1111, in __init__ module = __import__(self.modulename) File "/homes/morlet/aborghgr/local//lib/python2.3/site-packages/Numeric/Numeric.py", line 358, in ? from dotblas import dot, innerproduct, vdot File "/homes/morlet/aborghgr/local/lib/python2.3/site-packages/numarray/dotblas.py", line 5, in ? import generic as _gen File "/homes/morlet/aborghgr/local/lib/python2.3/site-packages/numarray/generic.py", line 13, in ? import numerictypes as _nt File "/homes/morlet/aborghgr/local/lib/python2.3/site-packages/numarray/numerictypes.py", line 168, in ? Byte = _register("Byte", Int8) File "/homes/morlet/aborghgr/local/lib/python2.3/site-packages/numarray/numerictypes.py", line 68, in _register raise ValueError("Type %s has already been registered" % name) ValueError: Type Byte has already been registered I don't have a clue as to what this means. Anyone care to help? P.S. Sorry about the double message, apparently I don't get my own posts, so I figured it lost. -- Alex Borghgraef

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sorting arrays with complex numbers
by Chris Fonnesbeck Sept. 13, 2005

Sept. 13, 2005

This may not be the appropriate place to post this question, but I'm sure someone has the answer. I am trying to extract the eigenvector with the largest corresponding eigenvalue from an array of eigenvectors. As you would expect, the array of eigenvalues contains complex numbers: >>> d array([ 0.6 +0.j , -0.16037672+0.27778062j, -0.16037672-0.27778062j, 0.32075343+0.j ]) When I try and call max() on this array, I get the following error: >>> max(d) Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: no ordering relation is defined for complex numbers What is the appropriate method for extracting the maximum value from this array? Thanks, Chris

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Numerical stability and Legendre polynomials
by Yaroslav Bulatov Sept. 9, 2005

Sept. 9, 2005

I'm trying to approximate a function using an expansion over few dozen of Legendre polynomials, but I'm running into a large numerical error. For instance the following special.legendre(30).integ()(1)-special.legendre(30).integ()(-1) evaluates to 2e-4, instead of 0 So my question is -- is such numerical error normal? Is it realistic to try to approximate a function using 30 Legendre polynomials? -- Yaroslav Bulatov bulatov(a)cs.oregonstate.edu Dearborn 102 Oregon State University Corvallis, OR

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Setting include dirs
by Alexander Borghgraef Sept. 8, 2005

Sept. 8, 2005

Hi all, I'm trying to install SciPy 0.3.2, but it gets stuck at the compilation of build/src/fortranobject.c. The problem seems to be that it doesn't find Numeric/arrayobject.h which is quite understandable since on the line before the error message I see the following: compile options: '-Ibuild/src -I/usr/include/python2.3 -c' Since I don't have root access to this machine, I installed Numeric locally, so the compile options should contain -I$HOME/local/inlcude/python2.3 . I did add the local directory to the PYTHON_INCLUDES environment variable, and the numpy info line at the beginning of the install run seems to find it too: numpy_info: FOUND: define_macros = [('NUMERIC_VERSION', '"\\"24.0b2\\""')] include_dirs = ['/homes/morlet/aborghgr/local/include/python2.3'] Strangely, it does not get included for compilation somehow. Any ideas on how to fix this? -- Alex Borghgraef

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Setting include dirs
by Alexander Borghgraef Sept. 7, 2005

Sept. 7, 2005

Hi all, I'm trying to install SciPy 0.3.2, but it gets stuck at the compilation of build/src/fortranobject.c. The problem seems to be that it doesn't find Numeric/arrayobject.h which is quite understandable since on the line before the error message I see the following: compile options: '-Ibuild/src -I/usr/include/python2.3 -c' Since I don't have root access to this machine, I installed Numeric locally, so the compile options should contain -I$HOME/local/inlcude/python2.3 . I did add the local directory to the PYTHON_INCLUDES environment variable, and the numpy info line at the beginning of the install run seems to find it too: numpy_info: FOUND: define_macros = [('NUMERIC_VERSION', '"\\"24.0b2\\""')] include_dirs = ['/homes/morlet/aborghgr/local/include/python2.3'] Strangely, it does not get included for compilation somehow. Any ideas on how to fix this? -- Alex Borghgraef

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