Re: [pymvpa] PyMVPA for Python 3 -- first report
My guess is that it is still related to how that AttributesCollector injects those additional attributes into the freshly constructed classes. I hope above clues would be of help it helped indeed ;) ciao, tiziano ___ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
Re: [pymvpa] PyMVPA for Python 3
MVPA_TESTS_QUICK=yes MVPA_TESTS_LOWMEM=yes make unittest-nonlabile [...] Let us know if that helps for a start. yep, it works fine this way, thanks. it even finds more unit tests (326) ;) ciao, tiziano ___ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
Accepted mdp 3.2+git78-g7db3c50-3 (source all)
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Format: 1.8 Date: Sat, 14 Apr 2012 14:22:00 +0200 Source: mdp Binary: python-mdp python3-mdp Architecture: source all Version: 3.2+git78-g7db3c50-3 Distribution: unstable Urgency: low Maintainer: Tiziano Zito opossumn...@gmail.com Changed-By: Tiziano Zito opossumn...@gmail.com Description: python-mdp - Modular toolkit for Data Processing python3-mdp - Modular toolkit for Data Processing Closes: 645584 Changes: mdp (3.2+git78-g7db3c50-3) unstable; urgency=low . * New upstream git snapshot * Add package for Python 3 (Closes: #645584) * Switch from cdbs to debhelper (= 7, squeeze) and dh_python* tools * Do not build depend on shogun-python-modular * Updated Vcs-* fields in source package * Added patches for easier backporting to squeeze and several Ubuntu releases * Upload sponsored by Yaroslav Halchenko Checksums-Sha1: fad8893a46145571025663ebc54223f333764bd2 1506 mdp_3.2+git78-g7db3c50-3.dsc 3b591313ba22d81fb71ed0ecd7b5c85b46e514dd 470409 mdp_3.2+git78-g7db3c50.orig.tar.gz 8bc05d40515301c062db36148b6f6898e3275a9e 5224 mdp_3.2+git78-g7db3c50-3.debian.tar.gz 63ae89e79353087a9dbdc059f7144348e61d90a6 481926 python-mdp_3.2+git78-g7db3c50-3_all.deb c5a86ff2c61fc88935792040038d59613c0ac7aa 469376 python3-mdp_3.2+git78-g7db3c50-3_all.deb Checksums-Sha256: c37870462b93754a26cc3bdecb23dcacb5b9a8f116b4087d7e91ea1cde946137 1506 mdp_3.2+git78-g7db3c50-3.dsc 59e9841170b1ef835bb004307bf9641e1108f06f350e78746b96a1ddf974d449 470409 mdp_3.2+git78-g7db3c50.orig.tar.gz ca6fe11ec775ed2a4ac42789e690e14728a2f399291c294f93f07ed23e220a23 5224 mdp_3.2+git78-g7db3c50-3.debian.tar.gz 3f570c908c91f932cd8f3ed3e5ac7668e3a956ee90fa5f2632d7f989aed06870 481926 python-mdp_3.2+git78-g7db3c50-3_all.deb 1074a656e068ec2af3d302b261c380bf34fd7054b319faf26d065b0cb2cd1b35 469376 python3-mdp_3.2+git78-g7db3c50-3_all.deb Files: 500de8daf7fe0fa3bac5766af188e83f 1506 python optional mdp_3.2+git78-g7db3c50-3.dsc 0abc9f7897ec950bee9e8c412f7cdc5c 470409 python optional mdp_3.2+git78-g7db3c50.orig.tar.gz 482b4738c45cf0b9f0608584d96a2990 5224 python optional mdp_3.2+git78-g7db3c50-3.debian.tar.gz ea68bff98bc22d69bce3e4bc22624586 481926 python optional python-mdp_3.2+git78-g7db3c50-3_all.deb 9775e937204003bfd884ca4ba19253dd 469376 python optional python3-mdp_3.2+git78-g7db3c50-3_all.deb -BEGIN PGP SIGNATURE- Version: GnuPG v1.4.11 (GNU/Linux) iEYEARECAAYFAk+MHDoACgkQjRFFY3XAJMjKGgCgzbKma73Yf8U9K3q8TUichJ4p 6/EAn3NU/bkEfFcbHufT0K83UAbYmeWy =MnW1 -END PGP SIGNATURE- Accepted: mdp_3.2+git78-g7db3c50-3.debian.tar.gz to main/m/mdp/mdp_3.2+git78-g7db3c50-3.debian.tar.gz mdp_3.2+git78-g7db3c50-3.dsc to main/m/mdp/mdp_3.2+git78-g7db3c50-3.dsc mdp_3.2+git78-g7db3c50.orig.tar.gz to main/m/mdp/mdp_3.2+git78-g7db3c50.orig.tar.gz python-mdp_3.2+git78-g7db3c50-3_all.deb to main/m/mdp/python-mdp_3.2+git78-g7db3c50-3_all.deb python3-mdp_3.2+git78-g7db3c50-3_all.deb to main/m/mdp/python3-mdp_3.2+git78-g7db3c50-3_all.deb -- To UNSUBSCRIBE, email to debian-devel-changes-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org Archive: http://lists.debian.org/e1skchb-0008vn...@franck.debian.org
[Numpy-discussion] [Reminder] Summer School Advanced Scientific Programming in Python in Kiel, Germany
Advanced Scientific Programming in Python = a Summer School by the G-Node and the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 2—7, 2012. Kiel, Germany. Preliminary Program === Day 0 (Sun Sept 2) — Best Programming Practices - Best Practices, Development Methodologies and the Zen of Python - Version control with git - Object-oriented programming design patterns Day 1 (Mon Sept 3) — Software Carpentry - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Best practices in data visualization - Programming in teams Day 2 (Tue Sept 4) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Advanced Python I: idioms, useful built-in data structures, generators Day 3 (Wed Sept 5) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 6) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Advanced Python II: decorators and context managers - Programming project Day 5 (Fri Sept 7) — Practical Software Development - Programming project - The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before 23:59 UTC, May 1, 2012. Notifications of acceptance will be sent by June 1, 2012. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate last time was around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. You are encouraged to go through the introductory material available on the website. Faculty === - Francesc Alted, Continuum Analytics Inc., USA - Pietro Berkes, Enthought Inc., UK - Valentin Haenel, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Technologit GbR, Germany - Bartosz Teleńczuk, Unité de Neurosciences Information et Complexité, Centre National de la Recherche Scientifique, France - Stéfan van der Walt, Helen Wills Neuroscience Institute, University of California Berkeley, USA - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus Neurotechnology, Germany - Niko Wilbert, TNG Technology Consulting GmbH, Germany - Tiziano Zito, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Organized by Christian T. Steigies and Christian Drews of the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel , and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org
Re: py.test is not in debian anymore
here a couple of short comments: - why do you build for all python versions and then only install for the default versions? - in override_dh_auto_test, it would probably be better not to introduce your own ENABLE_TESTS flag. you should rely on the DEB_BUILD_OPTIONS variable. something like this should do: ifeq (,$(filter nocheck,$(DEB_BUILD_OPTIONS))) - why did you choose to rename the py.test script for python3 to py3.test? I think the upstream convention of having py.test-2.6, py.test-2.7, py.test-3.2, etc., seems more sensible and also consistent with other packages (see for example ipython: ipython2.6 ipython2.7 ...) at the end one could have a py.test link to point to the py.test-2.x where x is the default (probably 7 for wheezy), and same thing for the python3 version, where py.test-3 may point to py.test-3.2 on wheezy. ciao, tiziano PS to the list: is this kind of conversation appropriate for debian-python or should it better stay private to keep the noise level low? On Wed 28 Mar, 18:28, Simon Chopin wrote: debian-python@lists.debian.org Cc-ed. On Wed, 28 Mar 2012 20:55:54 +0200, Tiziano Zito tiziano.z...@bccn-berlin.de wrote: hi simon, Hi ! I am a user of py.test and maintainer of the python-mdp package, which used to suggest python-py and used py.test. now that py.test is not contained in python-py anymore, I was thinking about filing an RFP for pytest. you already filed an ITP for that, which is owesome :) yarik, a seasoned DD in Cc, would be willing to sponsor, but being really busy, asked me to help him review your package. if you agree I could send you my comments about your package, so that with the help of yarik you can make it fit for a speedy upload to debian. what do you think? Only good things :-). The more review the package gets, the better it should become. Unfortunately, I have virtually no free time until next week, which means I will not be able to address any comment you could have ATM. As I intend to package pytest under the umbrella of the DPMT, the packaging is in its SVN, and I added an RFS for it on the TODO wiki page. Cheers, Simon -- To UNSUBSCRIBE, email to debian-python-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org Archive: http://lists.debian.org/20120329012827.57487A0204@beltira -- To UNSUBSCRIBE, email to debian-python-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org Archive: http://lists.debian.org/20120402133931.ga14...@bio230.biologie.hu-berlin.de
Re: FvwmRearrange bug if Style TitleAtLeft/TitleAtRight is used
On Mon 27 Feb, 18:02, Thomas Adam wrote: On Feb 27, 2012 5:34 PM, Tiziano Zito opossumn...@gmail.com wrote: Hi FVWM developers, first of all thank you for maintaining my window manager :) Patching this to use the TITLE_AT_* macros is the preferred way for this, as you can then get the frame geometry correctly. Can you patch it this way instead? It's easy enough. OK, I'll give it a try. Thank you for your prompt feedback, Tiziano
[Numpy-discussion] [ANN] Summer School Advanced Scientific Programming in Python in Kiel, Germany
Advanced Scientific Programming in Python = a Summer School by the G-Node and the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 2—7, 2012. Kiel, Germany. Preliminary Program === Day 0 (Sun Sept 2) — Best Programming Practices - Best Practices, Development Methodologies and the Zen of Python - Version control with git - Object-oriented programming design patterns Day 1 (Mon Sept 3) — Software Carpentry - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Best practices in data visualization - Programming in teams Day 2 (Tue Sept 4) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Advanced Python I: idioms, useful built-in data structures, generators Day 3 (Wed Sept 5) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 6) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Advanced Python II: decorators and context managers - Programming project Day 5 (Fri Sept 7) — Practical Software Development - Programming project - The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before 23:59 UTC, May 1, 2012. Notifications of acceptance will be sent by June 1, 2012. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate last time was around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. You are encouraged to go through the introductory material available on the website. Faculty === - Francesc Alted, Continuum Analytics Inc., USA - Pietro Berkes, Enthought Inc., UK - Valentin Haenel, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Technologit GbR, Germany - Bartosz Teleńczuk, Unité de Neurosciences Information et Complexité, Centre National de la Recherche Scientifique, France - Stéfan van der Walt, Helen Wills Neuroscience Institute, University of California Berkeley, USA - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus Neurotechnology, Germany - Niko Wilbert, TNG Technology Consulting GmbH, Germany - Tiziano Zito, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Organized by Christian T. Steigies and Christian Drews of the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel , and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org
[ANN] Summer School Advanced Scientific Programming in Python in Kiel, Germany
Advanced Scientific Programming in Python = a Summer School by the G-Node and the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 2—7, 2012. Kiel, Germany. Preliminary Program === Day 0 (Sun Sept 2) — Best Programming Practices - Best Practices, Development Methodologies and the Zen of Python - Version control with git - Object-oriented programming design patterns Day 1 (Mon Sept 3) — Software Carpentry - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Best practices in data visualization - Programming in teams Day 2 (Tue Sept 4) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Advanced Python I: idioms, useful built-in data structures, generators Day 3 (Wed Sept 5) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 6) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Advanced Python II: decorators and context managers - Programming project Day 5 (Fri Sept 7) — Practical Software Development - Programming project - The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before 23:59 UTC, May 1, 2012. Notifications of acceptance will be sent by June 1, 2012. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate last time was around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. You are encouraged to go through the introductory material available on the website. Faculty === - Francesc Alted, Continuum Analytics Inc., USA - Pietro Berkes, Enthought Inc., UK - Valentin Haenel, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Technologit GbR, Germany - Bartosz Teleńczuk, Unité de Neurosciences Information et Complexité, Centre National de la Recherche Scientifique, France - Stéfan van der Walt, Helen Wills Neuroscience Institute, University of California Berkeley, USA - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus Neurotechnology, Germany - Niko Wilbert, TNG Technology Consulting GmbH, Germany - Tiziano Zito, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Organized by Christian T. Steigies and Christian Drews of the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel , and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software
Accepted mdp 3.2+git28-g4243e9c-1 (source all)
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Format: 1.8 Date: Thu, 19 Jan 2012 18:32:19 +0100 Source: mdp Binary: python-mdp Architecture: source all Version: 3.2+git28-g4243e9c-1 Distribution: unstable Urgency: low Maintainer: Tiziano Zito opossumn...@gmail.com Changed-By: Tiziano Zito opossumn...@gmail.com Description: python-mdp - Modular toolkit for Data Processing Changes: mdp (3.2+git28-g4243e9c-1) unstable; urgency=low . [Tiziano Zito] * New git snapshot * More sklearn wrappers * Fix sklearn wrappers for version 0.10 * Fix reload support * Fix wrong sorting of eigenvectors in degenerate case for SVD * Silence sklearn warnings for version = 0.9 * Updated git repo information in README.Debian-source * Added shogun-python-modular to Build-Depends now that it is available on all major platforms * Updated debian-branch name in gbp.conf Checksums-Sha1: b7b63336edba8f8e6612f6056acabde4976ec188 1439 mdp_3.2+git28-g4243e9c-1.dsc dd28fdafc2df0c5a0161baab31c72d96722ac70d 466949 mdp_3.2+git28-g4243e9c.orig.tar.gz 9a718d124668885a07ff8020ba4c7ebfdec21f21 4255 mdp_3.2+git28-g4243e9c-1.debian.tar.gz dd178efd3d92b63cfecc24b172ad1baa60c4bad7 480630 python-mdp_3.2+git28-g4243e9c-1_all.deb Checksums-Sha256: 6b04884b240e13fce75a9560e5c4530211cfc9c3c247a3ab8efa9b5822344bb3 1439 mdp_3.2+git28-g4243e9c-1.dsc ad969692eab260bd776a5ff5e4657842b602ae33d015b3244b3124ec61432072 466949 mdp_3.2+git28-g4243e9c.orig.tar.gz 1b554a46f390361760801184257076416db5dce80ffb2db2c735dd851327d4a1 4255 mdp_3.2+git28-g4243e9c-1.debian.tar.gz e9b0dccbc7af1774484d5a7ac6346d6fcd1562e87b99b98ff56aa04f9b9ed01d 480630 python-mdp_3.2+git28-g4243e9c-1_all.deb Files: d58c56694d3e614117d8a8c14a94 1439 python optional mdp_3.2+git28-g4243e9c-1.dsc 3a105a506fb86e8f9b7af59c3c9f16a1 466949 python optional mdp_3.2+git28-g4243e9c.orig.tar.gz f682faa6556160cd3d81388942e4406a 4255 python optional mdp_3.2+git28-g4243e9c-1.debian.tar.gz 4f0f6b04959e046c93f9e45f0a6663dd 480630 python optional python-mdp_3.2+git28-g4243e9c-1_all.deb -BEGIN PGP SIGNATURE- Version: GnuPG v1.4.11 (GNU/Linux) iEYEARECAAYFAk8c3QUACgkQjRFFY3XAJMj+ygCfe7Xk7NOsv3ftI9XHLCMuEjYN JFIAnRDPu470wrE0xVwBmQVz6wopfgYq =VPVY -END PGP SIGNATURE- Accepted: mdp_3.2+git28-g4243e9c-1.debian.tar.gz to main/m/mdp/mdp_3.2+git28-g4243e9c-1.debian.tar.gz mdp_3.2+git28-g4243e9c-1.dsc to main/m/mdp/mdp_3.2+git28-g4243e9c-1.dsc mdp_3.2+git28-g4243e9c.orig.tar.gz to main/m/mdp/mdp_3.2+git28-g4243e9c.orig.tar.gz python-mdp_3.2+git28-g4243e9c-1_all.deb to main/m/mdp/python-mdp_3.2+git28-g4243e9c-1_all.deb -- To UNSUBSCRIBE, email to debian-devel-changes-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org Archive: http://lists.debian.org/e1rpblx-0006kp...@franck.debian.org
Bug#645584: Please support python3
Package: python-mdp Version: 3.2-1 Followup-For: Bug #645584 python-mdp depends on python-numpy. As soon as Python 3 package is available for python-numpy, I can try to prepare a Python 3 package for python-mdp. See: http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=601593 Best, Tiziano -- System Information: Debian Release: wheezy/sid APT prefers unstable APT policy: (500, 'unstable'), (101, 'experimental') Architecture: i386 (i686) Kernel: Linux 3.1.0-1-686-pae (SMP w/2 CPU cores) Locale: LANG=en_US.UTF-8, LC_CTYPE=en_US.UTF-8 (charmap=UTF-8) Shell: /bin/sh linked to /bin/dash Versions of packages python-mdp depends on: ii python2.7.2-9 ii python-numpy 1:1.5.1-3 ii python2.6 2.6.7-4 ii python2.7 2.7.2-12 Versions of packages python-mdp recommends: ii python-joblib 0.6.0~b3-1 ii python-libsvm 3.1-1 ii python-pp 1.6.1-1 ii python-scikits-learn none ii python-scipy 0.9.0+dfsg1-1+b2 ii shogun-python-modular 1.1.0-1 Versions of packages python-mdp suggests: ii python-py 1.3.4-2 -- To UNSUBSCRIBE, email to debian-bugs-dist-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org
[Numpy-discussion] ANN: MDP 3.2 released!
We are glad to announce release 3.2 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. What's new in version 3.2? -- - improved sklearn wrappers - update sklearn, shogun, and pp wrappers to new versions - do not leave temporary files around after testing - refactoring and cleaning up of HTML exporting features - improve export of signature and doc-string to public methods - fixed and updated FastICANode to closely resemble the original Matlab version (thanks to Ben Willmore) - support for new numpy version - new NeuralGasNode (thanks to Michael Schmuker) - several bug fixes and improvements We recommend all users to upgrade. Resources - Download: http://sourceforge.net/projects/mdp-toolkit/files Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users Acknowledgments --- We thank the contributors to this release: Michael Schmuker, Ben Willmore. The MDP developers, Pietro Berkes Zbigniew Jędrzejewski-Szmek Rike-Benjamin Schuppner Niko Wilbert Tiziano Zito ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Accepted mdp 3.2-1 (source all)
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Format: 1.8 Date: Mon, 24 Oct 2011 16:01:00 +0200 Source: mdp Binary: python-mdp Architecture: source all Version: 3.2-1 Distribution: unstable Urgency: low Maintainer: Tiziano Zito opossumn...@gmail.com Changed-By: Tiziano Zito opossumn...@gmail.com Description: python-mdp - Modular toolkit for Data Processing Closes: 645583 645585 Changes: mdp (3.2-1) unstable; urgency=low . [ Yaroslav Halchenko ] * Boosted policy compliance to 3.9.2 (no changes needed) * Tiziano now maintains -- I upload . [ Tiziano Zito ] * New upstream release * New release adds support for shogun 1.0 (Closes: #645583) * Use dh_python2 instead of deprecated python-support (Closes: #645585) * Introduced unittesting at package build time * Do not build-depend on shogun-python-modular until version 1.0.0 is available on non-amd64 platforms Checksums-Sha1: 81f1a4f843b91bbeab7c51797b9ddb96b882b94b 1311 mdp_3.2-1.dsc 699e1b9d2ca5b03073e16a5280b4614094b328c1 465298 mdp_3.2.orig.tar.gz dd6ad5a0bf20d5bd1b4f28916dd3fb3490a58fa6 4075 mdp_3.2-1.debian.tar.gz a820bce7833e4923e63baffce00cc47d34c93843 478816 python-mdp_3.2-1_all.deb Checksums-Sha256: 3a789ea39ffbb83044b5aa6a39b328be237b696cec41ac3a521b8c468f27e245 1311 mdp_3.2-1.dsc 113cb2e1894ffc59507ef0d980e9a981fc0e4e87e3b06760ebbd048f666bda56 465298 mdp_3.2.orig.tar.gz 36b9114279f9c41229e643df518855a902414b32b8e7f22c23b89117d7656c2b 4075 mdp_3.2-1.debian.tar.gz 7fcc6c45e7d3f5bdec91268908fbd5a69f6864636ae267c3f6b7e67a6f2f307e 478816 python-mdp_3.2-1_all.deb Files: db77cfa26abb6c8a92bd59df639b1740 1311 python optional mdp_3.2-1.dsc e29de353c74f2bd974396c96dc464e7d 465298 python optional mdp_3.2.orig.tar.gz 6f2d83fc2aee33e9873ad6cdf8e6c38a 4075 python optional mdp_3.2-1.debian.tar.gz d9f84505666787bf1571c3e95fd5f698 478816 python optional python-mdp_3.2-1_all.deb -BEGIN PGP SIGNATURE- Version: GnuPG v1.4.11 (GNU/Linux) iEYEARECAAYFAk6lpQgACgkQjRFFY3XAJMjHhgCgrwNOYckCOCTGVkTAnCNX6V1A YAwAoLZN3B8BiNh8RjLwnp1bs9O59D/A =0uAW -END PGP SIGNATURE- Accepted: mdp_3.2-1.debian.tar.gz to main/m/mdp/mdp_3.2-1.debian.tar.gz mdp_3.2-1.dsc to main/m/mdp/mdp_3.2-1.dsc mdp_3.2.orig.tar.gz to main/m/mdp/mdp_3.2.orig.tar.gz python-mdp_3.2-1_all.deb to main/m/mdp/python-mdp_3.2-1_all.deb -- To UNSUBSCRIBE, email to debian-devel-changes-requ...@lists.debian.org with a subject of unsubscribe. Trouble? Contact listmas...@lists.debian.org Archive: http://lists.debian.org/e1rioqt-0008d0...@franck.debian.org
ANN: MDP 3.2 released!
We are glad to announce release 3.2 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. What's new in version 3.2? -- - improved sklearn wrappers - update sklearn, shogun, and pp wrappers to new versions - do not leave temporary files around after testing - refactoring and cleaning up of HTML exporting features - improve export of signature and doc-string to public methods - fixed and updated FastICANode to closely resemble the original Matlab version (thanks to Ben Willmore) - support for new numpy version - new NeuralGasNode (thanks to Michael Schmuker) - several bug fixes and improvements We recommend all users to upgrade. Resources - Download: http://sourceforge.net/projects/mdp-toolkit/files Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users Acknowledgments --- We thank the contributors to this release: Michael Schmuker, Ben Willmore. The MDP developers, Pietro Berkes Zbigniew Jędrzejewski-Szmek Rike-Benjamin Schuppner Niko Wilbert Tiziano Zito -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] [ANN] EuroScipy 2011 - deadline extended
=== EuroScipy 2011 - Deadline Extended! === Deadline extended! You can submit your contribution until Friday May 13. - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[ANN] EuroScipy 2011 - deadline extended
=== EuroScipy 2011 - Deadline Extended! === Deadline extended! You can submit your contribution until Friday May 13. - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 ___ NumPy-Discussion mailing list numpy-discuss...@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] [ANN] EuroScipy 2011 - deadline approaching
= EuroScipy 2011 - Deadline Approaching = Beware: talk submission deadline is approaching. You can submit your contribution until Sunday May 8. - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[ANN] EuroScipy 2011 - deadline approaching
= EuroScipy 2011 - Deadline Approaching = Beware: talk submission deadline is approaching. You can submit your contribution until Sunday May 8. - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] [ANN] Summer School Advanced Scientific Programming in Python in St Andrews, UK
Advanced Scientific Programming in Python = a Summer School by the G-Node and the School of Psychology, University of St Andrews Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 11—16, 2011. St Andrews, UK. Preliminary Program === Day 0 (Sun Sept 11) — Best Programming Practices - Agile development Extreme Programming - Advanced Python: decorators, generators, context managers - Version control with git Day 1 (Mon Sept 12) — Software Carpentry - Object-oriented programming design patterns - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Programming in teams Day 2 (Tue Sept 13) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Best practices in data visualization Day 3 (Wed Sept 14) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 15) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Data serialization: from pickle to databases - Programming project Day 5 (Fri Sept 16) — Practical Software Development - Programming project - The Pac-Man Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before May 29, 2011. Notifications of acceptance will be sent by June 19, 2011. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate in past editions was around 30%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. Please consult the website for a list of introductory material. Faculty === - Francesc Alted, author of PyTables, Castelló de la Plana, Spain - Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA - Valentin Haenel, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, The Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Bernstein Center for Computational Neuroscience Berlin, Germany - Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus: Neurotechnology, Germany - Pauli Virtanen, Institute for Theoretical Physics and Astrophysics, University of Würzburg, Germany - Tiziano Zito, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Katharina Maria Zeiner and Manuel Spitschan of the School of Psychology, University of St Andrews, and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[ANN] Summer School Advanced Scientific Programming in Python in St Andrews, UK
Advanced Scientific Programming in Python = a Summer School by the G-Node and the School of Psychology, University of St Andrews Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 11—16, 2011. St Andrews, UK. Preliminary Program === Day 0 (Sun Sept 11) — Best Programming Practices - Agile development Extreme Programming - Advanced Python: decorators, generators, context managers - Version control with git Day 1 (Mon Sept 12) — Software Carpentry - Object-oriented programming design patterns - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Programming in teams Day 2 (Tue Sept 13) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Best practices in data visualization Day 3 (Wed Sept 14) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 15) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Data serialization: from pickle to databases - Programming project Day 5 (Fri Sept 16) — Practical Software Development - Programming project - The Pac-Man Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before May 29, 2011. Notifications of acceptance will be sent by June 19, 2011. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate in past editions was around 30%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. Please consult the website for a list of introductory material. Faculty === - Francesc Alted, author of PyTables, Castelló de la Plana, Spain - Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA - Valentin Haenel, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, The Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Bernstein Center for Computational Neuroscience Berlin, Germany - Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus: Neurotechnology, Germany - Pauli Virtanen, Institute for Theoretical Physics and Astrophysics, University of Würzburg, Germany - Tiziano Zito, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Katharina Maria Zeiner and Manuel Spitschan of the School of Psychology, University of St Andrews, and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] [Ann] EuroScipy 2011 - Call for papers
= Announcing EuroScipy 2011 = - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Ann] EuroScipy 2011 - Call for papers
= Announcing EuroScipy 2011 = - The 4th European meeting on Python in Science - **Paris, Ecole Normale Supérieure, August 25-28 2011** We are happy to announce the 4th EuroScipy meeting, in Paris, August 2011. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Main topics === - Presentations of scientific tools and libraries using the Python language, including but not limited to: - vector and array manipulation - parallel computing - scientific visualization - scientific data flow and persistence - algorithms implemented or exposed in Python - web applications and portals for science and engineering. - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Tutorials = There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Keynote Speaker: Fernando Perez === We are excited to welcome Fernando Perez (UC Berkeley, Helen Wills Neuroscience Institute, USA) as our keynote speaker. Fernando Perez is the original author of the enhanced interactive python shell IPython and a very active contributor to the Python for Science ecosystem. Important dates === Talk submission deadline: Sunday May 8 Program announced:Sunday May 29 Tutorials tracks: Thursday August 25 - Friday August 26 Conference track: Saturday August 27 - Sunday August 28 Call for papers === We are soliciting talks that discuss topics related to scientific computing using Python. These include applications, teaching, future development directions, and research. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission guidelines = - We solicit talk proposals in the form of a one-page long abstract. - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the EuroSciPy conference by at least one author. The one-page long abstracts are for conference planing and selection purposes only. We will later select papers for publication of post-proceedings in a peer-reviewed journal. How to submit an abstract = To submit a talk to the EuroScipy conference follow the instructions here: http://www.euroscipy.org/card/euroscipy2011_call_for_papers Organizers == Chairs: - Gaël Varoquaux (INSERM, Unicog team, and INRIA, Parietal team) - Nicolas Chauvat (Logilab) Local organization committee: - Emmanuelle Gouillart (Saint-Gobain Recherche) - Jean-Philippe Chauvat (Logilab) Tutorial chair: - Valentin Haenel (MKP, Technische Universität Berlin) Program committee: - Chair: Tiziano Zito (MKP, Technische Universität Berlin) - Romain Brette (ENS Paris, DEC) - Emmanuelle Gouillart (Saint-Gobain Recherche) - Eric Lebigot (Laboratoire Kastler Brossel, Université Pierre et Marie Curie) - Konrad Hinsen (Soleil Synchrotron, CNRS) - Hans Petter Langtangen (Simula laboratories) - Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute) - Mike Müller (Python Academy) - Didrik Pinte (Enthought Inc) - Marc Poinot (ONERA) - Christophe Pradal (CIRAD/INRIA, Virtual Plantes team) - Andreas Schreiber (DLR) - Stéfan van der Walt (University of Stellenbosch) Website === http://www.euroscipy.org/conference/euroscipy_2011 -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] MDP release 3.0
We are glad to announce release 3.0 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. What's new in version 3.0? -- - Python 3 support - New extensions: caching and gradient - Automatically generated wrappers for scikits.learn algorithms - Shogun and libsvm wrappers - New algorithms: convolution, several classifiers and several user-contributed nodes - Several new examples on the homepage - Improved and expanded tutorial - Several improvements and bug fixes - New license: MDP goes BSD! Resources - Download: http://sourceforge.net/projects/mdp-toolkit/files Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users Acknowledgments --- We thank the contributors to this release: Sven Dähne, Alberto Escalante, Valentin Haenel, Yaroslav Halchenko, Sebastian Höfer, Michael Hull, Samuel John, José Quesada, Ariel Rokem, Benjamin Schrauwen, David Verstraeten, Katharina Maria Zeiner. The MDP developers, Pietro Berkes Zbigniew Jędrzejewski-Szmek Rike-Benjamin Schuppner Niko Wilbert Tiziano Zito ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
MDP release 3.0
We are glad to announce release 3.0 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. What's new in version 3.0? -- - Python 3 support - New extensions: caching and gradient - Automatically generated wrappers for scikits.learn algorithms - Shogun and libsvm wrappers - New algorithms: convolution, several classifiers and several user-contributed nodes - Several new examples on the homepage - Improved and expanded tutorial - Several improvements and bug fixes - New license: MDP goes BSD! Resources - Download: http://sourceforge.net/projects/mdp-toolkit/files Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users Acknowledgments --- We thank the contributors to this release: Sven Dähne, Alberto Escalante, Valentin Haenel, Yaroslav Halchenko, Sebastian Höfer, Michael Hull, Samuel John, José Quesada, Ariel Rokem, Benjamin Schrauwen, David Verstraeten, Katharina Maria Zeiner. The MDP developers, Pietro Berkes Zbigniew Jędrzejewski-Szmek Rike-Benjamin Schuppner Niko Wilbert Tiziano Zito -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
[Numpy-discussion] [ANN] Reminder: Autumn School Advanced Scientific Programming in Python in Trento, It aly
Reminder: Application deadline is August 31st, 2010! === Advanced Scientific Programming in Python = an Autumn School by the G-Node, the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented. This school is targeted at Post-docs and PhD students from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. A basic knowledge of the Python language is assumed. Participants without prior experience with Python should work through the proposed introductory materials. Date and Location = October 4th—8th, 2010. Trento, Italy. Preliminary Program === Day 0 (Mon Oct 4) — Software Carpentry Advanced Python • Documenting code and using version control • Object-oriented programming, design patterns, and agile programming • Exception handling, lambdas, decorators, context managers, metaclasses Day 1 (Tue Oct 5) — Software Carpentry • Test-driven development, unit testing Quality Assurance • Debugging, profiling and benchmarking techniques • Data serialization: from pickle to databases Day 2 (Wed Oct 6) — Scientific Tools for Python • Advanced NumPy • The Quest for Speed (intro): Interfacing to C • Programming project Day 3 (Thu Oct 7) — The Quest for Speed • Writing parallel applications in Python • When parallelization does not help: the starving CPUs problem • Programming project Day 4 (Fri Oct 8) — Practical Software Development • Efficient programming in teams • Programming project • The Pac-Man Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects Applications You can apply on-line at http://www.g-node.org/python-autumnschool Applications must be submitted before August 31st, 2010. Notifications of acceptance will be sent by September 4th, 2010. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate in past editions was around 30%. Prerequisites = You are supposed to know the basics of Python to participate in the lectures! Look on the website for a list of introductory material. Faculty === • Francesc Alted, author of PyTables, Castelló de la Plana, Spain • Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA • Valentin Haenel, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany • Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland • Eilif Muller, The Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Switzerland • Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy • Rike-Benjamin Schuppner, Bernstein Center for Computational Neuroscience Berlin, Germany • Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Bastian Venthur, Berlin Institute of Technology and Bernstein Focus: Neurotechnology, Germany • Stéfan van der Walt, Applied Mathematics, University of Stellenbosch, South Africa • Tiziano Zito, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Paolo Avesani for the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler, and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://www.g-node.org/python-autumnschool Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug in dtype.__eq__ method ?
hi all,
we just noticed the following weird thing:
$ python
Python 2.6.6rc2 (r266rc2:84114, Aug 18 2010, 07:33:44)
[GCC 4.4.5 20100816 (prerelease)] on linux2
Type help, copyright, credits or license for more information.
import numpy
numpy.version.version
'2.0.0.dev8469'
numpy.dtype('float64') == None
True
numpy.dtype('float32') == None
False
is this a bug in the dtype.__eq__ method?
ciao,
tiziano
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Re: [Numpy-discussion] ANN: Numpy runs on Python 3
hi, it's probably obvious for most of the subscribers, but still: svn clone http://svn.scipy.org/svn/numpy/trunk/ numpy should actually read: svn checkout http://svn.scipy.org/svn/numpy/trunk/ numpy that said, I'm looking forward to the git migration ;-) ciao, tiziano (member of the PR department @ EuroScipy :) On Sat 10 Jul, 14:30, Pauli Virtanen wrote: Hi, As many of you probably already know, Numpy works fully on Python 3 and Python 2, with a *single code base*, since March. This work is scheduled to be included in the next releases 1.5 and 2.0. Porting Scipy to work on Python 3 has proved to be much less work, and will probably be finished soon. (Ongoing work is here: http:// github.com/cournape/scipy3/commits/py3k , http://github.com/pv/scipy-work/ commits/py3k ) For those who are interested in already starting to port their stuff to Python 3, you can use Numpy's SVN trunk version. Grab it: svn clone http://svn.scipy.org/svn/numpy/trunk/ numpy cd numpy python3 setup.py build An important point is that supporting Python 3 and Python 2 in the same code base can be done, and it is not very difficult either. It is also much preferable from the maintenance POV to creating separate branches for Python 2 and 3. We attempted to log changes needed in Numpy at http://projects.scipy.org/numpy/browser/trunk/doc/Py3K.txt which may be useful (although not completely up-to-date) information for people wanting to do make the same transition in their own code. (Announcement as recommended by our PR department @ EuroScipy :) -- Pauli Virtanen ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Autumn School Advanced Scientific Programming in Python in Trento, Italy
Advanced Scientific Programming in Python = an Autumn School by the G-Node, the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented. This school is targeted at Post-docs and PhD students from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. A basic knowledge of the Python language is assumed. Participants without prior experience with Python should work through the proposed introductory materials. Date and Location = October 4th—8th, 2010. Trento, Italy. Preliminary Program === Day 0 (Mon Oct 4) — Software Carpentry Advanced Python • Documenting code and using version control • Object-oriented programming, design patterns, and agile programming • Exception handling, lambdas, decorators, context managers, metaclasses Day 1 (Tue Oct 5) — Software Carpentry • Test-driven development, unit testing Quality Assurance • Debugging, profiling and benchmarking techniques • Data serialization: from pickle to databases Day 2 (Wed Oct 6) — Scientific Tools for Python • Advanced NumPy • The Quest for Speed (intro): Interfacing to C • Programming project Day 3 (Thu Oct 7) — The Quest for Speed • Writing parallel applications in Python • When parallelization does not help: the starving CPUs problem • Programming project Day 4 (Fri Oct 8) — Practical Software Development • Efficient programming in teams • Programming project • The Pac-Man Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects Applications You can apply on-line at http://www.g-node.org/python-autumnschool Applications must be submitted before August 31th, 2010. Notifications of acceptance will be sent by September 4th, 2010. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate in past editions was around 30%. Prerequisites = You are supposed to know the basics of Python to participate in the lectures! Look on the website for a list of introductory material. Faculty === • Francesc Alted, author of PyTables, Castelló de la Plana, Spain • Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA • Valentin Haenel, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany • Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland • Eilif Muller, The Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Switzerland • Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy • Rike-Benjamin Schuppner, Bernstein Center for Computational Neuroscience Berlin, Germany • Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Bastian Venthur, Berlin Institute of Technology and Bernstein Focus: Neurotechnology, Germany • Stéfan van der Walt, Applied Mathematics, University of Stellenbosch, South Africa • Tiziano Zito, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Paolo Avesani for the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler , and by Zbigniew JędrzejewscySzmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://www.g-node.org/python-autumnschool Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: MDP release 2.6 and MDP Sprint 2010
We are glad to announce release 2.6 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, JADE, and XSFA), Slow Feature Analysis, Restricted Boltzmann Machine, and Locally Linear Embedding. What's new in version 2.6? -- - Several new classifier nodes have been added. - A new node extension mechanism makes it possible to dynamically add methods or attributes for specific features to node classes, enabling aspect-oriented programming in MDP. Several MDP features (like parallelization) are now based on this mechanism, and users can add their own custom node extensions. - BiMDP is a large new package in MDP that introduces bidirectional data flows to MDP, including backpropagation and even loops. BiMDP also enables the transportation of additional data in flows via messages. - BiMDP includes a new flow inspection tool, that runs as as a graphical debugger in the webrowser to step through complex flows. It can be extended by users for the analysis and visualization of intermediate data. - As usual, tons of bug fixes The new additions in the library have been thoroughly tested but, as usual after a public release, we especially welcome user's feedback and bug reports. MDP Sprint 2010 --- Following our tradition of sprint-driven development, the team of the core developers decided to organize a programming sprint open to external participants. We invite in particular all users who implemented new algorithms and would like to see them integrated in MDP: you will work together with a core developer! More info: http://sourceforge.net/apps/mediawiki/mdp-toolkit/index.php?title=MDP_Sprint_2010 Resources - Download: http://sourceforge.net/projects/mdp-toolkit/files Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users -- Pietro Berkes Volen Center for Complex Systems Brandeis University Waltham, MA, USA Rike-Benjamin Schuppner Berlin, Germany Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Modelling of Cognitive Processes Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Advanced Scientific Programming in Python Winter School in Warsaw, Poland
Advanced Scientific Programming in Python a Winter School by the G-Node and University of Warsaw Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented. This winter school is targeted at Post-docs and PhD students from all areas. Substantial proficiency in Python or in another language (e.g. Java, C/C++, MATLAB, Mathematica) is absolutely required. An optional, one-day introduction to Python is offered to participants without prior experience with the language. Date and Location: February 8th — 12th, 2010. Warsaw, Poland. Preliminary Program: - Day 0 (Mon Feb 8) — [Optional] Dive into Python - Day 1 (Tue Feb 9) — Software Carpentry • Documenting code and using version control • Test-driven development and unit testing • Debugging, profiling and benchmarking techniques • Object-oriented programming, design patterns, and agile programming - Day 2 (Wed Feb 10) — Scientific Tools for Python • NumPy, SciPy, Matplotlib • Data serialization: from pickle to databases • Programming project in the afternoon - Day 3 (Thu Feb 11) — The Quest for Speed • Writing parallel applications in Python • When parallelization does not help: the starving CPUs problem • Programming project in the afternoon - Day 4 (Fri Feb 12) — Practical Software Development • Software design • Efficient programming in teams • Quality Assurance • Programming project final Applications: Applications should be sent before December 6th, 2009 to: python-wintersch...@g-node.org No fee is charged but participants should take care of travel, living, and accommodation expenses. Applications should include full contact information (name, affiliation, email phone), a *short* CV and a *short* statement addressing the following questions: • What is your educational background? • What experience do you have in programming? • Why do you think “Advanced Scientific Programming in Python” is an appropriate course for your skill profile? Candidates will be selected on the basis of their profile. Places are limited: early application is recommended. Notifications of acceptance will be sent by December 14th, 2009. Faculty • Francesc Alted, author of PyTables, Castelló de la Plana, Spain [Day 3] • Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA [Day 1] • Zbigniew Jędrzejewski-Szmek, Institute of Experimental Physics, University of Warsaw, Poland [Day 0] • Eilif Muller, Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne, Switzerland [Day 3] • Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany [Day 2] • Niko Wilbert, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany [Day 1] • Tiziano Zito, Bernstein Center for Computational Neuroscience, Berlin, Germany [Day 4] Organized by Piotr Durka, Joanna and Zbigniew Jędrzejewscy-Szmek (Institute of Experimental Physics, University of Warsaw), and Tiziano Zito (German Neuroinformatics Node of the INCF). Website: http://www.g-node.org/python-winterschool Contact: python-wintersch...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[ANN] Advanced Scientific Programming in Python Winter School in Warsaw, Poland
Advanced Scientific Programming in Python a Winter School by the G-Node and University of Warsaw Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented. This winter school is targeted at Post-docs and PhD students from all areas. Substantial proficiency in Python or in another language (e.g. Java, C/C++, MATLAB, Mathematica) is absolutely required. An optional, one-day introduction to Python is offered to participants without prior experience with the language. Date and Location: February 8th — 12th, 2010. Warsaw, Poland. Preliminary Program: - Day 0 (Mon Feb 8) — [Optional] Dive into Python - Day 1 (Tue Feb 9) — Software Carpentry • Documenting code and using version control • Test-driven development and unit testing • Debugging, profiling and benchmarking techniques • Object-oriented programming, design patterns, and agile programming - Day 2 (Wed Feb 10) — Scientific Tools for Python • NumPy, SciPy, Matplotlib • Data serialization: from pickle to databases • Programming project in the afternoon - Day 3 (Thu Feb 11) — The Quest for Speed • Writing parallel applications in Python • When parallelization does not help: the starving CPUs problem • Programming project in the afternoon - Day 4 (Fri Feb 12) — Practical Software Development • Software design • Efficient programming in teams • Quality Assurance • Programming project final Applications: Applications should be sent before December 6th, 2009 to: python-wintersch...@g-node.org No fee is charged but participants should take care of travel, living, and accommodation expenses. Applications should include full contact information (name, affiliation, email phone), a *short* CV and a *short* statement addressing the following questions: • What is your educational background? • What experience do you have in programming? • Why do you think “Advanced Scientific Programming in Python” is an appropriate course for your skill profile? Candidates will be selected on the basis of their profile. Places are limited: early application is recommended. Notifications of acceptance will be sent by December 14th, 2009. Faculty • Francesc Alted, author of PyTables, Castelló de la Plana, Spain [Day 3] • Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA [Day 1] • Zbigniew Jędrzejewski-Szmek, Institute of Experimental Physics, University of Warsaw, Poland [Day 0] • Eilif Muller, Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne, Switzerland [Day 3] • Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany [Day 2] • Niko Wilbert, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany [Day 1] • Tiziano Zito, Bernstein Center for Computational Neuroscience, Berlin, Germany [Day 4] Organized by Piotr Durka, Joanna and Zbigniew Jędrzejewscy-Szmek (Institute of Experimental Physics, University of Warsaw), and Tiziano Zito (German Neuroinformatics Node of the INCF). Website: http://www.g-node.org/python-winterschool Contact: python-wintersch...@g-node.org -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
Modular toolkit for Data Processing 2.5 released!
We are glad to announce release 2.5 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, JADE, and XSFA), Slow Feature Analysis, Restricted Boltzmann Machine, and Locally Linear Embedding. What's new in version 2.5? -- - New nodes for XSFA, Linear Regression, Histogram, Cutoffs - The parallel package has grown more features - Tons of bug fixes Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Volen Center for Complex Systems Brandeis University Waltham, MA, USA Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
Bug#521617: tls is broken in version 0.6.8
Package: libnss-ldapd Version: 0.6.8 Severity: normal I can confirm that the bug is more serious that just tls_reqcert never not working. We have here an openldap server with a self-signed certificate. Lenny clients with version 0.6.7 connect using tls without any problem. The relevant part of the nss-ldapd.conf file reads: ssl start_tls tls_checkpeer yes tls_cacertfile /etc/ssl/certs/bccnca.pem On a sid client with version 0.6.8 ssl start_tls does not work. The relevant part of the nss-ldapd.conf file reads: ssl start_tls tls_reqcert demand tls_cacertfile /etc/ssl/certs/bccnca.pem A debug session looking up a valid user on a working lenny client: nslcd: DEBUG: add_uri(ldap://ldap1) nslcd: DEBUG: add_uri(ldap://ldap2) nslcd: /etc/nss-ldapd.conf:30: option tls_checkpeer is currently untested (please report any successes) nslcd: /etc/nss-ldapd.conf:31: option tls_cacertfile is currently untested (please report any successes) nslcd: version 0.6.7 starting nslcd: DEBUG: unlink() of /var/run/nslcd/socket failed (ignored): No such file or directory nslcd: DEBUG: setgroups(0,NULL) done nslcd: DEBUG: setgid(120) done nslcd: DEBUG: setuid(113) done nslcd: accepting connections nslcd: [8b4567] DEBUG: connection from pid=12401 uid=0 gid=0 nslcd: [8b4567] DEBUG: nslcd_passwd_byname(tiziano) nslcd: [8b4567] DEBUG: myldap_search(base=dc=bccn-berlin,dc=de, filter=((objectClass=posixAccount)(uid=tiziano))) nslcd: [8b4567] DEBUG: simple anonymous bind to ldap://ldap1 nslcd: [8b4567] connected to LDAP server ldap://ldap1 nslcd: [8b4567] DEBUG: ldap_result(): end of results nslcd: [7b23c6] DEBUG: connection from pid=12401 uid=0 gid=0 nslcd: [7b23c6] DEBUG: nslcd_passwd_byuid(2061) [...] A debug session looking up the same user on the broken sid client with tls enabled: nslcd: DEBUG: add_uri(ldap://ldap1) nslcd: DEBUG: add_uri(ldap://ldap2) nslcd: /etc/nss-ldapd.conf:30: option tls_reqcert is currently untested (please report any successes) nslcd: /etc/nss-ldapd.conf:31: option tls_cacertfile is currently untested (please report any successes) nslcd: version 0.6.8 starting nslcd: DEBUG: unlink() of /var/run/nslcd/socket failed (ignored): No such file or directory nslcd: DEBUG: setgroups(0,NULL) done nslcd: DEBUG: setgid(122) done nslcd: DEBUG: setuid(112) done nslcd: accepting connections nslcd: [8b4567] DEBUG: connection from pid=22112 uid=0 gid=0 nslcd: [8b4567] DEBUG: nslcd_passwd_byname(tiziano) nslcd: [8b4567] DEBUG: myldap_search(base=dc=bccn-berlin,dc=de, filter=((objectClass=posixAccount)(uid=tiziano))) nslcd: [8b4567] ldap_start_tls_s() failed: Connect error: No such file or directory nslcd: [8b4567] failed to bind to LDAP server ldap://ldap1: Connect error: No such file or directory nslcd: [8b4567] ldap_start_tls_s() failed: Connect error: Success nslcd: [8b4567] failed to bind to LDAP server ldap://ldap2: Connect error: Success nslcd: [8b4567] no available LDAP server found, sleeping 1 seconds nslcd: [8b4567] no available LDAP server found [...] A debug session looking up the same user on the same broken sid client this time with tls disabled: nslcd: DEBUG: add_uri(ldap://ldap1) nslcd: DEBUG: add_uri(ldap://ldap2) nslcd: version 0.6.8 starting nslcd: DEBUG: unlink() of /var/run/nslcd/socket failed (ignored): No such file o r directory nslcd: DEBUG: setgroups(0,NULL) done nslcd: DEBUG: setgid(122) done nslcd: DEBUG: setuid(112) done nslcd: accepting connections nslcd: [8b4567] DEBUG: connection from pid=22121 uid=0 gid=0 nslcd: [8b4567] DEBUG: nslcd_passwd_byname(tiziano) nslcd: [8b4567] DEBUG: myldap_search(base=dc=bccn-berlin,dc=de, filter=((obj ectClass=posixAccount)(uid=tiziano))) nslcd: [8b4567] DEBUG: simple anonymous bind to ldap://ldap1 nslcd: [8b4567] connected to LDAP server ldap://ldap1 nslcd: [8b4567] DEBUG: ldap_result(): end of results nslcd: [7b23c6] DEBUG: connection from pid=22121 uid=0 gid=0 nslcd: [7b23c6] DEBUG: nslcd_passwd_byuid(2061) [...] If more info is needed, I'm happy to assist: we need to use TLS (LAN network can not be trusted). ciao, tiziano -- System Information: Debian Release: squeeze/sid APT prefers unstable APT policy: (500, 'unstable') Architecture: amd64 (x86_64) Kernel: Linux 2.6.26-2-amd64 (SMP w/4 CPU cores) Locale: LANG=en_US.UTF-8, LC_CTYPE=en_US.UTF-8 (charmap=UTF-8) Shell: /bin/sh linked to /bin/bash Versions of packages libnss-ldapd depends on: ii adduser 3.110 add and remove users and groups ii debconf [debconf-2.0 1.5.26 Debian configuration management sy ii libc62.9-7 GNU C Library: Shared libraries ii libgssapi-krb5-2 1.6.dfsg.4~beta1-13 MIT Kerberos runtime libraries - k ii libldap-2.4-22.4.15-1.1 OpenLDAP libraries ii libsasl2-2 2.1.22.dfsg1-23 Cyrus SASL - authentication abstra Versions of packages libnss-ldapd recommends: ii libpam-ldap 184-8 Pluggable Authentication Module fo pn nscd
Advanced Scientific Programming in Python Summer School in Berlin, Germany
Advanced Scientific Programming in Python a G-Node Summer School Many scientists spend much of their time writing, debugging, and maintaining software. But while techniques for doing this efficiently have been developed, only few scientists actually use them. As a result, they spend far too much time writing deficient code and reinventing the wheel instead of doing research. In this course we present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of the programming scientist. To spice up theory and foster our new skills in a real-world programming project, we will team up to develop an entertaining scientific computer game. We will use the Python programming language for the entire course. With a large collection of open-source scientific modules and all features of a full-fledged programming language, Python is rapidly gaining popularity in the neuroscience community. It enables the scientist to quickly develop powerful, efficient, and structured software and is becoming an essential tool for scientific computing. The summer school is targeted at Post-docs and PhD students from all areas of neuroscience. Substantial proficiency in Python or in another language (e.g. Java, C/C++, MATLAB, Mathematica) is absolutely required. An optional, one-day pre-course is offered to participants without Python experience to familiarize with the language. Date and Location - August 31st, 2009 -- September 4th, 2009. Berlin, Germany. Preliminary Program --- Day 0 (Mon Aug 31) -- [Optional] Dive into Python Day 1 (Tue Sep 1) -- Software Carpentry - Documenting code and using version control - Test-driven development unit testing - Debugging, profiling and benchmarking techniques - Object-oriented programming, design patterns and Extreme Programming Day 2 (Wed Sep 2) -- Scientific Tools for Python - NumPy, SciPy, Matplotlib, IPython - Neuroscience libraries - Programming project in the afternoon Day 3 (Thu Sep 3) -- Parallelization - Python multiprocessing for SMP machines - Distributed parallelization for cluster computing - Programming project in the afternoon Day 4 (Fri Sep 4) -- Practical Software Development - Software design - Efficient programming in teams - Quality Assurance - Finalizing the programming project Applications Applications should be sent before May 31st, 2009 to pythonsummersch...@bccnberlin.de. No fee is charged but participants should take care of travel, living, and accommodation expenses. Applications should include full contact information (name, affiliation, email phone), a short CV and a short statement addressing the following questions (maximum 500 words): - What is your educational background? - What experience do you have in programming? - Why do you think Advanced Scientific Programming in Python is an appropriate course for your skill profile? Candidates will be selected based on their profile. Places are limited: early application is recommended. Faculty --- Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA Jens Kremkow, Institut de Neurosciences Cognitives de la Méditerranée, CNRS, Marseille, France Eilif Muller, Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne, Switzerland Michael Schmuker, Neurobiology, Freie Universität Berlin, Germany Bartosz Telenczuk, Charité Universitätsmedizin Berlin, Germany Niko Wilbert, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Tiziano Zito, Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Michael Schmuker and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://www.g-node.org/Teaching Contact: python-summersch...@bccn-berlin.de -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
Re: [Numpy-discussion] Numpy 1.3 release date ?
what about fixing http://scipy.org/scipy/scipy/ticket/812 ? this is actually a scipy-numpy compatibility problem, where numpy is wrong IMO. it is a one-line fix, I think. thank you! tiziano ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Modular toolkit for Data Processing 2.4 released!
We are glad to announce release 2.4 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Restricted Boltzmann Machine, and Locally Linear Embedding. What's new in version 2.4? -- - The new version introduces a new parallel package to execute the MDP algorithms on multiple processors or machines. The package also offers an interface to develop customized schedulers and parallel algorithms. Old MDP scripts can be turned into their parallelized equivalent with one simple command. - The number of available algorithms is increased with the Locally Linear Embedding and Hessian eigenmaps algorithms to perform dimensionality reduction and manifold learning (many thanks to Jake VanderPlas for his contribution!) - Some more bug fixes, useful features, and code migration towards Python 3.0 Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Volen Center for Complex Systems Brandeis University Waltham, MA, USA Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Modular toolkit for Data Processing 2.4 released!
We are glad to announce release 2.4 of the Modular toolkit for Data Processing (MDP). MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Restricted Boltzmann Machine, and Locally Linear Embedding. What's new in version 2.4? -- - The new version introduces a new parallel package to execute the MDP algorithms on multiple processors or machines. The package also offers an interface to develop customized schedulers and parallel algorithms. Old MDP scripts can be turned into their parallelized equivalent with one simple command. - The number of available algorithms is increased with the Locally Linear Embedding and Hessian eigenmaps algorithms to perform dimensionality reduction and manifold learning (many thanks to Jake VanderPlas for his contribution!) - Some more bug fixes, useful features, and code migration towards Python 3.0 Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Volen Center for Complex Systems Brandeis University Waltham, MA, USA Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
Re: [Numpy-discussion] making numpy.dot faster
Hi, This seems to tell that numpy has been build without altas. Hum, maybe we need to work with the Debian guys to make sure that numpy is available with altas. we had recently a discussion regarding this issue on this mailinglist, see: http://groups.google.com/group/Numpy-discussion/browse_thread/thread/507e7722f99406fa/ and on the debian bug tracker: http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=489253 in debian testing and unstable numpy is now built with a patch that enables atlas support. I don't know the status of the ubuntu package, but I presume they may apply a similar patch until the numpy building system's checks are relaxed ina way that no patch is needed anymore. cheers, tiziano ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Bug#498229: Typo in /usr/lib/python2.5/site-packages/matplotlib/axes3d.py
Package: python-matplotlib
Version: 0.98.1-1
Severity: minor
Hi, in the package python-matplotlib I've just hit a typo in
/usr/share/pyshared/matplotlib/axes3d.py:
raise NotImplmentedError('axes3d is not supported in matplotlib-0.98.
You may want to try the 0.91.x maintenance branch')
should read NotImplementedError instead of NotImplmentedError.
To reproduce just try:
from matplotlib import axes3d
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/lib/python2.5/site-packages/matplotlib/axes3d.py, line
1, in module
raise NotImplmentedError('axes3d is not supported in
matplotlib-0.98. You may want to try the 0.91.x maintenance branch')
NameError: name 'NotImplmentedError' is not defined
not such a big deal, the import should fail with NotImplementedError
instead of NameError.
thank you!
tiziano
-- System Information:
Debian Release: lenny/sid
APT prefers testing
APT policy: (500, 'testing')
Architecture: i386 (i686)
Kernel: Linux 2.6.26-1-686-bigmem (SMP w/4 CPU cores)
Locale: LANG=en_US.ISO-8859-15, LC_CTYPE=en_US.ISO-8859-15 (charmap=ISO-8859-15)
Shell: /bin/sh linked to /bin/bash
Versions of packages python-matplotlib depends on:
ii dvipng 1.11-1convert DVI files to PNG graphics
ii libatk1.0-01.22.0-1 The ATK accessibility toolkit
ii libc6 2.7-13GNU C Library: Shared libraries
ii libcairo2 1.6.4-6 The Cairo 2D vector graphics libra
ii libfreetype6 2.3.7-2 FreeType 2 font engine, shared lib
ii libgcc11:4.3.1-9 GCC support library
ii libglib2.0-0 2.16.5-1 The GLib library of C routines
ii libgtk2.0-02.12.11-3 The GTK+ graphical user interface
ii libpango1.0-0 1.20.5-1 Layout and rendering of internatio
ii libpng12-0 1.2.27-1 PNG library - runtime
ii libstdc++6 4.3.1-9 The GNU Standard C++ Library v3
ii python 2.5.2-2 An interactive high-level object-o
ii python-cairo 1.4.12-1.1Python bindings for the Cairo vect
ii python-central 0.6.8 register and build utility for Pyt
ii python-configobj 4.5.2-1 a simple but powerful config file
ii python-dateutil1.4-1 powerful extensions to the standar
ii python-dev 2.5.2-2 Header files and a static library
ii python-enthought-trait 2.0.5-1 Manifest typing and reactive progr
ii python-excelerator 0.6.3a-3.1module for reading/writing Excel s
ii python-glade2 2.12.1-6 GTK+ bindings: Glade support
ii python-gobject 2.14.2-1 Python bindings for the GObject li
ii python-gtk22.12.1-6 Python bindings for the GTK+ widge
ii python-matplotlib-data 0.98.1-1 Python based plotting system (data
ii python-numpy 1:1.1.0-3 Numerical Python adds a fast array
ii python-pyparsing 1.5.0-1 Python parsing module
ii python-qt3 3.17.4-1 Qt3 bindings for Python
ii python-qt4 4.4.2-4 Python bindings for Qt4
ii python-tk 2.5.2-1 Tkinter - Writing Tk applications
ii python-tz 2008c-2 Python version of the Olson timezo
ii python-wxgtk2.62.6.3.2.2-2 wxWidgets Cross-platform C++ GUI t
ii tcl8.4 8.4.19-2 Tcl (the Tool Command Language) v8
ii tk8.4 8.4.19-2 Tk toolkit for Tcl and X11, v8.4 -
ii zlib1g 1:1.2.3.3.dfsg-12 compression library - runtime
python-matplotlib recommends no packages.
Versions of packages python-matplotlib suggests:
ii ipython0.8.4-1 enhanced interactive Python shell
pn python-matplotlib-doc none(no description available)
ii texlive-extra-utils2007.dfsg.2-3 TeX Live: TeX auxiliary programs
ii texlive-latex-extra2007.dfsg.3-2 TeX Live: LaTeX supplementary pack
-- no debconf information
--
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Typo in python-matplotlib
Hi, in the package python-matplotlib I've just hit a typo in
/usr/share/pyshared/matplotlib/axes3d.py:
raise NotImplmentedError('axes3d is not supported in matplotlib-0.98.
You may want to try the 0.91.x maintenance branch')
should read NotImplementedError instead of NotImplmentedError.
To reproduce just try:
from matplotlib import axes3d
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/lib/python2.5/site-packages/matplotlib/axes3d.py, line
1, in module
raise NotImplmentedError('axes3d is not supported in
matplotlib-0.98. You may want to try the 0.91.x maintenance branch')
NameError: name 'NotImplmentedError' is not defined
not such a big deal, the import should fail with NotImplementedError
instead of NameError. How is the policy for reporting such bugs
upstream? Should I simply go and report, or is there an official
spokesman of the DPMT?
thanks,
tiziano
--
To UNSUBSCRIBE, email to [EMAIL PROTECTED]
with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Python-modules-team] Bug#498229: Typo in /usr/lib/python2.5/site-packages/matplotlib/axes3d.py
Package: python-matplotlib
Version: 0.98.1-1
Severity: minor
Hi, in the package python-matplotlib I've just hit a typo in
/usr/share/pyshared/matplotlib/axes3d.py:
raise NotImplmentedError('axes3d is not supported in matplotlib-0.98.
You may want to try the 0.91.x maintenance branch')
should read NotImplementedError instead of NotImplmentedError.
To reproduce just try:
from matplotlib import axes3d
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/lib/python2.5/site-packages/matplotlib/axes3d.py, line
1, in module
raise NotImplmentedError('axes3d is not supported in
matplotlib-0.98. You may want to try the 0.91.x maintenance branch')
NameError: name 'NotImplmentedError' is not defined
not such a big deal, the import should fail with NotImplementedError
instead of NameError.
thank you!
tiziano
-- System Information:
Debian Release: lenny/sid
APT prefers testing
APT policy: (500, 'testing')
Architecture: i386 (i686)
Kernel: Linux 2.6.26-1-686-bigmem (SMP w/4 CPU cores)
Locale: LANG=en_US.ISO-8859-15, LC_CTYPE=en_US.ISO-8859-15 (charmap=ISO-8859-15)
Shell: /bin/sh linked to /bin/bash
Versions of packages python-matplotlib depends on:
ii dvipng 1.11-1convert DVI files to PNG graphics
ii libatk1.0-01.22.0-1 The ATK accessibility toolkit
ii libc6 2.7-13GNU C Library: Shared libraries
ii libcairo2 1.6.4-6 The Cairo 2D vector graphics libra
ii libfreetype6 2.3.7-2 FreeType 2 font engine, shared lib
ii libgcc11:4.3.1-9 GCC support library
ii libglib2.0-0 2.16.5-1 The GLib library of C routines
ii libgtk2.0-02.12.11-3 The GTK+ graphical user interface
ii libpango1.0-0 1.20.5-1 Layout and rendering of internatio
ii libpng12-0 1.2.27-1 PNG library - runtime
ii libstdc++6 4.3.1-9 The GNU Standard C++ Library v3
ii python 2.5.2-2 An interactive high-level object-o
ii python-cairo 1.4.12-1.1Python bindings for the Cairo vect
ii python-central 0.6.8 register and build utility for Pyt
ii python-configobj 4.5.2-1 a simple but powerful config file
ii python-dateutil1.4-1 powerful extensions to the standar
ii python-dev 2.5.2-2 Header files and a static library
ii python-enthought-trait 2.0.5-1 Manifest typing and reactive progr
ii python-excelerator 0.6.3a-3.1module for reading/writing Excel s
ii python-glade2 2.12.1-6 GTK+ bindings: Glade support
ii python-gobject 2.14.2-1 Python bindings for the GObject li
ii python-gtk22.12.1-6 Python bindings for the GTK+ widge
ii python-matplotlib-data 0.98.1-1 Python based plotting system (data
ii python-numpy 1:1.1.0-3 Numerical Python adds a fast array
ii python-pyparsing 1.5.0-1 Python parsing module
ii python-qt3 3.17.4-1 Qt3 bindings for Python
ii python-qt4 4.4.2-4 Python bindings for Qt4
ii python-tk 2.5.2-1 Tkinter - Writing Tk applications
ii python-tz 2008c-2 Python version of the Olson timezo
ii python-wxgtk2.62.6.3.2.2-2 wxWidgets Cross-platform C++ GUI t
ii tcl8.4 8.4.19-2 Tcl (the Tool Command Language) v8
ii tk8.4 8.4.19-2 Tk toolkit for Tcl and X11, v8.4 -
ii zlib1g 1:1.2.3.3.dfsg-12 compression library - runtime
python-matplotlib recommends no packages.
Versions of packages python-matplotlib suggests:
ii ipython0.8.4-1 enhanced interactive Python shell
pn python-matplotlib-doc none(no description available)
ii texlive-extra-utils2007.dfsg.2-3 TeX Live: TeX auxiliary programs
ii texlive-latex-extra2007.dfsg.3-2 TeX Live: LaTeX supplementary pack
-- no debconf information
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Re: [Numpy-discussion] Debian: numpy not building _dotblas.so
Hi numpy-devs, I was the one reporting the original bug about missing ATLAS support in the debian lenny python-numpy package. AFAICT the source python-numpy package in etch (numpy version 1.0.1) does not require atlas to build _dotblas.c, only lapack is needed. If you install the resulting binary package on a system where ATLAS is present, ATLAS libraries are used instead of plain lapack. So basically it was already working before the check for ATLAS was introduced into the numpy building system. Why should ATLAS now be required? It's not as trivial as just reverting that changeset, though. why is that? I mean, it was *working* before... thank you, tiziano ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Bug#489253: should numpy be built with atlas?
On 7/8/08, Matthias Klose [EMAIL PROTECTED] wrote: thanks for the update. Looking at the blas package, I see that the cblas library is included in libblas3. So it looks like the numpy check is wrong, testing for a package name, and not for a feature. This seems to explain why it did work in etch, and this should be fixed in python-numpy. Hi Ondrej, Hi Matthias. Removing the two lines in numpy/core/setup.py indeed seems to do the trick. Feel free to test the attached patch, generated against the python-numpy source package in sid. On my system it generates a numpy package with a _dotblas.so file correctly linked to lapack libs. If ATLAS is then installed, the ATLAS libraries are used instead. Ondrej: if the patch works, would you report it upstream? have a nice day! tiziano atlas.patch Description: Binary data
Bug#489253: Fw: should numpy be built with atlas?
Hi Ondrej, $ ./test_atlas.py Using ATLAS: 0.53141283989 $ wajig remove atlas3-base libatlas3gf-base $ ./test_atlas.py Using ATLAS: 1.64572000504 So it seems to work, even though the difference is not so big. the difference is not so big because the package contains a _dotblas.so file. When you remove the ATLAS libs, it simply uses the available lapack+blas routines, which are slower then ATLAS but still not so bad. If you build the package without the patch and without using ATLAS, not _dotblas.so file is present, and the slow down is much higher: $ aptitude search ~i~natlas i libatlas3gf-sse2- Automatically Tuned Linear Algebra Softwar $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/sse2/atlas/libblas.so.3gf (0xb79b6000) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7904000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb78dd000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb78d) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7775000) /lib/ld-linux.so.2 (0x8000) $ python test_atlas.py Using ATLAS: 0.539413928986 $ aptitude purge libatlas3gf-sse2 $ python test_atlas.py Using ATLAS: 1.90855002403 $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/libblas.so.3gf (0xb7f6) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7eae000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb7e87000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb7e7a000) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7d1f000) /lib/ld-linux.so.2 (0x8000) $ mv /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so.bak $ python test_atlas.py No ATLAS: 7.52080416679 bye, tiziano -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
Re: Fw: should numpy be built with atlas?
Hi Ondrej, $ ./test_atlas.py Using ATLAS: 0.53141283989 $ wajig remove atlas3-base libatlas3gf-base $ ./test_atlas.py Using ATLAS: 1.64572000504 So it seems to work, even though the difference is not so big. the difference is not so big because the package contains a _dotblas.so file. When you remove the ATLAS libs, it simply uses the available lapack+blas routines, which are slower then ATLAS but still not so bad. If you build the package without the patch and without using ATLAS, not _dotblas.so file is present, and the slow down is much higher: $ aptitude search ~i~natlas i libatlas3gf-sse2- Automatically Tuned Linear Algebra Softwar $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/sse2/atlas/libblas.so.3gf (0xb79b6000) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7904000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb78dd000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb78d) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7775000) /lib/ld-linux.so.2 (0x8000) $ python test_atlas.py Using ATLAS: 0.539413928986 $ aptitude purge libatlas3gf-sse2 $ python test_atlas.py Using ATLAS: 1.90855002403 $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/libblas.so.3gf (0xb7f6) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7eae000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb7e87000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb7e7a000) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7d1f000) /lib/ld-linux.so.2 (0x8000) $ mv /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so.bak $ python test_atlas.py No ATLAS: 7.52080416679 bye, tiziano -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
join DPMT?
Dear all, I would like to join the DPMT and help maintaining current packages. I've already helped a little bit for the python-numpy package and I am the author of two programs in debian (python-mdp and python-symeig, packages maintained by Yaroslav Halchenko). My login on alioth is tiziano-guest (btw, why *-guest?). Thank you! Tiziano ps: I didn't want to bother all of you with background information about me, if you need them I would me more than happy to deliver them in a different message... -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
Bug#489726: should numpy be built with atlas?
On 7/8/08, Matthias Klose [EMAIL PROTECTED] wrote: thanks for the update. Looking at the blas package, I see that the cblas library is included in libblas3. So it looks like the numpy check is wrong, testing for a package name, and not for a feature. This seems to explain why it did work in etch, and this should be fixed in python-numpy. Hi Ondrej, Hi Matthias. Removing the two lines in numpy/core/setup.py indeed seems to do the trick. Feel free to test the attached patch, generated against the python-numpy source package in sid. On my system it generates a numpy package with a _dotblas.so file correctly linked to lapack libs. If ATLAS is then installed, the ATLAS libraries are used instead. Ondrej: if the patch works, would you report it upstream? have a nice day! tiziano atlas.patch Description: Binary data
Bug#489726: Fw: should numpy be built with atlas?
Hi Ondrej, $ ./test_atlas.py Using ATLAS: 0.53141283989 $ wajig remove atlas3-base libatlas3gf-base $ ./test_atlas.py Using ATLAS: 1.64572000504 So it seems to work, even though the difference is not so big. the difference is not so big because the package contains a _dotblas.so file. When you remove the ATLAS libs, it simply uses the available lapack+blas routines, which are slower then ATLAS but still not so bad. If you build the package without the patch and without using ATLAS, not _dotblas.so file is present, and the slow down is much higher: $ aptitude search ~i~natlas i libatlas3gf-sse2- Automatically Tuned Linear Algebra Softwar $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/sse2/atlas/libblas.so.3gf (0xb79b6000) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7904000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb78dd000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb78d) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7775000) /lib/ld-linux.so.2 (0x8000) $ python test_atlas.py Using ATLAS: 0.539413928986 $ aptitude purge libatlas3gf-sse2 $ python test_atlas.py Using ATLAS: 1.90855002403 $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/libblas.so.3gf (0xb7f6) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7eae000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb7e87000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb7e7a000) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7d1f000) /lib/ld-linux.so.2 (0x8000) $ mv /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so.bak $ python test_atlas.py No ATLAS: 7.52080416679 bye, tiziano -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Python-modules-team] Bug#489253: should numpy be built with atlas?
On 7/8/08, Matthias Klose [EMAIL PROTECTED] wrote: thanks for the update. Looking at the blas package, I see that the cblas library is included in libblas3. So it looks like the numpy check is wrong, testing for a package name, and not for a feature. This seems to explain why it did work in etch, and this should be fixed in python-numpy. Hi Ondrej, Hi Matthias. Removing the two lines in numpy/core/setup.py indeed seems to do the trick. Feel free to test the attached patch, generated against the python-numpy source package in sid. On my system it generates a numpy package with a _dotblas.so file correctly linked to lapack libs. If ATLAS is then installed, the ATLAS libraries are used instead. Ondrej: if the patch works, would you report it upstream? have a nice day! tiziano atlas.patch Description: Binary data ___ Python-modules-team mailing list Python-modules-team@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/python-modules-team
[Python-modules-team] Bug#489253: Fw: should numpy be built with atlas?
Hi Ondrej, $ ./test_atlas.py Using ATLAS: 0.53141283989 $ wajig remove atlas3-base libatlas3gf-base $ ./test_atlas.py Using ATLAS: 1.64572000504 So it seems to work, even though the difference is not so big. the difference is not so big because the package contains a _dotblas.so file. When you remove the ATLAS libs, it simply uses the available lapack+blas routines, which are slower then ATLAS but still not so bad. If you build the package without the patch and without using ATLAS, not _dotblas.so file is present, and the slow down is much higher: $ aptitude search ~i~natlas i libatlas3gf-sse2- Automatically Tuned Linear Algebra Softwar $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/sse2/atlas/libblas.so.3gf (0xb79b6000) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7904000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb78dd000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb78d) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7775000) /lib/ld-linux.so.2 (0x8000) $ python test_atlas.py Using ATLAS: 0.539413928986 $ aptitude purge libatlas3gf-sse2 $ python test_atlas.py Using ATLAS: 1.90855002403 $ ldd /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so linux-gate.so.1 = (0xe000) libblas.so.3gf = /usr/lib/libblas.so.3gf (0xb7f6) libgfortran.so.3 = /usr/lib/libgfortran.so.3 (0xb7eae000) libm.so.6 = /lib/i686/cmov/libm.so.6 (0xb7e87000) libgcc_s.so.1 = /lib/libgcc_s.so.1 (0xb7e7a000) libc.so.6 = /lib/i686/cmov/libc.so.6 (0xb7d1f000) /lib/ld-linux.so.2 (0x8000) $ mv /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so /usr/lib/python2.5/site-packages/numpy/core/_dotblas.so.bak $ python test_atlas.py No ATLAS: 7.52080416679 bye, tiziano ___ Python-modules-team mailing list Python-modules-team@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/python-modules-team
Bug#489253: python-numpy: enable ATLAS support?
Hi Tiziano! Thanks for the bug report. Do you suggest this patch? [...] Unfortunately, I am not an expert in blas/lapack/atlas, I only remember there were some problems with that. The package compiles and seems to work fine, tests run. However, for example the generated dependencies of the resulting binary package are wrong: Depends: python ( 2.6), python (= 2.4), python-central (= 0.6.7), libc6 (= 2.7-1) so it doesn't seem to pickup atlas correctly. Maybe there are some other problems too. Would you be willing to look at this? If so, please join DPMT, so that you can edit the package and then feel free to fix it. If you prepare the package, so that it's ready for upload, I'll upload it. Hi Ondrej, I'll request to join the DPMT, but in the meanwhile this patch created a perfectly running python-numpy package with ATLAS support and right dependencies: Depends: python ( 2.6), python (= 2.4), python-central (=0.6.7), libatlas3gf-base | libatlas.so.3gf, libc6 (= 2.7-1), libgcc1 (= 1:4.1.1), libgfortran3 (= 4.3), liblapack3gf | liblapack.so.3gf | libatlas3gf-base $ svn di Index: debian/control === --- debian/control (revision 5836) +++ debian/control (working copy) @@ -3,7 +3,7 @@ Priority: optional Maintainer: Debian Python Modules Team [EMAIL PROTECTED] Uploaders: Marco Presi (Zufus) [EMAIL PROTECTED], Alexandre Fayolle [EMAIL PROTECTED], José Fonseca [EMAIL PROTECTED], Matthias Klose [EMAIL PROTECTED], Ondrej Certik [EMAIL PROTECTED], Kumar Appaiah [EMAIL PROTECTED] -Build-Depends: cdbs (= 0.4.43), python-all-dev, python-all-dbg, python-central (= 0.6), gfortran (= 4:4.2), libblas-dev [!arm !m68k], liblapack-dev [!arm !m68k], debhelper (= 5.0.38), patchutils, python-docutils, libfftw3-dev +Build-Depends: cdbs (= 0.4.43), python-all-dev, python-all-dbg, python-central (= 0.6), gfortran (= 4:4.2), libblas-dev [!arm !m68k], liblapack-dev [!arm !m68k], libatlas-base-dev [!arm !m68k] | libatlas-sse-dev [!arm !m68k] | libatlas-sse2-dev [!arm !m68k] | libatlas-3dnow-dev [!arm !m68k],debhelper (= 5.0.38), patchutils, python-docutils, libfftw3-dev Build-Conflicts: atlas3-base-dev XS-Python-Version: = 2.3 Standards-Version: 3.8.0 Index: debian/patches/series === --- debian/patches/series (revision 5836) +++ debian/patches/series (working copy) @@ -1,2 +1 @@ 01_fix_man_hyphens.patch -02_dontuse_lapack.diff Hope that helps, tiziano -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
Bug#476641: gkrellm: /proc/acpi Battery Interface Deprecated in 2.6.24, Must Use sysfs
Hi! This bug is still present after last kernel upgrade to linux-image-2.6.25-2 . Any chances to get this patch applied upstream before lenny freeze? thank you! tiziano -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Python-modules-team] Bug#489253: python-numpy: enable ATLAS support?
Hi Tiziano! Thanks for the bug report. Do you suggest this patch? [...] Unfortunately, I am not an expert in blas/lapack/atlas, I only remember there were some problems with that. The package compiles and seems to work fine, tests run. However, for example the generated dependencies of the resulting binary package are wrong: Depends: python ( 2.6), python (= 2.4), python-central (= 0.6.7), libc6 (= 2.7-1) so it doesn't seem to pickup atlas correctly. Maybe there are some other problems too. Would you be willing to look at this? If so, please join DPMT, so that you can edit the package and then feel free to fix it. If you prepare the package, so that it's ready for upload, I'll upload it. Hi Ondrej, I'll request to join the DPMT, but in the meanwhile this patch created a perfectly running python-numpy package with ATLAS support and right dependencies: Depends: python ( 2.6), python (= 2.4), python-central (=0.6.7), libatlas3gf-base | libatlas.so.3gf, libc6 (= 2.7-1), libgcc1 (= 1:4.1.1), libgfortran3 (= 4.3), liblapack3gf | liblapack.so.3gf | libatlas3gf-base $ svn di Index: debian/control === --- debian/control (revision 5836) +++ debian/control (working copy) @@ -3,7 +3,7 @@ Priority: optional Maintainer: Debian Python Modules Team python-modules-team@lists.alioth.debian.org Uploaders: Marco Presi (Zufus) [EMAIL PROTECTED], Alexandre Fayolle [EMAIL PROTECTED], José Fonseca [EMAIL PROTECTED], Matthias Klose [EMAIL PROTECTED], Ondrej Certik [EMAIL PROTECTED], Kumar Appaiah [EMAIL PROTECTED] -Build-Depends: cdbs (= 0.4.43), python-all-dev, python-all-dbg, python-central (= 0.6), gfortran (= 4:4.2), libblas-dev [!arm !m68k], liblapack-dev [!arm !m68k], debhelper (= 5.0.38), patchutils, python-docutils, libfftw3-dev +Build-Depends: cdbs (= 0.4.43), python-all-dev, python-all-dbg, python-central (= 0.6), gfortran (= 4:4.2), libblas-dev [!arm !m68k], liblapack-dev [!arm !m68k], libatlas-base-dev [!arm !m68k] | libatlas-sse-dev [!arm !m68k] | libatlas-sse2-dev [!arm !m68k] | libatlas-3dnow-dev [!arm !m68k],debhelper (= 5.0.38), patchutils, python-docutils, libfftw3-dev Build-Conflicts: atlas3-base-dev XS-Python-Version: = 2.3 Standards-Version: 3.8.0 Index: debian/patches/series === --- debian/patches/series (revision 5836) +++ debian/patches/series (working copy) @@ -1,2 +1 @@ 01_fix_man_hyphens.patch -02_dontuse_lapack.diff Hope that helps, tiziano ___ Python-modules-team mailing list Python-modules-team@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/python-modules-team
Bug#489253: python-numpy: enable ATLAS support?
Package: python-numpy Version: 1:1.1.0-1 Severity: normal dear maintainers, python-numpy is currently built without ATLAS support. Among other things this implies very slow matrix multiplication. ATLAS support was dropped during the gfortran transition (see http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=464784+ ). Dropping the 02_dontuse_lapack.diff patch and build-depend on atlas should re-enable ATLAS support. The reason for dropping ATLAS support was that ATLAS had not completed the gfortran transition yet. This issue seems to be solved now: http://buildd.debian.org/~jeroen/status/package.php?p=atlas Thank you! tiziano -- System Information: Debian Release: lenny/sid APT prefers unstable APT policy: (500, 'unstable') Architecture: i386 (i686) Kernel: Linux 2.6.24-1-686 (SMP w/1 CPU core) Locale: LANG=C, LC_CTYPE=C (charmap=ANSI_X3.4-1968) Shell: /bin/sh linked to /bin/bash Versions of packages python-numpy depends on: ii libatlas3gf-base [liblapack.s 3.6.0-21.5 Automatically Tuned Linear Algebra ii libatlas3gf-sse2 [liblapack.s 3.6.0-21.5 Automatically Tuned Linear Algebra ii libblas3gf [libblas.so.3gf] 1.2-1.6Basic Linear Algebra Subroutines 3 ii libc6 2.7-12 GNU C Library: Shared libraries ii libgcc1 1:4.3.1-4 GCC support library ii libgfortran3 4.3.1-4Runtime library for GNU Fortran ap ii liblapack3gf [liblapack.so.3g 3.1.1-0.4 library of linear algebra routines ii python2.5.2-1An interactive high-level object-o ii python-central0.6.7 register and build utility for Pyt python-numpy recommends no packages. -- no debconf information -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Python-modules-team] Bug#489253: python-numpy: enable ATLAS support?
Package: python-numpy Version: 1:1.1.0-1 Severity: normal dear maintainers, python-numpy is currently built without ATLAS support. Among other things this implies very slow matrix multiplication. ATLAS support was dropped during the gfortran transition (see http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=464784+ ). Dropping the 02_dontuse_lapack.diff patch and build-depend on atlas should re-enable ATLAS support. The reason for dropping ATLAS support was that ATLAS had not completed the gfortran transition yet. This issue seems to be solved now: http://buildd.debian.org/~jeroen/status/package.php?p=atlas Thank you! tiziano -- System Information: Debian Release: lenny/sid APT prefers unstable APT policy: (500, 'unstable') Architecture: i386 (i686) Kernel: Linux 2.6.24-1-686 (SMP w/1 CPU core) Locale: LANG=C, LC_CTYPE=C (charmap=ANSI_X3.4-1968) Shell: /bin/sh linked to /bin/bash Versions of packages python-numpy depends on: ii libatlas3gf-base [liblapack.s 3.6.0-21.5 Automatically Tuned Linear Algebra ii libatlas3gf-sse2 [liblapack.s 3.6.0-21.5 Automatically Tuned Linear Algebra ii libblas3gf [libblas.so.3gf] 1.2-1.6Basic Linear Algebra Subroutines 3 ii libc6 2.7-12 GNU C Library: Shared libraries ii libgcc1 1:4.3.1-4 GCC support library ii libgfortran3 4.3.1-4Runtime library for GNU Fortran ap ii liblapack3gf [liblapack.so.3g 3.1.1-0.4 library of linear algebra routines ii python2.5.2-1An interactive high-level object-o ii python-central0.6.7 register and build utility for Pyt python-numpy recommends no packages. -- no debconf information ___ Python-modules-team mailing list Python-modules-team@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/python-modules-team
[Numpy-discussion] ANN: MDP 2.3 released!
Dear NumPy and SciPy users, we are proud to announce release 2.3 of the Modular toolkit for Data Processing (MDP): a Python data processing framework. The base of readily available algorithms includes Principal Component Analysis (PCA and NIPALS), four flavors of Independent Component Analysis (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Independent Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, Factor Analysis, Restricted Boltzmann Machine, and many more. What's new in version 2.3? -- - Enhanced PCA nodes (with SVD, automatic dimensionality reduction, and iterative algorithms). - A complete implementation of the FastICA algorithm. - JADE and TDSEP nodes for more fun with ICA. - Restricted Boltzmann Machine nodes. - The new subpackage hinet allows combining nodes in arbitrary feed-forward network architectures with a HTML visualization tool. - The tutorial has been updated with a section on hierarchical networks. - MDP integrated into the official Debian repository as python-mdp. - A bunch of bug-fixes. Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Gatsby Computational Neuroscience Unit UCL London, United Kingdom Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
ANN: MDP 2.3 released!
Dear Pythonistas, we are proud to announce release 2.3 of the Modular toolkit for Data Processing (MDP): a Python data processing framework. The base of readily available algorithms includes Principal Component Analysis (PCA and NIPALS), four flavors of Independent Component Analysis (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Independent Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, Factor Analysis, Restricted Boltzmann Machine, and many more. What's new in version 2.3? -- - Enhanced PCA nodes (with SVD, automatic dimensionality reduction, and iterative algorithms). - A complete implementation of the FastICA algorithm. - JADE and TDSEP nodes for more fun with ICA. - Restricted Boltzmann Machine nodes. - The new subpackage hinet allows combining nodes in arbitrary feed-forward network architectures with a HTML visualization tool. - The tutorial has been updated with a section on hierarchical networks. - MDP integrated into the official Debian repository as python-mdp. - A bunch of bug-fixes. Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Gatsby Computational Neuroscience Unit UCL London, United Kingdom Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
Bug#405870: wake on lan broken
Package: sysvinit Version: 2.86.ds1-38 Followup-For: Bug #405870 Wake on LAN does not work here (Broadcom Corporation NetXtreme BCM5754 ethernet controller). ethtool reports that wake on lan is enabled $ ethtool eth0 ... Supports Wake-on: g Wake-on: d I suspect the issue is related to what reported in bug #405870. Wake on lan is enabled in the BIOS. If I shutdown the machine manually from the grub menu (i.e. before starting the OS), the network card correctly remains active (LED flashing). On the other hand, if I shutdown using halt, poweroff or shutdown commands, the network card is switched off, even if I set NETDOWN=no in /etc/default/halt , thus making WOL impossible. The original bug report seems to be quite old, any progress on this? Do I understand it correctly that the only workaround at the moment is recompiling halt from upstream source? thank you, tiziano -- System Information: Debian Release: 4.0 APT prefers stable APT policy: (500, 'stable') Architecture: i386 (i686) Shell: /bin/sh linked to /bin/bash Kernel: Linux 2.6.18-4-686 Locale: LANG=en_US.ISO-8859-15, LC_CTYPE=en_US.ISO-8859-15 (charmap=ISO-8859-15) Versions of packages sysvinit depends on: ii initscripts 2.86.ds1-38 Scripts for initializing and shutt ii libc6 2.3.6.ds1-13 GNU C Library: Shared libraries ii libselinux1 1.32-3 SELinux shared libraries ii libsepol1 1.14-2 Security Enhanced Linux policy lib ii sysv-rc 2.86.ds1-38 System-V-like runlevel change mech ii sysvinit-utils 2.86.ds1-38 System-V-like utilities sysvinit recommends no packages. -- no debconf information -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Numpy-discussion] Modular toolkit for Data Processing 2.1 released!
MDP version 2.1 and symeig 1.2 have been released! What's new in version 2.1? -- - Fully compatible with NumpPy 1.0, the first stable release of the descendant of the Numeric python extension module - symeig project resumed and updated - For increased speed, scipy and symeig are automatically used if available - New nodes: Independent Slow Feature Analysis and quadratic forms analysis algorithms - General improvements, several bug fixes, and code cleanups What is it? --- Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP consists of a collection of trainable supervised and unsupervised algorithms that can be combined into data processing flows. The base of readily available algorithms includes Principal Component Analysis, two flavors of Independent Component Analysis, Slow Feature Analysis, Independent Slow Feature Analysis, and many more. From the developer's perspective, MDP is a framework to make the implementation of new algorithms easier. MDP takes care of tedious tasks like numerical type and dimensionality checking, leaving the developer free to concentrate on the implementation of the training and execution phases. The new elements then seamlessly integrate with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user side together with the reusability of the implemented nodes make it also a valid educational tool. As its user base is increasing, MDP is becoming a common repository of user-supplied, freely available, Python-implemented data processing algorithms. The optional symeig module contains a Python wrapper for the LAPACK functions to solve the standard and generalized eigenvalue problems for symmetric (hermitian) positive definite matrices. Those specialized algorithms give an important speed-up with respect to the generic LAPACK eigenvalue problem solver used by NumPy. Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Tiziano Zito Institute for Theoretical Biology Humboldt-Universitaet zu Berlin Invalidenstrasse, 43 10115 Berlin, Germany Pietro Berkes Gatsby Computational Neuroscience Unit Alexandra House, 17 Queen Square London WC1N 3AR, United Kingdom ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[EuroPython] previous years conferences archive?
Dear Europython organizers, I enjoyed very much the last two conferences and I hope to attend the next conference in Vilnius! My question is: do we have an archive for the old europython conferences? Right now I can browse through the talks of the conference in Geneva from the www.europython.org site, but that, I guess, is going to change pretty soon. What about the conferences in Gothenburg, are the slides still available somewhere? thank you very much! Tiziano Zito Institute for Theoretical Biology Humboldt-Universitaet zu Berlin Invalidenstrasse, 43 D-10115 Berlin, Germany http://itb.biologie.hu-berlin.de/~zito/ room: 1306 phone: +49 30 2093-8630 fax: +49 30 2093-8801 ___ EuroPython mailing list EuroPython@python.org http://mail.python.org/mailman/listinfo/europython
Bug#402733: a2ps: segmentation fault
Package: a2ps Version: 1:4.13b.dfsg.1-1 Severity: grave Justification: renders package unusable a2ps segfaults when run without arguments, as well as when given an output file. Step to reproduce: - simply run a2ps - try: echo test | a2ps -o test.ps The package is at the moment not usable. Thank you for looking inot this! Tiziano -- System Information: Debian Release: 4.0 APT prefers testing APT policy: (500, 'testing') Architecture: i386 (i686) Shell: /bin/sh linked to /bin/bash Kernel: Linux 2.6.17-2-686 Locale: LANG=en_US.ISO-8859-15, LC_CTYPE=en_US.ISO-8859-15 (charmap=ISO-8859-15) Versions of packages a2ps depends on: ii libc62.3.6.ds1-8 GNU C Library: Shared libraries ii libpaper11.1.21 Library for handling paper charact Versions of packages a2ps recommends: ii bzip2 1.0.3-6high-quality block-sorting file co ii cupsys-bsd [lpr] 1.2.7-1Common UNIX Printing System(tm) - ii cupsys-client 1.2.7-1Common UNIX Printing System(tm) - ii psutils 1.17-24A collection of PostScript documen pn wdiff none (no description available) -- no debconf information -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
Bug#402733: a2ps: segmentation fault
Package: a2ps Version: 1:4.13b.dfsg.1-1 Severity: grave Justification: renders package unusable a2ps segfaults when run without arguments, as well as when given an output file. Step to reproduce: - simply run a2ps - try: echo test | a2ps -o test.ps The package is at the moment not usable. Thank you for looking inot this! Tiziano -- System Information: Debian Release: 4.0 APT prefers testing APT policy: (500, 'testing') Architecture: i386 (i686) Shell: /bin/sh linked to /bin/bash Kernel: Linux 2.6.17-2-686 Locale: LANG=en_US.ISO-8859-15, LC_CTYPE=en_US.ISO-8859-15 (charmap=ISO-8859-15) Versions of packages a2ps depends on: ii libc62.3.6.ds1-8 GNU C Library: Shared libraries ii libpaper11.1.21 Library for handling paper charact Versions of packages a2ps recommends: ii bzip2 1.0.3-6high-quality block-sorting file co ii cupsys-bsd [lpr] 1.2.7-1Common UNIX Printing System(tm) - ii cupsys-client 1.2.7-1Common UNIX Printing System(tm) - ii psutils 1.17-24A collection of PostScript documen pn wdiff none (no description available) -- no debconf information -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
[Numpy-discussion] MDP-2.0 released
MDP version 2.0 has been released! What is it? --- Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP consists of a collection of trainable supervised and unsupervised algorithms that can be combined into data processing flows. The base of readily available algorithms includes Principal Component Analysis, two flavors of Independent Component Analysis, Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, and Factor Analysis. From the developer's perspective, MDP is a framework to make the implementation of new algorithms easier. MDP takes care of tedious tasks like numerical type and dimensionality checking, leaving the developer free to concentrate on the implementation of the training and execution phases. The new elements then automatically integrate with the rest of the library. As its user base is increasing, MDP might be a good candidate for becoming a common repository of user-supplied, freely available, Python implemented data processing algorithms. Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 What's new in version 2.0? -- MDP 2.0 introduces some important structural changes. It is now possible to implement nodes with multiple training phases and even nodes with an undetermined number of phases. This allows for example the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, or others that need to iterate over a training phase until a convergence criterion is satisfied. The ability to train each phase using chunks of input data is maintained if the chunks are generated with iterators. Nodes that require supervised training can be defined in a very straightforward way by passing additional arguments (e.g., labels or a target output) to the 'train' method. New algorithms have been added, expanding the base of readily available basic data processing elements. MDP is now based exclusively on the NumPy Python numerical extension. -- Tiziano Zito Institute for Theoretical Biology Humboldt-Universitaet zu Berlin Invalidenstrasse, 43 D-10115 Berlin, Germany Pietro Berkes Gatsby Computational Neuroscience Unit Alexandra House, 17 Queen Square London WC1N 3AR, United Kingdom Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
MDP-2.0 released
MDP version 2.0 has been released! What is it? --- Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP consists of a collection of trainable supervised and unsupervised algorithms that can be combined into data processing flows. The base of readily available algorithms includes Principal Component Analysis, two flavors of Independent Component Analysis, Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, and Factor Analysis. From the developer's perspective, MDP is a framework to make the implementation of new algorithms easier. MDP takes care of tedious tasks like numerical type and dimensionality checking, leaving the developer free to concentrate on the implementation of the training and execution phases. The new elements then automatically integrate with the rest of the library. As its user base is increasing, MDP might be a good candidate for becoming a common repository of user-supplied, freely available, Python implemented data processing algorithms. Resources - Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 What's new in version 2.0? -- MDP 2.0 introduces some important structural changes. It is now possible to implement nodes with multiple training phases and even nodes with an undetermined number of phases. This allows for example the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, or others that need to iterate over a training phase until a convergence criterion is satisfied. The ability to train each phase using chunks of input data is maintained if the chunks are generated with iterators. Nodes that require supervised training can be defined in a very straightforward way by passing additional arguments (e.g., labels or a target output) to the 'train' method. New algorithms have been added, expanding the base of readily available basic data processing elements. MDP is now based exclusively on the NumPy Python numerical extension. -- Tiziano Zito Institute for Theoretical Biology Humboldt-Universitaet zu Berlin Invalidenstrasse, 43 D-10115 Berlin, Germany Pietro Berkes Gatsby Computational Neuroscience Unit Alexandra House, 17 Queen Square London WC1N 3AR, United Kingdom -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
