Mailman 3 May 2005 - Python-announce-list

[ANN] Release 0.37 of Task Coach
by Frank Niessink May 14, 2005

May 14, 2005

Hi all, I'm pleased to announce release 0.37 of Task Coach. New in this release: Bugs fixed: - Icons in tree view on Windows 2000 (1194654). I hope I fixed this, but since I have no access to Windows 2000 it's a bit difficult to test. Features added: - Columns in the task list view can be turned on/off by right-clicking on the column headers. - Tasks can be sorted either by due date or alphabetically (1177984). - More options when editing an effort record. - Used a new DatePickerCtrl (1191909). What is Task Coach? Task Coach is a simple task manager that allows for hierarchical tasks, i.e. tasks in tasks. Task Coach is open source (GPL) and is developed using Python and wxPython. You can download Task Coach from: http://taskcoach.niessink.com https://sourceforge.net/projects/taskcoach/ A binary installer is available for Windows XP, in addition to the source distribution. Note that Task Coach is alpha software, meaning that it is wise to back up your task file regularly, and especially when upgrading to a new release. Cheers, Frank

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pyExcelerator 0.5.1a released
by Roman V. Kiseliov May 14, 2005

May 14, 2005

Hi! I'm pleased to announce that pyExcelerator 0.5.1a is now available for download. ------------------------------------------------------- What can you do with pyExcelerator: Generating Excel 97+ files with Python 2.4+ (need decorators), importing Excel 95+ files, support for UNICODE in Excel files, using variety of formatting features and printing options, Excel files and OLE2 compound files dumper. No need in Windows/COM --------------------------------------------------------- 0.5.1a (14.05.2005) --------- * improved floating point decoding code (thanks to Dmitry Vasiliev) ---------------------------------------------------------- Also thanks for valuable discussion to /Oleg Ponomarev, Oleg Komkov and Thomas Guettler. / Regards, Roman V. Kiseliov roman(a)kiseliov.ru

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pyExcelerator 0.5.0a released (Python + FreeBSD + Excel 5/95/97/2k/XP/2k3 + UNICODE)
by Roman V. Kiseliov May 12, 2005

May 12, 2005

Hi! I'm pleased to announce that pyExcelerator 0.5.0a is now available for download. ------------------------------------------------------- What can you do with pyExcelerator: Generating Excel 97+ files with Python 2.4+ (need decorators), importing Excel 95+ files, support for UNICODE in Excel files, using variety of formatting features and printing options, Excel files and OLE2 compound files dumper. No need in Windows/COM -------------------------------------------------------- CHANGES: 0.4.0a (27.03.2005) --------- * First public version 0.5.0a (11.05.2005) --------- * pyExcelerator now can import Excel 5/95/97/2000/XP/2003 files. With imported information you can do what you want. See ./examples/xls2txt.py for reference. * fixed bug in MS OLE2 compound document dumper (not in writer :)) Bug causes dumper to combine links in sectors' allocation chain sectors contained in additional MSAT with SAT data. * Excel files dumper supports now Excel 5/95/97/2000/XP/2003 files and produces more informative output. ------------------------------------------------------------- DOWNLOAD: http://sourceforge.net/projects/pyexcelerator/ http://www.kiseliov.ru/downloads.html ------------------------------------------------------------- Regards, Roman V. Kiseliov, roman(a)kiseliov.ru

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The Python Challenge - for riddle lovers!
by pythonchallenge May 12, 2005

May 12, 2005

For the riddles' lovers amongst you, you are most invited to take part in The Python Challenge, the first programming riddle on the net. Every riddle can be solved by a bit of Python programming. It is a great and unique way to explore the "included batteries" of Python. You are most invited to take part in it at: http://www.pythonchallenge.com

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Europython update
by jacob＠cd.chalmers.se May 11, 2005

May 11, 2005

This is a news update about the Europython 2005 conference, to be held in Göteborg, Sweden 27-29 June - We have received a very nice array of talks this year, and we expect to be the biggest Python conference ever in terms of subjects covered. Many thanks to all the speakers who are putting their efforts into this. - A list of talks is now available,either through the "Track Overview" menu item on the Europython website, or directly at http://www.python-in-business.org/ep2005/atracks.chtml - Around the end of May we will be soliciting input from all attendees on what talks they aremost interested in. This information will be used in scheduling, in order to reduce the amounts of collisions between popular talks and in order to reduce the risk of overcrowding a small room with a talk that turns out to be very popular. - Early bird registrations end 15 May. Registration for the low cost accomodation close to the conference venue ends on the same day. We may have further accomodation available at a later date, but we can't make any promises. - Please help us become the biggest Python/Zope conference. Talk to your friends about Europython and write about the conference in your blog. The more people who show up, the more we can spend on getting the best and most interesting talks. - This year we have a special focus on Python newbies in the Tutorials track. If you have staff or customers who need a well rounded introduction to Python, send them to Europython 2005. For all conference details, go to: http://www.europython.org See you in Göteborg. EuroPython Team. About EuroPython: Europython is an annual conference for the Python and Zope communities. It circulates between different sites in Europe. Having started in Charleroi, Belgium, it is now in Göteborg, Sweden and will move to CERN in Swizerland next year. Europython is a community conference run by volunteers. --

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Dallas Ft. Worth Pythoneers' Meetings
by Jeff Rush May 11, 2005

May 11, 2005

Contents Within: [1] Evening social at Springcreek this Thursday [2] Programming session at Nerdbooks this Saturday [3] Club version control repository established [1] The DFW Pythoneers, an open discussion group based around the Python programming language, will be having its 6th get-together this week. We always meet the second-thursday of the month. The details are: When: Thursday, May 12 at 7:00 pm Where: Spring Creek BBQ (enclosed patio on side of building) 14941 Midway Road Addison TX 75001 (972) 385-0970 There is a map on the Meetup website at (the DFW group is chapter 10): http://python.meetup.com/10/ [2] And although it's short notice, we're also having our first programming session this Saturday from 2:00 pm to 5:00 pm at Nerdbooks.com in Richardson. I understand if a lot of you can't make it without more notice so we'll just be checking out the facilities, testing the network, setting up whiteboards and cruising the Python bookshelves. Bring your laptops. You can find directions to Nerdbooks at www.nerdbooks.com. We have a mailing list, separate from that at Meetup. You can find it at: http://www.taupro.com/lists And Python-specific resources and such on our new wiki at: http://python.taupro.com/ [3] I've also set up a Subversion-based version control repository at: http://python.taupro.com/repo/ I'd also like to ask those planning to participate in the coding sprints to prepare their laptops with the necessary software, to save time. Details are on the wiki at: http://python.taupro.com/SprintingSetup As we meet to begin some projects, I'll give out write-access passwords. The repository is publically readable so everyone can watch the projects. I hope to see some of you at Spring Creek BBQ or Nerdbooks this week. Jeff Rush, DFW Pythoneers Coordinator

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CHANGE BayPIGgies: May *THIRD* Thurs
by Aahz May 11, 2005

May 11, 2005

Reminder: We will *NOT* be meeting the *SECOND* Thursday (this week, May 12). Our May meeting will be the *THIRD* Thursday, May 19. This will be our first meeting at Google, with Alex Martelli's presention on design patterns. More details soon! -- Aahz (aahz(a)pythoncraft.com) <*> http://www.pythoncraft.com/ "And if that makes me an elitist...I couldn't be happier." --JMS

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SC-Corporate-ID release
by Bud P. Bruegger May 10, 2005

May 10, 2005

Dear all, I am very pleased to announce the release of SC-Corporate-ID. SC-Corporate-ID is a commercial Smart Card security system that can be extended by the user using the Python language. SC-Corporate-ID is written in Python and wxPython for the most part (except for the PCSC wrapper, the GINA dll and the crypto) on the PC side, and in .... other languages on the Smart Card side. The PCSC driver (PYCSC) and the crypto (PYCRYPTO) are public domain packages (PYCSC was slightly modified by us) SC-Corporate-ID is an off-the shelf package: you can use it as is; yet, as it is based on a set of libraries (SCF), it can be modified and customized by the company using it (the restriction being the capabilities of the code in the Smart Card which would have to be modified by us). SC-Corporate-ID can be used for PC Access, Data Security (file encryption/signature), Company Electronic Purse and Corporate Identification. Some of its possible extentions are Building Access and WEB Security. SC-Corporate-ID cards can be delivered pre-programmed (firwmare + data) or simply with the firmware so the user may handle its own cards issuance. Although it is a commercial product, SC-Corporate-ID intends to be a true low cost security solution for Corporations. SC-Corporate-ID will soon be followed by a School/University ID as well as an Health Card ID. You may find information on SC-Corporate-ID at www.snakecard.com. Best regards, Philippe Martin -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html ------------------------------------------------------------------------------------------------- Ing. Bud P. Bruegger, Ph.D. +39-0564-488577 (voice), -21139 (fax) Servizio Elaborazione Dati e-mail: bud(a)comune.grosseto.it Comune di Grosseto http://www.comune.grosseto.it/cie/ Via Ginori, 43 http://OpenPortalGuard.sf.net 58100 Grosseto (Tuscany, Italy) jabber: bud(a)amessage.info Free Software in Public Administration: not just a good idea, but a necessity Perfection is attained, not when there is nothing more to be added, but when there is nothing more to be taken away -- Antoine de Saint-Exupery

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PyDO-2.0a1 released
by Jacob Smullyan May 10, 2005

May 10, 2005

I'm pleased to announce the second alpha release of PyDO 2. What's New ---------- * PyDO2 is now in the PyDO2 package, to facilitate peaceful coexistence with PyDO1. * explicit schema support (as in 'myschema.mytable'). * The "table" attribute can now be left out if the class name is the name of the table. * "refetch" behavior has been refactored. * there are a bunch of classes to help in doing various kinds of joins. What it is ---------- PyDO is Drew Csillag's ORM (Object-Relational Mapper) database access library for Python that facilitates writing a Python database access layer. PyDO attempts to be simple, flexible, extensible, and unconstraining. PyDO 2 is a rewrite of the 1.x series distributed with SkunkWeb. It has several enhancements: * PyDO can now be used in multi-threaded or twisted-style asynchronous sitations, with or without a customizable connection pool. * PyDO objects are now dict subclasses, but also support attribute access to fields. * Projections -- subsets of the field list of a super-class -- are now supported by the PyDO.project() method. * Table attributes are now declared in a more concise way. * Overall, the API has been tightened and the code restructured. It also has several limitations: * PyDO 2 is alpha code. Bugs should be expected, and the API is not guaranteed to be stable. * Currently, PyDO 2 supports fewer database systems than PyDO 1 (PostgreSQL, SQLite, and MySQL), although adding new drivers should not be difficult. * PyDO 2 requires Python 2.4 or later. PyDO is dual GPL/BSD licensed. The source tarball is available at SkunkWeb's berlios site: https://developer.berlios.de/projects/skunkweb/ or, more directly: http://download.berlios.de/skunkweb/PyDO-2.0a1.tar.gz Questions pertaining to PyDO can be addressed to the SkunkWeb mailing list at sourceforge: http://lists.sourceforge.net/lists/listinfo/skunkweb-list Cheers, js -- Jacob Smullyan

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PyTables 1.0 released
by Francesc Altet May 10, 2005

May 10, 2005

========================= Announcing PyTables 1.0 ========================= The Carabos crew is very proud to announce the immediate availability of **PyTables release 1.0**. On this release you will find a series of exciting new features, being the most important the Undo/Redo capabilities, support for objects (and indexes!) with more than 2**31 rows, better I/O performance for Numeric objects, new time datatypes (useful for time-stamping fields), support for Octave HDF5 files and improved support for HDF5 native files. What it is ========== **PyTables** is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data (with support for full 64-bit file addressing). It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code, makes it a very easy-to-use tool for high performance data storage and retrieval. It is built on top of the HDF5 library and the numarray package, and provides containers for both heterogeneous data (``Table``) and homogeneous data (``Array``, ``EArray``) as well as containers for keeping lists of objects of variable length (like Unicode strings or general Python objects) in a very efficient way (``VLArray``). It also sports a series of filters allowing you to compress your data on-the-fly by using different compressors and compression enablers. But perhaps the more interesting features are its powerful browsing and searching capabilities that allow you to perform data selections over heterogeneous datasets exceeding gigabytes of data in just tenths of second. Besides, the PyTables I/O is buffered, implemented in C and carefully tuned so that you can reach much better performance with PyTables than with your own home-grown wrappings to the HDF5 library. Changes more in depth ===================== Improvements: - New Undo/Redo feature (i.e. integrated support for undoing and/or redoing actions). This functionality lets you to put marks in specific places of your data operations, so that you can make your HDF5 file pop back (undo) to a specific mark (for example for inspecting how your data looked at that point). You can also go forward to a more recent marker (redo). You can even do jumps to the marker you want using just one instruction. - Reading Numeric objects from ``*Array`` and ``Table`` (Numeric columns) objects have a 50-100x speedup. With that, Louis Wicker reported that a speed of 350 MB/s can be achieved with Numeric objects (on a SGI Altix with a Raid 5 disk array) while with numarrays, this speed approaches 900 MB/s. This improvement has been possible mainly due to a nice recipe from Todd Miller. Thanks Todd! - With PyTables 1.0 you can finally create Tables, EArrays and VLArrays with more than 2**31 (~ 2 thousand millions) rows, as well as retrieve them. Before PyTables 1.0, retrieving data on these beasts was not well supported, in part due to limitations in some slicing functions in Python (that rely on 32-bit adressing). So, we have made the necessary modifications in these functions to support 64-bit indexes and integrated them into PyTables. As a result, our tests shows that this feature works just fine. - As a consequence of the above, you can now index columns of tables with more than 2**31 rows. For instance, indexes have been created for integer columns with 10**10 (yeah, 10 thousand million) rows in less than 1 hour using an Opteron @ 1.6 GHz system (~ 1 hour and half with a Xeon Intel32 @ 2.5 GHz platform). Enjoy! - Now PyTables supports the native HDF5 time types, both 32-bit signed integer and 64-bit fixed point timestamps. They are mapped to ``Int32`` and ``Float64`` values for easy manipulation. See the documentation for the ``Time32Col`` and ``Time64Col`` classes. - Massive internal reorganization of the methods that deal with the hierarchy. Hopefully, that will enable a better understanding of the code for anybody wanting to add/modify features. - The opening and copying of files with large number of objects has been made faster by correcting a typo in ``Table._open()``. Thanks to Ashley Walsh for sending a patch for this. - Now, one can modify rank-0 (scalar) ``EArray`` datasets. Thanks to Norbert Nemec for providing a patch for this. - You are allowed from this version on to add non-valid natural naming names as node or attribute names. A warning is issued to warn (but not forbid) you in such a case. Of course, you have to use the ``getattr()`` function so as to access such invalid natural names. - The indexes of ``Table`` and ``*Array`` datasets can be of long type besides of integer type. However, indexes in slices are still restricted to regular integer type. - The concept of ``READ_ONLY`` system attributes has disappeared. You can change them now at your own risk! However, you still cannot remove or rename system attributes. - Now, one can do reads in-between write loops using ``table.row`` instances. This is thanks to a decoupling in I/O buffering: now there is a buffer for reading and other for writing, so that no collisions take place anymore. Fixes #1186892. - Support for Octave HDF5 output format. Even complex arrays are supported. Thanks to Edward C. Jones for reporting this format. Backward-incompatible changes: - The format of indexes has been changed and indexes in files created with PyTables pre-1.0 versions are ignored now. However, ``ptrepack`` can still save your life because it is able to convert your old files into the new indexing format. Also, if you copy the affected tables to other locations (by using ``Leaf.copy()``), it will regenerate your indexes with the new format for you. - The API has changed a little bit (nothing serious) for some methods. See ``RELEASE-NOTES.txt`` for more details. Bug fixes: - Added partial support for native HDF5 chunked datasets. They can be read now, and even extended, but only along the first extensible dimension. This limitation may be removed when multiple extensible dimensions are supported in PyTables. - Formerly, when the name of a column in a table was subsumed in another column name, PyTables crashed while retrieving information of the former column. That has been fixed. - A bug prevented the use of indexed columns of tables that were in other hierarchical level than root. This is solved now. - When a ``Group`` was renamed you were not able to modify its attributes. This has been fixed. - When whether ``Table.modifyColumns()`` or ``Table.modifyRows()`` were called, a subsequent call to ``Table.flush()`` didn't really flush the modified data to disk. This works as intended now. - Fixed some issues when iterating over ``*Array`` objects using the ``List`` or ``Tuple`` flavor. Important note for Python 2.4 and Windows users =============================================== If you are willing to use PyTables with Python 2.4 in Windows platforms, you will need to get the HDF5 library compiled for MSVC 7.1, aka .NET 2003. It can be found at: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win-net.ZIP Users of Python 2.3 on Windows will have to download the version of HDF5 compiled with MSVC 6.0 available in: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win.ZIP Where can PyTables be applied? ============================== PyTables is not designed to work as a relational database competitor, but rather as a teammate. If you want to work with large datasets of multidimensional data (for example, for multidimensional analysis), or just provide a categorized structure for some portions of your cluttered RDBS, then give PyTables a try. It works well for storing data from data acquisition systems (DAS), simulation software, network data monitoring systems (for example, traffic measurements of IP packets on routers), very large XML files, or for creating a centralized repository for system logs, to name only a few possible uses. What is a table? ================ A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and "strict data types" seem to be quite a strange requirement for a language like Python that supports dynamic data types, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. What is HDF5? ============= For those people who know nothing about HDF5, it is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms ========= We are using Linux on top of Intel32 as the main development platform, but PyTables should be easy to compile/install on other UNIX machines. This package has also been successfully compiled and tested on a FreeBSD 5.4 with Opteron64 processors, a UltraSparc platform with Solaris 7 and Solaris 8, a SGI Origin3000 with Itanium processors running IRIX 6.5 (using the gcc compiler), Microsoft Windows and MacOSX (10.2 although 10.3 should work fine as well). In particular, it has been thoroughly tested on 64-bit platforms, like Linux-64 on top of an Intel Itanium, AMD Opteron (in 64-bit mode) or PowerPC G5 (in 64-bit mode) where all the tests pass successfully. Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP (using the Microsoft Visual C compiler), but it should also work with other flavors as well. Web site ======== Go to the PyTables web site for more details: http://pytables.sourceforge.net/ To know more about the company behind the PyTables development, see: http://www.carabos.com/ Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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