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Re: [Pytables-users] ANN: PyTables 3.0 final
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From: Francesc A. <fa...@gm...> - 2013-06-02 21:09:32
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My congrats for the hard effort too. I am very pleased to see the PyTables project so healty and well managed. Thanks to all the developers, most specially Antonio and Anthony. You guys rock! Francesc El 02/06/2013 17:54, "Anthony Scopatz" <sc...@gm...> va escriure: > Congratulations All! > > This is a huge and important milestone for PyTables and I am glad to have > been a part of it! > > Be Well > Anthony > > > On Sat, Jun 1, 2013 at 6:33 AM, Antonio Valentino < > ant...@ti...> wrote: > >> =========================== >> Announcing PyTables 3.0.0 >> =========================== >> >> We are happy to announce PyTables 3.0.0. >> >> PyTables 3.0.0 comes after about 5 years from the last major release >> (2.0) and 7 months since the last stable release (2.4.0). >> >> This is new major release and an important milestone for the PyTables >> project since it provides the long waited support for Python 3.x, which >> has been around for 4 years. >> >> Almost all of the core numeric/scientific packages for Python already >> support Python 3 so we are very happy that now also PyTables can provide >> this important feature. >> >> >> What's new >> ========== >> >> A short summary of main new features: >> >> - Since this release, PyTables now provides full support to Python 3 >> - The entire code base is now more compliant with coding style >> guidelines described in PEP8. >> - Basic support for HDF5 drivers. It now is possible to open/create an >> HDF5 file using one of the SEC2, DIRECT, LOG, WINDOWS, STDIO or CORE >> drivers. >> - Basic support for in-memory image files. An HDF5 file can be set >> from or copied into a memory buffer. >> - Implemented methods to get/set the user block size in a HDF5 file. >> - All read methods now have an optional *out* argument that allows to >> pass a pre-allocated array to store data. >> - Added support for the floating point data types with extended >> precision (Float96, Float128, Complex192 and Complex256). >> - Consistent ``create_xxx()`` signatures. Now it is possible to create >> all data sets Array, CArray, EArray, VLArray, and Table from existing >> Python objects. >> - Complete rewrite of the `nodes.filenode` module. Now it is fully >> compliant with the interfaces defined in the standard `io` module. >> Only non-buffered binary I/O is supported currently. >> >> Please refer to the RELEASE_NOTES document for a more detailed list of >> changes in this release. >> >> As always, a large amount of bugs have been addressed and squashed as >> well. >> >> In case you want to know more in detail what has changed in this >> version, please refer to: http://pytables.github.io/release_notes.html >> >> You can download a source package with generated PDF and HTML docs, as >> well as binaries for Windows, from: >> http://sourceforge.net/projects/pytables/files/pytables/3.0.0 >> >> For an online version of the manual, visit: >> http://pytables.github.io/usersguide/index.html >> >> >> What it is? >> =========== >> >> PyTables is a library for managing hierarchical datasets and >> designed to efficiently cope with extremely large amounts of data with >> support for full 64-bit file addressing. PyTables runs on top of >> the HDF5 library and NumPy package for achieving maximum throughput and >> convenient use. PyTables includes OPSI, a new indexing technology, >> allowing to perform data lookups in tables exceeding 10 gigarows >> (10**10 rows) in less than a tenth of a second. >> >> >> Resources >> ========= >> >> About PyTables: http://www.pytables.org >> >> About the HDF5 library: http://hdfgroup.org/HDF5/ >> >> About NumPy: http://numpy.scipy.org/ >> >> >> Acknowledgments >> =============== >> >> Thanks to many users who provided feature improvements, patches, bug >> reports, support and suggestions. See the ``THANKS`` file in the >> distribution package for a (incomplete) list of contributors. Most >> specially, a lot of kudos go to the HDF5 and NumPy makers. >> Without them, PyTables simply would not exist. >> >> >> Share your experience >> ===================== >> >> Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. >> >> >> ---- >> >> **Enjoy data!** >> >> -- The PyTables Developers >> >> >> ------------------------------------------------------------------------------ >> Get 100% visibility into Java/.NET code with AppDynamics Lite >> It's a free troubleshooting tool designed for production >> Get down to code-level detail for bottlenecks, with <2% overhead. >> Download for free and get started troubleshooting in minutes. >> http://p.sf.net/sfu/appdyn_d2d_ap2 >> _______________________________________________ >> Pytables-users mailing list >> Pyt...@li... >> https://lists.sourceforge.net/lists/listinfo/pytables-users >> > > > > ------------------------------------------------------------------------------ > Get 100% visibility into Java/.NET code with AppDynamics Lite > It's a free troubleshooting tool designed for production > Get down to code-level detail for bottlenecks, with <2% overhead. > Download for free and get started troubleshooting in minutes. > http://p.sf.net/sfu/appdyn_d2d_ap2 > _______________________________________________ > Pytables-users mailing list > Pyt...@li... > https://lists.sourceforge.net/lists/listinfo/pytables-users > > |
