============================= Announcing PyTables 3.0.0b1 =============================
We are happy to announce PyTables 3.0.0b1. PyTables 3.0.0b1 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 that is being around for already 4 years now. Almost all the main 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 provides full support to Python 3 - The entire code base is now more compliant with coding style guidelines describe in the PEP8. - Basic support for HDF5 drivers. Now it 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). 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/3.0.0b1 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 Team ------------------------------------------------------------------------------ Try New Relic Now & We'll Send You this Cool Shirt New Relic is the only SaaS-based application performance monitoring service that delivers powerful full stack analytics. Optimize and monitor your browser, app, & servers with just a few lines of code. Try New Relic and get this awesome Nerd Life shirt! http://p.sf.net/sfu/newrelic_d2d_apr _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
