Re: [Pytables-announce] ANN: PyTables 2.3rc1 released

[Francesc A. <fa...@py...>]

Hey Antonio, that sounds terrific :)

Thanks for all the new Governance Team.  You all Josh, Anthony and Antonio
rocks!

Keep the good work!

Francesc

2011/9/11 Antonio Valentino <ant...@ti...>

> ===========================
>  Announcing PyTables 2.3rc1
> ===========================
>
> We are happy to announce PyTables 2.3.
> This release comes after abour 10 months of development and after that
> Francesc Altet, the creator of PyTables, ceased activities with the
> project.
>
> Thank you Francesc.
>
> Also the project has been moved to GitHub:
> http://github.com/PyTables/PyTables.
>
>
> What's new
> ==========
>
> The main new features in 2.3 series are:
>
> * PyTables now includes the codebase of PyTables Pro (now release under
> open
>  source license) gaining a lot of performance improvements and some new
>  features like:
>
>  - the new and powerful indexing engine: OPSI
>  - a fine-tuned LRU cache for both metadata (nodes) and regular data
>
> * The entire documentation set has been converted to ReStructuredTest and
>  Sphinx
>
> As always, a large amount of bugs have been addressed and squashed too.
>
> In case you want to know more in detail what has changed in this
> version, have a look at:
> http://pytables.github.com/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/2.3rc1
>
> For an on-line version of the manual, visit:
> http://pytables.github.com/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 1 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 (and numarray!)
> 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
>
>
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-- 
Francesc Alted