Re: [Pytables-users] ANN: PyTables 3.0 final

[Seref A. <ser...@gm...>]

Many thanks for keeping such a great piece of work up and running. I've
just seen some features in the release notes, features which I was going to
need in the very near future!
Great job!

Best regards
Seref Arikan



On Sat, Jun 1, 2013 at 12:33 PM, 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
>
>
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