[SciPy-User] ANN: PyTables 3.0
Sun Jun 2 10:59:17 CDT 2013
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.
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
- 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
- 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:
For an online version of the manual, visit:
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.
About PyTables: http://www.pytables.org
About the HDF5 library: http://hdfgroup.org/HDF5/
About NumPy: http://numpy.scipy.org/
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.
-- The PyTables Developers
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