Looks good. Only one test failure on win-amd64-py2.7 (attached).
Christoph
On 7/7/2012 11:47 AM, Antonio Valentino wrote:
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===========================
Announcing PyTables 2.4.0b1
===========================
We are happy to announce PyTables 2.4.0b1.
This is an incremental release which includes many changes to prepare
for future Python 3 support.
What's new
==========
This release includes support for the float16 data type and read-only
support for variable length string attributes.
The handling of HDF5 errors has been improved. The user will no
longer see HDF5 error stacks dumped to the console. All HDF5 error
messages are trapped and attached to a proper Python exception.
Now PyTables only supports HDF5 v1.8.4+. All the code has been updated
to the new HDF5 API. Supporting only HDF5 1.8 series is beneficial
for future development.
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.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/2.4.0b1
For an online 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 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 (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.
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PyTables version: 2.4.0b1
HDF5 version: 1.8.8
NumPy version: 1.6.2
Numexpr version: 2.0.1 (using VML/MKL 10.2.7)
Zlib version: 1.2.3 (in Python interpreter)
LZO version: 2.04 (Oct 31 2010)
BZIP2 version: 1.0.6 (6-Sept-2010)
Blosc version: 1.1.3 (2010-11-16)
Cython version: 0.16
Python version: 2.7.3 (default, Apr 10 2012, 23:24:47) [MSC v.1500 64 bit
(AMD64)]
Byte-ordering: little
Detected cores: 8
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Performing only a light (yet comprehensive) subset of the test suite.
If you want a more complete test, try passing the --heavy flag to this script
(or set the 'heavy' parameter in case you are using tables.test() call).
The whole suite will take more than 4 hours to complete on a relatively
modern CPU and around 512 MB of main memory.
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======================================================================
FAIL: test_enable_messages (tables.tests.test_basics.HDF5ErrorHandling)
----------------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python27-x64\lib\site-packages\tables\tests\common.py", line 259, in
newmethod
return oldmethod(self, *args, **kwargs)
File "X:\Python27-x64\lib\site-packages\tables\tests\test_basics.py", line
2445, in test_enable_messages
self.assertTrue("HDF5-DIAG" in stderr)
AssertionError: False is not true
----------------------------------------------------------------------
Ran 4987 tests in 144.628s
FAILED (failures=1)
------------------------------------------------------------------------------
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