The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.
Get an email when there's a new version of PyTables - Hierarchical datasets
| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| README.rst | 2014-03-25 | 2.2 kB | |
| tables-3.1.1.tar.gz | 2014-03-25 | 6.7 MB | |
| RELEASE_NOTES-3.1.1.txt | 2014-03-25 | 9.8 kB | |
| pytablesmanual-3.1.1-html.tar.gz | 2014-03-25 | 2.8 MB | |
| pytablesmanual-3.1.1.pdf | 2014-03-25 | 2.5 MB | |
| pytables-3.1.1.md5 | 2014-03-25 | 238 Bytes | |
| Totals: 6 Items | 12.0 MB | 0 | |
PyTables 3.1.1
This is a bug-fix release that addresses a critical bug that make PyTables unusable on some platforms.
What's new
- Fixed a critical bug that caused an exception at import time. The error was triggered when a bug in long-double detection is detected in the HDF5 library (see :issue:`275`) and numpy_ does not expose float96 or float128. Closes :issue:`344`.
- The internal Blosc_ library has been updated to version 1.3.5. This fixes a false buffer overrun condition that made c-blosc to fail, even if the problem was not real.
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/3.1.1
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.