python-tables
PyTables is a package for managing hierarchical datasets and designed to efficently cope with extremely large amounts of
data. PyTables is built on top of the HDF5 library and the NumPy package and features an object-oriented interface
that, combined with C-code generated from Pyrex sources, makes of it a fast, yet extremely easy to use tool for
interactively save and retrieve large amounts of data.
- Links to devel:langua...thon:numeric / python-tables
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Source Files
| Filename | Size | Changed |
|---|---|---|
| _multibuild | 0000000123 123 Bytes | |
| python-tables.changes | 0000044207 43.2 KB | |
| python-tables.spec | 0000004723 4.61 KB | |
| tables-3.10.1.tar.gz | 0004762413 4.54 MB |
Latest Revision
Eric Schirra (ecsos)
committed
(revision 3)
- Update to 3.10.1
## Bugfixes
* Fix version constraints for the numpy runtime requirements
* (#1204).
* For a mistake it didn't allow to use PyTables with numpy 2.x.
* Fix compatibility with PyPy (:issue:1205), Thanks to Michał
Górny.
## Improvements
* Upstream: Enforce numpy >= 2 as build constraint (see
discussion in #1200). -- not enforced in openSUSE
* Use tuple of plain ints for chunk info coordinates. Thanks to
Ivan Vilata-i-Balaguer.
* Enable faulthandler in tables.tests.test_all. Thanks to Eric
Larson.
- Release 3.10.0
* New direct chunking API which allows access to raw chunk data
skipping the HDF5 filter pipeline (cutting overhead, see
"Optimization tips" in User's Guide), as well as querying chunk
information (#1187). Thanks to Ivan Vilata and Francesc Alted.
This development was funded by a NumFOCUS grant.
## Improvements
* This release is finally compatible with NumPy 2 with wheels
being built against it so that they are still binary-compatible
with NumPy 1 installations (#1176, #1183, #1184, #1192, #1195,
#1160, #1172, #1185). NumPy >= 1.20 is required now. Thanks to
Antonio Valentino, Maximilian Linhoff and Eric Larson.
* Fix compatibility with Python 3.13 (#1166), Python >= 3.10
required. Cython 3.0.10 is required for building. Thanks to
Antonio Valentino.
* Add type hints to atom.py (#1079). This also narrows some
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