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.
- Developed at devel:languages:python:numeric
- Sources inherited from project openSUSE:Factory
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Source Files
Filename | Size | Changed |
---|---|---|
_multibuild | 0000000122 122 Bytes | |
python-tables.changes | 0000039695 38.8 KB | |
python-tables.spec | 0000004647 4.54 KB | |
tables-3.9.2.tar.gz | 0004683437 4.47 MB |
Revision 23 (latest revision is 28)
Ana Guerrero (anag+factory)
accepted
request 1130124
from
Steve Kowalik (StevenK)
(revision 23)
- Update to 3.9.2: * Minimum supported version for Python is 3.9 (see gh-1062). * New features + Apply optimized slice read to Blosc2-compressed CArray and EArray + Add basic API for column-level attributes as Col._v_col_attrs * Bugfixes + Fix the assembly of returned slice data in Blosc2 NDim optimized slice reads by using Blosc2’s b2nd_copy_buffer + Fix crash in Blosc2 optimized path with large tables + Fix compatibility with NumExpr v2.8.5 + Fix ptrepack failures with external links + Replace stderr messages with Python warnings + Fix Cython slice indexing under Python 3.12 + Fix unsafe temporary file creation in benchmark * Improvements + PyTables wheels now use a threadsafe build of the HDF5 library + Partial support for the future NumPy 2, with some tests still failing + Relax the reading of Blosc2 NDim to cope with datasets stored with other tools + Use H5Dchunk_iter (when available) to speed up walking over many chunks in a very large table, as well as with random reads + Improve setup.py (now using pyproject.toml as per PEP 518) and blosc2 discovery mechanism + Enable compatibility with Cython 3 + Handle py-cpuinfo missing in some platforms + Avoid NumPy >= 1.25 deprecations, use numpy.all, numpy.any, etc. instead. * Other changes + Drop compatibility with the obsolete HDF5 1.8 API + Require python-blosc2 >= 2.3.0 or c-blosc2 >= 2.11.0 + Assorted fixes to b2nd slicing optimization tips
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