ANN: PyTables v3.10.0 released

[Ivan Vilata i Balaguer]

============================
Announcing PyTables 3.10.0
============================

We are happy to announce PyTables 3.10.0.


What's new
==========

The new PyTables 3.10 release includes nearly nine months worth of
enhancements and fixes from many contributors. It includes the
long-awaited compatibility with NumPy 2, and it is also compatible with
Python 3.13. Wheels are available for Python 3.10-3.12 on Linux AMD64 &
ARM64, Windows AMD64, and macOS AMD64 & ARM64 (Apple Silicon, which was
added in this release). The wheels should be usable with both NumPy 1
and 2.

PyTables 3.10 also adds an all new direct chunking API to access raw
chunk data avoiding the overhead of the HDF5 filter pipeline (check out
the optimization tips in the User's Guide for help and benchmarks).
This development was funded by a NumFOCUS grant.

The documentation of the API has been enhanced with type annotations,
and its language improved with many fixes. The code itself contains
many bugfixes, and the build and continuous integration procedures have
been enhanced in various ways.

In case you want to know more in detail what has changed in this
version, please refer to: http://www.pytables.org/release_notes.html

You can install it via pip or download a source package with generated
PDF and HTML docs from:
https://github.com/PyTables/PyTables/releases/v3.10.0

For an online version of the manual, visit:
http://www.pytables.org/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.


Share your experience
=====================

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.


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**Enjoy data!**

-- The PyTables Developers