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Update latest release in doc
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FrancescAlted committed Oct 2, 2024
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26 changes: 14 additions & 12 deletions ANNOUNCE.rst
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Announcing Python-Blosc2 3.0.0-beta.3
Announcing Python-Blosc2 3.0.0-beta.4
=====================================

The Blosc development team is pleased to announce the second beta release of
Python-Blosc2 3.0.0. Here, we have fine tuned the performance of the new
evaluation engine, and extended it to operate with other instances than NDArray,
like the `C2Array` class and the new one `Proxy` (access to remote data with caching).
Documentation has been improved and we have added more examples and tutorials.
As always, we would like to get feedback from the community before the final release.
The Blosc development team is pleased to announce the fourth beta release of
Python-Blosc2 3.0.0. Here, documentation has been improved quite a lot and
we have added more examples and tutorials (thanks NumFOCUS for sponsoring this).
Also, there are new CParams, DParams and Storage dataclasses that allow for
a more flexible and powerful way to set parameters for the Blosc2 compressor.

Now, you can evaluate expressions like `a + sin(b) + 1` where `a` and `b`
are NDArray instances. This is a powerful feature that allows for
In new 3.0 release, you can evaluate expressions like `a + sin(b) + 1` where
`a` and `b` are NDArray instances. This is a powerful feature that allows for
efficient computations on compressed data, and supports advanced features
like reductions, filters, user-defined functions and broadcasting (still
in beta). See this `example <https://github.com/Blosc/python-blosc2/blob/main/examples/ndarray/eval_expr.py>`_.
in beta). See this
`example <https://github.com/Blosc/python-blosc2/blob/main/examples/ndarray/eval_expr.py>`_.

Also, we have added support for memory mapping in `SChunk` and `NDArray` instances.
This allows to map super-chunks stored in disk and access them as if they were in
memory. When combined with the evaluation engine, this feature allows for very
good performance when working with large datasets. See this `benchmark <https://github.com/Blosc/python-blosc2/blob/main/bench/ndarray/lazyarray-expr.ipynb>`_
good performance when working with large datasets. See this
`benchmark <https://github.com/Blosc/python-blosc2/blob/main/bench/ndarray/lazyarray-expr.ipynb>`_
(as it is a Jupyter notebook, you can easily run it in your own computer).

Last, but not least, we are using NumPy 2.x as the default for testing procedures
and builds. This means that our wheels are built against NumPy 2, so in case you want
to use NumPy 1.x, you will need to use NumPy 1.23.0 or later.

As always, we would like to get feedback from the community before the final release.
We are providing binary wheels that you can easily install from PyPI with:

pip install blosc2==3.0.0b1
pip install blosc2==3.0.0b4

For more info, you can have a look at the release notes in:

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6 changes: 6 additions & 0 deletions RELEASING.rst
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* Make sure that the tag is passing the tests in continuous integration (this
may take more than an hour).

* Update the latest release in the ``doc/python-blosc2.rst`` file with the new version
number (and date?). Do a commit::

git commit -a -m "Update latest release in doc"
git push

* Go to ``Blosc/blogsite`` repo, then to "Actions", click on the most recent
workflow run (at the top of the list), and then click on the "Re-run all
jobs" button to regenerate the documentation and check that it has been
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4 changes: 2 additions & 2 deletions doc/python-blosc2.rst
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<p style="text-align: center; color: black; background-color: rgba(230, 169, 9, 0.65);">
<a href="https://github.com/Blosc/python-blosc2/blob/main/RELEASE_NOTES.md"
style="font-size: 1.5em;">Version 3.0.0 beta3 released!</a>
style="font-size: 1.5em;">Version 3.0.0 beta4 released on 2024-10-02!</a>
<span style="display: inline-block; width: 20px;"></span>
<span style="font-family: monospace;">pip install blosc2==3.0.0b3</span>
<span style="font-family: monospace;">pip install blosc2==3.0.0b4</span>
</p>

.. raw:: html
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