ANN: Python-Blosc2 3.0.0 rc2 is out!

[Francesc Alted]

Announcing Python-Blosc2 3.0.0-rc2
==================================

The Blosc development team is pleased to announce the second release release of
Python-Blosc2 3.0.0. Here, we are streamlining the API and adding more
flexibility
to the compute engine (e.g. support for reductions in fields of struct arrays).
You can think of Python-Blosc2 3.0 as a replacement of numexpr, but better :-)

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==3.0.0rc2

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

https://github.com/Blosc/python-blosc2/releases

Docs and examples are available in the documentation site:

https://www.blosc.org/python-blosc2

What is it?
-----------

`C-Blosc2 <https://github.com/Blosc/c-blosc2>`_ is a blocking, shuffling and
lossless compression library meant for numerical data written in C. Blosc2
is the next generation of Blosc, an
`award-winning <https://www.blosc.org/posts/prize-push-Blosc2/>`_
library that has been around for more than a decade.

On top of C-Blosc2 we built Python-Blosc2, a Python wrapper that exposes the
C-Blosc2 API, plus many extensions that allow it to work transparently with
NumPy arrays, while performing advanced computations on compressed data that
can be stored either in-memory, on-disk or on the network (via the
`Caterva2 library <https://github.com/Blosc/Caterva2>`_).

Python-Blosc2 leverages both NumPy and numexpr for achieving great performance,
but with a twist. Among the main differences between the new computing engine
and NumPy or numexpr, you can find:

* Support for n-dim arrays that are compressed in-memory, on-disk or on the
network.
* High performance compression codecs, for integer, floating point, complex
booleans, string and structured data.
* Can perform many kind of math expressions, including reductions, indexing,
filters and more.
* Support for NumPy ufunc mechanism, allowing to mix and match NumPy and
Blosc2 computations.
* Excellent integration with Numba and Cython via User Defined Functions.
* Support for broadcasting operations. This is a powerful feature that
allows to perform operations on arrays of different shapes.
* Much better adherence to the NumPy casting rules than numexpr.
* Lazy expressions that are computed only when needed, and can be stored for
later use.
* Persistent reductions that can be updated incrementally.
* Support for proxies that allow to work with compressed data on local or
remote machines.

You can read some of our tutorials on how to perform advanced computations at:

https://www.blosc.org/python-blosc2/getting_started/tutorials

As well as the full documentation at:

https://www.blosc.org/python-blosc2

Finally, Python-Blosc2 aims to leverage the full C-Blosc2 functionality to
support a wide range of compression and decompression needs, including
metadata, serialization and other bells and whistles.

**Note:** Blosc2 is meant to be backward compatible with Blosc(1) data.
That means that it can read data generated with Blosc, but the opposite
is not true (i.e. there is no *forward* compatibility).

Sources repository
------------------

The sources and documentation are managed through github services at:

https://github.com/Blosc/python-blosc2

Python-Blosc2 is distributed using the BSD license, see
https://github.com/Blosc/python-blosc2/blob/main/LICENSE.txt
for details.

Mastodon feed
-------------

Please follow https://fosstodon.org/@Blosc2 to get informed about the latest
developments.


- Blosc Development Team
Compress better, compute bigger