Blosc2

===========================

* New bytedelta filter added. SIMD support for Intel and ARM platforms is there.
We have blogged about this: https://www.blosc.org/posts/bytedelta-enhance-compression-toolset.rst
Thanks to Aras Pranckevičius for inspiration and initial implementation.

* Minor improvements in BloscLZ, leading to better compression ratios in general.
BLoscLZ version bumped to 2.5.2.

* Updated internal zlib-ng to 2.0.7.

* Used `const` qualifier where possible in b2nd. Thanks to cf-natali.

* Updated to latest C-Blosc2 2.15.1.
Fixes SIGKILL issues when using the `blosc2` library in old Intel CPUs.

* Updated to latest C-Blosc2 2.15.0.

* Deprecated `LazyExpr.evaluate()`.

* Fixed `_check_rc` function. See https://github.com/Blosc/python-blosc2/issues/187.

* Protection when platforms have just one CPU. This caused the
internal number of threads to be 0, producing a division by zero.

* Updated to latest C-Blosc2 2.14.3.

* Updated to latest C-Blosc2 2.14.1. This was necessary to be able to
load dynamics plugins on Windows.

* [EXP] New evaluation engine (based on numexpr) for NDArray instances.
Now, you can evaluate expressions like `a + b + 1` where `a` and `b`
are NDArray instances. This is a powerful feature that allows for
efficient computations on compressed data. See this [example](https://github.com/Blosc/python-blosc2/blob/main/examples/ndarray/eval_expr.py) to see how this works.
Thanks to omaech for her help in the `pow` function.

* As a consequence of the above, there are many new functions to operate with
NDArray instances. See the function section in [NDArray API](https://www.blosc.org/python-blosc2/reference/ndarray_api.html#functions) for more information.

* Support for NumPy 2.0.0 is here! Now, the wheels are built with NumPy 2.0.0rc1.
Please tell us in case you see any issues with this new version.

* Add `**kwargs` to `load_tensor()` function. This allows to pass additional parameters
to the deserialization function. Thanks to jasam-sheja.

* Fix `vlmeta.to_dict()` not honoring tuple encoding. Thanks to ivilata.

* Check that chunks/blocks computation does not allow a 0 in blocks. Thanks to ivilata.

* Many improvements in ruff rules and others. Thanks to DimitriPapadopoulos.

* Remove printing large arrays in notebooks (they use too much RAM in recent versions of Jupyter notebook).

* Updated to latest C-Blosc2 2.14.0.