Release 0.6.6

Changes from 0.6.5 to 0.6.6

• Add arm64 wheels for macosx (this time for real).

Release 0.6.5

Changes from 0.6.4 to 0.6.5

• Add arm64 wheels for macosx.

Release 0.6.4

Changes from 0.6.3 to 0.6.4

• Add arm64 wheels and remove musl builds (NumPy not having them makes the build process too long).

Release 2.6.3

Changes from 0.6.2 to 0.6.3

• Use oldest-supported-numpy for maximum compatibility.

Release 0.6.2

Changes from 0.6.1 to 0.6.2

• Updated C-Blosc2 to 2.6.0.

Release 0.6.1

Changes from 0.5.2 to 0.6.1

• Support for Python prefilters and postfilters. With this, you can pre-process or post-process data in super-chunks automatically. This machinery is handled internally by C-Blosc2, so it is very efficient (although it cannot work in multi-thread mode due to the GIL). See the examples/ directory for different ways of using this.

• Support for fillers. This is a specialization of a prefilter, and it allows to use Python functions to create new super-chunks from different kind of inputs (NumPy, SChunk instances, scalars), allowing computations among them and getting the result automatically compressed. See a sample script in the examples/ directory.

• Lots of small improvements in the style, consistency and other glitches in the code. Thanks to Dimitri Papadopoulos for hist attention to detail.

• No need to compile C-Blosc2 tests, benchs or fuzzers. Compilation time is much shorter now.

• Added cratio, nbytes and cbytes properties to SChunk instances.

• Added setters for dparams and cparams attributes in SChunk.

Release 0.5.2

Changes from 0.5.1 to 0.5.2

• Honor nested cparams properties in kwargs. E.g. you can do:

blosc2.save_tensor(a, "test.bl2", mode="w",
                   filters=[blosc2.Filter.TRUNC_PREC, blosc2.Filter.BITSHUFFLE],
                   filters_meta=[13, 0],
                   codec=blosc2.Codec.LZ4,
                   clevel=9)

without a need to build a proper cparams dict first.

• C-Blosc2 upgraded to 2.4.3. It should improve cratio for BloscLZ in combination with bitshuffle.

• Prefer pack_tensor/save_tensor in benchs and examples

Release 0.5.1

Changes from 0.5.0 to 0.5.1

• Remove the testing of packing PyTorch or TensorFlow objects during wheels build.

Release 0.5.0

Changes from 0.4.1 to 0.5.0

• New pack_tensor, unpack_tensor, save_tensor and load_tensor functions for serializing/deserializing PyTorch and TensorFlow tensor objects. They also understand NumPy arrays, so these are the new recommended ones for serialization.

• pack_array2 do not modify the value of a possible cparams parameter anymore.

• The pack_array2 / save_array have changed the serialization format to follow the new standard introduced in pack_tensor. In the future pack_array2 / save_array will probably be deprecated, so please change to pack_tensor / save_tensor as soon as you can.

• The new 'standard' for serialization relies on using the __pack_tensor__ attribute as a vlmeta (variable length) metalayer.

Release 0.4.1

Changes from 0.4.0 to 0.4.1

• Add msgpack as a runtime requirement