Release 3.0.0 beta3

Changes from 3.0.0-beta.1 to 3.0.0-beta.3

• Revamped documentation. Now, it is more complete and has a better structure. Thanks to Oumaima Ech Chdig (@omaech), our newcomer to the Blosc team. Also, thanks to NumFOCUS for their support in this task.

• New Proxy class to access other arrays, while providing caching. This is useful for example when you have a big array, and you want to access a small part of it, but you want to cache the accessed data for later use. See its doc.

• Lazy expressions can accept proxies as operands.

• Read-ahead support for reading super-chunks from disk. This allows for overlapping reads and computations, which can be a big performance boost for some workloads.

• New BLOSC_LOW_MEM envar for keeping memory under a minimum while evaluating expressions. This makes it possible to evaluate expressions on very large arrays, even if the memory is limited (at the expense of performance).

• Fine tune block sizes for the internal compute engine.

• Better CPU cache size guessing for linux and macOS.

• Build tooling has been modernized and now uses pyproject.toml and scikit-build-core for managing dependencies and building the package. Thanks to @LecrisUT for the excellent guidance in this area.

• Many code cleanup and syntax improvements in code. Thanks to @DimitriPapadopoulos.

Release 2.7.1

Changes from 2.7.0 to 2.7.1

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

Release 3.0.0 beta 1

Changes from 2.6.2 to 3.0.0-beta.1

• 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, and supports advanced features like reductions, filters, user-defined functions and broadcasting (still in beta). See this example.

• As a consequence of the above, there are many new functions to operate with, and evaluate NDArray instances. See the function section docs for more information.

• Support for NumPy 2.0.0 is here! Now, the wheels are built with NumPy 2.0.0. If you want to use NumPy 1.x, you can still use it by installing NumPy 1.23 and up.

• 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. If curious, see some benchmarks here. Thanks to @JanSellner for the excellent implementation, both in the C and the Python libraries.

• Internal C-Blosc2 updated to 2.15.0.

• 32-bit platforms are officially unsupported now. If you need support for 32-bit platforms, please use python-blosc 1.x series.

Release 2.7.0

Changes from 2.6.2 to 2.7.0

• Updated to latest C-Blosc2 2.15.0.

• Deprecated LazyExpr.evaluate().

• Fixed _check_rc function. See #187.

Release 2.6.2

Changes from 2.6.1 to 2.6.2

• 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.

Release 2.6.1

Changes from 2.6.0 to 2.6.1

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

Release 2.6.0

Changes from 2.5.1 to 2.6.0

• [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 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 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.

Release 2.5.1

Changes from 2.5.0 to 2.5.1

• Updated to latest C-Blosc2 2.13.1.

• Fixed bug in b2nd.h.

Changes from 2.4.0 to 2.5.0

• Updated to latest C-Blosc2 2.13.0.

• Added the filter INT_TRUNC for integer truncation.

• Added some optimizations for zstd.

• Now the grok library is initialized when loading the
  plugin from C-Blosc2.

• Improved doc.

• Support for slices in blosc2.get_slice_nchunks() when using SChunk
  objects.

Release 2.4.0

Changes from 2.3.2 to 2.4.0

• Updated to latest C-Blosc2 2.12.0.

• Added blosc2.get_slice_nchunks() to get array of chunk
  indexes needed to get a slice of a Blosc2 container.

• Added grok codec plugin.

• Added imported target with pkg-config to support windows.

Release 2.3.2

Changes from 2.3.1 to 2.3.2

• Support for pathlib.Path objects in all the places where urlpath is
  used (e.g. blosc2.open()). Thanks to Marta Iborra.

• Included docs for SChunk.fill_special() and NDArray.dtype. Thanks
  to Francesc Alted.

• Upgrade to latest C-Blosc2 2.11.3. It fixes a bug preventing the use of
  typesize > 255 in frames. Now you can use a typesize up to 2**31-1.