Release 3.1.0

Changes from 3.0.0 to 3.1.0

Improvements

• Optimizations for the compute engine. Now, it is faster and uses less memory.
  In particular, careful attention has been paid to the memory handling, as
  this is the main bottleneck for the compute engine in many instances.

• Improved detection of CPU cache sizes for Linux and macOS. In particular,
  support for multi-CCX (AMD EPYC) and multi-socket systems has been implemented.
  Now, the library should be able to detect the cache sizes for most of the
  CPUs out there (specially on Linux).

• Optimization on NDArray slicing when the slice is a single chunk. This is a
  common operation when working with NDArray instances, and now it is faster.

New API functions and decorators

• New blosc2.evaluate() function for evaluating expressions on NDArray/NumPy
  instances. This a drop-in replacement of numexpr.evaluate(), but with the
  next improvements:

  • More functionality than numexpr (e.g. reductions).
  • Follow casting rules of NumPy more closely.
  • Use both NumPy arrays and Blosc2 NDArrays in the same expression.

  See here for more information.

• New blosc2.jit decorator for allowing NumPy expressions to be computed
  using the Blosc2 compute engine. This is a powerful feature that allows for
  efficient computations on compressed data, and supports advanced features like
  reductions, filters and broadcasting. See here for more information.

• Support out= in blosc2.mean(), blosc2.std() and blosc2.var() reductions
  (besides blosc2.sum() and blosc2.prod()).

Others

• Bumped to use latest C-Blosc2 sources (2.16.0).

• The cache for cpuinfo is now stored in ${HOME}/.cache/python-blosc2/cpuinfo.json
  instead of ${HOME}/.blosc2-cpuinfo.json; you can get rid of the latter, as
  the former is more standard (see PR #360). Thanks to Jonas Lundholm Bertelsen
  (@jonaslb).