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ANNOUNCE.rst

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Hi everyone,

This is primarily a maintenance release that fixes a number of newly discovered bugs. <INSERT VERSION SPECIFIC INFORMATION HERE PRIOR TO RELEASE>

Project documentation is now available at:

http://numexpr.readthedocs.io/

Announcing Numexpr 2.6.4

Changes from 2.6.4 to 2.6.5

  • Constants in where no longer generate an exception. Thanks to Mahdi Ben Jelloul for the fix.
  • Github user 'mamrehn' found and fixed several typos in the documentation, so thanks for that.
  • Import time was reduced from about 0.27 s to 0.1 s in order to speed imports for dependant modules such as pandas and pytables. Imports of pkg_resources were removed, and numexpr.cpu is now lazily imported. If you need CPU information, import numexpr.cpu_info.cpu as cpu. Thanks to Jason Sachs for pointing out the source of the slow-down.

Changes from 2.6.3 to 2.6.4

  • Christoph Gohlke noticed a lack of coverage for the 2.6.3 floor and ceil functions for MKL that caused seg-faults in test, so thanks to him for that.

Changes from 2.6.2 to 2.6.3

  • Documentation now available at numexpr.readthedocs.io
  • Support for floor() and ceil() functions added by Caleb P. Burns.
  • NumPy requirement increased from 1.6 to 1.7 due to changes in iterator flags (#245).
  • Sphinx autodocs support added for documentation on readthedocs.org.
  • Fixed a bug where complex constants would return an error, fixing problems with sympy when using NumExpr as a backend.
  • Fix for #277 whereby arrays of shape (1,...) would be reduced as if they were full reduction. Behavoir now matches that of NumPy.
  • String literals are automatically encoded into 'ascii' bytes for convience (see #281).

What's Numexpr?

Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python.

It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL:

https://github.com/pydata/numexpr/wiki/NumexprMKL

Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies.

Where I can find Numexpr?

The project is hosted at GitHub in:

https://github.com/pydata/numexpr

You can get the packages from PyPI as well (but not for RC releases):

http://pypi.python.org/pypi/numexpr

Documentation is hosted at:

http://numexpr.readthedocs.io/en/latest/

Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.

Enjoy data!