Hi everyone, This is primarily a maintenance release that fixes a number of newly discovered bugs. The NumPy requirement has increased from 1.6 to 1.7 due to changes with `numpy.nditer` flags. Thanks to Caleb P. Burns `ceil` and `floor` functions are now supported. Project documentation is now available at: http://numexpr.readthedocs.io/ ========================= Announcing Numexpr 2.6.3 ========================= 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! -- Robert McLeod, Ph.D. robbmcleod@gmail.com robbmcleod@protonmail.com