========================== Announcing Numexpr 2.6.7 ========================== Hi everyone, This is a bug-fix release. Thanks to Lehman Garrison for a fix that could result in memory leak-like behavior. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.6 to 2.6.7 --------------------------- - Thanks to Lehman Garrison for finding and fixing a bug that exhibited memory leak-like behavior. The use in `numexpr.evaluate` of `sys._getframe` combined with `.f_locals` from that frame object results an extra refcount on objects in the frame that calls `numexpr.evaluate`, and not `evaluate`'s frame. So if the calling frame remains in scope for a long time (such as a procedural script where `numexpr` is called from the base frame) garbage collection would never occur. - Imports for the `numexpr.test` submodule were made lazy in the `numexpr` module. 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 robert.mcleod@hitachi-hhtc.ca www.entropyreduction.al