Hi everyone, In 2.8.5 we have added a new function, `validate` which checks an expression `ex` for validity, for usage where the program is parsing a user input. There are also consequences for this sort of usage, since `eval(ex)` is called, and as such we do some string sanitization as described below. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.8.4 to 2.8.5 --------------------------- * A `validate` function has been added. This function checks the inputs, returning `None` on success or raising an exception on invalid inputs. This function was added as numerous projects seem to be using NumExpr for parsing user inputs. `re_evaluate` may be called directly following `validate`. * As an addendum to the use of NumExpr for parsing user inputs, is that NumExpr calls `eval` on the inputs. A regular expression is now applied to help sanitize the input expression string, forbidding '__', ':', and ';'. Attribute access is also banned except for '.r' for real and '.i' for imag. * Thanks to timbrist for a fix to behavior of NumExpr with integers to negative powers. NumExpr was pre-checking integer powers for negative values, which was both inefficient and caused parsing errors in some situations. Now NumExpr will simply return 0 as a result for such cases. While NumExpr generally tries to follow NumPy behavior, performance is also critical. * Thanks to peadar for some fixes to how NumExpr launches threads for embedded applications. * Thanks to de11n for making parsing of the `site.cfg` for MKL consistent among all shared platforms. 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 robbmcleod@gmail.com robert.mcleod@hitachi-hightech.com