(sorry for the double posting, if any) Dear pythraners and pythonistae (the latin plural for pythonista?) It is my pleasure to announce the release of Pythran 0.7.2, now available on - pypy: https://pypi.python.org/pypi/pythran - conda: https://anaconda.org/serge-sans-paille/pythran - github: https://github.com/serge-sans-paille/pythran Pythran is an ahead-of-time Python compiler with a focus on high-level scientific computing. It relies on a few non-intrusive annotations to provide (potentially) vectorized, parallel version of high level Python code, like the following, borrowed from stackoverflow [0]; #pythran export GrayScott(int, float, float, float, float) # ^ see the non intrusive, backward compatible annotation ^ import numpy as np def GrayScott(counts, Du, Dv, F, k): n = 300 U = np.zeros((n+2,n+2), dtype=np.float32) V = np.zeros((n+2,n+2), dtype=np.float32) u, v = U[1:-1,1:-1], V[1:-1,1:-1] r = 20 u[:] = 1.0 U[n/2-r:n/2+r,n/2-r:n/2+r] = 0.50 V[n/2-r:n/2+r,n/2-r:n/2+r] = 0.25 u += 0.15*np.random.random((n,n)) v += 0.15*np.random.random((n,n)) for i in range(counts): Lu = ( U[0:-2,1:-1] + U[1:-1,0:-2] - 4*U[1:-1,1:-1] + U[1:-1,2:] + U[2: ,1:-1] ) Lv = ( V[0:-2,1:-1] + V[1:-1,0:-2] - 4*V[1:-1,1:-1] + V[1:-1,2:] + V[2: ,1:-1] ) uvv = u*v*v u += Du*Lu - uvv + F*(1 - u) v += Dv*Lv + uvv - (F + k)*v return V This minor yet amazing version brings in more numpy function support [1], easier install, a conda build, tentative windows support, faster compilation time and generates generally faster native modules. Kudos to Pierrick for the hardwork and to all the bug reporters. You all help a lot to bring the motivation high. Special thanks to the OpenDreamKit[2] project for the financial support. Most of the new features have been implemented thanks to this funding! Changelog extract: 2015-10-13 Serge Guelton <serge.guelton@telecom-bretagne.eu> * Significantly decrease compilation time * Faster execution of numpy generalized slicing * Tentative conda support * Tentative Windows support (using Win Python) * Preserve original docstrings * Add __pythran__ global variable to pythran generated modules * Faster implementation of various itertools functions * Rely on distutils for module code compilation * Support most of numpy.random * Remove git and make dependency to install nt2 * Proper pip support instead of distuils * Remove dependency to boost.python * Remove dependency to tcmalloc * Pythonic library cleaning (less dependencies / header / splitting / mrpropering) * More lazy computations * More numpy function support (including dot on matrices, linalg.norm, mean) * Lot of code cleaning / refactoring (both in Python and C++) * Many bugfixes, thanks to all the bug reporters! [0] http://stackoverflow.com/questions/26823312/numba-or-cython-acceleration-in-... [1] https://pythonhosted.org/pythran/SUPPORT.html [2] http://opendreamkit.org/