(sorry for the double posting, if any) Dear pythraners and pythonists,
The pythran team (a great total of 2 active developers) is delighted to
announce the release of Pythran 0.7.4, available on the traditional
channels:
- pypi: https://pypi.python.org/pypi/pythran
- conda: https://anaconda.org/serge-sans-paille/pythran
- github: https://github.com/serge-sans-paille/pythran
As usual, here is a (new) code sample, once again adapted from a
stackoverflow question[0] that showcases pythran capability:
#pythran export check_mask(bool[][], bool[])
# ^~~~~~~ non intrusive top-level annotation
import numpy as np
# ^~~~~~ numpy support (partial)
def check_mask(db, out, mask=[1, 0, 1]):
for idx, line in enumerate(db):
target, vector = line[0], line[1:]
# ^~~~~ type destructuring, array view
if (mask == np.bitwise_and(mask, vector)).all():
# ^~~~~~~ optimization of high level construct
if target == 1:
out[idx] = 1
return out
Compiled with:
% pythran check_mask.py
And benchmarked with:
% python -m timeit -s 'n=10e3 ; import numpy as np;db =
np.array(np.random.randint(2, size=(n, 4)), dtype=bool); out =
np.zeros(int(n),dtype=bool); from eq import check_mask' 'check_mask(db, out)'
On average, the CPython version runs in 137 msec while the pythran version run
in 450us on my laptop :-)
Here is an extract of the changelog:
2016-01-05 Serge Guelton <serge.guel...@telecom-bretagne.eu>
* IPython's magic for pythran now supports extra compile flags
* Pythran's C++ output is compatible with Python3 and pythran3 can
compile it!
* More syntax checks (and less template traceback)
* Improved UI (multiline pythran exports, better setup.py...)
* Pythonic leaning / bugfixing (this tends to be a permanent item)
* More generic support for numpy's dtype
* Simpler install (no more boost.python deps, nor nt2 configuration)
* Faster compilation (no more boost.python deps, smarter pass manager)
* Better testing (gcc + clang)
Again, thanks a lot to Pierrick for his continuous top-quality work, and
to the OpenDreamKit[1] project that funded (most of) the recent developments!
Special thanks to @hainm, @nbecker, @pkoch, @fsteinmetz, @Suor for their
feedbacks. *You* give us the motivation to go on!
[0]
http://stackoverflow.com/questions/34500913/numba-slower-for-numpy-bitwise-and-on-boolean-arrays
[1] http://opendreamkit.org/
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