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From: serge guelton <serge.guelton@telecom-bretagne.eu>
Newsgroups: comp.lang.python.announce
Subject: Pythran 0.7.4 is out!
Date: Thu, 7 Jan 2016 20:51:39 +0100
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(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.guelton@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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