Dear all,
This is a cross post of stackoverflow[0].
I am interfacing a C++ data intense library with Python by
py++[1]/boost.python[2]. After profiling my program, I find 70% of run
time is spent on code like this:
ni = range(v2o.getHits())
tau = np.array([v2o.TofCorrectedTime[i] for i in ni])
q = np.array[v2o.getCharge()[i] for i in ni]
v2o.TofCorrectedTime is typed __array_1_float_2368[3] from
py++. v2o.getCharge() is typed _impl_details_range_iterator_[4] from
py++, too. Size being about 2000, the convertion from these py++ array
wrappers to numpy is slow:
In [42]: timeit np.array(v2o.TofCorrectedTime)
100 loops, best of 3: 2.52 ms per loop
In [43]: timeit np.array(v2o.getCharge())
100 loops, best of 3: 4.94 ms per loop
In [44]: timeit np.array([0]*2368)
1000 loops, best of 3: 310 µs per loop
In [45]: timeit np.array(np.zeros(2368))
100000 loops, best of 3: 4.41 µs per loop
I searched the web for a solution. The candidates are:
1. Cython[5] with memoryview[6]
2. pyublas[7]
3. Boost.NumPy[8]
Questions:
- Is cython/memoryview easy to be integrated with boost.python and
py++? I want to keep the rest of the library wrapper.
- Which one best suites my problem in terms of convertion overhead?
Thanks,
Benda
[0]:
http://stackoverflow.com/questions/20376021/exchange-numpy-array-with-boost-python-pyublas-or-boost-numpy
[1]: http://svn.code.sf.net/p/pygccxml/svn/pyplusplus_dev
[2]: http://www.boost.org/libs/python
[3]:
http://svn.code.sf.net/p/pygccxml/svn/pyplusplus_dev/pyplusplus/code_repository/array_1.py
[4]:
http://svn.code.sf.net/p/pygccxml/svn/pyplusplus_dev/pyplusplus/code_repository/return_range.py
[5]: http://cython.org
[6]: http://docs.cython.org/src/userguide/memoryviews.html
[7]: http://mathema.tician.de/software/pyublas/
[8]: https://github.com/ndarray/Boost.NumPy
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