On Mon, Oct 13, 2014 at 8:39 PM, Charles R Harris <charlesr.har...@gmail.com > wrote:
> > > On Mon, Oct 13, 2014 at 12:54 PM, Sebastian Berg < > sebast...@sipsolutions.net> wrote: > >> On Mo, 2014-10-13 at 13:35 +0200, Daniele Nicolodi wrote: >> > Hello, >> > >> > I have a C++ application that collects float, int or complex data in a >> > possibly quite large std::vector. The application has some SWIG >> > generated python wrappers that expose this vector to python. However, >> > the standard way in which SWIG exposes the data is to create a touple >> > and pass this to python, where it is very often converted to a numpy >> > array for processing. Of course this is not efficient. >> > >> > I would like therefore to generate a smarter python interface. In python >> > 3 I would without doubt go for implementing the buffer protocol, which >> > enables seamless interfacing with numpy. However, I need to support also >> > python 2 and there the buffer protocol is not as nice. >> >> Isn't the new buffer protocol in python 2.6 or 2.7 already? There is at >> least a memoryview object in python 2, which maybe could be used to the >> same effect? >> > > No memoryview in python2.6, but the older buffer protocol it there. Is > Boost Python an option? > > <snip> > > Chuck > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > Here's an idea on how to avoid the buffer protocol entirely. It won't be perfectly optimal, but it is a simple solution that may be good enough. It could be that the shape and type inference and the Python loops that are run when converting the large tuple to a NumPy array are the main sticking points. In that case, the primary bottleneck could be eliminated by looping and copying at the C level. You could use Cython and just copy the needed information manually into the array. Here's a rough outline of what the Cython file might look like: # distutils: language = c++ # distutils: libraries = (whatever shared library you need) # distutils: library_dirs = (folders where the libraries are) # distutils: include_dirs = (wherever the headers are) # cython: boundscheck = False # cython: wraparound = False import numpy as np from libcpp.vector cimport vector # Here do the call to your C++ library # This can be done by declaring the # functions or whatever else you need in # a cdef extern from "header file" block # and then calling the functions to get # the vector you need. cdef int[::1] arr_from_vector(vector[int] v): cdef: int i int[::1] a = np.empty(v.size(), int) for i in xrange(v.size()): a[i] = v[i] return np.asarray(a) def get_data(): # Run your C++ computation here. return arr_from_vector(v)
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
