Another library that can pass C++ arrays to/from python is pyrap (pyrap.googlecode.com). It is the python binding to the casacore package, so it maps (possibly strided) Python arrays to casa::Array objects.
Unlike the other C++ array classes I know (Blitz, boost::multi_array, vigra::MultiArray) the dimensionality of casa::Array is a runtime parameter, not a template parameter. It makes it easier to handle arrays (e.g. reading from a file, passing in from python) of which the dimensionality is unknown at compile time. Iteration through a strided casa::Array is as fast as boost::multi_array. casa::Array axes are Fortran ordered, while numpy is basically C ordered (numpy can handle Fortran order, but slower; furthermore it converts to C order as soon you do something like an addition). Therefore pyrap reverses the axes. Note that pyrap can also handle the conversion of a numpy array element to a basic C++ type (like float). pyrap is based on an old version of Boost_Python, so it does not use newer, handier features. Cheers, Ger van Diepen >>> Hans Meine <hans_me...@gmx.net> 4/21/2010 10:36 PM >>> Am Montag 08 März 2010 13:32:22 schrieb Pim Schellart: > we are working on a project for which it would be extremely useful if > numpy arrays could be passed as arguments to wrapped C++ methods. > On the website I cannot find any evidence that this is currently > supported by Boost Python. > Is this (or will this be) implemented? After all the recent discussion, I want to point to yet another (hopefully) interesting bit of code which I am involved with: Our "vigra" (computer vision) library now comes with numpy- and BPL-based python bindings. In addition to type-safe conversions between C++ and Python(*), this offers a vigra::MultiArray on the C++ side, which is an n-dimensional array class template, complete with n-dimensional iterators (using raw pointers for the innermost dimension to offer the best possible performance for unstrided arrays) and a lot of algorithms, i.e. pointwise transformation / [possibly type-coercing] copying / inspection etc. One particular feature Ulli is proud of is the automatic conversion to *subclasses* of ndarray, such that we can register and use e.g. 'Volume' python classes for 3D arrays. I really want to stress that VIGRA is a very versatile template library which should be able to work in conjunction with any other libraries and data structures, i.e. true generic code (usually you don't even need to compile VIGRA, if you are not interested in an image import/export library). So don't hesitate to give it a shot even if you are /not/ interested in any of its array / linalg algorithms. (Actually, the PythonArray template class should be even useful without boost::python, i.e. I am using it with SIP, too.) I don't think there will ever be just /one/ single solution for boost::python+numpy, since everyone seems to have different requirements/expectations w.r.t. available algorithms, static vs. dynamic element types etc. However, I do think that our vigranumpy approach could be very useful for some of this list's readers. HTH, Hans
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