On Wed, Sep 10, 2008 at 11:59 PM, Lane Brooks <[EMAIL PROTECTED]> wrote: > > When writing an numpy extension module, what is the preferred way to > deal with the all the possible types an ndarray can have? > > I have some data processing functions I need to implement and they need > to be generic and work for all the possible numerical dtypes. I do not > want to have to re-implement the same C-code for all the possible types, > so the way I approached it was to use a C++ template function to > implement the processing. Then I have a dispatching function that > checks the type of the input ndarray and calls the correct template. Is > there a better way? >
In scipy.sparse there is a 'sparsetools' extension module that uses C++ templates to support 14 (or so) numpy types. In sparsetools, SWIG typemaps are used to dispatch the correct template based on the numpy data type. For instance, a typemap decides to call foo<float> if the array has type PyArray_FLOAT. SWIG can be a little overwhelming, but it automates dispatching and can even do automatic upcasting for you. For instance, if your C++ function is foo(double, double) and you pass it (float,int) then SWIG can upcast both arguments to double before passing them into foo(). Also, you can force arrays to be C-contiguous (as opposed to strided or Fortran order) and have the correct endianness. Basically, you can use SWIG to tame the input numpy arrays. The sparsetools module is here: http://projects.scipy.org/scipy/scipy/browser/trunk/scipy/sparse/sparsetools The file numpy.i does most of the heavy lifiting. For each sparse matrix format, for instance 'csr', there is a separate SWIG file ('csr.i') that instantiates each function in the corresponding header file ('csr.h'). The output of 'swig -c++ -python csr.i' is the actual extension module ('csr_wrap.cxx'). If you look at one of these (massive!) files you can see how SWIG dispatches the appropriate function. Also, in order to support complex types (e.g. complex128), there is a wrapper in complex_ops.h that overloads the standard arithmetic operators for complex numbers. Then complex types are handled exactly as above. http://projects.scipy.org/scipy/scipy/browser/trunk/scipy/sparse/sparsetools/complex_ops.h Depending on your problem, SWIG may be overkill. OTOH you may be able to get everything to need from the sparsetools source code. Feel free to pillage it as you require :) Should you go the SWIG path, I can help explain some of the more cryptic parts. -- Nathan Bell [EMAIL PROTECTED] http://graphics.cs.uiuc.edu/~wnbell/ _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
