[Numpy-discussion] Dealing with types in extension modules

Nathan Bell wnbell@gmail....
Sat Sep 13 11:32:20 CDT 2008

On Wed, Sep 10, 2008 at 11:59 PM, Lane Brooks <lbrooks@mit.edu> wrote:
> implement the processing.  Then I have a dispatching function that
> 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:

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

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.


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 wnbell@gmail.com

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