[Numpy-discussion] Dealing with types in extension modules

[Lane Brooks]

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?

For example, suppose I have a processing template function:

template <typename T> int Resize(T *datai) {
...
}


Then in my dispatch function, I do:

  switch(PyArray_TYPE(bufi)) {
  case NPY_UBYTE:
    Resize<npy_ubyte>((npy_ubyte *) PyArray_DATA(bufi));
    break;
  case NPY_BYTE:
    Resize<npy_byte>((npy_byte *) PyArray_DATA(bufi));
    break;
  case NPY_USHORT:
    Resize<npy_ushort>((npy_ushort *) PyArray_DATA(bufi));
    break;
  case NPY_SHORT:
    Resize<npy_short>((npy_short *) PyArray_DATA(bufi));
    break;
  case NPY_UINT:
    Resize<npy_uint>((npy_uint *) PyArray_DATA(bufi));
    break;
  ....
  }