[Numpy-discussion] Dealing with types in extension modules
Lane Brooks
lbrooks@mit....
Wed Sep 10 19:06:41 CDT 2008
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;
....
}
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