[Numpy-discussion] Dealing with types in extension modules
Wed Sep 10 22:38:33 CDT 2008
Travis E. Oliphant wrote:
> Lane Brooks 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?
> You could store the functions in an array of function pointers and
> look-up the correct one using the typenum:
> with resize_funcs filled appropriately.
Would this require implementing a unique function for each of the
possible types, though? That is mostly what I want to avoid. I do not
want to have to implement 10 to 15 different functions that all do the
same exact thing but to different types of data. I guess with your
proposal I can still use templates to have a single function definition.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Numpy-discussion