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:
resize_funcs[PyArray_Type(buf1)](PyArray_DATA(bufi))
with resize_funcs filled appropriately.
-Travis
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.
Lane
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