Travis E. Oliphant wrote:
Lane Brooks wrote:
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.
You could have a default function which does type coercion or uses
type-indifferent code. It really depends on what you are doing.
But generally if you want to support multiple types you have to repeat
the code for that type, I don't know of anyway around that.
Also, you don't have to fill in all the functions (some could be NULL
and you could use coercion to another type or some other default
implementation as needed).
-Travis
Is such an approach preferred to templates? I think duplicating the
code for different types sound tedious and error-prone.
What is type-indifferent code? Is there a way, for example, to add or
multiple two variables in a type-indifferent way?
Thanks,
Lane
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