Hi Benoit, Since you have a function that takes two scalars to one scalar, it sounds to me as though you would be best off creating a ufunc. This will then handle the conversion to and looping over the arrays, etc for you. The documentation is available here: https://numpy.org/doc/1.18/user/c-info.ufunc-tutorial.html.
Regards, Eric On Tue, Mar 10, 2020 at 12:28 PM Benoit Gschwind <gschw...@gnu-log.net> wrote: > Hello, > > I writing a python binding of one of our library. The binding intend to > vectorize the function call. for exemple: > > double foo(double, double) will be bound to a python: > > <numpy.array of double> module.foo(<numpy.array>, <numpy.array>) > > and the function foo will be called like : > > for (int i = 0; i < size; ++i) > outarr[i] = foo(inarr0[i], inarr[1]); > > My question is about how can I handle type conversion of input array, > preferably in efficient manner, given that each input array may require > different input type. > > Currently I basically enforce the type and no type conversion is > performed. But I would like relax it. I thought of several possibility > starting from the obvious solution consisting on recasting in the inner > loop that would give: > > for (int i = 0; i < size; ++i) > if (inarr[i] need recast) > in0 = > recast(inarr[i]) > else > in0 = inarr[i] > [... same for all > inputs parameter ...] > outarr[i] = foo(in0, in1, ...); > > This solution is memory efficient, but not actually computationally > efficient. > > The second solution is to copy&recast the entire inputs arrays, but in > that case it's not memory efficient. And my final thought is to mix the > first and the second by chunking the second method, i.e. converting N > input in a raw, then applying the function to them en so on until all > the array is processed. > > Thus my questions are: > - there is another way to do what I want? > - there is an existing or recommended way to do it? > > And a side question, I use the PyArray_FROM_OTF, but I do not > understand well it's semantic. If I pass a python list, it is converted > to the desired type and requirement; when I pass a non-continuous array > it is converted to the continuous one; but when I pass a numpy array of > another type than the one specified I do not get the conversion. There > is a function that do the conversion unconditionally? Did I missed > something ? > > Thank you by advance for your help > > Best regards > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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