On Fri, Jun 27, 2025 at 5:29 PM Benjamin Root via NumPy-Discussion <[email protected]> wrote: > > I'm looking at a situation where I like to wrap a C++ function that takes two > doubles as inputs, and returns an error code, a position vector, and a > velocity vector so that I essentially would have a function signature of (N), > (N) -> (N), (N, 3), (N, 3). When I try to use np.vectorize() or > np.frompyfunc() on the python version of this function, I keep running into > issues where it wants to make the outputs into object arrays of tuples. And > looking at utilizing PyUFunc_FromFuncAndData, it isn't clear to me how I can > tell it to expect those two output arrays to have a size 3 outer dimension. > > Are ufuncs the wrong thing here? How should I go about this? Is it even > possible?
Ben, It looks like the simplest signature for your core operation would be (),()->(),(3),(3), with broadcasting taking care of higher dimensional inputs. Because not all the core shapes are scalars, that would require a *generalized* ufunc (gufunc). There is an open issue (https://github.com/numpy/numpy/issues/14020) with a request for a function to generate a gufunc from a Python function. numba has the @guvectorize decorator, but I haven't use it much, and in my few quick attempts just now, it appeared to not accept fixed integer sizes in the output shape. But wait to see if any numba gurus respond with a definitive answer about whether or not it can handle the shape signature (),()->(),(3),(3). You could implement the gufunc in a C or C++ extension module, if you don't mind the additional development effort and packaging hassle. I know that works--I've implemented quite a few gufuncs in ufunclab (https://github.com/WarrenWeckesser/ufunclab). Warren > > Thanks in advance, > Ben Root > _______________________________________________ > NumPy-Discussion mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/numpy-discussion.python.org > Member address: [email protected] _______________________________________________ NumPy-Discussion mailing list -- [email protected] To unsubscribe send an email to [email protected] https://mail.python.org/mailman3//lists/numpy-discussion.python.org Member address: [email protected]
