I would rather suggest .is_integer(integer_dtype) signature because knowing that 1e300 is an integer is not very useful in the numpy world, since this integer number is not representable as a numpy.integer dtype.
Note that in python assert not f.is_integer() or int(f) == f never fails because integers have unlimited precision but this does would not map into assert ( ~f_arr.is_integer() | (np.int64(f_arr) == f.arr) ).all() because of possible OverflowErrors. Stefano > On 31 Dec 2021, at 04:46, numpy-discussion-requ...@python.org wrote: > > Is adding arbitrary optional parameters a thing with ufuncs? I could easily > add upper and lower bounds checks. > > On Thu, Dec 30, 2021, 20:56 Brock Mendel <jbrockmen...@gmail.com > <mailto:jbrockmen...@gmail.com>> wrote: > At least some of the commenters on that StackOverflow page need a slightly > stronger check: not only is_integer(x), but also "np.iinfo(dtype).min <= x <= > np.info <http://np.info/>(dtype).max" for some particular dtype. i.e. "Can I > losslessly set these values into the array I already have?" > > >
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