Is there a guide on how to pacakage non-ufunc functions with multiple loops? Something like sort? It looks like there is no way of adding additional arguments to a ufunc as of yet.
On a related note, would it be more useful to have a function that returns the number of bits required to store a number, or -1 if it has a fractional part? Then you could just test something like ``(k := integer_bits(a)) < 64 & k > 0``. - Joe On Sat, Jan 1, 2022 at 5:55 AM Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote: > Stefano, > > That is an excellent point. Just to make sure I understand, would an > interface like `is_integer(a, int_dtype=None)` be satisfactory? That way, > there are no bounds by default (call it python integer bounds), but the > user can specify a limited type at will. An alternative would be something > like `is_integer(a, bits=None, unsigned=False)`. This would have the > advantage of testing against hypothetical types, which might be useful > sometimes, or just annoying. I could always allow a two-element tuple in as > an argument to the first version. > > While I completely agree with the idea behind adding this test, one big > question remains: can I add arbirary arguments to a ufunc? > > - Joe > > On Sat, Jan 1, 2022 at 5:41 AM Stefano Miccoli <stefano.micc...@polimi.it> > wrote: > >> 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> 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(dtype).max" for some particular >>> dtype. i.e. "Can I losslessly set these values into the array I already >>> have?" >>> >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list -- numpy-discussion@python.org >> To unsubscribe send an email to numpy-discussion-le...@python.org >> https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ >> Member address: jfoxrabinov...@gmail.com >> >
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