So what type should uint64 + int64 return? On Apr 12, 2016 7:17 PM, "Antony Lee" <antony....@berkeley.edu> wrote:
> This kind of issue (see also https://github.com/numpy/numpy/issues/3511) > has become more annoying now that indexing requires integers (indexing with > a float raises a VisibleDeprecationWarning). The argument "dividing an > uint by an int may give a result that does not fit in an uint nor in an > int" does not sound very convincing to me, after all even adding two > (sized) ints may give a result that does not fit in the same size, but > numpy does not upcast everything there: > > In [17]: np.int32(2**31 - 1) + np.int32(2**31 - 1) > Out[17]: -2 > > In [18]: type(np.int32(2**31 - 1) + np.int32(2**31 - 1)) > Out[18]: numpy.int32 > > > I'd think that overflowing operations should just overflow (and possibly > raise a warning via the seterr mechanism), but their possibility should not > be an argument for modifying the output type. > > Antony > > 2016-04-12 17:57 GMT-07:00 T J <tjhn...@gmail.com>: > >> Thanks Eric. >> >> Also relevant: https://github.com/numba/numba/issues/909 >> >> Looks like Numba has found a way to avoid this edge case. >> >> >> >> On Monday, April 4, 2016, Eric Firing <efir...@hawaii.edu> wrote: >> >>> On 2016/04/04 9:23 AM, T J wrote: >>> >>>> I'm on NumPy 1.10.4 (mkl). >>>> >>>> >>> np.uint(3) // 2 # 1.0 >>>> >>> 3 // 2 # 1 >>>> >>>> Is this behavior expected? It's certainly not desired from my >>>> perspective. If this is not a bug, could someone explain the rationale >>>> to me. >>>> >>>> Thanks. >>>> >>> >>> I agree that it's almost always undesirable; one would reasonably expect >>> some sort of int. Here's what I think is going on: >>> >>> The odd behavior occurs only with np.uint, which is np.uint64, and when >>> the denominator is a signed int. The problem is that if the denominator is >>> negative, the result will be negative, so it can't have the same type as >>> the first numerator. Furthermore, if the denominator is -1, the result >>> will be minus the numerator, and that can't be represented by np.uint or >>> np.int. Therefore the result is returned as np.float64. The promotion >>> rules are based on what *could* happen in an operation, not on what *is* >>> happening in a given instance. >>> >>> Eric >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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