On Tue, Feb 18, 2014 at 9:42 AM, Robert Kern <robert.k...@gmail.com> wrote: > On Tue, Feb 18, 2014 at 2:37 PM, <josef.p...@gmail.com> wrote: >> On Tue, Feb 18, 2014 at 8:53 AM, Robert Kern <robert.k...@gmail.com> wrote: >>> On Tue, Feb 18, 2014 at 1:46 PM, Nathaniel Smith <n...@pobox.com> wrote: >>>> On 18 Feb 2014 07:07, "Robert Kern" <robert.k...@gmail.com> wrote: >>>>> >>>>> On Tue, Feb 18, 2014 at 12:00 PM, Nathaniel Smith <n...@pobox.com> wrote: >>>>> > Perhaps integer power should raise an error on negative powers? That way >>>>> > people will at least be directed to use arr ** -1.0 instead of silently >>>>> > getting nonsense from arr ** -1. >>>>> >>>>> Controllable by np.seterr(invalid=...)? I could get behind that. >>>> >>>> I'm not sure the np.seterr part would work or be a good idea, given that we >>>> have no way to return or propagate NaN... I vote for just an unconditional >>>> error. >>> >>> <shrug> We issue configurable warning/error/ignore behavior for >>> integer 0/0 through this mechanism too without any NaNs. However, >>> that's `divide` and not `invalid`. Your point is taken that `invalid` >>> usually implies that a `NaN` is generated, though I don't think this >>> is ever stated anywhere. I just suggested `invalid` as that is usually >>> what we use for function domain violations. >> >> I thought 0/0 = 0 has been removed a few versions ago. Does numpy >> still have silent casting of nan to 0 in ints. > > There is no casting involved.
numpy still creates a return dtype, that holds the wrong result. > >> I thought invalid and divide error/warnings are for floating point >> when we want to signal that the outcome is nan or inf, not that we are >> casting and return a result that is just wrong. > > No, they are also for some integer operations without going through a > floating point intermediate. good that I don't like integers, I didn't see a bug because of this in years. at least for division, we can go into the __future__ with python 3.3: >>> np.array(0) ** np.array(-1) -2147483648 >>> np.divide(np.array(0), np.array(0)) nan >>> np.array(0) / np.array(0) nan Josef > > -- > Robert Kern > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
