On Tue, Sep 29, 2015 at 11:25 AM, Anne Archibald <archibald@astron.nl> wrote:
IEEE 754 has signum(NaN)->NaN. So does np.sign on floating-point arrays. Why should it be different for object arrays?
Anne
P.S. If you want exceptions when NaNs appear, that's what np.seterr is for. -A
I also think NaN should be treated the same way as floating point numbers (whatever that is). Otherwise it is difficult to remember when nan is essentially a float dtype or another dtype. (given that float is the smallest dtype that can hold a nan) Josef
On Tue, Sep 29, 2015 at 5:18 PM Freddy Rietdijk <freddyrietdijk@fridh.nl> wrote:
I wouldn't know of any valid output when applying the sign function to NaN. Therefore, I think it is correct to return a ValueError. Furthermore, I would prefer such an error over just returning NaN since it helps you locating where NaN is generated.
On Tue, Sep 29, 2015 at 5:13 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
Hi All,
Due to a recent commit, Numpy master now raises an error when applying the sign function to an object array containing NaN. Other options may be preferable, returning NaN for instance, so I would like to open the topic for discussion on the list.
Thoughts?
Chuck
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