[Numpy-discussion] Sign of NaN

[Charles R Harris]

On Tue, Sep 29, 2015 at 11:59 AM, Stephan Hoyer <shoyer at gmail.com> wrote:

> On Tue, Sep 29, 2015 at 8:13 AM, Charles R Harris <
> charlesr.harris at gmail.com> wrote:
>
>> 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.
>>
>
> We discussed this last month on the list and on GitHub:
> https://mail.scipy.org/pipermail/numpy-discussion/2015-August/073503.html
> https://github.com/numpy/numpy/issues/6265
> https://github.com/numpy/numpy/pull/6269/files
>
> The discussion was focused on what to do in the generic fallback case. Now
> that I think about this more, I think it makes sense to explicitly check
> for NaN in the unorderable case, and return NaN is the input is NaN. I
> would not return NaN in general from unorderable objects, though -- in
> general we should raise an error.
>
> It sounds like Allan has already fixed this in his PR, but it also would
> not be hard to add that logic to the existing code. Is this code in the
> NumPy 1.10?
>

No. NumPy 1.10 also has differing behavior between python 2 and python 3.
The reason I raise the question now is that current master has replace use
of PyObject_Compare by PyObject_RichCompare for both python 2 and 3. I
would be easy to extend it. I'm less sure of Allan's work, on a quick look
it seems more complicated.

charris at fc [~]$ python3
Python 3.4.2 (default, Jul  9 2015, 17:24:30)
[GCC 5.1.1 20150618 (Red Hat 5.1.1-4)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.sign(np.array([float('nan')]*3, np.object))
array([None, None, None], dtype=object)
>>>
charris at fc [~]$ python2
Python 2.7.10 (default, Jul  5 2015, 14:15:43)
[GCC 5.1.1 20150618 (Red Hat 5.1.1-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.sign(np.array([float('nan')]*3, np.object))
array([-1, -1, -1], dtype=object)

Chuck
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