On Sep 21, 2014, at 5:19 PM, Eric Firing <efir...@hawaii.edu> wrote:
> I think what you are missing is that the standard Python idiom for this
> use case is "if self._some_array is None:". This will continue to work,
> regardless of whether the object being checked is an ndarray or any
> other Python object.
That's an alternative, but I think it's a subtle distinction that will be lost
on many users. I still think that this is something that can easily trip up
many people; it's not clear from looking at the code that this is the behavior;
it's "hidden". At the very least, I strongly suggest that the warning point
this out, e.g.
"FutureWarning: comparison to `None` will result in an elementwise object
comparison in the future; use 'value is None' as an alternative."
Assume:
a = np.array([1, 2, 3, 4])
b = np.array([None, None, None, None])
What is the result of "a == None"? Is it "np.array([False, False, False,
False])"?
What about the second case? Is the result of "b == None" -> np.array([True,
True, True, True])? If so, then
if (b == None):
...
will always evaluate to "True" if b is "None" or *any* Numpy array, and that's
clearly unexpected behavior.
On Sep 21, 2014, at 9:30 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> That being said, I do wonder about related situations where the lhs of the
> equal sign might be an array, or it might be a None and you are comparing
> against another numpy array. In those situations, you aren't trying to
> compare against None, you are just checking if two objects are equivalent.
Right. With this change, using "==" with numpy arrays now sometimes means "are
these equivalent" and other times "element-wise comparison". The potential for
inadvertent bugs is far greater than what convenience this redefinition of a
very basic operator might offer. Any scenario where
(a == b) != (b == a)
is asking for trouble.
Cheers,
Demitri
_________________________________________
Demitri Muna
Department of Astronomy
An Ohio State University
http://trillianverse.org
http://scicoder.org
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