[Numpy-discussion] numpy type mismatch
Fri Jun 10 23:00:55 CDT 2011
On Fri, Jun 10, 2011 at 9:55 PM, Benjamin Root <firstname.lastname@example.org> wrote:
> On Fri, Jun 10, 2011 at 8:51 PM, Keith Goodman <email@example.com>wrote:
>> On Fri, Jun 10, 2011 at 6:35 PM, Charles R Harris
>> <firstname.lastname@example.org> wrote:
>> > On Fri, Jun 10, 2011 at 5:19 PM, Olivier Delalleau <email@example.com>
>> >> But isn't it a bug if numpy.dtype('i') != numpy.dtype('l') on a 32 bit
>> >> computer where both are int32?
>> > Maybe yes, maybe no ;) They have different descriptors, so from numpy's
>> > perspective they are different, but at the hardware/precision level they
>> > the same. It's more of a decision as to what != means in this case.
>> > numpy started as Numeric with only the c types the current behavior is
>> > consistent, but that doesn't mean it shouldn't change at some point.
>> Maybe this is the same question, but are you maybe yes, maybe no on this
>> >>> type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32
>> Ben, what happens if you put an axis in there? Like
>> >>> np.sum([[1, 2, 3], [4,5,6]], axis=0).dtype == np.int32
> The same thing happens as before.
>> Just wondering if this is another different-dtype-for-different-axis case.
> No, I think Chuck has it right and that this is the result of the recent
> cleanup work for ufunc casting rules. However, I am so entirely unfamiliar
> with ufuncs that I really don't know how to investigate this. Can we get
> Mark Wiebe's opinion on this? I suspect he might recognize the problem
> right away.
Yeah, it's basically a misfeature of NumPy which is apparently inherited
from Numeric. The type resolution changes to 1.6 changed where this would
pop up, but it's always been there in the code. I suspect that eliminating
the long type, and making it an alias to either int or longlong depending on
the platform, would fix all of this on all current platforms.
> Ben Root
> NumPy-Discussion mailing list
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