[Numpy-discussion] np.nan and ``is``
Fri Sep 19 13:39:32 CDT 2008
On Sep 19, 2008, at 7:52 PM, Christopher Barker wrote:
> I don't know the interning rules, but I do know that you should never
> count on them, then may not be consistent between implementations, or
> even different runs.
There are a few things that Python-the-language guarantees are singleton
objects which can be compared correctly with "is". Those are:
True, False, None
Otherwise there is no guarantee that two objects of a given type
which are "equal" in some sense of the word, are actually the same
As Chris pointed out, the C implementation does (as a performance
matter) have additional singletons. For example, the integers between
-5 to 257 are also singletons
#define NSMALLPOSINTS 257
#define NSMALLNEGINTS 5
/* References to small integers are saved in this array so that they
can be shared.
The integers that are saved are those in the range
-NSMALLNEGINTS (inclusive) to NSMALLPOSINTS (not inclusive).
static PyIntObject *small_ints[NSMALLNEGINTS + NSMALLPOSINTS];
This used to be -1 to 100 but some testing showed it was better
to extend the range somewhat.
There was also some performance testing about special-casing 0.0
and +/- 1.0 but I think it showed the results weren't worthwhile.
So, back to NaN. There's no guarantee NaN is a singleton
object, so testing with "is" almost certainly is wrong.
In fact, at the bit-level there are multiple NaNs. A
NaN (according to Wikipedia) fits the following bit pattern.
NaN: x11111111axxxxxxxxxxxxxxxxxxxxxx. x = undefined. If a = 1,
it is a quiet NaN, otherwise it is a signalling NaN.
are all NaN values.
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