[Numpy-discussion] "False" and "True" not singletons?

[Christopher Barker]

Hi all,

I've just discovered that "False" is not a singleton:

 >>> import numpy as N

 >>> f = N.all((1,2,0))
 >>> print f
False
 >>> id(f)
17316364
 >>> f is False
False
 >>> id(False)
3294768


Should it be?


This arose for me in some tests I'm using that check if a result is False:

self.failUnless ( (self.B1 == self.B3) is False )

I'm doing it this way, because I want to make sure that the __eq__ 
method really is returning "True" or "False", rather than a value that 
happens to evaluate to true or false, like 1, or an empty list, or whatever.

This is interesting to me because back when Python first introduced 
Booleans, I had thought they should be kept pure and not be subclasses 
of integers, and, in fact, "if" should only except boolean values. 
However this opinion was really a matter of my sense of purity, and this 
is the fist time I've run into a case that matters.

I suppose I'm not being pythonic -- I should really only care if the 
result evaluates true or false, but I don't feel like I'm testing right 
if values can slip though that shouldn't.

It does reinforce my opinion though -- whether zero, or an empty 
sequence or string should evaluate false really is a matter of specific 
application, not universal.

Anyway, should I just give up? Or should numpy return the same "True" 
and "False" (like None), or is there another solution?

  x == False

is close, but zero still slips through, even though an empty list does not:

 >>> 0 == False
True
 >>> [] == False
False


-Chris


-- 
Christopher Barker, Ph.D.
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