[Numpy-discussion] in the NA discussion, what can we agree on?
T J
tjhnson@gmail....
Fri Nov 4 22:33:34 CDT 2011
On Fri, Nov 4, 2011 at 8:03 PM, Nathaniel Smith <njs@pobox.com> wrote:
> On Fri, Nov 4, 2011 at 7:43 PM, T J <tjhnson@gmail.com> wrote:
> > On Fri, Nov 4, 2011 at 6:31 PM, Pauli Virtanen <pav@iki.fi> wrote:
> >> An acid test for proposed rules: given two arrays `a` and `b`,
> >>
> >> a = [1, 2, IGNORED(3), IGNORED(4)]
> >> b = [10, IGNORED(20), 30, IGNORED(40)]
> [...]
> > (A1) Does unmask(a+b) == unmask(b + a) ?
> >
> > Yes. They both equal:
> >
> > unmask([11, IGNORED(22), IGNORED(33), IGNORED(44)])
> > =
> > [11, 22, 33, 44]
>
> Again, I really don't think you're going to be able to sell an API where
> [2] + [IGNORED(20)] == [IGNORED(22)]
> I mean, it's not me you have to convince, it's Gary, Pierre, maybe
> Benjamin, Lluís, etc. So I could be wrong. But you might want to
> figure that out first before making plans based on this...
>
But this is how np.ma currently does it, except that it doesn't compute the
payload---it just calls it IGNORED.
And it seems that this generalizes the way people want it to:
>>> z = [2, 4] + [IGNORED(20), 3]
>>> z
[IGNORED(24), 7]
>>> z.sum(skip_ignored=True) # True could be the default
7
>>> z.sum(skip_ignored=False)
IGNORED(31)
I guess I am confused because it seems that you implicitly used this same
rule here:
Say we have
>>> a = np.array([1, IGNORED(2), 3])
>>> b = np.array([10, 20, 30])
(Here's I'm using IGNORED(2) to mean a value that is currently
ignored, but if you unmasked it it would have the value 2.)
Then we have:
# non-propagating **or** propagating, doesn't matter:
>>> a + 2
[3, IGNORED(2), 5]
That is, element-wise, you had to have done:
IGNORED(2) + 2 --> IGNORED(2).
I said it should be equal to IGNORED(4), but the result is still some form
of ignore. Sorry if I am missing the bigger picture at this point....its
late and a Fri.
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