> NAN and NA apparently fall into the PdS class.
>
Here is where I think we need ot be a bit more careful. It is true that we want
NAN and MISSING to propagate, but then we additionally want to ignore it
sometimes. This is precisely why we have functions like nansum. Although
people
are well-aware of this desire, I think this thread has largely conflated the
issues when discussing "propagation".
To push this forward a bit, can I propose that IGNORE behave as: PnC
>>> x = np.array([1, 2, 3])
>>> y = np.array([10, 20, 30])
>>> ignore(x[2])
>>> x
[1, IGNORED(2), 3]
>>> x + 2
[3, IGNORED(4), 5]
>>> x + y
[11, IGNORED(22), 33]
>>> z = x.sum()
>>> z
IGNORED(6)
>>> unignore(z)
>>> z
6
>>> x.sum(skipIGNORED=True)
4
When done in this fashion, I think it is perfectly fine for "masks to be
unmasked".
In my mind, IGNORED items should be skipped by default (i.e., skipIGNORED
seems redundant ... isn't that what ignoring is all about?). Thus I might
instead suggest the opposite (default) behavior at the end:
x = np.array([1, 2, 3])
y = np.array([10, 20, 30])
ignore(x[2])
x
[1, IGNORED(2), 3]
x + 2
[3, IGNORED(4), 5]
x + y
[11, IGNORED(22), 33]
z = x.sum()
z
4
unignore(x).sum()
6
x.sum(keepIGNORED=True)
6
(Obviously all the syntax is totally up for debate.)
-best
Gary
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