[Numpy-discussion] in the NA discussion, what can we agree on?
Gary Strangman
strang@nmr.mgh.harvard....
Fri Nov 4 15:38:05 CDT 2011
> On Fri, Nov 4, 2011 at 1:03 PM, Gary Strangman <strang@nmr.mgh.harvard.edu>
> wrote:
>
> 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
>
>
> 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.)
>
>
>
> I agree that it would be ideal if the default were to skip IGNORED values, but
> that behavior seems inconsistent with its propagation properties (such as when
> adding arrays with IGNORED values). To illustrate, when we did "x+2", we were
> stating that:
>
> IGNORED(2) + 2 == IGNORED(4)
>
> which means that we propagated the IGNORED value. If we were to skip them by
> default, then we'd have:
>
> IGNORED(2) + 2 == 2
>
> To be consistent, then it seems we also should have had:
>
> >>> x + 2
> [3, 2, 5]
>
> which I think we can agree is not so desirable. What this seems to come down to
> is that we tend to want different behavior when we are doing reductions, and that
> for IGNORED data, we want it to propagate in every situation except for a
> reduction (where we want to skip over it).
>
> I don't know if there is a well-defined way to distinguish reductions from the
> other operations. Would it hold for generalized ufuncs? Would it hold for other
> functions which might return arrays instead of scalars?
Ahhh, yes. That clearly explains the issue hung-up in my mind, and also
clarifies what I was getting at with the elementwise vs. reduction
distinction I made earlier today. Maybe this is a pickle in a jar with no
lid. I'll have to think about it ...
-best
Gary
The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.
More information about the NumPy-Discussion
mailing list