On 07/06/2011 02:38 PM, Christopher Jordan-Squire wrote:
On Wed, Jul 6, 2011 at 11:38 AM, Christopher Barker
<[email protected] <mailto:[email protected]>> wrote:
Christopher Jordan-Squire wrote:
> If we follow those rules for IGNORE for all computations, we
sometimes
> get some weird output. For example:
> [ [1, 2], [3, 4] ] * [ IGNORE, 7] = [ 15, 31 ]. (Where * is matrix
> multiply and not * with broadcasting.) Or should that sort of
operation
> through an error?
That should throw an error -- matrix computation is heavily influenced
by the shape and size of matrices, so I think IGNORES really don't
make
sense there.
If the IGNORES don't make sense in basic numpy computations then I'm
kinda confused why they'd be included at the numpy core level.
Nathaniel Smith wrote:
> It's exactly this transparency that worries Matthew and me -- we
feel
> that the alterNEP preserves it, and the NEP attempts to erase it. In
> the NEP, there are two totally different underlying data structures,
> but this difference is blurred at the Python level. The idea is that
> you shouldn't have to think about which you have, but if you
work with
> C/Fortran, then of course you do have to be constantly aware of the
> underlying implementation anyway.
I don't think this bothers me -- I think it's analogous to things in
numpy like Fortran order and non-contiguous arrays -- you can
ignore all
that when working in pure python when performance isn't critical, but
you need a deeper understanding if you want to work with the data in C
or Fortran or to tune performance in python.
So as long as there is an API to query and control how things work, I
like that it's hidden from simple python code.
-Chris
I'm similarly not too concerned about it. Performance seems finicky
when you're dealing with missing data, since a lot of arrays will
likely have to be copied over to other arrays containing only complete
data before being handed over to BLAS. My primary concern is that the
np.NA stuff 'just works'. Especially since I've never run into use
cases in statistics where the difference between IGNORE and NA mattered.
Exactly!
I have not been able to think of an real example where that difference
matters as the calculations are only on the 'valid' (ie non-missing and
non-masked) values.
Bruce
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