Hey all,
I've been waiting for Mark Wiebe to arrive in Austin where he will spend
several weeks, but I also know that masked arrays will be only one of the
things he and I are hoping to make head-way on while he is in Austin.
Nevertheless, we need to make progress on the masked array discussion and if we
want to finalize the masked array implementation we will need to finish the
design.
I've caught up on most of the discussion including Mark's NEP, Nathaniel's NEP
and other writings and the very-nice mailing list discussion that included a
somewhat detailed discussion on the algebra of IGNORED. I think there are
some things still to be decided. However, I think some things are pretty
clear:
1) Masked arrays are going to be fundamental in NumPy and these should
replace most people's use of numpy.ma. The numpy.ma code will remain as a
compatibility layer
2) The reality of #1 and NumPy's general philosophy to date means that
masked arrays in NumPy should support the common use-cases of masked arrays
(including getting and setting of the mask from the Python and C-layers).
However, the semantic of what the mask implies may change from what numpy.ma
uses to having a True value meaning selected.
3) There will be missing-data dtypes in NumPy. Likely only a limited
sub-set (string, bytes, int64, int32, float32, float64, complex64, complex32,
and object) with an API that allows more to be defined if desired. These will
most likely use Mark's nice machinery for managing the calculation structure
without requiring new C-level loops to be defined.
4) I'm still not sure about whether the IGNORED concept is necessary or
not. I really like the separation that was emphasized between implementation
(masks versus bit-patterns) and operations (propagating versus
non-propagating). Pauli even created another dimension which I don't totally
grok and therefore can't remember. Pauli? Do you still feel that is a
necessary construction? But, do we need the IGNORED concept to indicate what
amounts to different default key-word arguments to functions that operate on
NumPy arrays containing missing data (however that is represented)? My
current weak view is that it is not really necessary. But, I could be
convinced otherwise.
I think the good news is that given Mark's hard-work and Nathaniel's follow-up
we are really quite far along. I would love to get Nathaniel's opinion about
what remains un-done in the current NumPy code-base. I would also appreciate
knowing (from anyone with an interest) opinions of items 1-4 above and anything
else I've left out.
Thanks,
-Travis
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