They're unpacked to full floats on element access and packed back from
full floats on mutable access, which makes this all very nice and
transparent. Yay Python!
That's it for now, I'll postpone half-float and matrix types for later
when these are actually needed, as it needs extra testing for the
aligned variants too.
To not have to duplicate these for each and every case, enlarging the
surface for potential bugs. Also changing the signatures to number +
identifier, instead of identifier repeated number of times. Means the
compiler won't be able to deduplicate / overlap the literals in the
binary, but is much more maintainable.
For Numpy this causes the slices to be reported as actual typed Numpy
arrays instead of typeless tuples, which is good I guess? Not sure why
that wasn't the case before, or what is the difference, and how it will
behave for sparse types such as aligned matrices.
It was casting in the wrong direction, causing an unbound type to be
returned and also accessing the data totally wrong. Should have tested
this properly in the first place.
Otherwise people wouldn't be able to conveniently use os.path, which is
an undesirable pain point. And do this for all currently exposed APIs,
not just the AbstractImporter that caused a problem.
This reverts commit 28497ec2ca.
In concrete types. Yes. Well, some of them for now, I still need to
sleep on normalized and matrix types, how it makes the most sense to
expose those. Oh, and array attributes as well.
Only the double ones, exposed as floats, because the extra ALU required
by doubles is negligible to function call overhead. It'll be different
for non-scalar types, but here I use this.
Vladimír Vondruš