[Numpy-discussion] Rank-0 arrays - reprise
Matthew Brett
matthew.brett@gmail....
Sat Jan 5 06:15:55 CST 2013
Hi,
Following on from Nathaniel's explorations of the scalar - array
casting rules, some resources on rank-0 arrays.
The discussion that Nathaniel tracked down on "rank-0 arrays"; it also
makes reference to casting. The rank-0 arrays seem to have been one
way of solving the problem of maintaining array dtypes other than bool
/ float / int:
http://mail.scipy.org/pipermail/numpy-discussion/2002-September/001612.html
Quoting from an email from Travis in that thread, replying to an email
from Tim Hochberg:
http://mail.scipy.org/pipermail/numpy-discussion/2002-September/001647.html
<quote>
> Frankly, I have no idea what the implimentation details would be, but
> could we get rid of rank-0 arrays altogether? I have always simply found
> them strange and confusing... What are they really neccesary for
> (besides holding scalar values of different precision that standard
> Pyton scalars)?
With new coercion rules this becomes a possibility. Arguments against it
are that special rank-0 arrays behave as more consistent numbers with the
rest of Numeric than Python scalars. In other words they have a length
and a shape and one can right N-dimensional code that works the same even
when the result is a scalar.
Another advantage of having a Numeric scalar is that we can control the
behavior of floating point operations better.
e.g.
if only Python scalars were available and sum(a) returned 0, then
1 / sum(a) would behave as Python behaves (always raises error).
while with our own scalars
1 / sum(a) could potentially behave however the user wanted.
</quote>
There seemed then to be some impetus to remove rank-0 arrays and
replace them with Python scalar types with the various numpy
precisions :
http://mail.scipy.org/pipermail/numpy-discussion/2002-September/013983.html
Travis' recent email hints at something that seems similar, but I
don't understand what he means:
http://mail.scipy.org/pipermail/numpy-discussion/2012-December/064795.html
<quote>
Don't create array-scalars. Instead, make the data-type object a
meta-type object whose instances are the items returned from NumPy
arrays. There is no need for a separate array-scalar object and in
fact it's confusing to the type-system. I understand that now. I
did not understand that 5 years ago.
</quote>
Travis - can you expand?
I remember rank-0 arrays being confusing in that I sometimes get a
python scalar and sometimes a numpy scalar, and I may want a python
scalar, and have to special-case the rank-0 array, but I don't
remember precisely why I needed the python scalar. Any other comments
/ records of rank-0 arrays being confusing?
Best,
Matthew
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