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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
