Hi, On Sun, Jan 6, 2013 at 5:53 PM, Charles R Harris <charlesr.har...@gmail.com> wrote: > > > On Sat, Jan 5, 2013 at 2:31 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >> On 5 Jan 2013 12:16, "Matthew Brett" <matthew.br...@gmail.com> wrote: >> > >> > 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? >> >> Numpy has 3 partially overlapping concepts: >> >> A) scalars (what Travis calls "array scalars"): Things like "float64", >> "int32". These are ordinary Python classes; usually when you subscript >> an array, what you get back is an instance of one of these classes: >> >> In [1]: a = np.array([1, 2, 3]) >> >> In [2]: a[0] >> Out[2]: 1 >> >> In [3]: type(a[0]) >> Out[3]: numpy.int64 >> >> Note that even though they are called "array scalars", they have >> nothing to do with the actual ndarray type -- they are totally >> separate objects. >> >> B) dtypes: These are instances of class np.dtype. For every scalar >> type, there is a corresponding dtype object; plus you can create new >> dtype objects for things like record arrays (which correspond to >> scalars of type "np.void"; I don't really understand how void scalars >> work in detail): > > > While thinking about dtypes I started a post proposing that *all* arrays be > considered as special cases of void arrays. A void array is basically a > memory indexing construct combined with a view. > > <snip>
I'd be really interested to read that, I'm sure others would too, Cheers, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
