[Numpy-discussion] Rank-0 arrays - reprise
Sun Jan 6 12:16:32 CST 2013
On Sun, Jan 6, 2013 at 5:53 PM, Charles R Harris
> On Sat, Jan 5, 2013 at 2:31 PM, Nathaniel Smith <email@example.com> wrote:
>> On 5 Jan 2013 12:16, "Matthew Brett" <firstname.lastname@example.org> 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 : a = np.array([1, 2, 3])
>> In : a
>> Out: 1
>> In : type(a)
>> Out: 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.
I'd be really interested to read that, I'm sure others would too,
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