[Numpy-discussion] Rank-0 arrays - reprise

Matthew Brett matthew.brett@gmail....
Sat Jan 5 06:27:02 CST 2013


On Sat, Jan 5, 2013 at 12:15 PM, Matthew Brett <matthew.brett@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?
> 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?


Comments by Konrad Hinsen on desirable methods for rank-0 arrays, all
of which seem to have got into numpy:




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