[Matrix-SIG] Numeric Nits

[Konrad Hinsen]

> I'll concede that virtually all the operations should go ahead and return
> scalars. However, the original poster referred to '-array([5])' returning a
> scalar. I haven't been able to think of any situations where people are
> going to be using negative on a rank-0 array without expecting the result to
> also be a rank-0 array. The chance of the rank-0 array's produced this way
> getting loose and hurting people seems slim.

Fine, but wouldn't you also want addition, multiplication, etc. of
rank-0 arrays to return rank-0 arrays for consistency? And how should
the NumPy code decide if it was called with an explicit rank-0 array
or with a result of a previous operation that just happens to be a
rank-0 array and that the user wants converted to a scalar?

> Actually, I've disallowed this in JPython because it is,as you say, a
> kludge. There are however two ways of getting at the value in a rank-0
> array.
> 
> a[()]
> a[...]
> 
> I prefer the first (not just because it's shorter...).

I agree, but this is still far from obvious (try to write an
explanation for the NumPy tutorial!)

> This isn't a very good solution in many ways, but it's the best one I've
> seen so far. Using floats is enough of a pain that it would probably kill
> you in interactive mode, so I think the lack of support for floats is a
> legitimate complaint. I would love to see a better solution than this one
> though.

I'd prefer the
"special-float-type-which-is-higher-in-the-cast-hierarchy-than-double"
solution. At least the "weird" property is attached to arrays, and not
to a global variable.

Konrad.
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