Travis, after reading all the post on this thread, my comments Fist of all, I'm definitelly +1 on your suggestion. Below my rationale.
* I believe numpy scalars should provide all possible features needed to smooth the difference between mutable, indexable 0-d arrays and inmutable, non-indexable builtin Python numeric types. * Given that in the context of generic multi-dimensional array processing a 0-d array are more natural and useful concept that a Python 'int' and 'float', I really think that numpy scalars shoud follow as much as possible the behavior of 0-d arrays (of course, retaining inmutability). * Numpy scalars already have (thanks for that!) a very, very similar API to ndarrays. You can as for 'size', 'shape', etc ( BTW, why scalar.fill(x) does not generate any error????). Why do not add indexing as well? * However, I'm not sure about the proposal of supporting len(), I'm -0 on this point. Anyway, if this is added, then 0-d arrays should also have to support it. And then... len(scalar) or len(0-d-array) is going to return 0 (zero)? Regards. On 2/21/08, Travis E. Oliphant <[EMAIL PROTECTED]> wrote: > In writing some generic code, I've encountered situations where it would > reduce code complexity to allow NumPy scalars to be "indexed" in the > same number of limited ways, that 0-d arrays support. > > For example, 0-d arrays can be indexed with > > * Boolean masks > * Ellipses x[...] and x[..., newaxis] > * Empty tuple x[()] > > I think that numpy scalars should also be indexable in these particular > cases as well (read-only of course, i.e. no setting of the value would > be possible). > > This is an easy change to implement, and I don't think it would cause > any backward compatibility issues. > > Any opinions from the list? > > > Best regards, > > -Travis O. > > > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > -- Lisandro Dalcín --------------- Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC) Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PTLC - Güemes 3450, (3000) Santa Fe, Argentina Tel/Fax: +54-(0)342-451.1594 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
