Travis E. Oliphant wrote: > Hi everybody, > > 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. > > As for me I would be glad to see same behavior for numbers as for arrays at all, like it's implemented in MATLAB, i.e. >>a=80 >>disp(a) 80 >>disp(a(1,1)) 80 ok, for numpy having at least possibility to use a=array(80) print a[0] would be very convenient, now atleast_1d(a) is required very often, and sometimes errors occur only some times later, already during execution of user-installed code, when user usually pass several-variables arrays and some time later suddenly single-variable array have been encountered. I guess it could be implemented via a simple check: if user calls for a[0] and a is array of shape () (i.e. like a=array(80)) then return a[()] D.
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