El dl 19 de 03 del 2007 a les 15:59 -0400, en/na Matthew Koichi Grimes va escriure: > Francesc Altet wrote: > > with a > > rank-0 'recarr', 'recarr.x' should return a rank-0 array (for > > consistency), but it doesn't: > > > > In [74]:recarr=numpy.rec.array((1.0, 0, 3), dtype) > > In [75]:recarr.x > > Out[75]:1.0 > > In [76]:type(recarr.x) > > Out[76]:<type 'numpy.float64'> > > > > While I find this inconsistent, I'm not sure whether it should be be > > fixed or not because the boundaries between rank-0 and scalar objects > > are always a bit fuzzy. Travis can probably bring more light here. > > > Here's my case for "yes, it's a problem that should be fixed (please?)": > In my particular project, this inconsistency becomes a pervasive problem > when writing vectorized functions that are supposed to accept and return > tensors of various ranks. To accomodate the inconsistency, one has to > take what would have been a simple statement of the form: > > <snip> > output.x[...] = blah > </snip> > > and replace it with a bunch of boolean switches: > > <snip> > if output.ndim == 0: > output = output.reshape([1]) > outputWas0dim = True > else: > outputWas0dim = False > > output.x[...] = blah > > if outputWas0dim: > output = output.reshape([]) > > return output > </snip>
Yeah, I agree. In fact, one of the main reasons to keep rank-0 arrays around is to provide generality enough to address to these kind of problems in an elegant way. Cheers, -- Francesc Altet | Be careful about using the following code -- Carabos Coop. V. | I've only proven that it works, www.carabos.com | I haven't tested it. -- Donald Knuth _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
