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Hi Russ, Sorry for the delays in replying as I'm catching up on e-mail after vacation. I don't think a rank-0 array datatype would be a really good change. Perhaps changing the dataspace (which can have a rank of 0) for the attribute would be more appropriate? Quincey > We're developing an alternative straw-person draft of mapping netCDF > to HDF5, at least for the first deliverable of netCDF-3 on HDF5. > > Currently, we're thinking of storing the netCDF dimension IDs for a > netCDF variable in an HDF5 array attribute, _ncdimids, associated with > the HDF5 dataset corresponding to the variable. This works fine for > array variables, but scalar (rank 0) netCDF variables have no > associated dimension IDs. > > Various ways to encode this in HDF5 include: > > - no _ncdimids attribute for scalar variables > > - an _ncdimids attribute that is not an array > > - a rank-0 _ndimids array attribute. In this case, the rank of > _ncdimids would always correspond to the rank of the associated > variable, whether scalar or dimensioned. > > The latter is not currently possible in HDF5, but would be if rank-0 > arrays were supported. > > I don't know how disruptive it would be to permit rank-0 arrays as a > Datatype, so if it has lots of unpleasant side effects, please just > ignore this minor request. But if it's relatively easy, you might > find other uses for rank-0 arrays, for example, as something to return > to represent an empty result of a query that typically returns an > array. We've found that treating rank-k multidimensional arrays > pretty much the same for k = 0, 1, 2, ... makes some code simpler. > > --Russ >
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