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Still relevant.

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This is an ancient issue, but still - wondering if anyone here managed to hack together some workarounds?

I just got bit with this as well. I was basically using tuples of indices as coordinates in order to implement a multidimensional sparse array .

My workaround is to use plain dimension index_dim to index the points in the N-dimensional space that I actually populate, and to have several coordinates (say X,Y) that all have index_dim as their only dimension. It's easy enough to see what the coordinates are once you select a value along index_dim, but I have to go outside xarray to locate a populated point based on it's X,Y-coordinates, because I can't slice along those arrays as (A) they aren't aliased to a dimension (B) they have non-unique values.

I've come up with an ugly method for selecting by tuples of X,Y-coordinates:

pairs = zip(x_wanted,y_wanted)

pair2index = {(dataset.x[i].item(), dataset.y[i].item()):i for i in dataset.index_dim.data}

try:

     found_indices = [pair2index[p] for p in pairs]

     found = dataset.isel(index_dim=found_indices)

except KeyError:

     print "Coordinate {} not found in dataset.".format(p)

     raise

I'd also like to see better support for compound types, writing them for starters. I'll collect some information here:

• In the code @tfurf linked to (_nc4_values_and_dtype), an elif needs to be added to catch structured dtypes. I think they have kind == 'V'.

• dtype.builtin can be used to detect whether we are indeed dealing with a structured type. Namely dtype.builtin must be 0.

• The structured type must fist be added to the netCDF4.Dataset using its method createCompoundType. This must be done recursively, with the deepest levels first.

• The netCDF variable is created in prepare_variable, which calls _nc4_values_and_dtype. There, via self.ds we also have access to the netCDF4 Dataset to be used for the creation of the as mentioned above. However, is self.ds really the Dataset, or some NetCDF4.Group? In any case _nc4_values_and_dtype and its use in prepare_variable needs to be refactored, because we need access to the underlying netCDF4 Dataset.

Is there anything I've missed? Can someone shed light on whether self.ds in prepare_variable can be assumed to the underlying netCDF4 Dataset?