issue_comments

The intent with decoders is handle the invertible transformations between a raw file on disk and xarray's data model. So we will definitely need some transformations that act on a collection of variables (e.g., to decode coordinates vs data variables), but not in the form of a Dataset yet. These decoders/encoders will probably take/return three arguments: variables (an OrderedDict of xarray.Variable objects with both coordinates and data), dims (dict of dimension names to sizes) and (global) attrs.

@shoyer I'm writing a small helper that looks very much like a decoding filter, but I'm not sure it fits the VariableCoder model.

The intent is to use CF attributes to identify coordinate variables, for example {'units': 'degrees_north'} identify a 'latitude' coordinate, and apply a set of transformations to harmonise datasets from different providers: I rename the coordinates to a known name, for example 'lat' for latitude, and convert to a known unit, for example to 'Pa' for pressure.

To my understanding some of the transformations need to be at a DataArray / Dataset level, not Variable one, because I need to manipulate the names of the coordinate variables and keep the name of the dimensions in sync, for example I'm currently using DataArray.rename a lot, but I also have an example of using DataArray.swap_dims. A simplified example is:

python def harmonise_latitude(dataset): # (xr.Dataset) -> xr.Dataset for coord in dataset.coords.values(): if coord.attrs.get('units') == 'degrees_north': # update the other CF attributes that reference coord.name, for example 'coordinates'. return dataset.rename({coord.name: 'lat'}) return dataset

Would you be open to add support for Dataset -> Dataset decoding filters similar to the Variable -> Variable?

I'm going to merge this shortly if there are no objections.