issue_comments

@shoyer I favour option 2. as a stable solution, possibly with all arguments moved to keyword-only ones: * users don't need to import and additional class * users get the argument completion onopen_dataset * xarray does validation and mangling in the class and passes to the backends only the non default values

I'm for using the words decode/decoding but I'm against the use of CF.

What backend will do is map the on-disk representation of the data (typically optimised for space) to the in-memory representation preferred by xarray (typically optimised of easy of use). This mapping is especially tricky for time-like variables.

CF is one possible way to specify the map, but it is not the only one. Both the GRIB format and all the spatial formats supported by GDAL/rasterio can encode rich data and decoding has (typically) nothing to do with the CF conventions.

My preferred meaning for the decode_-options is: * True: the backend attempts to map the data to the xarray natural data types (np.datetime64, np.float with mask and scale) * False: the backend attempts to return a representation of the data as close as possible to the on-disk one

Typically when a user asks the backend not to decode they intend to insepct the content of the data file to understand why something is not mapping in the expected way.

As an example: in the case of GRIB time-like values are represented as integers like 20190101, but cfgrib at the moment is forced to convert them into a fake CF representation before passing them to xarray, and when using decode_times=False a GRIB user is presented with something that has nothing to do with the on-disk representation.

I see this as complementary to @alexamici's proposal in https://github.com/pydata/xarray/issues/4309#issuecomment-697952300, which I also like. I guess the main difference is moving decode_cf into the explicit signature of open_dataset, rather than leaving it in **kwargs.

Auto-completion is one reason to prefer a class, but enforced error checking and consistency between backends in the data model are also good reasons. In particular, it is important that users get an error if they mis-spell an argument name, e.g., open_dataset(path, decode_times=False) vs open_dataset(path, decode_time=False). We can definitely achieve this with putting decoding options into a dict, too, but we would need to be carefully to always validate the set of dict keys.

I guess cfgrib is an example of a backend with its own CF decoding options? This is indeed a tricky design question. I don't know if it's possible to make xarray.open_dataset() directly extensible in this way -- this could still be a reason for a user to use a backend-specific cfgrib.open_dataset() function.

I agree that we should add CF to the names. Can we use Decoders instead of DecodingOptions? So decode_cf and CFDecoders?

works for me!

Totally in favour of this. Option 2 does seem like a good intermediate step.

This proposal would make error handling easier (https://github.com/pydata/xarray/issues/3020)

I agree that we should add CF to the names. Can we use Decoders instead of DecodingOptions? So decode_cf and CFDecoders?