issue_comments: 234472262

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https://github.com/pydata/xarray/issues/432#issuecomment-234472262 https://api.github.com/repos/pydata/xarray/issues/432 234472262 MDEyOklzc3VlQ29tbWVudDIzNDQ3MjI2Mg== 1217238 2016-07-22T07:18:36Z 2016-07-22T07:18:36Z MEMBER

Yes, I think this is still of interest, though of course the devil is in the details. 1. Do we make this look closer to the xarray.Dataset data model (coords, data_vars, attrs, dims) or netCDF (variables, attributes, dimensions)? 2. If the later -- do we go so far as to encode all data types (e.g., dates and times) according to CF conventions? 3. Do we save data in the form of nested lists or in a numpy array? 4. Do we output directly output to JSON or just a dict? 5. Do we include dims or dimensions (providing dimension sizes) as a top level field/check? 6. How does the format differ for xarray.DataArray? Do we even bother with DataArray?

My inclinations: 1. Mirror xarray.Dataset 2. NA 3. Use nested lists of native Python types, e.g., generated with numpy's .tolist() method. 4. Just a dict, to preserve flexibility for different serialization formats. 5. Yes, sanity checks are important. 6. Probably not a bad idea to cover xarray.DataArray, too, but the format should be clearly distinct (not reusing variables as a top level key).

 
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