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https://github.com/pydata/xarray/issues/4118#issuecomment-845845472 https://api.github.com/repos/pydata/xarray/issues/4118 845845472 MDEyOklzc3VlQ29tbWVudDg0NTg0NTQ3Mg== 6772352 2021-05-21T10:15:13Z 2021-05-21T10:17:00Z NONE

A simple comment/question:

In xarray.Dataset, why not just use the Unix-path notation into a "flat" dict model?

Actually, netCDF4 implements this Unix-like path access to groups and variables: /path/to/group/variable.

All of the hierarchical stuff (e.g., getting a sub-Dataset from a random group) and conventions (e.g., dimensions scoping rule) would then be driven by the parsing of strings only. It's all about symbolic names (like in a file system right?) and there would be not any hierarchical data in memory anymore.

My question is then: Are there some tricky points for xarray.Dataset not to go this simple way?

Some related remarks: - About the attribute access to variables: I don't really know why this exist at all since it is all about mixing unrelated namespaces: (1) the class internals and (2) the user's variables one. Mixing namespaces seems very bad to me: it makes some variable names forbidden in order to avoid any collision between the two namespaces, it usually imply unnecessarily complex code with corner cases to deal with. - About netCDF4 being a self-described format: xarray API has open_dataset(filepath), but this function is unable to read the whole file in memory without getting help from a priori file content description, i.e., the names of the groups if you follow me. Considering xarray for simple tasks like geographical-selection-cropping, it seems to ignore the self-describing nature of netCDF4 format. As far as I can understand the situation, a "flat" model could be a good way to go.

 
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