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Add "datastores" to represent input data from zarr, npy, etc (#66)
Introduce new "datastores" concept where loading of input data from disk into `pytorch.Dataset` (`neural_lam.WeatherDataset`) is split into two layers: a) datastores that work with and return `xr.DataArray` objects for the whole time-series and b) `neural_lam.WeatherDataset` which consumes output from a datastore, takes care of time-sampling and produces `pytorch.Tensor`-based training samples. Currently, two kinds of datastores are implemented: 1) reading of zarr-based training datasets produced with `mllam-data-prep` and 2) reading of the npyfiles-based MEPS example dataset included with neural-lam `v0.1.0`. --------- Co-authored-by: SimonKamuk <[email protected]> Co-authored-by: joeloskarsson <[email protected]> Co-authored-by: Leif Denby <[email protected]> Co-authored-by: Joel Oskarsson <[email protected]> Co-authored-by: Simon Adamov <[email protected]> Co-authored-by: Simon Adamov <[email protected]> Co-authored-by: Kasper Hintz <[email protected]>
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