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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 <43374850+SimonKamuk@users.noreply.github.com> Co-authored-by: joeloskarsson <joel.oskarsson@liu.se> Co-authored-by: Leif Denby <lcd@dmi.dk> Co-authored-by: Joel Oskarsson <joel.oskarsson@outlook.com> Co-authored-by: Simon Adamov <simon.adamov@meteoswiss.ch> Co-authored-by: Simon Adamov <kode@mailbox.org> Co-authored-by: Kasper Hintz <kah@dmi.dk>
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