Add support for inverting transformation operations (mapping back to structure of source datasets)

[leifdenby]

Describe your changes

This PR adds support for taking a zarr dataset produced with mllam-data-prep and inverting the transformations applied to create it. This is implemented in mllam_data_prep.recreate_inputs(ds: xr.Dataset) which returns a dictionary of datasets Dict[str,xr.Dataset] where the is the name of each input defined in the config that was used to create the dataset.

The main motivation for implementing this functionality is to be able to easily take a forecast produced with neural-lam as an zarr dataset and invert back to the data-structure (individual variables on a regular grid) so that these can be used in downstream applications.

Below is an example that shows inverting where the DANRA training dataset included with mllam-data-prep and we just select the danra_heigth_levels to write to zarr:

Added parse to support parsing of the format string used for creating the coordinate values during stacking of variables and coordinates.

Issue Link

#85 should be merged first since it introduces a better handling of MultiIndex resulting from stacking coordinates, and using this better handling makes inverting the operation a lot simpler to handle.

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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