feat(graphs): add custom area weightings

[efare04]

Description

This PR adds a class which allows users to load custom area weightings from a variable in an Anemoi dataset. The behaviour of AnemoiDatasetVariableWeights is based on that of BaseAnemoiDatasetVariable, but isn't inherited directly from that class as that would set dtype = bool.

What problem does this change solve?

None of the existing classes to calculate area weightings are suitable for the tripolar ORCA025 grid, but we already have a file containing the cell areas. This information could be read directly from a variable in an Anemoi dataset into a node attribute, rather than calculating the area weightings within anemoi-graphs.

What issue or task does this change relate to?

#925

Additional notes

I've put this in area_weights.py, as we're intending to use it for area weightings, but this class could be used to create a custom node attribute for other purposes. Perhaps it would be worth renaming it (e.g. to AnemoiDatasetVariableAttribute) and moving it elsewhere, as its application isn't limited to area weights?

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📚 Documentation preview 📚: https://anemoi-training--926.org.readthedocs.build/en/926/

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📚 Documentation preview 📚: https://anemoi-graphs--926.org.readthedocs.build/en/926/

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📚 Documentation preview 📚: https://anemoi-models--926.org.readthedocs.build/en/926/

[JPXKQX]

Hi @efare04, this PR looks self-contained and provides some new functionality. Thanks for your contribution! I made a minor comment, but I'm happy to approve!

[efare04]

Hi @JPXKQX, thanks for the review! I've updated the docstring to remove the private method.

Just to quickly follow up on my original comment, I'm happy to rename and relocate AnemoiDatasetVariableWeights if you think a generic name would be more appropriate, given that it could be used for more than just area weights. No problem if you'd prefer to keep it as-is - just wanted to double-check before this gets merged.

[JPXKQX]

I think it's fine for now. If we identify any new use cases that reuse this functionality, we could consider abstracting it.