Refactor to take advantage of Pydantic validation #55
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…metadata attributes. natcap#29
phargogh
approved these changes
Nov 27, 2024
| try: | ||
| eband = existing_resource.get_band_description(band.index) | ||
| # TODO: rewrite this as __eq__ of BandSchema? | ||
| if (band.numpy_type, band.gdal_type, band.nodata) == ( | ||
| eband.numpy_type, eband.gdal_type, eband.nodata): | ||
| band = dataclasses.replace(band, **eband.__dict__) | ||
| updated_dict = band.model_dump() | eband.model_dump() |
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Fun! I was not aware that you could bitwise-or two dicts to take their union.
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This PR refactors the data models to inherit from Pydantic's
BaseModel.Pydantic is useful for type-checking (aka "validation"). And using
BaseModelinstead ofpydantic.dataclassesneatly works around the problem described in #54We're now validating metadata documents on
Resource.__init__, in other words, when a new dataset is described or an existing document is loaded from a file. And we also validate fields on assignment.One change to the metadata specification:
schemais a reserved attribute by Pydantic. So I renamed ourschemaattribute todata_model, and added a clause to do that migration when an existing yaml doc is loaded, if needed. We should do some future planning around metadata schema versioning.Fixes #54
Fixes #29