1.) Fixes an error where the magt data were labeled as alt and vice-versa for the historical baseline. The error was caused in the preprocessing of the data when converting from .asc data to GeoTIFF. This was rectified in the preprocessing Jupyter notebook. Data were re-processed and successfully ingested to the dev Rasdaman instance on Apollo.
2.) Adds a dictionary of "encodings" to the metadata that map the numerical axis identifiers used in the netCDF (0, 1, 2, etc.) that are necessary for the Rasdaman ingest. I did this because it could be a handy way for endpoints to access and query the data vs. manually reproducing some version in a luts.py or similar in an API endpoint. When you request the data, you also get what you need to parse it back into something useful or at least human-readable.
You can see those encodings via PetaScope in the "Coverage Metadata" drop-down:
This PR accomplishes two things:
1.) Fixes an error where the
magtdata were labeled asaltand vice-versa for the historical baseline. The error was caused in the preprocessing of the data when converting from .asc data to GeoTIFF. This was rectified in the preprocessing Jupyter notebook. Data were re-processed and successfully ingested to the dev Rasdaman instance on Apollo.2.) Adds a dictionary of "encodings" to the metadata that map the numerical axis identifiers used in the netCDF (0, 1, 2, etc.) that are necessary for the Rasdaman ingest. I did this because it could be a handy way for endpoints to access and query the data vs. manually reproducing some version in a
luts.pyor similar in an API endpoint. When you request the data, you also get what you need to parse it back into something useful or at least human-readable.You can see those encodings via PetaScope in the "Coverage Metadata" drop-down: