column-map-and-manifests

To map site-specific point names to pandas DataFrame columns, you load a JSON manifest to resolve site mappings and match site inputs with DataFrame columns for fault detection rule execution. This ensures correct rule application across different site configurations.

A JSON manifest in pandas data integration workflows provides the data structure needed to translate site-specific point identifiers into DataFrame columns. This enables seamless integration of custom data schemas with fault detection rules.

You convert BRICK ontology inputs to pandas columns by using dicts, manifests, or composite resolvers within a column mapping workflow. This allows precise site-aware data resolution for downstream processing.

Yes, you can use this column mapping approach with the open_fdd engine. It is designed to integrate directly with open_fdd to streamline YAML fault detection rule execution across different site configurations.

No specific external dependencies are required to resolve site mappings with JSON manifests, as the Skill operates independently. However, it depends on open-fdd's column map resolver modules to execute fault detection rules effectively.

The best way to handle flexible resolver chains for site-aware data resolution is using composite resolvers that combine dicts and JSON manifests. This approach supports dynamic mapping of custom data schemas to pandas DataFrame columns.