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generate_structured_odd()

Produce an ASAM OpenODD-compliant CSV describing the vehicle’s operating domain.
Structured ODD export
from nomadicml import NomadicML, DEFAULT_STRUCTURED_ODD_COLUMNS

client = NomadicML(api_key="your_api_key")

# Use the default schema or customise it before calling the export.
columns = [
    {
        "name": "timestamp",
        "prompt": "Log the timestamp in ISO 8601 format (placeholder date 2024-01-01).",
        "type": "YYYY-MM-DDTHH:MM:SSZ",
    },
    {
        "name": "scenery.road.type",
        "prompt": "The type of road the vehicle is on.",
        "type": "categorical",
        "literals": ["motorway", "rural", "urban_street", "parking_lot", "unpaved", "unknown"],
    },
]

odd = client.generate_structured_odd(
    video_id="VIDEO_ID",
    columns=columns or DEFAULT_STRUCTURED_ODD_COLUMNS,
)

print(odd["csv"])
print(odd.get("share_url"))
Required Parameters:
ParameterTypeDescription
video_idstrID of the analysed video whose operating domain you want to export
Optional Parameters:
ParameterTypeDefaultDescription
columnsSequence[StructuredOddColumn]DEFAULT_STRUCTURED_ODD_COLUMNSColumn definitions matching the UI schema (name, prompt, type, optional literals)
timeoutintclient defaultRequest timeout override in seconds
Returns: Dict containing:
  • csv: The generated CSV text.
  • columns: The resolved column schema (after validation).
  • reasoning_trace_path: Final Firestore path used for reasoning logs.
  • share_id / share_url: Optional sharing metadata if the backend stored the export.
  • processing_time: Time spent generating the export.
  • raw: Full backend response payload for additional introspection.