Missions

Missions, i.e., flights to be simulated by AEIC, are currently represented by the simple Mission class. The Mission class represents only flights following a great circle route between origin and destination airports. It cannot represent “as-flown” trajectories from ADS-B data or other sources.

class AEIC.missions.Mission(origin: str, destination: str, departure: pandas._libs.tslibs.timestamps.Timestamp, arrival: pandas._libs.tslibs.timestamps.Timestamp, load_factor: float, aircraft_type: str, flight_id: int | None = None)

aircraft_type: str: Aircraft type (ICAO code).

arrival: Timestamp: Arrival time (UTC).

departure: Timestamp: Departure time (UTC).

destination: str: IATA code of destination airport.

property destination_position: Position: Spatial position (3-D) of destination airport.

flight_id: int | None = None: Unique flight identifier from mission database (if available).

classmethod from_query_result(qr: QueryResult, load_factor: float = 1.0) → Mission

Create a Mission instance from a QueryResult instance.

This is used for generating missions from mission database queries.

classmethod from_toml(data: dict) → list[Mission]

Create a list of Mission instances from a TOML-like dictionary.

This is used for parsing sample mission data.

property gc_distance: float: Great circle distance between departure and arrival positions (m).

load_factor: float: Load factor (between 0 and 1).

origin: str: IATA code of origin airport.

property origin_position: Position: Spatial position (3-D) of origin airport.