traffic.algorithms.metadata

This module is about the inference of common metadata that is usually accessible on closed sources of information, but lacking in ADS-B data.

Classes here provide information about origin and destination airports, runways, possible diversions and emergency situations.

Airport detection

class traffic.algorithms.metadata.airports.AirportInferenceBase(*args, **kwargs)

class traffic.algorithms.metadata.airports.LandingAirportInference(dataset=None, warning_distance=None)

Returns the most probable landing airport based on the last location in the trajectory.

>>> from traffic.data.samples import belevingsvlucht
>>> belevingsvlucht.infer_airport("landing")
Airport(icao='EHAM', iata='AMS', name='Amsterdam Airport Schiphol', ...)

When data is missing near the ground, it may be relevant to specify a subset of airports as a keyword parameter.

>>> missing_data = belevingsvlucht.before("2018-05-30 20:00")
>>> missing_data.infer_airport("landing")
Airport(icao='NL-0092', name='De Kreupel Helipad', ...)

>>> from traffic.data import airports
>>> large_airports = airports.query("type == 'large_airport'")
>>> missing_data.infer_airport("landing", dataset=large_airports)
Airport(icao='EHAM', iata='AMS', name='Amsterdam Airport Schiphol', ...)

class traffic.algorithms.metadata.airports.TakeoffAirportInference(dataset=None, warning_distance=None)

Returns the most probable takeoff airport based on the first location in the trajectory.

>>> from traffic.data.samples import belevingsvlucht
>>> belevingsvlucht.infer_airport("takeoff")
Airport(icao='EHAM', iata='AMS', name='Amsterdam Airport Schiphol', ...)

When data is missing near the ground, it may be relevant to specify a subset of airports as a keyword parameter.

>>> missing_data = belevingsvlucht.after("2018-05-30 15:30")
>>> missing_data.infer_airport("takeoff")
Airport(icao='NL-0114', name='Netherlands Traffic Center Heliport', ...)

>>> from traffic.data import airports
>>> large_airports = airports.query("type == 'large_airport'")
>>> missing_data.infer_airport("takeoff", dataset=large_airports)
Airport(icao='EHAM', iata='AMS', name='Amsterdam Airport Schiphol', ...)

Flight plan inference

class traffic.algorithms.metadata.flightplan.FlightPlanBase(*args, **kwargs)

class traffic.algorithms.metadata.flightplan.FlightPlanInference(navaids=None, buffer=0.1)

This functions recomputes the most probable alignments on navigational points on the trajectory.

Parameters:

navaid –

By default, all navaids of the default database are considered, but limited to a buffered bounding box around the trajectory.

It should be good practice to limit the size of the navaid dataset: the smaller the data, the faster the computation will be.

>>> from traffic.data import navaids
>>> from traffic.data.samples import savan
>>> flight = savan["SAVAN01"]
>>> vor = navaids.query("type == 'VOR'")
>>> df = flight.infer_flightplan(vor)
>>> for _, line in df.iterrows():
...     print(f"aligned on {line.navaid} ({line.type}) for {line.duration}")
aligned on CFA (VOR) for 0 days 00:21:19
aligned on POI (VOR) for 0 days 00:15:42
aligned on CAN (VOR) for 0 days 00:14:38
aligned on DPE (VOR) for 0 days 00:16:19
aligned on CHW (VOR) for 0 days 00:15:45
aligned on BRY (VOR) for 0 days 00:19:27

For more insights about this example: Calibration flights with SAVAN trajectories

Once computed, the following Altair snippet may be useful to display the trajectory as a succession of segments:

import altair as alt

segments = (
    alt.Chart(df.drop(columns="duration")).encode(
        alt.X("start", title=None),
        alt.X2("stop"),
        alt.Y("navaid", sort="x", title=None),
        alt.Color("type", title="Navigational point"),
        alt.Tooltip(["navaid", "distance", "shift_mean"]),
    )
    .mark_bar(size=10)
    .configure_legend(
        orient="bottom",
        labelFontSize=14, titleFontSize=14, labelFont="Ubuntu"
    )
    .configure_axis(labelFontSize=14, labelFont="Ubuntu")
)