so6-file-converter

Convert SO6 flight traffic files into pandas DataFrames for data analysis.

Installation

pip install so6-file-converter

Requires Python 3.8+ and pandas (installed automatically).

Quick start

from so6_converter import so6_to_df, so6_to_df_simple, so6_to_df_waypoints

# Raw SO6 file, one row per segment
df_raw = so6_to_df("example.so6")

# One row per flight, variables stored as lists
df_simple = so6_to_df_simple("./route1/example.so6")

# One row per waypoint, duplicates removed
df_waypoints = so6_to_df_waypoints(r"C:\Users\folder1\folder2\example.so6")

Paths can be strings or pathlib.Path objects.

All three functions convert coordinates from minutes to decimal degrees and add a country_flow column built from the ICAO prefixes of origin and destination (e.g. EG_LE).

Functions

so6_to_df

Reads a raw SO6 file without altering its structure: one row per segment, so each row contains two waypoints (start and end of the segment).

Columns: segment_id, origin, destination, ac_type, time_ini_seg, time_end_seg, fl_ini, fl_end, status, callsign, date_ini, date_end, lat_ini, lon_ini, lat_end, lon_end, flightid, sequence, segment_length, segment_color, flown_distance (total distance of the flight), country_flow.

Typical uses: initial inspection of SO6 data, custom pipelines built on raw inputs.

so6_to_df_simple

Groups records by flight. Each row is a flight, and lat, long, alt, time and date are lists with the evolution of each variable along the route.

Columns: callsign, flightid, origin, destination, lat, long, alt, time, date, total_distance, ac_type, country_flow.

Typical uses: flight-level trajectory analysis.

so6_to_df_waypoints

Splits each segment into its two waypoints, one per row, and removes duplicates.

Columns: waypoint, callsign, flightid, origin, destination, lat, lon, alt, time, date, country_flow.

Typical uses: route reconstruction, airspace and network analysis, flight path visualization.

Development

pip install -e ".[test]"
pytest

License

MIT