Map your own column names

FuelEstimator.estimate() expects logical feature names (typecode, groundspeed, altitude, vertical_rate, …). When your frame uses different column names, map them with keyword arguments instead of renaming the frame.

The default names

If your columns already use the default names, no mapping is needed:

fe.estimate(flight)  # expects typecode, groundspeed, altitude, vertical_rate

Overriding names

Each keyword argument maps a logical feature to the column name in your frame. Map only what differs — anything you omit falls back to the default name:

result = fe.estimate(
    flight,
    typecode="FLPL_AIRC_TYPE",
    groundspeed="GRND_SPD_KT",
    altitude="ALTI_STD_FT",
    vertical_rate="VERT_SPD_FTMN",
    second="FLIGHT_TIME",
    airspeed="TRUE_AIR_SPD_KT",
    mass="MASS_KG",
)

Required vs optional features

Keyword	Required	Unit	Notes
`typecode`	✅	—	ICAO aircraft type (e.g. `A320`)
`groundspeed`	✅	kt
`altitude`	✅	ft
`vertical_rate`	✅	ft/min
`airspeed`	optional	kt	defaults to `groundspeed` if absent
`mass`	optional	kg	improves the estimate
`second`	optional	s	enables derivatives + `fuel_cumsum`
`d_altitude`	optional	—	pre-computed derivative (see Derivatives)
`d_groundspeed`	optional	—	pre-computed derivative
`d_airspeed`	optional	—	pre-computed derivative

What gets validated

The four required columns must exist under the mapped names, otherwise estimate() raises ValueError: Column '<name>' not found.
If you map second, that column must be a numeric (float or integer) dtype, otherwise estimate() raises ValueError: column for second must be float or integer.

Note

Unmapped optional features are simply skipped — passing a frame with no mass column is fine, the model uses its default mass handling.