fit_aero — DRIVE `ball` op

• Data kinds: signal × points × table → table

• Call: import fullseye as fs; fs.ledger.fit_aero(t, p, omega, bp: 'dict', *, iters: 'int' = 15, dt: 'float' = 0.001) -> 'dict' (to call the implementation directly, import ballistics; ballistics.fit_aero(t, p, omega, bp: 'dict', *, iters: 'int' = 15, dt: 'float' = 0.001) -> 'dict'; from the registry, opsdrive.get("fit_aero"))

Usage

Identifies the aerodynamic coefficients as well from a trajectory: the 8 parameters (p₀, v₀, C_d, C_L) by Gauss–Newton (ω known, C_L fitted as a constant). Drag goes with the 2nd power of speed and Magnus with ω × v, so C_L is hard to determine unless the trajectory is non-straight (with spin, long enough) (cond = condition number of the Jacobian is returned together with rms, so you can tell). Return value {"p0", "v0", "cd", "cl", "rms", "cond", "iters"}. For a ground-truth trajectory (made with constant C_L) it recovers to 1e-6 (gate).

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`ball`)

ball_params · impact_params · flight_vacuum · flight_ode · flight_simulate · flight_state_at · magnus_lift_coefficient · drag_coefficient_sphere

*Provenance: ballistics.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

fit_aero — DRIVE ball op