puck op• Data kinds: signal × matrix × table → table
• Call: import fullseye as fs; fs.ledger.puck_velocity_estimate(t, xy, table: 'dict', *, model: 'str' = 'coulomb', t_ref=None, iters: 'int' = 3) -> 'dict' (to call the implementation directly, import puck; puck.puck_velocity_estimate(t, xy, table: 'dict', *, model: 'str' = 'coulomb', t_ref=None, iters: 'int' = 3) -> 'dict'; from the registry, opsdrive.get("puck_velocity_estimate"))
Least-squares velocity from N positions: uniform-deceleration model with fixed direction û, p(τ) = p_ref + v_ref τ − ½ μg τ² û (τ = t − t_ref).
û is taken from v_ref, so 3 iterations are run (initial value = difference of the end points). "viscous" is linear in the basis (1 − e^{−kτ})/k, "none" is a straight line. Noise estimate: σ_pos from the residuals, the variance of v_ref from (AᵀA)⁻¹ (sigma_v, isotropic). Returns {"p_ref","v_ref","speed","u","t_ref","sigma_pos","sigma_v","sigma_p","resid_rms","n","model"}. N < 3 raises ValueError.
• 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.
• poc_air_hockey_intercept — py -3.11 examples/poc_air_hockey_intercept.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
puck)puck_table · puck_wall_bounce · puck_slide_predict · puck_state_at · puck_crossing_point · puck_mirror_path · puck_stop_distance · puck_camera
*Provenance: puck.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.