pegsym op• Data kinds: matrix × scalar × scalar → table
• Call: import fullseye as fs; fs.ledger.spiral_expected_tries(points, capture: 'float', rho: 'float', grid: 'int' = 161) -> 'dict' (to call the implementation directly, import pegsym; pegsym.spiral_expected_tries(points, capture: 'float', rho: 'float', grid: 'int' = 161) -> 'dict'; from the registry, opsdrive.get("spiral_expected_tries"))
Expected number of tries until a lateral offset, uniform over a disk of radius rho, is first captured (distance ≤ capture) when the point sequence points is tried in order. measured = mean over a grid inside the disk (grid × grid, points outside the circle discarded). closed = swept-area approximation: k points sweep area k·p·s, so radius r is reached at k = πr²/(ps), and if r is uniform over the disk, E = πρ²/(2ps) + 1/2 (p and s are estimated from the point sequence: s = median distance between adjacent points, p = per-turn increase of the radius of the outermost points). If some grid point is captured by no point, uncovered > 0 (a hole in the coverage). Raises ValueError: points are not (N ≥ 3, 2), capture or rho ≤ 0, grid < 11.
• 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_peg_symmetry_search — py -3.11 examples/poc_peg_symmetry_search.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
pegsym)polygon_peg · polygon_offset · polygon_fit_check · rotation_window · polygon_two_point_depth · polygon_coverage_image · plane_topview · polygon_yaw_read
*Provenance: pegsym.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.