preston_coefficient_fit — DRIVE polish op

• Data kinds: image2d × image2d → table

• Call: import fullseye as fs; fs.ledger.preston_coefficient_fit(depth_map, dwell_map, mask=None) -> 'dict' (to call the implementation directly, import polish; polish.preston_coefficient_fit(depth_map, dwell_map, mask=None) -> 'dict'; from the registry, opsdrive.get("preston_coefficient_fit"))

Usage

Solves depth map = k_p × dwell map (dwell_map = ∫ p |v_rel| dt [Pa·m] integrated with k_p = 1) by least squares and returns the Preston coefficient: the through-origin fit k_p, plus k_p_affine / offset with an intercept, r2, rms_residual, n. Without mask, the pixels whose dwell exceeds 5 % of the maximum are used. Raises ValueError: shapes differ or non-finite, fewer than 3 pixels, dwell is 0.

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)

• poc_polish_wipe_measure — py -3.11 examples/poc_polish_wipe_measure.py

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 (polish)

preston_pressure_kernel · preston_removal_map · preston_track_profile · wipe_band_width · raster_wipe_area · coat_image · coat_thickness_from_image · wipe_coverage

*Provenance: polish.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.