kyosoku_summary — DRIVE `town` op

• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.kyosoku_summary(path=None) -> 'Dict[str, object]' (to call the implementation directly, import drivetown; drivetown.kyosoku_summary(path=None) -> 'Dict[str, object]'; from the registry, opsdrive.get("kyosoku_summary"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

教則の場面の再現台帳(docs/drive/kyosoku_scenarios.json)を読み、category × status の件数表にする。

`path = None なら repo の docs/drive/kyosoku_scenarios.json`(このモジュールの場所から引く)。

返り値 ``{"total", "statuses" (順序つき), "by_status": {status: n}, "categories": [...] (件数の多い順),

"by_category": {category: {status: n}}, "table": [[category, n_reproduced, n_partial, n_pending, n_not_reproducible, total], ...]}``。

Raises `ValueError: ファイルが無い・JSON でない・scenarios` が無い・場面に id/category/status が無い・

status が :data:KYOSOKU_STATUSES に無い・id が重複。

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_driving_town — py -3.11 examples/poc_driving_town.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 (`town`)

town_chain · town_layout · town_world · town_centerline · town_stop_lines · town_rules · town_crossing_state · town_run

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

kyosoku_summary — DRIVE town op