DCCONSTRUCTIONS
e515ab1b8c
Preserve the completed teach-and-repeat laboratory stage: reference preparation, cascaded acquisition, local tracking and recovery, recording lifecycle, replay qualification, and persistent Rerun scene controls. Document the open grid-picking regression and Rerun upgrade contract. No autonomous driving or loop-closure optimization is claimed.
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
"""Two bounded negative checks using an already captured causal snapshot."""
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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from k1link.device_plugins.xgrids_k1.localization_source import extract_submap
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from k1link.missions.causal_replay import digest
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from k1link.missions.registration import path_hint
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from k1link.missions.registration_worker import run_registration
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def main():
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument("--replay", type=Path, required=True)
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p.add_argument("--reference-raw", type=Path, required=True)
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p.add_argument("--reference-planning", type=Path, required=True)
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p.add_argument("--output", type=Path, required=True)
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args = p.parse_args()
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base = json.loads((args.replay / "report.json").read_text())
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planning = json.loads(args.reference_planning.read_text())
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# Fixed third causal snapshot, never the final offline B fit.
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snapshot = args.replay / "step-003/registration-input.npz"
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query_path_file = snapshot.with_name("query-path.npy")
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files = {
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snapshot: base["artifacts"][str(snapshot.relative_to(args.replay))],
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query_path_file: base["artifacts"][str(query_path_file.relative_to(args.replay))],
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args.reference_raw: planning["source_digests"]["raw-transport-primary"],
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args.reference_raw.with_name("mqtt.metadata.jsonl"): planning["source_digests"][
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"raw-transport-index"
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],
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}
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for file, expected in files.items():
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if digest(file) != expected:
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raise ValueError("Control input digest mismatch.")
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args.output.mkdir(parents=True, exist_ok=False)
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with np.load(snapshot, allow_pickle=False) as data:
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reference, query, hint = data["reference"], data["query"], data["initial"]
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path = np.load(query_path_file, allow_pickle=False)
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distant = hint.copy()
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distant[:3, 3] += 1000
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far_dir = args.output / "far-seed"
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far_dir.mkdir()
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far = run_registration(far_dir, reference, query, distant)
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# This disjoint A interval was specified before executing the controls.
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poses = planning["poses"]
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start = next(i for i, x in enumerate(poses) if x["distance_m"] >= 130)
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end = next(i for i, x in enumerate(poses) if x["distance_m"] >= 155)
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wrong, meta = extract_submap(args.reference_raw, planning, start, end)
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wrong_path = np.array([x["position"] for x in poses[start : end + 1]])
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wrong_dir = args.output / "wrong-region"
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wrong_dir.mkdir()
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other = run_registration(wrong_dir, wrong, query, path_hint(wrong_path, path))
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def clean(value):
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return {k: v for k, v in value.items() if k != "matched_query_indices"}
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report = dict(
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schema_version="missioncore.causal-replay-controls/v1",
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source_step="step-003",
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reference_interval_m=[130, 155],
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reference_interval_indices=[start, end],
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reference_extraction=meta,
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results={"far-seed": clean(far), "wrong-region": clean(other)},
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input_digests={str(k): v for k, v in files.items()},
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source_integrity_verified=all(digest(k) == v for k, v in files.items()),
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vehicle_control=False,
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localization_confirmed=False,
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)
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report["artifacts"] = {
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str(x.relative_to(args.output)): digest(x) for x in args.output.rglob("*") if x.is_file()
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}
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(args.output / "report.json").write_text(json.dumps(report, allow_nan=False))
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print(
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json.dumps(
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{
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k: {
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j: v.get(j)
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for j in [
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"status",
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"reasons",
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"overlap",
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"inlier_rmse_m",
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"registration_seconds",
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]
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}
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for k, v in report["results"].items()
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}
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),
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flush=True,
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)
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if __name__ == "__main__":
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main()
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