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.
155 lines
6.3 KiB
Python
155 lines
6.3 KiB
Python
"""Frozen stationary-to-fresh functional experiment on independent archived walks."""
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import argparse
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import json
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import platform
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import shutil
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import time
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from pathlib import Path
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import numpy as np
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from k1link.artifacts import utc_now_iso
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from k1link.device_plugins.xgrids_k1.planning_replay import iter_planning_events
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from k1link.missions.causal_replay import digest
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from k1link.missions.stationary_bootstrap import BOOTSTRAP_POLICY
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from k1link.missions.stationary_replay import replay_stationary
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def write(path, value):
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path.write_text(json.dumps(value, indent=2, allow_nan=False))
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def code_hashes():
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root = Path(__file__).resolve().parents[1]
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paths = [
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Path(__file__).resolve(),
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*sorted((root / "src/k1link/missions").glob("*.py")),
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root / "src/k1link/device_plugins/xgrids_k1/planning_replay.py",
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root / "src/k1link/device_plugins/xgrids_k1/planning_live.py",
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root / "src/k1link/device_plugins/xgrids_k1/localization_source.py",
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root / "tests/test_stationary_bootstrap.py",
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]
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return {str(p.relative_to(root)): digest(p) for p in paths}
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--stage", choices=["prepare", "correct-entry", "wrong-region"], required=True
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)
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parser.add_argument("--predecessor", type=Path)
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parser.add_argument("--output", type=Path, required=True)
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args = parser.parse_args()
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root = args.output
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if args.stage == "prepare":
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previous = args.predecessor
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old = json.loads((previous / "manifest.json").read_text())
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inputs = {**old["input_digests"], str(previous / "reference.npz"): old["reference_sha256"]}
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inputs[str(previous / "manifest.json")] = digest(previous / "manifest.json")
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if any(digest(Path(p)) != sha for p, sha in inputs.items()):
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raise ValueError("Predecessor input digest mismatch.")
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root.mkdir(parents=True, exist_ok=False)
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code = code_hashes()
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for name in code:
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target = root / "executed-source" / name
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target.parent.mkdir(parents=True, exist_ok=True)
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shutil.copyfile(name, target)
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a_path = next(
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p
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for p in old["input_digests"]
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if p.endswith(".json")
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and json.loads(Path(p).read_text()).get("session_id") == old["reference_session"]
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)
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poses = json.loads(Path(a_path).read_text())["poses"]
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wrong_path = np.array([p["position"] for p in poses if 130 <= p["distance_m"] <= 155])
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with np.load(old["negative_file"], allow_pickle=False) as data:
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np.savez_compressed(
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root / "wrong-reference.npz", reference=data["reference"], reference_path=wrong_path
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)
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# The old archive supplies fixed A geometry only; no previous B fitted
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# transform, mask or future B pose is used as an initialization seed.
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inputs[str(root / "wrong-reference.npz")] = digest(root / "wrong-reference.npz")
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write(
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root / "manifest.json",
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dict(
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schema_version="missioncore.stationary-bootstrap-probe/v1",
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created_at_utc=utc_now_iso(),
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created_monotonic_ns=time.monotonic_ns(),
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query_raw=old["query_raw"],
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query_session=old["query_session"],
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reference_session=old["reference_session"],
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references={
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"correct-entry": str(previous / "reference.npz"),
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"wrong-region": str(root / "wrong-reference.npz"),
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},
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input_digests=inputs,
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implementation_sha256=code,
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protocol=BOOTSTRAP_POLICY,
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maximum_seconds=65.0,
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maximum_distance_m=40.0,
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expectations=dict(
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correct_entry="complete search -> provisional -> three fresh consistent fits",
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wrong_region="no current candidate or tracking",
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freshness="all validation observations after ready; windows have disjoint IDs",
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pace="original receipt clocks, 1x; no offline B heading or transform",
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),
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notes="Engineering qualification only. Frame continuity is unverified; "
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"one pre-ready receipt gap permits a hypothesis, not a tracking result. "
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"No new capture, device commands, UI changes or live activation.",
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vehicle_control=False,
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localization_confirmed=False,
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),
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)
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print(json.dumps(dict(stage="prepared", output=str(root))), flush=True)
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return
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manifest = json.loads((root / "manifest.json").read_text())
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inputs = manifest["input_digests"]
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code = code_hashes()
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if code != manifest["implementation_sha256"]:
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raise ValueError("Implementation changed since protocol freeze.")
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if any(digest(Path(p)) != sha for p, sha in inputs.items()):
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raise ValueError("Source changed before replay.")
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with np.load(manifest["references"][args.stage], allow_pickle=False) as data:
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reference, path = data["reference"], data["reference_path"]
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events = iter_planning_events(Path(manifest["query_raw"]), manifest["query_session"])
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report = replay_stationary(
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events,
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reference,
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path,
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root / args.stage,
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max_seconds=manifest["maximum_seconds"],
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max_distance=manifest["maximum_distance_m"],
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)
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report.update(
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input_digests=inputs,
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source_integrity_verified=all(digest(Path(p)) == h for p, h in inputs.items()),
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implementation_sha256=code,
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code_integrity_verified=code_hashes() == code,
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runtime=dict(
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system=platform.system(), machine=platform.machine(), python=platform.python_version()
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),
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)
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write(root / args.stage / "report.json", report)
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print(
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json.dumps(
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{
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k: report.get(k)
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for k in (
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"state",
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"first_prior_s",
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"first_candidate_s",
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"first_tracking_s",
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"transitions",
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"source_integrity_verified",
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"code_integrity_verified",
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)
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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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