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.
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
"""Repeat the frozen negative controls with exactly the new entry search policy."""
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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.artifacts import utc_now_iso
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from k1link.missions.causal_replay import digest
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from k1link.missions.entry_acquisition_worker import run_entry_acquisition
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def main():
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument("--controls", type=Path, required=True)
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p.add_argument("--replay", 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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prior = json.loads((args.controls / "report.json").read_text())
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replay = json.loads((args.replay / "report.json").read_text())
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path_file = args.replay / "step-003/query-path.npy"
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files = {path_file: replay["artifacts"]["step-003/query-path.npy"]}
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for name in ["wrong-region", "far-seed"]:
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relative = name + "/registration-input.npz"
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files[args.controls / relative] = prior["artifacts"][relative]
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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 changed.")
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query_path = np.load(path_file, allow_pickle=False)
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direction = next(
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p - query_path[0] for p in query_path[1:] if np.linalg.norm((p - query_path[0])[:2]) >= 3
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)
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args.output.mkdir(parents=True, exist_ok=False)
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report = dict(
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schema_version="missioncore.entry-controls/v1",
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created_at_utc=utc_now_iso(),
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source_step="step-003",
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results={},
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vehicle_control=False,
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localization_confirmed=False,
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input_digests={str(k): v for k, v in files.items()},
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)
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for name in ["wrong-region", "far-seed"]:
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with np.load(args.controls / name / "registration-input.npz", allow_pickle=False) as data:
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reference, query, initial = data["reference"], data["query"], data["initial"]
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directory = args.output / name
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directory.mkdir()
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result = run_entry_acquisition(
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directory, reference, query, initial, query_path[0], initial[:3, :3] @ direction
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)
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report["results"][name] = {k: v for k, v in result.items() if k != "matched_query_indices"}
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print(
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json.dumps(
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dict(
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control=name,
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status=result["status"],
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reasons=result["reasons"],
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hypotheses=len(result["initialization"]["attempts"]),
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clusters=result["initialization"]["clusters"],
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elapsed_s=result["initialization"]["elapsed_s"],
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)
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),
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flush=True,
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)
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report["source_integrity_verified"] = all(digest(k) == v for k, v in files.items())
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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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report["finished_at_utc"] = utc_now_iso()
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(args.output / "report.json").write_text(json.dumps(report, allow_nan=False))
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if __name__ == "__main__":
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main()
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