mirror of
https://github.com/firestar5683/StarPilot.git
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firestar5683
90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import csv
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import json
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from pathlib import Path
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Merge fingerprint-compatible speed-limit manual review queues.")
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parser.add_argument("inputs", type=Path, nargs="+", help="Queue directories or manual_review_queue.csv files.")
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parser.add_argument("--output-dir", type=Path, required=True, help="Destination queue directory.")
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return parser.parse_args()
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def queue_paths(input_path: Path) -> tuple[Path, Path]:
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resolved = input_path.expanduser().resolve()
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if resolved.is_dir():
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return resolved / "manual_review_queue.csv", resolved / "manual_review_summary.json"
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return resolved, resolved.with_name("manual_review_summary.json")
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def main() -> int:
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args = parse_args()
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rows_by_key: dict[str, dict[str, str]] = {}
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summaries_by_route: dict[str, dict[str, object]] = {}
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fieldnames: list[str] | None = None
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mining_fingerprint = ""
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model_fingerprint = ""
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for input_path in args.inputs:
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queue_path, summary_path = queue_paths(input_path)
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if not queue_path.is_file() or not summary_path.is_file():
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raise FileNotFoundError(f"Queue or summary missing for {input_path}")
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summary = json.loads(summary_path.read_text(encoding="utf-8"))
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current_mining = str(summary.get("mining_fingerprint", ""))
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current_model = str(summary.get("model_fingerprint", ""))
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if not current_mining or not current_model:
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raise RuntimeError(f"Queue is not fingerprinted: {input_path}")
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if mining_fingerprint and current_mining != mining_fingerprint:
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raise RuntimeError(f"Mining fingerprint mismatch: {input_path}")
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if model_fingerprint and current_model != model_fingerprint:
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raise RuntimeError(f"Model fingerprint mismatch: {input_path}")
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mining_fingerprint = current_mining
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model_fingerprint = current_model
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with queue_path.open("r", encoding="utf-8", newline="") as handle:
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reader = csv.DictReader(handle)
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current_fields = list(reader.fieldnames or [])
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if fieldnames is not None and current_fields != fieldnames:
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raise RuntimeError(f"Queue fields differ: {input_path}")
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fieldnames = current_fields
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for row in reader:
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key = row.get("record_key", "")
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if key:
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rows_by_key[key] = row
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for route_summary in summary.get("routes", []):
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route = str(route_summary.get("route", ""))
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if route:
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summaries_by_route[route] = route_summary
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rows = sorted(rows_by_key.values(), key=lambda row: (-float(row.get("review_priority") or 0.0), row["record_key"]))
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route_summaries = sorted(summaries_by_route.values(), key=lambda item: str(item["route"]))
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output_dir = args.output_dir.expanduser().resolve()
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output_dir.mkdir(parents=True, exist_ok=True)
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queue_output = output_dir / "manual_review_queue.csv"
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with queue_output.open("w", encoding="utf-8", newline="") as handle:
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writer = csv.DictWriter(handle, fieldnames=fieldnames or [])
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writer.writeheader()
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writer.writerows(rows)
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summary_output = output_dir / "manual_review_summary.json"
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summary_output.write_text(json.dumps({
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"mining_fingerprint": mining_fingerprint,
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"model_fingerprint": model_fingerprint,
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"manifest": str(queue_output),
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"rows": len(rows),
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"candidates": sum(row.get("detector_class") != "negative_empty" for row in rows),
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"negatives": sum(row.get("detector_class") == "negative_empty" for row in rows),
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"routes": route_summaries,
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}, indent=2, sort_keys=True) + "\n", encoding="utf-8")
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print(f"Merged {len(rows)} rows from {len(route_summaries)} routes into {queue_output}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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