engine/sim/rf/cli.py

from __future__ import annotations

import argparse
from pathlib import Path

from .calibration import BDR_MODEL_PRESETS, DEFAULT_CLASS_CONFIG, REFERENCE_CLASS
from .pipeline import generate_report, run_evolution_cli, run_pipeline, thresholds_from_json


def main() -> None:
    parser = argparse.ArgumentParser(description="RF mode pack v5.1/v6.0 engineering screening CLI")
    parser.add_argument("--n-designs", type=int, default=1000)
    parser.add_argument("--seed", type=int, default=7)
    parser.add_argument("--bdr-preset", choices=list(BDR_MODEL_PRESETS.keys()), default="nominal")
    parser.add_argument("--reference-class", default=REFERENCE_CLASS)
    parser.add_argument("--out-dir", default="rf_mode_pack_v5_1_outputs")
    parser.add_argument("--report-only", action="store_true")
    parser.add_argument("--skip-report", action="store_true")
    parser.add_argument("--class-config-json", default="")

    parser.add_argument("--run-ga", action="store_true")
    parser.add_argument("--ga-pop-size", type=int, default=300)
    parser.add_argument("--ga-generations", type=int, default=20)
    parser.add_argument("--ga-target-class", default="B_development")
    args = parser.parse_args()

    out_dir = Path(args.out_dir)

    class_config = DEFAULT_CLASS_CONFIG
    if args.class_config_json:
        class_config = thresholds_from_json(Path(args.class_config_json))

    if args.reference_class not in class_config:
        raise SystemExit(f"reference class '{args.reference_class}' is not in class config")

    if args.ga_target_class not in class_config:
        raise SystemExit(f"GA target class '{args.ga_target_class}' is not in class config")

    if args.report_only:
        report_path = out_dir / "rf_mode_pack_v5_1_report.md"
        generate_report(out_dir, report_path, class_order=list(class_config.keys()), reference_class=args.reference_class)
        print("Saved report to:", report_path)
        return

    if args.run_ga:
        run_evolution_cli(
            pop_size=args.ga_pop_size,
            generations=args.ga_generations,
            seed=args.seed,
            bdr_preset=args.bdr_preset,
            target_class=args.ga_target_class,
            out_dir=out_dir,
            skip_report=args.skip_report,
            class_config=class_config,
        )
        return

    run_pipeline(
        n_designs=args.n_designs,
        seed=args.seed,
        bdr_preset=args.bdr_preset,
        reference_class=args.reference_class,
        out_dir=out_dir,
        class_config=class_config,
        skip_report=args.skip_report,
    )

if __name__ == "__main__":
    main()