"""预处理原始 HDF5 数据集以供代理模型训练。

脚本根据实验 tag 或显式路径读取原始曲线数据，调用统一预处理函数完成参数变换、
曲线清洗/标准化、训练验证测试划分和 scaler 保存，输出后续训练脚本直接消费的
processed 数据文件。
"""

# pylint: disable=import-error,wrong-import-position

from __future__ import annotations

import argparse
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.append(str(ROOT))

from src.common.experiment_paths import normalize_tag, processed_path_for_tag
from src.data.preprocess import preprocess_dataset


def main() -> None:
    """把原始 HDF5 曲线数据切分、标准化并保存为训练用 pkl。"""
    parser = argparse.ArgumentParser(
        description="Preprocess HDF5 dataset for forward surrogate"
    )
    parser.add_argument(
        "--input",
        type=str,
        required=True,
        help="Path to the generated .h5 dataset",
    )
    parser.add_argument(
        "--output",
        type=str,
        default=None,
        help="Optional output .pkl path",
    )
    parser.add_argument(
        "--tag",
        type=str,
        default=None,
        help="Experiment tag for auto naming",
    )
    parser.add_argument("--test-size", type=float, default=0.15)
    parser.add_argument("--val-size", type=float, default=0.15)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument(
        "--no-param-feature-transform",
        action="store_true",
        help="Keep raw physical parameters before StandardScaler; default uses log/asinh features",
    )
    args = parser.parse_args()

    tag = normalize_tag(args.tag)

    output_path = (
        Path(args.output)
        if args.output is not None
        else processed_path_for_tag(tag)
    )

    preprocess_dataset(
        input_path=Path(args.input),
        output_path=output_path,
        test_size=args.test_size,
        val_size=args.val_size,
        random_seed=args.seed,
        use_param_feature_transform=not args.no_param_feature_transform,
    )


if __name__ == "__main__":
    main()