"""数据清洗脚本：修正原始数据中的问题"""

import json
from pathlib import Path

import numpy as np
import pandas as pd
import typer
from loguru import logger

from lnp_ml.config import RAW_DATA_DIR, INTERIM_DATA_DIR

APP_DIR = Path(__file__).resolve().parents[1] / "app"

app = typer.Typer()


@app.command()
def main(
    input_path: Path = RAW_DATA_DIR / "internal.xlsx",
    output_path: Path = INTERIM_DATA_DIR / "internal.csv",
):
    """
    预处理内部数据，按给药途径分组进行 z-score 标准化，对 size 列取 log。
    
    修正内容：
    1. 按给药途径分组进行 z-score 标准化
    2. 对 size 列取 log
    3. 将 z-score 的 mean/std 保存到 app/ 供推理时反推
    """
    logger.info(f"Loading data from {input_path}")
    df = pd.read_excel(input_path, header=2)
    logger.info(f"Loaded {len(df)} samples")

    # 分别对肌肉注射组和静脉注射组重新进行 z-score 标准化
    logger.info("Z-score normalizing delivery by Route_of_administration...")
    df["unnormalized_delivery"] = pd.to_numeric(df["unnormalized_delivery"], errors="coerce")

    # 计算并保存 per-route 统计量，用于推理时反推和评分归一化
    zscore_stats = {}
    for route, group in df.groupby("Route_of_administration"):
        vals = group["unnormalized_delivery"].dropna()
        if len(vals) > 1:
            zscore_stats[route] = {"mean": float(vals.mean()), "std": float(vals.std())}
            logger.info(f"  {route}: mean={vals.mean():.6f}, std={vals.std():.6f}, n={len(vals)}")

    df["quantified_delivery"] = (
        df.groupby("Route_of_administration")["unnormalized_delivery"]
          .transform(lambda x: (x - x.mean()) / x.std())
    )

    # 补充 quantified_delivery 的 per-route min/max（用于评分时归一化到 [0,1]）
    for route, group in df.groupby("Route_of_administration"):
        qd = group["quantified_delivery"].dropna()
        if len(qd) > 0 and route in zscore_stats:
            zscore_stats[route]["qd_min"] = float(qd.min())
            zscore_stats[route]["qd_max"] = float(qd.max())
            logger.info(f"  {route}: qd_min={qd.min():.6f}, qd_max={qd.max():.6f}")

    stats_path = APP_DIR / "delivery_zscore_stats.json"
    stats_path.parent.mkdir(parents=True, exist_ok=True)
    with open(stats_path, "w") as f:
        json.dump(zscore_stats, f, indent=2)
    logger.success(f"Saved delivery stats to {stats_path}")

    # 对 size 列取 log
    logger.info("Log-transforming size column...")
    df["size"] = pd.to_numeric(df["size"], errors="coerce")
    df["size"] = np.log(df["size"].replace(0, np.nan))  # 避免 log(0)

    # 保存
    output_path.parent.mkdir(parents=True, exist_ok=True)
    df.to_csv(output_path, index=False)
    logger.success(f"Saved cleaned data to {output_path}")


if __name__ == "__main__":
    app()