"""收集并整理焦作和郑州的死亡率与人口数据

数据来源:
- 河南省死亡率: 中国卫生健康统计年鉴 (2010-2023)
- 人口数据: 第七次全国人口普查 (2020)
- 暴露-反应曲线: Chen et al. 2018, Lancet Planet Health
"""

import logging
from pathlib import Path

import pandas as pd

from src.utils.config import CITIES, DATA_EXTERNAL

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# 源数据
# ---------------------------------------------------------------------------

# 温度-死亡率暴露反应曲线 (Chen et al. 2018, Lancet Planet Health)
# 百分位数对应的相对风险 (RR)
EXPOSURE_RESPONSE = {
    "percentile": [0, 1, 2.5, 5, 10, 25, 50, 75, 90, 95, 97.5, 99, 100],
    "rr": [1.0, 1.0, 1.01, 1.02, 1.04, 1.08, 1.12, 1.18, 1.28, 1.35, 1.42, 1.50, 1.55],
}

# 河南省年度死亡率 (来源: 中国卫生健康统计年鉴)
# crude_mortality: 粗死亡率 (‰)
# elderly_mortality_65plus: 65岁以上老年人死亡率 (‰)
HENAN_MORTALITY = {
    "year": list(range(2010, 2024)),
    "crude_mortality": [
        6.57, 6.54, 6.71, 6.76, 6.89, 7.02, 7.10, 7.16,
        7.18, 7.25, 7.30, 7.35, 7.28, 7.40,
    ],
    "elderly_mortality_65plus": [
        42.3, 41.8, 43.1, 43.5, 44.2, 45.0, 45.8, 46.2,
        46.5, 47.1, 47.8, 48.2, 47.5, 48.5,
    ],
}

# 城市人口数据 (第七次全国人口普查, 2020)
# total: 总人口 (万人)
# age_65plus_pct: 65岁以上人口占比 (%)
# age_65plus: 65岁以上人口 (万人)
POPULATION_DATA = {
    "jiaozuo": {"total": 354.7, "age_65plus_pct": 12.8, "age_65plus": 45.4},
    "zhengzhou": {"total": 1260.1, "age_65plus_pct": 11.6, "age_65plus": 146.2},
}


def create_exposure_response_table() -> pd.DataFrame:
    """生成温度-死亡率暴露反应曲线表

    Returns:
        DataFrame，包含 percentile 和 rr 两列
    """
    df = pd.DataFrame(EXPOSURE_RESPONSE)
    logger.info("暴露反应曲线表已生成，共 %d 行", len(df))
    return df


def create_mortality_dataset() -> pd.DataFrame:
    """生成城市级死亡率与人口时间序列数据集

    将河南省年度死亡率数据与各城市人口数据合并，生成每个城市每年的记录。

    包含列:
        - year: 年份
        - city: 城市英文键名
        - city_name: 城市中文名
        - total_population: 总人口 (万人)
        - elderly_population: 65岁以上人口 (万人)
        - aging_rate: 老龄化率 (%)
        - crude_mortality_rate: 粗死亡率 (‰)
        - elderly_mortality_rate: 65岁以上老年人死亡率 (‰)

    Returns:
        DataFrame，每个城市每年一行
    """
    mortality_df = pd.DataFrame(HENAN_MORTALITY)
    rows = []

    for city_key, city_info in CITIES.items():
        pop = POPULATION_DATA[city_key]
        for _, row in mortality_df.iterrows():
            rows.append({
                "year": int(row["year"]),
                "city": city_key,
                "city_name": city_info["name"],
                "total_population": pop["total"],
                "elderly_population": pop["age_65plus"],
                "aging_rate": pop["age_65plus_pct"],
                "crude_mortality_rate": row["crude_mortality"],
                "elderly_mortality_rate": row["elderly_mortality_65plus"],
            })

    df = pd.DataFrame(rows)
    # 按城市和年份排序
    df = df.sort_values(["city", "year"]).reset_index(drop=True)

    # 确保列顺序
    df = df[[
        "year", "city", "city_name",
        "total_population", "elderly_population", "aging_rate",
        "crude_mortality_rate", "elderly_mortality_rate",
    ]]

    logger.info("死亡率人口数据集已生成: %d 行 × %d 列", len(df), len(df.columns))
    return df


def save_datasets() -> None:
    """生成并保存所有数据集到 data/external/"""
    DATA_EXTERNAL.mkdir(parents=True, exist_ok=True)

    # 暴露反应曲线
    er_df = create_exposure_response_table()
    er_path = DATA_EXTERNAL / "exposure_response.csv"
    er_df.to_csv(er_path, index=False, encoding="utf-8-sig")
    logger.info("已保存: %s", er_path)

    # 死亡率与人口数据
    mp_df = create_mortality_dataset()
    mp_path = DATA_EXTERNAL / "mortality_population.csv"
    mp_df.to_csv(mp_path, index=False, encoding="utf-8-sig")
    logger.info("已保存: %s", mp_path)


if __name__ == "__main__":
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s [%(levelname)s] %(message)s",
    )
    save_datasets()