#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Excel服务部门层级信息补充脚本

功能：
1. 读取Excel文件，为每条记录添加"绑定服务部门"和"服务部门所属部门"两列
2. 根据层级规则和区名关键词自动判断上级部门
3. 生成统计报告并保存补充后的Excel文件
"""

import pandas as pd
import sys
from pathlib import Path
from collections import Counter


# 区名关键词映射表
REGION_KEYWORDS = {
    '禅城区': '禅城区服务部门',
    '南海区': '南海区服务部门',
    '顺德区': '顺德区服务部门',
    '三水区': '三水区服务部门',
    '高明区': '高明区服务部门'
}

MUNICIPAL_DEPT = '市级服务部门'


def extract_region_service_dept(unit_name):
    """
    从单位名称中提取区级服务部门

    Args:
        unit_name: 单位名称字符串

    Returns:
        对应的区级服务部门名称，如果未找到则返回None
    """
    if pd.isna(unit_name):
        return None

    unit_name_str = str(unit_name)

    # 按优先级顺序匹配区名关键词
    for keyword, service_dept in REGION_KEYWORDS.items():
        if keyword in unit_name_str:
            return service_dept

    return None


def determine_parent_department(unit_name, level_value):
    """
    确定服务部门所属部门

    规则：
    1. 如果层级值包含"市级" → 市级服务部门
    2. 如果单位名称包含区名关键词 → 对应区级服务部门
    3. 否则默认为市级服务部门（兜底策略）

    Args:
        unit_name: 单位名称
        level_value: 层级/风险提示/备注列的值

    Returns:
        服务部门所属部门名称
    """
    # 规则1: 检查层级值是否包含"市级"
    if pd.notna(level_value) and '市级' in str(level_value):
        return MUNICIPAL_DEPT

    # 规则2: 从单位名称中提取区级服务部门
    region_dept = extract_region_service_dept(unit_name)
    if region_dept:
        return region_dept

    # 规则3: 兜底策略 - 默认为市级服务部门
    return MUNICIPAL_DEPT


def supplement_excel_file(input_path, output_path):
    """
    补充Excel文件的部门层级信息

    Args:
        input_path: 输入Excel文件路径
        output_path: 输出Excel文件路径
    """
    print(f"[INFO] Reading Excel file: {input_path}")

    # 读取Excel文件
    try:
        df = pd.read_excel(input_path)
        print(f"[OK] Successfully read {len(df)} records")
    except Exception as e:
        print(f"[ERROR] Failed to read Excel file: {e}")
        sys.exit(1)

    # 显示原始列名
    print(f"\n[INFO] Original columns ({len(df.columns)} columns):")
    for i, col in enumerate(df.columns):
        print(f"  {i+1}. {col}")

    # 添加"绑定服务部门"列（直接复制"单位名称"列）
    # 假设第二列是"单位名称"
    unit_name_col = df.columns[1]  # 第二列
    df['绑定服务部门'] = df[unit_name_col]

    # 添加"服务部门所属部门"列（根据规则计算）
    level_col = df.columns[4]  # "层级/风险提示/备注"列（第5列）

    df['服务部门所属部门'] = df.apply(
        lambda row: determine_parent_department(row[unit_name_col], row[level_col]),
        axis=1
    )

    print(f"\n[OK] Added 2 columns:")
    print(f"  - Binding Service Department (copied from '{unit_name_col}')")
    print(f"  - Parent Service Department (calculated based on hierarchy rules)")

    # 生成统计报告
    print(f"\n[STATS] Parent Service Department Distribution:")
    parent_dept_counts = Counter(df['服务部门所属部门'])

    # 按标准顺序显示
    standard_order = [MUNICIPAL_DEPT] + list(REGION_KEYWORDS.values())
    for dept in standard_order:
        count = parent_dept_counts.get(dept, 0)
        percentage = (count / len(df) * 100) if len(df) > 0 else 0
        print(f"  {dept}: {count} 条 ({percentage:.1f}%)")

    # 检查无法匹配的记录
    unmapped_records = df[
        (~df[level_col].astype(str).str.contains('市级', na=False)) &
        (~df[unit_name_col].astype(str).str.contains('|'.join(REGION_KEYWORDS.keys()), na=False))
    ]

    if len(unmapped_records) > 0:
        print(f"\n[WARNING] Found {len(unmapped_records)} records that could not be matched by region keywords, assigned to Municipal Service Department by default:")
        print("  Sample records:")
        for idx, row in unmapped_records.head(5).iterrows():
            print(f"    - {row[unit_name_col]} (层级: {row[level_col]})")
        if len(unmapped_records) > 5:
            print(f"    ... 还有 {len(unmapped_records) - 5} 条")

    # 保存补充后的Excel文件
    print(f"\n[SAVE] Saving supplemented Excel file: {output_path}")
    try:
        df.to_excel(output_path, index=False, engine='openpyxl')
        print(f"[OK] Successfully saved {len(df)} records to {output_path}")
    except Exception as e:
        print(f"[ERROR] Failed to save Excel file: {e}")
        sys.exit(1)

    # 验证数据完整性
    print(f"\n[VERIFY] Data Integrity Validation:")
    null_binding = df['绑定服务部门'].isna().sum()
    null_parent = df['服务部门所属部门'].isna().sum()

    print(f"  Total records: {len(df)}")
    print(f"  Null values in Binding Service Department: {null_binding}")
    print(f"  Null values in Parent Service Department: {null_parent}")
    print(f"  Total columns: {len(df.columns)}")

    if null_binding == 0 and null_parent == 0:
        print(f"[OK] Data integrity validation passed")
    else:
        print(f"[WARNING] Found null values, please check data source")

    # 显示示例数据
    print(f"\n[DATA] Sample data (first 5 rows):")
    sample_cols = [unit_name_col, level_col, '绑定服务部门', '服务部门所属部门']
    print(df[sample_cols].head(10).to_string(index=False))

    return df


def main():
    """主函数"""
    # 定义文件路径
    base_path = Path(__file__).parent.parent
    input_file = base_path / 'data' / '法律风险提示管理员名单_合并-20260309.xlsx'
    output_file = base_path / 'data' / '法律风险提示管理员名单_合并-20260309_supplemented.xlsx'

    print("=" * 80)
    print("Excel服务部门层级信息补充工具")
    print("=" * 80)

    # 检查输入文件是否存在
    if not input_file.exists():
        print(f"[ERROR] Input file does not exist: {input_file}")
        sys.exit(1)

    # 执行补充处理
    df = supplement_excel_file(str(input_file), str(output_file))

    print("\n" + "=" * 80)
    print("[OK] Processing completed!")
    print(f"[FILE] Output file: {output_file}")
    print("=" * 80)


if __name__ == '__main__':
    main()