import re
from pathlib import Path

import pandas as pd

from utils.db_helper import DBHelper
from utils.constants import DOWNLOAD_DIR
from utils.parse_utils import convert_wan_to_yuan, extract_year_month_from_path, traverse_and_process

FUJIAN_CITY = {
"福州市": "350100",
"厦门市": "350200",
"莆田市": "350300",
"三明市": "350400",
"泉州市": "350500",
"漳州市": "350600",
"南平市": "350700",
"宁德市": "350900",
"龙岩市": "350800",
"平潭地区": "350128"
}


# 常量配置（新增路径正则校验）
PROV_CODE = "350000"
PROV_NAME = "福建省"
YEAR_PATTERN = re.compile(r"^\d{4}$")
MONTH_PATTERN = re.compile(r"^(0[1-9]|1[0-2])$")
download_dir = DOWNLOAD_DIR / "fujian"


def parse_excel(current_dir):
    """主解析入口（优化为单参数模式）
    Args:
        current_dir (str): 当前月份数据目录（格式：/年份/省份/月份）
    """
    current_path = Path(current_dir)
    year, month = extract_year_month_from_path(current_path)

    try:

        # 处理商品贸易数据
        current_file_path = next(current_path.glob("*分地市*"), None)
        process_region_trade(current_file_path, year, month)
        print(f"{current_dir}数据已全部成功处理")
    except Exception as e:
        print(f"处理失败：{current_dir}，错误：{str(e)}")
        raise


def process_region_trade(current_file_path, year, month):
    """处理地市贸易数据（增强1月逻辑 + 多sheet处理）"""

    # 动态选择列配置
    usecols = (
        list(range(7))
        if (year == 2023 and month <= 5)
        else [0, 3, 4, 7, 8, 11, 12]
    )
    #2023年5月之前的表格数据，单月数据在第二个sheet页
    sheet_index = 1 if (year == 2023 and month <= 5) else 0

    # 读取并处理主数据表
    current_df = load_and_process_data(
        current_file_path, year, month, usecols , sheet_index
    )

    # 数据库写入
    db = DBHelper()
    bulk_insert_data(
        db, current_df,
        conflict_cols=['crossborder_year_month', 'city_code'],
        update_cols=[
            'monthly_total', 'monthly_import', 'monthly_export',
            'yoy_import_export', 'yoy_import', 'yoy_export'
        ]
    )

    # 二月特殊处理逻辑
    if month == 2:
        print(f"根据2月表格生成{year}年1月数据...")
        handle_february_special_case(db, current_file_path, year, usecols)


def load_and_process_data(file_path, year, month, usecols, sheet_index = 0):
    """通用数据加载处理流程"""
    df = pd.read_excel(
        file_path,
        header=4,
        sheet_name=sheet_index,
        usecols=usecols,
        names=[
            'city_name', 'monthly_total', 'yoy_import_export',
            'monthly_export', 'yoy_export', 'monthly_import', 'yoy_import'
        ]
    )

    # 数据清洗流程
    df = (
        df.pipe(clean_city_names)
        .pipe(map_city_codes)
        .pipe(add_metadata, year, month)
        .pipe(convert_units)
    )
    return df


def handle_february_special_case(db, file_path, year, usecols):
    """二月数据处理特殊逻辑"""
    try:
        if year == 2023:
            process_2023_february(db, file_path, usecols)
        else:
            process_regular_february(db, file_path, year)
    except Exception as e:
        print(f"生成模拟1月数据失败: {str(e)}")


# ---------- 工具函数 ----------
def clean_city_names(df):
    """清洗城市名称"""
    df['city_name'] = (
        df['city_name']
        .str.replace(r'[（].*?[）]', '', regex=True)
        .str.strip()
    )
    return df


def map_city_codes(df):
    """映射城市编码"""
    df['city_code'] = df['city_name'].map(FUJIAN_CITY)
    return df[df['city_code'].notnull()].copy()


def add_metadata(df, year, month):
    """添加元数据字段"""
    return df.assign(
        crossborder_year=year,
        crossborder_year_month=f"{year}-{month:02d}",
        prov_code=PROV_CODE,
        prov_name=PROV_NAME
    )


def convert_units(df):
    """单位转换（万→元）"""
    for col in ['monthly_total', 'monthly_import', 'monthly_export']:
        df[col] = df[col].apply(convert_wan_to_yuan)
    return df


def bulk_insert_data(db, df, conflict_cols, update_cols):
    """批量数据插入"""
    db.bulk_insert(
        df,
        't_yujin_crossborder_prov_region_trade',
        conflict_columns=conflict_cols,
        update_columns=update_cols
    )


# ---------- 二月特殊处理逻辑 ----------
def process_2023_february(db, file_path, usecols):
    """2023年特殊处理逻辑"""
    # 读取双sheet数据
    ytd_df = load_sheet_data(file_path, sheet_index=0, usecols=usecols)
    current_df = load_sheet_data(file_path, sheet_index=1, usecols=usecols)

    # 合并计算差值
    merged = ytd_df.merge(
        current_df,
        on='city_code',
        suffixes=('_ytd', '_current')
    )

    # 生成一月数据
    january_df = create_january_data(merged, year=2023)

    bulk_insert_data(
        db, january_df,
        conflict_cols=['crossborder_year_month', 'city_code'],
        update_cols=[
            'monthly_total', 'monthly_import', 'monthly_export',
            'yoy_import_export', 'yoy_import', 'yoy_export'
        ]
    )


def process_regular_february(db, file_path, year):
    """常规年份二月处理"""
    df = pd.read_excel(
        file_path,
        header=4,
        usecols=[0, 1, 3, 4, 5, 7, 8, 9, 11, 12],
        names=[
            'city_name', 'ytd_monthly_total', 'monthly_total', 'yoy_import_export',
            'ytd_monthly_export', 'monthly_export', 'yoy_export',
            'ytd_monthly_import', 'monthly_import', 'yoy_import'
        ]
    )

    # 完整处理流程
    processed_df = (
        df.pipe(clean_city_names)
        .pipe(map_city_codes)
        .pipe(convert_special_units)
        .pipe(calculate_january_values)
        .pipe(add_metadata, year=year, month=1)
    )

    bulk_insert_data(
        db, processed_df,
        conflict_cols=['crossborder_year_month', 'city_code'],
        update_cols=[
            'monthly_total', 'monthly_import', 'monthly_export',
            'yoy_import_export', 'yoy_import', 'yoy_export'
        ]
    )


def load_sheet_data(file_path, sheet_index, usecols):
    """加载指定sheet数据"""
    df = pd.read_excel(
        file_path,
        sheet_name=sheet_index,
        header=4,
        usecols=usecols,
        names=[
            'city_name', 'monthly_total', 'yoy_import_export',
            'monthly_export', 'yoy_export', 'monthly_import', 'yoy_import'
        ]
    )
    return (
        df.pipe(clean_city_names)
        .pipe(map_city_codes)
        .pipe(convert_units)
    )

def create_january_data(merged_df, year):
    """生成一月数据（精确控制输出列）"""
    return (
        merged_df
        # 步骤1：计算新字段
        .assign(
            monthly_total=lambda x: x['monthly_total_ytd'] - x['monthly_total_current'],
            monthly_export=lambda x: x['monthly_export_ytd'] - x['monthly_export_current'],
            monthly_import=lambda x: x['monthly_import_ytd'] - x['monthly_import_current'],
            yoy_import_export=0.0,
            yoy_export=0.0,
            yoy_import=0.0,
            crossborder_year_month=f"{year}-01",
            city_name=lambda x:  x['city_name_current']
        )
        # 步骤2：精确选择输出列（关键修复）
        .reindex(columns=[
            'city_code', 'city_name',
            'monthly_total', 'monthly_export', 'monthly_import',
            'yoy_import_export', 'yoy_export', 'yoy_import',
            'crossborder_year_month'
        ])
        # 步骤3：合并元数据（确保字段完整）
        .assign(
            crossborder_year=year,
            prov_code=PROV_CODE,
            prov_name=PROV_NAME
        )
    )


def convert_special_units(df):
    """特殊单位转换"""
    for col in [
        'ytd_monthly_total', 'monthly_total',
        'ytd_monthly_export', 'monthly_export',
        'ytd_monthly_import', 'monthly_import'
    ]:
        df[col] = df[col].apply(convert_wan_to_yuan)
    return df


def calculate_january_values(df):
    """计算一月数值"""
    return df.assign(
        monthly_total=lambda x: x['ytd_monthly_total'] - x['monthly_total'],
        monthly_export=lambda x: x['ytd_monthly_export'] - x['monthly_export'],
        monthly_import=lambda x: x['ytd_monthly_import'] - x['monthly_import'],
        yoy_import_export=0.0,
        yoy_export=0.0,
        yoy_import=0.0
    ).drop(columns=[
            'ytd_monthly_total', 'ytd_monthly_export',
            'ytd_monthly_import'
    ])


# def clean_commodity_name(name):
#     return re.sub(r'[^\w\u4e00-\u9fa5]', '', str(name)).strip()



if __name__ == "__main__":
    traverse_and_process(download_dir, parse_excel, province_name="fujian")
    print("更新同比数据……")
    db_helper = DBHelper()
    db_helper.update_january_yoy()
    # parse_excel(download_dir/"2023"/"02")