"""美国物流价格数据访问类"""
import pandas as pd
from typing import Dict, List, Optional
from dataaccess.base_dao import BaseDAO


class USPriceDAO(BaseDAO):
    """美国物流价格数据访问"""

    def __init__(self):
        super().__init__("us_logistics_price")

    def get_all_companies(self) -> List[str]:
        """获取所有美国物流公司"""
        df = self.get_all()
        if df.empty:
            return []
        return df["company"].unique().tolist()

    def get_company_price(self, company: str) -> pd.DataFrame:
        """获取指定物流公司的价格数据"""
        return self.get_by_condition({"company": company})

    def get_company_config(self, company: str) -> Dict:
        """获取物流公司配置信息"""
        df = self.get_by_condition({"company": company})
        if df.empty:
            return {}

        config = {}
        for _, row in df.iterrows():
            key = row.get("config_key")
            value = row.get("config_value")
            if key:
                config[key] = value

        return config


class USPostcodeDAO(BaseDAO):
    """美国邮编分区数据访问"""

    def __init__(self):
        super().__init__("us_postcode_zone")

    def get_zone(self, postcode: str, port: str = "west") -> Optional[str]:
        """根据邮编获取分区"""
        postcode_5 = postcode[:5]
        df = self.get_by_condition({"postcode": postcode_5, "port": port})

        if df.empty:
            return None

        return df.iloc[0].get("zone")

    def get_remote_type(self, postcode: str) -> int:
        """获取偏远类型: 0-非偏远, 1-偏远, 2-超偏远, 3-超超偏远"""
        postcode_5 = postcode[:5]
        df = self.get_by_condition({"postcode": postcode_5})

        if df.empty:
            return 0

        return int(df.iloc[0].get("remote_type", 0))

    def is_contiguous(self, postcode: str) -> bool:
        """判断是否在本土范围内"""
        postcode_5 = postcode[:5]
        df = self.get_by_condition({"postcode": postcode_5})
        return not df.empty