sixGuoDjango/wechat_项目工具包/腾讯地图/腾讯地图工具.py

"""功能模块"""
import math
from typing import TypedDict, Union

import jmespath
import pydash
import requests


# WechatMapClass.haversine(lat1=32.
# 474664, lon1=119.893471, lat2=32.
# 471578, lon2=119.917068)
# WechatMapClass.addressToCoordinat
# eConversion(address="江苏省泰州市海陵区济川东路与青年南路交叉口东220米泰州万达广场(2号入口)")
# WechatMapClass.coordinateToAddressConversion(location="32.474664,119.893471")
# WechatMapClass.coordinateToAddressConversion(location="32.471578,119.917068")
class OriginPointOptional(TypedDict):
    经度: Union[float, str]
    纬度: Union[float, str]


from pydantic import BaseModel


class AdInfo(BaseModel):
    city: str
    adcode: int
    nation: str
    district: str
    province: str
    nation_code: int


class Location(BaseModel):
    lat: float
    lng: float


class Result(BaseModel):
    ip: str
    ad_info: AdInfo
    location: Location


class IP解析地址模型Response(BaseModel):
    result: Result
    status: int
    message: str


class wei地图工具:
    """注释"""
    
    key = "7SNBZ-FLN63-LRA3S-YJBAK-XSHQ3-TGF5E"  # 腾讯地图的key
    
    # key = "LREBZ-PH5R7-2NIXW-H7JDB-CVHTK-4YFUJ"  # 莫大帅注册地地图的腾讯地图的key
    
    @classmethod
    def 公式计算二点之间的距离(cls, orgin原点: OriginPointOptional, user原点: OriginPointOptional) -> float:
        """"""
        # 地球半径，单位为公里
        R = 6371.0
        # orgin原点: OriginPointOptional = {"纬度": 30.80377, "经度": 104.151812}
        # user原点: OriginPointOptional = {"纬度": 30.80377, "经度": 104.151812}
        # 将角度转换为弧度
        orgin纬度 = math.radians(orgin原点.get("纬度"))
        orgin经度 = math.radians(orgin原点.get("经度"))
        user纬度 = math.radians(user原点.get("纬度"))
        user经度 = math.radians(user原点.get("纬度"))
        
        # 经纬度差值
        经度差 = user经度 - orgin经度
        纬度差 = user纬度 - orgin纬度
        print(经度差, 纬度差)
        
        # Haversine公式
        a = math.sin(纬度差 / 2) ** 2 + math.cos(orgin纬度) * math.cos(user纬度) * math.sin(经度差 / 2) ** 2
        c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
        # 计算两点间的距离
        distance = R * c
        print(f"The distance between two points is {distance} km.")
        return distance
    
    @classmethod
    def 腾讯地图计算二点之间的距离(cls, toData终点: OriginPointOptional, fromData起点: OriginPointOptional):
        """"""
        fromStrDat = dict(sorted(fromData起点.items(), reverse=False))
        fromStr = ",".join(fromStrDat.values())
        toStrDat = dict(sorted(toData终点.items(), reverse=False))
        toStr = ",".join(toStrDat.values())
        print("排序后的值为", fromStr, toStr)
        data = {"key": cls.key, "from": fromStr, "to": toStr}
        url = f"https://apis.map.qq.com/ws/distance/v1/matrix?mode=driving"
        res_data = requests.post(url=url, data=data)
        print(res_data.json())
        res_json = res_data.json()  # type:  dict
        d = {
            "status": 0,
            "message": "Success",
            "request_id": "9435209f1ee84219ae7826e65f577331",
            "result": {
                "rows": [{
                    "elements": [{
                        "distance": 3550,  # 起点到终点的距离，单位：米
                        "duration": 651,  # 表示从起点到终点的结合路况的时间，秒为单位注：步行/骑行方式（不计算耗时）以及起终点附近没有道路造成无法计算时，不返回本此节点
                        }],
                    }],
                },
            }
        distance = jmespath.search(data=res_data.json(), expression="result.rows[0].elements[0].distance")
        return distance
    
    @classmethod
    def 地址转换为坐标(cls, address: str):
        """"""
        url = f"https://apis.map.qq.com/ws/geocoder/v1/?address={address}"
        res = requests.get(url=url, params={"key": cls.key}, )
        d = {
            "status": 0,
            "message": "Success",
            "request_id": "9b049a3ff13c4ac3bda3bb5a9fe0c0a8",
            "result": {
                "title": "泰州海陵万达广场",
                "location": {  # 解析到的坐标（GCJ02坐标系）
                    "lng": 119.917068,  # 经度
                    "lat": 32.471578,  # 纬度
                    },
                "ad_info": {  # 行政区划信息 object	是
                    "adcode": "321202",  # string	是	行政区划代码，规则详见：行政区划代码说明
                    },
                "address_components": {
                    "province": "江苏省",
                    "city": "泰州市",
                    "district": "海陵区",
                    "street": "济川东路",
                    "street_number": "",
                    },
                "similarity": 0.99,
                "deviation": 1000,
                "reliability": 7,  # 可信度参考：值范围 1 <低可信> - 10 <高可信>
                "level": 11,  # 解析精度级别，分为11个级别，一般>=9即可采用（定位到点，精度较高） 也可根据实际业务需求自行调整，完整取值表见下文。
                },
            }
    
    @classmethod
    def 经纬度转换为地址(cls, location):
        """"""
        pass
        url = f"https://apis.map.qq.com/ws/geocoder/v1/?location={location}"
        res = requests.get(url=url, params={"key": cls.key}, )
        return res.json()
    
    @classmethod
    def ip地址转为地址(cls, x_forwarded_for: str | bool, ):
        """"""
        url = "https://apis.map.qq.com/ws/location/v1/ip"
        response_data = requests.get(url=url, params={"key": cls.key, "ip": x_forwarded_for}, )
        data = response_data.json()  # type:  dict
        print(data)
        resData = pydash.omit(data, "request_id")
        d = {
            "status": 0,
            "message": "Success",
            "request_id": "8805bff3303f42dd8a1cbcca3701cf63",
            "result": {
                "ip": "240e:3a3:b201:3f10:185c:efdb:c5b0:b95c",
                "location": {
                    "lat": 32.49098,
                    "lng": 119.91956,
                    },
                "ad_info": {
                    "nation": "中国",
                    "province": "江苏省",
                    "city": "泰州市",
                    "district": "海陵区",
                    "adcode": 321202,
                    "nation_code": 156,
                    },
                },
            }
        # nation = jmespath.search(data=data, expression="result.ad_info.nation", )  # 国家
        # province = jmespath.search(data=data, expression="result.ad_info.province", )  # 省份
        # city = jmespath.search(data=data, expression="result.ad_info.city", )
        # district = jmespath.search(data=data, expression="result.ad_info.district", )  # 区
        # adcode = jmespath.search(data=data, expression="result.ad_info.adcode", )  # 区号
        # ip = jmespath.search(data=data, expression="result.ip", )
        # lat = jmespath.search(data=data, expression="result.location.lat", )
        # lng = jmespath.search(data=data, expression="result.location.lng", )
        
        return resData


if __name__ == "__main__":
    # wei地图工具.腾讯地图计算二点之间的距离(toData终点={"纬度": "30.80377", "经度": "104.151812"}, fromData起点={"纬度": "30.80377", "经度": "104.151812"})
    print(wei地图工具.ip地址转为地址(x_forwarded_for="119.84.150.100", ))