196 lines
7.6 KiB
Python
196 lines
7.6 KiB
Python
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"""功能模块"""
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import math
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from typing import TypedDict, Union
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import jmespath
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import pydash
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import requests
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# WechatMapClass.haversine(lat1=32.
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# 474664, lon1=119.893471, lat2=32.
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# 471578, lon2=119.917068)
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# WechatMapClass.addressToCoordinat
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# 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)")
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# WechatMapClass.coordinateToAddressConversion(location="32.474664,119.893471")
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# WechatMapClass.coordinateToAddressConversion(location="32.471578,119.917068")
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class OriginPointOptional(TypedDict):
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经度: Union[float, str]
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纬度: Union[float, str]
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from pydantic import BaseModel
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class AdInfo(BaseModel):
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city: str
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adcode: int
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nation: str
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district: str
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province: str
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nation_code: int
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class Location(BaseModel):
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lat: float
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lng: float
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class Result(BaseModel):
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ip: str
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ad_info: AdInfo
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location: Location
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class IP解析地址模型Response(BaseModel):
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result: Result
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status: int
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message: str
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class wei地图工具:
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"""注释"""
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key = "7SNBZ-FLN63-LRA3S-YJBAK-XSHQ3-TGF5E" # 腾讯地图的key
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# key = "LREBZ-PH5R7-2NIXW-H7JDB-CVHTK-4YFUJ" # 莫大帅注册地地图的腾讯地图的key
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@classmethod
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def 公式计算二点之间的距离(cls, orgin原点: OriginPointOptional, user原点: OriginPointOptional) -> float:
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""""""
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# 地球半径，单位为公里
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R = 6371.0
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# orgin原点: OriginPointOptional = {"纬度": 30.80377, "经度": 104.151812}
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# user原点: OriginPointOptional = {"纬度": 30.80377, "经度": 104.151812}
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# 将角度转换为弧度
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orgin纬度 = math.radians(orgin原点.get("纬度"))
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orgin经度 = math.radians(orgin原点.get("经度"))
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user纬度 = math.radians(user原点.get("纬度"))
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user经度 = math.radians(user原点.get("纬度"))
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# 经纬度差值
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经度差 = user经度 - orgin经度
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纬度差 = user纬度 - orgin纬度
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print(经度差, 纬度差)
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# Haversine公式
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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
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
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# 计算两点间的距离
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distance = R * c
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print(f"The distance between two points is {distance} km.")
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return distance
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@classmethod
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def 腾讯地图计算二点之间的距离(cls, toData终点: OriginPointOptional, fromData起点: OriginPointOptional):
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""""""
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fromStrDat = dict(sorted(fromData起点.items(), reverse=False))
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fromStr = ",".join(fromStrDat.values())
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toStrDat = dict(sorted(toData终点.items(), reverse=False))
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toStr = ",".join(toStrDat.values())
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print("排序后的值为", fromStr, toStr)
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data = {"key": cls.key, "from": fromStr, "to": toStr}
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url = f"https://apis.map.qq.com/ws/distance/v1/matrix?mode=driving"
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res_data = requests.post(url=url, data=data)
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print(res_data.json())
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res_json = res_data.json() # type: dict
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d = {
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'status': 0,
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'message': 'Success',
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'request_id': '9435209f1ee84219ae7826e65f577331',
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'result': {
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'rows': [{
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'elements': [{
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'distance': 3550, # 起点到终点的距离，单位：米
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'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
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}],
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}],
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},
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}
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distance = jmespath.search(data=res_data.json(), expression="result.rows[0].elements[0].distance")
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return distance
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@classmethod
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def 地址转换为坐标(cls, address: str):
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""""""
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url = f"https://apis.map.qq.com/ws/geocoder/v1/?address={address}"
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res = requests.get(url=url, params={"key": cls.key}, )
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d = {
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'status': 0,
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'message': 'Success',
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'request_id': '9b049a3ff13c4ac3bda3bb5a9fe0c0a8',
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'result': {
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'title': '泰州海陵万达广场',
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'location': { # 解析到的坐标（GCJ02坐标系）
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'lng': 119.917068, # 经度
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'lat': 32.471578, # 纬度
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},
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'ad_info': { # 行政区划信息 object 是
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'adcode': '321202', # string 是 行政区划代码，规则详见：行政区划代码说明
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},
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'address_components': {
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'province': '江苏省',
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'city': '泰州市',
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'district': '海陵区',
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'street': '济川东路',
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'street_number': '',
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},
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'similarity': 0.99,
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'deviation': 1000,
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'reliability': 7, # 可信度参考：值范围 1 <低可信> - 10 <高可信>
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'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。
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},
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}
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@classmethod
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def 经纬度转换为地址(cls, location):
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""""""
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pass
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url = f'https://apis.map.qq.com/ws/geocoder/v1/?location={location}'
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res = requests.get(url=url, params={"key": cls.key}, )
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return res.json()
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@classmethod
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def ip地址转为地址(cls, x_forwarded_for: str | bool, ):
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""""""
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url = "https://apis.map.qq.com/ws/location/v1/ip"
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response_data = requests.get(url=url, params={"key": cls.key, "ip": x_forwarded_for}, )
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data = response_data.json() # type: dict
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print(data)
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resData = pydash.omit(data, "request_id")
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d = {
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'status': 0,
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'message': 'Success',
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'request_id': '8805bff3303f42dd8a1cbcca3701cf63',
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'result': {
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'ip': '240e:3a3:b201:3f10:185c:efdb:c5b0:b95c',
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'location': {
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'lat': 32.49098,
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'lng': 119.91956,
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},
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'ad_info': {
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'nation': '中国',
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'province': '江苏省',
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'city': '泰州市',
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'district': '海陵区',
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'adcode': 321202,
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'nation_code': 156,
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},
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},
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}
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# nation = jmespath.search(data=data, expression="result.ad_info.nation", ) # 国家
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# province = jmespath.search(data=data, expression="result.ad_info.province", ) # 省份
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# city = jmespath.search(data=data, expression="result.ad_info.city", )
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# district = jmespath.search(data=data, expression="result.ad_info.district", ) # 区
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# adcode = jmespath.search(data=data, expression="result.ad_info.adcode", ) # 区号
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# ip = jmespath.search(data=data, expression="result.ip", )
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# lat = jmespath.search(data=data, expression="result.location.lat", )
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# lng = jmespath.search(data=data, expression="result.location.lng", )
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return resData
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if __name__ == '__main__':
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# 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"})
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print(wei地图工具.ip地址转为地址(x_forwarded_for="119.84.150.100", ))
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