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from idlelib import tooltip
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from math import *
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import queue
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import sys
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import time
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import os
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from datetime import datetime
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import pymssql
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import pandas as pd
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import numpy as np
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import datetime
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import math
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from folium import plugins
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import folium
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import os
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sys.path.append(r'C:\Users\Wayson\Desktop')
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from CarQuery import *
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from Arc import *
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from ArcMany import *
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arc_objects=[]
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Arc_many_count=[]
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Arc_many_objects=[]
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Arc_data2=[]
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# 返回两个gps点的实际距离，x,y与米的换算约为1:111000。不同的比例尺换算不一样
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def DistanceActual(geo1, geo2):
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return pow(pow(float(geo1[0]) - float(geo2[0]), 2) + pow(float(geo1[1]) - float(geo2[1]), 2), 0.5) * 111000
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# 如果起点定位数据跟道路的相差很远，则没有必要取该条arc
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def CheckNecessary(arc, car_record):
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if DistanceActual(arc.geometry_list[0], car_record.geo) > 1000:
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if DistanceActual(arc.geometry_list[-1], car_record.geo) > 1000:
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return False
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else:
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return True
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def CalCost(arc, car_record):
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cost = float("inf")
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# CheckNecessary函数首先排除与起点定位点距离相差远的路段。/LOU
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if CheckNecessary(arc, car_record):
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# 下面if是计算这条道路上所有点跟目标点的最短距离小于10m才返回True。我们认为起点定位精度为10m
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min_dist = float("inf")
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for geo1 in arc.geometry_list:
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tmp = DistanceActual(geo1, car_record.geo)
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if tmp < min_dist:
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min_dist = tmp
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cost = min_dist
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return cost
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# 给定car_geo和car_direction，根据最短距离，计算在所有道路的Cost，返回Cost最小的道路id
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def CalMinCostArcID(car_record):
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min_cost = float("inf")
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id = 0
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# arc_objects存储着所有边的Arc对象实例，即所有路段信息。/LOU
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for arc in arc_objects:
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cost = CalCost(arc, car_record)
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if cost < min_cost:
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min_cost = cost
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id = arc.id
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return id
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# 得到id为i的路段的密集点集合
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def ArcmanyRecordsDivide(Arcmany_i):
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Arcmany_index_start = sum(Arc_many_count[:Arcmany_i]) # 第i路段的起始索引位置
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Arcmany_index_end = sum(Arc_many_count[:Arcmany_i + 1]) # 第i路段的终止索引位置
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Arcmany_record_list = Arc_many_objects[Arcmany_index_start: Arcmany_index_end] # 每个路段的点集合
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Arcmany_geo_list = [Arcmany_record.geometry_list2 for Arcmany_record in Arcmany_record_list] # 每个路段的点的(x,y)集合
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return Arcmany_geo_list
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def swap_loc(list):
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temp = 0
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for i in range(0,len(list)):
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temp = list[i][1]
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list[i][1] = list[i][0]
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list[i][0] = temp
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# arc_objects里的路段经纬度更新为点集合
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for i in range(0, len(Arc_data2)): # 对每条路段循环
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del arc_objects[i].geometry_list[0:4] # 删除第i条路段原有的(x,y)信息
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Arcmany_record_list_i = ArcmanyRecordsDivide(i) # 提取第i条路段新的密集点(x,y)信息
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for j in range(0, len(Arcmany_record_list_i)): # 对第i条路段新的密集点(x,y)信息循环
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arc_objects[i].geometry_list.append(Arcmany_record_list_i[j]) # 在第i条路段(x,y)列加上 新的密集点(x,y)第j条信息
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