我想知道如何获得<2> GPS点之间的距离和方位。 我研究了半胱氨酸配方。 有人告诉我，我也可以使用相同的数据找到轴承。
一切都运转良好，但轴承还没有正常工作。轴承输出负值但应在0 - 360度之间。
设定数据应使水平方位96.02166666666666
并且是：
Start point: 53.32055555555556 , -1.7297222222222221
Bearing: 96.02166666666666
Distance: 2 km
Destination point: 53.31861111111111, -1.6997222222222223
Final bearing: 96.04555555555555
这是我的新代码：
from math import *
Aaltitude = 2000
Oppsite = 20000
lat1 = 53.32055555555556
lat2 = 53.31861111111111
lon1 = -1.7297222222222221
lon2 = -1.6997222222222223
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * atan2(sqrt(a), sqrt(1-a))
Base = 6371 * c
Bearing =atan2(cos(lat1)*sin(lat2)-sin(lat1)*cos(lat2)*cos(lon2-lon1), sin(lon2-lon1)*cos(lat2))
Bearing = degrees(Bearing)
print ""
print ""
print "--------------------"
print "Horizontal Distance:"
print Base
print "--------------------"
print "Bearing:"
print Bearing
print "--------------------"
Base2 = Base * 1000
distance = Base * 2 + Oppsite * 2 / 2
Caltitude = Oppsite - Aaltitude
a = Oppsite/Base
b = atan(a)
c = degrees(b)
distance = distance / 1000
print "The degree of vertical angle is:"
print c
print "--------------------"
print "The distance between the Balloon GPS and the Antenna GPS is:"
print distance
print "--------------------"
答案 0 :(得分：206)
这是一个Python版本：
from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r
答案 1 :(得分：6)
这些答案中的大多数都是“四舍五入”地球的半径。如果您针对其他距离计算器（例如geopy）检查这些，则这些功能将关闭。
这很有效：
from math import radians, cos, sin, asin, sqrt
def haversine(lat1, lon1, lat2, lon2):
R = 3959.87433 # this is in miles. For Earth radius in kilometers use 6372.8 km
dLat = radians(lat2 - lat1)
dLon = radians(lon2 - lon1)
lat1 = radians(lat1)
lat2 = radians(lat2)
a = sin(dLat/2)**2 + cos(lat1)*cos(lat2)*sin(dLon/2)**2
c = 2*asin(sqrt(a))
return R * c
# Usage
lon1 = -103.548851
lat1 = 32.0004311
lon2 = -103.6041946
lat2 = 33.374939
print(haversine(lat1, lon1, lat2, lon2))
答案 2 :(得分：4)
轴承计算不正确，您需要将输入交换为atan2。
bearing = atan2(sin(long2-long1)*cos(lat2), cos(lat1)*sin(lat2)-sin(lat1)*cos(lat2)*cos(long2-long1))
bearing = degrees(bearing)
bearing = (bearing + 360) % 360
这将为您提供正确的方位。
答案 3 :(得分：3)
您可以尝试以下操作：
from haversine import haversine
haversine((45.7597, 4.8422),(48.8567, 2.3508),miles = True)
243.71209416020253
答案 4 :(得分：2)
您可以通过添加360°来解决负面轴承问题。 不幸的是，对于正向轴承，这可能导致轴承大于360°。 这是模运算符的一个很好的候选者，所以你应该添加行
Bearing = (Bearing + 360) % 360
在方法结束时。
答案 5 :(得分：2)
还有一个矢量化实现，该实现允许使用4个numpy数组代替坐标的标量值：
def distance(s_lat, s_lng, e_lat, e_lng):
# approximate radius of earth in km
R = 6373.0
s_lat = s_lat*np.pi/180.0
s_lng = np.deg2rad(s_lng)
e_lat = np.deg2rad(e_lat)
e_lng = np.deg2rad(e_lng)
d = np.sin((e_lat - s_lat)/2)**2 + np.cos(s_lat)*np.cos(e_lat) * np.sin((e_lng - s_lng)/2)**2
return 2 * R * np.arcsin(np.sqrt(d))
答案 6 :(得分：1)
默认情况下，atan2中的Y是第一个参数。这是documentation。您需要切换输入以获得正确的方位角。
bearing = atan2(sin(lon2-lon1)*cos(lat2), cos(lat1)*sin(lat2)in(lat1)*cos(lat2)*cos(lon2-lon1))
bearing = degrees(bearing)
bearing = (bearing + 360) % 360
答案 7 :(得分：1)
这实际上提供了两种获得距离的方法。他们是Haversine和Vincentys。根据我的研究，我发现Vincentys相对准确。也可以使用import语句来实现。
答案 8 :(得分：0)
以下是计算距离和方位的两个函数，它们基于先前消息中的代码和https://gist.github.com/jeromer/2005586（为了清晰起见，两个函数的lat，lon格式的地理点添加了元组类型）。我测试了这两个功能，它们似乎正常工作。
#coding:UTF-8
from math import radians, cos, sin, asin, sqrt, atan2, degrees
def haversine(pointA, pointB):
if (type(pointA) != tuple) or (type(pointB) != tuple):
raise TypeError("Only tuples are supported as arguments")
lat1 = pointA[0]
lon1 = pointA[1]
lat2 = pointB[0]
lon2 = pointB[1]
# convert decimal degrees to radians
lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r
def initial_bearing(pointA, pointB):
if (type(pointA) != tuple) or (type(pointB) != tuple):
raise TypeError("Only tuples are supported as arguments")
lat1 = radians(pointA[0])
lat2 = radians(pointB[0])
diffLong = radians(pointB[1] - pointA[1])
x = sin(diffLong) * cos(lat2)
y = cos(lat1) * sin(lat2) - (sin(lat1)
* cos(lat2) * cos(diffLong))
initial_bearing = atan2(x, y)
# Now we have the initial bearing but math.atan2 return values
# from -180° to + 180° which is not what we want for a compass bearing
# The solution is to normalize the initial bearing as shown below
initial_bearing = degrees(initial_bearing)
compass_bearing = (initial_bearing + 360) % 360
return compass_bearing
pA = (46.2038,6.1530)
pB = (46.449, 30.690)
print haversine(pA, pB)
print initial_bearing(pA, pB)
答案 9 :(得分：0)
这是@Michael Dunn给出的Haversine公式的小数矢量化实现，比大型矢量提高了10到50倍。
from numpy import radians, cos, sin, arcsin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
#Convert decimal degrees to Radians:
lon1 = np.radians(lon1.values)
lat1 = np.radians(lat1.values)
lon2 = np.radians(lon2.values)
lat2 = np.radians(lat2.values)
#Implementing Haversine Formula:
dlon = np.subtract(lon2, lon1)
dlat = np.subtract(lat2, lat1)
a = np.add(np.power(np.sin(np.divide(dlat, 2)), 2),
np.multiply(np.cos(lat1),
np.multiply(np.cos(lat2),
np.power(np.sin(np.divide(dlon, 2)), 2))))
c = np.multiply(2, np.arcsin(np.sqrt(a)))
r = 6371
return c*r