在2022年，人们可以发布JavaScript和Python混合代码，使用最新的Python库，即地理库来解决这个问题。总的好处是，用户可以在运行在现代设备上的web页面上看到结果。

async function main(){ let pyodide = await loadPyodide(); await pyodide.loadPackage(["micropip"]); console.log(pyodide.runPythonAsync(` import micropip await micropip.install('geographiclib') from geographiclib.geodesic import Geodesic lat1 = 52.2296756; lon1 = 21.0122287; lat2 = 52.406374; lon2 = 16.9251681; ans = Geodesic.WGS84.Inverse(lat1, lon1, lat2, lon2) dkm = ans["s12"] / 1000 print("Geodesic solution", ans) print(f"Distance = {dkm:.4f} km.") `)); } main(); <script src="https://cdn.jsdelivr.net/pyodide/v0.21.0/full/pyodide.js"></script>

您可以使用Uber的H3,point_dist()函数来计算两个(纬度，经度)点之间的球面距离。我们可以设置返回单位('km'、'm'或'rads')。默认单位为km。

例子:

import h3

coords_1 = (52.2296756, 21.0122287)
coords_2 = (52.406374, 16.9251681)
distance = h3.point_dist(coords_1, coords_2, unit='m') # To get distance in meters

有多种方法来计算基于坐标的距离，即纬度和经度

安装和导入

from geopy import distance
from math import sin, cos, sqrt, atan2, radians
from sklearn.neighbors import DistanceMetric
import osrm
import numpy as np

定义坐标

lat1, lon1, lat2, lon2, R = 20.9467,72.9520, 21.1702, 72.8311, 6373.0
coordinates_from = [lat1, lon1]
coordinates_to = [lat2, lon2]

使用半正矢

dlon = radians(lon2) - radians(lon1)
dlat = radians(lat2) - radians(lat1)
    
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
    
distance_haversine_formula = R * c
print('distance using haversine formula: ', distance_haversine_formula)

使用哈弗辛和sklearn

dist = DistanceMetric.get_metric('haversine')
    
X = [[radians(lat1), radians(lon1)], [radians(lat2), radians(lon2)]]
distance_sklearn = R * dist.pairwise(X)
print('distance using sklearn: ', np.array(distance_sklearn).item(1))

使用OSRM

osrm_client = osrm.Client(host='http://router.project-osrm.org')
coordinates_osrm = [[lon1, lat1], [lon2, lat2]] # note that order is lon, lat
    
osrm_response = osrm_client.route(coordinates=coordinates_osrm, overview=osrm.overview.full)
dist_osrm = osrm_response.get('routes')[0].get('distance')/1000 # in km
print('distance using OSRM: ', dist_osrm)

使用geopy

distance_geopy = distance.distance(coordinates_from, coordinates_to).km
print('distance using geopy: ', distance_geopy)
    
distance_geopy_great_circle = distance.great_circle(coordinates_from, coordinates_to).km 
print('distance using geopy great circle: ', distance_geopy_great_circle)

输出

distance using haversine formula:  26.07547017310917
distance using sklearn:  27.847882224769783
distance using OSRM:  33.091699999999996
distance using geopy:  27.7528030550408
distance using geopy great circle:  27.839182219511834

在2022年，人们可以发布JavaScript和Python混合代码，使用最新的Python库，即地理库来解决这个问题。总的好处是，用户可以在运行在现代设备上的web页面上看到结果。

async function main(){ let pyodide = await loadPyodide(); await pyodide.loadPackage(["micropip"]); console.log(pyodide.runPythonAsync(` import micropip await micropip.install('geographiclib') from geographiclib.geodesic import Geodesic lat1 = 52.2296756; lon1 = 21.0122287; lat2 = 52.406374; lon2 = 16.9251681; ans = Geodesic.WGS84.Inverse(lat1, lon1, lat2, lon2) dkm = ans["s12"] / 1000 print("Geodesic solution", ans) print(f"Distance = {dkm:.4f} km.") `)); } main(); <script src="https://cdn.jsdelivr.net/pyodide/v0.21.0/full/pyodide.js"></script>

另一种有趣的方法是通过Pyodide和WebAssembly实现混合JavaScript和Python，使用Python的库Pandas和geographiclib来获得解决方案，这也是可行的。

我用Pandas做了额外的工作来准备输入数据，当输出可用时，将它们附加到解决方案列中。Pandas为常见需求提供了许多有用的输入/输出特性。它的toHtml方法可以方便地在网页上呈现最终的解决方案。

我发现这个答案中的代码在某些iPhone和iPad设备上执行不成功。但在较新的中端Android设备上，它运行得很好。

async function main(){ let pyodide = await loadPyodide(); await pyodide.loadPackage(["pandas", "micropip"]); console.log(pyodide.runPythonAsync(` import micropip import pandas as pd import js print("Pandas version: " + pd.__version__) await micropip.install('geographiclib') from geographiclib.geodesic import Geodesic import geographiclib as gl print("Geographiclib version: " + gl.__version__) data = {'Description': ['Answer to the question', 'Bangkok to Tokyo'], 'From_long': [21.0122287, 100.6], 'From_lat': [52.2296756, 13.8], 'To_long': [16.9251681, 139.76], 'To_lat': [52.406374, 35.69], 'Distance_km': [0, 0]} df1 = pd.DataFrame(data) collist = ['Description','From_long','From_lat','To_long','To_lat'] div2 = js.document.createElement("div") div2content = df1.to_html(buf=None, columns=collist, col_space=None, header=True, index=True) div2.innerHTML = div2content js.document.body.append(div2) arr="<i>by Swatchai</i>" def dkm(frLat,frLon,toLat,toLon): print("frLon,frLat,toLon,toLat:", frLon, "|", frLat, "|", toLon, "|", toLat) dist = Geodesic.WGS84.Inverse(frLat, frLon, toLat, toLon) return dist["s12"] / 1000 collist = ['Description','From_long','From_lat','To_long','To_lat','Distance_km'] dist = [] for ea in zip(df1['From_lat'].values, df1['From_long'].values, df1['To_lat'].values, df1['To_long'].values): ans = dkm(*ea) print("ans=", ans) dist.append(ans) df1['Distance_km'] = dist # Update content div2content = df1.to_html(buf=None, columns=collist, col_space=None, header=True, index=False) div2.innerHTML = div2content js.document.body.append(div2) # Using the haversine formula from math import sin, cos, sqrt, atan2, radians, asin # Approximate radius of earth in km from Wikipedia R = 6371 lat1 = radians(52.2296756) lon1 = radians(21.0122287) lat2 = radians(52.406374) lon2 = radians(16.9251681) # https://en.wikipedia.org/wiki/Haversine_formula def hav(angrad): return (1-cos(angrad))/2 h = hav(lat2-lat1)+cos(lat2)*cos(lat1)*hav(lon2-lon1) dist2 = 2*R*asin(sqrt(h)) print(f"Distance by haversine formula = {dist2:8.6f} km.") `)); } main(); <script src="https://cdn.jsdelivr.net/pyodide/v0.21.0/full/pyodide.js"></script> Pyodide implementation<br>

最简单的方法是用哈弗辛包装。

import haversine as hs

coord_1 = (lat, lon)
coord_2 = (lat, lon)
x = hs.haversine(coord_1, coord_2)
print(f'The distance is {x} km')