一组125个经纬度坐标和另一组137个经纬度坐标对比分布是否相似代码

这里提供一种可能的实现方式，假设经纬度坐标已经存储在两个列表 `coords1` 和 `coords2` 中，每个经纬度坐标以元组 `(longitude, latitude)` 的形式表示。

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

def compare_coordinate_distribution(coords1, coords2):
    """
    Compare the distribution of two sets of coordinates based on the
    Haversine distance between each pair of coordinates. Returns a float
    between 0 and 1, where 0 indicates completely different distributions
    and 1 indicates identical distributions.
    """
    distances1 = []
    distances2 = []
    for coord1 in coords1:
        for coord2 in coords2:
            distances1.append(haversine(coord1[0], coord1[1], coord2[0], coord2[1]))
            distances2.append(haversine(coord2[0], coord2[1], coord1[0], coord1[1]))
    avg_distance1 = sum(distances1) / len(distances1)
    avg_distance2 = sum(distances2) / len(distances2)
    return min(avg_distance1, avg_distance2) / max(avg_distance1, avg_distance2)

这里使用了 Haversine 公式计算两个经纬度坐标之间的距离。`compare_coordinate_distribution` 函数计算了两个坐标集合之间的距离分布比较，返回一个 float 值，表示相似度，值越大表示越相似。