python - 许多二维点之间的最短路径(Shapely LineString 中的旅行商？)

Question

我试图根据地面点测量创建河流横截面剖面图。当尝试从一系列具有公共(public) ID 的点创建一个 Shapely LineString 时，我意识到给定点的顺序真的很重要，因为 LineString 只会连接给定的点 ' indexwise'(按列表给定顺序连接点)。下面的代码说明了默认行为:

from shapely.geometry import Point, LineString
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt

# Generate random points
x=np.random.randint(0,100,10)
y=np.random.randint(0,50,10)
data = zip(x,y)

# Create Point and default LineString GeoSeries
gdf_point = gpd.GeoSeries([Point(j,k) for j,k in data])
gdf_line = gpd.GeoSeries(LineString(zip(x,y)))

# plot the points and "default" LineString
ax = gdf_line.plot(color='red')
gdf_point.plot(marker='*', color='green', markersize=5,ax=ax)

这将产生图像:

问题:Shapely 中是否有任何内置方法可以自动创建最符合逻辑(又名:最短、最简单、最不交叉) ,...) 穿过给定的随机二维点列表？

您可以在下面找到与默认行(红色)相比所需的行(绿色)。

Answer 1

这是解决我的横截面 LineString 简化问题的方法。但是，我的解决方案没有正确解决计算上更复杂的任务，即找到通过给定点的最终最短路径。正如评论者所建议的那样，有许多库和脚本可用于解决该特定问题，但如果有人想保持简单，您可以使用对我有用的东西。欢迎使用和评论!

def simplify_LineString(linestring):

    '''
    Function reorders LineString vertices in a way that they each vertix is followed by the nearest remaining vertix.
    Caution: This doesn't calculate the shortest possible path (travelling postman problem!) This function performs badly
    on very random points since it doesn't see the bigger picture.
    It is tested only with the positive cartesic coordinates. Feel free to upgrade and share a better function!

    Input must be Shapely LineString and function returns Shapely Linestring.

    '''

    from shapely.geometry import Point, LineString
    import math

    if not isinstance(linestring,LineString):
        raise IOError("Argument must be a LineString object!")

    #create a point lit
    points_list = list(linestring.coords)

    ####
    # DECIDE WHICH POINT TO START WITH - THE WESTMOST OR SOUTHMOST? (IT DEPENDS ON GENERAL DIRECTION OF ALL POINTS)
    ####
    points_we = sorted(points_list, key=lambda x: x[0])
    points_sn = sorted(points_list, key=lambda x: x[1])

    # calculate the the azimuth of general diretction
    westmost_point = points_we[0]
    eastmost_point = points_we[-1]

    deltay = eastmost_point[1] - westmost_point[1]
    deltax = eastmost_point[0] - westmost_point[0]

    alfa = math.degrees(math.atan2(deltay, deltax))
    azimut = (90 - alfa) % 360

    if (azimut > 45 and azimut < 135):
        #General direction is west-east
        points_list = points_we
    else:
        #general direction is south-north
        points_list = points_sn

    ####
    # ITERATIVELY FIND THE NEAREST VERTIX FOR THE EACH REMAINING VERTEX
    ####

    # Create a new, ordered points list, starting with the east or southmost point.
    ordered_points_list = points_list[:1]

    for iteration in range(0, len(points_list[1:])):

        current_point = ordered_points_list[-1]  # current point that we are looking the nearest neighour to
        possible_candidates = [i for i in points_list if i not in ordered_points_list]  # remaining (not yet sortet) points

        distance = 10000000000000000000000
        best_candidate = None
        for candidate in possible_candidates:
            current_distance = Point(current_point).distance(Point(candidate))
            if current_distance < distance:
                best_candidate = candidate
                distance = current_distance

        ordered_points_list.append(best_candidate)

    return LineString(ordered_points_list)