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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Tue Aug 2 12:38:31 2022 | |
| @author: syed | |
| """ | |
| import regex_spatial | |
| from utils import geoutil | |
| import geopandas as gpd | |
| import pandas as pd | |
| import re | |
| from shapely.geometry import Polygon,mapping | |
| import numpy as np | |
| from shapely.geometry import Polygon, MultiPoint, LineString, Point | |
| from shapely.geometry.base import geom_factory | |
| from shapely.geos import lgeos | |
| from geocoder import geo_level1 | |
| def get_between_coordinates(coordinates1, coordinates2, centroid1, centroid2): | |
| # 创建多边形对象 | |
| poly1 = Polygon(coordinates1) | |
| poly2 = Polygon(coordinates2) | |
| # 计算中心点连线 | |
| center_line = LineString([centroid1, centroid2]) | |
| def max_perpendicular_distance(poly, line): | |
| max_dist = 0 | |
| farthest_points = None | |
| for point in poly.exterior.coords: | |
| p = Point(point) | |
| # 计算点到中心线的垂直距离 | |
| dist = p.distance(line) | |
| if dist > max_dist: | |
| max_dist = dist | |
| farthest_points = p | |
| return max_dist * 2 # 计算直径（双倍最大垂直距离） | |
| # 计算两个区域的最大垂线距离 | |
| diameter1 = max_perpendicular_distance(poly1, center_line) | |
| diameter2 = max_perpendicular_distance(poly2, center_line) | |
| # 计算平均直径 R | |
| R = (diameter1 + diameter2) / 2 | |
| # 计算圆心（两个中心点的中点） | |
| midpoint = ((centroid1[0] + centroid2[0]) / 2, (centroid1[1] + centroid2[1]) / 2) | |
| # 生成圆形区域 | |
| circle = Point(midpoint).buffer(R / 2, resolution=100) | |
| # 获取圆的坐标 | |
| circle_coords = list(circle.exterior.coords) | |
| return [circle_coords], midpoint | |
| def get_angular_coordinates(coordinates, angle, distance): | |
| """ | |
| 计算基于输入地点坐标、方向角度和距离的目标点，并返回以该点为圆心的等面积圆形坐标集。 | |
| :param coordinates: 地点的边界坐标列表 | |
| :param angle: 方向角度（以正北为 0 度，顺时针方向） | |
| :param distance: 目标点距离（单位：公里） | |
| :return: 以目标点为圆心，等面积半径的圆形坐标集 | |
| """ | |
| # 计算输入地点的几何中心 | |
| poly = Polygon(coordinates) | |
| poly_center = poly.centroid | |
| lat, lon = poly_center.y, poly_center.x # 纬度在前，经度在后 | |
| # 计算地球上的 1 度经纬度距离（近似值） | |
| lat_km = 111.32 # 1 度纬度 ≈ 111.32 km | |
| lon_km = 111.32 * np.cos(np.radians(lat)) # 1 度经度 ≈ 111.32 × cos(纬度) | |
| # 计算输入区域的面积（近似 km²，需进一步优化投影转换） | |
| area_degrees = poly.area # 原始经纬度面积 | |
| area_km2 = area_degrees * (lat_km * lon_km) # 近似转换为 km² | |
| # 计算等面积圆的半径 r（单位 km） | |
| r_km = np.sqrt(area_km2 / np.pi) # 使得 πr² ≈ area_km2 | |
| # 计算方向向量（基于输入角度） | |
| angle_rad = np.radians(angle) # 转换为弧度 | |
| d_lat = (np.sin(angle_rad) * distance) / lat_km | |
| d_lon = (np.cos(angle_rad) * distance) / lon_km | |
| # 计算目标点 | |
| target_point = (lon + d_lon, lat + d_lat) | |
| # 计算以目标点为圆心，半径 r_km 的圆形坐标集 | |
| circle_points = [] | |
| for theta in np.linspace(0, 360, num=100): # 生成 100 个点构成圆形 | |
| theta_rad = np.radians(theta) | |
| d_lat = (np.sin(theta_rad) * r_km) / lat_km | |
| d_lon = (np.cos(theta_rad) * r_km) / lon_km | |
| circle_points.append((target_point[0] + d_lon, target_point[1] + d_lat)) | |
| return circle_points | |