随机化坐标

import numpy as np

# 生成均匀分布的随机坐标点
def generate_uniform_coordinates(num_points, x_range, y_range):
    x_coords = np.random.uniform(x_range[0], x_range[1], num_points)
    y_coords = np.random.uniform(y_range[0], y_range[1], num_points)
    return list(zip(x_coords, y_coords))

# 生成高斯分布的随机坐标点
def generate_gaussian_coordinates(num_points, x_mean, y_mean, x_std, y_std):
    x_coords = np.random.normal(x_mean, x_std, num_points)
    y_coords = np.random.normal(y_mean, y_std, num_points)
    return list(zip(x_coords, y_coords))

# 示例调用
uniform_points = generate_uniform_coordinates(100, (0, 100), (0, 100))
gaussian_points = generate_gaussian_coordinates(100, 50, 50, 20, 20)

print("Uniform Points:", uniform_points)
print("Gaussian Points:", gaussian_points)

import numpy as np

def generate_uniform_coordinates_within_range(num_points, x_range, y_range):
    x_coords = []
    y_coords = []
    while len(x_coords) < num_points:
        x = np.random.uniform(x_range[0], x_range[1])
        y = np.random.uniform(y_range[0], y_range[1])
        if x_range[0] <= x <= x_range[1] and y_range[0] <= y <= y_range[1]:
            x_coords.append(x)
            y_coords.append(y)
    return list(zip(x_coords, y_coords))

# 示例调用
points_within_range = generate_uniform_coordinates_within_range(100, (0, 100), (0, 100))
print("Points within Range:", points_within_range)