将MATLAB中的CircleFitByKasa转换为Python

import numpy as np
from scipy.optimize import minimize

def circle_fit_by_kasa(data):
    # 定义目标函数
    def objective(params):
        cx, cy, r = params
        distances = np.sqrt((data[:, 0] - cx)**2 + (data[:, 1] - cy)**2)
        return np.sum((distances - r)**2)

    # 初始化拟合参数
    cx_init = np.mean(data[:, 0])
    cy_init = np.mean(data[:, 1])
    r_init = np.sqrt(np.mean((data[:, 0] - cx_init)**2 + (data[:, 1] - cy_init)**2))
    params_init = [cx_init, cy_init, r_init]

    # 最小化目标函数
    result = minimize(objective, params_init, method='Nelder-Mead')

    # 提取拟合结果
    cx_fit, cy_fit, r_fit = result.x

    return cx_fit, cy_fit, r_fit

# 示例数据
data = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

# 进行拟合
cx_fit, cy_fit, r_fit = circle_fit_by_kasa(data)

# 打印拟合结果
print("拟合圆心坐标：({}, {})".format(cx_fit, cy_fit))
print("拟合半径：{}".format(r_fit))