# def make_func(letters,result):
#     global func_x
#     # print(letters,result)
#     draw_axis(-100, 100, 20)
#     letter = str(letters).split(',')
#     n = len(letter)-1
#     decimals = [round(random.uniform(-10, 10), 1) for _ in range(n)]
#     target_result = functional_formula(letter[1:],result,decimals)
#     # output.configure(state="normal")
#     # output.delete("1.0", tk.END)  # 删除当前输出框中的所有文本
#     # output.insert(tk.END, target_result)
#     # output.configure(state="disabled")
#     code_str = fitting('x', target_result)
#     # print(code_str)
#     target_func = create_func(code_str)  # 获取当前函数func
#     # # print(target_result)
#     # # 生成带噪声的待拟合数据
#     # np.random.seed(0)
#     # x_data = np.linspace(-100, 100, 50)
#     # y_data = target_func(x_data) + np.random.normal(0, 0.5, 50)
#     # # print(y_data)
#     # code_str = fitting(letters, result)
#     # print(code_str)
#     # func_x = create_func(code_str)  # 获取当前函数func
#     #
#     # # 初始参数的估计值
#     # initial_coeffs = [1.0 for _ in range(n)]
#     # # 使用最小二乘法拟合函数
#     # result = leastsq(residuals, initial_coeffs, args=(y_data, x_data))
#     # best_coeffs = result[0]
#     # # 打印拟合得到的系数
#     # print("拟合得到的系数：", best_coeffs)
#
#     # 绘制拟合结果
#     x_fit = np.linspace(-100, 100, 50)
#     y_fit = target_func(x_fit)
#
#     # plt.plot(x_data, y_data, 'bo', label='原始数据')
#     plt.plot(x_fit, y_fit, 'r-', label='拟合曲线')
#     plt.legend()
#     plt.xlabel('x')
#     plt.ylabel('y')
#     # plt.show()
#     plt.savefig('new_line.png')
#     # 清除当前图形状态
#     plt.clf()
#     show_image()
#
# # 定义误差函数，即最小二乘法的目标函数
# def residuals(coeffs, y, x):
#     return y - func_x(x, *coeffs)