我可以使用曲线拟合的不确定性(Python)绘制误差范围吗？

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# 定义拟合函数
def func(x, a, b, c):
    return a * np.exp(-b * x) + c

# 生成带有噪声的数据
x = np.linspace(0, 4, 50)
y = func(x, 2.5, 1.3, 0.5)
np.random.seed(0)
y_noise = 0.2 * np.random.normal(size=x.size)
ydata = y + y_noise

# 进行曲线拟合
popt, pcov = curve_fit(func, x, ydata)

# 计算置信区间
popt_err = np.sqrt(np.diag(pcov))
n_samples = 1000
popt_samples = np.random.multivariate_normal(popt, pcov, n_samples)

# 绘制拟合曲线和置信区间
plt.plot(x, y, 'r-', label='True curve')
plt.plot(x, func(x, *popt), 'b-', label='Best fit')
plt.fill_between(x, func(x, *(popt - popt_err)), func(x, *(popt + popt_err)), color='gray', alpha=0.3, label='Confidence interval')
for sample in popt_samples:
    plt.plot(x, func(x, *sample), 'k-', alpha=0.1)
plt.scatter(x, ydata, color='black', label='Data')
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()