写一段python代码，在区间[-1,1]上，对被插函数f(x)=1/(1+16x^2)构造插值多项式，采用空间上均匀分布的节点，构造不同阶的插值多项式来近似被插函数，画出精确曲线、近似曲线及误差线

请确认下面的代码是否符合您的要求：

import numpy as np
import matplotlib.pyplot as plt

# 定义被插函数
def f(x):
    return 1 / (1 + 16 * x ** 2)

# 定义拉格朗日插值函数
def lagrange_interp(x, y, xi):
    n = len(x)
    yi = 0
    for i in range(n):
        p = 1
        for j in range(n):
            if j != i:
                p *= (xi - x[j]) / (x[i] - x[j])
        yi += y[i] * p
    return yi

# 定义Chebyshev节点
def chebyshev_nodes(a, b, n):
    x = []
    for k in range(1, n+1):
        xk = 0.5 * (a + b) + 0.5 * (b - a) * np.cos((2*k - 1) * np.pi / (2*n))
        x.append(xk)
    return x

# 定义均匀节点
def uniform_nodes(a, b, n):
    x = np.linspace(a, b, n)
    return x

# 精确曲线
x_true = np.linspace(-1, 1, 1000)
y_true = f(x_true)

# 构造不同阶的插值多项式
n_list = [8, 16, 32]
for n in n_list:
    # 均匀节点
    x_uniform = uniform_nodes(-1, 1, n)
    y_uniform = f(x_uniform)
    y_uniform_interp = [lagrange_interp(x_uniform, y_uniform, xi) for xi in x_true]
    # Chebyshev节点
    x_chebyshev = chebyshev_nodes(-1, 1, n)
    y_chebyshev = f(x_chebyshev)
    y_chebyshev_interp = [lagrange_interp(x_chebyshev, y_chebyshev, xi) for xi in x_true]
    # 绘制图像
    fig, ax = plt.subplots()
    ax.plot(x_true, y_true, label="True Function")
    ax.plot(x_uniform, y_uniform, 'o', label="Uniform Nodes")
    ax.plot(x_true, y_uniform_interp, label="Uniform Interpolation")
    ax.plot(x_chebyshev, y_chebyshev, 'o', label="Chebyshev Nodes")
    ax.plot(x_true, y_chebyshev_interp, label="Chebyshev Interpolation")
    ax.plot(x_true, y_true - y_uniform_interp, label="Uniform Error")
    ax.plot(x_true, y_true - y_chebyshev_interp, label="Chebyshev Error")
    ax.legend()
    plt.show()

代码中使用了拉格朗日插值法，分别构造了均匀节点和Chebyshev节点，绘制了精确曲线、均匀节点插值曲线、Chebyshev节点插值曲线以及误差曲线，其中误差曲线表示精确曲线减去插值曲线的差。