制作一个seaborn线图，为每个点指定标准差/置信区间

我正在尝试制作一个具有平滑的置信区间的线图。看起来像这样的东西：

_{(source: pydata.org https://seaborn.pydata.org/_images/seaborn-lineplot-1.png)}

目前，我所做的是使用误差线来显示置信区间。所以我有 100 个 (x,y) 对，我将其传递给sns.lineplot它为我创建了一条线，然后每个点，我都有我想要绘制的标准差Sigma_new_vec.

axs[(e-1)//2, (e-1)%2].errorbar(x, y ,yerr = Sigma_new_vec, linestyle="None")
sns.lineplot(x='x', y='y', data = predicted_line, ax= axs[(e-1)//2, (e-1)%])
sns.lineplot(x='x', y='y', data = true_line, ax = axs[(e-1)//2, (e-1)%2] )

So currently what I have looks something like this, where I have confidence intervals for each of the 100 points, but I would like it to be smoothened out.

With @ImportanceOfBeingErnest 的 https://stackoverflow.com/users/4124317/importanceofbeingernest建议，我让它工作了！

lower_bound = [M_new - Sigma for M_new, Sigma in zip(M_new_vec, Sigma_new_vec)]
upper_bound = [M_new + Sigma for M_new, Sigma in zip(M_new_vec, Sigma_new_vec)]
plt.fill_between(x_axis, lower_bound, upper_bound, alpha=.3)

如果 numpy 可用：

import numpy as np
import matplotlib.pyplot as plt 

M_new_vec = np.array(M_new_vec)
Sigma_new_vec = np.array(Sigma_new_vec)

lower_bound = M_new_vec - Sigma_new_vec
upper_bound = M_new_vec + Sigma_new_vec

plt.fill_between(x_axis, lower_bound, upper_bound, alpha=.3)