Python 如何使用不确定性条或等效物创建三维曲面图？

目标是创建一个3D曲面图，以不确定性条或其他清晰的不确定性可视化为特征，如下所示：

目前，我有以下情节：

这是使用以下代码生成的：

from io import StringIO
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import seaborn as sns

data_string = StringIO(
    """
     V1, V2, V3, V4, V5
    110,111,109,107,108
    101,101,102,102,102
    102,102,103,103,103
    103,103,104,104,104
    100,101,100,100,100
    """
)
uncertainties_string = StringIO(
    """
    V1U,V2U,V3U,V4U,V5U
      5,  5,  5,  5,  7
      5,  5,  3,  5,  5
      6,  5,  5,  5,  5
      5,  6,  5,  2,  5
      5,  5,  5,  5,  5
    """
)

data          = pd.read_csv(data_string)
uncertainties = pd.read_csv(uncertainties_string)

df = data.unstack().reset_index()
df.columns = ["X", "Y", "Z"]

df['X'] = pd.Categorical(df['X'])
df['X'] = df['X'].cat.codes

fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.jet, linewidth=0.01)
fig.colorbar(surf)
plt.show()

---

我不确定这是否有内置功能，但我玩得很开心： 我为每个数据点制作了两个错误条（一个用于上面的部分，一个用于下面的部分），并用不同的

zorder

绘制它们：

# get errors into the same format as data
df_unc = uncertainties.unstack().reset_index()
df_unc.columns = ["X", "Y", "Z"]
df_unc['X'] = pd.Categorical(df_unc['X'])
df_unc['X'] = df_unc['X'].cat.codes

# compute lower and upper values for errorbars
df['z_low'] = df['Z'] - df_unc['Z']
df['z_high'] = df['Z'] + df_unc['Z']

# plot surface with middle value for zorder 
surf = ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.jet, linewidth=0.01, zorder=2)

# plot lines with lower and higher zorder, respectively
for ix, row in df.iterrows():
    ax.plot((row['Y'], row['Y']), (row['X'], row['X']), (row['z_low'], row['Z']), c='k', zorder=1)
    ax.plot((row['Y'], row['Y']), (row['X'], row['X']), (row['Z'], row['z_high']), c='k', zorder=3)