Python绘制等高线图时plt.clabel触发TypeError错误的解决求助

问题场景 #

尝试绘制简单等高线图，使用的样本数据如下：

1,1,0.008935424499213696
2,1,0.0003330092004034668
3,1,0.0007237526588141918
4,1,0.009105708450078964
5,1,0.005323910620063543
6,1,0.002217665547505021
7,1,0.02252855896949768
8,1,0.01543859951198101
9,1,0.005959200672805309
10,1,0.003394557628780603
11,1,0.004915744531899691
12,1,0.7103423476219177
13,1,4.735919952392578
14,143,6.962272167205811
15,143,9.117818832397461
16,143,10.14690971374512
17,143,11.10696411132812
18,143,13.1904764175415
19,143,13.69217777252197
20,143,12.86547660827637
21,143,11.97461032867432
22,143,11.42048358917236
23,143,10.77302360534668
24,143,8.853728294372559
25,143,7.49812650680542
26,143,5.019758224487305
27,143,0.771071195602417

使用的代码如下：

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
# load values
X_dat, Y_dat, Z_dat = np.loadtxt('file.txt', unpack=True, delimiter=',')

# Convert to numpy arrays
X, Y, Z, = np.array([]), np.array([]), np.array([])
for i in range(len(X_dat)):
    X = np.append(X, X_dat[i])
    Y = np.append(Y, Y_dat[i])
    Z = np.append(Z, Z_dat[i])

# create x-y points to be used in heatmap
xi = np.linspace(X.min(), X.max(), 290)
yi = np.linspace(Y.min(), Y.max(), 290)
# Interpolate for plotting
zi = griddata((X, Y), Z, (xi[None, :], yi[:, None]), method='cubic')

# Create the contour plot
levels = [0.0, 0.2, 0.5, 0.9, 1.5, 2.5, 3.5]
cp = plt.contour(xi, yi, zi, levels, cmap='RdGy')
plt.colorbar()
plt.show()
plt.clabel(cp, fmt='%2.1f', inline='true', fontsize=12)
plt.title('Contour Plot')
plt.xlabel('x (cm)')
plt.ylabel('y (cm)')

运行时触发错误：

TypeError: slice indices must be integers or None or have an __index__ method

错误由plt.clabel()触发。

问题原因 #

核心问题是**plt.show()的位置错误**：plt.show()会显示并关闭当前绘图窗口，窗口关闭后，contour对象cp已经失效，后续调用plt.clabel(cp)时就会出现切片索引相关的错误。

另外代码中存在冗余逻辑：np.loadtxt返回的本身就是numpy数组，不需要再通过循环逐个append转换。

修复方案 #

1. 调整plt.show()的位置，将其放在所有绘图修饰（标签、标题、刻度等）之后
2. 删除冗余的数组转换逻辑
3. 可选：补充处理插值后可能出现的NaN值（避免绘图警告）

修复后的完整代码：

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata

# 加载数据，直接得到numpy数组
X, Y, Z = np.loadtxt('file.txt', unpack=True, delimiter=',')

# 创建网格点
xi = np.linspace(X.min(), X.max(), 290)
yi = np.linspace(Y.min(), Y.max(), 290)
# 插值，替换NaN为0（可选，根据需求调整）
zi = griddata((X, Y), Z, (xi[None, :], yi[:, None]), method='cubic')
zi = np.nan_to_num(zi)

# 绘制等高线图
levels = [0.0, 0.2, 0.5, 0.9, 1.5, 2.5, 3.5]
cp = plt.contour(xi, yi, zi, levels, cmap='RdGy')

# 添加绘图修饰
plt.clabel(cp, fmt='%2.1f', inline=True, fontsize=12)
plt.title('Contour Plot')
plt.xlabel('x (cm)')
plt.ylabel('y (cm)')
plt.colorbar()

# 最后显示图形
plt.show()

说明 #

• 调整plt.show()顺序后，cp对象在窗口显示前是有效的，plt.clabel()可以正常为等高线添加标签
• np.nan_to_num(zi)用于处理插值后可能出现的NaN值，避免绘图时的警告信息
• 去掉了冗余的数组循环转换，直接使用np.loadtxt返回的数组，提升代码效率

内容的提问来源于stack exchange，提问作者Conny91