在matplotlib中用3d绘制imshow()图像

Question

如何imshow()在3D轴上绘制图像？我正在尝试这篇文章.在那篇文章中,表面图看起来与imshow()情节相同,但实际上它们不是.为了演示,我在这里采用了不同的数据:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# create a 21 x 21 vertex mesh
xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21))

# create vertices for a rotated mesh (3D rotation matrix)
X =  xx 
Y =  yy
Z =  10*np.ones(X.shape)

# create some dummy data (20 x 20) for the image
data = np.cos(xx) * np.cos(xx) + np.sin(yy) * np.sin(yy)

# create the figure
fig = plt.figure()

# show the reference image
ax1 = fig.add_subplot(121)
ax1.imshow(data, cmap=plt.cm.BrBG, interpolation='nearest', origin='lower', extent=[0,1,0,1])

# show the 3D rotated projection
ax2 = fig.add_subplot(122, projection='3d')
ax2.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=plt.cm.BrBG(data), shade=False)

这是我的情节:

Answer 1

我认为您在3D和2D表面颜色中的错误是由于表面颜色的数据归一化所致。如果使用来标准化传递给plot_surfacefacecolor 的数据，facecolors=plt.cm.BrBG(data/data.max())结果将更 接近您的期望。

如果您只是想要一个垂直于坐标轴的切片，而不是使用imshow，则可以使用contourf，从Matplotlib 1.1.0开始3D支持，

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib import cm

# create a 21 x 21 vertex mesh
xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21))

# create vertices for a rotated mesh (3D rotation matrix)
X =  xx 
Y =  yy
Z =  10*np.ones(X.shape)

# create some dummy data (20 x 20) for the image
data = np.cos(xx) * np.cos(xx) + np.sin(yy) * np.sin(yy)

# create the figure
fig = plt.figure()

# show the reference image
ax1 = fig.add_subplot(121)
ax1.imshow(data, cmap=plt.cm.BrBG, interpolation='nearest', origin='lower', extent=[0,1,0,1])

# show the 3D rotated projection
ax2 = fig.add_subplot(122, projection='3d')
cset = ax2.contourf(X, Y, data, 100, zdir='z', offset=0.5, cmap=cm.BrBG)

ax2.set_zlim((0.,1.))

plt.colorbar(cset)
plt.show()

此代码生成此图像：

尽管这不适用于imshow 解决方案更好的3D中任意位置的切片。