在matplotlib中以3D方式绘制imshow()图像 [英] Plotting a imshow() image in 3d in matplotlib

问题描述

如何在3d轴上绘制imshow()图像?我正在尝试与此帖子.在那篇文章中，表面图看起来与imshow()图相同，但实际上却不同.为了演示，在这里我采用了不同的数据:

How to plot a imshow() image in 3d axes? I was trying with this post. In that post, the surface plot looks same as imshow() plot but actually they are not. To demonstrate, here I took different data:

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:

推荐答案

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_surface facecolor的数据，则facecolors=plt.cm.BrBG(data/data.max())的结果将更接近您的期望.

I think your error in the 3D vs 2D surface colour is due to data normalisation in the surface colours. If you normalise the data passed to plot_surface facecolor with, facecolors=plt.cm.BrBG(data/data.max()) the results are closer to what you'd expect.

如果只希望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，该功能在3D中受支持

If you simply want a slice normal to a coordinate axis, instead of using imshow, you could use contourf, which is supported in 3D as of matplotlib 1.1.0,

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()

此代码生成以下图像:

尽管这对于在3D中imshow

Although this won't work for a slice at an arbitrary position in 3D where the imshow solution is better.

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