[英]pylab 3d scatter plots with 2d projections of plotted data
I am trying to create a simple 3D scatter plot but I want to also show a 2D projection of this data on the same figure. 我正在尝试创建一个简单的三维散点图,但我想在同一图上显示这些数据的二维投影。 This would allow to show a correlation between two of those 3 variables that might be hard to see in a 3D plot. 这将允许显示在3D图中可能难以看到的这3个变量中的两个之间的相关性。
I remember seeing this somewhere before but was not able to find it again. 我记得之前在某个地方看过这个,但却无法再找到它。
Here is some toy example: 这是一些玩具示例:
x= np.random.random(100)
y= np.random.random(100)
z= sin(x**2+y**2)
fig= figure()
ax= fig.add_subplot(111, projection= '3d')
ax.scatter(x,y,z)
You can add 2D projections of your 3D scatter data by using the plot
method and specifying zdir
: 您可以使用plot
方法并指定zdir
来添加3D散点图数据的2D投影:
import numpy as np
import matplotlib.pyplot as plt
x= np.random.random(100)
y= np.random.random(100)
z= np.sin(3*x**2+y**2)
fig= plt.figure()
ax= fig.add_subplot(111, projection= '3d')
ax.scatter(x,y,z)
ax.plot(x, z, 'r+', zdir='y', zs=1.5)
ax.plot(y, z, 'g+', zdir='x', zs=-0.5)
ax.plot(x, y, 'k+', zdir='z', zs=-1.5)
ax.set_xlim([-0.5, 1.5])
ax.set_ylim([-0.5, 1.5])
ax.set_zlim([-1.5, 1.5])
plt.show()
The other answer works with matplotlib 0.99, but 1.0 and later versions need something a bit different (this code checked with v1.3.1): 另一个答案适用于matplotlib 0.99,但1.0及更高版本需要一些不同的东西(此代码用v1.3.1检查):
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x= np.random.random(100)
y= np.random.random(100)
z= np.sin(3*x**2+y**2)
fig= plt.figure()
ax = Axes3D(fig)
ax.scatter(x,y,z)
ax.plot(x, z, 'r+', zdir='y', zs=1.5)
ax.plot(y, z, 'g+', zdir='x', zs=-0.5)
ax.plot(x, y, 'k+', zdir='z', zs=-1.5)
ax.set_xlim([-0.5, 1.5])
ax.set_ylim([-0.5, 1.5])
ax.set_zlim([-1.5, 1.5])
plt.show()
You can see what version of matplotlib you have by importing it and printing the version string: 您可以通过导入和打印版本字符串来查看matplotlib的版本:
import matplotlib
print matplotlib.__version__
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