preamble
When encountering three-dimensional data, three-dimensional images can give us a deeper understanding of the data. python's matplotlib library contains a rich set of three-dimensional plotting tools.
1. Creating 3D Axes Object Axes3D
There are two main ways to create Axes3D, one is to utilize the keywordprojection='3d'l to do so, and the other is achieved by taking the data from thempl_toolkits.mplot3dImporting objects into Axes3D to achieve this is all about generating objects in Axes3D with a 3D format.
#Method 1, using keywords from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D #Define the axes fig = () ax1 = (projection='3d') #ax = fig.add_subplot(111,projection='3d') #This method can also draw multiple subplots #Method two, utilizing the three-dimensional axis method from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Define image and 3D format axes fig=() ax2 = Axes3D(fig)
2. 3D curves and scattering points
Subsequently graph on the defined axes:
import numpy as np z = (0,13,1000) x = 5*(z) y = 5*(z) zd = 13*(100) xd = 5*(zd) yd = 5*(zd) ax1.scatter3D(xd,yd,zd, cmap='Blues') # Plotting scatterplots ax1.plot3D(x,y,z,'gray') # Plotting space curves ()
3. Three-dimensional surfaces
The next step is to draw three-dimensionalmeshes：
fig = () # Define new 3D axes ax3 = (projection='3d') #Define 3D data xx = (-5,5,0.5) yy = (-5,5,0.5) X, Y = (xx, yy) Z = (X)+(Y) #Mapping ax3.plot_surface(X,Y,Z,cmap='rainbow') #(X,Y,Z, zdim='z',offset=-2, cmap='rainbow) #contour map, to set offset, as the minimum value of Z ()
If you add the step size during rendering, you will get a sharper and more detailed image:ax3.plot_surface(X,Y,Z,rstride = 1, cstride = 1,cmap='rainbow')The row and cloum_stride are the sampling steps of the drawing in horizontal and vertical directions, and the smaller they are, the finer the drawing will be.
4. Contour lines
It is also possible to placecontour lineProjection onto different surfaces:
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#Define the axes
fig4 = ()
ax4 = (projection='3d')
# Generate 3D data
xx = (-5,5,0.1)
yy = (-5,5,0.1)
X, Y = (xx, yy)
Z = ((X**2+Y**2))
#Mapping
ax4.plot_surface(X,Y,Z,alpha=0.3,cmap='winter') # Generate surfaces, alpha is used to control transparency
(X,Y,Z,zdir='z', offset=-3,cmap="rainbow") # Generate a z-direction projection onto the x-y plane
(X,Y,Z,zdir='x', offset=-6,cmap="rainbow") # Generate x-direction projection to y-z plane
(X,Y,Z,zdir='y', offset=6,cmap="rainbow") # Generate a y-direction projection onto the x-z plane
#(X,Y,Z,zdir='y', offset=6,cmap="rainbow") #Generate y-direction projection fill, cast to x-z plane, contourf() function
#Set display range
ax4.set_xlabel('X')
ax4.set_xlim(-6, 4) # Pull apart the range of axes to show the projection
ax4.set_ylabel('Y')
ax4.set_ylim(-4, 6)
ax4.set_zlabel('Z')
ax4.set_zlim(-3, 3)
()
5. Randomized Scatterplot
You can use scatter() to generate scatter plots of various sizes and colors with the following parameters:
# Function Definition (x, y, s=None, #array scalar c=None, # Color sequence array, sequence marker=None, # Points of Style cmap=None, #colormap Color Styles norm=None, #Normalized Normalized color camp vmin=None, vmax=None, # corresponds to the normalized range above alpha=None, #Transparency linewidths=None, #Line width verts=None, # edgecolors=None, #Edge color data=None, **kwargs ) #ref:/api/_as_gen/
from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D #Define the axes fig4 = () ax4 = (projection='3d') # Generate 3D data xx = (20)*10-5 # Take 100 random numbers in the range of 5~5 yy = (20)*10-5 X, Y = (xx, yy) Z = ((X**2+Y**2)) #Mapping (X,Y,Z,alpha=0.3,c=(400),s=(10,20, size=(20, 40))) # Generate scatter. Use c to control the color sequence and s to control the size. #Set display range ()
Finish
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summarize
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