Python中的3D绘图命令~放到论文或PPT里太加分了

导语

很多情况下,为了能够观察到数据之间的内部的关系,可以使用绘图来更好的显示规律。

比如在下面的几张动图中,使用matplotlib中的三维显示命令,使得我们可以对于logistic回归网络的性能与相关参数有了更好的理解。

下面的动图显示了在训练网络时,不同的学习速率对于算法收敛之间的影响。

下面给出了绘制这些动态曲线的相关的python指令:

➤01 3D plot


1.基本语法

在安装matplotlib之后,自动安装有 mpl_toolkits.mplot3d。

#Importing Libraries import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d #3D Plotting fig = plt.figure() ax = plt.axes(projection="3d") #Labeling ax.set_xlabel('X Axes') ax.set_ylabel('Y Axes') ax.set_zlabel('Z Axes') plt.show()

2.Python Cmd

使用pythoncmd 插入相应的语句。

3.举例

(1) Ex1

#!/usr/local/bin/python # -*- coding: gbk -*- #****************************** # TEST2.PY -- by Dr. ZhuoQing 2020-11-16 # # Note: #****************************** from headm import * from mpl_toolkits.mplot3d import axes3d ax = plt.axes(projection='3d') x = [1,2,3,4,5,6,7,8,9] y = [2,3,4,6,7,8,9,5,1] z = [5,6,2,4,8,6,5,6,1] ax.plot3D(x,y,z) ax.set_xlabel('X Axes') ax.set_ylabel('Y Axes') ax.set_zlabel('Z Axes') plt.show() #------------------------------------------------------------ # END OF FILE : TEST2.PY #******************************

▲ 3D plot的演示

(2) Ex2

from mpl_toolkits.mplot3d import axes3d ax = plt.axes(projection='3d') angle = linspace(0, 2*pi*5, 400) x = cos(angle) y = sin(angle) z = linspace(0, 5, 400) ax.plot3D(x,y,z) ax.set_xlabel('X Axes') ax.set_ylabel('Y Axes') ax.set_zlabel('Z Axes') plt.show()

▲ 3D绘制的例子

(3) Ex3

import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D import numpy as np import matplotlib.pyplot as plt mpl.rcParams['legend.fontsize'] = 10 fig = plt.figure() ax = fig.gca(projection='3d') theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) z = np.linspace(-2, 2, 100) r = z**2 + 1 x = r * np.sin(theta) y = r * np.cos(theta) ax.plot(x, y, z, label='parametric curve') ax.legend() plt.show()

➤02 绘制Scatter


利用和上面的相同的绘制命令,将原来的plot3D修改成为 scatter即可。

from mpl_toolkits.mplot3d import axes3d ax = plt.axes(projection='3d') angle = linspace(0, 2*pi*5, 40) x = cos(angle) y = sin(angle) z = linspace(0, 5, 40) ax.scatter(x,y,z, color='b') ax.set_xlabel('X Axes') ax.set_ylabel('Y Axes') ax.set_zlabel('Z Axes') plt.show()

▲ Scatter 的例子

➤03 绘制3D Surface


(1) Ex1

▲ 3D surface例子

#!/usr/local/bin/python # -*- coding: gbk -*- #****************************** # TEST2.PY -- by Dr. ZhuoQing 2020-11-16 # # Note: #****************************** from headm import * from mpl_toolkits.mplot3d import axes3d ax = plt.axes(projection='3d') x = arange(-5, 5, 0.1) y = arange(-5, 5, 0.1) x,y = meshgrid(x, y) R = sqrt(x**2+y**2) z = sin(R) ax.plot_surface(x, y, z) ax.set_xlabel('X Axes') ax.set_ylabel('Y Axes') ax.set_zlabel('Z Axes') plt.show() #------------------------------------------------------------ # END OF FILE : TEST2.PY #******************************

▲ 3D 绘制Surface

▲ 绘制3D球表面

(2) 举例

''' *********** 3D surface (color map) *********** Demonstrates plotting a 3D surface colored with the coolwarm color map. The surface is made opaque by using antialiased=False. Also demonstrates using the LinearLocator and custom formatting for the z axis tick labels. ''' from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter import numpy as np fig = plt.figure() ax = fig.gca(projection='3d') # Make data. X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) # Plot the surface. surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False) # Customize the z axis. ax.set_zlim(-1.01, 1.01) ax.zaxis.set_major_locator(LinearLocator(10)) ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) # Add a color bar which maps values to colors. fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()

▲ 彩色表面绘制

end

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原文链接:https://blog.csdn.net/L010409/article/details/123061457

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