Matplotlib绘制常用三维图

￡神魔★判官ぃ 2024-04-06 10:22 9阅读 0赞

#### 目录 ####

*  1 三维图像的绘制简介
 *  2 三维线框图
 *  3 三维曲面图
 *  4 三维散点图
 *  5 三维柱状图
 *  6 三维折线图

--------------------

![在这里插入图片描述][271b08bb882346ce960d7f4c653aa540.gif_pic_center]

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## 1 三维图像的绘制简介 ##

Matplotlib使用和绘制二维图表：[https://blog.csdn.net/ZGL\_cyy/article/details/125705060][https_blog.csdn.net_ZGL_cyy_article_details_125705060]

matplotlib支持绘制三维线框图, 三维曲面图, 三维散点图. 需要使用axes3d提供3d坐标系.

from mpl_toolkits.mplot3d import axes3d
    ax3d = mp.gca(projection='3d')
    
    ax3d.plot_wireframe()	# 绘制3d线框图
    ax3d.plot_surface()		# 绘制3d曲面图
    ax3d.scatter()			# 绘制3d散点图

## 2 三维线框图 ##

ax3d.plot_wireframe(
        x, y, 	# x,y网格点坐标矩阵
        z, 		# z为每个坐标点的值
    	rstride=30,	# 行跨距
        cstride=30, # 列跨距
        linewidth=1,
        color=''
    )

案例演示:

"""
    三维线框图
    """
    import numpy as np
    import matplotlib.pyplot as mp
    from mpl_toolkits.mplot3d import axes3d
    
    # 生成网格点坐标矩阵
    n = 1000
    x, y = np.meshgrid(np.linspace(-3, 3, n),
    				   np.linspace(-3, 3, n))
    # 根据x,y 计算当前坐标下的z高度值
    z = (1-x/2 + x**5 + y**3) * np.exp(-x**2 -y**2)
    
    mp.figure('Wireframe', facecolor='lightgray')
    ax3d = mp.gca(projection='3d')
    ax3d.set_xlabel('X', fontsize=14)
    ax3d.set_ylabel('Y', fontsize=14)
    ax3d.set_zlabel('Z', fontsize=14)
    ax3d.plot_wireframe(x, y, z, rstride=10, 
    	cstride=10,color='dodgerblue')
    mp.show()

![在这里插入图片描述][ca8d1c1d57a64159808a7251d69bcb36.png]

## 3 三维曲面图 ##

ax3d.plot_surface(
        x, y, 	# x,y网格点坐标矩阵
        z, 		# z为每个坐标点的值
    	rstride=30,	# 行跨距
        cstride=30, # 列跨距
        cmap='jet'
    )

案例演示：

"""
    三维曲面图
    """
    import numpy as np
    import matplotlib.pyplot as mp
    from mpl_toolkits.mplot3d import axes3d
    
    # 生成网格点坐标矩阵
    n = 1000
    x, y = np.meshgrid(np.linspace(-3, 3, n),
    				   np.linspace(-3, 3, n))
    # 根据x,y 计算当前坐标下的z高度值
    z = (1-x/2 + x**5 + y**3) * np.exp(-x**2 -y**2)
    
    mp.figure('Surface', facecolor='lightgray')
    ax3d = mp.gca(projection='3d')
    ax3d.set_xlabel('X', fontsize=14)
    ax3d.set_ylabel('Y', fontsize=14)
    ax3d.set_zlabel('Z', fontsize=14)
    ax3d.plot_surface(x, y, z, rstride=50, 
    	cstride=50, cmap='jet')
    mp.show()

![在这里插入图片描述][c48872fd0d29476fabbb6c4625de7f44.png]

## 4 三维散点图 ##

ax3d.scatter(
    	x, y, z,  		 # x,y,z  确定一组散点坐标
        marker='',		 # 点型
        s = 60,			 # 点的大小
        edgecolor='',	 # 边缘色
        facecolor='',	 # 填充色
        zorder=3,		 # 绘制图层编号 
        c=d,			 # 设置过渡性颜色
        cmap='jet'		 # 颜色映射
    )
    12345678910

案例演示：

"""
    三维散点图
    """
    import numpy as np
    import matplotlib.pyplot as mp
    import mpl_toolkits.mplot3d as axes3d
    
    n = 500
    x = np.random.normal(0, 1, n)
    y = np.random.normal(0, 1, n)
    z = np.random.normal(0, 1, n)
    d = np.sqrt(x**2 + y**2 + z**2)
    
    mp.figure('3D Scatter')
    ax3d = mp.gca(projection='3d')
    ax3d.set_xlabel('X', fontsize=14)
    ax3d.set_ylabel('Y', fontsize=14)
    ax3d.set_zlabel('Z', fontsize=14)
    ax3d.scatter(x, y, z, s=60, alpha=0.6, 
    	c=d, cmap='jet')
    mp.show()

![在这里插入图片描述][9b4df75e8f574afc9bbcf4da63b71a01.png]

## 5 三维柱状图 ##

import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    #构造需要显示的值
    X=np.arange(0, 5, step=1)#X轴的坐标
    Y=np.arange(0, 9, step=1)#Y轴的坐标
    #设置每一个（X，Y）坐标所对应的Z轴的值，在这边Z（X，Y）=X+Y
    Z=np.zeros(shape=(5, 9))
    for i in range(5):
        for j in range(9):
            Z[i, j]=i+j
    xx, yy=np.meshgrid(X, Y)#网格化坐标
    X, Y=xx.ravel(), yy.ravel()#矩阵扁平化
    bottom=np.zeros_like(X)#设置柱状图的底端位值
    Z=Z.ravel()#扁平化矩阵
    width=height=1#每一个柱子的长和宽
    #绘图设置
    fig=plt.figure()
    ax=fig.gca(projection='3d')#三维坐标轴
    ax.bar3d(X, Y, bottom, width, height, Z, shade=True)#
    #坐标轴设置
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z(value)')
    plt.show()

![在这里插入图片描述][930dee2208d344b480d77109635a56b7.png]

## 6 三维折线图 ##

import numpy as np
    import matplotlib.pyplot as plt
    
    xs1 = np.ones(100)
    ys1 = range(1, 101)
    zs1 = np.zeros(100)
    
    xs2 = np.ones(100) * 2
    ys2 = range(1, 101)
    zs2 = np.zeros(100)
    
    xs3 = np.ones(100) * 3
    ys3 = range(1, 101)
    zs3 = np.zeros(100)
    for i in range(21, 100):
      zs3[i] = -0.22
    
    xs4 = np.ones(100) * 4
    ys4 = range(1, 101)
    zs4 = np.zeros(100)
    
    
    # Plot
    from mpl_toolkits.mplot3d import Axes3D
    fig = plt.figure()
    ax = Axes3D(fig)
    ax.plot(xs1, ys1, zs1, lw=2, color=[0.8, 0.33, 0])
    ax.plot(xs2, ys2, zs2, lw=2, color=[0.33, 0.8, 0])
    ax.plot(xs3, ys3, zs3, lw=2, color=[0, 0.5, 0.8])
    ax.plot(xs4, ys4, zs4, lw=2, color=[0.8, 0.1, 0.5])
    # ax.set_xlabel("X Axis")
    # ax.set_ylabel("Y Axis")
    # ax.set_zlabel("Z Axis")
    # ax.set_title("Addective fault simulation")
    plt.show()

![在这里插入图片描述][ed8ad2447b7241d1b13ea83a5897a241.png]

[271b08bb882346ce960d7f4c653aa540.gif_pic_center]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/be4fbd6420714b3dae1fd1f4bbc2f518.gif
[Java]: https://blog.csdn.net/ZGL_cyy/article/details/133549985
[https_blog.csdn.net_ZGL_cyy_article_details_125705060]: https://blog.csdn.net/ZGL_cyy/article/details/125705060
[ca8d1c1d57a64159808a7251d69bcb36.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/ed4bec28943b492d8788cb47f4697073.png
[c48872fd0d29476fabbb6c4625de7f44.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/ddc13a6bd8e74a5b988155b2ecee1737.png
[9b4df75e8f574afc9bbcf4da63b71a01.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/a959b08a388a47fe87bdcdbfbbb99b37.png
[930dee2208d344b480d77109635a56b7.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/3245dc90f23a4ff489d0fd8c1f3fa956.png
[ed8ad2447b7241d1b13ea83a5897a241.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/1ef5e4790b5c4a61b960b42d37e2c20d.png