3d图形在数据分析、数据建模、图形和图像处理等领域中都有着广泛的应用,下面将给大家介绍一下如何使用python进行3d图形的绘制,包括3d散点、3d表面、3d轮廓、3d直线(曲线)以及3d文字等的绘制。
准备工作:
python中绘制3d图形,依旧使用常用的绘图模块matplotlib,但需要安装mpl_toolkits工具包,安装方法如下:windows命令行进入到python安装目录下的scripts文件夹下,执行: pip install --upgrade matplotlib即可;linux环境下直接执行该命令。
安装好这个模块后,即可调用mpl_tookits下的mplot3d类进行3d图形的绘制。
下面以实例进行说明。
1、3d表面形状的绘制
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot( 111 , projection = '3d' ) # make data u = np.linspace( 0 , 2 * np.pi, 100 ) v = np.linspace( 0 , np.pi, 100 ) x = 10 * np.outer(np.cos(u), np.sin(v)) y = 10 * np.outer(np.sin(u), np.sin(v)) z = 10 * np.outer(np.ones(np.size(u)), np.cos(v)) # plot the surface ax.plot_surface(x, y, z, color = 'b' ) plt.show() |
球表面,结果如下:
2、3d直线(曲线)的绘制
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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() |
这段代码用于绘制一个螺旋状3d曲线,结果如下:
3、绘制3d轮廓
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from matplotlib import cm fig = plt.figure() ax = fig.gca(projection = '3d' ) x, y, z = axes3d.get_test_data( 0.05 ) cset = ax.contour(x, y, z, zdir = 'z' , offset = - 100 , cmap = cm.coolwarm) cset = ax.contour(x, y, z, zdir = 'x' , offset = - 40 , cmap = cm.coolwarm) cset = ax.contour(x, y, z, zdir = 'y' , offset = 40 , cmap = cm.coolwarm) ax.set_xlabel( 'x' ) ax.set_xlim( - 40 , 40 ) ax.set_ylabel( 'y' ) ax.set_ylim( - 40 , 40 ) ax.set_zlabel( 'z' ) ax.set_zlim( - 100 , 100 ) plt.show() |
绘制结果如下:
4、绘制3d直方图
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot( 111 , projection = '3d' ) x, y = np.random.rand( 2 , 100 ) * 4 hist, xedges, yedges = np.histogram2d(x, y, bins = 4 , range = [[ 0 , 4 ], [ 0 , 4 ]]) # construct arrays for the anchor positions of the 16 bars. # note: np.meshgrid gives arrays in (ny, nx) so we use 'f' to flatten xpos, # ypos in column-major order. for numpy >= 1.7, we could instead call meshgrid # with indexing='ij'. xpos, ypos = np.meshgrid(xedges[: - 1 ] + 0.25 , yedges[: - 1 ] + 0.25 ) xpos = xpos.flatten( 'f' ) ypos = ypos.flatten( 'f' ) zpos = np.zeros_like(xpos) # construct arrays with the dimensions for the 16 bars. dx = 0.5 * np.ones_like(zpos) dy = dx.copy() dz = hist.flatten() ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color = 'b' , zsort = 'average' ) plt.show() |
绘制结果如下:
5、绘制3d网状线
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot( 111 , projection = '3d' ) # grab some test data. x, y, z = axes3d.get_test_data( 0.05 ) # plot a basic wireframe. ax.plot_wireframe(x, y, z, rstride = 10 , cstride = 10 ) plt.show() |
绘制结果如下:
6、绘制3d三角面片图
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np n_radii = 8 n_angles = 36 # make radii and angles spaces (radius r=0 omitted to eliminate duplication). radii = np.linspace( 0.125 , 1.0 , n_radii) angles = np.linspace( 0 , 2 * np.pi, n_angles, endpoint = false) # repeat all angles for each radius. angles = np.repeat(angles[..., np.newaxis], n_radii, axis = 1 ) # convert polar (radii, angles) coords to cartesian (x, y) coords. # (0, 0) is manually added at this stage, so there will be no duplicate # points in the (x, y) plane. x = np.append( 0 , (radii * np.cos(angles)).flatten()) y = np.append( 0 , (radii * np.sin(angles)).flatten()) # compute z to make the pringle surface. z = np.sin( - x * y) fig = plt.figure() ax = fig.gca(projection = '3d' ) ax.plot_trisurf(x, y, z, linewidth = 0.2 , antialiased = true) plt.show( |
绘制结果如下:
7、绘制3d散点图
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np def randrange(n, vmin, vmax): ''''' helper function to make an array of random numbers having shape (n, ) with each number distributed uniform(vmin, vmax). ''' return (vmax - vmin) * np.random.rand(n) + vmin fig = plt.figure() ax = fig.add_subplot( 111 , projection = '3d' ) n = 100 # for each set of style and range settings, plot n random points in the box # defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh]. for c, m, zlow, zhigh in [( 'r' , 'o' , - 50 , - 25 ), ( 'b' , '^' , - 30 , - 5 )]: xs = randrange(n, 23 , 32 ) ys = randrange(n, 0 , 100 ) zs = randrange(n, zlow, zhigh) ax.scatter(xs, ys, zs, c = c, marker = m) ax.set_xlabel( 'x label' ) ax.set_ylabel( 'y label' ) ax.set_zlabel( 'z label' ) plt.show() |
绘制结果如下:
8、绘制3d文字
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt fig = plt.figure() ax = fig.gca(projection = '3d' ) # demo 1: zdir zdirs = (none, 'x' , 'y' , 'z' , ( 1 , 1 , 0 ), ( 1 , 1 , 1 )) xs = ( 1 , 4 , 4 , 9 , 4 , 1 ) ys = ( 2 , 5 , 8 , 10 , 1 , 2 ) zs = ( 10 , 3 , 8 , 9 , 1 , 8 ) for zdir, x, y, z in zip (zdirs, xs, ys, zs): label = '(%d, %d, %d), dir=%s' % (x, y, z, zdir) ax.text(x, y, z, label, zdir) # demo 2: color ax.text( 9 , 0 , 0 , "red" , color = 'red' ) # demo 3: text2d # placement 0, 0 would be the bottom left, 1, 1 would be the top right. ax.text2d( 0.05 , 0.95 , "2d text" , transform = ax.transaxes) # tweaking display region and labels ax.set_xlim( 0 , 10 ) ax.set_ylim( 0 , 10 ) ax.set_zlim( 0 , 10 ) ax.set_xlabel( 'x axis' ) ax.set_ylabel( 'y axis' ) ax.set_zlabel( 'z axis' ) plt.show( |
绘制结果如下:
9、3d条状图
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from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot( 111 , projection = '3d' ) for c, z in zip ([ 'r' , 'g' , 'b' , 'y' ], [ 30 , 20 , 10 , 0 ]): xs = np.arange( 20 ) ys = np.random.rand( 20 ) # you can provide either a single color or an array. to demonstrate this, # the first bar of each set will be colored cyan. cs = [c] * len (xs) cs[ 0 ] = 'c' ax.bar(xs, ys, zs = z, zdir = 'y' , color = cs, alpha = 0.8 ) ax.set_xlabel( 'x' ) ax.set_ylabel( 'y' ) ax.set_zlabel( 'z' ) plt.show() |
绘制结果如下:
以上所述是小编给大家介绍的python绘制3d图形,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对服务器之家网站的支持
原文链接:https://blog.csdn.net/guduruyu/article/details/78050268