#|Python--matplotlib绘3维图形 #|C++画图

文章目录

初始化
直线绘制（Line plots）
散点绘制（Scatter plots）
线框图（Wireframe plots）
表面图（Surface plots）
三角表面图（Tri-Surface plots）
等高线（Contour plots）
Bar plots（条形图）
子图绘制（subplot）
子图Subplot用法
补充：

初始化假设已经安装了matplotlib工具包。
利用matplotlib.figure.Figure创建一个图框：

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = fig.add_subplot(111, projection='3d')

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直线绘制（Line plots）基本用法：
ax.plot(x,y,z,label=’ ')
code:

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()

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散点绘制（Scatter plots）基本用法：
ax.scatter(xs, ys, zs, s=20, c=None, depthshade=True, *args, *kwargs)

xs,ys,zs：输入数据；
s:scatter点的尺寸
c:颜色，如c = 'r’就是红色；
depthshase:透明化，True为透明，默认为True，False为不透明
*args等为扩展变量，如maker = ‘o’，则scatter结果为’o‘的形状

code:

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()

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线框图（Wireframe plots）基本用法：
ax.plot_wireframe(X, Y, Z, *args, **kwargs)

X,Y,Z：输入数据
rstride:行步长
cstride:列步长
rcount:行数上限
ccount:列数上限

code:

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()

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表面图（Surface plots）基本用法：
ax.plot_surface(X, Y, Z, *args, **kwargs)

X,Y,Z：数据
rstride、cstride、rcount、ccount:同Wireframe plots定义
color:表面颜色
cmap:图层

code:

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()

三角表面图（Tri-Surface plots）基本用法：
ax.plot_trisurf(*args, **kwargs)

X,Y,Z:数据
其他参数类似surface-plot

code:

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()

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等高线（Contour plots）基本用法：
ax.contour(X, Y, Z, *args, **kwargs)
code :

from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from matplotlib import cm fig = plt.figure() ax = fig.add_subplot(111, projection='3d') X, Y, Z = axes3d.get_test_data(0.05) cset = ax.contour(X, Y, Z, cmap=cm.coolwarm) ax.clabel(cset, fontsize=9, inline=1) plt.show()

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二维的等高线，同样可以配合三维表面图一起绘制：
code:

from mpl_toolkits.mplot3d import axes3d 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) ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3) 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()

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也可以是三维等高线在二维平面的投影：

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) ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3) cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm) cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm) cset = ax.contourf(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()

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Bar plots（条形图）基本用法：
ax.bar(left, height, zs=0, zdir=‘z’, *args, **kwargs

x，y，zs = z，数据
zdir:条形图平面化的方向，具体可以对应代码理解。

代码：

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()

【#|Python--matplotlib绘3维图形】

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子图绘制（subplot） A-不同的2-D图形，分布在3-D空间，其实就是投影空间不空，对应code:

from mpl_toolkits.mplot3d import Axes3D import numpy as np import matplotlib.pyplot as plt fig = plt.figure() ax = fig.gca(projection='3d') # Plot a sin curve using the x and y axes. x = np.linspace(0, 1, 100) y = np.sin(x * 2 * np.pi) / 2 + 0.5 ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)') # Plot scatterplot data (20 2D points per colour) on the x and z axes. colors = ('r', 'g', 'b', 'k') x = np.random.sample(20*len(colors)) y = np.random.sample(20*len(colors)) c_list = [] for c in colors: c_list.append([c]*20) # By using zdir='y', the y value of these points is fixed to the zs value 0 # and the (x,y) points are plotted on the x and z axes. ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)') # Make legend, set axes limits and labels ax.legend() ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.set_zlim(0, 1) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z')

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子图Subplot用法与MATLAB不同的是，如果一个四子图效果，如：

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MATLAB： subplot(2,2,1) subplot(2,2,2) subplot(2,2,[3,4])

Python: subplot(2,2,1) subplot(2,2,2) subplot(2,1,2)

code:

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.axes3d import Axes3D, get_test_data from matplotlib import cm import numpy as np # set up a figure twice as wide as it is tall fig = plt.figure(figsize=plt.figaspect(0.5)) #=============== #First subplot #=============== # set up the axes for the first plot ax = fig.add_subplot(2, 2, 1, projection='3d') # plot a 3D surface like in the example mplot3d/surface3d_demo 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) surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False) ax.set_zlim(-1.01, 1.01) fig.colorbar(surf, shrink=0.5, aspect=10) #=============== # Second subplot #=============== # set up the axes for the second plot ax = fig.add_subplot(2,1,2, projection='3d') # plot a 3D wireframe like in the example mplot3d/wire3d_demo X, Y, Z = get_test_data(0.05) ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) plt.show()

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补充：文本注释的基本用法：

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()