plt画图工具(3d)
1.3d图形
生成3d图的方式
fig = plt.figure()
ax = fig.gca(projection='3d')
普通图形，设置x,y,z三个轴的数据
from matplotlib import cbook from matplotlib import cm from matplotlib.colors import LightSource import matplotlib.pyplot as plt import numpy as np with cbook.get_sample_data('jacksboro_fault_dem.npz') as file, \ np.load(file) as dem: z = dem['elevation'] nrows, ncols = z.shape x = np.linspace(dem['xmin'], dem['xmax'], ncols) y = np.linspace(dem['ymin'], dem['ymax'], nrows) x, y = np.meshgrid(x, y) region = np.s_[5:50, 5:50] x, y, z = x[region], y[region], z[region] fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) ls = LightSource(270, 45) # To use a custom hillshading mode, override the built-in shading and pass # in the rgb colors of the shaded surface calculated from "shade". rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft') surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb, linewidth=0, antialiased=False, shade=False) ax.set_xticks([]) ax.set_yticks([]) ax.set_zticks([]) fig.savefig("surface3d_frontpage.png", dpi=25)
2.2d图形嵌入3d图形中
通过设置zdir='y', 参数来设置图形嵌入位置
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') # Fixing random state for reproducibility np.random.seed(19680801) x = np.random.sample(20 * len(colors)) y = np.random.sample(20 * len(colors)) c_list = [] for c in colors: c_list.extend([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') # Customize the view angle so it's easier to see that the scatter points lie # on the plane y=0 ax.view_init(elev=20., azim=-35) plt.show()
3.多个2d图形嵌入3d中
zs参数设置嵌入的坐标位置
import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') colors = ['r', 'g', 'b', 'y'] yticks = [3, 2, 1, 0] for c, k in zip(colors, yticks): # Generate the random data for the y=k 'layer'. xs = np.arange(20) ys = np.random.rand(20) # You can provide either a single color or an array with the same length as # xs and ys. To demonstrate this, we color the first bar of each set cyan. cs = [c] * len(xs) cs[0] = 'c' # Plot the bar graph given by xs and ys on the plane y=k with 80% opacity. ax.bar(xs, ys, zs=k, zdir='y', color=cs, alpha=0.8) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') # On the y axis let's only label the discrete values that we have data for. ax.set_yticks(yticks) plt.show()
4.轮廓图
extend3d=True用于将轮廓也展示为3d形式
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.contourf(X, Y, Z, cmap=cm.coolwarm) ax.clabel(cset, fontsize=9, inline=1) plt.show()
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) # Plot contour curves cset = ax.contour(X, Y, Z, cmap=cm.coolwarm) ax.clabel(cset, fontsize=9, inline=1) plt.show()
5.3d超平面
ax.plot_surface
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) # Plot the 3D surface ax.plot_surface(X, Y, Z) ax.set_xlim(-40, 40) ax.set_ylim(-40, 40) ax.set_zlim(-100, 100) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') plt.show()
6.3d柱状图
ax.bar3d
import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) 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. xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25, indexing="ij") xpos = xpos.ravel() ypos = ypos.ravel() zpos = 0 # Construct arrays with the dimensions for the 16 bars. dx = dy = 0.5 * np.ones_like(zpos) dz = hist.ravel() ax.bar3d(xpos, ypos, zpos, dx, dy, dz, zsort='average') plt.show()
7.3d线性图
ax.plot(x, y, z, label='parametric curve')
import numpy as np import matplotlib.pyplot as plt plt.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()
8.抖动图
ax.quiver
import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.gca(projection='3d') # Make the grid x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2), np.arange(-0.8, 1, 0.2), np.arange(-0.8, 1, 0.8)) # Make the direction data for the arrows u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z) v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z) w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) * np.sin(np.pi * z)) ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True) plt.show()
9.3d散点图
ax.scatter
import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) 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 m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]: xs = randrange(n, 23, 32) ys = randrange(n, 0, 100) zs = randrange(n, zlow, zhigh) ax.scatter(xs, ys, zs, marker=m) ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label') plt.show()
10.voxels
import matplotlib.pyplot as plt import numpy as np # prepare some coordinates x, y, z = np.indices((8, 8, 8)) # draw cuboids in the top left and bottom right corners, and a link between them cube1 = (x < 3) & (y < 3) & (z < 3) cube2 = (x >= 5) & (y >= 5) & (z >= 5) link = abs(x - y) + abs(y - z) + abs(z - x) <= 2 # combine the objects into a single boolean array voxels = cube1 | cube2 | link # set the colors of each object colors = np.empty(voxels.shape, dtype=object) colors[link] = 'red' colors[cube1] = 'blue' colors[cube2] = 'green' # and plot everything fig = plt.figure() ax = fig.gca(projection='3d') ax.voxels(voxels, facecolors=colors, edgecolor='k') plt.show()
11.3d图中画函数
import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle, PathPatch from matplotlib.text import TextPath from matplotlib.transforms import Affine2D import mpl_toolkits.mplot3d.art3d as art3d def text3d(ax, xyz, s, zdir="z", size=None, angle=0, usetex=False, **kwargs): ''' Plots the string 's' on the axes 'ax', with position 'xyz', size 'size', and rotation angle 'angle'. 'zdir' gives the axis which is to be treated as the third dimension. usetex is a boolean indicating whether the string should be interpreted as latex or not. Any additional keyword arguments are passed on to transform_path. Note: zdir affects the interpretation of xyz. ''' x, y, z = xyz if zdir == "y": xy1, z1 = (x, z), y elif zdir == "x": xy1, z1 = (y, z), x else: xy1, z1 = (x, y), z text_path = TextPath((0, 0), s, size=size, usetex=usetex) trans = Affine2D().rotate(angle).translate(xy1[0], xy1[1]) p1 = PathPatch(trans.transform_path(text_path), **kwargs) ax.add_patch(p1) art3d.pathpatch_2d_to_3d(p1, z=z1, zdir=zdir) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Draw a circle on the x=0 'wall' p = Circle((5, 5), 3) ax.add_patch(p) art3d.pathpatch_2d_to_3d(p, z=0, zdir="x") # Manually label the axes text3d(ax, (4, -2, 0), "X-axis", zdir="z", size=.5, usetex=False, ec="none", fc="k") text3d(ax, (12, 4, 0), "Y-axis", zdir="z", size=.5, usetex=False, angle=np.pi / 2, ec="none", fc="k") text3d(ax, (12, 10, 4), "Z-axis", zdir="y", size=.5, usetex=False, angle=np.pi / 2, ec="none", fc="k") # Write a Latex formula on the z=0 'floor' text3d(ax, (1, 5, 0), r"$\displaystyle G_{\mu\nu} + \Lambda g_{\mu\nu} = " r"\frac{8\pi G}{c^4} T_{\mu\nu} $", zdir="z", size=1, usetex=True, ec="none", fc="k") ax.set_xlim(0, 10) ax.set_ylim(0, 10) ax.set_zlim(0, 10) plt.show()
