plt画图工具(3d)

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)

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()