python - 如何使用 matplotlib 为圆柱体的每个面添加颜色

Question

我正在尝试为圆柱体的每个面着色，但我不确定该怎么做，我尝试了以下方法:

for i in range(10):
    col.append([])


for i in range(10):
    for j in range(20):

        col[i].append(plt.cm.Blues(0.4))

 ax.plot_surface(X, Y, Z,facecolors = col,edgecolor = "red")

我希望为每张脸分配自己的颜色，所以我想我会为二维数组中的每张脸提供一组颜色。但这给出了一个错误:

in plot_surface
    colset.append(fcolors[rs][cs])
IndexError: list index out of range

完整代码如下:

import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.linalg import norm
from mpl_toolkits.mplot3d.art3d import Poly3DCollection


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
origin = np.array([0, 0, 0])

#axis and radius
p0 = np.array([1, 3, 2])
p1 = np.array([8, 5, 9])
R = 5

#vector in direction of axis
v = p1 - p0

#find magnitude of vector
mag = norm(v)

#unit vector in direction of axis
v = v / mag

#make some vector not in the same direction as v
not_v = np.array([1, 0, 0])
if (v == not_v).all():
not_v = np.array([0, 1, 0])

#make vector perpendicular to v
n1 = np.cross(v, not_v)
#normalize n1
n1 /= norm(n1)

#make unit vector perpendicular to v and n1
n2 = np.cross(v, n1)

#surface ranges over t from 0 to length of axis and 0 to 2*pi
t = np.linspace(0, mag, 200)
theta = np.linspace(0, 2 * np.pi, 100)

#use meshgrid to make 2d arrays
t, theta = np.meshgrid(t, theta)

#generate coordinates for surface
X, Y, Z = [p0[i] + v[i] * t + R * np.sin(theta) * n1[i] + R * np.cos(theta) *       n2[i] for i in [0, 1, 2]]
col = []
for i in range(10):
    col.append([])
for i in range(10):
    for j in range(20):

        col[i].append(plt.cm.Blues(0.4))

ax.plot_surface(X, Y, Z,facecolors = col,edgecolor = "red")

#plot axis
ax.plot(*zip(p0, p1), color = 'red')
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
ax.set_zlim(0, 10)
plt.axis('off')
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
plt.show()

Answer 1

您的 Z 数组的大小为 100x200，但您只指定了 10x20 颜色。制作 col(具有正确尺寸)的更快方法可能是这样的:

col1 = plt.cm.Blues(np.linspace(0,1,200)) # linear gradient along the t-axis
col1 = np.repeat(col1[np.newaxis,:, :], 100, axis=0) # expand over the theta-axis

col2 = plt.cm.Blues(np.linspace(0,1,100)) # linear gradient along the theta-axis
col2 = np.repeat(col2[:, np.newaxis, :], 200, axis=1) # expand over the t-axis

ax=plt.subplot(121, projection='3d')
ax.plot_surface(X, Y, Z, facecolors=col1)

ax=plt.subplot(122, projection='3d')
ax.plot_surface(X, Y, Z, facecolors=col2)

产生: