I want to plot 3d cuboid in python.
Input : center (3 points for the center) radius (3 radius values, one for each dimension)
Ideally it should be a wireframe plot(I need to see whats inside).I am not exactly sure how to go about this. Using python matplotlib or Mayavi is fine.
Thanks!
So far I have tried the following code ..but that only draws a cube
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from itertools import product, combinations
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect("equal")
#draw cube
r = [-1, 1]
for s, e in combinations(np.array(list(product(r,r,r))), 2):
if np.sum(np.abs(s-e)) == r[1]-r[0]:
ax.plot3D(*zip(s,e), color="b")
plt.show()
Whats missing in this code is that its only a cube(not a cuboid) and it's only centered around 0 (I actually want to provide the center)
After thinking a little bit I came up with this.Which seems right. Let me know if you think its not correct...this is the simplest possible way without installing myavi,pygame, povray (I had a hard time installing these on ipython, conda,my windows laptop)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from itertools import product, combinations
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect("equal")
#draw cube
r1 = [-1, 1]
r2 = [-2, 2]
r3 = [-3, 3]
center =[5,5,5]
for s, e in combinations(np.array(list(product(r1,r2,r3))), 2):
s=np.array(center)+np.array(s)
e=np.array(center)+np.array(e)
ax.scatter3D(*center, color="r")
if np.linalg.norm(s-e) == 2*r1[1] or np.linalg.norm(s-e) == 2*r2[1] or np.linalg.norm(s-e) == 2*r3[1]:
print zip(s,e)
ax.plot3D(*zip(s,e), color="b")
plt.show()
Here is a wireframe plot for a cuboid.
def plot_cuboid(center, size):
"""
Create a data array for cuboid plotting.
============= ================================================
Argument Description
============= ================================================
center center of the cuboid, triple
size size of the cuboid, triple, (x_length,y_width,z_height)
:type size: tuple, numpy.array, list
:param size: size of the cuboid, triple, (x_length,y_width,z_height)
:type center: tuple, numpy.array, list
:param center: center of the cuboid, triple, (x,y,z)
"""
# suppose axis direction: x: to left; y: to inside; z: to upper
# get the (left, outside, bottom) point
import numpy as np
ox, oy, oz = center
l, w, h = size
x = np.linspace(ox-l/2,ox+l/2,num=10)
y = np.linspace(oy-w/2,oy+w/2,num=10)
z = np.linspace(oz-h/2,oz+h/2,num=10)
x1, z1 = np.meshgrid(x, z)
y11 = np.ones_like(x1)*(oy-w/2)
y12 = np.ones_like(x1)*(oy+w/2)
x2, y2 = np.meshgrid(x, y)
z21 = np.ones_like(x2)*(oz-h/2)
z22 = np.ones_like(x2)*(oz+h/2)
y3, z3 = np.meshgrid(y, z)
x31 = np.ones_like(y3)*(ox-l/2)
x32 = np.ones_like(y3)*(ox+l/2)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
# outside surface
ax.plot_wireframe(x1, y11, z1, color='b', rstride=1, cstride=1, alpha=0.6)
# inside surface
ax.plot_wireframe(x1, y12, z1, color='b', rstride=1, cstride=1, alpha=0.6)
# bottom surface
ax.plot_wireframe(x2, y2, z21, color='b', rstride=1, cstride=1, alpha=0.6)
# upper surface
ax.plot_wireframe(x2, y2, z22, color='b', rstride=1, cstride=1, alpha=0.6)
# left surface
ax.plot_wireframe(x31, y3, z3, color='b', rstride=1, cstride=1, alpha=0.6)
# right surface
ax.plot_wireframe(x32, y3, z3, color='b', rstride=1, cstride=1, alpha=0.6)
ax.set_xlabel('X')
ax.set_xlim(-100, 100)
ax.set_ylabel('Y')
ax.set_ylim(-100, 100)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
def test():
center = [0, 0, 0]
length = 32 * 2
width = 50 * 2
height = 100 * 2
plot_cuboid(center, (length, width, height))
if __name__ == '__main__':
test()
Here is the result. plot cuboid
Everybody forgets about POVray that handles 3D very well. It doesn't render wireframe, though, but you can use a half-transparent texture to see what is inside of the box.
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
center='-1, -1, -1'
radius='1, 1, 1'
pov='camera { location <0, 2, -3> look_at <0, 1, 2> }\n\
light_source { <2, 4, -3> color rgb 1*1.5}\n\
background {color rgb <0.00, 0.00, 0.00>}\n\
box {<'+center+'>, < '+radius+'>\n\
pigment { color rgbt <0.67, 1.00, 0.39, 0.80> }\n\
rotate <52, 6, 0>\n\
scale 0.9\n\
translate <0, 1.2, 1>}\n\
'
f=open('scene.pov', 'w')
f.write(pov)
f.close()
os.system('povray +W400 +H300 +A +FN scene.pov')
Output "scene.png"
You need to read povray's documentation.
I have encountered the same question, and tried to give a answer as follows.
def cuboid_data(center, size):
"""
Create a data array for cuboid plotting.
============= ================================================
Argument Description
============= ================================================
center center of the cuboid, triple
size size of the cuboid, triple, (x_length,y_width,z_height)
:type size: tuple, numpy.array, list
:param size: size of the cuboid, triple, (x_length,y_width,z_height)
:type center: tuple, numpy.array, list
:param center: center of the cuboid, triple, (x,y,z)
"""
# suppose axis direction: x: to left; y: to inside; z: to upper
# get the (left, outside, bottom) point
o = [a - b / 2 for a, b in zip(center, size)]
# get the length, width, and height
l, w, h = size
x = [[o[0], o[0] + l, o[0] + l, o[0], o[0]], # x coordinate of points in bottom surface
[o[0], o[0] + l, o[0] + l, o[0], o[0]], # x coordinate of points in upper surface
[o[0], o[0] + l, o[0] + l, o[0], o[0]], # x coordinate of points in outside surface
[o[0], o[0] + l, o[0] + l, o[0], o[0]]] # x coordinate of points in inside surface
y = [[o[1], o[1], o[1] + w, o[1] + w, o[1]], # y coordinate of points in bottom surface
[o[1], o[1], o[1] + w, o[1] + w, o[1]], # y coordinate of points in upper surface
[o[1], o[1], o[1], o[1], o[1]], # y coordinate of points in outside surface
[o[1] + w, o[1] + w, o[1] + w, o[1] + w, o[1] + w]] # y coordinate of points in inside surface
z = [[o[2], o[2], o[2], o[2], o[2]], # z coordinate of points in bottom surface
[o[2] + h, o[2] + h, o[2] + h, o[2] + h, o[2] + h], # z coordinate of points in upper surface
[o[2], o[2], o[2] + h, o[2] + h, o[2]], # z coordinate of points in outside surface
[o[2], o[2], o[2] + h, o[2] + h, o[2]]] # z coordinate of points in inside surface
return x, y, z
def test():
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
center = [0, 0, 0]
length = 32 * 2
width = 50 * 2
height = 100 * 2
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = cuboid_data(center, (length, width, height))
ax.plot_surface(X, Y, Z, color='b', rstride=1, cstride=1, alpha=0.1)
ax.set_xlabel('X')
ax.set_xlim(-100, 100)
ax.set_ylabel('Y')
ax.set_ylim(-100, 100)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
if __name__ == '__main__':
test()
This is the result. matplotlib plot cuboid example
来源：https://stackoverflow.com/questions/30715083/python-plotting-a-wireframe-3d-cuboid