My code is:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
After plotting some points, when I use the plt.show() method then it displays a 3D axes system but there is only one octant. I need all 8 of them for my project. Is there any way to get them?
Thanks in advance.
It should put your data (presumably negative) in view when you plot it. However, it's worth knowing how to manually set the limits as well:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim(-1,1)
ax.set_ylim(-1,1)
ax.set_zlim(-1,1)
Related
I make 3d plots with matplotlib and I always get a weird frame with a normalized scale around my plot. Where does it come from and how can I get rid of it ?
Here is an example code that drives me to the problem :
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.linspace(0,10)
# ------------- Figure ---------------
fig, ax = plt.subplots(figsize = (9,6))
ax = fig.gca(projection='3d')
ax.plot(np.sin(x), np.cos(y), z)
plt.show()
And here is the result :
I use plt.subplots() because I want a figure with a 3D and a 2D plot side by side.
You call plt.subplots(...) and this, of course, instantiates an Axes, complete of horizontal and vertical spines, before Matplotlib is informed that you want a 3D enabled Axes.
When you later call plt.gca(...) it's too late…
Simply use
fig, ax = plt.subplots(figsize = (9,6), subplot_kw={"projection" : "3d"})
or
fig = plt.figure(figsize = (9,6))
ax = fig.add_subplot(111, projection='3d')
Addressing OP's comment
Figure.add_subplot is pretty flexible…
fig = plt.figure()
fig.add_subplot(1,5,(1,4), projection='3d')
fig.add_subplot(1,5,5)
fig.tight_layout()
plt.show()
I am trying to get a scatter plot with 2 axes (x,z) in a log scale using set_scale but that doesn't work.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
x=np.linspace(0,500,10)
y=np.linspace(0,1,10)
z=np.linspace(0,100000,10)
fig = plt.figure()
ax = Axes3D(fig)
ax.plot(x, y, z,'o', c='g')
ax.grid()
#ax.set_xscale('log')
#ax.set_zscale('log')
#ax.set_xlim([min(x), max(x)])
#ax.set_zlim([min(z), max(z)])
plt.show()
I have searched for this in google, but found solutions for 2d points in real time.How can I achieve this for stream of 3d point.
Here I should be able to add new points to plot.
I tried this, its just plots series of data. How to update?
You could just plot in interactive mode, for example the following keeps adding new points,
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')
plt.ion()
plt.show()
x = np.linspace(0.,np.pi*4.,100)
ax.set_xlim([0.,13.])
ax.set_ylim([-1.5,1.5])
ax.set_zlim([-1.5,1.5])
for i in x:
ax.scatter(i, np.sin(i), np.cos(i))
print(i)
plt.pause(0.01)
UPDATE: added example of labelling
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')
plt.ion()
plt.show()
lsp = np.linspace(0.,np.pi*4.,100)
ax.set_xlim([0.,13.])
ax.set_ylim([-1.5,1.5])
ax.set_zlim([-1.5,1.5])
for i, x in enumerate(lsp):
y = np.sin(x)
z = np.cos(x)
ax.scatter(x, y, z)
if i%10 == 0:
ax.text(x, y, z, str(np.round(x,3))+", "
+str(np.round(y,3))+", "
+str(np.round(z,3)))
plt.pause(0.01)
I am trying to create a plot composed of multiple wireframe spheres using matplotlib. I found a code fragment to plot one such sphere here so I thought it would be easy to extend it to multiple spheres by just calling plot_wireframe multiple times. I was wrong. Here's a code fragment:
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
x=np.cos(u)*np.sin(v)
y=np.sin(u)*np.sin(v)
z=np.cos(v)
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
spheres = [ [0,0,0,1], [3,0,0,1.6] ]
for v in spheres:
ax.plot_wireframe(x*v[3]+v[0], y*v[3]+v[1], z*v[3]+v[2],linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
If you run that code, the first plot will show a nice sphere, while in the second all the spheres are distorted and shifted. I searched to make sure plot_wireframe can be called multiple time on the same axis but couldn't find anything. Also, I'm a Python noob, but I don't think I'm doing anything wrong.
Thank you for the help!
Short answer: adjust the axes limits manually:
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
x=np.cos(u)*np.sin(v)
y=np.sin(u)*np.sin(v)
z=np.cos(v)
# I'm not sure what was this for.
'''
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
'''
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
spheres = [ [0,0,0,1], [3,0,0,1.6] ]
for v in spheres:
ax.plot_wireframe(x*v[3]+v[0], y*v[3]+v[1], z*v[3]+v[2],linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_xlim([0,7]) # Like so.
ax.set_ylim([-3,3])
ax.set_zlim([-3,3])
ax.set_aspect("equal")
plt.show()
Is there a way to turn of the grid for polar plots in matplotlib? I tried matplotlib.pyplot.rgrids([], []), but it doesn't work.
From your axes instance, call grid(False).
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.grid(False)
r = np.arange(0,1,0.001)
theta = 2*2*np.pi*r
ax.plot(theta,r)
plt.show()