There might be better ways, but at least you can always make a planar mesh and color it: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np # create a 21 x 21 vertex mesh xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21)) # create some dummy data (20 x 20) for the image data … Read more
In order to synchronize the rotation between two subplots in mplot3d you can connect the motion_notify_event to a function that reads the angles from rotated plot and applies it to the respective other plot. Here is an example from the gallery with the described functionality added. from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import … Read more
I did a 3d surface plot overlay on top of a background image once: If this is similar to what you want, I could try to make a working example out of it. Alternatively, if you just want to display an image in 3d space, you can use a surface plot: from pylab import * … Read more
It is a little complicated, but you can draw all the objects by the following code: 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): … Read more
# Hide grid lines ax.grid(False) # Hide axes ticks ax.set_xticks([]) ax.set_yticks([]) ax.set_zticks([]) Note, you need matplotlib>=1.2 for set_zticks() to work.
You can use matplotlib for this. matplotlib has a mplot3d module that will do exactly what you want. import matplotlib.pyplot as plt import random fig = plt.figure(figsize=(12, 12)) ax = fig.add_subplot(projection=’3d’) sequence_containing_x_vals = list(range(0, 100)) sequence_containing_y_vals = list(range(0, 100)) sequence_containing_z_vals = list(range(0, 100)) random.shuffle(sequence_containing_x_vals) random.shuffle(sequence_containing_y_vals) random.shuffle(sequence_containing_z_vals) ax.scatter(sequence_containing_x_vals, sequence_containing_y_vals, sequence_containing_z_vals) plt.show() The code above generates a … Read more
To add arrow patches to a 3D plot, the simple solution is to use FancyArrowPatch class defined in /matplotlib/patches.py. However, it only works for 2D plot (at the time of writing), as its posA and posB are supposed to be tuples of length 2. Therefore we create a new arrow patch class, name it Arrow3D, … Read more
This answer addresses the 4d surface plot problem. It uses matplotlib’s plot_surface function instead of plot_trisurf. Basically you want to reshape your x, y and z variables into 2d arrays of the same dimension. To add the fourth dimension as a colormap, you must supply another 2d array of the same dimension as your axes … Read more
The scatter plot in 3D is a mpl_toolkits.mplot3d.art3d.Path3DCollection object. This provides an attribute _offsets3d which hosts a tuple (x,y,z) and can be used to update the scatter points’ coordinates. Therefore it may be beneficial not to create the whole plot on every iteration of the animation, but instead only update its points. The following is … Read more
Just to elaborate on my comment above, matplotlib’s 3D plotting really isn’t intended for something as complex as isosurfaces. It’s meant to produce nice, publication-quality vector output for really simple 3D plots. It can’t handle complex 3D polygons, so even if implemented marching cubes yourself to create the isosurface, it wouldn’t render it properly. However, … Read more