Matplotlib Line3DCollection for time-varying colors

Question

I'm attempting to plot 3D line trajectories that evolve over time, and I would like the colors to change to show that passage of time (e.g. from light blue to dark blue). However, there is a distinct lack of tutorials for using matplotlib's Line3DCollection; this is the closest I could find, but all I'm getting is a white line.

Here's my code.

import matplotlib.pyplot as plot
from mpl_toolkits.mplot3d.axes3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Line3DCollection
import numpy as np

# X has shape (3, n)
c = np.linspace(0, 1., num = X.shape[1])[::-1]
a = np.ones(shape = c.shape[0])
r = zip(a, c, c, a) # an attempt to make red vary from light to dark

# r, which contains n tuples of the form (r,g,b,a), looks something like this:
# [(1.0, 1.0, 1.0, 1.0), 
# (1.0, 0.99998283232330165, 0.99998283232330165, 1.0),
# (1.0, 0.9999656646466033, 0.9999656646466033, 1.0),
# (1.0, 0.99994849696990495, 0.99994849696990495, 1.0),
# ..., 
# (1.0, 1.7167676698312416e-05, 1.7167676698312416e-05, 1.0),
# (1.0, 0.0, 0.0, 1.0)]

fig = plot.figure()
ax = fig.gca(projection = '3d')

points = np.array([X[0], X[1], X[2]]).T.reshape(-1, 1, 3)
segs = np.concatenate([points[:-1], points[1:]], axis = 1)
lc = Line3DCollection(segs, colors = r)
ax.add_collection3d(lc)

ax.set_xlim(-0.45, 0.45)
ax.set_ylim(-0.4, 0.5)
ax.set_zlim(-0.45, 0.45)

plot.show()

However, here's what I get:

Just a bunch of white line segments, no shift in the color. What am I doing wrong? Thanks!

Answer 1

Your code works just fine, here's a bit of a sample. Basically, this is your code with a custom X set.

fig = plot.figure();
ax = fig.gca(projection = '3d')
X = [(0,0,0,1,0),(0,0,1,0,0),(0,1,0,0,0)]
points = np.array([X[0], X[1], X[2]]).T.reshape(-1, 1, 3)
r = [(1.0, 1.0, 1.0, 1.0), (1.0, 0.75, 0.75, 1.0), (1.0, 0.5, 0.5, 1.0), (1.0, 0.25, 0.25, 1.0), (1.0, 0.0, 0.0, 1.0)];

segs = np.concatenate([points[:-1], points[1:]], axis = 1)
ax.add_collection(Line3DCollection(segs,colors=list(r)))

plot.show()

And the plot looks like this: