python - Matplotlib 3D scatter plot with color gradient

Question

How can I create a 3D plot with a color gradient for the points? See the example below, which works for a 2D scatter plot.

Edit (thanks to Chris): What I'm expecting to see from the 3D plot is a color gradient of the points ranging from red to green as in the 2D scatter plot. What I see in the 3D scatter plot are only red points.

Solution: for some reasons (related to the gradient example I copied elsewhere) I set xrange to len-1, which messes everything in the 3D plot.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Create Map
cm = plt.get_cmap("RdYlGn")

x = np.random.rand(30)
y = np.random.rand(30)
z = np.random.rand(30)
#col = [cm(float(i)/(29)) for i in xrange(29)] # BAD!!!
col = [cm(float(i)/(30)) for i in xrange(30)]

# 2D Plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, s=10, c=col, marker='o')  

# 3D Plot
fig = plt.figure()
ax3D = fig.add_subplot(111, projection='3d')
ax3D.scatter(x, y, z, s=10, c=col, marker='o')  

plt.show()

Answer 1

Here is an example for 3d scatter with gradient colors:

import matplotlib.cm as cmx
from mpl_toolkits.mplot3d import Axes3D
def scatter3d(x,y,z, cs, colorsMap='jet'):
    cm = plt.get_cmap(colorsMap)
    cNorm = matplotlib.colors.Normalize(vmin=min(cs), vmax=max(cs))
    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
    fig = plt.figure()
    ax = Axes3D(fig)
    ax.scatter(x, y, z, c=scalarMap.to_rgba(cs))
    scalarMap.set_array(cs)
    fig.colorbar(scalarMap)
    plt.show()

Of course, you can choose the scale to range between different values, like 0 and 1.