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.