3D Scatter Plot with Colorbar

Question

Borrowing from the example on the Matplotlib documentation page and slightly modifying the code,

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

def randrange(n, vmin, vmax):
    return (vmax-vmin)*np.random.rand(n) + vmin

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
n = 100
for c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
    xs = randrange(n, 23, 32)
    ys = randrange(n, 0, 100)
    zs = randrange(n, zl, zh)
    cs = randrange(n, 0, 100)
    ax.scatter(xs, ys, zs, c=cs, marker=m)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

Will give a 3D scatter plot with different colors for each point (random colors in this example). What's the correct way to add a colorbar to the figure, since adding in plt.colorbar() or ax.colorbar() doesn't seem to work.

Vola

what is cs in your example? – Phyllida

Answer 1

This produces a colorbar (though possibly not the one you need):

Replace this line:

ax.scatter(xs, ys, zs, c=cs, marker=m)

with

p = ax.scatter(xs, ys, zs, c=cs, marker=m)

then use

fig.colorbar(p)

near the end

Root

Answer 2

Using the above answer did not solve my problem. The colorbar colormap was not linked to the axes (note also the incorrect colorbar limits):

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

data = np.random.rand(3, 100)
x, y, z = data  # for show
c = np.arange(len(x)) / len(x)  # create some colours

p = ax.scatter(x, y, z, c=plt.cm.magma(0.5*c))
ax.set_xlabel('$\psi_1$')
ax.set_ylabel('$\Phi$')
ax.set_zlabel('$\psi_2$')

ax.set_box_aspect([np.ptp(i) for i in data])  # equal aspect ratio

fig.colorbar(p, ax=ax)

The solution (see here also) is to use cmap in ax.scatter:

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

data = np.random.rand(3, 100)
x, y, z = data  # for show
c = np.arange(len(x)) / len(x)  # create some colours

p = ax.scatter(x, y, z, c=0.5*c, cmap=plt.cm.magma)
ax.set_xlabel('$\psi_1$')
ax.set_ylabel('$\Phi$')
ax.set_zlabel('$\psi_2$')

ax.set_box_aspect([np.ptp(i) for i in data])  # equal aspect ratio

fig.colorbar(p, ax=ax)

Ur

You passed the values c (which ranges from 0. to 1.) through the cm.magma function, which altered all the values, and gave the impression of an incorrect range. I would not say that my answer was incorrect, but that your first attempt to change the colormap was incorrect. Your second answer correctly sets the colormap with the cmap argument. – Root

why is 0.5*c needed? – Pfeifer

It is not needed, but c is bounded [0..1] and 0.5 was added as a scaling factor to show that the scale bar displays appropriate units in the second plot, which is not the case in the first plot. – Ur