根据值在matplotlib中更改3D条形图中的条形颜色

Question

I have a 3D bar plot in matplotlib which consists of a total 165 bars and at the moment it is quite chaotic.

.

I would like to change the colour of the bars based on the discreet z-values: 0,1,2.

I know there is the option to change colour bar in 1D bar plots based on specific values by using masks as in Color matplotlib bar chart based on value .

And there is also a question on how to change bar colour based on values: Defining colors of Matplotlib 3D bar plot

I am not sure If i perfectly comprehend the given answer but I cannot make it work in this case.

Code is:

   data = [[0 0 0 2 0 0 1 2 0 0 0]
            [0 0 2 2 0 0 0 0 2 0 0]
            [1 0 2 2 1 2 0 0 2 0 2]
            [1 0 2 2 0 2 0 2 2 2 2]
            [2 2 2 2 2 2 2 2 2 2 2]
            [2 2 0 2 2 2 2 2 2 2 2]
            [0 2 2 0 2 2 2 2 2 2 2]
            [1 2 0 0 2 1 2 2 0 0 2]
            [0 0 2 1 0 0 2 0 0 0 0]
            [2 1 2 2 0 0 0 2 0 0 2]
            [2 2 2 0 2 0 0 0 2 2 2]
            [2 2 0 0 2 2 2 2 2 0 0]
            [2 2 1 2 0 0 0 2 2 2 0]
            [2 0 0 2 0 0 2 2 2 2 2]
            [2 0 0 2 0 2 2 2 2 2 2]]

   ly = len(data[0])
   lx = len(data[:,0])
   xpos = np.arange(0,lx,1)    # Set up a mesh of positions
   ypos = np.arange(0,ly,1)
   xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)

   xpos = xpos.flatten()   # Convert positions to 1D array
   ypos = ypos.flatten()
   zpos = np.zeros(lx*ly)

   dx = 0.5 * np.ones_like(zpos)
   dy = dx.copy()
   dz = data.flatten()


   ys = np.array([float(yi) for yi in y[1:]])

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

   # all blue bars
   #ax.bar3d(xpos,ypos,zpos, dx, dy, dz, color='b')

   # try changing color bars

   colors = ['r','g','b']
   for i in range(0,3):

       ax.bar3d(xpos[i], ypos[i], zpos[i], dx, dy, dz[i], alpha=0.1, 
                    color=colors[i])

   ax.set_xlabel('X')
   ax.set_ylabel('Y')
   ax.set_zlabel('Z')


plt.show()

Answer 1

As seen from the documentation of bar3d , color can be an array, with one color per bar.

This makes it quite easy to colorize all bars in a single call to bar3d ; we just need to convert the data array to an array of colors which can be done using a colormap,

colors = plt.cm.jet(data.flatten()/float(data.max()))

(Note, that a colormap takes values between 0 and 1, so we need to normalize the values into this range.)

Complete example:

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

data = np.array([ [0, 0, 0, 2, 0, 0, 1, 2, 0, 0, 0],
         [0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 0],
         [1, 0, 2, 2, 1, 2, 0, 0, 2, 0, 2],
         [1, 0, 2, 2, 0, 2, 0, 2, 2, 2, 2],
         [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
         [2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2],
         [0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2],
         [1, 2, 0, 0, 2, 1, 2, 2, 0, 0, 2],
         [0, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0],
         [2, 1, 2, 2, 0, 0, 0, 2, 0, 0, 2],
         [2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 2],
         [2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0],
         [2, 2, 1, 2, 0, 0, 0, 2, 2, 2, 0],
         [2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2],
         [2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 2]])


ypos, xpos  = np.indices(data.shape) 

xpos = xpos.flatten()   
ypos = ypos.flatten()
zpos = np.zeros(xpos.shape)

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

colors = plt.cm.jet(data.flatten()/float(data.max()))
ax.bar3d(xpos,ypos,zpos, .5,.5,data.flatten(), color=colors)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()