根据值更改matplotlib中3D条形图中的条形颜色 [英] Change bar color in a 3D bar plot in matplotlib based on value
问题描述
我在matplotlib中有一个3D条形图,其中总共有165条,现在非常混乱.
I have a 3D bar plot in matplotlib which consists of a total 165 bars and at the moment it is quite chaotic.
.
我想根据谨慎的z值(0、1、2)更改条形的颜色.
I would like to change the colour of the bars based on the discreet z-values: 0,1,2.
我知道可以使用还有一个关于如何根据值更改条形颜色的问题: 定义Matplotlib 3D条形图的颜色
And there is also a question on how to change bar colour based on values: Defining colors of Matplotlib 3D bar plot
我不确定我是否能完全理解给出的答案,但是在这种情况下我无法使其正常工作.
I am not sure If i perfectly comprehend the given answer but I cannot make it work in this case.
代码是:
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()
推荐答案
As seen from the documentation of bar3d, color
can be an array, with one color per bar.
这使得在一次调用bar3d
的所有条上着色变得非常容易;我们只需要将data
数组转换为可以使用颜色图完成的颜色数组
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()))
(请注意,颜色表的值介于0到1之间,因此我们需要将这些值归一化到该范围内.)
(Note, that a colormap takes values between 0 and 1, so we need to normalize the values into this range.)
完整示例:
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()
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