matplotlib 中 3d 条形图的颜色图应用于每个条形图

Question

有谁知道如何在 matplotlib 中轻松实现 3d 条形图的颜色映射？

以this 为例，如何根据颜色图更改每个条形？例如，短条应该主要是蓝色的，而较高的条则将它们的颜色从蓝色渐变到红色......

Answer 1

在物理科学中，想要一个所谓的 LEGO 情节是很常见的，我认为这就是原始用户想要的。 Kevin G 的回答很好，让我得到了最终结果。这是一个更高级的直方图，用于 x-y 散点数据，按高度着色：

xAmplitudes = np.random.exponential(10,10000) #your data here
yAmplitudes = np.random.normal(50,10,10000) #your other data here - must be same array length

x = np.array(xAmplitudes)   #turn x,y data into numpy arrays
y = np.array(yAmplitudes)   #useful for regular matplotlib arrays

fig = plt.figure()          #create a canvas, tell matplotlib it's 3d
ax = fig.add_subplot(111, projection='3d')

#make histogram stuff - set bins - I choose 20x20 because I have a lot of data
hist, xedges, yedges = np.histogram2d(x, y, bins=(20,20))
xpos, ypos = np.meshgrid(xedges[:-1]+xedges[1:], yedges[:-1]+yedges[1:])

xpos = xpos.flatten()/2.
ypos = ypos.flatten()/2.
zpos = np.zeros_like (xpos)

dx = xedges [1] - xedges [0]
dy = yedges [1] - yedges [0]
dz = hist.flatten()

cmap = cm.get_cmap('jet') # Get desired colormap - you can change this!
max_height = np.max(dz)   # get range of colorbars so we can normalize
min_height = np.min(dz)
# scale each z to [0,1], and get their rgb values
rgba = [cmap((k-min_height)/max_height) for k in dz] 

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=rgba, zsort='average')
plt.title("X vs. Y Amplitudes for ____ Data")
plt.xlabel("My X data source")
plt.ylabel("My Y data source")
plt.savefig("Your_title_goes_here")
plt.show()

注意：结果会因您选择的 bin 数量和使用的数据量而异。此代码需要您插入一些数据或生成随机线性数组。结果图如下，有两种不同的视角：

Answer 2

所以也许不是你正在寻找的东西（也许对你来说是一个很好的起点），但使用

Getting individual colors from a color map in matplotlib

可以为条形赋予不同的纯色：

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.cm as cm           # import colormap stuff!
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y = np.random.rand(2, 100) * 4
hist, xedges, yedges = np.histogram2d(x, y, bins=4, range=[[0, 4], [0, 4]])

# Construct arrays for the anchor positions of the 16 bars.
# Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten xpos,
# ypos in column-major order. For numpy >= 1.7, we could instead call meshgrid
# with indexing='ij'.
xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)

# Construct arrays with the dimensions for the 16 bars.
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = hist.flatten()

cmap = cm.get_cmap('jet') # Get desired colormap
max_height = np.max(dz)   # get range of colorbars
min_height = np.min(dz)

# scale each z to [0,1], and get their rgb values
rgba = [cmap((k-min_height)/max_height) for k in dz] 

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=rgba, zsort='average')

plt.show()

就我个人而言，我觉得这很丑陋！但是使用顺序颜色图可能看起来不会太糟糕 - https://matplotlib.org/examples/color/colormaps_reference.html