如何正确生成 3d 直方图

Question

这更多是关于 python 中 3d 直方图创建的一般问题。

我尝试在下面的代码中使用 X 和 Y 数组创建 3d 直方图

import matplotlib
import pylab
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm

def threedhist():
    X = [1, 3, 5, 8, 6, 7, 1, 2, 4, 5]
    Y = [3, 4, 3, 6, 5, 3, 1, 2, 3, 8]
    fig = pylab.figure()
    ax = Axes3D(fig)
    ax.hist([X, Y], bins=10, range=[[0, 10], [0, 10]])
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.zlabel('Frequency')
    plt.title('Histogram')
    plt.show()

但是，我收到以下错误

Traceback (most recent call last):
  File "<pyshell#0>", line 1, in <module>
    a3dhistogram()
  File "C:/Users/ckiser/Desktop/Projects/Tom/Python Files/threedhistogram.py", line 24, in a3dhistogram
    ax.hist([X, Y], bins=10, range=[[0, 10], [0, 10]])
  File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 7668, in hist
    m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
  File "C:\Python27\lib\site-packages\numpy\lib\function_base.py", line 169, in histogram
    mn, mx = [mi+0.0 for mi in range]
TypeError: can only concatenate list (not "float") to list

我尝试了行中带或不带“[”的代码 ax.hist([X, Y], bin=10, 范围=[[0, 10], [0, 10]]) 我也尝试过 numpy 的功能但没有成功 H, xedges, yedges = np.histogram2d(x, y, bins = (10, 10)) 我是否缺少步骤或参数？任何建议将不胜感激。

Answer 1

我将其发布在有关彩色 3D 条形图的相关线程中，但我认为它也与此相关，因为我无法在任一线程中找到我需要的完整答案。此代码为任何类型的 xy 数据生成直方图散点图。高度表示该箱中值的频率。因此，例如，如果您有许多 (x,y) = (20,20) 的数据点，那么它会很高且呈红色。如果箱中 (x,y) = (100,100) 的数据点很少，那么它会是低的和蓝色的。

注意：结果将根据您拥有的数据量以及为直方图选择的箱数而有很大差异。相应调整！

xAmplitudes = #your data here
yAmplitudes = #your other data here

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

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()

下面是我的大约 75k 数据点的结果。请注意，您可以拖放到不同的视角，并且可能希望保存多个视图以供演示和后代使用。

Answer 2

看看
https://matplotlib.org/stable/gallery/mplot3d/hist3d.html，这有一个工作示例脚本。

我已经改进了该链接处的代码，使其更像是直方图：

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [1, 3, 5, 8, 6, 7, 1, 2, 4, 5]
y = [3, 4, 3, 6, 5, 3, 1, 2, 3, 8]

hist, xedges, yedges = np.histogram2d(x, y, bins=(4,4))
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()

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')
plt.xlabel ("X")
plt.ylabel ("Y")

plt.show()

我不确定如何使用 Axes3D.hist () 来做到这一点。

Answer 3

在此答案中有一个针对散点的 2D 和 3D 直方图的解决方案。用法很简单：

points, sub = hist2d_scatter( radius, density, bins=4 )

points, sub = hist3d_scatter( temperature, density, radius, bins=4 )

其中 sub 是一个 matplotlib "Subplot" 实例（3D 或非 3D），并且 points 包含用于散点图的点。

Answer 4

我已添加到 @lxop 的答案以允许任意大小的存储桶：

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

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

x = np.array([0, 2, 5, 10, 2, 3, 5, 2, 8, 10, 11])
y = np.array([0, 2, 5, 10, 6, 4, 2, 2, 5, 10, 11])
# This example actually counts the number of unique elements.
binsOne = sorted(set(x))
binsTwo = sorted(set(y))
# Just change binsOne and binsTwo to lists.
hist, xedges, yedges = np.histogram2d(x, y, bins=[binsOne, binsTwo])

# The start of each bucket.
xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1])

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

# The width of each bucket.
dx, dy = np.meshgrid(xedges[1:] - xedges[:-1], yedges[1:] - yedges[:-1])

dx = dx.flatten()
dy = dy.flatten()
dz = hist.flatten()

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