matplotlib 3D数据热图

slmsl1lt 于 2023-05-01 发布在其他

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我想用我的3D数据生成热图。
我已经能够使用这些数据绘制trisurf。
有人能帮我做一个热图吗？我看了网上的教程，但他们似乎都很复杂的3D。我在这个网站上发现了一个在matplotlib中使用散点生成热图的方法，但它只有2D数据。
我的生成trisurf的代码是

from mpl_toolkits.mplot3d import Axes3D

from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np

n_angles = 36
n_radii = 8

# An array of radii
# Does not include radius r=0, this is to eliminate duplicate points
radii = np.linspace(0.125, 1.0, n_radii)

# An array of angles
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)

# Repeat all angles for each radius
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)

# Convert polar (radii, angles) coords to cartesian (x, y) coords
# (0, 0) is added here. There are no duplicate points in the (x, y) plane

x,y,z =np.loadtxt('output/flash_KR_endowment_duration_3D.dat',delimiter='\t',usecols=(0,1,2),unpack=True)
#x,y,z =np.loadtxt('output/disk_KR_endowment_duration_3D.dat',delimiter='\t',usecols=(0,1,2),unpack=True)

fig = plt.figure()
ax = fig.gca(projection='3d')
#fig.suptitle(suptitle, fontsize=12, fontweight='bold')

#ax.set_title("Disk Kryder's Rate-Endowment-Duration Plot",fontsize=12)
ax.set_title("Flash Kryder's Rate-Endowment-Duration Plot",fontsize=12)

ax.set_xlabel("Kryder's rate")
ax.set_ylabel("Duration")
ax.set_zlabel("Endowment")

surf = ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2)
fig.colorbar(surf, shrink=.7, aspect=20)

plt.show()

数据为3列。比如说X，Y，Z。我已经尝试过3D散点图与颜色。我在找热图。

matplotlib

来源：https://stackoverflow.com/questions/21978729/heatmap-with-3d-data

1条答案

按热度按时间

6g8kf2rb1#

如果你只想“使用三维来着色”，你可以这样做：

import pandas as pd
import numpy as np
import plotly.plotly as plotly
from plotly.graph_objs import Data, Heatmap

plotly.sign_in("username", "api_key") # this is annoying but you can get one after registering - free

# generate tridimentional data
pp = pd.Panel(np.random.rand(20, 20, 20))

# crunch (sum, average...) data along one axis
crunch = pp.sum(axis=0)

# now plot with plot.ly or matplotlib as you wish
data = Data([Heatmap(z=np.array(crunch))])
plotly.image.save_as(data, "filename.pdf")

结果-具有3D数据的第三变量作为颜色的热图：x1c 0d1x此外，您可以使用循环为每个轴组合绘图：

## Plot
# for each axis, sum data along axis, plot heatmap
# dict is axis:[x,y,z], where z is a count of that variable
desc = {0 : ["ax1", "ax2", "ax3"], 1 : ["ax1", "ax2", "ax3"], 2 : ["ax1", "ax2", "ax3"]}
for axis in xrange(3):
    # crunch (sum) data along one axis
    crunch = pp.sum(axis=axis)

    # now let's plot
    data = Data([Heatmap(
        z=np.array(crunch),
        x=crunch.columns,
        y=crunch.index)]
    )
    plotly.image.save_as(data,
        "heatmap_{0}_vs_{1}_count_of_{2}".format(desc[axis][0], desc[axis][1], desc[axis][2])
    )

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matplotlib 3D数据热图

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