在 Python 中将 4D 数据绘制为分层热图

Question

我想使用 (x,y,z) 坐标和基于颜色的第四维来创建分层热图，以与强度相关联。

每个与层相关的数据都位于一个包含 x、y、z 和 G 列的文本文件中。分隔符为白色 space。如果显示不正确，我们深表歉意。

XA 200 600 1200 1800 2400 3000 200 600 1200 1800 2400 3000

是啊 0 0 0 0 0 0 600 600 600 600 600 600

ZA 600 600 600 600 600 600 600 600 600 600 600 600

GA 1.27 1.54 1.49 1.34 1.27 1.25 1.28 1.96 1.12 1.06 1.06 1.06

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

data = np.load(filename)

x = np.linspace(0,2400,num=6)
y = np.linspace(0,2400,num=11)
X,Y=np.meshgrid(x,y)
Z = data[:,:,0] * 1e-3

plt.contourf(X,Y,Z)
plt.colorbar()

如何读取文本文件、创建和叠加沿 Z 轴的热图？

Answer 1

假设你有两个 txt 文件，即 data-z600.txt 和 data-z1200.txt，在与您的 python 脚本相同的文件夹，其内容完全

数据-z600.txt（你的）

XA YA ZA GA
200 0 600 1.27
600 0 600 1.54
1200 0 600 1.49
1800 0 600 1.34
2400 0 600 1.27
3000 0 600 1.25
200 600 600 1.28
600 600 600 1.96
1200 600 600 1.12
1800 600 600 1.06
2400 600 600 1.06
3000 600 600 1.06

和data-z1200.txt（故意发明的）

XA YA ZA GA
200 0 1200 1.31
600 0 1200 2
1200 0 1200 1.63
1800 0 1200 1.36
2400 0 1200 1.31
3000 0 1200 1.35
200 600 1200 1.38
600 600 1200 1.36
1200 600 1200 1.2
1800 600 1200 1.1
2400 600 1200 1.1
3000 600 1200 1.11

让我们导入所有需要的库

# libraries
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import scipy.interpolate as si
from matplotlib import cm
import pandas as pd
import numpy as np

并定义 grids_maker，一个函数，用于准备给定文件中包含的数据，此处通过 filepath 参数针对。

def grids_maker(filepath): # Get the data df = pd.read_csv(filepath, sep=' ') # Make things more legible xy = df[['XA', 'YA']] x = xy.XA y = xy.YA z = df.ZA g = df.GA reso_x = reso_y = 50 interp = 'cubic' # or 'nearest' or 'linear' # Convert the 4d-space's dimensions into grids grid_x, grid_y = np.mgrid[ x.min():x.max():1j*reso_x, y.min():y.max():1j*reso_y ] grid_z = si.griddata( xy, z.values, (grid_x, grid_y), method=interp ) grid_g = si.griddata( xy, g.values, (grid_x, grid_y), method=interp ) return { 'x' : grid_x, 'y' : grid_y, 'z' : grid_z, 'g' : grid_g, }

让我们对我们的文件列表使用 grids_maker 并获取每个文件的第 4 维的极值。

# Let's retrieve all files' contents fgrids = dict.fromkeys([ 'data-z600.txt', 'data-z1200.txt' ]) g_mins = [] g_maxs = [] for fpath in fgrids.keys(): fgrids[fpath] = grids = grids_maker(fpath) g_mins.append(grids['g'].min()) g_maxs.append(grids['g'].max())

让我们创建我们的（所有文件统一的）色标

# Create the 4th color-rendered dimension scam = plt.cm.ScalarMappable( norm=cm.colors.Normalize(min(g_mins), max(g_maxs)), cmap='jet' # see https://matplotlib.org/examples/color/colormaps_reference.html )

...最后 make/show 剧情

# Make the plot fig = plt.figure() ax = fig.gca(projection='3d') for grids in fgrids.values(): scam.set_array([]) ax.plot_surface( grids['x'], grids['y'], grids['z'], facecolors = scam.to_rgba(grids['g']), antialiased = True, rstride=1, cstride=1, alpha=None ) plt.show()

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