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与强度相关的分层热图。

每个与图层相关的数据都位于一个文本文件中，其中包含 x、y、z 和 G 列。分隔符是空格。如果显示不正确，请致歉。

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

是的 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，沿 Z 轴创建和叠加热图？

Answer 1

假设你有两个txt文件，分别是data-z600.txt和data-z1200.txt，和你的python脚本在同一个文件夹下，其内容完全一致

data-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，该函数负责准备给定文件中包含的数据，这里通过 filepath 参数。

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,
    }

让我们在文件列表上使用grids_maker 并获取每个文件第 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）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
)

...最后制作/显示情节

# 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()