在 Python 中将 4D 数据绘制为分层热图

本文介绍了在 Python 中将 4D 数据绘制为分层热图的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

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

I would like to create layered heatmaps using (x,y,z) coordinates and a fourth dimension, color-based, to correlate to intensity.

每个与图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.分隔符是空格.如果它没有正确呈现，我们深表歉意.

Each layer-related data sits in a text file with columns of x, y, z and G. The delimiter is white space. Apologies if it does not present properly.

XA20060012001800240030002006001200180024003000

是的000000600600600600600600

YA000000600600600600600600

ZA600600600600600600600600600600600600

遗传算法1.271.541.491.341.271.251.281.961.121.061.061.06

GA1.271.541.491.341.271.251.281.961.121.061.061.06

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、创建和沿 Z 轴叠加热图?

推荐答案

假设你有两个txt文件，分别是data-z600.txt和data-z1200.txt, 与你的 python 脚本在同一个文件夹中，其内容完全相同

Say you have two txt files, namely data-z600.txt and data-z1200.txt, in the same folder as your python script, whose contents are exactly

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

Let's import all the required libraries

# 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，这是一个负责准备包含在给定文件中的数据的函数，此处针对通过filepath 参数.

and define grids_maker, a function that does the job of preparing data contained in a given file, here targeted via the filepath argument.

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,
    }

让我们在我们的文件列表上使用 grids_maker 并获得每个文件的第 4 维的极值.

Let's use grids_maker over our list of files and get the extrema of each file's 4th dimension.

# 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

Let's create our (all-file unifying) color-scale

# 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/展示情节

... and finally make/show the plot

# 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()

这篇关于在 Python 中将 4D 数据绘制为分层热图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！