在 Python 中将 4D 数据绘制为分层热图 [英] Plot 4D data as layered heatmaps in Python
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
XA 200 600 1200 1800 2400 3000 200 600 1200 1800 2400 3000
是的000000600600600600600600
YA 0 0 0 0 0 0 600 600 600 600 600 600
ZA600600600600600600600600600600600600
ZA 600 600 600 600 600 600 600 600 600 600 600 600
遗传算法1.271.541.491.341.271.251.281.961.121.061.061.06
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
<小时>
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IT屋！
