三个.txt文件中的数据-循环到一个文件中，然后绘制一个图

我想要一个散点图来区分不同值之间的颜色。首先我需要分析我的数据。问题是我有一个FE模型，它导出与4个节点耦合的元素编号。这4个节点有4个坐标集，但如果4个元素共享1个节点，它将只为这4个结点提供1个坐标集。

最后我得到了三个.txt文件。

带元素编号的

.txt(以及我正在为绘图分析的数据(
.txt，其中包含元素编号和节点编号
.txt，带有节点坐标

是否可以创建一个连接这些数据点的循环？我想举一个例子，但我还没有做一个。我试过这种


from numpy import loadtxt
from fpdf import FPDF

Sek = ['Sektion_100']
I = 0
HeaderY =['nr.','LC','Element nr.','Myy','Nyy','MRd_y','URy']
HeaderX =['nr.','LC','Element nr.','Mxx','Nxx','MRd_x','URx']
#load the excel file
#header = loadtxt"LoadsArea_233.txt", unpack=False, skiprows=1)
pdf = FPDF(orientation = 'P', unit = 'mm', format = 'A4')
MaxURx =[]
MaxURy =[]
data = loadtxt("Loads/Sektion_150.txt", unpack=False, skiprows=1)
nr = data[:,1]
Mxx = data[:,2]
Nxx = -data[:,4]
Myy = data[:,3]
Nyy = -data[:,5]
topologi = loadtxt("Loads/Sektion_150_topologi.txt", unpack=False, skiprows=1)
nr1 = topologi[:,0]
node1 = topologi[:,1]
node2 = topologi[:,2]
node3 = topologi[:,3]
node4 = topologi[:,4]
knuder = loadtxt("Loads/Sektion_150_knuder.txt", unpack=False, skiprows=1)
nr2 = knuder[:,0]
x = knuder[:,1]
y = knuder[:,2]
z = knuder[:,3]

数据集图片

我在这里附上了我的数据集的图片。在"；Sektion_150_ Knuder"；NR=节点编号。我希望任何人都能找到解决这个问题的正确方向。

我找到了答案。

import xlwings as xw
import matplotlib.pyplot as plt
from math import pi
from numpy import loadtxt
import numpy as np
from fpdf import FPDF
import matplotlib as mpl
from matplotlib.ticker import ScalarFormatter


Sek = ['Sektion_100','Sektion_110','Sektion_120','Sektion_130','Sektion_140','Sektion_150']
I = 0
HeaderY =['nr.','LC','Element nr.','Myy','Nyy','MRd_y','URy']
HeaderX =['nr.','LC','Element nr.','Mxx','Nxx','MRd_x','URx']
#load the excel file
#header = loadtxt"LoadsArea_233.txt", unpack=False, skiprows=1)
pdf = FPDF(orientation = 'P', unit = 'mm', format = 'A4')
MaxURx =[]
MaxURy =[]

Elem = np.array(loadtxt("Element/Sektion_100_elements.txt", unpack=False, skiprows=1))
Node = np.array(loadtxt("Element/Sektion_100_nodes.txt", unpack=False, skiprows=1))
#Elem = np.array(loadtxt("Element/"+Sek+"_elements.txt", unpack=False, skiprows=1))
#Node = np.array(loadtxt("Element/"+Sek+"_nodes.txt", unpack=False, skiprows=1))
data = loadtxt("Loads/Sektion_100.txt", unpack=False, skiprows=1)
Mxx = data[:,2]
Nxx = -data[:,4]
Myy = data[:,3]
Nyy = -data[:,5]



R1x = []
R2x = []
MRdx = []
URx = []
R1y = []
R2y = []
MRdy = []
URy = []




min_nx = int(round(max(min(Nxx),-300),-1)-10)
max_nx = int(round(max(Nxx),-1)+10)
min_ny = int(round(min(Nyy),-1)-10)
max_ny = int(round(max(Nyy),-1)+10)
xrange = range(min_nx, max_nx+50, round((max_nx+50-min_nx)/20))
yrange = range(min_ny, max_ny+50, round((max_ny+50-min_ny)/20))

x2 =range(0,len(data),1)


wbx =xw.Book("Capacity\Sektion_100_L.xlsm", None, True)
sht_x1 = wbx.sheets["Beregning"]


for i in xrange:
kx = sht_x1.range("N25").value = i
Q1x = sht_x1["AV24"].value
Q2x = sht_x1["BC24"].value
R1x+=[Q1x]
R2x+=[Q2x]    


for i in x2:
if Myy[i] <= 0:

mrdx = np.interp(Nxx[i],xrange,R1x)
urx = Mxx[i] / mrdx
else:
mrdx = np.interp(Nxx[i],xrange,R2x)
urx = Mxx[i] / mrdx

MRdx += [round(mrdx,2)]
URx += [round(urx,2)]

TabelX=np.c_[data[:,[0,1,2,4]],MRdx,URx]


sort_tabelX  = np.flipud(TabelX[TabelX[:,5].argsort()])
LimX = 25

for i in x2:
if sort_tabelX[i,5] > 1.05 :
LimX = i+2
else:    
break
LimX = max(25,LimX)
TABx=np.c_[list(range(1,LimX+1)),sort_tabelX[0:LimX,:]]
TABx2 = np.unique(TABx[:,2])
print(TABx2)
print(len(TABx2))
#OUE=np.array(TABx2)
#np.savetxt("array1.txt", TABx2)




# %%
NumOUE = len(TABx2)
NumElem = len(Elem)

EleRange = range(0,NumElem)
OUERange = range(0,NumOUE)
EO = np.searchsorted(Elem[:,0], TABx2)   
print(EO) 
EleCorOx =[]
EleCorOy =[]
EleCorOz =[]
EleCorUx =[]
EleCorUy =[]
EleCorUz =[]
for i in EleRange:

Na = np.searchsorted(Node[:,0],Elem[i,1])   
Nb = np.searchsorted(Node[:,0],Elem[i,2])
Nc = np.searchsorted(Node[:,0],Elem[i,3])
Nd = np.searchsorted(Node[:,0],Elem[i,4])
print(Na,Nb,Nc,Nd)
if i in EO:
EleCorOx += [(Node[Na,1] + Node[Nb,1] + Node[Nc,1] + Node[Nd,1])/4]
EleCorOy += [(Node[Na,2] + Node[Nb,2] + Node[Nc,2] + Node[Nd,2])/4]
EleCorOz += [(Node[Na,3] + Node[Nb,3] + Node[Nc,3] + Node[Nd,3])/4]
else:
EleCorUx += [(Node[Na,1] + Node[Nb,1] + Node[Nc,1] + Node[Nd,1])/4]
EleCorUy += [(Node[Na,2] + Node[Nb,2] + Node[Nc,2] + Node[Nd,2])/4]
EleCorUz += [(Node[Na,3] + Node[Nb,3] + Node[Nc,3] + Node[Nd,3])/4]
fig = plt.figure()
fig.set_size_inches(20,10)
ax = fig.add_subplot(projection='3d')
ax.scatter3D(EleCorUx,EleCorUy,EleCorUz,color = 'Blue')  
ax.scatter3D(EleCorOx,EleCorOy,EleCorOz,color = 'red')
ax.set_zlim(0,27000)
plt.show()

此代码用于显示Sektion 100。小的变化给了我110、120、130等等的图。如果有人能用的话。

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