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| import os
import math
import numpy as np
import matplotlib.pyplot as plt
import scipy
class spectrum:
sw=np.array([])
v=np.array([])
su=np.array([])
tf=np.array([])
u=np.array([])
u0=np.array([])
u00=np.array([])
saT=np.array([])
sa=np.array([])
sv=np.array([])
sd=np.array([])
PGA=0
PGD=0
PGV=0
t=0
def __init__(self,path,t):
file_4=open(path,"r")
str4=""
for p in range(0,4):
str4=str4+file_4.readline()
data=file_4.readlines()
file_4.close()
lit=[]
for d in data:
tmpd=d.split()
for num_tmpd in range(0,len(tmpd)):
lit.append(float(tmpd[num_tmpd]))
self.sw=np.array(lit)
self.t=t
return
def newmark_beta(self,T,ksi=0.05,gamma=0.5,beta=0.25):
if T==0:
T=0.00000001
n=len(self.sw)
m=1.0
c=2*m*ksi*(2*np.pi/T)
u00=np.zeros(n)
u0=np.zeros(n)
u=np.zeros(n)
k=(2*np.pi/T)**2*m
u00[0]=-self.sw[0]-c*u0[0]-k*u[0]
a1=m/(beta*(self.t**2))+gamma*c/(beta*self.t)
a2=m/(beta*self.t)+(gamma/beta-1)*c
a3=(1/(2*beta)-1)*m+self.t*(gamma/(2*beta)-1)*c
k_hat=k+a1
for i in range(1,n):
p_hat=-self.sw[i]+a1*u[i-1]+a2*u0[i-1]+a3*u00[i-1]
u[i]=p_hat/k_hat
u0[i]=gamma/(beta*self.t)*(u[i]-u[i-1])+(1-gamma/beta)*u0[i-1]+self.t*(1-gamma/(2*beta))*u00[i-1]
u00[i]=1/(beta*(self.t**2))*(u[i]-u[i-1])-u0[i-1]/(beta*self.t)-(1/(2*beta)-1)*u00[i-1]
self.u=u
self.u0=u0
self.u00=u00+self.sw
return u,u0,u00+self.sw
def central_difference(self,T,ksi=0.05,gamma=0.5,beta=0.25):
m=1.0
n=len(self.sw)
u00=np.zeros(n)
u0=np.zeros(n)
u=np.zeros(n+1)
c=2*m*ksi*(2*np.pi/T)
k=(2*np.pi/T)**2*m
u00[0]=-self.sw[0]-c*u0[0]-k*u[0]
u_1=u[0]-self.t*u0[0]+self.t**2*u00[0]/2
k_hat=m/(self.t**2)+c/(2*self.t)
a=m/(self.t**2)-c/(2*self.t)
b=k-2*m/(self.t**2)
for i in range(n):
if i==1:
p_hat=-self.sw[i]-a*u_1-b*u[i]
else:
p_hat=-self.sw[i]-a*u[i-1]-b*u[i]
u[i+1]=p_hat/k_hat
u0[i]=(u[i+1]-u[i-1])/(2*self.t)
u00[i]=(u[i+1]-2*u[i]+u[i-1])/(self.t**2)
self.u=u[:n]
self.u0=u0
self.u00=u00+self.sw
return
def get_PGA(self):
self.PGA=max(abs(self.sw))
return max(abs(self.sw))
def get_PGV(self):
self.PGV=max(abs(self.v))
return max(abs(self.v))
def get_PGD(self):
self.PGD=max(abs(self.su))
return max(abs(self.su))
def get_sa(self,begin,end,step):
T=np.arange(begin,end,step)
sa=np.array([])
sv=np.array([])
su=np.array([])
for i in T:
u,v,a=self.newmark_beta(i)
sa=np.append(sa,max(abs(a)))
sv=np.append(sv,max(abs(v)))
su=np.append(su,max(abs(u)))
self.saT=T
self.sa=sa
self.sd=su
self.sv=sv
return
def get_v(self):
v=[]
for i in range(len(self.sw)):
v.append(np.trapz(self.sw[:i+1],dx=self.t))
self.v=np.array(v)
return v
def get_u(self):
v=self.get_v()
u=[]
for i in range(len(self.sw)):
u.append(np.trapz(v[:i+1],dx=self.t))
self.su=np.array(u)
return u
def tiaofu_sa(self,T,target):
u,v,a=self.newmark_beta(T)
sa=max(abs(a))
self.tf=self.sw
self.sw=self.sw*(target/sa)
self.get_v()
self.get_u()
return target/sa
def fypa(self,T,x,path):
y=np.loadtxt(path)
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(10,5))
plt.title("标准加速度反应谱")
self.get_PGA()
plt.plot(T,x/self.PGA,label="python")
plt.plot(T,y/self.PGA,label="SPECTR")
plt.xlabel("T(s)")
plt.ylabel("$\\beta$")
plt.legend()
def fypv(self,T,x,path):
y=np.loadtxt(path)/1000
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(10,5))
plt.title("标准速度反应谱")
self.get_PGV()
plt.plot(T,x/self.PGV,label="python")
plt.plot(T,y/self.PGV,label="SPECTR")
plt.xlabel("T(s)")
plt.ylabel("$\\beta$")
plt.legend()
def fypu(self,T,x,path):
y=np.loadtxt(path)/1000
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(10,5))
plt.title("标准位移反应谱")
self.get_PGD()
plt.plot(T,x/self.PGD,label="python")
plt.plot(T,y/self.PGD,label="SPECTR")
plt.xlabel("T(s)")
plt.ylabel("$\\beta$")
plt.legend()
def sw_a(self):
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus']=False
plt.figure(figsize=(10,5))
plt.title("地震波加速度时程")
t=np.linspace(0,len(self.sw)*self.t,len(self.sw))
plt.plot(t,self.sw)
plt.xlabel("t(s)")
plt.ylabel("a(g)")
|
