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fragilitycurve.py
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fragilitycurve.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @Author : Penghui Zhang
# @Email : [email protected]
import matplotlib.pyplot as plt
import numpy as np
import math
class FragilityCurve:
def __init__ (self, im, edp):
self.im = im
self.edp = edp
self.n = 100000 #蒙特卡洛抽样次数
self.dot = 30 #易损性曲线点数
def capcityModel (self):
#能力模型生成100000行的抽样矩阵
capacity = None
if self.edp.split('_')[1]=='cdrift':
sc = np.array([0.005, 0.01, 0.02, 0.025])
beltac = np.array([0.0025, 0.0025, 0.0046, 0.0046])
capacity = np.zeros((self.n, 4))
for i in range(self.n):
flag = 1
while flag:
randomArray = np.array([np.random.lognormal(mean=math.log(sc[0]), sigma=beltac[0], size=None),\
np.random.lognormal(mean=math.log(sc[1]), sigma=beltac[1], size=None),\
np.random.lognormal(mean=math.log(sc[2]), sigma=beltac[2], size=None),\
np.random.lognormal(mean=math.log(sc[3]), sigma=beltac[3], size=None)])
if randomArray[0]<randomArray[1]<randomArray[2]<randomArray[3]:
capacity[i,:] = randomArray.copy()
flag = 0
elif self.edp.split('_')[1]=='bdisp':
sc = np.array([0.15, 0.35])
beltac = np.array([0.35, 0.35])
capacity = np.zeros((self.n, 2))
for i in range(self.n):
flag = 1
while flag:
randomArray = np.array([np.random.lognormal(mean=math.log(sc[0]), sigma=beltac[0], size=None),\
np.random.lognormal(mean=math.log(sc[1]), sigma=beltac[1], size=None)])
if randomArray[0]<randomArray[1]:
capacity[i,:] = randomArray.copy()
flag = 0
elif self.edp.split('_')[1]=='adispa':
sc = np.array([0.01, 0.038, 0.077])
beltac = np.array([0.0007, 0.0009, 0.00085])
capacity = np.zeros((self.n, 3))
for i in range(self.n):
flag = 1
while flag:
randomArray = np.array([np.random.lognormal(mean=math.log(sc[0]), sigma=beltac[0], size=None),\
np.random.lognormal(mean=math.log(sc[1]), sigma=beltac[1], size=None),\
np.random.lognormal(mean=math.log(sc[2]), sigma=beltac[2], size=None)])
if randomArray[0]<randomArray[1]<randomArray[2]:
capacity[i,:] = randomArray.copy()
flag = 0
elif self.edp.split('_')[1]=='adispp':
sc = np.array([0.037, 0.147])
beltac = np.array([0.00046, 0.00046])
capacity = np.zeros((self.n, 2))
for i in range(self.n):
flag = 1
while flag:
randomArray = np.array([np.random.lognormal(mean=math.log(sc[0]), sigma=beltac[0], size=None),\
np.random.lognormal(mean=math.log(sc[1]), sigma=beltac[1], size=None)])
if randomArray[0]<randomArray[1]:
capacity[i,:] = randomArray.copy()
flag = 0
return capacity
def lmDemandModel (self, lnIM, intercept, coeffiction, beltad):
lnSd = intercept + coeffiction*lnIM
lmDemand = np.random.lognormal(mean=lnSd, sigma=beltad, size=(self.n,1))
return lmDemand
def mmToInches (self,mm):
#mm transform to inches
inches=mm*0.0393700787
return inches
def fragilityCurvePlot (self, imRange,lmIntercept, lmCoeffiction, lmBeltad):
imList = np.linspace(imRange[0], imRange[1], self.dot)
lnIMList = np.array([math.log(x) for x in imList])
if self.edp.split('_')[1] == 'cdrift':
lmfragility = np.zeros((self.dot, 4))
elif self.edp.split('_')[1] == 'bdisp':
lmfragility = np.zeros((self.dot, 2))
elif self.edp.split('_')[1] == 'adispa':
lmfragility = np.zeros((self.dot, 3))
elif self.edp.split('_')[1] == 'adispp':
lmfragility = np.zeros((self.dot, 2))
for i in range(self.dot):
lnIM = lnIMList[i]
capacity = self.capcityModel()
lmdemand = self.lmDemandModel (lnIM, lmIntercept, lmCoeffiction, lmBeltad)
lmfragility[i,:] = np.sum(lmdemand>capacity, axis=0)/self.n
print('lmfragility: ',lmfragility[i,:])
#画出易损性曲线
width=self.mmToInches(70)
height=self.mmToInches(50)
fig = plt.figure(facecolor="white", figsize=(width, height))
if self.edp.split('_')[1] == 'cdrift':
plt.plot(imList,lmfragility[:,0],color='red',linestyle=':',linewidth=1,label='LS1')
plt.plot(imList,lmfragility[:,1],color='red',linestyle='-.',linewidth=1,label='LS2')
plt.plot(imList,lmfragility[:,2],color='red',linestyle='--',linewidth=1,label='LS3')
plt.plot(imList,lmfragility[:,3],color='red',linestyle='-',linewidth=1,label='LS4')
elif self.edp.split('_')[1] == 'bdisp':
plt.plot(imList,lmfragility[:,0],color='red',linestyle=':',linewidth=1,label='LS1')
plt.plot(imList,lmfragility[:,1],color='red',linestyle='-.',linewidth=1,label='LS2')
elif self.edp.split('_')[1] == 'adispa':
plt.plot(imList,lmfragility[:,0],color='red',linestyle=':',linewidth=1,label='LS1')
plt.plot(imList,lmfragility[:,1],color='red',linestyle='-.',linewidth=1,label='LS2')
plt.plot(imList,lmfragility[:,2],color='red',linestyle='--',linewidth=1,label='LS3')
elif self.edp.split('_')[1] == 'adispp':
plt.plot(imList,lmfragility[:,0],color='red',linestyle=':',linewidth=1,label='LS1')
plt.plot(imList,lmfragility[:,1],color='red',linestyle='-.',linewidth=1,label='LS2')
plt.xlabel(self.im,size=8)
plt.ylabel('probability',size=8)
plt.xlim(imRange[0], imRange[1])
plt.ylim(0, 1)
plt.legend(loc='lower right',frameon=True,edgecolor='black',fontsize=6)
ax=plt.gca()
ax.tick_params(direction='in')
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
plt.savefig('fagilitycurve/'+self.edp+" FragilityCurve.png",dpi = 960, bbox_inches="tight")
plt.savefig('fagilitycurve/'+self.edp+" FragilityCurve.eps",dpi = 960, bbox_inches="tight")