def vulnerability_process(an_type, T, Gamma, w, EDPlim, dcroof, EDPvec, RDvec, SPO, bUthd, noBlg, g, MC, Sa_ratios, plot_feature, N, Tc, Td):
plotflag,linew,fontsize,units,iml = plot_feature[0:5]
LR50s = []
bLRs = []
for blg in range(0,noBlg):
LRs = []
# Vamvatsikos and Cornell's method
if np.array(bUthd[blg]).any()>0:
# If some damage criteria have uncertainty
# Monte Carlo realisations of damage criteria e derivation of fragility curves
# for each sample, mean loss ratios are computed at each IML from all samples
for i in range (0,MC):
dc_sample = np.zeros_like(dcroof[blg])
EDP_sample = np.zeros(len(dcroof[blg])+1)
for j in range(0,len(dcroof[blg])):
if bUthd[blg][j]>0:
EDP_sample[j+1] = np.random.lognormal(np.log(EDPlim[blg][j]),bUthd[blg][j],1)
while (EDP_sample[j+1]<=EDP_sample[j]):
EDP_sample[j+1] = np.random.lognormal(np.log(EDPlim[blg][j]),bUthd[blg][j],1)
else:
EDP_sample[j+1] = EDPlim[blg][j]
dc_sample[j] = np.interp(EDP_sample[j+1],EDPvec[blg],RDvec[blg]);
EDP_sample = EDP_sample[1::]
if an_type == 0:
[SaT50, bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), g, MC)
elif an_type==2: # Dolsek and Fajfar's method
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50,bTSa] = DFfragility(T[blg], Gamma[blg], EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), mc, r, g, Tc, Td, MC)
else:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm, idacr] = spo2ida_allT(mc,a,ac,r,mf,T[blg],pw,[0],filletstyle,N,linew,fontsize)
[SaT50,bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g, EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50)*Sa_ratios[blg], np.array(bTSa)*Sa_ratios[blg]
LRs.append(damage_to_loss(SaTlogmean_av,bTSa_av,iml,cd+'/NSP/inputs'))
else:
# If any damage criteria have uncertainty
if an_type == 0:
# Ruiz-Garcia Miranda's method the uncertainty in the damage criteria is already included in the total dispersion in a simplified way
[SaT50, bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], g, MC)
elif an_type == 2:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50,bTSa] = DFfragility(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], mc, r, g, Tc, Td, MC)
else:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm, idacr] = spo2ida_allT(mc,a,ac,r,mf,T[blg],pw,[0],filletstyle,N,linew,fontsize)
[SaT50,bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g, EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50)*Sa_ratios[blg], np.array(bTSa)*Sa_ratios[blg]
LR50 = damage_to_loss(SaTlogmean_av,bTSa_av,iml,cd+'/NSP/inputs')
LRs.append(LR50)
# Define Vulnerability curve for each building
LR50s.append(np.mean(LRs,axis = 0))
bLRs.append(np.std(LRs, axis = 0))
# Combine the Loss Ratios of each building in a single LR for each iml,
# with mean = mean(LRs) and std = std(LRs)
lr = np.array(LR50s)
bLRs = np.array(bLRs)
LR50 = np.average(lr, axis=0, weights=w) # weighted mean
w2 = np.array([np.repeat(ele,len(iml)) for ele in w])
bLR = np.sqrt(np.sum(np.multiply(np.power(bLRs,2)+np.power(lr-LR50,2),w2),axis=0))
return [LR50, bLR]
all = [var for var in globals() if var[0] != "_"]
for var in all:
del globals()[var]
clearall()
from rmtk.vulnerability.common.damage_to_loss import damage_to_loss
import numpy as np
import os
# <codecell>
iml = np.linspace(0.1,1,10)
theta = np.array([0.30359629923905929, 0.61648687596347196, 0.90233055223871583, 1.1590608046681703, 1.1590608046681703])
beta = np.array([0.0, 0.1343651091408001, 0.22569253428542574, 0.28024503952191621, 0.28024503952191621])
path = os.getcwd()
# <codecell>
#SaT = [np.log(ele) for ele in theta]
#bTSa = beta
SaT = np.log(theta)
LR = damage_to_loss(SaT,beta,iml,path)
# <codecell>
print LR
# <codecell>
def vulnerability_process(vuln, iml, log_meanSa, log_stSa):
if vuln == 1:
LR50 = damage_to_loss(log_meanSa, log_stSa, iml, cd + '/inputs/')
else:
LR50 = []
return LR50
from rmtk.vulnerability.common.damage_to_loss import damage_to_loss
import numpy as np
import os
# <codecell>
iml = np.linspace(0.1, 1, 10)
theta = np.array([
0.30359629923905929, 0.61648687596347196, 0.90233055223871583,
1.1590608046681703, 1.1590608046681703
])
beta = np.array([
0.0, 0.1343651091408001, 0.22569253428542574, 0.28024503952191621,
0.28024503952191621
])
path = os.getcwd()
# <codecell>
#SaT = [np.log(ele) for ele in theta]
#bTSa = beta
SaT = np.log(theta)
LR = damage_to_loss(SaT, beta, iml, path)
# <codecell>
print LR
# <codecell>
def vulnerability_process(an_type, T, Gamma, w, EDPlim, dcroof, EDPvec, RDvec,
SPO, bUthd, noBlg, g, MC, Sa_ratios, plot_feature, N,
Tc, Td):
plotflag, linew, fontsize, units, iml = plot_feature[0:5]
LR50s = []
bLRs = []
for blg in range(0, noBlg):
LRs = []
# Vamvatsikos and Cornell's method
if np.array(bUthd[blg]).any() > 0:
# If some damage criteria have uncertainty
# Monte Carlo realisations of damage criteria e derivation of fragility curves
# for each sample, mean loss ratios are computed at each IML from all samples
for i in range(0, MC):
dc_sample = np.zeros_like(dcroof[blg])
EDP_sample = np.zeros(len(dcroof[blg]) + 1)
for j in range(0, len(dcroof[blg])):
if bUthd[blg][j] > 0:
EDP_sample[j + 1] = np.random.lognormal(
np.log(EDPlim[blg][j]), bUthd[blg][j], 1)
while (EDP_sample[j + 1] <= EDP_sample[j]):
EDP_sample[j + 1] = np.random.lognormal(
np.log(EDPlim[blg][j]), bUthd[blg][j], 1)
else:
EDP_sample[j + 1] = EDPlim[blg][j]
dc_sample[j] = np.interp(EDP_sample[j + 1], EDPvec[blg],
RDvec[blg])
EDP_sample = EDP_sample[1::]
if an_type == 0:
[SaT50, bTSa
] = simplified_bilinear(T[blg], Gamma[blg], EDP_sample,
dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), g, MC)
elif an_type == 2: # Dolsek and Fajfar's method
[mc, a, ac, r, mf] = get_spo2ida_parameters(
SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50, bTSa] = DFfragility(T[blg], Gamma[blg], EDP_sample,
dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), mc,
r, g, Tc, Td, MC)
else:
[mc, a, ac, r,
mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm,
idacr] = spo2ida_allT(mc, a, ac, r, mf, T[blg], pw, [0],
filletstyle, N, linew, fontsize)
[SaT50,
bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g,
EDP_sample, dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(
SaT50) * Sa_ratios[blg], np.array(bTSa) * Sa_ratios[blg]
LRs.append(
damage_to_loss(SaTlogmean_av, bTSa_av, iml,
cd + '/NSP/inputs'))
else:
# If any damage criteria have uncertainty
if an_type == 0:
# Ruiz-Garcia Miranda's method the uncertainty in the damage criteria is already included in the total dispersion in a simplified way
[SaT50,
bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDPlim[blg],
dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg],
g, MC)
elif an_type == 2:
[mc, a, ac, r, mf] = get_spo2ida_parameters(
SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50, bTSa] = DFfragility(T[blg], Gamma[blg], EDPlim[blg],
dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg],
mc, r, g, Tc, Td, MC)
else:
[mc, a, ac, r,
mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm,
idacr] = spo2ida_allT(mc, a, ac, r, mf, T[blg], pw, [0],
filletstyle, N, linew, fontsize)
[SaT50,
bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g,
EDPlim[blg], dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg], MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50) * Sa_ratios[blg], np.array(
bTSa) * Sa_ratios[blg]
LR50 = damage_to_loss(SaTlogmean_av, bTSa_av, iml,
cd + '/NSP/inputs')
LRs.append(LR50)
# Define Vulnerability curve for each building
LR50s.append(np.mean(LRs, axis=0))
bLRs.append(np.std(LRs, axis=0))
# Combine the Loss Ratios of each building in a single LR for each iml,
# with mean = mean(LRs) and std = std(LRs)
lr = np.array(LR50s)
bLRs = np.array(bLRs)
LR50 = np.average(lr, axis=0, weights=w) # weighted mean
w2 = np.array([np.repeat(ele, len(iml)) for ele in w])
bLR = np.sqrt(
np.sum(np.multiply(np.power(bLRs, 2) + np.power(lr - LR50, 2), w2),
axis=0))
return [LR50, bLR]
def vulnerability_process(vuln,iml,log_meanSa,log_stSa):
if vuln == 1:
LR50 = damage_to_loss(log_meanSa, log_stSa, iml, cd+'/inputs/')
else:
LR50 = []
return LR50
