def lossModelling(event_loss_table_folder,save_ses_csv,save_elt_csv,total_cost,investigationTime,return_periods):
timeSES, ses = parse_ses.parse_ses(event_loss_table_folder,save_ses_csv)
event_loss_table = parse_elt.parse_elt(event_loss_table_folder,save_elt_csv)
losses, rateOfExceedance = estimateLosses(event_loss_table,investigationTime)
maxLoss, aal, aalr, lossLevels = estimateLossStatistics(losses,rateOfExceedance,investigationTime,total_cost,return_periods)
plotCurves(losses,rateOfExceedance,return_periods,lossLevels)
def exportInfo(event_loss_table_folder):
investigationTime, ses = parse_ses.parse_ses(event_loss_table_folder,
False)
event_loss_table = parse_elt.parse_elt(event_loss_table_folder, False)
losses, rateOfExceedance = estimateLosses(event_loss_table,
investigationTime)
results = open('results.txt', "w")
for loss in event_loss_table:
rup = ses[np.where(ses[:, 0] == loss[0]), :][0][0]
idrup = rup[0]
mag = rup[1]
lon = str(
(float(rup[6]) + float(rup[9]) + float(rup[12]) + float(rup[15])) /
4)
lat = str(
(float(rup[7]) + float(rup[10]) + float(rup[13]) + float(rup[16]))
/ 4)
depth = str(
(float(rup[8]) + float(rup[11]) + float(rup[14]) + float(rup[17]))
/ 4)
lossValue = loss[2]
results.write(idrup + ',' + mag + ',' + lon + ',' + lat + ',' + depth +
',' + lossValue + '\n')
def lossModelling(event_loss_table_folder,save_ses_csv,save_elt_csv,total_cost,return_periods):
investigationTime, ses = parse_ses.parse_ses(event_loss_table_folder,save_ses_csv)
# investigationTime = 170000
event_loss_table = parse_elt.parse_elt(event_loss_table_folder,save_elt_csv)
losses, rateOfExceedance = estimateLosses(event_loss_table,investigationTime)
maxLoss, aal, aalr, lossLevels = estimateLossStatistics(losses,rateOfExceedance,investigationTime,total_cost,return_periods)
plotCurves(losses,rateOfExceedance,return_periods,lossLevels)
def selectRuptures(event_loss_table_folder,return_periods,rups_for_return_period):
investigationTime, ses = parse_ses.parse_ses(event_loss_table_folder,False)
event_loss_table = parse_elt.parse_elt(event_loss_table_folder,False)
losses, rateOfExceedance = estimateLosses(event_loss_table,investigationTime)
if rups_for_return_period > 0:
ruptures = captureRuptures(losses,rateOfExceedance,ses,event_loss_table,return_periods,rups_for_return_period)
for i in range(len(ruptures)):
plotCapturedRuptures(ruptures[i],return_periods[i])
def selectRuptures(event_loss_table_folder,return_periods,rups_for_return_period):
investigationTime, ses = parse_ses.parse_ses(event_loss_table_folder,False)
event_loss_table = parse_elt.parse_elt(event_loss_table_folder,False)
losses, rateOfExceedance = estimateLosses(event_loss_table,investigationTime)
if rups_for_return_period > 0:
ruptures = captureRuptures(losses,rateOfExceedance,ses,event_loss_table,return_periods,rups_for_return_period)
for i in range(len(ruptures)):
plotCapturedRuptures(ruptures[i],return_periods[i])
def exportInfo(event_loss_table_folder):
investigationTime, ses = parse_ses.parse_ses(event_loss_table_folder,False)
event_loss_table = parse_elt.parse_elt(event_loss_table_folder,False)
losses, rateOfExceedance = estimateLosses(event_loss_table,investigationTime)
results = open('results.txt',"w")
for loss in event_loss_table:
rup = ses[np.where(ses[:,0]==loss[0]),:][0][0]
idrup = rup[0]
mag = rup[1]
lon = str((float(rup[6])+float(rup[9])+float(rup[12])+float(rup[15]))/4)
lat = str((float(rup[7])+float(rup[10])+float(rup[13])+float(rup[16]))/4)
depth = str((float(rup[8])+float(rup[11])+float(rup[14])+float(rup[17]))/4)
lossValue = loss[2]
results.write(idrup+','+mag+','+lon+','+lat+','+depth+','+lossValue+'\n')
print 'Loss for a return period of ' + str(
return_periods[i]) + ' years.'
print losses[idx]
else:
print 'Return period of ' + str(
return_periods[i]
) + ' years is above the time length of the stochastic event set.'
return maxLoss, aal, aalr, lossLevels
def estimateLosses(event_loss_table, investigationTime):
allLosses = map(float, event_loss_table[:, 2])
allLosses.sort(reverse=True)
rates = np.arange(1, len(allLosses) + 1) / investigationTime
return allLosses, rates
def find_nearest(array, value):
return (np.abs(array - value)).argmin()
#investigationTime, ses = parse_ses.parse_ses('Turkey',True)
event_loss_table = parse_elt.parse_elt('Turkey', True)
losses, rateOfExceedance = estimateLosses(event_loss_table, 132000)
maxLoss, aal, aalr, lossLevels = estimateLossStatistics(
losses, rateOfExceedance, 132000, 1.044E12, [475])
annual_rate_exc = 1.0/np.array(return_periods)
for i in range(len(annual_rate_exc)):
if annual_rate_exc[i] > min(rateOfExceedance):
idx = find_nearest(rateOfExceedance,annual_rate_exc[i])
lossLevels.append(losses[idx])
print 'Loss for a return period of '+str(return_periods[i])+' years.'
print losses[idx]
else:
print 'Return period of '+str(return_periods[i])+' years is above the time length of the stochastic event set.'
return maxLoss, aal, aalr, lossLevels
def estimateLosses(event_loss_table,investigationTime):
allLosses = map(float, event_loss_table[:,2])
allLosses.sort(reverse=True)
rates = np.arange(1,len(allLosses)+1)/investigationTime
return allLosses, rates
def find_nearest(array,value):
return (np.abs(array-value)).argmin()
#investigationTime, ses = parse_ses.parse_ses('Turkey',True)
event_loss_table = parse_elt.parse_elt('Turkey',True)
losses, rateOfExceedance = estimateLosses(event_loss_table,132000)
maxLoss, aal, aalr, lossLevels = estimateLossStatistics(losses,rateOfExceedance,132000, 1.044E12,[475])
