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08_outputDiagnostics batch.r
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08_outputDiagnostics batch.r
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#-------------------------------------------------------------------------------
# WKHELP
#
# Author: Niels Hintzen
# IMARES, The Netherland
#
# Performs an MSE of North Sea Herring under different TAC scenario's
#
# Date: 02-Sep-2012
#
# Build for R2.13.2
#-------------------------------------------------------------------------------
rm(list=ls())
#library(FLSAM)
library(MASS)
#library(msm)
wine <- F
library(FLCore)
#library(PBSadmb)
library(lattice)
library(MASS)
ac<-function(x) {return(as.character(x))}
an<-function(x) {return(as.numeric(x))}
path <- "W:/IMARES/Data/ICES-WG/WKMACLTMP/"
inPath <- "W:/IMARES/Data/ICES-WG/WKMACLTMP/Data/"
codePath <- "W:/IMARES/Data/ICES-WG/WKMACLTMP/R code/"
outPath <- "W:/IMARES/Data/ICES-WG/WKMACLTMP/Results/"
if(substr(R.Version()$os,1,3)== "lin"){
path <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",path)
inPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",inPath)
codePath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",codePath)
outPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",outPath)
outPathsave<-outPath
}
home<-F
if(home)
{
path <- "D://MSE/"
inPath <- "D://MSE/Data/"
codePath <- "D://MSE/R code/"
outPath <- "D://MSE/Results/"
}
source(paste(codePath,'functions.r',sep=""))
# load the true and observed stocks at the start of the simulation from :
RecRegime <- "srest"
#define year ranges
ShortT <- ac(2014:2018)
MidT <- ac(2019:2028)
LongT <- ac(2029:2052)
##-------------------------------------------------------------------------------
## Setup array to save results
##-------------------------------------------------------------------------------
diags<-data.frame(scenario=NA,RecRegime=NA,Iterations=NA,Btrigger=NA,Blim=NA,Ftarget=NA,
Risk1ShortT=NA,Risk1MidT=NA,Risk1LongT=NA,
Risk2ShortT=NA,Risk2MidT=NA,Risk2LongT=NA,
Risk3ShortT=NA,Risk3MidT=NA,Risk3LongT=NA,
SSBend=NA,
meanSSBShortT=NA,meanSSBMidT=NA,meanSSBLongT=NA,
Fend=NA,
meanFShortT=NA,meanFMidT=NA,meanFLongT=NA,
meanYieldShortTerm=NA,meanYieldMidTerm=NA,meanYieldLongTerm=NA,
meanrelTACIAV=NA,noIAVrestrictup=NA,noIAVrestrictdown=NA,TACup=NA,TACdown=NA,
SmeanAgeShortT=NA,SmeanAgeMidT=NA,SmeanAgeLongT=NA,
SmeanWeightShortT=NA,SmeanWeightMidT=NA,SmeanWeightLongT=NA)
##-------------------------------------------------------------------------------
## Load results
##-------------------------------------------------------------------------------
counter<-1
for (sc in c("2.0mt","2.2mt","2.4mt","2.6mt","2.8mt","3.0mt","3.2mt"))
{
for (opt in c(1:16,21))
{
scen <- paste("LTMP",sc,sep="")
TACvarlim <- T
Fvarlim <- T
BBscen <- "noBB"
LastRecOp <- "geom"
#
cat(scen,opt,"\n")
#for (LastRecOp in c("RCT3" ,"geom","SAM"))
#{
#scen <- c("LTMP")
#TACvarlim <- T
#Fvarlim <- T
#BBscen <- "AlternateBank"
#opt <- 1
##
#for (perm in c(F,T))
#{
#scen <- c("LTMP")
#TACvarlim <- T
#Fvarlim <- T
#BBscen <- "AlternateBank"
#opt <- 4
#LastRecOp <- "geom"
##
#
outPath<-outPathsave
perm<-T
if (perm) cat("!!! scenario with permanent changes")
ifelse (perm,outPathp <- paste(outPath,"perm",sep=""), outPathp <- outPath)
sc.name<-paste(scen,opt,"_TACvarlim",TACvarlim,"_Fvarlim",Fvarlim,"_",BBscen,"_LastRec",LastRecOp,sep="")
if(is.element(sc,c("2.8mt","3.0mt","3.2mt"))) outPath<-"/media/n/Projecten/WKMACLTMP/Results/"
outPath2<-paste(outPath,"HCR base case/",sc.name,"/",sep="")
source(paste(codePath,"07_scenarioDescription.r", sep=""))
mpPoints <- get(scen)[[which(names(get(scen))==paste("opt",opt,sep=""))]]
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_Finalf.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_Finalbiol.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_Finalstocks.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_Finalpercievedstocks.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_FinalTAC.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_FinalfSTF.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_FinalSSB.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_Finalfishery.RData", sep=""))
load(file=paste(outPath2,"/",scen,opt,mpPoints$FadultA,"_FinalmpPoints.RData", sep=""))
source(paste(codePath,"functions.r", sep=""))
source(paste(codePath,"04_forecastScenarios.r", sep=""))
load(file=paste(outPath,"settings.RData", sep=""))
for(i in 1:length(settings)) assign(x=names(settings)[i],value=settings[[i]])
cat("change projperiod here")
futureMaxYr<-histMinYr+length(biol@n[1,which(!is.na(biol@n[1,,,,,1])),,,,1])
projPeriod <- 2014:(futureMaxYr-1)
nits<-dim(f)[6]
print(counter)
print("question :")
print("is this better to use median or mean among stock replicates")
print("given that things may be multimodal due to different SR model")
Ref<-mpPoints
diags[counter,"scenario"] <- opt
diags[counter,"RecRegime"] <- RecType
diags[counter,"Iterations"] <- nits
diags[counter,"Btrigger"] <- Ref$Btrigger
diags[counter,"Blim"] <- Ref$Blim
diags[counter,"Ftarget"] <- Ref$Ftarget
diags[counter,"permanent"] <- perm
##-------------------------------------------------------------------------------
## Diagnostics on results
##-------------------------------------------------------------------------------
Btrg <- Ref$Btrigger
Blim <- Ref$Blim
Bpa <- Ref$Bpa
Ssb<-ssbb(biol,f,stockstore)
percSsb<-ssb(stockstore)
Fbar<-quantMeans((f[ac(4:8),]))
Fbar2<-quantMeans((harvest(stockstore)[ac(4:8),]))
# risk related to Blim .
risk<- apply(Ssb<mpPoints$Blim,c(1:5),sum)/nits
diags[counter,"Risk1ShortT"] <- mean(risk[,ShortT])*100 # percentage of iteration that reach Blim
diags[counter,"Risk1MidT"] <- mean(risk[,MidT]) *100
diags[counter,"Risk1LongT"] <- mean(risk[,LongT]) *100
diags[counter,"Risk2ShortT"] <- length(unique(which(Ssb[,ShortT]<Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100 # percentage of iteration that reach Blim
diags[counter,"Risk2MidT"] <- length(unique(which(Ssb[,MidT] <Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100
diags[counter,"Risk2LongT"] <- length(unique(which(Ssb[,LongT] <Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100
diags[counter,"Risk3ShortT"] <- max(risk[,ShortT])*100 # percentage of iteration that reach Blim
diags[counter,"Risk3MidT"] <- max(risk[,MidT]) *100
diags[counter,"Risk3LongT"] <- max(risk[,LongT]) *100
# risk related to Btrig .
risk<- apply(Ssb<mpPoints$Btrigger,c(1:5),sum)/nits
diags[counter,"RiskBT1ShortT"] <- mean(risk[,ShortT])*100 # percentage of iteration that reach Blim
diags[counter,"RiskBT1MidT"] <- mean(risk[,MidT]) *100
diags[counter,"RiskBT1LongT"] <- mean(risk[,LongT]) *100
diags[counter,"RiskBT2ShortT"] <- length(unique(which(Ssb[,ShortT]<Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100 # percentage of iteration that reach Blim
diags[counter,"RiskBT2MidT"] <- length(unique(which(Ssb[,MidT] <Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100
diags[counter,"RiskBT2LongT"] <- length(unique(which(Ssb[,LongT] <Blim,arr.ind=T)[,"dim6"]))/dims(Ssb)$iter *100
diags[counter,"RiskBT3ShortT"] <- max(risk[,ShortT])*100 # percentage of iteration that reach Blim
diags[counter,"RiskBT3MidT"] <- max(risk[,MidT]) *100
diags[counter,"RiskBT3LongT"] <- max(risk[,LongT]) *100
# number of times SSB<Btrigger
trig<- apply(Ssb<mpPoints$Btrigger,c(1:5),sum)
trig<- Ssb<mpPoints$Btrigger
diags[counter,"bellowBtrigShortT"] <- median(c(yearSums(trig[,ShortT])/length(ShortT)@.Data))
diags[counter,"bellowBtrigMidT"] <- median(c(yearSums(trig[,MidT])/length(MidT)@.Data))
diags[counter,"bellowBtrigLongT"] <- median(c(yearSums(trig[,LongT])/length(LongT)@.Data))
# stock and fishing mortality
diags[counter,"SSBend"] <- round(median(c(apply(Ssb[,ac(futureMaxYr-1)],3:6,mean,na.rm=T)))) # or round(iterMeans(Ssb[,ac(futureMaxYr-1)])) ?
diags[counter,"meanSSBLongT"] <- round(median(c(apply(Ssb[,LongT],3:6,mean,na.rm=T))))
diags[counter,"meanSSBMidT"] <- round(median(c(apply(Ssb[,MidT],3:6,mean,na.rm=T))))
diags[counter,"meanSSBShortT"] <- round(median(c(apply(Ssb[,ShortT],3:6,mean,na.rm=T))))
diags[counter,"meanFShortT"] <- round( median( yearMeans(Fbar[,ShortT])@.Data) ,3)
diags[counter,"meanFMidT"] <- round( median( yearMeans(Fbar[,MidT])@.Data) ,3)
diags[counter,"meanFLongT"] <- round( median( yearMeans(Fbar[,LongT])@.Data) ,3)
diags[counter,"Fend"] <- round( median (Fbar[,ac(futureMaxYr-1)]@.Data) ,3)
# difference between percieved and true stocks
diags[counter,"SSBabsBias"] <- round(apply( yearMeans(100*abs(percSsb[,ac(projPeriod)]-Ssb[,ac(projPeriod)])/Ssb[,ac(projPeriod)]),1:5,median,na.rm=T)@.Data,3)
diags[counter,"SSBBias"] <- round(apply( yearMeans(100*(percSsb[,ac(projPeriod)]-Ssb[,ac(projPeriod)])/Ssb[,ac(projPeriod)]),1:5,median,na.rm=T)@.Data,3)
diags[counter,"FbarabsBias"] <- round(apply( yearMeans(100*abs((Fbar2[,ac(projPeriod)])-(Fbar[,ac(projPeriod)]))/(Fbar[,ac(projPeriod)])),1:5,median,na.rm=T)@.Data,3)
diags[counter,"FbarBias"] <- round(apply( yearMeans(100*((Fbar2[,ac(projPeriod)])-(Fbar[,ac(projPeriod)]))/(Fbar[,ac(projPeriod)])),1:5,median,na.rm=T)@.Data,3)
# catches and quotas
diags[counter,"meanYieldShortTerm"] <- round(median(c(yearMeans((computeLandings(fishery)[,ShortT])))))
diags[counter,"meanYieldMidTerm"] <- round(median(c(yearMeans((computeLandings(fishery)[,MidT])))))
diags[counter,"meanYieldLongTerm"] <- round(median(c(yearMeans((computeLandings(fishery)[,LongT])))))
# mean age and weight in the catches and in the SSB
# mean age mature fish
sma<-quantSums(sweep((biol@n*biol@fec),c(1:6) ,c(0:12),"*"))/quantSums((biol@n*biol@fec))
# mean weight mature fish
smw<-quantSums((biol@n*biol@wt*biol@fec)) / quantSums((biol@n*biol@fec))
diags[counter,"SmeanAgeShortT"] <- round(median(c(yearMeans((sma[,ShortT])))),2)
diags[counter,"SmeanAgeMidT"] <- round(median(c(yearMeans((sma[,MidT])))),2)
diags[counter,"SmeanAgeLongT"] <- round(median(c(yearMeans((sma[,LongT])))),2)
diags[counter,"SmeanWeightShortT"] <- round(1000*median(c(yearMeans((smw[,ShortT])))),0)
diags[counter,"SmeanWeightMidT"] <- round(1000*median(c(yearMeans((smw[,MidT])))),0)
diags[counter,"SmeanWeightLongT"] <- round(1000*median(c(yearMeans((smw[,LongT])))),0)
# mean age based on the distribution of catches between age classes
yma<-quantSums(sweep((fishery@landings.n),c(1:6) ,c(0:12),"*"))/quantSums((fishery@landings.n))
# mean weight based on the distribution of catches between age classes
ymw<-quantSums((fishery@landings.n*fishery@landings.wt))/quantSums((fishery@landings.n))
diags[counter,"YmeanAgeShortT"] <- round(median(c(yearMeans((yma[,ShortT])))),2)
diags[counter,"YmeanAgeMidT"] <- round(median(c(yearMeans((yma[,MidT])))),2)
diags[counter,"YmeanAgeLongT"] <- round(median(c(yearMeans((yma[,LongT])))),2)
diags[counter,"YmeanWeightShortT"] <- round(1000*median(c(yearMeans((ymw[,ShortT])))),0)
diags[counter,"YmeanWeightMidT"] <- round(1000*median(c(yearMeans((ymw[,MidT])))),0)
diags[counter,"YmeanWeightLongT"] <- round(1000*median(c(yearMeans((ymw[,LongT])))),0)
# quota variability
diags[counter,"meanrelTACIAV"] <- round(median(c(apply(abs(TAC[,ac(projPeriod[2]:rev(projPeriod)[1])] - TAC[,ac(projPeriod[1]:rev(projPeriod)[2])]) /TAC[,ac(projPeriod[1]:rev(projPeriod)[2])] * 100,3:6,mean,na.rm=T))),3)
IAVUp <- which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)])] == 1.2* TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-1])],arr.ind=T)
IAVDown <- which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)])] == 0.8* TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-1])],arr.ind=T)
# #- Average number of times the IAV rule is applied upwards or downwards
diags[counter,"noIAVrestrictup"]<-0
if((nrow(IAVUp)) > 0 ){
a <- IAVUp
diags[counter,"noIAVrestrictup"] <- max(0,median(aggregate(a[,"dim2"],by=list(a[,"dim6"]),function(x){length(x)})$x),na.rm=T)
}
diags[counter,"noIAVrestrictdown"]<-0
if((nrow(IAVDown)) > 0 ){
a <- IAVDown
diags[counter,"noIAVrestrictdown"] <- max(0,median(aggregate(a[,"dim2"],by=list(a[,"dim6"]),function(x){length(x)})$x),na.rm=T)
}
#
# #- Which TAC of the runs go up and which go down
resUp <- which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)])] > TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-1])],arr.ind=T)
resDown <- which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)])] < TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-1])],arr.ind=T)
#
# #- Mean increase in TAC is TAC goes up, or mean decrease in TAC is TAC goes down
diags[counter,"TACup"] <- round(mean((TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)-1])] - TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-2])])@.Data[which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)-1])] > TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-2])])]))
diags[counter,"TACdown"] <- round(mean((TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-2])] - TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)-1])])@.Data[which(TAC[,ac(projPeriod[2]:projPeriod[length(projPeriod)-1])] < TAC[,ac(projPeriod[1]:projPeriod[length(projPeriod)-2])])]))
#
counter <- counter + 1
}
write.csv((diags),file=paste(outPathsave,"/tables_diags_HCR base case1000","_TACvarlim",TACvarlim,"_Fvarlim",Fvarlim,"_",BBscen,"_LastRec.csv",sep=""),row.names=T)
}
#write.csv(t(diags),file=paste(outPath,"/tables_diags",paste(scen,opt,"_TACvarlim",TACvarlim,"_Fvarlim",Fvarlim,"_",BBscen,"_LastRec",LastRecOp,sep=""),".csv",sep=""),row.names=T)