git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngineSmartExecutor@114113 82a268e6-3cf1-43bd-a215-b396298e98cf
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set.seed(999) ## for same random sequence
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#require(hacks)
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#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
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## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
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## For example
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## stock res yr ct
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## cap-icel Medium 1984 1234.32
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## filename <- "RAM_MSY.csv"
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##filename <- "ICESct2.csv"
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cat("Step 1","\n")
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TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
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filename <- "D20.csv"
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outfile <- "CatchMSY_Output.csv"
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outfile2 <- paste("NonProcessedSpecies.csv",sep="")
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#cdat <- read.csv2(filename, header=T, dec=".")
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cdat1 <- read.csv(filename)
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cat("\n", "File", filename, "read successfully","\n")
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cat("Step 2","\n")
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if(file.exists("cdat.RData"))
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{load("cdat.RData")} else
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{
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dim(cdat1)
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yrs=1950:2012
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# to set NA as 0
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cdat1[is.na(cdat1)] <- 0
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nrow <- length(cdat1[,1])
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ndatColn <- length(cdat1[1,c(-1:-12)])
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rownames(cdat1) <- NULL
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cdat <- NULL
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for(i in 1:nrow)
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{#i=1
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#a <- ctotal3[i,-1]
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tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
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species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
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yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])),
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res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
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cdat <- rbind(cdat,tmp)
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#edit(cdat)
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}
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}
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StockList=unique(as.character(cdat$stock))
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colnames(cdat)
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#stock_id <- unique(as.character(cdat$stock))
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#??
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# stock_id <- "cod-2224" ## for selecting individual stocks
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# stock=stock_id
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#??
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cat("Step 3","\n")
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## FUNCTIONS are going to be used subsequently
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.schaefer <- function(theta)
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{
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with(as.list(theta), { ## for all combinations of ri & ki
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bt=vector()
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ell = 0 ## initialize ell
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J=0 #Ye
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for (j in startbt)
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{
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if(ell == 0)
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{
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bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
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ell = 0
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if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
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ell = 1
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J=j # Ye
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}
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}
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return(list(ell=ell,J=J)) # Ye adding J=J
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})
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}
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sraMSY <-function(theta, N)
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{
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#This function conducts the stock reduction
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#analysis for N trials
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#args:
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# theta - a list object containing:
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# r (lower and upper bounds for r)
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# k (lower and upper bounds for k)
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# lambda (limits for current depletion)
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with(as.list(theta),
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{
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ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
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ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
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itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
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## assign ri, ki, and final biomass range to itheta
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M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
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i=1:N
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## prototype objective function
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get.ell=function(i) M[[i]]$ell
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ell = sapply(i, get.ell)
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get.J=function(i) M[[i]]$J # Ye
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J=sapply(i,get.J) # Ye
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return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
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})
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}
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getBiomass <- function(r, k, j)
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{
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BT <- NULL
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bt=vector()
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for (v in 1:length(r))
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{
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bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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BT=rbind(BT, t(t(bt)))
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}
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return(BT)
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}
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## The End of Functions section
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cat("Step 4","\n")
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stockLoop <- StockList
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# randomly select stocks from randomly selected 5 area codes first
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if(TestRUN)
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{
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set.seed(999)
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AreaCodeList <- unique(cdat1$AREA_Code)
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sampledAC <- sample(AreaCodeList,size=5,replace=F)
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stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
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}
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#setup counters
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counter1 <- 0
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counter2 <- 0
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cat("Step 4","\n")
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## Loop through stocks
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#write.table("x",file=outfile,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
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write.table("x",file=outfile2,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
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for(stock in stockLoop)
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{
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t0<-Sys.time()
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xr <- runif(1, 1.0, 10000)
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x1<-c(paste("processed",xr,sep=","))
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xr <- runif(1, 1.0, 10000)
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x2<-c(paste("non processed",xr,sep=","))
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#write.table(x1,file=outfile,append = T, row.names = FALSE,col.names=FALSE,sep=",")
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write.table(x2,file=outfile2,append = T, row.names = FALSE,col.names=FALSE,sep=",")
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cat("Elapsed: ",Sys.time()-t0," \n")
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}
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@ -1,178 +0,0 @@
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set.seed(999) ## for same random sequence
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#require(hacks)
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#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
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## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
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## For example
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## stock res yr ct
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## cap-icel Medium 1984 1234.32
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## filename <- "RAM_MSY.csv"
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##filename <- "ICESct2.csv"
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cat("Step 1","\n")
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TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
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filename <- "D20.csv"
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outfile <- "CatchMSY_Output1.csv"
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outfile2 <- paste("NonProcessedSpecies.csv",sep="")
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#cdat <- read.csv2(filename, header=T, dec=".")
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cdat1 <- read.csv(filename)
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cat("\n", "File", filename, "read successfully","\n")
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cat("Step 2","\n")
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if(file.exists("cdat.RData"))
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{load("cdat.RData")} else
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{
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dim(cdat1)
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yrs=1950:2012
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# to set NA as 0
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cdat1[is.na(cdat1)] <- 0
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nrow <- length(cdat1[,1])
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ndatColn <- length(cdat1[1,c(-1:-12)])
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rownames(cdat1) <- NULL
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cdat <- NULL
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for(i in 1:nrow)
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{#i=1
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#a <- ctotal3[i,-1]
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tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
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species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
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yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])),
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res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
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cdat <- rbind(cdat,tmp)
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#edit(cdat)
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}
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}
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StockList=unique(as.character(cdat$stock))
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colnames(cdat)
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#stock_id <- unique(as.character(cdat$stock))
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#??
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# stock_id <- "cod-2224" ## for selecting individual stocks
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# stock=stock_id
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#??
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cat("Step 3","\n")
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## FUNCTIONS are going to be used subsequently
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.schaefer <- function(theta)
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{
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with(as.list(theta), { ## for all combinations of ri & ki
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bt=vector()
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ell = 0 ## initialize ell
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J=0 #Ye
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for (j in startbt)
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{
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if(ell == 0)
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{
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bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
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ell = 0
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if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
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ell = 1
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J=j # Ye
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}
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}
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return(list(ell=ell,J=J)) # Ye adding J=J
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})
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}
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sraMSY <-function(theta, N)
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{
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#This function conducts the stock reduction
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#analysis for N trials
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#args:
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# theta - a list object containing:
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# r (lower and upper bounds for r)
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# k (lower and upper bounds for k)
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# lambda (limits for current depletion)
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with(as.list(theta),
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{
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ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
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ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
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itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
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## assign ri, ki, and final biomass range to itheta
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M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
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i=1:N
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## prototype objective function
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get.ell=function(i) M[[i]]$ell
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ell = sapply(i, get.ell)
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get.J=function(i) M[[i]]$J # Ye
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J=sapply(i,get.J) # Ye
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return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
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})
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}
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getBiomass <- function(r, k, j)
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{
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BT <- NULL
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bt=vector()
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for (v in 1:length(r))
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{
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bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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BT=rbind(BT, t(t(bt)))
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}
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return(BT)
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}
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## The End of Functions section
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cat("Step 4","\n")
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stockLoop <- StockList
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# randomly select stocks from randomly selected 5 area codes first
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if(TestRUN)
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{
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set.seed(999)
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AreaCodeList <- unique(cdat1$AREA_Code)
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sampledAC <- sample(AreaCodeList,size=5,replace=F)
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stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
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}
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#setup counters
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counter1 <- 0
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counter2 <- 0
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cat("Step 4","\n")
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## Loop through stocks
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write.table("x",file=outfile,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
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write.table("x",file=outfile2,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
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for(stock in stockLoop)
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{
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t0<-Sys.time()
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xr <- runif(1, 1.0, 10000)
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x1<-c(paste("processed",xr,sep=","))
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xr <- runif(1, 1.0, 10000)
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x2<-c(paste("non processed",xr,sep=","))
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write.table(x1,file=outfile,append = T, row.names = FALSE,col.names=FALSE,sep=",")
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write.table(x2,file=outfile2,append = T, row.names = FALSE,col.names=FALSE,sep=",")
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cat("Elapsed: ",Sys.time()-t0," \n")
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}
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@ -1,5 +0,0 @@
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#!/bin/sh
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# FAOMSY
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cd $1
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java -Xmx1024M -classpath ./:./c3p0-0.9.1.2.jar:./common-configuration-scanner-1.0.1-SNAPSHOT.jar:./common-encryption-1.0.1-3.5.0.jar:./common-gcore-resources-1.2.0-3.5.0.jar:./common-gcore-stubs-1.2.0-3.5.0.jar:./common-scope-1.2.1-SNAPSHOT.jar:./common-scope-maps-1.0.2-3.5.0.jar:./commons-collections-3.1.jar:./commons-io-1.2.jar:./discovery-client-1.0.1-3.5.0.jar:./dom4j-1.6.1.jar:./ecological-engine-1.8.1-SNAPSHOT.jar:./EcologicalEngineExecutor-1.6.4-SNAPSHOT.jar:./hibernate3.jar:./ic-client-1.0.1-3.5.0.jar:./jaxen-1.1.2.jar:./jta-1.1.jar:./log4j-1.2.16.jar:./mongo-java-driver-2.12.4.jar:./postgresql-8.4-702.jdbc4.jar:./slf4j-api-1.6.0.jar:./slf4j-log4j12-1.6.0.jar:./storage-manager-core-2.1.3-3.6.0.jar:./storage-manager-wrapper-2.1.0-3.5.0.jar:./xalan-2.6.0.jar:./xpp3_min-1.1.4c.jar:./xstream-1.3.1.jar:./YASMEEN-matcher-1.2.0.1.jar:./YASMEEN-parser-1.2.0.jar org.gcube.dataanalysis.executor.nodes.algorithms.FAOMSY $2 execution.output
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