From 70660bf2c1f502d78d7bd2960ec18488aae95a4b Mon Sep 17 00:00:00 2001 From: Gianpaolo Coro Date: Thu, 9 Apr 2015 11:06:09 +0000 Subject: [PATCH] git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngineSmartExecutor@113921 82a268e6-3cf1-43bd-a215-b396298e98cf --- PARALLEL_PROCESSING/CatchMSY_Dec2014.R | 328 ++++++++++++++++++++++--- 1 file changed, 295 insertions(+), 33 deletions(-) diff --git a/PARALLEL_PROCESSING/CatchMSY_Dec2014.R b/PARALLEL_PROCESSING/CatchMSY_Dec2014.R index 3db4ea9..51e6329 100644 --- a/PARALLEL_PROCESSING/CatchMSY_Dec2014.R +++ b/PARALLEL_PROCESSING/CatchMSY_Dec2014.R @@ -5,7 +5,7 @@ set.seed(999) ## for same random sequence ## 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 ## For example -## stock res yr ct +## stock res yr ct ## cap-icel Medium 1984 1234.32 ## filename <- "RAM_MSY.csv" @@ -27,27 +27,27 @@ if(file.exists("cdat.RData")) {load("cdat.RData")} else { - dim(cdat1) - yrs=1950:2012 + dim(cdat1) + yrs=1950:2012 + + # to set NA as 0 + cdat1[is.na(cdat1)] <- 0 + nrow <- length(cdat1[,1]) + ndatColn <- length(cdat1[1,c(-1:-12)]) + rownames(cdat1) <- NULL + + cdat <- NULL + for(i in 1:nrow) + {#i=1 + #a <- ctotal3[i,-1] + tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn), + species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn), + yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])), + res=rep(cdat1[i,"ResilienceIndex"],ndatColn)) - # to set NA as 0 - cdat1[is.na(cdat1)] <- 0 - nrow <- length(cdat1[,1]) - ndatColn <- length(cdat1[1,c(-1:-12)]) - rownames(cdat1) <- NULL - - cdat <- NULL - for(i in 1:nrow) - {#i=1 - #a <- ctotal3[i,-1] - tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn), - species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn), - yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])), - res=rep(cdat1[i,"ResilienceIndex"],ndatColn)) - - cdat <- rbind(cdat,tmp) - #edit(cdat) - } + cdat <- rbind(cdat,tmp) + #edit(cdat) + } } StockList=unique(as.character(cdat$stock)) @@ -161,18 +161,280 @@ counter2 <- 0 cat("Step 4","\n") ## Loop through stocks -#write.table("x",file=outfile,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",") -write.table("x",file=outfile2,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",") - for(stock in stockLoop) { t0<-Sys.time() - xr <- runif(1, 1.0, 10000) - x1<-c(paste("processed",xr,sep=",")) - xr <- runif(1, 1.0, 10000) - x2<-c(paste("non processed",xr,sep=",")) - #write.table(x1,file=outfile,append = T, row.names = FALSE,col.names=FALSE,sep=",") - write.table(x2,file=outfile2,append = T, row.names = FALSE,col.names=FALSE,sep=",") - - cat("Elapsed: ",Sys.time()-t0," \n") -} +##stock = "3845" # NB only for test single loop! + ## make graph file names: + b <- with(cdat1,cdat1[Stock_ID == stock,c(1,3,5,12)]) # Stock_ID,AREA_Names,Country,"Species" + bb <- do.call(paste,b) + + yr <- cdat$yr[as.character(cdat$stock)==stock] + ct <- as.numeric(cdat$ct[as.character(cdat$stock)==stock])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes + res <- unique(as.character(cdat$res[as.character(cdat$stock)==stock])) ## resilience from FishBase, if needed, enable in PARAMETER SECTION + nyr <- length(yr) ## number of years in the time series + + cat("\n","Stock",stock,"\n") + flush.console() + + ## PARAMETER SECTION + mvlen=3 + ma=function(x,n=mvlen){filter(x,rep(1/n,n),sides=1)} + + ## If resilience is to be used, delete ## in rows 1-4 below and set ## in row 5 below + start_r <- if(res == "Very low"){c(0.015, 0.1)}else{ + if(res == "Low") {c(0.05,0.5)}else { + if(res == "High") {c(0.6,1.5)}else {c(0.2,1)} + } + } + ## Medium, or default if no res is found + ##start_r <- c(0.5,1.5) ## disable this line if you use resilience + start_k <- c(max(ct),50*max(ct)) ## default for upper k e.g. 100 * max catch + ## startbio <- c(0.8,1) ## assumed biomass range at start of time series, as fraction of k + ##startbio <- if(ct[1]/max(ct) < 0.5) {c(0.5,0.9)} else {c(0.3,0.6)} ## use for batch processing + + ## NB: Yimin's new idea on 20Jan14 + startbio<- if(mean(ct[1:5])/max(ct) < 0.3) {c(0.6,0.95)} else { + if(mean(ct[1:5])/max(ct)>0.3&mean(ct[1:5])/max(ct)<0.6) {c(0.3,0.7)} else { + c(0.2,0.6)}} + + interyr <- yr[2] ## interim year within time series for which biomass estimate is available; set to yr[2] if no estimates are available + interbio <- c(0, 1) ## biomass range for interim year, as fraction of k; set to 0 and 1 if not available + ## finalbio <- c(0.8, 0.9) ## biomass range after last catches, as fraction of k + ## finalbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.3,0.7)} else {c(0.01,0.4)} ## use for batch processing + + ## Yimin's new stuff on 10Mar14 + #######> pre-classification + + pre.clas=ct + pre.clas[pre.clas==0]=0.1 + tx=ma(as.numeric(pre.clas),n=mvlen) + Myr=which.max(tx) + Maxc=pre.clas[which.max(tx)] + + + if(Myr==1)startbio=c(0.05,0.6)else + { + if (ct[1]/Maxc>=0.5) startbio=c(0.4,0.85) + else startbio=c(0.65,0.95) + } + + if (Myr==length(yr))finalbio=c(.4,.95) else # ie from fully to overexploited + { + if (tx[length(ct)]/Maxc>=0.5) finalbio=c(.4,.85) + else finalbio=c(.05,.6) + } + + +# if (Myr==length(yr))finalbio=c(.5,.9) +# #if (Myr=0.8) finalbio=c(.4,.8) else +# # if (tx[length(ct)]/Maxc>0.5) finalbio=c(.3,.7) else finalbio=c(.05,.6)} +# # below is the last used (20 Feb) +# if (Myr0.5) finalbio=c(.2,.8) +# else finalbio=c(.05,.6) +# } + + ##############< + n <- 30000 ## number of iterations, e.g. 100000 + sigR <- 0.0 ## process error; 0 if deterministic model; 0.05 reasonable value? 0.2 is too high + + startbt <- seq(startbio[1], startbio[2], by = 0.05) ## apply range of start biomass in steps of 0.05 + parbound <- list(r = start_r, k = start_k, lambda = finalbio, sigR) + + cat("Last year =",max(yr),", last catch =",1000*ct[nyr],"\n") + cat("Resilience =",res,"\n") + cat("Process error =", sigR,"\n") + cat("Assumed initial biomass (B/k) =", startbio[1],"-", startbio[2], " k","\n") + cat("Assumed intermediate biomass (B/k) in", interyr, " =", interbio[1],"-",interbio[2]," k","\n") + cat("Assumed final biomass (B/k) =", parbound$lambda[1],"-",parbound$lambda[2]," k","\n") + cat("Initial bounds for r =", parbound$r[1], "-", parbound$r[2],"\n") + cat("Initial bounds for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n") + + flush.console() + + ## MAIN + + R1 = sraMSY(parbound, n) + + ## Get statistics on r, k, MSY and determine new bounds for r and k + r1 <- R1$r[R1$ell==1] + k1 <- R1$k[R1$ell==1] + j1 <- R1$J[R1$ell==1] # Ye + msy1 <- r1*k1/4 + mean_msy1 <- exp(mean(log(msy1))) + max_k1a <- min(k1[r1<1.1*parbound$r[1]]) ## smallest k1 near initial lower bound of r + max_k1b <- max(k1[r1*k1/4=10) + { + ## set new upper bound of r to 1.2 max r1 + parbound$r[2] <- 1.2*max(r1) + ## set new lower bound for k to 0.9 min k1 and upper bound to max_k1 + parbound$k <- c(0.9 * min(k1), max_k1) + + cat("First MSY =", format(1000*mean_msy1, digits=3),"\n") + cat("First r =", format(exp(mean(log(r1))), digits=3),"\n") + cat("New upper bound for r =", format(parbound$r[2],digits=2),"\n") + cat("New range for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n") + + ## Repeat analysis with new r-k bounds + R1 = sraMSY(parbound, n) + + ## Get statistics on r, k and msy + r = R1$r[R1$ell==1] + k = R1$k[R1$ell==1] + j = R1$J[R1$ell==1] # Ye + msy = r * k / 4 + mean_ln_msy = mean(log(msy)) + + ############################################################## + ##> Ye + # BT=0 + + ## + R2<-getBiomass(r, k, j) + + #R2<-R2[-1,] + runs<-rep(1:length(r), each=nyr+1) + years=rep(yr[1]:(yr[length(yr)]+1),length=length(r)*(length(yr)+1)) + + runs=t(runs) + years=t(years) + stock_id=rep(stock,length(runs)) + R3<-cbind(as.numeric(runs), as.numeric(years), stock_id, as.numeric(R2) ) + + ## changed this, as otherwise biomass is the level of the factor below + R4<-data.frame(R3, stringsAsFactors=FALSE) + names(R4)<-c("Run", "Year", "Stock","Biomass") + + Bmsy_x<-k*0.5 + Run<-c(1:length(r)) + BMSY<-cbind(Run, Bmsy_x) + R5<-merge(R4, BMSY, by="Run", all.x=T, all.y=F) + R5$B_Bmsy<-as.numeric(paste(R5$Biomass))/R5$Bmsy_x + + ### B/Bmsy calculated for all feasible combinations of r,K,B0 + R6<-aggregate(log(B_Bmsy)~as.numeric(Year)+Stock, data=R5, + FUN=function(z){c(mean=mean(z),sd=sd(z),upr=exp(quantile(z, p=0.975)), + lwr=exp(quantile(z, p=0.025)), lwrQ=exp(quantile(z, p=0.25)), + uprQ=exp(quantile(z, p=0.75)))}) # from directly calculated from R5 becasue B_Bmsy has a lognormal dist + + R6<-data.frame(cbind(R6[,1:2],R6[,3][,1],R6[,3][,2],R6[,3][,3],R6[,3][,4],R6[,3][,5], R6[,3][,6])) + names(R6)<-c("Year", "Stock", "BoverBmsy", "BoverBmsySD","BoverBmsyUpper","BoverBmsyLower","BoverBmsylwrQ","BoverBmsyuprQ") + ##remove last entry as it is 1 greater than number of years + ## removed final year here for ease of dataframe output below + R6<-R6[-length(R6),] + ## geometric mean + GM_B_Bmsy<-exp(R6$BoverBmsy) + GM_B_BmsySD=R6$BoverBmsySD #add + ## arithmetic mean + M_B_Bmsy<-exp(R6$BoverBmsy+R6$BoverBmsySD^2/2) + + ### r,k, and MSY + + #del GM_B_Bmsy=c(rep(0,(min(yr)-1940)),GM_B_Bmsy) + #del GM_B_BmsySD=c(rep(0,(min(yr)-1940)),GM_B_BmsySD) ###### + #del M_B_Bmsy=c(rep(0,(min(yr)-1940)),M_B_Bmsy) + #del yr1=seq(1940,max(yr)) + + yr1=yr #add + + stockInfo <- with(cdat1,cdat1[Stock_ID==stock,1:12]) + temp=c(startbio[1],startbio[2],finalbio[1],finalbio[2],res, + mean(log(r)),sd(log(r)),mean(log(k)),sd(log(k)),mean(log(msy)), + sd(log(msy)),sigR,min(yr),max(yr),max(ct),length(r),GM_B_Bmsy,GM_B_BmsySD,M_B_Bmsy) + + #add, adding "GM_B_BmsySD" in the line above + + output=as.data.frame(matrix(temp,nrow=1)) + output <- cbind(stockInfo,output) + names(output) <- c(names(cdat1)[1:12],"startbio[1]","startbio[2]","finalbio[1]","finalbio[2]", + "res","mean(log(r))","sd(log(r))","mean(log(k))","sd(log(k))", + "mean(log(msy))","sd(log(msy))","sigR","min(yr)","max(yr)","max(ct)", + "length(r)",paste("GM_B_msy",yr1,sep="_"),paste("GM_B_msySD",yr1,sep="_"),paste("M_B_Bmsy",yr1,sep="_")) + + #add, adding "paste("GM_B_msySD",yr1,sep="_")"in the line above + + ######< Ye + ######################################################## + + ## plot MSY over catch data + pdf(paste(bb,"graph.pdf",sep="_")) + + par(mfcol=c(2,3)) + plot(yr, ct, type="l", ylim = c(0, max(ct)), xlab = "Year", + ylab = "Catch (1000 t)",main = paste("StockID",stock,sep=":")) + abline(h=exp(mean(log(msy))),col="red", lwd=2) + abline(h=exp(mean_ln_msy - 2 * sd(log(msy))),col="red") + abline(h=exp(mean_ln_msy + 2 * sd(log(msy))),col="red") + + hist(r, freq=F, xlim=c(0, 1.2 * max(r)), main = "") + abline(v=exp(mean(log(r))),col="red",lwd=2) + abline(v=exp(mean(log(r))-2*sd(log(r))),col="red") + abline(v=exp(mean(log(r))+2*sd(log(r))),col="red") + + plot(r1, k1, xlim = start_r, ylim = start_k, xlab="r", ylab="k (1000t)") + + hist(k, freq=F, xlim=c(0, 1.2 * max(k)), xlab="k (1000t)", main = "") + abline(v=exp(mean(log(k))),col="red", lwd=2) + abline(v=exp(mean(log(k))-2*sd(log(k))),col="red") + abline(v=exp(mean(log(k))+2*sd(log(k))),col="red") + + + plot(log(r), log(k),xlab="ln(r)",ylab="ln(k)") + abline(v=mean(log(r))) + abline(h=mean(log(k))) + abline(mean(log(msy))+log(4),-1, col="red",lwd=2) + abline(mean(log(msy))-2*sd(log(msy))+log(4),-1, col="red") + abline(mean(log(msy))+2*sd(log(msy))+log(4),-1, col="red") + + hist(msy, freq=F, xlim=c(0, 1.2 * max(msy)), xlab="MSY (1000t)",main = "") + abline(v=exp(mean(log(msy))),col="red", lwd=2) + abline(v=exp(mean_ln_msy - 2 * sd(log(msy))),col="red") + abline(v=exp(mean_ln_msy + 2 * sd(log(msy))),col="red") + + graphics.off() + + + cat("Possible combinations = ", length(r),"\n") + cat("geom. mean r =", format(exp(mean(log(r))),digits=3), "\n") + cat("r +/- 2 SD =", format(exp(mean(log(r))-2*sd(log(r))),digits=3),"-",format(exp(mean(log(r))+2*sd(log(r))),digits=3), "\n") + cat("geom. mean k =", format(1000*exp(mean(log(k))),digits=3), "\n") + cat("k +/- 2 SD =", format(1000*exp(mean(log(k))-2*sd(log(k))),digits=3),"-",format(1000*exp(mean(log(k))+2*sd(log(k))),digits=3), "\n") + cat("geom. mean MSY =", format(1000*exp(mean(log(msy))),digits=3),"\n") + cat("MSY +/- 2 SD =", format(1000*exp(mean_ln_msy - 2 * sd(log(msy))),digits=3), "-", format(1000*exp(mean_ln_msy + 2 * sd(log(msy))),digits=3), "\n") + + ## Write results into outfile, in append mode (no header in file, existing files will be continued) + ## output = data.frame(stock, sigR, startbio[1], startbio[2], interbio[1], interbio[2], finalbio[1], finalbio[2], min(yr), max(yr), res, max(ct), ct[1], ct[nyr], length(r), exp(mean(log(r))), sd(log(r)), min(r), quantile(r,0.05), quantile(r,0.25), median(r), quantile(r,0.75), quantile(r,0.95), max(r), exp(mean(log(k))), sd(log(k)), min(k), quantile(k, 0.05), quantile(k, 0.25), median(k), quantile(k, 0.75), quantile(k, 0.95), max(k), exp(mean(log(msy))), sd(log(msy)), min(msy), quantile(msy, 0.05), quantile(msy, 0.25), median(msy), quantile(msy, 0.75), quantile(msy, 0.95), max(msy)) + + #write.table(output, file = outfile, append = TRUE, sep = ";", dec = ".", row.names = FALSE, col.names = FALSE) + appendPar <- ifelse(counter2==0,F,T) + colnamePar <- ifelse(counter2==0,T,F) + write.table(output, file = outfile, append = appendPar, sep = ",", dec = ".", + row.names = FALSE, col.names = colnamePar) + + counter2 <- counter2 + 1 + + } +cat("Elapsed: ",Sys.time()-t0," \n") +} ## End of stock loop, get next stock or exit