# Long term daily mean of variables in htp-file #png(filename="xx.png", width=600, height=400, pointsize=12) x11() par(mfrow=c(2,1)) par(mar = c(5, 4, 4, 4) + 0.3) plot(1,type="n",ylim=c(0,20),xlim=c(1,3000),col="blue",lwd=2,cex.axis=1.4, cex.lab=1.2,ylab="mm",xlab="Jahr", xaxt="n",yaxt="n",axes=F) #grid(lty="dotdash",col="darkgrey") abline(v=seq(1,3000,by=100),h=seq(0,30,by=10),lty="dotted",col="lightgrey") axis(1,at=seq(1,30000,by=100),cex.axis=1.2) axis(2,at=seq(0,20,by=5),cex.axis=1.2) par(new = TRUE) # Add new plot plot(1,type="n",ylim=c(0,10),xlim=c(1,3000),col="green",lwd=4,cex.axis=1.4, cex.lab=1.2,ylab="mm",xlab="Jahr", xaxt="n",yaxt="n",axes=F) axis(side = 4, at=seq(0,10,by=5),cex.axis=1.2) # Add second axis mtext("LAI", side = 4, line = 3) # Add second axis label legend("top",inset=0.05,col=c("blue","green","lightblue","orange"),lwd=c(2,2,2,2), legend=c("Rain","LAI","GWR","ETa"), cex=1.0,bg="white",ncol=4) title(main="Burkina Faso Nouna") setwd("output/Res") for (k in 1:10000) { system("ps -fC swim | grep swim > swim_progress") checkSwim <- read.table("swim_progress") if(length(checkSwim) == 0) { break() } dataInOri<-read.table("htp.prn", skip=0,header=T) names(dataInOri)[1] <- "jahr" names(dataInOri)[2] <- "tag" names(dataInOri)[3] <- "SUB" names(dataInOri)[4] <- "HRU" names(dataInOri)[5] <- "prec" names(dataInOri)[6] <- "irr" names(dataInOri)[15] <- "LAI" names(dataInOri)[9] <- "PERC" names(dataInOri)[13] <- "ETa" names(dataInOri)[21] <- "WS" #Wasserstress names(dataInOri)[22] <- "g" names(dataInOri)[23] <- "SWE" #Soilwater dataIn <- subset(dataInOri, dataInOri$SUB=="1" & dataInOri$HRU=="1") mean.sim1 <- numeric() mean.sim2 <- numeric() mean.sim3 <- numeric() mean.sim4 <- numeric() mean.sim5 <- numeric() mean.sim6 <- numeric() mean.sim7 <- numeric() mean.sim8 <- numeric() # Mittelere Tage berechnen lines(dataIn$PERC,type="l",col="lightblue",lwd=2) lines(dataIn$ETa,type="l",col="orange",lwd=2) lines(dataIn$LAI,type="l",col="green",lwd=2) #box() Sys.sleep(5) } #for k loop for (i in 1:365) { mean.sim1[i]<-mean(dataIn$prec[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim2[i]<-mean(dataIn$LAI[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim3[i]<-mean(dataIn$PERC[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim4[i]<-mean(dataIn$ETa[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim5[i]<-mean(dataIn$WS[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim6[i]<-mean(dataIn$SWE[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim7[i]<-mean(dataIn$irr[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) mean.sim8[i]<-mean(dataIn$g[dataIn$jahr>1959 & dataIn$jahr<2020 & dataIn$tag==i]) } par(mar = c(5, 4, 4, 4) + 0.3) plot(mean.sim1,type="l",ylim=c(0,15),xlim=c(1,365),col="blue",lwd=2,cex.axis=1.4, cex.lab=1.2,ylab="mm",xlab="Jahr", xaxt="n",yaxt="n",axes=F) lines(mean.sim3,type="l",col="lightblue",lwd=2) lines(mean.sim4,type="l",col="orange",lwd=2) #lines(dataIn$LAI,type="l",col="green",lwd=2) #box() #grid(lty="dotdash",col="darkgrey") abline(v=seq(1,365,by=30),h=seq(0,15,by=5),lty="dotted",col="lightgrey") axis(1,at=seq(1,365,by=30),cex.axis=1.2) axis(2,at=seq(0,20,by=5),cex.axis=1.2) par(new = TRUE) # Add new plot plot(mean.sim2,type="l",ylim=c(0,10),xlim=c(1,365),col="green",lwd=4,cex.axis=1.4, cex.lab=1.2,ylab="mm",xlab="Jahr", xaxt="n",yaxt="n",axes=F) axis(side = 4, at=seq(0,10,by=5),cex.axis=1.2) # Add second axis mtext("LAI", side = 4, line = 3) # Add second axis label legend("top",inset=0.05,col=c("blue","green","lightblue","orange"),lwd=c(2,2,2,2), legend=c("Rain","LAI","GWR","ETa"), cex=1.0,bg="white",ncol=4) title("Kenya, Kisumu") while(names(dev.cur()) !='null device') Sys.sleep(1)