# Read file with data filenames

setwd("bayesmodel")
load(paste("bayes_recon_",numpc,".R",sep=""))

yhat <- list(yhat1, yhat2, yhat3)
yhatbar <- lapply(yhat, rowMeans)
all_idxD <- c(in_idxD, out_idxD)
mm <- min(unlist(lapply(yhat,min)))
MM <- max(unlist(lapply(yhat,max)))
sizes <- c( mean(apply(yhat[[1]], 1, quantile, prob=.975)-apply(yhat[[1]], 1, quantile, prob=.025)),
            mean(apply(yhat[[2]], 1, quantile, prob=.975)-apply(yhat[[2]], 1, quantile, prob=.025)),
            mean(apply(yhat[[3]], 1, quantile, prob=.975)-apply(yhat[[3]], 1, quantile, prob=.025)) )
sizerank <- rank(sizes)
vv <- mean(c( max(as.numeric(out_idxI)), min(as.numeric(in_idxI)) ))

# PLOT:
pdf(pdffile1, width=8, height=5)
par(oma=rep(0,4), mar=c(4,4,.7,.1))
plot(as.numeric(all_idxD), yhatbar[[1]], type="n", col=sizerank[1]+1, ylim=c(-1.5, 1.5), xlab="Year", ylab="Temperature Anomaly (C)",main=paste("Impact of proper proxy selection/No Tilj/OLS PC",numpc,sep=""),cex.main=0.8)
for(j in 1:dim(yhat[[3]])[2]){ 
lines(as.numeric(all_idxD), yhat[[3]][,j], col="grey")
}
for(j in 1:dim(yhat[[1]])[2]){ 
lines(as.numeric(all_idxD), yhat[[1]][,j], col=5) 
}
for(j in 1:dim(yhat[[2]])[2]){ 
lines(as.numeric(all_idxD), yhat[[2]][,j], col=colors()[47])
}

# Add mean of all reconstructions
lines(as.numeric(all_idxD), yhatbar[[1]], type="l", col=2, ylim=c(mm, MM), xlab="", ylab="")
tmplo <- loess(yhatbar[[1]]~as.numeric(all_idxD), span=.05)
tmpy <- predict(tmplo, as.numeric(all_idxD), se=TRUE) 
lines(as.numeric(all_idxD), tmpy$fit, col=2, lwd=2)

# Add CRU
lines(instrument[,1], instrument[,2], col=1)
tmplo <- loess(instrument[,2] ~instrument[,1], span=.33)
tmpy <- predict(tmplo, instrument[,1], se=TRUE) 
lines(instrument[,1], tmpy$fit, col=1, lwd=2)
abline(v=vv)

#legend(1300,1.5, legend=c("  "), col=c("red"), lwd=2)

dev.off()

setwd("../")