library(stockassessment)
source("src/common.R")
# load what has been saved
setwd("run")
for(f in dir(pattern="RData"))load(f) 
setwd("..")

basefit<-NULL
if(file.exists("baserun/model.RData")){
  local({load("baserun/model.RData"); basefit<<-fit})
}else{
  basefit <- fit
}
fits <- c(base=basefit,current=fit)

exfitname <- scan("conf/viewextra.cfg", what="", comment.char="#", quiet=TRUE)
for(nam in exfitname){
  local({
    fit<-urlLoadFit(paste0("https://www.stockassessment.org/datadisk/stockassessment/userdirs/user3/",nam,"/run/model.RData"))
    if(!is.null(fit)){
      i <- length(fits)
      fits[[i+1]] <<- fit
      names(fits)[i+1] <<- nam
    }else{
      warning(paste0("View extra stock ", nam, " not found or of incompatible format (skipped)"))
    }
  })
}

plotcounter<-1
tit.list<-list()

setcap<-function(title="", caption=""){   
 tit.list[length(tit.list)+1]<<-paste("# Title",plotcounter,"\n")
 tit.list[length(tit.list)+1]<<-paste(title,"\n")
 tit.list[length(tit.list)+1]<<-paste("# Caption",plotcounter,"\n")
 tit.list[length(tit.list)+1]<<-paste(caption,"\n")
 plotcounter<<-plotcounter+1 
}


############################## plots ##############################
plots<-function(){
  par(cex.lab=1, cex.axis=1, mar=c(5,5,1,1))
    
  if(exists("fits")){

    tsbplot(fits, addCI=TRUE)
    stampit(fit)
    setcap("Total stock biomass", "Total stock biomass. 
            Estimates from the current run and point wise 95% confidence 
            intervals are shown by line and shaded area.")

    ssbplot(fits, addCI=TRUE)
    stampit(fit)
    setcap("Spawning stock biomass", "Spawning stock biomass. 
            Estimates from the current run and point wise 95% confidence 
            intervals are shown by line and shaded area.")
    
    fbarplot(fits, addCI=TRUE)
    stampit(fit)
    setcap("Average fishing mortality", "Average fishing mortality for the shown age range. 
            Estimates from the current run and point wise 95% confidence 
            intervals are shown by line and shaded area.")

    recplot(fits, addCI=TRUE, las=0)
    stampit(fit)
    setcap("Recruitment", "Yearly resruitment. 
        Estimates from the current run and point wise 95% confidence 
        intervals are shown by line and shaded area.")

    catchplot(fits, addCI=TRUE, ylim = c(0, 6100))
    CW<-exp(fit$pl$logCW)[1:nrow(ntable(fit)),,1 ]
    CC<-rowSums(getFleet(fit, 1)*CW[,-1], na.rm=TRUE)
    points(fit$data$years, CC, lwd=3, cex=3, col="darkblue")
    ct <- catchtable(fit, obs.show = TRUE)
    points(as.integer(rownames(ct)), ct[, "sop.catch"], pch = 4, lwd = 2, cex = 1.2, col="darkblue")
    stampit(fit)
    setcap("Catch", "Total catch in weight. 
        Prediction from the current run and point wise 95% confidence 
        intervals are shown by line and shaded area. The yearly
        observed total catch weight (crosses) are calculated as Cy=sum(WayCay).")

    srplot(fit)
    stampit(fit)
    setcap("Spawner-resruits", "Estimated recruitment as a function of spawning stock biomass.")

   plot(ypr(fit))
   stampit(fit)
   setcap("Yield per Recruit", "Yield per recruit (solid line) and spawning stock biomass plotted against different levels of fishing")
   
    if(!all(fit$conf$obsCorStruct=="ID")){ 
      corplot(fit)			  
      setcap("Estimated correlations", "Estimates correlations between age groups for each fleet")
      stampit(fit)
    }
    
    for(f in 1:fit$data$noFleets){
      fitplot(fit, fleets=f)
      setcap("Fit to data", "Predicted line and observed points (log scale)")
      stampit(fit)
    }

## F by age

F<-faytable(fit)
    matplot(rownames(F),log(F),lty=1:ncol(F),col=1:ncol(F), type='l', xlab='Year', lwd=2)
    legend('topleft', col=1:ncol(F), lty=1:ncol(F), legend=colnames(F), bty='n', lwd=2)
    stampit(fit)
    setcap("Partial F")
    
    
 ## CW
 CW<-exp(fit$pl$logCW)[,,1]
 matplot(fit$dat$catchMeanWeight[,,1], xaxt = "n")
 axis(1,at=1:24, labels=c(2000:2023))
 matplot(CW, type="l", add=T)
 setcap("Cacth weight")
 
 
 ## SW
 SW<-exp(fit$pl$logSW)
 matplot(fit$dat$stockMeanWeight, xaxt = "n")
 axis(1,at=1:24, labels=c(2000:2023))
 matplot(SW, type="l", add=T)
 setcap("Stock weight")

## not relevant when using predvarlink      
#  Wplot<-function(fit){
#    cf <- fit$conf$keyVarObs
#    fn <- attr(fit$data, "fleetNames")
#    ages <- fit$conf$minAge:fit$conf$maxAge
#    pt <- partable(fit)
#    sd <- unname(exp(pt[grep("logSdLogObs",rownames(pt)),1]))
#    v<-cf
#    v[] <- c(NA,sd)[cf+2]
#    res<-data.frame(fleet=fn[as.vector(row(v))],name=paste0(fn[as.vector(row(v))]," age ",ages[as.vector(col(v))]), sd=as.vector(v))
#    res<-res[complete.cases(res),]
#    o<-order(res$sd)
#    res<-res[o,]
#    op<-par(mar=c(13,6,2,1))
#    W<-1/(res$sd^2)
#    barplot(W/max(W), ylim=c(0,1.1), names.arg=res$name,las=2, col=colors()[as.integer(as.factor(res$fleet))*10], ylab="relative weight"); box()
#   par(op)
#}

#    Wplot(fit)
#    setcap("Relative weight", "")
    
    
 # Selectivity of the Fishery
 
    # Selectivity of the Fishery
    cc<-rainbow(10)
    sel <- t(faytable(fit)/fbartable(fit)[,1])
    sel[is.na(sel)]<-0
    op <- par(mfrow=c(3,3), mai=c(0.4,0.5,0.2,0.2))
    age.sel<-as.integer(rownames(sel))
    cuts<-round(seq(1,dim(sel)[2],length=10))
    cuts[length(cuts)]<-cuts[length(cuts)]+1
    for(i in 1:(length(cuts)-1)){
      plot(age.sel, sel[,cuts[i]], type="l", xlab="", ylab="", lwd=1.5, ylim=c(0,max(sel)))
      for(y in (cuts[i]+1):(cuts[i+1]-1)) lines(age.sel, sel[,y], col=cc[y-cuts[i]], lwd=1.5)    
      legend("topleft",paste(c(colnames(sel)[cuts[i]:(cuts[i+1]-1)])), lty=rep(1,5),bty="n",
             col=c("black", cc[1:(cuts[i+1]-cuts[i]-1)]))
    }
    mtext("Age", 1, outer=T, line=1)
    mtext("F/Fbar", 2, outer=T, line=1)
    par(op)
    setcap("Selection pattern", "")


    mn.sel <- apply(sel,1,mean)
    sd.sel <- apply(sel,1,quantile,probs=c(0.025,0.975))
    plot(age.sel, mn.sel, ylim=c(0,max(sd.sel)), type="l", xlab='Age', ylab="F/Fbar", lwd=2)
    for(i in 1:length(age.sel)){
      lines(rep(age.sel[i],2), sd.sel[,i])
    }
    setcap("Selection pattern", "")   

    #Q<-fit$pl$logFpar
    #Qsd<-fit$plsd$logFpar
    #key<-fit$conf$keyLogFpar
    #fun<-function(x)if(x<0){NA}else{Q[x+1]}
    #FF<-Vectorize(fun)
    #ages<-fit$conf$minAge:fit$conf$maxAge
    #matplot(ages, exp(t(matrix(FF(key), nrow=5))), type="l", lwd=5, lty="solid", xlab="Ages", ylab="Q")
    #legend("topright", lwd=5, col=2:5, legend=attr(fit$data, "fleetNames")[2:5])
    #stampit(fit)

  }  
  
  if(exists("RES")){  
    plot(RES)
    setcap("One-observation-ahead residuals", "Standardized one-observation-ahead residuals.")
    stampit(fit)
    par(mfrow=c(1,1))
  }
  
   
  if(exists("RESP")){  
    plot(RESP)
    setcap("Process residuals", "Standardized single-joint-sample residuals of process increments")
    stampit(fit)
    par(mfrow=c(1,1))
  } 
 
  
  if(exists("LO")){  
    ssbplot(LO)
    setcap("Leaveout (SSB)", "")
    stampit(fit)
    
    fbarplot(LO)
    setcap("Leaveout (Average F)", "")
    stampit(fit)

    recplot(LO)
    setcap("Leaveout (Recruitment)", "")
    stampit(fit)

    catchplot(LO)
    setcap("Leaveout (Catch)", "")
    stampit(fit)
    
  } 
  
 # if(exists("RETRO")){
 #  ssbplot(RETRO, las=0, drop=1)
 #   setcap("Retrospective (SSB)", "")
 #   stampit(fit)
 # 
 #   fbarplot(RETRO, las=0, drop=1)
 #   setcap("Retrospective (Average F)", "")
 #   stampit(fit)
 # 
 #   recplot(RETRO, las=0, drop=1)
 #   setcap("Retrospective (Recruitment)", "")
 #   stampit(fit)
 # 
 #   catchplot(RETRO)
 #   setcap("Retrospective (Catch)", "")
 #   stampit(fit)
 # }


if(exists("RETRO")){
  rho <- round(mohn(RETRO,lag=0),3)
  ssbplot(RETRO, las=0, drop=0)
  setcap("Retrospective (SSB)", "")
  legend("topright", legend=substitute(rho==RHO, list(RHO=rho[2])), bty="n")
  stampit(fit)


  fbarplot(RETRO, las=0, drop=0)
  setcap("Retrospective (Average F)", "")
  legend("topright", legend=substitute(rho==RHO, list(RHO=rho[3])), bty="n")
  stampit(fit)

  recplot(RETRO, las=0, drop=0)
  setcap("Retrospective (Recruitment)", "")
  legend("topright", legend=substitute(rho==RHO, list(RHO=rho[1])), bty="n")
  stampit(fit)

  catchplot(RETRO)
  stampit(fit)
}
  
  
  
  if(exists("FC")){  
    lapply(FC, function(f){plot(f); title(attr(f,"label"), outer=TRUE, line=-1); stampit(fit)})
  }  
  
}


setwd('res')
file.remove(dir(pattern='png$'))
stamp<-gsub('-[[:digit:]]{4}$','',gsub(':','.',gsub(' ','-',gsub('^[[:alpha:]]{3} ','',date()))))
png(filename = paste(stamp,"_%03d.png", sep=''), width = 480, height = 480,
    units = "px", pointsize = 10, bg = "white")
  plots()    
dev.off()

writeLines(unlist(tit.list),'titles.cfg') 

png(filename = paste("big_",stamp,"_%03d.png", sep=''), width = 1200, height = 1200, 
    units = "px", pointsize = 20, bg = "white")
  plots()    
dev.off()

#pdf(onefile=FALSE, width = 8, height = 8)
#  plots()    
#dev.off()

file.remove(dir(pattern='html$'))

tsb<-tsbtable(fit)
colnames(tsb)<-c("TSB","Low", "High")
tab.summary <- cbind(summary(fit), tsb)
xtab(tab.summary, caption=paste('Table 1. Estimated recruitment, spawning stock biomass (SSB), 
     and average fishing mortality','.',sep=''), cornername='Year', 
     file=paste(stamp,'_tab1.html',sep=''), dec=c(0,0,0,0,0,0,3,3,3,0,0,0))

ftab <- faytable(fit)
xtab(ftab, caption=paste('Table 2. Estimated fishing mortality at age','.',sep=''), cornername='Year \ Age', 
     file=paste(stamp,'_tab2.html',sep=''), dec=rep(3,ncol(ftab)))

ntab <- ntable(fit)
xtab(ntab, caption=paste('Table 3. Estimated stock numbers at age','.',sep=''), cornername='Year \ Age', 
     file=paste(stamp,'_tab3.html',sep=''), dec=rep(0,ncol(ntab)))

ptab <- partable(fit)
xtab(ptab, caption=paste('Table 4. Table of model parameters','.',sep=''), cornername='Parameter name', 
     file=paste(stamp,'_tab4.html',sep=''), dec=rep(3,ncol(ptab)))

mtab <- modeltable(c(Current=fit, base=basefit))
mdec <- c(2,0,2,6)[1:ncol(mtab)]
xtab(mtab, caption=paste('Table 5. Model fitting','.',sep=''), cornername='Model', 
     file=paste(stamp,'_tab5.html',sep=''), dec=mdec)
     
sdState<-function(fit, y=max(fit$data$years)-1:0){
  idx <- names(fit$sdrep$value) == "logR"
  sdLogR<-fit$sdrep$sd[idx][fit$data$years%in%y]
  idx <- names(fit$sdrep$value) == "logssb"
  sdLogSSB<-fit$sdrep$sd[idx][fit$data$years%in%y]
  idx <- names(fit$sdrep$value) == "logfbar"
  sdLogF<-fit$sdrep$sd[idx][fit$data$years%in%y]
  ret<-cbind(sdLogR, sdLogSSB, sdLogF)
  rownames(ret)<-y
  colnames(ret)<-c("sd(log(R))", "sd(log(SSB))", "sd(log(Fbar))")
  return(ret)
}

sdtab <- sdState(fit)
xtab(sdtab, caption=paste('Table 6. Table of selected sd','.',sep=''), cornername='Year', 
     file=paste(stamp,'_tab6.html',sep=''), dec=rep(3,ncol(sdtab)))


if(exists("FC")){  
    ii<-0
    lapply(FC, function(f){
       ii<<-ii+1;
       tf<-attr(f,"tab");
       dec<-c(3,3,3,rep(0,ncol(tf)-3));
       xtab(tf, caption=paste0('Forecast table ',ii,'. ', attr(f,"label"),'.'), 
       cornername='Year', file=paste(stamp,'_tabX',ii,'.html',sep=''), dec=dec);      
       })
}  

setwd("..")