diff --git a/automatedSelEstimscript2.R b/automatedSelEstimscript2.R
new file mode 100644
index 0000000..896be8b
--- /dev/null
+++ b/automatedSelEstimscript2.R
@@ -0,0 +1,289 @@
+# Copyright INRA
+# author: Renaud VITALIS (2013)
+#  
+# renaud.vitalis@inra.fr
+# 
+# Mariana DAMAS-MORA (2023)
+#
+#fc52541@alunos.fc.ul.pt
+# This file is part of SelEstim.
+# 
+# SelEstim is a computer program whose purpose is to is to detect
+# and measure selection from gene frequency data.
+# 
+# SelEstim is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# SelEstim is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+#automatedselestimscript: Plots the specific selection coefficient of the makers in each position and the KLD values fpr each locus from the SelEstim results.
+#Usage: Rscript [path to the automatedselestimscript.R] [path to summary_delta.out] [path to calibration summary_delta.out path] [path to summary_sigma.out] [path to calibration map] [path to the output specific selection coefficient plot] [path to the kld values output plot]  
+#source script
+library(gplots)
+#adaptatition to get arguments
+args <- commandArgs(trailingOnly = TRUE)
+
+# Get arguments
+delta <- args[1]
+calibration_delta <- args[2]
+sigma <- args[3]
+output_coefficient <- args[4]
+output_kld <- args[5]
+#calibration_map <- args[6]
+
+plot.delta <- function(file = "summary_delta.out",map = "") {
+  
+  # 'map' is the name of a file that contains two columns only: the SNP ID and its position (in bp). Be careful to keep the same order of the SNPs as in the data file)
+  
+  palette(rich.colors(64))
+  output <- read.table(file,header = TRUE)
+  mean.delta <- output$mean
+  relative.delta <- (mean.delta / max(mean.delta))
+  n.snps <- length(mean.delta)
+  if (map != "") {
+    if(!file.exists(map)) {
+      stop(paste("The file ",map," does not exist...",sep = ""))
+    }
+    physical.map <- read.table(map,header = FALSE)
+    position <- physical.map[,2] / 1e6
+    sorted <- order(position)
+    plot(position[sorted],mean.delta[sorted],type = "n",xlab = "Position (Mb)",ylab = expression(Locus-specific~selection~coefficient~delta[j]),ylim = c(0,max(mean.delta) + 10))
+    segments(position[sorted],0,position[sorted],mean.delta[sorted],col = 1 + 63 * relative.delta[sorted],lwd = relative.delta[sorted])
+  }
+  else {
+    plot(seq(1,n.snps),mean.delta,type = "n",xlab = "Markers",ylab = expression(Locus-specific~selection~coefficient~delta[j]),ylim = c(0,max(mean.delta) + 10))
+    segments(seq(1,n.snps),0,seq(1,n.snps),mean.delta,col = 1 + 63 * relative.delta,lwd = relative.delta)
+  }
+}
+
+plot.kld <- function(file = "summary_delta.out",map = "",calibration_file = "calibration/summary_delta.out",limit,window.size,n.markers) {
+  
+  # 'map' is the name of a file that contains two columns only: the SNP ID and its position (in bp). Be careful to keep the same order of the SNPs as in the data file)
+  # 'limit' (optional) is used to compute the threshold value of the empirical distribution of the KLD used to calibrate the KLD;
+  #  e.g., if you chose limit = 0.01, then the 99\%-quantile of the KLD distribution from the pod analysis will be used as a decision criterion to discriminate between selection and neutrality.
+  # 'window.size (optional) is the size of the sliding window, which may be used to determine the regions that contain at least n.markers with KLD above the threshold determined by the 'limit' argument
+  # 'n.markers' (optional) is uded to determine the regions that contain at least 'n.markers' with KLD above the threshold determined by the 'limit' argument
+  
+  output <- read.table(file,header = TRUE)
+  kld <- output$KLD
+  n.snps <- length(kld)
+  if (map != "") {
+    if (!file.exists(map)) {
+      stop(paste("The file ",map," does not exist...",sep = ""))
+    }
+    physical.map <- read.table(map,header = F)
+    position <- physical.map[,2]
+    sorted <- order(position)
+    sorted.position <- position[sorted]
+    sorted.position.mb <- sorted.position / 1e6
+    plot(sorted.position.mb,kld[sorted],cex = 0.25,xlab = "Position (Mb)",ylab = "Kullback-Leibler divergence (KLD)",pch = 16,col = "grey",type = "n")
+    points(sorted.position.mb,kld[sorted],cex = 0.25,pch = 16,col = "grey")
+    if (!missing(window.size) & !missing(n.markers) & !missing(limit)) {
+      if (!file.exists(calibration_file)) {
+        stop(paste("The file ",calibration_file," does not exist...",sep = ""))
+      }
+      calibration <- read.table(calibration_file,header = TRUE)
+      kld.calibration <- calibration$KLD
+      threshold <- quantile(kld.calibration,(1 - limit))
+      outstanding.region <- vector("numeric",n.snps)
+      for (i in 1:n.snps) {
+        window <- abs(sorted.position[i] - sorted.position) <= (window.size / 2)
+        outstanding.region[i] <- length(kld[window][kld[window] >= threshold])
+      }
+      within <- (outstanding.region >= n.markers & kld >= threshold)
+      if (length(within[within]) > 0) {
+        segments(sorted.position.mb[within],0, sorted.position.mb[within],max(kld),col = rich.colors(1,alpha = 0.2),lwd = 2)
+      }
+      points(sorted.position.mb[within][kld[within] >= threshold],kld[within][kld[within] >= threshold],cex = 0.25,col = "black",pch = 8)
+    }
+    else {
+      if (!missing(limit)) {
+        if (!file.exists(calibration_file)) {
+          stop(paste("The file ",calibration_file," does not exist...",sep = ""))
+        }
+        calibration <- read.table(calibration_file,header = TRUE)
+        kld.calibration <- calibration$KLD
+        threshold <- quantile(kld.calibration,(1 - limit))
+        #				points(position.mb[sorted][kld[sorted] >= threshold],kld[sorted][kld[sorted] >= threshold],cex = 0.25,col = "black",pch = 8)
+        points(sorted.position.mb[kld[sorted] >= threshold],kld[sorted][kld[sorted] >= threshold],cex = 0.25,col = "black",pch = 8)
+      }
+    }
+  }
+  else {
+    plot(seq(1,n.snps),kld,cex = 0.25,xlab = "Markers",ylab = "Kullback-Leibler divergence (KLD)",pch = 16,col = "grey",type = "n")
+    points(seq(1,n.snps),kld,cex = 0.25,pch = 16,col = "grey")
+    if (!missing(limit)) {
+      if (!file.exists(calibration_file)) {
+        stop(paste("The file ",calibration_file," does not exist...",sep = ""))
+      }
+      calibration <- read.table(calibration_file,header = TRUE)
+      kld.calibration <- calibration$KLD
+      threshold <- quantile(kld.calibration,(1 - limit))
+      points(seq(1,n.snps)[kld >= threshold],kld[kld >= threshold],cex = 0.25,col = "black",pch = 8)
+      print(seq(1,n.snps)[kld>=threshold])
+      print(length(seq(1,n.snps)[kld>=threshold]))
+      
+    }
+  }
+}
+
+randomize.reference.allele <- function (infile = "",outfile = "",pool = FALSE) {
+  
+  # 'infile' is the original dataset
+  # 'outfile' is the dataset where reference alleles are chosen randomly
+  # 'pool' is an indicator variable that says whether the data consist in allele counts or reads (pooled data)
+  # the 'reference.allele' output file contains, for each locus, the reference allele chosen from the original data
+  
+  if (infile != "") {
+    if(!file.exists(infile)) {
+      stop(paste("The file ",infile," does not exist...",sep = ""))
+    }
+  }
+  else {
+    stop(paste("\n\tThe argument \"infile\" is missing, with no default value",sep = ""))
+  }
+  if (outfile == "") {
+    stop(paste("\n\tThe argument \"outfile\" is missing, with no default value",sep = ""))
+  }
+  skip.lines <- 2
+  if (pool) {
+    skip.lines <- skip.lines + 1
+  }
+  orig.data <- read.table(infile,skip = skip.lines)
+  dummy <- scan(infile,nmax = 2)
+  number.of.populations <- dummy[1]
+  number.of.loci <- dummy[2]
+  if (number.of.populations != (ncol(orig.data) / 2)) {
+    stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+  }
+  if (number.of.loci != nrow(orig.data)) {
+    stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+  }
+  new.data <- matrix(nrow = nrow(orig.data),ncol = ncol(orig.data))
+  flip <- vector(mode = "numeric",length = ncol(orig.data))
+  cpt <- 0
+  for (i in 1: number.of.populations) {
+    flip[c(1,2) + cpt] <- c(2,1) + cpt
+    cpt <- cpt + 2
+  }
+  mask <- sample(c(TRUE,FALSE),size = number.of.loci,replace = TRUE)
+  same <- seq(1,number.of.loci)[mask]
+  anti <- seq(1,number.of.loci)[!mask]
+  new.data[same,] <- as.matrix(orig.data[same,])
+  new.data[anti,] <- as.matrix(orig.data[anti,flip])
+  write.table(number.of.populations,file = outfile,row.names = FALSE,col.names = FALSE,sep = '\t')
+  write.table(number.of.loci,file = outfile,row.names = FALSE,col.names = FALSE,sep = '\t',append = TRUE)
+  if (pool) {
+    sample.size <- read.table(infile,nrows = 1,skip = 2)
+    write.table(sample.size,file = outfile,row.names = FALSE,col.names = FALSE,sep = '\t',append = TRUE)
+  }
+  write.table(new.data,file = outfile,row.names = FALSE,col.names = FALSE,sep = '\t',append = TRUE)
+  list.alleles <- 2 - mask
+  write.table(cbind(seq(1,number.of.loci),list.alleles),file = "reference.allele",quote = FALSE,row.names = FALSE,col.names = c("locus","allele"),sep = '\t')
+}
+
+compute.F_ST <- function(infile = "",pool = FALSE) {
+  
+  # 'infile' is the original dataset (in SelEstim format)
+  # 'pool' is an indicator variable that says whether the data consist in allele counts or reads (pooled data)
+  # The F_ST for pool-seq data is compuyed following Hivert et al. (in prep.)
+  # The compute.F_ST function results in a list of two elements: F_ST (a vector of locus-specific F_ST estimates) and F_ST_multilocus (the multilocus estimate of F_ST)
+  
+  if (infile != "") {
+    if(!file.exists(infile)) {
+      stop(paste("The file ",infile," does not exist...",sep = ""))
+    }
+  }
+  else {
+    stop(paste("\n\tThe argument \"infile\" is missing, with no default value",sep = ""))
+  }
+  if (!pool) {
+    skip.lines <- 2
+    counts <- read.table(infile,skip = skip.lines)
+    dummy <- scan(infile,nmax = 2)
+    number.of.populations <- dummy[1]
+    number.of.loci <- dummy[2]
+    if (number.of.populations != (ncol(counts) / 2)) {
+      stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+    }
+    if (number.of.loci != nrow(counts)) {
+      stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+    }
+    r <- ncol(counts) / 2
+    l <- seq(1,(2 * r),2)
+    ss <- counts[,l] + counts[,(l + 1)]	
+    ss2 <- rowSums((counts[,l] + counts[,(l + 1)])^2)
+    n <- rowSums(ss)
+    n_c <- (n - ss2 / n) / (r - 1.0)
+    p <- counts[,l] / ss
+    q <- counts[,(l + 1)] / ss
+    pbar <- rowSums(counts[,l]) / rowSums(ss)
+    qbar <- rowSums(counts[,(l + 1)]) / rowSums(ss) 
+    SSI <- rowSums(ss * (p - p^2) + ss * (q - q^2))
+    SSP <- rowSums(ss * (p - pbar)^2 + ss * (q - qbar)^2)		
+    MSI <- SSI / (n - r)
+    MSP <- SSP / (r - 1.0)
+  }
+  else {
+    skip.lines <- 3	
+    reads <- read.table(infile,skip = skip.lines)
+    dummy <- scan(infile,nmax = 2)
+    number.of.populations <- dummy[1]
+    number.of.loci <- dummy[2]
+    if (number.of.populations != (ncol(reads) / 2)) {
+      stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+    }
+    if (number.of.loci != nrow(reads)) {
+      stop(paste("\tProblem reading file \"infile\": perhaps the \"pool\" argument is misspecified",sep = ""))
+    }
+    n_i <- as.vector(read.table(infile,skip = (skip.lines - 1),nrows = 1),mode = "numeric")
+    if (length(n_i) != (ncol(reads) / 2)) {
+      stop(paste("\tProblem reading file \"infile\": the pool sizes are misspecified",sep = ""))
+    }
+    nbr_loci <- nrow(reads)
+    n_d <- ncol(reads) / 2
+    l <- seq(1,(2 * n_d),2)
+    mtrx.n_i <- matrix(n_i,nrow = nbr_loci,ncol = n_d,byrow = TRUE)
+    R_1_i <- reads[,l] + reads[,(l + 1)]
+    R_1 <- rowSums(R_1_i)
+    R_2 <- rowSums(R_1_i^2)
+    C_1 <- rowSums(R_1_i / mtrx.n_i + (mtrx.n_i - 1) / mtrx.n_i)
+    C_1.star <- rowSums(R_1_i * (R_1_i / mtrx.n_i + (mtrx.n_i - 1) / mtrx.n_i)) / R_1
+    n_c <- (R_1 - R_2 / R_1) / (C_1 - C_1.star)
+    SSI <- rowSums(reads[,l] - reads[,l]^2 / R_1_i + reads[,(l + 1)] - reads[,(l + 1)]^2 / R_1_i)	
+    SSP <- rowSums(R_1_i * ((reads[,l] / R_1_i) - (rowSums(reads[,l]) / R_1))^2 + R_1_i * ((reads[,(l + 1)] / R_1_i) - (rowSums(reads[,(l + 1)]) / R_1))^2)
+    MSI <- SSI / (R_1 - C_1)
+    MSP <- SSP / (C_1 - C_1.star)
+  }
+  F_ST <- (MSP - MSI)  / (MSP + (n_c - 1) * MSI)
+  F_ST_multilocus <- sum(MSP - MSI)  / sum(MSP + (n_c - 1) * MSI)
+  rslt <- list(F_ST = F_ST,F_ST_multilocus = F_ST_multilocus)
+  return(rslt)
+}
+#script from the manual
+png(paste(output_coefficient, ".png", sep=""))
+plot.delta(file = delta)
+dev.off()
+png(paste(output_kld, ".png", sep=""))
+plot.kld(file = delta)
+dev.off()
+png(paste(output_kld, ".png", sep=""))
+plot.kld(file = delta, calibration_file = calibration_delta, limit = 0.01)
+rslt <- read.table(delta, header = TRUE)
+top.snp <- which(rslt$KLD == max(rslt$KLD))
+top.snp
+#dev.off()
+#png(paste(output_kld, ".png", sep=""))
+#plot.kld(file = delta, calibration_file = calibration_delta, limit = 0.01)
+#abline(v = 4.867859,lty = 2)
+rslt$mean[top.snp]
+sigma <- read.table(sigma, header = TRUE)
+sigma$mean[which(sigma$locus == top.snp)]
+dev.off()
diff --git a/automatedbayescanplots.R b/automatedbayescanplots.R
new file mode 100644
index 0000000..f414fb2
--- /dev/null
+++ b/automatedbayescanplots.R
@@ -0,0 +1,66 @@
+#automatedbayescanplots: plots the FST of each loci and the density of loci with a given selection coefficient.
+#based on the file.g_fst and the file.g.sel outpputed by the selection detection program BayeScan.
+#Usage:
+# BayeScan_plots path to [plot_R.r] [file.g_fst] [g.sel] output_namefst output_namedensity
+#Variable to allow using only the files as inputs
+plot_bayescan <- function(res, FDR = 0.05, size = 1, pos = 0.35, highlight = NULL, name_highlighted = F, add_text = T)
+{
+  if (is.character(res))
+    res=read.table(res)
+  
+  colfstat=4
+  colq=colfstat-1
+  
+  highlight_rows=which(is.element(as.numeric(row.names(res)),highlight))
+  non_highlight_rows=setdiff(1:nrow(res),highlight_rows)
+  
+  outliers=as.integer(row.names(res[res[,colq]<=FDR,]))
+  
+  ok_outliers=TRUE
+  if (sum(res[,colq]<=FDR)==0)
+    ok_outliers=FALSE;
+  
+  res[res[,colq]<=0.0001,colq]=0.0001
+  
+  # plot
+  plot(log10(res[,colq]),res[,colfstat],xlim=rev(range(log10(res[,colq]))),xlab="log10(q value)",ylab=names(res[colfstat]),type="n")
+  points(log10(res[non_highlight_rows,colq]),res[non_highlight_rows,colfstat],pch=19,cex=size)
+  
+  if (name_highlighted) {
+    if (length(highlight_rows)>0) {
+      text(log10(res[highlight_rows,colq]),res[highlight_rows,colfstat],row.names(res[highlight_rows,]),col="red",cex=size*1.2,font=2)
+    }
+  }
+  else {
+    points(log10(res[highlight_rows,colq]),res[highlight_rows,colfstat],col="red",pch=19,cex=size)
+    # add names of loci over p and vertical line
+    if (ok_outliers & add_text) {
+      text(log10(res[res[,colq]<=FDR,][,colq])+pos*(round(runif(nrow(res[res[,colq]<=FDR,]),1,2))*2-3),res[res[,colq]<=FDR,][,colfstat],row.names(res[res[,colq]<=FDR,]),cex=size)
+    }
+  }
+  lines(c(log10(FDR),log10(FDR)),c(-1,1),lwd=2)
+  
+  return(list("outliers"=outliers,"nb_outliers"=length(outliers)))
+}
+args <- commandArgs(trailingOnly = TRUE)
+# Get arguments
+#sourcefile <- args[1]
+g_fst.txt <- args[1]
+g.sel <- args[2]
+output_namefst <- args[3]
+output_namedensity <- args[4]
+#Source plot_R.r
+#source(sourcefile::plot_bayescan(res, FDR = 0.05, size = 1, pos = 0.35, highlight = NULL, name_highlighted = F, add_text = T))
+#Plot FST
+png(paste(output_namefst, ".png", sep=""))
+plot_bayescan(g_fst.txt,0,FDR=0.05)
+dev.off()
+mydata=read.table(g.sel,colClasses="numeric")
+parameter="Fst1"
+#Plot density of loci with a given selection coefficient.
+png(paste(output_namedensity, ".png", sep=""))
+plot(density(mydata[[parameter]]), xlab=parameter,
+     main=paste(parameter,"posterior distribution"))
+dev.off()
+#install.packages("boa")
+#boa.hpd(mydata[[parameter]],0.05)
diff --git a/snp_pca_static.R b/snp_pca_static.R
old mode 100755
new mode 100644
index 2d988eb..4e7fe6e
--- a/snp_pca_static.R
+++ b/snp_pca_static.R
@@ -2,6 +2,10 @@
 # snp_pca.R performs a PCA using the SNPRelate R package using a VCF file
 # and an option populations files
 
+# Altered from the original: 
+#   displays the percentage of variance explained by each principal component in the axis labels
+#   slightly altered the pch and cls variables
+
 # Usage:
 # snp_pca.R vcf_file output_file_name popupations_file[optional]
 
@@ -28,6 +32,7 @@ print(pca)
 pc.percent<- pca$varprop * 100
 print(round(pc.percent, 2))
 
+
 # Open figure driver
 pdf(paste(output_name, ".pdf", sep=""))
 
@@ -41,8 +46,8 @@ if (!is.na(pops_file)) {
     EV1 = pca$eigenvect[,1],
     EV2 = pca$eigenvect[,2],
     stringsAsFactors=F)
-  cls <-rep(brewer.pal(n = 12, name = "Set3"), times=5)
-  pch_v <- rep(c(16, 15, 17, 18), each=12)
+  cls <-rep(brewer.pal(n = 5, name = "Set1"), times=5)
+  pch_v <- rep(c(16, 15, 17, 18), each=5)
   save(tab, file=paste(output_name, ".Rdata", sep=""))
   # par(mar =  c(5, 4, 4, 6) + 1.8)
   
@@ -53,9 +58,11 @@ if (!is.na(pops_file)) {
   leg_wid <- grconvertX(leg$rect$w, to='ndc') - grconvertX(0, to='ndc')
   
   par(omd=c(0, 1-leg_wid, 0, 1))
-  plot(tab$EV1, tab$EV2, col=cls[as.integer(tab$pop)], xlab="PC 1",
-    ylab="PC 2", pch=pch_v[as.numeric(tab$pop)], solid=.2, cex=1,
+  plot1 <- plot(tab$EV1, tab$EV2, col=cls[as.integer(tab$pop)], xlab=paste("PC 1: ", print(round(pc.percent[1],2)),"%", sep = ""),
+    ylab=paste("PC 2: ", print(round(pc.percent[2],2)),"%", sep = ""), pch=pch_v[as.numeric(tab$pop)], solid=.2, cex=1.2,
     clab=1, leg=T, bg="white")
+  
+#  text(tab$EV1 - 1, tab$EV2, labels=tab$sample.id)
 
   legend(par('usr')[2], par('usr')[4], legend=levels(tab$pop), bty='n', xpd=NA,
          col=cls[0:length(tab$pop)], pch=pch_v)