##Script used to calculate Trial-level Bias Scores.  Your mileage may vary with this script, depending on how your data
##is compiled.  Good luck!

##A few annotations:
##The variable "dir" is the directory where your result files are stored.  This script will read CSV files.
##The variable "Y" is the raw reaction time.
##The variable "Cond" is the trial condition.  Here, "0" is a neutral trial (no threat), "1" is a congruent trial
##	where the threat is cued, and "2" is an incongruent trial where the threat is not cued.
##You may want to adjust the "for (j in 1:5) {" line of code depending on the proximity you're aiming for
##	re: your trial-level bias.  This is set up to look for neighboring trials with five trials above or below.
##The output will spit out the data files with a new column for the TL-BS score for each eligible trial.  Check at least
##	one participant by hand so that you can be sure there are no glaring differences in the way your data-set was analyzed
##	by this script.  
##If you have any questions, drop a line at tdaniel@westfield.ma.edu.

dir <-"C:\\Users\\Alex\\Desktop\\Bardeen\\"
listing <- list.files(dir)
TotalSessions <- length(listing)
sess = 1

for (i in 1:TotalSessions){
	db <- paste(dir,listing[sess], sep="")
	data <- read.csv(db)

colnames(data)[34]<-"Y"
colnames(data)[46]<-"Cond"

df<- data.frame(data)

getclosest <- function(df, i) {
   if ((as.character(df$Cond[[i]]) == "0") | (as.character(df$Cond[[i]]) == "1")) {
      return (NA)
   } else {
      # df$Y[i]
      below <- NA
      above <- NA
      aj <- NA
      bj <- NA
      for (j in 1:5) {
          pos <- i - j
          if (pos < 1) break
          if (df$Cond[pos] == "1") {
            below <- df$Y[pos]
            bj <- j
            break
          }
      }
      for (j in 1:5) {
        pos <- i + j
        if (pos > nrow(df)) break 
        if (df$Cond[pos] == "1") {
           above <- df$Y[pos]
           aj <- j
           break
        }
      }
      temp = NA
      tempb = NA
      if (is.na(above) && is.na(below)) {
          return(NA)
      }
      if (!is.na(below) && !is.na(above)) {

        if (aj  < bj) {
           return(df$Y[i] - above)
        } else {
           return(df$Y[i] - below)
        }
      }
      if (!is.na(below)) {
        tempb <- (df$Y[i] - below)
        return (tempb)
      }
      if (!is.na(above)) { 
         tempa <- (df$Y[i] - above)
         return(tempa)
      }

    return (NA)
   }
}

getclosest2 <- function(df, i) {
   if ((as.character(df$Cond[[i]]) == "0") | (as.character(df$Cond[[i]]) == "2")) {
      return (NA)
   } else {
      # df$Y[i]
      below <- NA
      above <- NA
      aj <- NA
      bj <- NA
      for (j in 1:5) {
          pos <- i - j
          if (pos < 1) break
          if (df$Cond[pos] == "2") {
            below <- df$Y[pos]
            bj <- j
            break
          }
      }
      for (j in 1:5) {
        pos <- i + j
        if (pos > nrow(df)) break 
        if (df$Cond[pos] == "2") {
           above <- df$Y[pos]
           aj <- j
           break
        }
      }
      temp = NA
      tempb = NA
      if (is.na(above) && is.na(below)) {
          return(NA)
      }
      if (!is.na(below) && !is.na(above)) {

        if (aj  < bj) {
           return(above - df$Y[i])
        } else {
           return(below - df$Y[i])
        }
      }
      if (!is.na(below)) {
        tempb <- (below - df$Y[i])
        return (tempb)
      }
      if (!is.na(above)) { 
         tempa <- (above - df$Y[i])
         return(tempa)
      }

    return (NA)
   }
}

v <- c() # length=nrow(df))
for (i in 1:nrow(df)) { 
   v[i] <- getclosest(df, i)
}

w <- c() # length=nrow(df))
for (i in 1:nrow(df)) {
   w[i] <- getclosest2(df, i)
}

TrialBias = rowSums(cbind(v, w), na.rm=TRUE)

oka <- data.frame(df, TrialBias)

db2 <- paste(dir,"t",listing[sess], sep="")
write.table(oka,
	file=db2, row.names=F, col.names=T, append=T, sep=",")

data = NULL
df = NULL
oka = NULL
sess = sess + 1
}