NAMEOFOUTPUTFILE<-"output1.csv"

SuperAwesometrial <- read.delim2("input1.tabular", header=FALSE)
#once you've used the other script to turn the FASFA into a CSV, copypaste the filepath and name 
#of the csv into this line between the quote marks.  

SBF<-read.csv("input3.csv", stringsAsFactors = FALSE, header = FALSE)
SBF<-t(SBF)

PositiveMotifs <- read.csv("input2.csv", stringsAsFactors=FALSE)
#because of R reasons, it is required that the motifs in this file have blank cells instead of spaces where there is no letter in 
#the motif

YsToim<-rep("xY",times=nrow(PositiveMotifs))
PositiveMotifs[,11]<-YsToim



################################################################################################################################
#I have to paste them, then split and unlist them, then find the x and paste again
Positive9Letters<-PositiveMotifs[,4:18]
#head(Positive9Letters)
PositiveTrueMotifs<-c()

AccessionNumbers<-as.character(SBF[2:nrow(SBF),1])
AccessionNumbers<-AccessionNumbers[!is.na(AccessionNumbers)]
ALLPOSSIBLE<-SuperAwesometrial[,1]
ALLPOSSIBLE<-as.character(ALLPOSSIBLE)
################################################################################################################################

for (q in 1:nrow(Positive9Letters)) {
  LeftJust<-0
  RightJust<-0
  
  motifmotif<-Positive9Letters[q,]
  motifmotif<-paste(motifmotif, collapse = "",sep = "")
  
  motifmotif<-unlist(strsplit(motifmotif, split = ""))
  
  position <- match(x = "x", table = motifmotif)
  LeftJust<-position-1
  RightJust<-length(motifmotif)-position-1
  
  LeftSpaces<-rep(x=" ", times=(7-LeftJust))
  RightSpaces<-rep(x=" ", times=(7-RightJust))
  
  motifmotif<-motifmotif[!motifmotif %in% c("x")]
  
  motifmotif<-c(LeftSpaces,motifmotif,RightSpaces)
  motifmotif<-paste(motifmotif, collapse = "",sep = "")
  PositiveTrueMotifs<-c(PositiveTrueMotifs,motifmotif)
}



################################################################################################################################
allmotifs<-matrix(data=rep("Motifs", times= 1000000),ncol = 1)
thenames<-matrix(data=rep("AccessionNumbers", times= 1000000),ncol = 1)
################################################################################################################################

################################################################################################################################

#I need to preallocate these vectors.  I will find out how many y's there are total and then make the vector that many long
#Or what I need is two separate loops.  First loop finds all the accession number positions that Grep to the FASTA (which is called ALLPOSSIBLE)
#then take only those AAs from the fasta and count their y's, preallocate the vector for part 2 to that many y's
#those accessions and such as saved in a vector... this seems like it would be no faster actually

#then_that_are <- which(AccessionNumbers %in% ALLPOSSIBLE)

MotifNumber<-2

#TrueMotifNums<-which(ALLPOSSIBLE %in% AccessionNumbers)
#fihlodeANs<-c()
for (q in 1:length(AccessionNumbers)) {
  patterno<-as.character(AccessionNumbers[q])
  location<-sapply(ALLPOSSIBLE, grepl, pattern=patterno, fixed=TRUE)
  if (sum(location)>0){
    whereisit<-which(location %in% TRUE)
    for (u in 1:length(whereisit)) {
      i<-whereisit[u]
      name<-c()
      data<-c()
      name<-as.character(SuperAwesometrial[i,1])
      #the name of each protein is the first column 
      name<-sub(x=name, pattern=",", replacement="")
      #the names may contain commas, remove them
      data<-as.character(SuperAwesometrial[i,3])
      #the amino acids are stored in the third column
      data<-strsplit(data,"")
      #split them into their component letters
      data<-unlist(data)
      #turn them into a vector
      motif<-c()
      
      #this part below is where I can speed things up
      The_Ys<-data=="Y"
      #find any Y in the protein
      if (sum(The_Ys>0)){ #if there is at least one Y
        Where_are_they<-which(The_Ys %in% TRUE)
        for (z in 1:length(Where_are_they)) { #then for every Y, make a motif
          
          j<-Where_are_they[z]
          #for (j in 1:length(data)){
          #if ("Y" %in% data[j]){
          #if there is a Y aka Tyrosine in the data
          #allmotifs=rbind(allmotifs,data[(i-4):(i+4)])
          a <- j-7
          a<-ifelse(a<1, a <- 1, a <- a)
          # if (a<1){
          #   a <- 1
          # }
          b<-j+7
          b<-ifelse(b>length(data), b <- length(data), b <- 
                      b)
          # if (b>length(data)){
          #   b<-length(data)
          # }
          #take the motif that is +/- 4 from that Y, sanity checks so that values are never off the grid from the protein
          
          LeftSide<-7-(j-a)
          RightSide<-7-(b-j)
          #how is the motif justified?  Does it have exactly 4 letters to the left/right, or does it not?
          
          leftspaces<-rep(" ",times=LeftSide)
          rightspaces<-rep(" ",times=RightSide)
          #add blank spaces if the motif has less than 4 letters to the left/right
          
          
          motif<-(data[(a):(b)])
          motif<-c(leftspaces,motif,rightspaces)
          #save that motif, which is the Y and +/- 4 amino acids, including truncation
          
          # lens<-c(lens,length(motif))
          # leni<-c(leni,i)
          # lenj<-c(lenj,j)
          
          motif<-paste(motif, sep="", collapse="")
          #the 4 amino acids, put them back together into a single string
          motif<-matrix(data=c(motif),nrow = 1)
          namesss<-matrix(data=c(name),nrow = 1)
          #keep this motif and separately keep the name of the protein it came from
          
          # allmotifs<-rbind(allmotifs,motif)
          # thenames<-rbind(thenames,namesss)
          allmotifs[MotifNumber,1]<-motif
          thenames[MotifNumber,1]<-namesss
          MotifNumber<-MotifNumber+1
          
          #add names and motifs to a growing list
          
          # write.table(motif, file="TRIALTIALRIAALSKFDJSD.csv", quote=FALSE, sep=",",
          #             row.names=FALSE,col.names = FALSE, na="", append=TRUE)
          #and then write it into a csv, the sep is needed so that the two pieces of the data frame are separated
          #append has 1to equal true because this thing will loop around many times adding more and more data points
          #you must create a new filename/filepath with each new data you run
        }
        
      }
    }
  }
}




################################################################################################################################
################################################################################################################################
################################################################################################################################


# for (i in 1:nrow(SuperAwesometrial)){
# 
# }

names(allmotifs)<-thenames

truemotifs<-allmotifs[!duplicated(allmotifs)]
#truenames<-thenames[!duplicated(thenames)]
#remove duplicates from the motifs and names

#make the motifs and names into matrices


truemotifs<-truemotifs[!truemotifs %in% PositiveTrueMotifs]

outputfile<-cbind(names(truemotifs),truemotifs)

outputfile <- gsub(",","",outputfile)

write.table(outputfile, file=NAMEOFOUTPUTFILE, quote=FALSE, sep=",",
             row.names=FALSE,col.names = FALSE, na="", append=TRUE)