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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()

locations<-unique(grep(paste(AccessionNumbers,collapse="|"), ALLPOSSIBLE))

if (sum(locations)>0){
  whereisit<-locations
  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)