#Package check and install.
{
  check <- suppressWarnings(suppressMessages(require(reticulate)))
  
  if(!check){
    install.packages("reticulate")
    library(reticulate)
  }
  
  check <- suppressWarnings(suppressMessages(require(ggplot2)))
  
  if(!check){
    install.packages("ggplot2")
    library(ggplot2)
  }
  
  check <- suppressWarnings(suppressMessages(require(shiny)))
  
  if(!check){
    install.packages("shiny")
    library(shiny)
  }
  
  check <- suppressWarnings(suppressMessages(require(data.table)))
  
  if(!check){
    install.packages("data.table")
    library(data.table)
  }
  
  check <- suppressWarnings(suppressMessages(require(plotly)))
  
  if(!check){
    install.packages("plotly")
    library(plotly)
  }
  
  check <- suppressWarnings(suppressMessages(require(cowplot)))
  
  if(!check){
    install.packages("cowplot")
    library(cowplot)
  }
  
  check <- suppressWarnings(suppressMessages(require(enveomics.R)))
  
  if(!check){
    install.packages("enveomics.R")
    library(enveomics.R)
  }
  
  check <- suppressWarnings(suppressMessages(require(shinyBS)))
  
  if(!check){
    install.packages("shinyBS")
    library(shinyBS)
  }
  
  check <- suppressWarnings(suppressMessages(require(hms)))
  
  if(!check){
    install.packages("hms")
    library(hms)
  }
  
  check <- suppressWarnings(suppressMessages(require(easycsv)))
  
  if(!check){
    install.packages("easycsv")
    library(easycsv)
  }
  
  check <- suppressWarnings(suppressMessages(require(shinyalert)))
  
  if(!check){
    install.packages("shinyalert")
    library(shinyalert)
  }
  
  check <- suppressWarnings(suppressMessages(require(htmlwidgets)))
  
  if(!check){
    install.packages("htmlwidgets")
    library(htmlwidgets)
  }
  
  
}

#Helper functions
{
  #This will download whatever the current python script is. You have to run it before landing page.
  get_python <- function(){
    
    source_python("https://raw.githubusercontent.com/KGerhardt/Recplot_4/master/Recplot.py", envir = globalenv())
    
  }
  
  #Checks for necessary setup steps and makes the requisite installs as needed.
  prepare_environment <- function(){
    
    cat("Checking for Miniconda and installing if necessary...\n")
    try({
      install_miniconda()
    })
    
    #Checking for first-time use of recplots
    if(!"recruitment_plots" %in% conda_list()$name){
      cat("Creating Miniconda environment: 'recruitment_plots'\n")
      conda_create(envname = "recruitment_plots")
    }
    
    use_miniconda(condaenv = "recruitment_plots", required = T)
    
    if(get_sys() != "Windows"){
      if(py_module_available("pysam")){
        cat("Attempting to install pysam to recruitment_plots... ")
        try({
          py_install(packages = "pysam", envname = "recruitment_plots", pip = T)
          cat("Done!\n")
        }) 
      }else{
        cat("Pysam already installed. You probably shouldn't be seeing this warning. Did you call prepare_environment() twice?\n")
      }
      
      get_python()
      
    }
    
  }
  
  #Prepares the background miniconda env if necessary; otherwise, sets the environment and loads the python script functions
  initiate <- function(){
    cat("Initiating recruitment plot environment. Please wait a moment. ")
    
    tryCatch({
      
      use_miniconda(condaenv = "recruitment_plots", required = T )
      get_python()
      
    }, error = function(cond){
      
      cat("\nPerforming first-time setup. Wait a moment, please.\n")
      
      prepare_environment()
      
      return("Environment prepared.")
      
    }  )
  }
  
  #The python import is a space-efficient, but strcutrually awkward data object
  #List of 2 items: list of lists of contig starts, stops, assoc. counts per %ID bin, and %ID bins.
  #This function converts the structure into a recplot-ready data.table with appropriate labelling and returns some other key values for building the plots.
  pydat_to_recplot_dat <- function(extracted_MAG, contig_names){
    
    id_breaks <- unlist(extracted_MAG[[2]])
    #id_breaks <- paste0(id_breaks - id_width, "-", id_breaks)
    
    extracted_MAG <- extracted_MAG[[1]]
    names(extracted_MAG) = contig_names
    
    ends <- lapply(extracted_MAG, function(x){
      
      return(x[[2]])
      
    })
    
    maximum_table <- data.table(contig = names(ends), length = lapply(ends, max))
    maximum_table[, relative_end := cumsum(length) + 1:nrow(maximum_table) - 1]
    
    pos.max <- maximum_table$relative_end[nrow(maximum_table)]
    
    bp_unit <- c("(bp)", "(Kbp)", "(Mbp)", "(Gbp)")[findInterval(log10(pos.max), c(0,3,6,9,12,Inf))]
    bp_div <- c(1, 1e3, 1e6, 1e9)[findInterval(log10(pos.max), c(0,3,6,9,12,Inf))]
    
    ends <- unlist(ends)
    
    bins <- rbindlist(lapply(extracted_MAG, function(x){
      
      return(data.table(cbind(do.call(rbind, x[[3]]))))
      
    }))
    
    colnames(bins) = as.character(id_breaks)
    
    starts <- lapply(extracted_MAG, function(x){
      
      return(x[[1]])
      
    })
    #needs start, end, contig name for annotation, rel.pos absolute for plotting
    
    bins[, Start := unlist(starts)]
    
    bins[, End := ends]
    
    bins[, seq_pos := seq(1/bp_div, pos.max/bp_div , length.out = nrow(bins))]
    
    bins[, contig := rep(names(starts), times = lengths(starts))]
    
    bins <- melt.data.table(bins, id.vars = c("contig", "Start", "End", "seq_pos"))
    
    colnames(bins)[5:6] = c("Pct_ID_bin", "bp_count")
    
    bins[, Pct_ID_bin := as.numeric(levels(bins$Pct_ID_bin))[bins$Pct_ID_bin]]
    
    #return(bins)
    return(list(bins, bp_unit,bp_div, pos.max))
    
  }
  
  create_static_plot <- function(base, bp_unit, bp_div, pos_max, in_grp_min, id_break, width, linear, showpeaks, ends, trunc_behavior = "ends", trunc_degree = as.integer(75), ...){
    
    group.colors <- c(depth.in = "darkblue", depth.out = "lightblue", depth.in.nil = "darkblue", depth.out.nil = "lightblue")
    
    #This seems to be working.
    #Sets any starts < trunc degree to trunc_degree
    base <- base[Start < trunc_degree, Start := trunc_degree]
    
    base[, contig_len := max(End), by = contig]
    base[End == contig_len, End := End - trunc_degree, ]
    
    #Selects the bins at the end of each contig and subtracts trunc degree from it
    #base[base[, End > (max(End)-trunc_degree), by = contig]$V1, End := (End - trunc_degree),]
    
    
    #If the final bin was too small, removes it as unplottable.
    base <- base[Start <= End,]
    
    #fwrite(ends, "endings.txt", sep = "\t")
    
    #b2 <- base
    #setkeyv(b2, c("contig", "Start", "Pct_ID_bin"))
    #fwrite(b2, "base_inital_mod.txt", sep = "\t")
    
    #Allows for count normalization by bin width across all bins
    norm_factor <- min(base$End-base$Start) + 1
    
    #Lower left panel
    {    
    p <- ggplot(base, aes(x = seq_pos, y = Pct_ID_bin, fill=log10((bp_count * (norm_factor/(End-Start+1))))))+ 
      scale_fill_gradient(low = "white", high = "black",  na.value = "#EEF7FA")+
      ylab("Percent Identity") +
      xlab(paste("Position in Genome", bp_unit)) +
      scale_y_continuous(expand = c(0, 0)) +
      scale_x_continuous(expand = c(0, 0), limits = c(0, pos_max/bp_div), breaks = scales::pretty_breaks(n = 10)) +
      theme(legend.position = "none", 
            axis.line = element_line(colour = "black"),
            axis.title = element_text(size = 14),
            axis.text = element_text(size = 14)) +
      geom_raster()
    
    
    p <- p + annotate("rect", xmin = 0, xmax = pos_max/bp_div, 
                      ymin = in_grp_min, 
                      ymax = 100, fill = "darkblue", alpha = .15)
    
    read_rec_plot <- p + geom_vline(xintercept = ends$V1/bp_div[-nrow(ends)], col = "#AAAAAA40")
    }
    
    base[, group_label := ifelse(base$Pct_ID_bin-id_break >= in_grp_min, "depth.in", "depth.out")]
    setkeyv(base, c("group_label", "seq_pos"))
    
    #upper left panel
    {
    depth_data <- base[, sum(bp_count/(End-Start+1), na.rm = T), by = key(base)]
    colnames(depth_data)[3] = "count"
    
    ddSave <- base[, sum(bp_count, na.rm = T), by = key(base)]
    
    nil_depth_data <- depth_data[count == 0]
    nil_depth_data$group_label <- ifelse(nil_depth_data$group_label == "depth.in", "depth.in.nil", "depth.out.nil")
    
    seg_upper_bound <- min(depth_data$count[depth_data$count > 0])
    
    depth_data$count[depth_data$count == 0] <- NA
    
    seq_depth_chart <- ggplot(depth_data, aes(x = seq_pos, y = count, colour=group_label, group = group_label))+
      geom_step(alpha = 0.75) +
      scale_y_continuous(trans = "log10", labels = scales::scientific) +
      scale_x_continuous(expand=c(0,0), limits = c(0, pos_max/bp_div), breaks = scales::pretty_breaks(n = 10))+
      theme(legend.position = "none", 
            panel.border = element_blank(), 
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(), 
            axis.line = element_line(colour = "black"), 
            panel.background = element_blank(), 
            axis.title.x = element_blank(), 
            #axis.text.y = element_text(angle = 90, hjust = 0.5),
            axis.title = element_text(size = 14),
            axis.text = element_text(size = 14)) +
      scale_color_manual(values = group.colors) +
      ylab("Depth")
    
    if(nrow(nil_depth_data) > 0){
      seq_depth_chart <- seq_depth_chart + geom_segment(data = nil_depth_data, aes(x = seq_pos, xend = seq_pos, y = 0, yend = seg_upper_bound, color = group_label, group = group_label))
      
    }
    }
    
    #Seq. depth histograms (top right panel)
    {
      depth_data <- depth_data[count > 0]
      seqdepth.lim <- range(c(depth_data$count[depth_data[,group_label == "depth.in"]], depth_data$count[depth_data[,group_label == "depth.out"]])) * c(1/2, 2)
      hist_binwidth <- (log10(seqdepth.lim[2]/2) - log10(seqdepth.lim[1] * 2))/199
      
      depth_data[,count := log10(count)]
      depth_data$group_label <-factor(depth_data$group_label, levels = c("depth.out", "depth.in"))
      depth_data <- depth_data[order(group_label),]
      
      p4 <- ggplot(depth_data, aes(x = count, fill = group_label)) +
        geom_histogram(binwidth = hist_binwidth) +
        scale_fill_manual(values = group.colors) +
        scale_y_continuous(expand=c(0,0)) +
        theme(legend.position = "none", 
              panel.border = element_blank(), 
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank(), 
              axis.line = element_line(colour = "black"), 
              panel.background = element_blank(), 
              axis.ticks.y = element_blank(), 
              axis.text.y = element_blank(), 
              axis.text.x = element_text(colour = "white"),
              axis.ticks.x = element_blank(),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        xlab(label = element_blank()) +
        ylab(label = element_blank())
      
      if(showpeaks){
        
        enve.recplot2.__peakHist <- function
        ### Internal ancilliary function (see `enve.RecPlot2.Peak`).
        (x, mids, counts=TRUE){
          d.o <- x$param.hat
          if(length(x$log)==0) x$log <- FALSE
          if(x$log){
            d.o$x <- log(mids)
          }else{
            d.o$x <- mids
          }
          prob  <- do.call(paste('d', x$dist, sep=''), d.o)
          if(!counts) return(prob)
          if(length(x$values)>0) return(prob*length(x$values)/sum(prob))
          return(prob*x$n.hat/sum(prob))
        }
        
        h.breaks <- seq(log10(seqdepth.lim[1] * 2), log10(seqdepth.lim[2]/2), 
                        length.out = 200)
        h.mids <- (10^h.breaks[-1] + 10^h.breaks[-length(h.breaks)])/2
        
        min_info <- list()
        
        if(nrow(ends) > 1){
          ends[, adjust := c(-1, V1[1:nrow(ends)-1])+1]
        }else{
          ends[, adjust := 0]
        }
        
        base[, contiguous_end := End + ends$adjust[match(contig, ends$contig)]]
        
        min_info$pos.breaks = c(0, base$contiguous_end[match(ddSave$seq_pos[ddSave$group_label == "depth.in"], base$seq_pos)])
        
        min_info$pos.counts.in = ddSave$V1[ddSave$group_label == "depth.in"]
        
        #This is finicky
        
        try({
          
          peaks <- enve.recplot2.findPeaks(min_info)
          
          dpt <- signif(as.numeric(lapply(peaks, function(x) x$seq.depth)), 2)
          frx <- signif(100 * as.numeric(lapply(peaks,function(x) ifelse(length(x$values) == 0, x$n.hat, length(x$values))/x$n.total)), 2)
          
          if (peaks[[1]]$err.res < 0) {
            err <- paste(", LL:", signif(peaks[[1]]$err.res,3))
          }  else {
            err <- paste(", err:", signif(as.numeric(lapply(peaks, function(x) x$err.res)), 2))
          }
          labels <- paste(letters[1:length(peaks)], ". ", dpt, "X (", frx, "%", err, ")", sep = "")
          
          peak_counts <- lapply(peaks, enve.recplot2.__peakHist, h.mids)
          
          plot_breaks = h.breaks[-length(h.breaks)]
          
          gg_peak_info <- data.table(plot_breaks = rep(plot_breaks, length(peak_counts)), count = unlist(peak_counts), grp = rep(labels, each = length(plot_breaks)))
          
          p4 <- p4 + geom_line(data = gg_peak_info, aes(x = plot_breaks, y = count, color = grp, group = grp), inherit.aes = F, color = "red", lwd = 1.13)
          
        })
        
      }
      
      p4 <- p4 + coord_flip()
      
      if(showpeaks){
        
        try({
          o_max <- max(table(findInterval(depth_data$count[depth_data$group_label == "depth.in"], h.breaks)))*.75
          x_start = 0
          
          if(length(labels) > 0){
            for(i in labels){
              p4 <- p4 + annotate("text", label = i, y = o_max, x = x_start, size = 3)
              x_start = x_start - .185
            }
          }
        })
        
      }
      
      seq_depth_hist <- p4
      
      rm(p4)
      
    }
    
    #bp counts histogram (bottom right panel)
    {
      
      bp_data <- base[,sum(bp_count, na.rm = T), by = Pct_ID_bin]
      
      if(linear == 1){
        
        p4 <- ggplot(data = bp_data, aes(y = V1, x = Pct_ID_bin-(id_break))) +
          geom_step() +
          scale_y_continuous(expand = c(0,0), breaks = scales::pretty_breaks(n = 3)) + 
          scale_x_continuous(expand = c(0,0)) +
          theme(legend.position = "none", 
                panel.border = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(), 
                axis.line = element_line(colour = "black"), 
                panel.background = element_rect(fill = "#EEF7FA"), 
                axis.text.y = element_blank(), 
                axis.title.y = element_blank(), 
                axis.ticks.y = element_blank(),
                axis.title = element_text(size = 14),
                axis.text = element_text(size = 14))+
          ylab("Base Pair Count by % ID")
        
      } else {
        
        p4 <- ggplot(data = bp_data, aes(y = V1, x = Pct_ID_bin-(id_break))) +
          geom_step() +
          scale_y_continuous(expand = c(0,0), trans = "log10", breaks = scales::log_breaks(n = 4)) + 
          scale_x_continuous(expand = c(0,0)) +
          theme(legend.position = "none", 
                panel.border = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(), 
                axis.line = element_line(colour = "black"), 
                panel.background = element_rect(fill = "#EEF7FA"), 
                axis.text.y = element_blank(), 
                axis.title.y = element_blank(), 
                axis.ticks.y = element_blank(),
                axis.title = element_text(size = 14),
                axis.text = element_text(size = 14))+
          ylab("Base Pair Count by % ID")
        
      }
      
      bp_count_hist <- p4 + annotate("rect", xmin = in_grp_min, xmax = 100, ymin = 0, ymax = Inf, fill = "darkblue", alpha = .15) + coord_flip()
      
      rm(p4)
      
    }
    
    
    
    #I worry that the modify in place permanently whoopsies things, so I change it back just in case
    base[End == contig_len-trunc_degree, End := End + trunc_degree, ]
    base[, contig_len := NULL, ]
    
    overall_plot <- plot_grid(seq_depth_chart, seq_depth_hist, read_rec_plot, bp_count_hist, align = "hv", ncol = 2, rel_widths = c(2.7, 1), rel_heights = c(1, 2.3))
    
    return(overall_plot)
    
  }
  
  create_static_data <- function(base, bp_unit, bp_div, pos_max, in_grp_min, id_break, width, linear, showpeaks, ends, trunc_behavior = "ends", trunc_degree = as.integer(75), ...){

    setkeyv(base, c("contig", "Start", "Pct_ID_bin"))
    
    base[, contig_len := max(End), by = contig]
    base[End == contig_len, End := End - trunc_degree, ]
    
    returnable_base <- base
    
    #fwrite(base, "base_print_data.txt", sep = "\t")
    
    if("gene_name" %in% colnames(returnable_base)){
      
      #Allows for count normalization by bin width across all bins
      norm_factor <- min(returnable_base$End-returnable_base$Start) + 1
      
      returnable_base[, normalized_count := bp_count * (norm_factor/(End-Start+1)),]
      
      returnable_base[, seq_pos := NULL,]
      
      returnable_base[, id_lower := Pct_ID_bin - id_break, ]
      
      returnable_base[, group_label := ifelse(returnable_base$Pct_ID_bin-id_break >= in_grp_min, "in_group", "out_group"), ]
      
      returnable_base <- returnable_base[, list(contig, Start, End, id_lower, Pct_ID_bin, bp_count, normalized_count, group_label, gene_name, gene_start, gene_end, gene_strand, gene_annotation),]
      
      colnames(returnable_base)[1:8] = c("contig_name", "start_pos_in_contig", "end_pos_in_contig", "lower_pct_id", "upper_pct_id", "raw_count_of_bp_in_bin", "normalized_count_of_bp_in_bin", "pct_id_group")
      
      setkeyv(returnable_base, c("contig_name", "start_pos_in_contig", "end_pos_in_contig", "lower_pct_id", "upper_pct_id", "pct_id_group"))
      
      setkeyv(returnable_base, c("contig_name", "start_pos_in_contig", "pct_id_group"))
      
      #upper left panel
      
      depth_data <- returnable_base[, list(sum(raw_count_of_bp_in_bin/(end_pos_in_contig-start_pos_in_contig + 1), na.rm = T), unique(gene_name), unique(gene_start), unique(gene_end), unique(gene_strand), unique(gene_annotation)), by = key(returnable_base)]
      colnames(depth_data)[4:9] = c("average_sequencing_depth", "gene_name", "gene_start", "gene_end", "gene_strand", "gene_annotation")
      
      setkeyv(depth_data, c("contig_name", "pct_id_group", "start_pos_in_contig"))
      
    }else{
      #Allows for count normalization by bin width across all bins
      norm_factor <- min(returnable_base$End-returnable_base$Start) + 1
      
      returnable_base[, normalized_count := bp_count * (norm_factor/(End-Start+1)),]
      
      returnable_base[, seq_pos := NULL,]
      
      returnable_base[, id_lower := Pct_ID_bin - id_break, ]
      
      returnable_base[, group_label := ifelse(returnable_base$Pct_ID_bin-id_break >= in_grp_min, "in_group", "out_group"), ]
      
      returnable_base <- returnable_base[, list(contig, Start, End, id_lower, Pct_ID_bin, bp_count, normalized_count, group_label),]
      
      #todo
      #fwrite(returnable_base, "returnable_base_reorg.txt", sep = "\t")
      
      colnames(returnable_base) = c("contig_name", "start_pos_in_contig", "end_pos_in_contig", "lower_pct_id", "upper_pct_id", "raw_count_of_bp_in_bin", "normalized_count_of_bp_in_bin", "pct_id_group")
      
      setkeyv(returnable_base, c("contig_name", "start_pos_in_contig", "end_pos_in_contig", "lower_pct_id", "upper_pct_id", "pct_id_group"))
      
      setkeyv(returnable_base, c("contig_name", "start_pos_in_contig", "pct_id_group"))
      
      #upper left panel
      
      depth_data <- returnable_base[, sum(raw_count_of_bp_in_bin/(end_pos_in_contig-start_pos_in_contig + 1), na.rm = T), by = key(returnable_base)]
      colnames(depth_data)[ncol(depth_data)] = "average_sequencing_depth"
      
      setkeyv(depth_data, c("contig_name", "pct_id_group", "start_pos_in_contig"))
      
    }
    
    #I worry that the modify in place permanently whoopsies things, so I change it back just in case
    base[End == contig_len-trunc_degree, End := End + trunc_degree, ]
    base[, contig_len := NULL, ]
    
    return(list(returnable_base, depth_data))
    
  }
  
  gene_pydat_to_recplot_dat_prodigal <- function(prodigal_gene_mess){
    
    contigs <- names(prodigal_gene_mess)
    
    lengths <- unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(length(x[[1]]))
      
    })))
    
    pretty_data <- data.table(contig = rep(contigs, times = lengths))
    
    
    
    pretty_data[, gene_name := unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(x[[1]])
      
    }))) ]
    
    pretty_data[, gene_start := unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(x[[2]])
      
    }))) ]
    
    pretty_data[, gene_end := unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(x[[3]])
      
    }))) ]
    
    pretty_data[, strand  := unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(x[[4]])
      
    }))) ]
    pretty_data[, annotation  := unname(unlist(lapply(prodigal_gene_mess, function(x){
      
      return(x[[5]])
      
    }))) ]
    
    
    return(pretty_data)
    
    
  }
  
  #Wish I could add captions to the unix/osx versions
  choose_directory = function(caption = 'Select data directory') {
    if (exists('choose.dir')) {
      choose.dir(caption = caption) 
    } else {
      easycsv::choose_dir()
    }
  }
  
  #Wish I could add captions to the unix/osx versions
  choose_file = function(caption) {
    if (exists('choose.files')) {
      choose.files(caption = caption, multi = F)
    } else {
      file.choose(new = F)
    }
  }
  
  path_simplifier <- function(working_directory, file_path){
    
    file_path_t <- gsub("\\\\", "/", file_path)
    working_directory <- gsub("\\\\", "/", working_directory)
    working_directory <- paste0(working_directory, "/")
    
    if(substr(working_directory, 1, 12) == "Working in: "){
      working_directory <- substr(working_directory, 13, nchar(working_directory))
    }
    
    if(grepl(working_directory, file_path_t)){
      return(gsub(working_directory, "", file_path_t))
    }else{
      return(file_path)
    }
    
    
    
  }
  
}

#This is the GUI function
recplot_UI <- function(){
  #System/choices issue
  {
    system <- get_sys()
    
    format_choices <- c("Tabular BLAST" = "blast", "SAM" = "sam")
    
    if(system == "Windows"){
      format_choices <- c("Tabular BLAST" = "blast", "SAM" = "sam", "BAM" = "bam")
    }else{
      if(py_module_available("pysam") == T){
        format_choices <- c("Tabular BLAST" = "blast", "SAM" = "sam", "BAM" = "bam")
      }else{
        cat("Your OS supports pysam, but you do not have it installed. Recruitment Plots tries to install this, but it seems to have failed. Try 'pip install pysam' on the command line.\n")
      }
    }


    
    gene_choices <- c("Prodigal GFF" = "prodigal")
  }
  
  #UI
  {  
    ui <- fluidPage(
      
      tags$head(
        tags$style(
          HTML(".shiny-notification {
              height: 100px;
              width: 800px;
              position:fixed;
              top: calc(50% - 50px);;
              left: calc(50% - 400px);;
            }
           "
          )
        )
      ),
      
      useShinyalert(),
      
      tabsetPanel(id = "tabs",
                  tabPanel("Database Creation",
                           fluidRow(
                             #todo       
                             
                             column(1),
                             column(4, 
                                    h2("Create a new Database"),
                                    actionButton('dir', '(1) Choose Directory', icon = icon("folder-open")),
                                    
                                    textInput("cur_dir",label = NULL, value = paste("Working in:", getwd()), width = '100%'),
                                    
                                    br(),
                                    actionButton('contigs', '(2) Select Reference Genomes', icon = icon("file-upload")),
                                    checkboxInput("one_mag", label = "This is a single binned genome.", value = F),
                                    textInput("contig_file",label = NULL, value = "No genomes selected.", width = '100%'),
                                    br(),
                                    
                                    actionButton('reads', '(3) Select Mapped Reads', icon = icon("file-upload")),
                                    textInput("read_file",label = NULL, value = "No mapped read file selected.", width = '100%'),
                                    selectInput('fmt', 'Mapped Read Format', selected = "Tabular BLAST", choices = format_choices, width = '100%'),
                                    br(),
                                    
                                    actionButton('mags', '(Optional) Association File', icon = icon("file-upload")),
                                    actionButton("what_are_mags", "Info", icon = icon("question-circle")),
                                    textInput("mag_file",label = NULL, value = "No association file selected.", width = '100%'),
                                    
                                    br(),
                                    
                                    
                                    textInput("dbname",label = "(4) Name the database", value = "Enter name here.", width = '100%'),
                                    br(),
                                    actionButton('db' , "(5) Create database", icon("coins")),
                                    
                                    
                                    #Tooltips
                                    
                                    bsTooltip("dir", "(Optional) Select a working directory. The database and any saved plots will be placed here.", placement = "right"),
                                    bsTooltip("contigs", "Select a FASTA format file containing genome sequences or contigs.", placement = "right"),
                                    bsTooltip("one_mag", "If you binned an assembly and have a single multi-FASTA containing the sequences for this MAG as your reference genomes, check this box, leave the association file blank, and they will all appear on a single plot.", placement = "right"),
                                    bsTooltip("mags", "Select an association file. This is a file designed to support the visualization of genomes divided into multiple contigs. Click the info button to learn more.", placement = "right"),
                                    bsTooltip("reads", "Select a mapped read file. These reads should be mapped to the genomes in the reference genomes file selected above.", placement = "right"),
                                    bsTooltip("fmt", "Select the format of the mapped reads to be added to the new database.", placement = "right"),
                                    bsTooltip("dbname", "Name your database. A .db extension will be added to the end of the name you give it.", placement = "right"),
                                    bsTooltip("db", "Click me after selecting all input files to create your database.", placement = "right")
                             ),
                             column(1),
                             
                             column(5,
                                    h2("Build Report"),
                                    verbatimTextOutput("message")
                             ),
                             column(1)
                             
                           )
                  ),
                  
                  tabPanel("Database Management",
                           fluidRow(
                             column(1),
                             column(4, 
                                    h2("Work with an existing database"),
                                    actionButton('exist_db', 'Select an existing DB', icon = icon("coins")),
                                    textInput("exist_dbname",label = NULL, value = "No DB currently selected", width = '100%'),
                                    br(),
                                    h4("Add More Reads"),
                                    actionButton('add_sample', 'Select another sample to add?', icon = icon("file-upload")),
                                    actionButton("show_samps", "Show Samples", icon = icon("question-circle")),
                                    textInput("add_samp",label = NULL, value = "No new sample to add.", width = '100%'),
                                    selectInput('fmt_add', 'Mapped Read Format', selected = "Tabular BLAST", choices = format_choices, width = '100%'),
                                    actionButton('new_samp_commit', "Add this sample to the DB", icon = icon("coins")),
                                    br(),
                                    br(),
                                    h4("Add Genes"),
                                    actionButton('add_genes', 'Add genes to database?', icon = icon("file-upload")),
                                    textInput("add_gen", label = NULL, value = "No genes to add.", width = '100%'),
                                    selectInput('fmt_gen', 'Gene format', selected = "Prodigal GFF", choices = gene_choices, width = '100%'),
                                    actionButton('genes_commit', "Add these genes to the DB", icon = icon("coins")),
                                    actionButton('check_for_genes', "Check if genes have been added.", icon = icon("question-circle")),
                                    br(),
                                    br(),
                                    selectInput('task', 'Plot contigs or plot genes?', selected = "Contigs", choices = c("Contigs" = "contigs", "Genes" = "genes"), width = '100%'),
                                    
                                    bsTooltip("exist_db", "Select a database previously created with Recruitment Plot.", placement = "right"),
                                    bsTooltip("add_sample", "(Optional) Select another set of reads mapped to the same genomes to be added to the database.", placement = "right"),
                                    bsTooltip("fmt_add", "Select the format of the mapped reads to be added to the existing database.", placement = "right"),
                                    bsTooltip("add_genes", "(Optional) Add genes for existing genomes.", placement = "right"),
                                    bsTooltip("fmt_gen", "Select the format of the genes to be added to the existing database. Currently only Prodigal GFF format is supported.", placement = "right"),
                                    bsTooltip("new_samp_commit", "Once you have selected another set of mapped reads to add and chosen the format, click this to add the sample. The sample will not be added until you do.", placement = "right"),
                                    bsTooltip("genes_commit", "Once you have selected a set of genes to add and chosen the format (currently only Prodigal GFF), click this to add the genes. The genes will not be added until you do.", placement = "right"),
                                    bsTooltip("show_samps", "Display all samples currently in the selected database. Requires a database to be selected first.", placement = "right"),
                                    bsTooltip("check_for_genes", "Query the database for the presence of genes on the genomes.", placement = "right")
                                    
                                    
                             ),
                             column(1),
                             column(5,
                                    h2("Database Status"),
                                    verbatimTextOutput("message2")
                             ),
                             column(1)
                             
                           )
                  ),
                  
                  tabPanel("Recruitment Plot", 
                           sidebarPanel(
                             width = 3,
                             
                             h4("Choose a Genome"),
                             
                             selectInput("samples", "(1) Select a sample in the database", selected = NULL, choices = NULL),
                             
                             selectInput("mags_in_db", "(2) Select a genome in the sample", selected = NULL, choices = NULL),
                             
                             h4("Select Bin Resolution"),
                             
                             numericInput("height", "(3) Pct. ID Resolution", min = 0.05, max = 3, value = 0.5),
                             
                             #numericInput("low_bound", "(4) Minimum Pct. ID", min = 50, max = 95, value = 70),
                             
                             conditionalPanel(condition = "input.task == 'contigs'",
                                              
                                              numericInput("width", "(4) Genome Resolution", min = 75, max = 5000, value = 1000)
                                              
                             ),
                             conditionalPanel(condition = "input.task == 'genes'",
                                              
                                              selectInput("regions_stat", "(4) Display Control", choices = c("Genes Only" = 1, "Intergenic Regions Only" = 2, "Genes and long IGR" = 3, "All Regions" = 4), selected = 1)
                                              
                             ),
                             
                             h4("Load Selected Genome"),
                             
                             actionButton('get_a_mag', '(5) View Selected Genome', icon = icon("jedi-order")),
                             
                             h4("Fine Tuning (Interactive)"),
                             
                             numericInput("in_group_min_stat", "(6) In-Group Pct. ID", min = 50, max = 99.5, value = 95),
                             selectInput("linear_stat", "(7) BP Histogram Scale", choices = c("Linear" = 1, "Logarithmic" = 2), selected = 1),
                             
                             checkboxInput("show_peaks", "(8) Display Depth Peaks?"),
                             
                             textInput("pdf_name", "Name and save current plot."),
                             actionButton("print_stat", "Save to PDF", icon = icon("save")),
                             
                             #todo
                             actionButton("output_data_stat", "Save raw data", icon = icon("file")),
                             
                             bsTooltip("samples", "This menu contains a list of samples within the database. Select one, and the genome field will be populated with the genomes found in that sample.", placement = "right"),
                             bsTooltip("mags_in_db", "This menu contains the set of genomes in currently selected sample. Select one, then select resolution parameters.", placement = "right"),
                             bsTooltip("width", "Approximate number of base pairs in each genome window. The Recruitment Plot attempts to normalize bin width for each contig to this size. Lower values = higher resolution, but is slower. Higher values = lower resolution, but is faster.", placement = "right"),
                             bsTooltip("height", "Controls the resolution of percent identity to the reference. Lower values here will result in finer resolution, but will be slower. Hint: The default 0.5% window means a resolution of 1 base pair mismatch per 200 bases; finer resolution is probably uneccessary.", placement = "right"),
                             #bsTooltip("low_bound", "Reads mapping below this percent identity will not be included in the current recruitment plot.", placement = "right"),
                             bsTooltip("get_a_mag", "Click this to load the current Genome into the viewer and plot it. Please wait for the plot to appear after clicking this. This loads the data for all tabs.", placement = "right"),
                             bsTooltip("in_group_min_stat", "Controls the lower edge of the shaded region in the recruitment plot's lower panels. Reads mapping at or above this percent identity are regarded as the \"in-group\" for the Recruitment Plot, and are represented by the dark blue lines in the upper panels.", placement = "right"),
                             bsTooltip("linear_stat", "Causes the lower right panel to display base pair counts per percent identity bin in linear scale or log scale.", placement = "right"),
                             bsTooltip("print_stat", "After loading a plot (meaning you should be able to see it), add a name in the associated text box and then click this to print a PDF of the current plot", placement = "right"),
                             
                             #todo
                             bsTooltip("output_data_stat", "Outputs the data for the current plots to 2 tab-separated files corresponding to the bottom left and top left panels (recruitment and sequencing depth information).", placement = "right"),
                             
                             bsTooltip("show_peaks", "Calculate and overlay peaks for the depth of coverage histogram (top right panel)", placement = "right"),
                             
                             bsTooltip("regions_stat", "Controls the display of intergenic regions. Default shows only genes, IGR = InterGenic Region. 'Long IGRs' are intergenic regions > 6 bp in length.", placement = "right"),
                             
                             bsTooltip("recplot_main", "Bottom left panel: a 2-D histogram of the counts of base pairs falling into a bin defined by position in the genome (x-axis) and percent identity (y-axis) Bins are as wide as the genome resolution parameter                             if viewing contigs, and cover genes & intergenic regions in contiguous chunks if viewing genes. The shaded section is the current in-group, which is the dark blue line on the top two plots Top left panel: Average sequencing depth for each x-axis bin on in the bottom left panel. Dark blue corresponds to depth of coverage for bins in the in-group, and light blue to the out-group. Segments at the bottom of the plot have zero coverage. Top right panel: a histogram of the depths of coverage observed in the corresponding in/out group in the sequencing depths chart (top left). If peaks are selected, they correspond to the estimates of the genome's average sequencing depth. Bottom right panel: a histogram of the number of bases falling into each percent identity bin across the entire genome, displayed in linear or log scale depending on your selection.", trigger = "click", placement = "left")
                           ),
                           mainPanel(id = "recplot_main",
                                     #Spacing
                                     fluidRow(
                                       column(12,
                                              div(style = "height:60px;background-color: white;", "")
                                       )
                                     ),
                                     fluidRow(
                                       
                                       plotOutput("read_recruitment_plot", height = "850px")
                                       
                                     )
                                     
                                     
                           )
                  ),
                  tabPanel("Interactive Plot", 
                           sidebarPanel(
                             width = 3,
                             
                             h3("Choose a Genome"),
                             
                             selectInput("samples_interact", "(1) Select a sample in the database", selected = NULL, choices = NULL),
                             
                             selectInput("mags_in_db_interact", "(2) Select a Genome in the sample", selected = NULL, choices = NULL),
                             
                             h3("Select Bin Resolution"),
                             
                             
                             
                             numericInput("height_interact", "(3) Pct. ID Resolution", min = 0.05, max = 3, value = 0.5),
                             
                             #numericInput("low_bound_interact", "(4) Minimum Pct. ID", min = 50, max = 95, value = 70),
                             
                             conditionalPanel(condition = "input.task == 'contigs'",
                                              
                                              numericInput("width_interact", "(4) Genome Resolution", min = 75, max = 5000, value = 1000)
                                              
                             ),
                             
                             conditionalPanel(condition = "input.task == 'genes'",
                                              
                                              selectInput("regions_interact", "(4) Display Control", choices = c("Genes Only" = 1, "IGR Only" = 2, "Genes and long IGR" = 3, "All Regions" = 4), selected = 1)
                                              
                             ),
                             
                             h3("Load Selected Genome"),
                             
                             actionButton('get_a_mag_interact', '(5) View selected Genome', icon = icon("jedi-order")),
                             
                             h3("Fine Tuning (Interactive)"),
                             
                             numericInput("in_group_min_interact", "(6) In-Group Pct. ID", min = 50, max = 99.5, value = 95),
                             
                             textInput("pdf_name_interact", "Name and save current plot."),
                             actionButton("print_interact", "Save interactive HTML", icon = icon("save")),
                             
                             bsTooltip("pdf_name_interact", "Name the current interactive plot as an HTML page before saving it.", placement = "right"),
                             bsTooltip("print_interact", "After loading a plot (meaning you should be able to see it), add a name in the associated text box and then click this to save an HTML of the current", placement = "right"),
                             
                             bsTooltip("samples_interact", "This menu contains a list of samples within the database. Select one, and the genome field will be populated with the genomes found in that sample.", placement = "right"),
                             bsTooltip("mags_in_db_interact", "This menu contains the set of genomes in currently selected sample. Select one, then select resolution parameters.", placement = "right"),
                             bsTooltip("width_interact", "Approximate number of base pairs in each genome window. The Recruitment Plot attempts to normalize bin width for each contig to this size. Lower values = higher resolution, but is slower. Higher values = lower resolution, but is faster.", placement = "right"),
                             bsTooltip("height_interact", "Controls the resolution of percent identity to the reference. Lower values here will result in finer resolution, but will be slower. Hint: The default 0.5% window means a resolution of 1 base pair mismatch per 200 bases; finer resolution is probably uneccessary.", placement = "right"),
                             bsTooltip("low_bound_interact", "Reads mapping below this percent identity will not be included in the current recruitment plot.", placement = "right"),
                             bsTooltip("regions_interact", "Controls the display of intergenic regions. Default shows only genes, IGR = InterGenic Region. 'Long IGRs' are intergenic regions > 6 bp in length.", placement = "right"),
                             
                             bsTooltip("get_a_mag_interact", "Click this to load the current genome into the viewer and plot it. Please wait for the plot to appear after clicking this.", placement = "right"),
                             bsTooltip("in_group_min_interact", "Controls the lower edge of the shaded region in the recruitment plot's lower panels. Reads mapping at or above this percent identity are regarded as the \"in-group\" for the Recruitment Plot, and are represented by the dark blue lines in the upper panel.", placement = "right")
                           ),
                           mainPanel(
                             column(12, 
                                    plotlyOutput("Plotly_interactive", height = "850px")
                             )
                           )
                  )
      )
      #End fluid page
    )
    
  }
  
  return(ui)
}

recplot_server <- function(input, output, session) {
  
  initial_message <- "Welcome to Recruitment Plot!\nThis page allows you to take contigs and reads and create a database.\nPlease select the appropriate files using the options on the left.\nFile selection windows may pop up in the background, so please check!\n\nWhen you create your database, the Build Report will grey out for a short time - this is not an error.\nYour database is being built and the report will notify you when it has completed.\nIf the whole screen greys out, an error has occurred."
  initial_message2 <- "This page is for selecting an existing database to plot, or modifying an existing one.\nIf you just created a database, it should be loaded here.\nYou can add more mapped reads or genes to the database here."
  
  output$message <- renderText(initial_message)
  output$message2 <- renderText(initial_message2)
  
  directory <- "No directory selected. Try again?"
  reads <- "No reads selected. Try again?"
  contigs <- "No genomes selected. Try again?"
  mags <- "No association file selected. Try again?"
  new_samp <- "No new sample. Try again?"
  db <- "No existing database selected. Try again?"
  new_genes <- "No genes selected. Try again?"
  
  plotting_materials <- NA
  
  gene_data <- NA
  
  exist_db <- "No existing database selected. Try again?"
  
  samples_in_db <- "No database selected or built yet."
  
  #TODO - this is the contig end cutoff that removes the first & last 75 bp from consideration in avg. depth.
  #These will need to be later incorporated into the interactive inputs
  trunc_degree <- as.integer(75)
  
  #Database building
  observeEvent(input$what_are_mags, {
    
    initial_message <<- paste0(initial_message, "\n\nRecruitment plots were originally designed with metagenomes in mind.\nIn the case that a genome of interest is divided into several discrete genome\nsegments, the segments must be associated with the genome they all belong to.\nThe association file tells the recruitment plot that it should place these segments on the same plot.\nCheck the documentation for help with creating an association file for your contigs.\nIf you don't supply an association file, then a placeholder will be generated from your contigs\nand the recruitment plot will still function. Note: you still need to select contigs first.\n")
    
    output$message <- renderText(initial_message)
    
    
  })
  
  observeEvent(input$show_samps, {
    
    if(input$exist_dbname == "No existing database selected. Try again?" | input$exist_dbname == "No DB currently selected" | input$exist_dbname == "" ){
      
      initial_message2 <<- paste0(initial_message2, "\nYou need to select a database before I can show you the samples inside it.")
      
      output$message2 <- renderText(initial_message2)
      
    }else{
      
      samps <- unlist(assess_samples(input$exist_dbname))
      
      samps <- paste0(samps, collapse = "\n")
      
      initial_message2 <<- paste0(initial_message2, "\n\nSamples currently in the database:\n", samps)
      
      output$message2 <- renderText(initial_message2)
    }
    
    
  })
  
  observeEvent(input$check_for_genes, {
    
    if(input$exist_dbname == "No existing database selected. Try again?" | input$exist_dbname == "No DB currently selected" | input$exist_dbname == "" ){
      
      initial_message2 <<- paste0(initial_message2, "\nYou need to select a database before I can check for genes inside it.")
      
      output$message2 <- renderText(initial_message2)
      
    }else{
      
      if(!check_presence_of_genes(input$exist_dbname)){
        initial_message2 <<- paste0(initial_message2, "\nGenes not detected. You have to add genes to the database\nbefore plotting gene regions and annotations.")
        
        output$message2 <- renderText(initial_message2)
      }else{
        initial_message2 <<- paste0(initial_message2, "\nGenes detected! You can plot the genes in this data.")
        
        output$message2 <- renderText(initial_message2)
      }
      
    }
    
  })
  
  observeEvent(input$task, {
    
    #Add a don't-do-this if there's not a selected DB
    if(input$task == "genes"){
      
      if(input$exist_dbname == "" | input$exist_dbname == "No existing database selected. Try again?" | input$exist_dbname == "No DB currently selected"){
        
        initial_message2 <<- paste0(initial_message2, "\nYou have to select an existing database before selecting a plotting task.")
        
        output$message2 <- renderText(initial_message2)
        
        return(NA)
      }
      
      if(!check_presence_of_genes(input$exist_dbname)){
        
        shinyalert("No genes found", "You have to add genes to your database on this tab, first.", type = "error",
                   callbackR = function() { 
                     updateSelectInput(session, "task", label = 'Plot contigs or plot genes?', selected = "contigs", choices = c("Contigs" = "contigs", "Genes" = "genes"))
                   })
        
        initial_message2 <<- paste0(initial_message2, "\nGenes NOT found in database. Have you added them?")
        output$message2 <- renderText(initial_message2)
        
      }
    }else{
      return(NA)
    }
    
  })
  
  observeEvent(input$dir, {
    
    tryCatch({
      directory <<- choose_directory()
    },
    error = function(cond){
      directory <<- "No directory selected. Try again?"
      return(directory)
    })
    
    tryCatch({
      setwd(directory)
    },
    error = function(cond){
      directory <<- "No directory selected. Try again?"
      return(directory)
    })
    
    if(length(directory) == 0){
      directory <<- "No directory selected. Try again?"
    }
    
    
    if(directory == "No directory selected. Try again?"){
      updateTextInput(session, "cur_dir", value = paste(directory))
    }else{
      updateTextInput(session, "cur_dir", value = paste("Working in:", directory))
    }
    
  })
  
  observeEvent(input$reads, {
    
    detected_fmt = "none"
    
    tryCatch({
      reads <- choose_file(caption = "Select Mapped Reads")
    },
    error = function(cond){
      reads <- "No reads selected. Try again?"
      return(reads)
    })
    
    if(length(reads) == 0){
      reads <- "No reads selected. Try again?"
    }else{
      
      if(file.exists(reads)){
        detected_fmt = detect_file_format(reads)
        if(detected_fmt == "fasta"){
          shinyalert("This looks like a FASTA file!", "Are you sure these are mapped reads and NOT raw reads or genomes?", type = "error")
        }
        if(detected_fmt == "database"){
          shinyalert("This looks like a SQL Database!", "If this is an old Recruitment Plot database, go the the database management tab and select it there!", type = "error")
        }
        if(detected_fmt == "genes"){
          shinyalert("This looks like a GFF!", "Did you want to add predicted genes to your genomes? The file selection for that is on the Database Management tab!", type = "error")
        }
        if(detected_fmt == "assoc"){
          shinyalert("This looks like an association file!", "This type of file is meant to associate separate contigs with a parent binned genome. Are you sure these are mapped reads?", type = "error")
        }
        if(detected_fmt == "none"){
          shinyalert("I don't recognize this file!", "This doesn't look like the right kind of file! Are you sure this is a mapped read file?", type = "error")
        }
      }
    }
    
    updateSelectInput(session, "fmt", selected = "blast")
    updateTextInput(session, "read_file", value = reads)
    
    if(detected_fmt == "sam"){
      updateSelectInput(session, "fmt", selected = "sam")
    }
    
    #checks for pysam in case
    if(detected_fmt == "bam"){
      updateSelectInput(session, "fmt", selected = "bam")
    }
    
    
  })
  
  observeEvent(input$contigs, {
    detected_fmt = "none"
    
    tryCatch({
      contigs <- choose_file(caption = "Select Reference Genomes")
    },
    error = function(cond){
      contigs <- "No genomes selected. Try again?"
      return(contigs)
    })
    
    if(length(contigs) == 0){
      contigs <- "No genomes selected. Try again?"
    }else{
      if(file.exists(contigs)){
        detected_fmt = detect_file_format(contigs)
        if(detected_fmt == "sam" | detected_fmt == "bam" | detected_fmt == "blast"){
          shinyalert("This looks like mapped reads!", "Are you sure these are genomes?", type = "error")
        }
        if(detected_fmt == "database"){
          shinyalert("This looks like a SQL Database!", "If this is an old Recruitment Plot database, go the the database management tab and select it there!", type = "error")
        }
        if(detected_fmt == "none"){
          shinyalert("I don't recognize this file!", "This doesn't look like the right kind of file! Are you sure this is a mapped read file?", type = "error")
        }
        if(detected_fmt == "genes"){
          shinyalert("This looks like a a GFF!", "Did you want to add predicted genes to your genomes? The file selection for that is on the Database Management tab!", type = "error")
        }
        if(detected_fmt == "assoc"){
          shinyalert("This looks like an association file!", "This type of file is meant to associate separate contigs with a parent binned genome. Are you sure these are genomes?", type = "error")
        }
      }
    }
    
    updateTextInput(session, "contig_file", value = contigs)
  })
  
  observeEvent(input$mags, {
    detected_fmt = "none"
    
    tryCatch({
      mags <- choose_file(caption = "Select Contig-Genome Association File")
    },
    error = function(cond){
      mags <- "No association file selected. Try again?"
      return(mags)
    })
    if(length(mags) == 0){
      mags <- "No association file selected. Try again?"
    }else{
      if(file.exists(mags)){
        detected_fmt = detect_file_format(mags)
        if(detected_fmt == "sam" | detected_fmt == "bam" | detected_fmt == "blast"){
          shinyalert("This looks like mapped reads!", "Are you sure this is an association file?", type = "error")
        }
        if(detected_fmt == "database"){
          shinyalert("This looks like a SQL Database!", "If this is an old Recruitment Plot database, go the the database management tab and select it there!", type = "error")
        }
        if(detected_fmt == "none"){
          shinyalert("I don't recognize this file!", "You may have selected the wrong kind of file, or selected an association file with incorrect format.", type = "error")
        }
        if(detected_fmt == "genes"){
          shinyalert("This looks like a a GFF!", "Did you want to add predicted genes to your genomes? The file selection for that is on the Database Management tab!", type = "error")
        }
        if(detected_fmt == "fasta"){
          shinyalert("This looks like a FASTA file!", "Are you sure this is an association file and not genomes?", type = "error")
        }
      }
    }
    
    updateTextInput(session, "mag_file", value = mags)
  })
  
  observeEvent(input$db, {
    
    ready_to_make = TRUE
    needs_MAGs = F
    has_contigs = F
    
    #check to make sure that inputs are set and exist:
    if(input$read_file == "No reads selected." | input$read_file == "No reads selected. Try again?"){
      ready_to_make <- F
    }
    if(input$contig_file == "No genomes selected." | input$contig_file == "No genomes selected. Try again?"){
      ready_to_make <- F
    }
    if(input$mag_file == "No association file selected." | input$mag_file == "No association file selected. Try again?"){
      needs_MAGs <- T
    }
    
    
    if(!file.exists(input$read_file)){
      ready_to_make <- F
    }
    
    if(!file.exists(input$contig_file)){
      ready_to_make <- F
    }else{
      has_contigs <- T
    }
    
    if(needs_MAGs & has_contigs){
      #Update input waits to update the input until the end of the function
      #The name of the MAGs file has to be fed manually here.
      
      
      if(!ready_to_make){
        initial_message <<- paste0(initial_message, "\nNot all files have been selected or the files cannot be found. Please select a set of reads and contigs")
        
        output$message <- renderText(initial_message)
        return(NA)
      }
      
      
      if(input$dbname == "Enter name here."){
        initial_message <<- paste0(initial_message, "\nThe database needs a name. Please give it one!")
        
        output$message <- renderText(initial_message)
        ready_to_make <- F
      }
      
      if(!ready_to_make){
        #initial_message <<- paste0(initial_message)
        
        output$message <- renderText(initial_message)
      }else{
        #if no MAGs file supplied, do this
        
        #Create 1 to 1 contig to contig mags file.
        parse_to_mags_identical(input$contig_file, paste("automatically_generated_association_file.txt"))
        updateTextInput(session, "mag_file", value = "automatically_generated_association_file.txt")
        
        
        if(input$one_mag){
          temp <- fread("automatically_generated_association_file.txt", sep = "\t", header = F)
          temp[, V2 := path_simplifier(input$cur_dir, input$contig_file)]
          fwrite(temp, "automatically_generated_association_file.txt", sep = "\t", col.names = F)
          
          initial_message <<- paste0(initial_message, "\nAll sequences in your genomes file are being treated as part of one genome.")
          output$message <- renderText(initial_message)
        }
        
        
        initial_message <<- paste0(initial_message, "\n\nAssociation file generated automatically!")
        output$message <- renderText(initial_message)
        
        initial_message <<- paste0(initial_message, "\nDatabase in creation. Please wait...")
        
        output$message <- renderText(initial_message)
        
        #User feedback bar
        input_bigness <- hms(max(ceiling(round(file.size(input$read_file)/(1024^2)/15, 1)), 5))
        
        if(input$fmt == "bam"){
          input_bigness <- hms(max(ceiling(round(file.size(input$read_file)/(1024^2)/4, 1)), 5))
        }
        
        progress <- shiny::Progress$new()
        on.exit(progress$close())
        progress$set(message = "Making Database", value = 0.5, detail = paste("Expected time to completion:",input_bigness))
        
        
        sqldb_creation(contigs = input$contig_file, mags = "automatically_generated_association_file.txt", sample_reads = list(path_simplifier(input$cur_dir, input$read_file)), map_format = input$fmt, database = paste0(gsub(" ", "_", input$dbname), ".db"))
        
        
        #Gets rid of the silly non-progress bar
        progress$set(message = "Making Database", value = 1)
        
        updateTextInput(session, "dbname", value = gsub(" ", "_", input$dbname))
        
        initial_message <<- paste0(initial_message, "\nDatabase Built! You're done on this page.\nPlease go to the database management tab.")
        
        initial_message2 <<- paste0(initial_message2, "\n\nI have the database you just built loaded in.\nYou can go to view plots now, or add to it.\n")
        
        
        output$message <- renderText(initial_message)
        output$message2 <- renderText(initial_message2)
        
        updateTextInput(session, "exist_dbname", value = paste0(gsub(" ", "_", input$dbname), ".db"))
        
      }
      
    }else{
      
      #If a MAGs file WAS supplied, do this
      
      if(!ready_to_make){
        initial_message <<- paste0(initial_message, "\nNot all files have been selected or the files cannot be found. Please select a set of reads and contigs")
        
        output$message <- renderText(initial_message)
        return(NA)
      }
      
      if(input$dbname == "Enter name here.."){
        initial_message <<- paste0(initial_message, "\nThe database needs a name. Please give it one!")
        
        output$message <- renderText(initial_message)
        ready_to_make <- F
      }
      
      if(!ready_to_make){
        
        output$message <- renderText(initial_message)
      }else{
        initial_message <<- paste0(initial_message, "\n\nDatabase in creation. Please wait...")
        
        output$message <- renderText(initial_message)
        
        #User feedback bar
        input_bigness <- hms(max(ceiling(round(file.size(input$read_file)/(1024^2)/15, 1)), 5))
        progress <- shiny::Progress$new()
        on.exit(progress$close())
        progress$set(message = "Making Database", value = 0.5, detail = paste("Expected time to completion:",input_bigness))
        
        #todooooo
        #sqldb_creation(contigs = input$contig_file, mags = input$mag_file, sample_reads = list(path_simplifier(input$cur_dir, input$read_file)), map_format = input$fmt, database = paste0(gsub(" ", "_", input$dbname), ".db"))
        sqldb_creation(contigs = input$contig_file, mags = input$mag_file, sample_reads = list(input$read_file), map_format = input$fmt, database = paste0(gsub(" ", "_", input$dbname), ".db"))
        
        #Gets rid of the silly non-progress bar
        progress$set(message = "Making Database", value = 1)
        
        #No spaces allowed
        updateTextInput(session, "dbname", value = gsub(" ", "_", input$dbname))
        
        initial_message <<- paste0(initial_message, "\nDatabase Built!")
        
        output$message <- renderText(initial_message)
        
        updateTextInput(session, "exist_dbname", value = paste0(gsub(" ", "_", input$dbname), ".db"))
        
      }
    }
    
    
  })
  
  observeEvent(input$exist_db,{
    
    tryCatch({
      db <- choose_file(caption = "Select an Existing Recruitment Plot Database")
    },
    error = function(cond){
      db <- "No existing database selected. Try again?"
      return(db)
    })
    
    if(length(db) == 0){
      db <- "No existing database selected. Try again?"
    }else{
      if(file.exists(db)){
        if( detect_file_format(db) != "database"){
          shinyalert("This doesn't look like a database to me!", "Did you make this file with the Recruitment Plot database creation tab?", type = "error")
        }
      }
    }
    
    updateTextInput(session, "exist_dbname", value = db)
    
  })
  
  observeEvent(input$add_sample,{
    
    detected_fmt = "none"
    
    #Add a don't-do-this if there's not a selected DB
    if(input$exist_dbname == "" | input$exist_dbname == "No existing database selected. Try again?"){
      
      initial_message2 <<- paste0(initial_message2, "\nYou have to select an existing database first.")
      
      output$message2 <- renderText(initial_message2)
      return(NA)
    }
    
    tryCatch({
      new_samp <- choose_file(caption = "Select Additional Mapped Reads to Add")
    },
    error = function(cond){
      new_samp <- "No new sample. Try again?"
      return(new_samp)
    })
    
    if(length(new_samp) == 0){
      new_samp <- "No new sample. Try again?"
    }else{
      if(file.exists(new_samp)){
      detected_fmt = detect_file_format(new_samp)
      if(detected_fmt == "fasta"){
        shinyalert("This looks like a FASTA file!", "Are you sure these are mapped reads and NOT raw reads or genomes?", type = "error")
      }
      if(detected_fmt == "database"){
        shinyalert("This looks like a SQL Database!", "If this is an old Recruitment Plot database, select it up above instead!", type = "error")
      }
      if(detected_fmt == "genes"){
        shinyalert("This looks like a a GFF!", "Did you want to add predicted genes to your genomes? The file selection for that is down below!", type = "error")
      }
      if(detected_fmt == "none"){
        shinyalert("I don't recognize this file!", "This doesn't look like the right kind of file! Are you sure this is a mapped read file?", type = "error")
      }
      if(detected_fmt == "assoc"){
        shinyalert("This looks like an association file!", "This type of file is meant to associate separate contigs with a parent binned genome. Are you sure these are mapped reads?", type = "error")
      }
      }
    }
    
    
    updateTextInput(session, "add_samp", value = new_samp)
    
    updateSelectInput(session, "fmt_add", selected = "blast")
    
    if(detected_fmt == "sam"){
      updateSelectInput(session, "fmt_add", selected = "sam")
    }
    
    if(detected_fmt == "bam"){
      updateSelectInput(session, "fmt_add", selected = "bam")
    }
    
  })
  
  observeEvent(input$new_samp_commit, {
    
    if(input$exist_dbname == "No DB currently selected" | input$exist_dbname == "No existing database selected. Try again?"){
      initial_message2 <<- paste0(initial_message2, "\nYou cannot add a new sample without making a new database or choosing an existing one first.")
      
      output$message2 <- renderText(initial_message2)
      return(NA)
    }
    
    if(input$add_samp == "No new sample to add." | input$add_samp == "No new sample. Try again?"){
      
      initial_message2 <<- paste0(initial_message2, "\nYou must choose a sample before committing it to the database.")
      
      output$message2 <- renderText(initial_message2)
      
      return(NA)
      
    }
    
    initial_message2 <<- paste0(initial_message2, "\nAdding sample...")
    
    output$message2 <- renderText(initial_message2)
    
    #User feedback bar
    input_bigness <- hms(max(ceiling(round(file.size(input$add_samp)/(1024^2)/15, 1)), 5))
    
    if(input$fmt_add == "bam"){
      input_bigness <- hms(max(ceiling(round(file.size(input$add_samp)/(1024^2)/4, 1)), 5))
    }
    
    progress <- shiny::Progress$new()
    on.exit(progress$close())
    progress$set(message = "Adding Sample", value = 0.5, detail = paste("Expected time to completion:",input_bigness))
    
    add_sample(input$exist_dbname, list(path_simplifier(input$cur_dir, input$add_samp)), input$fmt_add)
    
    progress$set(message = "Adding Sample", value = 1)
    
    initial_message2 <<- paste0(initial_message2, "\nSample added!")
    
    output$message2 <- renderText(initial_message2)
    
    samples_in_db = assess_samples(input$exist_dbname)
    
    labels <- unlist(samples_in_db)
    
    samples_in_db <- unlist(samples_in_db)
    names(samples_in_db) = labels
    
    updateSelectInput(session, "samples", choices = samples_in_db)
    updateSelectInput(session, "samples_interact", choices = samples_in_db)
    
    
  })
  
  observeEvent(input$add_genes,{
    
    #Add a don't-do-this if there's not a selected DB
    if(input$exist_dbname == "" | input$exist_dbname == "No existing database selected. Try again?" | input$exist_dbname == "No DB currently selected"){
      initial_message2 <<- paste0(initial_message2, "\nYou have to select an existing database first.")
      
      output$message2 <- renderText(initial_message2)
      
      return(NA)
    }
    
    detected_fmt = "none"

    
    tryCatch({
      new_genes <- choose_file(caption = "Select Annotated Genes to Add")
    },
    error = function(cond){
      new_genes <- "No genes selected. Try again?"
      return(new_genes)
    })
    
    if(length(new_genes) == 0){
      new_genes <- "No genes selected. Try again?"
    }else{
      if(file.exists(new_genes)){
        detected_fmt = detect_file_format(new_genes)
        if(detected_fmt == "fasta"){
          shinyalert("This looks like a FASTA file!", "Are you sure this is a Prodigal GFF?", type = "error")
        }
        if(detected_fmt == "database"){
          shinyalert("This looks like a SQL Database!", "If this is an old Recruitment Plot database, it should be selected up above!", type = "error")
        }
        if(detected_fmt == "none"){
          shinyalert("I don't recognize this file!", "This doesn't look like the right kind of file! Are you sure this is a GFF made by Prodigal?", type = "error")
        }
        if(detected_fmt == "sam" | detected_fmt == "bam" | detected_fmt == "blast"){
          shinyalert("This file looks like mapped reads to me!", "Did you want to add a sample? The button fo that is up above!", type = "error")
        }
        if(detected_fmt == "assoc"){
          shinyalert("This looks like an association file!", "This type of file is meant to associate separate contigs with a parent binned genome. Are you sure this is a GFF made by Prodigal?", type = "error")
        }
      }
    }
    
    

    
    updateTextInput(session, "add_gen", value = new_genes)
    
  })
  
  observeEvent(input$genes_commit, {
    
    if(input$exist_dbname == "No DB currently selected" | input$exist_dbname == "No existing database selected. Try again?"){
      initial_message2 <<- paste0(initial_message2, "\nYou cannot add a new sample without making a new database or choosing an existing one first.")
      
      output$message2 <- renderText(initial_message2)
      
      return(NA)
    }
    
    if(input$add_gen == "No genes to add." | input$add_gen == "No genes selected. Try again?"){
      initial_message2 <<- paste0(initial_message2, "\nYou must choose genes before committing them to the database.")
      
      output$message2 <- renderText(initial_message2)
      
      return(NA)
      
    }
    
    
    add_genes_to_db(input$exist_dbname, path_simplifier(input$cur_dir, input$add_gen), input$fmt_gen)
    
    initial_message2 <<- paste0(initial_message2, "\nGenes added!")
    
    output$message2 <- renderText(initial_message2)
    
    
  })
  
  observeEvent(input$exist_dbname, {
    
    if(input$exist_dbname == "No DB currently selected" | input$exist_dbname == "No existing database selected. Try again?"){
      samples_in_db <- c("nothing selected"="Nothing Selected")
    }else{
      
      tryCatch({
        samples_in_db = assess_samples(input$exist_dbname)
        
      },
      error = function(cond){
        initial_message2 <<- paste0(initial_message2, "\nNo existing database selected. Try again?")
        
        output$message2 <- renderText(initial_message2)
        
        return(NA)
      })
      
      detected_fmt = detect_file_format(input$exist_dbname)
      
      if(detected_fmt != "database"){
        return(NA)
      }
      
      labels <- unlist(samples_in_db)
      
      #pretty names
      labels <- unlist(lapply(labels, function(x){
        
        str <- strsplit(x, split = "[/\\]")[[1]]
        
        return(str[length(str)])
        
      }))
      
      
      samples_in_db <- unlist(samples_in_db)
      names(samples_in_db) = labels
      
    }
    
    updateSelectInput(session, "samples", choices = c("Placeholder = placeholder"))
    updateSelectInput(session, "samples_interact", choices = c("Placeholder = placeholder"))
    
    updateSelectInput(session, "samples", choices = samples_in_db)
    updateSelectInput(session, "samples_interact", choices = samples_in_db)
    
    
  })
  
  #Plot pages
  
  observeEvent(input$samples, {
    
    
    if(input$exist_dbname == "No DB currently selected"){
      
      mags_in_samp <- c("nothing selected"="Nothing Selected")
      
    }else{
      
      if(input$samples == "" | input$samples == "Nothing Selected"){
        
        mags_in_samp <- c("nothing selected"="Nothing Selected")
        
      }else{
        mags_in_samp = unlist(assess_MAGs(input$exist_dbname, input$samples))
        
        labels <- mags_in_samp
        
        mags_in_samp <- unlist(mags_in_samp)
        
        names(mags_in_samp) = labels
        
      }
    }
    
    updateSelectInput(session, "mags_in_db", choices = mags_in_samp)
    updateSelectInput(session, "mags_in_db_interact", choices = mags_in_samp)
    
  })
  
  observeEvent(input$get_a_mag, {
    if(input$exist_dbname == "No DB currently selected" | input$samples == "Select a sample in the database" | input$mags_in_db == "Select a MAG in the sample"){
      
      shinyalert("No database currently loaded", "You must either create a new database or select an existing one first", type = "error")
      
      recplot_data <- data.frame(placeholder = 1)
    }else{
      
      progress <- shiny::Progress$new()
      on.exit(progress$close())
      progress$set(message = "Reading Database", value = 0.33, detail = "Please be patient")
      
      #I decided this wasn't worth having as a selection.
      lower_bound <- 70
      
      if(input$task == "contigs"){
        
        recplot_data <- extract_MAG_for_R(input$exist_dbname, input$samples, input$mags_in_db, input$width, input$height, lower_bound)
      }else{
        recplot_data <- extract_genes_MAG_for_R(input$exist_dbname, input$samples, input$mags_in_db, input$height, lower_bound)
        gene_data <<- gene_pydat_to_recplot_dat_prodigal(recplot_data[[3]])
        
      }
      
      progress$set(message = "Reading Database", value = 0.66, detail = "Formatting data for plotting")
      
      contig_names <- unlist(get_contig_names(input$exist_dbname, input$mags_in_db))
      
      plotting_materials <<- pydat_to_recplot_dat(recplot_data, contig_names)
      
      old_value <- input$in_group_min_stat
      
      updateNumericInput(session, "in_group_min_stat", value = old_value-1)
      updateNumericInput(session, "in_group_min_interact", value = old_value-1)
      
      updateNumericInput(session, "in_group_min_stat", value = old_value)
      updateNumericInput(session, "in_group_min_interact", value = old_value)
      
      progress$set(message = "Reading Database", value = 1, detail = "Done")
      
    }
    
  })
  
  observeEvent(input$get_a_mag_interact, {
    if(input$exist_dbname == "No DB currently selected" | input$samples == "Select a sample in the database" | input$mags_in_db == "Select a MAG in the sample"){
      shinyalert("No database currently loaded", "You must either create a new database or select an existing one first", type = "error")
      
      recplot_data <- data.frame(placeholder = 1)
    }else{
      
      progress <- shiny::Progress$new()
      on.exit(progress$close())
      progress$set(message = "Reading Database", value = 0.33, detail = "Please be patient")
      
      lower_bound <- 70
      
      if(input$task == "contigs"){
        recplot_data <- extract_MAG_for_R(input$exist_dbname, input$samples_interact, input$mags_in_db_interact, input$width_interact, input$height_interact, lower_bound)
      }else{
        recplot_data <- extract_genes_MAG_for_R(input$exist_dbname, input$samples_interact, input$mags_in_db_interact, input$height_interact, lower_bound)
        gene_data <<- gene_pydat_to_recplot_dat_prodigal(recplot_data[[3]])
      }
      
      progress$set(message = "Reading Database", value = 0.66, detail = "Formatting data for plotting")
      
      contig_names <- unlist(get_contig_names(input$exist_dbname, input$mags_in_db))
      
      plotting_materials <<- pydat_to_recplot_dat(recplot_data, contig_names)
      
      old_value <- input$in_group_min_stat
      
      updateNumericInput(session, "in_group_min_stat", value = old_value-1)
      updateNumericInput(session, "in_group_min_interact", value = old_value-1)
      
      updateNumericInput(session, "in_group_min_stat", value = old_value)
      updateNumericInput(session, "in_group_min_interact", value = old_value)
      
      progress$set(message = "Reading Database", value = 1, detail = "Done")
      
      
    }
    
  })
  
  observeEvent(input$print_stat, {
    
    if(is.na(plotting_materials)[1]){
      
      shinyalert("Cannot print plot.", "There is no genome currently loaded. You need to hit 'View Selected Genome' first.", type = "error")
      
      return(NA)
    }else{
      
      if(input$pdf_name == ""){
        
        shinyalert(
          "The output plot needs a name.", "You can enter a name here, then hit Save PDF button again to print it.", type = "input",
          callbackR = function(x) { 
            updateTextInput(session, "pdf_name", value = x)
          }
        )
        
        return(NA)
      }else{
        
        progress <- shiny::Progress$new()
        on.exit(progress$close())
        progress$set(message = "Printing plot to PDF", value = 0.3, detail = "Please be patient. Creating Plot.")
        
        base <- one_mag()
        
        req(!is.na(base))
        
        bp_unit <- base[[2]]
        bp_div <- base[[3]]
        pos_max <- base[[4]]
        base <- base[[1]]
        
        ending <- base[, max(End), by = contig]
        ending[, V1 := cumsum(V1) - 1 + 1:nrow(ending)]
        
        if(input$task == "genes"){
          
          #Genes only
          if(input$regions_stat == 1){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base[gene_annotation == "N/A", bp_count := NA,]
          }
          #IGR only
          if(input$regions_stat == 2){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base[gene_annotation != "N/A", bp_count := NA,]
          }
          #Genes + long IGR
          if(input$regions_stat == 3){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base <- base[End-Start+1 < 6, bp_count := NA,]
          }
          if(input$regions_stat == 4){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
          }
          
        }
        
        static_plot <- create_static_plot(base = base,
                                          bp_unit = bp_unit,
                                          bp_div = bp_div,
                                          pos_max = pos_max,
                                          in_grp_min = input$in_group_min_stat,
                                          id_break = input$height,
                                          width = input$width,
                                          linear = input$linear_stat,
                                          showpeaks= input$show_peaks,
                                          ends = ending
        )
        
        
          
          pretty_name <- strsplit(input$samples, split = "[/\\]")[[1]]
          
          pretty_name <- pretty_name[length(pretty_name)]
        
        title <- ggdraw() + 
          draw_label(
            paste("Read Recruitment Plot for sample:", pretty_name, "MAG:", input$mags_in_db),
            fontface = 'bold',
            x = 0,
            hjust = 0
          ) +
          theme(
            # add margin on the left of the drawing canvas,
            # so title is aligned with left edge of first plot
            plot.margin = margin(0, 0, 0, 7)
          )
        static_plot <- plot_grid(
          title, static_plot,
          ncol = 1,
          # rel_heights values control vertical title margins
          rel_heights = c(0.1, 1)
        )
        
        progress$set(message = "Printing plot to PDF", value = 0.66, detail = "Plot created. Printing to PDF")
        
        pdf(paste0(input$pdf_name, ".pdf"), height = 11, width = 17)
        
        suppressWarnings(print(static_plot))
        
        dev.off()
        
        
        progress$set(message = "Printing plot to PDF", value = 1, detail = "Please be patient.")
        
        
      }
      
    }
    
    
    
  })
  
  observeEvent(input$output_data_stat, {
    
    if(is.na(plotting_materials)[1]){
      
      shinyalert("Cannot output data.", "There is no genome currently loaded. You need to hit 'View Selected Genome' first.", type = "error")
      
      return(NA)
    }else{
      
      if(input$pdf_name == ""){
        
        shinyalert(
          "The output files need a name.", "You can enter a name here, then hit output data button again.", type = "input",
          callbackR = function(x) { 
            updateTextInput(session, "pdf_name", value = x)
          }
        )
        
        return(NA)
      }else{
        
        progress <- shiny::Progress$new()
        on.exit(progress$close())
        progress$set(message = "Printing data to tab-separated files", value = 0.3, detail = "Formatting data")
        
        base <- one_mag()
        
        req(!is.na(base))
        
        bp_unit <- base[[2]]
        bp_div <- base[[3]]
        pos_max <- base[[4]]
        base <- base[[1]]
        
        ending <- base[, max(End), by = contig]
        ending[, V1 := cumsum(V1) - 1 + 1:nrow(ending)]
        
        if(input$task == "genes"){
          
          #Genes only
          if(input$regions_stat == 1){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base[gene_annotation == "N/A", bp_count := NA,]
          }
          #IGR only
          if(input$regions_stat == 2){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base[gene_annotation != "N/A", bp_count := NA,]
          }
          #Genes + long IGR
          if(input$regions_stat == 3){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
            base <- base[End-Start+1 < 6, bp_count := NA,]
          }
          #all regions
          if(input$regions_stat == 4){
            base <- one_mag()[[1]]
            
            ratio <- nrow(base)/nrow(gene_data)
            
            setkeyv(base, c("contig", "Start"))
            setkey(gene_data, "contig")
            
            base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
            base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
            base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
            base[, gene_strand := rep(gene_data$strand, each = ratio) ]
            base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
            
          }
          
        }
        
        plot_data <- create_static_data(base = base,
                                        bp_unit = bp_unit,
                                        bp_div = bp_div,
                                        pos_max = pos_max,
                                        in_grp_min = input$in_group_min_stat,
                                        id_break = input$height,
                                        width = input$width,
                                        linear = input$linear_stat,
                                        showpeaks= input$show_peaks,
                                        ends = ending
        )
        
        
        
        progress$set(message = "Printing plot data", value = 0.66, detail = "Writing data")
        
        fwrite(plot_data[[1]], paste0(input$pdf_name, "_recruitment_data.tsv"), sep = "\t")
        fwrite(plot_data[[2]], paste0(input$pdf_name, "_sequencing_depth.tsv"), sep = "\t")
        
        
        progress$set(message = "Printing plot data", value = 1, detail = "Done.")
        
        
      }
      
    }
    
    
    
  })
  
  observeEvent(input$print_interact, {
    
    if(is.na(plotting_materials)[1]){
      
      shinyalert("Cannot print plot.", "There is no genome currently loaded. You need to hit 'View Selected Genome' first.", type = "error")
      
      return(NA)
      
    }else{
      
      if(input$pdf_name_interact == ""){
        
        shinyalert(
          "The output interactive plot needs a name.", "You can enter a name here, then hit the Save Plot button again to save it.", type = "input",
          callbackR = function(x) { 
            updateTextInput(session, "pdf_name_interact", value = x)
          }
        )
        
        return(NA)
        
      }else{
        
    #Reset this each time to make checking for it more consistent.
    warning_plot <- NA
    
    progress <- shiny::Progress$new()
    on.exit(progress$close())
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.10, detail = "Please be patient. Printing an interactive plot takes a bit.")
    
    base <- NULL
    
    base <- one_mag()
    
    req(!is.na(base))
    
    bp_unit <- base[[2]]
    bp_div <- base[[3]]
    pos_max <- base[[4]]
    base <- base[[1]]
    
    incoming_base <- base
    
    ends <- base[, max(End), by = contig]
    ends[, V1 := cumsum(V1) - 1 + 1:nrow(ends)]
    
    if(input$task == "contigs"){
      
      base <- base[Start < trunc_degree, Start := trunc_degree]
      
      #Selects the bins at the end of each contig and subtracts trunc degree from it
      base[, contig_len := max(End), by = contig]
      base[End == contig_len, End := End - trunc_degree, ]
      
      old_base <- base
      
      #If the final bin was too small, removes it.
      base <- base[Start <= End,]
      
      norm_factor <- min(base$End-base$Start) + 1
      
      widths <- base$End - base$Start + 1
      
      base$bp_count <- base$bp_count*(norm_factor/widths)
      
      p <- ggplot(base, aes(x = seq_pos, y = Pct_ID_bin, fill=log10(bp_count), text = paste0("Contig: ", contig,
                                                                                             "\nPos. in Contig: ", Start, "-", End,
                                                                                             "\nNorm. Bin Count: ", round(bp_count))))+ 
        scale_fill_gradient(low = "white", high = "black",  na.value = "#EEF7FA")+
        ylab("Percent Identity") +
        xlab(paste("Position in Genome", bp_unit)) +
        scale_y_continuous(expand = c(0, 0)) +
        scale_x_continuous(expand = c(0, 0)) +
        theme(legend.position = "none", 
              axis.line = element_line(colour = "black"),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        annotate("rect", xmin = 0, xmax = pos_max/bp_div, 
                 ymin = input$in_group_min_interact, 
                 ymax = 100, fill = "darkblue", alpha = .15)+
        geom_vline(xintercept = ends$V1/bp_div, col = "#AAAAAA40") +
        geom_raster()
      
      base[End == contig_len-trunc_degree, End := End + trunc_degree, ]
      base[, contig_len := NULL, ]
      
      #Reset
      base <- old_base
      
      #I  use the Z because the order matters for plotting the dark over the light color and that is determined by lexicographical ordering
      base$group_label <- ifelse(base$Pct_ID_bin-input$height >= input$in_group_min_interact, "depth.zin", "depth.out")
      
      setkeyv(base, c("group_label", "seq_pos"))
      
      depth_data <- base[, list(sum(bp_count/(End-Start+1)), unique(Start), unique(End), unique(contig)), by = key(base)]
      colnames(depth_data)[3:6] = c("count", "Start", "End", "contig")
      
      group.colors <- c(depth.zin = "darkblue", depth.out = "lightblue", depth.zin.nil = "darkblue", depth.out.nil = "lightblue")
      
      old_depth <- depth_data
      
      depth_data$count[depth_data$count == 0] <- NA
      
      #If bins need deleted at ends of contigs, this does so
      depth_data <- depth_data[Start <= End, ]
      
      seq_depth_chart <- ggplot(depth_data, aes(x = seq_pos, y = count, colour=group_label, group = group_label, text = paste0("Contig: ", contig,
                                                                                                                               "\nPos. in Contig: ", Start, "-", End,
                                                                                                                               "\nSeq. Depth: ", round(count))))+
        geom_step(alpha = 0.75) +
        scale_y_continuous(trans = "log10") +
        scale_x_continuous(expand=c(0,0), limits = c(0, pos_max/bp_div))+
        theme(legend.position = "none", 
              panel.border = element_blank(), 
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank(), 
              axis.line = element_line(colour = "black"), 
              panel.background = element_blank(), 
              axis.title.x = element_blank(), 
              #axis.text.y = element_text(angle = 90, hjust = 0.5),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        scale_color_manual(values = group.colors) +
        ylab("Log 10 Depth")
      
      base[End == contig_len-trunc_degree, End := End + trunc_degree, ]
      base[, contig_len := NULL, ]
      
    }else{
      
      if(is.na(gene_data)){
        
        #Check if there was a switch from contigs to genes without reloading
        warning_plot <- ggplot(data = NULL, aes(x = 1, y = 1, label = "This is not an error message.\nIt seems you switched from viewing contigs to genes.\nYour Recruitment Plot needs the gene data.\nPlease hit the 'View Selected Genome' button again."))+
          geom_text() +
          theme(panel.background = element_blank(),
                axis.title = element_blank(),
                axis.text = element_blank(),
                axis.ticks = element_blank())
        
        warning_plot <- ggplotly(warning_plot)
        
        progress$set(message = "Creating interactive Recruitment Plot", value = 1, detail = "Please be patient. The interactive plots take longer.")
        
        return(warning_plot)
        
        
      }
      
      #fixup alterations
      gene_reset <- gene_data
      
      ratio <- nrow(base)/nrow(gene_data)
      
      setkeyv(base, c("contig", "Start"))
      setkey(gene_data, "contig")
      
      base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
      base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
      base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
      base[, gene_strand := rep(gene_data$strand, each = ratio) ]
      base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
      
      #read rec plot
      base <- base[Start < trunc_degree, Start := trunc_degree]
      #Selects the bins at the end of each contig and subtracts trunc degree from it
      base[, contig_len := max(End), by = contig]
      base[End == contig_len, End := End - trunc_degree, ]
      
      gene_base <- base
      

      
      #Genes only
      if(input$regions_interact == 1){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation == "N/A"] <- NA
      }
      #IGR only
      if(input$regions_interact == 2){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation != "N/A"] <- NA
      }
      #Genes + long IGR
      if(input$regions_interact == 3){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$End-base$Start+1 < 6] <- NA
      }
      if(input$regions_interact == 4){
        base <- NULL
        base <- gene_base
        
        base$bp_count <- base$bp_count
      }
      
      norm_factor <- min(base$End-base$Start) + 1
      
      widths <- base$End - base$Start + 1
      
      base$bp_count <- base$bp_count*(norm_factor/widths)
      
      #If the final bin was too small, remeoves it.
      base <- base[Start <= End,]
      
      p <- ggplot(base, aes(x = seq_pos, y = Pct_ID_bin, fill=log10(bp_count), text = paste0("Contig: ", contig,
                                                                                             "\nPos. in Contig: ", Start, "-", End,
                                                                                             "\nNorm. Bin Count: ", round(bp_count),
                                                                                             "\nGene ID: ", gene_name,
                                                                                             "\nGene Range: ", gene_start, "-", gene_end,
                                                                                             "\nGene Strand: ", gene_strand,
                                                                                             "\nAnnotation: ", gene_annotation)))+ 
        scale_fill_gradient(low = "white", high = "black",  na.value = "#EEF7FA")+
        ylab("Percent Identity") +
        xlab(paste("Position in Genome", bp_unit)) +
        scale_y_continuous(expand = c(0, 0)) +
        scale_x_continuous(expand = c(0, 0), limits = c(0, pos_max/bp_div)) +
        theme(legend.position = "none", 
              axis.line = element_line(colour = "black"),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        annotate("rect", xmin = 0, xmax = pos_max/bp_div, 
                 ymin = input$in_group_min_interact, 
                 ymax = 100, fill = "darkblue", alpha = .15)+
        geom_vline(xintercept = ends$V1/bp_div, col = "#AAAAAA40") +
        geom_raster()
      
      
      #seqdepth chart
      base <- gene_base
      
      #I  use the Z because the order matters for plotting the dark over the light color and that is determined by lexicographical ordering
      base$group_label <- ifelse(base$Pct_ID_bin-input$height >= input$in_group_min_interact, "depth.zin", "depth.out")
      
      setkeyv(base, c("group_label", "seq_pos"))
      
      depth_data <- base[, list(sum(bp_count/(End-Start+1)), unique(Start), unique(End), unique(contig)), by = key(base)]
      colnames(depth_data)[3:6] = c("count", "Start", "End", "contig")
      
      group.colors <- c(depth.zin = "darkblue", depth.out = "lightblue", depth.zin.nil = "darkblue", depth.out.nil = "lightblue")
      
      depth_data$count[depth_data$count == 0] <- NA
      
      #If bins need deleted at ends of contigs, this does so
      depth_data <- depth_data[Start <= End, ]
      
      #reset
      gene_data <- gene_reset
      gene_data <- rbind(gene_data, gene_data)
      
      setkeyv(depth_data, c("group_label", "contig",  "Start", "End"))
      setkey(gene_data, "contig")
      
      depth_data[, gene_name := gene_data$gene_name]
      depth_data[, gene_start := gene_data$gene_start]
      depth_data[, gene_end := gene_data$gene_end]
      depth_data[, gene_strand := gene_data$strand]
      depth_data[, gene_annotation := gene_data$annotation]
      
      setkeyv(depth_data, c("group_label", "seq_pos"))
      
      gene_depth <- depth_data
      
      #Genes only
      if(input$regions_interact == 1){
        depth_data <- NULL
        depth_data <- gene_depth
        
        depth_data$count[depth_data$gene_annotation == "N/A"] <- NA
      }
      #IGR only
      if(input$regions_interact == 2){
        depth_data <- NULL
        depth_data <- gene_depth
        
        setkeyv(depth_data, c("group_label", "seq_pos"))
        
        depth_data$count[depth_data$gene_annotation != "N/A"] <- NA
      }
      #Genes + long IGR
      if(input$regions_interact == 3){
        depth_data <- NULL
        depth_data <- gene_depth
        
        setkeyv(depth_data, c("group_label", "seq_pos"))
        
        depth_data$count[depth_data$End-depth_data$Start + 1 < 6] <- NA
      }
      if(input$regions_interact == 4){
        depth_data <- NULL
        depth_data <- gene_depth
        
        setkeyv(depth_data, c("group_label", "seq_pos"))
        
        depth_data$count <- depth_data$count
      }
      
      #If bins need deleted at ends of contigs, this does so
      depth_data <- depth_data[Start <= End, ]
      
      seq_depth_chart <- ggplot(depth_data, aes(x = seq_pos, y = count, colour=group_label, group = group_label, text = paste0("Contig: ", contig,
                                                                                                                               "\nPos. in Contig: ", Start, "-", End,
                                                                                                                               "\nNorm. Bin Count: ", round(count),
                                                                                                                               "\nGene ID: ", gene_name,
                                                                                                                               "\nGene Range: ", gene_start, "-", gene_end,
                                                                                                                               "\nGene Strand: ", gene_strand,
                                                                                                                               "\nAnnotation: ", gene_annotation)))+
        geom_step(alpha = 0.75) +
        scale_y_continuous(trans = "log10") +
        scale_x_continuous(expand=c(0,0), limits = c(0, pos_max/bp_div))+
        theme(legend.position = "none", 
              panel.border = element_blank(), 
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank(), 
              axis.line = element_line(colour = "black"), 
              panel.background = element_blank(), 
              axis.title.x = element_blank(), 
              #axis.text.y = element_text(angle = 90, hjust = 0.5),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        scale_color_manual(values = group.colors) +
        ylab("Log 10 Depth")
      
    }
    
    a <- list(
      range = c(min(depth_data$count, na.rm = T), max(depth_data$count, na.rm = T)),
      showticklabels = TRUE,
      exponentformat = "e"
    )
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.5, detail = "Plots created. Making first plot interactive...")
    
    seq_depth_chart <- ggplotly(seq_depth_chart, dynamicTicks = T, tooltip = c("text")) %>% 
      layout(plot_bgcolor = "grey90", yaxis = a)
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.6, detail = "Making second plot interactive...")
    
    read_rec_plot <- ggplotly(p, dynamicTicks = T, tooltip = c("text")) %>% 
      layout(plot_bgcolor = "grey90") %>% 
      style(hoverinfo = "none", traces = c(1, 2))
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.7, detail = "Formatting plots")
    
    overplot <- subplot(list(seq_depth_chart, read_rec_plot), nrows = 2, shareX = T, heights = c(1/3, 2/3))
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.85, detail = "Saving plot")
    
    #todo
    saveWidget(overplot, paste0(input$pdf_name_interact, ".html"))
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 1, detail = "Done!")
    
    base <- NULL
    base<- incoming_base
    
    }
      
    }
    return(NA)
  })
  
  one_mag <- reactive({
    
    input$in_group_min_stat
    input$linear_stat
    input$regions_stat
    input$regions_interact
    input$task
    
    plotting_materials
    
  })
  
  #Static plots
  
  output$read_recruitment_plot <- renderPlot(suppressWarnings({
    
    progress <- shiny::Progress$new()
    on.exit(progress$close())
    progress$set(message = "Creating Recruitment Plot", value = 0.5, detail = "Please be patient")
    
    base <- one_mag()
    
    req(!is.na(base))
    
    bp_unit <- base[[2]]
    bp_div <- base[[3]]
    pos_max <- base[[4]]
    base <- base[[1]]
    
    ending <- base[, max(End), by = contig]
    ending[, V1 := cumsum(V1) - 1 + 1:nrow(ending)]
    
    if(input$task == "genes"){
      
      if(is.na(gene_data)){
        
        warning_plot <- ggplot(data = NULL, aes(x = 1, y = 1, label = "This is not an error message.\nIt seems you switched from viewing contigs to genes.\nYour Recruitment Plot needs the gene data.\nPlease hit the 'View Selected Genome' button again."))+
          geom_text(size = 6) +
          theme(panel.background = element_blank(),
                axis.title = element_blank(),
                axis.text = element_blank(),
                axis.ticks = element_blank())
        
        
        progress$set(message = "Creating Recruitment Plot", value = 0.5, detail = "Please be patient.")
        
        return(warning_plot)
      }
      
      ratio <- nrow(base)/nrow(gene_data)
      
      setkeyv(base, c("contig", "Start"))
      setkey(gene_data, "contig")
      
      base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
      base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
      base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
      base[, gene_strand := rep(gene_data$strand, each = ratio) ]
      base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
      
      gene_base <- base
      
      #Genes only
      if(input$regions_stat == 1){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation == "N/A"] <- NA
      }
      #IGR only
      if(input$regions_stat == 2){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation != "N/A"] <- NA
      }
      #Genes + long IGR
      if(input$regions_stat == 3){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$End-base$Start+1 < 6] <- NA
      }
      #All regions
      if(input$regions_stat == 4){
        base <- NULL
        base <- gene_base
        
        base$bp_count <- base$bp_count
      }
      
      if(sum(!is.na(base$bp_count)) == 0){
        static_plot <- ggplot(data.table(1, 1), aes(x = 1, y = 1, label = "No regions with valid counts were detected.\nIt's possible that no genes were\npredicted for this genome."))+
          geom_text(size = 18) +
          theme(plot.background = element_blank(),
                axis.text = element_blank(),
                axis.title = element_blank(),
                axis.ticks = element_blank(),
                axis.line = element_blank(),
                panel.grid = element_blank())
      }else{
        static_plot <- create_static_plot(base = base,
                                          bp_unit = bp_unit,
                                          bp_div = bp_div,
                                          pos_max = pos_max,
                                          in_grp_min = input$in_group_min_stat,
                                          id_break = input$height,
                                          width = input$width,
                                          linear = input$linear_stat,
                                          showpeaks= input$show_peaks,
                                          ends = ending
        )
      }
      
      progress$set(message = "Creating Recruitment Plot", value = 1, detail = "Please be patient")
      
      return(static_plot)
      
    }
    
    static_plot <- create_static_plot(base = base,
                                      bp_unit = bp_unit,
                                      bp_div = bp_div,
                                      pos_max = pos_max,
                                      in_grp_min = input$in_group_min_stat,
                                      id_break = input$height,
                                      width = input$width,
                                      linear = input$linear_stat,
                                      showpeaks= input$show_peaks,
                                      ends = ending
    )
    
    progress$set(message = "Creating Recruitment Plot", value = 1, detail = "Please be patient")
    
    return(static_plot)
    
  }))
  
  #Hover plots
  
  output$Plotly_interactive <- renderPlotly(suppressWarnings({
    
    #Reset this each time to make checking for it more consistent.
    warning_plot <- NA
    
    progress <- shiny::Progress$new()
    on.exit(progress$close())
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.16, detail = "Please be patient. The interactive plots take longer.")
    
    base <- one_mag()
    
    req(!is.na(base))
    
    bp_unit <- base[[2]]
    bp_div <- base[[3]]
    pos_max <- base[[4]]
    base <- base[[1]]
    
    incoming_base <- base
    
    ends <- base[, max(End), by = contig]
    ends[, V1 := cumsum(V1) - 1 + 1:nrow(ends)]
    
    if(input$task == "contigs"){
      
      base <- base[Start < trunc_degree, Start := trunc_degree]
      #Selects the bins at the end of each contig and subtracts trunc degree from it
      
      base[, contig_len := max(End), by = contig]
      base[End == contig_len, End := End - trunc_degree, ]
      
      trunc_base <- base
      
      #If the final bin was too small, removes it.
      base <- base[Start <= End,]
      
      norm_factor <- min(base$End-base$Start) + 1
      
      widths <- base$End - base$Start + 1
      
      base$bp_count <- base$bp_count*(norm_factor/widths)
      
      p <- ggplot(base, aes(x = seq_pos, y = Pct_ID_bin, fill=log10(bp_count), text = paste0("Contig: ", contig,
                                                                                             "\nPos. in Contig: ", Start, "-", End,
                                                                                             "\nNorm. Bin Count: ", round(bp_count))))+ 
        scale_fill_gradient(low = "white", high = "black",  na.value = "#EEF7FA")+
        ylab("Percent Identity") +
        xlab(paste("Position in Genome", bp_unit)) +
        scale_y_continuous(expand = c(0, 0)) +
        scale_x_continuous(expand = c(0, 0)) +
        theme(legend.position = "none", 
              axis.line = element_line(colour = "black"),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        annotate("rect", xmin = 0, xmax = pos_max/bp_div, 
                 ymin = input$in_group_min_interact, 
                 ymax = 100, fill = "darkblue", alpha = .15)+
        geom_vline(xintercept = ends$V1/bp_div, col = "#AAAAAA40") +
        geom_raster()
      
      base <- trunc_base
      
      #I  use the Z because the order matters for plotting the dark over the light color and that is determined by lexicographical ordering
      base$group_label <- ifelse(base$Pct_ID_bin-input$height >= input$in_group_min_interact, "depth.zin", "depth.out")
      
      setkeyv(base, c("group_label", "seq_pos"))
      
      depth_data <- base[, list(sum(bp_count/(End-Start+1)), unique(Start), unique(End), unique(contig)), by = key(base)]
      colnames(depth_data)[3:6] = c("count", "Start", "End", "contig")
      
      group.colors <- c(depth.zin = "darkblue", depth.out = "lightblue", depth.zin.nil = "darkblue", depth.out.nil = "lightblue")
      
      depth_data$count[depth_data$count == 0] <- NA
      
      #If bins need deleted at ends of contigs, this does so
      depth_data <- depth_data[Start <= End, ]
      
      seq_depth_chart <- ggplot(depth_data, aes(x = seq_pos, y = count, colour=group_label, group = group_label, text = paste0("Contig: ", contig,
                                                                                                                               "\nPos. in Contig: ", Start, "-", End,
                                                                                                                               "\nSeq. Depth: ", round(count))))+
        geom_step(alpha = 0.75) +
        scale_y_continuous(trans = "log10") +
        scale_x_continuous(expand=c(0,0), limits = c(0, pos_max/bp_div))+
        theme(legend.position = "none", 
              panel.border = element_blank(), 
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank(), 
              axis.line = element_line(colour = "black"), 
              panel.background = element_blank(), 
              axis.title.x = element_blank(), 
              #axis.text.y = element_text(angle = 90, hjust = 0.5),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        scale_color_manual(values = group.colors) +
        ylab("Log 10 Depth")
      
      
      #I worry that the modify in place permanently whoopsies things, so I change it back just in case
      base[End == contig_len-trunc_degree, End := End + trunc_degree, ]
      base[, contig_len := NULL, ]
      
      
    }else{
      
      if(is.na(gene_data)){
        
        #Check if there was a switch from contigs to genes without reloading
        warning_plot <- ggplot(data = NULL, aes(x = 1, y = 1, label = "This is not an error message.\nIt seems you switched from viewing contigs to genes.\nYour Recruitment Plot needs the gene data.\nPlease hit the 'View Selected Genome' button again."))+
          geom_text() +
          theme(panel.background = element_blank(),
                axis.title = element_blank(),
                axis.text = element_blank(),
                axis.ticks = element_blank())
        
        warning_plot <- ggplotly(warning_plot)
        
        progress$set(message = "Creating interactive Recruitment Plot", value = 1, detail = "Please be patient. The interactive plots take longer.")
        
        return(warning_plot)
        
        
      }
      
      #fixup alterations
      gene_reset <- gene_data
      #If the final bin was too small, removes it.
      #base <- base[Start <= End,]
      
      #fwrite(base, "after.tsv", sep ="\t")
      
      ratio <- nrow(base)/nrow(gene_data)
      
      setkeyv(base, c("contig", "Start"))
      setkey(gene_data, "contig")
      
      base[, gene_name := rep(gene_data$gene_name, each = ratio) ]
      base[, gene_start := rep(gene_data$gene_start, each = ratio) ]
      base[, gene_end := rep(gene_data$gene_end, each = ratio) ]
      base[, gene_strand := rep(gene_data$strand, each = ratio) ]
      base[, gene_annotation := rep(gene_data$annotation, each = ratio) ]
      
      base <- base[Start < trunc_degree, Start := trunc_degree]
      #Selects the bins at the end of each contig and subtracts trunc degree from it
      
      base[, contig_len := max(End), by = contig]
      base[End == contig_len, End := End - trunc_degree, ]
      
      gene_base <- base
      
      norm_factor <- min(base$End-base$Start) + 1
      
      widths <- base$End - base$Start + 1
      
      #fwrite(base, "before.tsv", sep ="\t")
      
      base$bp_count <- base$bp_count*(norm_factor/widths)
      
      #Genes only
      if(input$regions_interact == 1){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation == "N/A"] <- NA
      }
      #IGR only
      if(input$regions_interact == 2){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$gene_annotation != "N/A"] <- NA
      }
      #Genes + long IGR
      if(input$regions_interact == 3){
        base <- NULL
        base <- gene_base
        
        base$bp_count[base$End-base$Start+1 < 6] <- NA
      }
      #all regions
      if(input$regions_interact == 4){
        base <- NULL
        base <- gene_base
        
        base$bp_count <- base$bp_count
      }

      #If the final bin was too small, removes it.
      base <- base[Start <= End,]
      
      p <- ggplot(base, aes(x = seq_pos, y = Pct_ID_bin, fill=log10(bp_count), text = paste0("Contig: ", contig,
                                                                                             "\nPos. in Contig: ", Start, "-", End,
                                                                                             "\nNorm. Bin Count: ", round(bp_count),
                                                                                             "\nGene ID: ", gene_name,
                                                                                             "\nGene Range: ", gene_start, "-", gene_end,
                                                                                             "\nGene Strand: ", gene_strand,
                                                                                             "\nAnnotation: ", gene_annotation)))+ 
        scale_fill_gradient(low = "white", high = "black",  na.value = "#EEF7FA")+
        ylab("Percent Identity") +
        xlab(paste("Position in Genome", bp_unit)) +
        scale_y_continuous(expand = c(0, 0)) +
        scale_x_continuous(expand = c(0, 0), limits = c(0, pos_max/bp_div)) +
        theme(legend.position = "none", 
              axis.line = element_line(colour = "black"),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        annotate("rect", xmin = 0, xmax = pos_max/bp_div, 
                 ymin = input$in_group_min_interact, 
                 ymax = 100, fill = "darkblue", alpha = .15)+
        geom_vline(xintercept = ends$V1/bp_div, col = "#AAAAAA40") +
        geom_raster()
      
      
      #seqdepth chart
      base <- NULL
      base <- gene_base
      
      #I  use the Z because the order matters for plotting the dark over the light color and that is determined by lexicographical ordering
      base$group_label <- ifelse(base$Pct_ID_bin-input$height >= input$in_group_min_interact, "depth.zin", "depth.out")
      
      setkeyv(base, c("group_label", "seq_pos"))
      
      depth_data <- base[, list(sum(bp_count/(End-Start+1)), unique(Start), unique(End), unique(contig)), by = key(base)]
      colnames(depth_data)[3:6] = c("count", "Start", "End", "contig")
      
      group.colors <- c(depth.zin = "darkblue", depth.out = "lightblue", depth.zin.nil = "darkblue", depth.out.nil = "lightblue")
      
      depth_data$count[depth_data$count == 0] <- NA
      
      #If bins need deleted at ends of contigs, this does so
      #depth_data <- depth_data[Start <= End, ]
      
      
      #reset
      gene_data <- gene_reset
      gene_data <- rbind(gene_data, gene_data)
      
      setkeyv(depth_data, c("group_label", "contig",  "Start", "End"))
      setkey(gene_data, "contig")
      
      depth_data[, gene_name := gene_data$gene_name]
      depth_data[, gene_start := gene_data$gene_start]
      depth_data[, gene_end := gene_data$gene_end]
      depth_data[, gene_strand := gene_data$strand]
      depth_data[, gene_annotation := gene_data$annotation]
      
      setkeyv(depth_data, c("group_label", "seq_pos"))
      
      gene_depth <- depth_data
      
      #Genes only
      if(input$regions_interact == 1){
        depth_data <- NULL
        depth_data <- gene_depth
        
        depth_data$count[depth_data$gene_annotation == "N/A"] <- NA
      }
      #IGR only
      if(input$regions_interact == 2){
        depth_data <- NULL
        depth_data <- gene_depth
        
        depth_data$count[depth_data$gene_annotation != "N/A"] <- NA
      }
      #Genes + long IGR
      if(input$regions_interact == 3){
        depth_data <- NULL
        depth_data <- gene_depth
        
        depth_data$count[depth_data$End-depth_data$Start + 1 < 6] <- NA
      }
      if(input$regions_interact == 4){
        depth_data <- NULL
        depth_data <- gene_depth
        
        depth_data$count <- depth_data$count
      }
      
      #If bins need deleted at ends of contigs, this does so
      depth_data <- depth_data[Start <= End, ]
      
      seq_depth_chart <- ggplot(depth_data, aes(x = seq_pos, y = count, colour=group_label, group = group_label, text = paste0("Contig: ", contig,
                                                                                                                               "\nPos. in Contig: ", Start, "-", End,
                                                                                                                               "\nNorm. Bin Count: ", round(count),
                                                                                                                               "\nGene ID: ", gene_name,
                                                                                                                               "\nGene Range: ", gene_start, "-", gene_end,
                                                                                                                               "\nGene Strand: ", gene_strand,
                                                                                                                               "\nAnnotation: ", gene_annotation)))+
        geom_step(alpha = 0.75) +
        scale_y_continuous(trans = "log10") +
        scale_x_continuous(expand=c(0,0), limits = c(0, pos_max/bp_div))+
        theme(legend.position = "none", 
              panel.border = element_blank(), 
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank(), 
              axis.line = element_line(colour = "black"), 
              panel.background = element_blank(), 
              axis.title.x = element_blank(), 
              #axis.text.y = element_text(angle = 90, hjust = 0.5),
              axis.title = element_text(size = 14),
              axis.text = element_text(size = 14)) +
        scale_color_manual(values = group.colors) +
        ylab("Log 10 Depth")
    
    }
    
    a <- list(
      range = c(min(depth_data$count, na.rm = T), max(depth_data$count, na.rm = T)),
      showticklabels = TRUE,
      exponentformat = "e"
    )
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.5, detail = "Plots created. Making first plot interactive...")
    
    seq_depth_chart <- ggplotly(seq_depth_chart, dynamicTicks = T, tooltip = c("text")) %>% 
      layout(plot_bgcolor = "grey90", yaxis = a)
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.66, detail = "Making second plot interactive...")
    
    read_rec_plot <- ggplotly(p, dynamicTicks = T, tooltip = c("text")) %>% 
      layout(plot_bgcolor = "grey90") %>% 
      style(hoverinfo = "none", traces = c(1, 2))
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 0.83, detail = "Formatting plots")
    
    overplot <- subplot(list(seq_depth_chart, read_rec_plot), nrows = 2, shareX = T, heights = c(1/3, 2/3))
    
    progress$set(message = "Creating interactive Recruitment Plot", value = 1, detail = "Done!")
    
    base <- incoming_base
    
    return(overplot)
    
  }))
  
  #Consistency between plot panels
  
  observeEvent(input$tabs, {
    
    if(input$tabs == "Recruitment Plot"){
      if(!is.null(input$samples_interact)){
        updateSelectInput(session, "samples", selected = input$samples_interact)
      }
      if(!is.null(input$mags_in_db_interact)){
        updateSelectInput(session, "mags_in_db", selected = input$mags_in_db_interact)
      }
      updateNumericInput(session, "width", value = input$width_interact)
      updateNumericInput(session, "height", value = input$height_interact)
      #updateNumericInput(session, "low_bound", value = input$low_bound_interact)
      updateNumericInput(session, "in_group_min_stat", value = input$in_group_min_interact)
      updateSelectInput(session, "regions_stat", selected = input$regions_interact)
      updateTextInput(session, "pdf_name", value = input$pdf_name_interact)
    }
    if(input$tabs == "Interactive Plot"){
      if(!is.null(input$samples)){
        updateSelectInput(session, "samples_interact", selected = input$samples)
      }
      if(!is.null(input$mags_in_db)){
        updateSelectInput(session, "mags_in_db_interact", selected = input$mags_in_db)
      }
      updateNumericInput(session, "width_interact", value = input$width)
      updateNumericInput(session, "height_interact", value = input$height)
      #updateNumericInput(session, "low_bound_interact", value = input$low_bound)
      updateNumericInput(session, "in_group_min_interact", value = input$in_group_min_stat)
      updateSelectInput(session, "regions_interact", selected = input$regions_stat)
      updateTextInput(session, "pdf_name_interact", value = input$pdf_name)
    }
    
    
    
  })
  
  session$onSessionEnded(function() {
    
    cat("\nThank you for using Recruitment Plots!\n")
    cat("Any databases you created or plots you made are stored in: ")
    cat(getwd())
    cat("\n")
    stopApp()
  })
  
}

recplot_landing_page <- function(){
  
  initiate()
  
  cat("Recruitment plots ready.\n")
  
  if (interactive()) {
    runApp(list(ui = recplot_UI(), server = recplot_server), launch.browser = T)
  }
  
  
}

recplot_landing_page()