--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ks_distribution.R	Thu Jun 08 12:55:49 2017 -0400
@@ -0,0 +1,163 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages(library("optparse"))
+
+option_list <- list(
+    make_option(c("-c", "--components_input"), action="store", dest="components_input", help="Ks significant components input dataset"),
+    make_option(c("-k", "--kaks_input"), action="store", dest="kaks_input", help="KaKs analysis input dataset"),
+    make_option(c("-o", "--output"), action="store", dest="output", help="Output dataset")
+)
+
+parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
+args <- parse_args(parser, positional_arguments=TRUE)
+opt <- args$options
+
+
+get_num_components = function(components_data)
+{
+    # Get the max of the number_comp column.
+    number_comp = components_data[, 3]
+    num_components <- max(number_comp, na.rm=TRUE)
+    return(num_components)
+}
+
+get_pi_mu_var = function(components_data, num_components)
+{
+    # FixMe: enhance this to generically handle any integer value for num_components.
+    if (num_components == 1)
+    {
+        pi <- c(components_data[1, 9])
+        mu <- c(components_data[1, 7])
+        var <- c(components_data[1, 8])
+    }
+    else if (num_components == 2)
+    {
+        pi <- c(components_data[2, 9], components_data[3, 9])
+        mu <- c(components_data[2, 7], components_data[3, 7])
+        var <- c(components_data[2, 8], components_data[3, 8])
+    }
+    else if (num_components == 3)
+    {
+      pi <- c(components_data[4, 9], components_data[5, 9], components_data[6, 9])
+      mu <- c(components_data[4, 7], components_data[5, 7], components_data[6, 7])
+      var <- c(components_data[4, 8], components_data[5, 8], components_data[6, 8])
+    }
+    else if (num_components == 4)
+    {
+        pi <- c(components_data[7, 9], components_data[8, 9], components_data[9, 9], components_data[10, 9])
+        mu <- c(components_data[7, 7], components_data[8, 7], components_data[9, 7], components_data[10, 7])
+        var <- c(components_data[7, 8], components_data[8, 8], components_data[9, 8], components_data[10, 8])
+    }
+    else if (num_components == 5)
+    {
+        pi <- c(components_data[11, 9], components_data[12, 9], components_data[13, 9], components_data[14, 9], components_data[15, 9])
+        mu <- c(components_data[11, 7], components_data[12, 7], components_data[13, 7], components_data[14, 7], components_data[15, 7])
+        var <- c(components_data[11, 8], components_data[12, 8], components_data[13, 8], components_data[14, 8], components_data[15, 8])
+    }
+    else if (num_components == 6)
+    {
+        pi <- c(components_data[16, 9], components_data[17, 9], components_data[18, 9], components_data[19, 9], components_data[20, 9], components_data[21, 9])
+        mu <- c(components_data[16, 7], components_data[17, 7], components_data[18, 7], components_data[19, 7], components_data[20, 7], components_data[21, 7])
+        var <- c(components_data[16, 8], components_data[17, 8], components_data[18, 8], components_data[19, 8], components_data[20, 8], components_data[21, 8])
+    }
+    results = c(pi, mu, var)
+    return(results)
+}
+
+plot_ks<-function(kaks_input, output, pi, mu, var)
+{
+    # Start PDF device driver to save charts to output.
+    pdf(file=output, bg="white")
+    kaks <- read.table(file=kaks_input, header=T)
+    max_ks <- max(kaks$Ks, na.rm=TRUE)
+    # Change bin width
+    max_bin_range <- as.integer(max_ks / 0.05)
+    bin <- 0.05 * seq(0, (max_bin_range + 1 ))
+    kaks <- kaks[kaks$Ks<max_ks,]
+    h.kst <- hist(kaks$Ks, breaks=bin, plot=F)
+    nc <- h.kst$counts
+    vx <- h.kst$mids
+    ntot <- sum(nc)
+    # Set margin for plot bottom, left top, right.
+    par(mai=c(0.5, 0.5, 0, 0))
+    # Plot dimension in inches.
+    par(pin=c(3.0, 3.0))
+    g <- calculate_fitted_density(pi, mu, var, max_ks)
+    h <- ntot * 1.5 / sum(g)
+    vx <- seq(1, 100) * (max_ks / 100)
+    ymax <- max(nc)
+    barplot(nc, space=0.25, offset=0, width=0.04, xlim=c(0, max_ks), ylim=c(0, ymax), col="lightpink1", border="lightpink3")
+    # Add x-axis.
+    axis(1)
+    color <- c('red', 'yellow','green','black','blue', 'darkorange' )
+    for (i in 1:length(mu))
+    {
+       lines(vx, g[,i] * h, lwd=2, col=color[i])
+    }
+}
+
+calculate_fitted_density <- function(pi, mu, var, max_ks)
+{
+    comp <- length(pi)
+    var <- var/mu^2
+    mu <- log(mu)
+    # Calculate lognormal density.
+    vx <- seq(1, 100) * (max_ks / 100)
+    fx <- matrix(0, 100, comp)
+    for (i in 1:100)
+    {
+        for (j in 1:comp)
+        {
+           fx[i, j] <- pi[j] * dlnorm(vx[i], meanlog=mu[j], sdlog=(sqrt(var[j])))
+           if (is.nan(fx[i,j])) fx[i,j]<-0
+        }
+     }
+    return(fx)
+}
+
+# Read in the components data.
+components_data <- read.delim(opt$components_input, header=TRUE)
+# Get the number of components.
+num_components <- get_num_components(components_data)
+
+# Set pi, mu, var.
+items <- get_pi_mu_var(components_data, num_components)
+if (num_components == 1)
+{
+	pi <- items[1]
+	mu <- items[2]
+	var <- items[3]
+}
+if (num_components == 2)
+{
+	pi <- items[1:2]
+	mu <- items[3:4]
+	var <- items[5:6]
+}
+if (num_components == 3)
+{
+	pi <- items[1:3]
+	mu <- items[4:6]
+	var <- items[7:9]
+}
+if (num_components == 4)
+{
+	pi <- items[1:4]
+	mu <- items[5:8]
+	var <- items[9:12]
+}
+if (num_components == 5)
+{
+	pi <- items[1:5]
+	mu <- items[6:10]
+	var <- items[11:15]
+}
+if (num_components == 6)
+{
+	pi <- items[1:6]
+	mu <- items[7:12]
+	var <- items[13:18]
+}
+
+# Plot the output.
+plot_ks(opt$kaks_input, opt$output, pi, mu, var)