Merge pull request #4 from IBPA/master

bug fixes and gurobi setup script

R/common_OMMs.R

@@ -86,7 +86,7 @@ load_MMs <- function(filename) {
   MMs <- matrix(0, nrow = num_meals, ncol = length(uids), dimnames = list(1:num_meals, uids))
   for (i in 1:num_meals){
     line_cur <- all_lines[[i]]
-    df_cur <- data.frame(str_split(line_cur, ":"))
+    df_cur <- data.frame(str_split(line_cur, ":"), stringsAsFactors = FALSE)
     fids_cur <- unlist(unname(df_cur[1,]))
     proportions_cur <- unname(df_cur[2,])
     MMs[i,fids_cur] <- as.numeric(unlist(proportions_cur))

R/convert_to_friendlyname_OMMs.R

@@ -4,6 +4,7 @@
 suppressPackageStartupMessages(library("argparse"))
 library(stringr)
 
+# Parse and return command line arguments of this script
 get_args <- function() {
   parser <- ArgumentParser(description = "Use friendly food names instead of uids for the OMMs.")
   parser$add_argument("--glycanDB", required=TRUE,

R/setup_OMMs.R

@@ -0,0 +1,17 @@
+#!/usr/bin/env Rscript
+#' One-time setup script
+
+suppressPackageStartupMessages(library("argparse"))
+library(stringr)
+
+# Parse and return command line arguments of this script
+get_args <- function() {
+  parser <- ArgumentParser(description = "One-time setup for OMMs package.")
+  parser$add_argument("--gurobi-R-package", required=TRUE,
+                      help="The path to the gurobi R package.")
+  args <- parser$parse_args()
+  return(args)
+}
+
+args <- get_args()
+install.packages(args$gurobi_R_package, repos=NULL)

R/visualize_OMMs.R

@@ -46,7 +46,6 @@ source_from_here <- function(filename) {
 source_from_here("common_OMMs.R")
 source_from_here("theme_util_OMMs.R")
 
-
 args <- get_args()
 
 # 1) Load data
@@ -68,8 +67,8 @@ gPlot_bars <- ggplot(df_MM_melt, aes(x=id, y=Quantity, fill=Glycan))+
           my_base_theme
 
 print(gPlot_bars)
-ggsave("./results/Fig1_A.pdf", gPlot_bars, dpi = 400, width=8 , height = 4)
-ggsave("./results/Fig1_A.png", gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_A.pdf"), gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_A.png"), gPlot_bars, dpi = 400, width=8 , height = 4)
 
 # 3.B) Plot stacked bar-charts of mixed meals (proportions)
 df_MM <- data.frame(MM_vectors)
@@ -81,8 +80,8 @@ gPlot_bars <- ggplot(df_MM_melt, aes(x=id, y=Quantity, fill=Glycan))+
   scale_y_continuous("Glycan %weight per mg of wet sample", labels = scales::percent)+
   my_base_theme
 print(gPlot_bars)
-ggsave("./results/Fig1_B.pdf", gPlot_bars, dpi = 400, width=8 , height = 4)
-ggsave("./results/Fig1_B.png", gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_B.pdf"), gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_B.png"), gPlot_bars, dpi = 400, width=8 , height = 4)
 
 # 3.C) Plot stacked bar-charts of foods (proportions)
 df_food_vectors_vis <- data.frame(df_food_vectors)
@@ -96,8 +95,8 @@ gPlot_bars <- ggplot(df_food_vectors_melt, aes(x=id, y=Quantity, fill=Glycan))+
   scale_y_continuous("Glycan %weight per mg of wet sample", labels = scales::percent)+
   my_base_theme
 print(gPlot_bars)
-ggsave("./results/Fig1_C.pdf", gPlot_bars, dpi = 400, width=8 , height = 4)
-ggsave("./results/Fig1_C.png", gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_C.pdf"), gPlot_bars, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig1_C.png"), gPlot_bars, dpi = 400, width=8 , height = 4)
 
 # 4) Calculate information content iteratively
 df_vectors_all <- data.frame(MM_vectors)
@@ -120,8 +119,8 @@ gPlot <- ggplot(df_infos, aes(x=iter, y=infos))+
           scale_y_continuous("Glycan information content (entropy)", breaks = 0:round(max(df_infos$info)))+
           my_base_theme
 print(gPlot)
-ggsave("./results/Fig2.pdf", gPlot, dpi = 400, width=8 , height = 4)
-ggsave("./results/Fig2.png", gPlot, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig2.pdf"), gPlot, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig2.png"), gPlot, dpi = 400, width=8 , height = 4)
 
 # 6) Histogram for number of foods used in the mixed-meals
 df_hist <- data.frame(id=1:nrow(MMs), count = rowSums(MMs!=0))
@@ -132,6 +131,6 @@ gPlot_hist <- ggplot(df_hist, aes(count))+
                 my_base_theme
 sprintf("Mixed meals use an average %.2f foods.", mean(df_hist$count))
 print(gPlot_hist)
-ggsave("./results/Fig3.pdf", gPlot_hist, dpi = 400, width=8 , height = 4)
-ggsave("./results/Fig3.png", gPlot_hist, dpi = 400, width=8 , height = 4)
-print("Completed Successfully!")
+ggsave(file.path(args$output_dir,"Fig3.pdf"), gPlot_hist, dpi = 400, width=8 , height = 4)
+ggsave(file.path(args$output_dir,"Fig3.png"), gPlot_hist, dpi = 400, width=8 , height = 4)
+print("Completed Successfully!")