#!/usr/bin/env Rscript             
#########################################################
# TASK: formatting data that will be used for various plots
#########################################################
# load libraries and functions
library(data.table)
library(dplyr)
#########################################################
# FIXME (not urgent!): Dirty function return nothing, but creates global dfs
# plotting_data(): formatting data for plots
# input args: 
 ## input csv file
 ## lig cut off dist, default = 10 Ang
# output: None
# Side effects: global dfs (formatted and added columns)
  ## my_df
  ## my_df_u
  ## my_df_u_lig
  ## dup_muts

plotting_data <- function(infile_params, mcsm_lig_cutoff = 10) {
my_df       = data.frame()
my_df_u     = data.frame()
my_df_u_lig = data.frame()
dup_muts    = data.frame()
  
cat(paste0("Input file 1:", infile_params, '\n') )

# These globals are created by import_dirs()
#cat('columns based on variables:\n'
#    , drug
#    , '\n'
#    , dr_muts_col
#    , '\n'
#    , other_muts_col
#    , "\n"
#    , resistance_col
#    , '\n===============================================================')

#===========================
# Read file: struct params
#===========================
my_df = read.csv(infile_params, header = T)

cat("\nInput dimensions:", dim(my_df)) 

#==================================
# add foldx outcome category
# and foldx scaled values 

# This will enable to always have these variables available
# when calling for plots
#==================================

#------------------------------
# adding foldx scaled values
# scale data b/w -1 and 1
#------------------------------
n = which(colnames(my_df) == "ddg"); n 

my_min = min(my_df[,n]); my_min 
my_max = max(my_df[,n]); my_max 

my_df$foldx_scaled = ifelse(my_df[,n] < 0
                            , my_df[,n]/abs(my_min)
                            , my_df[,n]/my_max) 
# sanity check
my_min = min(my_df$foldx_scaled); my_min 
my_max = max(my_df$foldx_scaled); my_max

if (my_min == -1 && my_max == 1){
  cat("\nPASS: foldx ddg successfully scaled b/w -1 and 1"
      , "\nProceeding with assigning foldx outcome category")
}else{
  cat("\nFAIL: could not scale foldx ddg values"
      , "Aborting!")
}

#------------------------------
# adding foldx outcome category
# ddg<0 = "Stabilising" (-ve)
#------------------------------
c1 = table(my_df$ddg < 0)
my_df$foldx_outcome = ifelse(my_df$ddg < 0, "Stabilising", "Destabilising")
c2 = table(my_df$ddg < 0)

if ( all(c1 == c2) ){
  cat("\nPASS: foldx outcome successfully created")
}else{
  cat("\nFAIL: foldx outcome could not be created. Aborting!")
  exit()
}

#==================================
# extract unique mutation entries
#==================================

# check for duplicate mutations
if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformation)){
  cat(paste0("\nCAUTION:", " Duplicate mutations identified"
             , "\nExtracting these..."))
  #cat(my_df[duplicated(my_df$mutationinformation),])
  dup_muts = my_df[duplicated(my_df$mutationinformation),]
  dup_muts_nu = length(unique(dup_muts$mutationinformation))
  cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
             , "\nNo. of unique duplicate mutations:", dup_muts_nu
             , "\n\nExtracting df with unique mutations only"))
  my_df_u = my_df[!duplicated(my_df$mutationinformation),]
}else{
  cat(paste0("\nNo duplicate mutations detected"))
  my_df_u = my_df
}

upos = unique(my_df_u$position)
cat("\nDim of clean df:"); cat(dim(my_df_u))
cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
  
#===============================================
# extract mutations <10 Angstroms and symbol
#===============================================
table(my_df_u$ligand_distance<mcsm_lig_cutoff)

my_df_u_lig = my_df_u[my_df_u$ligand_distance <mcsm_lig_cutoff,]

cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10\u212b of the ligand\n"))

# return list of DFs

#return(list(my_df, my_df_u, my_df_u_lig, dup_muts))
#df_names = c("my_df", "my_df_u", "my_df_u_lig", "dup_muts")
all_df = list(my_df, my_df_u, my_df_u_lig, dup_muts)
#all_df = Map(setNames, all_df, df_names)

return(all_df)
}

########################################################################
#               end of data extraction and cleaning for plots          #
########################################################################