#!/usr/bin/env Rscript                                                  
    
#source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.

# read pdb file

# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs

# generate some distribution plots for inspection

#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))

#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = ""
#gene = ""

# # command line args
# spec = matrix(c(
#   "drug"   , "d", 1, "character",
#   "gene"   , "g", 1, "character"
# ), byrow = TRUE, ncol = 4)
# 
# opt = getopt(spec)
# 
# drug = opt$drug
# gene = opt$gene
# 
# if(is.null(drug)|is.null(gene)) {
#   stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
# }
#========================================================
cat(gene)
gene_match = paste0(gene,"_p."); cat(gene_match)
cat(gene_match)

#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))

#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb") 
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )

#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)

in_filename_mean_stability = paste0(tolower(gene), "_mean_ens_stability.csv")
infile_mean_stability = paste0(outdir_plots, "/", in_filename_mean_stability)
cat(paste0("Input file:", infile_mean_stability) )

#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_duet_mspdb = paste0(tolower(gene), "_complex_b_stab_ms.pdb") 
outfile_duet_mspdb = paste0(outdir_plots, "/", out_filename_duet_mspdb)
print(paste0("Output file:", outfile_duet_mspdb))

#%%===============================================================
#NOTE: duet here refers to the ensemble stability values

###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_stability, header = T)
str(my_df)

#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
                  , maxlines = -1
                  , multi = FALSE 
                  , rm.insert = FALSE
                  , rm.alt = TRUE
                  , ATOM.only = FALSE 
                  , hex = FALSE
                  , verbose = TRUE)

rm(in_filename_mean_stability, in_filename_pdb)

# assign separately for duet and ligand 
my_pdb_duet = my_pdb

#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]

# make a copy: required for downstream sanity checks
d2_duet = df_duet

# sanity checks: B factor
max(df_duet$b); min(df_duet$b)

#==================================================
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#==================================================
# Set the margin on all sides
par(oma = c(3,2,3,0)
    , mar = c(1,3,5,2)
    #, mfrow = c(3,2)
    #, mfrow = c(3,4))
    , mfrow  = c(3,2))


#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
     , xlab = "" 
     , main = "Bfactor stability")

plot(density(df_duet$b)
     , xlab = ""
     , main = "Bfactor stability")

#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============

#hist(my_df$averaged_duet
hist(my_df$avg_stability_scaled_pos_scaled
     , xlab = "" 
     , main = "mean stability values")

#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_stability_scaled_pos_scaled)
     , xlab = ""
     , main = "mean stability values")

#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check:  match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]

#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA

#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
df_duet$b = my_df$avg_stability_scaled_pos_scaled[match(df_duet$resno, my_df$position)]

#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet

# count number of 0"s in Bactor
sum(df_duet$b == 0)

# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep

# # sanity check: should be 0 and True
# # duet
# if ( (sum(df_duet$b == na_rep) == b_na_duet) {
#   print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
#   print("FAIL: NA replacement in df_duet NOT successful")
#   quit()
# }
# 
# max(df_duet$b); min(df_duet$b)
# 
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_stability_scaled_pos_scaled)) & (min(df_duet$b) == min(my_df$avg_stability_scaled_pos_scaled)) ){
#   print("PASS: B-factors replaced correctly in df_duet")
# } else {
#   print ("FAIL: To replace B-factors in df_duet")
#   quit()
# }

#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) &
     (dim(df_duet)[2] == dim(d2_duet)[2])
    ){
  print("PASS: Dims of both dfs as expected")
} else {
  print ("FAIL: Dims mismatch")
  quit()}

#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))

#=========
# step 5_P1
#=========
cat(paste0("output file duet mean stability pdb:", outfile_duet_mspdb))
write.pdb(my_pdb_duet, outfile_duet_mspdb)

#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
     , xlab = ""
     , main = "repalcedB duet")

plot(density(df_duet$b)
     , xlab = ""
     , main = "replacedB duet")

# graph titles
mtext(text = "Frequency"
      , side = 2
      , line = 0
      , outer = TRUE)

mtext(text = paste0(tolower(gene), ": stability distribution")
      , side = 3
      , line = 0
      , outer = TRUE)
#============================================

#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!