#!/usr/bin/env Rscript                                                  
source("~/git/LSHTM_analysis/config/gid.R")

source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")

#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised affinity values

#########################################################
# working dir and loading libraries

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

#========================================================
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/Writing/thesis/images/results/", tolower(gene))

#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
cat("plots will output to:", outdir_images)
#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb") 
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )

#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_lig_mspdb = paste0(tolower(gene), "_complex_b_lig_ms.pdb") 
outfile_lig_mspdb = paste0(outdir_images,out_filename_lig_mspdb)
print(paste0("Output file:", outfile_lig_mspdb))

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

###########################
# Read file: average affinity values
# or mcsm_normalised file
###########################
my_df_raw = merged_df3[, c("position", "ligand_distance", "avg_lig_affinity_scaled", "avg_lig_affinity")]
my_df_raw = my_df_raw[my_df_raw$ligand_distance<10,]

# avg by position on the SCALED values
my_df <- my_df_raw %>%
  group_by(position) %>%
  summarize(avg_ligaff_sc_pos = mean(avg_lig_affinity_scaled))

max(my_df$avg_ligaff_sc_pos)
min(my_df$avg_ligaff_sc_pos)

#############
# 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)

# assign separately for duet and ligand 
my_pdb_duet = my_pdb

#=========================================================
# Replacing B factor with mean affinity 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 affinity values
# 3: replaced B-factors with mean affinity 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 affinity")

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

#=============
# Row 2 plots: original mean affinity values
# affinity
#=============

#hist(my_df$averaged_duet
hist(my_df$avg_ligaff_sc_pos
     , xlab = "" 
     , main = "mean affinity values")

#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_ligaff_sc_pos)
     , xlab = ""
     , main = "mean affinity values")

################################################################


#=========
# step 1_P1
#=========

df_duet$b = my_df$avg_ligaff_sc_pos[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

#=========
# 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:  OUTPUT
#=========
cat(paste0("output file duet mean affinity pdb:", outfile_lig_mspdb))
write.pdb(my_pdb_duet, outfile_lig_mspdb)

# OUTPUT: position file
poscsvF = paste0(outdir_images, tolower(gene), "_ligaff_positions.csv")
cat(paste0("output file duet mean NA affinity POSITIONS:", poscsvF))

filtered_pos = toString(my_df$position)
write.table(filtered_pos, poscsvF, row.names = F, col.names = F )

#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with affinity 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), ": affinity distribution")
      , side = 3
      , line = 0
      , outer = TRUE)
#============================================