#!/usr/bin/env Rscript       
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting/")
getwd()

#########################################################
# TASK: To combine struct params and meta data for plotting
# Input csv files:
# 1) <gene>_all_params.csv
# 2) <gene>_meta_data.csv

# Output: 
# 1) muts with opposite effects on stability
# 2) large combined df including NAs for AF, OR,etc
# 		Dim: same no. of rows as gene associated meta_data_with_AFandOR
# 3) small combined df including NAs for AF, OR, etc.
# 		Dim: same as mcsm data
# 4) large combined df excluding NAs 
# 		Dim: dim(#1) - no. of NAs(AF|OR) + 1
# 5) small combined df excluding NAs
# 		Dim: dim(#2) - no. of unique NAs - 1
# This script is sourced from other .R scripts for plotting
#########################################################

##########################################################
# 				Installing and loading required packages 			 
##########################################################
#source("Header_TT.R")
require(data.table)
require(arsenal)
require(compare)
library(tidyverse)

source("plotting_data.R")

# should return the following dfs, directories and variables
# my_df
# my_df_u
# my_df_u_lig
# dup_muts

cat(paste0("Directories imported:"
           , "\ndatadir:", datadir
           , "\nindir:", indir
           , "\noutdir:", outdir
           , "\nplotdir:", plotdir))

cat(paste0("Variables imported:"
           , "\ndrug:", drug
           , "\ngene:", gene
           , "\ngene_match:", gene_match
           , "\nLength of upos:", length(upos)
           , "\nAngstrom symbol:", angstroms_symbol))       

# clear excess variable
rm(my_df, upos, dup_muts, my_df_u_lig)
#========================================================

#========================================================
#%% variable assignment: input and output paths & filenames
drug = "pyrazinamide"
gene = "pncA"
gene_match = paste0(gene,"_p.")
cat(gene_match)

#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
plotdir = paste0("~/git/Data", "/", drug, "/output/plots")
#===========
# input
#===========
#in_file1: output of plotting_data.R


# infile 2: gene associated meta data
#in_filename_gene_metadata = paste0(tolower(gene),  "_meta_data_with_AFandOR.csv")
in_filename_gene_metadata = paste0(tolower(gene),  "_metadata.csv")
infile_gene_metadata = paste0(outdir, "/", in_filename_gene_metadata)
cat(paste0("Input infile 2:", infile_gene_metadata))

#===========
# output
#===========
# mutations with opposite effects
out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)


#%%===============================================================
table(my_df_u$duet_outcome); sum(table(my_df_u$duet_outcome) )

# spelling Correction 1: DUET incase American spelling needed!
#my_df_u$duet_outcome[my_df_u$duet_outcome=="Stabilising"] <- "Stabilizing"
#my_df_u$duet_outcome[my_df_u$duet_outcome=="Destabilising"] <- "Destabilizing"


# spelling Correction 2: Ligand incase American spelling needed!
table(my_df_u$ligand_outcome); sum(table(my_df_u$ligand_outcome) )
#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Stabilising"] <- "Stabilizing"
#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Destabilising"] <- "Destabilizing"


# muts with opposing effects on protomer and ligand stability
table(my_df_u$duet_outcome != my_df_u$ligand_outcome)
changes = my_df_u[which(my_df_u$duet_outcome != my_df_u$ligand_outcome),]

# sanity check: redundant, but uber cautious!
dl_i = which(my_df_u$duet_outcome != my_df_u$ligand_outcome)
ld_i = which(my_df_u$ligand_outcome != my_df_u$duet_outcome)

cat("Identifying muts with opposite stability effects")
if(nrow(changes) == (table(my_df_u$duet_outcome != my_df_u$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
  cat("PASS: muts with opposite effects on stability and affinity correctly identified"
        , "\nNo. of such muts: ", nrow(changes))
}else {
  cat("FAIL: unsuccessful in extracting muts with changed stability effects")
}

#***************************
# write file: changed muts
write.csv(changes, outfile_opp_muts)

cat("Finished writing file for muts with opp effects:"
    , "\nFilename: ", outfile_opp_muts
    , "\nDim:", dim(changes))

# clear variables
rm(out_filename_opp_muts, outfile_opp_muts)
rm(changes, dl_i, ld_i)

# count na in each column
na_count = sapply(my_df_u, function(y) sum(length(which(is.na(y))))); na_count
df_ncols = ncol(my_df_u)

###########################
# 2: Read file: <gene>_meta data.csv
###########################
cat("Reading meta data file:", infile_gene_metadata)

gene_metadata <- read.csv(infile_gene_metadata
                      , stringsAsFactors = F
                      , header = T)
cat("Dim:", dim(gene_metadata))

#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# FIXME: remove
# counting NAs in AF, OR cols: 
if (identical(sum(is.na(my_df_u$or_mychisq))
              , sum(is.na(my_df_u$pval_fisher))
              , sum(is.na(my_df_u$af)))){
  cat("\nPASS: NA count match for OR, pvalue and AF\n")
  na_count = sum(is.na(my_df_u$af))
  cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_mychisq)))
} else{
  cat("\nFAIL: NA count mismatch"
      , "\nNA in OR: ", sum(is.na(my_df_u$or_mychisq))
      , "\nNA in pvalue: ", sum(is.na(my_df_u$pval_fisher))
      , "\nNA in AF:", sum(is.na(my_df_u$af)))
}


if (identical(sum(is.na(my_df_u$or_kin))
              , sum(is.na(my_df_u$pwald_kin))
              , sum(is.na(my_df_u$af_kin)))){
  cat("\nPASS: NA count match for OR, pvalue and AF\n from Kinship matrix calculations")
  na_count = sum(is.na(my_df_u$af_kin))
  cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_kin)))
} else{
  cat("\nFAIL: NA count mismatch"
      , "\nNA in OR: ", sum(is.na(my_df_u$or_kin))
      , "\nNA in pvalue: ", sum(is.na(my_df_u$pwald_kin))
      , "\nNA in AF:", sum(is.na(my_df_u$af_kin)))
}

#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# clear variables
rm(in_filename_gene_metadata, infile_gene_metadata)

str(gene_metadata)

# sort by position (same as my_df)
# earlier it was mutationinformation
#head(gene_metadata$mutationinformation)
#gene_metadata = gene_metadata[order(gene_metadata$mutationinformation),]
##head(gene_metadata$mutationinformation)

head(gene_metadata$position)
gene_metadata = gene_metadata[order(gene_metadata$position),]
head(gene_metadata$position)

###########################
# Merge 1: two dfs with NA
# merged_df2
###########################
head(my_df_u$mutationinformation)
head(gene_metadata$mutationinformation)

# Find common columns b/w two df
# FIXME: mutation has empty cell for some muts
merging_cols = intersect(colnames(my_df_u), colnames(gene_metadata))

cat(paste0("Merging dfs with NAs: big df (1-many relationship b/w id & mut)"
           , "\nNo. of merging cols:", length(merging_cols)
           , "\nMerging columns identified:"))
print(merging_cols)

# important checks!
table(nchar(my_df_u$mutationinformation))
table(nchar(my_df_u$wild_type))
table(nchar(my_df_u$mutant_type))
table(nchar(my_df_u$position))

#=============
# merged_df2: gene_metadata + my_df
#==============
# all.y  because x might contain non-structural positions!
merged_df2 = merge(x = gene_metadata
                  , y = my_df_u
                  , by = merging_cols
                  , all.y = T)

cat("Dim of merged_df2: ", dim(merged_df2))
head(merged_df2$position)

#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# FIXME: count how many unique muts have entries in meta data
# should PASS 
cat("Checking nrows in merged_df2")
if(nrow(gene_metadata) == nrow(merged_df2)){
  cat("PASS: nrow(merged_df2) = nrow (gene associated gene_metadata)"
      ,"\nExpected no. of rows: ",nrow(gene_metadata) 
      ,"\nGot no. of rows: ", nrow(merged_df2))
} else{
  cat("FAIL: nrow(merged_df2)!= nrow(gene associated gene_metadata)"
      , "\nExpected no. of rows after merge: ", nrow(gene_metadata)
      , "\nGot no. of rows: ", nrow(merged_df2)
      , "\nFinding discrepancy")
  merged_muts_u = unique(merged_df2$mutationinformation)
  meta_muts_u = unique(gene_metadata$mutationinformation)
    # find the index where it differs
  unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
}

#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# sort by position
head(merged_df2$position)
merged_df2 = merged_df2[order(merged_df2$position),]
head(merged_df2$position)

merged_df2v3 = merge(x = gene_metadata
                   , y = my_df_u
                   , by = merging_cols
                   , all = T) 

merged_df2v2 = merge(x = gene_metadata
                     , y = my_df_u
                     , by = merging_cols
                     , all.x = T) 
#!=!=!=!=!=!=!=!
#identical(merged_df2, merged_df2v2)

nrow(merged_df2[merged_df2$position==186,])
#!=!=!=!=!=!=!=!

# should  be False
identical(merged_df2, merged_df2v2)
table(merged_df2$position%in%merged_df2v2$position)

#!!!!!!!!!!! check why these differ

#########
# merge 3b (merged_df3):remove duplicated mutations
cat("Merging dfs without NAs: small df (removing muts with no AF|OR associated)"
    ,"\nCannot trust lineage info from this"
    ,"\nlinking col: mutationinforamtion"
    ,"\nfilename: merged_df3")

#==#=#=#=#=#=#
# Cannot trust lineage, country from this df as the same mutation
# can have many different lineages
# but this should be good for the numerical corr plots
#=#=#=#=#=#=#=
merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),] 
head(merged_df3$position); tail(merged_df3$position) # should be sorted

# sanity check
cat("Checking nrows in merged_df3")
if(nrow(my_df_u) == nrow(merged_df3)){
  cat("PASS: No. of rows match with my_df"
      ,"\nExpected no. of rows: ", nrow(my_df_u)
      ,"\nGot no. of rows: ", nrow(merged_df3))
} else {
  cat("FAIL: No. of rows mismatch"
      , "\nNo. of rows my_df: ", nrow(my_df_u)
      , "\nNo. of rows merged_df3: ", nrow(merged_df3))
  quit()
}

# counting NAs in AF, OR cols in merged_df3
# this is because mcsm has no AF, OR cols,
# so you cannot count NAs
if (identical(sum(is.na(merged_df3$or_kin))
              , sum(is.na(merged_df3$pwald_kin))
              , sum(is.na(merged_df3$af_kin)))){
  cat("PASS: NA count match for OR, pvalue and AF\n")
  na_count_df3 = sum(is.na(merged_df3$af_kin))
  cat("No. of NAs: ", sum(is.na(merged_df3$or_kin)))
} else{
  cat("FAIL: NA count mismatch"
      , "\nNA in OR: ", sum(is.na(merged_df3$or_kin))
      , "\nNA in pvalue: ", sum(is.na(merged_df3$pwald_kin))
      , "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
}

# check if the same or and afs are missing for 
if ( identical( which(is.na(merged_df2$or_mychisq)), which(is.na(merged_df2$or_kin)))
  && identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin)))
  && identical( which(is.na(merged_df2$pval_fisher)), which(is.na(merged_df2$pwald_kin))) ){
  cat('PASS: Indices match for mychisq and kin ors missing values')
} else{
  cat('Index mismatch: mychisq and kin ors missing indices match')
  quit()
}

###########################
# 4: merging two dfs: without NA
###########################
#########
# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
#########
cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
    ,"\nlinking col: Mutationinforamtion"
    ,"\nfilename: merged_df2_comp")

if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
  print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
  na_count_df2 = sum(is.na(merged_df2$af))
  merged_df2_comp = merged_df2[!is.na(merged_df2$af),] 
  # sanity check: no +-1 gymnastics
  cat("Checking nrows in merged_df2_comp")
  if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count_df2)){
    cat("\nPASS: No. of rows match"
        ,"\nDim of merged_df2_comp: "
        ,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
        , "\nNo. of rows: ", nrow(merged_df2_comp)
        , "\nNo. of cols: ", ncol(merged_df2_comp))
  }else{
    cat("FAIL: No. of rows mismatch"
        ,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
        ,"\nGot no. of rows: ", nrow(merged_df2_comp))
  }
}else{
  print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
}

#########
# merge 4b (merged_df3_comp): remove duplicate mutation information
#########
if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
  print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
  na_count_df3 = sum(is.na(merged_df3$af))
  #merged_df3_comp = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),] # a way
  merged_df3_comp = merged_df3[!is.na(merged_df3$af),] # another way
  cat("Checking nrows in merged_df3_comp")
  if(nrow(merged_df3_comp) == (nrow(merged_df3) - na_count_df3)){
    cat("\nPASS: No. of rows match"
        ,"\nDim of merged_df3_comp: "
        ,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
        , "\nNo. of rows: ", nrow(merged_df3_comp)
        , "\nNo. of cols: ", ncol(merged_df3_comp))
    }else{
    cat("FAIL: No. of rows mismatch"
        ,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
        ,"\nGot no. of rows: ", nrow(merged_df3_comp))
   }
} else{
  print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
} 
  
# alternate way of deriving merged_df3_comp
foo = merged_df3[!is.na(merged_df3$af),]
bar = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),]
# compare dfs: foo and merged_df3_com
all.equal(foo, bar)
#summary(comparedf(foo, bar))

#=============== end of combining df
#==============================================================
#################
# OPTIONAL: write ALL 4 output files 
#################
#outvars = c("merged_df2"
#             , "merged_df3"
#             , "merged_df2_comp"
#             , "merged_df3_comp")

#cat("Writing output files: "
#    , "\nPath:", outdir)

#for (i in outvars){
#  out_filename = paste0(i, ".csv")
#  outfile = paste0(outdir, "/", out_filename)
#  cat("Writing output file:"
#      ,"\nFilename: ", out_filename,"\n")
#  write.csv(get(i), outfile, row.names = FALSE)
#  cat("Finished writing: ", outfile
#      , "\nNo. of rows: ", nrow(get(i))
#      , "\nNo. of cols: ", ncol(get(i)), "\n")
#}

#*************************
# clear variables
rm(foo, bar, gene_metadata
   , in_filename_params, infile_params, merging_cols
   , merged_df2v2, merged_df2v3)

#============================= end of script