#!/usr/bin/env Rscript
#########################################################
# 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) - na_count_df2
# 5) small combined df excluding NAs
# 		Dim: dim(#2) - na_count_df3
# This script is sourced from other .R scripts for plotting
#########################################################
#=======================================================================
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting/")
getwd()

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)
#========================================================
#===========
# input
#===========
#in_file1: output of plotting_data.R
# my_df_u

# quick checks
head(my_df_u[, c("mutation")])

cols_to_extract  = c("mutationinformation", "mutation", "or_mychisq", "or_kin", "af", "af_kin")
foo = my_df_u[, colnames(my_df_u)%in% cols_to_extract]


table(which(is.na(my_df_u$af_kin)) == which(is.na(my_df_u$af)))

baz = read.csv(file.choose())

baz = cbind(my_df_u$mutation, my_df_u$or_mychisq, bar$mutation, bar$or_mychisq)
baz = as.data.frame(baz)
colnames(baz) = c("my_df_u_muts", "my_df_u_or", "real_muts", "real_or")
sum(is.na(baz$my_df_u_or)) == sum(is.na(my_df_u$or_mychisq))

cat("\nNo. of with NA in or_mychisq:", sum(is.na(my_df_u$or_mychisq))
    ,"\nNo. of NA in or_kin:" , sum(is.na(my_df_u$or_kin)))

# 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
#===========
# other variables that you can write
# primarily called by other scripts for plotting

# PS combined: 
# 1) merged_df2
# 2) merged_df2_comp
# 3) merged_df3
# 4) merged_df3_comp

# LIG combined: 
# 5) merged_df2_lig
# 6) merged_df2_comp_lig
# 7) merged_df3_lig
# 8) merged_df3_comp_lig

#%%===============================================================

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


# counting NAs in AF, OR cols
# or_mychisq
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)))
}

# or kin
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)))
}

str(gene_metadata)

# change category of ambiguos mutations
table(gene_metadata$mutation_info)

cols_to_extract2  = c("mutationinformation", "mutation", "mutation_info")
foo2 = gene_metadata[, colnames(gene_metadata)%in% cols_to_extract2]

dr_muts = foo2[foo2$mutation_info == dr_muts_col,]
other_muts = foo2[foo2$mutation_info == other_muts_col,]

common_muts = dr_muts[dr_muts$mutation%in%other_muts$mutation,]
#write.csv(common_muts, 'common_muts.csv')

# FIXME read properly
# "ambiguous_mut_names.csv"
#"pnca_p.gly108arg", "pnca_p.gly132ala", "pnca_p.val180phe"
ambiguous_muts = read.csv(file.choose())
ambiguous_muts_names = ambiguous_muts$mutation

common_muts_all = gene_metadata[gene_metadata$mutation%in%ambiguous_muts_names,]

gene_metadata2 = gene_metadata

if (gene_metadata$mutation_info[gene_metadata$mutation%in%ambiguous_muts_names] == other_muts_col){
  print('change me')
}
###################################################################
#                           combining: PS
###################################################################
# sort by position (same as my_df)
head(gene_metadata$position)
gene_metadata = gene_metadata[order(gene_metadata$position),]
head(gene_metadata$position)

#=========================
# Merge 1: merged_df2
# dfs with NAs in ORs
#=========================
head(my_df_u$mutationinformation)
head(gene_metadata$mutationinformation)

# Find common columns b/w two df
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))

# 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)

# sanity check 
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])
  quit()
}

#=========================
# Merge 2: merged_df3
# dfs with NAs in ORs
#
# 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
#=========================
# 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")

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()
}

#=========================
# Merge3: merged_df2_comp
# same as merge 1 but excluding NAs from ORs, etc.
#=========================
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")
}

#=========================
# Merge4: merged_df3_comp
# same as merge 2 but excluding NAs from ORs, etc or 
# 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))

#==============================================================
#################
# 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
   , in_filename_gene_metadata, infile_gene_metadata
   , merged_df2v2, merged_df2v3)
#*************************
#####################################################################
#                       Combining: LIG
#####################################################################

#=========================
# Merges 5-8
#=========================

merged_df2_lig = merged_df2[merged_df2$ligand_distance<10,]
merged_df2_comp_lig = merged_df2_comp[merged_df2_comp$ligand_distance<10,]

merged_df3_lig = merged_df3[merged_df3$ligand_distance<10,]
merged_df3_comp_lig = merged_df3_comp[merged_df3_comp$ligand_distance<10,]

# sanity check
if (nrow(merged_df3_lig) == nrow(my_df_u_lig)){
  print("PASS: verified merged_df3_lig")
}else{
  cat(paste0("FAIL: nrow mismatch for merged_df3_lig"
             , "\nExpected:", nrow(my_df_u_lig)
             , "\nGot:", nrow(merged_df3_lig)))
}

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