#!/usr/bin/env Rscript
#########################################################
# TASK: Get formatted data for plots
#=======================================================================
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()

source("Header_TT.R")
source("../functions/my_pairs_panel.R") # with lower panel turned off
source("../functions/plotting_globals.R")
source("../functions/plotting_data.R")
source("../functions/combining_dfs_plotting.R")

#********************
# cmd args passed 
# in from other scripts
# to call this
#********************
#drug = 'streptomycin'
#gene = 'gid'
#====================
# variables for lig
#====================

LigDist_colname = "ligand_distance"
LigDist_cutoff = 20

#===========
# input
#===========
#---------------------
# call: import_dirs()
#---------------------
import_dirs(drug, gene)

#---------------------------
# call: plotting_data()
#---------------------------
#if (!exists("infile_params") && exists("gene")){
if (!is.character(infile_params) && exists("gene")){ # when running as cmd
  #in_filename_params = paste0(tolower(gene), "_all_params.csv") 
  in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
  infile_params = paste0(outdir, "/", in_filename_params)
  cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
}

# Input 1: read <gene>_comb_afor.csv
cat("\nReading mcsm combined data file: ", infile_params)
mcsm_df = read.csv(infile_params, header = T)
pd_df = plotting_data(mcsm_df
                      , lig_dist_colname = LigDist_colname
                      , lig_dist_cutoff = LigDist_cutoff)

my_df       = pd_df[[1]] 
my_df_u     = pd_df[[2]] # this forms one of the input for combining_dfs_plotting()
my_df_u_lig = pd_df[[3]] 
dup_muts    = pd_df[[4]] 

#--------------------------------
# call: combining_dfs_plotting()
#--------------------------------
#if (!exists("infile_metadata") && exists("gene")){
if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
  in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
  infile_metadata = paste0(outdir, "/", in_filename_metadata)
  cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
}

# Input 2: read <gene>_meta data.csv
cat("\nReading meta data file: ", infile_metadata)

gene_metadata <- read.csv(infile_metadata
                          , stringsAsFactors = F
                          , header = T)

all_plot_dfs = combining_dfs_plotting(my_df_u
                                      , gene_metadata
                                      , lig_dist_colname = LigDist_colname
                                      , lig_dist_cutoff = LigDist_cutoff)

merged_df2          = all_plot_dfs[[1]]
merged_df3          = all_plot_dfs[[2]]
merged_df2_comp     = all_plot_dfs[[3]]
merged_df3_comp     = all_plot_dfs[[4]]
merged_df2_lig      = all_plot_dfs[[5]]
merged_df3_lig      = all_plot_dfs[[6]]
merged_df2_comp_lig = all_plot_dfs[[7]]
merged_df3_comp_lig = all_plot_dfs[[8]]

####################################################################
#                        Data for logoplots
####################################################################
#-------------------------
# choose df for logoplot
#-------------------------
logo_data = merged_df3
#logo_data = merged_df3_comp

# quick checks
colnames(logo_data)
str(logo_data)

c1 = unique(logo_data$position) 
nrow(logo_data)
cat("No. of rows in my_data:", nrow(logo_data)
    , "\nDistinct positions corresponding to snps:", length(c1)
    , "\n===========================================================")
#=======================================================================
#%% logo plots from dataframe

foo = logo_data[, c("position"
                      , "mutant_type","duet_scaled", "or_mychisq"
                      , "mut_prop_polarity", "mut_prop_water")] 

logo_data$log10or = log10(logo_data$or_mychisq)
logo_data_plot = logo_data[, c("position"
                            , "mutant_type", "or_mychisq", "log10or")]

logo_data_plot_or = logo_data[, c("position", "mutant_type", "or_mychisq")]
wide_df_or <- logo_data_plot_or %>% spread(position, or_mychisq, fill = 0.0)

wide_df_or = as.matrix(wide_df_or)
rownames(wide_df_or) = wide_df_or[,1]
dim(wide_df_or)
wide_df_or = wide_df_or[,-1]
str(wide_df_or)

position_or = as.numeric(colnames(wide_df_or))

#==================
# logo data: logOR
#==================
# extracting data with log10R
logo_data_plot_logor = logo_data[, c("position", "mutant_type", "log10or")]
wide_df_logor <- logo_data_plot_logor %>% spread(position, log10or, fill = 0.0)

wide_df_logor = as.matrix(wide_df_logor)

rownames(wide_df_logor) = wide_df_logor[,1]
wide_df_logor = subset(wide_df_logor, select = -c(1) )
colnames(wide_df_logor)
wide_df_logor_m = data.matrix(wide_df_logor)

rownames(wide_df_logor_m)
colnames(wide_df_logor_m)

position_logor = as.numeric(colnames(wide_df_logor_m))

#===============================
# logo data: multiple nsSNPs (>1)
#=================================
#require(data.table)

# get freq count of positions so you can subset freq<1
setDT(logo_data)[, mut_pos_occurrence := .N, by = .(position)] 

table(logo_data$position)
table(logo_data$mut_pos_occurrence)

max_mut = max(table(logo_data$position))

# extract freq_pos > 1
my_data_snp = logo_data[logo_data$mut_pos_occurrence!=1,] 
u = unique(my_data_snp$position)
max_mult_mut = max(table(my_data_snp$position))

if (nrow(my_data_snp) == nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]] ){
  
  cat("PASS: positions with  multiple muts extracted"
      , "\nNo. of mutations:", nrow(my_data_snp)
      , "\nNo. of positions:", length(u)
      , "\nMax no. of muts at any position", max_mult_mut)
}else{
  cat("FAIL: positions with multiple muts could NOT be extracted"
      , "\nExpected:",nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]]
      , "\nGot:", nrow(my_data_snp) )
}

cat("\nNo. of sites with only 1 mutations:", table(logo_data$mut_pos_occurrence)[[1]])

#--------------------------------------
# matrix for_mychisq mutant type
# frequency of mutant type by position
#---------------------------------------
table(my_data_snp$mutant_type, my_data_snp$position)
tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
class(tab_mt)

# unclass to convert to matrix
tab_mt = unclass(tab_mt)
tab_mt = as.matrix(tab_mt, rownames = T)

# should be TRUE
is.matrix(tab_mt)

rownames(tab_mt) #aa
colnames(tab_mt) #pos

#-------------------------------------
# matrix for wild type
# frequency of wild type by position
#-------------------------------------
tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
tab_wt = unclass(tab_wt)

# remove wt duplicates
wt = my_data_snp[, c("position", "wild_type")]
wt = wt[!duplicated(wt),]

tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1

rownames(tab_wt)
rownames(tab_wt)

identical(colnames(tab_mt), colnames(tab_wt))
identical(ncol(tab_mt), ncol(tab_wt))

########################################################################
#                           End of script
########################################################################