#!/usr/bin/env Rscript

#########################################################
# TASK: Get formatted data for plots
#########################################################
# working dir and loading libraries
getwd()
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/Header_TT.R")

#********************
# cmd args passed 
# in from other scripts
# to call this
#********************
# set drug and gene name

#==========================================
# variables for lig:
# comes from functions/plotting_globals.R
#==========================================

cat("\nGlobal variables for Ligand:"
    , "\nligand distance colname:", LigDist_colname
    , "\nligand distance cut off:", LigDist_cutoff)

#===========
# input
#===========
#--------------------------------------------
# call: import_dirs()
# comes from functions/plotting_globals.R
#--------------------------------------------
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") 
  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()

max_ang <- round(max(my_df_u[LigDist_colname]))
min_ang <- round(min(my_df_u[LigDist_colname]))

cat("\nLigand distance colname:", LigDist_colname
    , "\nThe max distance", gene, "structure df" , ":", max_ang, "\u212b"
    , "\nThe min distance", gene, "structure df" , ":", min_ang, "\u212b")

#--------------------------------
# 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)

cat("\nDim of meta data file: ", dim(gene_metadata))

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]]
#======================================================================
####################################################################
#                        Data for combining other dfs
####################################################################

#source("other_dfs_data.R")
# Fixed this at source i.e python script
# Moved: "other_dfs_data.R" to redundant/

####################################################################
#                        Data for subcols barplot (~heatmap)
####################################################################

#source("coloured_bp_data.R")
# Repurposed function so that params can be passed instead to generate
# data required for plotting.
# Moved "coloured_bp_data.R" to redundant/

####################################################################
#                        Data for logoplots
####################################################################

#source(paste0(plot_script_path, "logo_data.R"))
#s1 = c("\nSuccessfully sourced logo_data.R")
#cat(s1)
# input data is merged_df3
# so repurposed it into a function so params can be passed instead to generate
# data required for plotting.
# Moved "logo_data.R" to redundant/

source(paste0(plot_script_path, "logo_data_msa.R"))
s1 = c("\nSuccessfully sourced logo_data_msa.R")
cat(s1)

####################################################################
#                        Data for DM OM Plots: Long format dfs
####################################################################

#source("other_plots_data.R")

# source(paste0(plot_script_path, "dm_om_data.R"))
# 
# s2 = c("\nSuccessfully sourced other_plots_data.R")
# cat(s2)

####################################################################
#                  Data for Lineage barplots: WF and LF dfs
####################################################################
# 
# source(paste0(plot_script_path, "lineage_data.R"))
# 
# s3 = c("\nSuccessfully sourced lineage_data.R")
# cat(s3)

####################################################################
#                  Data for corr plots:
####################################################################
# make sure the above script works because merged_df2_combined is needed
# source(paste0(plot_script_path, "corr_data.R"))
# 
# s4 = c("\nSuccessfully sourced corr_data.R")
# cat(s4)

########################################################################
#                           End of script
########################################################################
# if (  all( length(s1), length(s2), length(s3), length(s4) ) > 0 ){
#  cat(
#   "\n##################################################"
#  , "\nSuccessful: get_plotting_dfs.R worked!"
#  , "\n###################################################\n") 
# } else {
#  cat(
#   "\n#################################################"
#  , "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
#  , "\n###################################################\n" )
#  }   
#  
########################################################################
# clear excess variables: from the global enviornment

vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))] 
vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))] 
vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]

rm(c1
   , fact_cols
   , infile_metadata
   , infile_params
   , vars0
   , vars1
   , vars2
   , vars3)