getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()

#########################################################
# TASK:

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

########################################################################
# 		Installing and loading required packages and functions		   #	
########################################################################

source("Header_TT.R")
source("barplot_colour_function.R")

########################################################################
#		 Read file: call script for combining df for PS			   	   #
########################################################################
#?????????????
#
########################################################
#%% 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")

#======
# input
#======
#in_filename = "mcsm_complex1_normalised.csv"
in_filename_params = paste0(tolower(gene), "_all_params.csv") 
infile_params = paste0(outdir, "/", in_filename_params)
cat(paste0("Input file:", infile_params) )

#=======
# output
#=======
subcols_bp_duet = "barplot_subcols_DUET.svg"
outPlot_subcols_bp_duet  =  paste0(outdir, "/plots/", subcols_bp_duet)

#%%===============================================================
###########################
# Read file: struct params
###########################
cat("Reading struct params including mcsm:", in_filename_params)

my_df = read.csv(infile_params
                 #, stringsAsFactors = F
                 , header = T)

cat("Input dimensions:", dim(my_df)) 

# clear variables
rm(in_filename_params, infile_params)

# quick checks
colnames(my_df)
str(my_df)

# check for duplicate mutations
if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformation)){
  cat(paste0("CAUTION:", " Duplicate mutations identified"
             , "\nExtracting these..."))
  dup_muts = my_df[duplicated(my_df$mutationinformation),]
  dup_muts_nu = length(unique(dup_muts$mutationinformation))
  cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
             , "\nNo. of unique duplicate mutations:", dup_muts_nu
             , "\n\nExtracting df with unique mutations only"))
  my_df_u = my_df[!duplicated(my_df$mutationinformation),]
}else{
  cat(paste0("No duplicate mutations detected"))
  my_df_u = my_df
}

upos = unique(my_df_u$position)
cat("Dim of clean df:"); cat(dim(my_df_u))
cat("\nNo. of unique mutational positions:"); cat(length(upos))

########################################################################
#               end of data extraction and cleaning for plots          #
########################################################################
#===================
# Data for plots
#===================
# REASSIGNMENT as necessary
df  = my_df_u

rm(my_df)

# sanity checks
upos = unique(df$position)

# should be a factor
is.factor(my_df$duet_outcome)
#[1] TRUE

table(df$duet_outcome)

# should be -1 and 1
min(df$duet_scaled)
max(df$duet_scaled)

tapply(df$duet_scaled, df$duet_outcome, min)
tapply(df$duet_scaled, df$duet_outcome, max)

#******************
# generate plot
#******************
#==========================
# Barplot with scores (unordered)
# corresponds to duet_outcome
# Stacked Barplot with colours: duet_outcome @ position coloured by 
# stability scores. This is a barplot where each bar corresponds 
# to a SNP and is coloured by its corresponding DUET stability value.
# Normalised values (range between -1 and 1 ) to aid visualisation
# NOTE: since barplot plots discrete values, colour = score, so number of
# colours will be equal to the no. of unique normalised scores 
# rather than a continuous scale
# will require generating the colour scale separately.
#============================

# My colour FUNCTION: based on group and subgroup
# in my case;
# df = df
# group = duet_outcome
# subgroup = normalised score i.e duet_scaled

# check unique values in normalised data
u = unique(df$duet_scaled) 

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Run this section if rounding is to be used
n = 3 
df$duet_scaledR = round(df$duet_scaled, n)
ur = unique(df$duet_scaledR)

# create an extra column called group which contains the "gp name and score" 
# so colours can be generated for each unique values in this column

#my_grp = df$duet_scaledR # rounding
my_grp = df$duet_scaled # no rounding
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

df$group <- paste0(df$duet_outcome, "_", my_grp, sep = "")

# Call the function to create the palette based on the group defined above
colours <- ColourPalleteMulti(df, "duet_outcome", "my_grp")
print(paste0("Colour palette generated for: ", length(colours), " colours"))
my_title = "Protein stability (DUET)"

# axis label size
my_xaxls = 13
my_yaxls = 15

# axes text size
my_xaxts = 15
my_yaxts = 15

#******************
# generate plot: NO axis colours
# no ordering of x-axis
#******************
# plot name and location
print(paste0("plot will be in:", outdir))
bp_subcols_duet = "barplot_coloured_PS.svg"
plot_bp_subcols_duet = paste0(outdir, "/plots/", bp_subcols_duet) 
print(paste0("plot name:", plot_bp_subcols_duet))

svg(plot_bp_subcols_duet, width = 26, height = 4)

g = ggplot(df, aes(factor(position, ordered = T)))
outPlot = g + 
  geom_bar(aes(fill = group), colour = "grey") +
  scale_fill_manual( values = colours
                     , guide = "none") +
  theme( axis.text.x = element_text(size = my_xaxls
                                    , angle = 90
                                    , hjust = 1
                                    , vjust = 0.4)
         , axis.text.y = element_text(size = my_yaxls 
                                      , angle = 0
                                      , hjust = 1
                                      , vjust = 0)
         , axis.title.x = element_text(size = my_xaxts)
         , axis.title.y = element_text(size = my_yaxts ) ) +
  labs(title = my_title
       , x = "position"
       , y = "Frequency")
       
print(outPlot)
dev.off()
# for sanity and good practice
rm(df)
#======================= end of plot
# axis colours labels
# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label