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

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

source("../combining_two_df.R")

#---------------------- PAY ATTENTION
# the above changes the working dir
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
#---------------------- PAY ATTENTION

#==========================
# This will return:

# df with NA:
# merged_df2
# merged_df3

# df without NA:
# merged_df2_comp
# merged_df3_comp
#===========================

###########################
# Data for DUET plots
# you need merged_df3
# or
# merged_df3_comp
# since these have unique SNPs
# I prefer to use the merged_df3
# because using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available
###########################

# uncomment as necessary
#<<<<<<<<<<<<<<<<<<<<<<<<<
# REASSIGNMENT
my_df  = merged_df3 
#my_df  = merged_df3_comp
#<<<<<<<<<<<<<<<<<<<<<<<<<

# delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)

# quick checks
colnames(my_df)
str(my_df)

# Ensure correct data type in columns to plot: need to be factor
# sanity check
is.factor(my_df$DUET_outcome)
my_df$DUET_outcome = as.factor(my_df$DUET_outcome)
is.factor(my_df$DUET_outcome)
#[1] TRUE

########################################################################
#               end of data extraction and cleaning for plots          #
########################################################################

#==========================
# 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.
#============================

#===================
# Data for plots
#===================

#<<<<<<<<<<<<<<<<<<<<<<<<
# REASSIGNMENT
df  = my_df 
#<<<<<<<<<<<<<<<<<<<<<<<<<

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$ratioDUET)
max(df$ratioDUET)

tapply(df$ratioDUET, df$DUET_outcome, min)
tapply(df$ratioDUET, df$DUET_outcome, max)

#******************
# generate plot
#******************

# set output dir for plots
getwd()
setwd("~/git/Data/pyrazinamide/output/plots")
getwd()

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

# Prepare data: round off ratioDUET scores
# round off to 3 significant digits:
# 323 if no rounding is performed: used to generate the original graph
# 287 if rounded to 3 places
# FIXME: check if reducing precicion creates any ML prob

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

# <<<<< -------------------------------------------
# Run this section if rounding is to be used
# specify number for rounding
n = 3 
df$ratioDUETR = round(df$ratioDUET, n)
u = unique(df$ratioDUETR)
# create an extra column called group which contains the "gp name and score" 
# so colours can be generated for each unique values in this column
my_grp = df$ratioDUETR
df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "")

# else 
# uncomment the below if rounding is not required

#my_grp = df$ratioDUET
#df$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")
my_title = "Protein stability (DUET)"

# axis label size
my_xaxls = 13
my_yaxls = 15

# axes text size
my_xaxts = 15
my_yaxts = 15

# no ordering of x-axis
g = ggplot(df, aes(factor(Position, ordered = T)))
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")

# 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