#!/usr/bin/env Rscript   
#########################################################
# TASK: Barplots
# basic barplots with outcome
# basic barplots with frequency of count of mutations
#########################################################
#=============
# Data: Input
#==============
source("~/git/LSHTM_analysis/config/rpob.R")
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")

cat("\nSourced plotting cols as well:", length(plotting_cols))

####################################################
class(merged_df3)

df3 = subset(merged_df3, select = -c(pos_count))

#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
cat("plots will output to:", outdir_images)

##########################################################
# blue, red bp
sts = 8
lts = 8
ats = 8
als = 8
ltis = 8
geom_ls = 2.2

#pos_count
subtitle_size = 8
geom_ls_pc = 2.2
leg_text_size = 8
axis_text_size = 8
axis_label_size = 8

###########################################################
#------------------------------
# plot default sizes
#------------------------------
#=========================
# Affinity outcome
# check this var: outcome_cols_affinity
# get from preformatting or put in globals
#==========================
DistCutOff
LigDist_colname  # = "ligand_distance" # from globals 
ppi2Dist_colname
naDist_colname

###########################################################
# get plotting data within the distance
df3_lig  = df3[df3[[LigDist_colname]]<DistCutOff,]
df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
df3_na   = df3[df3[[naDist_colname]]<DistCutOff,]
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)

#------------------------------
# barplot for ligand affinity:
# <10 Ang of ligand
#------------------------------
mLigP = stability_count_bp(plotdf = df3_lig
               , df_colname = "ligand_outcome"
               #, leg_title  = "mCSM-lig"
               #, bp_plot_title = paste(common_bp_title, "ligand")
               , yaxis_title = "Number of nsSNPs"
               , leg_position = "none"
               , subtitle_text = "mCSM\nLig"
               , bar_fill_values = c("#F8766D", "#00BFC4")
               , subtitle_colour= "black"
               , sts = sts
               , lts = lts
               , ats = ats
               , als = als
               , ltis = ltis
               , geom_ls = geom_ls
               )
mLigP
#------------------------------
# barplot for ligand affinity:
# <10 Ang of ligand
# mmCSM-lig: will be the same no. of sites but the effect will be different
#------------------------------
mmLigP = stability_count_bp(plotdf = df3_lig
                   , df_colname = "mmcsm_lig_outcome"
                   #, leg_title  = "mmCSM-lig"
                   #, label_categories = labels_mmlig
                   #, bp_plot_title = paste(common_bp_title, "ligand")
                   
                   , yaxis_title = ""
                   , leg_position = "none"
                   , subtitle_text = "mmCSM\nLig"
                   , bar_fill_values = c("#F8766D", "#00BFC4")
                   , subtitle_colour= "black"
                   , sts = sts
                   , lts = lts
                   , ats = ats
                   , als = als
                   , ltis = ltis
                   , geom_ls = geom_ls
                   )
mmLigP
#------------------------------
# barplot for ppi2 affinity
#  <10 Ang of interface
#------------------------------
if (tolower(gene)%in%geneL_ppi2){
    ppi2P = stability_count_bp(plotdf = df3_ppi2
                     , df_colname = "mcsm_ppi2_outcome"
                     #, leg_title  = "mCSM-ppi2"
                     #, label_categories = labels_ppi2
                     #, bp_plot_title = paste(common_bp_title, "PP-interface")
                     
                     , yaxis_title = "Number of nsSNPs"
                     , leg_position = "none"
                     , subtitle_text = "mCSM\nPPI2"
                     , bar_fill_values = c("#F8766D", "#00BFC4")
                     , subtitle_colour= "black"
                     , sts = sts
                     , lts = lts
                     , ats = ats
                     , als = als
                     , ltis = ltis
                     , geom_ls = geom_ls
                     )
  ppi2P
}
#----------------------------
# barplot for ppi2 affinity
#  <10 Ang of interface
#------------------------------
if (tolower(gene)%in%geneL_na){
  nca_distP = stability_count_bp(plotdf = df3_na
                             , df_colname = "mcsm_na_outcome"
                             #, leg_title  = "mCSM-NA"
                             #, label_categories = 
                             #, bp_plot_title = paste(common_bp_title, "Dist to NA")
                             
                             , yaxis_title = "Number of nsSNPs"
                             , leg_position = "none"
                             , subtitle_text = "mCSM\nNA"
                             , bar_fill_values = c("#F8766D", "#00BFC4")
                             , subtitle_colour= "black"
                             , sts = sts
                             , lts = lts
                             , ats = ats
                             , als = als
                             , ltis = ltis
                             , geom_ls = geom_ls
  )
  nca_distP
}

#####################################################################
# ------------------------------
# bp site site count: mCSM-lig
# < 10 Ang ligand
# ------------------------------
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)

posC_lig = site_snp_count_bp(plotdf = df3_lig
                  , df_colname = "position"
                  , xaxis_title = "Number of nsSNPs"
                  , yaxis_title = "Number of Sites"
                  , subtitle_colour = "chocolate4"
                  , subtitle_text = ""
                  , subtitle_size = subtitle_size
                  , geom_ls = geom_ls_pc
                  , leg_text_size = leg_text_size
                  , axis_text_size = axis_text_size
                  , axis_label_size = axis_label_size)

posC_lig
#------------------------------
# bp site site count: ppi2
# < 10 Ang interface
#------------------------------
if (tolower(gene)%in%geneL_ppi2){
  posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
                    , df_colname = "position"
                    , xaxis_title = "Number of nsSNPs"
                    , yaxis_title = "Number of Sites"
                    , subtitle_colour = "chocolate4"
                    , subtitle_text = ""
                    , subtitle_size = subtitle_size
                    , geom_ls = geom_ls_pc
                    , leg_text_size = leg_text_size
                    , axis_text_size = axis_text_size
                    , axis_label_size = axis_label_size)
  posC_ppi2
}

#------------------------------
# bp site site count: NCA dist
# < 10 Ang nca
#------------------------------
if (tolower(gene)%in%geneL_na){
  posC_nca = site_snp_count_bp(plotdf = df3_na
                                , df_colname = "position"
                                , xaxis_title = "Number of nsSNPs"
                                , yaxis_title = "Number of Sites"
                                , subtitle_colour = "chocolate4"
                                , subtitle_text = ""
                               , subtitle_size = subtitle_size
                               , geom_ls = geom_ls_pc
                               , leg_text_size = leg_text_size
                               , axis_text_size = axis_text_size
                               , axis_label_size = axis_label_size)
  posC_nca
}
#===============================================================
#------------------------------
# bp site site count: ALL
# <10 Ang ligand
#------------------------------
posC_all = site_snp_count_bp(plotdf = df3
                             , df_colname = "position"
                             , xaxis_title = "Number of nsSNPs"
                             , yaxis_title = "Number of Sites"
                             , subtitle_colour = "chocolate4"
                             , subtitle_text = "All mutations sites"
                             , subtitle_size = subtitle_size
                             , geom_ls = geom_ls_pc
                             , leg_text_size = leg_text_size
                             , axis_text_size = axis_text_size
                             , axis_label_size = axis_label_size)
posC_all
##################################################################
consurfP = stability_count_bp(plotdf = df3
                              , df_colname = "consurf_outcome"
                              #, leg_title = "ConSurf"
                              #, label_categories = labels_consurf
                              , yaxis_title = "Number of nsSNPs"
                              , leg_position = "top"
                              , subtitle_text = "ConSurf"
                              , bar_fill_values = consurf_colours # from globals
                              , subtitle_colour= "black"
                              , sts = sts
                              , lts = lts
                              , ats = ats
                              , als = als
                              , ltis = ltis
                              , geom_ls = geom_ls)

consurfP

##############################################################
sts_so = 10
lts_so = 10
ats_so = 10
als_so = 10
ltis_so = 10
geom_ls_so = 2.5
#===================
# Stability
#===================
# duetP
duetP = stability_count_bp(plotdf = df3
                           , df_colname = "duet_outcome"
                           , leg_title = "mCSM-DUET"
                           #, label_categories = labels_duet
                           , yaxis_title = "Number of nsSNPs"
                           , leg_position = "none"
                           , subtitle_text = "mCSM-DUET"
                           , bar_fill_values = c("#F8766D", "#00BFC4")
                           , subtitle_colour= "black"
                           , sts = sts_so
                           , lts = lts_so
                           , ats = ats_so
                           , als = als_so
                           , ltis = ltis_so
                           , geom_ls = geom_ls_so)
duetP

# foldx
foldxP = stability_count_bp(plotdf = df3
                            , df_colname = "foldx_outcome"
                            #, leg_title = "FoldX"
                            #, label_categories = labels_foldx
                            , yaxis_title = ""
                            , leg_position = "none"
                            , subtitle_text = "FoldX"
                            , bar_fill_values = c("#F8766D", "#00BFC4")
                            , sts = sts_so
                            , lts = lts_so
                            , ats = ats_so
                            , als = als_so
                            , ltis = ltis_so
                            , geom_ls = geom_ls_so)
foldxP

# deepddg
deepddgP = stability_count_bp(plotdf = df3
                              , df_colname = "deepddg_outcome"
                              #, leg_title = "DeepDDG"
                              #, label_categories = labels_deepddg
                              , yaxis_title = ""
                              , leg_position = "none"
                              , subtitle_text = "DeepDDG"
                              , bar_fill_values = c("#F8766D", "#00BFC4")
                              , sts = sts_so
                              , lts = lts_so
                              , ats = ats_so
                              , als = als_so
                              , ltis = ltis_so
                              , geom_ls = geom_ls_so)
deepddgP

# deepddg
dynamut2P = stability_count_bp(plotdf = df3
                               , df_colname = "ddg_dynamut2_outcome"
                               #, leg_title = "Dynamut2"
                               #, label_categories = labels_ddg_dynamut2_outcome
                               , yaxis_title = ""
                               , leg_position = "none"
                               , subtitle_text = "Dynamut2"
                               , bar_fill_values = c("#F8766D", "#00BFC4")
                               , sts = sts_so
                               , lts = lts_so
                               , ats = ats_so
                               , als = als_so
                               , ltis = ltis_so
                               , geom_ls = geom_ls_so)
dynamut2P

# provean
proveanP = stability_count_bp(plotdf = df3
                              , df_colname = "provean_outcome"
                              #, leg_title = "PROVEAN"
                              #, label_categories = labels_provean
                              , yaxis_title = "Number of nsSNPs"
                              , leg_position = "none" # top
                              , subtitle_text = "PROVEAN"
                              , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
                              , sts = sts_so
                              , lts = lts_so
                              , ats = ats_so
                              , als = als_so
                              , ltis = ltis_so
                              , geom_ls = geom_ls_so)
proveanP

# snap2
snap2P = stability_count_bp(plotdf = df3
                            , df_colname = "snap2_outcome"
                            #, leg_title = "SNAP2"
                            #, label_categories = labels_snap2
                            , yaxis_title = ""
                            , leg_position = "none" # top
                            , subtitle_text = "SNAP2"
                            , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
                            , sts = sts_so
                            , lts = lts_so
                            , ats = ats_so
                            , als = als_so
                            , ltis = ltis_so
                            , geom_ls = geom_ls_so)
snap2P
#####################################################################################