#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")

source("/home/tanu/git/LSHTM_analysis/my_header.R")
#########################################################
# TASK: Generate averaged stability values 
# across all stability tools
# for a given structure
#########################################################

#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
outfile_mean_ens_st_aff = paste0(outdir_images, "/5uhc_", tolower(gene)
                                , "_mean_ens_stability.csv")
print(paste0("Output file:", outfile_mean_ens_st_aff))

#%%===============================================================

#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
length(df3$mutationinformation)

# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)

# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)

# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))

#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)

length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
common_cols  = c("mutationinformation"
                 , "X5uhc_position"
                 , "dst_mode"
                 , "mutation_info_labels"
                 , "sensitivity"
                 , "X5uhc_position"
                 , "X5uhc_offset"
                 , "ligand_distance"
                 , "interface_dist")

all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]

#===================
# stability cols
#===================
raw_cols_stability =  c("duet_stability_change"
                        , "deepddg"
                        , "ddg_dynamut2"
                        , "ddg_foldx")

scaled_cols_stability = c("duet_scaled"       
                          , "deepddg_scaled"   
                          , "ddg_dynamut2_scaled"
                          , "foldx_scaled")

outcome_cols_stability = c("duet_outcome"
                           , "deepddg_outcome"
                           , "ddg_dynamut2_outcome"
                           , "foldx_outcome")

###########################################################
cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
                                                    , raw_cols_stability
                                                    , scaled_cols_stability
                                                    , outcome_cols_stability)]

cols_to_extract  = cols_to_consider[cols_to_consider%in%c(common_cols
                                                          , outcome_cols_stability)]
##############################################################
#####################
# Ensemble stability: outcome_cols_stability
#####################
# extract outcome cols and map numeric values to the categories
# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
df3_plot = df3[, cols_to_extract]

# assign numeric values to outcome
df3_plot[, outcome_cols_stability] <- sapply(df3_plot[, outcome_cols_stability]
                             , function(x){ifelse(x == "Destabilising", 0, 1)})
table(df3$duet_outcome)
table(df3_plot$duet_outcome)
#=====================================
# Stability (4 cols): average the scores
# across predictors ==> average by
# X5uhc_position ==> scale b/w -1 and 1

# column to average: ens_stability
#=====================================
cols_to_average = which(colnames(df3_plot)%in%outcome_cols_stability)

# ensemble average across predictors
df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])

head(df3_plot$X5uhc_position); head(df3_plot$mutationinformation)
head(df3_plot$ens_stability)
table(df3_plot$ens_stability)

# ensemble average of predictors by X5uhc_position
mean_ens_stability_by_position <- df3_plot %>%
  dplyr::group_by(X5uhc_position) %>%
  dplyr::summarize(avg_ens_stability = mean(ens_stability))

# REscale b/w -1 and 1
#en_stab_min = min(mean_ens_stability_by_position['avg_ens_stability'])
#en_stab_max = max(mean_ens_stability_by_position['avg_ens_stability']) 

# scale the average stability value between -1 and 1
# mean_ens_by_position['averaged_stability3_scaled'] = lapply(mean_ens_by_position['averaged_stability3']
#                                                        , function(x) ifelse(x < 0, x/abs(en3_min), x/en3_max))

mean_ens_stability_by_position['avg_ens_stability_scaled'] = lapply(mean_ens_stability_by_position['avg_ens_stability']
                                                                  , function(x) {
                                                                    scales::rescale(x, to  = c(-1,1)
                                                                                    #, from = c(en_stab_min,en_stab_max))
                                                                                    , from = c(0,1))
                                                                  })
cat(paste0('Average stability scores:\n'
           , head(mean_ens_stability_by_position['avg_ens_stability'])
           , '\n---------------------------------------------------------------'
           , '\nAverage stability scaled scores:\n'
           , head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))

# convert to a data frame
mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)

#FIXME: sanity checks
# TODO: predetermine the bounds
# l_bound_ens = min(mean_ens_stability_by_position['avg_ens_stability_scaled'])
# u_bound_ens = max(mean_ens_stability_by_position['avg_ens_stability_scaled'])
# 
# if ( (l_bound_ens == -1) && (u_bound_ens == 1) ){
#   cat(paste0("PASS: ensemble stability scores averaged by X5uhc_position and then scaled"
#         , "\nmin ensemble averaged stability: ", l_bound_ens
#         , "\nmax ensemble averaged stability: ", u_bound_ens))
# }else{
#   cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
#         , "\nmin ensemble averaged stability: ", l_bound_ens
#         , "\nmax ensemble averaged stability: ", u_bound_ens))
#   quit()
# } 
##################################################################
# output
#write.csv(combined_df, outfile_mean_ens_st_aff
write.csv(mean_ens_stability_by_position
          , outfile_mean_ens_st_aff
          , row.names = F)
cat("Finished writing file:\n"
    , outfile_mean_ens_st_aff
    , "\nNo. of rows:", nrow(mean_ens_stability_by_position)
    , "\nNo. of cols:", ncol(mean_ens_stability_by_position))

# end of script
#===============================================================