#########################################################
# 1b: Define function: coloured barplot by subgroup
# LINK: https://stackoverflow.com/questions/49818271/stacked-barplot-with-colour-gradients-for-each-bar
#########################################################
#source("~/git/LSHTM_analysis/scripts/functions/generate_distance_colour_map.R")

ColourPalleteMulti = function(df, group, subgroup){
  
  # Find how many colour categories to create and the number of colours in each
  categories <- aggregate(as.formula(paste(subgroup, group, sep="~" ))
                          , df
                          , function(x) length(unique(x)))
  #  return(categories) }
  
  category.start <- (scales::hue_pal(l = 100)(nrow(categories))) # Set the top of the colour pallete
  
  category.end  <- (scales::hue_pal(l = 40)(nrow(categories))) # set the bottom
  
  #return(category.start); return(category.end)}
  
  # Build Colour pallette
  colours <- unlist(lapply(1:nrow(categories),
                           function(i){
                             colorRampPalette(colors = c(category.start[i]
                                                         , category.end[i]))(categories[i,2])}))
  return(colours)
}
#########################################################################

########################
# Generate bp with
# colour palette derived
# from the data using 
# above function
#########################

bp_stability_hmap <- function(plot_df = merged_df3
                              , xvar_colname = "position"
                              , yvar_colname = 'duet_scaled' #FIXME: temp, remove
                              #, bar_col_colname = "group"
                              , stability_colname = "duet_scaled"
                              , stability_outcome_colname = "duet_outcome"
                              , p_title = "DUET"  # "Protein stability (DUET)"
                              , my_xaxls = 12 # x-axis label size
                              , my_yaxls = 20 # y-axis label size
                              , my_xaxts = 18 # x-axis text size
                              , my_yaxts = 20 # y-axis text size
                              , my_pts  = 20  # plot-title size
                              , my_xlab = "Position"
                              , my_ylab = "No. of nsSNPs"
                              
                              # Custom 2: x-axis: geom tiles ~ lig distance
                              #, A_xvar_lig = T
                              , lig_dist_colname = LigDist_colname # from globals
                              , tpos0 = 0 # 0 is a magic number that does my sensible default
                              , tW0 = 1
                              , tH0 = 0.2
                              
                              
                              
)
{
  ################################################
  # Custom 2: x-axis geom tiles ~ lig distance
  ################################################
  
  #=========================
  # Build data with colours
  # ~ ligand distance
  #=========================
  plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
  
  # order the df by position and ensure it is a factor
  plot_df = plot_df[order(plot_df[[xvar_colname]]), ]
  plot_df[[xvar_colname]] = factor(plot_df[[xvar_colname]])
  
  #cat("\nSneak peak:\n")
  head(data.frame( plot_df[[xvar_colname]], plot_df[[stability_colname]] ) )
  
  # stability values isolated to help with generating column called: 'group'
  my_grp = plot_df[[stability_colname]]
  cat( "\nLength of nsSNPs:", length(my_grp)
       , "\nLength of unique values for nsSNPs:", length(unique(my_grp)) )
  
  # Add col: 'group'
  plot_df$group = paste0(plot_df[[stability_outcome_colname]], "_", my_grp, sep = "")
  
  # check unique values in normalised data
  cat("\nNo. of unique values in", stability_colname, "no rounding:"
      , length(unique(plot_df[[stability_colname]])))
  
  # Call the function to create the palette based on the group defined above
  #subcols_ps
  subcols_bp_hmap = ColourPalleteMulti(plot_df, stability_outcome_colname, stability_colname)
  
  cat("\nNo. of sub colours generated:", length(subcols_bp_hmap))
  
  
  #-------------------------------
  # Generate the subcols barplot
  #-------------------------------
  cowplot::plot_grid(
    ggplot(plot_df, aes_string(x = xvar_colname
                               # , ordered = T)
    )) +
      geom_bar(aes(fill = group)
               , colour = "grey") +
      
      scale_fill_manual( values = subcols_bp_hmap
                         , guide = "none") +
      # scale_x_discrete("Position", labels=factor(plot_df$position)) +
      
      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_blank()
        #, axis.title.x = element_text(size = my_xaxts)
        , axis.title.y = element_text(size = my_yaxts )
        , plot.title = element_text(size = my_pts
                                    , hjust = 0.5)) +
      
      labs(title = p_title
           , x = my_xlab
           , y = my_ylab),
    NULL,
    ggplot(plot_df, 
           aes(x=factor(position), # THIS STUPID FUCKING FACTOR THING
           )
    ) +
      geom_tile(aes(y=0), 
                fill=plot_df$ligD_colours) +
      scale_x_discrete("Position", labels=factor(plot_df$position)) +
      theme_nothing() +
      theme(plot.background = element_rect(fill = "transparent", colour=NA),
            plot.margin = margin(t=0,b=0)) +
      labs(x = NULL, y = NULL), #end of distance-heat-bar
    NULL,
    position_annotation(plot_df),
    #generate_distance_legend(plot_df),
    ncol = 1,
    align = "v",
    rel_heights = c(10,-0.1,1,-0.1,1)
    #rel_widths = c(9/10, 0.4/10)
  )
  
}
bp_stability_hmap(small_df3)