library(shinycssloaders)
library(DT)
library(NGLVieweR)
library(hash)

load_target_globals=function(target){
  cat(paste0("Reloading Target: ", target))
  source(paste0("/srv/shiny-server/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file
  
  invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv))
  invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv))
  invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv))
  invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv))
  invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv))
  lapply(
    c(
      "duet",
      "mcsm_lig",
      "foldx",
      "deepddg",
      "dynamut2",
      "consurf",
      "snap2",
      "provean",
      "dist_gen",
      "mcsm_ppi2"#,
      #"mcsm_na"
    ), function(x){
      wf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv")
      wf_var=paste0("wf_",x)
      if (file.exists(wf_filename)){
        invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH
      }
      lf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv")
      lf_var=paste0(target, "_lf_",x)
      if (file.exists(lf_filename)){
        invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH
      }
    }
  )
}
# populate target-specific *_unified_msa vars
load_msa_global=function(gene){
  drug=target_map[[gene]]
  in_filename_msa = paste0(tolower(gene), "_msa.csv") 
  infile_msa = paste0("/srv/shiny-server/git/Data/", drug, "/output/", in_filename_msa)
  print(infile_msa)
  msa1 = read.csv(infile_msa, header = F)
  msa_seq = msa1$V1
  
  infile_fasta = paste0("/srv/shiny-server/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta")
  print(infile_fasta)
  msa2 = read.csv(infile_fasta, header = F)
  wt_seq = msa2$V1
  
  target_name=paste0(gene, '_unified_msa')
  #print(target_name)
  invisible(assign(target_name, list(msa_seq = msa_seq, wt_seq = wt_seq), envir = .GlobalEnv))
}

#### Local Functions ####
# Generate a conditionalPanel() for a given graph function and sidebar name combination
generate_conditionalPanel = function(tab_name, plot_function, plot_df){
  # e.g.: list("lin_count_bp_diversity", "Lineage diversity count")
  cond=paste0("input.sidebar == '", tab_name, "'")
  conditionalPanel(condition=cond, box(
    title=tab_name
    , status = "info"
    , width=NULL
    , plotOutput(plot_function
                 , click = "plot_click") %>% withSpinner(color="#0dc5c1")
    # , plotOutput(plot_function, click = "plot_click")
  )
  )
}

# FIXME: passing in the per-plot params is broken
lin_sc=function(x, all_lineages = F, ...){
  lf_var = get(paste0(x,"_lin_lf"))
  wf_var = get(paste0(x,"_lin_wf"))
  cowplot::plot_grid(lin_count_bp_diversity(wf_var, all_lineages, ...), lin_count_bp(lf_var, all_lineages, ...))
}


options(shiny.port = 8000)
options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
options(shiny.launch.browser = FALSE)
options(width=120)
options(DT.options = list(scrollX = TRUE))


################ STATIC GLOBALS ONLY ##############
# never quite sure where "outdir" gets set :-|

# using dataframes instead of lists lets us avoid use of map()
plot_functions_df=data.frame(
  tab_name=c(
    "LogoP SNP",
    "Lineage Sample Count",
    "Site SNP count",
    "Stability SNP by site",
    "DM OM Plots",
    "Correlation",
    "Lineage Distribution",
    "Consurf",
    "LogoP OR",
    "LogoP ED"
  ),
  plot_function=c(
    "LogoPlotSnps",
    "lin_sc",
    "site_snp_count_bp",
    "bp_stability_hmap",
    "lf_bp2",
    "my_corr_pairs",
    "lineage_distP",
    "wideP_consurf3",
    "LogoPlotCustomH",
    "LogoPlotMSA"
  ),
  plot_df=c(
    "mutable_df3"  ,
    "lin_lf",
    "mutable_df3",
    "merged_df3" ,
    "lf_duet" ,
    "corr_df_m3_f",
    "merged_df2",
    "merged_df3",
    "merged_df2",
    "unified_msa"
  )
)

stability_boxes_df=data.frame(
  outcome_colname=c("duet_outcome",
                    "foldx_outcome",
                    "deepddg_outcome",
                    "ddg_dynamut2_outcome",
                    "mcsm_na_outcome",
                    "mcsm_ppi2_outcome",
                    "snap2_outcome",
                    "consurf_outcome",
                    "avg_stability_outcome"),
  stability_type=c(
    "DUET",
    "FoldX",
    "DeepDDG",
    "Dynamut2",
    "mCSM-NA",
    "mCSM-ppi2",
    "SNAP2",
    "Consurf",
    "Average"
  ),
  stability_colname=c(
    "duet_scaled",
    "foldx_scaled",
    "deepddg_scaled",
    "ddg_dynamut2_scaled",
    "mcsm_na_scaled",
    "mcsm_ppi2_scaled",
    "snap2_scaled",
    "consurf_scaled",
    "avg_stability_scaled"
  )
)

table_columns = c(
  "position",
  "mutationinformation",
  "sensitivity",
  "ligand_distance",
  "avg_lig_affinity",
  "avg_lig_affinity_outcome",
  "avg_stability",
  "avg_stability_outcome",
  "or_mychisq",
  "maf",
  "snap2_outcome",
  "consurf_outcome",
  "provean_outcome",
  "rsa", 
  "kd_values" ,
  "rd_values"
)

logoPlotSchemes <- list("chemistry"
                        , "taylor"
                        , "hydrophobicity"
                        , "clustalx")
dm_om_methods = c("DUET ΔΔG"
                  , "Consurf"
                  , "Deepddg ΔΔG"
                  , "Dynamut2 ΔΔG"
                  , "FoldX ΔΔG"
                  , "Ligand affinity (log fold change)"
                  , "mCSM-NA affinity ΔΔG"
                  , "SNAP2")
dm_om_map = hash(c(
  "DUET ΔΔG"
  , "Consurf"      
  , "Deepddg ΔΔG"  
  , "Dynamut2 ΔΔG" 
  , "FoldX ΔΔG"    
  , "Ligand affinity (log fold change)"
  , "mCSM-NA affinity ΔΔG"
  , "SNAP2"
), c(
  "lf_duet"
  ,"lf_consurf"
  ,"lf_deepddg"
  ,"lf_dynamut2"
  ,"lf_foldx"
  ,"lf_mcsm_lig"
  ,"lf_mcsm_na"
  ,"lf_snap2"
)
)
#### target_map: handy gene/drug mapping hash ####
target_map = hash(
  c(
    "alr",
    "gid",
    "embb",
    "pnca",
    "rpob",
    "katg"),
  c(
    "cycloserine",
    "streptomycin",
    "ethambutol",
    "pyrazinamide",
    "rifampicin",
    "isoniazid")
)

# load E V E R Y T H I N G
lapply(c(
  "alr",
  "embb",
  "gid",
  "katg",
  "pnca",
  "rpob"
),function(x){
  invisible(load_target_globals(x))
  invisible(load_msa_global(x)) # turn off to speed up start time at the expense of "LogoP ED"
}
)

consurf_palette1 = c("0" = "yellow2"
                     , "1" = "cyan1"
                     , "2" = "steelblue2"
                     , "3" = "cadetblue2"
                     , "4" = "paleturquoise2"
                     , "5" = "thistle3"
                     , "6" = "thistle2"
                     , "7" = "plum2"
                     , "8" = "maroon"
                     , "9" = "violetred2")

consurf_palette2 =  c("0" = "yellow2"
                      , "1" = "forestgreen"
                      , "2" = "seagreen3"
                      , "3" = "palegreen1"
                      , "4" = "darkseagreen2"
                      , "5" = "thistle3"
                      , "6" = "lightpink1"
                      , "7" = "orchid3"
                      , "8" = "orchid4"
                      , "9" = "darkorchid4")

# decreasing levels mess legend
# consurf_colours_LEVEL = c(
#    "0" = rgb(1.00,1.00,0.59)
#   , "9" = rgb(0.63,0.16,0.37)
#   , "8" = rgb(0.94,0.49,0.67)
#   , "7" = rgb(0.98,0.78,0.86)
#   , "6" = rgb(0.98,0.92,0.96)
#   , "5" = rgb(1.00,1.00,1.00)
#   , "4" = rgb(0.84,0.94,0.94)
#   , "3" = rgb(0.65,0.86,0.90)
#   , "2" = rgb(0.29,0.69,0.75)
#   , "1" = rgb(0.04,0.49,0.51)
#   )

consurf_colours = c(
  "0" = rgb(1.00,1.00,0.59)
  , "1" = rgb(0.04,0.49,0.51)
  , "2" = rgb(0.29,0.69,0.75)
  , "3" = rgb(0.65,0.86,0.90)
  , "4" = rgb(0.84,0.94,0.94)
  , "5" = rgb(1.00,1.00,1.00)
  , "6" = rgb(0.98,0.92,0.96)
  , "7" = rgb(0.98,0.78,0.86)
  , "8" = rgb(0.94,0.49,0.67)
  , "9" = rgb(0.63,0.16,0.37)
)