#Part 1 create summary tables and inputs files for figure in part 2 and part 3.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 03/23/2014  

#PROJECT: Environmental Layers project  

#TO DO:

# - 

  #16) multiple_region <- TRUE #PARAM 12

  #17) countries_shp <-"/data/project/layers/commons/NEX_data/countries.shp" #PARAM 13, copy this on NEX too

  #18) plot_region <- TRUE

  #20) threshold_missing_day <- c(367,365,300,200) #PARM18

  ##OUTPUTS

  #1)

  in_dir1 <- list_param_run_assessment_prediction$in_dir1 

  y_var_name <- list_param_run_assessment_prediction$y_var_name # e.g. dailyTmax" #PARAM3

  interpolation_method <- list_param_run_assessment_prediction$interpolation_method #c("gam_CAI") #PARAM4

  out_prefix <- list_param_run_assessment_prediction$out_prefix #output suffix e.g."run_global_analyses_pred_12282015" #PARAM5

  multiple_region <- list_param_run_assessment_prediction$multiple_region #PARAM16

  countries_shp <- list_param_run_assessment_prediction$countries_shp #PARAM17

  plot_region <- list_param_run_assessment_prediction$plot_region #PARAM18

  threshold_missing_day <- list_param_run_assessment_prediction$threshold_missing_day #PARM20

  ########################## START SCRIPT #########################################

  ##raster_prediction object : contains testing and training stations with RMSE and model object

  in_dir_list_tmp <- file.path(in_dir_list,year_predicted)

  list_names_tile_coord <- lapply(list_raster_obj_files,FUN=function(x){basename(dirname(x))})

  list_names_tile_id <- paste("tile",1:length(list_raster_obj_files),sep="_")

  names(list_raster_obj_files)<- list_names_tile_id

  lf_sub_sampling_obj_daily_files_tmp <- lapply(1:length(lf_sub_sampling_obj_daily_files),FUN=function(i,x){val <- x[[i]];if(length(val)==0){val<-0};val},x=lf_sub_sampling_obj_daily_files)

  df_tile_processed$sub_sampling_clim  <- unlist(lf_sub_sampling_obj_files_tmp)

  df_tile_processed$sub_sampling_daily  <- unlist(lf_sub_sampling_obj_daily_files_tmp)

  #lf_sub_sampling_obj_files

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

  #### Table 1: Average accuracy metrics per tile and predictions

  long<- as.numeric(lapply(1:length(tx),function(i,x){x[[i]][2]},x=tx))

  summary_metrics_v_NA$lat <- lat

  summary_metrics_v_NA$lon <- long

  write.table(as.data.frame(summary_metrics_v_NA),

              file=file.path(out_dir,paste("summary_metrics_v2_NA_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

                        FUN=function(i,x,y){rep(y[i],nrow(x[[i]]))},x=tb_diagnostic_v_tmp,y=names(tb_diagnostic_v_tmp))

  tb_diagnostic_v_NA$tile_id <- unlist(tile_id_tmp) #adding identifier for tile

  tb_diagnostic_v_NA <- merge(tb_diagnostic_v_NA,df_tile_processed[,1:2],by="tile_id")

  write.table((tb_diagnostic_v_NA),

              file=file.path(out_dir,paste("tb_diagnostic_v_NA_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

  tb_month_diagnostic_s_NA <- merge(tb_month_diagnostic_s_NA,df_tile_processed[,1:2],by="tile_id")

  date_f<-strptime(tb_month_diagnostic_s_NA$date, "%Y%m%d")   # interpolation date being processed

  write.table((tb_month_diagnostic_s_NA),

              file=file.path(out_dir,paste("tb_month_diagnostic_s_NA_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

  tb_diagnostic_s_NA$tile_id <- unlist(tile_id_tmp) #adding identifier for tile

  tb_diagnostic_s_NA <- merge(tb_diagnostic_s_NA,df_tile_processed[,1:2],by="tile_id")

  write.table((tb_diagnostic_s_NA),

              file=file.path(out_dir,paste("tb_diagnostic_s_NA_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

  list_outfiles[[4]] <- file.path(out_dir,paste("tb_diagnostic_s_NA_",year_predicted,"_",out_prefix,".txt",sep=""))

                    FUN=function(i,x){rep(names(x)[i],nrow(x[[i]]))},x=data_month_tmp)

  data_month_NAM <- do.call(rbind.fill,data_month_tmp) #combined data_month for "NAM" North America

  data_month_NAM$tile_id <- unlist(tile_id)

  write.table((data_month_NAM),

              file=file.path(out_dir,paste("data_month_s_NAM_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

  list_outfiles[[5]] <- file.path(out_dir,paste("data_month_s_NAM_",year_predicted,"_",out_prefix,".txt",sep=""))

                    FUN=function(i,x){rep(names(x)[i],nrow(x[[i]]))},x=data_day_s_tmp)

  data_day_s_NAM <- do.call(rbind.fill,data_day_s_tmp) #combined data_month for "NAM" North America

  data_day_s_NAM$tile_id <- unlist(tile_id)

  tile_id <- lapply(1:length(data_day_v_tmp),

                    FUN=function(i,x){rep(names(x)[i],nrow(x[[i]]))},x=data_day_v_tmp)

  data_day_v_NAM <- do.call(rbind.fill,data_day_v_tmp) #combined data_month for "NAM" North America

  data_day_v_NAM$tile_id <- unlist(tile_id)

  write.table((data_day_s_NAM),

              file=file.path(out_dir,paste("data_day_s_NAM_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

                      FUN=function(i,x){try(rep(names(x)[i],nrow(x[[i]])))},x=data_month_v_subsampling_tmp)

    data_month_v_subsmapling_NAM <- do.call(rbind.fill,ddata_month_v_subsampling_tmp) #combined data_month for "NAM" North America

    data_month_v_subsampling_NAM$tile_id <- unlist(tile_id)

    write.table((data_month_v_subsampling_NAM),

                file=file.path(out_dir,paste("data_month_v_subsampling_NAM_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

    list_outfiles[[8]] <- file.path(out_dir,paste("data_month_v_subsampling_NAM_",year_predicted,"_",out_prefix,".txt",sep=""))

  pred_data_month_info <- do.call(rbind,lapply(pred_data_info_tmp,function(x){x$pred_data_month_info}))

  pred_data_day_info <- do.call(rbind,lapply(pred_data_info_tmp,function(x){x$pred_data_day_info}))

  #putput inforamtion in csv !!

  write.table(pred_data_month_info,

              file=file.path(out_dir,paste("pred_data_month_info_",year_predicted,"_",out_prefix,".txt",sep="")),sep=",")

  df_tiles_all$tile_coord <- l_shp

  #names(df_tiles_all) <- "list_shp_world"

  names(df_tiles_all) <- c("shp_files","tile_coord")

  matching_index <- match(basename(in_dir_list),l_shp)

  list_shp_reg_files <- list_shp_world[matching_index]

  df_tile_processed$shp_files <-list_shp_reg_files

  long<- as.numeric(lapply(1:length(tx),function(i,x){x[[i]][2]},x=tx))

  df_tile_processed$lat <- lat

  df_tile_processed$lon <- long

  #put that list in the df_processed and also the centroids!!

  write.table(df_tile_processed,

  df_assessment_files <- data.frame(filename=outfiles_names,files=unlist(list_outfiles),

                                    reg=region_name,year=year_predicted)

  ###Prepare files for copying back?

  write.table(df_assessment_files,

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

  ####### PART 5: run plotting functions to produce figures

  #interpolation_method <- c("gam_CAI") #PARAM2, already set

  out_suffix <- out_prefix #PARAM3

  #out_dir <-  #PARAM4, already set

  #mosaic_plot <- FALSE #PARAM6

  #if daily mosaics NULL then mosaicas all days of the year

  #day_to_mosaic <- c("19920101","19920102","19920103") #PARAM7

  #CRS_locs_WGS84 already set

  #file_format <- ".rst" #PARAM 9, already set

  #NA_flag_val <- -9999 #PARAM 11, already set

  #multiple_region <- TRUE #PARAM 12

  #countries_shp <-"/data/project/layers/commons/NEX_data/countries.shp" #PARAM 13, copy this on NEX too

  #plot_region <- TRUE

  #num_cores <- 6 #PARAM 14, already set

  #use previous files produced in step 1a and stored in a data.frame

  #threshold_missing_day <- c(367,365,300,200) #PARM18

                      out_dir, create_out_dir_param, mosaic_plot, proj_str, file_format, NA_value,

                      multiple_region, countries_shp, plot_region, num_cores, 

                      "out_dir","create_out_dir_param","mosaic_plot","proj_str","file_format","NA_value",

                      "multiple_region","countries_shp","plot_region","num_cores", 

  df_assessment_figures_files <- run_assessment_plotting_prediction_fun(list_param_run_assessment_plotting) 

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