#MODIFIED ON: 01/02/2016            

  #This function assesses results from prediction of climate variables. 

  #Predictions are run on the NEX-NASA computer by tiles for a given year.

  #The function collects information by region and tiles and generate tables of accuracy by tiles/regions.

  #There are currently five processing:

  #reg1 (North Am), reg2(Europe),reg3(Asia), reg4 (South Am), reg5 (Africa), reg6 (Australia-Asia)

  ##There are 20 inputs to this function.

  ##INPUTS:

  #1) in_dir1 : location of root directory containging tiles

  #8) proj_str: coordinates  CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84, #PARAM8

  #13) plotting_figures: if TRUE, figures are generated from tables using assessment part2 script 

  ##Parameters related to assessment part 2: plot functions

  #14) mosaic_plot <- FALSE #PARAM6

  #15) day_to_mosaic <- c("19920101","19920102","19920103") #PARAM7,#if daily mosaics NULL then mosaic all days of the year

  #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

  #19) reg_modified <- TRUE #PARAM 15

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

  ##OUTPUTS

  #1)

  #2)

  #3)

  #

  #

  ###Loading R library and packages   

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

  create_out_dir_param <-list_param_run_assessment_prediction$create_out_dir_param #if TRUE output dir created #PARAM7

  proj_str <- list_param_run_assessment_prediction$proj_str # CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84, #PARAM8

  list_year_predicted <- list_param_run_assessment_prediction$list_year_predicted # 1984:2004

  plotting_figures <- list_param_run_assessment_prediction$plotting_figures #if true run part2 of assessment

  ##for plotting assessment function

  mosaic_plot <- list_param_run_assessment_prediction$mosaic_plot  #PARAM14

  day_to_mosaic <- list_param_run_assessment_prediction$day_to_mosaic #PARAM15

  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

  reg_modified <- list_param_run_assessment_prediction$reg_modified #PARAM19

  threshold_missing_day <- list_param_run_assessment_prediction$threshold_missing_day #PARM20

  ##### Table 5: monthly station information used in training

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

  ############## Collect information from assessment ##########

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

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

  in_dir <- out_dir #PARAM 0

  #y_var_name <- "dailyTmax" #PARAM1 , already set

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

  out_suffix <- out_prefix #PARAM3

  #out_dir <-  #PARAM4, already set

  #create_out_dir_param <- FALSE #PARAM 5, already created and 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

  proj_str<- CRS_WGS84 #PARAM 8 #check this parameter

  #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

  region_name <- c("reg4") #reference region to merge if necessary, if world all the regions are together #PARAM 16

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

  #df_assessment_files, #PARAM 17, set in the script

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

  list_param_run_assessment_plotting <- list(y_var_name, interpolation_method, out_suffix, 

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

                      multiple_region, countries_shp, plot_region, num_cores, 

                      region_name, df_assessment_files, threshold_missing_day) 

  names(list_param_run_assessment_plotting) <- c("y_var_name","interpolation_method","out_suffix", 

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

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

                      "region_name","df_assessment_files","threshold_missing_day") 

  df_assessment_figures_files <- run_assessment_plotting_prediction_fun(list_param_run_assessment_plotting) 

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

  ##### Prepare objet to return ####

  assessment_obj <- list(df_assessment_files, df_assessment_figures_files)

  names(assessment_obj) <- c("df_assessment_files", "df_assessment_figures_files")

  return(assessment_obj)