#9) pred_mod_name : model name used e.g. "mod1" #PARAM 9

  #10) var_pred : variable for use in residuals mapping (e.g. "res_mod1") #PARAM 10

  #11) create_out_dir_param: if TRUE then create a new dir #PARAM 11

  #13) proj_str :porjection used by tiles e.g. CRS_WGS84 #PARAM 13

  #14) file_format: output file format used for raster eg ".tif" #PARAM 14

  #15) NA_value: NA value used e.g. -9999 #PARAM 15

  create_out_dir_param <- list_param_run_mosaicing_prediction$create_out_dir_param # FALSE #PARAM 12

  #if daily mosaics NULL then mosaicas all days of the year #PARAM 13

  #CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84 #CONSTANT1

  #CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84

  ####### PART 1: Read in data and process data ########

  #out_dir <- in_dir #PARAM 11

  #in_dir_tiles <- file.path(in_dir,"tiles") #this is valid both for Atlas and NEX

  NA_flag_val <- NA_value #PARAM 16

  #in_dir <- file.path(in_dir,region_name)

  #out_dir <- in_dir

  if(create_out_dir_param==TRUE){

    out_dir_tmp <- file.path(out_dir,"mosaic")

    out_dir <- create_dir_fun(out_dir_tmp,out_suffix)

    setwd(out_dir)

  }else{

  setwd(out_dir)

  ### Read in assessment and accuracy files

  df_assessment_files <- read.table(df_assessment_files_name,stringsAsFactors=F,sep=",")

  tb_v_accuracy_name <- df_assessment_files$files[2] 

  tb_s_accuracy_name <- df_assessment_files$files[4] 

  tb_s_month_accuracy_name <- df_assessment_files$files[3] 

  data_month_s_name <- df_assessment_files$files[5] 

  ##data_month_v_name <- file.path(in_dir,basename(df_assessment_files$files[8])) 

  # accuracy table by tiles

  tb <- read.table(tb_v_accuracy_name,sep=",")

  tb_s <- read.table(tb_s_accuracy_name,sep=",")

  data_day_v <- read.table(data_day_v_name,sep=",") #daily training stations by dates and tiles

  df_tile_processed <- read.table(df_tile_processed_name,sep=",")

  #this part needs to be improve make this a function and use multicore to loop through files...

  #give a range of dates to run...

  }

  #reg_lf_mosaic[[k]] <- list.files(path=file.path(in_dir_tiles_tmp),pattern=paste(".*.",day_to_mosaic[i],".*.tif$",sep=""),full.names=T,recursive=T)

  ##################### PART 2: produce the mosaic ##################

  #This is is assuming a list of file for a region!! 

  #this is where the main function for mosaicing region starts!!

  #use reg4 to test the code for now, redo later for any regions!!!

  #There a 28 files for reg4, South America

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

  #Plot as quick check

  #r1 <- raster(lf_mosaic[[1]][1]) 

  #plot(r1)

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

  ### Now create accuracy surfaces from residuals...

  #NA_flag_val 

  #file_format 

  out_dir_str <- out_dir

  days_to_process <- day_to_mosaic

  df_tile_processed$path_NEX <- as.character(df_tile_processed$path_NEX) 

  df_tile_processed$reg <- basename(dirname(df_tile_processed$path_NEX))

  ##took 31 minutes to generate the residuals maps for each tiles (28) for region 4

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

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

  #### Mosaic tiles for the variable predicted and accuracy metric