list_tiles_predicted_masked <- list_param$list_tiles_predicted_masked

  df_missing_tiles_day <- list_param$df_missing_tiles_day    

  r_overlap_m <- list_param$r_overlap_m

  num_cores <- list_param$num_cores # 6 #PARAM 14

  region_name <- list_param$region_name #<- "world" #PARAM 15

  NA_flag_val <-list_param$NA_flag_val

  in_dir1 <- list_param$in_dir1 

  region_name <- list_param$region_name #e.g. c("reg23","reg4") #run only for one region

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

  out_dir <- list_param$out_dir #<- "/nobackupp8/bparmen1/" #PARAM6

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

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

  ##for plotting assessment function

  day_to_mosaic_range <- list_param$day_to_mosaic_range #PARAM15

  countries_shp <- list_param$countries_shp #PARAM17

  plotting_figures <- list_param$plotting_figures #PARAM18

  pred_mod_name <- list_param$pred_mod_name

  metric_name <- list_param$metric_name

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

  out_dir <- in_dir

  if(create_out_dir_param==TRUE){

    out_dir <- create_dir_fun(out_dir,out_suffix)

  #list_outfiles <- vector("list", length=35) #collect names of output files, this should be dynamic?

  #list_outfiles_names <- vector("list", length=35) #collect names of output files

  in_dir1_reg <- file.path(in_dir1,region_name)

  list_outfiles <- vector("list", length=14) #collect names of output files

  #gam_CAI_dailyTmax_predicted_mod1_0_1_20001231_30_1-39.7_165.1.tif

  #undebug(check_missing)

  test_missing <- try(lapply(1:length(list_lf_raster_tif_tiles),function(i){check_missing(lf=list_lf_raster_tif_tiles[[i]], 

                                                                      pattern_str=NULL,

                                                                      in_dir=out_dir,

                                                                      out_dir=".")}))

  df_time_series <- test_missing[[1]]$df_time_series

  head(df_time_series)

  table(df_time_series$missing)

  table(df_time_series$year)

  #####

  #Now combine df_time_series in one table

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

  #### Step 2: examine overlap

  path_to_shp <- dirname(countries_shp)

  layer_name <- sub(".shp","",basename(countries_shp))

  #centroids_pts <- tmp_pts 

  r_mask <- raster(infile_mask)

  ## find overlap

  #http://gis.stackexchange.com/questions/156660/loop-to-check-multiple-polygons-overlap-in-r

    }

    #

  }

  names(shps_tiles) <- basename(unlist(in_dir_reg))

  r_ref <- raster(list_lf_raster_tif_tiles[[1]][1])

  tile_spdf <- shps_tiles[[1]]

  tile_coord <- basename(in_dir_reg[1])

  date_val <- df_missing$date[1]

  #undebug(rasterize_tile_day)

  #list_predicted <- mclapply(1:6,

  #         FUN=rasterize_tile_day,

  #         list_spdf=shps_tiles,

           mc.cores = num_cores)

  ##check that everything is correct:

  ### Make a list of file

  out_suffix_str_tmp <- paste0(region_name,"_",out_suffix)

  writeLines(unlist(list_predicted),con=filename_list_predicted) #weights files to mosaic 

  #out_mosaic_name_weights_m <- r_weights_sum_raster_name <- file.path(out_dir,paste("r_weights_sum_m_",mosaic_method,"_weighted_mean_",out_suffix,".tif",sep=""))

  #out_mosaic_name_prod_weights_m <- r_weights_sum_raster_name <- file.path(out_dir,paste("r_prod_weights_sum_m_",mosaic_method,"_weighted_mean_",out_suffix,".tif",sep=""))

  out_mosaic_name_predicted_m  <- file.path(out_dir_str,paste("r_overlap_sum_m_",out_suffix_str_tmp,".tif",sep=""))