#MODIFIED ON: 04/07/2016            

  #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)

  if(layers_option=="res_testing"){

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

  ##Revised on 04/07 for three dates, it took 40 minutes

  #### PART 2: GENERATE MOSAIC FOR LIST OF FILES #####

    mosaic_method <- "use_edge_weights" #this is distance from edge

    out_suffix_tmp <- paste(interpolation_method,y_var_name,day_to_mosaic[i],out_suffix,sep="_")

    #debug(mosaicFiles)

    #can also loop through methods!!!

    #python_bin <- "/usr/bin/" #python gdal bin, on Atlas NCEAS

    #python_bin <- "/nobackupp6/aguzman4/climateLayers/sharedModules/bin" #on NEX

    #gdal_merge_sum_noDataTest.py

    mosaic_edge_obj_prediction <- mosaicFiles(lf_mosaic[[i]],

    #runs in 15-16 minutes for 3 dates and mosaicing of 28 tiles...  

    ## Now accuracy based on center of centroids

    mosaic_method <- "use_edge_weights" #this is distance from edge

    #Adding metric name in the name...

    out_suffix_tmp <- paste(interpolation_method,metric_name,day_to_mosaic[i],out_suffix,sep="_")

    #debug(mosaicFiles)

    #can also loop through methods!!!

    mosaic_edge_obj_accuracy <- mosaicFiles(lf_accuracy_raster[[i]],

    ##Took 39 minutes for 28 tiles and one date...!!!  

    list_mosaic_obj[[i]] <- list(prediction=mosaic_edge_obj_prediction,accuracy=mosaic_edge_obj_accuracy)

    #for now add data_day_s in the name!!

    mosaic_method <- "use_edge_weights" #this is distance from edge

    out_suffix_tmp <- paste(interpolation_method,"kriged_residuals","data_day_s",day_to_mosaic[i],out_suffix,sep="_")

    #lf_tmp<-list.files(pattern="*kriged_residuals.*.tif",full.names=T)

    lf_tmp <- lf_accuracy_residuals_raster[[i]]

    #lf_accuracy_residuals_raster[[i]]

    #debug(mosaicFiles)

    mosaic_edge_obj_residuals <- mosaicFiles(lf_tmp,

    #Took 11 to 12 minues for one day and 28 tiles in region 4

    list_mosaic_obj[[i]] <- list(prediction=mosaic_edge_obj_prediction,accuracy=mosaic_edge_obj_accuracy,mosaic_edge_obj_residuals)

    #}

    ##End of mosaicing function for region predictions

  }

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

  ###### PART 2: Analysis and figures for the outputs of mosaic function #####

  #### compute and aspect and slope with figures

  #list_lf_mosaic_obj <- vector("list",length(day_to_mosaic))

  #lf_mean_mosaic <- vector("list",length(mosaicing_method))#2methods only

  #l_method_mosaic <- vector("list",length(mosaicing_method))

  #list_out_suffix <- vector("list",length(mosaicing_method))

  #for(i in 1:length(day_to_mosaic)){

  #  list_lf_mosaic_obj[[i]] <- list.files(path=out_dir,pattern=paste("*",day_to_mosaic[i],

  #                                                                   "_.*.RData",sep=""))

  #  lf_mean_mosaic[[i]] <- unlist(lapply(list_lf_mosaic_obj[[i]],function(x){load_obj(x)[["mean_mosaic"]]}))

  #  l_method_mosaic[[i]] <- paste(unlist(lapply(list_lf_mosaic_obj[[i]],function(x){load_obj(x)[["method"]]})),day_to_mosaic[i],sep="_")

  #  list_out_suffix[[i]] <- unlist(paste(l_method_mosaic[[i]],day_to_mosaic[[i]],out_suffix,sep="_"))

  #}

  #if(plot_figures==TRUE){

    #list_param_plot_mosaic <- list(lf_mosaic=unlist(lf_mean_mosaic),

    #                               method=unlist(l_method_mosaic),

    #                               out_suffix=unlist(list_out_suffix))

    #plot and produce png movie...

    #plot_mosaic(1,list_param=list_param_plot_mosaic)

    #num_cores <- 1

    #l_png_files <- mclapply(1:length(unlist(lf_mean_mosaic)),FUN=plot_mosaic,

    #                        list_param= list_param_plot_mosaic,

    #                        mc.preschedule=FALSE,mc.cores = num_cores)

    #lf_plot<- list.files(pattern="r_m_use.*.mask.*.tif$")

    #lf_mean_mosaic <- lf_plot

    #list_param_plot_mosaic <- list(lf_raster_fname=unlist(lf_mean_mosaic[1:2]),

    #                               screenRast=TRUE,

    #                               l_dates=day_to_mosaic,

    #                               out_dir_str=out_dir,

    #                               out_prefix_str <- "dailyTmax_",

    #                               out_suffix_str=out_suffix)

    #debug(plot_screen_raster_val)

    #plot_screen_raster_val(1,list_param_plot_mosaic)

    #num_cores <- 2

    #l_png_files <- mclapply(1:length(unlist(lf_mean_mosaic)[1:2]),FUN=plot_screen_raster_val,

    #                        list_param= list_param_plot_mosaic,

    #                        mc.preschedule=FALSE,mc.cores = num_cores)

    #list_param_plot_mosaic <- list(lf_raster_fname=unlist(lf_mean_mosaic[4:6]),

    #                               screenRast=FALSE,

    #                               l_dates=day_to_mosaic,

    #                               out_dir_str=out_dir,

    #                               out_prefix_str <- paste("rmse_",out_suffix,sep=""),

    #                               out_suffix_str=paste("rmse_",out_suffix,sep=""))

    #num_cores <- 3

    #l_png_files_rmse <- mclapply(1:length(unlist(lf_mean_mosaic)[4:6]),FUN=plot_screen_raster_val,

    #                             list_param= list_param_plot_mosaic,

    #                             mc.preschedule=FALSE,mc.cores = num_cores)

  #}

  #list of mosaiced files