#The interpolation is done first at the monthly add delta.

#AUTHOR: Benoit Parmentier                                                                        

#Change this to allow explicitly arguments...

#Arguments: 

  }

  calc_val_metrics_rast <-function(df,y_ref,pred_names){

    list_metrics<-vector("list",length(pred_names))

    list_residuals<-vector("list",length(pred_names))

    return(accuracy_obj)

  }  

  #PARSING INPUT PARAMETERS

  day_list<- list_param$rast_day_year_list[[i]]

  #Change to results_mod_obj[[i]]$data_s to make it less specific

  data_v <- method_mod_obj[[i]]$data_v

  data_s <- method_mod_obj[[i]]$data_s

  ## Now create the stack

boxplot_from_tb <-function(tb_diagnostic,metric_names,out_prefix){

  #now boxplots and mean per models

  mod_names<-sort(unique(tb_diagnostic$pred_mod)) #models that have accuracy metrics

  t<-melt(tb_diagnostic,

          #measure=mod_var, 

  tb_mod_list<-lapply(mod_names, function(k) subset(tb, pred_mod==k)) #this creates a list of 5 based on models names

  names(tb_mod_list)<-mod_names

  #mod_metrics<-do.call(cbind,tb_mod_list)

  mod_metrics<-do.call(cbindX,tb_mod_list)

  test_names<-lapply(1:length(mod_names),function(k) paste(names(tb_mod_list[[1]]),mod_names[k],sep="_"))

  names(mod_metrics)<-unlist(test_names)

## Function to display metrics by months/seasons

boxplot_month_from_tb <-function(tb_diagnostic,metric_names,out_prefix){

  #Add code here...

}