calc_stat_from_raster_prediction_obj <-function(raster_prediction_obj,stat,training=FALSE){

  #Calculate statistics from validation and training out of raster_prediction_obj

  #If training is TRUE, then using training dataset

  #extract relevant information

  if(training==TRUE){

    tb <- extract_from_list_obj(raster_prediction_obj$validation_mod_obj,"metrics_s")

    rownames(tb)<-NULL #remove row names

  }else{

    tb <- raster_prediction_obj$tb_diagnostic_v

  }

  #Now summarize

calc_stat_month_from_raster_prediction_obj <-function(raster_prediction_obj,stat){

  #Calculate monthly averages from tb

  #Function

  #tb must have a month column month

  add_month_tag<-function(tb){

    date<-strptime(tb$date, "%Y%m%d")   # interpolation date being processed

    month<-strftime(date, "%m")          # current month of the date being processed

  }

  ## Beging ##

  tb<-raster_prediction_obj$tb_diagnostic_v

  tb$month<-add_month_tag(tb)

  t<-melt(tb,

          measure=c("mae","rmse","r","me","m50"), 

          id=c("pred_mod","month"),

          na.rm=T)

  stat_tb<-cast(t,pred_mod+month~variable,stat)

  return(stat_tb)

}