#5)possibilty of running GAM+FUSION or GAM+CAI and other options added

#The interpolation is done first at the monthly time scale then delta surfaces are added.

#AUTHOR: Benoit Parmentier                                                                        

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#568--     

#

# TO DO:

  t1<-proc.time()

  j=12

  #browser() #Missing out_path for gam_fusion list param!!!

    list_param_runClim_KGFusion<-list(j,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, out_prefix,out_path)

    names(list_param_runClim_KGFusion)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","out_prefix","out_path")

    #source(file.path(script_path,"GAM_fusion_function_multisampling_03122013.R"))

    clim_method_mod_obj<-mclapply(1:12, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement

    #clim_method_mod_obj<-mclapply(1:6, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement

    save(clim_method_mod_obj,file= file.path(out_path,paste(interpolation_method,"_mod_",y_var_name,out_prefix,".RData",sep="")))

    list_tmp<-vector("list",length(clim_method_mod_obj))

    for (i in 1:length(clim_method_mod_obj)){

    clim_yearlist<-list_tmp

  }

  if (interpolation_method=="gam_CAI"){

    list_param_runClim_KGCAI<-list(j,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, out_prefix,out_path)

    names(list_param_runClim_KGCAI)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","out_prefix","out_path")

      file=log_fname,sep="\n")

  #TODO : Same call for all functions!!! Replace by one "if" for all multi time scale methods...

    #input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn

    i<-1

    list_param_run_prediction_daily_deviation <-list(i,clim_yearlist,sampling_obj,dst,var,y_var_name, interpolation_method,out_prefix,out_path)

    method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data_day),list_param=list_param_run_prediction_daily_deviation,run_prediction_daily_deviation,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement

    save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))

  #TODO : Same call for all functions!!! Replace by one "if" for all daily single time scale methods...

  if (interpolation_method=="gam_daily"){

  names(list_param_validation)<-c("list_index","rast_day_year_list","method_mod_obj","y_var_name","out_prefix", "out_path") #same names for any method

  validation_mod_obj <-mclapply(1:length(method_mod_obj), list_param=list_param_validation, calculate_accuracy_metrics,mc.preschedule=FALSE,mc.cores = 9) 

  save(validation_mod_obj,file= file.path(out_path,paste(interpolation_method,"_validation_mod_obj_",y_var_name,out_prefix,".RData",sep="")))

  t2<-proc.time()-t1

  cat(as.character(t2),file=log_fname,sep="\n", append=TRUE)

  ################### PREPARE RETURN OBJECT ###############

  #Will add more information to be returned

    raster_prediction_obj<-list(clim_method_mod_obj,method_mod_obj,validation_mod_obj,tb_diagnostic_v,

                                summary_metrics_v,summary_month_metrics_v)

    names(raster_prediction_obj)<-c("clim_method_mod_obj","method_mod_obj","validation_mod_obj","tb_diagnostic_v",

  #### STEP3:  NOW FIT AND PREDICT  MODEL

  list_formulas<-lapply(list_models,as.formula,env=.GlobalEnv) #mulitple arguments passed to lapply!!

  #Now generate file names for the predictions...

  names(list_out_filename)<-cname  

  for (k in 1:length(list_out_filename)){

    #j indicate which month is predicted, var indicates TMIN or TMAX

    data_name<-paste(var,"_bias_LST_month_",j,"_",cname[k],"_",prop_month,

                     "_",run_samp,sep="")

    list_out_filename[[k]]<-raster_name

  }

  #Some modles will not be predicted...remove them

  rast_bias_list<-rast_bias_list[!sapply(rast_bias_list,is.null)] #remove NULL elements in list

    clim_fus_rast<-LST-subset(mod_rast,k)

    data_name<-paste(var,"_clim_LST_month_",j,"_",names(rast_clim_list)[k],"_",prop_month,

                     "_",run_samp,sep="")

    rast_clim_list[[k]]<-raster_name

    writeRaster(clim_fus_rast, filename=raster_name,overwrite=TRUE)  #Wri

  }