#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:

  #7)var: variable being interpolated-TMIN or TMAX

  #8)out_prefix

  #9)CRS_locs_WGS84

  #

  #6 parameters for sampling function

  #10)seed_number

  var<-list_param_data_prep$var

  out_prefix<-list_param_data_prep$out_prefix

  CRS_locs_WGS84<-list_param_data_prep$CRS_locs_WGS84

  #6 parameters for sampling function

  seed_number<-list_param_raster_prediction$seed_number

  out_path<-list_param_raster_prediction$out_path

  script_path<-list_param_raster_prediction$script_path

  interpolation_method<-list_param_raster_prediction$interpolation_method

  setwd(out_path)

  if (var=="TMAX"){

    y_var_name<-"dailyTmax"                                       

  }

  if (var=="TMIN"){

    y_var_name<-"dailyTmin"                                       

  }

  ############### READING INPUTS: DAILY STATION DATA AND OTEHR DATASETS  #################

  CRS_interp<-proj4string(ghcn)                       #Storing projection information (ellipsoid, datum,etc.)

  #dates2 <-readLines(file.path(in_path,dates_selected)) #dates to be predicted, now read directly from the file

  if (dates_selected==""){

    dates<-as.character(sort(unique(ghcn$date))) #dates to be predicted 

  names(s_raster)<-covar_names               #Assigning names to the raster layers: making sure it is included in the extraction

  #Reading monthly data

  ########### CREATE SAMPLING -TRAINING AND TESTING STATIONS ###########

  if (interpolation_method=="gam_daily"){

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

    i<-1

    #test <- runGAM_day_fun(1,list_param_run_prediction_gam_daily)

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

    list_param_run_prediction_gwr_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path)

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

    #test <- run_interp_day_fun(1,list_param_run_prediction_gwr_daily)

    #method_mod_obj<-mclapply(1:9,list_param=list_param_run_prediction_gwr_daily,run_interp_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement

    #method_mod_obj<-mclapply(1:18,list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement

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

  }

  #use %in% instead of "|" operator

  if (interpolation_method=="gam_daily" | interpolation_method=="kriging_daily" | interpolation_method=="gwr_daily"){

    raster_prediction_obj<-list(method_mod_obj,validation_mod_obj,tb_diagnostic_v,