# gam_daily, kriging_daily,gwr_daily,gam_CAI,gam_fusion,kriging_fusion,gwr_fusion and other options added.

#For multiple time scale methods, 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:

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

  #12)step

  #13)constant

  #14)prop_minmax

  #

  #6 additional parameters for monthly climatology and more

  ###Loading R library and packages     

  prop_minmax<-list_param_raster_prediction$prop_minmax

  dates_selected<-list_param_raster_prediction$dates_selected

  #6 additional parameters for monthly climatology and more

  list_models<-list_param_raster_prediction$list_models

  lst_avg<-list_param_raster_prediction$lst_avg

  cat("Starting script process time:",file=log_fname,sep="\n",append=TRUE)

  cat(as.character(time1),file=log_fname,sep="\n",append=TRUE)    

  ghcn<-readOGR(dsn=dirname(infile_daily),layer=sub(".shp","",basename(infile_daily)))

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

  #Reading monthly data

  dst<-readOGR(dsn=dirname(infile_monthly),layer=sub(".shp","",basename(infile_monthly)))

  #dates #list of dates for prediction

  #ghcn_name<-"ghcn" #infile daily data 

  #list_param_sampling<-list(seed_number,nb_sample,step,constant,prop_minmax,dates,ghcn_name)

  names(list_param_sampling)<-c("seed_number","nb_sample","step","constant","prop_minmax","dates","ghcn")

  sampling_obj<-sampling_training_testing(list_param_sampling)

  ########### PREDICT FOR MONTHLY SCALE  ##################

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

  #if (interpolation_method=="gam_fusion"){

  if (interpolation_method %in% c("gam_fusion","kriging_fusion","gwr_fusion")){

    #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

  }

  if (interpolation_method %in% c("gam_CAI","kriging_CAI", "gwr_CAI")){

    #test<-runClim_KGCAI(1,list_param=list_param_runClim_KGCAI)

    #gamclim_fus_mod<-mclapply(1:6, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) 

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

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

  #The methods could be defined earlier as constant??

  if (interpolation_method %in% c("gam_CAI","kriging_CAI","gwr_CAI","gam_fusion","kriging_fusion","gwr_fusion")){

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

    i<-1

    #test<-mclapply(1:18, runGAMFusion,list_param=list_param_runGAMFusion,mc.preschedule=FALSE,mc.cores = 9)

    #test<-runGAMFusion(1,list_param=list_param_runGAMFusion)

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

  }

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

    #test <- runGAM_day_fun(1,list_param_run_prediction_gam_daily)

    #method_mod_obj<-mclapply(1:11,list_param=list_param_run_prediction_gam_daily,runGAM_day_fun,mc.preschedule=FALSE,mc.cores = 11) #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="")))

    list_param_run_prediction_kriging_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_kriging_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 <- runKriging_day_fun(1,list_param_run_prediction_kriging_daily)

    #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(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