# 5)runGAMFusion <- function(i,list_param) : daily step for fusion method, perform daily prediction

#

#AUTHOR: Benoit Parmentier                                                                       

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363--   

##Comments and TODO:

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

  #8)y_var_name: output name, not used at this stage

  #9)out_prefix

  #The output is a list of four shapefile names produced by the function:

  #1) clim: list of output names for raster climatogies 

  #2) data_month: monthly training data for bias surface modeling

  var<-list_param$var

  y_var_name<-list_param$y_var_name

  out_prefix<-list_param$out_prefix

  #Model and response variable can be changed without affecting the script

  prop_month<-0 #proportion retained for validation

  run_samp<-1

    #j indicate which month is predicted

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

                     "_",run_samp,sep="")

    list_out_filename[[k]]<-raster_name

  }

  #clim_rast<-LST-bias_rast

  data_name<-paste(var,"_clim_month_",j,"_",model_name,"_",prop_month,

                   "_",run_samp,sep="")

  writeRaster(clim_rast, filename=raster_name_clim,overwrite=TRUE)  #Writing the data in a raster file format...(IDRISI)

  #Adding to current objects

  clim_obj<-list(rast_clim_list,data_month,mod_list,list_formulas)

  names(clim_obj)<-c("clim","data_month","mod","formulas")

  return(clim_obj) 

}

  var<-list_param$var

  y_var_name<-list_param$y_var_name

  out_prefix<-list_param$out_prefix

  #Model and response variable can be changed without affecting the script

  prop_month<-0 #proportion retained for validation

  run_samp<-1 #This option can be added later on if/when neeeded

    #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

  }

    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

  }

  #Saving kriged surface in raster images

  data_name<-paste(var,"_bias_LST_month_",j,"_",model_name,"_",prop_month,

                   "_",run_samp,sep="")

  writeRaster(bias_rast, filename=raster_name_bias,overwrite=TRUE)  #Writing the data in a raster file format...(IDRISI)

  #now climatology layer

  clim_rast<-LST-bias_rast

  data_name<-paste(var,"_clim_LST_month_",j,"_",model_name,"_",prop_month,

                   "_",run_samp,sep="")

  writeRaster(clim_rast, filename=raster_name_clim,overwrite=TRUE)  #Writing the data in a raster file format...(IDRISI)

  #Adding to current objects

  clim_obj<-list(rast_bias_list,rast_clim_list,data_month,mod_list,list_formulas)

  names(clim_obj)<-c("bias","clim","data_month","mod","formulas")

  return(clim_obj)

}

  #5)var: variable predicted -TMAX or TMIN

  #6)y_var_name: name of the variable predicted - dailyTMax, dailyTMin

  #7)out_prefix

  #

  #The output is a list of four shapefile names produced by the function:

  #1) list_temp: y_var_name

  y_var_name<-list_param$y_var_name

  out_prefix<-list_param$out_prefix

  dst<-list_param$dst #monthly station dataset

  ##########

  # STEP 1 - Read in information and get traing and testing stations

  #Saving kriged surface in raster images

  data_name<-paste("daily_delta_",y_var_name,"_",sampling_dat$date[i],"_",sampling_dat$prop[i],

                   "_",sampling_dat$run_samp[i],sep="")

  writeRaster(daily_delta_rast, filename=raster_name_delta,overwrite=TRUE)  #Writing the data in a raster file format...(IDRISI)

  #Now predict daily after having selected the relevant month

    data_name<-paste(y_var_name,"_predicted_",names(rast_clim_list)[j],"_",

                     sampling_dat$date[i],"_",sampling_dat$prop[i],

                     "_",sampling_dat$run_samp[i],sep="")

    writeRaster(temp_predicted, filename=raster_name,overwrite=TRUE) 

    temp_list[[j]]<-raster_name

  }

  obj_names<-c(y_var_name,"clim","delta","data_s","data_v",

               "sampling_dat",model_name)

  names(delta_obj)<-obj_names 

  return(delta_obj)

}