#This script contains 5 functions used in the interpolation of temperature in the specfied study/processing area:                             

# 1)predict_raster_model<-function(in_models,r_stack,out_filename)                                                             

# 2)fit_models<-function(list_formulas,data_training)           

# 4)runClim_KGFusion<-function(j,list_param) function for monthly step (climatology) in the fusion method

# 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:

####

#TODO:

#Add log file and calculate time and sizes for processes-outputs

  #Make this a function with multiple argument that can be used by mcmapply??

  #Arguments: 

  LST_name<-lst_avg[j] # name of LST month to be matched

  data_month$LST<-data_month[[LST_name]]

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

  #TMax to model...

  if (var=="TMIN"){   

    data_month$y_var<-data_month$TMin #Adding TMin as the variable modeled

  }

  mod_list<-fit_models(list_formulas,data_month) #only gam at this stage

  cname<-paste("mod",1:length(mod_list),sep="") #change to more meaningful name?

  names(mod_list)<-cname

  s_raster<-dropLayer(s_raster,pos)      # If it exists drop layer

  LST<-subset(s_raster,LST_name)

  names(LST)<-"LST"

  s_raster<-addLayer(s_raster,LST)            #Adding current month

  #Now generate file names for the predictions...

    #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

  }

  #now predict values for raster image...

  rast_clim_list<-predict_raster_model(mod_list,s_raster,list_out_filename)

  names(rast_clim_list)<-cname

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

  #Adding Kriging for Climatology options

  #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) 

}

## Run function for kriging...?

  #Arguments: 

  #1)index: loop list index for individual run/fit

  #2)clim_year_list: list of climatology files for all models...(12*nb of models)

  #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

    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

  }