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

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

# 3)runClimCAI<-function(j) : not working yet

#

#AUTHOR: Benoit Parmentier                                                                       

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

##Comments and TODO:

#This script is meant to be for general processing tile by tile or region by region.

# Note that the functions are called from GAM_fusion_analysis_raster_prediction_mutlisampling.R.

# This will be expanded to other methods.

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

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

  }

  if (var=="TMIN"){   

  }

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

  dst<-list_param$dst #monthly station dataset

  ##########

  date<-strptime(sampling_dat$date[i], "%Y%m%d")   # interpolation date being processed

  month<-strftime(date, "%m")          # current month of the date being processed

    modst$LSTD_bias <- modst$LST-modst$TMax; #That is the difference between the monthly LST mean and monthly station mean    

  }

  #This may be unnecessary since LSTD_bias is already in dst?? check the info

  #Clearn out this part: make this a function call

  x<-as.data.frame(data_v)

  d<-as.data.frame(data_s)

  for (j in 1:nrow(x)){

    if (x$value[j]== -999.9){

      x$value[j]<-NA

      d$value[j]<-NA

    }

  }

  pos<-match("value",names(d)) #Find column with name "value"

  #names(d)[pos]<-c("dailyTmax")

  names(d)[pos]<-y_var_name

  names(x)[pos]<-y_var_name

  names(d)[pos]<-c("id")

  names(x)[pos]<-c("id")

  dmoday <-merge(modst,d,by="id",suffixes=c("",".y2"))  

  xmoday <-merge(modst,x,by="id",suffixes=c("",".y2"))  

  var_pat<-grep(mod_pat,names(dmoday),value=FALSE) # using grep with "value" extracts the matching names

  mod_pat<-glob2rx("*.y2")   

  var_pat<-grep(mod_pat,names(xmoday),value=FALSE) # using grep with "value" extracts the matching names

  xmoday<-xmoday[,-var_pat] #Removing duplicate columns

  data_v<-xmoday

  ##########

  # STEP 3 - interpolate daily delta across space

  #Change to take into account TMin and TMax

  if (var=="TMIN"){

  }

  if (var=="TMAX"){

    daily_delta<-dmoday$dailyTmax-dmoday$TMax

  #Saving kriged surface in raster images

  data_name<-paste("daily_delta_",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

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

               "sampling_dat",model_name)

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

  return(delta_obj)