##################  Functions for use in the raster prediction stage   #######################################

############################ Interpolation in a given tile/region ##########################################

#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)           

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

# 4)runClim_KGFusion<-function(j,list_param)

# 5)runGAMFusion <- function(i,list_param) 

#

#AUTHOR: Benoit Parmentier                                                                       

#DATE: 03/12/2013                                                                                 

#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.

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

runClim_KGFusion<-function(j,list_param){

  #Arguments: 

  #1)list_index: j 

  #2)covar_rast: covariates raster images used in the modeling

  #3)covar_names: names of input variables 

  #4)lst_avg: list of LST climatogy names, may be removed later on

  #5)list_models: list input models for bias calculation

  #6)dst: data at the monthly time scale

  #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

  #3) mod: list of model objects fitted 

  #4) formulas: list of formulas used in bias modeling

  ### PARSING INPUT ARGUMENTS

  #list_param_runGAMFusion<-list(i,clim_yearlist,sampling_obj,var,y_var_name, out_prefix)

  index<-list_param$j

  s_raster<-list_param$covar_rast

  covar_names<-list_param$covar_names

  lst_avg<-list_param$lst_avg

  list_models<-list_param$list_models

  dst<-list_param$dst #monthly station dataset

  var<-list_param$var

  y_var_name<-list_param$y_var_name

  out_prefix<-list_param$out_prefix

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

  #### STEP 2: PREPARE DATA

  #names(s_raster)<-covar_names

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

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

  #3)sampling_obj: contains, data per date/fit, sampling information

  #4)dst: data at the monthly time scale

  #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

  #2) rast_clim_list: list of files for temperature climatology predictions

  #3) delta: list of files for temperature delta predictions

  #4) data_s: training data

  #5) data_v: testing data

  #6) sampling_dat: sampling information for the current prediction (date,proportion of holdout and sample number)

  #7) mod_kr: kriging delta fit, field package model object