#STAGE 4: Raster prediction: run interpolation method -- gam fusion, gam CAI, ...

#DATE: 03/12/2013                                                                                 

stages_to_run<-c(1,2,3,4,5) #May decide on antoher strategy later on...

#####SCRIPT USED FOR THE PREDICTIONS

#master_script_temp_03052013.R

#IN MASTER SCRIPT:

#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_02062013.R

#source(file.path(script_path,"Database_stations_covariates_processing_function_03052013.R"))

#source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03052013.R"))

#source(file.path(script_path,"results_interpolation_date_output_analyses_03052013.R"))

#CALLED FROM GAM FUSION ANALYSIS RASTER PREDICTION

#source(file.path(script_path,"sampling_script_functions_03052013.R"))

#source(file.path(script_path,"GAM_fusion_function_multisampling_03052013.R")) #Include GAM_CAI

#source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_02262013.R"))

############ STAGE 1: LST Climatology ###############

if (stages_to_run[1]==1){

  #Call run through python

  #/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_01252013b.py

}

if (stages_to_run[2]==2){

  #Transform into function...

  #/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_02062013.R

}

infile_covariates<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script and used in stage 3 and stage 4

#Prepare parameters for for raster prediction... 

#Collect parameters from the previous stage: data preparation stage

#3 parameters from output

infile_monthly<-list_outfiles$monthly_covar_ghcn_data #outile4 from database_covar script

infile_daily<-list_outfiles$daily_covar_ghcn_data  #outfile3 from database_covar script

infile_locs<- list_outfiles$loc_stations_ghcn #outfile2? from database covar script

list_param_data_prep<-list(infile_monthly,infile_daily,infile_locs,infile_covariates,covar_names,var,out_prefix,CRS_locs_WGS84)

names(list_param_data_prep)<-c("infile_monthly","infile_daily","infile_locs","infile_covariates","covar_names","var","out_prefix","CRS_locs_WGS84")

#Set additional parameters

#Input for sampling function...

seed_number<- 100  #if seed zero then no seed?     

nb_sample<-1           #number of time random sampling must be repeated for every hold out proportion

step<-0         

constant<-0             #if value 1 then use the same samples as date one for the all set of dates

prop_minmax<-c(0.3,0.3)  #if prop_min=prop_max and step=0 then predicitons are done for the number of dates...

infile_dates<-"list_365_dates_04212012.txt"

#Models to run...this can be change for each run

list_models<-c("y_var ~ s(elev_1)",

               "y_var ~ s(LST)",

               "y_var ~ s(elev_1,LST)",

               "y_var ~ s(lat) + s(lon)+ s(elev_1)",

               "y_var ~ s(lat,lon,elev_1)",

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST)", 

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC2)",

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC6)", 

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(DISTOC)")

#Choose interpolation method...

interpolation_method<-c("gam_fusion","gam_CAI") #other otpions to be added later

#Default name of LST avg to be matched               

lst_avg<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12")  

in_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data"

#Create on the fly output folder...

out_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data"

script_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/"

#Collect all parameters in a list

list_param_raster_prediction<-list(list_param_data_prep,

                                seed_number,nb_sample,step,constant,prop_minmax,infile_dates,

                                list_models,lst_avg,in_path,out_path,script_path,

                                interpolation_method)

names(list_param_raster_prediction)<-c("list_param_data_prep",

                                "seed_number","nb_sample","step","constant","prop_minmax","infile_dates",

                                "list_models","lst_avg","in_path","out_path","script_path",

                                "interpolation_method")

#Source file

source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03122013b.R"))

#Make the function call

raster_prediction_gam_fus_obj <-raster_prediction_gam_fusion(list_param_raster_prediction)