#STAGE 5: Output analyses: assessment of results for specific dates...

#

#AUTHOR: Benoit Parmentier                                                                       

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   

source(file.path(script_path,"sampling_script_functions_03122013.R"))

source(file.path(script_path,"GAM_fusion_function_multisampling_07302013.R")) #Include GAM_CAI

source(file.path(script_path,"interpolation_method_day_function_multisampling_07052013.R")) #Include GAM_day

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

#stages_to_run<-c(0,2,3,4,5) #May decide on antoher strategy later on...

stages_to_run<-c(0,2,3,4,5) #MRun only raster fitting, prediction and assessemnt (providing lst averages, covar brick and met stations)

#If stage 2 is skipped then use previous covar object

covar_obj_file<-"/data/project/layers/commons/data_workflow/output_data_365d_gam_fus_lst_test_run_07172013/covar_obj__365d_gam_fus_lst_test_run_07172013.RData"

#If stage 3 is skipped then use previous met_stations object

met_stations_outfiles_obj_file<-"/data/project/layers/commons/data_workflow/output_data_365d_gam_fus_lst_test_run_07172013/met_stations_outfiles_obj_gam_fusion__365d_gam_fus_lst_test_run_07172013.RData"

var<-"TMAX" # variable being interpolated

out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     

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

#interpolation_method<-c("kriging_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_CAI") #other otpions to be added later

#interpolation_method<-c("gam_daily") #other otpions to be added later

#interpolation_method<-c("gwr_daily") #other otpions to be added later

out_path<-"/home/parmentier/Data/IPLANT_project/Oregon_interpolation/Oregon_03142013/output_data"

#Set additional parameters

#Input for sampling function...

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

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

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

screen_data_training<-FALSE #screen training data for NA and use same input training for all models fitted

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

#               "y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,LC1)",

#               "y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,CANHGHT)")

#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")  

############## STAGE 5: OUTPUT ANALYSES ##################

date_selected_results<-c("20100101") 

list_param_results_analyses<-list(out_path,script_path,raster_prediction_obj,interpolation_method,

                                  covar_obj,date_selected_results,var,out_prefix)

names(list_param_results_analyses)<-c("out_path","script_path","raster_prediction_obj","interpolation_method",