# R code developed by:
# International Research Institute for Climate and Society
# Columbia University in the City of New York, USA
# Last update: Jully 3, 2013
# --------------------------------------------------------------------------------
# Dependancies : (see each function for further details)
# 		-- Function "verify_mod.R" from package "verification", slightly modified
# 		-- Package "ROCR"
# 		-- Functions "ContinuousScores.R" and "ProbalisticScores.R"
# 		-- Files "Directory_Communication/XXX_ens_correct_MonthlyMean_Start_Month_k_Pred_Month_k+l_SimulatedCases.csv"" for all 1=<k=<12 and 0=<l=<2 generated by Scilab function Verification_MonthlyMean.sce
# --------------------------------------------------------------------------------
# Actions :
#		-- For the four models, compute the continuous and probalistic scores in several configurations
# --------------------------------------------------------------------------------

###################
# Lauch on scilab :
# exec('~/Scilab/Verification/Verification_MonthlyMean.sci')
###################

#library(verification,lib.loc="/home/paulb/R/x86_64-redhat-linux-gnu-library/2.15")

Directory_Functions<-"/Volumes/shared_data/paulb/R/Functions/Verification/"
Directory_Communication <- "/Volumes/shared_data/paulb/Results/Communication/Verification/"
Directory_Output <- "/Volumes/shared_data/paulb/Results/Results/Skills/" #Directory where you want your output to be stocked. 

source(paste(Directory_Functions,"verify_mod.R",sep=""))
source(paste(Directory_Functions,"ContinuousScores.R",sep=""))
source(paste(Directory_Functions,"ProbalisticScores.R",sep=""))

ens <- "ensemble" 
	
for (XXX in c("WCT", "MAC", "AM", "ABP")) { # Select the model to be tested
	for (cor in c("uncorrected","corrected")) { # choose \corrected\" or \"uncorrected\""
		for (method in c("SMC","SLC")){
			if (cor == "uncorrected") {correct <- cor}
			if (cor == "corrected") {correct <- paste(cor, "_", method, sep = "")}
			for (anomalies in c(TRUE,FALSE)) {
				# Computation of the continuous scores for mean values of the model ensemble
				ContinuousScores(Directory_Communication, Directory_Output, XXX, ens, correct, anomalies)
			}
			#Computation of the probalistic scores
			ProbalisticScores(Directory_Communication, Directory_Output, XXX, ens, correct)
		}
	}
}

# ens<-"single"

# for (XXX in c("MultiModel_threshold=0","MultiModel_threshold=0.1","MultiModel_threshold=0.01","MultiModel_threshold=0.001","MultiModel_Mean")) { # Select the model to be tested
	# for (cor in c("corrected")) { # choose \corrected\" or \"uncorrected\""
		# for (method in c("SMC","SLC")){
		# if (cor == "uncorrected") {correct <- cor}
		# if (cor == "corrected") {correct <- paste(cor, "_", method, sep = "")}
		# for (anomalies in c(%T,%F)) {

			# # Computation of the continuous scores for mean values of the model ensemble

			# ContinuousScores(Directory_Communication, Directory_Output, XXX, ens, correct,anomalies)
		# }
	# }
# }