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

## CODE FOR MONTE CARLO SIMULATIONS OF "RED NOISE" SERIES FOR ALL PROXIES IN SPECIFIED MBH NETWORK
## DOES THIS FOR EACH ORIGINAL MBH PROXY SET USED

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

##LIBRARY CALL AND EXISTING VARIABLE REMOVAL

rm(list=ls())

library(waveslim)


##SET UP VARIABLES

#Specify file path (between quotation marks) to location of proxy data file to be used

FILE_PATH<-paste("SPECIFY_FULL_PATH_TO_APPROPRIATE_WA/MBH_PROXY_NETWORK_FILE")


nyear=581  # 1400 network used 
proxies=22
j=1400


NM<-1000  # number of Monte Carlo iterations


##READING IN OF REAL-WORLD PROXY DATA


Proxy_network<-scan(paste(FILE_PATH,"data",j,".txt",sep=""))
Proxy_network<-matrix(Proxy_network,nrow=nyear,ncol=proxies, byrow=TRUE)
	dim(Proxy_network)


N<-nrow(Proxy_network)
n<-ncol(Proxy_network)



#DETERMINATION OF ACFs FOR PROXY SERIES

	Data1<-array(rep(NA,N*n), dim=c(N,n))

		for (l in 1:ncol(Proxy_network)){
			Data1[,l]<-acf(Proxy_network[,l][!is.na(Proxy_network[,l])],N)[[1]][1:N]
			} #l


#CALCULATION OF SCALING FACTORS TO BE USED IN "NM" LOOP

	std_network<-apply(Proxy_network,2,sd)
	std_networkmatrix<-array(dim=c(N,n))

		for (o in 1:N){
		std_networkmatrix[o,]<-std_network}


#RED NOISE SERIES SIMULATION LOOP
 

	Proxy_network_sim<-array (rep(NA,NM*nyear*n), dim=c(nyear,NM,n) )

      	for (q in 1:NM) {

		for (g in 1:n) {
		Proxy_network_sim[,q,g]<-hosking.sim(N,Data1[,g])
     			} #g
 

#    ADDITIONS OF SCALING BY STANDARD DEVIATION OF "Proxy_network" MATRIX SERIES TO OUTPUT OF "hosking.sim"
#    ALGORITHM ABOVE
		
	Proxy_network_sim[,q,]<-std_networkmatrix*Proxy_network_sim[,q,]

		} #q

#write(Proxy_network_sim[,1:NM,], ncol=nyear, file = paste(FILE_PATH,"Proxy_network_sim",j,".dat",sep="")) #data saved as: cols = years, rows = first by MC reps (blocks of 1000) and then by number of proxies;  NOTE:  these files are generally more than 100 megabytes in size and take time to write, which is why they are commented out as a default to start. 



#**************************** END ***********************************