# This routine is for doing biPlots of time series data that were not sampled at exact
# same time.  It is based on two series of data.  It searches times in first series and then
# identifies closest data point in second series that within some minimum distance of first point.  Does
# not assume any particular ordering of the time series. maxx is the maximum amount of time that 
# program is allowed to search for matching timepoint in second series and lagg allows 
# search for points at some lagged distance (and is added to maxx)
# 
# zz and zzz specify which columns to use as y in each of the two data sets.  Time(x) is assumed to
# be the first column
# detre=1 means detrend series using locfit function and plot residuals
#
plotst <-function(x1,x2,zz=2,zzz=2,maxx=10,lagg=0,detre=1) {
tempx1_as.matrix(x1)
tempx2_as.matrix(x2)
xname1_name.cols(x1)
xname2_name.cols(x2)
		txa_as.vector(tempx1[,1][!is.na(tempx1[,1])])
		txb_as.vector(tempx2[,1][!is.na(tempx2[,1])])
		tya_as.vector(tempx1[,zz][!is.na(tempx1[,zz])])
		tyb_as.vector(tempx2[,zzz][!is.na(tempx2[,zzz])])
## Detrend by locfit.raw	
if(detre==1) {
		fit_locfit.raw(txa,tya,alpha=.2,deg=c(0,3))
		tya_residuals(fit)
		fit_locfit.raw(txb,tyb,alpha=.2,deg=c(0,3))
		tyb_residuals(fit)
}



pairsa_length(txa)
pairsb_length(txb)
maxx_maxx+lagg
prs_ matrix(NA,nrow=pairsa,ncol=2)
	for(i in 1:pairsa){
		prs[i,1]_tya[i]		
		diff_0
		y2val_NA
		p1_txa[i]
		cmin_1000
	for(j in 1:pairsb){
		p2_txb[j]
		diff_abs(p1-p2)
		lagt_p1-p2
		if (diff<=cmin & lagt >=lagg) {
			y2val_tyb[j]
			cmin_diff			  } 					
if (cmin<= maxx) {prs[i,2]_y2val}
		
# Below bracket closes pairsb loop
}

# Below bracket closes pairsa loop

}
#closes scipt
exportData(prs,"Plotout.xls",type="EXCEL")
plot(prs,xlab = xname1[zz], ylab = xname2[zzz])
title(paste(xname2[zzz],"as a function of ",xname1[zz]," lagged by ", as.character(lagg),"Years"))
}