Nothing
## we analyse the connected only heavy-heavy contributions to
## the eta correlation matrix
## we need the following flavour combinations
flavour.strings <- array(c("cscs","cssc","sccs","scsc", "cscc","csss","sccc","scss",
"cccs","ccsc","sscs","sssc", "cccc","ccss","sscc","ssss"),
dim=c(4,2,2))
elements.strings <- array(c("ss", "sp", "ps", "pp"), dim=c(2,2))
## mapping needed to create the filenames of the input files
## mapping is needed because of gamma_5 trick etc.
flavour.mapping <- function(s) {
return(paste(substr(s,1,1), substr(s,4,4), substr(s,2,2), substr(s,3,3), sep=""))
}
flavour.factors <- array(c(+.25,-.25,-.25,+.25, +.25,+.25,-.25,-.25,
-.25,+.25,-.25,+.25, +.25,+.25,+.25,+.25),
dim=c(4,2,2))
## the following have to be chosen for the heavyheavy code
gamma.indices <- array(c(4,4,4,4, 3,3,3,3,
2,2,2,2, 1,1,1,1),
dim=c(4,2,2))
## and this here for libcvcpp (might change in the future...)
##gamma.indices <- array(c(5,5,5,5, 7,7,7,7,
## 6,6,6,6, 1,1,1,1),
## dim=c(4,2,2))
# set reread = TRUE when you want to read the data again
reread <- FALSE
if(!file.exists("Cmatrix.Rdata") || reread) {
for(i in c(1:2)) {
for(j in c(1:2)) {
for(k in c(1:4)) {
files <- getorderedfilelist(basename=paste("outprcvn.", flavour.mapping(flavour.strings[k,i,j]), ".", sep=""))
## set skip to 1 for libcvcpp
cmicor <- readcmidatafiles(files, skip=0, verbose=TRUE)
assign(flavour.strings[k,i,j], extract.obs(cmicor, vec.obs=c(gamma.indices[k,i,j])))
if(k == 1) {
assign("tmp", eval(as.name(flavour.strings[k,i,j])))
tmp <- mul.cf(tmp, flavour.factors[k,i,j])
}
else {
tmp <- add.cf(tmp, eval(as.name(flavour.strings[k,i,j])), a=1., b=flavour.factors[k,i,j])
}
}
assign(elements.strings[i,j], tmp)
rm(tmp)
}
}
## now we coerce to obtain the full matrix
## note that here we have smearing as fastest index
Cmatrix <- c(eval(as.name(elements.strings[1,1])), eval(as.name(elements.strings[1,2])),
eval(as.name(elements.strings[2,1])), eval(as.name(elements.strings[2,2])))
## we bootstrap the matrix and save
Cmatrix <- bootstrap.cf(Cmatrix, boot.R=400, boot.l=2)
save(Cmatrix, file="Cmatrix.Rdata")
}
load("Cmatrix.Rdata")
## we use element.order to bring the matrix into the right order
Cmatrix.bootstrap.gevp <- bootstrap.gevp(Cmatrix, matrix.size=4,
element.order=c(
1,2,5,6,
3,4,7,8,
9,11,13,14,
10,12,15,16))
## solve the GEVP
etass.pc1 <- gevp2cf(Cmatrix.bootstrap.gevp, id=1)
etass.pc1.effectivemass <- bootstrap.effectivemass(cf=etass.pc1, type="acosh")
etass.pc1.effectivemass <- fit.effectivemass(etass.pc1.effectivemass, t1=12, t2=23, useCov=TRUE)
plot(etass.pc1.effectivemass, ylim=c(0.2,0.4))
summary(etass.pc1.effectivemass)
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