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prepare_data.R
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prepare_data.R
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library(shiny)
############################################
#####PartI: set parameters
############################################
#target <- 'LHS1140_TERRA_2season'
target <- 'HD26965_KECK_noout'
star <- gsub('_.+','',target)
#star <- 'CoRoT7_HARPS_TERRA'
#star <- 'GJ699'
#star <- 'GJ699'
#star <- 'HD172555'
include.error <- FALSE
np <- 0
Nma <- 1
ofac <- 2
Np <- 0
Inds <- 0
subtract.manual <- FALSE#TRUE
cummulative <- FALSE#TRUE
Ncum <- 0
opt.type <- 'sl'#nl(nonlinear fitting all parameters; i.e. without using the formula ) or sl(semi-linear fitting)
#model.type <- 'auto'#manually setting the number of MA components and differential RVs
model.type <- 'man'#automatically determine the optimal noise model
tol <- 1e-16#numerical fitting precision tolerance
trend <- TRUE
#trend <- TRUE
Nap <- 1#number of aperture
Nc <- 0#1
per.types <- c('MLP')#the periodograms to calculate
#per.types <- c('BFP','MLP','BGLS','GLST','GLS','LS')
per.type.seq <- per.types[1]
sequential <- TRUE
#sequential <- FALSE
quantify <- TRUE
Np.max <- 3
if(!sequential) Np.max <- 1
###To select the numbers of differential RVs and MA components, it is better not to include calibration data.
leg.pos <- 'topright'
MLP.type <- 'sub'#subtract the correlated noise from the data and then marginalize the non-correlated-noise parameters
#MLP.type <- 'assign'#fix the correlated noise parameters at their optimal values and then marginalize the other parameters
############################################
#####PartII: load data
############################################
#f <- paste0('data/',star,'_HARPS_TERRA.dat')
#if(!file.exists(f)){
#f <- paste0('data/',star,'_HARPS.dat')
#}
loading <- FALSE
Nw <- 1
if(loading){
#file <- 'keppure_priormt_12eppar1_Ndata221_quantifyTRUE_1per1_Pmin1Pmax3689_Nw1ABTRUE_Nap0_modedata_HD41248_HARPS_TERRA_1AP1_ervab0ap_nosim_1planet_ARMAp0q1_0momc0_Nsamp2000000_tem1_acc2.3_pretem0.311P25.6d_negLmax506'
# file <- 'keppure_priormt_talklike_Ndata221_quantifyTRUE_1per1_Pmin0.5Pmax7378_Nw1ABTRUE_Nap0_modedata_HD41248_HARPS_TERRA_1AP1_ervab0ap_smallerrFALSE_3planet_ARMA01_0momc3.2_Nsamp10000000_tem1_acc0.12_pretem1P13.4d26.2d290.4d_negLmax474'
# file <- 'keppure_priormt_talklike_Ndata805_quantifyTRUE_1per1_Pmin1Pmax21315_Nw6ABFALSE_Nap0_modedata_GJ699_TERRA_1AP1_ervab3ap_smallerrFALSE_2planet_ARMA02_0momc0_Nsamp8000000_tem1_acc7.1_pretem1P437d232.5d_negLmax1794'
file <- 'keppure_priormt_talkpost_Ndata805_quantifyTRUE_1per1_Pmin0.1Pmax21315_Nw6ABFALSE_Nap0_modedata_GJ699_TERRA_1AP1_ervab3ap_smallerrFALSE_1planet_ARMA02_0momc0_Nsamp4000000_tem1_acc9.4_pretem1P231.6d_negLmax1816'
load(paste0('../output/',star,'/',file,'.Robj'),envir=e <- new.env())
Nw <- e$Nw
if(Nw>1){
res.all <- c()
trv.all <- c()
RV.all <- c()
eRV.all <- c()
par.data <- e$par.data
data.names <- names(par.data)
for(j in 1:Nw){
if(!grepl('LICK',data.names[j])){
res.all <- c(res.all,par.data[[j]]$RV2)
RV.all <- c(RV.all,par.data[[j]]$RV)
eRV.all <- c(eRV.all,par.data[[j]]$eRV)
trv.all <- c(trv.all,par.data[[j]]$trv)
}
}
inds <- sort(trv.all,index.return=TRUE)$ix
trv.all <- trv.all[inds]
RV.all <- RV.all[inds]
eRV.all <- eRV.all[inds]
res.all <- res.all[inds]
sigs <- e$Popt
tab <- cbind(trv.all,res.all,eRV.all)
}else{
tab <- cbind(e$trv,e$res,e$eRV)
}
Np <- e$Np
}else{
# file <- paste0('../data/aperture/',star,'/',star,'_HARPS.dat')
file <- paste0('../data/aperture/',star,'/',target,'.dat')
if(!file.exists(file)) file <- paste0('../data/aperture/',star,'/',target,'.vels')
if(!file.exists(file)) file <- paste0('data/',target,'.dat')
tab <- read.table(file,header=TRUE,check.names=FALSE)
cat('read file:',file,'\n')
if(include.error){
Nproxy <- (ncol(tab)-3)/2
tab <- cbind(tab[,1:3],tab[,2+(1:Nproxy)*2])
if(cummulative){
r <- c()
for(j in 1:Nproxy){
r <- c(r,abs(cor(tab[,2],tab[,3+j])))
}
ind <- sort(r,decreasing=TRUE,index.return=TRUE)$ix
tab <- cbind(tab[,1:3],tab[,3+ind])
}
}else{
Nproxy <- ncol(tab)-3
}
}
if(ncol(tab)==3){
Indices <- NA
Inds <- 0
}else{
Indices <- as.matrix(tab[,3:ncol(tab),drop=FALSE])
}
#NI0 <- NI <- ncol(tab)-3
############################################
#####PartIII: prepare data
############################################
########################
####set up
########################
#update <- FALSE
#opt.types <- c('full','part','wr','rw','rep')
fmax <- 1/1.2#Pmin=0.5d
fmin <- 1/(max(tab[,1])-min(tab[,1]))
mar.type <- 'part'
Ndata <- nrow(tab)
#####rescale indices
t <- tab[,1]
y <- tab[,2]
dy <- tab[,3]
plot.proxy <- 'NA'
if(plot.proxy=='window'){
y <- rep(1,length(t))
# dy <- rep(0.1,length(t))
per.types <- 'LS'
sequential <- FALSE
Nma <- 0
Inds <- 0
}
data <- cbind(t,y,dy)
NI <- length(which(Inds!=0))
#model.type <- 'MA'#'MA' or 'auto'
cat('Ndata=',Ndata,'; Nma=',Nma,'; NI=',NI,'; Nap=',Nap,'; opt.type=',opt.type,'; model.type=',model.type,'; ofac=',ofac,'; tol=',tol,'\n')
#