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profuse.R
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profuse.R
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############################################ profuseAllStar.R ############################################
profuseAllStarFound2Fit = function(image,
sigma = NULL,
locs = NULL,
segim = NULL,
magzero = 0,
resamp = NULL,
psf_dim = c(51,51),
star_con = 2,
star_con_fit = TRUE,
star_circ = TRUE,
rough = FALSE,
star_dom_mag = NULL,
Nstar = 4,
...) {
message(' Running ProFound')
if(!requireNamespace("ProFound", quietly = TRUE)){stop('The ProFound package is required to run this function!')}
image[image < quantile(image, 0.01, na.rm=TRUE)*10] = NA
image[image > quantile(image, 0.99, na.rm=TRUE)*10] = NA
if(is.null(segim)){
mini_profound = ProFound::profoundProFound(
image = image,
sky = 0,
redosky = FALSE,
magzero = magzero,
verbose = FALSE,
boundstats = TRUE,
...
)
segim = mini_profound$segim
}else{
mini_profound = ProFound::profoundProFound(
image = image,
segim = segim,
sky = 0,
redosky = FALSE,
magzero = magzero,
verbose = FALSE,
boundstats = TRUE,
iters = 0,
...
)
}
if(is.null(sigma)){
gain = ProFound::profoundGainEst(image, objects = mini_profound$objects, sky = 0)
sigma = ProFound::profoundMakeSigma(
image = image,
objects = mini_profound$objects,
gain = gain,
sky = 0,
skyRMS = mini_profound$skyRMS,
plot = FALSE
)
}
if(!is.null(resamp)){
if(resamp > 1){
sigma = sigma*resamp
}
}
if(is.null(locs)){
if(is.null(star_dom_mag)){
star_dom_mag = median(mini_profound$segstats$mag, na.rm=TRUE)
}
cut_R50 = median(mini_profound$segstats[mini_profound$segstats$mag < star_dom_mag,'R50'], na.rm=TRUE)
locs = mini_profound$segstats[mini_profound$segstats$mag < star_dom_mag & mini_profound$segstats$R50 < cut_R50,c("xcen","ycen")]
}
locs = as.matrix(rbind(locs))
segID_tar = unique(mini_profound$segim[locs])
segID_tar[segID_tar > 0]
if(length(segID_tar) == 0){
message('All stars are detected as sky (lower skycut)!')
return(NULL)
}
segID_tar = mini_profound$segstats[mini_profound$segstats$segID %in% segID_tar & mini_profound$segstats$Nobject==0 & mini_profound$segstats$Nborder==0 & mini_profound$segstats$Nmask==0,'segID']
if(length(segID_tar) < Nstar){
segID_tar = unique(mini_profound$segim[locs])
segID_tar[segID_tar > 0]
segID_tar = mini_profound$segstats[mini_profound$segstats$segID %in% segID_tar & mini_profound$segstats$Nobject==0,'segID']
}
if(length(segID_tar) > Nstar){
segID_tar = segID_tar[1:Nstar]
}else{
Nstar = length(segID_tar)
}
if(Nstar == 0){
message('All stars too nearby other objects!')
return(NULL)
}
loc_tar = which(mini_profound$segstats$segID %in% segID_tar)
xcen = mini_profound$segstats[loc_tar, 'xcen']
ycen = mini_profound$segstats[loc_tar, 'ycen']
region = matrix(mini_profound$segim %in% segID_tar, nrow=dim(image)[1], ncol=dim(image)[2])
gridNy = ceiling(sqrt(Nstar))
gridNx = ceiling(Nstar/gridNy)
grid = expand.grid((1:gridNx - 0.5) * psf_dim[1], (1:gridNy - 0.5) * psf_dim[2])
image_psf = matrix(0, nrow=psf_dim[1]*gridNx, ncol=psf_dim[2]*gridNy)
sigma_psf = matrix(0, nrow=psf_dim[1]*gridNx, ncol=psf_dim[2]*gridNy)
region_psf = matrix(0, nrow=psf_dim[1]*gridNx, ncol=psf_dim[2]*gridNy)
for(i in 1:Nstar){
subx = 1:psf_dim[1] + grid[i,1] - psf_dim[1]/2
suby = 1:psf_dim[2] + grid[i,2] - psf_dim[2]/2
image_psf[subx,suby] = magcutout(image, loc=c(xcen[i],ycen[i]), box=psf_dim)$image
sigma_psf[subx,suby] = magcutout(sigma, loc=c(xcen[i],ycen[i]), box=psf_dim)$image
region_psf[subx,suby] = magcutout(region, loc=c(xcen[i],ycen[i]), box=psf_dim)$image
}
region_psf[is.na(image_psf) | is.na(region_psf)] = 0
if(star_circ){
ang = 0
axrat = 1
}else{
ang = median(mini_profound$segstats[loc_tar, 'ang'],na.rm=TRUE)
axrat = median(mini_profound$segstats[loc_tar, 'axrat'],na.rm=TRUE)
}
size = median(mini_profound$segstats[loc_tar, 'R50'],na.rm=TRUE)
modellist = list(
moffat = list(
xcen = grid[1:Nstar,1],
ycen = grid[1:Nstar,2],
mag = mini_profound$segstats[loc_tar, 'mag'],
fwhm = rep(size, Nstar),
con = rep(star_con, Nstar),
ang = rep(ang, Nstar),
axrat = rep(axrat, Nstar)
)
)
tofit = list(
moffat = list(
xcen = rep(TRUE,Nstar),
ycen = rep(TRUE,Nstar),
mag = rep(TRUE,Nstar),
fwhm = c(TRUE, rep(NA, Nstar-1)),
con = c(star_con_fit, rep(NA, Nstar-1)),
ang = c(!star_circ, rep(NA, Nstar-1)),
axrat = c(!star_circ, rep(NA, Nstar-1))
)
)
tolog = list(
moffat = list(
xcen = rep(FALSE, Nstar),
ycen = rep(FALSE, Nstar),
mag = rep(FALSE, Nstar),
fwhm = rep(TRUE, Nstar), #fwhm is best fit in log space
con = rep(TRUE, Nstar), #con is best fit in log space
ang = rep(FALSE, Nstar),
axrat = rep(TRUE, Nstar) #axrat is best fit in log space
)
)
intervals = list(moffat = list(
xcen = rep(list(c(-50, dim(image)[1] + 50)), Nstar),
ycen = rep(list(c(-50, dim(image)[2] + 50)), Nstar),
mag = rep(list(c(0, 40)), Nstar),
fwhm = rep(list(c(0.5, size*4)), Nstar),
con = rep(list(c(1, 10)), Nstar),
ang = rep(list(c(-180, 360)), Nstar),
axrat = rep(list(c(0.1, 1)), Nstar)
))
Data = profitSetupData(
image = image_psf,
region = region_psf,
sigma = sigma_psf,
segim = segim,
psf = NULL,
modellist = modellist,
tofit = tofit,
tolog = tolog,
intervals = intervals,
magzero = magzero,
algo.func = 'LD',
verbose = FALSE,
rough = rough
)
Data$Nmod = Nstar
return(invisible(list(profound = mini_profound, Data = Data)))
}
profuseAllStarDoFit = function(image,
sigma = NULL,
locs = NULL,
magzero = 0,
psf_dim = c(51,51),
rough = FALSE,
plot = FALSE,
seed = 666,
...) {
timestart = proc.time()[3] # start timer
call = match.call(expand.dots=TRUE)
message('Running Found2Fit')
found2fit = profuseAllStarFound2Fit(
image = image,
sigma = sigma,
locs = locs,
magzero = magzero,
psf_dim = psf_dim,
rough = rough,
...
)
Data = found2fit$Data
if (plot) {
profitLikeModel(parm = Data$init,
Data = Data,
makeplots = TRUE)
legend('topright', legend = 'Start')
}
lowers = unlist(Data$intervals)[c(T, F)]
lowers[unlist(Data$tolog) == T] = log10(lowers[unlist(Data$tolog) == T])
lowers = lowers[which(unlist(Data$tofit))]
uppers = unlist(Data$intervals)[c(F, T)]
uppers[unlist(Data$tolog) == T] = log10(uppers[unlist(Data$tolog) == T])
uppers = uppers[which(unlist(Data$tofit))]
message('Running Highlander')
if(!requireNamespace("ProFound", quietly = TRUE)){stop('The Highlander package is required to run this function!')}
highfit = Highlander::Highlander(
parm = Data$init,
Data = Data,
likefunc = profitLikeModel,
seed = seed,
lower = lowers,
upper = uppers,
applyintervals = FALSE,
applyconstraints = FALSE
)
names(highfit$parm) = names(Data$init)
if (plot) {
profitLikeModel(highfit$parm, Data = Data, makeplots = TRUE)
legend('topright', legend = 'After')
}
highfit$profound = found2fit$profound
highfit$Data = Data
highfit$initmodel = profitRemakeModellist(Data$init, Data = Data)
highfit$finalmodel = profitRemakeModellist(highfit$parm, Data = Data)
highfit$parm[highfit$parm < lowers] = lowers[highfit$parm < lowers]
highfit$parm[highfit$parm > uppers] = uppers[highfit$parm > uppers]
highfit$error = apply(highfit$LD_last$Posterior1,
MARGIN = 2,
FUN = 'sd')
if(psf_dim[1] %% 2 == 0){psf_dim[1] = psf_dim[1] + 1}
if(psf_dim[2] %% 2 == 0){psf_dim[2] = psf_dim[2] + 1}
temp_modellist = list(
moffat = list(
xcen = psf_dim[1]/2,
ycen = psf_dim[2]/2,
mag = 0,
fwhm = highfit$finalmodel$modellist$moffat$fwhm[1],
con = highfit$finalmodel$modellist$moffat$con[1],
ang = highfit$finalmodel$modellist$moffat$ang[1],
axrat = highfit$finalmodel$modellist$moffat$axrat[1]
)
)
highfit$psf = profitMakeModel(temp_modellist, dim=psf_dim)
highfit$psf_modellist = temp_modellist
psf_fluxcheck = sum(highfit$psf$z)
if(psf_fluxcheck < 0.95){
message('WARNING: psf output image contains less than 95% of the total model flux! Consider increasing the size of psf_dim.')
}
if(psf_fluxcheck > 0.999){
message('WARNING: psf output image contains more than 99.9% of the total model flux! Consider decreasing the size of psf_dim.')
}
highfit$psf = highfit$psf$z / sum(highfit$psf$z)
highfit$psf_fluxcheck
highfit$time = (proc.time()[3]-timestart)/60
highfit$date = date()
highfit$call = call
highfit$ProFit.version = packageVersion('ProFit')
highfit$ProFound.version = packageVersion('ProFound')
highfit$Highlander.version = packageVersion('Highlander')
highfit$R.version = R.version
return(highfit)
}
############################################ profuseFound2Fit.R ############################################
profuseFound2Fit = function(image,
sigma = NULL,
loc = NULL,
segim = NULL,
Ncomp = 2,
cutbox = dim(image),
psf = NULL,
magdiff = 2.5,
magzero = 0,
gain = NULL,
resamp = NULL,
loc_use = FALSE,
loc_fit = TRUE,
mag_fit = TRUE,
sing_nser = 2,
bulge_nser = 4,
disk_nser = 1,
sing_nser_fit = TRUE,
bulge_nser_fit = FALSE,
disk_nser_fit = FALSE,
bulge_circ = TRUE,
nser_upper=5.3,
star_rough = TRUE,
fit_rough = FALSE,
psf_dim = c(51, 51),
star_con = 2,
star_con_fit = TRUE,
star_circ = TRUE,
offset = NULL,
tightcrop = TRUE,
deblend_extra = TRUE,
fit_extra = FALSE,
pos_delta=10,
...) {
if(Ncomp == 0.5){psf = NULL}
if((Ncomp >= 1 & is.null(psf)) | (Ncomp == 0 & is.null(psf))){
message('Need PSF for Ncomp >= 1 or Ncomp == 0. Running AllStarDoFit!')
psf = profuseAllStarDoFit(image = image,
resamp = resamp,
psf_dim = psf_dim,
star_con = star_con,
star_con_fit = star_con_fit,
star_circ = star_circ,
magzero = magzero,
rough = star_rough,
skycut = 2, #works well for stars
SBdilate = 2)$psf #works well for stars
}
image[image < quantile(image, 0.01, na.rm=TRUE)*10] = NA
#image[image > quantile(image, 0.99, na.rm=TRUE)*10] = NA
if(!is.null(loc)){
cutim = magicaxis::magcutout(image, loc = loc, box = cutbox)
loc_cut = cutim$loc
cutim = cutim$image
}else{
loc_cut = dim(image) / 2
cutim = image
}
if (!is.null(sigma) & !is.null(loc)) {
cutsigma = magicaxis::magcutout(sigma, loc = loc, box = cutbox)$image
} else{
cutsigma = NULL
}
if(is.null(segim)){
cutseg = NULL
}else if(dim(segim)[1] == dim(cutim)[1] & dim(segim)[2] == dim(cutim)[2]){
cutseg = segim
}else if (dim(segim)[1] == dim(image)[1] & dim(segim)[2] == dim(image)[2] & !is.null(loc)) {
cutseg = magicaxis::magcutout(segim, loc = loc, box = cutbox)$image
}else{
message('No input segim that matches the input image- will create one using ProFound!')
cutseg = NULL
}
loc = loc_cut
message(' Running ProFound')
if(!requireNamespace("ProFound", quietly = TRUE)){stop('The ProFound package is required to run this function!')}
if(is.null(cutseg)){
mini_profound = ProFound::profoundProFound(
image = cutim,
sky = 0,
redosky = FALSE,
nearstats = TRUE,
groupby = 'segim',
magzero = magzero,
verbose = FALSE,
...
)
cutseg = mini_profound$segim
}else{
mini_profound = ProFound::profoundProFound(
image = cutim,
segim = cutseg,
sky = 0,
redosky = FALSE,
nearstats = TRUE,
groupby = 'segim',
magzero = magzero,
verbose = FALSE,
iters = 0,
...
)
}
if (is.null(cutsigma)) {
if(is.null(gain)){
gain = ProFound::profoundGainEst(cutim, objects = mini_profound$objects, sky = 0)
}
cutsigma = ProFound::profoundMakeSigma(
image = cutim,
objects = mini_profound$objects,
gain = gain,
sky = 0,
skyRMS = mini_profound$skyRMS,
plot = FALSE
)
}
if(!is.null(resamp)){
if(resamp > 1){
cutsigma = cutsigma*resamp
}
}
if(deblend_extra & fit_extra==FALSE){
cutim = ProFound::profoundFluxDeblend(mini_profound, image_reweight=TRUE)$image
}
segID_tar = mini_profound$segim[cutbox[1] / 2, cutbox[2] / 2]
if (segID_tar == 0) {
message('Target appears to be sky! Consider using different ProFound parameters.')
return(NULL)
}
loc_tar = which(mini_profound$segstats$segID == segID_tar)
magID_tar = mini_profound$segstats[mini_profound$segstats$segID == segID_tar, 'mag']
if(fit_extra){
segID_ext = unlist(mini_profound$near$nearID[mini_profound$near$segID == segID_tar])
if (length(segID_ext) > 0) {
loc_ext = match(segID_ext, mini_profound$segstats$segID)
loc_ext = loc_ext[which(mini_profound$segstats[loc_ext, "mag"] < magID_tar + magdiff)]
segID_ext = mini_profound$segstats[loc_ext, 'segID']
N_ext = length(loc_ext)
} else{
N_ext = 0
}
}else{
segID_ext = {}
N_ext = 0
}
region = matrix(mini_profound$segim %in% c(segID_tar, segID_ext), nrow=cutbox[1], ncol=cutbox[2])
regionlim = which(region, arr.ind=TRUE)
if(tightcrop){
xlo = min(regionlim[,1])
xhi = max(regionlim[,1])
ylo = min(regionlim[,2])
yhi = max(regionlim[,2])
cutim = cutim[xlo:xhi, ylo:yhi]
region = region[xlo:xhi, ylo:yhi]
cutsigma = cutsigma[xlo:xhi, ylo:yhi]
if(loc_use){
xcen = loc[1] - xlo + 1
ycen = loc[2] - ylo + 1
xcen_int = xcen + c(-pos_delta,pos_delta)
ycen_int = ycen + c(-pos_delta,pos_delta)
}else{
xcen = mini_profound$segstats[loc_tar, 'xmax'] - xlo + 1
ycen = mini_profound$segstats[loc_tar, 'ymax'] - ylo + 1
xcen_int = xcen + c(-pos_delta,pos_delta)
ycen_int = ycen + c(-pos_delta,pos_delta)
}
}else{
xlo = 1L
ylo = 1L
xcen = mini_profound$segstats[loc_tar, 'xmax']
ycen = mini_profound$segstats[loc_tar, 'ymax']
xcen_int = xcen + c(-pos_delta,pos_delta)
ycen_int = ycen + c(-pos_delta,pos_delta)
}
if (Ncomp == 0) {
modellist = list(
pointsource = list(
xcen = mini_profound$segstats[loc_tar, 'xmax'],
ycen = mini_profound$segstats[loc_tar, 'ymax'],
mag = mini_profound$segstats[loc_tar, 'mag']
)
)
}else if(Ncomp == 0.5) {
if(star_circ){
ang = 0
axrat = 1
}else{
ang = mini_profound$segstats[loc_tar, 'ang']
axrat = mini_profound$segstats[loc_tar, 'axrat']
}
modellist = list(
moffat = list(
xcen = xcen,
ycen = ycen,
mag = mini_profound$segstats[loc_tar, 'mag'],
fwhm = mini_profound$segstats[loc_tar, 'R50'] * 2,
con = star_con,
ang = ang,
axrat = axrat
)
)
} else if (Ncomp == 1) {
modellist = list(
sersic = list(
xcen = xcen,
ycen = ycen,
mag = mini_profound$segstats[loc_tar, 'mag'],
re = mini_profound$segstats[loc_tar, 'R50'],
nser = sing_nser,
ang = mini_profound$segstats[loc_tar, 'ang'],
axrat = mini_profound$segstats[loc_tar, 'axrat']
)
)
} else if (Ncomp == 2) {
modellist = list(
sersic = list(
xcen = rep(xcen, 2),
ycen = rep(ycen, 2),
mag = rep(mini_profound$segstats[loc_tar, 'mag'], 2) + 0.752575,
re = mini_profound$segstats[loc_tar, 'R50'] * c(0.5, 1.5),
nser = c(bulge_nser, disk_nser),
ang = c(ifelse(bulge_circ, 0, mini_profound$segstats[loc_tar, 'ang']), mini_profound$segstats[loc_tar, 'ang']),
axrat = c(1, mini_profound$segstats[loc_tar, 'axrat'])
)
)
} else if (Ncomp == 1.5) {
modellist = list(
pointsource = list(
xcen = xcen,
ycen = ycen,
mag = mini_profound$segstats[loc_tar, 'mag'] + 0.752575
),
sersic = list(
xcen = xcen,
ycen = ycen,
mag = mini_profound$segstats[loc_tar, 'mag'] + 0.752575,
re = mini_profound$segstats[loc_tar, 'R50'],
nser = disk_nser,
ang = mini_profound$segstats[loc_tar, 'ang'],
axrat = mini_profound$segstats[loc_tar, 'axrat']
)
)
} else if (Ncomp == 3) {
modellist = list(
sersic = list(
xcen = rep(xcen, 3),
ycen = rep(ycen, 3),
mag = rep(mini_profound$segstats[loc_tar, 'mag'], 3) + 0.4771213,
re = mini_profound$segstats[loc_tar, 'R50'] * c(0.5, 2, 1),
nser = c(bulge_nser, disk_nser, disk_nser),
ang = c(ifelse(bulge_circ, 0, mini_profound$segstats[loc_tar, 'ang']), rep(mini_profound$segstats[loc_tar, 'ang'],2)),
axrat = c(1, rep(mini_profound$segstats[loc_tar, 'axrat'],2))
)
)
}
if (Ncomp == 0) {
tofit = list(
pointsource = list(
xcen = loc_fit,
ycen = loc_fit,
mag = mag_fit
)
)
constraints = NULL
}else if (Ncomp == 0.5) {
tofit = list(
moffat = list(
xcen = loc_fit,
ycen = loc_fit,
mag = mag_fit,
fwhm = TRUE,
con = star_con_fit,
ang = !star_circ,
axrat = !star_circ
)
)
constraints = NULL
}else if (Ncomp == 1) {
tofit = list(
sersic = list(
xcen = loc_fit,
ycen = loc_fit,
mag = mag_fit,
re = TRUE,
nser = sing_nser_fit,
ang = TRUE,
axrat = TRUE
)
)
constraints = NULL
} else if (Ncomp == 2) {
tofit = list(sersic = list(
xcen = c(loc_fit, NA), #The NA couples the components together
ycen = c(loc_fit, NA), #The NA couples the components together
mag = rep(mag_fit, 2),
re = rep(TRUE, 2),
nser = c(bulge_nser_fit, disk_nser_fit),
ang = c(!bulge_circ, TRUE),
axrat = c(!bulge_circ, TRUE)
))
constraints = NULL
} else if (Ncomp == 1.5) {
tofit = list(
pointsource = list(
xcen = FALSE,
ycen = FALSE,
mag = mag_fit
),
sersic = list(
xcen = loc_fit,
ycen = loc_fit,
mag = mag_fit,
re = TRUE,
nser = disk_nser_fit,
ang = TRUE,
axrat = TRUE
)
)
constraints = .constraints_func1_5
} else if (Ncomp == 3) {
tofit = list(sersic = list(
xcen = c(loc_fit, NA, NA), #The NA couples the components together
ycen = c(loc_fit, NA, NA), #The NA couples the components together
mag = rep(mag_fit, 3),
re = rep(TRUE, 3),
nser = c(bulge_nser_fit, disk_nser_fit, NA),
ang = c(!bulge_circ, TRUE, NA),
axrat = c(!bulge_circ, TRUE, NA)
))
constraints = NULL
}
if (Ncomp == 0) {
tolog = list(
pointsource = list(
xcen = FALSE,
ycen = FALSE,
mag = FALSE
)
)
constraints = NULL
}else if (Ncomp == 0.5) {
tolog = list(
moffat = list(
xcen = rep(FALSE, Ncomp),
ycen = rep(FALSE, Ncomp),
mag = rep(FALSE, Ncomp),
fwhm = rep(TRUE, Ncomp),
#fwhm is best fit in log space
con = rep(TRUE, Ncomp),
#con is best fit in log space
ang = rep(FALSE, Ncomp),
axrat = rep(TRUE, Ncomp) #axrat is best fit in log space
)
)
} else if (Ncomp == 1 | Ncomp == 2) {
tolog = list(
sersic = list(
xcen = rep(FALSE, Ncomp),
ycen = rep(FALSE, Ncomp),
mag = rep(FALSE, Ncomp),
re = rep(TRUE, Ncomp),
#re is best fit in log space
nser = rep(TRUE, Ncomp),
#nser is best fit in log space
ang = rep(FALSE, Ncomp),
axrat = rep(TRUE, Ncomp) #axrat is best fit in log space
)
)
} else if (Ncomp == 1.5) {
tolog = list(
pointsource = list(
xcen = FALSE,
ycen = FALSE,
mag = FALSE
),
sersic = list(
xcen = FALSE,
ycen = FALSE,
mag = FALSE,
re = TRUE,
#re is best fit in log space
nser = TRUE,
#nser is best fit in log space
ang = FALSE,
axrat = TRUE #axrat is best fit in log space
)
)
} else if (Ncomp == 3) {
tolog = list(
sersic = list(
xcen = rep(FALSE, 3),
ycen = rep(FALSE, 3),
mag = rep(FALSE, 3),
re = rep(TRUE, 3),
#re is best fit in log space
nser = rep(TRUE, 3),
#nser is best fit in log space
ang = rep(FALSE, 3),
axrat = rep(TRUE, 3) #axrat is best fit in log space
)
)
}
#maxsize = sqrt(dim(cutim)[1]^2 + dim(cutim)[2]^2)
maxsize = mini_profound$segstats[loc_tar, 'R50'] * 4
if (Ncomp == 0) {
intervals = list(pointsource = list(
xcen = list(xcen_int),
ycen = list(ycen_int),
mag = list(c(0, 40))
))
} else if (Ncomp == 0.5) {
intervals = list(moffat = list(
xcen = list(xcen_int),
ycen = list(ycen_int),
mag = list(c(0, 40)),
fwhm = list(c(0.5, maxsize)),
con = list(c(1, 10)),
ang = list(c(-180, 360)),
axrat = list(c(0.5, 1))
))
} else if (Ncomp == 1) {
intervals = list(sersic = list(
xcen = list(xcen_int),
ycen = list(ycen_int),
mag = list(c(0, 40)),
re = list(c(1, maxsize)),
nser = list(c(0.5, nser_upper)),
ang = list(c(-180, 360)),
axrat = list(c(0.01, 1))
))
} else if (Ncomp == 2) {
intervals = list(
sersic = list(
xcen = list(xcen_int, xcen_int),
ycen = list(ycen_int, ycen_int),
mag = list(c(0, 40), c(0, 40)),
re = list(c(1, maxsize), c(1, maxsize)),
nser = list(c(2, nser_upper), c(0.5, 2)),
ang = list(c(-180, 360), c(-180, 360)),
axrat = list(c(0.01, 1), c(0.01, 1))
)
)
} else if (Ncomp == 1.5) {
intervals = list(
pointsource = list(
xcen = list(xcen_int),
ycen = list(ycen_int),
mag = list(c(0, 40))
),
sersic = list(
xcen = list(xcen_int),
ycen = list(ycen_int),
mag = list(c(0, 40)),
re = list(c(1, maxsize)),
nser = list(c(0.5, nser_upper)),
ang = list(c(-180, 360)),
axrat = list(c(0.01, 1))
)
)
} else if (Ncomp == 3) {
intervals = list(
sersic = list(
xcen = list(xcen_int, xcen_int, xcen_int),
ycen = list(ycen_int, ycen_int, ycen_int),
mag = list(c(0, 40), c(0, 40), c(0, 40)),
re = list(c(1, maxsize), c(1, maxsize), c(1, maxsize)),
nser = list(c(2, nser_upper), c(0.5, 2), c(0.5, 2)),
ang = list(c(-180, 360), c(-180, 360), c(-180, 360)),
axrat = list(c(0.01, 1), c(0.01, 1), c(0.01, 1))
)
)
}
if (fit_extra & N_ext > 0) {
modellist = c(modellist,
list(
sersic = list(
xcen = mini_profound$segstats[loc_ext, 'xmax'] - xlo + 1L,
ycen = mini_profound$segstats[loc_ext, 'ymax'] - ylo + 1L,
mag = mini_profound$segstats[loc_ext, 'mag'],
re = mini_profound$segstats[loc_ext, 'R50'],
nser = rep(2, N_ext),
ang = mini_profound$segstats[loc_ext, 'ang'],
axrat = mini_profound$segstats[loc_ext, 'axrat']
)
)
)
tofit = c(tofit,
list(
sersic = list(
xcen = rep(FALSE, N_ext),
ycen = rep(FALSE, N_ext),
mag = rep(TRUE, N_ext),
re = rep(TRUE, N_ext),
nser = rep(TRUE, N_ext),
ang = rep(FALSE, N_ext),
axrat = rep(TRUE, N_ext)
)
)
)
tolog = c(tolog,
list(
sersic = list(
xcen = rep(FALSE, N_ext),
ycen = rep(FALSE, N_ext),
mag = rep(FALSE, N_ext),
re = rep(TRUE, N_ext),
#re is best fit in log space
nser = rep(TRUE, N_ext),
#nser is best fit in log space
ang = rep(FALSE, N_ext),
axrat = rep(TRUE, N_ext) #axrat is best fit in log space
)
)
)
maxsize = max(mini_profound$segstats[loc_ext, 'R50']*4, na.rm=TRUE)
intervals = c(intervals,
list(
sersic = list(
xcen = rep(list(xcen_int), N_ext),
ycen = rep(list(ycen_int), N_ext),
mag = rep(list(c(0, 40)), N_ext),
re = rep(list(c(1, maxsize)), N_ext),
nser = rep(list(c(0.5, nser_upper)), N_ext),
ang = rep(list(c(-180, 360)), N_ext),
axrat = rep(list(c(0.01, 1)), N_ext)
)
)
)
}
Data = profitSetupData(
image = cutim,
region = region,
sigma = cutsigma,
segim = cutseg,
psf = psf,
modellist = modellist,
tofit = tofit,
tolog = tolog,
intervals = intervals,
constraints = constraints,
magzero = magzero,
algo.func = 'LD',
verbose = FALSE,
offset = offset,
rough = fit_rough
)
Data$Nmod = Ncomp + N_ext
return(invisible(list(profound = mini_profound, Data = Data)))
}
profuseDoFit = function(image,
sigma = NULL,
loc = NULL,
F2F = NULL,
Ncomp = 2,
cutbox = dim(image),
psf = NULL,
magdiff = 2.5,
magzero = 0,
psf_dim = c(51,51),
star_rough = TRUE,
fit_rough = FALSE,
plot = FALSE,
seed = 666,
optim_iters = 2,
Niters = c(1000,1000),
NfinalMCMC = Niters[2],
...) {
timestart = proc.time()[3] # start timer
#call = match.call(expand.dots=TRUE)
if(is.null(F2F)){
message('Running Found2Fit')
F2F = profuseFound2Fit(
image = image,
sigma = sigma,
loc = loc,
Ncomp = Ncomp,
cutbox = cutbox,
psf = psf,
magdiff = magdiff,
magzero = magzero,
fit_rough = fit_rough,
...
)
}else{
F2F = profuseRegenPSF_F2F(F2F)
}
Data = F2F$Data
if (plot) {
profitLikeModel(parm = Data$init,
Data = Data,
makeplots = TRUE)
legend('topright', legend = 'Start')
}
lowers = unlist(Data$intervals)[c(T, F)]
lowers[unlist(Data$tolog) == T] = log10(lowers[unlist(Data$tolog) == T])
lowers = lowers[which(unlist(Data$tofit))]
uppers = unlist(Data$intervals)[c(F, T)]
uppers[unlist(Data$tolog) == T] = log10(uppers[unlist(Data$tolog) == T])
uppers = uppers[which(unlist(Data$tofit))]
message('Running Highlander')
if(!requireNamespace("ProFound", quietly = TRUE)){stop('The Highlander package is required to run this function!')}
highfit = Highlander::Highlander(
parm = Data$init,
Data = Data,
likefunc = profitLikeModel,
seed = seed,
lower = lowers,
upper = uppers,
applyintervals = FALSE,
applyconstraints = FALSE,
optim_iters = optim_iters,
Niters = Niters,
NfinalMCMC = NfinalMCMC,
parm.names = Data$parm.names
)
names(highfit$parm) = names(Data$init)
if (plot) {
profitLikeModel(highfit$parm, Data = Data, makeplots = TRUE)
legend('topright', legend = 'After')
}
highfit$profound = F2F$profound
highfit$Data = Data
highfit$initmodel = profitRemakeModellist(Data$init, Data = Data)
highfit$finalmodel = profitRemakeModellist(highfit$parm, Data = Data)
highfit$parm[highfit$parm < lowers] = lowers[highfit$parm < lowers]
highfit$parm[highfit$parm > uppers] = uppers[highfit$parm > uppers]
highfit$error = apply(highfit$LD_last$Posterior1,
MARGIN = 2,
FUN = 'sd')
if(Ncomp == 0.5){
if(psf_dim[1] %% 2 == 0){psf_dim[1] = psf_dim[1] + 1}
if(psf_dim[2] %% 2 == 0){psf_dim[2] = psf_dim[2] + 1}
temp_modellist = highfit$finalmodel$modellist[[1]]
temp_modellist$moffat$mag = 0
temp_modellist$moffat$xcen = psf_dim[1]/2
temp_modellist$moffat$ycen = psf_dim[2]/2
highfit$psf = profitMakeModel(temp_modellist, dim=psf_dim)
highfit$psf_modellist = temp_modellist
psf_fluxcheck = sum(highfit$psf$z)
if(psf_fluxcheck < 0.95){
message('WARNING: psf output image contains less than 95% of the total model flux! Consider increasing the size of psf_dim.')