Log scat scale added

R/profuseMultiBand.R

@@ -28,6 +28,8 @@ profuseMultiBandFound2Fit = function(image_list,
                                     star_con = 2,
                                     star_con_fit = TRUE,
                                     star_circ = TRUE,
+                                    log_scat_scale = FALSE,
+                                    offset_list = NULL,
                                     tightcrop = TRUE,
                                     wave = NULL,
                                     smooth.parm = NULL,
@@ -48,6 +50,37 @@ profuseMultiBandFound2Fit = function(image_list,
     magzero = rep(magzero, Nim)
   }
 
+  if(is.null(offset_list)){
+    offset_list = vector("list", Nim)
+  }else{
+    if(!is.list(offset_list)){
+      stop("offset_list must be a list when provided.")
+    }
+    if(length(offset_list) == 1){
+      offset_list = rep(list(offset_list[[1]]), Nim)
+    }
+    if(length(offset_list) != Nim){
+      stop("offset_list must be NULL, length 1, or the same length as image_list.")
+    }
+    for(i in 1:Nim){
+      if(!is.null(offset_list[[i]]) && (!is.numeric(offset_list[[i]]) || length(offset_list[[i]]) != 2)){
+        stop("Each non-NULL offset_list element must be a numeric [X,Y] vector of length 2.")
+      }
+    }
+  }
+
+  if('log_scat_scale' %in% parm_global){
+    stop('log_scat_scale should not be in parm_global, instead set log_scat_scale argument to TRUE')
+  }
+
+  if (isTRUE(log_scat_scale)) {
+    if (!is.null(parm_global) && !is.character(parm_global)) {
+      stop("When log_scat_scale=TRUE, parm_global must be NULL or a character vector of parameter names.")
+    }
+    # Put log_scat_scale at the end of parm_global
+    parm_global = c(parm_global, "log_scat_scale")
+  }
+
   for(i in 1:Nim){
     if(autoclip){
       image_med = median(image_list[[i]], na.rm=TRUE)
@@ -153,6 +186,7 @@ profuseMultiBandFound2Fit = function(image_list,
                              disk_nser_fit = disk_nser_fit,
                              bulge_circ =  bulge_circ,
                              nser_upper = nser_upper,
+                             log_scat_scale = log_scat_scale,
                              tightcrop = FALSE,
                              fit_extra = FALSE,
                              autoclip = FALSE,
@@ -266,7 +300,9 @@ profuseMultiBandFound2Fit = function(image_list,
       constraints = F2Fstack$Data$constraints,
       magzero = magzero[i],
       algo.func = 'LD',
+      log_scat_scale = log_scat_scale,
       verbose = FALSE,
+      offset = offset_list[[i]],
       rough = fit_rough,
       nbenchconv = nbenchconv
     )
@@ -406,8 +442,13 @@ profuseMultiBandDoFit = function(image_list,
     lower_profit[logged_profit] = log10(lower_profit[logged_profit])
     upper_profit[logged_profit] = log10(upper_profit[logged_profit])
 
-    lower = c(lower_profit, MF2F$intervals_ProSpect$lo)
-    upper = c(upper_profit, MF2F$intervals_ProSpect$hi)
+    if('log_scat_scale' %in% MF2F$parm.names){
+      lower = c(lower_profit, -2, MF2F$intervals_ProSpect$lo)
+      upper = c(upper_profit, 1, MF2F$intervals_ProSpect$hi)
+    }else{
+      lower = c(lower_profit, MF2F$intervals_ProSpect$lo)
+      upper = c(upper_profit, MF2F$intervals_ProSpect$hi)
+    }
 
   }else{
     #This implies we are in smooth.spline fitting mode, i.e. not using ProSpect (we don't use this)
@@ -417,7 +458,7 @@ profuseMultiBandDoFit = function(image_list,
   }
 
   message('Running Highander on multi-band data')
-  if(!requireNamespace("ProFound", quietly = TRUE)){stop('The Highander package is required to run this function!')}
+  if(!requireNamespace("Highlander", quietly = TRUE)){stop('The Highlander package is required to run this function!')}
   highfit = Highlander::Highlander(
     parm = MF2F$init,
     Data = MF2F,

R/profuseMultiImage.R

@@ -10,8 +10,9 @@ profuseMultiImageFound2Fit = function(
     magzero = NULL,
     gain = NULL,
     resamp = NULL,
-    tightcrop = TRUE,
+    log_scat_scale = FALSE,
     offset_list = NULL,
+    tightcrop = TRUE,
     ...
 ){
   Nim = length(image_list)
@@ -82,8 +83,9 @@ profuseMultiImageFound2Fit = function(
                            magzero = magzero[[i]],
                            gain = gain[[i]],
                            resamp = resamp[[i]],
-                           tightcrop = tightcrop,
+                           log_scat_scale = log_scat_scale,
                            offset = offset_list[[i]],
+                           tightcrop = tightcrop,
                            ...
     )$Data
 

R/profuseSingleImage.R

@@ -28,6 +28,7 @@ profuseFound2Fit = function(image,
                            star_con = 2,
                            star_con_fit = TRUE,
                            star_circ = TRUE,
+                           log_scat_scale = FALSE,
                            offset = NULL,
                            tightcrop = TRUE,
                            deblend_extra = FALSE,
@@ -724,6 +725,7 @@ profuseFound2Fit = function(image,
     constraints = constraints,
     magzero = magzero,
     algo.func = 'LD',
+    log_scat_scale = log_scat_scale,
     verbose = FALSE,
     offset = offset,
     rough = fit_rough,

man/profuseFound2Fit.Rd

@@ -16,8 +16,8 @@ profuseFound2Fit(image, sigma = NULL, segim = NULL, mask = NULL, Ncomp = 2,
   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 = FALSE, fit_extra = FALSE,
-  pos_delta = 10, autoclip = TRUE, roughpedestal = TRUE, ...)
+  log_scat_scale = FALSE, offset = NULL, tightcrop = TRUE, deblend_extra = FALSE,
+  fit_extra = FALSE, pos_delta = 10, autoclip = TRUE, roughpedestal = TRUE, ...)
 
 profuseDoFit(image, F2F = NULL, Ncomp = 2, psf = NULL, magzero = NULL,
   psf_dim = c(51,51), plot = FALSE, seed = 666, optim_iters = 5,
@@ -117,6 +117,9 @@ Logical; optional, should the Moffat profile concentration index be optimised du
 }
   \item{star_circ}{
 Logical; optional, should the star be forced to be circular? If TRUE this means the \option{axrat} is fixed to be 1. This is only relevant if the \option{psf} has not been provided and we need to calculate ourself using \code{\link{profuseAllStarDoFit}}.
+}
+  \item{log_scat_scale}{
+Logical; optional. If TRUE, include a log scatter scale hyper-parameter in the likelihood evaluation (see \code{profitLikeModel}) to improve sampling when the image likelihood is under/over-dispersed.
 }
   \item{offset}{
 Numeric vector; optional. Offset argument to be passed to \code{\link{profitSetupData}} (see description there).

man/profuseMultiBand.Rd

@@ -17,9 +17,10 @@ profuseMultiBandFound2Fit(image_list, segim_list = NULL, segim_global = NULL,
   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, tightcrop = TRUE, wave = NULL, smooth.parm = NULL,
-  parm_ProSpect = NULL, data_ProSpect = NULL, logged_ProSpect = NULL,
-  intervals_ProSpect = NULL, autoclip = TRUE, roughpedestal = TRUE, ...)
+  star_circ = TRUE, log_scat_scale = FALSE, offset_list = NULL, tightcrop = TRUE,
+  wave = NULL, smooth.parm = NULL, parm_ProSpect = NULL, data_ProSpect = NULL,
+  logged_ProSpect = NULL, intervals_ProSpect = NULL, autoclip = TRUE,
+  roughpedestal = TRUE, ...)
 
 profuseMultiBandDoFit(image_list, MF2F = NULL, parm_global = c("sersic.xcen1",
   "sersic.ycen1", "sersic.re1", "sersic.ang2", "sersic.axrat2"), Ncomp = 2,
@@ -120,6 +121,12 @@ Logical; optional, should the Moffat profile concentration index be optimised du
 }
   \item{star_circ}{
 Logical; optional, should the star be forced to be circular? If TRUE this means the \option{axrat} is fixed to be 1. This is only relevant if the \option{psf} has not been provided and we need to calculate ourself using \code{\link{profuseAllStarDoFit}}.
+}
+  \item{log_scat_scale}{
+Logical; optional. If TRUE, include a log scatter scale hyper-parameter in the likelihood evaluation (see \code{profitLikeModel}) to improve sampling when the image likelihood is under/over-dispersed.
+}
+  \item{offset_list}{
+List; optional, per-band offsets to pass into \code{\link{profitSetupData}} \option{offset}. This can be used to track known [X,Y] positional shifts between images so a shared physical model is correctly projected into each band. If NULL (default) then no offsets are used. If length 1 it is recycled to all bands. Otherwise, \option{offset_list} must have the same length as \option{image_list}. Each non-NULL element should be a numeric [X,Y] vector of length 2.
 }
   \item{tightcrop}{
 Logical; optional, should the image be cut down to the tightest fitting box that fully contains the segmentation map? This might be a good idea if the parent image is very large (cuts down on memory allocation etc), but might be a bad idea if you want to know the exact position of your source with the frame, e.g. \option{xcen} and \option{ycen} in the \option{modellist} are relative to the new tightly cropped image not the original cropped image.

man/profuseMultiImage.Rd

@@ -12,7 +12,8 @@ Functions to easily take users from multiple image pixel data all the way to ful
 \usage{
 profuseMultiImageFound2Fit(image_list, segim_list = NULL, mask_list = NULL,
   Ncomp = 2, loc = NULL, cutbox = NULL, psf_list = NULL, nbenchconv = 0L,
-  magzero = NULL, gain = NULL, resamp = NULL, tightcrop = TRUE, offset_list = NULL, ...)
+  magzero = NULL, gain = NULL, resamp = NULL, log_scat_scale = FALSE,
+  offset_list = NULL, tightcrop = TRUE, ...)
 
 profuseMultiImageDoFit(image_list, F2F = NULL, Ncomp = 2, psf_list = NULL,
   magzero = NULL, psf_dim = c(51, 51), plot = FALSE, seed = 666,
@@ -57,11 +58,14 @@ Numeric vector; optional, gains to pass into \code{\link{profuseFound2Fit}} \opt
   \item{resamp}{
 Numeric scalar; optional, resampling factor to pass into \code{\link{profuseFound2Fit}} \option{resamp}.
 }
-  \item{tightcrop}{
-Logical; optional, should the image be cut down to the tightest fitting box that fully contains the segmentation map? This might be a good idea if the parent image is very large (cuts down on memory allocation etc), but might be a bad idea if you want to know the exact position of your source with the frame, e.g. \option{xcen} and \option{ycen} in the \option{modellist} are relative to the new tightly cropped image not the original cropped image.
+  \item{log_scat_scale}{
+Logical; optional. If TRUE, include a log scatter scale hyper-parameter in the likelihood evaluation (see \code{profitLikeModel}) to improve sampling when the image likelihood is under/over-dispersed.
 }
   \item{offset_list}{
 List; optional, offsets to pass into \code{\link{profuseFound2Fit}} \option{offset}.
+}
+  \item{tightcrop}{
+Logical; optional, should the image be cut down to the tightest fitting box that fully contains the segmentation map? This might be a good idea if the parent image is very large (cuts down on memory allocation etc), but might be a bad idea if you want to know the exact position of your source with the frame, e.g. \option{xcen} and \option{ycen} in the \option{modellist} are relative to the new tightly cropped image not the original cropped image.
 }
   \item{psf_dim}{
 Integer vector; optional, the dimensions of the output PSF generated when fitting a star with \option{Ncomp} = 0.5. The PSF will be centrally located in the image. The dimensions will be forced to be odd (adding 1 if required) to ensure the PSF mode is at the centre of the middle pixel.