ProFit (version 1.3.3)

profitDataSetOptionsFromBenchmarks: Setup ProFit Data Options from Benchmarks

Description

This is a utility function to set integration and convolution options for a profit.data previously set up by profitSetupData based on the results from a profit.benchmark generated by profitDataBenchmark.

Usage

profitDataSetOptionsFromBenchmarks(Data, benchmarks)

Arguments

Data

List; required, a object (list) of class profit.data as generated by profitSetupData.

benchmarks

List; required, a object (list) of class profit.data.benchmark as generated by profitDataBenchmark.

Value

List of class profit.data, with integration and convolution results set to the best-performing methods from benchmarks; all other options are unchanged from the original Data.

Details

Besides being called by profitSetupData when benchmarking is requested, users may want to call this function to re-do benchmarks for an existing profit.data object, either when loading a saved profit.data from disk or simply to change any of the benchmark arguments. This function does not perform benchmarking.

See Also

profitSetupData, profitDataBenchmark, profitBenchmark

Examples

# NOT RUN {
# Load ProFit example data

# There are 2 data source options: KiDS or SDSS (the galaxies are the same)

datasource='KiDS' 

# Now we can extract out the example files we have available for fitting by checking the
# contents of the directory containing the example FITS files:

data('ExampleInit')
ExampleFiles=list.files(system.file("extdata",datasource,package="ProFit"))
ExampleIDs=unlist(strsplit(ExampleFiles[grep('fitim',ExampleFiles)],'fitim.fits'))
print(ExampleIDs)

# There are 10 example galaxies included. Here we run example 1:

useID=ExampleIDs[1]

box = c(160,160)
image = magcutout(readFITS(system.file("extdata", paste(datasource,'/',useID,'fitim.fits',sep=''),
package="ProFit"))$imDat, box = box)$image
sigma = magcutout(readFITS(system.file("extdata", paste(datasource,'/',useID,'sigma.fits',sep=''),
package="ProFit"))$imDat, box = box)$image
segim = magcutout(readFITS(system.file("extdata", paste(datasource,'/',useID,'segim.fits',sep=''),
package="ProFit"))$imDat, box = box)$image
psf = magcutout(readFITS(system.file("extdata", paste(datasource,'/',useID,'psfim.fits',sep=''),
package="ProFit"))$imDat, box = c(21,21))$image

# Very rough model (not meant to look too good yet):

useIDnum=as.integer(strsplit(useID,'G')[[1]][2])
useloc=which(ExampleInit$CATAID==useIDnum)

# For our initial model we treat component 1 as the putitive bulge and componet 2 as
# the putitive disk. We are going to attempt a fit where the disk is forced to have
# nser=1 and the bulge has an axial ratio of 1.

modellist=list(
  sersic=list(
    xcen= c(dim(image)[1]/2, dim(image)[1]/2),
    ycen= c(dim(image)[2]/2, dim(image)[2]/2),
    mag= c(ExampleInit$sersic.mag1[useloc], ExampleInit$sersic.mag2[useloc]),
    re= c(ExampleInit$sersic.re1[useloc], ExampleInit$sersic.re2[useloc])*
      if(datasource=='KiDS'){1}else{0.2/0.339},
    nser= c(ExampleInit$sersic.nser1[useloc], 1),  #Disk is initially nser=1
    ang= c(ExampleInit$sersic.ang2[useloc], ExampleInit$sersic.ang2[useloc]),
    axrat= c(1, ExampleInit$sersic.axrat2[useloc]),  #Bulge is initially axrat=1
    box=c(0, 0)
  )
)

# The pure model (no PSF):
magimage(profitMakeModel(modellist,dim=dim(image)))

# The original image:
magimage(image)

# The convolved model (with PSF):
magimage(profitMakeModel(modellist,dim=dim(image),psf=psf))

# What should we be fitting:

tofit=list(
  sersic=list(
    xcen= c(TRUE,NA), #We fit for xcen and tie the two together
    ycen= c(TRUE,NA), #We fit for ycen and tie the two together
    mag= c(TRUE,TRUE), #Fit for both
    re= c(TRUE,TRUE), #Fit for both
    nser= c(TRUE,FALSE), #Fit for bulge
    ang= c(FALSE,TRUE), #Fit for disk
    axrat= c(FALSE,TRUE), #Fit for disk
    box= c(FALSE,FALSE) #Fit for neither
  )
)

# What parameters should be fitted in log space:

tolog=list(
  sersic=list(
    xcen= c(FALSE,FALSE),
    ycen= c(FALSE,FALSE),
    mag= c(FALSE,FALSE),
    re= c(TRUE,TRUE), #re is best fit in log space
    nser= c(TRUE,TRUE), #nser is best fit in log space
    ang= c(FALSE,FALSE),
    axrat= c(TRUE,TRUE), #axrat is best fit in log space
    box= c(FALSE,FALSE)
  )
)

# Setup the profit.data

openclenvs = data.frame()

Data=profitSetupData(image=image, sigma=sigma, segim=segim, psf=psf,
  modellist=modellist, tofit=tofit, tolog=tolog, magzero=0, algo.func='optim', verbose=TRUE,
  nbenchmark = 1L, benchconvmethods = "brute",
  benchintmethods = "brute", benchopenclenvs = openclenvs,
  finesample=4L, printbenchmark = TRUE)

system.time(profitLikeModel(parm=Data$init, Data=Data))

benchmarks = profitDataBenchmark(modellist = Data$modellist, calcregion = Data$calcregion,
  imgdim = dim(Data$image), finesample = Data$finesample, psf = Data$psf, fitpsf = Data$fitpsf,
  nbenchmark = 1L, benchconvmethods = profitAvailableConvolvers(),
  benchintmethods = profitAvailableIntegrators(), benchopenclenvs = openclenvs,
  printbenchmark = TRUE)

Data = profitDataSetOptionsFromBenchmarks(Data, benchmarks)

system.time(profitLikeModel(parm=Data$init, Data=Data))
# }