profitDataSetOptionsFromBenchmark: Setup ProFit Data Options from Benchmarks

In ProFit: Fit Projected 2D Profiles to Galaxy Images

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.

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.

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.

Author(s)

Dan Taranu

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))

## End(Not run)

ProFit documentation built on Nov. 11, 2019, 5:07 p.m.