def rmcrimg(obj):
iraf.lacos_im(input='f' + obj,
output='crf' + obj,
outmask='mask' + obj,
niter=4,
verbose='No')
print(obj + ' finished.')
def run_lacosmic(filename, sigclip, sigfrac, objlim, niter, sigclip_pf):
"""Runs ``IRAF/LACosmic`` over an FLT file.
Parameters:
filename : string
Name of the FITS file, including the path.
sigclip : float
Detection limit for cosmic rays.
sigfrac : float
Detection limit for adjacent pixels.
objlim : int
Max number of objects desired in image.
niter : int
Number of iterations of cosmic ray finder.
sigclip_pf : float
Detection limit for cosmic rays in Post-Flashed images.
Set to 0.0 if no Post-Flashed data.
Returns:
nothing
Outputs:
``IRAF/LACosmic`` cleaned FITS file,
``<file rootname>.clean.fits``.
"""
filename = str(filename)
sigclip = float(sigclip)
sigfrac = float(sigfrac)
objlim = int(objlim)
niter = int(niter)
sigclip_pf = float(sigclip_pf)
# Read the header for whether the image is post-flashed.
fits_file = fits.open(filename)
flshcorr = fits_file[0].header['FLSHCORR']
fits_file.close()
if sigclip_pf == 0.0 or flshcorr == 'OMIT':
sigclip = sigclip
print 'FLSHCORR set to OMIT.'
elif flshcorr == 'COMPLETE':
sigclip = sigclip_pf
print 'FLSHCORR set to COMPLETE.'
iraf.lacos_im(filename+'[1]', \
filename.split('.fits')[0]+'.clean.fits', \
filename.split('.fits')[0]+'.mask.fits', \
gain=1.5, \
readn=3.0, \
sigclip=sigclip, \
sigfrac=sigfrac, \
objlim=objlim, \
niter=niter)
def p60sub(refimage, inlist, ra=-1, dec=-1, dx=-1, dy=-1, \
nsx=5, nsy=5, sx=1, sy=1, sat1=60000, sat2=60000, min=5, ms=130, dback=1, \
dspace=2, hms=9, hss=15, thresh=20, seeing=5.0, scale=0.0, tol=0.005, \
matchlist="", goodmatch=2.0, aorder=2, nstars=30, outim="", MAX=900, \
order=3, clobber=globclob):
""" subtraction pipeline for one object/filter P60 data """
# Parse inputs
infiles = iraffiles(inlist)
# Find location (in pixels) of central object if specified
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
if not ((ra == -1) and (dec == -1)):
[xcenpix, ycenpix] = imwcs2pix(refimage, ra, dec)
else:
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[ra, dec] = impix2wcs(refimage, xcenpix, ycenpix)
# Trim the reference image
if (dx == -1):
xl = 1
xu = nx
else:
xl = int(xcenpix - (dx / 2.0))
xu = int(xcenpix + (dx / 2.0))
if (dy == -1):
yl = 1
yu = ny
else:
yl = int(ycenpix - (dy / 2.0))
yu = int(ycenpix + (dy / 2.0))
check_exist('t' + refimage, 'w', clobber)
iraf.imcopy(refimage + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
't' + refimage)
# Big Loop
for image in infiles:
# Separate image rootname from extension
imageroot = image.split('.')[0]
# Check to make sure the WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# If so, then shift all the images appropriately
else:
xsh = xcenpix - imwcs2pix(image, ra, dec)[0]
ysh = ycenpix - imwcs2pix(image, ra, dec)[1]
check_exist('s' + image, 'w', clobber)
iraf.imshift(image, 's' + image, xsh, ysh)
# Make sure sky subtraction is not done
[skysub, skybkg] = get_head(image, ['SKYSUB', 'SKYBKG'])
if (skysub == 1):
iraf.imarith('s' + image, '+', skybkg, 's' + image)
# Trim the images
check_exist('ts' + image, 'w', clobber)
iraf.imcopy('s' + image + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
'ts' + image)
# Cosmic Ray Cleaning
iraf.lacos_im('ts%s' % image, 'cts%s' % image, 'ts%s_crmask' % \
imageroot, gain=2.2, readn=5.3, niter=3)
# Register the images
os.system(
'$REDUCTION/imregister_new.pl t%s cts%s -sat %i -thresh %i -s %f -scale %f -tol %f %s -o %i -g %i -n %i -out cts%s.shift.fits -max %i'
% (refimage, image, sat1, thresh, seeing, scale, tol, matchlist,
order, goodmatch, nstars, imageroot, MAX))
# Write the configuration parameters for subtraction
pfile = open('default_config', 'w+')
pfile.write('nstamps_x %i\n' % nsx)
pfile.write('nstamps_y %i\n' % nsy)
pfile.write('sub_x %i\n' % sx)
pfile.write('sub_y %i\n' % sy)
pfile.write('half_mesh_size %i\n' % hms)
pfile.write('half_stamp_size %i\n' % hss)
pfile.write('deg_bg %i\n' % dback)
pfile.write('saturation1 %f\n' % sat1)
pfile.write('saturation2 %f\n' % sat2)
pfile.write('pix_min %i\n' % min)
pfile.write('min_stamp_center %i\n' % ms)
pfile.write('ngauss 3\n')
pfile.write('deg_gauss1 6\n')
pfile.write('deg_gauss2 4\n')
pfile.write('deg_gauss3 2\n')
pfile.write('sigma_gauss1 0.7\n')
pfile.write('sigma_gauss2 1.5\n')
pfile.write('sigma_gauss3 2.0\n')
pfile.write('deg_spatial %i\n' % dspace)
pfile.write('reverse 0\n')
pfile.write('stampsbyxy 0\n')
pfile.close()
# Run the subtraction
os.system("$REDUCTION/ISIS/bin/mrj_phot t%s cts%s.shift.fits" \
% (refimage, imageroot))
# Clean up files
os.system("mv conv.fits cts%s.shift.sub.fits" % imageroot)
os.system("mv conv0.fits cts%s.shift.conv.fits" % imageroot)
# Return
print 'Exiting successfully'
return
def iqp60(inpat,endhow="never",endwhen="",focmode="all",
logfile="",clobber=globclob,verbose=globver):
""" intelligent processing of P60 data files """
# Defaults
twait=30
reduced={}
filtkey="FILTER"
biaskey="IMGTYPE"
biasre="BIAS"
biasroot="Bias"
biaspfx="b"
flatkey="IMGTYPE"
flatre="DOMEFLAT"
flatpre="Flat-"
flatpfx="f"
bpm0root="BPM0"
bpmroot="BPM"
binkey="CCDSUM"
detseckey="DETSEC"
binroikey="BINROI"
binroidef="A"
nfocus=10
focuskey="IMGTYPE"
focusre="^FOCUS"
fseqkey="FOCUSSEQ"
skipkey="IMGTYPE"
skipre="SAOFOCUS"
statsec=""
reqkeys=['IMGTYPE','OBJECT','FILTER','BINROI',
'CCDSUM','DETSEC','ROISEC']
sigma=3.0
minraw=0.0
maxraw=65535.0
satval=50000.0
masksfx="mask"
rawpix=0.3787
zptkey="ZEROPT"
zpukey="ZEROPTU"
# For now:
#catmags={'B':'BMAG','V':'BMAG','g':'BMAG',
# 'R':'RMAG','r':'RMAG','I':'IMAG',
# 'i':'IMAG','ip':'IMAG','z':'IMAG',
# 'zp':'IMAG','zl':'IMAG','zs':'IMAG'}
## In the future, once Sloan mags are written to the ubcone output:
catmags={'B':'BMAG','V':'VMAG','g':'GPMAG',
'R':'RMAG','r':'RPMAG','I':'IMAG',
'i':'IMAG','ip':'IPMAG','z':'ZPMAG',
'zp':'ZPMAG','zl':'ZPMAG','zs':'ZPMAG',
'upr':'UPMAG','gpr':'GPMAG','rpr':'RPMAG',
'ipr':'IPMAG','zpr':'ZPMAG'}
exptmkey="EXPTIME"
exptmstd=60.0
minzpts={}
minskys={}
fwhm0=1.5
gain=2.3
readn=7.5
crsfx="crm"
statfile="Status_Science.txt"
# Parse end-condition "time"
if endhow=="time":
re1=re.search("^(\d+):(\d+)",endwhen)
if re1:
tnow=time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime=time.mktime([tnow[0],tnow[1],tnow[2],
int(re1.group(1)),int(re1.group(2)),0,
tnow[6],tnow[7],0]) - time.timezone
if reftime<time.time():
reftime+=86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow="never"
# Parse focmode setting
fonly=0
if focmode=="nofocus":
skipre="FOCUS"
elif focmode=="focus":
fonly=1
skipre="SCIENCE"
# Parse logfile setting
if len(logfile)>0:
check_exist(logfile,'w',yes)
try:
log=open(logfile,'w')
sys.stdout=log
except:
print "Failed to open logfile %s for writing" % logfile
# Setup ccdproc options
ccdproc=iraf.ccdred.ccdproc
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
ccdproc.interactive=no
ccdproc.function="chebyshev"
ccdproc.order=3
ccdproc.sample="*"
ccdproc.naverage=3
ccdproc.niterate=2
ccdproc.low_reject=3.0
ccdproc.high_reject=3.0
ccdproc.grow=0.0
# Demosaic all the files
allfiles=glob.glob(inpat)
alllist=','.join(allfiles)
iraf.iqmosaic(alllist,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="CCDSIZE,DETSEC,ROISEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN,CCDPROC",
clobber=yes,verbose=verbose)
iraf.add_wcs(alllist,instrument="p60new",binkey=binkey)
# Set ccdproc params for bias-subtraction only
ccdproc.overscan=no
ccdproc.trim=no
ccdproc.zerocor=yes
# Determine if we need to make calibration files
dobias=no
doflats=no
for image in allfiles:
# Criteria for bias image
biasval,flatval=get_head(image,[biaskey,flatkey])
if re.search(biasre,biasval,re.I):
dobias=yes
reduced[image]=yes
elif re.search(flatre,flatval,re.I):
doflats=yes
reduced[image]=yes
# Calibrations
if (dobias or doflats) and not fonly:
iraf.iqcals(alllist,dobias=dobias,biasproc=no,
biaskey=biaskey,biasre=biasre,biasroot=biasroot,
doflats=doflats,flatproc=yes,flatkey=flatkey,
flatre=flatre,filtkey=filtkey,flatpre=flatpre,
flatscale="mode",statsec="",normflat=yes,
dobpm=no,bpmroot=bpmroot,classkey=binroikey,
classdef=binroidef,mosaic=no,
clobber=clobber,verbose=verbose)
# Delete binroi-specific BPMs and flatfields
oldbpms=glob.glob(bpmroot+'-'+'*.pl')
for oldbpm in oldbpms:
iraf.imdel(oldbpm,verify=no,go_ahead=yes)
oldflats=glob.glob(flatpre+'*-*.fits')
for oldflat in oldflats:
iraf.imdel(oldflat,verify=no,go_ahead=yes)
# Clip all flatfields and update BPM as we go
bpm0name=bpm0root+'.pl'
bpmname=bpmroot+'.pl'
if os.path.exists(bpmname):
check_exist(bpm0name,'w',clobber=yes)
iraf.imrename(bpmname,bpm0name,verbose=no)
allflats=glob.glob(flatpre+'*.fits')
for flatimg in allflats:
iraf.iqclip(flatimg,lthresh=0.75,hthresh=1.25,bookend=no,
replace=1.0,maskin=bpm0name,maskout=bpmname,
maskval=1,clobber=yes,verbose=no)
iraf.imdel(bpm0name,verify=no,go_ahead=yes)
iraf.imrename(bpmname,bpm0name,verbose=no)
update_head(flatimg,"CLIPFLAT",1,"Has flatfield been clipped?")
update_head(flatimg,"CLIPREPL",1.0,
"Replacement value for clipped pixels")
iraf.imrename(bpm0name,bpmname,verbose=no)
##################################################
# Lists of focusfiles
allfocus=[]
focusfiles=[]
# Should additional calibrations be taken later on...
biasfiles=[]
flatfiles=[]
# Big Loop
done=no
while not done:
# Parse inputs
allfiles=glob.glob(inpat)
newfiles=[]
for image in allfiles:
if not reduced.has_key(image):
# Exclude Bias & Flats
if re.search(biasroot,image,re.I) or \
re.search(flatpre,image,re.I):
reduced[image]=yes
continue
# Exclude calibration files (for now)
biasval,flatval=get_head(image,[biaskey,flatkey])
if re.search(biasre,biasval,re.I):
biasfiles.append(image)
reduced[image]=yes
elif re.search(flatre,flatval,re.I):
flatfiles.append(image)
reduced[image]=yes
# Exclude "skippable" files
skipval=get_head(image,skipkey)
if re.search(skipre,skipval,re.I):
reduced[image]=yes
# Queue file for processing
else:
reduced[image]=no
newfiles.append(image)
elif not reduced[image]:
# Attempted reduction before, but failed?
newfiles.append(image)
if newfiles:
# Brief pause to avoid file conflicts (?)
time.sleep(3.0)
# Demosaic
newlist=','.join(newfiles)
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
iraf.iqmosaic(newlist,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,verbose=verbose)
iraf.add_wcs(newlist,instrument="p60new",binkey=binkey)
# Reset ccdproc params for bias-subtraction only
ccdproc.overscan=no
ccdproc.biassec=""
ccdproc.trim=no
ccdproc.trimsec=""
ccdproc.zerocor=yes
ccdproc.flatcor=no
##############################
# Process new images
for image in newfiles:
# Required keyword check
if not check_head(image,reqkeys):
print "Image %s is missing required keywords" % image
continue
# Attempt processing only once per image
reduced[image]=yes
if verbose:
print "Reducing new image %s" % image
# Track useful status information
scistat={}
# Prepare for processing
pix=rawpix
binroi=binroidef
biasname=biasroot+'.fits'
bpmname=bpmroot+'.pl'
filt=get_head(image,filtkey)
flatname=flatpre+filt+".fits"
# Calibration files for BINROI setting
if check_head(image,binroikey):
binroi=get_head(image,binroikey)
if binroi != binroidef:
# Get the right calibration files
fullbias=biasname
biasname=biasroot+'-'+binroi+'.fits'
fullbpm =bpmroot+'.pl'
bpmname =bpmroot+'-'+binroi+'.pl'
fullflat=flatname
flatname=flatpre+filt+'-'+binroi+".fits"
# Details of this binning/roi combo
[binval,detsec]=get_head(image,[binkey,detseckey])
binning=p60parsebin(binval)
roi=p60parseroi(detsec)
# Pixel scale (no anisotropic binning, sorry)
pix=binning[0]*rawpix
# Check for existance of bias; make it if we have to
if not os.path.exists(biasname):
print ("Failed to find bias for %s=%s; "+ \
"constructing one from full-frame bias") % \
(binroikey,binroi)
p60roiflat(fullbias,biasname,binning,roi)
# Produce the flatfield if necessary
if not os.path.exists(flatname):
p60roiflat(fullflat,flatname,binning,roi)
# Produce the bad-pixel mask if necessary
if not os.path.exists(bpmname):
# Get BPM into FITS format
bpm1=iraf.mktemp("iqbpm")+".fits"
iraf.imcopy(fullbpm,bpm1,verbose=no)
# Adjust BPM for binning/ROI
bpm2=iraf.mktemp("iqbpm")+".fits"
p60roibpm(bpm1,bpm2,binning,roi)
# Convert BPM to PL format
iraf.imcopy(bpm2,bpmname,verbose=no)
# Delete temporary files
iraf.imdel(bpm1,verify=no,go_ahead=yes)
iraf.imdel(bpm2,verify=no,go_ahead=yes)
# Bias subtraction
check_exist(biasname,"r")
ccdproc.zero=biasname
image1=biaspfx+image
check_exist(image1,'w',clobber)
iraf.ccdproc(image,output=image1,Stdout=1)
# Flatfielding
check_exist(flatname,"r")
iraf.iqflatten(image1,flatname,outpfx=flatpfx,
normflat=no,clipflat=no,
statsec=statsec,subsky=yes,
vignflat=no,clobber=yes,verbose=no)
imagef=flatpfx+image1
# Clip (bookend) flattened & sky-subtracted image
skybkg=float(get_head(image1,'SKYBKG'))
iraf.iqclip(image1,lthresh=minraw-skybkg,hthresh=maxraw-skybkg,
bookend=yes,maskin="",maskout="",clobber=yes,
verbose=no)
# Rename the file, if everything is well-behaved
reim=re.search('^%s%s(.+[^r])r?\.fits' % (flatpfx,biaspfx),
imagef,re.I)
if reim:
image2=reim.group(1)+'p.fits'
check_exist(image2,"w",yes)
iraf.imrename(imagef,image2,verbose=verbose)
else:
image2=imagef
# Set bad pixels to zero
update_head(image2,'BPM',bpmname,"Bad Pixel Mask")
iraf.iqmask(image2,mask='!BPM',method='constant',value='0.0',
clobber=yes,verbose=no)
# Defringing happens later, in bunches
# Cosmic Ray Rejection
tmpimg=iraf.mktemp("iqcr")+".fits"
check_exist("%s_%s.fits" % (image2[:15],crsfx),"w",clobber)
iraf.lacos_im(image2,tmpimg,"%s_%s.fits" % (image2[:15],crsfx),
gain=gain,readn=readn,skyval=skybkg,sigclip=4.5,
sigfrac=0.5,objlim=1.0,niter=1)
iraf.imdel(image2,verify=no,go_ahead=yes)
iraf.imcopy(tmpimg,image2,verbose=no)
iraf.imdel(tmpimg,verify=no,go_ahead=yes)
# Object-detection
if scistat.has_key("SEEING_%s" % file):
try:
fwhm=float(scistat["SEEING_%s" % file])
except:
fwhm=fwhm0
else:
fwhm=fwhm0
iraf.iqobjs(image2,sigma,satval,skyval="!SKYBKG",masksfx=masksfx,
wtimage="",fwhm=fwhm,pix=rawpix,aperture=2*fwhm/rawpix,
gain=gain,minlim=no,clobber=yes,verbose=no)
# Track some status information
nstars=get_head(image2,'NSTARS')
scistat['NSTARS']=nstars
if minskys.has_key(filt):
minsky=minskys[filt]
scistat["SKYBKG_%s" % filt]=skybkg/minsky
# Is it a focus frame?
focusval=get_head(image2,focuskey)
# Focus frame actions
if re.search(focusre,focusval,re.I):
focusfiles.append(image2)
# Find its place in the sequence
focusseq=get_head(image2,fseqkey)
ref=re.search("(\d+)of(\d+)",focusseq,re.I)
if ref:
ifocus=int(ref.group(1))
nfocus=int(ref.group(2))
# Non-focus frame actions
else:
# Make a new keyword for mosaic objects
objkey='OBJECT'
if check_head(image2,'MSCSIZE'):
mscsize=get_head(image2,'MSCSIZE')
mscdims=mscsize.split(',')
if int(mscdims[0])>1 or int(mscdims[1])>1:
object=get_head(image2,'OBJECT')
mscposn=get_head(image2,'MSCPOSN')
objmsc=object+'-'+mscposn
update_head(image2,'OBJMSC',objmsc,
'Object Name/Mosaic Position')
objkey='OBJMSC'
# Refine WCS (also calculates seeing in arcsec)
iraf.iqwcs(image2,objkey=objkey,rakey='RA',
deckey='DEC',pixtol=0.05,starfile='!STARFILE',
nstar=40,catalog='web',ubhost="localhost",
diffuse=yes,clobber=yes,verbose=verbose,
nstarmax=40)
# Retry if unsuccessful
if not check_head(image2,'IQWCS'):
iraf.iqwcs(image2,objkey=objkey,rakey='RA',
deckey='DEC',pixtol=0.05,starfile="!STARFILE",
nstar=40,catalog='web',ubhost="localhost",
diffuse=yes,clobber=yes,verbose=verbose,
nstarmax=1000)
# Calculate further quantities if WCS was successful
extinct='INDEF'
if check_head(image2,'IQWCS'):
scistat['WCSGOOD'] = 1
scistat["SEEING_%s" % filt]=get_head(image2,'SEEING')
[rapt,dcpt,lenx,leny]=get_head(image2,
['RA','DEC','NAXIS1','NAXIS2'])
[[ractr,dcctr]]=impix2wcs(image2,0.5*(1+float(lenx)),
0.5*(1+float(leny)))
coopt=astrocoords(rapt,dcpt)
cooctr=astrocoords(ractr,dcctr)
[offra,offdc]=radecdiff(coopt.radeg(),coopt.dcdeg(),
cooctr.radeg(),cooctr.dcdeg())
update_head(image2,'PTOFFSET',"%.2f %.2f" % \
(offra,offdc),
"Offset from nominal pointing (arcsec)")
scistat['PTOFFSET']="%.2f,%.2f" % (offra,offdc)
# Get zero-point against SDSS/NOMAD/USNO-B
if catmags.has_key(filt):
if check_head(image2,'OBJMSC'):
object=get_head(image2,'OBJMSC')
else:
object=get_head(image2,'OBJECT')
zpcat=""
# See if attempt already made for SDSS
if not (os.path.exists("%s.sdss" % object)):
getsdss(image2, "%s.sdss" % object)
# If successful, set to SDSS
sdssfile=open("%s.sdss" % object)
if (len(sdssfile.readlines())>2):
catalog="%s.sdss" % object
zpcat="SDSS"
# If SDSS unsuccessful, update USNO-B
if not zpcat:
update_usnob("%s.cat" % object,
outfile="%s.reg" % object)
catalog="%s.reg" % object
zpcat="USNO-B"
# Update image header
update_head(image2,"ZPCAT",zpcat)
# Run the zeropoint calculation
iraf.iqzeropt(image2,catmags[filt],
starfile="!STARFILE",catalog=catalog,
pixtol=3.0,useflags=yes,maxnum=50,
method="mean",rejout=1,fencelim=0.50,
sigma=1.5,maxfrac=0.25,
zptkey=zptkey,zpukey=zpukey,
clobber=yes,verbose=verbose)
# Use that to get an extinction estimate
zeropt,zeroptu,exptime=get_head(image2,[zptkey,
zpukey,exptmkey])
zeropt=float(zeropt)
zeroptu=float(zeroptu)
exptime=float(exptime)
scistat["ZEROPT_%s" % filt] = \
zeropt - 2.5*math.log10(exptime/exptmstd)
if minzpts.has_key(filt):
extinct = zeropt - \
2.5*math.log10(exptime/exptmstd) - \
minzpts[filt]
scistat["EXTINCT_%s" % filt]=extinct
# Fix the SEEING keyword if WCS fit was not successful
else:
scistat['WCSGOOD'] = 0
try:
seepix=float(get_head(image2,'SEEPIX'))
seeing= "%.3f" % (pix*seepix)
except:
seeing='INDEF'
update_head(image2,'SEEING',seeing,
"Estimated seeing in arcsec or INDEF")
# Write Archive Keywords
update_head(image2,'PROCESSD',1,
"Image has been processed by iqp60")
update_head(image2,'PROCVER',version,
"Version number of iqp60 used")
update_head(image2,'EXTINCT',extinct,
"Estimated mags extinction or INDEF")
update_head(image2,'PROCPROB','None',
"Problems encountered in iqp60 processing")
# Update status file
p60status(statfile,image2,scistat)
# Clean up
check_exist(image1,"w",yes)
##############################
# If a set of focus images have been processed, find best focus
if len(focusfiles)>=nfocus:
print "%d focus images have been processed" % len(focusfiles)
# Pick a starfile
focusfiles.sort()
medfile=focusfiles[len(focusfiles)/2]
starfile=get_head(medfile,'STARFILE')
# Update seeing values in all images based on good
# stars in the starfile.
ffiles=','.join(focusfiles)
iraf.iqseeing(ffiles,stars=starfile,
seekey="SEEING",method="acorr",useflags=yes,
skipstars=0,usestars=10,boxsize=64,
strictbox=no,imgsec="[940:1980,64:1980]",
update=yes,clobber=yes,verbose=no)
# Grab focus positions and seeing values
focx=[]
focy=[]
for image in focusfiles:
[fpos,seep]=list2float(get_head(image,['FOCUSPOS','SEEING']))
fpos=float(fpos)
seep=float(seep)
focx.append(fpos)
pix=rawpix
if check_head(image,binkey):
binval=get_head(image,binkey)
binels=binval.split()
binning=[int(binels[0]),int(binels[1])]
# Let's hope we don't ever do asymmetric binning
pix=binning[0]*rawpix
focy.append(pix*seep)
update_head(image,'SEEING',pix*seep,
"Estimated seeing in arcsec")
# Choose best-seeing as minimum of these
bestsee=min(focy)
bestfoc=focx[focy.index(bestsee)]
# This was too much verbosity for Brad
if verbose and 0:
print "Focus settings: "+liststr(focx,"%.2f")
print "Seeing values: "+liststr(focy,"%.1f")
print
print "Best focus: %.2f" % bestfoc
# Expunge the focusfiles list
allfocus.append(focusfiles)
focusfiles=[]
##############################
# Test end conditions
if endhow=="never":
done=no
elif endhow=="once":
done=yes
elif endhow=="time":
if time.time()>reftime:
done=yes
# Wait a little while
if not done:
time.sleep(twait)
zerocor=1,
darkcor=0,
flatcor=1)
#remove cosmic ray automatically
iraf.stsdas()
iraf.unlearn('lacos_im')
image_spectrum = [
inf_file[j][0] for j in range(len(inf_file))
if inf_file[j][-1] in ['o', 'c']
]
[
iraf.lacos_im(image_spectrum[j],
image_spectrum[j].replace('.fit', '_clean.fit'),
image_spectrum[j].replace('.fit', '_clean.pl'),
gain=Gain,
readn=Ron,
sigclip=4,
sigfrac=2,
niter=3) for j in range(len(image_spectrum))
]
pl_files = find_files(Path_Folder_Obj, '_clean.pl')
[os.remove(pl_files[j]) for j in range(len(pl_files))]
[os.remove(image_spectrum[j]) for j in range(len(image_spectrum))]
[
iraf.imcopy(image_spectrum[j].replace('.fit', '_clean.fit'),
image_spectrum[j]) for j in range(len(image_spectrum))
]
clean_files = find_files(Path_Folder_Obj, '_clean.fit')
[os.remove(clean_files[j]) for j in range(len(clean_files))]
fits_file = find_files(Path_Folder_Obj, '.fits')
[os.remove(fits_file[j]) for j in range(len(fits_file))]
def iq_ratir(chip="C1", filt="i", reflist="sdss.reg"):
'''Process RATIR data for given chip and filter'''
# Check and see if bias frame exists. If not, create one
if not os.path.exists("Bias-%s.fits" % chip):
imlist = glob.glob("[0-9]*T[0-9]*%sb.fits" % chip)
hdr = pyfits.getheader(imlist[0])
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = 'median'
zerocombine.reject = 'avsigclip'
zerocombine.ccdtype = ''
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = 'none'
zerocombine.statsec = '*'
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = 0.0
zerocombine.gain = hdr['SOFTGAIN']
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(imlist)
zerocombine(input=bstr, output="Bias-%s.fits" % chip)
# Bias subtract flat frames
imlist = glob.glob("[0-9]*T[0-9]*%sf.fits" % chip)
flis = []
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("b%s" % im)):
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = no
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="b%s" % im)
flis.append("b%s" % im)
elif (hdr["FILTER"] == filt):
flis.append("b%s" % im)
# Create flat field
if not os.path.exists("Flat-%s-%s.fits" % (chip, filt)):
glis = []
for im in flis:
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat(im)
if (iraf.iterstat.median <
RATIRSAT[chip]) and (iraf.iterstat.median > 1000.0):
glis.append(im)
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = 'median'
flatcombine.reject = 'avsigclip'
flatcombine.ccdtype = ''
flatcombine.scale = 'median'
flatcombine.statsec = ''
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = 0.0
flatcombine.gain = hdr["SOFTGAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
# Run flatcombine
fstr = ",".join(glis[:10])
flatcombine(fstr, output="Flat-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("Flat-%s-%s.fits" % (chip, filt))
iraf.imarith("Flat-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Flat-%s-%s.fits" % (chip, filt))
# Bias subtract and flat-field science images
imlist = glob.glob("[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("fb%s" % im)):
hdr = pyfits.getheader(im)
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = yes
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.flat = "Flat-%s-%s.fits" % (chip, filt)
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="fb%s" % im)
# Create sky frame (including dark current!)
if not os.path.exists("Sky-%s-%s.fits" % (chip, filt)):
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
hdr = pyfits.getheader(imlist[0])
slis = []
for im in imlist:
if (hdr["FILTER"] == filt):
slis.append(im)
# Set up combine
imcombine = iraf.immatch.imcombine
imcombine.headers = ""
imcombine.bpmasks = ""
imcombine.rejmasks = ""
imcombine.nrejmasks = ""
imcombine.expmasks = ""
imcombine.sigmas = ""
imcombine.combine = "median"
imcombine.reject = "avsigclip"
imcombine.project = no
imcombine.outtype = "real"
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "median"
imcombine.zero = "none"
imcombine.weight = "none"
imcombine.statsec = ""
imcombine.lsigma = 3.0
imcombine.hsigma = 3.0
imcombine.rdnoise = 0.0
imcombine.gain = hdr["SOFTGAIN"]
sstr = ",".join(slis[:10])
imcombine(sstr, "Sky-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat("Sky-%s-%s.fits" % (chip, filt))
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Sky-%s-%s.fits" % (chip, filt))
# Subtract sky frame (and dark!)
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("s%s" % im):
iraf.iterstat(im)
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "*",
iraf.iterstat.median, "temp.fits")
iraf.imarith(im, "-", "temp.fits", "s%s" % im)
os.remove("temp.fits")
fimg = pyfits.open("s%s" % im, "update")
fimg[0].header["SKYMED"] = iraf.iterstat.median
fimg[0].header["SKYSIG"] = iraf.iterstat.sigma
fimg[0].header["SKYSUB"] = 1
fimg.flush()
# Remove cosmic rays
imlist = glob.glob("sfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("c%s" % im):
fimg = pyfits.open(im)
iraf.lacos_im(im,
"c%s" % im,
"c%s.mask.fits" % im[:-5],
gain=hdr['SOFTGAIN'],
readn=0.0,
statsec="*,*",
skyval=fimg[0].header["SKYMED"],
sigclip=4.5,
sigfrac=0.5,
objlim=1.0,
niter=3,
verbose=no)
# Add WCS keywords
clis = glob.glob("csfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in clis:
fimg = pyfits.open(im, mode='update')
if fimg[0].header.get("RA") == None:
ra0 = fimg[0].header["STRCURA"]
dec0 = fimg[0].header["STRCUDE"]
nax = [fimg[0].header["NAXIS1"], fimg[0].header["NAXIS2"]]
fimg[0].header["RA"] = ra0
fimg[0].header["DEC"] = dec0
fimg[0].header["PIXSCALE"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL1"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL2"] = RATIRPIXSCALE[chip]
fimg[0].header["CTYPE1"] = "RA---TAN"
fimg[0].header["CTYPE2"] = "DEC--TAN"
fimg[0].header["WCSDIM"] = 2
fimg[0].header["WAT0_001"] = "system=image"
fimg[0].header["WAT1_001"] = "wtype=tan axtype=ra"
fimg[0].header["WAT2_001"] = "wtype=tan axtype=dec"
fimg[0].header["LTM1_1"] = 1.0
fimg[0].header["LTM2_2"] = 1.0
fimg[0].header["CRPIX1"] = nax[0] / 2.0
fimg[0].header["CRPIX2"] = nax[1] / 2.0
fimg[0].header["CRVAL1"] = ra0
fimg[0].header["CRVAL2"] = dec0
fimg[0].header["CD1_1"] = -RATIRPIXSCALE[chip] / 3600.0
fimg[0].header["CD1_2"] = 0.0
fimg[0].header["CD2_1"] = 0.0
fimg[0].header["CD2_2"] = RATIRPIXSCALE[chip] / 3600.0
fimg.flush()
# Crude astrometry
for im in clis:
if not os.path.exists("a%s" % im):
os.system("python %s %s" % (PYASTROM, im))
# Refine Astrometry
o1 = open("daofind.param", "w")
o1.write(
"NUMBER\nXWIN_IMAGE\nYWIN_IMAGE\nMAG_AUTO\nFLAGS\nA_IMAGE\nB_IMAGE\n")
o1.write("ELONGATION\nFWHM_IMAGE\nXWIN_WORLD\nYWIN_WORLD\n")
o1.write(
"ERRAWIN_IMAGE\nERRBWIN_IMAGE\nERRTHETAWIN_IMAGE\nERRAWIN_WORLD\n")
o1.write(
"ERRBWIN_WORLD\nERRTHETAWIN_WORLD\nFLUX_AUTO\nFLUX_RADIUS\nFLUXERR_AUTO"
)
o1.close()
o2 = open("default.conv", "w")
o2.write(
"CONV NORM\n# 5x5 convolution mask of a gaussian PSF with FWHM = 3.0 pixels.\n0.092163 0.221178 0.296069 0.221178 0.092163\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.296069 0.710525 0.951108 0.710525 0.296069\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.092163 0.221178 0.296069 0.221178 0.092163"
)
o2.close()
alis = glob.glob("acsfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in alis:
# Detect sources
os.system(
"sex -CATALOG_NAME %s.cat -CATALOG_TYPE FITS_LDAC -PARAMETERS_NAME daofind.param -DETECT_THRESH 2.0 -ANALYSIS_THRESH 2.0 -GAIN_KEY SOFTGAIN -PIXEL_SCALE 0 %s"
% (im[:-5], im))
# Run scamp for alignment
imlist = " ".join(alis)
os.system(
"scamp -ASTREF_CATALOG SDSS-R7 -DISTORTDEG 1 -SOLVE_PHOTOM N -SN_THRESHOLDS 3.0,10.0 -CHECKPLOT_DEV NULL %s"
% imlist.replace(".fits", ".cat"))
# Update header
for im in alis:
os.system("missfits %s" % im)
os.system("rm %s.head %s.back" % (im[:-5], im))
# Find good images for coadd
slis = []
for im in alis:
hdr = pyfits.getheader(im)
rms1 = hdr["ASTRRMS1"]
rms2 = hdr["ASTRRMS2"]
if (rms1 < 1.0e-4) and (rms1 > 5.0e-6) and (rms2 < 1.0e-4) and (
rms2 > 5.0e-6):
slis.append(im)
# Initial (unweighted) coadd
sstr = " ".join(slis)
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Identify sources in the coadded frame
hdr = pyfits.getheader("coadd.fits")
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=20.0,
wtimage="coadd.weight.fits")
hdr = pyfits.getheader("coadd.fits")
cpsfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=cpsfdiam,
wtimage="coadd.weight.fits")
refstars1 = Starlist("coadd.fits.stars")
refstars1.pix2wcs("coadd.fits")
truemags = np.array(refstars1.mags())
maglist = []
errlist = []
# (Relative) Zeropoints for individual images
for im in slis:
hdr = pyfits.getheader(im)
iqpkg.iqobjs(im,
3.0,
RATIRSAT[chip],
skyval="!SKYMED",
pix=RATIRPIXSCALE[chip],
gain=hdr["SOFTGAIN"],
aperture=20.0)
hdr = pyfits.getheader(im)
psfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs(im,
3.0,
RATIRSAT[chip],
skyval="!SKYMED",
pix=RATIRPIXSCALE[chip],
gain=hdr["SOFTGAIN"],
aperture=psfdiam)
stars = Starlist("%s.stars" % im)
refstars1.wcs2pix(im)
a, b = stars.match(refstars1, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars1))
newwts = np.zeros(len(refstars1))
for i in range(len(refstars1)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01), 2)
continue
maglist.append(newmags)
errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
# Update headers
medzp = np.median(zpts)
for i in range(len(slis)):
im = slis[i]
fimg = pyfits.open(im, mode="update")
fimg[0].header["RELZPT"] = zpts[i]
fimg[0].header["RELZPTSC"] = scatt[i]
fimg[0].header["RELZPRMS"] = rms[i]
fimg[0].header["FLXSCALE"] = 1.0 / np.power(10,
(zpts[i] - medzp) / 2.5)
fimg.flush()
# Final coadd
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Calibration
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=cpsfdiam,
wtimage="coadd.weight.fits")
stars = Starlist("coadd.fits.stars")
refstars = Starlist(reflist)
refstars.wcs2pix("coadd.fits")
refstars.set_mag("%sMAG" % filt.upper())
truemags = np.array(refstars.mags())
maglist = []
errlist = []
a, b = stars.match(refstars, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars))
newwts = np.zeros(len(refstars))
for i in range(len(refstars)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01), 2)
continue
maglist.append(newmags)
errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
fimg = pyfits.open("coadd.fits", mode="update")
fimg[0].header["ABSZPT"] = zpts[0] + 25
fimg[0].header["ABSZPTSC"] = scatt[0]
fimg[0].header["ABZPRMS"] = rms[0]
print "Zeropoint for coadded image: %.3f" % (25.0 + zpts[0])
print "Robust scatter for coadded image: %.3f" % scatt[0]
print "RMS for coadded image: %.3f" % rms[0]
def p60sub(refimage, inlist, ra=-1, dec=-1, dx=-1, dy=-1, \
nsx=5, nsy=5, sx=1, sy=1, sat1=60000, sat2=60000, min=5, ms=130, dback=1, \
dspace=2, hms=9, hss=15, thresh=20, seeing=5.0, scale=0.0, tol=0.005, \
matchlist="", goodmatch=2.0, aorder=2, nstars=30, outim="", MAX=900, \
order=3, clobber=globclob):
""" subtraction pipeline for one object/filter P60 data """
# Parse inputs
infiles = iraffiles(inlist)
# Find location (in pixels) of central object if specified
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
if not ((ra == -1) and (dec == -1)):
[xcenpix, ycenpix] = imwcs2pix(refimage, ra, dec)
else:
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[ra, dec] = impix2wcs(refimage, xcenpix, ycenpix)
# Trim the reference image
if (dx == -1):
xl = 1
xu = nx
else:
xl = int(xcenpix - (dx / 2.0))
xu = int(xcenpix + (dx / 2.0))
if (dy == -1):
yl = 1
yu = ny
else:
yl = int(ycenpix - (dy / 2.0))
yu = int(ycenpix + (dy / 2.0))
check_exist('t' + refimage, 'w', clobber)
iraf.imcopy(refimage + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
't' + refimage)
# Big Loop
for image in infiles:
# Separate image rootname from extension
imageroot = image.split('.')[0]
# Check to make sure the WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# If so, then shift all the images appropriately
else:
xsh = xcenpix - imwcs2pix(image, ra, dec)[0]
ysh = ycenpix - imwcs2pix(image, ra, dec)[1]
check_exist('s' + image, 'w', clobber)
iraf.imshift(image, 's' + image, xsh, ysh)
# Make sure sky subtraction is not done
[skysub, skybkg] = get_head(image, ['SKYSUB', 'SKYBKG'])
if (skysub == 1):
iraf.imarith('s'+image,'+',skybkg,'s'+image)
# Trim the images
check_exist('ts' + image, 'w', clobber)
iraf.imcopy('s' + image + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
'ts' + image)
# Cosmic Ray Cleaning
iraf.lacos_im('ts%s' % image, 'cts%s' % image, 'ts%s_crmask' % \
imageroot, gain=2.2, readn=5.3, niter=3)
# Register the images
os.system('$REDUCTION/imregister_new.pl t%s cts%s -sat %i -thresh %i -s %f -scale %f -tol %f %s -o %i -g %i -n %i -out cts%s.shift.fits -max %i' % (refimage, image, sat1, thresh, seeing, scale, tol, matchlist, order, goodmatch, nstars, imageroot, MAX))
# Write the configuration parameters for subtraction
pfile = open('default_config', 'w+')
pfile.write('nstamps_x %i\n' % nsx)
pfile.write('nstamps_y %i\n' % nsy)
pfile.write('sub_x %i\n' % sx)
pfile.write('sub_y %i\n' % sy)
pfile.write('half_mesh_size %i\n' % hms)
pfile.write('half_stamp_size %i\n' % hss)
pfile.write('deg_bg %i\n' % dback)
pfile.write('saturation1 %f\n' % sat1)
pfile.write('saturation2 %f\n' % sat2)
pfile.write('pix_min %i\n' % min)
pfile.write('min_stamp_center %i\n' % ms)
pfile.write('ngauss 3\n')
pfile.write('deg_gauss1 6\n')
pfile.write('deg_gauss2 4\n')
pfile.write('deg_gauss3 2\n')
pfile.write('sigma_gauss1 0.7\n')
pfile.write('sigma_gauss2 1.5\n')
pfile.write('sigma_gauss3 2.0\n')
pfile.write('deg_spatial %i\n' % dspace)
pfile.write('reverse 0\n')
pfile.write('stampsbyxy 0\n')
pfile.close()
# Run the subtraction
os.system("$REDUCTION/ISIS/bin/mrj_phot t%s cts%s.shift.fits" \
% (refimage, imageroot))
# Clean up files
os.system("mv conv.fits cts%s.shift.sub.fits" % imageroot)
os.system("mv conv0.fits cts%s.shift.conv.fits" % imageroot)
# Return
print 'Exiting successfully'
return
def iq_ratir(chip="C1", filt="i", reflist="sdss.reg"):
'''Process RATIR data for given chip and filter'''
# Check and see if bias frame exists. If not, create one
if not os.path.exists("Bias-%s.fits" % chip):
imlist = glob.glob("[0-9]*T[0-9]*%sb.fits" % chip)
hdr = pyfits.getheader(imlist[0])
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = 'median'
zerocombine.reject = 'avsigclip'
zerocombine.ccdtype = ''
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = 'none'
zerocombine.statsec = '*'
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = 0.0
zerocombine.gain = hdr['SOFTGAIN']
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(imlist)
zerocombine(input=bstr, output="Bias-%s.fits" % chip)
# Bias subtract flat frames
imlist = glob.glob("[0-9]*T[0-9]*%sf.fits" % chip)
flis = []
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("b%s" % im)):
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = no
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="b%s" % im)
flis.append("b%s" % im)
elif (hdr["FILTER"] == filt):
flis.append("b%s" % im)
# Create flat field
if not os.path.exists("Flat-%s-%s.fits" % (chip, filt)):
glis = []
for im in flis:
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat(im)
if (iraf.iterstat.median < RATIRSAT[chip]) and (iraf.iterstat.median > 1000.0):
glis.append(im)
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = 'median'
flatcombine.reject = 'avsigclip'
flatcombine.ccdtype = ''
flatcombine.scale = 'median'
flatcombine.statsec = ''
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = 0.0
flatcombine.gain = hdr["SOFTGAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
# Run flatcombine
fstr = ",".join(glis[:10])
flatcombine(fstr, output="Flat-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("Flat-%s-%s.fits" % (chip, filt))
iraf.imarith("Flat-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Flat-%s-%s.fits" % (chip, filt))
# Bias subtract and flat-field science images
imlist = glob.glob("[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("fb%s" % im)):
hdr = pyfits.getheader(im)
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = yes
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.flat = "Flat-%s-%s.fits" % (chip, filt)
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="fb%s" % im)
# Create sky frame (including dark current!)
if not os.path.exists("Sky-%s-%s.fits" % (chip, filt)):
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
hdr = pyfits.getheader(imlist[0])
slis = []
for im in imlist:
if (hdr["FILTER"] == filt):
slis.append(im)
# Set up combine
imcombine = iraf.immatch.imcombine
imcombine.headers = ""
imcombine.bpmasks = ""
imcombine.rejmasks = ""
imcombine.nrejmasks = ""
imcombine.expmasks = ""
imcombine.sigmas = ""
imcombine.combine = "median"
imcombine.reject = "avsigclip"
imcombine.project = no
imcombine.outtype = "real"
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "median"
imcombine.zero = "none"
imcombine.weight = "none"
imcombine.statsec = ""
imcombine.lsigma = 3.0
imcombine.hsigma = 3.0
imcombine.rdnoise = 0.0
imcombine.gain = hdr["SOFTGAIN"]
sstr = ",".join(slis[:10])
imcombine(sstr, "Sky-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat("Sky-%s-%s.fits" % (chip, filt))
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Sky-%s-%s.fits" % (chip, filt))
# Subtract sky frame (and dark!)
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("s%s" % im):
iraf.iterstat(im)
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "*", iraf.iterstat.median,
"temp.fits")
iraf.imarith(im, "-", "temp.fits", "s%s" % im)
os.remove("temp.fits")
fimg = pyfits.open("s%s" % im, "update")
fimg[0].header["SKYMED"] = iraf.iterstat.median
fimg[0].header["SKYSIG"] = iraf.iterstat.sigma
fimg[0].header["SKYSUB"] = 1
fimg.flush()
# Remove cosmic rays
imlist = glob.glob("sfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("c%s" % im):
fimg = pyfits.open(im)
iraf.lacos_im(im, "c%s" % im, "c%s.mask.fits" % im[:-5],
gain=hdr['SOFTGAIN'], readn=0.0, statsec="*,*",
skyval=fimg[0].header["SKYMED"], sigclip=4.5,
sigfrac=0.5, objlim=1.0, niter=3, verbose=no)
# Add WCS keywords
clis = glob.glob("csfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in clis:
fimg = pyfits.open(im, mode='update')
if fimg[0].header.get("RA")==None:
ra0 = fimg[0].header["STRCURA"]; dec0 = fimg[0].header["STRCUDE"]
nax = [fimg[0].header["NAXIS1"], fimg[0].header["NAXIS2"]]
fimg[0].header["RA"] = ra0
fimg[0].header["DEC"] = dec0
fimg[0].header["PIXSCALE"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL1"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL2"] = RATIRPIXSCALE[chip]
fimg[0].header["CTYPE1"] = "RA---TAN"
fimg[0].header["CTYPE2"] = "DEC--TAN"
fimg[0].header["WCSDIM"] = 2
fimg[0].header["WAT0_001"] = "system=image"
fimg[0].header["WAT1_001"] = "wtype=tan axtype=ra"
fimg[0].header["WAT2_001"] = "wtype=tan axtype=dec"
fimg[0].header["LTM1_1"] = 1.0
fimg[0].header["LTM2_2"] = 1.0
fimg[0].header["CRPIX1"] = nax[0] / 2.0
fimg[0].header["CRPIX2"] = nax[1] / 2.0
fimg[0].header["CRVAL1"] = ra0
fimg[0].header["CRVAL2"] = dec0
fimg[0].header["CD1_1"] = -RATIRPIXSCALE[chip] / 3600.0
fimg[0].header["CD1_2"] = 0.0
fimg[0].header["CD2_1"] = 0.0
fimg[0].header["CD2_2"] = RATIRPIXSCALE[chip] / 3600.0
fimg.flush()
# Crude astrometry
for im in clis:
if not os.path.exists("a%s" % im):
os.system("python %s %s" % (PYASTROM, im))
# Refine Astrometry
o1 = open("daofind.param", "w")
o1.write("NUMBER\nXWIN_IMAGE\nYWIN_IMAGE\nMAG_AUTO\nFLAGS\nA_IMAGE\nB_IMAGE\n")
o1.write("ELONGATION\nFWHM_IMAGE\nXWIN_WORLD\nYWIN_WORLD\n")
o1.write("ERRAWIN_IMAGE\nERRBWIN_IMAGE\nERRTHETAWIN_IMAGE\nERRAWIN_WORLD\n")
o1.write("ERRBWIN_WORLD\nERRTHETAWIN_WORLD\nFLUX_AUTO\nFLUX_RADIUS\nFLUXERR_AUTO")
o1.close()
o2 = open("default.conv", "w")
o2.write("CONV NORM\n# 5x5 convolution mask of a gaussian PSF with FWHM = 3.0 pixels.\n0.092163 0.221178 0.296069 0.221178 0.092163\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.296069 0.710525 0.951108 0.710525 0.296069\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.092163 0.221178 0.296069 0.221178 0.092163")
o2.close()
alis = glob.glob("acsfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in alis:
# Detect sources
os.system("sex -CATALOG_NAME %s.cat -CATALOG_TYPE FITS_LDAC -PARAMETERS_NAME daofind.param -DETECT_THRESH 2.0 -ANALYSIS_THRESH 2.0 -GAIN_KEY SOFTGAIN -PIXEL_SCALE 0 %s" % (im[:-5], im))
# Run scamp for alignment
imlist = " ".join(alis)
os.system("scamp -ASTREF_CATALOG SDSS-R7 -DISTORTDEG 1 -SOLVE_PHOTOM N -SN_THRESHOLDS 3.0,10.0 -CHECKPLOT_DEV NULL %s" % imlist.replace(".fits", ".cat"))
# Update header
for im in alis:
os.system("missfits %s" % im)
os.system("rm %s.head %s.back" % (im[:-5], im))
# Find good images for coadd
slis = []
for im in alis:
hdr = pyfits.getheader(im)
rms1 = hdr["ASTRRMS1"]; rms2 = hdr["ASTRRMS2"]
if (rms1 < 1.0e-4) and (rms1 > 5.0e-6) and (rms2 < 1.0e-4) and (rms2 > 5.0e-6):
slis.append(im)
# Initial (unweighted) coadd
sstr = " ".join(slis)
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Identify sources in the coadded frame
hdr = pyfits.getheader("coadd.fits")
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=20.0,
wtimage="coadd.weight.fits")
hdr = pyfits.getheader("coadd.fits")
cpsfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=cpsfdiam,
wtimage="coadd.weight.fits")
refstars1 = Starlist("coadd.fits.stars")
refstars1.pix2wcs("coadd.fits")
truemags = np.array(refstars1.mags())
maglist = []; errlist = []
# (Relative) Zeropoints for individual images
for im in slis:
hdr = pyfits.getheader(im)
iqpkg.iqobjs(im, 3.0, RATIRSAT[chip], skyval="!SKYMED",
pix=RATIRPIXSCALE[chip], gain=hdr["SOFTGAIN"], aperture=20.0)
hdr = pyfits.getheader(im)
psfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs(im, 3.0, RATIRSAT[chip], skyval="!SKYMED",
pix=RATIRPIXSCALE[chip], gain=hdr["SOFTGAIN"], aperture=psfdiam)
stars = Starlist("%s.stars" % im)
refstars1.wcs2pix(im)
a,b = stars.match(refstars1,tol=10.0,maxnum=1000)
newmags = np.zeros(len(refstars1)); newwts = np.zeros(len(refstars1))
for i in range(len(refstars1)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01),2)
continue
maglist.append(newmags); errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
# Update headers
medzp = np.median(zpts)
for i in range(len(slis)):
im = slis[i]
fimg = pyfits.open(im, mode="update")
fimg[0].header["RELZPT"] = zpts[i]
fimg[0].header["RELZPTSC"] = scatt[i]
fimg[0].header["RELZPRMS"] = rms[i]
fimg[0].header["FLXSCALE"] = 1.0 / np.power(10, (zpts[i] - medzp) / 2.5)
fimg.flush()
# Final coadd
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Calibration
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=cpsfdiam,
wtimage="coadd.weight.fits")
stars = Starlist("coadd.fits.stars")
refstars = Starlist(reflist)
refstars.wcs2pix("coadd.fits")
refstars.set_mag("%sMAG" % filt.upper())
truemags = np.array(refstars.mags())
maglist = []; errlist = []
a,b = stars.match(refstars, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars)); newwts = np.zeros(len(refstars))
for i in range(len(refstars)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01),2)
continue
maglist.append(newmags); errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
fimg = pyfits.open("coadd.fits", mode="update")
fimg[0].header["ABSZPT"] = zpts[0] + 25
fimg[0].header["ABSZPTSC"] = scatt[0]
fimg[0].header["ABZPRMS"] = rms[0]
print "Zeropoint for coadded image: %.3f" % (25.0+zpts[0])
print "Robust scatter for coadded image: %.3f" % scatt[0]
print "RMS for coadded image: %.3f" % rms[0]