def psf_star_fitting_2runs(imagebi, psfstarfile,photfilesuffix,satmag, psfcleaningradius=10.):
print '!!!!!!!!!!!!'+str(psfcleaningradius)
print "Fitting the PSF model for file: " + imagebi
iraf.daophot.verify = False
#needs to be set here because it is different for each amplifier
sky,skydev=get_sky(imagebi)
iraf.datapars.datamin=sky-5*skydev
iraf.datapars.sigma=skydev
#use a smaller radius for nstar and substar to substract the "core only" of close neighbours
original_psfrad=iraf.daopars.psfrad
iraf.daopars.psfrad = psfcleaningradius
print '!!!!!!!!!!!!'+str(psfcleaningradius)
print '!!!!!!!!!!!!'+str(iraf.daopars.psfrad)
# get inital list of psf stars (either selected by hand or let iraf select) and make a first-round psf model:
psf_star_fitting_initial(imagebi, psfstarfile,photfilesuffix,satmag)
# find neighbors of psf stars which are in the fitting radius.
iraf.nstar(imagebi,get_last_iraf(imagebi+'.psg'),'default','default','default', verbose = False)
# subtract the first-round psf model of those neighboring stars from the psf stars (so that wings of psf stars are star-free).
iraf.substar(imagebi,get_last_iraf(imagebi+'.nst'), get_last_iraf(imagebi+'.pst'), get_last_iraf(imagebi+'.psf',suffix='fits'), 'default', verbose=True, Stdout=1)
#then reset psfrad to original value
iraf.daopars.psfrad = original_psfrad
# now get the second-round psf model by fitting the neighbor-subtracted psf stars.
iraf.daophot.psf(get_last_iraf(imagebi+'.sub',suffix='fits'), imagebi+photfilesuffix,get_last_iraf(imagebi+'.pst'), get_next_iraf(imagebi+'.psf',suffix='fits'), get_next_iraf(imagebi+'.pst'), get_next_iraf(imagebi+'.psg'), interactive=False,verify=False,nclean=10)
def compute_psf_image(params,g,psf_deg=1,psf_rad=8,
star_file='phot.mags',psf_image='psf.fits',edge_dist=5):
iraf.digiphot()
iraf.daophot()
fp = params.loc_output+os.path.sep
f_im = g.image*g.mask
f = fp+'temp.ref.fits'
write_image(f_im,f)
g.fw = np.max([1.5,g.fw])
g.fw = np.min([0.5*params.psf_max_radius,g.fw])
logfile = fp+'psf.log'
fd = fits.getdata(f)
xmax = fd.shape[0] - edge_dist
ymax = fd.shape[1] - edge_dist
for d in ['temp.stars','temp.phot','temp.phot1','temp.phot2','temp.pst',
'temp.opst','temp.opst2',
'temp.psf.fits','temp.psf1.fits','temp.psf2.fits','temp.psg',
'temp.psg2','temp.psg3','temp.psg5','temp.rej','temp.rej2',
'temp.sub.fits','temp.sub1.fits',
'temp.sub2.fits','temp.opst1','temp.opst3','temp.rej3',
'temp.nst','temp.stars1','ref.mags',psf_image,'temp.als',
'temp.als2']:
if os.path.exists(fp+d):
os.remove(fp+d)
# locate stars
iraf.daofind(image=f,output=fp+'temp.stars',interactive='no',verify='no',
threshold=3,sigma=params.star_detect_sigma,fwhmpsf=g.fw,
datamin=1,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,
noise='poisson')
if params.star_file:
als_recenter = 'no'
all_template_stars = np.genfromtxt(params.star_file)
all_new_stars = np.genfromtxt(fp+'temp.stars')
if all_new_stars.shape[0] > params.star_file_number_match:
new_stars = all_new_stars[all_new_stars[:,2].argsort()][:params.star_file_number_match]
else:
new_stars = all_new_stars
if all_template_stars.shape[0] > params.star_file_number_match:
template_stars = all_template_stars[all_template_stars[:,3].argsort()][:params.star_file_number_match]
else:
template_stars = all_template_stars
tx, ty = compute_xy_shift(new_stars,template_stars[:,1:3],0.5,
degree=params.star_file_transform_degree)
if params.star_file_has_magnitudes:
star_positions = all_template_stars[:,1:4]
xx = (star_positions[:,0]-np.mean(new_stars[:,0]))/np.mean(new_stars[:,0])
yy = (star_positions[:,1]-np.mean(new_stars[:,1]))/np.mean(new_stars[:,1])
for m in range(params.star_file_transform_degree+1):
for n in range(params.star_file_transform_degree+1-m):
star_positions[:,0] += tx[m,n]* xx**m * yy**n
star_positions[:,1] += ty[m,n]* xx**m * yy**n
np.savetxt(fp+'temp.stars.1',star_positions,fmt='%10.3f %10.3f %10.3f')
else:
star_positions = all_template_stars[:,1:3]
xx = (star_positions[:,0]-np.mean(new_stars[:,0]))/np.mean(new_stars[:,0])
yy = (star_positions[:,1]-np.mean(new_stars[:,1]))/np.mean(new_stars[:,1])
for m in range(params.star_file_transform_degree+1):
for n in range(params.star_file_transform_degree+1-m):
star_positions[:,0] += tx[m,n]* xx**m * yy**n
star_positions[:,1] += ty[m,n]* xx**m * yy**n
np.savetxt(fp+'temp.stars.1',star_positions,fmt='%10.3f %10.3f')
all_template_stars[:,1] = star_positions[:,0]
all_template_stars[:,2] = star_positions[:,1]
else:
als_recenter = 'yes'
star_positions = np.genfromtxt(fp+'temp.stars')
np.savetxt(fp+'temp.stars.1',star_positions[:,:2],fmt='%10.3f %10.3f')
iraf.phot(image=f,output=fp+'temp.phot',coords=fp+'temp.stars.1',interactive='no',
verify='no',
sigma=params.star_detect_sigma,fwhmpsf=g.fw,apertures=g.fw,
datamin=1,
datamax=2*params.pixel_max,epadu=params.gain,annulus=3*g.fw,
dannulus=3.0,
readnoise=params.readnoise,noise='poisson')
print 'fw = ',g.fw
#fw = np.max([4.0,fw])
#print 'fw = ',fw
# select PSF stars
iraf.pstselect(image=f,photfile=fp+'temp.phot',pstfile=fp+'temp.pst',maxnpsf=40,
interactive='no',verify='no',datamin=1,fitrad=2.0,
datamax=params.pixel_max,epadu=params.gain,psfrad=np.max([3.0,g.fw]),
readnoise=params.readnoise,noise='poisson')
if params.star_file and params.star_file_has_magnitudes:
# We don't need to do the photometry - only make the PSF
# Initial PSF estimate to generate PSF groups
#psfrad=3*np.max([g.fw,1.8])
iraf.psf(image=f,photfile=fp+'temp.phot',pstfile=fp+'temp.pst',psfimage=fp+'temp.psf',
function=params.psf_profile_type,opstfile=fp+'temp.opst',
groupfile=fp+'temp.psg',
interactive='no',
verify='no',varorder=0 ,psfrad=2*np.max([g.fw,1.8]),
datamin=-10000,datamax=0.95*params.pixel_max,
scale=1.0)
# construct a file of the psf neighbour stars
slist = []
psf_stars = np.loadtxt(fp+'temp.opst',usecols=(0,1,2))
for star in range(psf_stars.shape[0]):
xp = psf_stars[star,1]
yp = psf_stars[star,2]
xmin = np.max([np.int(xp-10*g.fw),0])
xmax = np.min([np.int(xp+10*g.fw),f_im.shape[0]])
ymin = np.max([np.int(yp-10*g.fw),0])
ymax = np.min([np.int(yp+10*g.fw),f_im.shape[1]])
p = star_positions[np.logical_and(np.logical_and(star_positions[:,0]>xmin,
star_positions[:,0]<xmax),
np.logical_and(star_positions[:,1]>ymin,
star_positions[:,1]<ymax))]
slist.append(p)
group_stars = np.concatenate(slist)
np.savetxt(fp+'temp.nst',group_stars,fmt='%10.3f %10.3f %10.3f')
# subtract PSF star neighbours
iraf.substar(image=f,photfile=fp+'temp.nst',psfimage=fp+'temp.psf',
exfile=fp+'temp.opst',fitrad=2.0,
subimage=fp+'temp.sub1',verify='no',datamin=1,
datamax=params.pixel_max,epadu=params.gain,
readnoise=params.readnoise,noise='poisson')
# final PSF
iraf.psf(image=fp+'temp.sub1',photfile=fp+'temp.phot',pstfile=fp+'temp.opst',
psfimage=psf_image,psfrad=2*g.fw,
function=params.psf_profile_type,opstfile=fp+'temp.opst2',
groupfile=fp+'temp.psg2',
interactive='no',
verify='no',varorder=0,
datamin=1,datamax=0.95*params.pixel_max,
scale=1.0)
np.savetxt(fp+'ref.mags',all_template_stars,fmt='%7d %10.3f %10.3f %10.3f')
stars = all_template_stars
else:
# initial PSF estimate
iraf.psf(image=f,photfile=fp+'temp.phot',pstfile=fp+'temp.pst',psfimage=fp+'temp.psf',
function=params.psf_profile_type,opstfile=fp+'temp.opst',
groupfile=fp+'temp.psg1',
interactive='no',
verify='no',varorder=0 ,psfrad=2*g.fw,
datamin=1,datamax=0.95*params.pixel_max,
scale=1.0)
# separation distance of near neighbours
separation = np.max([rewrite_psg(fp+'temp.psg1',fp+'temp.psg2'),3])
print 'separation = ',separation
# subtract all stars using truncated PSF
iraf.allstar(image=f,photfile=fp+'temp.phot',psfimage=fp+'temp.psf',
allstarfile=fp+'temp.als',rejfile='',
subimage=fp+'temp.sub',verify='no',psfrad=2*g.fw,fitrad=2.0,
recenter='yes',groupsky='yes',fitsky='yes',sannulus=7,wsannulus=10,
datamin=1,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,
noise='poisson')
if params.star_file:
os.system('cp '+fp+'temp.phot '+fp+'temp.phot2')
else:
# locate new stars
iraf.daofind(image=fp+'temp.sub',output=fp+'temp.stars1',interactive='no',verify='no',
threshold=3,sigma=params.star_detect_sigma,fwhmpsf=2*g.fw,
datamin=1,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,
noise='poisson')
# magnitudes for new stars
iraf.phot(image=fp+'temp.sub',output=fp+'temp.phot1',coords=fp+'temp.stars1',
interactive='no',
verify='no',sigma=params.star_detect_sigma,
fwhmpsf=g.fw,datamin=1,
datamax=params.pixel_max,epadu=params.gain,
readnoise=params.readnoise,noise='poisson')
# join star lists together
iraf.pconcat(infiles=fp+'temp.phot,'+fp+'temp.phot1',outfile=fp+'temp.phot2')
# new PSF estimate to generate PSF groups
iraf.psf(image=f,photfile=fp+'temp.phot2',pstfile=fp+'temp.pst',psfimage=fp+'temp.psf2',
function=params.psf_profile_type,opstfile=fp+'temp.opst2',
groupfile=fp+'temp.psg3',
interactive='no',
verify='no',varorder=0 ,psfrad=2*g.fw,
datamin=-10000,datamax=0.95*params.pixel_max,
scale=1.0)
# magnitudes for PSF group stars
iraf.nstar(image=f,groupfile=fp+'temp.psg3',psfimage=fp+'temp.psf2',
nstarfile=fp+'temp.nst',
rejfile='',verify='no',psfrad=2*g.fw,fitrad=2.0,
recenter='no',
groupsky='yes',fitsky='yes',sannulus=7,wsannulus=10,
datamin=1,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,noise='poisson')
# subtract PSF star neighbours
iraf.substar(image=f,photfile=fp+'temp.nst',psfimage=fp+'temp.psf2',
exfile=fp+'temp.opst2',fitrad=2.0,
subimage=fp+'temp.sub1',verify='no',datamin=1,
datamax=params.pixel_max,epadu=params.gain,
readnoise=params.readnoise,noise='poisson')
# final PSF
iraf.psf(image=fp+'temp.sub1',photfile=fp+'temp.phot2',
pstfile=fp+'temp.opst2',
psfimage=psf_image,psfrad=2*g.fw,
function=params.psf_profile_type,opstfile=fp+'temp.opst3',
groupfile=fp+'temp.psg5',
interactive='no',
verify='no',varorder=0,
datamin=1,datamax=0.95*params.pixel_max,
scale=1.0)
# final photometry
iraf.allstar(image=g.fullname,photfile=fp+'temp.phot2',psfimage=psf_image,
allstarfile=fp+'temp.als2',rejfile='',
subimage=fp+'temp.sub2',verify='no',psfrad=2*g.fw,
recenter=als_recenter,groupsky='yes',fitsky='yes',sannulus=7,
wsannulus=10,fitrad=2.0,
datamin=params.pixel_min,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,
noise='poisson')
psfmag = 10.0
for line in open(fp+'temp.als2','r'):
sline = line.split()
if sline[1] == 'PSFMAG':
psfmag = float(sline[3])
break
if params.star_file:
iraf.psort(infiles=fp+'temp.als2',field='ID')
os.system('cp '+fp+'temp.als2 '+fp+'temp.als3')
else:
selection = 'XCE >= '+str(edge_dist)+' && XCE <= '+str(xmax)+' && YCE >= '+str(edge_dist)+' && YCE <= '+str(ymax)+' && MAG != INDEF'
iraf.pselect(infiles=fp+'temp.als2',outfiles=fp+'temp.als3',expr=selection)
iraf.psort(infiles=fp+'temp.als3',field='MAG')
iraf.prenumber(infile=fp+'temp.als3')
s = iraf.pdump(infiles=fp+'temp.als3',Stdout=1,
fields='ID,XCENTER,YCENTER,MAG,MERR,MSKY,SHARPNESS,CHI',expr='yes')
sf = [k.replace('INDEF','22.00') for k in s]
stars = np.zeros([len(sf),5])
for i, line in enumerate(sf):
stars[i,:] = np.array(map(float,sf[i].split()[1:6]))
s = iraf.pdump(infiles=fp+'temp.als3',Stdout=1,
fields='ID,XCENTER,YCENTER,MAG,MERR,SHARPNESS,CHI,MSKY',expr='yes')
sf = [k.replace('INDEF','22.00') for k in s]
with open(fp+'ref.mags','w') as fid:
for s in sf:
fid.write(s+'\n')
return stars
pst1.write(fn[:-5] + '.pst.0',
format='ascii.commented_header',
delimiter='\t',
comment='#N ID XCENTER YCENTER \
MAG MSKY\n#U ## pixels pixels magnitudes counts\n#F %-9d %-10.3f %-10.3f %-12.3f %-15.7g\n#'
)
original = sys.stdout
try:
sys.stdout = open(fn[:-5] + '.psf1.out', 'w')
iraf.psf(fn[:-5],photfile=fn[:-5]+'.mag.1',pstfile=fn[:-5]+'.pst.0',psfimage=fn[:-5]+'.psf.1',opstfile=fn[:-5]+'.pst.1', \
groupfile=fn[:-5]+'.psg.1',verify='no',interactive='no',plotfile=fn[:-5]+'.psf1.plots')
iraf.allstar(fn[:-5],photfile=fn[:-5]+'.psg.1',psfimage=fn[:-5]+'.psf.1',allstarfile=fn[:-5]+'.als.1',rejfile=fn[:-5]+ \
'.arj.1',subimage=fn[:-5]+'.sub.1',verify='no',verbos='no')
iraf.substar(fn[:-5],photfile=fn[:-5]+'.als.1',exfile=fn[:-5]+'.pst.1',psfimage=fn[:-5]+'.psf.1',subimage=fn[:-5]+ \
'.sub.11',verify='no',verbose='no')
sys.stdout = open(fn[:-5] + '.psf2.out', 'w')
iraf.psf(fn[:-5]+'.sub.11',photfile=fn[:-5]+'.mag.1',pstfile=fn[:-5]+'.pst.1',psfimage=fn[:-5]+'.psf.2', \
opstfile=fn[:-5]+'.pst.2', groupfile=fn[:-5]+'.psg.2',verify='no',interactive='no')
iraf.allstar(fn[:-5],photfile=fn[:-5]+'.psg.2',psfimage=fn[:-5]+'.psf.2',allstarfile=fn[:-5]+'.als.2',rejfile=fn[:-5]+ \
'.arj.2',subimage=fn[:-5]+'.sub.2',verify='no',verbos='no')
iraf.substar(fn[:-5],photfile=fn[:-5]+'.als.2',exfile=fn[:-5]+'.pst.2',psfimage=fn[:-5]+'.psf.2',subimage=fn[:-5]+ \
'.sub.22',verify='no',verbose='no')
sys.stdout = open(fn[:-5] + '.psf3.out', 'w')
iraf.psf(fn[:-5]+'.sub.22',photfile=fn[:-5]+'.mag.1',pstfile=fn[:-5]+'.pst.2',psfimage=fn[:-5]+'.psf.3',opstfile \
=fn[:-5]+'.pst.3', groupfile=fn[:-5]+'.psg.3',verify='no',interactive='no')
except iraf.IrafError, e:
sys.stdout = original
print fn
def psfphot(inlist,
ra,
dec,
reffilt,
interact,
fwhm,
readnoise,
gain,
threshold,
refimage=None,
starfile=None,
maxnpsf=5,
clobber=globclob,
verbose=globver,
skykey='SKYBKG',
filtkey='FILTER',
pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult = 5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall = 3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles = iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage == None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot = 's' + refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0, refimage)
#setup for keywords
if gain == "!GAIN":
try:
gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try:
readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image, "r")
# Grab image root name
root = image.split('.')[0]
# Map image to reference image
if not (image == refimage):
[nx, ny] = get_head(image, ['NAXIS1', 'NAXIS2'])
stars = Starlist(get_head(image, 'STARFILE'))
refstars = Starlist(get_head(refimage, 'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match, refmatch = stars.match(refstars, useflags=yes, tol=10.0)
nstars = len(match)
if not (nstars > 2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile = open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval, refmatch[i].yval,
match[i].xval, match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,
'%s.geodb' % root,
1.0,
nx,
1.0,
ny,
verbose=no,
interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,
's%s' % root,
'%s.geodb' % root,
'%s.match' % root,
geometry="geometric",
boundary="constant",
verbose=no)
else:
iraf.imcopy(image, 's%s' % root)
root = 's%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 0.90 * saturate
iraf.datapars.readnoise = readval
iraf.datapars.epadu = gainval
iraf.datapars.filter = filtkey
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image == refimage and starfile == None:
iraf.daophot.daofind(root,
'refimage.coo.1',
threshold=threshold,
verify=no,
verbose=verbose)
elif image == refimage:
shutil.copy(starfile, 'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,
'default',
refstarsfile,
maxnpsf,
interactive=yes,
verify=no,
verbose=verbose)
#fit the psf
iraf.psf(root,
'default',
refstarsfile,
'default',
'default',
'default',
interactive=interact,
verify=no,
verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root + ".psg.1"
iraf.nstar(root,
groupingfile,
'default',
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,
'default',
refstarsfile,
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root + ".sub.1"
iraf.psf(subtractedimage,
root + ".nst.1",
refstarsfile,
'%s.psf.2' % root,
'%s.pst.2' % root,
'%s.psg.2' % root,
interactive=interact,
verify=no,
verbose=verbose)
#Need to make sure SN was detected by daofind
stars = Starlist('%s.mag.1' % root)
SN = Star(name='SN', radeg=ra, dcdeg=dec, fwhm=2.0, fwhmw=2.0)
SNlis = Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0]) == 0):
#No match - need to add to daofind file
print "No match!"
coofile = open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' %
(SNlis[0].xval, SNlis[0].yval, 99.999, 0.500, 0.000,
0.000, 999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=psfmult * fwhm,
verify=no,
verbose=verbose)
# allstar run
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
def psfphot(inlist, ra, dec, reffilt, interact, fwhm, readnoise, gain,
threshold,refimage=None,starfile=None,maxnpsf=5,
clobber=globclob,verbose=globver,skykey='SKYBKG',
filtkey='FILTER',pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult=5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall=3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles=iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage==None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot='s'+refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0,refimage)
#setup for keywords
if gain == "!GAIN":
try: gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try: readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image,"r")
# Grab image root name
root=image.split('.')[0]
# Map image to reference image
if not (image==refimage):
[nx,ny]=get_head(image,['NAXIS1','NAXIS2'])
stars=Starlist(get_head(image,'STARFILE'))
refstars=Starlist(get_head(refimage,'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match,refmatch=stars.match(refstars,useflags=yes,tol=10.0)
nstars=len(match)
if not (nstars>2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile=open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval,refmatch[i].yval,
match[i].xval,match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,'%s.geodb' % root,1.0,nx,1.0,ny,
verbose=no,interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,'s%s' % root,'%s.geodb' % root,
'%s.match' % root,geometry="geometric",
boundary="constant",verbose=no)
else:
iraf.imcopy(image,'s%s' % root)
root='s%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf=fwhm
iraf.datapars.sigma=iraf.iterstat.sigma
iraf.datapars.datamin=iraf.iterstat.median-10*iraf.iterstat.sigma
iraf.datapars.datamax=0.90*saturate
iraf.datapars.readnoise=readval
iraf.datapars.epadu=gainval
iraf.datapars.filter=filtkey
iraf.daopars.psfrad=psfmult*fwhm
iraf.daopars.fitrad=fwhm
iraf.daopars.function="gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image==refimage and starfile==None:
iraf.daophot.daofind(root,'refimage.coo.1',threshold=threshold,verify=no,
verbose=verbose)
elif image==refimage:
shutil.copy(starfile,'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=fwhm,verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,'default',refstarsfile,maxnpsf,
interactive=yes,verify=no,verbose=verbose)
#fit the psf
iraf.psf(root,'default',refstarsfile,'default','default','default',
interactive=interact,verify=no,verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root+".psg.1"
iraf.nstar(root,groupingfile,'default','default','default',
psfrad= psfmultsmall * fwhm,verify=no,verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,'default',refstarsfile,'default','default',
psfrad=psfmultsmall*fwhm,verify=no,verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root+".sub.1"
iraf.psf(subtractedimage,root+".nst.1",refstarsfile,'%s.psf.2' % root,
'%s.pst.2' % root,'%s.psg.2' % root,interactive=interact,
verify=no,verbose=verbose)
#Need to make sure SN was detected by daofind
stars=Starlist('%s.mag.1' % root)
SN=Star(name='SN',radeg=ra,dcdeg=dec,fwhm=2.0,fwhmw=2.0)
SNlis=Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0])==0):
#No match - need to add to daofind file
print "No match!"
coofile=open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' % (SNlis[0].xval, SNlis[0].yval,99.999,0.500,0.000,0.000,999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=psfmult*fwhm,
verify=no,verbose=verbose)
# allstar run
iraf.allstar(root,'default','default','default','default','default',
verify=no,verbose=verbose)
