def subtract_stars(args):
""" Routine to subtract a set of stars defined by the user. We will model the PSF of some suitable stars in the
images, then use that """
# First substitute the names of the image in args.image by the corresponding astroim object, with more information
args.image = astroim.Astroim(args.image)
# Do photometry on the stars to be used to model the PSF
phot_modstars, phot_subtstars = apply_phot(args)
pst_file = apply_pst(args, phot_modstars)
psf_file = apply_psf(args, phot_modstars, pst_file)
hdr = args.image.header # for short
seeing, sigma = utils.get_from_header(args.image.im_name, hdr.seeingk, hdr.sigmak)
utils.if_exists_remove(args.output)
iraf.allstar(
args.image.im_name,
photfile=phot_subtstars,
psfimage=psf_file,
subimage=args.output,
psfrad=6 * seeing,
fitrad=3 * seeing,
wcsin="physical",
fitsky="yes",
sannulus=4 * seeing,
wsannulus=3 * seeing,
verify="no",
)
def daofind(filelist, psfimage):
iraf.datapars.setParam("exposure", "EXPTIME")
iraf.datapars.setParam("airmass", "AIRMASS")
iraf.datapars.setParam("filter", "INSFILTE")
iraf.datapars.setParam("ccdread", "RDNOISE")
iraf.datapars.setParam("gain", "GAIN")
iraf.daopars.setParam("fitrad", "5.853")
iraf.daopars.setParam("psfrad", "18.56")
with open(filelist) as file1:
for line in file1:
filename = line.strip()
y,z = np.loadtxt(filename + "-daoedit", usecols=[3,4], unpack=True)
sigma = np.mean(y)
fwhm = np.mean(z)
iraf.datapars.setParam("fwhmpsf", "%s" % str(fwhm) )
iraf.datapars.setParam("sigma", "%s" % str(sigma) )
iraf.allstar.setParam("image","%s" % filename )
iraf.allstar.setParam("photfile","%s.mag" % filename )
iraf.allstar.setParam("psfimage",psfimage )
iraf.allstar.setParam("allstarfile","%s.als"%filename )
iraf.allstar.setParam("rejfile","%s.arj"%filename )
iraf.allstar.setParam("subimage","%s.sub" %filename )
iraf.allstar(mode="h")
def run_allstar(imageFile, psfimage):
iraf.allstar.unlearn()
iraf.allstar(image=imageFile,\
photfile=imageFile+".mags.1",\
psfimage=psfimage, \
allstarfile=imageFile+".als.1",\
rejfile=imageFile+".arj.1",\
subimage=imageFile+".sub.1")
def run_allstar(imageFile, psfimage):
iraf.allstar.unlearn()
iraf.allstar(image=imageFile,\
photfile=imageFile+".mags.1",\
psfimage=psfimage, \
allstarfile=imageFile+".als.1",\
rejfile=imageFile+".arj.1",\
subimage=imageFile+".sub.1")
def simplepsfphot(image,coords,psf,refstar,centre=True,vary=False):
"""PSF photometry, with a given PSF file in psf used for every image"""
iraf.dele('temp.mag*')
iraf.dele('temp.als')
iraf.dele('temp.sub.fits')
if centre:
xsh,ysh = recentre(image,refstar)
print "Fine Centring: ", xsh,ysh
else: xsh,ysh = 0,0
if vary:
setaperture(image,refstar)
shift_file_coords(coords,xsh,ysh,'tempcoords2',sort='als')
iraf.phot(image,'tempcoords2','temp.mag2',inter="no",calgorithm='none',
mode='h',verify='no',update='no',verbose='no')
iraf.allstar(image,'temp.mag2',psf,'temp.als','temp.mag.arj','temp.sub.fits',
mode='h',verify='no',update='no',verbose='no')
out = iraf.pdump('temp.als','id,mag,merr,msky','yes',Stdout=1)
return out
def psfphot(image,coords,pststars,refstar,centre=True,vary=False):
"""PSF photometry. Centering is through phot on refstar.
Assume coords is a .als file for now. Recentering is always done
for the reference star, never for the targets."""
iraf.dele('temp.mag*')
iraf.dele('temp.psf.fits')
iraf.dele('temp.als')
if centre:
xsh,ysh = recentre(image,refstar)
print "Fine Centring: ", xsh,ysh
else: xsh,ysh = 0,0
if vary:
setaperture(image,refstar)
shift_file_coords(coords,xsh,ysh,'tempcoords2',sort='als')
shift_file_coords(pststars,xsh,ysh,'temppst2',sort='pst')
iraf.phot(image,'tempcoords2','temp.mag2',inter="no",calgorithm='none',
mode='h',verify='no',update='no',verbose='no')
iraf.psf(image,'temp.mag2','temppst2','temp.psf','temp.mag.pst','temp.mag.psg',
inter='no',mode='h',verify='no',update='no',verbose='no')
iraf.allstar(image,'temp.mag2','temp.psf','temp.als','temp.mag.arj',"default",
mode='h',verify='no',update='no',verbose='no')
out = iraf.pdump('temp.als','id,mag,merr,msky','yes',Stdout=1)
return out
def fitsn(img,
imgpsf,
coordlist,
_recenter,
fwhm0,
original,
sn,
residual,
_show,
_interactive,
z11='',
z22='',
midpt='',
size=7,
apco0=0,
dmax=51000,
dmin=-500):
import lsc
lsc.util.delete("apori")
lsc.util.delete(img + ".sn.mag")
#################################
from pyraf import iraf
import string
iraf.imcoords(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
a1 = int(fwhm0)
a2 = int(2. * fwhm0 + .5)
a3 = int(3. * fwhm0 + .5)
a4 = int(4. * fwhm0 + .5)
ap = str(a1) + "," + str(a2) + "," + str(a3)
########################################
if _recenter: answ = 'yes'
else: answ = 'no'
#########################################
hdr = lsc.util.readhdr(img + '.fits')
_gain = lsc.util.readkey3(hdr, 'gain')
_ron = lsc.util.readkey3(hdr, 'ron')
_exptime = lsc.util.readkey3(hdr, 'exptime')
_datamin = dmin
_datamax = dmax
iraf.noao.digiphot.daophot.photpars.zmag = 0
iraf.noao.digiphot.daophot.datapars.readnoi = _gain #1.4 #_ron
iraf.noao.digiphot.daophot.datapars.epadu = _ron # 13 #_gain
iraf.noao.digiphot.daophot.datapars.datamin = _datamin # -100 #_datamin
iraf.noao.digiphot.daophot.datapars.datamax = _datamax
iraf.noao.daophot.fitskypars.annulus = a3
iraf.noao.daophot.photpars.apertures = ap
iraf.noao.digiphot.daophot.datapars.exposure = 'exptime'
iraf.noao.digiphot.daophot.datapars.airmass = 'airmass'
iraf.noao.digiphot.daophot.datapars.filter = 'filter2'
iraf.noao.digiphot.daophot.daopars.psfrad = a4
# modify fitrad to 3 fwhm to see if works better
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0 #* 3
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
# iraf.noao.digiphot.daophot.centerpars.cbox = 0
iraf.noao.digiphot.daophot.centerpars.cbox = 4
iraf.noao.digiphot.daophot.centerpars.calgori = 'gauss'
# fitskypars.salgorithm = "constant"
# fitskypars.skyvalue = 0
print '\n### recentering: ' + str(answ)
if _show:
iraf.noao.digiphot.daophot.phot(original,
coordlist,
"apori",
veri='no')
iraf.noao.digiphot.daophot.phot(sn,
coordlist,
img + ".sn.mag",
veri='no')
else:
iraf.noao.digiphot.daophot.phot(original,
coordlist,
"apori",
veri='no',
verb='no')
iraf.noao.digiphot.daophot.phot(sn,
coordlist,
img + ".sn.mag",
veri='no',
verb='no')
lsc.util.delete(img + ".sn.als")
print sn, imgpsf, img
iraf.allstar(sn,
img + ".sn.mag",
imgpsf,
img + ".sn.als",
"",
residual,
veri='no',
verb='no')
lsc.util.delete("snfit.fits")
iraf.imarith(sn + '.fits', "-", residual + '.fits', "snfit.fits")
lsc.util.delete("skyfit.fits")
iraf.imarith(original + '.fits', "-", "snfit.fits", "skyfit.fits")
iraf.txsort(img + ".sn.als", "ID")
tmptbl = iraf.txdump(img + ".sn.als",
"mag,merr,xcenter,ycenter",
expr='yes',
Stdout=1)
magerr, fitmag, centx, centy = [], [], [], []
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0])) #-2.5*log10(_exptime))
except:
fitmag.append(string.split(i)[0])
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append(string.split(i)[1])
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl = iraf.txdump("apori", "mag", expr='yes', Stdout=1)
apori1, apori2, apori3 = [], [], []
for i in tmptbl:
try:
apori1.append(float(string.split(i)[0])) #-2.5*log10(_exptime))
except:
apori1.append(string.split(i)[0])
try:
apori2.append(float(string.split(i)[1])) #-2.5*log10(_exptime))
except:
apori2.append(string.split(i)[1])
try:
apori3.append(float(string.split(i)[2])) #-2.5*log10(_exptime))
except:
apori3.append(string.split(i)[2])
iraf.txsort(img + ".sn.mag", "YCENTER")
tmptbl = iraf.txdump(img + ".sn.mag", "mag,magerr", expr='yes', Stdout=1)
if _show:
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ", a1, " ", a2, " ", a3, " ", a1, " ", a2, " ", a3
apmag1, apmag2, apmag3, truemag = [], [], [], []
for i in range(len(tmptbl)):
try:
apmag1.append(float(string.split(
tmptbl[i])[0])) #-2.5*log10(_exptime))
except:
apmag1.append(9999)
try:
apmag2.append(float(string.split(
tmptbl[i])[1])) #-2.5*log10(_exptime))
except:
apmag2.append(9999)
try:
apmag3.append(float(string.split(
tmptbl[i])[2])) #-2.5*log10(_exptime))
except:
apmag3.append(9999)
try:
truemag.append(fitmag[i] + float(apco0))
except:
truemag.append('INDEF')
if _show:
print i, apori1[i], apori2[i], apori3[i], apmag1[i], apmag2[
i], apmag3[i], fitmag[i], truemag[i], magerr[i]
if _show:
print "********************************************************************"
if _show:
print midpt, z11, z22
_tmp1, _tmp2, goon = lsc.util.display_image(original + '.fits',
1,
z11,
z22,
False,
_xcen=.25,
_ycen=.25,
_xsize=.3,
_ysize=.3)
z01 = float(z11) - float(midpt)
z02 = float(z22) - float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('snfit.fits',
1,
z01,
z02,
False,
_xcen=.25,
_ycen=.75,
_xsize=.3,
_ysize=.3,
_erase='no')
lsc.util.delete("tmptbl")
tmptbl0 = iraf.txdump(img + ".sn.als",
"xcen,ycen",
expr='yes',
Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
lra = int((2 * float(size) * float(fwhm0)) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -1 * int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('skyfit.fits',
1,
z11,
z22,
False,
_xcen=.75,
_ycen=.25,
_xsize=.3,
_ysize=.3,
_erase='no')
s1 = 1
s2 = -1 * int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy
def performPhotometry(task, logger):
#iraf.prcacheOff()
[iraf.unlearn(t) for t in ('phot','pstselect','psf','allstar')]
iraf.set(imtype="fits,noinherit") # set image output format
iraf.set(clobber="yes")
hdu=pyfits.open(task['images'])[0]
hdr = hdu.header
imdata = hdu.data
for key,value in task['fits'].iteritems():
task[key] = hdr.get(value,1)
#Sextractor to find stars; add an object for the force detect
logger.info('Running SExtractor on [%s]' % os.path.basename(task['images']))
sex = sextractor.SExtractor()
makeSexConfig(sex,task)
sex.run(task['images'])
catalog = sex.catalog()
#Set up image parameters
MIN_X = max(1,int(task['numpixx']*task['filtfactor']))
MIN_Y = max(1,int(task['numpixy']*task['filtfactor']))
MAX_X = int(task['numpixx']*(1-task['filtfactor']))
MAX_Y = int(task['numpixy']*(1-task['filtfactor']))
AREAXY = '[%s:%s,%s:%s]' % (MIN_X, MAX_X, MIN_Y, MAX_Y)
AREANO = '[%s:%s,%s:%s]' % (MIN_X, MAX_X-2*MIN_X, MIN_Y, MAX_Y-2*MIN_Y)
try:
task['pixscale'] = abs(hdr.get('CD1_1'))*3600.
except TypeError:
task['pixscale'] = abs(hdr.get('CDELT1'))*3600.
task['seeing'] = np.median( sorted([i['FWHM_IMAGE'] for i in catalog])[:int(-len(catalog)*0.5)] ) #Take the median of the "bottom" 50% of objects
logger.info('--> %s SExtractor detected bright objects in the field' % (len(catalog),) )
logger.info('--> %0.2f median FWHM of bright objects in the field, in arcsec' % (task['seeing']*task['pixscale'],))
task['objects'] = [(i['ALPHA_J2000'],i['DELTA_J2000']) for i in catalog]
task['objects'].append(task['objwcs'])
task['objectlist'] = open(os.path.join(task['output_directory'],'objectlist'),'w')
task['objectlist'].write('\n'.join([' %s %s' % (i[0],i[1]) for i in task['objects']]))
task['objectlist'].close()
logger.info('Running iraf.imstat')
irafoutput = iraf.imstat(images=task['images']+AREANO,fields='midpt,min,max,stddev', format=0, Stdout=1)
task['nimgs'] = hdr.get('NIMGS',1)
task['gain'] *= task['nimgs']*2/3.
task['ron'] *= np.sqrt(task['nimgs'])/task['nimgs']*constants.INTERPSM[task['band']]
task['datamean'], task['datamin'], task['datamax'], task['datastdev'] = map(float, irafoutput[0].split())
irafoutput = iraf.imstat(images=task['images'],fields='stddev,midpt',nclip=25,format=0,cache='yes',Stdout=1)
task['skynoise'], task['datamean'] = map(float, irafoutput[0].split() )
task['skynoise'] *= constants.INTERPSM[task['band']]
task['airmass'] = hdr.get('AIRMASS',1)
task['zmag'] -= (float(task['airmass'])-1.0)*constants.extinction_coefficients[task['band']]
task['match_proximity'] = 2.5 * task['seeing']
logger.info('--> %5.2f counts: Sky noise, corrected for drizzle imcombine' % task['skynoise'])
logger.info('--> %5.2f Median count value, after background subtraction' % task['datamean'])
logger.info('--> %5.2f Airmass' % task['airmass'])
#prepare temp files that iraf will use
for filename in ('photfile','pstfile','psfimg','opstfile','groupfile','allstarfile','rejfile','subimage'):
task[filename] = open(os.path.join(task['output_directory'],filename),'w')
task[filename].close()
#iraf.phot to get APP magnitudes
logger.info('Running iraf.apphot.phot')
#apsizes = [i*task['faperture']*task['seeing'] for i in (0.4,0.5,0.6,0.8,1.0,1.2,1.5,2.0,2.5,3.0)]
#irafapsizes = ','.join(['%.2f' % i for i in apsizes])
irafapsizes = '%0.2f' % (task['faperture']*task['seeing'])
kwargs = dict(image=task['images'],coords=task['objectlist'].name,
output=task['photfile'].name,
interac='no',scale=1,
fwhmpsf=task['seeing'],
wcsin='world', wcsout='physical',
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
aperture= irafapsizes,
zmag=task['zmag'],
annulus=task['fannulus']*task['seeing'],
dannulus=task['fdannulus']*task['seeing'],
calgorithm='gauss',
cbox = 1.5*task['seeing'],
maxshift=2.0*task['seeing'],
mode="h",Stdout=1,verify=0)
iraf.phot(**kwargs)
if task['band'] not in constants.infrared:
#iraf.pstselect to choose objects for PSF modelling
logger.info('Running iraf.daophot.pstselect')
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,pstfile=task['pstfile'].name,
maxnpsf=task['pstnumber'],
wcsin='physical',
wcsout='physical',
interac="no",verify='no',scale=1,
fwhmpsf=task['seeing'],
datamin=0,
datamax=task['datamax'],
psfrad=3.0*task['seeing'],
fitrad=1.0*task['seeing'],
recente='yes',
nclean=task['nclean'],
mode="h",Stdout=1)
iraf.pstselect(**kwargs)
#iraf.psf to model PSF
logger.info('Running iraf.daophot.psf')
kwargs = dict( image=task['images'],
photfile=task['photfile'].name,
pstfile=task['pstfile'].name,
psfimage=task['psfimg'].name,
opstfile=task['opstfile'].name,
groupfile=task['groupfile'].name,
wcsin='physical',wcsout='physical',
interac="no",verify="no",scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
saturat='no',
psfrad=3.0*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
nclean=task['nclean'],
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.psf(**kwargs)
logger.info('Running iraf.daophot.allstar')
#iraf.allstars to compute PSF photometry; recenter with recenter='yes', mergerad=<value> to avoid duplicate detection
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,
wcsin='physical',
wcsout='physical',
psfimage=task['psfimg'].name,
allstarf=task['allstarfile'].name,
rejfile=task['rejfile'].name,
subimage=task['subimage'].name,
verbose=1,verify='no',scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
psfrad=3.*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
recenter='yes',
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.allstar(**kwargs)
#Parse both photometry, convert to RA,DEC,MAG,MAGERR
logger.info('iraf tasks complete. Parsing results and calibrating')
photometry = {}
photometry['APP'] = iraf.txdump(textfiles=task['photfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
if task['band'] not in constants.infrared:
photometry['PSF'] = iraf.txdump(textfiles=task['allstarfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
for phototype in photometry:
kwargs = dict(input='STDIN',
output='STDOUT',
insystem='%s physical' % task['images'],
outsystem='%s world' % task['images'],
ilatuni='physical',
ilnguni='physical',
olnguni='degrees',
olatuni='degrees',
ilngfor='%10.7f',
ilatfor='%10.7f',
olngfor='%10.5f',
olatfor='%10.5f',
Stdin=photometry[phototype],Stdout=1)
photometry[phototype] = [i.split() for i in iraf.skyctran(**kwargs) if i and not i.startswith('#') and 'INDEF' not in i]
photometry[phototype] = [map(float,(i[4],i[5],i[2],i[3])) for i in photometry[phototype] ] #Now we have [(ra,dec,'mag','mageerr'),...]
results = calibrate((task['objwcs'][0],task['objwcs'][1]),task,photometry,logger)
# if 'PSF' not in results:
return results
def psfphot(image,
coofile,
ot,
wtimage="",
varorder=1,
clobber=globclob,
verbose=globver,
pixtol=3.0,
maxnpsf=25):
""" 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()
iraf.digiphot()
iraf.daophot()
# Detect stars
iqobjs("%s.sub.fits" % image[:-5],
1.5,
12000.0,
wtimage=wtimage,
skyval="0.0")
root = image[:-5]
[gain, rnoise, fwhm] = get_head(image, ["GAIN", "READN", "SEEPIX"])
fwhm = float(fwhm)
rnoise = float(rnoise)
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 = 0.50 * saturate
iraf.datapars.readnoise = rnoise
iraf.datapars.epadu = gain
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
iraf.daopars.varorder = varorder
# Reference stars file
stars = Starlist(coofile)
stars.wcs2pix(image)
outf = open("%s.coo.1" % image[:-5], "w")
for star in stars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
#Aperture photometry
iraf.daophot.phot(root,
'default',
'default',
apertures=fwhm,
verify=no,
interac=no,
verbose=verbose)
iraf.datapars.datamax = 0.50 * saturate
iraf.pstselect(root,
'default',
'default',
maxnpsf,
interactive=no,
verify=no,
verbose=verbose)
iraf.psf(root,
'default',
'default',
'default',
'default',
'default',
interactive=no,
showplots=no,
verify=no,
verbose=verbose)
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
# Prep for subtracted image
iraf.iterstat("%s.sub.fits" % root)
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = saturate
# Look for source at OT location
substars = Starlist("%s.sub.fits.stars" % image[:-5])
otstars = Starlist(ot)
otstars.wcs2pix("%s.sub.fits" % image[:-5])
smatch, omatch = substars.match(otstars, tol=pixtol, useflags=no)
# Generate coo file
otcoo = open("%s.sub.coo.1" % image[:-5], "w")
if len(smatch) == 0:
otcoo.write("%10.3f%10.3f\n" % (otstars[0].xval, otstars[0].yval))
else:
otcoo.write("%10.3f%10.3f\n" % (smatch[0].xval, smatch[0].yval))
otcoo.close()
iraf.daophot.phot("%s.sub.fits" % root,
"%s.sub.coo.1" % image[:-5],
'default',
'default',
apertures=fwhm,
calgorithm="none",
interac=no,
verify=no,
verbose=verbose)
if len(smatch) == 0:
print "No match in subtracted image: %s.sub.fits" % root
else:
iraf.allstar("%s.sub.fits" % root,
'default',
"%s.psf.1.fits" % root,
'default',
'default',
'default',
verify=no,
verbose=no)
return
def fitsn(img,imgpsf,coordlist,_recenter,fwhm0,original,sn,residual,_show,_interactive,dmax,dmin,z11='',z22='',midpt='',size=7,apco0=0):
import lsc
lsc.util.delete("apori")
lsc.util.delete(img+".sn.mag")
#################################
from pyraf import iraf
import string
iraf.imcoords(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
a1 = int(fwhm0)
a2 = int(2.*fwhm0+.5)
a3 = int(3.*fwhm0+.5)
a4 = int(4.*fwhm0+.5)
ap = str(a1)+","+str(a2)+","+str(a3)
########################################
if _recenter: answ='yes'
else: answ='no'
#########################################
hdr=lsc.util.readhdr(img+'.fits')
_gain=lsc.util.readkey3(hdr,'gain')
_ron=lsc.util.readkey3(hdr,'ron')
_exptime=lsc.util.readkey3(hdr,'exptime')
iraf.noao.digiphot.daophot.photpars.zmag = 0
iraf.noao.digiphot.daophot.datapars.readnoi = _gain
iraf.noao.digiphot.daophot.datapars.epadu = _ron
iraf.noao.digiphot.daophot.datapars.datamin = dmin
iraf.noao.digiphot.daophot.datapars.datamax = dmax
iraf.noao.daophot.fitskypars.annulus=a3
iraf.noao.daophot.photpars.apertures = ap
iraf.noao.digiphot.daophot.datapars.exposure = 'exptime'
iraf.noao.digiphot.daophot.datapars.airmass = 'airmass'
iraf.noao.digiphot.daophot.datapars.filter = 'filter2'
iraf.noao.digiphot.daophot.daopars.psfrad = a4
# modify fitrad to 3 fwhm to see if works better
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0 #* 3
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
# iraf.noao.digiphot.daophot.centerpars.cbox = 0
iraf.noao.digiphot.daophot.centerpars.cbox = 4
iraf.noao.digiphot.daophot.centerpars.calgori = 'gauss'
# fitskypars.salgorithm = "constant"
# fitskypars.skyvalue = 0
print '\n### recentering: '+str(answ)
if _show:
iraf.noao.digiphot.daophot.phot(original,coordlist,"apori",veri='no')
iraf.noao.digiphot.daophot.phot(sn,coordlist,img+".sn.mag",veri='no')
else:
iraf.noao.digiphot.daophot.phot(original,coordlist,"apori",veri='no',verb='no')
iraf.noao.digiphot.daophot.phot(sn,coordlist,img+".sn.mag",veri='no',verb='no')
lsc.util.delete(img+".sn.als")
print sn,imgpsf,img
iraf.allstar(sn,img+".sn.mag",imgpsf,img+".sn.als","",residual,veri='no',verb='no')
lsc.util.delete("snfit.fits")
iraf.imarith(sn+'.fits',"-",residual+'.fits',"snfit.fits")
lsc.util.delete("skyfit.fits")
iraf.imarith(original+'.fits',"-","snfit.fits","skyfit.fits")
iraf.txsort(img+".sn.als","ID")
tmptbl = iraf.txdump(img+".sn.als","mag,merr,xcenter,ycenter",expr='yes', Stdout=1)
magerr,fitmag,centx,centy=[],[],[],[]
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0]))#-2.5*log10(_exptime))
except:
fitmag.append(string.split(i)[0])
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append(string.split(i)[1])
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl=iraf.txdump("apori","mag",expr='yes', Stdout=1)
apori1,apori2,apori3=[],[],[]
for i in tmptbl:
try:
apori1.append(float(string.split(i)[0]))#-2.5*log10(_exptime))
except:
apori1.append(string.split(i)[0])
try:
apori2.append(float(string.split(i)[1]))#-2.5*log10(_exptime))
except:
apori2.append(string.split(i)[1])
try:
apori3.append(float(string.split(i)[2]))#-2.5*log10(_exptime))
except:
apori3.append(string.split(i)[2])
iraf.txsort(img+".sn.mag","YCENTER")
tmptbl=iraf.txdump(img+".sn.mag","mag,magerr",expr='yes', Stdout=1)
if _show:
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ",a1," ",a2," ",a3," ",a1," ",a2," ",a3
apmag1,apmag2,apmag3,truemag=[],[],[],[]
for i in range(len(tmptbl)):
try:
apmag1.append(float(string.split(tmptbl[i])[0]))#-2.5*log10(_exptime))
except:
apmag1.append(9999)
try:
apmag2.append(float(string.split(tmptbl[i])[1]))#-2.5*log10(_exptime))
except:
apmag2.append(9999)
try:
apmag3.append(float(string.split(tmptbl[i])[2]))#-2.5*log10(_exptime))
except:
apmag3.append(9999)
try:
truemag.append(fitmag[i]+float(apco0))
except:
truemag.append('INDEF')
if _show:
print i,apori1[i],apori2[i],apori3[i],apmag1[i],apmag2[i],apmag3[i],fitmag[i],truemag[i],magerr[i]
if _show:
print "********************************************************************"
if _show:
print midpt,z11,z22
_tmp1,_tmp2,goon=lsc.util.display_image(original+'.fits',1, z11, z22, False, _xcen=.25, _ycen=.25, _xsize=.3, _ysize=.3)
z01 = float(z11)-float(midpt)
z02 = float(z22)-float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" ORIGINAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('snfit.fits',1, z01, z02, False, _xcen=.25, _ycen=.75, _xsize=.3, _ysize=.3, _erase='no')
lsc.util.delete("tmptbl")
tmptbl0=iraf.txdump(img+".sn.als","xcen,ycen",expr='yes',Stdout=1)
ff=open('tmptbl','w')
for i in tmptbl0:
ff.write(i+'\n')
ff.close()
lra = int((2*float(size)*float(fwhm0))*2)
iraf.tvmark(1,"tmptbl",autol='no',mark="circle", number='yes',nyoffset=lra,radi=a2,txsize=2,inter='no')
s1 = 1
s2 = -1*int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" FITTED")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('skyfit.fits',1, z11, z22, False, _xcen=.75, _ycen=.25, _xsize=.3, _ysize=.3, _erase='no')
s1 = 1
s2 = -1*int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" RESIDUAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
return apori1,apori2,apori3,apmag1,apmag2,apmag3,fitmag,truemag,magerr,centx,centy
print "...merging phot lists"
print "========================================="
iraf.pfmerge(inphotfi=str(flnm + '.sub.' + str(i - 1) + '.als' + ',' +
runnm + '.mag'),
outphotf=runnm + '.tot.mag')
iraf.prenumber(runnm + '.tot.mag')
print "========================================="
print "...running allstar for", runnm + '.fits'
print "========================================="
iraf.daopars.setParam('recenter', 'yes')
tas = time.time()
iraf.allstar(image=initimg,
photfile=runnm + '.tot.mag',
psfimage=flnm + '.psf.fits',
allstarf=runnm + '.als',
rejfile=runnm + '.arj',
subimage=flnm + '.sub.' + str(i + 1) + '.fits',
cache='no',
verify='no') # ALLSTAR on original sub.0
tae = time.time()
times[runnm + '_allstar'] = tae - tas
# removing the merged phot file (not needed for analysis)
os.remove(runnm + '.tot.mag')
print "\n>>> END OF DAOLOOP #" + str(i)
# Creating the total list of positions for all stars (after running allstar) for input in other routines (e.g. Ha photometry)
transform(str(runnm) + ".als", str(flnm) + ".final.list.coo")
# Printing parameters to check
pstfile.write("#U ## pixels pixels magnitudes counts \\\n")
pstfile.write("#F %-9d %-10.3f %-10.3f %-12.3f %-15.7g \n")
pstfile.write("#\n")
np.savetxt(pstfile,g.take([-1,0,1,2,3],axis=1),fmt=['%-9d','%-10.3f','%-10.3f','%-12.3f','%-15.7g'])
pstfile.close()
iraf.daopars.setParam('matchra',fwhm)
iraf.daopars.setParam('psfrad',4*fwhm+1)
iraf.daopars.setParam('fitrad',fwhm)
iraf.daopars.setParam('sannulu',2*fwhm)
iraf.daopars.setParam('wsannul',4*fwhm)
iraf.psf.setParam('image',FitsFileName)
iraf.psf(mode='h')
iraf.seepsf(psfimage=FitsFileName+'.psf.1.fits',image=base+'psfim'+ext+'.fits',magnitu='18.0')
iraf.allstar.setParam('image',FitsFileName)
iraf.allstar(mode='h',verbose='no')
if os.path.exists(base+'allstartmp') == True: os.remove(base+'allstartmp')
iraf.txdump(textfile=FitsFileName+'.als.1',fields='xcenter,ycenter,mag,merr,id',expr='mag\
!= INDEF && merr != INDEF', Stdout=base+'allstartmp')
outmags = np.loadtxt(base+'allstartmp')
if os.path.exists(base+'allstartmp') == True: os.remove(base+'allstartmp')
if int(ext) <= 4:
shifty = 0.0
shiftx = (float(ext) - 1.)*sx
else:
shifty = sy
shiftx = (float(ext) - 5.)*sx
outmags[:,0] = outmags[:,0] + shiftx
def errore(size, truemag, fwhm0, leng0):
from pyraf import iraf
import string, os, sys
dartf = 100
while dartf >= size - 1:
artfac0 = raw_input(
'>>> Dispersion of artificial star positions (in units of FWHM) [1] '
)
if not artfac0: artfac0 = 1
try:
artfac0 = float(artfac0)
if float(artfac0) >= size - 1:
print '!!! WARNING: ' + str(
artfac0) + ' too large (max ' + str(size) + '- 1)'
print 'try again....'
else:
dartf = artfac0
except:
print '#### WARNING: ' + str(artfac0) + ' should be a number !!!!'
print 'try again....'
iraf.delete("tmpar?", ve='no')
i = 0
tmpart = []
while i <= 8:
iraf.delete(
"reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?,artskyfit.fit?",
ve='no')
artrad = fwhm0 / 2.
#artseed = artseed+1234
artx = int(i / 3.) - 1
if i <= 2: arty = artx + i
if 3 <= i <= 5: arty = artx - 1 + i - 3
if i >= 6: arty = artx - 2 + i - 6
ff = open(img + ".sn.coo", 'r')
ss = ff.readline()
ff.close()
xbb = float(string.split(ss)[0])
ybb = float(string.split(ss)[1])
xbb = xbb + artx * fwhm0 * artfac0
ybb = ybb + arty * fwhm0 * artfac0
print xbb, ybb
iraf.delete("artlist.coo", ve='no')
ff = open("artlist.coo", 'w')
ff.write(str(xbb) + ' ' + str(ybb) + ' ' + str(truemag[0]) + " 1")
ff.close()
xb1 = int(float(xbb) - fwhm0 * float(leng0) / 2)
xb2 = int(float(xbb) + fwhm0 * float(leng0) / 2)
yb1 = int(float(ybb) - fwhm0 * float(leng0) / 2)
yb2 = int(float(ybb) + fwhm0 * float(leng0) / 2)
sec = "1 " + str(xb1) + " 1 " + str(nay) + '\n'
sec = sec + str(xb2) + ' ' + str(nax) + " 1 " + str(nay) + '\n'
sec = sec + str(xb1) + ' ' + str(xb2) + " 1 " + str(yb1) + '\n'
sec = sec + str(xb1) + ' ' + str(xb2) + ' ' + str(yb2) + ' ' + str(
nay) + '\n'
ff = open('sec', 'w')
ff.write(sec)
ff.close()
iraf.delete("reserr.art", ve='no')
iraf.delete("artlist.mag", ve='no')
iraf.delete("artlist.als", ve='no')
iraf.addstar("skyfit",
"artlist.coo",
imgpsf,
"reserr",
nstar=1,
veri='no',
simple='yes')
iraf.imsurfit("reserr",
"artbg",
xorder=xbgord0,
yorder=ybgord0,
regions="section",
section="sec")
iraf.imarith("reserr", "-", "artbg", "artstar")
iraf.phot("artstar", "artlist.coo", "artlist.mag", veri='no')
iraf.allstar("artstar",
"artlist.mag",
imgpsf,
"artlist.als",
"",
"artres",
veri='no',
verb='no')
iraf.imarith("artstar", "-", "artres", "artfit")
iraf.imarith("reserr", "-", "artfit", "artskyfit")
if i == 0: era = 'yes'
else: era = 'no'
artx = .5 + .25 * artx
arty = .5 + .25 * arty
iraf.display("artskyfit",
1,
fill='yes',
erase=era,
xcen=artx,
ycen=arty,
xsize=.25,
ysize=.25)
try:
tmpart.append(
float(
iraf.txdump("artlist.als", "mag", expr='yes',
Stdout=1)[0]))
except:
pass
i = i + 1
for i in tmpart:
print i
print " ########## "
try:
media = mean(array(tmpart))
arterr = std(array(tmpart))
arterr2 = std(
compress((average(tmpart) - std(tmpart) < array(tmpart)) &
(array(tmpart) < average(tmpart) + std(tmpart)),
array(tmpart)))
except:
media = 0
arterr = 0
arterr2 = 0
print '### average = %6.6s \t arterr= %6.6s ' % (str(media), str(arterr))
print '### %6.6s \t (error at 1 sigma rejection) ' % (str(arterr2))
iraf.delete(
"reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?,artskyfit.fit?",
ve='no')
iraf.delete("reserr.art", ve='no')
iraf.delete("artlist.*", ve='no')
return arterr2, arterr
(mag1['ID'][ind[0]], mag1['XINIT'][ind[0]], mag1['YINIT'][ind[0]],
mag1['MAG'][ind[0]], mag1['MSKY'][ind[0]]))
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:
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(image,
clobber=globclob,
verbose=globver,
pixtol=3.0,
maxnpsf=5,
interact=yes):
""" 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()
# Detect stars
iqpkg.iqobjs(image, 3.0, 50000.0, wtimage="", skyval="!MEDSKY")
root = image[:-5]
[gain, rnoise, fwhm] = get_head(image, ["GAIN", "READNOI", "SEEPIX"])
fwhm = float(fwhm)
rnoise = float(rnoise)
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 = 70000.0
iraf.datapars.readnoise = rnoise
iraf.datapars.epadu = gain
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
# coo file
stars = Starlist("%s.stars" % image)
outf = open("%s.coo.1" % image[:-5], "w")
for star in stars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
#initial photometry
iraf.daophot.phot(root,
'default',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
iraf.datapars.datamax = 30000.0
iraf.pstselect(root,
'default',
'default',
maxnpsf,
interactive=yes,
verify=no,
verbose=verbose)
iraf.psf(root,
'default',
'default',
'default',
'default',
'default',
interactive=interact,
verify=no,
verbose=verbose)
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
iraf.iterstat("%s.sub.fits" % root)
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 70000.0
iraf.daophot.phot("%s.sub.fits" % root,
"SN.coo",
'default',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
iraf.datapars.datamax = 30000.0
iraf.daopars.fitrad = fwhm * 2.0
iraf.allstar("%s.sub.fits" % root,
'default',
"%s.psf.1.fits" % root,
'default',
'default',
'default',
verify=no,
verbose=no)
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 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
def fitsn(_recenter, img, imgpsf, fwhm0, apco0, z22, z11, midpt, size, nor,
_values, DM):
from pyraf import iraf
import string, os, sys
from numpy import log10
a1 = int(fwhm0 + .5)
a2 = int(2. * fwhm0 + .5)
a3 = int(3. * fwhm0 + .5)
a4 = int(4. * fwhm0 + .5)
a5 = int(5. * fwhm0 + .5)
ap = str(a1) + "," + str(a2) + "," + str(a3)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
zmag = 0
iraf.noao.digiphot.daophot.photpars.zmag = zmag
iraf.delete("apori", verify='no')
iraf.delete(img + ".sn.mag", verify='no')
iraf.noao.digiphot.daophot.phot("original",
img + ".sn.coo",
"apori",
veri='no')
iraf.noao.digiphot.daophot.phot("sn",
img + ".sn.coo",
img + ".sn.mag",
veri='no')
iraf.noao.digiphot.daophot.daopars.psfrad = a4
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0
iraf.noao.digiphot.daophot.daopars.fitsky = 'no'
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
if _recenter:
answ = raw_input(">>> recentering for targets [yes/no] ? [yes] ")
if not answ: answ = 'yes'
else:
answ = 'yes'
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
iraf.delete(img + ".sn.als", verify='no')
iraf.allstar("sn",
img + ".sn.mag",
imgpsf,
img + ".sn.als",
"",
"residual",
veri='no',
verb='no')
iraf.delete("snfit.fits", verify='no')
iraf.imarith("sn", "-", "residual", "snfit")
iraf.delete("skyfit.fits", verify='no')
iraf.imarith("original", "-", "snfit", "skyfit")
iraf.txsort(img + ".sn.als", "ID")
tmptbl = iraf.txdump(img + ".sn.als",
"mag,merr,xcenter,ycenter",
expr='yes',
Stdout=1)
magerr, fitmag, centx, centy = [], [], [], []
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0])) #+2.5*log10(nor))
except:
fitmag.append('INDEF')
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append('INDEF')
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl = iraf.txdump("apori", "mag", expr='yes', Stdout=1)
apori1, apori2, apori3 = [], [], []
for i in tmptbl:
try:
apori1.append(
float(string.split(i)[0]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori1.append('INDEF')
try:
apori2.append(
float(string.split(i)[1]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori2.append('INDEF')
try:
apori3.append(
float(string.split(i)[2]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori3.append('INDEF')
iraf.txsort(img + ".sn.mag", "YCENTER")
tmptbl = iraf.txdump(img + ".sn.mag", "mag,magerr", expr='yes', Stdout=1)
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ", a1, " ", a2, " ", a3, " ", a1, " ", a2, " ", a3
apmag1, apmag2, apmag3, truemag = [], [], [], []
for i in range(len(tmptbl)):
try:
apmag1.append(
float(string.split(tmptbl[i])[0]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apmag1.append('INDEF')
try:
apmag2.append(
float(string.split(tmptbl[i])[1]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apmag2.append('INDEF')
try:
apmag3.append(
float(string.split(tmptbl[i])[2]) - float(_values) -
float(DM)) #+2.5*log10(no)
except:
apmag3.append('INDEF')
try:
truemag.append(
float(fitmag[i]) + float(apco0) + 2.5 * log10(nor) -
float(_values) - float(DM))
except:
truemag.append('INDEF')
print i, apori1[i], apori2[i], apori3[i], apmag1[i], apmag2[i], apmag3[
i], fitmag[i], truemag[i], magerr[i]
print "********************************************************************"
iraf.display("original",
1,
fill='yes',
xcen=.25,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
z01 = z11 - midpt
z02 = z22 - midpt
s1 = 1
s2 = -int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("snfit",
1,
erase='no',
fill='yes',
xcen=.25,
ycen=.75,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z02,
z1=z01)
iraf.delete("tmptbl", ve='no')
tmptbl0 = iraf.txdump(img + ".sn.als", "xcen,ycen", expr='yes', Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
lra = int((2 * float(size) * float(fwhm0)) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -1 * int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("skyfit",
1,
erase='no',
fill='yes',
xcen=.75,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
s1 = 1
s2 = -1 * int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
print '###'
print '### magnitude scaled to target exposure time using: ' + str(
2.5 * log10(nor))
print '### fit magnitude corrected with aperture correction: ' + str(
float(apco0))
print '### magnitude scaled to target using DM: ' + str(DM)
print '###'
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy